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Deng Y, Wang H, He L. CT radiomics to differentiate between Wilms tumor and clear cell sarcoma of the kidney in children. BMC Med Imaging 2024; 24:13. [PMID: 38182986 PMCID: PMC10768092 DOI: 10.1186/s12880-023-01184-2] [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: 05/29/2023] [Accepted: 12/15/2023] [Indexed: 01/07/2024] Open
Abstract
BACKGROUND To investigate the role of CT radiomics in distinguishing Wilms tumor (WT) from clear cell sarcoma of the kidney (CCSK) in pediatric patients. METHODS We retrospectively enrolled 83 cases of WT and 33 cases of CCSK. These cases were randomly stratified into a training set (n = 81) and a test set (n = 35). Several imaging features from the nephrographic phase were analyzed, including the maximum tumor diameter, the ratio of the maximum CT value of the tumor solid portion to the mean CT value of the contralateral renal vein (CTmax/CT renal vein), and the presence of dilated peritumoral cysts. Radiomics features from corticomedullary phase were extracted, selected, and subsequently integrated into a logistic regression model. We evaluated the model's performance using the area under the curve (AUC), 95% confidence interval (CI), and accuracy. RESULTS In the training set, there were statistically significant differences in the maximum tumor diameter (P = 0.021) and the presence of dilated peritumoral cysts (P = 0.005) between WT and CCSK, whereas in the test set, no statistically significant differences were observed (P > 0.05). The radiomics model, constructed using four radiomics features, demonstrated strong performance in the training set with an AUC of 0.889 (95% CI: 0.811-0.967) and an accuracy of 0.864. Upon evaluation using fivefold cross-validation in the training set, the AUC remained high at 0.863 (95% CI: 0.774-0.952), with an accuracy of 0.852. In the test set, the radiomics model achieved an AUC of 0.792 (95% CI: 0.616-0.968) and an accuracy of 0.857. CONCLUSION CT radiomics proves to be diagnostically valuable for distinguishing between WT and CCSK in pediatric cases.
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Affiliation(s)
- Yaxin Deng
- Department of Radiology, Children's Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Pediatrics, Chongqing, 400014, China
| | - Haoru Wang
- Department of Radiology, Children's Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Pediatrics, Chongqing, 400014, China
| | - Ling He
- Department of Radiology, Children's Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Pediatrics, Chongqing, 400014, China.
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Zhu Y, Li H, Huang Y, Fu W, Wang S, Sun N, Dong D, Tian J, Peng Y. CT-based identification of pediatric non-Wilms tumors using convolutional neural networks at a single center. Pediatr Res 2023; 94:1104-1110. [PMID: 36959318 DOI: 10.1038/s41390-023-02553-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/29/2021] [Revised: 12/21/2022] [Accepted: 01/05/2023] [Indexed: 03/25/2023]
Abstract
BACKGROUND Deep learning (DL) is more and more widely used in children's medical treatment. In this study, we have developed a computed tomography (CT)-based DL model for identifying undiagnosed non-Wilms tumors (nWTs) from pediatric renal tumors. METHODS This study collected and analyzed the preoperative clinical data and CT images of pediatric renal tumor patients diagnosed by our center from 2008 to 2020, and established a DL model to identify nWTs noninvasively. RESULTS A total of 364 children who had been confirmed by histopathology with renal tumors from our center were enrolled, including 269 Wilms tumors (WTs) and 95 nWTs. For DL model development, all cases were randomly allocated to training set (218 cases), validation set (73 cases), and test set (73 cases). In the test set, the DL model achieved area under the curve of 0.831 (95% CI: 0.712-0.951) in discriminating WTs from nWTs, with the accuracy, sensitivity, and specificity of 0.781, 0.563, and 0.842, respectively. The sensitivity of our model was higher than a radiologist with 15 years of experience. CONCLUSIONS We presented a DL model for identifying undiagnosed nWTs from pediatric renal tumors, with the potential to improve the image-based diagnosis. IMPACT Deep learning model was used for the first time to identify pediatric renal tumors in this study. Deep learning model can identify non-Wilms tumors from pediatric renal tumors. Deep learning model based on computed tomography images can improve tumor diagnosis rate.
