Predicting Pancreatic Cancer in New-Onset Diabetes Cohort Using a Novel Model With Integrated Clinical and Genetic Indicators: A Large-Scale Prospective Cohort Study

IF 2.9 2区医学 Q2 ONCOLOGY

Cancer Medicine Pub Date : 2024-11-11 DOI:10.1002/cam4.70388

Yongji Sun, Chaowen Hu, Sien Hu, Hongxia Xu, Jiali Gong, Yixuan Wu, Yiqun Fan, Changming Lv, Tianyu Song, Jianyao Lou, Kai Zhang, Jian Wu, Xiawei Li, Yulian Wu

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Abstract

Introduction

Individuals who develop new-onset diabetes have been identified as a high-risk cohort for pancreatic cancer (PC), exhibiting an incidence rate nearly 8 times higher than the general population. Hence, the targeted screening of this specific cohort presents a promising opportunity for early pancreatic cancer detection. We aimed to develop and validate a novel model capable of identifying high-risk individuals among those with new-onset diabetes.

Methods

Employing the UK Biobank cohort, we focused on those developing new-onset diabetes during follow-up. Genetic and clinical characteristics available at registration were considered as candidate predictors. We conducted univariate regression analysis to identify potential indicators and used a 5-fold cross-validation method to select optimal predictors for model development. Five machine learning algorithms were used for model development.

Results

Among 12,735 patients with new-onset diabetes, 100 (0.8%) were diagnosed with PC within 2 years. The final model (area under the curve, 0.897; 95% confidence interval, 0.865–0.929) included 5 clinical predictors and 24 single nucleotide polymorphisms. Two threshold cut-offs were established: 1.28% and 5.26%. The recommended 1.28% cut-off, based on model performance, reduces definitive testing to 13% of the total population while capturing 76% of PC cases. The high-risk threshold is 5.26%. Utilizing this threshold, only 2% of the population needs definitive testing, capturing nearly half of PC cases.

Conclusions

We, for the first time, combined clinical and genetic data to develop and validate a model to determine the risk of pancreatic cancer in patients with new-onset diabetes using machine learning algorithms. By reducing the number of unnecessary tests while ensuring that a substantial proportion of high-risk patients are identified, this tool has the potential to improve patient outcomes and optimize healthcare sources.

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利用综合临床和遗传指标的新型模型预测新发糖尿病队列中的胰腺癌：大规模前瞻性队列研究》。

导言：新发糖尿病患者已被确定为胰腺癌（PC）的高危人群，其发病率比普通人群高出近 8 倍。因此，对这一特定人群进行有针对性的筛查为早期胰腺癌检测带来了希望。我们的目标是开发并验证一种新型模型，该模型能够识别新发糖尿病患者中的高危人群：方法：我们利用英国生物库队列，重点研究随访期间新发糖尿病患者。登记时的遗传和临床特征被视为候选预测因子。我们进行了单变量回归分析，以确定潜在指标，并使用 5 倍交叉验证法选择最佳预测因子进行模型开发。模型开发中使用了五种机器学习算法：在12735名新发糖尿病患者中，有100人（0.8%）在2年内被诊断出患有PC。最终模型（曲线下面积，0.897；95% 置信区间，0.865-0.929）包括 5 个临床预测因子和 24 个单核苷酸多态性。确定了两个临界值：1.28% 和 5.26%。根据模型的性能，推荐的 1.28% 临界值可将确定性检测减少到总人口的 13%，同时捕获 76% 的 PC 病例。高风险临界值为 5.26%。利用这一阈值，仅有 2% 的人群需要进行确诊检测，从而捕获了近一半的 PC 病例：我们首次将临床和基因数据结合起来，开发并验证了一种利用机器学习算法确定新发糖尿病患者胰腺癌风险的模型。通过减少不必要的检测次数，同时确保识别出相当比例的高危患者，该工具有望改善患者的预后并优化医疗资源。

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.