Graduate College, Dalian Medical University, Dalian, China.
Department of Radiology, Changzhou Second People's Hospital, Changzhou, China.
BMC Gastroenterol. 2023 Aug 10;23(1):274. doi: 10.1186/s12876-023-02902-4.
This study aimed to evaluate the predictive value of computed tomography (CT) texture features in the treatment response of patients with advanced pancreatic cancer (APC) receiving palliative chemotherapy.
This study enrolled 84 patients with APC treated with first-line chemotherapy and conducted texture analysis on primary pancreatic tumors. 59 patients and 25 were randomly assigned to the training and validation cohorts at a ratio of 7:3. The treatment response to chemotherapy was evaluated according to the Response Evaluation Criteria in Solid Tumors (RECIST1.1). The patients were divided into progressive and non-progressive groups. The least absolute shrinkage selection operator (LASSO) was applied for feature selection in the training cohort and a radiomics signature (RS) was calculated. A nomogram was developed based on a multivariate logistic regression model incorporating the RS and carbohydrate antigen 19-9 (CA19-9), and was internally validated using the C-index and calibration plot. We performed the decision curve analysis (DCA) and clinical impact curve analysis to reflect the clinical utility of the nomogram. The nomogram was further externally confirmed in the validation cohort.
The multivariate logistic regression analysis indicated that the RS and CA19-9 were independent predictors (P < 0.05), and a trend was found for chemotherapy between progressive and non-progressive groups. The nomogram incorporating RS, CA19-9 and chemotherapy showed favorable discriminative ability in the training (C-index = 0.802) and validation (C-index = 0.920) cohorts. The nomogram demonstrated favorable clinical utility.
The RS of significant texture features was significantly associated with the early treatment effect of patients with APC treated with chemotherapy. Based on the RS, CA19-9 and chemotherapy, the nomogram provided a promising way to predict chemotherapeutic effects for APC patients.
本研究旨在评估 CT 纹理特征在接受姑息化疗的晚期胰腺癌（APC）患者治疗反应中的预测价值。
本研究纳入了 84 例接受一线化疗的 APC 患者，并对原发胰腺肿瘤进行纹理分析。59 例和 25 例患者按 7:3 的比例随机分配到训练集和验证集中。根据实体瘤反应评估标准（RECIST1.1）评估化疗的治疗反应。将患者分为进展组和非进展组。在训练集中应用最小绝对收缩和选择算子（LASSO）进行特征选择，并计算放射组学特征（RS）。基于包含 RS 和 CA19-9 的多变量逻辑回归模型开发了一个列线图，并通过 C 指数和校准图进行内部验证。我们进行了决策曲线分析（DCA）和临床影响曲线分析，以反映列线图的临床实用性。该列线图在验证队列中进一步进行了外部验证。
多变量逻辑回归分析表明，RS 和 CA19-9 是独立的预测因素（P<0.05），并且在进展组和非进展组之间存在化疗的趋势。包含 RS、CA19-9 和化疗的列线图在训练（C 指数=0.802）和验证（C 指数=0.920）队列中具有良好的判别能力。该列线图具有良好的临床实用性。
具有显著纹理特征的 RS 与接受化疗的 APC 患者的早期治疗效果显著相关。基于 RS、CA19-9 和化疗，该列线图为预测 APC 患者化疗效果提供了一种有前途的方法。
