Multicentric reticulohistiocytosis with oral and laryngeal involvement in association with autoimmune/inflammatory syndrome induced by adjuvants (ASIA): expanding the spectrum of two uncommon entities.

Multicentric reticulohistiocytosis (MRH) is the most frequent entity in the group of reticulohistiocytoses. It is usually accompanied by a symmetrical erosive polyarthritis and is frequently associated with cancer and autoimmune disorders. Autoimmune syndrome induced by adjuvants (ASIA) is an inflammatory syndrome triggered by adjuvants such as those contained in vaccines or by silicone implants. Here we report a 71-years old female with a history of breast cancer treated with surgery and subsequent prosthesis who developed a systemic hyperinflammatory syndrome including seronegative symmetric polyarthritis, multiple skin lesions and two large nodular lesions in the oral cavity and larynx. Clinical picture was consistent with a clinical diagnosis of ASIA, with breast implant rupture and/or vaccination against SARS-CoV-2 as possible triggers. Histopathology of skin, oral and laryngeal nodules revealed cutaneous/mucous and submucosal infiltration of large epithelioid mononuclear or binucleated cells with fine granular ground glass-like cytoplasm and round to kidney-shaped nuclei with prominent nucleoli, without atypical features or relevant pleomorphism, accompanied by sparse giant cells and lymphocytes. These cells stained positive for CD68 and CD45 and negative for S100, CD1a, and markers of epithelial or neural/melanocytic differentiation, altogether consistent with a diagnosis of reticulohistiocytosis. Clinic-pathological correlation allowed the final diagnosis of MRH. To our knowledge, this is the first report of a co-occurrence of MRH with ASIA and this is relevant to broaden the spectrum of those both rare diseases.

Comparing deep learning and pathologist quantification of cell-level PD-L1 expression in non-small cell lung cancer whole-slide images.

Programmed death-ligand 1 (PD-L1) expression is currently used in the clinic to assess eligibility for immune-checkpoint inhibitors via the tumor proportion score (TPS), but its efficacy is limited by high interobserver variability. Multiple papers have presented systems for the automatic quantification of TPS, but none report on the task of determining cell-level PD-L1 expression and often reserve their evaluation to a single PD-L1 monoclonal antibody or clinical center. In this paper, we report on a deep learning algorithm for detecting PD-L1 negative and positive tumor cells at a cellular level and evaluate it on a cell-level reference standard established by six readers on a multi-centric, multi PD-L1 assay dataset. This reference standard also provides for the first time a benchmark for computer vision algorithms. In addition, in line with other papers, we also evaluate our algorithm at slide-level by measuring the agreement between the algorithm and six pathologists on TPS quantification. We find a moderately low interobserver agreement at cell-level level (mean reader-reader F1 score = 0.68) which our algorithm sits slightly under (mean reader-AI F1 score = 0.55), especially for cases from the clinical center not included in the training set. Despite this, we find good AI-pathologist agreement on quantifying TPS compared to the interobserver agreement (mean reader-reader Cohen's kappa = 0.54, 95% CI 0.26-0.81, mean reader-AI kappa = 0.49, 95% CI 0.27-0.72). In conclusion, our deep learning algorithm demonstrates promise in detecting PD-L1 expression at a cellular level and exhibits favorable agreement with pathologists in quantifying the tumor proportion score (TPS). We publicly release our models for use via the Grand-Challenge platform.

Artificial intelligence-based algorithms for the diagnosis of prostate cancer: A systematic review.

OBJECTIVES: The high incidence of prostate cancer causes prostatic samples to significantly affect pathology laboratories workflow and turnaround times (TATs). Whole-slide imaging (WSI) and artificial intelligence (AI) have both gained approval for primary diagnosis in prostate pathology, providing physicians with novel tools for their daily routine. METHODS: A systematic review according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines was carried out in electronic databases to gather the available evidence on the application of AI-based algorithms to prostate cancer. RESULTS: Of 6290 articles, 80 were included, mostly (59%) dealing with biopsy specimens. Glass slides were digitized to WSI in most studies (89%), roughly two-thirds of which (66%) exploited convolutional neural networks for computational analysis. The algorithms achieved good to excellent results about cancer detection and grading, along with significantly reduced TATs. Furthermore, several studies showed a relevant correlation between AI-identified histologic features and prognostic predictive variables such as biochemical recurrence, extraprostatic extension, perineural invasion, and disease-free survival. CONCLUSIONS: The published evidence suggests that AI can be reliably used for prostate cancer detection and grading, assisting pathologists in the time-consuming screening of slides. Further technologic improvement would help widening AI's adoption in prostate pathology, as well as expanding its prognostic predictive potential.