Preoperative CT-based radiomics combined with intraoperative frozen section is predictive of invasive adenocarcinoma in pulmonary nodules: a multicenter study.
Objectives: Develop a CT-based radiomics model and combine it with frozen section (FS) and clinical data to distinguish invasive adenocarcinomas (IA) from preinvasive lesions/minimally invasive adenocarcinomas (PM).Methods: This multicenter study cohort of 623 lung adenocarcinomas was split into tra...
| Publicado en: | European Radiology Vol. 30; no. 5; pp. 2680 - 2692 |
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| Autores principales: | , , , , , , , , , , , , |
| Formato: | research Journal Article |
| Publicado: |
Springer Nature
May2020
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=142738706&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 142738706 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 09387994 NPH jtl: European Radiology issn: 09387994 maglogo: N pubinfo: dt: May2020 vid: 30 iid: 5 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 142738706 142738706 NLM32006165 142738706 10.1007/s00330-019-06597-8 NLM32006165 142738706 ppf: 2680 ppct: 12 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Preoperative CT-based radiomics combined with intraoperative frozen section is predictive of invasive adenocarcinoma in pulmonary nodules: a multicenter study. aug: au: Wu, Guangyao Woodruff, Henry C. Sanduleanu, Sebastian Refaee, Turkey Jochems, Arthur Leijenaar, Ralph Gietema, Hester Shen, Jing Wang, Rui Xiong, Jingtong Bian, Jie Wu, Jianlin Lambin, Philippe affil: The D-Lab: Department of Precision Medicine, GROW - School for Oncology and Developmental Biology, Maastricht University, Maastricht, The Netherlands sug: subj: Solitary Pulmonary Nodule Adenocarcinoma in Situ Lung Neoplasms Preoperative Care Adenocarcinoma in Situ Surgery Lung Neoplasms Pathology ROC Curve Human Lung Neoplasms Surgery Middle Age Adenocarcinoma in Situ Pathology Frozen Sections Tomography, X-Ray Computed Methods Retrospective Design Male Female Pharmacokinetics Solitary Pulmonary Nodule Pathology Validation Studies Comparative Studies Evaluation Research Multicenter Studies Funding Source Middle Aged: 45-64 years Male Female ab: Objectives: Develop a CT-based radiomics model and combine it with frozen section (FS) and clinical data to distinguish invasive adenocarcinomas (IA) from preinvasive lesions/minimally invasive adenocarcinomas (PM).Methods: This multicenter study cohort of 623 lung adenocarcinomas was split into training (n = 331), testing (n = 143), and external validation dataset (n = 149). Random forest models were built using selected radiomics features, results from FS, lesion volume, clinical and semantic features, and combinations thereof. The area under the receiver operator characteristic curves (AUC) was used to evaluate model performances. The diagnosis accuracy, calibration, and decision curves of models were tested.Results: The radiomics-based model shows good predictive performance and diagnostic accuracy for distinguishing IA from PM, with AUCs of 0.89, 0.89, and 0.88, in the training, testing, and validation datasets, respectively, and with corresponding accuracies of 0.82, 0.79, and 0.85. Adding lesion volume and FS significantly increases the performance of the model with AUCs of 0.96, 0.97, and 0.96, and with accuracies of 0.91, 0.94, and 0.93 in the three datasets. There is no significant difference in AUC between the FS model enriched with radiomics and volume against an FS model enriched with volume alone, while the former has higher accuracy. The model combining all available information shows minor non-significant improvements in AUC and accuracy compared with an FS model enriched with radiomics and volume.Conclusions: Radiomics signatures are potential biomarkers for the risk of IA, especially in combination with FS, and could help guide surgical strategy for pulmonary nodules patients.Key Points: • A CT-based radiomics model may be a valuable tool for preoperative prediction of invasive adenocarcinoma for patients with pulmonary nodules. • Radiomics combined with frozen sections could help in guiding surgery strategy for patients with pulmonary nodules. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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