CT‐based deep multi‐label learning prediction model for outcome in patients with oropharyngeal squamous cell carcinoma

作者：Baoqiang Ma, Jiapan Guo, Tian‐Tian Zhai, A. van der Schaaf, R.J.H.M. Steenbakkers, Lisanne V. van Dijk, Stefan Both, Johannes A. Langendijk, Weichuan Zhang, Bingjiang Qiu, Peter M. A. van Ooijen, Nanna M. Sijtsema · 发表于：Medical Physics · 年份：2023 · DOI：10.1002/mp.16465 · 被引用次数：13 · 研究领域：Head and Neck Cancer Studies、Radiomics and Machine Learning in Medical Imaging、Esophageal Cancer Research and Treatment

BACKGROUND: Personalized treatment is increasingly required for oropharyngeal squamous cell carcinoma (OPSCC) patients due to emerging new cancer subtypes and treatment options. Outcome prediction model can help identify low or high-risk patients who may be suitable to receive de-escalation or intensified treatment approaches. PURPOSE: To develop a deep learning (DL)-based model for predicting multiple and associated efficacy endpoints in OPSCC patients based on computed tomography (CT). METHODS: Two patient cohorts were used in this study: a development cohort consisting of 524 OPSCC patients (70% for training and 30% for independent testing) and an external test cohort of 396 patients. Pre-treatment CT-scans with the gross primary tumor volume contours (GTVt) and clinical parameters were available to predict endpoints, including 2-year local control (LC), regional control (RC), locoregional control (LRC), distant metastasis-free survival (DMFS), disease-specific survival (DSS), overall survival (OS), and disease-free survival (DFS). We proposed DL outcome prediction models with the multi-label learning (MLL) strategy that integrates the associations of different endpoints based on clinical factors and CT-scans. RESULTS: The multi-label learning models outperformed the models that were developed based on a single endpoint for all endpoints especially with high AUCs ≥ 0.80 for 2-year RC, DMFS, DSS, OS, and DFS in the internal independent test set and for all endpoints except ...