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Background <p>This study tested the diagnostic accuracy of multiparametric magnetic resonance imaging (mpMRI) in restaging locally advanced gastric cancer after neoadjuvant therapy (NAT) using pathologic T stage (ypT) and pathologic N stage (ypN) as the reference standard.</p> Methods <p>Between August 2022 and September 2023, the study enrolled a prospective cohort of 70 gastric cancer patients who underwent NAT and subsequent surgical resection. MRI procedures, including DLSB T2-weighted imaging (T2WI), ZOOMit diffusion-weighted imaging (DWI), and XD-VIBE dynamic contrast-enhanced imaging (DCE), were performed after NAT and before surgery. Four abdominal radiologists independently assigned radiologic T stage (yrT) and radiologic N stage (yrN) based on individual and combined sequences. Inter-reader agreement was quantified using Kendall's coefficient. Diagnostic accuracy was determined by comparing MRI assessments and pathologic outcomes, with pairwise comparisons analyzed via the McNemar test. Subgroup analysis evaluated the performance in identifying good responders to NAT.</p> Results <p>Inter-reader agreement was almost perfect for T restaging and substantial for N restaging. Diagnostic accuracy for T restaging was 0.432 using DLSB-T2WI, 0.586 using ZOOMit DWI, 0.557 using XD-VIBE DCE, and 0.586 using mpMRI. The accuracy demonstrated by DWI, DCE and mpMRI was superior to that of T2WI (all <i>P</i> &lt; 0.05). For N restaging, the accuracy of the mpMRI protocol was 0.443. Notably, mpMRI achieved an AUC of 0.879 (95% confidence interval 0.835–0.915) for differentiating ypT0-1 tumors.</p> Conclusions <p>Advanced mpMRI strategies can serve as a valuable tool for restaging gastric cancer after NAT. Accurately differentiating good responders to neoadjuvant therapy through mpMRI holds significant clinical implications for personalized treatment strategies and improved patient outcomes.</p>

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Advanced Multiparametric MRI Strategies for Tumor Restaging After Neoadjuvant Therapy in Locally Advanced Gastric Cancer

  • 期刊论文

  • 发表日期：

    2025年2月3日

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  • Ya-Jun Hou,
  • Zi-Tong Sang,
  • Qiong Li,
  • Qiu-Xia Feng,
  • Jing Wu,
  • Marcel Dominik Nickel,
  • Yi-Cheng Hsu,
  • Wei-Zhi Wang,
  • Chen-Jiang Wu,
  • Hao Xu,
  • Xi-Sheng Liu

摘要

Background

This study tested the diagnostic accuracy of multiparametric magnetic resonance imaging (mpMRI) in restaging locally advanced gastric cancer after neoadjuvant therapy (NAT) using pathologic T stage (ypT) and pathologic N stage (ypN) as the reference standard.

Methods

Between August 2022 and September 2023, the study enrolled a prospective cohort of 70 gastric cancer patients who underwent NAT and subsequent surgical resection. MRI procedures, including DLSB T2-weighted imaging (T2WI), ZOOMit diffusion-weighted imaging (DWI), and XD-VIBE dynamic contrast-enhanced imaging (DCE), were performed after NAT and before surgery. Four abdominal radiologists independently assigned radiologic T stage (yrT) and radiologic N stage (yrN) based on individual and combined sequences. Inter-reader agreement was quantified using Kendall's coefficient. Diagnostic accuracy was determined by comparing MRI assessments and pathologic outcomes, with pairwise comparisons analyzed via the McNemar test. Subgroup analysis evaluated the performance in identifying good responders to NAT.

Results

Inter-reader agreement was almost perfect for T restaging and substantial for N restaging. Diagnostic accuracy for T restaging was 0.432 using DLSB-T2WI, 0.586 using ZOOMit DWI, 0.557 using XD-VIBE DCE, and 0.586 using mpMRI. The accuracy demonstrated by DWI, DCE and mpMRI was superior to that of T2WI (all P < 0.05). For N restaging, the accuracy of the mpMRI protocol was 0.443. Notably, mpMRI achieved an AUC of 0.879 (95% confidence interval 0.835–0.915) for differentiating ypT0-1 tumors.

Conclusions

Advanced mpMRI strategies can serve as a valuable tool for restaging gastric cancer after NAT. Accurately differentiating good responders to neoadjuvant therapy through mpMRI holds significant clinical implications for personalized treatment strategies and improved patient outcomes.