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Song H, Wang X, Wu R, Liu W. The influence of manual segmentation strategies and different phases selection on machine learning-based computed tomography in renal tumors: a systematic review and meta-analysis. LA RADIOLOGIA MEDICA 2024:10.1007/s11547-024-01825-8. [PMID: 38740709 DOI: 10.1007/s11547-024-01825-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/04/2023] [Accepted: 04/29/2024] [Indexed: 05/16/2024]
Abstract
BACKGROUND Delineating the region/volume of interest (ROI/VOI) and selecting the phases are of importance in developing machine learning (ML). The results will change when choosing different methods of drawing the ROI/VOI and selecting different phases. However, there is no related standard for delineating the ROI/VOI and selecting the phases in renal tumors to develop ML based on computed tomography (CT). METHODS The PubMed and Web of Science were searched for related studies published until March 1, 2023. Inclusion criteria were studies that developed ML models in renal tumors from CT images. And the binary diagnostic accuracy data were extracted to obtain the outcomes, such as sensitivity (SE), specificity (SP), accuracy (ACC), and area under the curve (AUC). RESULTS Twenty-three papers were included in the meta-analysis with a pooled SE of 87% (95% CI 85-88%), SP of 82% (95% CI 79-85%), and AUC of 91% (95% CI 89-93%) in phases; a pooled SE of 82% (95% CI 80-84%), SP of 85% (95% CI 83-86%), and AUC of 90% (95% CI 88-93%) in phases combined with delineating strategies, respectively. In all different combinations, the contour-focused and single phase produce the highest AUC of 93% (95% CI 90-95%). In subgroup analyses (sample size, year of publication, and geographical distribution), the performance was acceptable on phases and phases combined strategies. CONCLUSIONS To explore the effect of manual segmentation strategies and different phases selection on ML-based CT, we find that the method of single phase (CMP or NP) combined with contour-focused was considered a better strategy compared to the other strategies.
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Affiliation(s)
- Honghao Song
- Department of Pediatric Surgery, Shandong Provincial Hospital, Shandong University, Jinan, 250021, Shandong, People's Republic of China
| | - Xiaoqing Wang
- Department of Pediatric Surgery, Shandong Provincial Hospital Affiliated to Shandong First Medical University, 324 Jingwu Street, Jinan, 250021, Shandong, People's Republic of China
- Post-doctoral Research Station of Clinical Medicine, Liaocheng People's Hospital, Liaocheng, 252004, Shandong, People's Republic of China
| | - Rongde Wu
- Department of Pediatric Surgery, Shandong Provincial Hospital Affiliated to Shandong First Medical University, 324 Jingwu Street, Jinan, 250021, Shandong, People's Republic of China
| | - Wei Liu
- Department of Pediatric Surgery, Shandong Provincial Hospital Affiliated to Shandong First Medical University, 324 Jingwu Street, Jinan, 250021, Shandong, People's Republic of China.
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Pan L, Chen M, Sun J, Jin P, Ding J, Cai P, Chen J, Xing W. Prediction of Fuhrman grade of renal clear cell carcinoma by multimodal MRI radiomics: a retrospective study. Clin Radiol 2024; 79:e273-e281. [PMID: 38065776 DOI: 10.1016/j.crad.2023.11.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2023] [Revised: 10/16/2023] [Accepted: 11/05/2023] [Indexed: 01/02/2024]
Abstract
AIM To explore the value of multimodal magnetic resonance imaging (MRI) radiomics combined with traditional radiologist-defined semantic characteristics and conventional (cMRI) and functional MRI (fMRI) texture features in predicting Fuhrman grade of clear cell renal cell carcinoma (ccRCC). MATERIALS AND METHODS The data of 89 patients with histopathologically proven ccRCC (low-grade, 54; high-grade, 35) were collected. Texture features were extracted from cMRI (T1- and T2-weighted imaging) and fMRI (Dixon-MRI; blood-oxygen-level dependent [BOLD]-MRI; and susceptibility-weighted imaging [SWI]) images, and the traditional characteristics (TC) were evaluated. Logistic regression analysis was performed to develop models based on TC, cMRI, and fMRI texture features for grading. Receiver operating characteristic (ROC) curve analysis and leave-group-out cross-validation (LGOCV) were performed to test the reliability of combined models. RESULTS Two T2-weighted imaging-based, two Dixon_W-based, one Dixon_F-based, one BOLD-based, and three SWI-based texture features, and three TC were extracted for feature selection. TC, cMRI, fMRI, cMRI+fMRI, cMRI+TC, fMRI+TC, and cMRI+fMRI+TC models were constructed. The AUC of the cMRI+fMRI+TC model for differentiating high- from low-grade ccRCC was 0.74, with 81.42% accuracy, 75.93% sensitivity, and 91.43% specificity. The fMRI+TC model exhibited a performance similar to that of the cMRI+fMRI+TC model (p>0.05). The areas under the curve (AUCs) of the fMRI+TC and cMRI+fMRI+TC models were significantly higher than those of the other five models (all p<0.05). For the cMRI+fMRI+TC model, the mean accuracy was 85.40% after 100 LGOCV for the test sets. CONCLUSION Multimodal MRI radiomics combined with TC, cMRI, and fMRI texture features may be a reliable quantitative approach for differentiating high-grade ccRCC from low-grade ccRCC.
