A systematic review of prognosis predictive role of radiomics in pancreatic cancer: heterogeneity markers or statistical tricks?
Objectives: We aimed to systematically evaluate the prognostic prediction accuracy of radiomics features extracted from pre-treatment imaging in patients with pancreatic ductal adenocarcinoma (PDAC).Methods: Radiomics literature on overall survival (OS) prediction of PDAC were all included in this s...
| Published in: | European Radiology Vol. 32; no. 12; pp. 8443 - 8453 |
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| Main Authors: | , , , , , , , |
| Format: | diagnostic images pictorial research systematic review tables/charts Journal Article |
| Published: |
Springer Nature
Dec2022
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=160459226&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 160459226 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 09387994 NPH jtl: European Radiology issn: 09387994 maglogo: N pubinfo: dt: Dec2022 vid: 32 iid: 12 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 160459226 158233348 160459226 NLM35904618 160459226 10.1007/s00330-022-08922-0 NLM35904618 160459226 ppf: 8443 ppct: 10 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: A systematic review of prognosis predictive role of radiomics in pancreatic cancer: heterogeneity markers or statistical tricks? aug: au: Gao, Yuhan Cheng, Sihang Zhu, Liang Wang, Qin Deng, Wenyi Sun, Zhaoyong Wang, Shitian Xue, Huadan affil: Department of Radiology, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Shuaifuyuan No.1, Wangfujing Street, Dongcheng District, 100730, Beijing, China sug: subj: Carcinoma, Ductal Pancreatic Neoplasms Radiomics Retrospective Design Prospective Studies Human Prognosis Meta Analysis Systematic Review Funding Source ab: Objectives: We aimed to systematically evaluate the prognostic prediction accuracy of radiomics features extracted from pre-treatment imaging in patients with pancreatic ductal adenocarcinoma (PDAC).Methods: Radiomics literature on overall survival (OS) prediction of PDAC were all included in this systematic review. A further meta-analysis was performed on the effect size of first-order entropy. Methodological quality and risk of bias of the included studies were assessed by the radiomics quality score (RQS) and prediction model risk of bias assessment tool (PROBAST).Results: Twenty-three studies were finally identified in this review. Two (8.7%) studies compared prognosis prediction ability between radiomics model and TNM staging model by C-index, and both showed a better performance of the radiomics. Twenty-one (91.3%) studies reported significant predictive values of radiomics features. Nine (39.1%) studies were included in the meta-analysis, and it showed a significant correlation between first-order entropy and OS (HR 1.66, 95%CI 1.18-2.34). RQS assessment revealed validation was only performed in 5 (21.7%) studies on internal datasets and 2 (8.7%) studies on external datasets. PROBAST showed that 22 (95.7%) studies have a high risk of bias in participants because of the retrospective study design.Conclusion: First-order entropy was significantly associated with OS and might improve the accuracy of PDAC prognosis prediction. Existing studies were poorly validated, and it should be noted in future studies. Modification of PROBAST for radiomics studies is necessary since the strict requirements of prospective study design may not be applicable to the demand for a large sample size in the model construction stage.Key Points: • Radiomics based on the primary lesion holds great potential for prognosis prediction. First-order entropy was significantly associated with the overall survival of PDAC and might improve the accuracy of current PDAC prognosis prediction. • We strongly recommend that at least an internal validation should be conducted in any radiomics study. Attention should be paid to the complex relationships between radiomics features. • Due to the close relationship between radiomics and big data, the strict requirement of prospective study design in PROABST may not be appropriate for radiomics studies. A balance between study types and sample sizes for radiomics studies needs to be found in the model construction stage. pubtype: Academic Journal doctype: diagnostic images meta analysis pictorial research systematic review tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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