# Deep Learning Based HPV Status Prediction for Oropharyngeal Cancer Patients

arXiv: https://arxiv.org/abs/2011.08555 · HTML: https://mlsanity.com/paper/2011.08555/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Nov 2020.

## Built on by (most cited first, 3 of 3)

- [Video Pretraining Advances 3D Deep Learning on Chest CT Tasks](https://mlsanity.com/paper/2304.00546/) (2023)
- [HEad and neCK TumOR (HECKTOR) 2025: Benchmark of Segmentation, Diagnosis, and Prognosis in Multimodal PET/CT](https://mlsanity.com/paper/2606.20143/) (2026)
- [Graph data modelling for outcome prediction in oropharyngeal cancer patients](https://mlsanity.com/paper/2310.02931/) (2023)

## Abstract (arXiv)

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.