期刊
FRONTIERS IN ONCOLOGY
卷 12, 期 -, 页码 -出版社
FRONTIERS MEDIA SA
DOI: 10.3389/fonc.2022.824509
关键词
nasopharyngeal carcinoma; magnetic resonance imaging; radiomics; induction chemotherapy; concurrent chemoradiotherapy
类别
资金
- National Natural Science Foundation of China [81871337]
- Natural Science Foundation of Zhejiang Province [Y22H185692, LGF18H160018, LY16H180007]
- Zhejiang Provincial Health Science and Technology Project [2018KY304, 2020RC092, 2021RC108]
- Hangzhou Medical and Health Science and Technology Project [A20200507]
This study established an MRI radiomics model and a Delta radiomics model to predict tumor retraction after induction chemotherapy combined with concurrent chemoradiotherapy for primary nasopharyngeal carcinoma in non-endemic areas and validated their efficacy.
ObjectiveWe aimed to establish an MRI radiomics model and a Delta radiomics model to predict tumor retraction after induction chemotherapy (IC) combined with concurrent chemoradiotherapy (CCRT) for primary nasopharyngeal carcinoma (NPC) in non-endemic areas and to validate its efficacy. MethodsA total of 272 patients (155 in the training set, 66 in the internal validation set, and 51 in the external validation set) with biopsy pathologically confirmed primary NPC who were screened for pretreatment MRI were retrospectively collected. The NPC tumor was delineated as a region of interest in the two sequenced images of MRI before treatment and after IC, followed by radiomics feature extraction. With the use of maximum relevance minimum redundancy (mRMR) and least absolute shrinkage and selection operator (LASSO) algorithms, logistic regression was performed to establish pretreatment MRI radiomics and pre- and post-IC Delta radiomics models. The optimal Youden's index was taken; the receiver operating characteristic (ROC) curve, calibration curve, and decision curve were drawn to evaluate the predictive efficacy of different models. ResultsSeven optimal feature subsets were selected from the pretreatment MRI radiomics model, and twelve optimal subsets were selected from the Delta radiomics model. The area under the ROC curve, accuracy, sensitivity, specificity, negative predictive value (NPV), and positive predictive value (PPV) of the MRI radiomics model were 0.865, 0.827, 0.837, 0.813, 0.776, and 0.865, respectively; the corresponding indicators of the Delta radiomics model were 0.941, 0.883, 0.793, 0.968, 0.833, and 0.958, respectively. ConclusionThe pretreatment MRI radiomics model and pre- and post-IC Delta radiomics models could predict the IC-CCRT response of NPC in non-epidemic areas.
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