Extraction parameter optimized radiomics for neoadjuvant chemotherapy response prognosis in advanced nasopharyngeal carcinoma
•MRI radiomics is promising for NAC early response prediction in NPC patients.•Predictive performance could be improved by the optimized strategy.•The model could help with NPC individualized treatment. Neoadjuvant Chemotherapy (NAC) followed by concurrent chemoradiotherapy (CCRT) is promising in im...
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Veröffentlicht in: | Clinical and translational radiation oncology 2022-03, Vol.33, p.37-44 |
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Zusammenfassung: | •MRI radiomics is promising for NAC early response prediction in NPC patients.•Predictive performance could be improved by the optimized strategy.•The model could help with NPC individualized treatment.
Neoadjuvant Chemotherapy (NAC) followed by concurrent chemoradiotherapy (CCRT) is promising in improving the survival rate for advanced nasopharyngeal carcinoma (NPC) patients relative to CCRT alone. However, not all patients respond well to NAC. Therefore, we aimed to develop and evaluate a modified radiomics model for the NAC response prognosis in NPC patients.
A total of 165 patients with biopsy-proven locally advanced NPC were retrospectively selected from the database of our hospital. 85 out of them were for training and cross-validation, while the other 80 patients were for independent testing. All patients were treated with NAC and underwent MRI inspection, including T1-weighted (T1), T2-weighted (T2), and contrast-enhanced T1-weighted (T1-cs) sequences before and after two cycles of NAC. We classified the patients into the response or non-response groups by the Response Evaluation Criteria in Solid Tumors 1.1 (RECIST 1.1). Radiomics features were extracted from the primary and lymph node gross tumor volume in each sequence. To further improve the predictive performance, the permutation of multiple combinations of extraction parameters has first ever been investigated in the NAC prognosis for NPC patients. The model was constructed by logistic regression and cross-validated by bootstrapping with a resampling number of 1000. Independent testing was also implemented. In addition, we also applied an imbalance-adjusted bootstrap strategy to decrease the bias of small samples.
For the cross-validation cohort, the resultant AUC, sensitivity, and specificity in terms of 95% confidence interval were 0.948 ± 0.004, 0.849 ± 0.005, and 0.840 ± 0.010. For the independent testing cohort, the model reached an AUC of 0.925, a sensitivity of 0.821, and a specificity of 0.792. There was a significant difference in the estimated radiomics score between the response and non-response groups (P |
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ISSN: | 2405-6308 2405-6308 |
DOI: | 10.1016/j.ctro.2021.12.005 |