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TitleDevelopment and validation of a preoperative CT-based radiomic nomogram to predict pathology invasiveness in patients with a solitary pulmonary nodule: a machine learning approach, multicenter, diagnostic study
Creator
Date Issued2022-03-01
Source PublicationEuropean Radiology
ISSN0938-7994
Volume32Issue:3Pages:1983-1996
Abstract

Objectives: To develop and validate a preoperative CT-based nomogram combined with radiomic and clinical–radiological signatures to distinguish preinvasive lesions from pulmonary invasive lesions. Methods: This was a retrospective, diagnostic study conducted from August 1, 2018, to May 1, 2020, at three centers. Patients with a solitary pulmonary nodule were enrolled in the GDPH center and were divided into two groups (7:3) randomly: development (n = 149) and internal validation (n = 54). The SYSMH center and the ZSLC Center formed an external validation cohort of 170 patients. The least absolute shrinkage and selection operator (LASSO) algorithm and logistic regression analysis were used to feature signatures and transform them into models. Results: The study comprised 373 individuals from three independent centers (female: 225/373, 60.3%; median [IQR] age, 57.0 [48.0–65.0] years). The AUCs for the combined radiomic signature selected from the nodular area and the perinodular area were 0.93, 0.91, and 0.90 in the three cohorts. The nomogram combining the clinical and combined radiomic signatures could accurately predict interstitial invasion in patients with a solitary pulmonary nodule (AUC, 0.94, 0.90, 0.92) in the three cohorts, respectively. The radiomic nomogram outperformed any clinical or radiomic signature in terms of clinical predictive abilities, according to a decision curve analysis and the Akaike information criteria. Conclusions: This study demonstrated that a nomogram constructed by identified clinical–radiological signatures and combined radiomic signatures has the potential to precisely predict pathology invasiveness. Key Points: • The radiomic signature from the perinodular area has the potential to predict pathology invasiveness of the solitary pulmonary nodule. • The new radiomic nomogram was useful in clinical decision-making associated with personalized surgical intervention and therapeutic regimen selection in patients with early-stage non-small-cell lung cancer.

KeywordAlgorithms Lung Nomograms Solitary pulmonary nodule Tomography, X-ray computed
DOI10.1007/s00330-021-08268-z
URLView source
Indexed BySCIE
Language英语English
WOS Research AreaRadiology, Nuclear Medicine & Medical Imaging
WOS SubjectRadiology, Nuclear Medicine & Medical Imaging
WOS IDWOS:000707536800001
Scopus ID2-s2.0-85117138076
Citation statistics
Cited Times:34[WOS]   [WOS Record]     [Related Records in WOS]
Document TypeJournal article
Identifierhttp://repository.uic.edu.cn/handle/39GCC9TT/8361
CollectionBeijing Normal-Hong Kong Baptist University
Corresponding AuthorQiao, Guibin
Affiliation
1.Division of Thoracic Surgery,Guangdong Provincial People’s Hospital & Guangdong Academy of Medical Sciences,The Second School of Clinical Medicine,Southern Medical University,Shantou University Medical College,Guangzhou,China
2.Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation,Department of Medical Oncology,Phase I Clinical Trial Centre,Sun Yat-Sen Memorial Hospital,Sun Yat-Sen University,Guangzhou,China
3.AI & Digital Media Concentration Program,Division of Science and Technology,Beijing Normal University-Hong Kong Baptist University United International College,Zhuhai,China
4.Department of Radiology,Zhoushan Hospital,Zhoushan City,Zhejiang Province,China
5.Department of Pathology,Zhoushan Hospital,Zhoushan City,Zhejiang Province,China
6.Department of Radiology,Department of PET Center,Guangdong Provincial People’s Hospital & Guangdong Academy of Medical Sciences,Guangzhou,China
7.Department of Pathology,Guangdong Provincial People’s Hospital & Guangdong Academy of Medical Sciences,Guangzhou,China
8.Department of Clinical Oncology,Queen Elizabeth Hospital,Hong Kong
9.Department of Respiratory and Critical Care Medicine,Beijing Institute of Respiratory Medicine,Beijing Chao-Yang Hospital,Capital Medical University,Beijing,China
10.Clinical Medicine,Zhongshan School of Medicine,Yat-Sen University,Guangzhou,China
11.Department of Health Technology and Informatics,The Hong Kong Polytechnic University,Hong Kong
Recommended Citation
GB/T 7714
Huang, Luyu,Lin, Weihuan,Xie, Daipenget al. Development and validation of a preoperative CT-based radiomic nomogram to predict pathology invasiveness in patients with a solitary pulmonary nodule: a machine learning approach, multicenter, diagnostic study[J]. European Radiology, 2022, 32(3): 1983-1996.
APA Huang, Luyu., Lin, Weihuan., Xie, Daipeng., Yu, Yunfang., Cao, Hanbo., .. & Zhou, Haiyu. (2022). Development and validation of a preoperative CT-based radiomic nomogram to predict pathology invasiveness in patients with a solitary pulmonary nodule: a machine learning approach, multicenter, diagnostic study. European Radiology, 32(3), 1983-1996.
MLA Huang, Luyu,et al."Development and validation of a preoperative CT-based radiomic nomogram to predict pathology invasiveness in patients with a solitary pulmonary nodule: a machine learning approach, multicenter, diagnostic study". European Radiology 32.3(2022): 1983-1996.
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