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Affiliation(s)
- L Pan
- Department of Radiology, Third Affiliated Hospital of Soochow University, Changzhou 213003, Jiangsu, China
| | - M Chen
- Department of Radiology, Third Affiliated Hospital of Soochow University, Changzhou 213003, Jiangsu, China
| | - J Sun
- Department of Radiology, Third Affiliated Hospital of Soochow University, Changzhou 213003, Jiangsu, China
| | - P Jin
- Department of Public Health, Nanjing Medical University, Nanjing 211166, Jiangsu, China
| | - J Ding
- Department of Radiology, Third Affiliated Hospital of Soochow University, Changzhou 213003, Jiangsu, China
| | - P Cai
- Department of Radiology, Third Affiliated Hospital of Soochow University, Changzhou 213003, Jiangsu, China
| | - J Chen
- Department of Radiology, Third Affiliated Hospital of Soochow University, Changzhou 213003, Jiangsu, China.
| | - W Xing
- Department of Radiology, Third Affiliated Hospital of Soochow University, Changzhou 213003, Jiangsu, China.
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Yang S, Jian Y, Yang F, Liu R, Zhang W, Wang J, Tan X, Wu J, Chen Y, Zhou X. Radiomics analysis based on single phase and different phase combinations of radiomics features from tri-phasic CT to distinguish renal oncocytoma from chromophobe renal cell carcinoma. Abdom Radiol (NY) 2024; 49:182-191. [PMID: 37907684 DOI: 10.1007/s00261-023-04053-2] [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: 06/14/2023] [Revised: 09/10/2023] [Accepted: 09/11/2023] [Indexed: 11/02/2023]
Abstract
OBJECTIVES To investigate different radiomics models based on single phase and the different phase combinations of radiomics features from 3D tri-phasic CT to distinguish RO from chRCC. METHODS A total of 96 patients (30 RO and 66 chRCC) were enrolled in this study. Radiomics features were extracted from unenhanced phase (UP), corticomedullary phase (CMP), and nephrographic phase (NP) CT images. Feature selection was based on the least absolute shrinkage and selection operator regression (LASSO) method. The selected features were used to develop different radiomics models using logistic regression (LR) analysis, including model 1 (UP), model 2(CMP), model 3(NP), model 4(UP+CMP), model 5(UP+NP), model 6(CMP+NP), and model 7(UP+CMP+NP). The radiomics model demonstrating the highest discrimination performance was utilized to construct the combined model (model 8) with clinical factors. A nomogram based on the model 8 was established. To evaluate the diagnostic performance of the different models, the receiver operating characteristic (ROC) curve and decision curve analysis (DCA) were used. Delong's test was utilized to assess the statistical significance of the AUC improvement across the models. RESULTS Among the seven radiomics models, model 7 exhibited the highest AUC of 0.84 (95% CI 0.69, 0.99), and model 7 demonstrated a significantly superior AUC compared to the other radiomics models (all P < 0.05). The AUC values of radiomics models based on two phases (model4, mode5, mode6) were greater than the models based on single phase (model1, mode2, mode3) (all P < 0.05). Model 3 illustrated the best performance of the three radiomics models based on single phase with an AUC of 0.76 (95% CI 0.57, 099). Model 6 illustrated the best performance of the three radiomics models based on two-phases combination with an AUC of 0.83 (0.66, 0.99). Model 8 achieved an AUC of 0.93 (95% CI 0.83, 1.00) which is higher than those all radiomics models. CONCLUSION Radiomics models based on combination of radiomics features from UP, CMP, and NP can be a useful and promising technique to differentiate RO from chRCC. Moreover, the model combining clinical factors and radiomics features showed better classification performance to distinguish them.
