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Open AccessArticle

Deep Learning Based HPV Status Prediction for Oropharyngeal Cancer Patients

1
Institute of Radiation Medicine, Helmholtz Zentrum München, 85764 Munich, Germany
2
Department of Radiation Oncology, School of Medicine and Klinikum Rechts der Isar, Technical University of Munich (TUM), 81675 Munich, Germany
3
Physics Department, Technical University of Munich, 85748 Garching, Germany
4
Deutsches Konsortium für Translationale Krebsforschung (DKTK), Partner Site, 81377 Munich, Germany
*
Author to whom correspondence should be addressed.
Academic Editor: Ognjen Arandjelovic
Cancers 2021, 13(4), 786; https://doi.org/10.3390/cancers13040786
Received: 28 January 2021 / Revised: 7 February 2021 / Accepted: 9 February 2021 / Published: 13 February 2021
(This article belongs to the Special Issue Machine Learning Techniques in Cancer)
Determination of human papillomavirus (HPV) status for oropharyngeal cancer patients depicts a essential diagnostic factor and is important for treatment decisions. Current histological methods are invasive, time consuming and costly. We tested the ability of deep learning models for HPV status testing based on routinely acquired diagnostic CT images. A network trained for sports video clip classification was modified and then fine tuned for HPV status prediction. In this way, very basic information about image structures is induced into the model before training is started, while still allowing for exploitation of full 3D information in the CT images. Usage of this approach helps the network to cope with a small number of training examples and makes it more robust. For comparison, two other models were trained, one not relying on a pre-training task and another one pre-trained on 2D Data. The pre-trained video model preformed best.
Infection with the human papillomavirus (HPV) has been identified as a major risk factor for oropharyngeal cancer (OPC). HPV-related OPCs have been shown to be more radiosensitive and to have a reduced risk for cancer related death. Hence, the histological determination of HPV status of cancer patients depicts an essential diagnostic factor. We investigated the ability of deep learning models for imaging based HPV status detection. To overcome the problem of small medical datasets, we used a transfer learning approach. A 3D convolutional network pre-trained on sports video clips was fine-tuned, such that full 3D information in the CT images could be exploited. The video pre-trained model was able to differentiate HPV-positive from HPV-negative cases, with an area under the receiver operating characteristic curve (AUC) of 0.81 for an external test set. In comparison to a 3D convolutional neural network (CNN) trained from scratch and a 2D architecture pre-trained on ImageNet, the video pre-trained model performed best. Deep learning models are capable of CT image-based HPV status determination. Video based pre-training has the ability to improve training for 3D medical data, but further studies are needed for verification. View Full-Text
Keywords: HPV status; oropharyngeal cancer; deep learning; transfer learning; machine learning HPV status; oropharyngeal cancer; deep learning; transfer learning; machine learning
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MDPI and ACS Style

Lang, D.M.; Peeken, J.C.; Combs, S.E.; Wilkens, J.J.; Bartzsch, S. Deep Learning Based HPV Status Prediction for Oropharyngeal Cancer Patients. Cancers 2021, 13, 786. https://doi.org/10.3390/cancers13040786

AMA Style

Lang DM, Peeken JC, Combs SE, Wilkens JJ, Bartzsch S. Deep Learning Based HPV Status Prediction for Oropharyngeal Cancer Patients. Cancers. 2021; 13(4):786. https://doi.org/10.3390/cancers13040786

Chicago/Turabian Style

Lang, Daniel M.; Peeken, Jan C.; Combs, Stephanie E.; Wilkens, Jan J.; Bartzsch, Stefan. 2021. "Deep Learning Based HPV Status Prediction for Oropharyngeal Cancer Patients" Cancers 13, no. 4: 786. https://doi.org/10.3390/cancers13040786

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Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

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