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Lu C, Xia Y, Han J, Chen W, Qiao X, Gao R, Jiang X. Multiphase comparative study for WHO/ISUP nuclear grading diagnostic model based on enhanced CT images of clear cell renal cell carcinoma. Sci Rep 2024; 14:12043. [PMID: 38802547 PMCID: PMC11130204 DOI: 10.1038/s41598-024-60921-x] [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: 02/08/2024] [Accepted: 04/29/2024] [Indexed: 05/29/2024] Open
Abstract
To compare and analyze the diagnostic value of different enhancement stages in distinguishing low and high nuclear grade clear cell renal cell carcinoma (ccRCC) based on enhanced computed tomography (CT) images by building machine learning classifiers. A total of 51 patients (Dateset1, including 41 low-grade and 10 high-grade) and 27 patients (Independent Dateset2, including 16 low-grade and 11 high-grade) with pathologically proven ccRCC were enrolled in this retrospective study. Radiomic features were extracted from the corticomedullary phase (CMP), nephrographic phase (NP), and excretory phase (EP) CT images, and selected using the recursive feature elimination cross-validation (RFECV) algorithm, the group differences were assessed using T-test and Mann-Whitney U test for continuous variables. The support vector machine (SVM), random forest (RF), XGBoost (XGB), VGG11, ResNet18, and GoogLeNet classifiers are established to distinguish low-grade and high-grade ccRCC. The classifiers based on CT images of NP (Dateset1, RF: AUC = 0.82 ± 0.05, ResNet18: AUC = 0.81 ± 0.02; Dateset2, XGB: AUC = 0.95 ± 0.02, ResNet18: AUC = 0.87 ± 0.07) obtained the best performance and robustness in distinguishing low-grade and high-grade ccRCC, while the EP-based classifier performance in poorer results. The CT images of enhanced phase NP had the best performance in diagnosing low and high nuclear grade ccRCC. Firstorder_Kurtosis and firstorder_90Percentile feature play a vital role in the classification task.
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Affiliation(s)
- Chenyang Lu
- School of Control Science and Engineering, Shandong University, Jinan, 250100, People's Republic of China
| | - Yangyang Xia
- Key Laboratory of Urinary Precision Diagnosis and Treatment, Department of Urology, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan, 250012, People's Republic of China
| | - Jiamin Han
- School of Control Science and Engineering, Shandong University, Jinan, 250100, People's Republic of China
| | - Wei Chen
- Department of Radiology, Shandong First Medical University and Shandong Academy of Medical Sciences, Taian, People's Republic of China
| | - Xu Qiao
- School of Control Science and Engineering, Shandong University, Jinan, 250100, People's Republic of China.
- Department of Radiology, Shandong First Medical University and Shandong Academy of Medical Sciences, Taian, People's Republic of China.
| | - Rui Gao
- School of Control Science and Engineering, Shandong University, Jinan, 250100, People's Republic of China.
| | - Xuewen Jiang
- Key Laboratory of Urinary Precision Diagnosis and Treatment, Department of Urology, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan, 250012, People's Republic of China.
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Alhussaini AJ, Steele JD, Jawli A, Nabi G. Radiomics Machine Learning Analysis of Clear Cell Renal Cell Carcinoma for Tumour Grade Prediction Based on Intra-Tumoural Sub-Region Heterogeneity. Cancers (Basel) 2024; 16:1454. [PMID: 38672536 PMCID: PMC11048006 DOI: 10.3390/cancers16081454] [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: 02/06/2024] [Revised: 03/22/2024] [Accepted: 04/03/2024] [Indexed: 04/28/2024] Open
Abstract
BACKGROUND Renal cancers are among the top ten causes of cancer-specific mortality, of which the ccRCC subtype is responsible for most cases. The grading of ccRCC is important in determining tumour aggressiveness and clinical management. OBJECTIVES The objectives of this research were to predict the WHO/ISUP grade of ccRCC pre-operatively and characterise the heterogeneity of tumour sub-regions using radiomics and ML models, including comparison with pre-operative biopsy-determined grading in a sub-group. METHODS Data were obtained from multiple institutions across two countries, including 391 patients with pathologically proven ccRCC. For analysis, the data were separated into four cohorts. Cohorts 1 and 2 included data from the respective institutions from the two countries, cohort 3 was the combined data from both cohort 1 and 2, and cohort 4 was a subset of cohort 1, for which both the biopsy and subsequent histology from resection (partial or total nephrectomy) were available. 3D image segmentation was carried out to derive a voxel of interest (VOI) mask. Radiomics features were then extracted from the contrast-enhanced images, and the data were normalised. The Pearson correlation coefficient and the XGBoost model were used to reduce the dimensionality of the features. Thereafter, 11 ML algorithms were implemented for the purpose of predicting the ccRCC grade and characterising the heterogeneity of sub-regions in the tumours. RESULTS For cohort 1, the 50% tumour core and 25% tumour periphery exhibited the best performance, with an average AUC of 77.9% and 78.6%, respectively. The 50% tumour core presented the highest performance in cohorts 2 and 3, with average AUC values of 87.6% and 76.9%, respectively. With the 25% periphery, cohort 4 showed AUC values of 95.0% and 80.0% for grade prediction when using internal and external validation, respectively, while biopsy histology had an AUC of 31.0% for the classification with the final grade of resection histology as a reference standard. The CatBoost classifier was the best for each of the four cohorts with an average AUC of 80.0%, 86.5%, 77.0% and 90.3% for cohorts 1, 2, 3 and 4 respectively. CONCLUSIONS Radiomics signatures combined with ML have the potential to predict the WHO/ISUP grade of ccRCC with superior performance, when compared to pre-operative biopsy. Moreover, tumour sub-regions contain useful information that should be analysed independently when determining the tumour grade. Therefore, it is possible to distinguish the grade of ccRCC pre-operatively to improve patient care and management.
