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Yang YC, Wu JJ, Shi F, Ren QG, Jiang QJ, Guan S, Tang XQ, Meng XS. Sub-regional Radiomics Analysis for Predicting Metastasis Risk in Clear Cell Renal Cell Carcinoma: A Multicenter Retrospective Study. Acad Radiol 2024:S1076-6332(24)00569-5. [PMID: 39147643 DOI: 10.1016/j.acra.2024.08.006] [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: 05/24/2024] [Revised: 08/01/2024] [Accepted: 08/03/2024] [Indexed: 08/17/2024]
Abstract
RATIONALE AND OBJECTIVES Clear cell renal cell carcinoma (ccRCC) is the most common malignant neoplasm affecting the kidney, exhibiting a dismal prognosis in metastatic instances. Elucidating the composition of ccRCC holds promise for the discovery of highly sensitive biomarkers. Our objective was to utilize habitat imaging techniques and integrate multimodal data to precisely predict the risk of metastasis, ultimately enabling early intervention and enhancing patient survival rates. MATERIAL AND METHODS A retrospective analysis was performed on a cohort of 263 patients with ccRCC from three hospitals between April 2013 and March 2021. Preoperative CT images, ultrasound images, and clinical data were comprehensively analyzed. Patients from two campuses of Qilu Hospital of Shandong University were assigned to the training dataset, while the third hospital served as the independent testing dataset. A robust consensus clustering method was used to classify the primary tumor space into distinct sub-regions (i.e., habitats) using contrast-enhanced CT images. Radiomic features were extracted from these tumor sub-regions and subsequently reduced to identify meaningful features for constructing a predictive model for ccRCC metastasis risk assessment. In addition, the potential value of radiomics in predicting ccRCC metastasis risk was explored by integrating ultrasound image features and clinical data to construct and compare alternative models. RESULTS In this study, we performed k-means clustering within the tumor region to generate three distinct tumor subregions. We quantified the Hounsfiled Unit (HU) value, volume fraction, and distribution of high- and low-risk groups in each subregion. Our investigation focused on 252 patients with Habitat1 + Habitat3 to assess the discriminative power of these two subregions. We then developed a risk prediction model for ccRCC metastasis risk classification based on radiomic features extracted from CT and ultrasound images, and clinical data. The Combined model and the CT_Habitat3 model showed AUC values of 0.935 [95%CI: 0.902-0.968] and 0.934 [95%CI: 0.902-0.966], respectively, in the training dataset, while in the independent testing dataset, they achieved AUC values of 0.891 [95%CI: 0.794-0.988] and 0.903 [95%CI: 0.819-0.987], respectively. CONCLUSION We have identified a non-invasive imaging predictor and the proposed sub-regional radiomics model can accurately predict the risk of metastasis in ccRCC. This predictive tool has potential for clinical application to refine individualized treatment strategies for patients with ccRCC.
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Affiliation(s)
- You Chang Yang
- Department of Radiology, Qilu Hospital (Qingdao), Cheeloo College of Medicine, Shandong University, Shandong Province, China.
| | - Jiao Jiao Wu
- Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China.
| | - Feng Shi
- Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China.
| | - Qing Guo Ren
- Department of Radiology, Qilu Hospital (Qingdao), Cheeloo College of Medicine, Shandong University, Shandong Province, China.
| | - Qing Jun Jiang
- Department of Radiology, Qilu Hospital (Qingdao), Cheeloo College of Medicine, Shandong University, Shandong Province, China.
| | - Shuai Guan
- Department of Radiology, Qilu Hospital (Qingdao), Cheeloo College of Medicine, Shandong University, Shandong Province, China.
| | - Xiao Qiang Tang
- Department of Radiology, The Affiliated Changzhou No. 2 People's Hospital of Nanjing Medical University, Changzhou, China.
| | - Xiang Shui Meng
- Department of Radiology, Qilu Hospital (Qingdao), Cheeloo College of Medicine, Shandong University, Shandong Province, China.
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Liu Q, Liu S, Mao Y, Kang X, Yu M, Chen G. Machine learning model to preoperatively predict T2/T3 staging of laryngeal and hypopharyngeal cancer based on the CT radiomic signature. Eur Radiol 2024; 34:5349-5359. [PMID: 38206403 DOI: 10.1007/s00330-023-10557-8] [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: 10/01/2023] [Revised: 11/28/2023] [Accepted: 12/06/2023] [Indexed: 01/12/2024]
Abstract
OBJECTIVES To develop and assess a radiomics-based prediction model for distinguishing T2/T3 staging of laryngeal and hypopharyngeal squamous cell carcinoma (LHSCC) METHODS: A total of 118 patients with pathologically proven LHSCC were enrolled in this retrospective study. We performed feature processing based on 851 radiomic features derived from contrast-enhanced CT images and established multiple radiomic models by combining three feature selection methods and seven machine learning classifiers. The area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity were used to assess the performance of the models. The radiomic signature obtained from the optimal model and statistically significant morphological image characteristics were incorporated into the predictive nomogram. The performance of the nomogram was assessed by calibration curve and decision curve analysis. RESULTS Using analysis of variance (ANOVA) feature selection and logistic regression (LR) classifier produced the best model. The AUCs of the training, validation, and test sets were 0.919, 0.857, and 0.817, respectively. A nomogram based on the model integrating the radiomic signature and a morphological imaging characteristic (suspicious thyroid cartilage invasion) exhibited C-indexes of 0.899 (95% confidence interval (CI) 0.843-0.955), fitting well in calibration curves (p > 0.05). Decision curve analysis further confirmed the clinical usefulness of the nomogram. CONCLUSIONS The nomogram based on the radiomics model derived from contrast-enhanced CT images had good diagnostic performance for distinguishing T2/T3 staging of LHSCC. CLINICAL RELEVANCE STATEMENT Accurate T2/T3 staging assessment of LHSCC aids in determining whether laryngectomy or laryngeal preservation therapy should be performed. The nomogram based on the radiomics model derived from contrast-enhanced CT images has the potential to predict the T2/T3 staging of LHSCC, which can provide a non-invasive and robust approach for guiding the optimization of clinical decision-making. KEY POINTS • Combining analysis of variance with logistic regression yielded the optimal radiomic model. • A nomogram based on the CT-radiomic signature has good performance for differentiating T2 from T3 staging of laryngeal and hypopharyngeal squamous cell carcinoma. • It provides a non-invasive and robust approach for guiding the optimization of clinical decision-making.
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Affiliation(s)
- Qianhan Liu
- Department of Radiology, The Affiliated Hospital of Southwest Medical University, No. 23 Tai Ping Street, Luzhou, 646000, Sichuan, China
| | - Shengdan Liu
- Department of Radiology, The Affiliated Hospital of Southwest Medical University, No. 23 Tai Ping Street, Luzhou, 646000, Sichuan, China
| | - Yu Mao
- Department of Radiology, The Affiliated Hospital of Southwest Medical University, No. 23 Tai Ping Street, Luzhou, 646000, Sichuan, China
| | - Xuefeng Kang
- Department of Radiology, The Affiliated Hospital of Southwest Medical University, No. 23 Tai Ping Street, Luzhou, 646000, Sichuan, China
| | - Mingling Yu
- Department of Radiology, The Affiliated Hospital of Southwest Medical University, No. 23 Tai Ping Street, Luzhou, 646000, Sichuan, China
| | - Guangxiang Chen
- Department of Radiology, The Affiliated Hospital of Southwest Medical University, No. 23 Tai Ping Street, Luzhou, 646000, Sichuan, China.
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Li X, Lin J, Qi H, Dai C, Guo Y, Lin D, Zhou J. Radiomics predict the WHO/ISUP nuclear grade and survival in clear cell renal cell carcinoma. Insights Imaging 2024; 15:175. [PMID: 38992169 PMCID: PMC11239644 DOI: 10.1186/s13244-024-01739-z] [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: 02/26/2024] [Accepted: 06/08/2024] [Indexed: 07/13/2024] Open
Abstract
OBJECTIVES This study aimed to assess the predictive value of radiomics derived from intratumoral and peritumoral regions and to develop a radiomics nomogram to predict preoperative nuclear grade and overall survival (OS) in patients with clear cell renal cell carcinoma (ccRCC). METHODS The study included 395 patients with ccRCC from our institution. The patients in Center A (anonymous) institution were randomly divided into a training cohort (n = 284) and an internal validation cohort (n = 71). An external validation cohort comprising 40 patients from Center B also was included. Computed tomography (CT) radiomics features were extracted from the internal area of the tumor (IAT) and IAT combined peritumoral areas of the tumor at 3 mm (PAT 3 mm) and 5 mm (PAT 5 mm). Independent predictors from both clinical and radiomics scores (Radscore) were used to construct a radiomics nomogram. Kaplan-Meier analysis with a log-rank test was performed to evaluate the correlation between factors and OS. RESULTS The PAT 5-mm radiomics model (RM) exhibited exceptional predictive capability for grading, achieving an area under the curves of 0.80, 0.80, and 0.90 in the training, internal validation, and external validation cohorts. The nomogram and RM gained from the PAT 5-mm region were more clinically useful than the clinical model. The association between OS and predicted nuclear grade derived from the PAT 5-mm Radscore and the nomogram-predicted score was statistically significant (p < 0.05). CONCLUSION The CT-based radiomics and nomograms showed valuable predictive capabilities for the World Health Organization/International Society of Urological Pathology grade and OS in patients with ccRCC. CRITICAL RELEVANCE STATEMENT The intratumoral and peritumoral radiomics are feasible and promising to predict nuclear grade and overall survival in patients with clear cell renal cell carcinoma, which can contribute to the development of personalized preoperative treatment strategies. KEY POINTS The multi-regional radiomics features are associated with clear cell renal cell carcinoma (ccRCC) grading and prognosis. The combination of intratumoral and peritumoral 5 mm regional features demonstrated superior predictive performance for grading. The nomogram and radiomics models have a broad range of clinical applications.
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Affiliation(s)
- Xiaoxia Li
- Department of Radiology, Zhongshan Hospital (Xiamen), Fudan University, Xiamen, 361015, China
| | - Jinglai Lin
- Department of Urology, Zhongshan Hospital (Xiamen), Fudan University, Xiamen, 361015, China
| | - Hongliang Qi
- Department of Clinical Engineering, Southern Medical University, Nanfang Hospital, Guangzhou, 510515, China
| | - Chenchen Dai
- Department of Radiology, Zhongshan Hospital, Fudan University, No 180, Fenglin Road, Xuhui District, Shanghai, 200032, China
| | - Yi Guo
- Department of Radiology, Zhongshan Hospital (Xiamen), Fudan University, Xiamen, 361015, China
| | - Dengqiang Lin
- Department of Urology, Zhongshan Hospital (Xiamen), Fudan University, Xiamen, 361015, China.
| | - Jianjun Zhou
- Department of Radiology, Zhongshan Hospital (Xiamen), Fudan University, Xiamen, 361015, China.
- Department of Radiology, Zhongshan Hospital, Fudan University, No 180, Fenglin Road, Xuhui District, Shanghai, 200032, China.
- Xiamen Municipal Clinical Research Center for Medical Imaging, Xiamen, 361015, China.
- Fujian Province Key Clinical Specialty for Medical Imaging, Xiamen, 361015, China.
- Xiamen Key Laboratory of Clinical Transformation of Imaging Big Data and Artificial Intelligence, Xiamen, 361015, China.
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Yang Y, Wang J, Ren Q, Yu R, Yuan Z, Jiang Q, Guan S, Tang X, Duan T, Meng X. Multimodal data integration using machine learning to predict the risk of clear cell renal cancer metastasis: a retrospective multicentre study. Abdom Radiol (NY) 2024; 49:2311-2324. [PMID: 38879708 DOI: 10.1007/s00261-024-04418-1] [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: 03/17/2024] [Revised: 05/24/2024] [Accepted: 05/29/2024] [Indexed: 07/30/2024]
Abstract
PURPOSE To develop and validate a predictive combined model for metastasis in patients with clear cell renal cell carcinoma (ccRCC) by integrating multimodal data. MATERIALS AND METHODS In this retrospective study, the clinical and imaging data (CT and ultrasound) of patients with ccRCC confirmed by pathology from three tertiary hospitals in different regions were collected from January 2013 to January 2023. We developed three models, including a clinical model, a radiomics model, and a combined model. The performance of the model was determined based on its discriminative power and clinical utility. The evaluation indicators included area under the receiver operating characteristic curve (AUC) value, accuracy, sensitivity, specificity, negative predictive value, positive predictive value and decision curve analysis (DCA) curve. RESULTS A total of 251 patients were evaluated. Patients (n = 166) from Shandong University Qilu Hospital (Jinan) were divided into the training cohort, of which 50 patients developed metastases; patients (n = 37) from Shandong University Qilu Hospital (Qingdao) were used as internal testing, of which 15 patients developed metastases; patients (n = 48) from Changzhou Second People's Hospital were used as external testing, of which 13 patients developed metastases. In the training set, the combined model showed the highest performance (AUC, 0.924) in predicting lymph node metastasis (LNM), while the clinical and radiomics models both had AUCs of 0.845 and 0.870, respectively. In the internal testing, the combined model had the highest performance (AUC, 0.877) for predicting LNM, while the AUCs of the clinical and radiomics models were 0.726 and 0.836, respectively. In the external testing, the combined model had the highest performance (AUC, 0.849) for predicting LNM, while the AUCs of the clinical and radiomics models were 0.708 and 0.804, respectively. The DCA curve showed that the combined model had a significant prediction probability in predicting the risk of LNM in ccRCC patients compared with the clinical model or the radiomics model. CONCLUSION The combined model was superior to the clinical and radiomics models in predicting LNM in ccRCC patients.
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Affiliation(s)
- YouChang Yang
- Department of Radiology, Qilu Hospital (Qingdao), Cheeloo College of Medicine, Shandong University, Qingdao, 266035, China
| | - JiaJia Wang
- Department of Radiology, Qilu Hospital of Shandong University, Jinan, China
| | - QingGuo Ren
- Department of Radiology, Qilu Hospital (Qingdao), Cheeloo College of Medicine, Shandong University, Qingdao, 266035, China
| | - Rong Yu
- Shandong University of Traditional Chinese Medicine, Jinan, China
| | - ZiYi Yuan
- Department of Radiology, Qilu Hospital of Shandong University, Jinan, China
| | - QingJun Jiang
- Department of Radiology, Qilu Hospital (Qingdao), Cheeloo College of Medicine, Shandong University, Qingdao, 266035, China
| | - Shuai Guan
- Department of Radiology, Qilu Hospital (Qingdao), Cheeloo College of Medicine, Shandong University, Qingdao, 266035, China
| | - XiaoQiang Tang
- Department of Radiology, The Affiliated Changzhou No. 2 People's Hospital of Nanjing Medical University, Changzhou, China
| | - TongTong Duan
- Department of Ultrasound, The Affiliated Changzhou No. 2 People's Hospital of Nanjing Medical University, Changzhou, China
| | - XiangShui Meng
- Department of Radiology, Qilu Hospital (Qingdao), Cheeloo College of Medicine, Shandong University, Qingdao, 266035, China.
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Deng LE, Guo M, Deng Y, Pan Y, Wang X, Maduraiveeran G, Liu J, Lu C. MOF-Based Platform for Kidney Diseases: Advances, Challenges, and Prospects. Pharmaceutics 2024; 16:793. [PMID: 38931914 PMCID: PMC11207304 DOI: 10.3390/pharmaceutics16060793] [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: 04/30/2024] [Revised: 06/03/2024] [Accepted: 06/04/2024] [Indexed: 06/28/2024] Open
Abstract
Kidney diseases are important diseases that affect human health worldwide. According to the 2020 World Health Organization (WHO) report, kidney diseases have become the top 10 causes of death. Strengthening the prevention, primary diagnosis, and action of kidney-related diseases is of great significance in maintaining human health and improving the quality of life. It is increasingly challenging to address clinical needs with the present technologies for diagnosing and treating renal illness. Fortunately, metal-organic frameworks (MOFs) have shown great promise in the diagnosis and treatment of kidney diseases. This review summarizes the research progress of MOFs in the diagnosis and treatment of renal disease in recent years. Firstly, we introduce the basic structure and properties of MOFs. Secondly, we focus on the utilization of MOFs in the diagnosis and treatment of kidney diseases. In the diagnosis of kidney disease, MOFs are usually designed as biosensors to detect biomarkers related to kidney disease. In the treatment of kidney disease, MOFs can not only be used as an effective adsorbent for uremic toxins during hemodialysis but also as a precise treatment of intelligent drug delivery carriers. They can also be combined with nano-chelation technology to solve the problem of the imbalance of trace elements in kidney disease. Finally, we describe the current challenges and prospects of MOFs in the diagnosis and treatment of kidney diseases.
