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Published on: January 22, 2018
Treatment-specific CT Radiomics Models To Predict Response To Neoadjuvant Therapy And Explore Individualized
Jiaxin Liu1, Xujie Gao1, Tingting Ma2
1Department of Radiology, Tianjin Medical University Cancer Institute & Hospital, National Clinical Research Center for Cancer, Tianjin, China (J.L., X.G., X.L., L.Y., L.W., J.X., Z.Y.); Tianjin's Clinical Research Center for Cancer, Tianjin, China (J.L., X.G., T.M., X.L., L.Y., L.W., X.D., J.X., Z.Y.); State Key Laboratory of Druggability Evaluation and Systematic Translational Medicine, Tianjin, China (J.L., X.G., T.M., X.L., L.Y., L.W., X.D., J.X., Z.Y.); Key Laboratory of Cancer Immunology and Biotherapy, Tianjin, China (J.L., X.G., T.M., X.L., L.Y., L.W., X.D., J.X., Z.Y.); Tianjin Key Laboratory of Digestive Cancer, Tianjin, China (J.L., X.G., T.M., X.L., L.Y., L.W., X.D., J.X., Z.Y.).
Rationale And Objectives:
To develop and compare general and treatment-specific radiomics models based on pretreatment computed tomography (CT) for predicting pathological response to neoadjuvant therapy (NAT) in gastric cancer (GC), and to explore a dual-score framework for individualized treatment selection.
Materials And Methods:
This retrospective study included 405 patients with GC who underwent neoadjuvant chemotherapy (NAC) or neoadjuvant immunochemotherapy (NAIC) followed by radical gastrectomy, comprising 337 in the development cohort and 68 in a temporal test cohort. The development cohort was randomly divided into training (n = 235) and validation (n = 102) sets. Radiomics features were extracted from portal venous-phase CT images. Four machine learning classifiers were used to construct general and treatment-specific models. Treatment-specific models were cross-applied to generate paired NAC and NAIC response probabilities.
Results:
In validation, the general model achieved an AUC of 0.679 (NAC, 0.732; NAIC, 0.659), whereas the NAC-specific and NAIC-specific models achieved AUCs of 0.770 and 0.753. Repeated random-split analyses more frequently favored treatment-specific models. In the temporal test cohort, the NAIC-specific model outperformed the general model (AUC, 0.707 vs 0.626), whereas the NAC-specific model showed no advantage (AUC, 0.563 vs 0.625). In the dual-score framework, patients who received model-recommended treatment showed higher pathological response rates (NAC-recommended: 46.4% vs 23.3%, p = 0.043; NAIC-recommended: 40.5% vs 19.4%, p < 0.0001).
Conclusion:
Treatment-specific radiomics models showed better discrimination than the general model for predicting pathological response to NAT in gastric cancer. The dual-score framework may provide an exploratory approach for individualized treatment selection.
