Development of a predictive radiomics model for lymph node metastases in pre-surgical CT-based stage IA non-small cell lung cancer
| Autor: | Yuan-yuan Wang, Lining Cong, Zhenzhou Hou, Hui Feng, Mengdi Cong, Gaofeng Shi, Jia-Liang Ren, Qian Xu |
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| Rok vydání: | 2020 |
| Předmět: |
Male
0301 basic medicine Pulmonary and Respiratory Medicine Cancer Research medicine.medical_specialty Lung Neoplasms 03 medical and health sciences 0302 clinical medicine Radiomics Carcinoma Non-Small-Cell Lung medicine Humans Lymph node Neoplasm Staging Retrospective Studies Receiver operating characteristic business.industry Retrospective cohort study Venous phase Middle Aged 030104 developmental biology medicine.anatomical_structure ROC Curve Oncology Lymphatic Metastasis 030220 oncology & carcinogenesis Stage IA non-small cell lung cancer Female Lymph Nodes Radiology Non small cell Tomography X-Ray Computed business Selection operator Algorithms Follow-Up Studies |
| Zdroj: | Lung Cancer. 139:73-79 |
| ISSN: | 0169-5002 |
| DOI: | 10.1016/j.lungcan.2019.11.003 |
| Popis: | Objectives To develop and validate predictive models using clinical parameters, radiomic features and a combination of both for lymph node metastasis (LNM) in pre-surgical CT-based stage IA non-small cell lung cancer (NSCLC) patients. Methods This retrospective study included 649 pre-surgical CT-based stage IA NSCLC patients from our hospital. One hundred and thirty-eight (21 %) of the 649 patients had LNM after surgery. A total of 396 radiomic features were extracted from the venous phase contrast enhanced computed tomography (CECT). The training group included 455 patients (97 with and 358 without LNM) and the testing group included 194 patients (41 with and 153 without LNM). The least absolute shrinkage and selection operator (LASSO) algorithm was used for radiomic feature selection. The random forest (RF) was used for model development. Three models (a clinical model, a radiomics model, and a combined model) were developed to predict LNM in early stage NSCLC patients. The area under the receiver operating characteristic (ROC) curve (AUC) value and decision curve analysis were used to evaluate the performance in LNM status (with or without LNM) using the three models. Results The ROC analysis (also decision curve analysis) showed predictive performance for LNM of the radiomics model (AUC values for training and testing, respectively 0.898 and 0.851) and of the combined model (0.911 and 0.860, respectively). Both performed better than the clinical model (0.739 and 0.614, respectively; delong test p-values both Conclusion A radiomics model using the venous phase of CE-CT has potential for predicting LNM in pre-surgical CT-based stage IA NSCLC patients. |
| Databáze: | OpenAIRE |
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