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Virarkar M, Daoud T, Sun J, Montanarella M, Menendez-Santos M, Mahmoud H, Saleh M, Bhosale P. MRI Radiomics Data Analysis for Differentiation between Malignant Mixed Müllerian Tumors and Endometrial Carcinoma. Cancers (Basel) 2024; 16:2647. [PMID: 39123375 PMCID: PMC11312193 DOI: 10.3390/cancers16152647] [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: 06/22/2024] [Revised: 07/17/2024] [Accepted: 07/23/2024] [Indexed: 08/12/2024] Open
Abstract
The objective of this study was to compare the quantitative radiomics data between malignant mixed Müllerian tumors (MMMTs) and endometrial carcinoma (EC) and identify texture features associated with overall survival (OS). This study included 61 patients (36 with EC and 25 with MMMTs) and analyzed various radiomic features and gray-level co-occurrence matrix (GLCM) features. These variables and patient clinicopathologic characteristics were compared between EC and MMMTs using the Wilcoxon Rank sum and Fisher's exact test. The area under the curve of the receiving operating characteristics (AUC ROC) was calculated for univariate analysis in predicting EC status. Logistic regression with elastic net regularization was performed for texture feature selection. This study showed that skewness (p = 0.045) and tumor volume (p = 0.007) significantly differed between EC and MMMTs. The range of cluster shade, the angular variance of cluster shade, and the range of the sum of squares variance were significant predictors of EC status (p ≤ 0.05). The regularized Cox regression analysis identified the "256 Angular Variance of Energy" texture feature as significantly associated with OS independently of the EC/MMMT grouping (p = 0.004). The volume and texture features of the tumor region may help distinguish between EC and MMMTs and predict patient outcomes.
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Affiliation(s)
- Mayur Virarkar
- Department of Radiology, University of Florida College of Medicine-Jacksonville, Jacksonville, FL 32209, USA; (M.V.); (M.M.)
| | - Taher Daoud
- Department of Radiology, University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA; (T.D.); (J.S.); (H.M.); (M.S.); (P.B.)
| | - Jia Sun
- Department of Radiology, University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA; (T.D.); (J.S.); (H.M.); (M.S.); (P.B.)
| | - Matthew Montanarella
- Department of Radiology, University of Florida College of Medicine-Jacksonville, Jacksonville, FL 32209, USA; (M.V.); (M.M.)
| | - Manuel Menendez-Santos
- Department of Radiology, University of Florida College of Medicine-Jacksonville, Jacksonville, FL 32209, USA; (M.V.); (M.M.)
| | - Hagar Mahmoud
- Department of Radiology, University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA; (T.D.); (J.S.); (H.M.); (M.S.); (P.B.)
| | - Mohammed Saleh
- Department of Radiology, University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA; (T.D.); (J.S.); (H.M.); (M.S.); (P.B.)
| | - Priya Bhosale
- Department of Radiology, University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA; (T.D.); (J.S.); (H.M.); (M.S.); (P.B.)
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Wang Y, Liu W, Chen Z, Zang Y, Xu L, Dai Z, Zhou Y, Zhu J. A noninvasive method for predicting clinically significant prostate cancer using magnetic resonance imaging combined with PRKY promoter methylation level: a machine learning study. BMC Med Imaging 2024; 24:60. [PMID: 38468226 DOI: 10.1186/s12880-024-01236-1] [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: 12/18/2023] [Accepted: 02/29/2024] [Indexed: 03/13/2024] Open
Abstract
BACKGROUND Traditional process for clinically significant prostate cancer (csPCA) diagnosis relies on invasive biopsy and may bring pain and complications. Radiomic features of magnetic resonance imaging MRI and methylation of the PRKY promoter were found to be associated with prostate cancer. METHODS Fifty-four Patients who underwent prostate biopsy or photoselective vaporization of the prostate (PVP) from 2022 to 2023 were selected for this study, and their clinical data, blood samples and MRI images were obtained before the operation. Methylation level of two PRKY promoter sites, cg05618150 and cg05163709, were tested through bisulfite sequencing PCR (BSP). The PI-RADS score of each patient was estimated and the region of interest (ROI) was delineated by 2 experienced radiologists. After being extracted by a plug-in of 3D-slicer, radiomic features were selected through LASSCO regression and t-test. Selected radiomic features, methylation levels and clinical data were used for model construction through the random forest (RF) algorithm, and the predictive efficiency was analyzed by the area under the receiver operation characteristic (ROC) curve (AUC). RESULTS Methylation level of the site, cg05618150, was observed to be associated with prostate cancer, for which the AUC was 0.74. The AUC of T2WI in csPCA prediction was 0.84, which was higher than that of the apparent diffusion coefficient ADC (AUC = 0.81). The model combined with T2WI and clinical data reached an AUC of 0.94. The AUC of the T2WI-clinic-methylation-combined model was 0.97, which was greater than that of the model combined with the PI-RADS score, clinical data and PRKY promoter methylation levels (AUC = 0.86). CONCLUSIONS The model combining with radiomic features, clinical data and PRKY promoter methylation levels based on machine learning had high predictive efficiency in csPCA diagnosis.
