Unknown

Dataset Information

0

Predicting Progression-Free Survival Using MRI-Based Radiomics for Patients With Nonmetastatic Nasopharyngeal Carcinoma.


ABSTRACT: Objectives: This study aimed to explore the predictive value of MRI-based radiomic model for progression-free survival (PFS) in nonmetastatic nasopharyngeal carcinoma (NPC). Methods: A total of 327 nonmetastatic NPC patients [training cohort (n = 230) and validation cohort (n = 97)] were enrolled. The clinical and MRI data were collected. The least absolute shrinkage selection operator (LASSO) and recursive feature elimination (RFE) were used to select radiomic features. Five models [Model 1: clinical data, Model 2: overall stage, Model 3: radiomics, Model 4: radiomics + overall stage, Model 5: radiomics + overall stage + Epstein-Barr virus (EBV) DNA] were constructed. The prognostic performances of these models were evaluated by Harrell's concordance index (C-index). The Kaplan-Meier method was applied for the survival analysis. Results: Model 5 incorporating radiomics, overall stage, and EBV DNA yielded the highest C-indices for predicting PFS in comparison with Model 1, Model 2, Model 3, and Model 4 (training cohorts: 0.805 vs. 0.766 vs. 0.749 vs. 0.641 vs. 0.563, validation cohorts: 0.874 vs. 0.839 vs. 836 vs. 0.689 vs. 0.456). The survival curve showed that the high-risk group yielded a lower PFS than the low-risk group. Conclusions: The model incorporating radiomics, overall stage, and EBV DNA showed better performance for predicting PFS in nonmetastatic NPC patients.

SUBMITTER: Shen H 

PROVIDER: S-EPMC7235342 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

altmetric image

Publications

Predicting Progression-Free Survival Using MRI-Based Radiomics for Patients With Nonmetastatic Nasopharyngeal Carcinoma.

Shen Hesong H   Wang Yu Y   Liu Daihong D   Lv Rongfei R   Huang Yuanying Y   Peng Chao C   Jiang Shixi S   Wang Ying Y   He Yongpeng Y   Lan Xiaosong X   Huang Hong H   Sun Jianqing J   Zhang Jiuquan J  

Frontiers in oncology 20200512


<b>Objectives:</b> This study aimed to explore the predictive value of MRI-based radiomic model for progression-free survival (PFS) in nonmetastatic nasopharyngeal carcinoma (NPC). <b>Methods:</b> A total of 327 nonmetastatic NPC patients [training cohort (<i>n</i> = 230) and validation cohort (<i>n</i> = 97)] were enrolled. The clinical and MRI data were collected. The least absolute shrinkage selection operator (LASSO) and recursive feature elimination (RFE) were used to select radiomic featur  ...[more]

Similar Datasets

| S-EPMC9256909 | biostudies-literature
| S-EPMC8800208 | biostudies-literature
| S-EPMC9372795 | biostudies-literature
| S-EPMC8017427 | biostudies-literature
| S-EPMC5641145 | biostudies-literature
| S-EPMC7601980 | biostudies-literature
| S-EPMC9261049 | biostudies-literature
| S-EPMC8833585 | biostudies-literature
| S-EPMC10088158 | biostudies-literature
| S-EPMC6805774 | biostudies-literature