Evidence map›Paper›PMID 33102242›Full record

ArticleFrontiers in oncology2020

Deep Learning Predicts Overall Survival of Patients With Unresectable Hepatocellular Carcinoma Treated by Transarterial Chemoembolization Plus Sorafenib.

Lei Zhang, Wei Xia, Zhi-Ping Yan, Jun-Hui Sun, Bin-Yan Zhong, Zhong-Heng Hou, Min-Jie Yang, Guan-Hui Zhou, Wan-Sheng Wang, Xing-Yu Zhao and 9 more

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In one paragraph

Article in Frontiers in oncology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
27citing papers in PubMed, 2 pooled it
–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

27 citing papers in PubMed, 2 syntheses or guidelines pooled it.

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  15. Update on the Use of Artificial Intelligence in Hepatobiliary MR Imaging.Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine · 2023
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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

19 authors.

Lei ZhangDepartment of Interventional Radiology, The First Affiliated Hospital of Soochow University, Suzhou, China.
Wei XiaSuzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, China.
Zhi-Ping YanDepartment of Interventional Radiology, Zhongshan Hospital, Fudan University, Shanghai, China.
Jun-Hui SunHepatobiliary and Pancreatic Interventional Treatment Center, Division of Hepatobiliary and Pancreatic Surgery, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.
Bin-Yan ZhongDepartment of Interventional Radiology, The First Affiliated Hospital of Soochow University, Suzhou, China.
Zhong-Heng HouDepartment of Interventional Radiology, The First Affiliated Hospital of Soochow University, Suzhou, China.
Min-Jie YangDepartment of Interventional Radiology, Zhongshan Hospital, Fudan University, Shanghai, China.
Guan-Hui ZhouHepatobiliary and Pancreatic Interventional Treatment Center, Division of Hepatobiliary and Pancreatic Surgery, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.
Wan-Sheng WangDepartment of Interventional Radiology, The First Affiliated Hospital of Soochow University, Suzhou, China.
Xing-Yu ZhaoSuzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, China.
Jun-Ming JianSuzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, China.
Peng HuangDepartment of Interventional Radiology, The First Affiliated Hospital of Soochow University, Suzhou, China.
Rui ZhangSuzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, China.
Shen ZhangDepartment of Interventional Radiology, The First Affiliated Hospital of Soochow University, Suzhou, China.
Jia-Yi ZhangSuzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, China.
Zhi LiDepartment of Interventional Radiology, The First Affiliated Hospital of Soochow University, Suzhou, China.
Xiao-Li ZhuDepartment of Interventional Radiology, The First Affiliated Hospital of Soochow University, Suzhou, China.
Xin GaoSuzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, China.
Cai-Fang NiDepartment of Interventional Radiology, The First Affiliated Hospital of Soochow University, Suzhou, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesTo develop and validate a deep learning-based overall survival (OS) prediction model in patients with hepatocellular carcinoma (HCC) treated with transarterial chemoembolization (TACE) plus sorafenib.

methodsThis retrospective multicenter study consisted of 201 patients with treatment-naïve, unresectable HCC who were treated with TACE plus sorafenib. Data from 120 patients were used as the training set for model development. A deep learning signature was constructed using the deep image features from preoperative contrast-enhanced computed tomography images. An integrated nomogram was built using Cox regression by combining the deep learning signature and clinical features. The deep learning signature and nomograms were also externally validated in an independent validation set of 81 patients. C-index was used to evaluate the performance of OS prediction.

resultsThe median OS of the entire set was 19.2 months and no significant difference was found between the training and validation cohort (18.6 months vs. 19.5 months,

conclusionThe deep learning signature provided significant added value to clinical features in the development of an integrated nomogram which may act as a potential tool for individual prognosis prediction and identifying HCC patients who may benefit from the combination therapy of TACE plus sorafenib.

Indexed as

biomarkerdeep learninghepatocellular carcinomasorafenibtransarterial chemoembolization

Identifiers

PMID33102242
PMCPMC7556271

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.