MRI‐Based Back Propagation Neural Network Model as a Powerful Tool for Predicting the Response to Induction Chemotherapy in Locoregionally Advanced Nasopharyngeal Carcinoma. Issue 2 (30th December 2021)

• Record Type: Journal Article
• Authors: Liao, Hai; Chen, Xiaobo; Lu, Shaolu; Jin, Guanqiao; Pei, Wei; Li, Ye; Wei, Yunyun; Huang, Xia; Wang, Chenghuan; Liang, Xueli; Bao, Huayan; Liu, Lidong; Su, Danke
• Is Part Of: Journal of magnetic resonance imaging. Volume 56:Issue 2(2022)
• Page Start: 547
• Page End: 559
• Publication Date: 2021-12-30
• Subjects: nasopharyngeal carcinoma -- magnetic resonance imaging -- radiomics -- induction chemotherapy -- response prediction -- back propagation neural network; Magnetic resonance imaging -- Periodicals; 616
• DOI: 10.1002/jmri.28047
• Languages: English
• ISSNs: 1053-1807

Abstract

Abstract : Background: Pretreatment individualized assessment of tumor response to induction chemotherapy (ICT) is a need in locoregionally advanced nasopharyngeal carcinoma (LANPC). Imaging method plays vital role in tumor response assessment. However, powerful imaging method for ICT response prediction in LANPC is insufficient. Purpose: To establish a robust model for predicting response to ICT in LANPC by comparing the performance of back propagation neural network (BPNN) model with logistic regression model. Study Type: Retrospective. Population: A total of 286 LANPC patients were assigned to training ( N = 200, 43.8 ± 10.9 years, 152 male) and testing ( N = 86, 43.5 ± 11.3 years, 57 male) cohorts. Field Strength/Sequence: T2 ‐weighted imaging, contrast enhanced‐T1 ‐weighted imaging using fast spin echo sequences at 1.5 T scanner. Assessment: Predictive clinical factors were selected by univariate and multivariate logistic models. Radiomic features were screened by interclass correlation coefficient, single‐factor analysis, and the least absolute shrinkage selection operator (LASSO). Four models based on clinical factors (Model clinic ), radiomics features (Model radiomics ), and clinical factors + radiomics signatures using logistic (Model combined ), and BPNN (Model BPNN ) methods were established, and model performances were compared. Statistical Tests: Student's t ‐test, Mann–Whitney U ‐test, and Chi‐square test or Fisher's exact test were used for comparison analysis. The performance of models was assessed by area under the receiver operating characteristic (ROC) curve (AUC) and Delong test. P < 0.05 was considered statistical significance. Results: Three significant clinical factors: Epstein–Barr virus‐DNA (odds ratio [OR] = 1.748; 95% confidence interval [CI], 0.969–3.171), sex (OR = 2.883; 95% CI, 1.364–6.745), and T stage (OR = 1.853; 95% CI, 1.201–3.052) were identified via univariate and multivariate logistic models. Twenty‐four radiomics features were associated with treatment response. Model BPNN demonstrated the highest performance among Model combined, Model radiomics, and Model clinic (AUC of training cohort: 0.917 vs. 0.808 vs. 0.795 vs. 0.707; testing cohort: 0.897 vs. 0.755 vs. 0.698 vs. 0.695). Conclusion: A machine‐learning approach using BPNN showed better ability than logistic regression model to predict tumor response to ICT in LANPC. Evidence Level: 3 Technical Efficacy: Stage 2