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Affiliation(s)
- Abeer J. Alhussaini
- Division of Imaging Sciences and Technology, School of Medicine, Ninewells Hospital, University of Dundee, Dundee DD1 9SY, UK
- Department of Clinical Radiology, Al-Amiri Hospital, Ministry of Health, Sulaibikhat 1300, Kuwait
| | - J. Douglas Steele
- Division of Imaging Sciences and Technology, School of Medicine, Ninewells Hospital, University of Dundee, Dundee DD1 9SY, UK
| | - Adel Jawli
- Division of Imaging Sciences and Technology, School of Medicine, Ninewells Hospital, University of Dundee, Dundee DD1 9SY, UK
- Department of Clinical Radiology, Sheikh Jaber Al-Ahmad Al-Sabah Hospital, Ministry of Health, Sulaibikhat 1300, Kuwait
| | - Ghulam Nabi
- Division of Imaging Sciences and Technology, School of Medicine, Ninewells Hospital, University of Dundee, Dundee DD1 9SY, UK
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Bai Y, An ZC, Li F, Du LF, Xie TW, Zhang XP, Cai YY. Deep learning using contrast-enhanced ultrasound images to predict the nuclear grade of clear cell renal cell carcinoma. World J Urol 2024; 42:184. [PMID: 38512539 DOI: 10.1007/s00345-024-04889-3] [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: 11/28/2023] [Accepted: 02/12/2024] [Indexed: 03/23/2024] Open
Abstract
PURPOSE To assess the effectiveness of a deep learning model using contrastenhanced ultrasound (CEUS) images in distinguishing between low-grade (grade I and II) and high-grade (grade III and IV) clear cell renal cell carcinoma (ccRCC). METHODS A retrospective study was conducted using CEUS images of 177 Fuhrmangraded ccRCCs (93 low-grade and 84 high-grade) from May 2017 to December 2020. A total of 6412 CEUS images were captured from the videos and normalized for subsequent analysis. A deep learning model using the RepVGG architecture was proposed to differentiate between low-grade and high-grade ccRCC. The model's performance was evaluated based on sensitivity, specificity, positive predictive value, negative predictive value and area under the receiver operating characteristic curve (AUC). Class activation mapping (CAM) was used to visualize the specific areas that contribute to the model's predictions. RESULTS For discriminating high-grade ccRCC from low-grade, the deep learning model achieved a sensitivity of 74.8%, specificity of 79.1%, accuracy of 77.0%, and an AUC of 0.852 in the test set. CONCLUSION The deep learning model based on CEUS images can accurately differentiate between low-grade and high-grade ccRCC in a non-invasive manner.
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Affiliation(s)
- Yun Bai
- Department of Ultrasound, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, No. 85/86 Wujin Road, Shanghai, 200080, China
| | - Zi-Chen An
- Department of Ultrasound, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, No. 85/86 Wujin Road, Shanghai, 200080, China
| | - Fan Li
- Department of Ultrasound, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, No. 85/86 Wujin Road, Shanghai, 200080, China
| | - Lian-Fang Du
- Department of Ultrasound, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, No. 85/86 Wujin Road, Shanghai, 200080, China
| | - Tian-Wu Xie
- Institute of Radiation Medicine, Fudan University, No. 2094 Xietu Road, Shanghai, 200032, China.
| | - Xi-Peng Zhang
- School of Computer Science and Technology, Taiyuan Normal University, No. 319 Daxue Street, Taiyuan, 030619, China.
| | - Ying-Yu Cai
- Department of Ultrasound, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, No. 85/86 Wujin Road, Shanghai, 200080, China.
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Shi Y, Ni L, Pei J, Zhan H, Li H, Zhang D, Wang L. Collateral vessels on preoperative enhanced computed tomography for predicting pathological grade of clear cell renal cell carcinoma: A retrospective study. Eur J Radiol 2024; 170:111240. [PMID: 38043383 DOI: 10.1016/j.ejrad.2023.111240] [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: 09/13/2023] [Revised: 11/02/2023] [Accepted: 11/26/2023] [Indexed: 12/05/2023]
Abstract
OBJECTIVES To retrospectively evaluate the association between the presence of collateral vessels and grade of clear cell renal cell carcinoma (ccRCC) and whether the presence of collateral vessels could serve as a predictor to differentiate high- and low-grade ccRCC. MATERIALS AND METHODS From May 2018 to September 2022, a total of 160 ccRCC patients with pathological diagnosis were enrolled in this study. Patients were divided into a high-grade group and a low-grade group according to World Health Organization/International Society of Urological Pathology (WHO/ISUP) grading system. The significant variables were extracted based on the univariate analyses using Student t test, Mann-Whitney U test, Chi-square test or Fisher's exact test. Multivariate logistic regression analyses were performed to determine independent factors among extracted variables. We calculated the sensitivity, specificity and their 95% confidence intervals (CI) of collateral vessels for predicting high WHO/ISUP grade to quantify its predictive performance. Furthermore, to investigate the additional predictive contribution of collateral vessels, a primary model and a control model were constructed to predict WHO/ISUP grade. The primary model included all extracted significant variables and the control model included significant variables except collateral vessels. RESULTS The proportion of ccRCC patients with collateral vessels was significantly larger in high-grade ccRCC than those in low-grade ccRCC (87.5 % vs. 26.8 %, P < 0.001). Multivariate logistic regression analyses showed that the presence of collateral vessels was an independent predictor for high WHO/ISUP grade (P < 0.001). The sensitivity and specificity of the presence of collateral vessels for differentiating high- and low-grade ccRCC were 87.5 % (95 % CI 0.753-0.941) and 73.2 % (95 % CI 0.643-0.806) respectively. Including collateral vessels in predictive model improves predictive performance for WHO/ISUP grade, increasing the area under the curve (AUC) value from 0.889 to 0.914. CONCLUSION The presence of collateral vessels has high sensitivity and specificity for differentiating high- and low-grade ccRCC and can improve the predictive performance for high WHO/ISUP grade.
