A randomized phase 2 network trial of tivantinib plus cetuximab versus cetuximab in patients with recurrent/metastatic head and neck squamous cell carcinoma

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/154935/1/cncr32762.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/154935/2/cncr32762_am.pd

A randomized phase 2 study of temsirolimus and cetuximab versus temsirolimus alone in recurrent/metastatic, cetuximab‐resistant head and neck cancer: The MAESTRO study

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/155947/1/cncr32929_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/155947/2/cncr32929.pd

Uncertainty-Informed Deep Learning Models Enable High-Confidence Predictions for Digital Histopathology

A model's ability to express its own predictive uncertainty is an essential attribute for maintaining clinical user confidence as computational biomarkers are deployed into real-world medical settings. In the domain of cancer digital histopathology, we describe a novel, clinically-oriented approach to uncertainty quantification (UQ) for whole-slide images, estimating uncertainty using dropout and calculating thresholds on training data to establish cutoffs for low- and high-confidence predictions. We train models to identify lung adenocarcinoma vs. squamous cell carcinoma and show that high-confidence predictions outperform predictions without UQ, in both cross-validation and testing on two large external datasets spanning multiple institutions. Our testing strategy closely approximates real-world application, with predictions generated on unsupervised, unannotated slides using predetermined thresholds. Furthermore, we show that UQ thresholding remains reliable in the setting of domain shift, with accurate high-confidence predictions of adenocarcinoma vs. squamous cell carcinoma for out-of-distribution, non-lung cancer cohorts

Immune-related adverse events are associated with improved response, progression-free survival, and overall survival for patients with head and neck cancer receiving immune checkpoint inhibitors.

BACKGROUND: The authors hypothesized that patients developing immune-related adverse events (irAEs) while receiving immune checkpoint inhibition (ICI) for recurrent/metastatic head and neck cancer (HNC) would have improved oncologic outcomes. METHODS: Patients with recurrent/metastatic HNC received ICI at 2 centers. Univariate and multivariate logistic regression, Kaplan-Meier methods, and Cox proportional hazards regression were used to associate the irAE status with the overall response rate (ORR), progression-free survival (PFS), and overall survival (OS) in cohort 1 (n = 108). These outcomes were also analyzed in an independent cohort of patients receiving ICI (cohort 2; 47 evaluable for irAEs). RESULTS: The median follow-up was 8.4 months for patients treated in cohort 1. Sixty irAEs occurred in 49 of 108 patients with 5 grade 3 or higher irAEs (10.2%). ORR was higher for irAE+ patients (30.6%) in comparison with irAE- patients (12.3%; P = .02). The median PFS was 6.9 months for irAE+ patients and 2.1 months for irAE- patients (P = .0004), and the median OS was 12.5 and 6.8 months, respectively (P = .007). Experiencing 1 or more irAEs remained associated with ORR (P = .03), PFS (P = .003), and OS (P = .004) in multivariate analyses. The association between development of irAEs and prolonged OS persisted in a 22-week landmark analysis (P = .049). The association between development of irAEs and favorable outcomes was verified in cohort 2. CONCLUSIONS: The development of irAEs was strongly associated with an ICI benefit, including overall response, PFS, and OS, in 2 separate cohorts of patients with recurrent/metastatic HNC

Development and Validation of a Decision Analytical Model for Posttreatment Surveillance for Patients with Oropharyngeal Carcinoma

Importance: Clinical practice regarding posttreatment radiologic surveillance for patients with oropharyngeal carcinoma (OPC) is neither adapted to individual patient risk nor fully evidence based.Objectives: To construct a microsimulation model for posttreatment OPC progression and use it to optimize surveillance strategies while accounting for both tumor stage and human papillomavirus (HPV) status.Design, Setting, and Participants: In this decision analytical modeling study, a Markov model of 3-year posttreatment patient trajectories was created. The training data source was the American College of Surgeon's National Cancer Database from 2010 to 2015. The external validation data set was the 2016 International Collaboration on Oropharyngeal Cancer Network for Staging (ICON-S) study. Training data comprised 2159 patients with OPC treated with primary radiotherapy who had known HPV status and disease staging information. Patients with American Joint Committee on Cancer, 7th edition stage III to IVB disease and those with clinical metastases during the time of primary treatment were included. Data were analyzed from August 1 to October 31, 2020.Main Outcomes and Measures: Main outcomes included disease stage and HPV status, specific disease transition probabilities, and latency of surveillance regimens, defined as time between recurrence incidence and disease discovery.Results: Training data consisted of 2159 total patients (1708 men [79.1%]; median age, 59.6 years [range, 40-90 years]; 401 with stage III disease, 1415 with stage IVA disease, and 343 with stage IVB disease). Cohorts predominantly had HPV-negative disease (1606 [74.4%]). With model-optimized regimens, recurrent disease was discovered a mean of 0.6 months (95% CI, 0.5-0.8 months) earlier than with a standard surveillance regimen based on current clinical guidelines. Recurrent disease was discovered using the optimized regimens without significant reduction in sensitivity. Compared with strategies based on reimbursement guidelines, the model-optimized regimens found disease a mean of 1.8 months (95% CI, 1.3-2.3 months) earlier.Conclusions and Relevance: Optimized, risk-stratified surveillance regimens consistently outperformed nonoptimized strategies. These gains were obtained without requiring any additional imaging studies. This approach to risk-stratified surveillance optimization is generalizable to a broad range of tumor types and risk factors.</p