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Hu Z, Li L, Sui A, Wu G, Wang Y, Shi Z, Yu J, Chen L, Yang G, Sun Y. OCIF: automatically learning the optimized clinical information fusion method for computer-aided diagnosis tasks. Int J Comput Assist Radiol Surg 2023; 18:2273-2286. [PMID: 37603163 DOI: 10.1007/s11548-023-02985-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2022] [Accepted: 06/12/2023] [Indexed: 08/22/2023]
Abstract
PURPOSE In computer-aided diagnosis, the fusion of image features extracted from neural networks and clinical information is crucial to improve diagnostic accuracy. How to integrate low-dimensional clinical information (LDCF) with high-dimensional network features (HDNF) is an urgent problem to be solved. We offer a new network search framework to address this problem, which can provide optimized LDCF fusion and efficient dimensionality reduction in HDNF. METHODS OCIF innovatively uses Gaussian process optimization to explore the search space for the number of fully connected (FC) layers, the number of neurons in each FC layer, the activation function, the dropout factor, and whether to add clinical information to each FC layer. Moreover, OCIF employs transfer learning to reduce the training parameter space and improve search efficiency. To evaluate the effectiveness of the proposed OCIF, we utilized three popular end-to-end overall survival (OS) time prediction models to predict the three classes. RESULTS Our experimental results show that applying OCIF to a classical computer-aided diagnosis neural network can improve classification accuracy. Experiments on the 2020 BRATS dataset prove that OCIF achieves satisfactory performance, with an accuracy of 0.684, precision of 0.735, recall of 0.684, and F1-score of 0.675 on the OS time prediction task. CONCLUSION OCIF effectively and creatively combines clinical information and network features, leveraging both clinical information and image features to enhance the accuracy of the final diagnosis. Our experiments demonstrate that the use of OCIF can significantly improve computer-aided diagnosis accuracy, and the approach has the potential to be extended to other medical classification tasks as well.
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Affiliation(s)
- Zhaoyu Hu
- School of Information Science and Technology, Fudan University, Shanghai, China
| | - Leyin Li
- School of Information Science and Technology, Fudan University, Shanghai, China
| | - An Sui
- School of Information Science and Technology, Fudan University, Shanghai, China
| | - Guoqing Wu
- School of Information Science and Technology, Fudan University, Shanghai, China
| | - Yuanyuan Wang
- School of Information Science and Technology, Fudan University, Shanghai, China
| | - Zhifeng Shi
- Department of Neurosurgery Huashan Hospital, Fudan University, Shanghai, China
| | - Jinhua Yu
- School of Information Science and Technology, Fudan University, Shanghai, China
| | - Liang Chen
- Department of Neurosurgery Huashan Hospital, Fudan University, Shanghai, China.
| | - Guiguan Yang
- Xingguo County People's Hospital, Ganzhou, Jiangxi, China.
| | - Yuhao Sun
- Department of Neurosurgery Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
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Ren J, Zhai X, Yin H, Zhou F, Hu Y, Wang K, Yan R, Han D. Multimodality MRI Radiomics Based on Machine Learning for Identifying True Tumor Recurrence and Treatment-Related Effects in Patients with Postoperative Glioma. Neurol Ther 2023; 12:1729-1743. [PMID: 37488335 PMCID: PMC10444917 DOI: 10.1007/s40120-023-00524-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/20/2023] [Accepted: 06/28/2023] [Indexed: 07/26/2023] Open
Abstract
INTRODUCTION Conventional magnetic resonance imaging (MRI) features have difficulty distinguishing glioma true tumor recurrence (TuR) from treatment-related effects (TrE). We aimed to develop a machine-learning model based on multimodality MRI radiomics to help improve the efficiency of identifying glioma TuR. METHODS A total of 131 patients were enrolled and randomly divided into the training set (n = 91) and the test set (n = 40). Radiomic features were extracted from the postoperative enhancement (PoE) region and edema (ED) region from four routine MRI sequences. After analyses of Spearman's rank correlation coefficient, and least absolute shrinkage and selection operator, the key radiomic features were selected to construct support vector machine (SVM) and k-nearest neighbor (KNN) models. Decision curve analysis (DCA) and receiver operating characteristic (ROC) curves were used to analyze the performance. RESULTS The PoE model had a significantly higher area under curve (AUC) than the ED model (p < 0.05). Among the models constructed with a single sequence, the model using PoE regional features from CE-T1WI was superior to other models, with an AUC of 0.905 for SVM and 0.899 for KNN. In multimodality models, the PoE model outperformed the ED model with an AUC of 0.931 for SVM and 0.896 for KNN. The multimodality model, which combined routine sequences and the whole regional features, showed a slightly better performance with an AUC of 0.965 for SVM and 0.955 for KNN. Decision curve analysis showed the good clinical utility of multimodal radiomics models. CONCLUSIONS Multimodality radiomics can identify glioma TuR and TrE, potentially aiding clinical decision-making for individualized treatment. And edematous regions may provide useful information for recognizing recurrence. RETROSPECTIVELY REGISTERED 2021.04.15, No:2020039.