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Affiliation(s)
- Yupeng Zhu
- Department of Radiology, MOE Key Laboratory of Major Diseases in Children, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing, 100045, China
- Department of Radiology, Peking University Third Hospital, Beijing, 100191, China
| | - Hailin Li
- Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, School of Medicine and Engineering, Beihang University, Beijing, 100191, China
- CAS Key Laboratory of Molecular Imaging, the State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China
| | - Yangyue Huang
- Department of Pediatric Urology, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing, 100045, China
| | - Wangxing Fu
- Department of Radiology, MOE Key Laboratory of Major Diseases in Children, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing, 100045, China
| | - Siwen Wang
- CAS Key Laboratory of Molecular Imaging, the State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Ning Sun
- Department of Pediatric Urology, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing, 100045, China.
| | - Di Dong
- CAS Key Laboratory of Molecular Imaging, the State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China.
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, 100049, China.
| | - Jie Tian
- Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, School of Medicine and Engineering, Beihang University, Beijing, 100191, China.
- CAS Key Laboratory of Molecular Imaging, the State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China.
- Engineering Research Center of Molecular and Neuro Imaging of Ministry of Education, School of Life Science and Technology, Xidian University, Xi'an, Shaanxi, 710126, China.
- Zhuhai Precision Medical Center, Zhuhai People's Hospital (affiliated with Jinan University), Zhuhai, 519000, China.
| | - Yun Peng
- Department of Radiology, MOE Key Laboratory of Major Diseases in Children, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing, 100045, China.
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Ge XY, Lan ZK, Lan QQ, Lin HS, Wang GD, Chen J. Diagnostic accuracy of ultrasound-based multimodal radiomics modeling for fibrosis detection in chronic kidney disease. Eur Radiol 2023; 33:2386-2398. [PMID: 36454259 PMCID: PMC10017610 DOI: 10.1007/s00330-022-09268-3] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2022] [Revised: 08/15/2022] [Accepted: 10/24/2022] [Indexed: 12/02/2022]
Abstract
OBJECTIVES To predict kidney fibrosis in patients with chronic kidney disease using radiomics of two-dimensional ultrasound (B-mode) and Sound Touch Elastography (STE) images in combination with clinical features. METHODS The Mindray Resona 7 ultrasonic diagnostic apparatus with SC5-1U convex array probe (bandwidth frequency of 1-5 MHz) was used to perform two-dimensional ultrasound and STE software. The severity of cortical tubulointerstitial fibrosis was divided into three grades: mild interstitial fibrosis and tubular atrophy (IFTA), fibrotic area < 25%; moderate IFTA, fibrotic area 26-50%; and severe IFTA, fibrotic area > 50%. After extracting radiomics from B-mode and STE images in these patients, we analyzed two classification schemes: mild versus moderate-to-severe IFTA, and mild-to-moderate versus severe IFTA. A nomogram was constructed based on multiple logistic regression analyses, combining clinical and radiomics. The performance of the nomogram for differentiation was evaluated using receiver operating characteristic (ROC), calibration, and decision curves. RESULTS A total of 150 patients undergoing kidney biopsy were enrolled (mild IFTA: n = 74; moderate IFTA: n = 33; severe IFTA: n = 43) and randomized into training (n = 105) and validation cohorts (n = 45). To differentiate between mild and moderate-to-severe IFTA, a nomogram incorporating STE radiomics, albumin, and estimated glomerular filtration (eGFR) rate achieved an area under the ROC curve (AUC) of 0.91 (95% confidence interval [CI]: 0.85-0.97) and 0.85 (95% CI: 0.77-0.98) in the training and validation cohorts, respectively. Between mild-to-moderate and severe IFTA, the nomogram incorporating B-mode and STE radiomics features, age, and eGFR achieved an AUC of 0.93 (95% CI: 0.89-0.98) and 0.83 (95% CI: 0.70-0.95) in the training and validation cohorts, respectively. Finally, we performed a decision curve analysis and found that the nomogram using both radiomics and clinical features exhibited better predictability than any other model (DeLong test, p < 0.05 for the training and validation cohorts). CONCLUSION A nomogram based on two-dimensional ultrasound and STE radiomics and clinical features served as a non-invasive tool capable of differentiating kidney fibrosis of different severities. KEY POINTS • Radiomics calculated based on the ultrasound imaging may be used to predict the severities of kidney fibrosis. • Radiomics may be used to identify clinical features associated with the progression of tubulointerstitial fibrosis in patients with CKD. • Non-invasive ultrasound imaging-based radiomics method with accuracy aids in detecting renal fibrosis with different IFTA severities.
