Evidence map›Paper›PMID 39841230›Full record

ArticleAbdominal radiology (New York)2025

CT-based machine learning radiomics predicts Ki-67 expression level and its relationship with overall survival in resectable pancreatic ductal adenocarcinoma.

Jiahao Chen, Zhuangxuan Ma, Yamin Xu, Jieqiong Ge, Hongfei Yao, Chunjing Li, Xiao Hu, Yunlong Pu, Ming Li, Chongyi Jiang

Abstract read
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Article in Abdominal radiology (New York), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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1citing papers in PubMed
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1 · What the graph read from it

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3 · Its place in the literature

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1 citing paper in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

10 authors.

Jiahao Chen *Department of Hepato-Biliary-Pancreatic Surgery, Huadong Hospital affiliated to Fudan University, Shanghai, China.
Zhuangxuan Ma *Department of Radiology, Huadong Hospital Affiliated to Fudan University, Shanghai, China.
Yamin Xu *Department of Hepato-Biliary-Pancreatic Surgery, Huadong Hospital affiliated to Fudan University, Shanghai, China.
Jieqiong GeDepartment of Hepato-Biliary-Pancreatic Surgery, Huadong Hospital affiliated to Fudan University, Shanghai, China.
Hongfei YaoDepartment of Hepato-Biliary-Pancreatic Surgery, Huadong Hospital affiliated to Fudan University, Shanghai, China.
Chunjing LiDepartment of Hepato-Biliary-Pancreatic Surgery, Huadong Hospital affiliated to Fudan University, Shanghai, China.
Xiao HuDepartment of Hepato-Biliary-Pancreatic Surgery, Huadong Hospital affiliated to Fudan University, Shanghai, China.
Yunlong PuDepartment of Hepato-Biliary-Pancreatic Surgery, Huadong Hospital affiliated to Fudan University, Shanghai, China.
Ming LiDepartment of Radiology, Huadong Hospital Affiliated to Fudan University, Shanghai, China. minli77@163.com.
Chongyi JiangDepartment of Hepato-Biliary-Pancreatic Surgery, Huadong Hospital affiliated to Fudan University, Shanghai, China. jiangzhongyi9@sina.com.

Funding

Excellent Academic Leaders of Shanghai 2022XD042Medical Engineering Jiont Fund of Fudan University XM03231533Medical Engineering Jiont Fund of Fudan University yg2022-22National Key Research and Development Program 2022YFF1203301National Natural Science Foundation of China 61976238National Natural Science Foundation of China 82372968Science and Technology Planning Project of Shanghai Science and Technology Commission 22Y11910700Youth Medical Talents-Medical Imaging Practitioner Program AB83030002019004
6 · The paper itself

Abstract

backgroundThe prognostic prediction of pancreatic ductal adenocarcinoma (PDAC) remains challenging. This study aimed to develop a radiomics model to predict Ki-67 expression status in PDAC patients using radiomics features from dual-phase enhanced CT, and integrated clinical characteristics to create a radiomics-clinical nomogram for prognostic prediction.

methodsIn this retrospective study, data were collected from 124 PDAC patients treated surgically at a single center, from January 2017 to March 2023. Patients were categorized according to the Ki-67 expression rate. Radiomics features were extracted from arterial and portal venous phase CT images using 3D Slicer v5.0.3. A radiomics model was formulated and validated to predict the Ki-67 expression, and a nomogram combining clinical indicators and the radiomics model was developed to predict 1, 2 and 3 year overall survival (OS).

resultsThe optimal Ki-67 expression rate cutoff was identified as 50%, with significant OS differences. The developed radiomics model showed good predictive ability with area under the curves of 0.806 and 0.801 in the training and validation groups, respectively. High radiomics score, elevated carbohydrate antigen 19-9 (CA19-9), and receipt of adjuvant chemotherapy were identified as independent prognostic factors for OS. The radiomics-clinical nomogram accurately predicted 1, 2 and 3 year OS in PDAC patients.

conclusionsThe radiomics-clinical nomogram provides a non-invasive and efficient method for predicting Ki-67 expression and overall survival in PDAC patients, which could potentially guide clinical decision-making and improve patient outcomes.

Indexed as

Carcinoma, Pancreatic DuctalKi-67 AntigenMachine LearningPancreatic NeoplasmsTomography, X-Ray ComputedAdultAgedAged, 80 and overFemaleHumansMaleMiddle AgedNomogramsPredictive Value of TestsPrognosisRadiomicsKi-67 AntigenKi-67NomogramOverall survivalPancreatic ductal adenocarcinomaRadiomics

Identifiers

PMID39841230

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.