Digital pathology
View article: Digital Spatial Pathway Mapping Reveals Prognostic Tumor States in Head and Neck Cancer
Digital Spatial Pathway Mapping Reveals Prognostic Tumor States in Head and Neck Cancer Open
Head and neck squamous cell carcinoma (HNSCC) is a morphologically and molecularly heterogeneous disease with limited effectiveness of genotype-informed therapies. Transcriptome-derived estimates of signaling pathway activity carry prognos…
View article: Seeing Beyond the Microscope: Artificial Intelligence and Fluorescence Confocal Digital Imaging in Pediatric Surgical Pathology
Seeing Beyond the Microscope: Artificial Intelligence and Fluorescence Confocal Digital Imaging in Pediatric Surgical Pathology Open
Background: Digital pathology (DP) combined with fluorescence confocal microscopy (FCM) allows rapid tissue assessment while preserving specimens. Artificial intelligence (AI) and large language models (LLMs) may enhance diagnostic workflo…
View article: Comparative Analysis of Pathology Foundation Models for Automated Detection of Tertiary Lymphoid Structures in H&E-Stained Digital Pathology Images
Comparative Analysis of Pathology Foundation Models for Automated Detection of Tertiary Lymphoid Structures in H&E-Stained Digital Pathology Images Open
Tertiary lymphoid structures (TLS) have been observed in solid tumors and have been associated with better outcomes in patients treated with immunotherapy, but their dynamic nature makes identifying TLS in clinical samples challenging. Rec…
View article: Advancements in real-time oncology diagnosis: harnessing AI and image fusion techniques
Advancements in real-time oncology diagnosis: harnessing AI and image fusion techniques Open
Real-time computer-aided diagnosis using artificial intelligence (AI), with images, can help oncologists diagnose cancer with high accuracy and in an early phase. It explores various real-time techniques, encompassing technical solutions, …
View article: Supplementary Vignette 2 from Building Tools for Machine Learning and Artificial Intelligence in Cancer Research: Best Practices and a Case Study with the PathML Toolkit for Computational Pathology
Supplementary Vignette 2 from Building Tools for Machine Learning and Artificial Intelligence in Cancer Research: Best Practices and a Case Study with the PathML Toolkit for Computational Pathology Open
In this vignette, we demonstrate a practical example of a complete PathML workflow for analyzing brightfield H&E images.
View article: Gleeson 2019 Automatic Gleason grading of prostate cancer in digital histopathology
Gleeson 2019 Automatic Gleason grading of prostate cancer in digital histopathology Open
These files are an Archive of the "Gleason 2019 Challenge". Please cite the original sources material and literature for the purpose of publication, and please use the original source download if/while it remains online. Original Source: h…
View article: Supplementary Vignette 3 from Building Tools for Machine Learning and Artificial Intelligence in Cancer Research: Best Practices and a Case Study with the PathML Toolkit for Computational Pathology
Supplementary Vignette 3 from Building Tools for Machine Learning and Artificial Intelligence in Cancer Research: Best Practices and a Case Study with the PathML Toolkit for Computational Pathology Open
In this vignette, we demonstrate a practical example of a complete PathML workflow for analyzing immunofluorescence images
View article: Supplementary Vignette 1 from Building Tools for Machine Learning and Artificial Intelligence in Cancer Research: Best Practices and a Case Study with the PathML Toolkit for Computational Pathology
Supplementary Vignette 1 from Building Tools for Machine Learning and Artificial Intelligence in Cancer Research: Best Practices and a Case Study with the PathML Toolkit for Computational Pathology Open
In this vignette, we use 15 publicly available images of a wide range of imaging modalities and file formats to demonstrate how PathML supports loading all of them under a simple, standardized syntax.
