Wang Fangqing, Sun Yang, Xu Jianwei, Chen Yufan, Zhang Hui, Yin Guotao, Yu Dexin
Department of Radiology, Qilu Hospital, Shandong University, Jinan, China.
Department of Radiology, Liaocheng Traditional Chinese Medicine Hospital, Liaocheng, China.
Insights Imaging. 2025 Sep 12;16(1):190. doi: 10.1186/s13244-025-02036-z.
To develop a CT radiomics model for predicting fibrosis grade in pancreatic ductal adenocarcinoma (PDAC) and to investigate the underlying prognosis value and biological basis.
Patients with resected PDAC were retrospectively included from three institutions. Evaluating tumor fibrosis content using fibrotic pixels proportion through Masson staining of postoperative pathological sections. Radiomics features from preoperative contrast-enhanced CT (CECT) were extracted and used to develop models in the training cohort. The diagnosis performance was further validated in the two test cohorts. The outcome cohort, including patients with advanced PDAC undergoing neoadjuvant chemotherapy, was used to evaluate the predictive value of the model for overall survival (OS) and disease-free survival (DFS), which were investigated using the Kaplan-Meier method and log-rank test. RNA sequencing data from a prospective biological basis cohort were conducted to explore the biological processes underlying the radiomics model.
Among 215 patients (median age 60.89 years, 142 men) used for radiomics modeling, 132 (61.40%) were confirmed as high fibrosis content. The combined phase (CP) radiomics model, which included all CECT radiomics features, showed the best performance for predicting fibrosis grade, with AUCs of 0.831, 0.785, and 0.746 in training, internal test, and external test cohorts. OS (p = 0.011) and DFS (p = 0.022) can be categorized using the CP radiomics model in the outcome cohort. RNA-seq indicated that different CP models were associated with fibrotic production and remodeling processes.
The CP radiomics model showed the best performance in predicting fibrosis grades in PDAC.
Fibrosis grading is of prognostic and neoadjuvant chemotherapy efficacy evaluation significance, and the CT-based combined phase radiomics model established in our study will facilitate risk stratification and selection of personalized treatment strategies for patients. Furthermore, underlying biological processes demonstrated in the radiomics model will offer valuable insights into their interpretability and clinical translation.
Fibrosis grading is of prognostic significance in pancreatic ductal adenocarcinoma (PDAC), but lacks a reliable preoperative assessment. The CT-based combined phase (CP) radiomics model predicts fibrosis grading effectively in PDAC. The CP radiomics model demonstrated prognostic and neoadjuvant chemotherapy efficacy evaluation value and underlying biological processes, which related fibrotic production and remodeling processes.
建立一种用于预测胰腺导管腺癌（PDAC）纤维化分级的CT影像组学模型，并研究其潜在的预后价值和生物学基础。
回顾性纳入来自三个机构的接受PDAC切除术的患者。通过术后病理切片的Masson染色，利用纤维化像素比例评估肿瘤纤维化含量。从术前对比增强CT（CECT）中提取影像组学特征，并用于在训练队列中建立模型。在两个测试队列中进一步验证诊断性能。结局队列包括接受新辅助化疗的晚期PDAC患者，用于评估该模型对总生存期（OS）和无病生存期（DFS）的预测价值，采用Kaplan-Meier法和对数秩检验进行研究。对来自前瞻性生物学基础队列的RNA测序数据进行分析，以探索影像组学模型背后的生物学过程。
在用于影像组学建模的215例患者（中位年龄60.89岁，男性142例）中，132例（61.40%）被确认为高纤维化含量。包含所有CECT影像组学特征的联合期相（CP）影像组学模型在预测纤维化分级方面表现最佳，在训练、内部测试和外部测试队列中的AUC分别为0.831、0.785和0.746。在结局队列中，CP影像组学模型可对OS（p = 0.011）和DFS（p = 0.022）进行分类。RNA测序表明，不同的CP模型与纤维化产生和重塑过程相关。
CP影像组学模型在预测PDAC纤维化分级方面表现最佳。
纤维化分级对预后和新辅助化疗疗效评估具有重要意义，我们研究中建立的基于CT的联合期相影像组学模型将有助于对患者进行风险分层和选择个性化治疗策略。此外，影像组学模型中显示的潜在生物学过程将为其可解释性和临床转化提供有价值的见解。
纤维化分级在胰腺导管腺癌（PDAC）中具有预后意义，但缺乏可靠的术前评估。基于CT的联合期相（CP）影像组学模型可有效预测PDAC的纤维化分级。CP影像组学模型显示出预后和新辅助化疗疗效评估价值以及潜在的生物学过程，这些过程与纤维化产生和重塑过程相关。
