Performance of an AI-powered visualization software platform for precision surgery in breast cancer patients

AdMaPlace March 16, 2025

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Performance of an AI-powered visualization software platform for precision surgery in breast cancer patients

Introduction

Breast cancer is the most common solid-tumor cancer worldwide, with 1 in 8 U.S. women developing invasive breast cancer over the course of their lifetime¹. Surgery persists as the main treatment modality in the management of early-stage invasive breast cancer². Breast-conserving surgery (BCS) is an area of growing interest for the treatment of breast cancer, where it has been shown to have physical and psychological benefits that improve patient satisfaction and quality of life³. The surgical landscape has been transformed by numerous clinical trials that have demonstrated that BCS can offer reduced morbidity and improved cosmetic outcomes for women, without compromising long-term survival^4,5,6,7 or satisfaction⁸.

Many factors unique to individual patients⁹, such as medical comorbidities, play a critical role in influencing treatment modality. However, there remain key spatial aspects of the tumor influencing operative decisions, including tumor size, proximity to local chest structures, multifocality, excision volume, tumor-to-breast-volume ratio, and gland density². The relationship of the tumor to the nipple and skin are of particular importance when considering nipple- and skin-sparing mastectomy, respectively, and the oncological safety of immediate breast reconstruction¹⁰. Moreover, the relationship of the tumor to the chest wall is relevant when weighing breast conservation and mastectomy¹¹. Even the width of surgical margins represents an important decision point for surgeons. Margins that are too wide may yield poor cosmetic results¹², while margins that are too narrow risk incomplete resection and disease recurrence¹³.

Despite significant progress, re-excision rates secondary to positive margins nevertheless remain at levels roughly double^14,15 the 10% goal set by the American Society of Breast Surgeons^16,17. When formulating a surgical plan, the surgeon is challenged with integrating key features of the patient’s anatomy, as well as tumor and tumor microenvironment features in multiple dimensions (spatially, temporally, metabolically, etc.), to mentally approximate the tumor location, size, and the overall extent of disease. One way for surgeons to visualize these features is dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI), which over the last decade has emerged as a standard for breast imaging to aid in the assessment of these morphometric dimensions¹⁸. Not only does DCE-MRI yield more precise tumor characterization compared to mammography, but this modality also allows for the visualization of tumor morphology in the context of breast, chest wall, and adjacent structures in three-dimensional (3D) space¹⁸.

Although DCE-MRI alone offers enhanced capabilities over standard MRI practices, further improvement in visualization and in assessing tumor characteristics are achievable by computational spatial analysis of the DCE-MRI data. The medical imaging field broadly refers to the identification and mapping of these specific tissue types as “segmentation” – delimiting the boundaries between distinct anatomical features, such as tumor, blood vessels, or bone. These models can facilitate the planning of the surgical approach, teaching of surgery residents, and communicating with patients. These models have proven especially valuable for planning surgeries in which the preservation of normal parenchyma is critically important for maintaining quality of life and physiologic function in a variety of tumor types^19,20,21,22. 3D models can also help map the tumor compartment to its vascular supply, thereby aiding in the surgical planning of resection margins that are dictated by the organ’s regional arterial territory^23,24. Importantly, studies that examined clinical outcomes resulting from surgeries performed using 3D modeling guidance report early evidence of improved surgical results and patient benefit²⁵, including in randomized clinical trials²⁶.

Here, we investigate the performance and utility of TumorSight Viz and its corresponding data analysis pipeline, which together provide detailed 3D spatial insights into anatomical structures, landmarks, and their relative positioning based on data extracted from routine standard-of-care DCE-MRI²⁷ (Supplementary Fig. 1). TumorSight Viz is an FDA-cleared medical device that assists in clinical decision-making for early-stage, biopsy-proven breast cancer, prior to neoadjuvant therapy. The platform generates 3D visualization maps of breast tumors alongside 5 additional tissue types, including fibroglandular breast tissue, adipose tissue, skin, chest wall, and blood vessels²⁷. TumorSight Viz provides a surgical roadmap of key anatomical landmarks as well as qualitative visualizations, and further defines automated quantitative assessments such as tumor-to-landmark distances and breast quadrant localization. We assessed the platform’s performance against board-certified radiologists on a single institution cohort (n = 100). We show that TumorSight Viz segmentations and related surgical margin visualizations are comparable to those approved by experts, and that its automated methods for tumor and tumor-to-landmark measurements are accurate.

Results

Datasets for assessment of TumorSight Viz performance

To assess TumorSight Viz performance, we assembled a cohort of breast cancer patients whose data was not used during the training and testing phases of TumorSight Viz development. This cohort therefore represents a truly independent validation set. The subjects in this cohort were selected via a retrospective search of the hospital radiology database for women with early-stage breast cancer who had diagnostic DCE-MRIs between January 1, 2001 and December 31, 2020. Here, “early stage” was defined as having no clinical or radiologic evidence of distant metastatic disease at the time of treatment initiation (i.e., the patients were cM0 at the time of treatment initiation). Patients who completed standard-of-care neoadjuvant chemotherapy and subsequently underwent surgery for pathologic evaluation of treatment response were included. Patients whose MRIs had major artifacts or other imaging issues were excluded. Examples of issues that warranted exclusion are lack of fat suppression, incomplete DICOM series, and non-correctable registration errors. The age at diagnosis, race, ethnicity, cancer subtype, clinical T stage, and clinical N stage were abstracted from the hospital medical records, when available. Our work complies with the Checklist for Artificial Intelligence in Medical Imaging guidelines prescribed by the Radiological Society of North America²⁸. An overview of our study is provided in Figs. 1 and 2. An overview of the core segmentation model development (described extensively elsewhere²⁷) is provided in Supplementary Fig. 1, and statistical analysis of these datasets is provided in Table 1.

