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A radiomics model of predicting tumor volume change of patients with stage III non-small cell lung cancer after radiotherapy.

ABSTRACT: To predict the volume change of stage III NSCLC after radiotherapy with 60 Gy.This retrospective study included two independent cohorts, a train cohort of 192 patients, and a test cohort of 31 patients. We developed a radiomics model based on radiomics features and clinical variables. LIFEx package was used to extract radiomics texture features from CT images. The classification method was logistic regression analysis and feature selection was performed by correlation coefficients. Performance metrics of logistic regression include accuracy, precision, the receiver operating characteristic curves, and recall.The combination features of clinical variables and radiomics can predict the tumor volume change after radiotherapy with 88.7% accuracy (88.6% precision, 88.7% recall, and 88.7% ROC area).Radiomics features combined with medical knowledge have a great potential to predict accurately tumor volume change of stage III NSCLC after radiotherapy with 60 Gy.

SUBMITTER: Yan M

PROVIDER: S-EPMC10453712 | biostudies-literature | 2021 Jan-Mar

REPOSITORIES: biostudies-literature

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A radiomics model of predicting tumor volume change of patients with stage III non-small cell lung cancer after radiotherapy.

Yan Mengmeng M Wang Weidong W

Science progress 20210101 1

To predict the volume change of stage III NSCLC after radiotherapy with 60 Gy.This retrospective study included two independent cohorts, a train cohort of 192 patients, and a test cohort of 31 patients. We developed a radiomics model based on radiomics features and clinical variables. LIFEx package was used to extract radiomics texture features from CT images. The classification method was logistic regression analysis and feature selection was performed by correlation coefficients. Performance m ...[more]

PMID: 33687294

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Project description:ObjectivesStereotactic body radiotherapy (SBRT) is a treatment option for patients with early-stage non-small cell lung cancer (NSCLC) who are unfit for surgery. Some patients may experience distant metastasis. This study aimed to develop and validate a radiomics model for predicting distant metastasis in patients with early-stage NSCLC treated with SBRT.MethodsPatients at five institutions were enrolled in this study. Radiomics features were extracted based on the PET/CT images. After feature selection in the training set (from Tianjin), CT-based and PET-based radiomics signatures were built. Models based on CT and PET signatures were built and validated using external datasets (from Zhejiang, Zhengzhou, Shandong, and Shanghai). An integrated model that included CT and PET radiomic signatures was developed. The performance of the proposed model was evaluated in terms of its discrimination, calibration, and clinical utility. Multivariate logistic regression was used to calculate the probability of distant metastases. The cutoff value was obtained using the receiver operator characteristic curve (ROC), and the patients were divided into high- and low-risk groups. Kaplan-Meier analysis was used to evaluate the distant metastasis-free survival (DMFS) of different risk groups.ResultsIn total, 228 patients were enrolled. The median follow-up time was 31.4 (2.0-111.4) months. The model based on CT radiomics signatures had an area under the curve (AUC) of 0.819 in the training set (n = 139) and 0.786 in the external dataset (n = 89). The PET radiomics model had an AUC of 0.763 for the training set and 0.804 for the external dataset. The model combining CT and PET radiomics had an AUC of 0.835 for the training set and 0.819 for the external dataset. The combined model showed a moderate calibration and a positive net benefit. When the probability of distant metastasis was greater than 0.19, the patient was considered to be at high risk. The DMFS of patients with high- and low-risk was significantly stratified (P < 0.001).ConclusionsThe proposed PET/CT radiomics model can be used to predict distant metastasis in patients with early-stage NSCLC treated with SBRT and provide a reference for clinical decision-making. In this study, the model was established by combining CT and PET radiomics signatures in a moderate-quantity training cohort of early-stage NSCLC patients treated with SBRT and was successfully validated in independent cohorts. Physicians could use this easy-to-use model to assess the risk of distant metastasis after SBRT. Identifying subgroups of patients with different risk factors for distant metastasis is useful for guiding personalized treatment approaches.

Project description:Background: There was rare studies on prognosis of pulmonary venous CTC and early or advanced NSCLC patients. We want to investigate whether CTCs and the subtype of it can predict the prognosis of NSCLC patients. Patients and Methods: One hundred and fourteen patients with stage I-III NSCLC were included CanPatrol™ CTC analysis. PD-L1 expression level were detected in CTC of pulmonary vein. PD-L1, number of CTC in pulmonary, CTC's subtype, clinical characteristics, prognosis of patients were analyzed. Results: 110/114 (96.5%) patients could be found CTCs in pulmonary vein, 58/114 (50.9%) patients had CTC≥15/ml in pulmonary vein, 53/110 patients (48.2%) were defined as having MCTC subtype and 56/110 patient were found have PD-L1 (+) CTC in pulmonary vein. Multivariate analyses showed that PVCTC, MCTC, and stage were independent factors of DFS (P < 0.05). No OS difference was found between number of CTC (P = 0.33) and other CTC factors (P > 0.05), only stage was independent factor of OS (P = 0.019). There were decreases of CTC number and MCTC number in EGFR mutant subgroup (P = 0.0009 and P = 0.007). There were increases of CTC (P = 0.0217), MCTC (P = 0.0041), and PD-L1 (+) CTC (P = 0.0002) number in KRAS mutant subgroup. There was increase of MCTC (P =0.0323) number in BRAF mutant. There were fewer CTCs in pulmonary vein for patients with EGFR mutant than in patients with full wild-type gene (P = 0.0346). There were more PD-L1 positive CTCs in pulmonary vein for patients with ALK rearrangement, KRAS mutant, BRAF mutant, or ROS1 mutant than in patients with full wild-type gene (P = 0.0610, P = 0.0003, P = 0.032, and P = 0.0237). There were more mesenchymal CTCs in pulmonary vein for patients with KRAS mutant and BRAF mutant than in patients with full wild-type gene (P = 0.073 and P = 0.0381). There were fewer mesenchymal CTCs in pulmonary vein for patients with EGFR mutant than in patients with full wild-type gene (P = 0.0898). Conclusions: The patients with high number of CTCs, MCTCs, or PD-L1 (+) CTCs in pulmonary vein experienced poor prognosis of DFS. There are obvious correlations between the CTC subtype of NSCLC and the gene subgroups of tumor tissue.

Dataset Information

A radiomics model of predicting tumor volume change of patients with stage III non-small cell lung cancer after radiotherapy.

Publications

A radiomics model of predicting tumor volume change of patients with stage III non-small cell lung cancer after radiotherapy.

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