Fully Automated Tumor Bud Assessment in Hematoxylin and Eosin-Stained Whole Slide Images of Colorectal Cancer.

Tumor budding (TB), the presence of single cells or small clusters of up to 4 tumor cells at the invasive front of colorectal cancer (CRC), is a proven risk factor for adverse outcomes. International definitions are necessary to reduce interobserver variability. According to the current international guidelines, hotspots at the invasive front should be counted in hematoxylin and eosin (H&E)-stained slides. This is time-consuming and prone to interobserver variability; therefore, there is a need for computer-aided diagnosis solutions. In this study, we report an artificial intelligence-based method for detecting TB in H&E-stained whole slide images. We propose a fully automated pipeline to identify the tumor border, detect tumor buds, characterize them based on the number of tumor cells, and produce a TB density map to identify the TB hotspot. The method outputs the TB count in the hotspot as a computational biomarker. We show that the proposed automated TB detection workflow performs on par with a panel of 5 pathologists at detecting tumor buds and that the hotspot-based TB count is an independent prognosticator in both the univariate and the multivariate analysis, validated on a cohort of n = 981 patients with CRC. Computer-aided detection of tumor buds based on deep learning can perform on par with expert pathologists for the detection and quantification of tumor buds in H&E-stained CRC histopathology slides, strongly facilitating the introduction of budding as an independent prognosticator in clinical routine and clinical trials.

Interobserver agreement of a gastric adenocarcinoma tumor regression grading system that incorporates assessment of lymph nodes.

Perioperative chemotherapy is increasingly used in combination with surgery for the treatment of patients with locally advanced, resectable gastric cancer. Histologic tumor regression grade (TRG) has emerged as an important prognostic factor; however, a common standard for its evaluation is lacking. Moreover, the clinical significance of regressive changes in metastatic lymph nodes (LNs) remains unclear. We conducted an international study to examine the interobserver agreement of a TRG system that is based on the Becker system for the primary tumors and additionally incorporates regression grading in LNs. Twenty observers at different levels of experience evaluated the TRG in 60 histologic slides (30 primary tumors and 30 LNs) based on the following criteria: for primary tumors, grade 1 represented complete response (no residual tumor), grade 2 represented <10%, grade 3 represented 10-50%, and grade 4 represented >50% residual tumor, as described by Becker et al. For LNs, grade "a" represented complete, grade "b" represented partial, and grade "c" represented no regression. The interobserver agreement was estimated using the Kendall's coefficient of concordance (W). Regarding primary tumors, agreement was good irrespective of the level of experience, reaching a W-value of 0.70 overall, 0.71 among subspecialized, and 0.71 among nonsubspecialized observers. Regarding LNs, interobserver agreement was moderate to good, with W-values of 0.52 overall, 0.64 among subspecialized, and 0.45 among nonsubspecialized observers. These findings indicate that the combination of the Becker TRG system with a three-tiered grading of regression in LNs generates a system that is reproducible. Future studies should investigate whether the additional information of TRG in LNs adds to the prognostic value of histologic regression grading in gastric cancer specimens.

Controversies in the pathological assessment of colorectal cancer.

Pathologic assessment of colorectal cancer specimens plays an essential role in patient management, informing prognosis and contributing to therapeutic decision making. The tumor-node-metastasis (TNM) staging system is a key component of the colorectal cancer pathology report and provides important prognostic information. However there is significant variation in outcome of patients within the same tumor stage. Many other histological features such as tumor budding, vascular invasion, perineural invasion, tumor grade and rectal tumor regression grade that may be of prognostic value are not part of TNM staging. Assessment of extramural tumor deposits and peritoneal involvement contributes to TNM staging but there are some difficulties with the definition of both of these features. Controversies in colorectal cancer pathology reporting include the subjective nature of some of the elements assessed, poor reporting rates and reproducibility and the need for standardized examination protocols and reporting. Molecular pathology is becoming increasingly important in prognostication and prediction of response to targeted therapies but accurate morphology still has a key role to play in colorectal cancer pathology reporting.

Standardization of whole slide image morphologic assessment with definition of a new application: Digital slide dynamic morphometry.

BACKGROUND: In histopathology, the quantitative assessment of various morphologic features is based on methods originally conceived on specific areas observed through the microscope used. Failure to reproduce the same reference field of view using a different microscope will change the score assessed. Visualization of a digital slide on a screen through a dedicated viewer allows selection of the magnification. However, the field of view is rectangular, unlike the circular field of optical microscopy. In addition, the size of the selected area is not evident, and must be calculated. MATERIALS AND METHODS: A digital slide morphometric system was conceived to reproduce the various methods published for assessing tumor budding in colorectal cancer. Eighteen international experts in colorectal cancer were invited to participate in a web-based study by assessing tumor budding with five different methods in 100 digital slides. RESULTS: The specific areas to be tested by each method were marked by colored circles. The areas were grouped in a target-like pattern and then saved as an .xml file. When a digital slide was opened, the .xml file was imported in order to perform the measurements. Since the morphometric tool is composed of layers that can be freely moved on top of the digital slide, the technique was named digital slide dynamic morphometry. Twelve investigators completed the task, the majority of them performing the multiple evaluations of each of the cases in less than 12 minutes. CONCLUSIONS: Digital slide dynamic morphometry has various potential applications and might be a useful tool for the assessment of histologic parameters originally conceived for optical microscopy that need to be quantified.