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Affiliation(s)
- Li-Er Deng
- Department of Nephrology, Dongguan Traditional Chinese Medicine Hospital, Dongguan 523000, China
| | - Manli Guo
- Dongguan Key Laboratory of Drug Design and Formulation Technology, Guangdong Provincial Key Laboratory of Medical Molecular Diagnostics, Guangdong Medical University, Dongguan 523808, China
| | - Yijun Deng
- Dongguan Key Laboratory of Drug Design and Formulation Technology, Guangdong Provincial Key Laboratory of Medical Molecular Diagnostics, Guangdong Medical University, Dongguan 523808, China
| | - Ying Pan
- Dongguan Key Laboratory of Drug Design and Formulation Technology, Guangdong Provincial Key Laboratory of Medical Molecular Diagnostics, Guangdong Medical University, Dongguan 523808, China
| | - Xiaoxiong Wang
- School of Materials and Environmental Engineering, Shenzhen Polytechnic University, Shenzhen 518055, China
| | - Govindhan Maduraiveeran
- Materials Electrochemistry Laboratory, Department of Chemistry, SRM Institute of Science and Technology, Kattankulathur 603 203, Tamil Nadu, India;
| | - Jianqiang Liu
- Dongguan Key Laboratory of Drug Design and Formulation Technology, Guangdong Provincial Key Laboratory of Medical Molecular Diagnostics, Guangdong Medical University, Dongguan 523808, China
| | - Chengyu Lu
- Dongguan Key Laboratory of Drug Design and Formulation Technology, Guangdong Provincial Key Laboratory of Medical Molecular Diagnostics, Guangdong Medical University, Dongguan 523808, China
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Zhang H, Yin F, Chen M, Qi A, Yang L, Wen G. CT-based radiomics model using stability selection for predicting the World Health Organization/International Society of Urological Pathology grade of clear cell renal cell carcinoma. Br J Radiol 2024; 97:1169-1179. [PMID: 38688660 PMCID: PMC11135802 DOI: 10.1093/bjr/tqae078] [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: 04/20/2023] [Revised: 11/15/2023] [Accepted: 04/08/2024] [Indexed: 05/02/2024] Open
Abstract
OBJECTIVES This study aimed to develop a model to predict World Health Organization/International Society of Urological Pathology (WHO/ISUP) low-grade or high-grade clear cell renal cell carcinoma (ccRCC) using 3D multiphase enhanced CT radiomics features (RFs). METHODS CT data of 138 low-grade and 60 high-grade ccRCC cases were included. RFs were extracted from four CT phases: non-contrast phase (NCP), corticomedullary phase, nephrographic phase, and excretory phase (EP). Models were developed using various combinations of RFs and subjected to cross-validation. RESULTS There were 107 RFs extracted from each phase of the CT images. The NCP-EP model had the best overall predictive value (AUC = 0.78), but did not significantly differ from that of the NCP model (AUC = 0.76). By considering the predictive ability of the model, the level of radiation exposure, and model simplicity, the overall best model was the Conventional image and clinical features (CICFs)-NCP model (AUC = 0.77; sensitivity 0.75, specificity 0.69, positive predictive value 0.85, negative predictive value 0.54, accuracy 0.73). The second-best model was the NCP model (AUC = 0.76). CONCLUSIONS Combining clinical features with unenhanced CT images of the kidneys seems to be optimal for prediction of WHO/ISUP grade of ccRCC. This noninvasive method may assist in guiding more accurate treatment decisions for ccRCC. ADVANCES IN KNOWLEDGE This study innovatively employed stability selection for RFs, enhancing model reliability. The CICFs-NCP model's simplicity and efficacy mark a significant advancement, offering a practical tool for clinical decision-making in ccRCC management.
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Affiliation(s)
- Haijie Zhang
- Nuclear Medicine Department, Center of PET/CT, Shenzhen Second People's Hospital, Shenzhen 518052, China
| | - Fu Yin
- School of Electronic and Communication Engineering, Shenzhen Polytechnic University, Shenzhen 518052, China
| | - Menglin Chen
- Medical Imaging Department, Nanfang Hospital, Southern Medical University, Guangzhou 510515, China
| | - Anqi Qi
- Medical Imaging Department, Nanfang Hospital, Southern Medical University, Guangzhou 510515, China
| | - Liyang Yang
- Medical Imaging Department, Nanfang Hospital, Southern Medical University, Guangzhou 510515, China
| | - Ge Wen
- Medical Imaging Department, Nanfang Hospital, Southern Medical University, Guangzhou 510515, China
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Wang N, Bing X, Li Y, Yao J, Dai Z, Yu D, Ouyang A. Study of radiomics based on dual-energy CT for nuclear grading and T-staging in renal clear cell carcinoma. Medicine (Baltimore) 2024; 103:e37288. [PMID: 38457546 PMCID: PMC10919525 DOI: 10.1097/md.0000000000037288] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/03/2023] [Revised: 12/23/2023] [Accepted: 01/25/2024] [Indexed: 03/10/2024] Open
Abstract
INTRODUCTION Clear cell renal cell carcinoma (ccRCC) is the most lethal subtype of renal cell carcinoma with a high invasive potential. Radiomics has attracted much attention in predicting the preoperative T-staging and nuclear grade of ccRCC. OBJECTIVE The objective was to evaluate the efficacy of dual-energy computed tomography (DECT) radiomics in predicting ccRCC grade and T-stage while optimizing the models. METHODS 200 ccRCC patients underwent preoperative DECT scanning and were randomized into training and validation cohorts. Radiomics models based on 70 KeV, 100 KeV, 150 KeV, iodine-based material decomposition images (IMDI), virtual noncontrasted images (VNC), mixed energy images (MEI) and MEI + IMDI were established for grading and T-staging. Receiver operating characteristic analysis and decision curve analysis (DCA) were performed. The area under the curve (AUC) values were compared using Delong test. RESULTS For grading, the AUC values of these models ranged from 0.64 to 0.97 during training and from 0.54 to 0.72 during validation. In the validation cohort, the performance of MEI + IMDI model was optimal, with an AUC of 0.72, sensitivity of 0.71, and specificity of 0.70. The AUC value for the 70 KeV model was higher than those for the 100 KeV, 150 KeV, and MEI models. For T-staging, these models achieved AUC values of 0.83 to 1.00 in training and 0.59 to 0.82 in validation. The validation cohort demonstrated AUCs of 0.82 and 0.70, sensitivities of 0.71 and 0.71, and specificities of 0.80 and 0.60 for the MEI + IMDI and IMDI models, respectively. In terms of grading and T-staging, the MEI + IMDI model had the highest AUC in validation, with IMDI coming in second. There were statistically significant differences between the MEI + IMDI model and the 70 KeV, 100 KeV, 150 KeV, MEI, and VNC models in terms of grading (P < .05) and staging (P ≤ .001). DCA showed that both MEI + IDMI and IDMI models outperformed other models in predicting grade and stage of ccRCC. CONCLUSIONS DECT radiomics models were helpful in grading and T-staging of ccRCC. The combined model of MEI + IMDI achieved favorable results.
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Affiliation(s)
- Ning Wang
- Department of Radiology, Central Hospital Affiliated to Shandong First Medical University, Jinan 250013, Shandong Province, P. R. China
| | - Xue Bing
- Department of Radiology, Central Hospital Affiliated to Shandong First Medical University, Jinan 250013, Shandong Province, P. R. China
| | - Yuhan Li
- Department of Radiology, Longkou Traditional Chinese Medicine Hospital, Yantai 265700, Shandong Province, P. R. China
| | - Jian Yao
- Department of Radiology, Central Hospital Affiliated to Shandong First Medical University, Jinan 250013, Shandong Province, P. R. China
| | - Zhengjun Dai
- Scientific Research Department, Huiying Medical Technology Co., Ltd, Beijing 100192, P. R. China
| | - Dexin Yu
- Department of Radiology, Qilu Hospital of Shandong University, Jinan 250012, Shandong Province, P. R. China
| | - Aimei Ouyang
- Department of Radiology, Central Hospital Affiliated to Shandong First Medical University, Jinan 250013, Shandong Province, P. R. China
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Yang S, Jian Y, Yang F, Liu R, Zhang W, Wang J, Tan X, Wu J, Chen Y, Zhou X. Radiomics analysis based on single phase and different phase combinations of radiomics features from tri-phasic CT to distinguish renal oncocytoma from chromophobe renal cell carcinoma. Abdom Radiol (NY) 2024; 49:182-191. [PMID: 37907684 DOI: 10.1007/s00261-023-04053-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2023] [Revised: 09/10/2023] [Accepted: 09/11/2023] [Indexed: 11/02/2023]
Abstract
OBJECTIVES To investigate different radiomics models based on single phase and the different phase combinations of radiomics features from 3D tri-phasic CT to distinguish RO from chRCC. METHODS A total of 96 patients (30 RO and 66 chRCC) were enrolled in this study. Radiomics features were extracted from unenhanced phase (UP), corticomedullary phase (CMP), and nephrographic phase (NP) CT images. Feature selection was based on the least absolute shrinkage and selection operator regression (LASSO) method. The selected features were used to develop different radiomics models using logistic regression (LR) analysis, including model 1 (UP), model 2(CMP), model 3(NP), model 4(UP+CMP), model 5(UP+NP), model 6(CMP+NP), and model 7(UP+CMP+NP). The radiomics model demonstrating the highest discrimination performance was utilized to construct the combined model (model 8) with clinical factors. A nomogram based on the model 8 was established. To evaluate the diagnostic performance of the different models, the receiver operating characteristic (ROC) curve and decision curve analysis (DCA) were used. Delong's test was utilized to assess the statistical significance of the AUC improvement across the models. RESULTS Among the seven radiomics models, model 7 exhibited the highest AUC of 0.84 (95% CI 0.69, 0.99), and model 7 demonstrated a significantly superior AUC compared to the other radiomics models (all P < 0.05). The AUC values of radiomics models based on two phases (model4, mode5, mode6) were greater than the models based on single phase (model1, mode2, mode3) (all P < 0.05). Model 3 illustrated the best performance of the three radiomics models based on single phase with an AUC of 0.76 (95% CI 0.57, 099). Model 6 illustrated the best performance of the three radiomics models based on two-phases combination with an AUC of 0.83 (0.66, 0.99). Model 8 achieved an AUC of 0.93 (95% CI 0.83, 1.00) which is higher than those all radiomics models. CONCLUSION Radiomics models based on combination of radiomics features from UP, CMP, and NP can be a useful and promising technique to differentiate RO from chRCC. Moreover, the model combining clinical factors and radiomics features showed better classification performance to distinguish them.
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Affiliation(s)
- Suping Yang
- Department of Radiology, The Second Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Yuanxi Jian
- Department of Radiology, The Second Affiliated Hospital of Kunming Medical University, Kunming, China.
| | - Fan Yang
- Department of Radiology, The Second Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Rui Liu
- Department of Radiology, The Second Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Wenqing Zhang
- Department of Radiology, The Second Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Jiaping Wang
- Department of Radiology, The Second Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Xin Tan
- Department of Radiology, The Second Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Junlin Wu
- Department of Radiology, The Second Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Yuan Chen
- Department of Radiology, The Second Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Xiaowen Zhou
- Department of Radiology, The Second Affiliated Hospital of Kunming Medical University, Kunming, China
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Dionese M, Pierantoni F, Bezzon E, Cumerlato E, Bimbatti D, Basso U, Maruzzo M, Zagonel V. Role of enhancement modifications in evaluating tumor response to immunotherapy in metastatic renal cell carcinoma. TUMORI JOURNAL 2023; 109:562-569. [PMID: 37501595 DOI: 10.1177/03008916231188157] [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] [Indexed: 07/29/2023]
Abstract
INTRODUCTION Evaluation of tumor response according only to dimensional criteria may underestimate treatment benefit in patients treated for metastatic renal cell carcinoma (RCC). In this study we evaluated the role of lesion enhancement modifications and Choi criteria in patients affected by renal cell carcinoma treated with immunotherapy. METHODS We collected data of 60 consecutive patients (with a total of 154 measurable lesions) treated with immunotherapy (nivolumab or ipilimumab plus nivolumab) at a single Institution. We evaluated tumour response using both RECIST1.1 criteria and Choi criteria at the first radiological assessment; we subsequently associated response with progression free survival and overall survival. RESULTS Choi criteria found a higher rate of objective response compared to RECIST criteria (38.3% vs 18.3%). An objective response according to both criteria was associated with longer progression free survival and overall survival. Response rate for Choi did not vary according to lesion site. CONCLUSION Choi criteria seemed to be able to predict clinical benefit in a higher proportion of patients with renal cell carcinoma treated with immunotherapy than RECIST criteria. Partial response according to RECIST was confirmed as a predictor of longer progression-free survival and overall survival.
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Affiliation(s)
- Michele Dionese
- Oncology 1 Unit, Veneto Institute of Oncology, IOV - IRCCS, Padua, Italy
| | - Francesco Pierantoni
- Department of Surgery, Oncology and Gastroenterology, University of Padua., Padua, Italy
- Oncology 3 Unit, Veneto Institute of Oncology, IOV - IRCCS, Castelfranco Veneto, Italy
| | - Elisabetta Bezzon
- Radiology Unit, Veneto Institute of Oncology, IOV - IRCCS, Padua, Italy
| | | | - Davide Bimbatti
- Oncology 1 Unit, Veneto Institute of Oncology, IOV - IRCCS, Padua, Italy
| | - Umberto Basso
- Oncology 1 Unit, Veneto Institute of Oncology, IOV - IRCCS, Padua, Italy
| | - Marco Maruzzo
- Oncology 1 Unit, Veneto Institute of Oncology, IOV - IRCCS, Padua, Italy
| | - Vittorina Zagonel
- Oncology 1 Unit, Veneto Institute of Oncology, IOV - IRCCS, Padua, Italy
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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 T, Ming Y, Xu J, Jin K, Huang C, Duan M, Li K, Liu Y, Lv Y, Zhang J, Huang Z. Radiomics and Ki-67 index predict survival in clear cell renal cell carcinoma. Br J Radiol 2023; 96:20230187. [PMID: 37393531 PMCID: PMC10546454 DOI: 10.1259/bjr.20230187] [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: 02/22/2023] [Revised: 06/13/2023] [Accepted: 06/23/2023] [Indexed: 07/03/2023] Open
Abstract
OBJECTIVE To develop and validate predictive models based on Ki-67 index, radiomics, and Ki-67 index combined with radiomics for survival analysis of patients with clear cell renal cell carcinoma. METHODS This study enrolled 148 patients who were pathologically diagnosed as ccRCC between March 2010 and December 2018 at our institute. All tissue sections were collected and immunohistochemical staining was performed to calculate Ki-67 index. All patients were randomly divided into the training and validation sets in a 7:3 ratio. Regions of interests (ROIs) were segmented manually. Radiomics features were selected from ROIs in unenhanced, corticomedullary, and nephrographic phases. Multivariate Cox models based on the Ki-67 index and radiomics and univariate Cox models based on the Ki-67 index or radiomics alone were built; the predictive power was evaluated by the concordance (C)-index, integrated area under the curve, and integrated Brier Score. RESULTS Five features were selected to establish the prediction models of radiomics and combined model. The C-indexes of Ki-67 index model, radiomics model, and combined model were 0.741, 0.718, and 0.782 for disease-free survival (DFS); 0.941, 0.866, and 0.963 for overall survival, respectively. The predictive power of combined model was the best in both training and validation sets. CONCLUSION The survival prediction performance of combined model was better than Ki-67 model or radiomics model. The combined model is a promising tool for predicting the prognosis of patients with ccRCC in the future. ADVANCES IN KNOWLEDGE Both Ki-67 and radiomics have showed giant potential in prognosis prediction. There are few studies to investigate the predictive ability of Ki-67 combined with radiomics. This study intended to build a combined model and provide a reliable prognosis for ccRCC in clinical practice.
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Affiliation(s)
- Tong Zhang
- Department of Radiology, Jinan City People's Hospital, Jinan, Shandong, China
| | - Ying Ming
- Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Jingxu Xu
- Department of Research Collaboration, R&D center, Beijing Deepwise & League of PHD Technology Co.Ltd, Beijing, China
| | - Ke Jin
- Department of Research Collaboration, R&D center, Beijing Deepwise & League of PHD Technology Co.Ltd, Beijing, China
| | - Chencui Huang
- Department of Research Collaboration, R&D center, Beijing Deepwise & League of PHD Technology Co.Ltd, Beijing, China
| | - Mingguang Duan
- Department of Radiology, Jinan City People's Hospital, Jinan, Shandong, China
| | - Kaiguo Li
- Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Shandong, China
| | - Yuanwei Liu
- Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Shandong, China
| | - Yonghui Lv
- Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Shandong, China
| | - Jie Zhang
- Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Shandong, China
| | - Zhaoqin Huang
- Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Shandong, China
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12
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Zhou Z, Qian X, Hu J, Geng C, Zhang Y, Dou X, Che T, Zhu J, Dai Y. Multi-phase-combined CECT radiomics models for Fuhrman grade prediction of clear cell renal cell carcinoma. Front Oncol 2023; 13:1167328. [PMID: 37692840 PMCID: PMC10485140 DOI: 10.3389/fonc.2023.1167328] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2023] [Accepted: 07/24/2023] [Indexed: 09/12/2023] Open
Abstract
Objective This study aimed to evaluate the effectiveness of multi-phase-combined contrast-enhanced CT (CECT) radiomics methods for noninvasive Fuhrman grade prediction of clear cell renal cell carcinoma (ccRCC). Methods A total of 187 patients with four-phase CECT images were retrospectively enrolled and then were categorized into training cohort (n=126) and testing cohort (n=61). All patients were confirmed as ccRCC by histopathological reports. A total of 110 3D classical radiomics features were extracted from each phase of CECT for individual ccRCC lesion, and contrast-enhanced variation features were also calculated as derived radiomics features. These features were concatenated together, and redundant features were removed by Pearson correlation analysis. The discriminative features were selected by minimum redundancy maximum relevance method (mRMR) and then input into a C-support vector classifier to build multi-phase-combined CECT radiomics models. The prediction performance was evaluated by the area under the curve (AUC) of receiver operating characteristic (ROC). Results The multi-phase-combined CECT radiomics model showed the best prediction performance (AUC=0.777) than the single-phase CECT radiomics model (AUC=0.711) in the testing cohort (p value=0.039). Conclusion The multi-phase-combined CECT radiomics model is a potential effective way to noninvasively predict Fuhrman grade of ccRCC. The concatenation of first-order features and texture features extracted from corticomedullary phase and nephrographic phase are discriminative feature representations.
