Systematically higher Ki67 scores on core biopsy samples compared to corresponding resection specimen in breast cancer: a multi-operator and multi-institutional study.

Ki67 has potential clinical importance in breast cancer but has yet to see broad acceptance due to inter-laboratory variability. Here we tested an open source and calibrated automated digital image analysis (DIA) platform to: (i) investigate the comparability of Ki67 measurement across corresponding core biopsy and resection specimen cases, and (ii) assess section to section differences in Ki67 scoring. Two sets of 60 previously stained slides containing 30 core-cut biopsy and 30 corresponding resection specimens from 30 estrogen receptor-positive breast cancer patients were sent to 17 participating labs for automated assessment of average Ki67 expression. The blocks were centrally cut and immunohistochemically (IHC) stained for Ki67 (MIB-1 antibody). The QuPath platform was used to evaluate tumoral Ki67 expression. Calibration of the DIA method was performed as in published studies. A guideline for building an automated Ki67 scoring algorithm was sent to participating labs. Very high correlation and no systematic error (p = 0.08) was found between consecutive Ki67 IHC sections. Ki67 scores were higher for core biopsy slides compared to paired whole sections from resections (p ≤ 0.001; median difference: 5.31%). The systematic discrepancy between core biopsy and corresponding whole sections was likely due to pre-analytical factors (tissue handling, fixation). Therefore, Ki67 IHC should be tested on core biopsy samples to best reflect the biological status of the tumor.

Feasibility of a deep learning algorithm to distinguish large cell neuroendocrine from small cell lung carcinoma in cytology specimens.

INTRODUCTION: Distinguishing small cell lung carcinoma (SCLC) from large cell neuroendocrine carcinoma (LCNEC) in cytology is challenging. Our aim was to design a deep learning algorithm for classifying high-grade neuroendocrine carcinomas in fine needle aspirations. METHODS: Archival cytology cases of high-grade neuroendocrine carcinoma (17 small cell, 13 large cell, 10 mixed/unclassifiable) were retrieved. Each case included smears (Diff-Quik® and Papanicolaou stains) and cell block or concomitant core biopsies (haematoxylin and eosin [H&E] stain). All slides (n = 114) were scanned at 40× magnification, randomised and split into training (11 large, nine small) and test (two large, eight small, 10 mixed) groups. Tumour was annotated using QuPath and exported as JPEG image tiles. Three distinct deep learning convolutional neural networks, one for each preparation/stain, were designed to classify each tile and provide an overall diagnosis for each slide. RESULTS: The H&E-trained algorithm correctly classified 7/8 (87.5%) SCLC cases and 2/2 (100%) LCNEC cases. The Papanicolaou stain algorithm correctly classified 6/7 (85.7%) SCLC. and 1/1 (100%) LCNEC cases. The algorithm trained on Diff-Quik® stained images correctly classified 7/8 (87.5%) SCLC and 1/1 (100%) LCNEC cases. CONCLUSION: Using open source software, it was feasible to design a deep learning algorithm to distinguish between SCLC and LCNEC. The algorithm showed high precision in distinguishing between these two categories on H&E sectioned material and direct smears. Although the dataset was limited, our deep learning models show promising results in the classification of LCNEC and SCLC. Additional work using a larger dataset is necessary to improve the algorithm's performance.

Systematic Review of the Use of Telepathology During Intraoperative Consultation.

OBJECTIVE: To compare studies that used telepathology systems vs conventional microscopy for intraoperative consultation (frozen-section) diagnosis. METHODS: A total of 56 telepathology studies with 13,996 cases in aggregate were identified through database searches. RESULTS: The concordance of telepathology with the reference standard was generally excellent, with a weighted mean of 96.9%. In comparison, we identified seven studies using conventional intraoperative consultation that showed a weighted mean concordance of 98.3%. Evaluation of the risk of bias showed that most of these studies were low risk. CONCLUSIONS: Despite limitations such as variation in reporting and publication bias, this systematic review provides strong support for the safety of using telepathology for intraoperative consultations.

Review of the use of telepathology for intraoperative consultation.

INTRODUCTION: The use of telepathology in intraoperative consultations has been increasing due to limited time and availability of pathologists, and the demand for increased access to pathology subspecialists in difficult cases. The five main categories of telepathology are (1) static, (2) dynamic, (3) robotic, (4) whole slide imaging (WSI), and (5) hybrid methods. The majority of these methods have been found to offer diagnostic accuracy rates similar to conventional microscopy, at the cost of slightly prolonged time to evaluate slides. AREAS COVERED: Herein we discuss the salient features of each telepathology method and provide examples of their performance reported in the literature. EXPERT COMMENTARY: Telepathology systems from any of the aforementioned categories can be employed to achieve timely and accurate diagnoses as long as they meet clinical needs and are validated for the intended use case. The decision to purchase a particular system depends on the clinical application, specific needs and budget of the laboratory, as well as the personal preference of the telepathologists involved. The adoption of telepathology practice is likely to expand in order to meet the increasing demand for subspecialist consultation and as technology advances to improve diagnostic accuracy and workflow.