Scholarlink

Purpose <p>To investigate the value of habitat imaging employing baseline dual-layer spectral CT (DLCT) for preoperative prediction of recurrence in pancreatic ductal adenocarcinoma (PDAC) after radical resection, and explore the relationship with pathological tumor-stroma ratio (TSR).</p> Methods <p>Resectable PDAC patients underwent multiphase DLCT examinations preoperatively were retrospectively enrolled and randomly allocated into training and validation cohorts. Extracellular volume (ECV) fraction and arterial enhancement fraction (AEF) maps were generated using spectral-based images. Voxels of tumor from ECV and AEF maps were clustered into different habitats through <i>K</i>-means algorithm. Habitat quantitative parameters were extracted. Clinical-radiological, habitat, and combined models for predicting recurrence free survival (RFS) were constructed using Cox regression analyses. Model performance was assessed through <i>c</i>-index and time-dependent receiver operating characteristic (ROC) analysis. Kaplan–Meier method was used to assess recurrence rate. Spearman’s correlation analysis and multiple linear regression were used to evaluate the relationship between TSR and habitat parameters and build prediction model.</p> Results <p>A total of 136 patients were finally included. The fraction of habitat 1 (<i>f</i><sub>1</sub>), fraction of habitat 4 (<i>f</i><sub>4</sub>), CA19-9 &gt; 180 U/mL, and rim-enhancement were used to construct the combined model. Combined model demonstrated superior performance than clinical-radiological model with <i>c</i>-index of 0.912 and 0.899 in training and validation cohorts, respectively. Time-dependent ROC analysis revealed areas under the curve of the combined model for predicting RFS were all above 0.85. The predicted-high-risk group had significantly shorter RFS than predicted-low-risk group in both training and validation group (both <i>p</i> &lt; 0.001). <i>f</i><sub>1</sub> and <i>f</i><sub>4</sub> were significantly associated with TSR and could be used to predict TSR quantitatively.</p> Conclusion <p>The combined model, integrating habitat quantitative parameters, CA19-9 and rim-enhancement, provides a noninvasive approach for predicting the risk of recurrence in PDAC preoperatively. Habitat quantitative parameters could be used to quantitative predict pathological TSR noninvasively.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Baseline dual-layer spectral CT-based habitat analysis for preoperative prediction of recurrence in pancreatic cancer after radical resection and its association with tumor-stroma ratio

  • 期刊论文

  • 发表日期：

    2025年12月1日

查看全文
  • Wei Cai,
  • Yongjian Zhu,
  • Dengfeng Li,
  • Bingzhi Wang,
  • Xiaohong Ma,
  • Xinming Zhao

摘要

Purpose

To investigate the value of habitat imaging employing baseline dual-layer spectral CT (DLCT) for preoperative prediction of recurrence in pancreatic ductal adenocarcinoma (PDAC) after radical resection, and explore the relationship with pathological tumor-stroma ratio (TSR).

Methods

Resectable PDAC patients underwent multiphase DLCT examinations preoperatively were retrospectively enrolled and randomly allocated into training and validation cohorts. Extracellular volume (ECV) fraction and arterial enhancement fraction (AEF) maps were generated using spectral-based images. Voxels of tumor from ECV and AEF maps were clustered into different habitats through K-means algorithm. Habitat quantitative parameters were extracted. Clinical-radiological, habitat, and combined models for predicting recurrence free survival (RFS) were constructed using Cox regression analyses. Model performance was assessed through c-index and time-dependent receiver operating characteristic (ROC) analysis. Kaplan–Meier method was used to assess recurrence rate. Spearman’s correlation analysis and multiple linear regression were used to evaluate the relationship between TSR and habitat parameters and build prediction model.

Results

A total of 136 patients were finally included. The fraction of habitat 1 (f1), fraction of habitat 4 (f4), CA19-9 > 180 U/mL, and rim-enhancement were used to construct the combined model. Combined model demonstrated superior performance than clinical-radiological model with c-index of 0.912 and 0.899 in training and validation cohorts, respectively. Time-dependent ROC analysis revealed areas under the curve of the combined model for predicting RFS were all above 0.85. The predicted-high-risk group had significantly shorter RFS than predicted-low-risk group in both training and validation group (both p < 0.001). f1 and f4 were significantly associated with TSR and could be used to predict TSR quantitatively.

Conclusion

The combined model, integrating habitat quantitative parameters, CA19-9 and rim-enhancement, provides a noninvasive approach for predicting the risk of recurrence in PDAC preoperatively. Habitat quantitative parameters could be used to quantitative predict pathological TSR noninvasively.