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Affiliation(s)
- Zhiyong Zhou
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, Jiangsu, China
| | - Xusheng Qian
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, Jiangsu, China
- School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, Suzhou, Jiangsu, China
| | - Jisu Hu
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, Jiangsu, China
- School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, Suzhou, Jiangsu, China
| | - Chen Geng
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, Jiangsu, China
| | - Yongsheng Zhang
- Department of Pathology, Second Affiliated Hospital of Soochow University, Suzhou, Jiangsu, China
| | - Xin Dou
- Department of Radiology, Second Affiliated Hospital of Soochow University, Suzhou, Jiangsu, China
| | - Tuanjie Che
- Key Laboratory of Functional Genomic and Molecular Diagnosis of Gansu Province, Lanzhou, Gansu, China
- Suzhou Science & Technology Town Hospital, Gusu School, Nanjing Medical University, Suzhou, Jiangsu, China
| | - Jianbing Zhu
- Suzhou Science & Technology Town Hospital, Gusu School, Nanjing Medical University, Suzhou, Jiangsu, China
| | - Yakang Dai
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, Jiangsu, China
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Mamdouh Farghaly H, Abd El-Hafeez T. A high-quality feature selection method based on frequent and correlated items for text classification. Soft comput 2023; 27:11259-11274. [DOI: 10.1007/s00500-023-08587-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/12/2023] [Indexed: 09/02/2023]
Abstract
AbstractThe feature selection problem is a significant challenge in pattern recognition, especially for classification tasks. The quality of the selected features plays a critical role in building effective models, and poor-quality data can make this process more difficult. This work explores the use of association analysis in data mining to select meaningful features, addressing the issue of duplicated information in the selected features. A novel feature selection technique for text classification is proposed, based on frequent and correlated items. This method considers both relevance and feature interactions, using association as a metric to evaluate the relationship between the target and features. The technique was tested using the SMS spam collecting dataset from the UCI machine learning repository and compared with well-known feature selection methods. The results showed that the proposed technique effectively reduced redundant information while achieving high accuracy (95.155%) using only 6% of the features.
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Wang L, Wu H, Li J, Wang Y, Zhang T, Tan Z, Liu Y, Wang H, Sun L, Xu D, Tu J. Diagnostic value of pseudocapsule ring hyperenhancement in contrast-enhanced ultrasound in renal focal lesions. Ir J Med Sci 2023; 192:1631-1636. [PMID: 36907918 DOI: 10.1007/s11845-023-03320-x] [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: 01/02/2023] [Accepted: 02/13/2023] [Indexed: 03/14/2023]
Abstract
OBJECT To investigate the diagnostic value of pseudocapsule ring hyperenhancement in contrast-enhanced ultrasound (CEUS) of focal renal lesions. METHOD Ninety eligible patients admitted were selected as the study subjects. The incidence rate of annular hyperenhancement in different benign and malignant focal lesions during angiography and the shape of annular enhancement were counted. The correlation between the occurrence of benign and malignant renal tumors and annular hyperenhancement and unenhanced areas was also analyzed. RESULTS Capsule enhancement was observed in 56 cases, including 50 cases of malignant tumors (89.3%, 50/56) and 6 cases of benign tumors (10.7%, 6/56). Pearson correlation analysis showed that renal malignancy was positively associated with the occurrence of pseudocapsule ring hyperenhancement (γ = 0.489, P < 0.001). Benign and malignant renal tumors were positively correlated with the occurrence of non-enhancing areas (γ = 0.215, P = 0.042). The thickness and peak intensity of pseudocapsule ring enhancement in renal malignant tumors were significantly higher than those in benign tumors (all P < 0.001), while the time to peak was significantly lower than that in benign tumors (P < 0.001). The results of receiver operating characteristic (ROC) curve showed that the ring enhancement thickness, time to peak, and area under ROC curve of peak intensity of tumor reached more than 0.9. CONCLUSION In CEUS, the pseudocapsule of focal renal lesions is a characteristic feature of renal malignant tumors, which can be used as a differential basis for benign and malignant tumors to improve the accuracy of benign and malignant renal tumors.
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Affiliation(s)
- Ligang Wang
- Cancer Center, Department of Ultrasound Medicine, Zhejiang Provincial People's Hospital, Affiliated People's Hospital, Hangzhou Medical College, Zhejiang Province, Hangzhou, 310000, People's Republic of China
| | - Hao Wu
- Department of Ultrasound Medicine, The Second Affiliated Hospital of Zhejiang Chinese Medical University, Zhejiang Province, Hangzhou, 310000, People's Republic of China
| | - Jianchun Li
- Cancer Center, Department of Ultrasound Medicine, Zhejiang Provincial People's Hospital, Affiliated People's Hospital, Hangzhou Medical College, Zhejiang Province, Hangzhou, 310000, People's Republic of China
| | - Ying Wang
- Health Management Center, Health Promotion Center, Zhejiang Provincial People's Hospital, Affiliated People's Hospital, Hangzhou Medical College, Zhejiang Province, Hangzhou, 310000, People's Republic of China
| | - Tingting Zhang
- Bengbu Medical College, Anhui Province, 230030, Bengbu, People's Republic of China
| | - Zhaowang Tan
- Bengbu Medical College, Anhui Province, 230030, Bengbu, People's Republic of China
| | - Yang Liu
- Cancer Center, Department of Ultrasound Medicine, Zhejiang Provincial People's Hospital, Affiliated People's Hospital, Hangzhou Medical College, Zhejiang Province, Hangzhou, 310000, People's Republic of China
| | - Han Wang
- Ultrasound Department, Zhejiang Hospital affiliated to Zhejiang, University School of Medicine, Zhejiang Province, Hangzhou, 310013, People's Republic of China
| | - Litao Sun
- Cancer Center, Department of Ultrasound Medicine, Zhejiang Provincial People's Hospital, Affiliated People's Hospital, Hangzhou Medical College, Zhejiang Province, Hangzhou, 310000, People's Republic of China.
| | - Dong Xu
- Department of Ultrasound Medicine, Cancer HospitalAffiliated to, University of Chinese Academy of Sciences (Zhejiang Cancer Hospital) , Zhejiang Province, Hangzhou, 310000, People's Republic of China.
| | - Jianfeng Tu
- Emergency and Critical Care Center, Department of Emergency Medicine, Zhejiang Provincial People's Hospital, Affiliated People's Hospital, Hangzhou Medical College, Zhejiang Province, Hangzhou, 310000, People's Republic of China.
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Dehghani Firouzabadi F, Gopal N, Hasani A, Homayounieh F, Li X, Jones EC, Yazdian Anari P, Turkbey E, Malayeri AA. CT radiomics for differentiating fat poor angiomyolipoma from clear cell renal cell carcinoma: Systematic review and meta-analysis. PLoS One 2023; 18:e0287299. [PMID: 37498830 PMCID: PMC10374097 DOI: 10.1371/journal.pone.0287299] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Accepted: 06/03/2023] [Indexed: 07/29/2023] Open
Abstract
PURPOSE Differentiation of fat-poor angiomyolipoma (fp-AMLs) from renal cell carcinoma (RCC) is often not possible from just visual interpretation of conventional cross-sectional imaging, typically requiring biopsy or surgery for diagnostic confirmation. However, radiomics has the potential to characterize renal masses without the need for invasive procedures. Here, we conducted a systematic review on the accuracy of CT radiomics in distinguishing fp-AMLs from RCCs. METHODS We conducted a search using PubMed/MEDLINE, Google Scholar, Cochrane Library, Embase, and Web of Science for studies published from January 2011-2022 that utilized CT radiomics to discriminate between fp-AMLs and RCCs. A random-effects model was applied for the meta-analysis according to the heterogeneity level. Furthermore, subgroup analyses (group 1: RCCs vs. fp-AML, and group 2: ccRCC vs. fp-AML), and quality assessment were also conducted to explore the possible effect of interstudy differences. To evaluate CT radiomics performance, the pooled sensitivity, specificity, and diagnostic odds ratio (DOR) were assessed. This study is registered with PROSPERO (CRD42022311034). RESULTS Our literature search identified 10 studies with 1456 lesions in 1437 patients. Pooled sensitivity was 0.779 [95% CI: 0.562-0.907] and 0.817 [95% CI: 0.663-0.910] for groups 1 and 2, respectively. Pooled specificity was 0.933 [95% CI: 0.814-0.978]and 0.926 [95% CI: 0.854-0.964] for groups 1 and 2, respectively. Also, our findings showed higher sensitivity and specificity of 0.858 [95% CI: 0.742-0.927] and 0.886 [95% CI: 0.819-0.930] for detecting ccRCC from fp-AML in the unenhanced phase of CT scan as compared to the corticomedullary and nephrogenic phases of CT scan. CONCLUSION This study suggested that radiomic features derived from CT has high sensitivity and specificity in differentiating RCCs vs. fp-AML, particularly in detecting ccRCCs vs. fp-AML. Also, an unenhanced CT scan showed the highest specificity and sensitivity as compared to contrast CT scan phases. Differentiating between fp-AML and RCC often is not possible without biopsy or surgery; radiomics has the potential to obviate these invasive procedures due to its high diagnostic accuracy.
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Affiliation(s)
- Fatemeh Dehghani Firouzabadi
- Radiology Department, National Institutes of Health, Clinical Center (CC), Bethesda, Maryland, United States of America
| | - Nikhil Gopal
- Urology Department, National Cancer Institutes (NCI), Clinical Center, National Institutes of Health, Bethesda, Maryland, United States of America
| | - Amir Hasani
- Radiology Department, National Institutes of Health, Clinical Center (CC), Bethesda, Maryland, United States of America
| | - Fatemeh Homayounieh
- Radiology Department, National Institutes of Health, Clinical Center (CC), Bethesda, Maryland, United States of America
| | - Xiaobai Li
- Biostatistics and Clinical Epidemiology Service, NIH Clinical Center, Bethesda, MD, United States of America
| | - Elizabeth C Jones
- Radiology Department, National Institutes of Health, Clinical Center (CC), Bethesda, Maryland, United States of America
| | - Pouria Yazdian Anari
- Radiology Department, National Institutes of Health, Clinical Center (CC), Bethesda, Maryland, United States of America
| | - Evrim Turkbey
- Radiology Department, National Institutes of Health, Clinical Center (CC), Bethesda, Maryland, United States of America
| | - Ashkan A Malayeri
- Radiology Department, National Institutes of Health, Clinical Center (CC), Bethesda, Maryland, United States of America
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Shehata M, Abouelkheir RT, Gayhart M, Van Bogaert E, Abou El-Ghar M, Dwyer AC, Ouseph R, Yousaf J, Ghazal M, Contractor S, El-Baz A. Role of AI and Radiomic Markers in Early Diagnosis of Renal Cancer and Clinical Outcome Prediction: A Brief Review. Cancers (Basel) 2023; 15:2835. [PMID: 37345172 DOI: 10.3390/cancers15102835] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2023] [Revised: 05/10/2023] [Accepted: 05/17/2023] [Indexed: 06/23/2023] Open
Abstract
Globally, renal cancer (RC) is the 10th most common cancer among men and women. The new era of artificial intelligence (AI) and radiomics have allowed the development of AI-based computer-aided diagnostic/prediction (AI-based CAD/CAP) systems, which have shown promise for the diagnosis of RC (i.e., subtyping, grading, and staging) and prediction of clinical outcomes at an early stage. This will absolutely help reduce diagnosis time, enhance diagnostic abilities, reduce invasiveness, and provide guidance for appropriate management procedures to avoid the burden of unresponsive treatment plans. This survey mainly has three primary aims. The first aim is to highlight the most recent technical diagnostic studies developed in the last decade, with their findings and limitations, that have taken the advantages of AI and radiomic markers derived from either computed tomography (CT) or magnetic resonance (MR) images to develop AI-based CAD systems for accurate diagnosis of renal tumors at an early stage. The second aim is to highlight the few studies that have utilized AI and radiomic markers, with their findings and limitations, to predict patients' clinical outcome/treatment response, including possible recurrence after treatment, overall survival, and progression-free survival in patients with renal tumors. The promising findings of the aforementioned studies motivated us to highlight the optimal AI-based radiomic makers that are correlated with the diagnosis of renal tumors and prediction/assessment of patients' clinical outcomes. Finally, we conclude with a discussion and possible future avenues for improving diagnostic and treatment prediction performance.
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Affiliation(s)
- Mohamed Shehata
- Department of Bioengineering, University of Louisville, Louisville, KY 40292, USA
| | - Rasha T Abouelkheir
- Department of Radiology, Urology and Nephrology Center, Mansoura University, Mansoura 35516, Egypt
| | | | - Eric Van Bogaert
- Department of Radiology, University of Louisville, Louisville, KY 40202, USA
| | - Mohamed Abou El-Ghar
- Department of Radiology, Urology and Nephrology Center, Mansoura University, Mansoura 35516, Egypt
| | - Amy C Dwyer
- Kidney Disease Program, University of Louisville, Louisville, KY 40202, USA
| | - Rosemary Ouseph
- Kidney Disease Program, University of Louisville, Louisville, KY 40202, USA
| | - Jawad Yousaf
- Electrical, Computer, and Biomedical Engineering Department, Abu Dhabi University, Abu Dhabi 59911, United Arab Emirates
| | - Mohammed Ghazal
- Electrical, Computer, and Biomedical Engineering Department, Abu Dhabi University, Abu Dhabi 59911, United Arab Emirates
| | - Sohail Contractor
- Department of Radiology, University of Louisville, Louisville, KY 40202, USA
| | - Ayman El-Baz
- Department of Bioengineering, University of Louisville, Louisville, KY 40292, USA
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Chung A, Raman SS. Radiologist's Disease: Imaging for Renal Cancer. Urol Clin North Am 2023; 50:161-180. [PMID: 36948664 DOI: 10.1016/j.ucl.2023.01.006] [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: 03/22/2023]
Abstract
There is a clear benefit of imaging-based differentiation of small indeterminate masses to its subtypes of clear cell renal cell carcinoma (RCC), chromophobe RCC, papillary RCC, fat poor angiomyolipoma and oncocytoma because it helps determine the next step options for the patients. The work thus far in radiology has explored different parameters in computed tomography, MRI, and contrast-enhanced ultrasound with the discovery of many reliable imaging features that suggest certain tissue subtypes. Likert score-based risk stratification systems can help determine management, and new techniques such as perfusion, radiogenomics, single-photon emission tomography, and artificial intelligence can add to the imaging-based evaluation of indeterminate renal masses.
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Affiliation(s)
- Alex Chung
- Department of Radiology, David Geffen School of Medicine at UCLA, Los Angeles, CA 90095, USA
| | - Steven S Raman
- David Geffen School of Medicine at UCLA, 757 Westwood Bl, RRMC, Los Angeles, CA, USA.
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Mamdouh Farghaly H, Shams MY, Abd El-Hafeez T. Hepatitis C Virus prediction based on machine learning framework: a real-world case study in Egypt. Knowl Inf Syst 2023. [DOI: 10.1007/s10115-023-01851-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/06/2023]
Abstract
AbstractPrediction and classification of diseases are essential in medical science, as it attempts to immune the spread of the disease and discover the infected regions from the early stages. Machine learning (ML) approaches are commonly used for predicting and classifying diseases that are precisely utilized as an efficient tool for doctors and specialists. This paper proposes a prediction framework based on ML approaches to predict Hepatitis C Virus among healthcare workers in Egypt. We utilized real-world data from the National Liver Institute, founded at Menoufiya University (Menoufiya, Egypt). The collected dataset consists of 859 patients with 12 different features. To ensure the robustness and reliability of the proposed framework, we performed two scenarios: the first without feature selection and the second after the features are selected based on sequential forward selection (SFS). Furthermore, the feature subset selected based on the generated features from SFS is evaluated. Naïve Bayes, random forest (RF), K-nearest neighbor, and logistic regression are utilized as induction algorithms and classifiers for model evaluation. Then, the effect of parameter tuning on learning techniques is measured. The experimental results indicated that the proposed framework achieved higher accuracies after SFS selection than without feature selection. Moreover, the RF classifier achieved 94.06% accuracy with a minimum learning elapsed time of 0.54 s. Finally, after adjusting the hyperparameter values of the RF classifier, the classification accuracy is improved to 94.88% using only four features.
