Advancing Ki67 hotspot detection in breast cancer: a comparative analysis of automated digital image analysis algorithms.

Aim: Manual detection and scoring of Ki67 hotspots is difficult and prone to variability, limiting its clinical utility. Automated hotspot detection and scoring by digital image analysis (DIA) could improve the assessment of the Ki67 hotspot proliferation index (PI). This study compared the clinical performance of Ki67 hotspot detection and scoring DIA algorithms based on virtual dual staining (VDS) and deep learning (DL) with manual Ki67 hotspot PI assessment.

A Pipeline for Evaluation of Machine Learning/Artificial Intelligence Models to Quantify Programmed Death Ligand 1 Immunohistochemistry.

Immunohistochemistry (IHC) is used to guide treatment decisions in multiple cancer types. For treatment with checkpoint inhibitors, programmed death ligand 1 (PD-L1) IHC is used as a companion diagnostic. However, the scoring of PD-L1 is complicated by its expression in cancer and immune cells.

Image-based multiplex immune profiling of cancer tissues: translational implications. A report of the International Immuno-oncology Biomarker Working Group on Breast Cancer.

Recent advances in the field of immuno-oncology have brought transformative changes in the management of cancer patients. The immune profile of tumours has been found to have key value in predicting disease prognosis and treatment response in various cancers. Multiplex immunohistochemistry and immunofluorescence have emerged as potent tools for the simultaneous detection of multiple protein biomarkers in a single tissue section, thereby expanding opportunities for molecular and immune profiling while preserving tissue samples.

Pitfalls in machine learning-based assessment of tumor-infiltrating lymphocytes in breast cancer: A report of the International Immuno-Oncology Biomarker Working Group on Breast Cancer.

The clinical significance of the tumor-immune interaction in breast cancer is now established, and tumor-infiltrating lymphocytes (TILs) have emerged as predictive and prognostic biomarkers for patients with triple-negative (estrogen receptor, progesterone receptor, and HER2-negative) breast cancer and HER2-positive breast cancer. How computational assessments of TILs might complement manual TIL assessment in trial and daily practices is currently debated. Recent efforts to use machine learning (ML) to automatically evaluate TILs have shown promising results.

Spatial analyses of immune cell infiltration in cancer: current methods and future directions: A report of the International Immuno-Oncology Biomarker Working Group on Breast Cancer.

Modern histologic imaging platforms coupled with machine learning methods have provided new opportunities to map the spatial distribution of immune cells in the tumor microenvironment. However, there exists no standardized method for describing or analyzing spatial immune cell data, and most reported spatial analyses are rudimentary. In this review, we provide an overview of two approaches for reporting and analyzing spatial data (raster versus vector-based).

Digital image analysis and assisted reading of the HER2 score display reduced concordance: pitfalls in the categorisation of HER2-low breast cancer.

Aims: Digital image analysis (DIA) is used increasingly as an assisting tool to evaluate biomarkers, including human epidermal growth factor receptor 2 (HER2) in invasive breast cancer (BC). DIA can assist pathologists in HER2 evaluation by presenting quantitative information about the HER2 staining in APP assisted reading (AR). Concurrently, the HER2-low category (HER2-1+/2+ without HER2 gene amplification) has gained prominence due to newly developed antibody-drug conjugates.

Development of a method for digital assessment of tumor regression grade in patients with rectal cancer following neoadjuvant therapy.

Neoadjuvant chemo-radiotherapy (nCRT) followed by surgical resection is the standard treatment strategy in patients with locally advanced rectal cancer (RC). The pathological effect of nCRT is assessed by determining the tumor regression grade (TRG) of the resected tumor. Various methods exist for assessing TRG and all are performed manually by the pathologist with an accompanying risk of interobserver variation.

Automated Quantification of sTIL Density with H&E-Based Digital Image Analysis Has Prognostic Potential in Triple-Negative Breast Cancers.

Triple-negative breast cancer (TNBC) is an aggressive and difficult-to-treat cancer type that represents approximately 15% of all breast cancers. Recently, stromal tumor-infiltrating lymphocytes (sTIL) resurfaced as a strong prognostic biomarker for overall survival (OS) for TNBC patients. Manual assessment has innate limitations that hinder clinical adoption, and the International Immuno-Oncology Biomarker Working Group (TIL-WG) has therefore envisioned that computational assessment of sTIL could overcome these limitations and recommended that any algorithm should follow the manual guidelines where appropriate.

Histological disease activity in patients with microscopic colitis is not related to clinical disease activity or long-term prognosis.

Background: Microscopic colitis (MC) is a common cause of chronic watery diarrhea. Biopsies with characteristic histological features are crucial for establishing the diagnosis. The two main subtypes are collagenous colitis (CC) and lymphocytic colitis (LC) but incomplete forms exist.

Quantifying intraepithelial lymphocytes and subepithelial collagen band in microscopic colitis, extracting insights into the interrelationship of lymphocytic and collagenous colitis.

Microscopic colitis (MC) is the umbrella term for the conditions termed lymphocytic colitis (LC) and collagenous colitis (CC). LC with thickening of the subepithelial collagen band or CC with increased number of intraepithelial T- lymphocytes (IELs) is often seen in MC and may lead to difficulties in correct histological classification. We investigated the extent of overlapping features of CC and LC in 60 cases of MC by measuring the exact thickness of the subepithelial collagen band in Van Gieson stained slides and quantifying number of IELs in CD3 stained slides by digital image analysis.

Pitfalls in assessing stromal tumor infiltrating lymphocytes (sTILs) in breast cancer.

Stromal tumor-infiltrating lymphocytes (sTILs) are important prognostic and predictive biomarkers in triple-negative (TNBC) and HER2-positive breast cancer. Incorporating sTILs into clinical practice necessitates reproducible assessment. Previously developed standardized scoring guidelines have been widely embraced by the clinical and research communities.

Report on computational assessment of Tumor Infiltrating Lymphocytes from the International Immuno-Oncology Biomarker Working Group.

Assessment of tumor-infiltrating lymphocytes (TILs) is increasingly recognized as an integral part of the prognostic workflow in triple-negative (TNBC) and HER2-positive breast cancer, as well as many other solid tumors. This recognition has come about thanks to standardized visual reporting guidelines, which helped to reduce inter-reader variability. Now, there are ripe opportunities to employ computational methods that extract spatio-morphologic predictive features, enabling computer-aided diagnostics.

From Detection of Individual Metastases to Classification of Lymph Node Status at the Patient Level: The CAMELYON17 Challenge.

Automated detection of cancer metastases in lymph nodes has the potential to improve the assessment of prognosis for patients. To enable fair comparison between the algorithms for this purpose, we set up the CAMELYON17 challenge in conjunction with the IEEE International Symposium on Biomedical Imaging 2017 Conference in Melbourne. Over 300 participants registered on the challenge website, of which 23 teams submitted a total of 37 algorithms before the initial deadline.