Xanthogranulomatous Colitis masquerading as carcinoma of colon

Xanthogranulomatous inflammation (XGI) is an uncommon pathological diagnosis involving various organ systems, the most common being the gall bladder and kidney. It can masquerade as a malignant mass thus, requiring a clinical suspicion for accurate and timely diagnosis. A 65-year-old woman presented with acute onset of obstipation and vomiting suggesting acute obstruction. Contrast enhanced computed tomography of abdomen revealed a solid irregular mass in the ascending colon with large necrotic areas and surrounding enlarged nodes suggestive of malignancy arising from right colon. Right hemi-colectomy was performed. Histopathology of the surgical specimen showed florid inflammatory infiltrate with collection of histiocytes, lymphocytes and polymorphs. Further immunohistochemistry was conducted, and CD68 and CD45 were found to be positive and pan-cytokeratin was negative. A clinico-pathological diagnosis was thus established to be xanthogranulomatous colitis

MoNuSAC2020:A Multi-Organ Nuclei Segmentation and Classification Challenge

Detecting various types of cells in and around the tumor matrix holds a special significance in characterizing the tumor micro-environment for cancer prognostication and research. Automating the tasks of detecting, segmenting, and classifying nuclei can free up the pathologists' time for higher value tasks and reduce errors due to fatigue and subjectivity. To encourage the computer vision research community to develop and test algorithms for these tasks, we prepared a large and diverse dataset of nucleus boundary annotations and class labels. The dataset has over 46,000 nuclei from 37 hospitals, 71 patients, four organs, and four nucleus types. We also organized a challenge around this dataset as a satellite event at the International Symposium on Biomedical Imaging (ISBI) in April 2020. The challenge saw a wide participation from across the world, and the top methods were able to match inter-human concordance for the challenge metric. In this paper, we summarize the dataset and the key findings of the challenge, including the commonalities and differences between the methods developed by various participants. We have released the MoNuSAC2020 dataset to the public

Xanthogranulomatous Colitis masquerading as carcinoma of colon

Xanthogranulomatous inflammation (XGI) is an uncommon pathological diagnosis involving various organ systems, the most common being the gall bladder and kidney. It can masquerade as a malignant mass thus, requiring a clinical suspicion for accurate and timely diagnosis. A 65-year-old woman presented with acute onset of obstipation and vomiting suggesting acute obstruction. Contrast enhanced computed tomography of abdomen revealed a solid irregular mass in the ascending colon with large necrotic areas and surrounding enlarged nodes suggestive of malignancy arising from right colon. Right hemi-colectomy was performed. Histopathology of the surgical specimen showed florid inflammatory infiltrate with collection of histiocytes, lymphocytes and polymorphs. Further immunohistochemistry was conducted, and CD68 and CD45 were found to be positive and pan-cytokeratin was negative. A clinico-pathological diagnosis was thus established to be xanthogranulomatous colitis

Concordance in breast cancer grading by artificial intelligence on whole slide images compares with a multi-institutional cohort of breast pathologists

Context: Breast carcinoma grade, as determined by the Nottingham Grading System (NGS), is an important criterion for determining prognosis. The NGS is based on 3 parameters: tubule formation (TF), nuclear pleomorphism (NP), and mitotic count (MC). The advent of digital pathology and artificial intelligence (AI) have increased interest in virtual microscopy using digital whole slide imaging (WSI) more broadly. Objective: To compare concordance in breast carcinoma grading between AI and a multi-institutional group of breast pathologists using digital WSI. Design: We have developed an automated NGS framework using deep learning. Six pathologists and AI independently reviewed a digitally scanned slide from 137 invasive carcinomas and assigned a grade based on scoring of the TF, NP, and MC. Results: Interobserver agreement for the pathologists and AI for overall grade was moderate (κ = 0.471). Agreement was good (κ = 0.681), moderate (κ = 0.442), and fair (κ = 0.368) for grades 1, 3, and 2, respectively. Observer pair concordance for AI and individual pathologists ranged from fair to good (κ = 0.313-0.606). Perfect agreement was observed in 25 cases (27.4%). Interobserver agreement for the individual components was best for TF (κ = 0.471 each) followed by NP (κ = 0.342) and was worst for MC (κ = 0.233). There were no observed differences in concordance amongst pathologists alone versus pathologists + AI. Conclusions: Ours is the first study comparing concordance in breast carcinoma grading between a multi-institutional group of pathologists using virtual microscopy to a newly developed WSI AI methodology. Using explainable methods, AI demonstrated similar concordance to pathologists alone