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Affiliation(s)
- Suping Yang
- Department of Radiology, The Second Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Yuanxi Jian
- Department of Radiology, The Second Affiliated Hospital of Kunming Medical University, Kunming, China.
| | - Fan Yang
- Department of Radiology, The Second Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Rui Liu
- Department of Radiology, The Second Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Wenqing Zhang
- Department of Radiology, The Second Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Jiaping Wang
- Department of Radiology, The Second Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Xin Tan
- Department of Radiology, The Second Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Junlin Wu
- Department of Radiology, The Second Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Yuan Chen
- Department of Radiology, The Second Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Xiaowen Zhou
- Department of Radiology, The Second Affiliated Hospital of Kunming Medical University, Kunming, China
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Yang H, Wu K, Liu H, Wu P, Yuan Y, Wang L, Liu Y, Zeng H, Li J, Liu W, Wu S. An automated surgical decision-making framework for partial or radical nephrectomy based on 3D-CT multi-level anatomical features in renal cell carcinoma. Eur Radiol 2023; 33:7532-7541. [PMID: 37289245 PMCID: PMC10598088 DOI: 10.1007/s00330-023-09812-9] [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: 02/09/2023] [Revised: 03/14/2023] [Accepted: 03/27/2023] [Indexed: 06/09/2023]
Abstract
OBJECTIVES To determine whether 3D-CT multi-level anatomical features can provide a more accurate prediction of surgical decision-making for partial or radical nephrectomy in renal cell carcinoma. METHODS This is a retrospective study based on multi-center cohorts. A total of 473 participants with pathologically proved renal cell carcinoma were split into the internal training and the external testing set. The training set contains 412 cases from five open-source cohorts and two local hospitals. The external testing set includes 61 participants from another local hospital. The proposed automatic analytic framework contains the following modules: a 3D kidney and tumor segmentation model constructed by 3D-UNet, a multi-level feature extractor based on the region of interest, and a partial or radical nephrectomy prediction classifier by XGBoost. The fivefold cross-validation strategy was used to get a robust model. A quantitative model interpretation method called the Shapley Additive Explanations was conducted to explore the contribution of each feature. RESULTS In the prediction of partial versus radical nephrectomy, the combination of multi-level features achieved better performance than any single-level feature. For the internal validation, the AUROC was 0.93 ± 0.1, 0.94 ± 0.1, 0.93 ± 0.1, 0.93 ± 0.1, and 0.93 ± 0.1, respectively, as determined by the fivefold cross-validation. The AUROC from the optimal model was 0.82 ± 0.1 in the external testing set. The tumor shape Maximum 3D Diameter plays the most vital role in the model decision. CONCLUSIONS The automated surgical decision framework for partial or radical nephrectomy based on 3D-CT multi-level anatomical features exhibits robust performance in renal cell carcinoma. The framework points the way towards guiding surgery through medical images and machine learning. CLINICAL RELEVANCE STATEMENT We proposed an automated analytic framework that can assist surgeons in partial or radical nephrectomy decision-making. The framework points the way towards guiding surgery through medical images and machine learning. KEY POINTS • The 3D-CT multi-level anatomical features provide a more accurate prediction of surgical decision-making for partial or radical nephrectomy in renal cell carcinoma. • The data from multicenter study and a strict fivefold cross-validation strategy, both internal validation set and external testing set, can be easily transferred to different tasks of new datasets. • The quantitative decomposition of the prediction model was conducted to explore the contribution of each extracted feature.
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Affiliation(s)
- Huancheng Yang
- Teaching Center of Shenzhen Luohu Hospital, Shantou University Medical College, Shantou, 515000, China
- Department of Urology, The Third Affiliated Hospital of Shenzhen University (Luohu Hospital Group), Shenzhen, 518000, China
- Shantou University Medical College, Shantou University, Shantou, 515000, China
- Shenzhen Following Precision Medical Research Institute, Luohu Hospital Group, Shenzhen, 51800, China
| | - Kai Wu
- Department of Urology, The Third Affiliated Hospital of Shenzhen University (Luohu Hospital Group), Shenzhen, 518000, China
- Shenzhen Following Precision Medical Research Institute, Luohu Hospital Group, Shenzhen, 51800, China
| | - Hanlin Liu
- Department of Radiology, The Third Affiliated Hospital of Shenzhen University (Luohu Hospital Group), Shenzhen, 518000, China
| | - Peng Wu
- Department of Urology, The Third Affiliated Hospital of Shenzhen University (Luohu Hospital Group), Shenzhen, 518000, China
- Shenzhen Following Precision Medical Research Institute, Luohu Hospital Group, Shenzhen, 51800, China
| | - Yangguang Yuan
- Department of Radiology, The Third Affiliated Hospital of Shenzhen University (Luohu Hospital Group), Shenzhen, 518000, China
| | - Lei Wang
- Department of Radiology, The Third Affiliated Hospital of Shenzhen University (Luohu Hospital Group), Shenzhen, 518000, China
| | - Yaru Liu
- Department of Radiology, The Third Affiliated Hospital of Shenzhen University (Luohu Hospital Group), Shenzhen, 518000, China
| | - Haoyang Zeng
- Teaching Center of Shenzhen Luohu Hospital, Shantou University Medical College, Shantou, 515000, China
- Shantou University Medical College, Shantou University, Shantou, 515000, China
| | - Junkai Li
- Teaching Center of Shenzhen Luohu Hospital, Shantou University Medical College, Shantou, 515000, China
- Shantou University Medical College, Shantou University, Shantou, 515000, China
| | - Weihao Liu
- Teaching Center of Shenzhen Luohu Hospital, Shantou University Medical College, Shantou, 515000, China
- Shantou University Medical College, Shantou University, Shantou, 515000, China
| | - Song Wu
- Teaching Center of Shenzhen Luohu Hospital, Shantou University Medical College, Shantou, 515000, China.
