Abdominal radiology (New York) · 2025
CT-based delta-radiomics nomogram to assess tumor regression grade in locally advanced gastric cancer patients following neoadjuvant chemotherapy.
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Chen Shulan, Xiao Zhe, Jin Jiayi, Wang Hanzhe, Miao Shouliang, Tang Kun, Chen Yimin, Shao Linfeng, Zheng Xiangwu
Аннотация
PURPOSE: To develop and validate a delta computed tomography radiomics (delCT-RS) based nomogram for accurate preoperative prediction of tumor regression grade (TRG) in locally advanced gastric cancer (LAGC) patients following neoadjuvant chemotherapy (NAC).
METHODS: This retrospective study enrolled 147 LAGC patients. Two delineation strategies were compared: (1) contouring both the primary tumor and the largest lymph node (P + L) as regions of interest (ROIs), and (2) contouring only the primary tumor (P). Subsequently, radiomic features were extracted to construct corresponding radiomic models. This study compared the predictive accuracy of delCT-RS signatures to conventional single-phase radiomic signatures for TRG assessment. Then, delCT-RS signatures and clinical variables were combined into a nomogram. Finally, the prediction performance of nomogram was comprehensively evaluated.
RESULTS: In assessing tumor response, delCT-RS outperformed single-phase radiomic signatures. Notably, delta computed tomography delCT-RS P + L demonstrated superior accuracy to delCT-RS P (delCT-RS P + L vs. delCT-RS area under the curve (AUC): training cohort: 0.805 vs. 0.727; validation cohort: 0.795 vs. 0.655). The nomogram, combining delCT-RS P + L and clinical factors, achieved optimal performance among all models (training cohort AUC = 0.841; validation cohort AUC = 0.817). (p < 0.05) CONCLUSION: In this study, we innovatively employed a method that simultaneously delineated the primary tumor and the largest lymph node. This model can accurately predict TRG, effectively identify LAGC patients who can benefit from NAC, and provide scientific support for individualized treatment.
METHODS: This retrospective study enrolled 147 LAGC patients. Two delineation strategies were compared: (1) contouring both the primary tumor and the largest lymph node (P + L) as regions of interest (ROIs), and (2) contouring only the primary tumor (P). Subsequently, radiomic features were extracted to construct corresponding radiomic models. This study compared the predictive accuracy of delCT-RS signatures to conventional single-phase radiomic signatures for TRG assessment. Then, delCT-RS signatures and clinical variables were combined into a nomogram. Finally, the prediction performance of nomogram was comprehensively evaluated.
RESULTS: In assessing tumor response, delCT-RS outperformed single-phase radiomic signatures. Notably, delta computed tomography delCT-RS P + L demonstrated superior accuracy to delCT-RS P (delCT-RS P + L vs. delCT-RS area under the curve (AUC): training cohort: 0.805 vs. 0.727; validation cohort: 0.795 vs. 0.655). The nomogram, combining delCT-RS P + L and clinical factors, achieved optimal performance among all models (training cohort AUC = 0.841; validation cohort AUC = 0.817). (p < 0.05) CONCLUSION: In this study, we innovatively employed a method that simultaneously delineated the primary tumor and the largest lymph node. This model can accurately predict TRG, effectively identify LAGC patients who can benefit from NAC, and provide scientific support for individualized treatment.