Based on CT radiomics model for predicting the response to first-line chemotherapy of diffuse large B-cell lymphoma.
10.3760/cma.j.cn112152-20220628-00459
- Author:
Man Xin YIN
1
;
Qiao Na SU
1
;
Xin SONG
2
;
Jian Xin ZHANG
3
Author Information
1. Department of Medical imaging, Shanxi Medical University, Taiyuan 030013, China.
2. Department of Public Health, Shanxi Medical University, Taiyuan 030013, China.
3. Shanxi Province Cancer Hospital/Shanxi Hospital Affiliated to Cancer Hospital, Chinese Academy of Medical Sciences/Cancer Hospital Affiliated to Shanxi Medical University, Taiyuan 030013, China.
- Publication Type:Journal Article
- Keywords:
Chemotherapy efficacy;
Diffuse large B-cell lymphoma;
Nomogram model;
Radiomics
- MeSH:
Humans;
Retrospective Studies;
Lymphoma, Large B-Cell, Diffuse/drug therapy*;
Algorithms;
Niacinamide;
Tomography, X-Ray Computed
- From:
Chinese Journal of Oncology
2023;45(5):438-444
- CountryChina
- Language:Chinese
-
Abstract:
Objective: To investigate the potential value of CT Radiomics model in predicting the response to first-line chemotherapy in diffuse large B-cell lymphoma (DLBCL). Methods: Pre-treatment CT images and clinical data of DLBCL patients treated at Shanxi Cancer Hospital from January 2013 to May 2018 were retrospectively analyzed and divided into refractory patients (73 cases) and non-refractory patients (57 cases) according to the Lugano 2014 efficacy evaluation criteria. The least absolute shrinkage and selection operator (LASSO) regression algorithm, univariate and multivariate logistic regression analyses were used to screen out clinical factors and CT radiomics features associated with efficacy response, followed by radiomics model and nomogram model. Receiver operating characteristic (ROC) curve, calibration curve and clinical decision curve were used to evaluate the models in terms of the diagnostic efficacy, calibration and clinical value in predicting chemotherapy response. Results: Based on pre-chemotherapy CT images, 850 CT texture features were extracted from each patient, and 6 features highly correlated with the first-line chemotherapy effect of DLBCL were selected, including 1 first order feature, 1 gray level co-occurence matrix, 3 grey level dependence matrix, 1 neighboring grey tone difference matrix. Then, the corresponding radiomics model was established, whose ROC curves showed AUC values of 0.82 (95% CI: 0.76-0.89) and 0.73 (95% CI: 0.60-0.86) in the training and validation groups, respectively. The nomogram model, built by combining validated clinical factors (Ann Arbor stage, serum LDH level) and CT radiomics features, showed an AUC of 0.95 (95% CI: 0.90-0.99) and 0.91 (95% CI: 0.82-1.00) in the training group and the validation group, respectively, with significantly better diagnostic efficacy than that of the radiomics model. In addition, the calibration curve and clinical decision curve showed that the nomogram model had good consistency and high clinical value in the assessment of DLBCL efficacy. Conclusion: The nomogram model based on clinical factors and radiomics features shows potential clinical value in predicting the response to first-line chemotherapy of DLBCL patients.