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Affiliation(s)
- Xin-Yue Ge
- Department of Medical Ultrasound, Fourth Affiliated Hospital of Guangxi Medical University, Liuzhou, Guangxi, China
| | - Zhong-Kai Lan
- Department of Medical Ultrasound, Liuzhou People's Hospital Affiliated to Guangxi Medical University, Liuzhou, Guangxi, China
| | - Qiao-Qing Lan
- Department of Radiology, First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China
| | - Hua-Shan Lin
- Department of Pharmaceutical Diagnosis, GE Healthcare, Changsha, 410005, China
| | - Guo-Dong Wang
- Department of Oncology, Fourth Affiliated Hospital of Guangxi Medical University, Liuzhou, Guangxi, China.
| | - Jing Chen
- Department of Medical Ultrasound, Fourth Affiliated Hospital of Guangxi Medical University, Liuzhou, Guangxi, China.
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Bandara MS, Gurunayaka B, Lakraj G, Pallewatte A, Siribaddana S, Wansapura J. Ultrasound Based Radiomics Features of Chronic Kidney Disease. Acad Radiol 2022; 29:229-235. [PMID: 33589307 DOI: 10.1016/j.acra.2021.01.006] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2020] [Revised: 01/02/2021] [Accepted: 01/04/2021] [Indexed: 12/13/2022]
Abstract
RATIONALE AND OBJECTIVES Interstitial fibrosis, common to most chronic kidney diseases, can potentially affect the speckle patterns of kidney ultrasound (US). Here we use Radiomics features derived from US images to identify kidneys with chronic kidney disease. MATERIALS AND METHODS B-mode US without speckle reduction was performed on a cohort of CKD patients (n = 75) and healthy subjects (n = 27). Images of the patients with renal cysts, agenesis and calculi were excluded. After background subtraction, regions of interest were selected from each kidney. Four hundred and sixty-five Radiomics features including first and second-order gray level statistics were calculated on the selected regions. Second-order features were also calculated on wavelet transformed images. A random forest model was used to identify the most important features that can differentiate healthy and diseased kidneys. The ten most important features, based on the Gini index, were used to train a support vector machine. Synthetic minority oversampling technique was used to remove over fitting. RESULTS Wavelet transformed, Gray Level Run Length Matrix based Normalized Run Length Non-uniformity, WT (LH) (GRLN) was identified as the most significant feature in differentiating CKD and healthy kidneys (accuracy - 0.85, sensitivity - 1.0). The mean WT (LH) GRLN of healthy kidneys (0.40 ± 0.01) was significantly higher (p < 0.01) than that of the CKD kidneys (0.24 ± 0.01). According to the Gini Index, the differentiability of WT (LH) GRLN was highest when the long axis of the kidney was oriented perpendicular to the columns of the image matrix. CONCLUSION Radiomics features based on wavelet transformation are sensitive to directionality of US speckle patters and can be successfully used to differentiate CKD and healthy US kidney images.
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Affiliation(s)
| | - Buddika Gurunayaka
- Department of Radiology, Teaching Hospital Anuradhapura, Anuradhapura, Sri Lanka
| | - Gamage Lakraj
- Department of Statistic, University of Colombo, Colombo, Sri Lanka
| | - Aruna Pallewatte
- Department of Neuroradiology, National Hospital of Sri Lanka, Colombo, Sri Lanka
| | - Sisira Siribaddana
- Department of Medicine, Faculty of Medicine and Allied Sciences, Rajarata University of Sri Lanka, Anuradhapura, Sri Lanka
| | - Janaka Wansapura
- Department of Physics, University of Colombo, Colombo, Sri Lanka; Advanced Imaging Research Center, UT Southwestern Medical center, 5323 Harry Hines Blvd, Dallas, TX.