Table 1 Cohort demographic and clinical characteristics

Full size table

Patient characteristics in the validation study

Our validation set was composed of 98 women comprising 100 cases (in which 2 women presented with bilateral breast cancer, see Methods – Sample Size Determination and Acceptance Criteria) (Table 1). The validation set was a random subsample of a patient cohort from the University of Alabama (UAB), obtained under a waiver from the organization’s Institutional Review Board (IRB). The sampling was balanced with respect to estrogen receptor status, progesterone receptor status, human epidermal growth factor receptor status, grade, T stage, laterality, tumor volume, tumor longest dimension, and MRI resolution within the overall UAB cohort (not the overall dataset). Each woman in this set had a confirmatory bilateral DCE-MRI and completed a neoadjuvant chemotherapy regimen at UAB. In each validation set, the average age at diagnosis was 50 years. The distribution by race was 70% white, 25% black, and 3% Asian.

Invasive ductal carcinoma (IDC) was by far the predominant histologic type, accounting for 80% of tumors in the validation cohort. Most women presented at clinical tumor stage of cT3 and clinical nodal stage of cN0. Tumor and multi-tissue labels were generated in TumorSight Viz using an AI labeling procedure for all DCE-MRIs. The performance of these automatic labels and metrics was then assessed relative to metrics determined by practicing radiologists.

Summary of methodology

Our validation methodology necessitated complete, voxel-level labeling of DCE-MRIs encompassing 6 different tissues and air. Because making these assignments would have been a Herculean task for two practicing radiologists to perform on a large patient reference dataset, we instead used physician experts to approve labels produced by trained lay people, as performed in previous validation studies²⁹. Additionally, tumor dimensions automatically extracted from radiologist-approved segmentations correlated well with direct measurements made by the radiologists. This similarity was reflected in comparable correlations between radiologist vs. TumorSight Viz segmentations as compared to inter-radiologist correlations. Collectively, these findings verify the high quality of radiologist-approved annotations and support their use as proxies for radiologist-generated annotations.

Automated nipple localization

The nipple is a critical surgical landmark, serving as a key point of reference in terms of breast surgery type and feasibility assessment³⁰. Using TumorSight Viz 3D renderings, we developed a simple heuristic for localizing a single voxel representing the nipple. For each breast, we first find the center of mass of all voxels laying within 8 mm of at least one voxel each of air, skin, and fibroglandular tissue, and then offset this point by 6 mm in the posterior direction.

Clinical metrics calculations

Having established the ability to label key landmarks, including skin, chest, and nipple, we introduced additional algorithms into TumorSight Viz for automating clinically-relevant measurements that can provide surgeons with a quantitative understanding of tumor topology and tumor-landmark distances²⁷.

Axis-aligned tumor dimensions

We automated calculation of the longest axis-aligned tumor dimension and tumor bounding box volume by first computing the longest tumor dimension in the transverse, coronal, and sagittal planes using the tumor label map. The longest axis-aligned dimension is the maximum of those dimensions, and the bounding box volume is their product. The longest axis-aligned dimension and bounding box volume was calculated for the entire tumor, as well as for the dominant tumor mass. The dominant mass was operationally defined as the largest contiguous collection of “tumor” voxels. These measurements are expected to be identical for unifocal tumors but different for multifocal tumors.

To assess relative tumor volume (e.g., as a percent of total breast volume), we implemented a series of volume calculation algorithms. The tumor volume algorithm first counts the tumor voxels and then multiplies by the voxel volume. The breast volume algorithm first computes a “breast box” (see Methods, Breast Box Construction) around the breast and then sums voxel volumes of all valid breast tissues (i.e., skin, adipose tissue, fibroglandular tissue, blood vessels, and tumor) within the breast box.

Tumor-to-landmark distances

The automatic tumor-to-landmark distance calculations were made by first extracting the coordinates of the vertices of each voxel of the tumor and landmark. The shortest distance between the tumor and landmark vertices, as well as the tumor-to-skin, tumor-to-nipple, and tumor-to-chest distances, were computed using the KDTree class, as implemented in the SciPy Python package.

Breast quadrant localization

The breast quadrant localization algorithm partitions the breast into 4 quadrants with equal volumes of breast tissue with planes passing through the nipple (see Methods, Breast Quadrant Localization). The quadrant in which the center of mass of the tumor falls defines the breast quadrant localization of the tumor.

Surgical margin determination

Visualizations of the putative resection volume to be removed during tumor excision can better inform the surgical approach. TumorSight Viz features the ability to display the tumor convex hull surrounding the tumor map, which serves as a general uniform-distance boundary around the tumor. We allowed the distance margin between the tumor and convex hull to be adjustable up to 2 cm, so that the surgeon may consider different surgical margin scenarios. For multifocal tumors, we included the ability to view separate convex hulls around each tumor (a “multicentric” convex hull). This feature is paired with the option to view multifocal tumors with a single convex hull around the entire tumor excision volume (a “unicentric” convex hull), which may aid in deciding between an en bloc resection and piecemeal excisions of each tumor nodule.

Convex hull generation

The TumorSight Viz unicentric convex hull algorithm generates a new tumor mask whose voxel centers are the corners of the original tumor mask, extracting a two-dimensional (2D) triangular surface mesh from the new mask using the marching cubes algorithm in scikit-image, translating the vertices in the mesh along their normal vector by a user-defined margin, and building the convex hull around the translated vertices. The multicentric convex hull algorithm applies the same procedure, albeit separately to each non-contiguous tumor region identified by the scipy.ndimage’s label method. After the hulls are generated, the algorithm checks for overlapping hulls by testing whether each vertex in each hull lies within any other hull. The procedure is applied to all pairs of convex hulls, and all overlapping convex hulls are merged. (See Methods, Unicentric & Multicentric Convex Hulls for additional details).

