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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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Gui Y, Zhang J. Research Progress of Artificial Intelligence in the Grading and Classification of Meningiomas. Acad Radiol 2024:S1076-6332(24)00073-4. [PMID: 38413314 DOI: 10.1016/j.acra.2024.02.003] [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: 12/02/2023] [Revised: 02/02/2024] [Accepted: 02/02/2024] [Indexed: 02/29/2024]
Abstract
A meningioma is a common primary central nervous system tumor. The histological features of meningiomas vary significantly depending on the grade and subtype, leading to differences in treatment and prognosis. Therefore, early diagnosis, grading, and typing of meningiomas are crucial for developing comprehensive and individualized diagnosis and treatment plans. The advancement of artificial intelligence (AI) in medical imaging, particularly radiomics and deep learning (DL), has contributed to the increasing research on meningioma grading and classification. These techniques are fast and accurate, involve fully automated learning, are non-invasive and objective, enable the efficient and non-invasive prediction of meningioma grades and classifications, and provide valuable assistance in clinical treatment and prognosis. This article provides a summary and analysis of the research progress in radiomics and DL for meningioma grading and classification. It also highlights the existing research findings, limitations, and suggestions for future improvement, aiming to facilitate the future application of AI in the diagnosis and treatment of meningioma.
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Affiliation(s)
- Yuan Gui
- Department of Radiology, the fifth affiliated hospital of zunyi medical university, zhufengdadao No.1439, Doumen District, Zhuhai, China
| | - Jing Zhang
- Department of Radiology, the fifth affiliated hospital of zunyi medical university, zhufengdadao No.1439, Doumen District, Zhuhai, China.
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Zhang M, Ye Z, Yuan E, Lv X, Zhang Y, Tan Y, Xia C, Tang J, Huang J, Li Z. Imaging-based deep learning in kidney diseases: recent progress and future prospects. Insights Imaging 2024; 15:50. [PMID: 38360904 PMCID: PMC10869329 DOI: 10.1186/s13244-024-01636-5] [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: 11/19/2023] [Accepted: 01/27/2024] [Indexed: 02/17/2024] Open
Abstract
Kidney diseases result from various causes, which can generally be divided into neoplastic and non-neoplastic diseases. Deep learning based on medical imaging is an established methodology for further data mining and an evolving field of expertise, which provides the possibility for precise management of kidney diseases. Recently, imaging-based deep learning has been widely applied to many clinical scenarios of kidney diseases including organ segmentation, lesion detection, differential diagnosis, surgical planning, and prognosis prediction, which can provide support for disease diagnosis and management. In this review, we will introduce the basic methodology of imaging-based deep learning and its recent clinical applications in neoplastic and non-neoplastic kidney diseases. Additionally, we further discuss its current challenges and future prospects and conclude that achieving data balance, addressing heterogeneity, and managing data size remain challenges for imaging-based deep learning. Meanwhile, the interpretability of algorithms, ethical risks, and barriers of bias assessment are also issues that require consideration in future development. We hope to provide urologists, nephrologists, and radiologists with clear ideas about imaging-based deep learning and reveal its great potential in clinical practice.Critical relevance statement The wide clinical applications of imaging-based deep learning in kidney diseases can help doctors to diagnose, treat, and manage patients with neoplastic or non-neoplastic renal diseases.Key points• Imaging-based deep learning is widely applied to neoplastic and non-neoplastic renal diseases.• Imaging-based deep learning improves the accuracy of the delineation, diagnosis, and evaluation of kidney diseases.• The small dataset, various lesion sizes, and so on are still challenges for deep learning.
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Affiliation(s)
- Meng Zhang
- Department of Radiology, West China Hospital, Sichuan University, No. 37 Guoxue Alley, Chengdu, 610041, China
- Medical Equipment Innovation Research Center, West China Hospital, Sichuan University, No. 37 Guoxue Alley, Chengdu, 610041, China
- Med+X Center for Manufacturing, West China Hospital, Sichuan University, No. 37 Guoxue Alley, Chengdu, 610041, China
| | - Zheng Ye
- Department of Radiology, West China Hospital, Sichuan University, No. 37 Guoxue Alley, Chengdu, 610041, China
| | - Enyu Yuan
- Department of Radiology, West China Hospital, Sichuan University, No. 37 Guoxue Alley, Chengdu, 610041, China
| | - Xinyang Lv
- Department of Radiology, West China Hospital, Sichuan University, No. 37 Guoxue Alley, Chengdu, 610041, China
| | - Yiteng Zhang
- Department of Radiology, West China Hospital, Sichuan University, No. 37 Guoxue Alley, Chengdu, 610041, China
| | - Yuqi Tan
- Department of Radiology, West China Hospital, Sichuan University, No. 37 Guoxue Alley, Chengdu, 610041, China
| | - Chunchao Xia
- Department of Radiology, West China Hospital, Sichuan University, No. 37 Guoxue Alley, Chengdu, 610041, China
| | - Jing Tang
- Department of Radiology, West China Hospital, Sichuan University, No. 37 Guoxue Alley, Chengdu, 610041, China.
| | - Jin Huang
- Medical Equipment Innovation Research Center, West China Hospital, Sichuan University, No. 37 Guoxue Alley, Chengdu, 610041, China.
- Med+X Center for Manufacturing, West China Hospital, Sichuan University, No. 37 Guoxue Alley, Chengdu, 610041, China.
| | - Zhenlin Li
- Department of Radiology, West China Hospital, Sichuan University, No. 37 Guoxue Alley, Chengdu, 610041, 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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Hammer S, Nunes DW, Hammer M, Zeman F, Akers M, Götz A, Balla A, Doppler MC, Fellner C, Platz Batista da Silva N, Thurn S, Verloh N, Stroszczynski C, Wohlgemuth WA, Palm C, Uller W. Deep learning-based differentiation of peripheral high-flow and low-flow vascular malformations in T2-weighted short tau inversion recovery MRI. Clin Hemorheol Microcirc 2024:CH232071. [PMID: 38306026 DOI: 10.3233/ch-232071] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/03/2024]
Abstract
BACKGROUND Differentiation of high-flow from low-flow vascular malformations (VMs) is crucial for therapeutic management of this orphan disease. OBJECTIVE A convolutional neural network (CNN) was evaluated for differentiation of peripheral vascular malformations (VMs) on T2-weighted short tau inversion recovery (STIR) MRI. METHODS 527 MRIs (386 low-flow and 141 high-flow VMs) were randomly divided into training, validation and test set for this single-center study. 1) Results of the CNN's diagnostic performance were compared with that of two expert and four junior radiologists. 2) The influence of CNN's prediction on the radiologists' performance and diagnostic certainty was evaluated. 3) Junior radiologists' performance after self-training was compared with that of the CNN. RESULTS Compared with the expert radiologists the CNN achieved similar accuracy (92% vs. 97%, p = 0.11), sensitivity (80% vs. 93%, p = 0.16) and specificity (97% vs. 100%, p = 0.50). In comparison to the junior radiologists, the CNN had a higher specificity and accuracy (97% vs. 80%, p < 0.001; 92% vs. 77%, p < 0.001). CNN assistance had no significant influence on their diagnostic performance and certainty. After self-training, the junior radiologists' specificity and accuracy improved and were comparable to that of the CNN. CONCLUSIONS Diagnostic performance of the CNN for differentiating high-flow from low-flow VM was comparable to that of expert radiologists. CNN did not significantly improve the simulated daily practice of junior radiologists, self-training was more effective.
