PuSH - Publikationsserver des Helmholtz Zentrums München

Lombardo, E.* ; Hess-Rieger, J. ; Kurz, C.* ; Riboldi, M.* ; Marschner, S.* ; Baumeister, P. ; Lauber, K. ; Pflugradt, U. ; Walch, A.K. ; Canis, M. ; Klauschen, F.* ; Zitzelsberger, H. ; Belka, C. ; Landry, G.* ; Unger, K.

DeepClassPathway: Molecular pathway aware classification using explainable deep learning.

Eur. J. Cancer 176, 41-49 (2022)
Open Access Green: Postprint online verfügbar 10/2023
OBJECTIVE: HPV-associated head and neck cancer is correlated with favorable prognosis; however, its underlying biology is not fully understood. We propose an explainable convolutional neural network (CNN) classifier, DeepClassPathway, that predicts HPV-status and allows patient-specific identification of molecular pathways driving classifier decisions. METHODS: The CNN was trained to classify HPV-status on transcriptome data from 264 (13% HPV-positive) and tested on 85 (25% HPV-positive) head and neck squamous carcinoma patients after transformation into 2D-treemaps representing molecular pathways. Grad-CAM saliency was used to quantify pathways contribution to individual CNN decisions. Model stability was assessed by shuffling pathways within 2D-images. RESULTS: The classification performance of the CNN-ensembles achieved ROC-AUC/PR-AUC of 0.96/0.90 for all treemap variants. Quantification of the averaged pathway saliency heatmaps consistently identified KRAS, spermatogenesis, bile acid metabolism, and inflammation signaling pathways as the four most informative for classifying HPV-positive patients and MYC targets, epithelial-mesenchymal transition, and protein secretion pathways for HPV-negative patients. CONCLUSION: We have developed and applied an explainable CNN classification approach to transcriptome data from an oncology cohort with typical sample size that allows classification while accounting for the importance of molecular pathways in individual-level decisions.
Weitere Metriken?
Zusatzinfos bearbeiten [➜Einloggen]
Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Schlagwörter Cnn ; Deep Learning ; Grad-cam ; Hnscc ; Hpv ; Transcriptome
ISSN (print) / ISBN 0959-8049
e-ISSN 1879-0852
Quellenangaben Band: 176, Heft: , Seiten: 41-49 Artikelnummer: , Supplement: ,
Verlag Elsevier
Begutachtungsstatus Peer reviewed
Institut(e) Research Unit Radiation Cytogenetics (ZYTO)
CCG Personalized Radiotherapy in Head and Neck Cancer (KKG-KRT)
Research Unit Analytical Pathology (AAP)
Förderungen Bundesministerium für Bildung und Forschung