A deep-learning workflow to predict upper tract urothelial carcinoma protein-based subtypes from H&E slides supporting the prioritization of patients for molecular testing.

Autor: Angeloni M; Institute of Pathology, University Hospital Erlangen-Nürnberg, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany.; Comprehensive Cancer Center Erlangen-EMN (CCC ER-EMN), Erlangen, Germany.; Bavarian Cancer Research Center (BZKF), Erlangen, Germany., van Doeveren T; Department of Urology, Erasmus MC Urothelial Cancer Research Group, Rotterdam, The Netherlands., Lindner S; Institute of Pathology, University Hospital Erlangen-Nürnberg, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany.; Comprehensive Cancer Center Erlangen-EMN (CCC ER-EMN), Erlangen, Germany.; Bavarian Cancer Research Center (BZKF), Erlangen, Germany., Volland P; Institute of Pathology, University Hospital Erlangen-Nürnberg, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany.; Comprehensive Cancer Center Erlangen-EMN (CCC ER-EMN), Erlangen, Germany.; Bavarian Cancer Research Center (BZKF), Erlangen, Germany., Schmelmer J; Institute of Pathology, University Hospital Erlangen-Nürnberg, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany.; Comprehensive Cancer Center Erlangen-EMN (CCC ER-EMN), Erlangen, Germany.; Bavarian Cancer Research Center (BZKF), Erlangen, Germany., Foersch S; Institute of Pathology, University Medical Center Mainz, Mainz, Germany., Matek C; Institute of Pathology, University Hospital Erlangen-Nürnberg, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany.; Comprehensive Cancer Center Erlangen-EMN (CCC ER-EMN), Erlangen, Germany.; Bavarian Cancer Research Center (BZKF), Erlangen, Germany., Stoehr R; Institute of Pathology, University Hospital Erlangen-Nürnberg, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany.; Comprehensive Cancer Center Erlangen-EMN (CCC ER-EMN), Erlangen, Germany.; Bavarian Cancer Research Center (BZKF), Erlangen, Germany., Geppert CI; Institute of Pathology, University Hospital Erlangen-Nürnberg, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany.; Comprehensive Cancer Center Erlangen-EMN (CCC ER-EMN), Erlangen, Germany.; Bavarian Cancer Research Center (BZKF), Erlangen, Germany., Heers H; Department of Urology, Philipps-Universität Marburg, Marburg, Germany., Wach S; Comprehensive Cancer Center Erlangen-EMN (CCC ER-EMN), Erlangen, Germany.; Bavarian Cancer Research Center (BZKF), Erlangen, Germany.; Department of Urology and Pediatric Urology, University Hospital Erlangen, Friedrich-Alexander Universität Erlangen-Nürnberg (FAU), Erlangen, Germany., Taubert H; Comprehensive Cancer Center Erlangen-EMN (CCC ER-EMN), Erlangen, Germany.; Bavarian Cancer Research Center (BZKF), Erlangen, Germany.; Department of Urology and Pediatric Urology, University Hospital Erlangen, Friedrich-Alexander Universität Erlangen-Nürnberg (FAU), Erlangen, Germany., Sikic D; Comprehensive Cancer Center Erlangen-EMN (CCC ER-EMN), Erlangen, Germany.; Bavarian Cancer Research Center (BZKF), Erlangen, Germany.; Department of Urology and Pediatric Urology, University Hospital Erlangen, Friedrich-Alexander Universität Erlangen-Nürnberg (FAU), Erlangen, Germany., Wullich B; Comprehensive Cancer Center Erlangen-EMN (CCC ER-EMN), Erlangen, Germany.; Bavarian Cancer Research Center (BZKF), Erlangen, Germany.; Department of Urology and Pediatric Urology, University Hospital Erlangen, Friedrich-Alexander Universität Erlangen-Nürnberg (FAU), Erlangen, Germany., van Leenders GJ; Department of Pathology, Erasmus MC Cancer Institute, University Medical Centre, Rotterdam, the Netherlands., Zaburdaev V; Department of Biology, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany.; Max-Planck-Zentrum für Physik und Medizin, Erlangen, Germany., Eckstein M; Institute of Pathology, University Hospital Erlangen-Nürnberg, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany.; Comprehensive Cancer Center