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Affiliation(s)
- Simone Hammer
- Department of Radiology, Medical Center Universityof Regensburg, Faculty of Medicine, University of Regensburg, Regensburg, Germany
| | - Danilo Weber Nunes
- Regensburg Medical Image Computing (ReMIC), Ostbayerische Technische Hochschule Regensburg (OTH Regensburg), Regensburg, Germany
| | - Michael Hammer
- Regensburg Medical Image Computing (ReMIC), Ostbayerische Technische Hochschule Regensburg (OTH Regensburg), Regensburg, Germany
| | - Florian Zeman
- Center for Clinical Trials, Medical Center University of Regensburg, Faculty of Medicine, University of Regensburg, Regensburg, Germany
| | - Michael Akers
- Department of Radiology, Medical Center Universityof Regensburg, Faculty of Medicine, University of Regensburg, Regensburg, Germany
| | - Andrea Götz
- Department of Radiology, Medical Center Universityof Regensburg, Faculty of Medicine, University of Regensburg, Regensburg, Germany
| | - Annika Balla
- Department of Radiology, Medical Center Universityof Regensburg, Faculty of Medicine, University of Regensburg, Regensburg, Germany
| | - Michael Christian Doppler
- Department of Diagnostic and Interventional Radiology, Medical Center University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Claudia Fellner
- Department of Radiology, Medical Center Universityof Regensburg, Faculty of Medicine, University of Regensburg, Regensburg, Germany
| | - Natascha Platz Batista da Silva
- Department of Radiology, Medical Center Universityof Regensburg, Faculty of Medicine, University of Regensburg, Regensburg, Germany
| | - Sylvia Thurn
- Department of Radiology, Medical Center Universityof Regensburg, Faculty of Medicine, University of Regensburg, Regensburg, Germany
| | - Niklas Verloh
- Department of Diagnostic and Interventional Radiology, Medical Center University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Christian Stroszczynski
- Department of Radiology, Medical Center Universityof Regensburg, Faculty of Medicine, University of Regensburg, Regensburg, Germany
| | - Walter Alexander Wohlgemuth
- Department of Radiology, Medical Center University of Halle (Saale), Faculty of Medicine, University of Halle (Saale), Halle, Germany
| | - Christoph Palm
- Regensburg Medical Image Computing (ReMIC), Ostbayerische Technische Hochschule Regensburg (OTH Regensburg), Regensburg, Germany
- Regensburg Center of Biomedical Engineering (RCBE), OTH Regensburg and University of Regensburg, Regensburg, Germany
| | - Wibke Uller
- Department of Diagnostic and Interventional Radiology, Medical Center University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
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Yang H, Liu H, Lin J, Xiao H, Guo Y, Mei H, Ding Q, Yuan Y, Lai X, Wu K, Wu S. An automatic texture feature analysis framework of renal tumor: surgical, pathological, and molecular evaluation based on multi-phase abdominal CT. Eur Radiol 2024; 34:355-366. [PMID: 37528301 DOI: 10.1007/s00330-023-10016-4] [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: 04/19/2023] [Revised: 06/06/2023] [Accepted: 06/12/2023] [Indexed: 08/03/2023]
Abstract
OBJECTIVES To determine whether the texture feature analysis of multi-phase abdominal CT can provide a robust prediction of benign and malignant, histological subtype, pathological stage, nephrectomy risk, pathological grade, and Ki67 index in renal tumor. METHODS A total of 1051 participants with renal tumor were split into the internal cohort (850 patients from four different hospitals) and the external testing cohort (201 patients from another local hospital). The proposed framework comprised a 3D-kidney and tumor segmentation model by 3D-UNet, a feature extractor for the regions of interest based on radiomics and image dimension reduction, and the six classifiers by XGBoost. A quantitative model interpretation method called SHAP was used to explore the contribution of each feature. RESULTS The proposed multi-phase abdominal CT model provides robust prediction for benign and malignant, histological subtype, pathological stage, nephrectomy risk, pathological grade, and Ki67 index in the internal validation set, with the AUROC values of 0.88 ± 0.1, 0.90 ± 0.1, 0.91 ± 0.1, 0.89 ± 0.1, 0.84 ± 0.1, and 0.88 ± 0.1, respectively. The external testing set also showed impressive results, with AUROC values of 0.83 ± 0.1, 0.83 ± 0.1, 0.85 ± 0.1, 0.81 ± 0.1, 0.79 ± 0.1, and 0.81 ± 0.1, respectively. The radiomics feature including the first-order statistics, the tumor size-related morphology, and the shape-related tumor features contributed most to the model predictions. CONCLUSIONS Automatic texture feature analysis of abdominal multi-phase CT provides reliable predictions for multi-tasks, suggesting the potential usage of clinical application. CLINICAL RELEVANCE STATEMENT The automatic texture feature analysis framework, based on multi-phase abdominal CT, provides robust and reliable predictions for multi-tasks. These valuable insights can serve as a guiding tool for clinical diagnosis and treatment, making medical imaging an essential component in the process. KEY POINTS • The automatic texture feature analysis framework based on multi-phase abdominal CT can provide more accurate prediction of benign and malignant, histological subtype, pathological stage, nephrectomy risk, pathological grade, and Ki67 index in renal tumor. • The quantitative decomposition of the prediction model was conducted to explore the contribution of the extracted feature. • The study involving 1051 patients from 5 medical centers, along with a heterogeneous external data testing strategy, can be seamlessly transferred to various tasks involving new datasets.
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Affiliation(s)
- Huancheng Yang
- Luohu Clinical Institute, Shantou University Medical College, Shantou, 515000, China
- Department of Urology, The Third Affiliated Hospital of Shenzhen University (Luohu Hospital Group), Shenzhen, 518000, China
- Shenzhen Following Precision Medical Research Institute, The Third Affiliated Hospital of Shenzhen University (Luohu Hospital Group), Shenzhen, 518000, China
- Shantou University Medical College, Shantou University, Shantou, 515000, China
| | - Hanlin Liu
- Department of Radiology, The Third Affiliated Hospital of Shenzhen University (Luohu Hospital Group), Shenzhen, 518000, China
| | - Jiashan Lin
- Luohu Clinical Institute, Shantou University Medical College, Shantou, 515000, China
- Shantou University Medical College, Shantou University, Shantou, 515000, China
- Department of Urology, Peking University Shenzhen Hospital, Shenzhen, 518036, China
| | - Hongwei Xiao
- Department of Urology, The Third Affiliated Hospital of Shenzhen University (Luohu Hospital Group), Shenzhen, 518000, China
- Shenzhen Following Precision Medical Research Institute, The Third Affiliated Hospital of Shenzhen University (Luohu Hospital Group), Shenzhen, 518000, China
| | - Yiqi Guo
- Luohu Clinical Institute, Shantou University Medical College, Shantou, 515000, China
- Shantou University Medical College, Shantou University, Shantou, 515000, China
| | - Hangru Mei
- Department of Urology, The Third Affiliated Hospital of Shenzhen University (Luohu Hospital Group), Shenzhen, 518000, China
- Shenzhen Following Precision Medical Research Institute, The Third Affiliated Hospital of Shenzhen University (Luohu Hospital Group), Shenzhen, 518000, China
| | - Qiuxia Ding
- Department of Urology, The Third Affiliated Hospital of Shenzhen University (Luohu Hospital Group), Shenzhen, 518000, China
- Shenzhen Following Precision Medical Research Institute, The Third Affiliated Hospital of Shenzhen University (Luohu Hospital Group), Shenzhen, 518000, China
| | - Yangguang Yuan
- Department of Radiology, The Third Affiliated Hospital of Shenzhen University (Luohu Hospital Group), Shenzhen, 518000, China
| | - Xiaohui Lai
- Department of Radiology, The Third Affiliated Hospital of Shenzhen University (Luohu Hospital Group), Shenzhen, 518000, 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, The Third Affiliated Hospital of Shenzhen University (Luohu Hospital Group), Shenzhen, 518000, China.
| | - Song Wu
- Luohu Clinical Institute, Shantou University Medical College, Shantou, 515000, China.
- Shenzhen Following Precision Medical Research Institute, The Third Affiliated Hospital of Shenzhen University (Luohu Hospital Group), Shenzhen, 518000, China.
- Shantou University Medical College, Shantou University, Shantou, 515000, China.
- Department of Urology, Health Science Center, South China Hospital, Shenzhen University, Shenzhen, 518116, China.
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Sim KC, Han NY, Cho Y, Sung DJ, Park BJ, Kim MJ, Han YE. Machine Learning-Based Magnetic Resonance Radiomics Analysis for Predicting Low- and High-Grade Clear Cell Renal Cell Carcinoma. J Comput Assist Tomogr 2023; 47:873-881. [PMID: 37948361 DOI: 10.1097/rct.0000000000001453] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2023]
Abstract
PURPOSE To explore whether high- and low-grade clear cell renal cell carcinomas (ccRCC) can be distinguished using radiomics features extracted from magnetic resonance imaging. METHODS In this retrospective study, 154 patients with pathologically proven clear ccRCC underwent contrast-enhanced 3 T magnetic resonance imaging and were assigned to the development (n = 122) and test (n = 32) cohorts in a temporal-split setup. A total of 834 radiomics features were extracted from whole-tumor volumes using 3 sequences: T2-weighted imaging (T2WI), diffusion-weighted imaging, and contrast-enhanced T1-weighted imaging. A random forest regressor was used to extract important radiomics features that were subsequently used for model development using the random forest algorithm. Tumor size, apparent diffusion coefficient value, and percentage of tumor-to-renal parenchymal signal intensity drop in the tumors were recorded by 2 radiologists for quantitative analysis. The area under the receiver operating characteristic curve (AUC) was generated to predict ccRCC grade. RESULTS In the development cohort, the T2WI-based radiomics model demonstrated the highest performance (AUC, 0.82). The T2WI-based radiomics and radiologic feature hybrid model showed AUCs of 0.79 and 0.83, respectively. In the test cohort, the T2WI-based radiomics model achieved an AUC of 0.82. The range of AUCs of the hybrid model of T2WI-based radiomics and radiologic features was 0.73 to 0.80. CONCLUSION Magnetic resonance imaging-based classifier models using radiomics features and machine learning showed satisfactory diagnostic performance in distinguishing between high- and low-grade ccRCC, thereby serving as a helpful noninvasive tool for predicting ccRCC grade.
