Development and Validation of a Predictive Model for Occult Liver Metastasis in Pancreatic Ductal Adenocarcinoma Using Subjective Imaging and Clinical Data

IF 3.1 2区医学 Q2 ONCOLOGY

Cancer Medicine Pub Date : 2025-09-29 DOI:10.1002/cam4.71280

Jia-Bei Liu, Qian-Biao Gu, Jia He, Die-Juan Liu, Jia-Lu Long, Hao Li, Peng Liu

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引用次数: 0

Abstract

Background

Pancreatic ductal adenocarcinoma (PDAC) is highly lethal, with liver metastases leading to poorer outcomes. Occult liver metastases (OLM), undetected by initial imaging, complicate treatment and diminish survival rates. We aimed to develop and validate a predictive model for occult liver metastasis in pancreatic cancer, which is crucial for effective preoperative planning.

Methods

A total of 142 patients with PDAC were retrospectively analyzed between January 1, 2020, and December 31, 2023. Malignant cases were confirmed by pathology, and benign cases were confirmed by pathology or follow-up. Patients were randomly divided into training and validation cohorts at a ratio of 7:3. Factors associated with OLM in PDAC were identified using a stepwise approach, beginning with univariate and followed by multivariate logistic regression analyses. Logistic regression was used to develop clinical, radiological, and combined models, with performance evaluated using the area under the curve (AUC). A nomogram was constructed, and calibration and decision curves were generated. Additionally, machine learning models (RF, SVM, XGBoost) were employed, with AUC and variable importance plots used to evaluate their performance.

Results

Two clinical and four radiological features independently predicted OLM. The combined model achieved an AUC of 0.86 (training) and 0.84 (validation), outperforming clinical (AUC: 0.73, 0.75) and radiological models (AUC: 0.81, 0.75). Machine learning models showed AUCs of 0.787 (RF), 0.850 (SVM), and 0.851 (XGBoost) in the validation cohort. Decision and calibration curves confirmed the combined model's reliability and clinical utility.

Conclusion

The combined model incorporating clinical and radiological features offers a simple, cost-effective tool to identify PDAC patients at high risk for OLMs, supporting informed surgical decisions and improved outcomes. Integrating clinical and radiological markers enhances early detection and personalized care in PDAC management.

Abstract Image

查看原文本刊更多论文

基于主观影像学和临床资料的胰腺导管腺癌隐匿性肝转移预测模型的建立和验证。

背景：胰腺导管腺癌（PDAC）具有高致死率，肝转移导致预后较差。隐匿性肝转移（OLM），未被早期影像学发现，使治疗复杂化并降低生存率。我们的目的是建立和验证胰腺癌隐匿性肝转移的预测模型，这对有效的术前规划至关重要。方法：回顾性分析2020年1月1日至2023年12月31日期间共142例PDAC患者。恶性病例经病理确诊，良性病例经病理或随访确诊。患者按7:3的比例随机分为训练组和验证组。与PDAC中OLM相关的因素采用逐步方法确定，从单变量开始，然后进行多变量逻辑回归分析。使用逻辑回归建立临床、放射学和联合模型，并使用曲线下面积（AUC）评估性能。构造了模态图，生成了标定曲线和决策曲线。此外，采用机器学习模型（RF， SVM, XGBoost），并使用AUC和可变重要性图来评估其性能。结果：两项临床和四项放射学特征独立预测OLM。联合模型的AUC分别为0.86（训练）和0.84（验证），优于临床模型（AUC: 0.73, 0.75）和放射学模型（AUC: 0.81, 0.75）。机器学习模型在验证队列中的auc为0.787 （RF）、0.850 （SVM）和0.851 （XGBoost）。决策曲线和校准曲线证实了联合模型的可靠性和临床实用性。结论：结合临床和影像学特征的联合模型提供了一种简单、经济的工具来识别有olm高风险的PDAC患者，支持明智的手术决策和改善的结果。整合临床和放射标志物可提高PDAC管理的早期发现和个性化护理。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Cancer Medicine ONCOLOGY-

CiteScore

5.50

自引率

2.50%

发文量

907

审稿时长

19 weeks

期刊介绍： Cancer Medicine is a peer-reviewed, open access, interdisciplinary journal providing rapid publication of research from global biomedical researchers across the cancer sciences. The journal will consider submissions from all oncologic specialties, including, but not limited to, the following areas: Clinical Cancer Research Translational research ∙ clinical trials ∙ chemotherapy ∙ radiation therapy ∙ surgical therapy ∙ clinical observations ∙ clinical guidelines ∙ genetic consultation ∙ ethical considerations Cancer Biology: Molecular biology ∙ cellular biology ∙ molecular genetics ∙ genomics ∙ immunology ∙ epigenetics ∙ metabolic studies ∙ proteomics ∙ cytopathology ∙ carcinogenesis ∙ drug discovery and delivery. Cancer Prevention: Behavioral science ∙ psychosocial studies ∙ screening ∙ nutrition ∙ epidemiology and prevention ∙ community outreach. Bioinformatics: Gene expressions profiles ∙ gene regulation networks ∙ genome bioinformatics ∙ pathwayanalysis ∙ prognostic biomarkers. Cancer Medicine publishes original research articles, systematic reviews, meta-analyses, and research methods papers, along with invited editorials and commentaries. Original research papers must report well-conducted research with conclusions supported by the data presented in the paper.