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Copenhagen, Denmark
Onsite/Online

ESTRO 2022

Session Item

Tumour radiobiology
Poster (digital)
Radiobiology
Regression and deep learning for transcriptome-based HPV-status prediction in head and neck cancer
Kristian Unger, Germany
PO-1824

Abstract

Regression and deep learning for transcriptome-based HPV-status prediction in head and neck cancer
Authors:

Kristian Unger1,2,3, Elia Lombardo3,6, Julia Hess4,5,3, Christopher Kurz3,6, Marco Riboldi6, Sebastian Marschner3,2, Philipp Baumeister7,2, Kirsten Lauber3,2, Ulrike Pflugradt3,2, Axel Walch8,2, Martin Canis7,2, Frederick Klauschen9,10, Horst Zitzelsberger11,2,3, Claus Belka3,2,10, Guillaume Landry3,12

1Helmholtz Zentrum München, German Research Center for Environmental Health GmbH, Radiation Cytogenetics, Neuherberg, Germany; 2Helmholtz Zentrum München, German Research Center for Environmental Health GmbH, Clinical Cooperation Group "Personalized Radiotherapy in Head and Neck Cancer", Neuherberg, Germany; 3University Hospital, LMU Munich, Department of Radiation Oncology, München, Germany; 4Helmholtz Zentrum München, German Research Center for Environmental Health GmbH, German Research Center for Environmental Health GmbH, Neuherberg, Germany; 5 Helmholtz Zentrum München, German Research Center for Environmental Health GmbH, Clinical Cooperation Group "Personalized Radiotherapy in Head and Neck Cancer", Neuherberg, Germany; 6Faculty of Physics, Ludwig-Maximilians-Universität München, Department of Medical Physics, Garching, Germany; 7University Hospital, LMU Munich, Department of Otorhinolaryngology, München, Germany; 8Helmholtz Zentrum München, German Research Center for Environmental Health GmbH, Research Unit Analytical Pathology, Neuherberg, Germany; 9Faculty of Medicine, Ludwig-Maximilians-University of Munich, Institute of Pathology, München, Germany; 10German Cancer Consortium (DKTK), Partner Site Munich, München, Germany; 11Helmholtz Zentrum München, German Research Center for Environmental Health GmbH, Research Unit Radiation Cytogenetics, Neuherberg, Germany; 12Faculty of Physics, Ludwig-Maximilians-Universität München, Department of Medical Physics, München, Germany

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Purpose or Objective

HPV-status is a known prognostic factor for therapy outcome of head and neck squamous cell carcinomas.

Material and Methods

In this study we used transcriptome data of 348 patients with known HPV-status to build and compare the performance of regression-based and 2D Convolutional Neural Network (CNN) models for HPV prediction. For the CNN, transcriptome data was reorganized as 2D treemap images representing MSigDB Hallmark pathways. The treemaps were built three times in three different ways to assess the stability of the 2D-CNN. The features for the linear regression model were selected using Lasso on the training set.

Results

Applied on an unseen testing set comprising 25 % of the full patient dataset, the CNN achieved test ROC-AUCs/PR-AUCs of 0.95/0.87, 0.93/0.82 and 0.93/0.81 for the three variants of input treemaps respectively, while the regression model achieved a ROC-AUC/PR-AUC of 0.92/0.81.

Conclusion

Therefore, we conclude that accurate predictions of HPV-status can be made with both models. However, the advantage over linear regression is that the deep learning model allows for functional interpretation through visualization of saliency maps computed using the Grad-CAM method.