View article: Gleeson 2019 Automatic Gleason grading of prostate cancer in digital histopathology
Gleeson 2019 Automatic Gleason grading of prostate cancer in digital histopathology Open
These files are an Archive of the "Gleason 2019 Challenge". Please cite the original sources material and literature for the purpose of publication, and please use the original source download if/while it remains online. Original Source: h…
View article: PREreview of "Tracking Inflammation in Real Time Following Vaccination: Validation of a Novel Individualized Digital Inflammatory Biomarker Relative to Serum Biomarkers"
PREreview of "Tracking Inflammation in Real Time Following Vaccination: Validation of a Novel Individualized Digital Inflammatory Biomarker Relative to Serum Biomarkers" Open
This Zenodo record is a permanently preserved version of a PREreview. You can view the complete PREreview at https://prereview.org/reviews/17704962. Summary The preprint "Tracking Inflammation in Real Time Following Vaccination: Validation…
View article: Machine learning driven multiomics analysis identifies disulfidptosis associated molecular subtypes in ovarian cancer
Machine learning driven multiomics analysis identifies disulfidptosis associated molecular subtypes in ovarian cancer Open
Precision oncology enables molecularly guided cancer therapy through multi-omics profiling, AI-driven classification, and biomarker-targeted interventions. Disulfidptosis has emerged as a promising therapeutic target, yet no ovarian cancer…
View article: PREreview of "Tracking Inflammation in Real Time Following Vaccination: Validation of a Novel Individualized Digital Inflammatory Biomarker Relative to Serum Biomarkers"
PREreview of "Tracking Inflammation in Real Time Following Vaccination: Validation of a Novel Individualized Digital Inflammatory Biomarker Relative to Serum Biomarkers" Open
This Zenodo record is a permanently preserved version of a PREreview. You can view the complete PREreview at https://prereview.org/reviews/17704962. SummaryThe preprint "Tracking Inflammation in Real Time Followi…
View article: Supplementary Figure S3 from Phase II Trial of Pembrolizumab and Anti-CD3 x Anti-HER2 Bispecific Antibody-Armed Activated T Cells in Metastatic Castration-Resistant Prostate Cancer
Supplementary Figure S3 from Phase II Trial of Pembrolizumab and Anti-CD3 x Anti-HER2 Bispecific Antibody-Armed Activated T Cells in Metastatic Castration-Resistant Prostate Cancer Open
A-C. FFPE sections of PC were stained by multiplex staining panel for CD4, CD8, FoxP3 (T regulatory cells), CD19 (B cells), CD68 (macrophages). Stained sections were imaged on the Hamamatsu slide scanner NanoZoomer S360 and images were vie…
View article: Statistical models to characterize colon tumor stiffness heterogeneity through representative atomic force microscopy maps
Statistical models to characterize colon tumor stiffness heterogeneity through representative atomic force microscopy maps Open
The study of the impact of physical forces and on cells has emerged as a fertile field of investigation. Applications in oncology are especially transformative since tumor stiffness was found associated with fundamental processes such as t…
View article: Leveraging Foundation Models for Histological Grading in Cutaneous Squamous Cell Carcinoma using PathFMTools
Leveraging Foundation Models for Histological Grading in Cutaneous Squamous Cell Carcinoma using PathFMTools Open
Despite the promise of computational pathology foundation models, adapting them to specific clinical tasks remains challenging due to the complexity of whole-slide image (WSI) processing, the opacity of learned features, and the wide range…
View article: Leveraging Foundation Models for Histological Grading in Cutaneous Squamous Cell Carcinoma using PathFMTools
Leveraging Foundation Models for Histological Grading in Cutaneous Squamous Cell Carcinoma using PathFMTools Open
Despite the promise of computational pathology foundation models, adapting them to specific clinical tasks remains challenging due to the complexity of whole-slide image (WSI) processing, the opacity of learned features, and the wide range…
View article: Imaging and pathology correlation in schwannomatosis: insights from a case series
Imaging and pathology correlation in schwannomatosis: insights from a case series Open
View article: DuXplore: A Dual-Hierarchical Deep Learning Model for Prognostic Prediction of Hepatocellular Carcinoma in Digital Pathology
DuXplore: A Dual-Hierarchical Deep Learning Model for Prognostic Prediction of Hepatocellular Carcinoma in Digital Pathology Open
Background: Spatial heterogeneity in tumor tissue has been linked to patient prognosis. To exploit both structural and semantic cues in whole slide images (WSIs), we propose Dual eXplanatory Framework (DuXplore), a dual-branch deep learnin…
View article: "PR-IHC-40X: Progesterone Receptor Immunohistochemistry Dataset for Breast Cancer Diagnosis"
"PR-IHC-40X: Progesterone Receptor Immunohistochemistry Dataset for Breast Cancer Diagnosis" Open
"PR-IHC-40x dataset comprises a high-resolution collection of regions of interest (ROI) images and corresponding ground truth (GT) annotation for progesterone receptor (PR) immunohistochemistry (IHC) analysis in breast cancer pathology. We…
