Evidence map›Paper›PMID 40960514›Full record

ArticleAbdominal radiology (New York)2026

CT-based delta-radiomics nomogram to assess tumor regression grade in locally advanced gastric cancer patients following neoadjuvant chemotherapy.

Shulan Chen, Zhe Xiao, Jiayi Jin, Hanzhe Wang, Shouliang Miao, Kun Tang, Yimin Chen, Linfeng Shao, Xiangwu Zheng

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Article in Abdominal radiology (New York), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 · Who and what money

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9 authors.

Shulan ChenDepartment of Radiology, First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China. 15967564876@163.com.
Zhe XiaoDepartment of Radiology, First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China.
Jiayi JinWenzhou Medical University, Wenzhou, China.
Hanzhe WangDepartment of Radiology, First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China.
Shouliang MiaoDepartment of Radiology, First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China.
Kun TangDepartment of Nuclear Medicine, First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China.
Yimin ChenDepartment of Radiology, First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China.
Linfeng ShaoDepartment of Radiology, First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China.
Xiangwu ZhengDepartment of Radiology, First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China. zxwu111@sina.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeTo 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).

methodsThis 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.

resultsIn 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)

conclusionIn 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.

Indexed as

Neoadjuvant TherapyNomogramsStomach NeoplasmsTomography, X-Ray ComputedAdultAgedChemotherapy, AdjuvantFemaleHumansMaleMiddle AgedNeoplasm GradingRadiomicsRetrospective StudiesDelta computed tomography radiomicsLocally advanced gastric cancerNeoadjuvant chemotherapyTumor regression grade

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.