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Affiliation(s)
- Ki Choon Sim
- From the Department of Radiology, Korea University Anam Hospital, Korea University College of Medicine
| | - Na Yeon Han
- From the Department of Radiology, Korea University Anam Hospital, Korea University College of Medicine
| | - Yongwon Cho
- Department of Radiology and AI Center, Korea University Anam Hospital, Korea University College of Medicine, Seoul, Republic of Korea
| | - Deuk Jae Sung
- From the Department of Radiology, Korea University Anam Hospital, Korea University College of Medicine
| | - Beom Jin Park
- From the Department of Radiology, Korea University Anam Hospital, Korea University College of Medicine
| | - Min Ju Kim
- From the Department of Radiology, Korea University Anam Hospital, Korea University College of Medicine
| | - Yeo Eun Han
- From the Department of Radiology, Korea University Anam Hospital, Korea University College of Medicine
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Zhang X, Zhang G, Xu L, Bai X, Zhang J, Chen L, Lu X, Yu S, Jin Z, Sun H. Prediction of World Health Organization /International Society of Urological Pathology (WHO/ISUP) Pathological Grading of Clear Cell Renal Cell Carcinoma by Dual-Layer Spectral CT. Acad Radiol 2023; 30:2321-2328. [PMID: 36543688 DOI: 10.1016/j.acra.2022.12.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2022] [Revised: 11/27/2022] [Accepted: 12/02/2022] [Indexed: 12/24/2022]
Abstract
RATIONALE AND OBJECTIVES To evaluate whether the dual-layer spectral computed tomography urography (DL-CTU) images could predict WHO/ISUP pathological grading of clear cell renal cell carcinoma (ccRCC). MATERIALS AND METHODS We retrospectively included patients (n = 50) with pathologically confirmed ccRCC who underwent preoperative DL-CTU (from October 2017 to February 2021). They were divided into low-grade (WHO/ISUP 1/2, n = 30) and high-grade groups (WHO/ISUP 3/4, n = 20). The lesion size, attenuation (HU), iodine concentration (IC), normalized IC(NIC), and other quantitative characteristics were compared between the two groups. HU, IC, and NIC were obtained by plotting ROI with two different methods (circular ROI in the solid component or irregular ROI along the tumor edge containing tumor necrotic components). Receiver operating characteristic curves and multivariable model were used to evaluate the ability of parameters to predict WHO/ISUP grade. RESULTS Transverse diameter (TD) of low-grade tumors was smaller, and HU in the non-contrast phase of the second method (HU-U-2) was lower than that of high-grade tumors (34.21±15.14 mm vs. 46.50 ± 20.68 mm, 27.33 ± 6.65 HU vs. 31.36 ± 6.09 HU, p< 0.05). The NIC in the nephrographic phase by the two methods (NIC-N-1 and NIC-N-2) of low-grade was higher than that of the high-grade group (0.78± 0.19 vs.0.58 ± 0.22, 0.73 ± 0.42 vs. 0.46 ± 0.22, p< 0.05). The final multivariable model composed of TD, HU-U-2, and NIC-N-1 could predict ccRCC grade with the area under the curve, sensitivity, specificity, and accuracy of 0.852, 70%, 90%, and 82%. CONCLUSION Quantitative indicators in DL-CTU images could help predict the WHO/ISUP grade of ccRCC.
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Affiliation(s)
- Xiaoxiao Zhang
- Department of Radiology, State Key Laboratory of Complex Severe and Rare Disease, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, BJ, P.R.China
| | - Gumuyang Zhang
- Department of Radiology, State Key Laboratory of Complex Severe and Rare Disease, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, BJ, P.R.China
| | - Lili Xu
- Department of Radiology, State Key Laboratory of Complex Severe and Rare Disease, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, BJ, P.R.China
| | - Xin Bai
- Department of Radiology, State Key Laboratory of Complex Severe and Rare Disease, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, BJ, P.R.China
| | - Jiahui Zhang
- Department of Radiology, State Key Laboratory of Complex Severe and Rare Disease, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, BJ, P.R.China
| | - Li Chen
- Department of Radiology, State Key Laboratory of Complex Severe and Rare Disease, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, BJ, P.R.China
| | - Xiaomei Lu
- CT Clinical Science, Philips Healthcare, Beijing, BJ, P.R.China
| | - Shenghui Yu
- CT Clinical Science, Philips Healthcare, Beijing, BJ, P.R.China
| | - Zhengyu Jin
- Department of Radiology, State Key Laboratory of Complex Severe and Rare Disease, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, BJ, P.R.China; National Center for Quality Control of Radiology, Beijing BJ, P.R.China
| | - Hao Sun
- Department of Radiology, State Key Laboratory of Complex Severe and Rare Disease, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, BJ, P.R.China; National Center for Quality Control of Radiology, Beijing BJ, P.R.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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Mahmud S, Abbas TO, Mushtak A, Prithula J, Chowdhury MEH. Kidney Cancer Diagnosis and Surgery Selection by Machine Learning from CT Scans Combined with Clinical Metadata. Cancers (Basel) 2023; 15:3189. [PMID: 37370799 DOI: 10.3390/cancers15123189] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2023] [Revised: 05/30/2023] [Accepted: 06/07/2023] [Indexed: 06/29/2023] Open
Abstract
Kidney cancers are one of the most common malignancies worldwide. Accurate diagnosis is a critical step in the management of kidney cancer patients and is influenced by multiple factors including tumor size or volume, cancer types and stages, etc. For malignant tumors, partial or radical surgery of the kidney might be required, but for clinicians, the basis for making this decision is often unclear. Partial nephrectomy could result in patient death due to cancer if kidney removal was necessary, whereas radical nephrectomy in less severe cases could resign patients to lifelong dialysis or need for future transplantation without sufficient cause. Using machine learning to consider clinical data alongside computed tomography images could potentially help resolve some of these surgical ambiguities, by enabling a more robust classification of kidney cancers and selection of optimal surgical approaches. In this study, we used the publicly available KiTS dataset of contrast-enhanced CT images and corresponding patient metadata to differentiate four major classes of kidney cancer: clear cell (ccRCC), chromophobe (chRCC), papillary (pRCC) renal cell carcinoma, and oncocytoma (ONC). We rationalized these data to overcome the high field of view (FoV), extract tumor regions of interest (ROIs), classify patients using deep machine-learning models, and extract/post-process CT image features for combination with clinical data. Regardless of marked data imbalance, our combined approach achieved a high level of performance (85.66% accuracy, 84.18% precision, 85.66% recall, and 84.92% F1-score). When selecting surgical procedures for malignant tumors (RCC), our method proved even more reliable (90.63% accuracy, 90.83% precision, 90.61% recall, and 90.50% F1-score). Using feature ranking, we confirmed that tumor volume and cancer stage are the most relevant clinical features for predicting surgical procedures. Once fully mature, the approach we propose could be used to assist surgeons in performing nephrectomies by guiding the choices of optimal procedures in individual patients with kidney cancer.
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Affiliation(s)
- Sakib Mahmud
- Department of Electrical Engineering, Qatar University, Doha 2713, Qatar
| | - Tariq O Abbas
- Urology Division, Surgery Department, Sidra Medicine, Doha 26999, Qatar
- Department of Surgery, Weill Cornell Medicine-Qatar, Doha 24811, Qatar
- College of Medicine, Qatar University, Doha 2713, Qatar
| | - Adam Mushtak
- Clinical Imaging Department, Hamad Medical Corporation, Doha 3050, Qatar
| | - Johayra Prithula
- Department of Electrical and Electronics Engineering, University of Dhaka, Dhaka 1000, Bangladesh
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Panic J, Defeudis A, Balestra G, Giannini V, Rosati S. Normalization Strategies in Multi-Center Radiomics Abdominal MRI: Systematic Review and Meta-Analyses. IEEE OPEN JOURNAL OF ENGINEERING IN MEDICINE AND BIOLOGY 2023; 4:67-76. [PMID: 37283773 PMCID: PMC10241248 DOI: 10.1109/ojemb.2023.3271455] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2023] [Revised: 03/18/2023] [Accepted: 04/25/2023] [Indexed: 06/08/2023] Open
Abstract
Goal: Artificial intelligence applied to medical image analysis has been extensively used to develop non-invasive diagnostic and prognostic signatures. However, these imaging biomarkers should be largely validated on multi-center datasets to prove their robustness before they can be introduced into clinical practice. The main challenge is represented by the great and unavoidable image variability which is usually addressed using different pre-processing techniques including spatial, intensity and feature normalization. The purpose of this study is to systematically summarize normalization methods and to evaluate their correlation with the radiomics model performances through meta-analyses. This review is carried out according to the PRISMA statement: 4777 papers were collected, but only 74 were included. Two meta-analyses were carried out according to two clinical aims: characterization and prediction of response. Findings of this review demonstrated that there are some commonly used normalization approaches, but not a commonly agreed pipeline that can allow to improve performance and to bridge the gap between bench and bedside.