- Shenzhen Following Precision Medical Research Institute, Luohu Hospital Group, Shenzhen, 51800, China.
- Department of Urology, South China Hospital, Health Science Center, Shenzhen University, Shenzhen, 518116, China.
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Ludwig DR, Thacker Y, Luo C, Narra A, Mintz AJ, Siegel CL. CT-derived textural analysis parameters discriminate high-attenuation renal cysts from solid renal neoplasms. Clin Radiol 2023; 78:e782-e790. [PMID: 37586966 DOI: 10.1016/j.crad.2023.07.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2023] [Revised: 05/15/2023] [Accepted: 07/05/2023] [Indexed: 08/18/2023]
Abstract
AIM To assess the utility of textural features on computed tomography (CT) to differentiate high-attenuation cysts from solid renal neoplasms among indeterminate renal lesions detected incidentally on CT. MATERIALS AND METHODS Patients were included if they had an indeterminate renal lesion on CT that was subsequently characterised on ultrasound or magnetic resonance imaging (MRI). Up to three lesions per patient were included if they had a size ≥10 mm and density of 20-70 HU on unenhanced CT or any single phase of contrast-enhanced CT. Cases were categorised as benign or most likely benign cysts (Bosniak II and IIF) versus indeterminate (Bosniak III), mixed solid and cystic (Bosniak IV), or solid renal lesions. A random forest model was generated using 95 textural parameters and four clinical parameters for each lesion. RESULTS Two hundred and thirty-four patients were included who had a total of 278 lesions. Of these, 193 (69%) were benign or most likely benign cysts and 85 (31%) were indeterminate, mixed cystic and solid, or solid renal lesions. The random forest model had an area under the curve of 0.71 (95% confidence interval [CI]: 0.65, 0.78), with a sensitivity and specificity of 81.2% and 38.9%, respectively. CONCLUSION A multivariate model including textural and clinical parameters had moderate overall performance for discriminating benign or likely benign cysts from indeterminate, mixed solid and cystic, or solid renal lesions. This study serves as a proof of concept and may reduce the need for further follow-up by characterising a significant portion of indeterminate lesions on CT as benign.
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Affiliation(s)
- D R Ludwig
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, Saint Louis, MO, USA.
