Evidence map›Paper›PMID 35286532›Full record

ArticleAnnals of surgical oncology2022

Predicting Genomic Alterations of Phosphatidylinositol-3 Kinase Signaling in Hepatocellular Carcinoma: A Radiogenomics Study Based on Next-Generation Sequencing and Contrast-Enhanced CT.

Haotian Liao, Hanyu Jiang, Yuntian Chen, Ting Duan, Ting Yang, Miaofei Han, Zhong Xue, Feng Shi, Kefei Yuan, Mustafa R Bashir and 3 more

Abstract read
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In one paragraph

Article in Annals of surgical oncology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed
2.1field-weighted citation impact, top 13% of its field
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

11 citing papers in PubMed, 16 citations in OpenAlex.

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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

13 authors at 6 institutions in 2 countries.

Haotian Liao *Department of Liver Surgery & Liver Transplantation, State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University and Collaborative Innovation Center of Biotherapy, Chengdu, China.
Hanyu Jiang *Department of Radiology, West China Hospital, Sichuan University, Chengdu, China.
Yuntian ChenDepartment of Radiology, West China Hospital, Sichuan University, Chengdu, China.
Ting DuanDepartment of Radiology, West China Hospital, Sichuan University, Chengdu, China.
Ting YangDepartment of Radiology, West China Hospital, Sichuan University, Chengdu, China.
Miaofei HanDepartment of Research and Development, Shanghai United Imaging Intelligence Co., Ltd, Shanghai, China.
Zhong XueDepartment of Research and Development, Shanghai United Imaging Intelligence Co., Ltd, Shanghai, China.
Feng ShiDepartment of Research and Development, Shanghai United Imaging Intelligence Co., Ltd, Shanghai, China.
Kefei YuanDepartment of Liver Surgery & Liver Transplantation, State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University and Collaborative Innovation Center of Biotherapy, Chengdu, China.
Mustafa R BashirDepartment of Radiology and Center for Advanced Magnetic Resonance Development, Duke University Medical Center, Durham, NC, USA.
Dinggang ShenDepartment of Research and Development, Shanghai United Imaging Intelligence Co., Ltd, Shanghai, China. Dinggang.Shen@gmail.com.
Bin SongDepartment of Radiology, West China Hospital, Sichuan University, Chengdu, China. songb_radiology@163.com.
Yong ZengDepartment of Liver Surgery & Liver Transplantation, State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University and Collaborative Innovation Center of Biotherapy, Chengdu, China. zeng_y@scu.edu.cn.
Sichuan University · CNUnited Imaging Healthcare (China) · CNState Key Laboratory of BiotherapyDuke University Hospital · USShanghaiTech University · CNWest China Hospital of Sichuan University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundExploring the genomic landscape of hepatocellular carcinoma (HCC) provides clues for therapeutic decision-making. Phosphatidylinositol-3 kinase (PI3K) signaling is one of the key pathways regulating HCC aggressiveness, and its genomic alterations have been correlated with sorafenib response. In this study, we aimed to predict somatic mutations of the PI3K signaling pathway in HCC samples through machine-learning-based radiomic analysis.

methodsHCC patients who underwent next-generation sequencing and preoperative contrast-enhanced CT were recruited from West China Hospital and The Cancer Genome Atlas for model training and validation, respectively. Radiomic features were extracted from volumes of interest (VOIs) covering the tumor (VOI

resultsA total of 132 HCC patients (mean age: 61.1 ± 14.7 years; 108 men) were enrolled. In the training set, the AUCs of radiomic signatures based on single CT phases were moderate (AUC 0.694-0.771). In the external validation set, the radiomic signature based on VOI

conclusionsMachine-learning-based radiomic analysis had potential for characterizing alterations of PI3K signaling in HCC and could help identify potential candidates for sorafenib treatment.

Identifiers

PMID35286532
OpenAlexW4220653848

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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.