Evidence map›Paper›PMID 40658136›Full record

ArticleAbdominal radiology (New York)2026

Dual-layer spectral detector CT: a non-invasive tool for simultaneously predicting Ki-67 proliferation status and p53 mutations in pancreatic ductal adenocarcinoma.

Jiaxin Yuan, Jiawei Liu, Mingjie Chen, Yangdi Wang, Minghao Zhang, Liqin Wang, Zhenpeng Peng, Luyong Wei, Siya Shi, Yanji Luo

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Article in Abdominal radiology (New York), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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10 authors.

Jiaxin Yuan *First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China.
Jiawei Liu *First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China.
Mingjie Chen *First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China.
Yangdi WangFirst Affiliated Hospital of Sun Yat-sen University, Guangzhou, China.
Minghao ZhangFirst Affiliated Hospital of Sun Yat-sen University, Guangzhou, China.
Liqin WangFirst Affiliated Hospital of Sun Yat-sen University, Guangzhou, China.
Zhenpeng PengFirst Affiliated Hospital of Sun Yat-sen University, Guangzhou, China.
Luyong Wei *First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China. weily26@mail.sysu.edu.cn.
Siya Shi *First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China. shisy@mail.sysu.edu.cn.
Yanji Luo *First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China. luoyj26@mail.sysu.edu.cn.

Funding

National Natural Science Foundation of China 82472096Natural Science Foundation of Guangdong Province 2023A1515011304Natural Science Foundation of Guangdong Province 2024A1515011968
6 · The paper itself

Abstract

objectivesTo evaluate the value of dual-layer spectral detector CT (DLCT) for predicting Ki-67 proliferation status and p53 mutations in pancreatic ductal adenocarcinoma (PDAC). MATERIALS &

methodsThis retrospective study included untreated patients with pathologically confirmed PDAC who underwent DLCT between June 2019 and September 2023. Independent relevant clinical-radiological features and quantitative parameters for predicting Ki-67 proliferation status and p53 mutations were identified using multivariate logistic regression analysis. The diagnostic performances of independent variables were evaluated using receiver operating characteristic curves.

resultsWe included 92 patients (60.19 ± 11.22 years old, 61 males). There were 40 patients with high Ki-67 expression (Ki-67 ≥ 25%) and 43 patients positive for p53 mutation. The radiologists showed substantial or near-perfect inter- and intra-observer agreement in evaluating quantitative parameters. Normalised iodine concentration in the arterial phase (nICa) was the only independent predictor of Ki-67 proliferation status (p < 0.001; adjusted odds ratio [OR], 2.33; 95% confidence interval [CI]: 1.46, 3.74; cut-off, 0.0910) and p53 mutations (p < 0.001; adjusted OR, 1.35; 95% CI: 1.07, 1.71; cut-off, 0.0905) according to the multivariate logistic analysis. nICa showed satisfactory performance when using the chosen rounded cut-off value of 0.090 (easily applicable in clinical practice) in predicting Ki-67 proliferation (area under the receiver operating characteristic curve [AUC], 0.812; sensitivity, 87.5%; specificity, 75.0%) and p53 mutations (AUC, 0.731; sensitivity, 76.7%; specificity, 69.4%).

conclusionThe DLCT parameter nICa enables simultaneous and non-invasive prediction of both Ki-67 proliferation status and p53 mutations in PDAC with satisfactory diagnostic efficacy.

Indexed as

Carcinoma, Pancreatic DuctalKi-67 AntigenPancreatic NeoplasmsTomography, X-Ray ComputedTumor Suppressor Protein p53AgedCell ProliferationFemaleHumansMaleMiddle AgedMutationRetrospective StudiesSensitivity and SpecificityKi-67 AntigenTumor Suppressor Protein p53Dual-layer spectral detector CTKi-67p53Pancreatic ductal adenocarcinoma

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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.