Quantitative Nuclear Grading: An Objective, Artificial Intelligence–Facilitated Foundation for Grading Noninvasive Papillary Urothelial Carcinoma

Slotman, Ava; Xu, Minqi; Lindale, Katherine; Hardy, Céline; Winkowski, Dan; Baird, Regan; Chen, Lina; Lal, Priti; Kwast, Theodorus van der; Jackson, Chelsea; Gooding, R. J.; Berman, David M.

doi:10.1016/j.labinv.2023.100155

Cited by 4 publications

(1 citation statement)

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“…In various cancer types, including breast cancer and melanocyte lesions, mitosis has been successfully detected using CPATH methods [ 81 , 82 ]. Although one study used automated mitosis detection for objective grading in BC, there is more untapped potential in this field [ 83 ]. The automated highlighting of mitotic figures in BC WSIs has the potential to decrease inter- or intraobserver variability in diagnosis.…”

Section: Discussionmentioning

confidence: 99%

Artificial Intelligence in Digital Pathology for Bladder Cancer: Hype or Hope? A Systematic Review

Khoraminia,

Fuster,

Kanwal

et al. 2023

Cancers

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Bladder cancer (BC) diagnosis and prediction of prognosis are hindered by subjective pathological evaluation, which may cause misdiagnosis and under-/over-treatment. Computational pathology (CPATH) can identify clinical outcome predictors, offering an objective approach to improve prognosis. However, a systematic review of CPATH in BC literature is lacking. Therefore, we present a comprehensive overview of studies that used CPATH in BC, analyzing 33 out of 2285 identified studies. Most studies analyzed regions of interest to distinguish normal versus tumor tissue and identify tumor grade/stage and tissue types (e.g., urothelium, stroma, and muscle). The cell’s nuclear area, shape irregularity, and roundness were the most promising markers to predict recurrence and survival based on selected regions of interest, with >80% accuracy. CPATH identified molecular subtypes by detecting features, e.g., papillary structures, hyperchromatic, and pleomorphic nuclei. Combining clinicopathological and image-derived features improved recurrence and survival prediction. However, due to the lack of outcome interpretability and independent test datasets, robustness and clinical applicability could not be ensured. The current literature demonstrates that CPATH holds the potential to improve BC diagnosis and prediction of prognosis. However, more robust, interpretable, accurate models and larger datasets—representative of clinical scenarios—are needed to address artificial intelligence’s reliability, robustness, and black box challenge.

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Section: Discussionmentioning

confidence: 99%