Radiologist review process establishing ground-truth in the validation set

The 100 cases in the validation dataset were reviewed by 2 breast-specialized radiologists (R1 and R2) for the purpose of selecting quality annotations for ground-truthing (Fig. 2). R1 approved 67 of 91 (73.6%) tumor annotations, while R2 approved 84 of 96 (87.5%). Of the 100 cases, 97 cases featured a reading from either R1 or R2. Some cases were not reviewed because of software issues, trouble finding the lesion, or other issues during the review process. Both radiologists reviewed a common set of 90 tumor annotations. For 83 (92%) of these, at least one radiologist believed that the tumor annotation was of acceptable quality. This finding demonstrates that our annotation pipeline generates viable tumor annotations for use by clinical radiologists, in most cases. For 64 (71%) cases in the common set of 90, both radiologists approved of the quality of the annotations (pre-adjudication). Additionally, both radiologists rejected 7 (8%) cases in the common set. The overall concordance between radiologists was 79% and Gwet’s AC1 was 0.7, both of which indicate that there is a modest degree of variability between even expert human radiological readers when assessing clinical DCE-MRI. Our methodology demonstrates comparable efficacy when assessed against these radiologist performance metrics.

To adjudicate the 19 (21%) discordant cases, the radiologists reviewed the MRIs and annotations together. A consensus was reached to approve 10 (53%) cases and reject 9 (47%). Ultimately, 74 (82%) cases in the common set of 90 were approved by both radiologists (post-adjudication).

A similar vetting procedure was carried out for selecting high-quality multi-tissue annotations for ground-truthing. R1 approved 87 of 90 (97%) reviewed annotations, while R2 approved 88 of 93 (95%) reviewed annotations. Both radiologists reviewed a common set of 86 annotations. Of these, all 86 (100%) were approved by at least one radiologist and 80 (93%) were approved by both radiologists. This finding indicates that our annotation pipeline generates good quality multi-tissue labels for most cases. The overall concordance between radiologists was 93% and Gwet’s AC1 was 0.93, both of which indicate high reliability of radiologists in the vetting of multi-tissue labels.

When considering the tumor and multi-tissue annotations together, R1 and R2 approved both segmentations for 70 (78%) and 68 (73%) cases, respectively. A total of 64 (71%) full MRI annotations were approved by both radiologists in the adjudicated dataset.

Performance validation of TumorSight Viz 3D tumor label maps

We provide a compendium of representative images demonstrating overall trends observed in these studies, focusing on both the broad successes of the segmentation model (Fig. 3), as well as areas for improvement (Fig. 4). TumorSight Viz renders clear depictions of these tumor segmentations, which can readily be overlaid on the corresponding MRI data along with segmentations of surrounding tissues.

**Fig. 3: Radiologist-verified depictions of early-stage breast cancer in the UAB validation cohort, as rendered in TumorSight Viz.**

**Fig. 4: Rejected or adjudicated TumorSight Viz depictions for early-stage breast cancer in the UAB validation cohort.**

We further assessed the accuracy of TumorSight Viz automatically-generated 3D tumor maps by analysing the degree of overlap with radiologist-approved segmentations (Fig. 5), while also determining the interquartile ranges (IQR) representing the span between the first (25th percentile) and third (75th percentile) quartiles.

**Fig. 5: Establishing validation set ground-truth.**

The median Sørensen–Dice coefficient (Dice) (see Methods) was 75.6% (IQR: 58.6–85.1) for the validation set. For comparison, the median Dice was 85.2% (IQR: 78.1–89.0) for the training set (n = 358) and 83.4% (IQR: 74.2–86.2) in the test set (n = 46).

In addition to applying Dice as a measure of bulk tumor segmentation quality³¹, we utilized the Hausdorff distance (see Methods) as a measure of accuracy in delineating the tumor border³². The median Hausdorff distance was 15.1 mm (IQR: 7.8–27.9) for the validation set (Fig. 6). For comparison, the median Hausdorff distance was 10.2 mm (IQR: 6.7–19.1) for the training set (n = 358) and 13.2 mm (IQR: 8.6–26.5) for the test set (n = 46).

Performance validation of TumorSight Viz clinical metrics

We assessed the performance of the automated metric determination methods implemented in TumorSight Viz. Summary statistics for automated and manual measurements can be found in Supplementary Table 1. For the longest tumor dimension, TumorSight Viz measurements were linearly related to each radiologist’s direct measurements (Fig. 7). The Pearson correlation coefficients were 0.78 for both radiologists. The correlation between the radiologists was 0.90, indicating significant agreement between radiologists. In addition to evaluating the correlation with ground-truth, we also examined the magnitude of the differences from ground-truth (Supplementary Table 2). The median absolute error in longest tumor dimension between TumorSight Viz and the mean of the 2 radiologists was 6.4 mm (IQR: 2.6–12.4) and 6.3 mm (IQR: 2.8–14.8), respectively. The measurements by radiologists were closer together, with a median difference of 3.2 mm (IQR: 1.6–6.7). These findings demonstrate that TumorSight Viz generally yields suitable metrics for downstream clinical applications.

**Fig. 7: Scatterplot matrix of maximum longest dimension and bounding box volume with Pearson correlation.**

The log-transformed volumes of TumorSight Viz tumor bounding boxes also showed a linear relationship with computed volumes from direct measurements by radiologists. The correlation coefficients were 0.75 and 0.75 with respect to radiologists R1 and R2, respectively, and 0.94 between radiologists. The median absolute error between TumorSight Viz and radiologist-assigned volumes were 9.5 cm³ (IQR: 2.5–50.9) for R1 and 10.7 cm³ (IQR: 2.1–42.4) for R2, whereas the median error between the radiologists was 5.6 cm³ (IQR: 1.5–22.9).