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Affiliation(s)
- Yufei Wang
- Department of Urology, The Second Affiliated Hospital of Soochow University, Suzhou, Jiangsu Province, 215000, China
| | - Weifeng Liu
- Department of Urology, The Second Affiliated Hospital of Soochow University, Suzhou, Jiangsu Province, 215000, China
| | - Zeyu Chen
- Department of Urology, The Second Affiliated Hospital of Soochow University, Suzhou, Jiangsu Province, 215000, China
| | - Yachen Zang
- Department of Urology, The Second Affiliated Hospital of Soochow University, Suzhou, Jiangsu Province, 215000, China
| | - Lijun Xu
- Department of Urology, The Second Affiliated Hospital of Soochow University, Suzhou, Jiangsu Province, 215000, China
| | - Zheng Dai
- Department of Urology, Hefei First People's Hopital, Hefei, Anhui Province, 230000, China.
| | - Yibin Zhou
- Department of Urology, The Second Affiliated Hospital of Soochow University, Suzhou, Jiangsu Province, 215000, China.
| | - Jin Zhu
- Department of Urology, The Second Affiliated Hospital of Soochow University, Suzhou, Jiangsu Province, 215000, China.
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Pan L, Chen M, Sun J, Jin P, Ding J, Cai P, Chen J, Xing W. Prediction of Fuhrman grade of renal clear cell carcinoma by multimodal MRI radiomics: a retrospective study. Clin Radiol 2024; 79:e273-e281. [PMID: 38065776 DOI: 10.1016/j.crad.2023.11.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2023] [Revised: 10/16/2023] [Accepted: 11/05/2023] [Indexed: 01/02/2024]
Abstract
AIM To explore the value of multimodal magnetic resonance imaging (MRI) radiomics combined with traditional radiologist-defined semantic characteristics and conventional (cMRI) and functional MRI (fMRI) texture features in predicting Fuhrman grade of clear cell renal cell carcinoma (ccRCC). MATERIALS AND METHODS The data of 89 patients with histopathologically proven ccRCC (low-grade, 54; high-grade, 35) were collected. Texture features were extracted from cMRI (T1- and T2-weighted imaging) and fMRI (Dixon-MRI; blood-oxygen-level dependent [BOLD]-MRI; and susceptibility-weighted imaging [SWI]) images, and the traditional characteristics (TC) were evaluated. Logistic regression analysis was performed to develop models based on TC, cMRI, and fMRI texture features for grading. Receiver operating characteristic (ROC) curve analysis and leave-group-out cross-validation (LGOCV) were performed to test the reliability of combined models. RESULTS Two T2-weighted imaging-based, two Dixon_W-based, one Dixon_F-based, one BOLD-based, and three SWI-based texture features, and three TC were extracted for feature selection. TC, cMRI, fMRI, cMRI+fMRI, cMRI+TC, fMRI+TC, and cMRI+fMRI+TC models were constructed. The AUC of the cMRI+fMRI+TC model for differentiating high- from low-grade ccRCC was 0.74, with 81.42% accuracy, 75.93% sensitivity, and 91.43% specificity. The fMRI+TC model exhibited a performance similar to that of the cMRI+fMRI+TC model (p>0.05). The areas under the curve (AUCs) of the fMRI+TC and cMRI+fMRI+TC models were significantly higher than those of the other five models (all p<0.05). For the cMRI+fMRI+TC model, the mean accuracy was 85.40% after 100 LGOCV for the test sets. CONCLUSION Multimodal MRI radiomics combined with TC, cMRI, and fMRI texture features may be a reliable quantitative approach for differentiating high-grade ccRCC from low-grade ccRCC.