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Affiliation(s)
- Jovana Panic
- Department of Surgical Science, and Polytechnic of Turin, Department of Electronics and TelecommunicationsUniversity of Turin10129TurinItaly
| | - Arianna Defeudis
- Department of Surgical ScienceUniversity of Turin10129TurinItaly
- Candiolo Cancer InstituteFPO-IRCCS10060CandioloItaly
| | - Gabriella Balestra
- Department of Electronics and TelecommunicationsPolytechnic of Turin10129TurinItaly
| | - Valentina Giannini
- Department of Surgical ScienceUniversity of Turin10129TurinItaly
- Candiolo Cancer InstituteFPO-IRCCS10060CandioloItaly
| | - Samanta Rosati
- Department of Electronics and TelecommunicationsPolytechnic of Turin10129TurinItaly
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13
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Lin Z, Yang W, Zhang W, Jiang C, Chu J, Yang J, Yuan X. Recognizing pathology of renal tumor from macroscopic cross-section image by deep learning. Biomed Eng Online 2023; 22:3. [PMID: 36670469 PMCID: PMC9854121 DOI: 10.1186/s12938-023-01064-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2022] [Accepted: 01/09/2023] [Indexed: 01/22/2023] Open
Abstract
OBJECTIVES This study aims to develop and evaluate the deep learning-based classification model for recognizing the pathology of renal tumor from macroscopic cross-section image. METHODS A total of 467 pathology-confirmed patients who received radical nephrectomy or partial nephrectomy were retrospectively enrolled. The experiment of distinguishing malignant and benign renal tumor are conducted followed by performing the multi-subtypes classification models for recognizing four subtypes of benign tumor and four subtypes of malignant tumors, respectively. The classification models used the same backbone networks which are based on the convolutional neural network (CNN), including EfficientNet-B4, ResNet-18, and VGG-16. The performance of the classification models was evaluated by area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and accuracy. Besides, we performed the quantitative comparison among these CNN models. RESULTS For the model to differentiate the malignant tumor from the benign tumor, three CNN models all obtained relatively satisfactory performance and the highest AUC was achieved by the ResNet-18 model (AUC = 0.9226). There is not statistically significance between EfficientNet-B4 and ResNet-18 architectures and both of them are significantly statistically better than the VGG-16 model. The micro-averaged AUC, macro-averaged sensitivity, macro-averaged specificity, and micro-averaged accuracy for the VGG-16 model to distinguish the malignant tumor subtypes achieved 0.9398, 0.5774, 0.8660, and 0.7917, respectively. The performance of the EfficientNet-B4 is not better than that of VGG-16 in terms of micro-averaged AUC except for other metrics. For the models to recognize the benign tumor subtypes, the EfficientNet-B4 ranked the best performance, but had no significantly statistical difference with other two models with respect to micro-averaged AUC. CONCLUSIONS The classification results were relatively satisfactory, which showed the potential for clinical application when analyzing the renal tumor macroscopic cross-section images. Automatically distinguishing the malignant tumor from benign tumor and identifying the subtypes pathology of renal tumor could make the patient-management process more efficient.
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Affiliation(s)
- Zefang Lin
- grid.258164.c0000 0004 1790 3548Zhuhai Interventional Medical Center, Zhuhai Precision Medical Center, Zhuhai People’s Hospital, Zhuhai Hospital Affiliated with Jinan University, Jinan University, Zhuhai, China
| | - Weihong Yang
- grid.258164.c0000 0004 1790 3548Department of Medical Equipment Engineering, Zhuhai People’s Hospital, Zhuhai Hospital Affiliated with Jinan University, Jinan University, Zhuhai, China
| | - Wenqiang Zhang
- grid.258164.c0000 0004 1790 3548Department of Urology, Zhuhai People’s Hospital, Zhuhai Hospital Affiliated with Jinan University, Jinan University, Zhuhai, China
| | - Chao Jiang
- Nursing Department, Guizhou Aerospace Hospital, Zunyi, China
| | - Jing Chu
- grid.258164.c0000 0004 1790 3548Department of Urology, Zhuhai People’s Hospital, Zhuhai Hospital Affiliated with Jinan University, Jinan University, Zhuhai, China
| | - Jing Yang
- grid.258164.c0000 0004 1790 3548Department of Pathology, Zhuhai People’s Hospital, Zhuhai Hospital Affiliated with Jinan University, Jinan University, Zhuhai, China
| | - Xiaoxu Yuan
- grid.258164.c0000 0004 1790 3548Department of Urology, Zhuhai People’s Hospital, Zhuhai Hospital Affiliated with Jinan University, Jinan University, Zhuhai, China
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Deep Learning to Classify AL versus ATTR Cardiac Amyloidosis MR Images. Biomedicines 2023; 11:biomedicines11010193. [PMID: 36672702 PMCID: PMC9855341 DOI: 10.3390/biomedicines11010193] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2022] [Revised: 12/18/2022] [Accepted: 01/11/2023] [Indexed: 01/13/2023] Open
Abstract
The aim of this work was to compare the classification of cardiac MR-images of AL versus ATTR amyloidosis by neural networks and by experienced human readers. Cine-MR images and late gadolinium enhancement (LGE) images of 120 patients were studied (70 AL and 50 TTR). A VGG16 convolutional neural network (CNN) was trained with a 5-fold cross validation process, taking care to strictly distribute images of a given patient in either the training group or the test group. The analysis was performed at the patient level by averaging the predictions obtained for each image. The classification accuracy obtained between AL and ATTR amyloidosis was 0.750 for cine-CNN, 0.611 for Gado-CNN and between 0.617 and 0.675 for human readers. The corresponding AUC of the ROC curve was 0.839 for cine-CNN, 0.679 for gado-CNN (p < 0.004 vs. cine) and 0.714 for the best human reader (p < 0.007 vs. cine). Logistic regression with cine-CNN and gado-CNN, as well as analysis focused on the specific orientation plane, did not change the overall results. We conclude that cine-CNN leads to significantly better discrimination between AL and ATTR amyloidosis as compared to gado-CNN or human readers, but with lower performance than reported in studies where visual diagnosis is easy, and is currently suboptimal for clinical practice.
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Posada Calderon L, Eismann L, Reese SW, Reznik E, Hakimi AA. Advances in Imaging-Based Biomarkers in Renal Cell Carcinoma: A Critical Analysis of the Current Literature. Cancers (Basel) 2023; 15:cancers15020354. [PMID: 36672304 PMCID: PMC9856305 DOI: 10.3390/cancers15020354] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2022] [Revised: 12/31/2022] [Accepted: 01/03/2023] [Indexed: 01/06/2023] Open
Abstract
Cross-sectional imaging is the standard diagnostic tool to determine underlying biology in renal masses, which is crucial for subsequent treatment. Currently, standard CT imaging is limited in its ability to differentiate benign from malignant disease. Therefore, various modalities have been investigated to identify imaging-based parameters to improve the noninvasive diagnosis of renal masses and renal cell carcinoma (RCC) subtypes. MRI was reported to predict grading of RCC and to identify RCC subtypes, and has been shown in a small cohort to predict the response to targeted therapy. Dynamic imaging is promising for the staging and diagnosis of RCC. PET/CT radiotracers, such as 18F-fluorodeoxyglucose (FDG), 124I-cG250, radiolabeled prostate-specific membrane antigen (PSMA), and 11C-acetate, have been reported to improve the identification of histology, grading, detection of metastasis, and assessment of response to systemic therapy, and to predict oncological outcomes. Moreover, 99Tc-sestamibi and SPECT scans have shown promising results in distinguishing low-grade RCC from benign lesions. Radiomics has been used to further characterize renal masses based on semantic and textural analyses. In preliminary studies, integrated machine learning algorithms using radiomics proved to be more accurate in distinguishing benign from malignant renal masses compared to radiologists' interpretations. Radiomics and radiogenomics are used to complement risk classification models to predict oncological outcomes. Imaging-based biomarkers hold strong potential in RCC, but require standardization and external validation before integration into clinical routines.
