Hepatic Tuberculosis Mimicking Biliary Cystadenoma: A Radiological Dilemma

Primary involvement of liver in tuberculosis is a rare entity. It is difficult to diagnose in absence of previous history of tuberculosis or concurrent pulmonary involvement. It is usually misdiagnosed as neoplastic liver lesion, which misdirects the treatment protocol and delays proper treatment. Here we are presenting a case of 36-year-old male patient with vague right upper quadrant abdominal pain. All the laboratory values were within normal limits. Radiological investigations were in favor of biliary cystadenoma but final diagnosis was primary focal involvement of liver in tuberculosis which was histopathologically proven to be tuberculous granulomas on biopsy of the resected mass

Rhinosporidiosis: A Rare Cause of Proptosis and an Imaging Dilemma for Sinonasal Masses

Background. Rhinosporidiosis is a common disease entity in tropical countries; however, it can be encountered in other parts of the world as well due to increasing medical tourism. It may mimic other more malignant and vigorous pathologies of the involved part. Case Report. We present a case of a 36-year-old male presenting with proptosis due to involvement of nasolacrimal duct which is rare. We will discuss typical CT and MRI features of the disease which were present in the case. Conclusion. For a surgeon and a radiologist, this is a necessary differential to be kept in mind for sinonasal masses. CT and MRI are invaluable investigations. However, FNAC is confirmatory. Both clinical and radiological aspects are required to reach correct diagnosis

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