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Rickard M, Fernandez N, Blais AS, Shalabi A, Amirabadi A, Traubici J, Lee W, Gleason J, Brzezinski J, Lorenzo AJ. Volumetric assessment of unaffected parenchyma and Wilms' tumours: analysis of response to chemotherapy and surgery using a semi-automated segmentation algorithm in children with renal neoplasms. BJU Int 2020; 125:695-701. [PMID: 32012416 DOI: 10.1111/bju.15026] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Abstract
OBJECTIVE To present our proof of concept with semi-automatic image recognition/segmentation technology for calculation of tumour/parenchyma volume. METHODS We reviewed Wilms' tumours (WTs) between 2000 and 2018, capturing computed tomography images at baseline, after neoadjuvant chemotherapy (NaC) and postoperatively. Images were uploaded into MATLAB-3-D volumetric image processing software. The program was trained by two clinicians who supervised the demarcation of tumour and parenchyma, followed by automatic recognition and delineation of tumour margins on serial imaging, and differentiation from uninvolved renal parenchyma. Volume was automatically calculated for both. RESULTS During the study period, 98 patients were identified. Of these, based on image quality and availability, 32 (38 affected moieties) were selected. Most patients (65%) were girls, diagnosed at age 50 ± 37 months of age. NaC was employed in 64% of patients. Surgical management included 27 radical and 11 partial nephrectomies. Automated volume assessment demonstrated objective response to NaC for unilateral and bilateral tumours (68 ± 20% and 53 ± 39%, respectively), as well as preservation on uninvolved parenchyma with partial nephrectomy (70 ± 46 cm3 at presentation to 57 ± 41 cm3 post-surgery). CONCLUSION Volumetric analysis is feasible and allows objective assessment of tumour and parenchyma volume in response to chemotherapy and surgery. Our data show changes after therapy that may be otherwise difficult to quantify. Use of such technology may improve surgical planning and quantification of response to treatment, as well as serving as a tool to predict renal reserve and long-term changes in renal function.
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Affiliation(s)
- Mandy Rickard
- Division of Urology, Hospital for Sick Children and Department of Surgery, University of Toronto, Toronto, ON, Canada
| | - Nicolas Fernandez
- Division of Urology, Hospital for Sick Children and Department of Surgery, University of Toronto, Toronto, ON, Canada.,Division of Urology, Hospital Universitario San Ignacio, Pontificia Universidad Javeriana, Bogota, Colombia.,Department of Urology, Fundacion Santa Fe de Bogota, Universidad de los Andes, Bogota, Colombia
| | - Anne-Sophie Blais
- Division of Urology, Hospital for Sick Children and Department of Surgery, University of Toronto, Toronto, ON, Canada.,Division of Urology, Centre Hospitalier Universitaire de Quebec, Quebec City, QC, Canada
| | - Ahmed Shalabi
- Department of Physics and Astronomy, University of Waterloo, Waterloo, ON, Canada
| | - Afsaneh Amirabadi
- Department of Diagnostic Imaging, Hospital for Sick Children, Toronto, ON, Canada
| | - Jeffrey Traubici
- Department of Diagnostic Imaging, Hospital for Sick Children, Toronto, ON, Canada
| | - Wayne Lee
- Department of Diagnostic Imaging, Hospital for Sick Children, Toronto, ON, Canada
| | - Joseph Gleason
- Department of Urology, University of Tennessee Health Science Center, Memphis, TN, USA.,Division of Paediatric Urology, LeBonheur Children's Hospital, Memphis, TN, USA.,Department of Surgery, St Jude Children's Research Hospital, Memphis, TN, USA
| | - Jack Brzezinski
- Division of Haematology and Oncology, Department of Paediatrics, Hospital for Sick Children, Toronto, ON, Canada
| | - Armando J Lorenzo
- Division of Urology, Hospital for Sick Children and Department of Surgery, University of Toronto, Toronto, ON, Canada
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