A magnetic resonance imaging (MRI)-based deep learning radiomics model predicts recurrence-free survival in lung cancer patients after surgical resection of brain metastases.
Authors
Affiliations (8)
Affiliations (8)
- Department of Radiology, Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, No. 107 Yanjiang Road West, Guangzhou 510120, China.
- Department of Radiology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, 651 Dongfeng East Road, Guangzhou, 510060, China.
- Department of Radiology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, 1 Swan Lake Road, Hefei, 230036, China.
- Department of Radiology, The Tenth Affiliated Hospital of Southern Medical University, No. 78 Wandao Road, Wanjiang Street, Dongguan People's Hospital, Dongguan, 523059, China.
- Department of Radiology, Third Affiliated Hospital of Sun Yat-Sen University, No. 2693 Huangpu Road, Guangzhou, 510630, China.
- Harvard T.H. Chan School of Public Health, 655 Huntington Ave, Boston, MA 02115, USA.
- Department of Radiology, Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, No. 107 Yanjiang Road West, Guangzhou 510120, China. Electronic address: [email protected].
- Department of Radiology, Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, No. 107 Yanjiang Road West, Guangzhou 510120, China. Electronic address: [email protected].
Abstract
To develop and validate a magnetic resonance imaging (MRI)-based deep learning radiomics model (DLRM) to predict recurrence-free survival (RFS) in lung cancer patients after surgical resection of brain metastases (BrMs). A total of 215 lung cancer patients with BrMs confirmed by surgical pathology were retrospectively included in five centres, 167 patients were assigned to the training cohort, and 48 to the external test cohort. All patients underwent regular follow-up brain MRIs. Clinical and morphological MRI models for predicting RFS were built using univariate and multivariate Cox regressions, respectively. Handcrafted and deep learning (DL) signatures were constructed from BrMs pretreatment MR images using the least absolute shrinkage and selection operator (LASSO) method, respectively. A DLRM was established by integrating the clinical and morphological MRI predictors, handcrafted and DL signatures based on the multivariate Cox regression coefficients. The Harrell C-index, area under the receiver operating characteristic curve (AUC), and Kaplan-Meier's survival analysis were used to evaluate model performance. The DLRM showed satisfactory performance in predicting RFS and 6- to 18-month intracranial recurrence in lung cancer patients after BrMs resection, achieving a C-index of 0.79 and AUCs of 0.84-0.90 in the training set and a C-index of 0.74 and AUCs of 0.71-0.85 in the external test set. The DLRM outperformed the clinical model, morphological MRI model, handcrafted signature, DL signature, and clinical-morphological MRI model in predicting RFS (P < 0.05). The DLRM successfully classified patients into high-risk and low-risk intracranial recurrence groups (P < 0.001). This MRI-based DLRM could predict RFS in lung cancer patients after surgical resection of BrMs.