| | - Y Thacker
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, Saint Louis, MO, USA
| | - C Luo
- Division of Public Health Sciences, Washington University School of Medicine, Saint Louis, MO, USA
| | - A Narra
- St George's University School of Medicine, Grenada, West Indies
| | - A J Mintz
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, Saint Louis, MO, USA
| | - C L Siegel
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, Saint Louis, MO, USA
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Nie P, Liu S, Zhou R, Li X, Zhi K, Wang Y, Dai Z, Zhao L, Wang N, Zhao X, Li X, Cheng N, Wang Y, Chen C, Xu Y, Yang G. A preoperative CT-based deep learning radiomics model in predicting the stage, size, grade and necrosis score and outcome in localized clear cell renal cell carcinoma: A multicenter study. Eur J Radiol 2023; 166:111018. [PMID: 37562222 DOI: 10.1016/j.ejrad.2023.111018] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2023] [Revised: 07/08/2023] [Accepted: 07/28/2023] [Indexed: 08/12/2023]
Abstract
BACKGROUND AND PURPOSE The Stage, Size, Grade and Necrosis (SSIGN) score is the most commonly used prognostic model in clear cell renal cell carcinoma (ccRCC) patients. It is a great challenge to preoperatively predict SSIGN score and outcome of ccRCC patients. The aim of this study was to develop and validate a CT-based deep learning radiomics model (DLRM) for predicting SSIGN score and outcome in localized ccRCC. METHODS A multicenter 784 (training cohort/ test 1 cohort / test 2 cohort, 475/204/105) localized ccRCC patients were enrolled. Radiomics signature (RS), deep learning signature (DLS), and DLRM incorporating radiomics and deep learning features were developed for predicting SSIGN score. Model performance was evaluated with area under the receiver operating characteristic curve (AUC). Kaplan-Meier survival analysis was used to assess the association of the model-predicted SSIGN with cancer-specific survival (CSS). Harrell's concordance index (C-index) was calculated to assess the CSS predictive accuracy of these models. RESULTS The DLRM achieved higher micro-average/macro-average AUCs (0.913/0.850, and 0.969/0.942, respectively in test 1 cohort and test 2 cohort) than the RS and DLS did for the prediction of SSIGN score. The CSS showed significant differences among the DLRM-predicted risk groups. The DLRM achieved higher C-indices (0.827 and 0.824, respectively in test 1 cohort and test 2 cohort) than the RS and DLS did in predicting CSS for localized ccRCC patients. CONCLUSION The DLRM can accurately predict the SSIGN score and outcome in localized ccRCC.
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Affiliation(s)
- Pei Nie
- Department of Radiology, the Affiliated Hospital of Qingdao University, Qingdao, Shandong, China
| | - Shihe Liu
- Department of Radiology, the Affiliated Hospital of Qingdao University, Qingdao, Shandong, China
| | - Ruizhi Zhou
- Department of Radiology, the Affiliated Hospital of Qingdao University, Qingdao, Shandong, China
| | - Xiaoli Li
- Department of Radiology, the Affiliated Hospital of Qingdao University, Qingdao, Shandong, China
| | - Kaiyue Zhi
- Department of Radiology, the Affiliated Hospital of Qingdao University, Qingdao, Shandong, China
| | | | - Zhengjun Dai
- Scientific Research Department, Huiying Medical Technology Co., Ltd, Beijing, China
| | - Lianzi Zhao
- Department of Radiation Oncology, Fudan University Shanghai Cancer Center, Shanghai, China
| | - Ning Wang
- Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, China
| | - Xia Zhao
- Department of Radiology, the Affiliated Hospital of Shandong University of Traditional Chinese Medicine, Jinan, Shandong, China
| | - Xianjun Li
- Department of Nuclear Medicine, Weifang People's Hospital, Weifang, Shandong, China
| | - Nan Cheng
- Department of Medical Imaging, the Affiliated Hospital of Jining Medical College, Jining, Shandong, China
| | - Yicong Wang
- Department of Nuclear Medicine, Binzhou Medical University Hospital, Binzhou, Shandong, China
| | - Chengcheng Chen
- Department of Radiology, Rizhao People's Hospital, Rizhao, Shandong, China
| | - Yuchao Xu
- School of Nuclear Science and Technology, University of South China, Hengyang, Hunan, China.
| | - Guangjie Yang
- Department of Nuclear Medicine, the Affiliated Hospital of Qingdao University, Qingdao, Shandong, China.
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Zhou Z, Qian X, Hu J, Geng C, Zhang Y, Dou X, Che T, Zhu J, Dai Y. Multi-phase-combined CECT radiomics models for Fuhrman grade prediction of clear cell renal cell carcinoma. Front Oncol 2023; 13:1167328. [PMID: 37692840 PMCID: PMC10485140 DOI: 10.3389/fonc.2023.1167328] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2023] [Accepted: 07/24/2023] [Indexed: 09/12/2023] Open
Abstract
Objective This study aimed to evaluate the effectiveness of multi-phase-combined contrast-enhanced CT (CECT) radiomics methods for noninvasive Fuhrman grade prediction of clear cell renal cell carcinoma (ccRCC). Methods A total of 187 patients with four-phase CECT images were retrospectively enrolled and then were categorized into training cohort (n=126) and testing cohort (n=61). All patients were confirmed as ccRCC by histopathological reports. A total of 110 3D classical radiomics features were extracted from each phase of CECT for individual ccRCC lesion, and contrast-enhanced variation features were also calculated as derived radiomics features. These features were concatenated together, and redundant features were removed by Pearson correlation analysis. The discriminative features were selected by minimum redundancy maximum relevance method (mRMR) and then input into a C-support vector classifier to build multi-phase-combined CECT radiomics models. The prediction performance was evaluated by the area under the curve (AUC) of receiver operating characteristic (ROC). Results The multi-phase-combined CECT radiomics model showed the best prediction performance (AUC=0.777) than the single-phase CECT radiomics model (AUC=0.711) in the testing cohort (p value=0.039). Conclusion The multi-phase-combined CECT radiomics model is a potential effective way to noninvasively predict Fuhrman grade of ccRCC. The concatenation of first-order features and texture features extracted from corticomedullary phase and nephrographic phase are discriminative feature representations.