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Affiliation(s)
- Yuting Shi
- Department of Radiology, The Second Affiliated Hospital of Anhui Medical University, No.678 Furong Road, Hefei, Anhui, China; Medical Imaging Research Center, Anhui Medical University, No.678 Furong Road, Hefei, Anhui, China
| | - Liangping Ni
- Department of Radiology, The Second Affiliated Hospital of Anhui Medical University, No.678 Furong Road, Hefei, Anhui, China; Medical Imaging Research Center, Anhui Medical University, No.678 Furong Road, Hefei, Anhui, China
| | - Jinxia Pei
- Department of Radiology, The Second Affiliated Hospital of Anhui Medical University, No.678 Furong Road, Hefei, Anhui, China; Medical Imaging Research Center, Anhui Medical University, No.678 Furong Road, Hefei, Anhui, China
| | - Hao Zhan
- Department of Radiology, The Second Affiliated Hospital of Anhui Medical University, No.678 Furong Road, Hefei, Anhui, China; Medical Imaging Research Center, Anhui Medical University, No.678 Furong Road, Hefei, Anhui, China
| | - Huan Li
- Department of Radiology, The Second Affiliated Hospital of Anhui Medical University, No.678 Furong Road, Hefei, Anhui, China; Medical Imaging Research Center, Anhui Medical University, No.678 Furong Road, Hefei, Anhui, China
| | - Dai Zhang
- Department of Radiology, The Second Affiliated Hospital of Anhui Medical University, No.678 Furong Road, Hefei, Anhui, China; Medical Imaging Research Center, Anhui Medical University, No.678 Furong Road, Hefei, Anhui, China.
| | - Longsheng Wang
- Department of Radiology, The Second Affiliated Hospital of Anhui Medical University, No.678 Furong Road, Hefei, Anhui, China; Medical Imaging Research Center, Anhui Medical University, No.678 Furong Road, Hefei, Anhui, China.
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Nie P, Yang G, Wang Y, Xu Y, Yan L, Zhang M, Zhao L, Wang N, Zhao X, Li X, Cheng N, Wang Y, Chen C, Wang N, Duan S, Wang X, Wang Z. A CT-based deep learning radiomics nomogram outperforms the existing prognostic models for outcome prediction in clear cell renal cell carcinoma: a multicenter study. Eur Radiol 2023; 33:8858-8868. [PMID: 37389608 DOI: 10.1007/s00330-023-09869-6] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2022] [Revised: 04/27/2023] [Accepted: 05/02/2023] [Indexed: 07/01/2023]
Abstract
OBJECTIVES To develop and validate a CT-based deep learning radiomics nomogram (DLRN) for outcome prediction in clear cell renal cell carcinoma (ccRCC), and its performance was compared with the Stage, Size, Grade, and Necrosis (SSIGN) score, the University of California, Los Angeles, Integrated Staging System (UISS), the Memorial Sloan-Kettering Cancer Center (MSKCC), and the International Metastatic Renal Cell Database Consortium (IMDC). METHODS A multicenter of 799 localized (training/ test cohort, 558/241) and 45 metastatic ccRCC patients were studied. A DLRN was developed for predicting recurrence-free survival (RFS) in localized ccRCC patients, and another DLRN was developed for predicting overall survival (OS) in metastatic ccRCC patients. The performance of the two DLRNs was compared with that of the SSIGN, UISS, MSKCC, and IMDC. Model performance was assessed with Kaplan-Meier curves, time-dependent area under the curve (time-AUC), Harrell's concordance index (C-index), and decision curve analysis (DCA). RESULTS In the test cohort, the DLRN achieved higher time-AUCs (0.921, 0.911, and 0.900 for 1, 3, and 5 years, respectively), C-index (0.883), and net benefit than SSIGN and UISS in predicting RFS for localized ccRCC patients. The DLRN provided higher time-AUCs (0.594, 0.649, and 0.754 for 1, 3, and 5 years, respectively) than MSKCC and IMDC in predicting OS for metastatic ccRCC patients. CONCLUSIONS The DLRN can accurately predict outcomes and outperformed the existing prognostic models in ccRCC patients. CLINICAL RELEVANCE STATEMENT This deep learning radiomics nomogram may facilitate individualized treatment, surveillance, and adjuvant trial design for patients with clear cell renal cell carcinoma. KEY POINTS • SSIGN, UISS, MSKCC, and IMDC may be insufficient for outcome prediction in ccRCC patients. • Radiomics and deep learning allow for the characterization of tumor heterogeneity. • The CT-based deep learning radiomics nomogram outperforms the existing prognostic models in ccRCC outcome prediction.
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Affiliation(s)
- Pei Nie
- Department of Radiology, the Affiliated Hospital of Qingdao University, Qingdao, Shandong, China
| | - Guangjie Yang
- Department of Nuclear Medicine, the Affiliated Hospital of Qingdao University, No. 59, Haier Road, Qingdao, 266061, Shandong, China
| | | | - Yuchao Xu
- School of Nuclear Science and Technology, University of South China, Hengyang, Hunan, China
| | - Lei Yan
- Department of Nuclear Medicine, the Affiliated Hospital of Qingdao University, No. 59, Haier Road, Qingdao, 266061, Shandong, China
| | - Mingxin Zhang
- Department of Urology, the Affiliated Hospital of Qingdao University, Qingdao, Shandong, 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, No. 324 Jingwu Road, Jinan, 250000, 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
| | - Nan Wang
- Department of Nuclear Medicine, Yantai Yuhuangding Hospital, Yantai, Shandong, China
| | | | - Ximing Wang
- Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, No. 324 Jingwu Road, Jinan, 250000, Shandong, China.
| | - Zhenguang Wang
- Department of Nuclear Medicine, the Affiliated Hospital of Qingdao University, No. 59, Haier Road, Qingdao, 266061, Shandong, China.
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Wang Z, Zhang X, Wang X, Li J, Zhang Y, Zhang T, Xu S, Jiao W, Niu H. Deep learning techniques for imaging diagnosis of renal cell carcinoma: current and emerging trends. Front Oncol 2023; 13:1152622. [PMID: 37727213 PMCID: PMC10505614 DOI: 10.3389/fonc.2023.1152622] [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: 01/28/2023] [Accepted: 08/11/2023] [Indexed: 09/21/2023] Open
Abstract
This study summarizes the latest achievements, challenges, and future research directions in deep learning technologies for the diagnosis of renal cell carcinoma (RCC). This is the first review of deep learning in RCC applications. This review aims to show that deep learning technologies hold great promise in the field of RCC diagnosis, and we look forward to more research results to meet us for the mutual benefit of renal cell carcinoma patients. Medical imaging plays an important role in the early detection of renal cell carcinoma (RCC), as well as in the monitoring and evaluation of RCC during treatment. The most commonly used technologies such as contrast enhanced computed tomography (CECT), ultrasound and magnetic resonance imaging (MRI) are now digitalized, allowing deep learning to be applied to them. Deep learning is one of the fastest growing fields in the direction of medical imaging, with rapidly emerging applications that have changed the traditional medical treatment paradigm. With the help of deep learning-based medical imaging tools, clinicians can diagnose and evaluate renal tumors more accurately and quickly. This paper describes the application of deep learning-based imaging techniques in RCC assessment and provides a comprehensive review.