We also evaluated TumorSight Viz automated measurements of tumor volume, tumor-to-breast-volume ratio, tumor-to-nipple distance, tumor-to-skin distance, tumor-to-chest distance, and the breast quadrant position of the tumor (Supplementary Table 3). The ground-truth for these were extracted from the radiologist-approved segmentations. The median absolute difference was 2.0 cm³ (IQR: 0.9–5.7) for tumor volume and 0.002 (IQR: 0.001–0.008) for tumor-to-breast-volume ratio. Distances from tumor to surgical landmarks were also accurate, with median errors of 2.3 mm (IQR: 0.8–8.3) for tumor-to-nipple distance, 1.0 mm (IQR: 0.5–2.8) for tumor-to-skin, and 1.3 mm (IQR: 0.6–3.6) for tumor-to-chest distance. There was 81% concordance on the quadrant localization of the primary tumor. We observed 89% concordance at localize the breast quadrant of the primary tumor.

Similar performance was observed with analyses applied to the extent of disease (Supplementary Tables 1 and 2 and Supplementary Fig. 2).

Performance validation of TumorSight Viz convex hulls

To assess the accuracy of TumorSight Viz in delineating convex hulls, the Dice score was computed with respect to ground-truth convex hulls generated from radiologist-approved annotations (Supplementary Table 3). The median Dice was 81.5% (IQR: 67.3–90.8), indicating a high degree of overlap between TumorSight Viz and ground-truth convex hulls generated with the unicentric approach. A similar Dice of 81.9% (IQR: 72.0–91.1) was found with a multicentric hull. The results suggest that TumorSight Viz can generate accurate convex hulls around virtual 3D models of both unifocal and multifocal breast tumors.

Performance validation of TumorSight Viz relative to T stage and cancer subtype

We additionally assessed TumorSight Viz performance relative to breast cancer T stage (T1, T2, T3) (Supplementary Table 4, Supplementary Figs. 3 and 4) and subtype (IDC, ILC, Other) (Supplementary Table 5, Supplementary Figs. 5 and 6). As expected, the observed error generally increased as a function of T stage across all comparisons, such that larger dimension values for later T stages had larger absolute errors. IDC and invasive lobular carcinoma (ILC) showed similar absolute errors, indicating that the segmentation model is robust to distinct pathological presentations of disease, particularly the more diffuse or morphologically complex presentation of ILC^33,34. In contrast, “Other” cancer types had notably larger errors, likely due to insufficient sampling of these subtypes during model development and the small sample size in the validation set.

Failure modes for tumor segmentation

We identified the 10 cases with the largest absolute difference between tumor volumes extracted from TumorSight Viz tumor annotations as compared to the radiologist-approved tumor annotations. We found that 9 cases had 1 or more of the following: heterogeneous or large degrees of fibroglandular tissue, moderate or marked background parenchymal enhancement, or non-mass enhancement. 7 cases had 2 or more from the above list. When looking at the 10 cases with the smallest absolute differences in tumor volume, we found that only 2 cases showed a single issue: heterogenous fibroglandular tissue. None had moderate or marked background parenchymal enhancement or non-mass enhancement. These results indicate the specific areas in which our model requires further refinement, helping to direct future software development.

Discussion

TumorSight Viz is a medical device software platform that assists in clinical decision-making through detailed 3D renderings of tumors in their anatomic context. The core technology employs a computer vision model developed on a large dataset of de-identified breast cancer patient DCE-MRIs. TumorSight Viz uses a deep-learning model that segments breast tumors and surrounding anatomy on DCE-MRIs to render 3D visualizations (Fig. 1)²⁷. While others have previously developed 3D breast tumor segmentation models^{17,35,36,37,38,39,40}, the TumorSight Viz platform offers several benefits. TumorSight Viz segments and visually maps 5 additional tissue types, providing surgeons with a “lay of the land” of the tumor with key anatomical landmarks (Fig. 1, right-top). Multi-tissue segmentation provides 3D visualizations of a tumor and its context within the body, and also enable automated quantitative assessments including tumor-to-landmark distances and breast quadrant localization (Fig. 1, right-mid). The observed level of precision in the platform’s measurements is on the same scale as the spatial resolution of MRI, which typically ranges from 0.7 to 1.6 mm depending on scanner type^41,42, indicating that device performance meets resolution standards for surgical precision that are inherent to clinical MRI data. The results support the relevance of TumorSight Viz generated measurements compared to real-world surgical implementation.

TumorSight Viz expands upon the existing shortlist of software platforms for DCE-MRI visualization, including DynaCAD and CADstream^43,44. TumorSight Viz represents the first stage of a broader effort to incorporate the wealth of patient data and emerging treatment response forecasting into a therapeutic pipeline customized on a per patient basis. The platform integrates with a suite of precision cancer analysis software, spanning from disease diagnosis, drug response forecasting and dose optimization, clinical and surgical decisional support, and long-term disease recurrence prediction. TumorSight Viz therefore represents an initial phase in establishing this broader end-to-end treatment platform for patients.

The workflow enabled by TumorSight Viz supports broad medical initiatives such as Cancer Moonshot, which aim to enhance personalized cancer treatment and intervention through an expanded toolkit of precision medicine techniques and tools. TumorSight Viz incorporates surgical clinical decision support functionalities to help visualize hypothetical resection margins, as represented by convex hulls around the 3D tumor map (Fig. 1, right-bottom). Such guiding margins can form a foundational surgical guideline which demarcate the minimal boundary for tumor removal. The convex hull can be expanded to increase the boundary of tissue removal to minimize risk of recurrence depending on the margin of safety required and the likelihood of achieving desired cosmetic outcomes. Several risk factors for positive surgical margins have been identified^45,46,47, offering objective avenues for adjusting both pre- and intra-operative imaging and surgical procedures.