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Abstract
Computed tomography (CT) of the abdomen is usually appropriate for the initial imaging of many urinary tract diseases, due to its wide availability, fast scanning and acquisition of thin slices and isotropic data, that allow the creation of multiplanar reformatted and three-dimensional reconstructed images of excellent anatomic details. Non-enhanced CT remains the standard imaging modality for assessing renal colic. The technique allows the detection of nearly all types of urinary calculi and the estimation of stone burden. CT is the primary diagnostic tool for the characterization of an indeterminate renal mass, including both cystic and solid tumors. It is also the modality of choice for staging a primary renal tumor. Urolithiasis and urinary tract malignancies represent the main urogenic causes of hematuria. CT urography (CTU) improves the visualization of both the upper and lower urinary tract and is recommended for the investigation of gross hematuria and microscopic hematuria, in patients with predisposing factors for urologic malignancies. CTU is highly accurate in the detection and staging of upper tract urothelial malignancies. CT represents the most commonly used technique for the detection and staging of bladder carcinoma and the diagnostic efficacy of CT staging improves with more advanced disease. Nevertheless, it has limited accuracy in differentiating non-muscle invasive bladder carcinoma from muscle-invasive bladder carcinoma. In this review, clinical indications and the optimal imaging technique for CT of the urinary tract is reviewed. The CT features of common urologic diseases, including ureterolithiasis, renal tumors and urothelial carcinomas are discussed.
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Radiogenomics in Renal Cancer Management-Current Evidence and Future Prospects. Int J Mol Sci 2023; 24:ijms24054615. [PMID: 36902045 PMCID: PMC10003020 DOI: 10.3390/ijms24054615] [Citation(s) in RCA: 41] [Impact Index Per Article: 41.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2023] [Revised: 02/21/2023] [Accepted: 02/22/2023] [Indexed: 03/03/2023] Open
Abstract
Renal cancer management is challenging from diagnosis to treatment and follow-up. In cases of small renal masses and cystic lesions the differential diagnosis of benign or malignant tissues has potential pitfalls when imaging or even renal biopsy is applied. The recent artificial intelligence, imaging techniques, and genomics advancements have the ability to help clinicians set the stratification risk, treatment selection, follow-up strategy, and prognosis of the disease. The combination of radiomics features and genomics data has achieved good results but is currently limited by the retrospective design and the small number of patients included in clinical trials. The road ahead for radiogenomics is open to new, well-designed prospective studies, with large cohorts of patients required to validate previously obtained results and enter clinical practice.
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Yao Y, Jia C, Zhang H, Mou Y, Wang C, Han X, Yu P, Mao N, Song X. Applying a nomogram based on preoperative CT to predict early recurrence of laryngeal squamous cell carcinoma after surgery. JOURNAL OF X-RAY SCIENCE AND TECHNOLOGY 2023; 31:435-452. [PMID: 36806538 DOI: 10.3233/xst-221320] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/16/2023]
Abstract
PURPOSE To identify the value of a computed tomography (CT)-based radiomics model to predict probability of early recurrence (ER) in patients diagnosed with laryngeal squamous cell carcinoma (LSCC) after surgery. MATERIALS AND METHOD Pre-operative CT scans of 140 LSCC patients treated by surgery are reviewed and selected. These patients are randomly split into the training set (n = 97) and test set (n = 43). The regions of interest of each patient were delineated manually by two senior radiologists. Radiomics features are extracted from CT images acquired in non-enhanced, arterial, and venous phases. Variance threshold, one-way ANOVA, and least absolute shrinkage and selection operator algorithm are used for feature selection. Then, radiomics models are built with five algorithms namely, k-nearest neighbor (KNN), logistic regression (LR), linear support vector machine (LSVM), radial basis function SVM (RSVM), and polynomial SVM (PSVM). Clinical factors are selected using univariate and multivariate logistic regressions. Last, a radiomics nomogram incorporating the radiomics signature and clinical factors is built to predict ER and its efficiency is evaluated by receiver operating characteristic (ROC) curve and calibration curve. Decision curve analysis (DCA) is also used to evaluate clinical usefulness. RESULTS Four features are remarkably associated with ER in patients with LSCC. Applying to test set, the area under the ROC curves (AUCs) of KNN, LR, LSVM, RSVM, and PSVM are 0.936, 0.855, 0.845, 0.829, and 0.794, respectively. The radiomics nomogram shows better discrimination (with AUC: 0.939, 95% CI: 0.867-0.989) than the best radiomics model and the clinical model. Predicted and actual ERs in the calibration curves are in good agreement. DCA shows that the radiomics nomogram is clinically useful. CONCLUSION The radiomics nomogram, as a noninvasive prediction tool, exhibits favorable performance for ER prediction of LSCC patients after surgery.
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Affiliation(s)
- Yao Yao
- Department of Otorhinolaryngology, Head and Neck Surgery, Yantai Yuhuangding Hospital, Qingdao University, Yantai, Shandong, China
- Shandong Provincial Clinical Research Center for Otorhinolaryngologic Diseases, Yantai, Shandong, China
| | - Chuanliang Jia
- Department of Otorhinolaryngology, Head and Neck Surgery, Yantai Yuhuangding Hospital, Qingdao University, Yantai, Shandong, China
- Shandong Provincial Clinical Research Center for Otorhinolaryngologic Diseases, Yantai, Shandong, China
- Big data and Artificial Intelligence Laboratory, Yantai Yuhuangding Hospital, Qingdao University, Yantai, Shandong, China
| | - Haicheng Zhang
- Big data and Artificial Intelligence Laboratory, Yantai Yuhuangding Hospital, Qingdao University, Yantai, Shandong, China
- Department of Radiology, Yantai Yuhuangding Hospital, Qingdao University, Yantai, Shandong, China
| | - Yakui Mou
- Department of Otorhinolaryngology, Head and Neck Surgery, Yantai Yuhuangding Hospital, Qingdao University, Yantai, Shandong, China
- Shandong Provincial Clinical Research Center for Otorhinolaryngologic Diseases, Yantai, Shandong, China
| | - Cai Wang
- Department of Otorhinolaryngology, Head and Neck Surgery, Yantai Yuhuangding Hospital, Qingdao University, Yantai, Shandong, China
- Shandong Provincial Clinical Research Center for Otorhinolaryngologic Diseases, Yantai, Shandong, China
| | - Xiao Han
- Department of Otorhinolaryngology, Head and Neck Surgery, Yantai Yuhuangding Hospital, Qingdao University, Yantai, Shandong, China
- Shandong Provincial Clinical Research Center for Otorhinolaryngologic Diseases, Yantai, Shandong, China
| | - Pengyi Yu
- Department of Otorhinolaryngology, Head and Neck Surgery, Yantai Yuhuangding Hospital, Qingdao University, Yantai, Shandong, China
- Shandong Provincial Clinical Research Center for Otorhinolaryngologic Diseases, Yantai, Shandong, China
| | - Ning Mao
- Big data and Artificial Intelligence Laboratory, Yantai Yuhuangding Hospital, Qingdao University, Yantai, Shandong, China
- Department of Radiology, Yantai Yuhuangding Hospital, Qingdao University, Yantai, Shandong, China
| | - Xicheng Song
- Department of Otorhinolaryngology, Head and Neck Surgery, Yantai Yuhuangding Hospital, Qingdao University, Yantai, Shandong, China
- Shandong Provincial Clinical Research Center for Otorhinolaryngologic Diseases, Yantai, Shandong, China
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Cheng D, Abudikeranmu Y, Tuerdi B. Differentiation of Clear Cell and Non-clear-cell Renal Cell Carcinoma through CT-based Radiomics Models and Nomogram. Curr Med Imaging 2023; 19:1005-1017. [PMID: 36411581 PMCID: PMC10556396 DOI: 10.2174/1573405619666221121164235] [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: 03/18/2022] [Revised: 09/12/2022] [Accepted: 10/17/2022] [Indexed: 11/23/2022]
Abstract
PURPOSE The aim of the study was to investigate the feasibility of discriminating between clear-cell renal cell carcinoma (ccRCC) and non-clear-cell renal cell carcinoma (non-ccRCC) via radiomics models and nomogram. METHODS The retrospective study included 147 patients (ccRCC=100, non-ccRCC=47) who underwent enhanced CT before surgery. CT images of the corticomedullary phase (CMP) were collected and features from the images were extracted. The data were randomly grouped into training and validation sets according to 7:3, and then the training set was normalized to extract the normalization rule for the training set, and then the rule was applied to the validation set. First, the T-test, T'-test or Wilcoxon rank-sum test were executed in the training set data to keep the statistically different parameters, and then the optimal features were picked based on the least absolute shrinkage and selection operator (LASSO) algorithm. Five machine learning (ML) models were trained to differentiate ccRCC from noccRCC, rad+cli nomogram was constructed based on clinical factors and radscore (radiomics score), and the performance of the classifier was mainly measured by area under the curve (AUC), accuracy, sensitivity, specificity, and F1. Finally, the ROC curves and radar plots were plotted according to the five performance parameters. RESULTS 1130 radiomics features were extracted, there were 736 radiomics features with statistical differences were obtained, and 4 features were finally selected after the LASSO algorithm. In the validation set of this study, three of the five ML models (logistic regression, random forest and support vector machine) had excellent performance (AUC 0.9-1.0) and two models (adaptive boosting and decision tree) had good performance (AUC 0.7-0.9), all with accuracy ≥ 0.800. The rad+cli nomogram performance was found excellent in both the training set (AUC = 0.982,0.963-1.000, accuracy=0.941) and the validation set (AUC = 0.949,0.885-1.000, accuracy=0.911). The random forest model with perfect performance (AUC = 1, accuracy=1) was found superior compared to the model performance in the training set. The rad+cli nomogram model prevailed in the comparison of the model's performance in the validation set. CONCLUSION The ML models and nomogram can be used to identify the relatively common pathological subtypes in clinic and provide some reference for clinicians.
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Affiliation(s)
- Delu Cheng
- Department of Radiology, People's Hospital of Xinjiang Uygur Autonomous Region, Urumqi 83000, China
- Department of Radiology, Liaocheng Traditional Chinese Medicine Hospital, Liaocheng, Shandong 252000, China
| | - Yeerxiati Abudikeranmu
- Department of Radiology, People's Hospital of Xinjiang Uygur Autonomous Region, Urumqi 83000, China
| | - Batuer Tuerdi
- Department of Radiology, People's Hospital of Xinjiang Uygur Autonomous Region, Urumqi 83000, China
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23
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Ferro M, Crocetto F, Barone B, del Giudice F, Maggi M, Lucarelli G, Busetto GM, Autorino R, Marchioni M, Cantiello F, Crocerossa F, Luzzago S, Piccinelli M, Mistretta FA, Tozzi M, Schips L, Falagario UG, Veccia A, Vartolomei MD, Musi G, de Cobelli O, Montanari E, Tătaru OS. Artificial intelligence and radiomics in evaluation of kidney lesions: a comprehensive literature review. Ther Adv Urol 2023; 15:17562872231164803. [PMID: 37113657 PMCID: PMC10126666 DOI: 10.1177/17562872231164803] [Citation(s) in RCA: 35] [Impact Index Per Article: 35.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2022] [Accepted: 03/04/2023] [Indexed: 04/29/2023] Open
Abstract
Radiomics and artificial intelligence (AI) may increase the differentiation of benign from malignant kidney lesions, differentiation of angiomyolipoma (AML) from renal cell carcinoma (RCC), differentiation of oncocytoma from RCC, differentiation of different subtypes of RCC, to predict Fuhrman grade, to predict gene mutation through molecular biomarkers and to predict treatment response in metastatic RCC undergoing immunotherapy. Neural networks analyze imaging data. Statistical, geometrical, textural features derived are giving quantitative data of contour, internal heterogeneity and gray zone features of lesions. A comprehensive literature review was performed, until July 2022. Studies investigating the diagnostic value of radiomics in differentiation of renal lesions, grade prediction, gene alterations, molecular biomarkers and ongoing clinical trials have been analyzed. The application of AI and radiomics could lead to improved sensitivity, specificity, accuracy in detecting and differentiating between renal lesions. Standardization of scanner protocols will improve preoperative differentiation between benign, low-risk cancers and clinically significant renal cancers and holds the premises to enhance the diagnostic ability of imaging tools to characterize renal lesions.
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Affiliation(s)
| | - Felice Crocetto
- Department of Neurosciences and Reproductive
Sciences and Odontostomatology, University of Naples Federico II, Naples,
Italy
| | - Biagio Barone
- Department of Neurosciences and Reproductive
Sciences and Odontostomatology, University of Naples Federico II, Naples,
Italy
| | - Francesco del Giudice
- Department of Maternal Infant and Urologic
Sciences, Policlinico Umberto I Hospital, Sapienza University of Rome, Rome,
Italy
| | - Martina Maggi
- Department of Maternal Infant and Urologic
Sciences, Policlinico Umberto I Hospital, Sapienza University of Rome, Rome,
Italy
| | - Giuseppe Lucarelli
- Urology, Andrology and Kidney Transplantation
Unit, Department of Emergency and Organ Transplantation, University of Bari,
Bari, Italy
| | - Gian Maria Busetto
- Department of Urology and Organ
Transplantation, University of Foggia, Foggia, Italy
| | | | - Michele Marchioni
- Department of Medical, Oral and
Biotechnological Sciences, Urology Unit, SS Annunziata Hospital, G.
d’Annunzio University of Chieti, Chieti, Italy
- Department of Urology, ASL Abruzzo 2, Chieti,
Italy
| | - Francesco Cantiello
- Department of Urology, Magna Graecia
University of Catanzaro, Catanzaro, Italy
| | - Fabio Crocerossa
- Department of Urology, Magna Graecia
University of Catanzaro, Catanzaro, Italy
| | - Stefano Luzzago
- Department of Urology, IEO – European
Institute of Oncology, IRCCS – Istituto di Ricovero e Cura a Carattere
Scientifico, Milan, Italy
- Università degli Studi di Milano, Milan,
Italy
| | - Mattia Piccinelli
- Cancer Prognostics and Health Outcomes Unit,
Division of Urology, University of Montréal Health Center, Montréal, QC,
Canada
- Department of Urology, IEO – European
Institute of Oncology, IRCCS – Istituto di Ricovero e Cura a Carattere
Scientifico, Milan, Italy
| | - Francesco Alessandro Mistretta
- Department of Urology, IEO – European
Institute of Oncology, IRCCS – Istituto di Ricovero e Cura a Carattere
Scientifico, Milan, Italy
- Department of Oncology and
Hematology-Oncology, Università degli Studi di Milano, Milan, Italy
| | - Marco Tozzi
- Department of Urology, IEO – European
Institute of Oncology, IRCCS – Istituto di Ricovero e Cura a Carattere
Scientifico, Milan, Italy
- Università degli Studi di Milano, Milan,
Italy
| | - Luigi Schips
- Department of Medical, Oral and
Biotechnological Sciences, Urology Unit, SS Annunziata Hospital, G.
d’Annunzio University of Chieti, Chieti, Italy
| | | | - Alessandro Veccia
- Urology Unit, Azienda Ospedaliera
Universitaria Integrata Verona, University of Verona, Verona, Italy
| | - Mihai Dorin Vartolomei
- Department of Cell and Molecular Biology,
George Emil Palade University of Medicine, Pharmacy, Science and Technology
of Târgu Mures, Târgu Mures, Romania
- Department of Urology, Medical University of
Vienna, Vienna, Austria
| | - Gennaro Musi
- Department of Urology, IEO – European
Institute of Oncology, IRCCS – Istituto di Ricovero e Cura a Carattere
Scientifico, Milan, Italy
- Department of Oncology and
Hematology-Oncology, Università degli Studi di Milano, Milan, Italy
| | - Ottavio de Cobelli
- Department of Urology, IEO – European
Institute of Oncology, IRCCS – Istituto di Ricovero e Cura a Carattere
Scientifico, Milan, Italy
- Department of Oncology and
Hematology-Oncology, Università degli Studi di Milano, Milan, Italy
| | - Emanuele Montanari
- Department of Urology, Foundation IRCCS Ca’
Granda – Ospedale Maggiore Policlinico, Department of Clinical Sciences and
Community Health, University of Milan, Milan, Italy
| | - Octavian Sabin Tătaru
- Institution Organizing University Doctoral
Studies (IOSUD), George Emil Palade University of Medicine, Pharmacy,
Science and Technology of Târgu Mures, Târgu Mures, Romania
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Wei H, Shao Z, Tai J, Fu F, Lv C, Guo Z, Wu Y, Chen L, Bai Y, Wu Q, Yu X, Mu X, Shao F, Wang M. Analysis of CT signs, radiomic features and clinical characteristics for delta variant COVID-19 patients with different vaccination status. BMC Med Imaging 2022; 22:209. [PMID: 36447133 PMCID: PMC9832212 DOI: 10.1186/s12880-022-00937-9] [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: 07/14/2022] [Accepted: 11/14/2022] [Indexed: 11/30/2022] Open
Abstract
OBJECTIVE To explore the characteristics of peripheral blood, high resolution computed tomography (HRCT) imaging and the radiomics signature (RadScore) in patients infected with delta variant virus under different coronavirus disease (COVID-19) vaccination status. METHODS 123 patients with delta variant virus infection collected from November 1, 2021 to March 1, 2022 were analyzed retrospectively. According to COVID-19 vaccination Status, they were divided into three groups: Unvaccinated group, partially vaccinated group and full vaccination group. The peripheral blood, chest HRCT manifestations and RadScore of each group were analyzed and compared. RESULTS The mean lymphocyte count 1.22 ± 0.49 × 10^9/L, CT score 7.29 ± 3.48, RadScore 0.75 ± 0.63 in the unvaccinated group; The mean lymphocyte count 1.55 ± 0.70 × 10^9/L, CT score 5.27 ± 2.72, RadScore 1.03 ± 0.46 in the partially vaccinated group; The mean lymphocyte count 1.87 ± 0.70 × 10^9/L, CT score 3.59 ± 3.14, RadScore 1.23 ± 0.29 in the fully vaccinated group. There were significant differences in lymphocyte count, CT score and RadScore among the three groups (all p < 0.05); Compared with the other two groups, the lung lesions in the unvaccinated group were more involved in multiple lobes, of which 26 cases involved the whole lung. CONCLUSIONS Through the analysis of clinical features, pulmonary imaging features and radiomics, we confirmed the positive effect of COVID-19 vaccine on pulmonary inflammatory symptoms and lymphocyte count (immune system) during delta mutant infection.
