Machine learning based on clinico-biological features integrated 18 F-FDG PET/CT radiomics for distinguishing squamous c
- PDF / 1,312,211 Bytes
- 12 Pages / 595.276 x 790.866 pts Page_size
- 49 Downloads / 182 Views
ORIGINAL ARTICLE
Machine learning based on clinico-biological features integrated 18 F-FDG PET/CT radiomics for distinguishing squamous cell carcinoma from adenocarcinoma of lung Caiyue Ren 2,3 & Jianping Zhang 1,4,5,6 & Ming Qi 1,4,5,6 & Jiangang Zhang 2,3 & Yingjian Zhang 1,3,4,5,6 & Shaoli Song 1,3,4,5,6 & Yun Sun 2,3,7 & Jingyi Cheng 1,3,4,5,6 Received: 23 May 2020 / Accepted: 1 October 2020 # The Author(s) 2020
Abstract Purpose To develop and validate a clinico-biological features and 18F-fluorodeoxyglucose (FDG) positron emission tomography/computed tomography (PET/CT) radiomic-based nomogram via machine learning for the pretherapy prediction of discriminating between adenocarcinoma (ADC) and squamous cell carcinoma (SCC) in non-small cell lung cancer (NSCLC). Methods A total of 315 NSCLC patients confirmed by postoperative pathology between January 2017 and June 2019 were retrospectively analyzed and randomly divided into the training (n = 220) and validation (n = 95) sets. Preoperative clinical factors, serum tumor markers, and PET, and CT radiomic features were analyzed. Prediction models were developed using the least absolute shrinkage and selection operator (LASSO) regression analysis. The performance of the models was evaluated and compared by the area under receiver-operator characteristic (ROC) curve (AUC) and DeLong test. The clinical utility of the models was determined via decision curve analysis (DCA). Then, a nomogram was developed based on the model with the best predictive efficiency and clinical utility and was validated using the calibration plots. Results In total, 122 SCC and 193 ADC patients were enrolled in this study. Four independent prediction models were separately developed to differentiate SCC from ADC using clinical factors-tumor markers, PET radiomics, CT These authors contributed equally to this work. This article is part of the Topical Collection on Oncology - Chest Electronic supplementary material The online version of this article (https://doi.org/10.1007/s00259-020-05065-6) contains supplementary material, which is available to authorized users. * Yun Sun [email protected]
1
Department of Nuclear Medicine, Shanghai Proton and Heavy Ion Center, Fudan University Cancer Hospital, Shanghai 201321, China
* Jingyi Cheng [email protected]
2
Department of Nuclear Medicine, Shanghai Proton and Heavy Ion Center, Shanghai 201315, China
Caiyue Ren [email protected]
3
Shanghai Engineering Research Center of Proton and Heavy Ion Radiation Therapy, Shanghai, China
4
Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, China
5
Center for Biomedical Imaging, Fudan University, Shanghai 200032, China
6
Shanghai Engineering Research Center for Molecular Imaging Probes, Shanghai 200032, China
7
Department of Research and Development, Shanghai Proton and Heavy Ion Center, Shanghai 201321, China
Jianping Zhang [email protected] Ming Qi [email protected] Jiangang Zhang [email protected] Yingjian Zhang yingjian.zhang
Data Loading...