View article: Assessing Tumour Budding in Lung Squamous Cell Carcinoma: A Comparative Analysis of Digital Whole Slide Imaging and Light Microscopy
Assessing Tumour Budding in Lung Squamous Cell Carcinoma: A Comparative Analysis of Digital Whole Slide Imaging and Light Microscopy Open
Introduction Tumour budding (TB) is an emerging prognostic marker in solid cancers and has recently been endorsed by the IASLC as a component of grading in lung squamous cell carcinoma (LUSC). With the growing adoption of digital pathology…
View article: TCGA-150-Pathology-Benchmark: External Validation Dataset for "The Digital Registrar"
TCGA-150-Pathology-Benchmark: External Validation Dataset for "The Digital Registrar" Open
This dataset contains the Independent External Validation (IEV) cohort used in the study: A Multicancer AI Framework for Comprehensive Cancer Surveillance from Pathology Reports Submitted to npj Digital Medicine. It comprises 150 de-identi…
View article: TCGA-150-Pathology-Benchmark: External Validation Dataset for "The Digital Registrar"
TCGA-150-Pathology-Benchmark: External Validation Dataset for "The Digital Registrar" Open
This dataset contains the Independent External Validation (IEV) cohort used in the study: A Multicancer AI Framework for Comprehensive Cancer Surveillance from Pathology Reports Submitted to npj Digital Me…
View article: A Lightweight, Interpretable Deep Learning System for Automated Detection of Cervical Adenocarcinoma In Situ (AIS)
A Lightweight, Interpretable Deep Learning System for Automated Detection of Cervical Adenocarcinoma In Situ (AIS) Open
Cervical adenocarcinoma in situ (AIS) is a critical premalignant lesion whose accurate histopathological diagnosis is challenging. Early detection is essential to prevent progression to invasive cervical adenocarcinoma. In this study, we d…
View article: A Lightweight, Interpretable Deep Learning System for Automated Detection of Cervical Adenocarcinoma In Situ (AIS)
A Lightweight, Interpretable Deep Learning System for Automated Detection of Cervical Adenocarcinoma In Situ (AIS) Open
Cervical adenocarcinoma in situ (AIS) is a critical premalignant lesion whose accurate histopathological diagnosis is challenging. Early detection is essential to prevent progression to invasive cervical adenocarcinoma. In this study, we d…
View article: Integration of Tear Fluid Biomarkers and Machine Learning for the Early Detection of Orbital Inflammatory Disorders
Integration of Tear Fluid Biomarkers and Machine Learning for the Early Detection of Orbital Inflammatory Disorders Open
The integration of tear fluid biomarkers and machine learning holds great promise for early detection and prognostication of orbital inflammatory disorders (OID) such as Graves’ orbitopathy and nonspecific orbital inflammation.. A hybrid d…
View article: Multi scale deep learning quantifies Ki67 index in breast cancer histopathology images
Multi scale deep learning quantifies Ki67 index in breast cancer histopathology images Open
The Ki67 proliferation index (PI) serves as a crucial prognostic indicator in clinical settings, widely utilized for evaluating breast cancer progression and forecasting chemotherapy efficacy. Nonetheless, conventional manual PI estimation…
View article: Rapid cancer diagnosis using deep learning–powered label-free subcellular-resolution photoacoustic histology
Rapid cancer diagnosis using deep learning–powered label-free subcellular-resolution photoacoustic histology Open
Traditional hematoxylin and eosin staining in formalin-fixed paraffin-embedded sections, while essential for diagnostic pathology, is time-consuming, labor intensive, and prone to artifacts that can obscure critical histological details. L…
View article: Multimodal deep learning framework integrating multiphase CT and histopathological whole slide imaging for predicting recurrence in ccRCC
Multimodal deep learning framework integrating multiphase CT and histopathological whole slide imaging for predicting recurrence in ccRCC Open
ccRCC is an aggressive, heterogeneous tumor with a poor prognosis. Prognostic assessments need multi-modal data. Radiological images have limits, while pathological images offer micro-level details. Integrating these for ccRCC outcome pred…
View article: Prognostic value of manual and digital PD-L1 expression in pT3 and pT4 colon cancer
Prognostic value of manual and digital PD-L1 expression in pT3 and pT4 colon cancer Open
Our results suggest that high PD-L1 expression is associated with longer OS. In future studies examining PD-L1 expression as a prognostic biomarker in CC, assessing PD-L1 expression using a digital approach or the CPS can be recommended.
View article: Enhancing Histological Cancer Cell Detection: Integrating XAI with Deep Learning for Improved Accuracy, Interpretability, and Clinical Trust
Enhancing Histological Cancer Cell Detection: Integrating XAI with Deep Learning for Improved Accuracy, Interpretability, and Clinical Trust Open
A precise and early histological diagnosis is critical for effective cancer treatment, reducing cancer-associated disability, and improving long-term quality of life. However, traditional deep learning (DL) models often function as black b…