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Affiliation(s)
- Zhiyong Zhou
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, Jiangsu, China
| | - Xusheng Qian
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, Jiangsu, China
- School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, Suzhou, Jiangsu, China
| | - Jisu Hu
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, Jiangsu, China
- School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, Suzhou, Jiangsu, China
| | - Chen Geng
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, Jiangsu, China
| | - Yongsheng Zhang
- Department of Pathology, Second Affiliated Hospital of Soochow University, Suzhou, Jiangsu, China
| | - Xin Dou
- Department of Radiology, Second Affiliated Hospital of Soochow University, Suzhou, Jiangsu, China
| | - Tuanjie Che
- Key Laboratory of Functional Genomic and Molecular Diagnosis of Gansu Province, Lanzhou, Gansu, China
- Suzhou Science & Technology Town Hospital, Gusu School, Nanjing Medical University, Suzhou, Jiangsu, China
| | - Jianbing Zhu
- Suzhou Science & Technology Town Hospital, Gusu School, Nanjing Medical University, Suzhou, Jiangsu, China
| | - Yakang Dai
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, Jiangsu, China
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8
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Shehata M, Abouelkheir RT, Gayhart M, Van Bogaert E, Abou El-Ghar M, Dwyer AC, Ouseph R, Yousaf J, Ghazal M, Contractor S, El-Baz A. Role of AI and Radiomic Markers in Early Diagnosis of Renal Cancer and Clinical Outcome Prediction: A Brief Review. Cancers (Basel) 2023; 15:2835. [PMID: 37345172 DOI: 10.3390/cancers15102835] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2023] [Revised: 05/10/2023] [Accepted: 05/17/2023] [Indexed: 06/23/2023] Open
Abstract
Globally, renal cancer (RC) is the 10th most common cancer among men and women. The new era of artificial intelligence (AI) and radiomics have allowed the development of AI-based computer-aided diagnostic/prediction (AI-based CAD/CAP) systems, which have shown promise for the diagnosis of RC (i.e., subtyping, grading, and staging) and prediction of clinical outcomes at an early stage. This will absolutely help reduce diagnosis time, enhance diagnostic abilities, reduce invasiveness, and provide guidance for appropriate management procedures to avoid the burden of unresponsive treatment plans. This survey mainly has three primary aims. The first aim is to highlight the most recent technical diagnostic studies developed in the last decade, with their findings and limitations, that have taken the advantages of AI and radiomic markers derived from either computed tomography (CT) or magnetic resonance (MR) images to develop AI-based CAD systems for accurate diagnosis of renal tumors at an early stage. The second aim is to highlight the few studies that have utilized AI and radiomic markers, with their findings and limitations, to predict patients' clinical outcome/treatment response, including possible recurrence after treatment, overall survival, and progression-free survival in patients with renal tumors. The promising findings of the aforementioned studies motivated us to highlight the optimal AI-based radiomic makers that are correlated with the diagnosis of renal tumors and prediction/assessment of patients' clinical outcomes. Finally, we conclude with a discussion and possible future avenues for improving diagnostic and treatment prediction performance.