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Affiliation(s)
- Zijie Wang
- Department of Vascular Intervention, ShengLi Oilfield Center Hospital, Dongying, China
| | - Xiaofei Zhang
- Department of Education and Training, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Xinning Wang
- Department of Urology, Affiliated Hospital of Qingdao University, Qingdao, China
| | - Jianfei Li
- Extenics Specialized Committee, Chinese Association of Artificial Intelligence (ESCCAAI), Beijing, China
| | - Yuhao Zhang
- Department of Urology, Affiliated Hospital of Qingdao University, Qingdao, China
| | - Tianwei Zhang
- Department of Urology, Affiliated Hospital of Qingdao University, Qingdao, China
| | - Shang Xu
- Department of Urology, Affiliated Hospital of Qingdao University, Qingdao, China
| | - Wei Jiao
- Department of Urology, Affiliated Hospital of Qingdao University, Qingdao, China
| | - Haitao Niu
- Department of Urology, Affiliated Hospital of Qingdao University, Qingdao, China
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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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Garnier C, Ferrer L, Vargas J, Gallinato O, Jambon E, Le Bras Y, Bernhard JC, Colin T, Grenier N, Marcelin C. A CT-Based Clinical, Radiological and Radiomic Machine Learning Model for Predicting Malignancy of Solid Renal Tumors (UroCCR-75). Diagnostics (Basel) 2023; 13:2548. [PMID: 37568911 PMCID: PMC10417436 DOI: 10.3390/diagnostics13152548] [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: 02/21/2023] [Revised: 07/05/2023] [Accepted: 07/25/2023] [Indexed: 08/13/2023] Open
Abstract
BACKGROUND Differentiating benign from malignant renal tumors is important for patient management, and it may be improved by quantitative CT features analysis including radiomic. PURPOSE This study aimed to compare performances of machine learning models using bio-clinical, conventional radiologic and 3D-radiomic features for the differentiation of benign and malignant solid renal tumors using pre-operative multiphasic contrast-enhanced CT examinations. MATERIALS AND METHODS A unicentric retrospective analysis of prospectively acquired data from a national kidney cancer database was conducted between January 2016 and December 2020. Histologic findings were obtained by robotic-assisted partial nephrectomy. Lesion images were semi-automatically segmented, allowing for a 3D-radiomic features extraction in the nephrographic phase. Conventional radiologic parameters such as shape, content and enhancement were combined in the analysis. Biological and clinical features were obtained from the national database. Eight machine learning (ML) models were trained and validated using a ten-fold cross-validation. Predictive performances were evaluated comparing sensitivity, specificity, accuracy and AUC. RESULTS A total of 122 patients with 132 renal lesions, including 111 renal cell carcinomas (RCCs) (111/132, 84%) and 21 benign tumors (21/132, 16%), were evaluated (58 +/- 14 years, men 74%). Unilaterality (100/111, 90% vs. 13/21, 62%; p = 0.02), necrosis (81/111, 73% vs. 8/21, 38%; p = 0.02), lower values of tumor/cortex ratio at portal time (0.61 vs. 0.74, p = 0.01) and higher variation of tumor/cortex ratio between arterial and portal times (0.22 vs. 0.05, p = 0.008) were associated with malignancy. A total of 35 radiomics features were selected, and "intensity mean value" was associated with RCCs in multivariate analysis (OR = 0.99). After ten-fold cross-validation, a C5.0Tree model was retained for its predictive performances, yielding a sensitivity of 95%, specificity of 42%, accuracy of 87% and AUC of 0.74. CONCLUSION Our machine learning-based model combining clinical, radiologic and radiomics features from multiphasic contrast-enhanced CT scans may help differentiate benign from malignant solid renal tumors.
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Affiliation(s)
- Cassandre Garnier
- Department of Imaging and Interventional Radiology, Hôpital Pellegrin, Place Amélie-Raba-Léon, 33076 Bordeaux, France
| | - Loïc Ferrer
- SOPHiA GENETICS, Multimodal Research, Cité de la Photonique—Bâtiment GIENAH, 11 Avenue de Canteranne, 33600 Pessac, France; (L.F.); (J.V.); (O.G.); (T.C.)
| | - Jennifer Vargas
- SOPHiA GENETICS, Multimodal Research, Cité de la Photonique—Bâtiment GIENAH, 11 Avenue de Canteranne, 33600 Pessac, France; (L.F.); (J.V.); (O.G.); (T.C.)
| | - Olivier Gallinato
- SOPHiA GENETICS, Multimodal Research, Cité de la Photonique—Bâtiment GIENAH, 11 Avenue de Canteranne, 33600 Pessac, France; (L.F.); (J.V.); (O.G.); (T.C.)
| | - Eva Jambon
- Department of Imaging and Interventional Radiology, Hôpital Pellegrin, Place Amélie-Raba-Léon, 33076 Bordeaux, France
| | - Yann Le Bras
- Department of Imaging and Interventional Radiology, Hôpital Pellegrin, Place Amélie-Raba-Léon, 33076 Bordeaux, France
| | | | - Thierry Colin
- SOPHiA GENETICS, Multimodal Research, Cité de la Photonique—Bâtiment GIENAH, 11 Avenue de Canteranne, 33600 Pessac, France; (L.F.); (J.V.); (O.G.); (T.C.)
