Xiaobin Liu1,2, Chuanqi Sun3, Miaomiao Long2, Yining Yang4, Peng Lin5, Shuang Xia2, Wen Shen6. 1. Department of Radiology, First Central Clinical College, Tianjin Medical University, Qixiangtai Road No. 22, Heping District, Tianjin, 300070, China. 2. Department of Radiology, Tianjin Medical Imaging Institute, Tianjin First Central Hospital, School of Medicine, Nankai University, Fukang Road No. 24, Nankai District, Tianjin, 300192, China. 3. Department of Biomedical Engineering, Guangzhou Medical University, Xinzao Road No. 1, Panyu District, Guangzhou, 511436, China. 4. Department of Radiotherapy, Tianjin First Central Hospital, School of Medicine, Nankai University, Fukang Road No. 24, Nankai District, Tianjin, 300192, China. 5. Department of Otorhinolaryngology Head and Neck Surgery, Tianjin First Central Hospital, School of Medicine, Nankai University, Fukang Road No. 24, Nankai District, Tianjin, 300192, China. 6. Department of Radiology, Tianjin Medical Imaging Institute, Tianjin First Central Hospital, School of Medicine, Nankai University, Fukang Road No. 24, Nankai District, Tianjin, 300192, China. shen_hos@163.com.
Abstract
PURPOSE: To establish and validate a radiomics signature for stratifying the risk of progression-free survival (PFS) in patients with locally advanced hypopharyngeal carcinoma (LAHC) undergoing induction chemotherapy (IC). METHODS: We extracted radiomics features from baseline contrast-enhanced computed tomography (CECT) images. We enrolled 112 LAHC patients (78 in the training cohort and 34 in the validation cohort). We used cox regression model and random survival forests variable hunting (RSFVH) algorithm for feature selection and radiomics signature building. The radiomics signature was established in the training cohort and tested in the validation cohort. We used the Kaplan-Meier analysis and log-rank test to evaluate the ability of radiomics signature in PFS risk stratification among patients with different IC responses and constructed a radiomics nomogram to predict individual PFS risk. RESULTS: The radiomics signature performed well in stratifying patients into highrisk and low-risk groups of progression in both the training and validation cohorts. The radiomics nomogram showed good discriminative ability for predicting PFS. Survival outcome analysis of subsets indicated that the radiomics signature performed well in stratifying the risk of PFS in patients with LAHC with different IC responses. CONCLUSIONS: The radiomics signature was a pretreatment predictor for PFS in patients with LAHC who exhibited different responses to IC.
PURPOSE: To establish and validate a radiomics signature for stratifying the risk of progression-free survival (PFS) in patients with locally advanced hypopharyngeal carcinoma (LAHC) undergoing induction chemotherapy (IC). METHODS: We extracted radiomics features from baseline contrast-enhanced computed tomography (CECT) images. We enrolled 112 LAHC patients (78 in the training cohort and 34 in the validation cohort). We used cox regression model and random survival forests variable hunting (RSFVH) algorithm for feature selection and radiomics signature building. The radiomics signature was established in the training cohort and tested in the validation cohort. We used the Kaplan-Meier analysis and log-rank test to evaluate the ability of radiomics signature in PFS risk stratification among patients with different IC responses and constructed a radiomics nomogram to predict individual PFS risk. RESULTS: The radiomics signature performed well in stratifying patients into highrisk and low-risk groups of progression in both the training and validation cohorts. The radiomics nomogram showed good discriminative ability for predicting PFS. Survival outcome analysis of subsets indicated that the radiomics signature performed well in stratifying the risk of PFS in patients with LAHC with different IC responses. CONCLUSIONS: The radiomics signature was a pretreatment predictor for PFS in patients with LAHC who exhibited different responses to IC.
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