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Affiliation(s)
- Lina Posada Calderon
- Urology Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Lennert Eismann
- Urology Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Stephen W. Reese
- Urology Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Ed Reznik
- Computational Oncology, Department of Epidemiology & Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Abraham Ari Hakimi
- Urology Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
- Correspondence:
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Li Y, Gao X, Tang X, Lin S, Pang H. Research on automatic classification technology of kidney tumor and normal kidney tissue based on computed tomography radiomics. Front Oncol 2023; 13:1013085. [PMID: 36910615 PMCID: PMC9998940 DOI: 10.3389/fonc.2023.1013085] [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: 08/06/2022] [Accepted: 02/13/2023] [Indexed: 03/14/2023] Open
Abstract
Purpose By using a radiomics-based approach, multiple radiomics features can be extracted from regions of interest in computed tomography (CT) images, which may be applied to automatically classify kidney tumors and normal kidney tissues. The study proposes a method based on CT radiomics and aims to use extracted radiomics features to automatically classify of kidney tumors and normal kidney tissues and to establish an automatic classification model. Methods CT data were retrieved from the 2019 Kidney and Kidney Tumor Segmentation Challenge (KiTS19) in The Cancer Imaging Archive (TCIA) open access database. Arterial phase-enhanced CT images from 210 cases were used to establish an automatic classification model. These CT images of patients were randomly divided into training (168 cases) and test (42 cases) sets. Furthermore, the radiomics features of gross tumor volume (GTV) and normal kidney tissues in the training set were extracted and screened, and a binary logistic regression model was established. For the test set, the radiomic features and cutoff value of P were consistent with the training set. Results Three radiomics features were selected to establish the binary logistic regression model. The accuracy (ACC), sensitivity (SENS), specificity (SPEC), area under the curve (AUC), and Youden index of the training and test sets based on the CT radiomics classification model were all higher than 0.85. Conclusion The automatic classification model of kidney tumors and normal kidney tissues based on CT radiomics exhibited good classification ability. Kidney tumors could be distinguished from normal kidney tissues. This study may complement automated tumor delineation techniques and warrants further research.
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Affiliation(s)
- Yunfei Li
- Department of Oncology, The Affiliated Hospital of Southwest Medical University, Luzhou, China
| | - Xinrui Gao
- Department of Oncology, The Affiliated Hospital of Southwest Medical University, Luzhou, China
| | - Xuemei Tang
- Department of Oncology, The Affiliated Hospital of Southwest Medical University, Luzhou, China
| | - Sheng Lin
- Department of Oncology, The Affiliated Hospital of Southwest Medical University, Luzhou, China
| | - Haowen Pang
- Department of Oncology, The Affiliated Hospital of Southwest Medical University, Luzhou, China
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Kong J, He Y, Zhu X, Shao P, Xu Y, Chen Y, Coatrieux JL, Yang G. BKC-Net: Bi-Knowledge Contrastive Learning for renal tumor diagnosis on 3D CT images. Knowl Based Syst 2022. [DOI: 10.1016/j.knosys.2022.109369] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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Roussel E, Capitanio U, Kutikov A, Oosterwijk E, Pedrosa I, Rowe SP, Gorin MA. Novel Imaging Methods for Renal Mass Characterization: A Collaborative Review. Eur Urol 2022; 81:476-488. [PMID: 35216855 PMCID: PMC9844544 DOI: 10.1016/j.eururo.2022.01.040] [Citation(s) in RCA: 40] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2021] [Revised: 01/08/2022] [Accepted: 01/21/2022] [Indexed: 01/19/2023]
Abstract
CONTEXT The incidental detection of localized renal masses has been rising steadily, but a significant proportion of these tumors are benign or indolent and, in most cases, do not require treatment. At the present time, a majority of patients with an incidentally detected renal tumor undergo treatment for the presumption of cancer, leading to a significant number of unnecessary surgical interventions that can result in complications including loss of renal function. Thus, there exists a clinical need for improved tools to aid in the pretreatment characterization of renal tumors to inform patient management. OBJECTIVE To systematically review the evidence on noninvasive, imaging-based tools for solid renal mass characterization. EVIDENCE ACQUISITION The MEDLINE database was systematically searched for relevant studies on novel imaging techniques and interpretative tools for the characterization of solid renal masses, published in the past 10 yr. EVIDENCE SYNTHESIS Over the past decade, several novel imaging tools have offered promise for the improved characterization of indeterminate renal masses. Technologies of particular note include multiparametric magnetic resonance imaging of the kidney, molecular imaging with targeted radiopharmaceutical agents, and use of radiomics as well as artificial intelligence to enhance the interpretation of imaging studies. Among these, 99mTc-sestamibi single photon emission computed tomography/computed tomography (CT) for the identification of benign renal oncocytomas and hybrid oncocytic chromophobe tumors, and positron emission tomography/CT imaging with radiolabeled girentuximab for the identification of clear cell renal cell carcinoma, are likely to be closest to implementation in clinical practice. CONCLUSIONS A number of novel imaging tools stand poised to aid in the noninvasive characterization of indeterminate renal masses. In the future, these tools may aid in patient management by providing a comprehensive virtual biopsy, complete with information on tumor histology, underlying molecular abnormalities, and ultimately disease prognosis. PATIENT SUMMARY Not all renal tumors require treatment, as a significant proportion are either benign or have limited metastatic potential. Several innovative imaging tools have shown promise for their ability to improve the characterization of renal tumors and provide guidance in terms of patient management.
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Affiliation(s)
- Eduard Roussel
- Department of Urology, University Hospitals Leuven, Leuven, Belgium
| | - Umberto Capitanio
- Department of Urology, University Vita-Salute, San Raffaele Scientific Institute, Milan, Italy; Division of Experimental Oncology, URI, Urological Research Institute, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Alexander Kutikov
- Division of Urology, Department of Surgery, Fox Chase Cancer Center, Temple University Health System, Philadelphia, PA, USA
| | - Egbert Oosterwijk
- Department of Urology, Radboud University Medical Center, Radboud Institute for Molecular Life Sciences (RIMLS), Nijmegen, The Netherlands
| | - Ivan Pedrosa
- Department of Radiology, University of Texas Southwestern Medical Center, Dallas, TX, USA; Advanced Imaging Research Center. University of Texas Southwestern Medical Center, Dallas, TX, USA; Department of Urology, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Steven P Rowe
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA; The James Buchanan Brady Urological Institute and Department of Urology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Michael A Gorin
- Urology Associates and UPMC Western Maryland, Cumberland, MD, USA; Department of Urology, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA.
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Wu K, Wu P, Yang K, Li Z, Kong S, Yu L, Zhang E, Liu H, Guo Q, Wu S. A comprehensive texture feature analysis framework of renal cell carcinoma: pathological, prognostic, and genomic evaluation based on CT images. Eur Radiol 2022; 32:2255-2265. [PMID: 34800150 DOI: 10.1007/s00330-021-08353-3] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2021] [Revised: 09/16/2021] [Accepted: 09/22/2021] [Indexed: 02/06/2023]
Abstract
OBJECTIVES We tried to realize accurate pathological classification, assessment of prognosis, and genomic molecular typing of renal cell carcinoma by CT texture feature analysis. To determine whether CT texture features can perform accurate pathological classification and evaluation of prognosis and genomic characteristics in renal cell carcinoma. METHODS Patients with renal cell carcinoma from five open-source cohorts were analyzed retrospectively in this study. These data were randomly split to train and test machine learning algorithms to segment the lesion, predict the histological subtype, tumor stage, and pathological grade. Dice coefficient and performance metrics such as accuracy and AUC were calculated to evaluate the segmentation and classification model. Quantitative decomposition of the predictive model was conducted to explore the contribution of each feature. Besides, survival analysis and the statistical correlation between CT texture features, pathological, and genomic signatures were investigated. RESULTS A total of 569 enhanced CT images of 443 patients (mean age 59.4, 278 males) were included in the analysis. In the segmentation task, the mean dice coefficient was 0.96 for the kidney and 0.88 for the cancer region. For classification of histologic subtype, tumor stage, and pathological grade, the model was on a par with radiologists and the AUC was 0.83 [Formula: see text] 0.1, 0.80 [Formula: see text] 0.1, and 0.77 [Formula: see text] 0.1 at 95% confidence intervals, respectively. Moreover, specific quantitative CT features related to clinical prognosis were identified. A strong statistical correlation (R2 = 0.83) between the feature crosses and genomic characteristics was shown. The structural equation modeling confirmed significant associations between CT features, pathological (β = - 0.75), and molecular subtype (β = - 0.30). CONCLUSIONS The framework illustrates high performance in the pathological classification of renal cell carcinoma. Prognosis and genomic characteristics can be inferred by quantitative image analysis. KEY POINTS • The analytical framework exhibits high-performance pathological classification of renal cell carcinoma and is on a par with human radiologists. • Quantitative decomposition of the predictive model shows that specific texture features contribute to histologic subtype and tumor stage classification. • Structural equation modeling shows the associations of genomic characteristics to CT texture features. Overall survival and molecular characteristics can be inferred by quantitative CT texture analysis in renal cell carcinoma.