Recent studies have suggested the safety of oncoplastic BCS for selected women with multifocal breast tumors⁴⁸. As such, the ability of TumorSight Viz to depict separate convex hulls for tumor foci, or a single convex hull around the entire tumor, may aid in deciding between an en bloc resection or separate excisions of each tumor focus. We found that convex hulls generated by TumorSight Viz were comparable to those generated from radiologist-approved tumor segmentations. Additional validation is needed, but the early assessment of TumorSight Viz to approximate surgical margins is promising.

The ability to rapidly assess and annotate these 3D label maps from routine standard of care medical imaging obtained for breast cancer patients must accurately reflect the breast tumor(s) and their local anatomy (Fig. 1, left), and it is paramount that these depictions faithfully reflect radiological interpretation of the patient’s true anatomy (Fig. 1, right-top). In the current study, we found a high degree of agreement between the ground-truth radiologist image assessment and the automatic TumorSight Viz breast segmentation assessment, as shown by 2 complimentary evaluation metrics. Dice analysis revealed significant volumetric overlap of bulk tumor segmentations between human readers and the segmentation model. Additionally, the Hausdorff distance demonstrated that tumor borders were accurately delineated, in the 1–3 mm range. These results align with findings demonstrating that 2 mm margins minimize degrees of recurrence⁴⁹, suggesting a suitable range for surgical performance. Accurate demarcation of tumor borders is particularly relevant for assigning surgical margins and gauging chest wall or skin invasion for staging purposes. TumorSight Viz performance metrics demonstrate the utility of TumorSight Viz in facilitating these crucial aspects of surgical planning.

A widely-applicable surgical clinical decision support tool should generalize across institutions with varying scanners and imaging protocols in order to facilitate deployment across numerous institutions and clinical settings. The validation dataset used in this study was derived from an institution that did not contribute to the training and testing datasets. As expected, a modest reduction in performance was observed with the use of data from an unseen institution, a practical reality in nearly all machine learning studies. However, given the strong performance of the model on a distinct dataset, this validation study ultimately demonstrates the segmentation model’s broad generalizability for 3D tumor renderings and analysis. A major strength of our study is our use of full 3D tumor segmentations to validate our virtual tumor model. Alternative methods that rely on 2D slices, maximum intensity projections, or regions of interest have been used, but these approaches suffer from information loss relative to 3D annotations^36,50,51. The TumorSight Viz methodology extracts morphological features within a 3D spatial lattice, rather than interpolating detail from 2D imaging, thereby enhancing informed treatment planning efforts.

Analysis of radiological features (“radiomics”) in medical imaging is a rapidly-expanding field, with the overarching goal of using image properties as the basis for disease status analysis and therapy response prediction^52,53,54. TumorSight Viz allows researchers to explore the radiological and radiomic features of breast tissues and tumors as a starting point for disease diagnostics and clinical decision support. The automated 3D tissue map further allows for extraction of radiomic features with minimal labor, enabling researchers to use these in downstream applications.

TumorSight Viz was designed to better enable economical cancer surgical decision-making, thus helping to combat the continued rise in patient surgical costs despite a lack of commensurate reimbursement⁵⁵. Hospitals are expected to shift towards solutions that enable surgeons to make better and more precise decisions pre-operatively, with the goal of guiding better anticipation of treatment outcomes. TumorSight Viz is poised to help expedite these surgical efforts, therefore holding promise for reducing uncertainty, improving patient care decisions, and reducing costs. The platform’s extensive visualization and decisional support tools minimize intra-operative decisions, shifting this planning to informed discussions between patients and physicians during the consultation phase.

The multi-institutional nature of the segmentation model’s training and testing sets provides the basis for broad generalizability of TumorSight Viz performance. However, there are notable limitations inherent to our training data. Our study cohort was limited to subjects eligible for neoadjuvant chemotherapy. Heterogenous or extreme fibroglandular tissue, moderate or marked background parenchymal enhancement, and non-mass enhancement (all known radiological challenges when reading MRI) were particularly associated with errors in tumor volume. We note that the model training and testing sets were biased toward cT2 tumors (~60%), whereas the UAB cohort was biased toward cT3 tumors (~70%). This discrepancy between the tumor types used in model training and subsequent application in this study may have resulted in some loss of performance on the UAB cohort. However, given the overall strong performance of the model on a distinct dataset, this validation study demonstrates the segmentation model’s broad applicability in 3D tumor renderings and analysis. Future multi-institutional prospective studies aimed at extensively assessing the generalizability and broader utility of TumorSight Viz, as well as efforts piloting clinical implementation, are ongoing.

The TumorSight Viz platform is designed to aid surgeons in planning their approach in breast cancer treatment. As a step towards the goal of comprehensive surgical-planning support, TumorSight Viz features precision 3D renderings of tumors in the context of key anatomical structures, quantitative measures of tumor morphology and tumor-landmark relationships, as well as visual approximations of resection margins. Many of the functionalities introduced in this study are derived from the platform’s multi-tissue 3D label map that encompasses the tumor and all clinically relevant tissues. Enhanced qualitative and quantitative capabilities for assessing surgical approach and decision-making requires high performance in accurately labeling all key anatomical structures. TumorSight Viz offers to streamline surgical procedures, and thus enable surgeons to better assess surgical treatment options and facilitate outcomes tailored for every patient and their corresponding unique anatomy. TumorSight Viz will continue to be updated and expanded as development proceeds for other indications and organ systems.

Methods

Ethics approval and consent to participate

De-identified patient data from the University of Alabama (UAB) were used for this study, for which an IRB waiver was granted. The investigators complied with all relevant ethical regulations and those specified under the Declaration of Helsinki.

Validation dataset annotations

For all cases, ground-truth tumor labels were generated using a hybrid AI-human approach. We first automatically generated preliminary tumor labels using an AI-powered labeling tool²⁷. These labels were then edited, when necessary, by trained laypeople to better mask the tumor on raw MRIs. Fully-labeled DCE-MRIs were produced by merging the tumor labels (i.e., as derived from verified automatic tumor segmentations or trained layperson annotators) with the automatic multi-tissue labels generated by TumorSight Viz. We refer to these as “AI-assisted tumor and multi-tissue labels” throughout this manuscript.

