Evidence map›Paper›PMID 37606762›Full record

ArticleJournal of cancer research and clinical oncology2023

Radiomics-guided prognostic assessment of early-stage hepatocellular carcinoma recurrence post-radical resection.

Qu Xie, Zeyin Zhao, Yanzhen Yang, Dan Long, Cong Luo

Open access · greenAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of cancer research and clinical oncology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed, 1 pooled it
1.2field-weighted citation impact, top 20% 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

5 citing papers in PubMed, 1 synthesis or guideline pooled it, 6 citations in OpenAlex.

  1. Pooled it
  2. Article
  3. Review
  4. Article
  5. Review
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

5 authors at 2 institutions in 1 country.

Qu Xie *Department of Hepato-Pancreato-Biliary & Gastric Medical Oncology, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, 310022, Zhejiang, China.
Zeyin Zhao *Molecular Science and Biomedicine Laboratory (MBL), State Key Laboratory of Chemo/Biosensing and Chemometrics, College of Chemistry and Chemical Engineering, College of Biology, Aptamer Engineering Center of Hunan Province, Hunan University, Changsha, 410082, Hunan, China.
Yanzhen YangDepartment of Hepato-Pancreato-Biliary & Gastric Medical Oncology, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, 310022, Zhejiang, China.
Dan LongDepartment of Radiology, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, 310022, Zhejiang, China.
Cong LuoDepartment of Hepato-Pancreato-Biliary & Gastric Medical Oncology, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, 310022, Zhejiang, China. lw939291@126.com.
Zhejiang Cancer Hospital · CNWenzhou Medical University · CN

Funding

the Zhejiang Traditional Chinese Medicine Scientific Research Fund Grant 20212B037Zhejiang Basic Public Welfare Research Program Grant LGF22H160084
6 · The paper itself

Abstract

purposeThe prognosis of early-stage hepatocellular carcinoma (HCC) patients after radical resection has received widespread attention, but reliable prediction methods are lacking. Radiomics derived from enhanced computed tomography (CT) imaging offers a potential avenue for practical prognostication in HCC patients.

methodsWe recruited early-stage HCC patients undergoing radical resection. Statistical analyses were performed to identify clinicopathological and radiomic features linked to recurrence. Clinical, radiomic, and combined models (incorporating clinicopathological and radiomic features) were built using four algorithms. The performance of these models was scrutinized via fivefold cross-validation, with evaluation metrics including the area under the curve (AUC), accuracy (ACC), sensitivity (SEN), and specificity (SPE) being calculated and compared. Ultimately, an integrated nomogram was devised by combining independent clinicopathological predictors with the Radscore.

resultsFrom January 2016 through December 2020, HCC recurrence was observed in 167 cases (64.5%), with a median time to recurrence of 26.7 months following initial resection. Combined models outperformed those solely relying on clinicopathological or radiomic features. Notably, among the combined models, those employing support vector machine (SVM) algorithms exhibited the most promising predictive outcomes (AUC: 0.840 (95% Confidence interval (CI): [0.696, 0.984]), ACC: 0.805, SEN: 0.849, SPE: 0.733). Hepatitis B infection, tumour size > 5 cm, and alpha-fetoprotein (AFP) > 400 ng/mL were identified as independent recurrence predictors and were subsequently amalgamated with the Radscore to create a visually intuitive nomogram, delivering robust and reliable predictive performance.

conclusionMachine learning models amalgamating clinicopathological and radiomic features provide a valuable tool for clinicians to predict postoperative HCC recurrence, thereby informing early preventative strategies.

Indexed as

Carcinoma, HepatocellularLiver NeoplasmsAlgorithmsHumansNomogramsPrognosisRetrospective StudiesHepatocellular carcinomaMachine learningRadical resectionRadiomicsRecurrence

Identifiers

PMID37606762
PMCPMC11797481
OpenAlexW4386046071

What OpenQuestion holds

Textmetadata
Read underepoch 390

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.