Evidence map›Paper›PMID 38051358›Full record

ArticleAbdominal radiology (New York)2024

Prediction of preoperative microvascular invasion by dynamic radiomic analysis based on contrast-enhanced computed tomography.

Zhenghao Zhou, Tianyi Xia, Teng Zhang, Mingyang Du, Jiarui Zhong, Yunzhi Huang, Kai Xuan, Geyang Xu, Zhuo Wan, Shenghong Ju and 1 more

Abstract read
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In one paragraph

Article in Abdominal radiology (New York), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
13citing 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

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

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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

11 authors.

Zhenghao Zhou *School of Artificial Intelligence, Institute for AI in Medicine, Nanjing University of Information Science and Technology, Nanjing, 210044, China.
Tianyi Xia *Jiangsu Key Laboratory of Molecular and Functional Imaging, Department of Radiology, School of Medicine, Zhongda Hospital, Southeast University, 87 Ding Jia Qiao Road, Nanjing, 210009, China.
Teng ZhangSchool of Artificial Intelligence, Institute for AI in Medicine, Nanjing University of Information Science and Technology, Nanjing, 210044, China.
Mingyang DuCerebrovascular Disease Treatment Center, Nanjing Brain Hospital Affiliated to Brain Hospital of Nanjing Medical University, Nanjing, 210029, China.
Jiarui ZhongJiangsu Key Laboratory of Molecular and Functional Imaging, Department of Radiology, School of Medicine, Zhongda Hospital, Southeast University, 87 Ding Jia Qiao Road, Nanjing, 210009, China.
Yunzhi HuangSchool of Artificial Intelligence, Institute for AI in Medicine, Nanjing University of Information Science and Technology, Nanjing, 210044, China.
Kai XuanSchool of Artificial Intelligence, Institute for AI in Medicine, Nanjing University of Information Science and Technology, Nanjing, 210044, China.
Geyang XuInformation School, University of Washington, Seattle, WA, 98195, USA.
Zhuo WanSchool of Artificial Intelligence, Institute for AI in Medicine, Nanjing University of Information Science and Technology, Nanjing, 210044, China.
Shenghong JuJiangsu Key Laboratory of Molecular and Functional Imaging, Department of Radiology, School of Medicine, Zhongda Hospital, Southeast University, 87 Ding Jia Qiao Road, Nanjing, 210009, China. jsh@seu.edu.cn.ORCID http://orcid.org/0000-0001-5041-7865
Jun XuSchool of Artificial Intelligence, Institute for AI in Medicine, Nanjing University of Information Science and Technology, Nanjing, 210044, China. jxu@nuist.edu.cn.ORCID http://orcid.org/0000-0001-5315-8811

Funding

National Natural Science Foundation of China 62101365National Natural Science Foundation of China 62171230National Natural Science Foundation of China 62301263National Natural Science Foundation of China 82302291National Natural Science Foundation of China 82302352National Natural Science Foundation of China 91959207National Natural Science Foundation of China 92159301
6 · The paper itself

Abstract

purposeMicrovascular invasion (MVI) is a common complication of hepatocellular carcinoma (HCC) surgery, which is an important predictor of reduced surgical prognosis. This study aimed to develop a fully automated diagnostic model to predict pre-surgical MVI based on four-phase dynamic CT images.

methodsA total of 140 patients with HCC from two centers were retrospectively included (training set, n = 98; testing set, n = 42). All CT phases were aligned to the portal venous phase, and were then used to train a deep-learning model for liver tumor segmentation. Radiomics features were extracted from the tumor areas of original CT phases and pairwise subtraction images, as well as peritumoral features. Lastly, linear discriminant analysis (LDA) models were trained based on clinical features, radiomics features, and hybrid features, respectively. Models were evaluated by area under curve (AUC), accuracy, sensitivity, specificity, positive and negative predictive values (PPV and NPV).

resultsOverall, 86 and 54 patients with MVI- (age, 55.92 ± 9.62 years; 68 men) and MVI+ (age, 53.59 ± 11.47 years; 43 men) were included. Average dice coefficients of liver tumor segmentation were 0.89 and 0.82 in training and testing sets, respectively. The model based on radiomics (AUC = 0.865, 95% CI: 0.725-0.951) showed slightly better performance than that based on clinical features (AUC = 0.841, 95% CI: 0.696-0.936). The classification model based on hybrid features achieved better performance in both training (AUC = 0.955, 95% CI: 0.893-0.987) and testing sets (AUC = 0.913, 95% CI: 0.785-0.978), compared with models based on clinical and radiomics features (p-value < 0.05). Moreover, the hybrid model also provided the best accuracy (0.857), sensitivity (0.875), and NPV (0.917).

conclusionThe classification model based on multimodal intra- and peri-tumoral radiomics features can well predict HCC patients with MVI.

Indexed as

Carcinoma, HepatocellularLiver NeoplasmsAdultAgedHumansMaleMiddle AgedRadiomicsRetrospective StudiesTomography, X-Ray ComputedDynamic enhanced CTHepatocellular carcinomaMVIPeritumoral radiomicsSubtraction image

Identifiers

PMID38051358

What OpenQuestion holds

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Registered trials

None linked

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.