Radiologist-approved ground-truth annotation

To ensure performance of TumorSight Viz was validated against clinical standards, ground-truth labels were reviewed by 2 board-certified radiologists. Both radiologists are fellowship-trained in breast imaging and have 8 years of experience each. The workflow for ground-truth generation in this study is shown in Fig. 2.

To prevent biasing of reader review, radiologists made measurements of tumor dimensions on raw DCE-MRI data prior to being shown either AI-assisted or automatic TumorSight Viz segmentations/annotations. After measurement determination, radiologists were then asked to review AI-assisted segmentations and mark these as accept/reject for both the tumor and multi-tissue segmentations. The multi-tissue segmentation was considered as a unit, such that the entirety of the 5 tissue segmentations were either accepted or rejected in their totality. The tumor approve/reject and multi-tissue approve/reject decisions were not linked or otherwise dependent on one another. Radiologists were additionally asked to review automatic segmentations of the tumor as generated by TumorSight Viz. They were instructed to base their decision on whether the tumor regions were annotated completely and exclusively. Cases with discordant ratings were adjudicated at a consensus conference between the radiologists. Only tumor annotations that were ultimately approved by both radiologists were used for ground-truthing in this study. Radiologists were asked to approve/reject TumorSight Viz segmentations, but did not manually modify the segmentations.

A similar procedure was carried out for vetting multi-tissue annotations. Radiologists were instructed to consider under- or over-segmentation of fibroglandular and adipose tissue, unsegmented major vessels, and patches of unsegmented skin in their review of the multi-tissue segmentation before ultimately accepting or rejecting it. There was no adjudication process for discordant reviews of multi-tissue annotations.

We found a high degree of agreement between the automatic measurements on approved segmentations and direct measurements on MRI (See Results – Performance Validation). We proceeded to use the radiologist-approved annotations as ground-truth measurements, allowing us to compute ground-truth tumor volume, tumor-to-breast-volume ratio, tumor localization, tumor-to-nipple distance, tumor-to-skin distance, and tumor-to chest distance.

Similarly, ground-truth unicentric and multicentric convex hulls were generated using radiologist-approved tumor annotations.

Breast box construction

The breast box was constructed using 5 key points that define the boundaries of the box. We initially identified the center of mass of the segmented breast tissues and the most anterior point along the image midline. Using these, we defined a new point that is a fixed distance (6 mm) posterior to the anterior point and aligned with the center of mass in the medial-lateral direction. We then found the 2 points on the surface of the labeled skin that are closest to the 6 mm fixed-distance point in both the transverse and sagittal projections (4 points total). The remaining fifth point was determined as the most anterior point on the surface of the skin. These 5 points define an initial guess for the extent of the breast in the horizontal, longitudinal, and anterior directions.

Next, checks were made to ensure no segmented breast tissue was excluded from the box. For each of the 5 points, if the corresponding box plane intersects breast tissue, the point is moved along the skin boundary in the direction that increases the box volume until no intersection occurs. As an example, although a point within the inframammary crease may define the inferior extent of the initial bounding box, a small portion of the breast tissue may actually be inferior to it. By iteratively moving the point along the surface of the skin in the inferior direction, we can adjust its location to a point slightly below the crease such that no more breast tissue is excluded. Once all these corrections are made, the extents of the final box in each direction are defined.

Breast quadrant localization

Breast quadrants were computed using the nipple location, the breast box, and the tumor segmentation. Initially, the breast was split into 4 quadrants using the transverse and sagittal planes. However, by allowing the sagittal plane to rotate slightly around the superior-inferior axis, we obtained better quadrant agreement with our clinical data. The rotation was done by finding the rotation angle that results in equal amounts of breast tissue (defined as tissue within the breast box) on each side of the plane. Additionally, the plane is defined to always pass through the nipple. Once the breast is split into the 4 quadrants, the quadrant containing the tumor center of mass is identified, and the fraction of tumor volume in each quadrant is determined.

Unicentric Hulls

To compute the convex hull around a tumor, we utilize scipy.spatial’s ConvexHull method. This method takes a set of points (an (N, 3) array of 3D point coordinates) and creates a convex hull object defined by vertices, facets, and neighbor information.

The points for which we create the convex hull are not the set of points defining the tumor volume, but are instead the points defining the tumor surface. This surface is constructed using skimage.measure’s marching_cubes method, which provides attributes for the vertices and surface normals. This allows us to find the convex hull of the tumor plus a surgical margin, by letting us first expand the tumor surface by moving its vertices away from the tumor surface (along the surface normals) and then computing the convex hull of these new surface vertex positions.

Multicentric Hulls

Although we can treat all tumor voxels as a single set of points and compute their “unicentric” convex hull, in cases where the tumor is made up of multiple, disconnected regions, we might be more interested in the spatial distribution of their individual convex hulls. We call this the “multicentric” tumor convex hull.

We first cluster the tumor voxels using scipy.ndimage’s label method. This uses a given structure element to find clusters of 1 s in a binary mask. This algorithm works by finding isolated regions of tumors voxels that do not have any neighboring tumor voxels (i.e., even a one-voxel thick layer of separation will result in two separate regions being identified). The algorithm gives each cluster a unique label and makes it possible to analyze each cluster of points individually. We then compute the convex hull of each cluster.

In cases in which adding a surgical margin to each cluster’s expanded surface causes them to overlap, we treat the overlapping surfaces as one and compute their combined hull (with the chosen margin). Overlapping surfaces are determined by checking if any of the vertices in one convex hull lie within another.

