Development and validation of method to predict pathology invasiveness in patients with a solitary pulmonary nodule

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Abstract

AIM: To develop and validate a preoperative CT-based nomogram combined with radiomic and clinical–radiological signatures to distinguish preinvasive lesions from pulmonary invasive lesions.

MATERIALS AND 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 threeabilities, according to a decision curve analysis and the Akaike information criteria.

CONCLUSION: This study demonstrated that a nomogram constructed by identified clinical–radiological signatures and combined radiomic signatures has the potential to precisely predict pathology invasiveness.

This article is a translation of the article by Huang L, Lin W, Xie D, 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. Eur Radiol. 2022;32(3):1983–1996. doi: 10.1007/s00330-021-08268-z

This article is licensed under a Creative Commons Attribution 4.0 International License Creative Commons Attribution 4.0 ( https://creativecommons.org/licenses/by/4.0/) .

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About the authors

Luyu Huang

Shantou University Medical College

Email: guibinqiao@126.com
ORCID iD: 0000-0002-5791-4781

MD

China, Guangzhou

Weihuan Lin

Shantou University Medical College

Email: guibinqiao@126.com
ORCID iD: 0000-0002-2982-3284

MD

China, Guangzhou

Daipeng Xie

Shantou University Medical College

Email: guibinqiao@126.com
ORCID iD: 0000-0003-1470-9945

MD

China, Guangzhou

Yunfang Yu

Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University; Beijing Normal University-Hong Kong Baptist University United International College

Email: guibinqiao@126.com
ORCID iD: 0000-0003-2579-6220

Associate Professor

China, Guangzhou; Zhuhai

Hanbo Cao

Zhoushan Hospital

Email: guibinqiao@126.com
ORCID iD: 0000-0001-9268-497X

MD

China, Zhoushan City, Zhejiang Province

Guoqing Liao

Shantou University Medical College

Email: guibinqiao@126.com
ORCID iD: 0000-0003-2593-4902

MD

China, Guangzhou

Shaowei Wu

Shantou University Medical College

Email: guibinqiao@126.com
ORCID iD: 0000-0002-8786-4375

Ph.D., Professor

China, Guangzhou

Lintong Yao

Shantou University Medical College

Email: guibinqiao@126.com
ORCID iD: 0009-0008-0382-5047

MD

China, Guangzhou

Zhaoyu Wang

Zhoushan Hospital

Email: guibinqiao@126.com

MD

China, Zhoushan City, Zhejiang Province

Mei Wang

Guangdong Provincial People’s Hospital & Guangdong Academy of Medical Sciences

Email: 281406196@gg.com
China, Guangzhou

Siyun Wang

Guangdong Provincial People’s Hospital & Guangdong Academy of Medical Sciences

Email: guibinqiao@126.com
ORCID iD: 0000-0001-7052-4430

MD

China, Guangzhou

Guangyi Wang

Guangdong Provincial People’s Hospital & Guangdong Academy of Medical Sciences

Email: wangguangyi@gdph.org.cn

MD

China, Guangzhou

Dongkun Zhang

Shantou University Medical College

Email: guibinqiao@126.com

MD

China, Guangzhou

Su Yao

Queen Elizabeth Hospital

Email: guibinqiao@126.com

MD

China, Hong Kong

Zifan He

Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University

Email: guibinqiao@126.com

MD

China, Guangzhou

William Chi-Shing Cho

Queen Elizabeth Hospital

Email: williamcscho@gmail.com

MD, Ph.D.

China, Hong Kong

Duo Chen

Capital Medical University

Email: guibinqiao@126.com

MD

China, Beijing

Zhengjie Zhang

Shantou University Medical College

Email: guibinqiao@126.com

MD

China, Guangzhou

Wanshan Li

Yat-Sen University

Email: guibinqiao@126.com
ORCID iD: 0009-0007-2940-8033

MD

China, Guangzhou

Guibin Qiao

Shantou University Medical College

Author for correspondence.
Email: guibinqiao@126.com
ORCID iD: 0000-0001-9200-9317

MD

China, Guangzhou

Lawrence Wing-Chi Chan

The Hong Kong Polytechnic University

Email: wing.chi.chan@polyu.edu.hk
ORCID iD: 0000-0002-2163-389X

MD

China, Hong Kong

Haiyu Zhou

Shantou University Medical College

Email: zhouhaiyu@gdph.org.cn
ORCID iD: 0000-0002-3328-6792

Ph.D. (Oncology)

China, Guangzhou

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3. Fig. 1. Patient recruitment process at three centers. Note. GDPH — Guangdong Provincial people’ s Hospital; SYSMH — Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University; ZSLC — Zhoushan Lung Cancer Institution; КТ — computed tomography; ПИП — pre-invasive lesions; ИП — invasion lesions; RS-C, combined radiomic signature selected from the nodular area and perinodular area.

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4. Fig. 2. Overall radiomic workflow and pipeline in this study. a — CT image (transverse section) in a 58-year-old male patient with a 1.5-cm solitary pulmonary nodule in the right upper lung (dotted box) on contrast-enhanced CT and biopsy confirmed as lung adenocarcinoma. b — two regions of interest (ROIs) were constructed into volumes of interests (VOIs), and radiomic features were extracted from two VOIs. c — radiomic features were selected by the LASSO algorithm and constructed into a radiomic signature. d — discrimination and calibration of the nomogram which was formed by the clinical–radiological and combined radiomic signatures.

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5. Fig. 3. Nomogram based on the radiomic and clinical–radiological signatures. The nomogram based on RS-C and the clinical–radiological signature to predict pathology invasiveness. RS-C — combined radiomic signature selected from the nodular area and perinodular area, C-R — clinical–radiological.

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6. Fig. 4. ROC curves of the nomogram and models in the development and validation cohorts. a — ROC curves of the nomogram in the development and validation cohorts; b — ROC curves of five models in the development cohort; c — ROC curves of five models in the internal validation cohorts; d — ROC curves of five models in the external validation cohorts. ROC, receiver operating characteristic; RS1 — radiomic signature selected from the nodular area; RS2 — radiomic signature selected from the perinodular area; RS-C, combined radiomic signatures selected from the nodular area and perinodular area; C-R, clinical–radiological.

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7. Fig. 5. Decision curve analysis for the nomogram and signatures in the development and validation cohorts. Decision curve analysis for the nomogram and signatures in the development ( a ), internal ( b ), and external ( c ) validation cohorts. DCA, decision curve analysis; RS-C — combined radiomic signature selected from the nodular area and perinodular area; C-R — clinical–radiological.

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Copyright (c) 2024 Huang L., Lin W., Xie D., Yu Y., Cao H., Liao G., Wu S., Yao L., Wang Z., Wang M., Wang S., Wang G., Zhang D., Yao S., He Z., Cho W.C., Chen D., Zhang Z., Li W., Qiao G., Chan L.W., Zhou H.

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