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PurposeThe use of postoperative radiotherapy (PORT) in patients with oral squamous cell carcinoma (OCSCC) lacks clear boundaries due to the non‐negligible toxicity accompanying its remarkable cancer‐killing effect. This study aims at validating the ability of deep learning models to develop individualized PORT recommendations for patients with OCSCC and quantifying the impact of patient characteristics on treatment selection.MethodsParticipants were categorized into two groups based on alignment between model‐recommended and actual treatment regimens, with their overall survival compared. Inverse probability treatment weighting was used to reduce bias, and a mixed‐effects multivariate linear regression illustrated how baseline characteristics influenced PORT selection.Results4990 patients with OCSCC met the inclusion criteria. Deep Survival regression with Mixture Effects (DSME) demonstrated the best performance among all the models and National Comprehensive Cancer Network guidelines. The efficacy of PORT is enhanced as the lymph node ratio (LNR) increases. Similar enhancements in efficacy are observed in patients with advanced age, large tumors, multiple positive lymph nodes, tongue involvement, and stage IVA. Early‐stage (stage 0–II) OCSCC may safely omit PORT.ConclusionsThis is the first study to incorporate LNR as a tumor character to make personalized recommendations for patients. DSME can effectively identify potential beneficiaries of PORT and provide quantifiable survival benefits.
PurposeThe use of postoperative radiotherapy (PORT) in patients with oral squamous cell carcinoma (OCSCC) lacks clear boundaries due to the non‐negligible toxicity accompanying its remarkable cancer‐killing effect. This study aims at validating the ability of deep learning models to develop individualized PORT recommendations for patients with OCSCC and quantifying the impact of patient characteristics on treatment selection.MethodsParticipants were categorized into two groups based on alignment between model‐recommended and actual treatment regimens, with their overall survival compared. Inverse probability treatment weighting was used to reduce bias, and a mixed‐effects multivariate linear regression illustrated how baseline characteristics influenced PORT selection.Results4990 patients with OCSCC met the inclusion criteria. Deep Survival regression with Mixture Effects (DSME) demonstrated the best performance among all the models and National Comprehensive Cancer Network guidelines. The efficacy of PORT is enhanced as the lymph node ratio (LNR) increases. Similar enhancements in efficacy are observed in patients with advanced age, large tumors, multiple positive lymph nodes, tongue involvement, and stage IVA. Early‐stage (stage 0–II) OCSCC may safely omit PORT.ConclusionsThis is the first study to incorporate LNR as a tumor character to make personalized recommendations for patients. DSME can effectively identify potential beneficiaries of PORT and provide quantifiable survival benefits.
The approval and effectiveness of immune checkpoint inhibitors in head-and-neck squamous cell carcinoma (HNSCC) highlights the role of the immune system in this tumor entity. HNSCCs not only interacts with the immune system in the tumor tissue, but also induce systemic effects that may be additionally influenced by further factors such as the microbiome. Nonetheless, reliable immunological biomarkers that predict treatment response and outcome in HNSCC patients are lacking. The currently available biomarkers are mainly limited to analyses from tumor biopsies, while biomarkers from liquid biopsies, such as peripheral blood are not well-established. Thus, the here presented trial aims to identify interactions of intra-tumoral and systemic immune responses and to define prognostic immune signatures. Consequently, not only samples from the tumor tissue, but also from peripheral blood and the microbiome will be studied/are being evaluated and correlated with the clinical outcome. In this prospective, multi-center trial, 1000 HNSCC patients and 100 patients in the control cohort with non-tumor head-and-neck surgery will be enrolled. The local immune status from of the tumor and the microbiome will be sampled before treatment. In addition, the systemic immune status from peripheral blood will be analyzed before and after surgery and after the adjuvant and definitive radio-chemotherapy (RCT). Clinical baseline characteristics and outcome will additionally be collected. Data mining and modelling approaches will finally be applied to identify interactions of local and systemic immune parameters and to define prognostic immune signatures based on the evaluated immune markers. Approval from the institutional review board of the Friedrich-Alexander-Universität Erlangen-Nürnberg was granted in December 2021 (application number 21-440-B). By now, 150 patients have been enrolled in the intervention cohort. The results will be disseminated to the scientific audience and the general public via presentations at conferences and publication in peer-reviewed journals.
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