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Yu Z, Zhihui Q, Linrui L, Long L, Qibing W. Machine Learning-Based Models for Assessing Postoperative Risk Factors in Patients with Cervical Cancer. Acad Radiol 2024; 31:1410-1418. [PMID: 37891091 DOI: 10.1016/j.acra.2023.09.031] [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: 08/22/2023] [Revised: 09/21/2023] [Accepted: 09/21/2023] [Indexed: 10/29/2023]
Abstract
RATIONALE AND OBJECTIVES To investigate the value of machine learning-based radiomics, intravoxel incoherent motion (IVIM) diffusion-weighted imaging and its combined model in predicting the postoperative risk factors of parametrial infiltration (PI), lymph node metastasis (LNM), deep muscle invasion (DMI), lymph-vascular space invasion (LVSI), pathological type (PT), differentiation degree (DD), and Ki-67 expression level in patients with cervical cancer. MATERIALS AND METHODS The data of 180 patients with cervical cancer were retrospectively analyzed and randomized 2:1 into a training and validation group. The IVIM-DWI and radiomics parameters of primary lesions were measured in all patients. Seven machine learning methods were used to calculate the optimal radiomics score (Rad-score), which was combined with IVIM-DWI and clinical parameters to construct nomograms for predicting the risk factors of cervical cancer, with internal and external validation. RESULTS The diagnostic efficacy of the nomograms based on clinical and imaging parameters was significantly better than MRI assessment alone. The area under the curve (AUC) of nomograms and MRI for the assessment of PI, LNM, and DMI were 0.981 vs 0.868, 0.848 vs 0.639, and 0.896 vs 0.780, respectively. Nomograms also performed well in the assessment of LVSI, PT, DD, and Ki-67 expression levels, with AUC of 0.796, 0.854, 0.806, 0.839 and 0.840, 0.856, 0.810, 0.832 in the training and validation groups. CONCLUSION Machine learning-based nomograms can serve as a useful tool for assessing postoperative risk factors in patients with cervical cancer.
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Affiliation(s)
- Zhang Yu
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, 81 Meishan Road, Hefei, Anhui 230000, China (Z.Y., Q.Z., L.L., W.Q.); Department of Radiology, West Branch of the First Affiliated Hospital of the University of Science and Technology of China, Hefei, Anhui 230001, China (Z.Y., L.L.)
| | - Qin Zhihui
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, 81 Meishan Road, Hefei, Anhui 230000, China (Z.Y., Q.Z., L.L., W.Q.)
| | - Li Linrui
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, 81 Meishan Road, Hefei, Anhui 230000, China (Z.Y., Q.Z., L.L., W.Q.); Department of Radiology, West Branch of the First Affiliated Hospital of the University of Science and Technology of China, Hefei, Anhui 230001, China (Z.Y., L.L.)
| | - Liu Long
- Department of Hepatobiliary and Pancreatic Surgery, The Second Hospital of Zhejiang University, Binjiang District, Zhejiang 310000, China (L.L.)
| | - Wu Qibing
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, 81 Meishan Road, Hefei, Anhui 230000, China (Z.Y., Q.Z., L.L., W.Q.).
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Fu H, Shen Z, Lai R, Zhou T, Huang Y, Zhao S, Mo R, Cai M, Jiang S, Wang J, Du B, Qian C, Chen Y, Yan F, Xiang X, Li R, Xie Q. Clinic-radiomics model using liver magnetic resonance imaging helps predict chronicity of drug-induced liver injury. Hepatol Int 2023; 17:1626-1636. [PMID: 37188998 DOI: 10.1007/s12072-023-10539-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/14/2023] [Accepted: 04/08/2023] [Indexed: 05/17/2023]
Abstract
BACKGROUND AND AIMS Some drug-induced liver injury (DILI) cases may become chronic, even after drug withdrawal. Radiomics can predict liver disease progression. We established and validated a predictive model incorporating the clinical characteristics and radiomics features for predicting chronic DILI. METHODS One hundred sixty-eight DILI patients who underwent liver gadolinium-diethylenetriamine pentaacetate-enhanced magnetic resonance imaging were recruited. The patients were clinically diagnosed using the Roussel Uclaf causality assessment method. Patients who progressed to chronicity or recovery were randomly divided into the training (70%) and validation (30%) cohorts, respectively. Hepatic T1-weighted images were segmented to extract 1672 radiomics features. Least absolute shrinkage and selection operator regression was used for feature selection, and Rad-score was constructed using support vector machines. Multivariable logistic regression analysis was performed to build a clinic-radiomics model incorporating clinical characteristics and Rad-scores. The clinic-radiomics model was evaluated for its discrimination, calibration, and clinical usefulness in the independent validation set. RESULTS Of 1672 radiomics features, 28 were selected to develop the Rad-score. Cholestatic/mixed patterns and Rad-score were independent risk factors of chronic DILI. The clinic-radiomics model, including the Rad-score and injury patterns, distinguished chronic from recovered DILI patients in the training (area under the receiver operating characteristic curve [AUROC]: 0.89, 95% confidence interval [95% CI]: 0.87-0.92) and validation (AUROC: 0.88, 95% CI: 0.83-0.91) cohorts with good calibration and great clinical utility. CONCLUSION The clinic-radiomics model yielded sufficient accuracy for predicting chronic DILI, providing a practical and non-invasive tool for managing DILI patients.