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Affiliation(s)
- L Pan
- Department of Radiology, Third Affiliated Hospital of Soochow University, Changzhou 213003, Jiangsu, China
| | - M Chen
- Department of Radiology, Third Affiliated Hospital of Soochow University, Changzhou 213003, Jiangsu, China
| | - J Sun
- Department of Radiology, Third Affiliated Hospital of Soochow University, Changzhou 213003, Jiangsu, China
| | - P Jin
- Department of Public Health, Nanjing Medical University, Nanjing 211166, Jiangsu, China
| | - J Ding
- Department of Radiology, Third Affiliated Hospital of Soochow University, Changzhou 213003, Jiangsu, China
| | - P Cai
- Department of Radiology, Third Affiliated Hospital of Soochow University, Changzhou 213003, Jiangsu, China
| | - J Chen
- Department of Radiology, Third Affiliated Hospital of Soochow University, Changzhou 213003, Jiangsu, China.
| | - W Xing
- Department of Radiology, Third Affiliated Hospital of Soochow University, Changzhou 213003, Jiangsu, China.
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Wu M, Zhang JL. MR Perfusion Imaging for Kidney Disease. Magn Reson Imaging Clin N Am 2024; 32:161-170. [PMID: 38007278 DOI: 10.1016/j.mric.2023.09.004] [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: 11/27/2023]
Abstract
Renal perfusion reflects overall function of a kidney. As an important indicator of kidney diseases, renal perfusion can be noninvasively measured by multiple methods of MR imaging, such as dynamic contrast-enhanced MR imaging, intravoxel incoherent motion analysis, and arterial spin labeling method. In this article we introduce the principle of the methods, review their recent technical improvements, and then focus on summarizing recent applications of the methods in assessing various renal diseases. By this review, we demonstrate the capability and clinical potential of the imaging methods, with the hope of accelerating their adoption to clinical practice.
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Affiliation(s)
- Mingyan Wu
- Central Research Institute, UIH Group, Shanghai, China; School of Biomedical Engineering Building, Room 409, 393 Huaxia Middle Road, Shanghai 201210, China
| | - Jeff L Zhang
- School of Biomedical Engineering, ShanghaiTech University, Room 409, School of Biomedical Engineering Building, 393 Huaxia Middle Road, Shanghai 201210, China.
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Wang S, Zhu C, Jin Y, Yu H, Wu L, Zhang A, Wang B, Zhai J. A multi-model based on radiogenomics and deep learning techniques associated with histological grade and survival in clear cell renal cell carcinoma. Insights Imaging 2023; 14:207. [PMID: 38010567 PMCID: PMC10682311 DOI: 10.1186/s13244-023-01557-9] [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: 08/29/2023] [Accepted: 11/01/2023] [Indexed: 11/29/2023] Open
Abstract
OBJECTIVES This study aims to evaluate the efficacy of multi-model incorporated by radiomics, deep learning, and transcriptomics features for predicting pathological grade and survival in patients with clear cell renal cell carcinoma (ccRCC). METHODS In this study, data were collected from 177 ccRCC patients, including radiomics features, deep learning (DL) features, and RNA sequencing data. Diagnostic models were then created using these data through least absolute shrinkage and selection operator (LASSO) analysis. Additionally, a multi-model was developed by combining radiomics, DL, and transcriptomics features. The prognostic performance of the multi-model was evaluated based on progression-free survival (PFS) and overall survival (OS) outcomes, assessed using Harrell's concordance index (C-index). Furthermore, we conducted an analysis to investigate the relationship between the multi-model and immune cell infiltration. RESULTS The multi-model demonstrated favorable performance in discriminating pathological grade, with area under the ROC curve (AUC) values of 0.946 (95% CI: 0.912-0.980) and 0.864 (95% CI: 0.734-0.994) in the training and testing cohorts, respectively. Additionally, it exhibited statistically significant prognostic performance for predicting PFS and OS. Furthermore, the high-grade group displayed a higher abundance of immune cells compared to the low-grade group. CONCLUSIONS The multi-model incorporated radiomics, DL, and transcriptomics features demonstrated promising performance in predicting pathological grade and prognosis in patients with ccRCC. CRITICAL RELEVANCE STATEMENT We developed a multi-model to predict the grade and survival in clear cell renal cell carcinoma and explored the molecular biological significance of the multi-model of different histological grades. KEY POINTS 1. The multi-model achieved an AUC of 0.864 for assessing pathological grade. 2. The multi-model exhibited an association with survival in ccRCC patients. 3. The high-grade group demonstrated a greater abundance of immune cells.