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Affiliation(s)
- Huanhuan Wei
- grid.414011.10000 0004 1808 090XAcademy of Medical Sciences, The People’s Hospital of Zhengzhou University, 7 Weiwu Road, Zhengzhou, 450003 Henan China
| | - Zehua Shao
- grid.414011.10000 0004 1808 090XHeart Center of Zhengzhou University People’s Hospital, Henan Provincial People’s Hospital, Zhengzhou, 450003 Henan People’s Republic of China
| | - Jianqing Tai
- grid.417239.aThe First People’s Hospital of Zhengzhou, 56 East Street, Zhengzhou, 450004 Henan China
| | - Fangfang Fu
- grid.414011.10000 0004 1808 090XDepartment of Medical Imaging, Henan Provincial People’s Hospital & the People’s Hospital of Zhengzhou University, 7 Weiwu Road, Zhengzhou, 450003 Henan China
| | - Chuanjian Lv
- grid.414011.10000 0004 1808 090XDepartment of Medical Imaging, Henan Provincial People’s Hospital & the People’s Hospital of Zhengzhou University, 7 Weiwu Road, Zhengzhou, 450003 Henan China
| | - Zhiping Guo
- grid.417239.aThe First People’s Hospital of Zhengzhou, 56 East Street, Zhengzhou, 450004 Henan China
| | - Yaping Wu
- grid.414011.10000 0004 1808 090XDepartment of Medical Imaging, Henan Provincial People’s Hospital & the People’s Hospital of Zhengzhou University, 7 Weiwu Road, Zhengzhou, 450003 Henan China
| | - Lijuan Chen
- grid.414011.10000 0004 1808 090XDepartment of Medical Imaging, Henan Provincial People’s Hospital & the People’s Hospital of Zhengzhou University, 7 Weiwu Road, Zhengzhou, 450003 Henan China
| | - Yan Bai
- grid.414011.10000 0004 1808 090XDepartment of Medical Imaging, Henan Provincial People’s Hospital & the People’s Hospital of Zhengzhou University, 7 Weiwu Road, Zhengzhou, 450003 Henan China
| | - Qingxia Wu
- Beijing United Imaging Research Institute of Intelligent Imaging, Beijing, 100089 China
| | - Xuan Yu
- grid.414011.10000 0004 1808 090XDepartment of Medical Imaging, Henan Provincial People’s Hospital & the People’s Hospital of Zhengzhou University, 7 Weiwu Road, Zhengzhou, 450003 Henan China
| | - Xinling Mu
- grid.417239.aThe First People’s Hospital of Zhengzhou, 56 East Street, Zhengzhou, 450004 Henan China
| | - Fengmin Shao
- grid.414011.10000 0004 1808 090XDepartment of Medical Imaging, Henan Provincial People’s Hospital & the People’s Hospital of Zhengzhou University, 7 Weiwu Road, Zhengzhou, 450003 Henan China
| | - Meiyun Wang
- grid.414011.10000 0004 1808 090XAcademy of Medical Sciences, The People’s Hospital of Zhengzhou University, 7 Weiwu Road, Zhengzhou, 450003 Henan China ,grid.414011.10000 0004 1808 090XDepartment of Medical Imaging, Henan Provincial People’s Hospital & the People’s Hospital of Zhengzhou University, 7 Weiwu Road, Zhengzhou, 450003 Henan China
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Yin F, Zhang H, Qi A, Zhu Z, Yang L, Wen G, Xie W. An exploratory study of CT radiomics using differential network feature selection for WHO/ISUP grading and progression-free survival prediction of clear cell renal cell carcinoma. Front Oncol 2022; 12:979613. [DOI: 10.3389/fonc.2022.979613] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2022] [Accepted: 10/11/2022] [Indexed: 11/13/2022] Open
Abstract
ObjectivesTo explore the feasibility of predicting the World Health Organization/International Society of Urological Pathology (WHO/ISUP) grade and progression-free survival (PFS) of clear cell renal cell cancer (ccRCC) using the radiomics features (RFs) based on the differential network feature selection (FS) method using the maximum-entropy probability model (MEPM).Methods175 ccRCC patients were divided into a training set (125) and a test set (50). The non-contrast phase (NCP), cortico-medullary phase, nephrographic phase, excretory phase phases, and all-phase WHO/ISUP grade prediction models were constructed based on a new differential network FS method using the MEPM. The diagnostic performance of the best phase model was compared with the other state-of-the-art machine learning models and the clinical models. The RFs of the best phase model were used for survival analysis and visualized using risk scores and nomograms. The performance of the above models was tested in both cross-validated and independent validation and checked by the Hosmer-Lemeshow test.ResultsThe NCP RFs model was the best phase model, with an AUC of 0.89 in the test set, and performed superior to other machine learning models and the clinical models (all p <0.05). Kaplan-Meier survival analysis, univariate and multivariate cox regression results, and risk score analyses showed the NCP RFs could predict PFS well (almost all p < 0.05). The nomogram model incorporated the best two RFs and showed good discrimination, a C-index of 0.71 and 0.69 in the training and test set, and good calibration.ConclusionThe NCP CT-based RFs selected by differential network FS could predict the WHO/ISUP grade and PFS of RCC.
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Hu X, Wang Z, Chen H, Zhao A, Sun N, Deng C. Diagnosing, Typing, and Staging of Renal Cell Carcinoma by Designer Matrix-Based Urinary Metabolic Analysis. Anal Chem 2022; 94:14846-14853. [PMID: 36260912 DOI: 10.1021/acs.analchem.2c01563] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Molecular diagnosing, typing, and staging have been considered to be the ideal alternatives of imaging-based detection methods in clinics. Designer matrix-based analytical tools, with high speed, throughout, efficiency and low/noninvasiveness, have attracted much attention recently for in vitro metabolite detection. Herein, we develop an advanced metabolic analysis tool based on highly porous metal oxides derived from available metal-organic frameworks (MOFs), which elaborately inherit the morphology and porosity of MOFs and newly incorporate laser adsorption capacity of metal oxides. Through optimized conditions, direct high-quality fingerprinting spectra in 0.5 μL of urine are acquired. Using these fingerprinting spectra, we can discriminate the renal cell carcinoma (RCC) from healthy controls with higher than 0.99 of area under the curve (AUC) values (R2Y(cum) = 0.744, Q2 (cum) = 0.880), as well, from patients with other tumors (R2Y(cum) = 0.748, Q2(cum) = 0.871). We also realize the typing of three RCC subtypes, including clear cell RCC, chromophobe RCC (R2Y(cum) = 0.620, Q2(cum) = 0.656), and the staging of RCC (R2Y(cum) = 0.755, Q2(cum) = 0.857). Moreover, the tumor sizes (threshold value is 3 cm) can be remarkably recognized by this advanced metabolic analysis tool (R2Y(cum) = 0.710, Q2(cum) = 0.787). Our work brings a bright prospect for designer matrix-based analytical tools in disease diagnosis, typing and staging.
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Affiliation(s)
- Xufang Hu
- Department of Gastroenterology and Hepatology, Zhongshan Hospital, and Department of Chemistry, Fudan University, Shanghai 200032, China
| | - Zongping Wang
- Department of Urology, Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Hangzhou 310000, China
| | - Haolin Chen
- Department of Gastroenterology and Hepatology, Zhongshan Hospital, and Department of Chemistry, Fudan University, Shanghai 200032, China
| | - An Zhao
- Experimental Research Center, Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Hangzhou 310000, China.,Institute of Cancer and Basic Medicine (ICBM), Chinese Academy of Sciences, Hangzhou 310022, China
| | - Nianrong Sun
- Department of Gastroenterology and Hepatology, Zhongshan Hospital, and Department of Chemistry, Fudan University, Shanghai 200032, China
| | - Chunhui Deng
- Department of Gastroenterology and Hepatology, Zhongshan Hospital, and Department of Chemistry, Fudan University, Shanghai 200032, China
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27
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Yu Z, Ding J, Pang H, Fang H, He F, Xu C, Li X, Ren K. A triple-classification for differentiating renal oncocytoma from renal cell carcinoma subtypes and CK7 expression evaluation: a radiomics analysis. BMC Urol 2022; 22:147. [PMID: 36096829 PMCID: PMC9469588 DOI: 10.1186/s12894-022-01099-0] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2022] [Accepted: 08/29/2022] [Indexed: 11/10/2022] Open
Abstract
Background To investigate the value of computed tomography (CT)-based radiomics model analysis in differentiating renal oncocytoma (RO) from renal cell carcinoma subtypes (chromophobe renal cell carcinoma, clear cell carcinoma) and predicting the expression of Cytokeratin 7 (CK7). Methods In this retrospective study, radiomics was applied for patients with RO, chRCC and ccRCC who underwent surgery between January 2013 and December 2019 comprised the training cohort, and the testing cohort was collected between January and October 2020. The corticomedullary (CMP) and nephrographic phases (NP) were manually segmented, and radiomics texture parameters were extracted. Support vector machine was generated from CMP and NP after feature selection. Shapley additive explanations were applied to interpret the radiomics features. A radiomics signature was built using the selected features from the two phases, and the radiomics nomogram was constructed by incorporating the radiomics features and clinical factors. Receiver operating characteristic curve was calculated to evaluate the above models in the two sets. Furthermore, Rad-score was used for correlation analysis with CK7. Results A total of 123 patients with RO, chRCC and ccRCC were analyzed in the training cohort and 57 patients in the testing cohort. Subsequently, 396 radiomics features were selected from each phase. The radiomics features combining two phases yielded the highest area under the curve values of 0.941 and 0.935 in the training and testing sets, respectively. The Pearson’s correlation coefficient was statistically significant between Rad-score and CK7. Conclusion We proposed a non-invasive and individualized CT-based radiomics nomogram to differentiation among RO, chRCC and ccRCC preoperatively and predict the immunohistochemical protein expression for accurate clinical diagnosis and treatment decision. Supplementary Information The online version contains supplementary material available at 10.1186/s12894-022-01099-0.
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Affiliation(s)
- Ziyang Yu
- School of Medicine, Xiamen University, Xiamen, Fujian Province, China
| | - Jie Ding
- Radiology, Xiang'an Hospital of Xiamen University, Xiamen, China
| | - Huize Pang
- Department of Radiology, First Affiliated Hospital of China Medical University, Shenyang, Liaoning Province, China
| | - Hongkun Fang
- School of Medicine, Xiamen University, Xiamen, Fujian Province, China
| | - Furong He
- School of Medicine, Xiamen University, Xiamen, Fujian Province, China
| | - Chenxi Xu
- School of Medicine, Xiamen University, Xiamen, Fujian Province, China
| | - Xuedan Li
- Department of Radiology, First Affiliated Hospital of China Medical University, Shenyang, Liaoning Province, China.
| | - Ke Ren
- School of Medicine, Xiamen University, Xiamen, Fujian Province, China. .,Department of Radiology, First Affiliated Hospital of China Medical University, Shenyang, Liaoning Province, China. .,Radiology, Xiang'an Hospital of Xiamen University, Xiamen, China.
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Alhussaini AJ, Steele JD, Nabi G. Comparative Analysis for the Distinction of Chromophobe Renal Cell Carcinoma from Renal Oncocytoma in Computed Tomography Imaging Using Machine Learning Radiomics Analysis. Cancers (Basel) 2022; 14:3609. [PMID: 35892868 PMCID: PMC9332006 DOI: 10.3390/cancers14153609] [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: 06/23/2022] [Revised: 07/13/2022] [Accepted: 07/19/2022] [Indexed: 02/04/2023] Open
Abstract
Background: ChRCC and RO are two types of rarely occurring renal tumors that are difficult to distinguish from one another based on morphological features alone. They differ in prognosis, with ChRCC capable of progressing and metastasizing, but RO is benign. This means discrimination of the two tumors is of crucial importance. Objectives: The purpose of this research was to develop and comprehensively evaluate predictive models that can discriminate between ChRCC and RO tumors using Computed Tomography (CT) scans and ML-Radiomics texture analysis methods. Methods: Data were obtained from 78 pathologically confirmed renal masses, scanned at two institutions. Data from the two institutions were combined to form a third set resulting in three data cohorts, i.e., cohort 1, 2 and combined. Contrast-enhanced scans were used and the axial cross-sectional slices of each tumor were extracted from the 3D data using a semi-automatic segmentation technique for both 2D and 3D scans. Radiomics features were extracted before and after applying filters and the dimensions of the radiomic features reduced using the least absolute shrinkage and selection operator (LASSO) method. Synthetic minority oversampling technique (SMOTE) was applied to avoid class imbalance. Five ML algorithms were used to train models for predictive classification and evaluated using 5-fold cross-validation. Results: The number of selected features with good model performance was 20, 40 and 6 for cohorts 1, 2 and combined, respectively. The best model performance in cohorts 1, 2 and combined had an excellent Area Under the Curve (AUC) of 1.00 ± 0.000, 1.00 ± 0.000 and 0.87 ± 0.073, respectively. Conclusions: ML-based radiomics signatures are potentially useful for distinguishing ChRCC and RO tumors, with a reliable level of performance for both 2D and 3D scanning.
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Affiliation(s)
- Abeer J. Alhussaini
- Division of Imaging Sciences and Technology, School of Medicine, Ninewells Hospital, University of Dundee, Dundee DD1 9SY, UK;
- Department of Medical Imaging, Al-Amiri Hospital, Ministry of Health, Sulaibikhat 1300, Kuwait
| | - J. Douglas Steele
- Division of Imaging Sciences and Technology, School of Medicine, Ninewells Hospital, University of Dundee, Dundee DD1 9SY, UK;
| | - Ghulam Nabi
- Division of Imaging Sciences and Technology, School of Medicine, Ninewells Hospital, University of Dundee, Dundee DD1 9SY, UK;
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Xiao F, Sun R, Sun W, Xu D, Lan L, Li H, Liu H, Xu H. Prediction of potential severe COVID-19 patients based on CT radiomics: a retrospective study. Med Phys 2022; 49:5886-5898. [PMID: 35837868 PMCID: PMC9349830 DOI: 10.1002/mp.15841] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2022] [Revised: 05/26/2022] [Accepted: 06/20/2022] [Indexed: 01/08/2023] Open
Abstract
PURPOSE Coronavirus disease 2019 (COVID-19) is a recently declared worldwide pandemic. Triaging of patients into severe and non-severe could further help in targeted management. "Potential severe patients" is a category of patients who did not have severe symptoms at their initial diagnosis, but eventually progressed to be severe patients and are easily overlooked in the early stage. This work aimed to develop and evaluate a CT-based radiomics signature for the prediction of these potential severe COVID-19 patients. METHODS 150 COVID-19 patients were enrolled and randomly divided into cross-validation and independent test sets. First, Their clinical characteristics were screened using the univariate and multivariate logistic regression step by step. Then, radiomics features were extracted from the lesions on their chest CT images. Subsequently, the Inter- and intra-class correlation coefficients (ICC) analysis, minimum-redundancy maximum-relevance (mRMR) selection and the least absolute shrinkage and selection operator (LASSO) algorithm were used step by step for feature selection and construction of a radiomics signature. Finally, the screened clinical risk factors and constructed radiomics signature were combined for the Combined model and Radiomics+Clinics nomogram construction. The predictive performance of the Radiomics and Combined models were evaluated and compared using receiver operating characteristic curve (ROC) analysis, Hosmer-Lemeshow test and Delong test. RESULTS Clinical characteristics analysis resulted in the screening of five clinical risk factors. The combination of ICC, mRMR and LASSO methods resulted in the selection of ten radiomics features, which made up the radiomics signature. The differences in the radiomics signature between the potential severe and non-severe groups in cross-validation set and test sets were both p < 0.001. All Radiomics and Combined models showed a very good predictive performance with the accuracy and AUC of nearly or above 0.9. Additionally, we found no significant difference in the predictive performance between these two models. CONCLUSIONS A CT-based radiomics signature for the prediction of potential severe COVID-19 patients was constructed and evaluated. Constructed Radiomics and Combined model showed good feasibility and accuracy. The Radiomics+Clinical nomogram, acted as a useful tool, may assist clinicians to better identify potential severe cases to target their management in the COVID-19 pandemic prevention and control. This article is protected by copyright. All rights reserved.
