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Pretreatment MR imaging radiomics signatures for response prediction to induction chemotherapy in patients with nasopharyngeal carcinoma.
European Journal of Radiology 2018 January
PURPOSE: This study aimed to investigate the capability of magnetic resonance (MR) imaging radiomics signatures for pretreatment prediction of early response to induction chemotherapy in patients with nasopharyngeal carcinoma (NPC).
MATERIALS AND METHODS: This was a retrospective study consisting of 120 patients with biopsy-proven NPC (stage II-IV). Texture features were extracted from the pretreatment morphological MR images for each case. Radiomics signatures were obtained with the least absolute shrinkage and selection operator method (LASSO) logistic regression model. The association between the radiomics signatures and the early response to induction chemotherapy was explored.
RESULTS: From the contrast-enhanced T1-weighted MR imaging (CE T1WI), 5 features were selected by the LASSO model. The radiomics signature categorised patients with NPC into response and nonresponse groups (P<0.001). The area under the receiver operating characteristic curve values (AUC), sensitivity, specificity, positive predictive value (PPV) and negative predictive value(NPV) were 0.715(95% CI 0.699-0.731), 0.940, 0.500, 0.568 and 0.897 respectively, where non-responders are true-positives. The AUC of 1000 bootstrap internal validation was 0.715. Furthermore, when the features of T1-weighted MR imaging (T1WI), T2-weighted MR imaging (T2WI), T2-weighted fat-suppressed MR imaging (T2WI FS) and CE T1WI were analysed together, 15 features were selected to develop the radiomics signature. The performance of this radiomics signature was better than that developed only from CE T1WI (P<0.05). The AUC value was 0.822(95% CI 0.809-0.835) with sensitivity of 0.980, specificity of 0.529, PPV of 0.593 and NPV of 0.949. The AUC of 1000 bootstrap analysis was 0.821. From T1WI, T2WI, and T2WI FS images separately, no valuable features were selected.
CONCLUSIONS: Pretreatment morphological MR imaging radiomics signatures can predict early response to induction chemotherapy in patients with NPC.
MATERIALS AND METHODS: This was a retrospective study consisting of 120 patients with biopsy-proven NPC (stage II-IV). Texture features were extracted from the pretreatment morphological MR images for each case. Radiomics signatures were obtained with the least absolute shrinkage and selection operator method (LASSO) logistic regression model. The association between the radiomics signatures and the early response to induction chemotherapy was explored.
RESULTS: From the contrast-enhanced T1-weighted MR imaging (CE T1WI), 5 features were selected by the LASSO model. The radiomics signature categorised patients with NPC into response and nonresponse groups (P<0.001). The area under the receiver operating characteristic curve values (AUC), sensitivity, specificity, positive predictive value (PPV) and negative predictive value(NPV) were 0.715(95% CI 0.699-0.731), 0.940, 0.500, 0.568 and 0.897 respectively, where non-responders are true-positives. The AUC of 1000 bootstrap internal validation was 0.715. Furthermore, when the features of T1-weighted MR imaging (T1WI), T2-weighted MR imaging (T2WI), T2-weighted fat-suppressed MR imaging (T2WI FS) and CE T1WI were analysed together, 15 features were selected to develop the radiomics signature. The performance of this radiomics signature was better than that developed only from CE T1WI (P<0.05). The AUC value was 0.822(95% CI 0.809-0.835) with sensitivity of 0.980, specificity of 0.529, PPV of 0.593 and NPV of 0.949. The AUC of 1000 bootstrap analysis was 0.821. From T1WI, T2WI, and T2WI FS images separately, no valuable features were selected.
CONCLUSIONS: Pretreatment morphological MR imaging radiomics signatures can predict early response to induction chemotherapy in patients with NPC.
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