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Affiliation(s)
- Shihui Wang
- Department of Radiology, The First Affiliated Hospital of Wannan Medical College, Wuhu, People's Republic of China
| | - Chao Zhu
- Department of Radiology, The First Affiliated Hospital of Wannan Medical College, Wuhu, People's Republic of China
| | - Yidong Jin
- School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, People's Republic of China
| | - Hongqing Yu
- Department of Radiology, The First Affiliated Hospital of Wannan Medical College, Wuhu, People's Republic of China
| | - Lili Wu
- Department of Radiology, The First Affiliated Hospital of Wannan Medical College, Wuhu, People's Republic of China
| | - Aijuan Zhang
- Department of Radiology, The First Affiliated Hospital of Wannan Medical College, Wuhu, People's Republic of China
| | - Beibei Wang
- Department of Radiology, The First Affiliated Hospital of Wannan Medical College, Wuhu, People's Republic of China
| | - Jian Zhai
- Department of Radiology, The First Affiliated Hospital of Wannan Medical College, Wuhu, People's Republic of China.
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Chartier S, Arif-Tiwari H. MR Virtual Biopsy of Solid Renal Masses: An Algorithmic Approach. Cancers (Basel) 2023; 15:2799. [PMID: 37345136 DOI: 10.3390/cancers15102799] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2023] [Revised: 05/12/2023] [Accepted: 05/12/2023] [Indexed: 06/23/2023] Open
Abstract
Between 1983 and 2002, the incidence of solid renal tumors increased from 7.1 to 10.8 cases per 100,000. This is in large part due to the increase in the volume of ultrasound and cross-sectional imaging, although a majority of solid renal tumors are still found incidentally. Ultrasound and computed tomography (CT) have been the mainstay of renal mass screening and diagnosis but recent advances in magnetic resonance (MR) technology have made this the optimal choice when diagnosing and staging renal tumors. Our purpose in writing this review is to survey the modern MR imaging approach to benign and malignant solid renal tumors, consolidate the various imaging findings into an easy-to-read reference, and provide an imaging-based, algorithmic approach to renal mass characterization for clinicians. MR is at the forefront of renal mass characterization, surpassing ultrasound and CT in its ability to describe multiple tissue parameters and predict tumor biology. Cutting-edge MR protocols and the integration of diagnostic algorithms can improve patient outcomes, allowing the imager to narrow the differential and better guide oncologic and surgical management.
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Affiliation(s)
- Stephane Chartier
- Department of Medical Imaging, College of Medicine, The University of Arizona, Tucson, AZ 85724, USA
| | - Hina Arif-Tiwari
- Department of Medical Imaging, College of Medicine, The University of Arizona, Tucson, AZ 85724, USA
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van der Beek JN, Artunduaga M, Schenk JP, Eklund MJ, Smith EA, Lederman HM, Warwick AB, Littooij AS, Khanna G. Similarities and controversies in imaging of pediatric renal tumors: A SIOP-RTSG and COG collaboration. Pediatr Blood Cancer 2022; 70 Suppl 2:e30080. [PMID: 36349564 DOI: 10.1002/pbc.30080] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/19/2022] [Revised: 10/03/2022] [Accepted: 10/11/2022] [Indexed: 11/11/2022]
Abstract
Malignant renal tumors are rare in children, and Wilms tumors (WTs) are the most common subtype. Imaging plays an essential role in the diagnosis, staging, and follow-up of these patients. Initial workup for staging is mainly performed by cross-sectional imaging modalities such as computed tomography (CT) and magnetic resonance imaging (MRI). Imaging approach within the two core international groups, the Children's Oncology Group (COG, North America) and the International Society of Pediatric Oncology - Renal Tumor Study Group (SIOP-RTSG, Europe), differs. Whereas abdominal ultrasound (US) is used for the initial diagnosis of a suspected pediatric renal tumor globally, COG protocols support the use of CT or MRI for locoregional staging, contrary to the preference for MRI over CT for abdominopelvic evaluation within the SIOP-RTSG. The purpose of this manuscript is to summarize current imaging approaches, highlighting differences and similarities within these core international groups, while focusing on future innovative efforts and collaboration within the HARMONICA initiative.