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Affiliation(s)
- Feng Xiao
- Department of Radiology, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Rongqing Sun
- Department of Radiology, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Wenbo Sun
- Department of Radiology, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Dan Xu
- Department of Radiology, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Lan Lan
- Department of Radiology, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Huan Li
- Department of Radiology, Zhongnan Hospital of Wuhan University, Wuhan, China
| | | | - Haibo Xu
- Department of Radiology, Zhongnan Hospital of Wuhan University, Wuhan, China
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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: 52] [Impact Index Per Article: 26.0] [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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Zheng X, Xiao C, Xie Z, Liu L, Chen Y. Prediction Models for Prognosis of Femoral Neck–Fracture Patients 6 Months after Total Hip Arthroplasty. Int J Gen Med 2022; 15:4339-4356. [PMID: 35480995 PMCID: PMC9037899 DOI: 10.2147/ijgm.s347425] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2021] [Accepted: 03/02/2022] [Indexed: 11/23/2022] Open
Abstract
Purpose To establish prediction models for 6-month prognosis in femoral neck–fracture patients receiving total hip arthroplasty (THA). Patients and Methods In total, 182 computed tomography image pairs from 85 patients were collected and divided into a training set (n=127) and testing set (n=55). Least absolute shrinkage–selection operator regression was used for selecting optimal predictors. A random-forest algorithm was used to establish the prediction models, which were evaluated for accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and area under the curve (AUC). Results The best model in this study was constructed based on demographic data, preoperative laboratory indicators, and three preoperative radiomic features. In the random-forest model, activated partial thromboplastin time, a preoperative radiomic feature (maximum diameter), and fibrinogen were important variables correlating with patient outcomes. The AUC, sensitivity, specificity, PPV, NPV, and accuracy in the training set were 0.986 (95% CI 0.971–1), 0.925 (95% CI 0.862–0.988), 0.983 (95% CI 0.951–1.016), 0.984 (95% CI 0.953–1.014), 0.922 (95% CI 0.856–0.988), and 0.953 (95% CI 0.916–0.990), respectively. The AUC, sensitivity, specificity, PPV, NPV, and accuracy in the testing set were 0.949 (95% CI 0.885–1), 0.767 (95% CI 0.615–0.918), 1 (95% CI 1–1), 1 (95% CI 1–1), 0.781 (95% CI 0.638–0.924), and 0.873 (95% CI 0.785–0.961), respectively. Conclusion The model based on demographic, preoperative clinical, and preoperative radiomic data showed the best predictive ability for 6-month prognosis in the femoral neck–fracture patients receiving THA.
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Affiliation(s)
- Xiaofeng Zheng
- Department of Orthopedics, Third Hospital of Mianyang Sichuan Mental Health Center, Mianyang, Sichuan, 621000, People’s Republic of China
| | - Cong Xiao
- Department of Orthopedics, Third Hospital of Mianyang Sichuan Mental Health Center, Mianyang, Sichuan, 621000, People’s Republic of China
| | - Zhuocheng Xie
- Department of Orthopedics, Sichuan Science City Hospital, Mianyang, Sichuan, 621000, People’s Republic of China
| | - Lijuan Liu
- Department of Orthopedics, Third Hospital of Mianyang Sichuan Mental Health Center, Mianyang, Sichuan, 621000, People’s Republic of China
| | - Yinhua Chen
- Department of Orthopedics, Third Hospital of Mianyang Sichuan Mental Health Center, Mianyang, Sichuan, 621000, People’s Republic of China
- Correspondence: Yinhua Chen, Department of Orthopedics, Third Hospital of Mianyang Sichuan Mental Health Center, 190 East Jiannan Road, Youxian, Mianyang, Sichuan, 621000, People’s Republic of China, Tel +86-139-9014-7729, Email
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Ming Y, Chen X, Xu J, Zhan H, Zhang J, Ma T, Huang C, Liu Z, Huang Z. A combined postoperative nomogram for survival prediction in clear cell renal carcinoma. Abdom Radiol (NY) 2022; 47:297-309. [PMID: 34647146 DOI: 10.1007/s00261-021-03293-4] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2021] [Revised: 09/19/2021] [Accepted: 09/20/2021] [Indexed: 10/20/2022]
Abstract
PURPOSE To investigate and validate the prognostic value of nomogram models for predicting disease-free survival (DFS) and overall survival (OS) in patients with clear cell renal cell carcinoma (ccRCC). METHODS In this retrospective study, 223 patients (age 54.38 ± 10.93 years) with pathologically confirmed ccRCC who underwent resection and lymph node dissection between March 2010 and September 2018 were investigated. All patients were randomly divided into training (n = 155) and validation (n = 68) cohorts. Radiomics features were extracted from computed tomography (CT) images in the unenhanced, corticomedullary, and nephrographic phases. Radiomic score was calculated and combined with clinicopathological factors for model construction and nomogram development. Clinicopathological factors and imaging features were collected at initial diagnosis. Univariate and multivariate Cox proportional hazards regression analyses were used to evaluate the relationship between the radiomics signature and prognosis outcomes. RESULTS There were four prognostic factors for predicting DFS and five factors for predicting OS in our nomogram model (P < 0.05). The radiomics signature correlated independently with DFS (hazard ratio = 27; P < 0.001) and OS (hazard ratio = 25; P < 0.001). The nomogram showed excellent performance (C-index = 0.825) for predicting DFS. The combined nomogram also showed the highest C-index for OS (C-index = 0.943), which was verified in the validation dataset. CONCLUSION The combined nomogram model based on radiomics, clinicopathological factors, and preoperative CT features can accurately perform prognosis and survival analysis and can potentially be used for preoperative non-invasive survival prediction in ccRCC patients.
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Tong X, Li J. Noninvasively predict the micro-vascular invasion and histopathological grade of hepatocellular carcinoma with CT-derived radiomics. Eur J Radiol Open 2022; 9:100424. [PMID: 35600083 PMCID: PMC9120240 DOI: 10.1016/j.ejro.2022.100424] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2022] [Revised: 04/14/2022] [Accepted: 04/30/2022] [Indexed: 11/01/2022] Open
Abstract
Objectives Methods Results Conclusion
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Yu B, Huang C, Fan X, Li F, Zhang J, Song Z, Zhi N, Ding J. Application of MR Imaging Features in Differentiation of Renal Changes in Patients With Stage III Type 2 Diabetic Nephropathy and Normal Subjects. Front Endocrinol (Lausanne) 2022; 13:846407. [PMID: 35600605 PMCID: PMC9114464 DOI: 10.3389/fendo.2022.846407] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/31/2021] [Accepted: 03/21/2022] [Indexed: 11/19/2022] Open
Abstract
OBJECTIVE The objective of the study was to explore the value of MRI texture features based on T1WI, T2-FS and diffusion-weighted imaging (DWI) in differentiation of renal changes in patients with stage III type 2 diabetic nephropathy (DN) and normal subjects. MATERIALS AND METHODS A retrospective analysis was performed to analyze 44 healthy volunteers (group A) and 40 patients with stage III type 2 diabetic nephropathy (group B) with microalbuminuria. Urinary albumin to creatinine ratio (ACR) <30 mg/g, estimated glomerular filtration rate (eGFR) in the range of 60-120 ml/(min 1.73 m2), and randomly divided into primary cohort and test cohort. Conventional MRI and DWI of kidney were performed using 1.5 T magnetic resonance imaging (MRI). The outline of the renal parenchyma was manually labeled in fat-suppressed T2-weighted imaging (FS-T2WI), and PyRadiomics was used to extract radiomics features. The radiomics features were then selected by the least absolute shrinkage and selection operator (LASSO) method. RESULTS There was a significant difference in sex and body mass index (BMI) (P <0.05) in the primary cohort, with no significant difference in age. In the final results, the wavelet and Laplacian-Gaussian filtering are used to extract 1,892 image features from the original T1WI image, and the LASSO algorithm is used for selection. One first-order feature and six texture features are selected through 10 cross-validations. In the mass, 1,638 imaging extracts features from the original T2WI image.1 first-order feature and 5 texture features were selected. A total of 1,241 imaging features were extracted from the original ADC images, and 5 texture features were selected. Using LASSO-Logistic regression analysis, 10 features were selected for modeling, and a combined diagnosis model of diabetic nephropathy based on texture features was established. The average unit cost in the logistic regression model was 0.98, the 95% confidence interval for the predictive efficacy was 0.9486-1.0, specificity 0.97 and precision 0.93, particularly. ROC curves also revealed that the model could distinguish with high sensitivity of at least 92%. CONCLUSION In consequence, the texture features based on MR have broad application prospects in the early detection of DN as a relatively simple and noninvasive tool without contrast media administration.
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Affiliation(s)
- Baoting Yu
- Department of Radiology, China–Japan Union Hospital of Jilin University, Changchun, China
| | - Chencui Huang
- Department of Research Collaboration, R&D Center, Beijing Deepwise & League of PHD Technology Co., Ltd., Beijing, China
| | - Xiaofei Fan
- Department of Radiology, China–Japan Union Hospital of Jilin University, Changchun, China
| | - Feng Li
- Department of Research Collaboration, R&D Center, Beijing Deepwise & League of PHD Technology Co., Ltd., Beijing, China
| | - Jianzhong Zhang
- Department of Radiology, China–Japan Union Hospital of Jilin University, Changchun, China
| | - Zihan Song
- Department of Radiology, Chang Chun Central Hospital, Changchun, China
| | - Nan Zhi
- Department of Radiology, China–Japan Union Hospital of Jilin University, Changchun, China
| | - Jun Ding
- Department of Radiology, China–Japan Union Hospital of Jilin University, Changchun, China
- *Correspondence: Jun Ding,
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Yu W, Liang G, Zeng L, Yang Y, Wu Y. Accuracy of CT texture analysis for differentiating low-grade and high-grade renal cell carcinoma: systematic review and meta-analysis. BMJ Open 2021; 11:e051470. [PMID: 34937716 PMCID: PMC8704996 DOI: 10.1136/bmjopen-2021-051470] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/29/2022] Open
Abstract
OBJECTIVES This study aimed to assess the accuracy of CT texture analysis (CTTA) for differentiating low-grade and high-grade renal cell carcinoma (RCC). DESIGN Systematic review and meta-analysis. DATA SOURCES PubMed, Cochrane Library, Embase, Web of Science, OVID Medline, Science Direct and Springer were searched to identify the included studies. ELIGIBILITY CRITERIA FOR INCLUDING STUDIES Clinical studies that report about the accuracy of CTTA in differentiating low-grade and high-grade RCC. METHODS Multiple databases were searched to identify studies from their inception to 20 October 2021. Two radiologists independently extracted data from the primary studies. The pooled sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR) and diagnostic OR (DOR) were calculated to assess CTTA performance. The summary receiver operating characteristic (SROC) curve was plotted, and the area under the curve (AUC) was calculated to evaluate the accuracy of CTTA in grading RCC. RESULTS This meta-analysis included 11 studies, with 1603 lesions observed in 1601 patients. Values of the pooled sensitivity, specificity, PLR, NLR, DOR were 0.79 (95% CI 0.73 to 0.84), 0.84 (95% CI 0.81 to 0.87), 5.1 (95% CI 4.0 to 6.4), 0.24 (95% CI 0.19 to 0.32) and 21 (95% CI 13 to 33), respectively. The SROC curve showed that the AUC was 0.88 (95% CI 0.84 to 0.90). Deeks' test found no significant publication bias among the studies (p=0.42). CONCLUSIONS The findings of this meta-analysis suggest that CTTA has a high accuracy in differentiating low-grade and high-grade RCC. A standardised methodology and large sample-based study are necessary to certain the diagnostic accuracy of CTTA in RCC grading for clinical decision making.
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Affiliation(s)
- Wei Yu
- Department of Radiology, Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan, China
| | - Gao Liang
- Department of Radiology, Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan, China
| | - Lichuan Zeng
- Department of Radiology, Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan, China
| | - Yang Yang
- Department of Radiology, Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan, China
| | - Yinghua Wu
- Department of Radiology, Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan, China
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Xv Y, Lv F, Guo H, Liu Z, Luo D, Liu J, Gou X, He W, Xiao M, Zheng Y. A CT-Based Radiomics Nomogram Integrated With Clinic-Radiological Features for Preoperatively Predicting WHO/ISUP Grade of Clear Cell Renal Cell Carcinoma. Front Oncol 2021; 11:712554. [PMID: 34926241 PMCID: PMC8677659 DOI: 10.3389/fonc.2021.712554] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2021] [Accepted: 11/02/2021] [Indexed: 11/29/2022] Open
Abstract
Objective This study aims to develop and validate a CT-based radiomics nomogram integrated with clinic-radiological factors for preoperatively differentiating high-grade from low-grade clear cell renal cell carcinomas (CCRCCs). Methods 370 patients with complete clinical, pathological, and CT image data were enrolled in this retrospective study, and were randomly divided into training and testing sets with a 7:3 ratio. Radiomics features were extracted from nephrographic phase (NP) contrast-enhanced images, and then a radiomics model was constructed by the selected radiomics features using a multivariable logistic regression combined with the most suitable feature selection algorithm determined by the comparison among least absolute shrinkage and selection operator (LASSO), recursive feature elimination (RFE) and ReliefF. A clinical model was established using clinical and radiological features. A radiomics nomogram was constructed by integrating the radiomics signature and independent clinic-radiological features. Performance of these three models was assessed using receiver operating characteristic (ROC) curve analysis and decision curve analysis (DCA). Results Using multivariate logistic regression analysis, three clinic-radiological features including intratumoral necrosis (OR=3.00, 95% CI=1.30-6.90, p=0.049), intratumoral angiogenesis (OR=3.28, 95% CI=1.22-8.78, p=0.018), and perinephric metastasis (OR=2.90, 95% CI=1.03-8.17, p=0.044) were found to be independent predictors of WHO/ISUP grade in CCRCC. Incorporating the above clinic-radiological predictors and radiomics signature constructed by LASSO, a CT-based radiomics nomogram was developed, and presented better predictive performance than clinic-radiological model and radiomics signature model, with an AUC of 0.891 (95% CI=0.832-0.962) and 0.843 (95% CI=0.718-0.975) in the training and testing sets, respectively. DCA indicated that the nomogram has potential clinical usefulness. Conclusion The CT-based radiomics nomogram is a promising tool to predict WHO/ISUP grade of CCRCC preoperatively and noninvasively.
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Affiliation(s)
- Yingjie Xv
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China.,Department of Urology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Fajin Lv
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Haoming Guo
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Zhaojun Liu
- Department of Urology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Di Luo
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China.,Department of Urology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Jing Liu
- Department of Urology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Xin Gou
- Department of Urology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Weiyang He
- Department of Urology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Mingzhao Xiao
- Department of Urology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Yineng Zheng
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
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Xv Y, Lv F, Guo H, Zhou X, Tan H, Xiao M, Zheng Y. Machine learning-based CT radiomics approach for predicting WHO/ISUP nuclear grade of clear cell renal cell carcinoma: an exploratory and comparative study. Insights Imaging 2021; 12:170. [PMID: 34800179 PMCID: PMC8605949 DOI: 10.1186/s13244-021-01107-1] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2021] [Accepted: 10/09/2021] [Indexed: 12/14/2022] Open
Abstract
Purpose To investigate the predictive performance of machine learning-based CT radiomics for differentiating between low- and high-nuclear grade of clear cell renal cell carcinomas (CCRCCs). Methods This retrospective study enrolled 406 patients with pathologically confirmed low- and high-nuclear grade of CCRCCs according to the WHO/ISUP grading system, which were divided into the training and testing cohorts. Radiomics features were extracted from nephrographic-phase CT images using PyRadiomics. A support vector machine (SVM) combined with three feature selection algorithms such as least absolute shrinkage and selection operator (LASSO), recursive feature elimination (RFE), and ReliefF was performed to determine the most suitable classification model, respectively. Clinicoradiological, radiomics, and combined models were constructed using the radiological and clinical characteristics with significant differences between the groups, selected radiomics features, and a combination of both, respectively. Model performance was evaluated by receiver operating characteristic (ROC) curve, calibration curve, and decision curve analyses. Results SVM-ReliefF algorithm outperformed SVM-LASSO and SVM-RFE in distinguishing low- from high-grade CCRCCs. The combined model showed better prediction performance than the clinicoradiological and radiomics models (p < 0.05, DeLong test), which achieved the highest efficacy, with an area under the ROC curve (AUC) value of 0.887 (95% confidence interval [CI] 0.798–0.952), 0.859 (95% CI 0.748–0.935), and 0.828 (95% CI 0.731–0.929) in the training, validation, and testing cohorts, respectively. The calibration and decision curves also indicated the favorable performance of the combined model. Conclusion A combined model incorporating the radiomics features and clinicoradiological characteristics can better predict the WHO/ISUP nuclear grade of CCRCC preoperatively, thus providing effective and noninvasive assessment. Supplementary Information The online version contains supplementary material available at 10.1186/s13244-021-01107-1.
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Affiliation(s)
- Yingjie Xv
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, No. 1 Youyi Road, Chongqing, 400016, Yuzhong, China.,Department of Urology, The First Affiliated Hospital of Chongqing Medical University, No. 1 Youyi Road, Chongqing, 400016, Yuzhong, China
| | - Fajin Lv
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, No. 1 Youyi Road, Chongqing, 400016, Yuzhong, China
| | - Haoming Guo
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, No. 1 Youyi Road, Chongqing, 400016, Yuzhong, China
| | - Xiang Zhou
- Department of Urology, The First Affiliated Hospital of Chongqing Medical University, No. 1 Youyi Road, Chongqing, 400016, Yuzhong, China
| | - Hao Tan
- Department of Urology, The First Affiliated Hospital of Chongqing Medical University, No. 1 Youyi Road, Chongqing, 400016, Yuzhong, China
| | - Mingzhao Xiao
- Department of Urology, The First Affiliated Hospital of Chongqing Medical University, No. 1 Youyi Road, Chongqing, 400016, Yuzhong, China.
| | - Yineng Zheng
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, No. 1 Youyi Road, Chongqing, 400016, Yuzhong, China.
