LITERATURE UPDATE
with light microscopy (LM) for reporting histopathology slides including breast and bowel cancer screening samples. 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 specialty groups), using both LM and DP, with the order randomly assigned and six 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.
Triple-negative medullary breast cancer (haematoxylin and eosin [H&E] staining).
authors believe that Sectra is, at the time of writing, the best developed option, but this could change in the very near future as other systems improve their capabilities. All synoptic reporting systems performed impressively. Specifics regarding quality and abilities of different components will change rapidly with time, but large pathology practices considering such a transition should be aware of the issues discussed and evaluate the most current generation to arrive at appropriate conclusions.
Closing the loop – the role of pathologists in digital and computational pathology research Rau TT, Cross W, Lastra RR, Lo RC, Matoso A, Herrington CS. J Pathol Clin Res. 2024 Mar; 10 (2): e12366. doi: 10.1002/2056-4538.12366.
An increasing number of manuscripts related to digital and computational pathology are being submitted to the Journal of Pathology: Clinical Research as part of the continuous evolution from digital imaging and algorithm-based digital pathology to computational pathology and artificial intelligence. However, despite these technological advances, tissue analysis still relies heavily on pathologists’ annotations. There are three crucial elements to the pathologist’s role during annotation tasks: granularity, time constraints, and responsibility for the interpretation of computational results. Granularity
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involves detailed annotations, including case level, regional, and cellular features; and integration of attributions from different sources. Time constraints due to pathologist shortages have led to the development of techniques to expedite annotation tasks from cell-level attributions up to so-called unsupervised learning. The impact of pathologists may seem diminished, but their role is crucial in providing ground truth and connecting pathological knowledge generation with computational advancements. Measures to display results back to pathologists and reflections about correctly applied diagnostic criteria are mandatory to maintain fidelity during human-machine interactions. Collaboration and iterative processes, such as human-in-the-loop machine learning are key for continuous improvement, ensuring the pathologist’s involvement in evaluating computational results and closing the loop for clinical applicability. The journal is interested particularly in the clinical diagnostic application of computational pathology and invites submissions that address the issues raised in this editorial.
Digital pathology for reporting histopathology samples, including cancer screening samples – definitive evidence from a multisite study Azam AS, Tsang YW, Thirlwall J et al. Histopathology. 2024 Apr; 84 (5): 847–62. doi: 10.1111/his.15129.
This study reports a definitive multicentre comparison of digital pathology (DP)
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 specialty 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.
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.
Computational pathology: A survey review and the way forward Hosseini MS, Bejnordi BE, Trinh VQ et al. J Pathol Inform. 2024 Jan 14; 15: 100357. doi: 10.1016/
j.jpi.2023.100357. eCollection 2024 Dec.
Computational pathology (CPath) is an interdisciplinary science that augments developments of computational approaches to analyse and model medical histopathology images. The main objective for CPath is to develop infrastructure and workflows of digital diagnostics as an assistive CAD system for clinical pathology, facilitating transformational changes in the diagnosis
MAY 2024
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