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Affiliation(s)
- Mohamed Shehata
- Department of Bioengineering, University of Louisville, Louisville, KY 40292, USA
| | - Rasha T Abouelkheir
- Department of Radiology, Urology and Nephrology Center, Mansoura University, Mansoura 35516, Egypt
| | | | - Eric Van Bogaert
- Department of Radiology, University of Louisville, Louisville, KY 40202, USA
| | - Mohamed Abou El-Ghar
- Department of Radiology, Urology and Nephrology Center, Mansoura University, Mansoura 35516, Egypt
| | - Amy C Dwyer
- Kidney Disease Program, University of Louisville, Louisville, KY 40202, USA
| | - Rosemary Ouseph
- Kidney Disease Program, University of Louisville, Louisville, KY 40202, USA
| | - Jawad Yousaf
- Electrical, Computer, and Biomedical Engineering Department, Abu Dhabi University, Abu Dhabi 59911, United Arab Emirates
| | - Mohammed Ghazal
- Electrical, Computer, and Biomedical Engineering Department, Abu Dhabi University, Abu Dhabi 59911, United Arab Emirates
| | - Sohail Contractor
- Department of Radiology, University of Louisville, Louisville, KY 40202, USA
| | - Ayman El-Baz
- Department of Bioengineering, University of Louisville, Louisville, KY 40292, USA
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Zhu L, Huang R, Zhou Z, Fan Q, Yan J, Wan X, Zhao X, He Y, Dong F. Prediction of Renal Function 1 Year After Transplantation Using Machine Learning Methods Based on Ultrasound Radiomics Combined With Clinical and Imaging Features. ULTRASONIC IMAGING 2023; 45:85-96. [PMID: 36932907 DOI: 10.1177/01617346231162910] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/18/2023]
Abstract
Kidney transplantation is the most effective treatment for advanced chronic kidney disease (CKD). If the prognosis of transplantation can be predicted early after transplantation, it might improve the long-term survival of patients with transplanted kidneys. Currently, studies on the assessment and prediction of renal function by radiomics are limited. Therefore, the present study aimed to explore the value of ultrasound (US)-based imaging and radiomics features, combined with clinical features to develop and validate the models for predicting transplanted kidney function after 1 year (TKF-1Y) using different machine learning algorithms. A total of 189 patients were included and classified into the abnormal TKF-1Y group, and the normal TKF-1Y group based on their estimated glomerular filtration rate (eGFR) levels 1 year after transplantation. The radiomics features were derived from the US images of each case. Three machine learning methods were employed to establish different models for predicting TKF-1Y using selected clinical and US imaging as well as radiomics features from the training set. Two US imaging, four clinical, and six radiomics features were selected. Then, the clinical (including clinical and US image features), radiomics, and combined models were developed. The area under the curves (AUCs) of the models was 0.62 to 0.82 within the test set. Combined models showed statistically higher AUCs than the radiomics models (all p-values <.05). The prediction performance of different models was not significantly affected by the different machine learning algorithms (all p-values >.05). In conclusion, US imaging features combined with clinical features could predict TKF-1Y and yield an incremental value over radiomics features. A model integrating all available features may further improve the predictive efficacy. Different machine learning algorithms may not have a significant impact on the predictive performance of the model.
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Affiliation(s)
- Lili Zhu
- Department of Ultrasound, the First Affiliated Hospital of Soochow University, Suzhou City, Jiangsu Province, P.R. China
| | - Renjun Huang
- Department of Radiology, the First Affiliated Hospital of Soochow University, Suzhou City, Jiangsu Province, P.R. China
| | - Zhiyong Zhou
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou City, Jiangsu Province, P.R. China
| | - Qingmin Fan
- Department of Ultrasound, the First Affiliated Hospital of Soochow University, Suzhou City, Jiangsu Province, P.R. China
| | - Junchen Yan
- Department of Ultrasound, the First Affiliated Hospital of Soochow University, Suzhou City, Jiangsu Province, P.R. China
| | - Xiaojing Wan
- Department of Ultrasound, the First Affiliated Hospital of Soochow University, Suzhou City, Jiangsu Province, P.R. China
| | - Xiaojun Zhao
- Department of Urology, the First Affiliated Hospital of Soochow University, Suzhou City, Jiangsu Province, P.R. China
| | - Yao He
- Suzhou Key Laboratory of Nanotechnology and Biomedicine, Jiangsu Key Laboratory for Carbon-Based Functional Materials & Devices, Institute of Functional Nano & Soft Materials (FUNSOM), Soochow University, Suzhou City, Jiangsu Province, P.R. China
| | - Fenglin Dong
- Department of Ultrasound, the First Affiliated Hospital of Soochow University, Suzhou City, Jiangsu Province, P.R. China
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Yu W, Liang G, Zeng L, Yang Y, Wu Y. Accuracy of CT texture analysis for differentiating low-grade and high-grade renal cell carcinoma: systematic review and meta-analysis. BMJ Open 2021; 11:e051470. [PMID: 34937716 PMCID: PMC8704996 DOI: 10.1136/bmjopen-2021-051470] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/29/2022] Open
Abstract