| | - Nicolas Grenier
- Department of Imaging and Interventional Radiology, Hôpital Pellegrin, Place Amélie-Raba-Léon, 33076 Bordeaux, France
| | - Clément Marcelin
- Department of Imaging and Interventional Radiology, Hôpital Pellegrin, Place Amélie-Raba-Léon, 33076 Bordeaux, France
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Liu X, Shi J, Li Z, Huang Y, Zhang Z, Zhang C. The Present and Future of Artificial Intelligence in Urological Cancer. J Clin Med 2023; 12:4995. [PMID: 37568397 PMCID: PMC10419644 DOI: 10.3390/jcm12154995] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2023] [Revised: 07/10/2023] [Accepted: 07/27/2023] [Indexed: 08/13/2023] Open
Abstract
Artificial intelligence has drawn more and more attention for both research and application in the field of medicine. It has considerable potential for urological cancer detection, therapy, and prognosis prediction due to its ability to choose features in data to complete a particular task autonomously. Although the clinical application of AI is still immature and faces drawbacks such as insufficient data and a lack of prospective clinical trials, AI will play an essential role in individualization and the whole management of cancers as research progresses. In this review, we summarize the applications and studies of AI in major urological cancers, including tumor diagnosis, treatment, and prognosis prediction. Moreover, we discuss the current challenges and future applications of AI.
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Affiliation(s)
| | | | | | | | - Zhihong Zhang
- Tianjin Institute of Urology, The Second Hospital of Tianjin Medical University, Tianjin 300211, China; (X.L.)
| | - Changwen Zhang
- Tianjin Institute of Urology, The Second Hospital of Tianjin Medical University, Tianjin 300211, China; (X.L.)
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10
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Yang M, He X, Xu L, Liu M, Deng J, Cheng X, Wei Y, Li Q, Wan S, Zhang F, Wu L, Wang X, Song B, Liu M. CT-based transformer model for non-invasively predicting the Fuhrman nuclear grade of clear cell renal cell carcinoma. Front Oncol 2022; 12:961779. [PMID: 36249050 PMCID: PMC9555088 DOI: 10.3389/fonc.2022.961779] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2022] [Accepted: 08/29/2022] [Indexed: 11/13/2022] Open
Abstract
Background Clear cell Renal Cell Carcinoma (ccRCC) is the most common malignant tumor in the urinary system and the predominant subtype of malignant renal tumors with high mortality. Biopsy is the main examination to determine ccRCC grade, but it can lead to unavoidable complications and sampling bias. Therefore, non-invasive technology (e.g., CT examination) for ccRCC grading is attracting more and more attention. However, noise labels on CT images containing multiple grades but only one label make prediction difficult. However, noise labels exist in CT images, which contain multiple grades but only one label, making prediction difficult. Aim We proposed a Transformer-based deep learning algorithm with CT images to improve the diagnostic accuracy of grading prediction and to improve the diagnostic accuracy of ccRCC grading. Methods We integrate different training models to improve robustness and predict Fuhrman nuclear grade. Then, we conducted experiments on a collected ccRCC dataset containing 759 patients and used average classification accuracy, sensitivity, specificity, and AreaUnderCurve as indicators to evaluate the quality of research. In the comparative experiments, we further performed various current deep learning algorithms to show the advantages of the proposed method. We collected patients with pathologically proven ccRCC diagnosed from April 2010 to December 2018 as the training and internal test dataset, containing 759 patients. We propose a transformer-based network architecture that efficiently employs convolutional neural networks (CNNs) and self-attention mechanisms to extract a persuasive feature automatically. And then, a nonlinear classifier is applied to classify. We integrate different training models to improve the accuracy and robustness of the model. The average classification accuracy, sensitivity, specificity, and area under curve are used as indicators to evaluate the quality of a model. Results The mean accuracy, sensitivity, specificity, and Area Under Curve achieved by CNN were 82.3%, 89.4%, 83.2%, and 85.7%, respectively. In contrast, the proposed Transformer-based model obtains a mean accuracy of 87.1% with a sensitivity of 91.3%, a specificity of 85.3%, and an Area Under Curve (AUC) of 90.3%. The integrated model acquires a better performance (86.5% ACC and an AUC of 91.2%). Conclusion A transformer-based network performs better than traditional deep learning algorithms in terms of the accuracy of ccRCC prediction. Meanwhile, the transformer has a certain advantage in dealing with noise labels existing in CT images of ccRCC. This method is promising to be applied to other medical tasks (e.g., the grade of neurogliomas and meningiomas).
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Affiliation(s)
- Meiyi Yang
- Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou, China
- School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China
| | - Xiaopeng He
- Department of Radiology, Affiliated Hospital of Southwest Medical University, Luzhou, China
| | - Lifeng Xu
- Quzhou Affiliated Hospital of Wenzhou Medical University, Quzhou People’s Hospital, Quzhou, China
| | - Minghui Liu
- School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China
| | - Jiali Deng
- School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China
| | - Xuan Cheng
- School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China
| | - Yi Wei
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
| | - Qian Li
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
| | - Shang Wan
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
| | - Feng Zhang
- Quzhou Affiliated Hospital of Wenzhou Medical University, Quzhou People’s Hospital, Quzhou, China
| | - Lei Wu
- School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China
| | - Xiaomin Wang
- School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China
| | - Bin Song
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
- *Correspondence: Ming Liu, ; Bin Song,
| | - Ming Liu
- Quzhou Affiliated Hospital of Wenzhou Medical University, Quzhou People’s Hospital, Quzhou, China
- *Correspondence: Ming Liu, ; Bin Song,
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11
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Xu L, Yang C, Zhang F, Cheng X, Wei Y, Fan S, Liu M, He X, Deng J, Xie T, Wang X, Liu M, Song B. Deep Learning Using CT Images to Grade Clear Cell Renal Cell Carcinoma: Development and Validation of a Prediction Model. Cancers (Basel) 2022; 14:cancers14112574. [PMID: 35681555 PMCID: PMC9179576 DOI: 10.3390/cancers14112574] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2022] [Revised: 04/21/2022] [Accepted: 04/29/2022] [Indexed: 02/06/2023] Open
Abstract
Simple Summary Clear cell renal cell carcinoma (ccRCC) pathologic grade identification is essential to both monitoring patients’ conditions and constructing individualized subsequent treatment strategies. However, biopsies are typically used to obtain the pathological grade, entailing tremendous physical and mental suffering as well as heavy economic burden, not to mention the increased risk of complications. Our study explores a new way to provide grade assessment of ccRCC on the basis of the individual’s appearance on CT images. A deep learning (DL) method that includes self-supervised learning is constructed to identify patients with high grade for ccRCC. We confirmed that our grading network can accurately differentiate between different grades of CT scans of ccRCC patients using a cohort of 706 patients from West China Hospital. The promising diagnostic performance indicates that our DL framework is an effective, non-invasive and labor-saving method for decoding CT images, offering a valuable means for ccRCC grade stratification and individualized patient treatment. Abstract This retrospective study aimed to develop and validate deep-learning-based models for grading clear cell renal cell carcinoma (ccRCC) patients. A cohort enrolling 706 patients (n = 706) with pathologically verified ccRCC was used in this study. A temporal split was applied to verify our models: the first 83.9% of the cases (years 2010–2017) for development and the last 16.1% (year 2018–2019) for validation (development cohort: n = 592; validation cohort: n = 114). Here, we demonstrated a deep learning(DL) framework initialized by a self-supervised pre-training method, developed with the addition of mixed loss strategy and sample reweighting to identify patients with high grade for ccRCC. Four types of DL networks were developed separately and further combined with different weights for better prediction. The single DL model achieved up to an area under curve (AUC) of 0.864 in the validation cohort, while the ensembled model yielded the best predictive performance with an AUC of 0.882. These findings confirms that our DL approach performs either favorably or comparably in terms of grade assessment of ccRCC with biopsies whilst enjoying the non-invasive and labor-saving property.
