Objective With good loco-regional control, disease failure in p16-positive oropharyngeal squamous cell carcinoma (OPSCC) mainly results from distant metastasis (DM). Our objective was to characterize the patterns and clinical outcomes of DM in p16-positive OPSCC and compare these to patients with p16-negative disease. Methods Primary OPSCC patients who developed DM after completing surgical or non-surgical treatment were identified and p16 status was evaluated. Patterns of DM and post-DM progression-free (PFS) and disease-specific survival (DSS) were assessed. Results Forty-one of the 66 (62%) patients with DM were p16-positive. DM patterns were not statistically different by p16 status. However, p16-positive patients developed DM later in their course and had longer survival. All p16-negative patients either had progression or died within 24 months of DM detection whereas the 2-year post-DM PFS in the p16-positive group was 20% (95% CI:8–32.5%,p=0.003). The 3-year post-DM disease-specific survival (DSS) estimate in the p16-positive patients was 16% (95%CI: 7–18%) while all p16-negative patients died within 34 months (p<0.001). p16-negativity, loco-regional disease, and no/palliative versus curative intent treatment were all associated with reduced post-DM DSS in multivariate analysis. Conclusions The DM pattern did not differ remarkably between p16-positive and negative OPSCC patients in our practice. In p16-positive OPSCC with pulmonary oligometastatic disease, curative intent treatment and optimized locoregional control for the index primary prolonged survival.

Background With most centers reporting excellent disease control following transoral surgery for human papillomavirus (HPV)‐related oropharyngeal cancer, data are still lacking regarding management and outcomes in patients who relapse. We describe the treatment outcomes after curative intent therapy for locoregional and distant relapse following transoral surgery for HPV‐related oropharyngeal squamous cell carcinoma (OPSCC). Methods A single institution retrospective study in which patients were identified with curatively treated loco‐regional or single site distant relapse of HPV‐related OPSCC treated initially with transoral surgery (TOS) between 1996 and 2014. Results Among 279 patients, 38 (13.6%) relapsed. 21 (7.5%) had local‐regional relapse and 18 were treated with curative intent. Three‐year overall survival for loco‐regional recurrence with curatively intended treatment was 75.3% (95% confidence intervals 54.3‐96.0) with a median follow‐up 33 (range: 5‐65) months. Among 4 patients with isolated distant metastasis who were treated with curative intent, all subsequently developed second relapse of disease. Conclusion Salvage curative treatment of isolated local‐regional relapse after TOS is associated with favorable prognosis and should be offered in appropriate candidates.

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High-resolution histopathological images have rich characteristics of cancer tissues and cells. Recent studies have shown that digital pathology analysis can aid clinical decision-making by identifying metastases, subtyping and grading tumors, and predicting clinical outcomes. Still, the analysis of digital histologic images remains challenging due to the imbalance of the training data, the intrinsic complexity of histology characteristics of tumor tissue, and the extremely heavy computation burden for processing extremely high-resolution whole slide imaging (WSI) images. In this study, we developed a new deep learning-based classification framework that addresses these unique challenges to support clinical decision-making. The proposed method is motivated by our recently developed adversarial learning strategy with two major innovations. First, an image pre-processing module was designed to process the high-resolution histology images to reduce computational burden and keep informative features, alleviating the risk of overfitting issues when training the network. Second, recently developed StyleGAN2 with powerful generative capability was employed to recognize complex texture patterns and stain information in histology images and learn deep classification-relevant information, further improving the classification and reconstruction performance of our method. The experimental results on three different histology image datasets for different classification tasks demonstrated superior classification performance compared to traditional deep learning-based methods, and the generality of the proposed method to be applied to various applications.

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Purpose: Existing methods to segment the tumor boundary using PET images have focused on intensity thresholding, which suffers from ambiguity with respect to the threshold selection. In this paper, we present a novel, semi‐automatic method for PET head & neck tumor delineation utilizing an energy minimization method—Mumford‐shah active contours. Method and Materials: Mumford‐shah method is a region‐based active contour model which enjoys a number of attractive properties, such as greater robustness to noise than most edge‐based approaches, and flexible initial contour placement. The proposed process begins when an experienced radiation oncologist manually draws a small initial curve (or surface) inside the desired tumor. Then the Mumford‐shah active contour model is utilized to expend this initialization to reach an equilibrium state and form the final tumor boundary. In this model, the region‐based information is for guiding the energy minimization process. The entire process is adaptive to each tumor and independent of the manual drawn initializations. Results: We applied this method to a set of simulated, phantom, and clinical PET images. For simulated and phantom images, the results were quantitatively compared with the known sphere sizes. The results show that our method is reasonably robust with errors less than 2.0 mm in the diameter and volume overlap metric higher than 90.0% between the detected volumes and the actual volumes. The results on clinical PET images were deemed reasonably accurate through visual appraisal by an experienced head‐and‐neck radiation oncologist. Conclusion: An energy minimization method based on Mumford‐shah active contours is applied to adaptive, reproducible, and accurate tumor delineation in PET. Experimental results on simulated, phantom, and clinical PET images demonstrate the robustness, accuracy, reproducibility, and its potential usefulness in clinical radiation therapy planning.