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Affiliation(s)
- Kai Wu
- Department of Urology, The Third Affiliated Hospital of Shenzhen University (Luohu Hospital Group), Shenzhen, 518001, China
- Shenzhen Following Precision Medical Research Institute, Luohu Hospital Group, Shenzhen, 518001, China
| | - Peng Wu
- Department of Urology, The Third Affiliated Hospital of Shenzhen University (Luohu Hospital Group), Shenzhen, 518001, China
- Shenzhen Following Precision Medical Research Institute, Luohu Hospital Group, Shenzhen, 518001, China
| | - Kai Yang
- Department of Urology, South China Hospital, Health Science Center, Shenzhen University, Shenzhen, 518116, China
| | - Zhe Li
- Department of Urology, The Third Affiliated Hospital of Shenzhen University (Luohu Hospital Group), Shenzhen, 518001, China
- Shenzhen Following Precision Medical Research Institute, Luohu Hospital Group, Shenzhen, 518001, China
| | - Sijia Kong
- Department of Urology, The Third Affiliated Hospital of Shenzhen University (Luohu Hospital Group), Shenzhen, 518001, China
- Shenzhen Following Precision Medical Research Institute, Luohu Hospital Group, Shenzhen, 518001, China
| | - Lu Yu
- Department of Urology, The Third Affiliated Hospital of Shenzhen University (Luohu Hospital Group), Shenzhen, 518001, China
- Shenzhen Following Precision Medical Research Institute, Luohu Hospital Group, Shenzhen, 518001, China
| | - Enpu Zhang
- Department of Urology, The Third Affiliated Hospital of Shenzhen University (Luohu Hospital Group), Shenzhen, 518001, China
- Shenzhen Following Precision Medical Research Institute, Luohu Hospital Group, Shenzhen, 518001, China
| | - Hanlin Liu
- Department of Urology, The Third Affiliated Hospital of Shenzhen University (Luohu Hospital Group), Shenzhen, 518001, China
- Shenzhen Following Precision Medical Research Institute, Luohu Hospital Group, Shenzhen, 518001, China
| | - Qing Guo
- Department of Urology, The Third Affiliated Hospital of Shenzhen University (Luohu Hospital Group), Shenzhen, 518001, China
- Shenzhen Following Precision Medical Research Institute, Luohu Hospital Group, Shenzhen, 518001, China
| | - Song Wu
- Department of Urology, The Third Affiliated Hospital of Shenzhen University (Luohu Hospital Group), Shenzhen, 518001, China
- Department of Urology, South China Hospital, Health Science Center, Shenzhen University, Shenzhen, 518116, China
- Teaching Center of Shenzhen Luohu Hospital, Shantou University Medical College, Shantou, 515041, China
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20
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Multiparametric Functional MRI of the Kidney: Current State and Future Trends with Deep Learning Approaches. ROFO-FORTSCHR RONTG 2022; 194:983-992. [PMID: 35272360 DOI: 10.1055/a-1775-8633] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Abstract
BACKGROUND Until today, assessment of renal function has remained a challenge for modern medicine. In many cases, kidney diseases accompanied by a decrease in renal function remain undetected and unsolved, since neither laboratory tests nor imaging diagnostics provide adequate information on kidney status. In recent years, developments in the field of functional magnetic resonance imaging with application to abdominal organs have opened new possibilities combining anatomic imaging with multiparametric functional information. The multiparametric approach enables the measurement of perfusion, diffusion, oxygenation, and tissue characterization in one examination, thus providing more comprehensive insight into pathophysiological processes of diseases as well as effects of therapeutic interventions. However, application of multiparametric fMRI in the kidneys is still restricted mainly to research areas and transfer to the clinical routine is still outstanding. One of the major challenges is the lack of a standardized protocol for acquisition and postprocessing including efficient strategies for data analysis. This article provides an overview of the most common fMRI techniques with application to the kidney together with new approaches regarding data analysis with deep learning. METHODS This article implies a selective literature review using the literature database PubMed in May 2021 supplemented by our own experiences in this field. RESULTS AND CONCLUSION Functional multiparametric MRI is a promising technique for assessing renal function in a more comprehensive approach by combining multiple parameters such as perfusion, diffusion, and BOLD imaging. New approaches with the application of deep learning techniques could substantially contribute to overcoming the challenge of handling the quantity of data and developing more efficient data postprocessing and analysis protocols. Thus, it can be hoped that multiparametric fMRI protocols can be sufficiently optimized to be used for routine renal examination and to assist clinicians in the diagnostics, monitoring, and treatment of kidney diseases in the future. KEY POINTS · Multiparametric fMRI is a technique performed without the use of radiation, contrast media, and invasive methods.. · Multiparametric fMRI provides more comprehensive insight into pathophysiological processes of kidney diseases by combining functional and structural parameters.. · For broader acceptance of fMRI biomarkers, there is a need for standardization of acquisition, postprocessing, and analysis protocols as well as more prospective studies.. · Deep learning techniques could significantly contribute to an optimization of data acquisition and the postprocessing and interpretation of larger quantities of data.. CITATION FORMAT · Zhang C, Schwartz M, Küstner T et al. Multiparametric Functional MRI of the Kidney: Current State and Future Trends with Deep Learning Approaches. Fortschr Röntgenstr 2022; DOI: 10.1055/a-1775-8633.
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21
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Germain P, Vardazaryan A, Padoy N, Labani A, Roy C, Schindler TH, El Ghannudi S. Deep Learning Supplants Visual Analysis by Experienced Operators for the Diagnosis of Cardiac Amyloidosis by Cine-CMR. Diagnostics (Basel) 2021; 12:diagnostics12010069. [PMID: 35054236 PMCID: PMC8774777 DOI: 10.3390/diagnostics12010069] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2021] [Revised: 12/24/2021] [Accepted: 12/27/2021] [Indexed: 12/03/2022] Open
Abstract
Background: Diagnosing cardiac amyloidosis (CA) from cine-CMR (cardiac magnetic resonance) alone is not reliable. In this study, we tested if a convolutional neural network (CNN) could outperform the visual diagnosis of experienced operators. Method: 119 patients with cardiac amyloidosis and 122 patients with left ventricular hypertrophy (LVH) of other origins were retrospectively selected. Diastolic and systolic cine-CMR images were preprocessed and labeled. A dual-input visual geometry group (VGG ) model was used for binary image classification. All images belonging to the same patient were distributed in the same set. Accuracy and area under the curve (AUC) were calculated per frame and per patient from a 40% held-out test set. Results were compared to a visual analysis assessed by three experienced operators. Results: frame-based comparisons between humans and a CNN provided an accuracy of 0.605 vs. 0.746 (p < 0.0008) and an AUC of 0.630 vs. 0.824 (p < 0.0001). Patient-based comparisons provided an accuracy of 0.660 vs. 0.825 (p < 0.008) and an AUC of 0.727 vs. 0.895 (p < 0.002). Conclusion: based on cine-CMR images alone, a CNN is able to discriminate cardiac amyloidosis from LVH of other origins better than experienced human operators (15 to 20 points more in absolute value for accuracy and AUC), demonstrating a unique capability to identify what the eyes cannot see through classical radiological analysis.
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Affiliation(s)
- Philippe Germain
- Department of Radiology, Nouvel Hopital Civil, University Hospital, 67000 Strasbourg, France; (A.L.); (C.R.); (S.E.G.)
- Correspondence:
| | - Armine Vardazaryan
- ICube, University of Strasbourg, CNRS, 67000 Strasbourg, France; (A.V.); (N.P.)
- IHU (Institut Hopitalo-Universitaire), 67000 Strasbourg, France
| | - Nicolas Padoy
- ICube, University of Strasbourg, CNRS, 67000 Strasbourg, France; (A.V.); (N.P.)
- IHU (Institut Hopitalo-Universitaire), 67000 Strasbourg, France
| | - Aissam Labani
- Department of Radiology, Nouvel Hopital Civil, University Hospital, 67000 Strasbourg, France; (A.L.); (C.R.); (S.E.G.)
| | - Catherine Roy
- Department of Radiology, Nouvel Hopital Civil, University Hospital, 67000 Strasbourg, France; (A.L.); (C.R.); (S.E.G.)
| | - Thomas Hellmut Schindler
- Mallinckrodt Institute of Radiology, Division of Nuclear Medicine, Washington University School of Medicine, Saint Louis, MO 63110, USA;
| | - Soraya El Ghannudi
- Department of Radiology, Nouvel Hopital Civil, University Hospital, 67000 Strasbourg, France; (A.L.); (C.R.); (S.E.G.)
