Evidence map›Paper›PMID 42844287›Full record

Observational studyNature communications2026

Deep learning CT signature for predicting early liver metastases in pancreatic ductal adenocarcinoma.

Ben Zhao, Zhifang Gong, Wenbo Xiao, Ming Chen, Shan Huang, Liwen Zhu, Tianyi Xia, Yingxin Sun, Wentao Yang, Mengqiu Liu and 19 more

Abstract readMulticenter StudyObservational Study
PubMed Full text at PMC Publisher
In one paragraph

Observational study in Nature communications, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

29 authors.

Ben Zhao *Cultivation and Construction Site of the State Key Laboratory of Intelligent Imaging and Interventional Medicine, Department of Radiology, Zhongda Hospital, Medical School of Southeast University, Nanjing, China.
Zhifang Gong *School of Biological Science and Medical Engineering, Southeast University, Nanjing, China.ORCID 0009-0001-8044-3789
Wenbo Xiao *Department of Radiology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.ORCID 0000-0001-7124-1096
Ming ChenDepartment of Medical Oncology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.
Shan HuangDepartment of Radiology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.
Liwen ZhuCultivation and Construction Site of the State Key Laboratory of Intelligent Imaging and Interventional Medicine, Department of Radiology, Zhongda Hospital, Medical School of Southeast University, Nanjing, China.
Tianyi XiaCultivation and Construction Site of the State Key Laboratory of Intelligent Imaging and Interventional Medicine, Department of Radiology, Zhongda Hospital, Medical School of Southeast University, Nanjing, China.
Yingxin SunCultivation and Construction Site of the State Key Laboratory of Intelligent Imaging and Interventional Medicine, Department of Radiology, Zhongda Hospital, Medical School of Southeast University, Nanjing, China.
Wentao YangCultivation and Construction Site of the State Key Laboratory of Intelligent Imaging and Interventional Medicine, Department of Radiology, Zhongda Hospital, Medical School of Southeast University, Nanjing, China.
Mengqiu LiuDepartment of Radiology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China.
Shuai MingDepartment of Radiology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China.
Yaoyao YuCultivation and Construction Site of the State Key Laboratory of Intelligent Imaging and Interventional Medicine, Department of Radiology, Zhongda Hospital, Medical School of Southeast University, Nanjing, China.
Ridong LiDepartment of Medical Imaging, Clinic Medical School, Yangzhou University, Northern Jiangsu Province Hospital, Yangzhou, China.
Zixin XiaoCultivation and Construction Site of the State Key Laboratory of Intelligent Imaging and Interventional Medicine, Department of Radiology, Zhongda Hospital, Medical School of Southeast University, Nanjing, China.
Xiaoxuan XuCultivation and Construction Site of the State Key Laboratory of Intelligent Imaging and Interventional Medicine, Department of Radiology, Zhongda Hospital, Medical School of Southeast University, Nanjing, China.
Huayu ChenCultivation and Construction Site of the State Key Laboratory of Intelligent Imaging and Interventional Medicine, Department of Radiology, Zhongda Hospital, Medical School of Southeast University, Nanjing, China.
Yang SongMR Research Collaboration Team, Siemens Healthineers Ltd, Shanghai, China.
Tianyu TangCultivation and Construction Site of the State Key Laboratory of Intelligent Imaging and Interventional Medicine, Department of Radiology, Zhongda Hospital, Medical School of Southeast University, Nanjing, China.
Chunqiang LuCultivation and Construction Site of the State Key Laboratory of Intelligent Imaging and Interventional Medicine, Department of Radiology, Zhongda Hospital, Medical School of Southeast University, Nanjing, China.
Di ChangCultivation and Construction Site of the State Key Laboratory of Intelligent Imaging and Interventional Medicine, Department of Radiology, Zhongda Hospital, Medical School of Southeast University, Nanjing, China.
Zebin XiaoCultivation and Construction Site of the State Key Laboratory of Intelligent Imaging and Interventional Medicine, Department of Radiology, Zhongda Hospital, Medical School of Southeast University, Nanjing, China.ORCID 0000-0002-3311-7403
Yuancheng WangCultivation and Construction Site of the State Key Laboratory of Intelligent Imaging and Interventional Medicine, Department of Radiology, Zhongda Hospital, Medical School of Southeast University, Nanjing, China.
Ying LiuDepartment of Radiology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China.
Jing YeDepartment of Medical Imaging, Clinic Medical School, Yangzhou University, Northern Jiangsu Province Hospital, Yangzhou, China.
Yunjun YangDepartment of Radiology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China.
Wei WeiDepartment of Radiology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China.
Guangquan ZhouSchool of Biological Science and Medical Engineering, Southeast University, Nanjing, China.
Qi ZhangDepartment of Hepatobiliary and Pancreatic Surgery, The First Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China.ORCID 0000-0002-6096-0690
Shenghong JuCultivation and Construction Site of the State Key Laboratory of Intelligent Imaging and Interventional Medicine, Department of Radiology, Zhongda Hospital, Medical School of Southeast University, Nanjing, China. jsh@seu.edu.cn.ORCID 0000-0001-5041-7865

Funding

National Natural Science Foundation of China (National Science Foundation of China) 82330060National Natural Science Foundation of China (National Science Foundation of China) 823B2040National Natural Science Foundation of China (National Science Foundation of China) 82402355National Natural Science Foundation of China (National Science Foundation of China) 82427803National Natural Science Foundation of China (National Science Foundation of China) 92359304
6 · The paper itself

Abstract

Accurately predicting the risk of early liver metastases (ELM) and identifying patients who are most likely to benefit from neoadjuvant therapy (NAT) are critical for pancreatic ductal adenocarcinoma (PDAC). Here, we develop a Mamba-based predictive model that integrates imaging features from both the primary pancreatic tumor and the liver to assess the risk of ELM. The model is evaluated in a multi-institutional cohort of 1063 PDAC patients and demonstrates robust performance in predicting ELM (AUCs: 0.806-0.890). Besides, model-defined high-risk patients exhibit significantly shorter progression-free survival (PFS: HR = 1.93, p < 0.001) and overall survival (OS: HR = 1.89, p < 0.001). Notably, NAT confers significant OS benefits in model-defined high-risk patients (17.4 vs. 34.1 months; p < 0.001), even after propensity score matching (p = 0.004), while no survival benefit occurs in low-risk patients. Radiotranscriptomic analyses further reveal relative biological aggressiveness in the high-risk group. Overall, our proposed Mamba-based framework enables accurate prediction of ELM and may serve as a clinically actionable tool for identifying PDAC patients most likely to benefit from NAT.

Indexed as

Carcinoma, Pancreatic DuctalDeep LearningLiver NeoplasmsPancreatic NeoplasmsTomography, X-Ray ComputedAgedFemaleHumansMaleMiddle AgedNeoadjuvant TherapyPrognosis

Identifiers

PMID42844287
PMCPMC13646268

What OpenQuestion holds

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Registered trials

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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.