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Affiliation(s)
- Lifeng Xu
- The Quzhou Affiliated Hospital of Wenzhou Medical University, Quzhou People’s Hospital, Quzhou 324000, China; (L.X.); (F.Z.)
- Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou 324000, China; (C.Y.); (X.C.); (S.F.); (M.L.); (J.D.); (T.X.); (X.W.); (M.L.)
| | - Chun Yang
- Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou 324000, China; (C.Y.); (X.C.); (S.F.); (M.L.); (J.D.); (T.X.); (X.W.); (M.L.)
- University of Electronic Science and Technology of China, Chengdu 610000, China
| | - Feng Zhang
- The Quzhou Affiliated Hospital of Wenzhou Medical University, Quzhou People’s Hospital, Quzhou 324000, China; (L.X.); (F.Z.)
| | - Xuan Cheng
- Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou 324000, China; (C.Y.); (X.C.); (S.F.); (M.L.); (J.D.); (T.X.); (X.W.); (M.L.)
- University of Electronic Science and Technology of China, Chengdu 610000, China
| | - Yi Wei
- West China Hospital, Sichuan University, Chengdu 610000, China;
| | - Shixiao Fan
- Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou 324000, China; (C.Y.); (X.C.); (S.F.); (M.L.); (J.D.); (T.X.); (X.W.); (M.L.)
- University of Electronic Science and Technology of China, Chengdu 610000, China
| | - Minghui Liu
- Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou 324000, China; (C.Y.); (X.C.); (S.F.); (M.L.); (J.D.); (T.X.); (X.W.); (M.L.)
- University of Electronic Science and Technology of China, Chengdu 610000, China
| | - Xiaopeng He
- West China Hospital, Sichuan University, Chengdu 610000, China;
- Affiliated Hospital of Southwest Medical University, Luzhou 646000, China
- Correspondence: (X.H.); (B.S.)
| | - Jiali Deng
- Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou 324000, China; (C.Y.); (X.C.); (S.F.); (M.L.); (J.D.); (T.X.); (X.W.); (M.L.)
- University of Electronic Science and Technology of China, Chengdu 610000, China
| | - Tianshu Xie
- Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou 324000, China; (C.Y.); (X.C.); (S.F.); (M.L.); (J.D.); (T.X.); (X.W.); (M.L.)
- University of Electronic Science and Technology of China, Chengdu 610000, China
| | - Xiaomin Wang
- Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou 324000, China; (C.Y.); (X.C.); (S.F.); (M.L.); (J.D.); (T.X.); (X.W.); (M.L.)
- University of Electronic Science and Technology of China, Chengdu 610000, China
| | - Ming Liu
- Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou 324000, China; (C.Y.); (X.C.); (S.F.); (M.L.); (J.D.); (T.X.); (X.W.); (M.L.)
- University of Electronic Science and Technology of China, Chengdu 610000, China
| | - Bin Song
- West China Hospital, Sichuan University, Chengdu 610000, China;
- Correspondence: (X.H.); (B.S.)
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Yu AC, Mohajer B, Eng J. External Validation of Deep Learning Algorithms for Radiologic Diagnosis: A Systematic Review. Radiol Artif Intell 2022; 4:e210064. [PMID: 35652114 DOI: 10.1148/ryai.210064] [Citation(s) in RCA: 80] [Impact Index Per Article: 40.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2021] [Revised: 03/09/2022] [Accepted: 04/12/2022] [Indexed: 01/17/2023]
Abstract
Purpose To assess generalizability of published deep learning (DL) algorithms for radiologic diagnosis. Materials and Methods In this systematic review, the PubMed database was searched for peer-reviewed studies of DL algorithms for image-based radiologic diagnosis that included external validation, published from January 1, 2015, through April 1, 2021. Studies using nonimaging features or incorporating non-DL methods for feature extraction or classification were excluded. Two reviewers independently evaluated studies for inclusion, and any discrepancies were resolved by consensus. Internal and external performance measures and pertinent study characteristics were extracted, and relationships among these data were examined using nonparametric statistics. Results Eighty-three studies reporting 86 algorithms were included. The vast majority (70 of 86, 81%) reported at least some decrease in external performance compared with internal performance, with nearly half (42 of 86, 49%) reporting at least a modest decrease (≥0.05 on the unit scale) and nearly a quarter (21 of 86, 24%) reporting a substantial decrease (≥0.10 on the unit scale). No study characteristics were found to be associated with the difference between internal and external performance. Conclusion Among published external validation studies of DL algorithms for image-based radiologic diagnosis, the vast majority demonstrated diminished algorithm performance on the external dataset, with some reporting a substantial performance decrease.Keywords: Meta-Analysis, Computer Applications-Detection/Diagnosis, Neural Networks, Computer Applications-General (Informatics), Epidemiology, Technology Assessment, Diagnosis, Informatics Supplemental material is available for this article. © RSNA, 2022.
