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Kong C, Lai L, Jin X, Chen W, Ding J, Zheng L, Zhang D, Ying X, Chen X, Chen M, Tu J, Ji J. Machine Learning Classifier for Preoperative Prediction of Early Recurrence After Bronchial Arterial Chemoembolization Treatment in Lung Cancer Patients. Acad Radiol 2023; 30:2880-2893. [PMID: 37225529 DOI: 10.1016/j.acra.2023.04.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2023] [Revised: 04/12/2023] [Accepted: 04/13/2023] [Indexed: 05/26/2023]
Abstract
RATIONALE AND OBJECTIVES Bronchial arterial chemoembolization (BACE) was deemed as an effective and safe approach for advanced standard treatment-ineligible/rejected lung cancer patients. However, the therapeutic outcome of BACE varies greatly and there is no reliable prognostic tool in clinical practice. This study aimed to investigate the effectiveness of radiomics features in predicting tumor recurrence after BACE treatment in lung cancer patients. MATERIALS AND METHODS A total of 116 patients with pathologically confirmed lung cancer who received BACE treatment were retrospectively recruited. All patients underwent contrast-enhanced CT within 2 weeks before BACE treatment and were followed up for more than 6 months. We conducted a machine learning-based characterization of each lesion on the preoperative contrast-enhanced CT images. In the training cohort, recurrence-related radiomics features were screened by least absolute shrinkage and selection operator (LASSO) regression. Three predictive radiomics signatures were built with linear discriminant analysis (LDA), support vector machine (SVM) and logistic regression (LR) algorithms, respectively. Univariate and multivariate LR analyses were performed to select the independent clinical predictors for recurrence. The radiomics signature with best predictive performance was integrated with the clinical predictors to form a combined model, which was visualized as a nomogram. The performance of the combined model was assessed by receiver operating characteristic curve (ROC), calibration curve, and decision curve analysis (DCA). RESULTS Nine recurrence-related radiomics features were screened out, and three radiomics signatures (RadscoreLDA, RadscoreSVM and RadscoreLR) were built based on these features. Patients were classified into the low-risk and high-risk groups based on the optimal threshold of three signatures. Progression-free survival (PFS) analysis showed that patients of low-risk group achieved longer PFS than patients of high-risk group (P < 0.05). The combined model including RadscoreLDA and independent clinical predictors (tumor size, carcinoembryonic antigen and pro-gastrin releasing peptide) achieved the best predictive performance for recurrence after BACE treatment. It yields AUCs of 0.865 and 0.867 in the training and validation cohorts, with accuracy (ACC) of 0.804 and 0.750, respectively. Calibration curves indicated that the probability of recurrence predicted by the model fits well with the actual recurrence probability. DCA showed that the radiomics nomogram was clinically useful. CONCLUSION The radiomics and clinical predictors-based nomogram can predict tumor recurrence after BACE treatment effectively, which allowing oncologists to identify potential recurrence and enable better patient management and clinical decision-making.
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Affiliation(s)
- Chunli Kong
- Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, Institute of Imaging Diagnosis and Minimally Invasive Intervention Research, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui 323000, China (C.K., L.L., X.J., W.C., J.D., L.Z., D.Z., X.Y., X.C., M.C., J.T., J.J.); Clinical College of The Affiliated Central Hospital, School of Medicine, Lishui University, Lishui 323000, China (C.K., L.L., W.C., J.D., L.Z., D.Z., X.Y., X.C., M.C., J.T., J.J.); Department of Radiology, Lishui Hospital of Zhejiang University, Lishui 323000, China (C.K., L.L., W.C., L.Z., D.Z., X.Y., X.C., M.C., J.T., J.J.)
| | - Linqiang Lai
- Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, Institute of Imaging Diagnosis and Minimally Invasive Intervention Research, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui 323000, China (C.K., L.L., X.J., W.C., J.D., L.Z., D.Z., X.Y., X.C., M.C., J.T., J.J.); Clinical College of The Affiliated Central Hospital, School of Medicine, Lishui University, Lishui 323000, China (C.K., L.L., W.C., J.D., L.Z., D.Z., X.Y., X.C., M.C., J.T., J.J.); Department of Radiology, Lishui Hospital of Zhejiang University, Lishui 323000, China (C.K., L.L., W.C., L.Z., D.Z., X.Y., X.C., M.C., J.T., J.J.)
| | - Xiaofeng Jin
- Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, Institute of Imaging Diagnosis and Minimally Invasive Intervention Research, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui 323000, China (C.K., L.L., X.J., W.C., J.D., L.Z., D.Z., X.Y., X.C., M.C., J.T., J.J.)
