AI-Augmented Pathology for Head and Neck Squamous Lesions Improves Non-HN Pathologist Agreement to Expert Level

Abstract Importance Diagnosis of head and neck squamous dysplasias and carcinomas is challenging, with a moderate inter-rater agreement. Nowadays, new artificial intelligence (AI) models are developed to automatically detect and grade lesions, but their contribution to the performance of pathologists hasn’t been assessed. Objective To evaluate the contribution of our AI tool in assisting pathologists in diagnosing squamous dysplasia and carcinoma in the head and neck region. Design, Setting, and Participants We evaluated the effectiveness of our previously described AI model, which combines an automatic classification of laryngeal and pharyngeal squamous lesions with a confidence score, on a panel of eight pathologists coming from different backgrounds and with different levels of experience on a subset of 115 slides. Main Outcomes and Measures The main outcome was the inter-rater agreement, measured by the weighted linear kappa. Other outcomes on diagnostic efficiency were assessed using paired t tests. Results AI-Assistance significantly improved the inter-rater agreement (linear kappa 0.73, 95%CI [0.711-0.748] with assistance versus 0.675, 95%CI [0.579-0.765] without assistance, p < 0.001). The agreement was even better on high confidence predictions (mean linear kappa 0.809, 95%CI [0.784-0.834] for assisted review, versus 0.731, 95%CI [0.681-0.781] non-assisted, p = 0.018). These improvements were particularly strong for non-specialized and younger pathologists. Hence, the AI-Assistance enabled the panel to perform on par with the expert panel described in the literature. Conclusions and Relevance Our AI-Assistance is of great value for helping pathologists in the difficult task of diagnosing squamous dysplasias and carcinomas, improving for the first time the inter-rater agreement. It demonstrates the possibility of a truly Augmented Pathology in complex tasks such as the classification of head and neck squamous lesions

Carbon footprint evaluation of routine anatomic pathology practices using eco-audit: Current status and mitigation strategies

International audienceBecause of global warming, integrating an ecological dimension to the practice of pathology is crucial, as already performed in other medical specialties. The aim of the study was to estimate the greenhouse gas emissions (GHE) of one biopsy/surgical specimen hematoxylin-phloxine-saffron (HPS) slide, one frozen section examination, and one immunohistochemistry (IHC) slide, and to propose environmentally friendly recommendations.The different steps of the pathological procedures, from sample transport to slide/block destruction, were considered for the GHE evaluation. Consumables and reagents used at each step were noted, as well as the electricity consumption of the premises/equipment, specific wastes, and staff travel. The GHE analyses were performed according to the eco-audit method.The GHE attributable to one HPS slide of a biopsy, one HPS slide of a surgical specimen, one frozen section examination, and one IHC slide were 0.589 kgCO2e (CO2 equivalent), 0.618 kgCO2e, 1.481 kgCO2e, and 0.363 kgCO2e, respectively. For a one-year activity in the pathology department, the GHE for HPS was estimated at 91,897 kgCO2e, equivalent to 422,321 km of thermal car ride. The main GHE sources were staff travel for one biopsy HPS slide (34.2 %) and one surgical specimen HPS slide (32.6 %), materials (39.1 %) and staff travel (27.2 %) for one frozen section examination, and staff travel (28.2 %) and reagents and their packaging (44.9 %) for one IHC slide.The present study highlighted the most significant sources of GHE for routine practices in a pathology department, underlining that, from pathologists to policy makers, each actor has a responsibility to implement sustainable changes

Can AI predict epithelial lesion categories via automated analysis of cervical biopsies: The TissueNet challenge?

The French Society of Pathology (SFP) organized its first data challenge in 2020 with the help of the Health Data Hub (HDH). The organization of this event first consisted of recruiting nearly 5000 cervical biopsy slides obtained from 20 pathology centers. After ensuring that patients did not refuse to include their slides in the project, the slides were anonymized, digitized, and annotated by expert pathologists, and finally uploaded to a data challenge platform for competitors from around the world. Competing teams had to develop algorithms that could distinguish 4 diagnostic classes in cervical epithelial lesions. Among the many submissions from competitors, the best algorithms achieved an overall score close to 95%. The final part of the competition lasted only 6 weeks, and the goal of SFP and HDH is now to allow for the collection to be published in open access for the scientific community. In this report, we have performed a “post-competition analysis” of the results. We first described the algorithmic pipelines of 3 top competitors. We then analyzed several difficult cases that even the top competitors could not predict correctly. A medical committee of several expert pathologists looked for possible explanations for these erroneous results by reviewing the images, and we present their findings here targeted for a large audience of pathologists and data scientists in the field of digital pathology

Distinct regulations driving YAP1 expression loss in poroma, porocarcinoma and RB1 ‐deficient skin carcinoma

International audienceAims: Recently, YAP1 fusion genes have been demonstrated in eccrine poroma and porocarcinoma, and the diagnostic use of YAP1 immunohistochemistry has been highlighted in this setting. In other organs, loss of YAP1 expression can reflect YAP1 rearrangement or transcriptional repression, notably through RB1 inactivation. In this context, our objective was to re-evaluate the performance of YAP1 immunohistochemistry for the diagnosis of poroma and porocarcinoma.Methods and results: The expression of the C-terminal part of the YAP1 protein was evaluated by immunohistochemistry in 543 cutaneous epithelial tumours, including 27 poromas, 14 porocarcinomas and 502 other cutaneous tumours. Tumours that showed a lack of expression of YAP1 were further investigated for Rb by immunohistochemistry and for fusion transcripts by real-time PCR (YAP1::MAML2 and YAP1::NUTM1). The absence of YAP1 expression was observed in 24 cases of poroma (89%), 10 porocarcinoma (72%), 162 Merkel cell carcinoma (98%), 14 squamous cell carcinoma (SCC) (15%), one trichoblastoma and one sebaceoma. Fusions of YAP1 were detected in only 16 cases of poroma (n = 66%), 10 porocarcinoma (71%) all lacking YAP1 expression, and in one sebaceoma. The loss of Rb expression was detected in all cases except one of YAP1-deficient SCC (n = 14), such tumours showing significant morphological overlap with porocarcinoma. In-vitro experiments in HaCat cells showed that RB1 knockdown resulted in repression of YAP1 protein expression.Conclusion: In addition to gene fusion, we report that transcriptional repression of YAP1 can be observed in skin tumours with RB1 inactivation, including MCC and a subset of SCC