Development and validation of 18F-FDG PET/CT radiomics-based nomogram to predict visceral pleural invasion in solid lung adenocarcinoma

被引:0
作者
Cui, Nan [1 ]
Li, Jiatong [1 ]
Jiang, Zhiyun [2 ]
Long, Zhiping [3 ]
Liu, Wei [1 ]
Yao, Hongyang [1 ]
Li, Mingshan [1 ]
Li, Wei [4 ]
Wang, Kezheng [1 ]
机构
[1] Harbin Med Univ, Canc Hosp, PET CT MRI Dept, 150 Haping Rd, Harbin 150081, Heilongjiang, Peoples R China
[2] Harbin Med Univ, Canc Hosp, Radiol Dept, 150 Haping Rd, Harbin 150081, Heilongjiang, Peoples R China
[3] Harbin Med Univ, Sch Publ Hlth, Dept Epidemiol, 157 Baojian Rd, Harbin 150081, Heilongjiang, Peoples R China
[4] Harbin Med Univ, Harbin Med Univ, Intervent Vasc Surg Dept, Affiliated Hosp 4, 37 Yiyuan Rd, Harbin 150001, Heilongjiang, Peoples R China
关键词
F-18-FDG; Visceral pleural invasion; Lung adenocarcinoma; Positron Emission Tomography; Radiomics; CANCER; CLASSIFICATION; DIAGNOSIS; EDITION; IMPACT; TUMORS; CM;
D O I
10.1007/s12149-023-01861-w
中图分类号
R8 [特种医学]; R445 [影像诊断学];
学科分类号
1002 ; 100207 ; 1009 ;
摘要
ObjectivesThis study aimed to establish a radiomics model based on F-18-FDG PET/CT images to predict visceral pleural invasion (VPI) of solid lung adenocarcinoma preoperatively.MethodsWe retrospectively enrolled 165 solid lung adenocarcinoma patients confirmed by histopathology with F-18-FDG PET/CT images. Patients were divided into training and validation at a ratio of 0.7. To find significant VPI predictors, we collected clinicopathological information and metabolic parameters measured from PET/CT images. Three-dimensional (3D) radiomics features were extracted from each PET and CT volume of interest (VOI). Receiver operating characteristic (ROC) curve was performed to determine the performance of the model. Accuracy, sensitivity, specificity and area under curve (AUC) were calculated. Finally, their performance was evaluated by concordance index (C-index) and decision curve analysis (DCA) in training and validation cohorts.Results165 patients were divided into training cohort (n = 116) and validation cohort (n = 49). Multivariate analysis showed that histology grade, maximum standardized uptake value (SUVmax), distance from the lesion to the pleura (DLP) and the radiomics features had statistically significant differences between patients with and without VPI (P < 0.05). A nomogram was developed based on the logistic regression method. The accuracy of ROC curve analysis of this model was 75.86% in the training cohort (AUC: 0.867; C-index: 0.867; sensitivity: 0.694; specificity: 0.889) and the accuracy rate in validation cohort was 71.55% (AUC: 0.889; C-index: 0.819; sensitivity: 0.654; specificity: 0.739).ConclusionsA PET/CT-based radiomics model was developed with SUVmax, histology grade, DLP, and radiomics features. It can be easily used for individualized VPI prediction.
引用
收藏
页码:605 / 617
页数:13
相关论文
共 50 条
[31]   Radiomics-clinical nomogram based on pretreatment 18F-FDG PET-CT radiomics features for individualized prediction of local failure in nasopharyngeal carcinoma [J].
Ding, Jianming ;
Li, Zirong ;
Lin, Yuhao ;
Huang, Chaoxiong ;
Chen, Jiawei ;
Hong, Jiabiao ;
Fei, Zhaodong ;
Zhou, Qichao ;
Chen, Chuanben .
SCIENTIFIC REPORTS, 2023, 13 (01)
[32]   Value of preoperative 18F-FDG PET/CT and HRCT in predicting the differentiation degree of lung adenocarcinoma dominated by solid density [J].
Chen, Xiaolin ;
Li, Ping ;
Zhang, Minghui ;
Wang, Xuewei ;
Wang, Dalong .
PEERJ, 2023, 11
[33]   Preoperative clinical-radiomics nomogram for microvascular invasion prediction in hepatocellular carcinoma using 18F-FDG PET/CT [J].
Wang, Yutao ;
Luo, Shuying ;
Jin, Gehui ;
Fu, Randi ;
Yu, Zhongfei ;
Zhang, Jian .
BMC MEDICAL IMAGING, 2022, 22 (01)
[34]   The role of 18F-FDG PET/CT radiomics in lymphoma [J].
Alessio Rizzo ;
Elizabeth Katherine Anna Triumbari ;
Roberto Gatta ;
Luca Boldrini ;
Manuela Racca ;
Marius Mayerhoefer ;
Salvatore Annunziata .
Clinical and Translational Imaging, 2021, 9 :589-598
[35]   Explainable 18F-FDG PET/CT radiomics model for predicting EGFR mutation status in lung adenocarcinoma: a two-center study [J].
Zuo, Yan ;
Liu, Qiufang ;
Li, Nan ;
Li, Panli ;
Fang, Yichong ;
Bian, Linjie ;
Zhang, Jianping ;
Song, Shaoli .
JOURNAL OF CANCER RESEARCH AND CLINICAL ONCOLOGY, 2024, 150 (10)
[36]   18F-FDG PET/CT-based radiomics nomogram for the preoperative prediction of lymph node metastasis in gastric cancer [J].
Xue, Xiu-qing ;
Yu, Wen-Ji ;
Shi, Xun ;
Shao, Xiao-Liang ;
Wang, Yue-Tao .
FRONTIERS IN ONCOLOGY, 2022, 12
[37]   PD-L1 in Lung Adenocarcinoma: Insights into the Role of 18F-FDG PET/CT [J].
Cui, Yan ;
Li, Xuena ;
Du, Bulin ;
Diao, Yao ;
Li, Yaming .
CANCER MANAGEMENT AND RESEARCH, 2020, 12 :6385-6395
[38]   Diagnostic Value of 18F-FDG PET/CT- Based Radiomics Nomogram in Bone Marrow Involvement of Pediatric Neuroblastoma [J].
Feng, Lijuan ;
Yang, Xu ;
Lu, Xia ;
Kan, Ying ;
Wang, Chao ;
Zhang, Hui ;
Wang, Wei ;
Yang, Jigang .
ACADEMIC RADIOLOGY, 2023, 30 (05) :940-951
[39]   Classification of solid pulmonary nodules using a machine-learning nomogram based on 18F-FDG PET/CT radiomics integrated clinicobiological features [J].
Ren, Caiyue ;
Xu, Mingxia ;
Zhang, Jiangang ;
Zhang, Fuquan ;
Song, Shaoli ;
Sun, Yun ;
Wu, Kailiang ;
Cheng, Jingyi .
ANNALS OF TRANSLATIONAL MEDICINE, 2022,
[40]   Predicting PD-L1 in Lung Adenocarcinoma Using 18F-FDG PET/CT Radiomic Features [J].
Zhang, Huiyuan ;
Meng, Xiangxi ;
Wang, Zhe ;
Zhou, Xin ;
Liu, Yang ;
Li, Nan .
DIAGNOSTICS, 2025, 15 (05)