- Department of Nuclear Medicine, Nouvel Hopital Civil, University Hospital, 67000 Strasbourg, France
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22
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Chen XY, Zhang Y, Chen YX, Huang ZQ, Xia XY, Yan YX, Xu MP, Chen W, Wang XL, Chen QL. MRI-Based Grading of Clear Cell Renal Cell Carcinoma Using a Machine Learning Classifier. Front Oncol 2021; 11:708655. [PMID: 34660276 PMCID: PMC8517330 DOI: 10.3389/fonc.2021.708655] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2021] [Accepted: 09/15/2021] [Indexed: 11/14/2022] Open
Abstract
Objective To develop a machine learning (ML)-based classifier for discriminating between low-grade (ISUP I-II) and high-grade (ISUP III-IV) clear cell renal cell carcinomas (ccRCCs) using MRI textures. Materials and Methods We retrospectively evaluated a total of 99 patients (with 61 low-grade and 38 high-grade ccRCCs), who were randomly divided into a training set (n = 70) and a validation set (n = 29). Regions of interest (ROIs) of all tumors were manually drawn three times by a radiologist at the maximum lesion level of the cross-sectional CMP sequence images. The quantitative texture analysis software, MaZda, was used to extract texture features, including histograms, co-occurrence matrixes, run-length matrixes, gradient models, and autoregressive models. Reproducibility of the texture features was assessed with the intra-class correlation coefficient (ICC). Features were chosen based on their importance coefficients in a random forest model, while the multi-layer perceptron algorithm was used to build a classifier on the training set, which was later evaluated with the validation set. Results The ICCs of 257 texture features were equal to or higher than 0.80 (0.828–0.998. Six features, namely Kurtosis, 135dr_RLNonUni, Horzl_GLevNonU, 135dr_GLevNonU, S(4,4)Entropy, and S(0,5)SumEntrp, were chosen to develop the multi-layer perceptron classifier. A three-layer perceptron model, which has 229 nodes in the hidden layer, was trained on the training set. The accuracy of the model was 95.7% with the training set and 86.2% with the validation set. The areas under the receiver operating curves were 0.997 and 0.758 for the training and validation sets, respectively. Conclusions A machine learning-based grading model was developed that can aid in the clinical diagnosis of clear cell renal cell carcinoma using MRI images.
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Affiliation(s)
- Xin-Yuan Chen
- Department of Rehabilitation Medicine, The First Affiliated Hospital of Fujian Medical University, Fuzhou, China
| | - Yu Zhang
- Department of Radiology, The First Affiliated Hospital of Fujian Medical University, Fuzhou, China.,Department of Radiology, Xiangyang Central Hospital, Affiliated Hospital of Hubei University of Arts and Science, Xiangyang, China
| | - Yu-Xing Chen
- Department of Bioinformatics, School of Basic Medical Sciences, School of Medical Technology and Engineering, Key Laboratory of Medical Bioinformatics, Key Laboratory of Ministry of Education for Gastrointestinal Cancer, Fujian Medical University, Fuzhou, China
| | - Zi-Qiang Huang
- Department of Radiology, The First Affiliated Hospital of Fujian Medical University, Fuzhou, China
| | - Xiao-Yue Xia
- Department of Radiology, The First Affiliated Hospital of Fujian Medical University, Fuzhou, China
| | - Yi-Xin Yan
- Department of Bioinformatics, School of Basic Medical Sciences, School of Medical Technology and Engineering, Key Laboratory of Medical Bioinformatics, Key Laboratory of Ministry of Education for Gastrointestinal Cancer, Fujian Medical University, Fuzhou, China
| | - Mo-Ping Xu
- Department of Bioinformatics, School of Basic Medical Sciences, School of Medical Technology and Engineering, Key Laboratory of Medical Bioinformatics, Key Laboratory of Ministry of Education for Gastrointestinal Cancer, Fujian Medical University, Fuzhou, China
| | - Wen Chen
- Department of Bioinformatics, School of Basic Medical Sciences, School of Medical Technology and Engineering, Key Laboratory of Medical Bioinformatics, Key Laboratory of Ministry of Education for Gastrointestinal Cancer, Fujian Medical University, Fuzhou, China
| | - Xian-Long Wang
- Department of Bioinformatics, School of Basic Medical Sciences, School of Medical Technology and Engineering, Key Laboratory of Medical Bioinformatics, Key Laboratory of Ministry of Education for Gastrointestinal Cancer, Fujian Medical University, Fuzhou, China
| | - Qun-Lin Chen
- Department of Radiology, The First Affiliated Hospital of Fujian Medical University, Fuzhou, China
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23
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Choi JW, Hu R, Zhao Y, Purkayastha S, Wu J, McGirr AJ, Stavropoulos SW, Silva AC, Soulen MC, Palmer MB, Zhang PJL, Zhu C, Ahn SH, Bai HX. Preoperative prediction of the stage, size, grade, and necrosis score in clear cell renal cell carcinoma using MRI-based radiomics. Abdom Radiol (NY) 2021; 46:2656-2664. [PMID: 33386910 DOI: 10.1007/s00261-020-02876-x] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2020] [Revised: 11/15/2020] [Accepted: 11/18/2020] [Indexed: 12/26/2022]
Abstract
PURPOSE Clear cell renal cell carcinoma (ccRCC) is the most common subtype of renal cell carcinoma. Currently, there is a lack of noninvasive methods to stratify ccRCC prognosis prior to any invasive therapies. The purpose of this study was to preoperatively predict the tumor stage, size, grade, and necrosis (SSIGN) score of ccRCC using MRI-based radiomics. METHODS A multicenter cohort of 364 histopathologically confirmed ccRCC patients (272 low [< 4] and 92 high [≥ 4] SSIGN score) with preoperative T2-weighted and T1-contrast-enhanced MRI were retrospectively identified and divided into training (254 patients) and testing sets (110 patients). The performance of a manually optimized radiomics model was assessed by measuring accuracy, sensitivity, specificity, area under receiver operating characteristic curve (AUROC), and area under precision-recall curve (AUPRC) on an independent test set, which was not included in model training. Lastly, its performance was compared to that of a machine learning pipeline, Tree-Based Pipeline Optimization Tool (TPOT). RESULTS The manually optimized radiomics model using Random Forest classification and Analysis of Variance feature selection methods achieved an AUROC of 0.89, AUPRC of 0.81, accuracy of 0.89 (95% CI 0.816-0.937), specificity of 0.95 (95% CI 0.875-0.984), and sensitivity of 0.72 (95% CI 0.537-0.852) on the test set. The TPOT using Extra Trees Classifier achieved an AUROC of 0.94, AUPRC of 0.83, accuracy of 0.89 (95% CI 0.816-0.937), specificity of 0.95 (95% CI 0.875-0.984), and sensitivity of 0.72 (95% CI 0.537-0.852) on the test set. CONCLUSION Preoperative MR radiomics can accurately predict SSIGN score of ccRCC, suggesting its promise as a prognostic tool that can be used in conjunction with diagnostic markers.
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Affiliation(s)
- Ji Whae Choi
- Warren Alpert Medical School, Brown University, Providence, RI, 02903, USA.
- Department of Diagnostic Imaging, Rhode Island Hospital, Providence, RI, 02903, USA.
| | - Rong Hu
- School of Computer Science and Engineering, Central South University, Changsha, 410083, China
- Hunan Province Engineering Technology Research Center of Computer Vision and Intelligent Medical Treatment, Changsha, 410083, China
- Joint Laboratory of Mobile Health, Ministry of Education and China Mobile, Hunan, 410083, China
| | - Yijun Zhao
- Department of Radiology, Second Xiangya Hospital of Central South University, Changsha, Hunan, 410011, China
| | - Subhanik Purkayastha
- Warren Alpert Medical School, Brown University, Providence, RI, 02903, USA
- Department of Diagnostic Imaging, Rhode Island Hospital, Providence, RI, 02903, USA
| | - Jing Wu
- Department of Diagnostic Imaging, Rhode Island Hospital, Providence, RI, 02903, USA
- Department of Radiology, Second Xiangya Hospital of Central South University, Changsha, Hunan, 410011, China
| | - Aidan J McGirr
- Department of Radiology, Mayo Clinic Hospital, Scottsdale, AZ, 85054, USA
| | - S William Stavropoulos
- Department of Radiology, Hospital of the University of Pennsylvania, Philadelphia, 19104, PA, USA
| | - Alvin C Silva
- Department of Radiology, Mayo Clinic Hospital, Scottsdale, AZ, 85054, USA
| | - Michael C Soulen
- Department of Radiology, Hospital of the University of Pennsylvania, Philadelphia, 19104, PA, USA
| | - Matthew B Palmer
- Department of Pathology and Laboratory Medicine, Hospital of the University of Pennsylvania, Philadelphia, 19104, PA, USA
| | - Paul J L Zhang
- Department of Pathology and Laboratory Medicine, Hospital of the University of Pennsylvania, Philadelphia, 19104, PA, USA
| | - Chengzhang Zhu
- Joint Laboratory of Mobile Health, Ministry of Education and China Mobile, Hunan, 410083, China
- College of Literature and Journalism, Central South University, Changsha, 410083, China
| | - Sun Ho Ahn
- Warren Alpert Medical School, Brown University, Providence, RI, 02903, USA
- Department of Diagnostic Imaging, Rhode Island Hospital, Providence, RI, 02903, USA
| | - Harrison X Bai
- Warren Alpert Medical School, Brown University, Providence, RI, 02903, USA
- Department of Diagnostic Imaging, Rhode Island Hospital, Providence, RI, 02903, USA
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24
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Yi X, Xiao Q, Zeng F, Yin H, Li Z, Qian C, Wang C, Lei G, Xu Q, Li C, Li M, Gong G, Zee C, Guan X, Liu L, Chen BT. Computed Tomography Radiomics for Predicting Pathological Grade of Renal Cell Carcinoma. Front Oncol 2021; 10:570396. [PMID: 33585193 PMCID: PMC7873602 DOI: 10.3389/fonc.2020.570396] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2020] [Accepted: 12/08/2020] [Indexed: 12/16/2022] Open
Abstract
Background Clear cell renal cell carcinoma (ccRCC) is the most common renal cancer and it has the worst prognosis among all renal cancers. However, traditional radiological characteristics on computed tomography (CT) scans of ccRCC have been insufficient to predict the pathological grade of ccRCC before surgery. Methods Patients with ccRCC were retrospectively enrolled into this study and were separated into two groups according to the World Health Organization (WHO)/International Society of Urological Pathology (ISUP) grading system, i.e., low-grade (Grade I and II) group and high-grade (Grade III and IV) group. Traditional CT radiological characteristics such as tumor size, pre- and post-enhancing CT densities were assessed. In addition, radiomic texture analysis based on the CT imaging of the ccRCC were also performed. A CT-based machine learning method combining the traditional radiological characteristics and radiomic features was used in the predictive modeling for differentiating the low-grade from the high-grade ccRCC. Model performance was evaluated with the receiver operating characteristic curve (ROC) analysis. Results A total of 264 patients with pathologically confirmed ccRCC were included in this study. In this cohort, 206 patients had the low-grade tumors and 58 had the high-grade tumors. The model built with traditional radiological characteristics achieved an area under the curve (AUC) of 0.9175 (95% CI: 0.8765–0.9585) and 0.8088 (95% CI: 0.7064–0.9113) in differentiating the low-grade from the high-grade ccRCC for the training cohort and the validation cohort respectively. The model built with the radiomic textural features yielded an AUC value of 0.8170 (95% CI: 0.7353–0.8987) and 0.8017 (95% CI: 0.6878–0.9157) for the training cohort and the validation cohort, respectively. The combined model integrating both the traditional radiological characteristics and the radiomic textural features achieved the highest efficacy, with an AUC of 0.9235 (95% CI: 0.8646–0.9824) and an AUC of 0.9099 (95% CI: 0.8324–0.9873) for the training cohort and validation cohort, respectively. Conclusion We developed a machine learning radiomic model achieving a satisfying performance in differentiating the low-grade from the high-grade ccRCC. Our study presented a potentially useful non-invasive imaging-focused method to predict the pathological grade of renal cancers prior to surgery.
