Digital pathology for reporting histopathology samples, including cancer screening samples - definitive evidence from a multisite study.

AIMS: To conduct a definitive multicentre comparison of digital pathology (DP) with light microscopy (LM) for reporting histopathology slides including breast and bowel cancer screening samples. METHODS: A total of 2024 cases (608 breast, 607 GI, 609 skin, 200 renal) were studied, including 207 breast and 250 bowel cancer screening samples. Cases were examined by four pathologists (16 study pathologists across the four speciality groups), using both LM and DP, with the order randomly assigned and 6 weeks between viewings. Reports were compared for clinical management concordance (CMC), meaning identical diagnoses plus differences which do not affect patient management. Percentage CMCs were computed using logistic regression models with crossed random-effects terms for case and pathologist. The obtained percentage CMCs were referenced to 98.3% calculated from previous studies. RESULTS: For all cases LM versus DP comparisons showed the CMC rates were 99.95% [95% confidence interval (CI) = 99.90-99.97] and 98.96 (95% CI = 98.42-99.32) for cancer screening samples. In speciality groups CMC for LM versus DP showed: breast 99.40% (99.06-99.62) overall and 96.27% (94.63-97.43) for cancer screening samples; [gastrointestinal (GI) = 99.96% (99.89-99.99)] overall and 99.93% (99.68-99.98) for bowel cancer screening samples; skin 99.99% (99.92-100.0); renal 99.99% (99.57-100.0). Analysis of clinically significant differences revealed discrepancies in areas where interobserver variability is known to be high, in reads performed with both modalities and without apparent trends to either. CONCLUSIONS: Comparing LM and DP CMC, overall rates exceed the reference 98.3%, providing compelling evidence that pathologists provide equivalent results for both routine and cancer screening samples irrespective of the modality used.

Validity assessment of a simulation module for robot-assisted thoracic lobectomy.

BACKGROUND: Training for robot-assisted thoracic lobectomy remains an issue, prompting the development of virtual reality simulators. Our aim was to assess the construct and face validity of a new thoracic lobectomy module on the RobotiX Mentor, a robotic surgery simulator. We also aimed to determine the acceptability and feasibility of implementation into training. METHODS: This prospective, observational, and comparative study recruited novice (n = 16), intermediate (n = 9), and expert (n = 5) participants from King's College London, the 25th European Conference on General Thoracic Surgery, and the Society of Robotic Surgery conference 2018. Each participant completed two familiarization tasks followed by the Guided Robotic Lobectomy module and an evaluation questionnaire. Outcome measures were compared using Mann-Whitney U tests. RESULTS: Construct validity was demonstrated in 12/21 performance evaluation metrics. Significant differences between groups were found in all metrics including: time taken to complete module, vascular injury, respect for tissue, number of stapler firings, time instruments out of view, number of instrument collisions, and number of movements. Participants deemed aspects of the simulator (mean 3/5) and module (3/5) as realistic and rated the simulator as both acceptable (3.8/5) and feasible (3.8/5) for robotic surgical training. CONCLUSIONS: Face validity, acceptability, and feasibility were established for the thoracic lobectomy module of the RobotiX Mentor simulator. Moderate evidence of construct validity was also demonstrated. With further work, this simulation module could help to reduce the initial part of the learning curve for trainees and decrease the risk of errors during live training.