An independent assessment of an artificial intelligence system for prostate cancer detection shows strong diagnostic accuracy.

Prostate cancer is a leading cause of morbidity and mortality for adult males in the US. The diagnosis of prostate carcinoma is usually made on prostate core needle biopsies obtained through a transrectal approach. These biopsies may account for a significant portion of the pathologists' workload, yet variability in the experience and expertise, as well as fatigue of the pathologist may adversely affect the reliability of cancer detection. Machine-learning algorithms are increasingly being developed as tools to aid and improve diagnostic accuracy in anatomic pathology. The Paige Prostate AI-based digital diagnostic is one such tool trained on the digital slide archive of New York's Memorial Sloan Kettering Cancer Center (MSKCC) that categorizes a prostate biopsy whole-slide image as either "Suspicious" or "Not Suspicious" for prostatic adenocarcinoma. To evaluate the performance of this program on prostate biopsies secured, processed, and independently diagnosed at an unrelated institution, we used Paige Prostate to review 1876 prostate core biopsy whole-slide images (WSIs) from our practice at Yale Medicine. Paige Prostate categorizations were compared to the pathology diagnosis originally rendered on the glass slides for each core biopsy. Discrepancies between the rendered diagnosis and categorization by Paige Prostate were each manually reviewed by pathologists with specialized genitourinary pathology expertise. Paige Prostate showed a sensitivity of 97.7% and positive predictive value of 97.9%, and a specificity of 99.3% and negative predictive value of 99.2% in identifying core biopsies with cancer in a data set derived from an independent institution. Areas for improvement were identified in Paige Prostate's handling of poor quality scans. Overall, these results demonstrate the feasibility of porting a machine-learning algorithm to an institution remote from its training set, and highlight the potential of such algorithms as a powerful workflow tool for the evaluation of prostate core biopsies in surgical pathology practices.

Mollaret's meningitis: cytopathologic analysis of fourteen cases.

Mollaret's meningitis (MM) is a rare disease of benign nature characterized by recurrent episodes of aseptic meningitis. Cerebrospinal fluid (CSF) examination remains the sole diagnostic modality. Eighteen CSF samples from 14 patients were studied along with the clinical data. Specimens were prepared by cytocentrifugation and Millipore filtration and were stained with Diff-Quik and Papanicolaou stains. Eight patients were men and six were women, with an age range of 17-74 yr (mean age 37 yr). Most common clinical presentation was recurrent episodes of headaches and photophobia followed by a sustained mild fever lasting 5-7 days. The CSF showed markedly increased cellularity with pleocytosis. The differential count showed predominant monocytosis ranging from 84% to 100% (mean 96). In our series, two patients had herpes simplex virus type 2 (HSV-2) DNA detected by polymerase chain reaction (PCR) in the CSF. The monocytes were seen predominantly singly, but three cases showed a strong tendency to aggregate in small groups. Phenotypically, these cells had bean-shaped bilobed nuclei as well as multiple deep nuclear clefts depicting the so-called "footprint" appearance. In four cases, multiple blunt-tipped cytoplasmic pseudopods were noted. Degenerated monocytes with the appearance of the so-called "ghost cells" were noted in one-half of the cases. Background cells were mostly small mature lymphocytes; however, one-half of cases showed a significant amount of plasma cells and/or polymorphonuclear leukocytes (PMNs). Lysed blood with hemosiderin-laden macrophages and numerous leptomeningeal cells were seen in two cases. CSF examination of MM presents a spectrum of cytomorphologic features. When interpreted in light of the appropriate clinical setting. the latter, although nonspecific, provides an accurate diagnosis. The differential diagnosis includes various degenerative, inflammatory/infectious, and lymphoproliferative disorders of the central nervous system.