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Affiliation(s)
- Haoshuang Fu
- Department of Infectious Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Zhehan Shen
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
- College of Health Science and Technology, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Rongtao Lai
- Department of Infectious Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Tianhui Zhou
- Department of Infectious Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Yan Huang
- Department of Infectious Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Shuang Zhao
- Department of Infectious Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Ruidong Mo
- Department of Infectious Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Minghao Cai
- Department of Infectious Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Shaowen Jiang
- Department of Infectious Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Jiexiao Wang
- Department of Infectious Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Bingying Du
- Department of Infectious Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Cong Qian
- Department of Infectious Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Yaoxing Chen
- Department of Infectious Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Fuhua Yan
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
- College of Health Science and Technology, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Xiaogang Xiang
- Department of Infectious Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China.
| | - Ruokun Li
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China.
- College of Health Science and Technology, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
| | - Qing Xie
- Department of Infectious Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China.
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Zhang Y, Liu L, Zhang K, Su R, Jia H, Qian L, Dong J. Nomograms Combining Clinical and Imaging Parameters to Predict Recurrence and Disease-free Survival After Concurrent Chemoradiotherapy in Patients With Locally Advanced Cervical Cancer. Acad Radiol 2023; 30:499-508. [PMID: 36050264 DOI: 10.1016/j.acra.2022.08.002] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2022] [Revised: 07/31/2022] [Accepted: 08/01/2022] [Indexed: 01/27/2023]
Abstract
PURPOSES To investigate the value of nomograms based on clinical prognostic factors (CPF), intravoxel incoherent motion diffusion weighted imaging (IVIM-DWI) and MRI-derived radiomics in predicting recurrence and disease-free survival (DFS) after concurrent chemoradiotherapy (CCRT) for locally advanced cervical cancer (LACC). METHODS Retrospective analysis of data from 115 patients with ⅠB-ⅣA cervical cancer who underwent CCRT and had been followed up consistently. All patients were randomized 2:1 into training and validation groups. Pre-treatment IVIM-DWI parameters (ADC-value, D-value, D*-value and f-value) and pre- and post-treatment three-dimensional radiomics parameters (from axial T2WI) of primary lesions were measured. The LASSO algorithm and Logistic regression analysis were used to filter texture features and calculate radiomics score (Rad-score). Multivariate Logistic and Cox regression analysis was used to construct nomograms to predict recurrence and DFS for patients with LACC after CCRT respectively, with internal and external validation. RESULTS External beam radiotherapy dose, f-value, pre-treatment and post-treatment Rad-score were independent prognostic factors for recurrence and DFS in patients with cervical cancer, forming Model1 and Model2, with OR values of 0.480, 1.318, 3.071, 3.200 and HR values of 0.322, 3.372, 5.138, 7.204. The area under the curve (AUC) of Model1 for predicting recurrence of cervical cancer was 0.977, with internal and external validation C-indexes of 0.977 and 0.962. The AUC for Model2 predicting disease-free survival (DFS) at 1, 3, and 5 years was 0.895, 0.888 and 0.916 respectively, with internal and external C-indexes of 0.860 and 0.892. The decision curves analysis and clinical impact curves further indicate the high predictive efficiency and stability of nomograms. CONCLUSION The nomograms based on clinical, IVIM-DWI and radiomics parameters have high clinical value in predicting recurrence and DFS of patients with LACC after CCRT and can provide a reference for prognostic assessment and individualized treatment of cervical cancer patients.
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Affiliation(s)
- Yu Zhang
- Department of Radiation Oncology, Anhui Provincial Hospital Affiliated to Anhui Medical University, Hefei, Anhui, 230001, China
| | - Long Liu
- Department of Hepatobiliary Surgery, Taizhou Hospital of Zhejiang University, Taizhou, Zhejiang, China
| | - Kaiyue Zhang
- Department of Radiation Oncology, Anhui Provincial Hospital Affiliated to Anhui Medical University, Hefei, Anhui, 230001, China
| | - Rixin Su
- Department of Radiation Oncology, Anhui Provincial Hospital Affiliated to Anhui Medical University, Hefei, Anhui, 230001, China
| | - Haodong Jia
- Department of Radiation Oncology, Anhui Provincial Hospital Affiliated to Anhui Medical University, Hefei, Anhui, 230001, China; Department of Radiology, the First Affiliated Hospital of University of Science and Technology of China, Hefei, Anhui, China
| | - Liting Qian
- Department of Radiation Oncology, Anhui Provincial Hospital Affiliated to Anhui Medical University, Hefei, Anhui, 230001, China
| | - Jiangning Dong
- Department of Radiation Oncology, Anhui Provincial Hospital Affiliated to Anhui Medical University, Hefei, Anhui, 230001, China; Department of Radiology, the First Affiliated Hospital of University of Science and Technology of China, Hefei, Anhui, China.
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Elkady RM. Radiomics Analysis in Evaluation of Cervical Cancer: A Further Step on the Road. Acad Radiol 2022; 29:1141-1142. [PMID: 35307261 DOI: 10.1016/j.acra.2022.02.018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2022] [Accepted: 02/19/2022] [Indexed: 11/30/2022]
Affiliation(s)
- Reem M Elkady
- Department of radiology, Faculty of medicine, Assiut University, Assiut, Egypt & Department of radiology and medical imaging, College of medicine, Taibah University, Madinah, Saudi Arabia.
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