| | - Weiyue Chen
- Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, Institute of Imaging Diagnosis and Minimally Invasive Intervention Research, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui 323000, China (C.K., L.L., X.J., W.C., J.D., L.Z., D.Z., X.Y., X.C., M.C., J.T., J.J.); Clinical College of The Affiliated Central Hospital, School of Medicine, Lishui University, Lishui 323000, China (C.K., L.L., W.C., J.D., L.Z., D.Z., X.Y., X.C., M.C., J.T., J.J.); Department of Radiology, Lishui Hospital of Zhejiang University, Lishui 323000, China (C.K., L.L., W.C., L.Z., D.Z., X.Y., X.C., M.C., J.T., J.J.)
| | - Jiayi Ding
- Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, Institute of Imaging Diagnosis and Minimally Invasive Intervention Research, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui 323000, China (C.K., L.L., X.J., W.C., J.D., L.Z., D.Z., X.Y., X.C., M.C., J.T., J.J.); Clinical College of The Affiliated Central Hospital, School of Medicine, Lishui University, Lishui 323000, China (C.K., L.L., W.C., J.D., L.Z., D.Z., X.Y., X.C., M.C., J.T., J.J.)
| | - Liyun Zheng
- Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, Institute of Imaging Diagnosis and Minimally Invasive Intervention Research, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui 323000, China (C.K., L.L., X.J., W.C., J.D., L.Z., D.Z., X.Y., X.C., M.C., J.T., J.J.); Clinical College of The Affiliated Central Hospital, School of Medicine, Lishui University, Lishui 323000, China (C.K., L.L., W.C., J.D., L.Z., D.Z., X.Y., X.C., M.C., J.T., J.J.); Department of Radiology, Lishui Hospital of Zhejiang University, Lishui 323000, China (C.K., L.L., W.C., L.Z., D.Z., X.Y., X.C., M.C., J.T., J.J.)
| | - Dengke Zhang
- Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, Institute of Imaging Diagnosis and Minimally Invasive Intervention Research, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui 323000, China (C.K., L.L., X.J., W.C., J.D., L.Z., D.Z., X.Y., X.C., M.C., J.T., J.J.); Clinical College of The Affiliated Central Hospital, School of Medicine, Lishui University, Lishui 323000, China (C.K., L.L., W.C., J.D., L.Z., D.Z., X.Y., X.C., M.C., J.T., J.J.); Department of Radiology, Lishui Hospital of Zhejiang University, Lishui 323000, China (C.K., L.L., W.C., L.Z., D.Z., X.Y., X.C., M.C., J.T., J.J.)
| | - Xihui Ying
- Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, Institute of Imaging Diagnosis and Minimally Invasive Intervention Research, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui 323000, China (C.K., L.L., X.J., W.C., J.D., L.Z., D.Z., X.Y., X.C., M.C., J.T., J.J.); Clinical College of The Affiliated Central Hospital, School of Medicine, Lishui University, Lishui 323000, China (C.K., L.L., W.C., J.D., L.Z., D.Z., X.Y., X.C., M.C., J.T., J.J.); Department of Radiology, Lishui Hospital of Zhejiang University, Lishui 323000, China (C.K., L.L., W.C., L.Z., D.Z., X.Y., X.C., M.C., J.T., J.J.)
| | - Xiaoxiao Chen
- Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, Institute of Imaging Diagnosis and Minimally Invasive Intervention Research, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui 323000, China (C.K., L.L., X.J., W.C., J.D., L.Z., D.Z., X.Y., X.C., M.C., J.T., J.J.); Clinical College of The Affiliated Central Hospital, School of Medicine, Lishui University, Lishui 323000, China (C.K., L.L., W.C., J.D., L.Z., D.Z., X.Y., X.C., M.C., J.T., J.J.); Department of Radiology, Lishui Hospital of Zhejiang University, Lishui 323000, China (C.K., L.L., W.C., L.Z., D.Z., X.Y., X.C., M.C., J.T., J.J.)
| | - Minjiang Chen
- Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, Institute of Imaging Diagnosis and Minimally Invasive Intervention Research, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui 323000, China (C.K., L.L., X.J., W.C., J.D., L.Z., D.Z., X.Y., X.C., M.C., J.T., J.J.); Clinical College of The Affiliated Central Hospital, School of Medicine, Lishui University, Lishui 323000, China (C.K., L.L., W.C., J.D., L.Z., D.Z., X.Y., X.C., M.C., J.T., J.J.); Department of Radiology, Lishui Hospital of Zhejiang University, Lishui 323000, China (C.K., L.L., W.C., L.Z., D.Z., X.Y., X.C., M.C., J.T., J.J.)
| | - Jianfei Tu
- Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, Institute of Imaging Diagnosis and Minimally Invasive Intervention Research, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui 323000, China (C.K., L.L., X.J., W.C., J.D., L.Z., D.Z., X.Y., X.C., M.C., J.T., J.J.); Clinical College of The Affiliated Central Hospital, School of Medicine, Lishui University, Lishui 323000, China (C.K., L.L., W.C., J.D., L.Z., D.Z., X.Y., X.C., M.C., J.T., J.J.); Department of Radiology, Lishui Hospital of Zhejiang University, Lishui 323000, China (C.K., L.L., W.C., L.Z., D.Z., X.Y., X.C., M.C., J.T., J.J.)