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Affiliation(s)
- Alice C Yu
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, 1800 Orleans St, Baltimore, MD 21287
| | - Bahram Mohajer
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, 1800 Orleans St, Baltimore, MD 21287
| | - John Eng
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, 1800 Orleans St, Baltimore, MD 21287
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Xu Q, Zhu Q, Liu H, Chang L, Duan S, Dou W, Li S, Ye J. Differentiating Benign from Malignant Renal Tumors Using T2- and Diffusion-Weighted Images: A Comparison of Deep Learning and Radiomics Models Versus Assessment from Radiologists. J Magn Reson Imaging 2021; 55:1251-1259. [PMID: 34462986 DOI: 10.1002/jmri.27900] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2021] [Revised: 08/14/2021] [Accepted: 08/17/2021] [Indexed: 12/18/2022] Open
Abstract
BACKGROUND Differentiating benign from malignant renal tumors is important for selection of the most effective treatment. PURPOSE To develop magnetic resonance imaging (MRI)-based deep learning (DL) models for differentiation of benign and malignant renal tumors and to compare their discrimination performance with the performance of radiomics models and assessment by radiologists. STUDY TYPE Retrospective. POPULATION A total of 217 patients were randomly assigned to a training cohort (N = 173) or a testing cohort (N = 44). FIELD STRENGTH/SEQUENCE Diffusion-weighted imaging (DWI) and fast spin-echo sequence T2-weighted imaging (T2WI) at 3.0T. ASSESSMENT A radiologist manually labeled the region of interest (ROI) on each image. Three DL models using ResNet-18 architecture and three radiomics models using random forest were developed using T2WI alone, DWI alone, and a combination of the two image sets to discriminate between benign and malignant renal tumors. The diagnostic performance of two radiologists was assessed based on professional experience. We also compared the performance of each model and the radiologists. STATISTICAL TESTS The area under the receiver operating characteristic (ROC) curve (AUC) was used to assess the performance of each model and the radiologists. P < 0.05 indicated statistical significance. RESULTS The AUC of the DL models based on T2WI, DWI, and the combination was 0.906, 0.846, and 0.925 in the testing cohorts, respectively. The AUC of the combination DL model was significantly better than that of the models based on individual sequences (0.925 > 0.906, 0.925 > 0.846). The AUC of the radiomics models based on T2WI, DWI, and the combination was 0.824, 0.742, and 0.826 in the testing cohorts, respectively. The AUC of two radiologists was 0.724 and 0.667 in the testing cohorts. CONCLUSION Thus, the MRI-based DL model is useful for differentiating benign from malignant renal tumors in clinic, and the DL model based on T2WI + DWI had the best performance. LEVEL OF EVIDENCE 3 TECHNICAL EFFICACY STAGE: 2.
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Affiliation(s)
- Qing Xu
- Department of Medical Imaging, Clinic Medical School, Yangzhou University, Northern Jiangsu Province Hospital, Yangzhou, China
| | - QingQiang Zhu
- Department of Medical Imaging, Clinic Medical School, Yangzhou University, Northern Jiangsu Province Hospital, Yangzhou, China
| | - Hao Liu
- Yizhun Medical AI, Beijing, China
| | | | | | | | - SaiYang Li
- Department of Urology, The Third Affiliated Hospital of Soochow University, Changzhou, China
| | - Jing Ye
- Department of Medical Imaging, Clinic Medical School, Yangzhou University, Northern Jiangsu Province Hospital, Yangzhou, China
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Kuusk T, Neves JB, Tran M, Bex A. Radiomics to better characterize small renal masses. World J Urol 2021; 39:2861-2868. [PMID: 33495866 DOI: 10.1007/s00345-021-03602-y] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2020] [Accepted: 01/11/2021] [Indexed: 12/23/2022] Open
Abstract
PURPOSE Radiomics is a specific field of medical research that uses programmable recognition tools to extract objective information from standard images to combine with clinical data, with the aim of improving diagnostic, prognostic, and predictive accuracy beyond standard visual interpretation. We performed a narrative review of radiomic applications that may support improved characterization of small renal masses (SRM). The main focus of the review was to identify and discuss methods which may accurately differentiate benign from malignant renal masses, specifically between renal cell carcinoma (RCC) subtypes and from angiomyolipoma without visible fat (fat-poor AML) and oncocytoma. Furthermore, prediction of grade, sarcomatoid features, and gene mutations would be of importance in terms of potential clinical utility in prognostic stratification and selecting personalised patient management strategies. METHODS A detailed search of original articles was performed using the PubMed-MEDLINE database until 20 September 2020 to identify the English literature relevant to radiomics applications in renal tumour assessment. In total, 42 articles were included in the analysis in 3 main categories related to SRM: prediction of benign versus malignant SRM, subtypes, and nuclear grade, and other features of aggressiveness. CONCLUSION Overall, studies reported the superiority of radiomics over expert radiological assessment, but were mainly of retrospective design and therefore of low-quality evidence. However, it is clear that radiomics is an attractive modality that has the potential to improve the non-invasive diagnostic accuracy of SRM imaging and prediction of its natural behaviour. Further prospective validation studies of radiomics are needed to augment management algorithms of SRM.
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Affiliation(s)
- Teele Kuusk
- Urology Department, Darent Valley Hospital, Dartford and Gravesham NHS Trust, Dartford, UK
- Specialist Centre for Kidney Cancer, Royal Free London NHS Foundation Trust, London, UK
| | - Joana B Neves
- Specialist Centre for Kidney Cancer, Royal Free London NHS Foundation Trust, London, UK
| | - Maxine Tran
- Specialist Centre for Kidney Cancer, Royal Free London NHS Foundation Trust, London, UK
- UCL Division of Surgery and Interventional Science, London, UK
| | - Axel Bex
- Specialist Centre for Kidney Cancer, Royal Free London NHS Foundation Trust, London, UK.