Evaluation of tumorsight viz 3D label maps, clinical metrics, and Convex Hulls

To assess the quality of TumorSight Viz tumor segmentations with respect to ground-truth segmentations, the following 2 complimentary evaluation metrics were computed. First, we utilized the Sørensen–Dice coefficient (Dice), which is widely used in the computer vision literature to gauge the similarity of 2 segmentations and is defined as:

$${Dice}=frac{2{TP}}{2{TP}+{FP}+{FN}}$$

(1)

where TP, FP, and FN are the pixel-wise numbers of true positives, false positives, and false negatives, respectively. Second, we used the Hausdorff distance d_H, which is defined as:

$${d}_{H}(X,,Y)=max left{{su}{p}_{xin X}{rm{d}}left(x,Yright),{su}{p}_{yin Y}{rm{d}}left(X,yright)right}$$

(2)

where (dleft(a,Bright)=,mathop{inf }limits_{bin B}{d}left(a,bright)), (d) is a distance measure from point (ain X) to subset (Bsubseteq X), and (X) and (Y) are tumor boundary pixels in the TumorSight Viz and ground-truth segmentations, respectively. It is a measure of how far apart 2 sets are from each other and has been previously used to evaluate the performance of segmentation networks in accurately delineating tumor boundaries⁵⁶.

The accuracies of TumorSight Viz tumor dimension and bounding box volume measurements were assessed using the intra-class correlation, Pearson correlation, absolute error, and relative error with respect to each radiologist’s direct measurements. These statistics were also computed between the 2 radiologist’s measurements to serve as benchmarks for evaluating TumorSight performance. Similar assessments were performed to estimate the agreement between measurements by radiologists and measurements extracted from radiologist-approved annotations to affirm the quality of the annotations.

The accuracy of TumorSight Viz measurements of tumor volume, tumor-to-breast-volume ratio, tumor-to-nipple distance, tumor-to-skin distance, and tumor-to chest distance was assessed using the signed, absolute, and relative errors compared to ground-truth segmentations. Performance in breast quadrant localization was quantified using concordance, and the accuracy of convex hulls was evaluated using the Dice score. In each case, the reference was provided by the corresponding metric extracted from radiologist-approved segmentations. Interquartile ranges (IQR) representing the span between the first (25^th percentile) and third (75^th percentile) quartiles were also calculated.

Sample size determination and acceptance criteria

The percentage of cases approved by radiologists was calculated to assess the performance of our internal AI-assisted annotation pipeline for tumor and multi-tissue segmentations. Based on an early pilot study, a 65% approval rate was considered acceptable. In order to exclude 65% from the lower bound of a one-sided exact confidence interval (CI) of the approval rate of segmentations, while assuming a true approval rate of 75% or higher, a sample size of at least 88 cases was required. To account for anticipated missing reviews (due to issues with software, trouble finding the lesion, etc.), we arrived at a final sample size of 100 cases.

The concordance and Gwet’s AC1 between radiologist approval/rejection calls was calculated to evaluate the reliability of radiologists at vetting ground-truth for both the tumor and multi-tissue segmentations.

Categories: Breast cancer, Surgical oncology

Tags: Breast cancer, Surgical oncology

Targeting of TAMs: can we be more clever than cancer cells?

With increasing incidence and geography, cancer is one of the leading causes of death, reduced quality of life and disability worldwide. Principal progress in the development of new anticancer therapies, in improving the efficiency of immunotherapeutic tools, and in the personification of conventional therapies needs to consider cancer-specific and patient-specific programming of innate immunity. Intratumoral TAMs and their precursors, resident macrophages and monocytes, are principal regulators of tumor progression and therapy resistance. Our review summarizes the accumulated evidence for the subpopulations of TAMs and their increasing number of biomarkers, indicating their predictive value for the clinical parameters of carcinogenesis and therapy resistance, with a focus on solid cancers of non-infectious etiology. We present the state-of-the-art knowledge about the tumor-supporting functions of TAMs at all stages of tumor progression and highlight biomarkers, recently identified by single-cell and spatial analytical methods, that discriminate between tumor-promoting and tumor-inhibiting TAMs, where both subtypes express a combination of prototype M1 and M2 genes. Our review focuses on novel mechanisms involved in the crosstalk among epigenetic, signaling, transcriptional and metabolic pathways in TAMs. Particular attention has been given to the recently identified link between cancer cell metabolism and the epigenetic programming of TAMs by histone lactylation, which can be responsible for the unlimited protumoral programming of TAMs. Finally, we explain how TAMs interfere with currently used anticancer therapeutics and summarize the most advanced data from clinical trials, which we divide into four categories: inhibition of TAM survival and differentiation, inhibition of monocyte/TAM recruitment into tumors, functional reprogramming of TAMs, and genetic enhancement of macrophages.

AdMaPlace March 9, 2025

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Stromal architecture and fibroblast subpopulations with opposing effects on outcomes in hepatocellular carcinoma

Dissecting the spatial heterogeneity of cancer-associated fibroblasts (CAFs) is vital for understanding tumor biology and therapeutic design. By combining pathological image analysis with spatial proteomics, we revealed two stromal archetypes in hepatocellular carcinoma (HCC) with different biological functions and extracellular matrix compositions. Using paired single-cell RNA and epigenomic sequencing with Stereo-seq, we revealed two fibroblast subsets CAF-FAP and CAF-C7, whose spatial enrichment strongly correlated with the two stromal archetypes and opposing patient prognosis. We discovered two functional units, one is the intratumor inflammatory hub featured by CAF-FAP plus CD8_PDCD1 proximity and the other is the marginal wound-healing hub with CAF-C7 plus Macrophage_SPP1 co-localization. Inhibiting CAF-FAP combined with anti-PD-1 in orthotopic HCC models led to improved tumor regression than either monotherapy. Collectively, our findings suggest stroma-targeted strategies for HCC based on defined stromal archetypes, raising the concept that CAFs change their transcriptional program and intercellular crosstalk according to the spatial context.

