Objective: Routine haematoxylin and eosin (H&E) photomicrographs from human papillomavirus-associated oropharyngeal squamous cell carcinomas (HPV+OpSCC) contain a wealth of prognostic information. In this study, we set out to develop a high content image analysis workflow to quantify features of H&E images from HPV+OpSCC patients to identify prognostic features which can be used for prediction of patient outcomes. Methods: We have developed a dedicated image analysis workflow using open-source software, for single-cell segmentation and classification. This workflow was applied to a set of 567 images from diagnostic H&E slides in a retrospective cohort of HPV+OpSCC patients with favourable (n = 29) and unfavourable (n = 29) outcomes. Using our method, we have identified 31 quantitative prognostic features which were quantified in each sample and used to train a neural network model to predict patient outcomes. The model was validated by k-fold cross-validation using 10 folds and a test set. Results: Univariate and multivariate statistical analyses revealed significant differences between the two patient outcome groups in 31 and 16 variables respectively (P<0.05). The neural network model had an overall accuracy of 78.8% and 77.7% in recognising favourable and unfavourable prognosis patients when applied to the test set and k-fold cross-validation respectively. Conclusion: Our open-source H&E analysis workflow and model can predict HPV+OpSCC outcomes with promising accuracy. Our work supports the use of machine learning in digital pathology to exploit clinically relevant features in routine diagnostic pathology without additional biomarkers.

The authors have declared no competing interest.

This study did not receive any funding.

I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.

Yes

The details of the IRB/oversight body that provided approval or exemption for the research described are given below:

The Outer North East London Research Ethics Committee of the National Research Ethics Service gave ethical approval for this work. (Reference: 10/H0701/27)

I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals.

Yes

I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).

Yes

I have followed all appropriate research reporting guidelines and uploaded the relevant EQUATOR Network research reporting checklist(s) and other pertinent material as supplementary files, if applicable.

Yes