- UCL Division of Surgery and Interventional Science, London, UK.
- Surgical Oncology Division, Urology Department, The Netherlands Cancer Institute, Amsterdam, The Netherlands.
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Lee M, Wei S, Anaokar J, Uzzo R, Kutikov A. Kidney cancer management 3.0: can artificial intelligence make us better? Curr Opin Urol 2021; 31:409-415. [PMID: 33882560 DOI: 10.1097/mou.0000000000000881] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
PURPOSE OF REVIEW Artificial intelligence holds tremendous potential for disrupting clinical medicine. Here we review the current role of artificial intelligence in the kidney cancer space. RECENT FINDINGS Machine learning and deep learning algorithms have been developed using information extracted from radiomic, histopathologic, and genomic datasets of patients with renal masses. SUMMARY Although artificial intelligence applications in medicine are still in their infancy, they already hold immediate promise to improve accuracy of renal mass characterization, grade, and prognostication. As algorithms become more robust and generalizable, artificial intelligence is poised to significantly disrupt kidney cancer care.
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Affiliation(s)
| | | | - Jordan Anaokar
- Department of Diagnostic Imaging, Fox Chase Cancer Center, Philadelphia, Pennsylvania, USA
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Campi R, Stewart GD, Staehler M, Dabestani S, Kuczyk MA, Shuch BM, Finelli A, Bex A, Ljungberg B, Capitanio U. Novel Liquid Biomarkers and Innovative Imaging for Kidney Cancer Diagnosis: What Can Be Implemented in Our Practice Today? A Systematic Review of the Literature. Eur Urol Oncol 2021; 4:22-41. [DOI: 10.1016/j.euo.2020.12.011] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2020] [Revised: 11/26/2020] [Accepted: 12/14/2020] [Indexed: 12/12/2022]
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17
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Wang J, Zhu H, Wang SH, Zhang YD. A Review of Deep Learning on Medical Image Analysis. MOBILE NETWORKS AND APPLICATIONS 2021; 26:351-380. [DOI: 10.1007/s11036-020-01672-7] [Citation(s) in RCA: 43] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 10/20/2020] [Indexed: 08/30/2023]
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Woolford SJ, D'Angelo S, Curtis EM, Parsons CM, Ward KA, Dennison EM, Patel HP, Cooper C, Harvey NC. COVID-19 and associations with frailty and multimorbidity: a prospective analysis of UK Biobank participants. Aging Clin Exp Res 2020; 32:1897-1905. [PMID: 32705587 PMCID: PMC7377312 DOI: 10.1007/s40520-020-01653-6] [Citation(s) in RCA: 43] [Impact Index Per Article: 10.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2020] [Accepted: 07/07/2020] [Indexed: 01/13/2023]
Abstract
BACKGROUND Frailty and multimorbidity have been suggested as risk factors for severe COVID-19 disease. AIMS We investigated, in the UK Biobank, whether frailty and multimorbidity were associated with risk of hospitalisation with COVID-19. METHODS 502,640 participants aged 40-69 years at baseline (54-79 years at COVID-19 testing) were recruited across UK during 2006-10. A modified assessment of frailty using Fried's classification was generated from baseline data. COVID-19 test results (England) were available for 16/03/2020-01/06/2020, mostly taken in hospital settings. Logistic regression was used to discern associations between frailty, multimorbidity and COVID-19 diagnoses, after adjusting for sex, age, BMI, ethnicity, education, smoking and number of comorbidity groupings, comparing COVID-19 positive, COVID-19 negative and non-tested groups. RESULTS 4510 participants were tested for COVID-19 (positive = 1326, negative = 3184). 497,996 participants were not tested. Compared to the non-tested group, after adjustment, COVID-19 positive participants were more likely to be frail (OR = 1.4 [95%CI = 1.1, 1.8]), report slow walking speed (OR = 1.3 [1.1, 1.6]), report two or more falls in the past year (OR = 1.3 [1.0, 1.5]) and be multimorbid (≥ 4 comorbidity groupings vs 0-1: OR = 1.9 [1.5, 2.3]). However, similar strength of associations were apparent when comparing COVID-19 negative and non-tested groups. However, frailty and multimorbidity were not associated with COVID-19 diagnoses, when comparing COVID-19 positive and COVID-19 negative participants. DISCUSSION AND CONCLUSIONS Frailty and multimorbidity do not appear to aid risk stratification, in terms of positive versus negative results of COVID-19 testing. Investigation of the prognostic value of these markers for adverse clinical sequelae following COVID-19 disease is urgently needed.
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Affiliation(s)
- S J Woolford
- MRC Lifecourse Epidemiology Unit, University of Southampton, Southampton, UK
| | - S D'Angelo
- MRC Lifecourse Epidemiology Unit, University of Southampton, Southampton, UK
| | - E M Curtis
- MRC Lifecourse Epidemiology Unit, University of Southampton, Southampton, UK
| | - C M Parsons
- MRC Lifecourse Epidemiology Unit, University of Southampton, Southampton, UK
| | - K A Ward
- MRC Lifecourse Epidemiology Unit, University of Southampton, Southampton, UK
| | - E M Dennison
- MRC Lifecourse Epidemiology Unit, University of Southampton, Southampton, UK
| | - H P Patel
- MRC Lifecourse Epidemiology Unit, University of Southampton, Southampton, UK
- Medicine for Older People, University Hospital Southampton, Southampton, UK
- Academic Geriatric Medicine, University of Southampton, Southampton, UK
- NIHR Southampton Biomedical Research Centre, University of Southampton and University Hospital Southampton NHS Foundation Trust, Southampton, UK
| | - C Cooper
- MRC Lifecourse Epidemiology Unit, University of Southampton, Southampton, UK
- NIHR Southampton Biomedical Research Centre, University of Southampton and University Hospital Southampton NHS Foundation Trust, Southampton, UK
- NIHR Biomedical Research Centre, University of Oxford, Oxford, UK
| | - N C Harvey
- MRC Lifecourse Epidemiology Unit, University of Southampton, Southampton, UK.
- NIHR Southampton Biomedical Research Centre, University of Southampton and University Hospital Southampton NHS Foundation Trust, Southampton, UK.
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