Enhancing mitosis quantification and detection in meningiomas with computational digital pathology.

Mitosis is a critical criterion for meningioma grading. However, pathologists' assessment of mitoses is subject to significant inter-observer variation due to challenges in locating mitosis hotspots and accurately detecting mitotic figures. To address this issue, we leverage digital pathology and propose a computational strategy to enhance pathologists' mitosis assessment. The strategy has two components: (1) A depth-first search algorithm that quantifies the mathematically maximum mitotic count in 10 consecutive high-power fields, which can enhance the preciseness, especially in cases with borderline mitotic count. (2) Implementing a collaborative sphere to group a set of pathologists to detect mitoses under each high-power field, which can mitigate subjective random errors in mitosis detection originating from individual detection errors. By depth-first search algorithm (1) , we analyzed 19 meningioma slides and discovered that the proposed algorithm upgraded two borderline cases verified at consensus conferences. This improvement is attributed to the algorithm's ability to quantify the mitotic count more comprehensively compared to other conventional methods of counting mitoses. In implementing a collaborative sphere (2) , we evaluated the correctness of mitosis detection from grouped pathologists and/or pathology residents, where each member of the group annotated a set of 48 high-power field images for mitotic figures independently. We report that groups with sizes of three can achieve an average precision of 0.897 and sensitivity of 0.699 in mitosis detection, which is higher than an average pathologist in this study (precision: 0.750, sensitivity: 0.667). The proposed computational strategy can be integrated with artificial intelligence workflow, which envisions the future of achieving a rapid and robust mitosis assessment by interactive assisting algorithms that can ultimately benefit patient management.

From Neck Pain to Sarcoidosis: The Interesting Association.

We report the case of a 31-year-old male patient, presenting to the emergency department (ED) with a 6-week history of left-sided lateral neck pain, along with a minor localized swelling. A few weeks after the beginning of his complaints, he contracted a mild coronavirus disease 2019 (COVID-19). Upon examination, his aches were defined as carotidynia; thus, proper radiologic evaluation was carried out. While ultrasound (US) and magnetic resonance imaging (MRI) scans showed evident signs of left common carotid (LCC) vasculitis, computed tomography angiography (CTA) and positron emission tomography-CT (PET-CT) scans revealed no vascular findings. Unexpected hypermetabolic hilar and mediastinal lymphadenopathy was found on PET-CT, necessitating lymph node biopsy. Pathology results displayed noncaseating granulomas. Besides, angiotensin-converting enzyme (ACE) levels in blood were high. Sarcoidosis, with concurrent LCC vasculitis, was diagnosed, and corticosteroid therapy was started. Shortly thereafter, remarkable recovery ensued.

Spatial Intratumoral Heterogeneity Expression of PD-L1 Antigen in Head and Neck Squamous Cell Carcinoma.

INTRODUCTION: Immune-checkpoint inhibitors have demonstrated a significant survival benefit in metastatic and non-resectable head and neck squamous cell carcinoma (HNSCC). Patients with a combined positivity score (CPS) of 20 and higher benefit the most from therapy. Inaccurate definition of the CPS category might lead to the incorrect stratification of patients to immunotherapy. This study's main aim was to investigate programmed death-ligand 1 (PD-L1) antigen expression in HNSCC in diverse clinical situations and histological settings. MATERIALS AND METHODS: This is a prospective cohort study conducted in a tertiary referral medical center. Tissues were investigated for PD-L1 expression using the FDA-approved 22C3 immunohistochemistry assay (Dako). We analyzed potential associations between the CPS category and meaningful demographic, clinical, and outcome metrics. Furthermore, we investigated morphologically separate sites for CPS scores in whole surgical tissue specimens and matched preoperative biopsies. RESULTS: We analyzed 36 patients, of whom 26 had oral cavity SCC and 10 had laryngeal SCC. The overall, disease-specific, and progression-free survival of the HNSCC group of patients were not associated with the CPS category (p = 0.45, p = 0.31, and p = 0.88, respectively). There was a significant (18%, 95% CI 0.65-0.9) inconsistency between the CPS category determined in biopsies versus whole carcinoma analyses. We also found an uneven distribution of whole-tumor CPS attributed to spatial carcinoma invasiveness, tumor differentiation, and inflammatory cell infiltration heterogeneity. DISCUSSION AND CONCLUSIONS: Our data suggest that careful selection of tumor area for CPS analysis is important. PD-L1 antigen expression, clinically represented by CPS, may be up- or down-categorized in different clinical and pathological circumstances. The high whole-tissue CPS category scatter may clinically result in potential treatment modifications. We argue that CPS analysis requires not only adequacy (at least 100 viable tumor cells), but also correct representation of the tumor microenvironment.