The advantages of the digital methodology are well known. In this paper, we provide a detailed description of the process for the digital transformation of the pathology laboratory at IPATIMUP, the major modifications that operate throughout the processing pipeline, and the advantages of its implementation. The model of digital workflow implementation at IPATIMUP demonstrates that careful planning and adoption of simple measures related to time, space, and sample management can be adopted by any pathology laboratory to achieve higher quality and easy digital transformation.

Paige Prostate is a clinical-grade artificial intelligence tool designed to assist the pathologist in detecting, grading, and quantifying prostate cancer. In this work, a cohort of 105 prostate core needle biopsies (CNBs) was evaluated through digital pathology. Then, we compared the diagnostic performance of four pathologists diagnosing prostatic CNB unaided and, in a second phase, assisted by Paige Prostate. In phase 1, pathologists had a diagnostic accuracy for prostate cancer of 95.00%, maintaining their performance in phase 2 (93.81%), with an intraobserver concordance rate between phases of 98.81%. In phase 2, pathologists reported atypical small acinar proliferation (ASAP) less often (about 30% less). Additionally, they requested significantly fewer immunohistochemistry (IHC) studies (about 20% less) and second opinions (about 40% less). The median time required for reading and reporting each slide was about 20% lower in phase 2, in both negative and cancer cases. Lastly, the average total agreement with the software performance was observed in about 70% of the cases, being significantly higher in negative cases (about 90%) than in cancer cases (about 30%). Most of the diagnostic discordances occurred in distinguishing negative cases with ASAP from small foci of well-differentiated (less than 1.5 mm) acinar adenocarcinoma. In conclusion, the synergic usage of Paige Prostate contributes to a significant decrease in IHC studies, second opinion requests, and time for reporting while maintaining highly accurate diagnostic standards.

Background: The coronavirus disease-2019 pandemic has forced health systems to undergo dynamic changes. This study aims to evaluate the impact of the pre-lockdown and of the lockdown period on the surgical activity of a Portuguese Orthopaedic and Traumatology Department and to compare it with the homologous period of 2019. Methods: The surgical activity between March 2 and May 2, 2020 and that of the homologous period of 2019 were analyzed and compared. Additionally, the impact of national and institutional measures was analyzed. Results: There was a decrease in elective surgeries, from 587 to 100. In 2020, 59.3% of all surgeries were urgent and 48.4% were trauma whereas in 2019 there were 25.5% urgent and 23.0% trauma surgeries (P < .001 and P < .001, respectively). There was no difference in the mean of proximal hip fractures operated per week (P = .310), even when analyzing only the lockdown period (P = .102). However, proximal hip fractures corresponded to significantly higher proportion of surgeries in 2020 (P = .04). Hand and tendon injuries significantly reduced in 2020, as were sports-related trauma surgeries. Mean number of days until surgery was significantly lower in 2020 (2020:1.6 ± 2.1, 2019: 2.2 ± 2.5, P = .012). Conclusion: Governmental and institutional measures had high impact on the production and on the epidemiology of trauma. While resumption of elective surgery is needed, lessons from these measures may help in the response to a possible second wave.

No abstract

Breast cancer is the most common malignancy in women worldwide, and is responsible for more than half a million deaths each year. The appropriate therapy depends on the evaluation of the expression of various biomarkers, such as the human epidermal growth factor receptor 2 (HER2) transmembrane protein, through specialized techniques, such as immunohistochemistry or in situ hybridization. In this work, we present the HER2 on hematoxylin and eosin (HEROHE) challenge, a parallel event of the 16th European Congress on Digital Pathology, which aimed to predict the HER2 status in breast cancer based only on hematoxylin–eosin-stained tissue samples, thus avoiding specialized techniques. The challenge consisted of a large, annotated, whole-slide images dataset (509), specifically collected for the challenge. Models for predicting HER2 status were presented by 21 teams worldwide. The best-performing models are presented by detailing the network architectures and key parameters. Methods are compared and approaches, core methodologies, and software choices contrasted. Different evaluation metrics are discussed, as well as the performance of the presented models for each of these metrics. Potential differences in ranking that would result from different choices of evaluation metrics highlight the need for careful consideration at the time of their selection, as the results show that some metrics may misrepresent the true potential of a model to solve the problem for which it was developed. The HEROHE dataset remains publicly available to promote advances in the field of computational pathology.