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Affiliation(s)
- Xiaoping Yi
- Department of Radiology, Xiangya Hospital, Central South University, Changsha, China
| | - Qiao Xiao
- Department of Urology, Xiangya Hospital, Central South University, Changsha, China
| | - Feiyue Zeng
- Department of Radiology, Xiangya Hospital, Central South University, Changsha, China
| | - Hongling Yin
- Department of Pathology, Xiangya Hospital, Central South University, Changsha, China
| | - Zan Li
- Xiangya School of Medicine, Central-South University, Changsha, China
| | - Cheng Qian
- Xiangya School of Medicine, Central-South University, Changsha, China
| | - Cikui Wang
- Department of Urology, Xiangya Hospital, Central South University, Changsha, China
| | - Guangwu Lei
- Department of Radiology, Xiangya Hospital, Central South University, Changsha, China
| | - Qingsong Xu
- School of Mathematics and Statistics, Central South University, Changsha, China
| | - Chuanquan Li
- School of Mathematics and Statistics, Central South University, Changsha, China
| | - Minghao Li
- Department of Urology, Xiangya Hospital, Central South University, Changsha, China
| | - Guanghui Gong
- Department of Pathology, Xiangya Hospital, Central South University, Changsha, China
| | - Chishing Zee
- Department of Radiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
| | - Xiao Guan
- Department of Urology, Xiangya Hospital, Central South University, Changsha, China
| | - Longfei Liu
- Department of Urology, Xiangya Hospital, Central South University, Changsha, China
| | - Bihong T Chen
- Department of Diagnostic Radiology, City of Hope National Medical Center, Duarte, CA, United States
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He Y, Pan I, Bao B, Halsey K, Chang M, Liu H, Peng S, Sebro RA, Guan J, Yi T, Delworth AT, Eweje F, States LJ, Zhang PJ, Zhang Z, Wu J, Peng X, Bai HX. Deep learning-based classification of primary bone tumors on radiographs: A preliminary study. EBioMedicine 2020; 62:103121. [PMID: 33232868 PMCID: PMC7689511 DOI: 10.1016/j.ebiom.2020.103121] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2020] [Revised: 10/19/2020] [Accepted: 10/26/2020] [Indexed: 12/18/2022] Open
Abstract
BACKGROUND To develop a deep learning model to classify primary bone tumors from preoperative radiographs and compare performance with radiologists. METHODS A total of 1356 patients (2899 images) with histologically confirmed primary bone tumors and pre-operative radiographs were identified from five institutions' pathology databases. Manual cropping was performed by radiologists to label the lesions. Binary discriminatory capacity (benign versus not-benign and malignant versus not-malignant) and three-way classification (benign versus intermediate versus malignant) performance of our model were evaluated. The generalizability of our model was investigated on data from external test set. Final model performance was compared with interpretation from five radiologists of varying level of experience using the Permutations tests. FINDINGS For benign vs. not benign, model achieved area under curve (AUC) of 0•894 and 0•877 on cross-validation and external testing, respectively. For malignant vs. not malignant, model achieved AUC of 0•907 and 0•916 on cross-validation and external testing, respectively. For three-way classification, model achieved 72•1% accuracy vs. 74•6% and 72•1% for the two subspecialists on cross-validation (p = 0•03 and p = 0•52, respectively). On external testing, model achieved 73•4% accuracy vs. 69•3%, 73•4%, 73•1%, 67•9%, and 63•4% for the two subspecialists and three junior radiologists (p = 0•14, p = 0•89, p = 0•93, p = 0•02, p < 0•01 for radiologists 1-5, respectively). INTERPRETATION Deep learning can classify primary bone tumors using conventional radiographs in a multi-institutional dataset with similar accuracy compared to subspecialists, and better performance than junior radiologists. FUNDING The project described was supported by RSNA Research & Education Foundation, through grant number RSCH2004 to Harrison X. Bai.
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Affiliation(s)
- Yu He
- Department of Radiology, The Second Xiangya Hospital of Central South University, No.139 Middle Renmin Road, Changsha, Hunan 410011, PR China
| | - Ian Pan
- Department of Diagnostic Imaging, Warren Alpert Medical School of Brown University, Providence 02912, USA
| | - Bingting Bao
- Department of Radiology, The Second Xiangya Hospital of Central South University, No.139 Middle Renmin Road, Changsha, Hunan 410011, PR China
| | - Kasey Halsey
- Department of Diagnostic Imaging, Warren Alpert Medical School of Brown University, Providence 02912, USA
| | | | - Hui Liu
- Department of Radiology, The Second Xiangya Hospital of Central South University, No.139 Middle Renmin Road, Changsha, Hunan 410011, PR China
| | - Shuping Peng
- Department of Radiology, The Second Xiangya Hospital of Central South University, No.139 Middle Renmin Road, Changsha, Hunan 410011, PR China
| | - Ronnie A Sebro
- Musculoskeletal Imaging, Department of Radiology, University of Pennsylvania, Philadelphia 19104, USA
| | - Jing Guan
- Department of Radiology, The Second Xiangya Hospital of Central South University, No.139 Middle Renmin Road, Changsha, Hunan 410011, PR China
| | - Thomas Yi
- Warren Alpert Medical School of Brown University, Providence 02903, USA
| | | | - Feyisope Eweje
- Perelman School of Medicine at the University of Pennsylvania, Philadelphia 19104, USA
| | - Lisa J States
- Department of Radiology, Children's Hospital of Philadelphia, 19104, USA
| | - Paul J Zhang
- Department of Pathology and Laboratory Medicine, Hospital of the University of Pennsylvania, Philadelphia 19104, USA
| | - Zishu Zhang
- Department of Radiology, The Second Xiangya Hospital of Central South University, No.139 Middle Renmin Road, Changsha, Hunan 410011, PR China
| | - Jing Wu
- Department of Radiology, The Second Xiangya Hospital of Central South University, No.139 Middle Renmin Road, Changsha, Hunan 410011, PR China.
| | - Xianjing Peng
- Department of Radiology, Xiangya Hospital, Central South University, No.87 Xiangya Road, Changsha, Hunan 410008, PR China.
| | - Harrison X Bai
- Department of Diagnostic Imaging, Warren Alpert Medical School of Brown University, Providence 02912, USA.
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Yerram NK, Ball MW. EDITORIAL COMMENT. Urology 2020; 144:156-157. [DOI: 10.1016/j.urology.2020.05.095] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2020] [Accepted: 05/17/2020] [Indexed: 11/25/2022]
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