OBJECTIVES This study aimed to assess the accuracy of CT texture analysis (CTTA) for differentiating low-grade and high-grade renal cell carcinoma (RCC). DESIGN Systematic review and meta-analysis. DATA SOURCES PubMed, Cochrane Library, Embase, Web of Science, OVID Medline, Science Direct and Springer were searched to identify the included studies. ELIGIBILITY CRITERIA FOR INCLUDING STUDIES Clinical studies that report about the accuracy of CTTA in differentiating low-grade and high-grade RCC. METHODS Multiple databases were searched to identify studies from their inception to 20 October 2021. Two radiologists independently extracted data from the primary studies. The pooled sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR) and diagnostic OR (DOR) were calculated to assess CTTA performance. The summary receiver operating characteristic (SROC) curve was plotted, and the area under the curve (AUC) was calculated to evaluate the accuracy of CTTA in grading RCC. RESULTS This meta-analysis included 11 studies, with 1603 lesions observed in 1601 patients. Values of the pooled sensitivity, specificity, PLR, NLR, DOR were 0.79 (95% CI 0.73 to 0.84), 0.84 (95% CI 0.81 to 0.87), 5.1 (95% CI 4.0 to 6.4), 0.24 (95% CI 0.19 to 0.32) and 21 (95% CI 13 to 33), respectively. The SROC curve showed that the AUC was 0.88 (95% CI 0.84 to 0.90). Deeks' test found no significant publication bias among the studies (p=0.42). CONCLUSIONS The findings of this meta-analysis suggest that CTTA has a high accuracy in differentiating low-grade and high-grade RCC. A standardised methodology and large sample-based study are necessary to certain the diagnostic accuracy of CTTA in RCC grading for clinical decision making.
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Affiliation(s)
- Wei Yu
- Department of Radiology, Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan, China
| | - Gao Liang
- Department of Radiology, Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan, China
| | - Lichuan Zeng
- Department of Radiology, Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan, China
| | - Yang Yang
- Department of Radiology, Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan, China
| | - Yinghua Wu
- Department of Radiology, Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan, China
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Xv Y, Lv F, Guo H, Liu Z, Luo D, Liu J, Gou X, He W, Xiao M, Zheng Y. A CT-Based Radiomics Nomogram Integrated With Clinic-Radiological Features for Preoperatively Predicting WHO/ISUP Grade of Clear Cell Renal Cell Carcinoma. Front Oncol 2021; 11:712554. [PMID: 34926241 PMCID: PMC8677659 DOI: 10.3389/fonc.2021.712554] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2021] [Accepted: 11/02/2021] [Indexed: 11/29/2022] Open
Abstract
Objective This study aims to develop and validate a CT-based radiomics nomogram integrated with clinic-radiological factors for preoperatively differentiating high-grade from low-grade clear cell renal cell carcinomas (CCRCCs). Methods 370 patients with complete clinical, pathological, and CT image data were enrolled in this retrospective study, and were randomly divided into training and testing sets with a 7:3 ratio. Radiomics features were extracted from nephrographic phase (NP) contrast-enhanced images, and then a radiomics model was constructed by the selected radiomics features using a multivariable logistic regression combined with the most suitable feature selection algorithm determined by the comparison among least absolute shrinkage and selection operator (LASSO), recursive feature elimination (RFE) and ReliefF. A clinical model was established using clinical and radiological features. A radiomics nomogram was constructed by integrating the radiomics signature and independent clinic-radiological features. Performance of these three models was assessed using receiver operating characteristic (ROC) curve analysis and decision curve analysis (DCA). Results Using multivariate logistic regression analysis, three clinic-radiological features including intratumoral necrosis (OR=3.00, 95% CI=1.30-6.90, p=0.049), intratumoral angiogenesis (OR=3.28, 95% CI=1.22-8.78, p=0.018), and perinephric metastasis (OR=2.90, 95% CI=1.03-8.17, p=0.044) were found to be independent predictors of WHO/ISUP grade in CCRCC. Incorporating the above clinic-radiological predictors and radiomics signature constructed by LASSO, a CT-based radiomics nomogram was developed, and presented better predictive performance than clinic-radiological model and radiomics signature model, with an AUC of 0.891 (95% CI=0.832-0.962) and 0.843 (95% CI=0.718-0.975) in the training and testing sets, respectively. DCA indicated that the nomogram has potential clinical usefulness. Conclusion The CT-based radiomics nomogram is a promising tool to predict WHO/ISUP grade of CCRCC preoperatively and noninvasively.
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Affiliation(s)
- Yingjie Xv
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China.,Department of Urology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Fajin Lv
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Haoming Guo
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Zhaojun Liu
- Department of Urology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Di Luo
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China.,Department of Urology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Jing Liu
- Department of Urology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Xin Gou
- Department of Urology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Weiyang He
- Department of Urology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Mingzhao Xiao
- Department of Urology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Yineng Zheng
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
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