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Wang X, Song G, Jiang H, Zheng L, Pang P, Xu J. Can texture analysis based on single unenhanced CT accurately predict the WHO/ISUP grading of localized clear cell renal cell carcinoma? Abdom Radiol (NY) 2021; 46:4289-4300. [PMID: 33909090 DOI: 10.1007/s00261-021-03090-z] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2021] [Revised: 04/08/2021] [Accepted: 04/10/2021] [Indexed: 12/22/2022]
Abstract
OBJECTIVE The purpose was to investigate the value of texture analysis in predicting the World Health Organization (WHO)/International Society of Urological Pathology (ISUP) grading of localized clear cell renal cell carcinoma (ccRCC) based on unenhanced CT (UECT). MATERIALS AND METHODS Pathologically confirmed subjects (n = 104) with localized ccRCC who received UECT scanning were collected retrospectively for this study. All cases were classified into low grade (n = 53) and high grade (n = 51) according to the WHO/ISUP grading and were randomly divided into training set and test set as a ratio of 7:3. Using 3D-ROI segmentation on UECT images and extracted ninety-three texture features (first-order, gray-level co-occurrence matrix [GLCM], gray-level run length matrix [GLRLM], gray-level size zone matrix [GLSZM], neighboring gray tone difference matrix [NGTDM] and gray-level dependence matrix [GLDM] features). Univariate analysis and the least absolute shrinkage selection operator (LASSO) regression were used for feature dimension reduction, and logistic regression classifier was used to develop the prediction model. Using receiver operating characteristic (ROC) curve, bar chart and calibration curve to evaluate the performance of the prediction model. RESULTS Dimension reduction screened out eight optimal texture features (maximum, median, dependence variance [DV], long run emphasis [LRE], run entropy [RE], gray-level non-uniformity [GLN], gray-level variance [GLV] and large area low gray-level emphasis [LALGLE]), and then the prediction model was developed according to the linear combination of these features. The accuracy, sensitivity, specificity, and AUC of the model in training set were 86.1%, 91.4%, 81.1%, and 0.937, respectively. The accuracy, sensitivity, specificity, and AUC of the model in test set were 81.2%, 81.2%, 81.2%, and 0.844, respectively. The calibration curves showed good calibration both in training set and test set (P > 0.05). CONCLUSION This study has demonstrated that the radiomics model based on UECT texture analysis could accurately evaluate the WHO/ISUP grading of localized ccRCC.
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Affiliation(s)
- Xu Wang
- Department of Radiology, Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), No 1, Banshan East Road, Hangzhou, 310022, Zhejiang, China
- Institute of Cancer and Basic Medicine, Chinese Academy of Sciences, No 1, Banshan East Road, Hangzhou, 310022, Zhejiang, China
| | - Ge Song
- Department of Radiology, Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), No 1, Banshan East Road, Hangzhou, 310022, Zhejiang, China
- Institute of Cancer and Basic Medicine, Chinese Academy of Sciences, No 1, Banshan East Road, Hangzhou, 310022, Zhejiang, China
| | - Haitao Jiang
- Department of Radiology, Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), No 1, Banshan East Road, Hangzhou, 310022, Zhejiang, China.
- Institute of Cancer and Basic Medicine, Chinese Academy of Sciences, No 1, Banshan East Road, Hangzhou, 310022, Zhejiang, China.
| | - Linfeng Zheng
- Institute of Cancer and Basic Medicine, Chinese Academy of Sciences, No 1, Banshan East Road, Hangzhou, 310022, Zhejiang, China
- Department of Pathology, Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), No 1, Banshan East Road, Hangzhou, 310022, Zhejiang, China
| | | | - Jingjing Xu
- Institute of Cancer and Basic Medicine, Chinese Academy of Sciences, No 1, Banshan East Road, Hangzhou, 310022, Zhejiang, China
- Department of Pathology, Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), No 1, Banshan East Road, Hangzhou, 310022, Zhejiang, China
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Prediction of histologic grade and type of small (< 4 cm) papillary renal cell carcinomas using texture and neural network analysis: a feasibility study. Abdom Radiol (NY) 2021; 46:4266-4277. [PMID: 33813624 DOI: 10.1007/s00261-021-03044-5] [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: 11/29/2020] [Revised: 03/01/2021] [Accepted: 03/05/2021] [Indexed: 10/21/2022]
Abstract
OBJECTIVE To predict the histologic grade and type of small papillary renal cell carcinomas (pRCCs) using texture analysis and machine learning algorithms. METHODS This was a retrospective HIPAA-compliant study. 24 noncontrast (NC), 22 corticomedullary (CM) phase, and 24 nephrographic (NG) phase CTs of small (< 4 cm) surgically resected pRCCs were identified. Surgical pathology classified the tumors as low- or high-Fuhrman histologic grade and type 1 or 2. The axial image with the largest cross-sectional tumor area was exported and segmented. Six histogram and 31 texture (20 gray-level co-occurrences and 11 gray-level run-lengths) features were calculated for each tumor in each phase. Feature values in low- versus high-grade and type 1 versus 2 pRCCs were compared. Area under the receiver operating curve (AUC) was calculated for each feature to assess prediction of histologic grade and type of pRCCs in each phase. Histogram, texture, and combined histogram and texture feature sets were used to train and test three classification algorithms (support vector machine (SVM), random forest, and histogram-based gradient boosting decision tree (HGBDT)) with stratified shuffle splits and threefold cross-validation; AUCs were calculated for each algorithm in each phase to assess prediction of histologic grade and type of pRCCs. RESULTS Individual histogram and texture features did not have statistically significant differences between low- and high-grade or type 1 and type 2 pRCCs across all phases. Individual features had low predictive power for tumor grade or type in all phases (AUC < 0.70). HGBDT was highly accurate at predicting pRCC histologic grade and type using histogram, texture or combined histogram and texture feature data from the CM phase (AUCs = 0.97-1.0). All algorithms had highest AUCs using CM phase feature data sets; AUCs decreased using feature sets from NC or NG phases. CONCLUSIONS The histologic grade and type of small pRCCs can be predicted with classification algorithms using CM histogram and texture features, which outperform NC and NG phase image data. The accurate prediction of pRCC histologic grade and type may be able to further guide management of patients with small (< 4 cm) pRCCs being considered for active surveillance.
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Stanzione A, Ricciardi C, Cuocolo R, Romeo V, Petrone J, Sarnataro M, Mainenti PP, Improta G, De Rosa F, Insabato L, Brunetti A, Maurea S. MRI Radiomics for the Prediction of Fuhrman Grade in Clear Cell Renal Cell Carcinoma: a Machine Learning Exploratory Study. J Digit Imaging 2021; 33:879-887. [PMID: 32314070 DOI: 10.1007/s10278-020-00336-y] [Citation(s) in RCA: 32] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023] Open
Abstract
The Fuhrman nuclear grade is a recognized prognostic factor for patients with clear cell renal cell carcinoma (CCRCC) and its pre-treatment evaluation significantly affects decision-making in terms of management. In this study, we aimed to assess the feasibility of a combined approach of radiomics and machine learning based on MR images for a non-invasive prediction of Fuhrman grade, specifically differentiation of high- from low-grade tumor and grade assessment. Images acquired on a 3-Tesla scanner (T2-weighted and post-contrast) from 32 patients (20 with low-grade and 12 with high-grade tumor) were annotated to generate volumes of interest enclosing CCRCC lesions. After image resampling, normalization, and filtering, 2438 features were extracted. A two-step feature reduction process was used to between 1 and 7 features depending on the algorithm employed. A J48 decision tree alone and in combination with ensemble learning methods were used. In the differentiation between high- and low-grade tumors, all the ensemble methods achieved an accuracy greater than 90%. On the other end, the best results in terms of accuracy (84.4%) in the assessment of tumor grade were achieved by the random forest. These evidences support the hypothesis that a combined radiomic and machine learning approach based on MR images could represent a feasible tool for the prediction of Fuhrman grade in patients affected by CCRCC.
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Affiliation(s)
- Arnaldo Stanzione
- Department of Advanced Biomedical Sciences, University of Naples "Federico II", Via S. Pansini, 5, 80123, Naples, Italy
| | - Carlo Ricciardi
- Department of Advanced Biomedical Sciences, University of Naples "Federico II", Via S. Pansini, 5, 80123, Naples, Italy
| | - Renato Cuocolo
- Department of Advanced Biomedical Sciences, University of Naples "Federico II", Via S. Pansini, 5, 80123, Naples, Italy.
| | - Valeria Romeo
- Department of Advanced Biomedical Sciences, University of Naples "Federico II", Via S. Pansini, 5, 80123, Naples, Italy
| | - Jessica Petrone
- Department of Advanced Biomedical Sciences, University of Naples "Federico II", Via S. Pansini, 5, 80123, Naples, Italy
| | - Michela Sarnataro
- Department of Advanced Biomedical Sciences, University of Naples "Federico II", Via S. Pansini, 5, 80123, Naples, Italy
| | - Pier Paolo Mainenti
- Institute of Biostructures and Bioimaging of the National Research Council (CNR), Naples, Italy
| | - Giovanni Improta
- Department of Public Health, University of Naples "Federico II", Naples, Italy
| | - Filippo De Rosa
- Department of Advanced Biomedical Sciences, University of Naples "Federico II", Via S. Pansini, 5, 80123, Naples, Italy
| | - Luigi Insabato
- Department of Advanced Biomedical Sciences, University of Naples "Federico II", Via S. Pansini, 5, 80123, Naples, Italy
| | - Arturo Brunetti
- Department of Advanced Biomedical Sciences, University of Naples "Federico II", Via S. Pansini, 5, 80123, Naples, Italy
| | - Simone Maurea
- Department of Advanced Biomedical Sciences, University of Naples "Federico II", Via S. Pansini, 5, 80123, Naples, Italy
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CT Radiomics for the Prediction of Synchronous Distant Metastasis in Clear Cell Renal Cell Carcinoma. J Comput Assist Tomogr 2021; 45:696-703. [PMID: 34347707 DOI: 10.1097/rct.0000000000001211] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
PURPOSE The aim of this study was to construct and verify a computed tomography (CT) radiomics model for preoperative prediction of synchronous distant metastasis (SDM) in clear cell renal cell carcinoma (ccRCC) patients. METHODS Overall, 172 patients with ccRCC were enrolled in the present research. Contrast-enhanced CT images were manually sketched, and 2994 quantitative radiomic features were extracted. The radiomic features were then normalized and subjected to hypothesis testing. Least absolute shrinkage and selection operator (LASSO) was applied to dimension reduction, feature selection, and model construction. The performance of the predictive model was validated through analysis of the receiver operating characteristic curve. Multivariate and subgroup analyses were performed to verify the radiomic score as an independent predictor of SDM. RESULTS The patients randomized into a training (n = 104) and a validation (n = 68) cohort in a 6:4 ratio. Through dimension reduction using LASSO regression, 9 radiomic features were used for the construction of the SDM prediction model. The model yielded moderate performance in both the training (area under the curve, 0.89; 95% confidence interval, 0.81-0.97) and the validation cohort (area under the curve, 0.83; 95% confidence interval, 0.69-0.95). Multivariate analysis showed that the CT radiomic signature was an independent risk factor for clinical parameters of ccRCC. Subgroup analysis revealed a significant connection between the SDM and radiomic signature, except for the lower pole of the kidney subgroup. CONCLUSIONS The CT-based radiomics model could be used as a noninvasive, personalized approach for SDM prediction in patients with ccRCC.
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Kuusk T, Neves JB, Tran M, Bex A. Radiomics to better characterize small renal masses. World J Urol 2021; 39:2861-2868. [PMID: 33495866 DOI: 10.1007/s00345-021-03602-y] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2020] [Accepted: 01/11/2021] [Indexed: 12/23/2022] Open
Abstract
PURPOSE Radiomics is a specific field of medical research that uses programmable recognition tools to extract objective information from standard images to combine with clinical data, with the aim of improving diagnostic, prognostic, and predictive accuracy beyond standard visual interpretation. We performed a narrative review of radiomic applications that may support improved characterization of small renal masses (SRM). The main focus of the review was to identify and discuss methods which may accurately differentiate benign from malignant renal masses, specifically between renal cell carcinoma (RCC) subtypes and from angiomyolipoma without visible fat (fat-poor AML) and oncocytoma. Furthermore, prediction of grade, sarcomatoid features, and gene mutations would be of importance in terms of potential clinical utility in prognostic stratification and selecting personalised patient management strategies. METHODS A detailed search of original articles was performed using the PubMed-MEDLINE database until 20 September 2020 to identify the English literature relevant to radiomics applications in renal tumour assessment. In total, 42 articles were included in the analysis in 3 main categories related to SRM: prediction of benign versus malignant SRM, subtypes, and nuclear grade, and other features of aggressiveness. CONCLUSION Overall, studies reported the superiority of radiomics over expert radiological assessment, but were mainly of retrospective design and therefore of low-quality evidence. However, it is clear that radiomics is an attractive modality that has the potential to improve the non-invasive diagnostic accuracy of SRM imaging and prediction of its natural behaviour. Further prospective validation studies of radiomics are needed to augment management algorithms of SRM.
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Affiliation(s)
- Teele Kuusk
- Urology Department, Darent Valley Hospital, Dartford and Gravesham NHS Trust, Dartford, UK
- Specialist Centre for Kidney Cancer, Royal Free London NHS Foundation Trust, London, UK
| | - Joana B Neves
- Specialist Centre for Kidney Cancer, Royal Free London NHS Foundation Trust, London, UK
| | - Maxine Tran
- Specialist Centre for Kidney Cancer, Royal Free London NHS Foundation Trust, London, UK
- UCL Division of Surgery and Interventional Science, London, UK
| | - Axel Bex
- Specialist Centre for Kidney Cancer, Royal Free London NHS Foundation Trust, London, UK.
- UCL Division of Surgery and Interventional Science, London, UK.
- Surgical Oncology Division, Urology Department, The Netherlands Cancer Institute, Amsterdam, The Netherlands.
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Gao R, Qin H, Lin P, Ma C, Li C, Wen R, Huang J, Wan D, Wen D, Liang Y, Huang J, Li X, Wang X, Chen G, He Y, Yang H. Development and Validation of a Radiomic Nomogram for Predicting the Prognosis of Kidney Renal Clear Cell Carcinoma. Front Oncol 2021; 11:613668. [PMID: 34295804 PMCID: PMC8290524 DOI: 10.3389/fonc.2021.613668] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2020] [Accepted: 06/01/2021] [Indexed: 12/12/2022] Open
Abstract
Purpose The present study aims to comprehensively investigate the prognostic value of a radiomic nomogram that integrates contrast-enhanced computed tomography (CECT) radiomic signature and clinicopathological parameters in kidney renal clear cell carcinoma (KIRC). Methods A total of 136 and 78 KIRC patients from the training and validation cohorts were included in the retrospective study. The intraclass correlation coefficient (ICC) was used to assess reproducibility of radiomic feature extraction. Univariate Cox analysis and least absolute shrinkage and selection operator (LASSO) as well as multivariate Cox analysis were utilized to construct radiomic signature and clinical signature in the training cohort. A prognostic nomogram was established containing a radiomic signature and clinicopathological parameters by using a multivariate Cox analysis. The predictive ability of the nomogram [relative operating characteristic curve (ROC), concordance index (C-index), Hosmer–Lemeshow test, and calibration curve] was evaluated in the training cohort and validated in the validation cohort. Patients were split into high- and low-risk groups, and the Kaplan–Meier (KM) method was conducted to identify the forecasting ability of the established models. In addition, genes related with the radiomic risk score were determined by weighted correlation network analysis (WGCNA) and were used to conduct functional analysis. Results A total of 2,944 radiomic features were acquired from the tumor volumes of interest (VOIs) of CECT images. The radiomic signature, including ten selected features, and the clinical signature, including three selected clinical variables, showed good performance in the training and validation cohorts [area under the curve (AUC), 0.897 and 0.712 for the radiomic signature; 0.827 and 0.822 for the clinical signature, respectively]. The radiomic prognostic nomogram showed favorable performance and calibration in the training cohort (AUC, 0.896, C-index, 0.846), which was verified in the validation cohort (AUC, 0.768). KM curves indicated that the progression-free interval (PFI) time was dramatically shorter in the high-risk group than in the low-risk group. The functional analysis indicated that radiomic signature was significantly associated with T cell activation. Conclusions The nomogram combined with CECT radiomic and clinicopathological signatures exhibits excellent power in predicting the PFI of KIRC patients, which may aid in clinical management and prognostic evaluation of cancer patients.
