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Background p16 positive oropharyngeal squamous cell carcinoma (OPSCC) patients are potentially cured with definitive treatment. However, there are currently no reliable biomarkers of treatment failure in p16 positive OPSCC. Pathologist-based visual assessment of tumor cell multinucleation has been shown to be independently prognostic of disease-free survival in p16 positive OPSCC. However, its quantification is time-intensive, subjective, and at risk of interobserver variability. Methods We present a deep learning-based metric, the multi-nucleation index (MuNI), for prognostication in p16 positive OPSCC. This approach quantifies tumor multi-nucleation from digitally scanned hematoxylin eosin (H&E)-stained slides. Representative H&E whole slide images from 1,094 previously untreated p16 positive OPSCC patients were acquired from six institutions for optimizing and validating MuNI. Results MuNI was prognostic for disease-free (DFS), overall (OS), or distant metastasis-free (DMFS) survival in p16 positive OPSCC with HRs of 1.78(95%CI:1.37-2.30), 1.94(1.44-2.60), and 1.88(1.43-2.47), respectively, independent of age, smoking status, treatment type, and T/Ncategories in multivariable analyses. It was also prognostic for DFS, OS, and DMFS in OPSCC patients at stages I and III. Conclusion MuNI holds promise as a low-cost, tissue non-destructive, H&E stain based digital biomarker test for counseling, treatment, and surveillance of p16 positive OPSCC patients. These data support further confirmation of MuNI in prospective trials.

Background HPV-associated oropharyngeal squamous cell carcinoma (OPCSCC) has excellent control rates compared to non-virally associated OPSCC. Multiple trials are actively testing whether de-escalation of treatment intensity for these patients can maintain oncologic equipoise while reducing treatment related toxicity. We have developed OP-TIL, a biomarker that characterizes the spatial interplay between tumor-infiltrating lymphocytes (TILs) and surrounding cells in histology images. Herein, we sought to test whether OP-TIL can segregate stage I HPV-associated OPSCC patients into low-risk and high-risk groups and aid in patient selection for de-escalation clinical trials. Methods Association between OP-TIL and patient outcome was explored on whole slide H&E images from 439 stage I HPV-associated OPSCC patients across six institutional cohorts. One institutional cohort (n = 94) was used to identify the most prognostic features and train a Cox regression model to predict risk of recurrence and death. Survival analysis was used to validate the algorithm as a biomarker of recurrence/death in the remaining five cohorts (n = 345). All statistical tests were 2-sided. Results OP-TIL separated stage I HPV-associated OPSCC patients with ≤30 pack-year smoking history into low-risk (2-year disease-free survival [DFS] = 94.2%; 5-year DFS= 88.4%) and high-risk (2-year DFS = 82.5%; 5-year DFS = 74.2%) groups (hazard ratio = 2.56, 95% confidence interval = 1.52–4.32, P < .001), even after adjusting for age, smoking status, T and N-classification, and treatment modality on multivariate analysis for DFS (hazard ratio = 2.27, 95% confidence interval = 1.32–3.94, P = .003). Conclusions OP-TIL can identify stage I HPV-associated OPSCC patients likely to be poor candidates for treatment de-escalation. Following validation on previously completed multi-institutional clinical trials, OP-TIL has the potential to be a biomarker, beyond clinical stage and HPV status, that can be used clinically to optimize patient selection for de-escalation.

Despite known histological, biological, and clinical differences between lung adenocarcinoma (LUAD) and squamous cell carcinoma (LUSC), relatively little is known about the spatial differences in their corresponding immune contextures. Our study of over 1000 LUAD and LUSC tumors revealed that computationally derived patterns of tumor-infiltrating lymphocytes (TILs) on H&E images were different between LUAD (N = 421) and LUSC (N = 438), with TIL density being prognostic of overall survival in LUAD and spatial arrangement being more prognostically relevant in LUSC. In addition, the LUAD-specific TIL signature was associated with OS in an external validation set of 100 NSCLC treated with more than six different neoadjuvant chemotherapy regimens, and predictive of response to therapy in the clinical trial CA209-057 (n = 303). In LUAD, the prognostic TIL signature was primarily comprised of CD4+ T and CD8+ T cells, whereas in LUSC, the immune patterns were comprised of CD4+ T, CD8+ T, and CD20+ B cells. In both subtypes, prognostic TIL features were associated with transcriptomics-derived immune scores and biological pathways implicated in immune recognition, response, and evasion. Our results suggest the need for histologic subtype-specific TIL-based models for stratifying survival risk and predicting response to therapy. Our findings suggest that predictive models for response to therapy will need to account for the unique morphologic and molecular immune patterns as a function of histologic subtype of NSCLC.

Lung diseases carry a significant burden of morbidity and mortality worldwide. The advent of digital pathology (DP) and an increase in computational power have led to the development of artificial intelligence (AI)‐based tools that can assist pathologists and pulmonologists in improving clinical workflow and patient management. While previous works have explored the advances in computational approaches for breast, prostate, and head and neck cancers, there has been a growing interest in applying these technologies to lung diseases as well. The application of AI tools on radiology images for better characterization of indeterminate lung nodules, fibrotic lung disease, and lung cancer risk stratification has been well documented. In this article, we discuss methodologies used to build AI tools in lung DP, describing the various hand‐crafted and deep learning‐based unsupervised feature approaches. Next, we review AI tools across a wide spectrum of lung diseases including cancer, tuberculosis, idiopathic pulmonary fibrosis, and COVID‐19. We discuss the utility of novel imaging biomarkers for different types of clinical problems including quantification of biomarkers like PD‐L1, lung disease diagnosis, risk stratification, and prediction of response to treatments such as immune checkpoint inhibitors. We also look briefly at some emerging applications of AI tools in lung DP such as multimodal data analysis, 3D pathology, and transplant rejection. Lastly, we discuss the future of DP‐based AI tools, describing the challenges with regulatory approval, developing reimbursement models, planning clinical deployment, and addressing AI biases. © 2022 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.