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Affiliation(s)
- Jinfa Ren
- Department of MR, The First Affiliated Hospital of Xinxiang Medical University, No.88 Health Road, Weihui, 453100, China
| | - Xiaoyang Zhai
- Department of MR, The First Affiliated Hospital of Xinxiang Medical University, No.88 Health Road, Weihui, 453100, China
| | - Huijia Yin
- Department of MR, The First Affiliated Hospital of Xinxiang Medical University, No.88 Health Road, Weihui, 453100, China
| | - Fengmei Zhou
- Department of MR, The First Affiliated Hospital of Xinxiang Medical University, No.88 Health Road, Weihui, 453100, China
| | - Ying Hu
- Department of Radiology, The First Affiliated Hospital of Xinxiang Medical University, Weihui, China
| | - Kaiyu Wang
- MR Research China, GE Healthcare, Beijing, China
| | - Ruifang Yan
- Department of MR, The First Affiliated Hospital of Xinxiang Medical University, No.88 Health Road, Weihui, 453100, China
| | - Dongming Han
- Department of MR, The First Affiliated Hospital of Xinxiang Medical University, No.88 Health Road, Weihui, 453100, China.
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Perillo T, de Giorgi M, Papace UM, Serino A, Cuocolo R, Manto A. Current role of machine learning and radiogenomics in precision neuro-oncology. EXPLORATION OF TARGETED ANTI-TUMOR THERAPY 2023; 4:545-555. [PMID: 37720347 PMCID: PMC10501892 DOI: 10.37349/etat.2023.00151] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2022] [Accepted: 04/20/2023] [Indexed: 09/19/2023] Open
Abstract
In the past few years, artificial intelligence (AI) has been increasingly used to create tools that can enhance workflow in medicine. In particular, neuro-oncology has benefited from the use of AI and especially machine learning (ML) and radiogenomics, which are subfields of AI. ML can be used to develop algorithms that dynamically learn from available medical data in order to automatically do specific tasks. On the other hand, radiogenomics can identify relationships between tumor genetics and imaging features, thus possibly giving new insights into the pathophysiology of tumors. Therefore, ML and radiogenomics could help treatment tailoring, which is crucial in personalized neuro-oncology. The aim of this review is to illustrate current and possible future applications of ML and radiomics in neuro-oncology.
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Affiliation(s)
- Teresa Perillo
- Department of Neuroradiology, “Umberto I” Hospital, 84014 Norcera Inferiore, Italy
| | - Marco de Giorgi
- Department of Advanced Biomedical Sciences, University of Naples “Federico II”, 80138 Naples, Italy
| | - Umberto Maria Papace
- Department of Advanced Biomedical Sciences, University of Naples “Federico II”, 80138 Naples, Italy
| | - Antonietta Serino
- Department of Neuroradiology, “Umberto I” Hospital, 84014 Norcera Inferiore, Italy
| | - Renato Cuocolo
- Department of Medicine, Surgery, and Dentistry, University of Salerno, 84084 Fisciano, Italy
| | - Andrea Manto
- Department of Neuroradiology, “Umberto I” Hospital, 84014 Norcera Inferiore, Italy
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Wang L, Ma X, Feng B, Wang S, Liang M, Li D, Wang S, Zhao X. Multi-Sequence MR-Based Radiomics Signature for Predicting Early Recurrence in Solitary Hepatocellular Carcinoma ≤5 cm. Front Oncol 2022; 12:899404. [PMID: 35756618 PMCID: PMC9213728 DOI: 10.3389/fonc.2022.899404] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2022] [Accepted: 05/17/2022] [Indexed: 01/27/2023] Open
Abstract
Purpose To investigate the value of radiomics features derived from preoperative multi-sequence MR images for predicting early recurrence (ER) in patients with solitary hepatocellular carcinoma (HCC) ≤5 cm. Methods One hundred and ninety HCC patients were enrolled and allocated to training and validation sets (n = 133:57). The clinical–radiological model was established by significant clinical risk characteristics and qualitative imaging features. The radiomics model was constructed using the least absolute shrinkage and selection operator (LASSO) logistic regression algorithm in the training set. The combined model was formed by integrating the clinical–radiological risk factors and selected radiomics features. The predictive performance was assessed by the area under the receiver operating characteristic curve (AUC). Results Arterial peritumoral hyperenhancement, non-smooth tumor margin, satellite nodules, cirrhosis, serosal invasion, and albumin showed a significant correlation with ER. The AUC of the clinical–radiological model was 0.77 (95% CI: 0.69–0.85) and 0.76 (95% CI: 0.64–0.88) in the training and validation sets, respectively. The radiomics model constructed using 12 radiomics features selected by LASSO regression had an AUC of 0.85 (95% CI: 0.79–0.91) and 0.84 (95% CI: 0.73–0.95) in the training and validation sets, respectively. The combined model further improved the prediction performance compared with the clinical–radiological model, increasing AUC to 0.90 (95% CI: 0.85–0.95) in the training set and 0.88 (95% CI: 0.80–0.97) in the validation set (p < 0.001 and p = 0.012, respectively). The calibration curve fits well with the standard curve. Conclusions The predictive model incorporated the clinical–radiological risk factors and radiomics features that could adequately predict the individualized ER risk in patients with solitary HCC ≤5 cm.
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Affiliation(s)
- Leyao Wang
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Xiaohong Ma
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Bing Feng
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Shuang Wang
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Meng Liang
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Dengfeng Li
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Sicong Wang
- Magnetic Resonance Imaging Research, General Electric Healthcare, Beijing, China
| | - Xinming Zhao
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
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