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Affiliation(s)
- Ruizhi Gao
- Department of Medical Ultrasound, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Hui Qin
- Department of Medical Ultrasound, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Peng Lin
- Department of Medical Ultrasound, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Chenjun Ma
- Department of Urology, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Chengyang Li
- Department of Urology, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Rong Wen
- Department of Medical Ultrasound, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Jing Huang
- Department of Medical Ultrasound, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Da Wan
- Department of Medical Ultrasound, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Dongyue Wen
- Department of Medical Ultrasound, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Yiqiong Liang
- Department of Radiology, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Jiang Huang
- Department of Radiology, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Xin Li
- GE Healthcare Global Research, GE, Shanghai, China
| | - Xinrong Wang
- GE Healthcare Global Research, GE, Shanghai, China
| | - Gang Chen
- Department of Pathology, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Yun He
- Department of Medical Ultrasound, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Hong Yang
- Department of Medical Ultrasound, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
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Radiomics models based on enhanced computed tomography to distinguish clear cell from non-clear cell renal cell carcinomas. Sci Rep 2021; 11:13729. [PMID: 34215760 PMCID: PMC8253856 DOI: 10.1038/s41598-021-93069-z] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2020] [Accepted: 06/17/2021] [Indexed: 12/17/2022] Open
Abstract
This study was to assess the effect of the predictive model for distinguishing clear cell RCC (ccRCC) from non-clear cell RCC (non-ccRCC) by establishing predictive radiomic models based on enhanced-computed tomography (CT) images of renal cell carcinoma (RCC). A total of 190 cases with RCC confirmed by pathology were retrospectively analyzed, with the patients being randomly divided into two groups, including the training set and testing set according to the ratio of 7:3. A total of 396 radiomic features were computationally obtained and analyzed with the Correlation between features, Univariate Logistics and Multivariate Logistics. Finally, 4 features were selected, and three machine models (Random Forest (RF), Support Vector Machine (SVM) and Logistic Regression (LR)) were established to discriminate RCC subtypes. The radiomics performance was compared with that of radiologist diagnosis. In the testing set, the RF model had an area under the curve (AUC) value of 0.909, a sensitivity of 0.956, and a specificity of 0.538. The SVM model had an AUC value of 0.841, a sensitivity of 1.0, and a specificity of 0.231, in the testing set. The LR model had an AUC value of 0.906, a sensitivity of 0.956, and a specificity of 0.692, in the testing set. The sensitivity and specificity of radiologist diagnosis to differentiate ccRCC from non-ccRCC were 0.850 and 0.581, respectively, with the AUC value of the radiologist diagnosis as 0.69. In conclusion, radiomics models based on CT imaging data show promise for augmenting radiological diagnosis in renal cancer, especially for differentiating ccRCC from non-ccRCC.
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CT-based peritumoral radiomics signatures for malignancy grading of clear cell renal cell carcinoma. Abdom Radiol (NY) 2021; 46:2690-2698. [PMID: 33427908 DOI: 10.1007/s00261-020-02890-z] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2020] [Revised: 11/26/2020] [Accepted: 11/28/2020] [Indexed: 10/22/2022]
Abstract
OBJECTIVE To evaluate the efficiency of CT-based peritumoral radiomics signatures of clear cell renal cell carcinoma (ccRCC) for malignancy grading in preoperative prediction. MATERIALS AND METHODS 203 patients with pathologically confirmed as ccRCC were retrospectively enrolled in this study. All patients were categorized into training set (n = 122) and validation set (n = 81). For each patient, two types of volumes of interest (VOI) were masked on CT images. One type of VOIs was defined as the tumor mass volume (TMV), which was masked by radiologists delineating the outline of all contiguous slices of the entire tumor, while the other type defined as the peritumoral tumor volume (PTV), which was automatically created by an image morphological method. 1760 radiomics features were calculated from each VOI, and then the discriminative radiomics features were selected by Pearson correlation analysis for reproducibility and redundancy. These selected features were investigated their validity for building radiomics signatures by mRMR feature ranking method. Finally, the top ranked features, which were used as radiomics signatures, were input into a classifier for malignancy grading. The prediction performance was evaluated by receiver operating characteristic (ROC) curve in an independent validation cohort. RESULTS The radiomics signatures of PTV showed a better performance on malignancy grade prediction of ccRCC with AUC of 0.807 (95% CI 0.800-0.834) in train data and 0.848 (95% CI 0.760-0.936) in validation data, while the radiomics signatures of TMV with AUC of 0.773 (95% CI 0.744-0.802) in train data and 0.810 (95% CI 0.706-0.914) in validation data. CONCLUSION The CT-based peritumoral radiomics signature is a potential way to be used as a noninvasive tool to preoperatively predict the malignancy grades of ccRCC.
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Pei X, Wang P, Ren JL, Yin XP, Ma LY, Wang Y, Ma X, Gao BL. Comparison of Different Machine Models Based on Contrast-Enhanced Computed Tomography Radiomic Features to Differentiate High From Low Grade Clear Cell Renal Cell Carcinomas. Front Oncol 2021; 11:659969. [PMID: 34123817 PMCID: PMC8187849 DOI: 10.3389/fonc.2021.659969] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2021] [Accepted: 04/28/2021] [Indexed: 01/03/2023] Open
Abstract
Purpose This study was to investigate the role of different radiomics models with enhanced computed tomography (CT) scan in differentiating low from high grade renal clear cell carcinomas. Materials and Methods CT data of 190 cases with pathologically confirmed renal cell carcinomas were collected and divided into the training set and testing set according to different time periods, with 122 cases in the training set and 68 cases in the testing set. The region of interest (ROI) was delineated layer by layer. Results A total of 402 radiomics features were extracted for analysis. Six of the radiomic parameters were deemed very valuable by univariate analysis, rank sum test, LASSO cross validation and correlation analysis. From these six features, multivariate logistic regression model, support vector machine (SVM), and decision tree model were established for analysis. The performance of each model was evaluated by AUC value on the ROC curve and decision curve analysis (DCA). Among the three prediction models, the SVM model showed a high predictive efficiency. The AUC values of the training set and the testing set were 0.84 and 0.83, respectively, which were significantly higher than those of the decision tree model and the multivariate logistic regression model. The DCA revealed a better predictive performance in the SVM model that possessed the highest degree of coincidence. Conclusion Radiomics analysis using the SVM radiomics model has highly efficiency in discriminating high- and low-grade clear cell renal cell carcinomas.
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Affiliation(s)
- Xu Pei
- CT/MRI Room, Affiliated Hospital of Hebei University, Baoding, China
| | - Ping Wang
- CT/MRI Room, Affiliated Hospital of Hebei University, Baoding, China
| | - Jia-Liang Ren
- Department of Pharmaceutical Diagnostics, GE Healthcare China (Shanghai) Co Ltd., Shanghai, China
| | - Xiao-Ping Yin
- CT/MRI Room, Affiliated Hospital of Hebei University, Baoding, China.,Key Laboratory of Cancer Radiotherapy and Chemotherapy Mechanism and Regulations, Baoding, China
| | - Lu-Yao Ma
- CT/MRI Room, Affiliated Hospital of Hebei University, Baoding, China
| | - Yun Wang
- CT/MRI Room, Affiliated Hospital of Hebei University, Baoding, China
| | - Xi Ma
- CT/MRI Room, Affiliated Hospital of Hebei University, Baoding, China
| | - Bu-Lang Gao
- CT/MRI Room, Affiliated Hospital of Hebei University, Baoding, China
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Tsili AC, Andriotis E, Gkeli MG, Krokidis M, Stasinopoulou M, Varkarakis IM, Moulopoulos LA. The role of imaging in the management of renal masses. Eur J Radiol 2021; 141:109777. [PMID: 34020173 DOI: 10.1016/j.ejrad.2021.109777] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2021] [Revised: 05/09/2021] [Accepted: 05/14/2021] [Indexed: 12/26/2022]
Abstract
The wide availability of cross-sectional imaging is responsible for the increased detection of small, usually asymptomatic renal masses. More than 50 % of renal cell carcinomas (RCCs) represent incidental findings on noninvasive imaging. Multimodality imaging, including conventional US, contrast-enhanced US (CEUS), CT and multiparametric MRI (mpMRI) is pivotal in diagnosing and characterizing a renal mass, but also provides information regarding its prognosis, therapeutic management, and follow-up. In this review, imaging data for renal masses that urologists need for accurate treatment planning will be discussed. The role of US, CEUS, CT and mpMRI in the detection and characterization of renal masses, RCC staging and follow-up of surgically treated or untreated localized RCC will be presented. The role of percutaneous image-guided ablation in the management of RCC will be also reviewed.
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Affiliation(s)
- Athina C Tsili
- Department of Clinical Radiology, School of Health Sciences, Faculty of Medicine, University of Ioannina, 45110, Ioannina, Greece.
| | - Efthimios Andriotis
- Department of Newer Imaging Methods of Tomography, General Anti-Cancer Hospital Agios Savvas, 11522, Athens, Greece.
| | - Myrsini G Gkeli
- 1st Department of Radiology, General Anti-Cancer Hospital Agios Savvas, 11522, Athens, Greece.
| | - Miltiadis Krokidis
- 1st Department of Radiology, School of Medicine, National and Kapodistrian University of Athens, Areteion Hospital, 11528, Athens, Greece; Department of Diagnostic, Interventional and Pediatric Radiology, Inselspital Bern University Hospital, University of Bern, 3010, Bern, Switzerland.
| | - Myrsini Stasinopoulou
- Department of Newer Imaging Methods of Tomography, General Anti-Cancer Hospital Agios Savvas, 11522, Athens, Greece.
| | - Ioannis M Varkarakis
- 2nd Department of Urology, National and Kapodistrian University of Athens, Sismanoglio Hospital, 15126, Athens, Greece.
| | - Lia-Angela Moulopoulos
- 1st Department of Radiology, School of Medicine, National and Kapodistrian University of Athens, Areteion Hospital, 11528, Athens, Greece.
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Han D, Yu Y, He T, Yu N, Dang S, Wu H, Ren J, Duan X. Effect of radiomics from different virtual monochromatic images in dual-energy spectral CT on the WHO/ISUP classification of clear cell renal cell carcinoma. Clin Radiol 2021; 76:627.e23-627.e29. [PMID: 33985770 DOI: 10.1016/j.crad.2021.02.033] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2020] [Accepted: 02/10/2021] [Indexed: 12/24/2022]
Abstract
AIM To investigate the effect of radiomics obtained from different virtual monochromatic images (VMIs) in dual-energy spectral computed tomography (CT) on the World Health Organization/International Association for Urological Pathology (WHO/ISUP) classification of clear cell renal cell carcinoma (ccRCC). MATERIALS AND METHODS A retrospective study of 99 ccRCC patients who underwent contrast-enhanced dual-energy CT was undertaken. ccRCC was confirmed at surgery or biopsy and graded according to the WHO/ISUP pathological grading criteria as low grade (n=68, grade I and II) or high grade (n=31, grade III and IV). Radiomics risk scores (RRSs) for differentiating high and low grades of ccRCC were constructed from 11 sets of VMI in (40-140 keV, 10 keV interval) the cortical phase. Receiver operating characteristic (ROC) curves were drawn and the area under the curves (AUCs) was calculated to evaluate the discriminatory power of RRS for each VMI. The Hosmer-Lemeshow test was used to evaluate the goodness-of-fit of each model and the decision curve was used to analyse its net benefit to patients. RESULTS The AUC values for distinguishing low-from high-grade ccRCC with RRS of 40-140 keV VMIs were all >0.920. The Hosmer-Lemeshow test showed that the p-values of RRS of VMIs were >0.05, suggesting good fits. In the decision curve analysis, RRS from the 40-140 keV VMIs had similar decision curves and provided better net benefits than considering all patients either as high-grade or low-grade. CONCLUSIONS The RRS obtained from multiple VMIs in dual-energy spectral CT have high diagnostic efficiencies for distinguishing between low- and high-grade ccRCC with no significant differences between different VMIs.
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Affiliation(s)
- D Han
- Department of Medical Image, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Y Yu
- Department of Radiology, Affiliated Hospital of Shaanxi University of Chinese Medicine, Xianyang, Shaanxi, China
| | - T He
- Department of Radiology, Affiliated Hospital of Shaanxi University of Chinese Medicine, Xianyang, Shaanxi, China
| | - N Yu
- Department of Radiology, Affiliated Hospital of Shaanxi University of Chinese Medicine, Xianyang, Shaanxi, China
| | - S Dang
- Department of Medical Image, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - H Wu
- Pathology Department, Affiliated Hospital of Shaanxi University of Chinese Medicine, Xianyang, Shaanxi, China
| | - J Ren
- GE Healthcare China, Beijing, China
| | - X Duan
- Department of Medical Image, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China.
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Xiao F, Sun R, Sun W, Xu D, Lan L, Li H, Liu H, Xu H. Radiomics analysis of chest CT to predict the overall survival for the severe patients of COVID-19 pneumonia. Phys Med Biol 2021; 66. [PMID: 33845467 DOI: 10.1088/1361-6560/abf717] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2021] [Accepted: 04/12/2021] [Indexed: 01/12/2023]
Abstract
Personalized assessment and treatment of severe patients with COVID-19 pneumonia have greatly affected the prognosis and survival of these patients. This study aimed to develop the radiomics models as the potential biomarkers to estimate the overall survival (OS) for the COVID-19 severe patients. A total of 74 COVID-19 severe patients were enrolled in this study, and 30 of them died during the follow-up period. First, the clinical risk factors of the patients were analyzed. Then, two radiomics signatures were constructed based on two segmented volumes of interest of whole lung area and lesion area. Two combination models were built depend on whether the clinic risk factors were used and/or whether two radiomics signatures were combined. Kaplan-Meier analysis were performed for validating two radiomics signatures and C-index was used to evaluated the predictive performance of all radiomics signatures and combination models. Finally, a radiomics nomogram combining radiomics signatures with clinical risk factors was developed for predicting personalized OS, and then assessed with respect to the calibration curve. Three clinical risk factors were found, included age, malignancy and highest temperature that influence OS. Both two radiomics signatures could effectively stratify the risk of OS in COVID-19 severe patients. The predictive performance of the combination model with two radiomics signatures was better than that only one radiomics signature was used, and became better when three clinical risk factors were interpolated. Calibration curves showed good agreement in both 15 d survival and 30 d survival between the estimation with the constructed nomogram and actual observation. Both two constructed radiomics signatures can act as the potential biomarkers for risk stratification of OS in COVID-19 severe patients. The radiomics+clinical nomogram generated might serve as a potential tool to guide personalized treatment and care for these patients.
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Affiliation(s)
- Feng Xiao
- Department of Radiology, Zhongnan Hospital of Wuhan University, Wuhan, 430071, Hubei, People's Republic of China
| | - Rongqing Sun
- Department of Radiology, Zhongnan Hospital of Wuhan University, Wuhan, 430071, Hubei, People's Republic of China
| | - Wenbo Sun
- Department of Radiology, Zhongnan Hospital of Wuhan University, Wuhan, 430071, Hubei, People's Republic of China
| | - Dan Xu
- Department of Radiology, Zhongnan Hospital of Wuhan University, Wuhan, 430071, Hubei, People's Republic of China
| | - Lan Lan
- Department of Radiology, Zhongnan Hospital of Wuhan University, Wuhan, 430071, Hubei, People's Republic of China
| | - Huan Li
- Department of Radiology, Zhongnan Hospital of Wuhan University, Wuhan, 430071, Hubei, People's Republic of China
| | - Huan Liu
- GE Healthcare, Shanghai, 201203, People's Republic of China
| | - Haibo Xu
- Department of Radiology, Zhongnan Hospital of Wuhan University, Wuhan, 430071, Hubei, People's Republic of China
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50
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Hussain MA, Hamarneh G, Garbi R. Learnable image histograms-based deep radiomics for renal cell carcinoma grading and staging. Comput Med Imaging Graph 2021; 90:101924. [PMID: 33895621 DOI: 10.1016/j.compmedimag.2021.101924] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2020] [Revised: 01/07/2021] [Accepted: 04/05/2021] [Indexed: 12/11/2022]
Abstract
Fuhrman cancer grading and tumor-node-metastasis (TNM) cancer staging systems are typically used by clinicians in the treatment planning of renal cell carcinoma (RCC), a common cancer in men and women worldwide. Pathologists typically use percutaneous renal biopsy for RCC grading, while staging is performed by volumetric medical image analysis before renal surgery. Recent studies suggest that clinicians can effectively perform these classification tasks non-invasively by analyzing image texture features of RCC from computed tomography (CT) data. However, image feature identification for RCC grading and staging often relies on laborious manual processes, which is error prone and time-intensive. To address this challenge, this paper proposes a learnable image histogram in the deep neural network framework that can learn task-specific image histograms with variable bin centers and widths. The proposed approach enables learning statistical context features from raw medical data, which cannot be performed by a conventional convolutional neural network (CNN). The linear basis function of our learnable image histogram is piece-wise differentiable, enabling back-propagating errors to update the variable bin centers and widths during training. This novel approach can segregate the CT textures of an RCC in different intensity spectra, which enables efficient Fuhrman low (I/II) and high (III/IV) grading as well as RCC low (I/II) and high (III/IV) staging. The proposed method is validated on a clinical CT dataset of 159 patients from The Cancer Imaging Archive (TCIA) database, and it demonstrates 80% and 83% accuracy in RCC grading and staging, respectively.
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Affiliation(s)
| | - Ghassan Hamarneh
- Medical Image Analysis Lab, Simon Fraser University, Burnaby, BC V5A 1S6, Canada.
| | - Rafeef Garbi
- BiSICL, University of British Columbia, Vancouver, BC V6T 1Z4, Canada.
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