Erlangen-EMN (CCC ER-EMN), Erlangen, Germany.; Bavarian Cancer Research Center (BZKF), Erlangen, Germany., Hartmann A; Institute of Pathology, University Hospital Erlangen-Nürnberg, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany.; Comprehensive Cancer Center Erlangen-EMN (CCC ER-EMN), Erlangen, Germany.; Bavarian Cancer Research Center (BZKF), Erlangen, Germany., Boormans JL; Department of Urology, Erasmus MC Urothelial Cancer Research Group, Rotterdam, The Netherlands., Ferrazzi F; Institute of Pathology, University Hospital Erlangen-Nürnberg, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany.; Comprehensive Cancer Center Erlangen-EMN (CCC ER-EMN), Erlangen, Germany.; Bavarian Cancer Research Center (BZKF), Erlangen, Germany.; Department of Nephropathology, Institute of Pathology, University Hospital Erlangen-Nürnberg, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany., Bahlinger V; Institute of Pathology, University Hospital Erlangen-Nürnberg, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany.; Comprehensive Cancer Center Erlangen-EMN (CCC ER-EMN), Erlangen, Germany.; Bavarian Cancer Research Center (BZKF), Erlangen, Germany.; Department of Pathology and Neuropathology, University Hospital and Comprehensive Cancer Center Tübingen, Tübingen, Germany.
Jazyk: angličtina
Zdroj: The journal of pathology. Clinical research [J Pathol Clin Res] 2024 Mar; Vol. 10 (2), pp. e12369.
DOI: 10.1002/2056-4538.12369
Abstrakt: Upper tract urothelial carcinoma (UTUC) is a rare and aggressive, yet understudied, urothelial carcinoma (UC). The more frequent UC of the bladder comprises several molecular subtypes, associated with different targeted therapies and overlapping with protein-based subtypes. However, if and how these findings extend to UTUC remains unclear. Artificial intelligence-based approaches could help elucidate UTUC's biology and extend access to targeted treatments to a wider patient audience. Here, UTUC protein-based subtypes were identified, and a deep-learning (DL) workflow was developed to predict them directly from routine histopathological H&E slides. Protein-based subtypes in a retrospective cohort of 163 invasive tumors were assigned by hierarchical clustering of the immunohistochemical expression of three luminal (FOXA1, GATA3, and CK20) and three basal (CD44, CK5, and CK14) markers. Cluster analysis identified distinctive luminal (N = 80) and basal (N = 42) subtypes. The luminal subtype mostly included pushing, papillary tumors, whereas the basal subtype diffusely infiltrating, non-papillary tumors. DL model building relied on a transfer-learning approach by fine-tuning a pre-trained ResNet50. Classification performance was measured via three-fold repeated cross-validation. A mean area under the receiver operating characteristic curve of 0.83 (95% CI: 0.67-0.99), 0.8 (95% CI: 0.62-0.99), and 0.81 (95% CI: 0.65-0.96) was reached in the three repetitions. High-confidence DL-based predicted subtypes showed significant associations (p < 0.001) with morphological features, i.e. tumor type, histological subtypes, and infiltration type. Furthermore, a significant association was found with programmed cell death ligand 1 (PD-L1) combined positive score (p < 0.001) and FGFR3 mutational status (p = 0.002), with high-confidence basal predictions containing a higher proportion of PD-L1 positive samples and high-confidence luminal predictions a higher proportion of FGFR3-mutated samples. Testing of the DL model on an independent cohort highlighted the importance to accommodate histological subtypes. Taken together, our DL workflow can predict protein-based UTUC subtypes, associated with the presence of targetable alterations, directly from H&E slides.
(© 2024 The Authors. The Journal of Pathology: Clinical Research published by The Pathological Society of Great Britain and Ireland and John Wiley & Sons Ltd.)
Databáze: MEDLINE
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