AdMaPlace March 9, 2025

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Rapid brain tumor classification from sparse epigenomic data

Although the intraoperative molecular diagnosis of the approximately 100 known brain tumor entities described to date has been a goal of neuropathology for the past decade, achieving this within a clinically relevant timeframe of under 1 h after biopsy collection remains elusive. Advances in third-generation sequencing have brought this goal closer, but established machine learning techniques rely on computationally intensive methods, making them impractical for live diagnostic workflows in clinical applications. Here we present MethyLYZR, a naive Bayesian framework enabling fully tractable, live classification of cancer epigenomes. For evaluation, we used nanopore sequencing to classify over 200 brain tumor samples, including 10 sequenced in a clinical setting next to the operating room, achieving highly accurate results within 15 min of sequencing. MethyLYZR can be run in parallel with an ongoing nanopore experiment with negligible computational overhead. Therefore, the only limiting factors for even faster time to results are DNA extraction time and the nanopore sequencer’s maximum parallel throughput. Although more evidence from prospective studies is needed, our study suggests the potential applicability of MethyLYZR for live molecular classification of nervous system malignancies using nanopore sequencing not only for the neurosurgical intraoperative use case but also for other oncologic indications and the classification of tumors from cell-free DNA in liquid biopsies.

AdMaPlace March 13, 2025

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The spatial coupling and its influencing mechanism between rural human-habitat heritage and key rural tourism villages in China

Exploring the influencing factors and its influencing mechanism of the spatial coupling between rural human-habitat heritage (RHH) and key rural tourism villages (RTV) at county scale from the perspective of space can expand the theoretical research on the spatial coupling mechanism between RHH and RTV, and further provide theoretical reference and data support for the coordinated development and high-quality development of RHH and RTV in China. At the same time, previous studies have failed to systematically analyze the influencing factors and influencing mechanisms of the spatial coupling between RHH and RTV at the county scale, which restricted decision makers from formulating coordinated development measures between RHH and RTV at the macro level. In this study, bivariate spatial autocorrelation model and spatial coupling coordination model were used to quantitatively analyze the spatial coupling level between RHH and RTV at the county scale in China. Then, the linear regression (OLS) model, geographically weighted regression (GWR) model, and optimal parameter GeoDetector (OPGD) model were integrated to systematically analyze the linear influencing, spatial heterogeneity effect and interactive effect of natural environment and socioeconomic factors on the spatial coupling level between RHH and RTV in China, and explore the interactive influencing mechanism. The results show that the spatial coupling level of RHH and RTV in China show a significant east-west differentiation. There were 2024, 473, 293, 55 and 6 areas of severe, moderate, mild, basic and moderate coordination between RHH and RTV, respectively. Among them, severe and moderate discoordination areas are mainly distributed in Northeast China, arid and semi-arid areas in Western China, plateau areas in Southwest China, densely populated urban agglomerations and plains agricultural areas in the Middle East China. Mild discoordination areas and basic and moderate coordination areas are mainly located in transition zones in mountainous and plain areas, economically developed mountainous and hilly counties along the southeastern coast, and coastal tourist cities. Economic and population factors are the fundamental factors that affect the spatial coupling between RHH and RTV. Rural tourism facilities and rural public service facilities are important external driving forces for the coupling development of RHH and RTV, and Sociocultural environment factors are the important internal driving forces. Different surface forms, different climate conditions and different ecological environment conditions can form different natural textures and spatial organizations. Suitable climate conditions, sufficient water sources and ecological environment conditions can form more suitable rural settlement construction conditions and production and living conditions, and ultimately affect the protection and activation of rural human settlement heritage and the development and layout of key tourist villages. The spatial coupling relationship between RHH and RTV is the result of the complex interaction between the natural directivity law caused by natural environmental factors and the humanistic directivity law caused by human social and economic activities.

AdMaPlace March 16, 2025

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Higher income is associated with greater life satisfaction, and more stress

Is there a cost to our well-being from increased affluence? Drawing upon responses from 2.05 million U.S. adults from the Gallup Daily Poll from 2008 to 2017 we find that with household income above ~$63,000 respondents are more likely to experience stress. This contrasts with the trend below this threshold, where at higher income the prevalence of stress decreases. Such a turning point for stress was also found for population sub-groups, divided by gender, race, and political affiliation. Further, we find that respondents who report prior-day stress have lower life satisfaction for all income and sub-group categories compared to the respondents who do not report prior-day stress. We find suggestive evidence that among the more satisfied, healthier, socially connected, and those not suffering basic needs deprivations, this turn-around in stress prevalence starts at lower values of income and stress. We hypothesize that stress at higher income values relates to lifestyle factors associated with affluence, rather than from known well-being deprivations related to good health and social conditions, which may arise even at lower income values if conventional needs are met.

AdMaPlace March 10, 2025

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Introduction

Results

Datasets for assessment of TumorSight Viz performance

Patient characteristics in the validation study

Summary of methodology

Automated nipple localization

Clinical metrics calculations

Axis-aligned tumor dimensions

Tumor-to-landmark distances

Breast quadrant localization

Surgical margin determination

Convex hull generation

Radiologist review process establishing ground-truth in the validation set

Performance validation of TumorSight Viz 3D tumor label maps

Performance validation of TumorSight Viz clinical metrics

Performance validation of TumorSight Viz convex hulls

Performance validation of TumorSight Viz relative to T stage and cancer subtype

Failure modes for tumor segmentation

Discussion

Methods

Ethics approval and consent to participate

Validation dataset annotations

Radiologist-approved ground-truth annotation

Breast box construction

Breast quadrant localization

Unicentric Hulls

Multicentric Hulls

Evaluation of tumorsight viz 3D label maps, clinical metrics, and Convex Hulls

Sample size determination and acceptance criteria

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