| | - Jiansong Ji
- Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, Institute of Imaging Diagnosis and Minimally Invasive Intervention Research, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui 323000, China (C.K., L.L., X.J., W.C., J.D., L.Z., D.Z., X.Y., X.C., M.C., J.T., J.J.); Clinical College of The Affiliated Central Hospital, School of Medicine, Lishui University, Lishui 323000, China (C.K., L.L., W.C., J.D., L.Z., D.Z., X.Y., X.C., M.C., J.T., J.J.); Department of Radiology, Lishui Hospital of Zhejiang University, Lishui 323000, China (C.K., L.L., W.C., L.Z., D.Z., X.Y., X.C., M.C., J.T., J.J.).
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Ming W, Zhu Y, Bai Y, Gu W, Li F, Hu Z, Xia T, Dai Z, Yu X, Li H, Gu Y, Yuan S, Zhang R, Li H, Zhu W, Ding J, Sun X, Liu Y, Liu H, Liu X. Radiogenomics analysis reveals the associations of dynamic contrast-enhanced–MRI features with gene expression characteristics, PAM50 subtypes, and prognosis of breast cancer. Front Oncol 2022; 12:943326. [PMID: 35965527 PMCID: PMC9366134 DOI: 10.3389/fonc.2022.943326] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2022] [Accepted: 06/29/2022] [Indexed: 11/17/2022] Open
Abstract
Background To investigate reliable associations between dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) features and gene expression characteristics in breast cancer (BC) and to develop and validate classifiers for predicting PAM50 subtypes and prognosis from DCE-MRI non-invasively. Methods Two radiogenomics cohorts with paired DCE-MRI and RNA-sequencing (RNA-seq) data were collected from local and public databases and divided into discovery (n = 174) and validation cohorts (n = 72). Six external datasets (n = 1,443) were used for prognostic validation. Spatial–temporal features of DCE-MRI were extracted, normalized properly, and associated with gene expression to identify the imaging features that can indicate subtypes and prognosis. Results Expression of genes including RBP4, MYBL2, and LINC00993 correlated significantly with DCE-MRI features (q-value < 0.05). Importantly, genes in the cell cycle pathway exhibited a significant association with imaging features (p-value < 0.001). With eight imaging-associated genes (CHEK1, TTK, CDC45, BUB1B, PLK1, E2F1, CDC20, and CDC25A), we developed a radiogenomics prognostic signature that can distinguish BC outcomes in multiple datasets well. High expression of the signature indicated a poor prognosis (p-values < 0.01). Based on DCE-MRI features, we established classifiers to predict BC clinical receptors, PAM50 subtypes, and prognostic gene sets. The imaging-based machine learning classifiers performed well in the independent dataset (areas under the receiver operating characteristic curve (AUCs) of 0.8361, 0.809, 0.7742, and 0.7277 for estrogen receptor (ER), human epidermal growth factor receptor 2 (HER2)-enriched, basal-like, and obtained radiogenomics signature). Furthermore, we developed a prognostic model directly using DCE-MRI features (p-value < 0.0001). Conclusions Our results identified the DCE-MRI features that are robust and associated with the gene expression in BC and displayed the possibility of using the features to predict clinical receptors and PAM50 subtypes and to indicate BC prognosis.
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Affiliation(s)
- Wenlong Ming
- State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing, China
| | - Yanhui Zhu
- Department of Breast Surgery, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Yunfei Bai
- State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing, China
| | - Wanjun Gu
- State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing, China
- Collaborative Innovation Center of Jiangsu Province of Cancer Prevention and Treatment of Chinese Medicine, School of Artificial Intelligence and Information Technology, Nanjing University of Chinese Medicine, Nanjing, China
| | - Fuyu Li
- State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing, China
| | - Zixi Hu
- State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing, China
| | - Tiansong Xia
- Department of Breast Surgery, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Zuolei Dai
- Department of Information, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Xiafei Yu
- Department of Breast Surgery, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Huamei Li
- State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing, China
| | - Yu Gu
- State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing, China
| | - Shaoxun Yuan
- State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing, China
| | - Rongxin Zhang
- State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing, China
| | - Haitao Li
- State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing, China
| | - Wenyong Zhu
- State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing, China
| | - Jianing Ding
- State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing, China
| | - Xiao Sun
- State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing, China
| | - Yun Liu
- Department of Information, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
- *Correspondence: Yun Liu, ; Hongde Liu, ; Xiaoan Liu,
| | - Hongde Liu
- State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing, China
- *Correspondence: Yun Liu, ; Hongde Liu, ; Xiaoan Liu,
| | - Xiaoan Liu
- Department of Breast Surgery, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
- *Correspondence: Yun Liu, ; Hongde Liu, ; Xiaoan Liu,
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