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Affiliation(s)
- Justine N van der Beek
- Department of Radiology and Nuclear Medicine, University Medical Center Utrecht/Wilhelmina Children's Hospital, Utrecht University, Utrecht, The Netherlands.,Princess Máxima Center for Pediatric Oncology, Utrecht, The Netherlands
| | - Maddy Artunduaga
- Pediatric Radiology Division, Department of Radiology, University of Texas Southwestern Medical Center, Children's Health Medical Center, Dallas, Texas, USA
| | - Jens-Peter Schenk
- Clinic of Diagnostic and Interventional Radiology, Division of Pediatric Radiology, Heidelberg University Hospital, Heidelberg, Germany
| | - Meryle J Eklund
- Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, South Carolina, USA
| | - Ethan A Smith
- Department of Radiology, University of Cincinnati College of Medicine, Cincinnati, Ohio, USA.,Department of Radiology, Cincinnati Children's Hospital, Cincinnati, Ohio, USA
| | - Henrique M Lederman
- Department of Diagnostic Imaging, Escola Paulista de Medicina, UNIFESP, São Paulo, Brazil
| | - Anne B Warwick
- Department of Pediatrics, F. Edward Hébert School of Medicine, Uniformed Services University, Bethesda, Maryland, USA
| | - Annemieke S Littooij
- Department of Radiology and Nuclear Medicine, University Medical Center Utrecht/Wilhelmina Children's Hospital, Utrecht University, Utrecht, The Netherlands.,Princess Máxima Center for Pediatric Oncology, Utrecht, The Netherlands
| | - Geetika Khanna
- Department of Radiology & Imaging Sciences, Emory University, Children's Healthcare of Atlanta, Atlanta, Georgia, USA
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Dwivedi DK, Jagannathan NR. Emerging MR methods for improved diagnosis of prostate cancer by multiparametric MRI. MAGMA (NEW YORK, N.Y.) 2022; 35:587-608. [PMID: 35867236 DOI: 10.1007/s10334-022-01031-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/04/2021] [Revised: 06/28/2022] [Accepted: 07/08/2022] [Indexed: 06/15/2023]
Abstract
Current challenges of using serum prostate-specific antigen (PSA) level-based screening, such as the increased false positive rate, inability to detect clinically significant prostate cancer (PCa) with random biopsy, multifocality in PCa, and the molecular heterogeneity of PCa, can be addressed by integrating advanced multiparametric MR imaging (mpMRI) approaches into the diagnostic workup of PCa. The standard method for diagnosing PCa is a transrectal ultrasonography (TRUS)-guided systematic prostate biopsy, but it suffers from sampling errors and frequently fails to detect clinically significant PCa. mpMRI not only increases the detection of clinically significant PCa, but it also helps to reduce unnecessary biopsies because of its high negative predictive value. Furthermore, non-Cartesian image acquisition and compressed sensing have resulted in faster MR acquisition with improved signal-to-noise ratio, which can be used in quantitative MRI methods such as dynamic contrast-enhanced (DCE)-MRI. With the growing emphasis on the role of pre-biopsy mpMRI in the evaluation of PCa, there is an increased demand for innovative MRI methods that can improve PCa grading, detect clinically significant PCa, and biopsy guidance. To meet these demands, in addition to routine T1-weighted, T2-weighted, DCE-MRI, diffusion MRI, and MR spectroscopy, several new MR methods such as restriction spectrum imaging, vascular, extracellular, and restricted diffusion for cytometry in tumors (VERDICT) method, hybrid multi-dimensional MRI, luminal water imaging, and MR fingerprinting have been developed for a better characterization of the disease. Further, with the increasing interest in combining MR data with clinical and genomic data, there is a growing interest in utilizing radiomics and radiogenomics approaches. These big data can also be utilized in the development of computer-aided diagnostic tools, including automatic segmentation and the detection of clinically significant PCa using machine learning methods.
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Affiliation(s)
- Durgesh Kumar Dwivedi
- Department of Radiodiagnosis, King George Medical University, Lucknow, UP, 226 003, India.
| | - Naranamangalam R Jagannathan
- Department of Radiology, Chettinad Hospital and Research Institute, Chettinad Academy of Research and Education, Kelambakkam, TN, 603 103, India.
- Department of Radiology, Sri Ramachandra Institute of Higher Education and Research, Chennai, TN, 600 116, India.
- Department of Electrical Engineering, Indian Institute Technology Madras, Chennai, TN, 600 036, India.
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Deep Segmentation Networks for Segmenting Kidneys and Detecting Kidney Stones in Unenhanced Abdominal CT Images. Diagnostics (Basel) 2022; 12:diagnostics12081788. [PMID: 35892498 PMCID: PMC9330428 DOI: 10.3390/diagnostics12081788] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2022] [Revised: 07/20/2022] [Accepted: 07/20/2022] [Indexed: 11/17/2022] Open
Abstract
Recent breakthroughs of deep learning algorithms in medical imaging, automated detection, and segmentation techniques for renal (kidney) in abdominal computed tomography (CT) images have been limited. Radiomics and machine learning analyses of renal diseases rely on the automatic segmentation of kidneys in CT images. Inspired by this, our primary aim is to utilize deep semantic segmentation learning models with a proposed training scheme to achieve precise and accurate segmentation outcomes. Moreover, this work aims to provide the community with an open-source, unenhanced abdominal CT dataset for training and testing the deep learning segmentation networks to segment kidneys and detect kidney stones. Five variations of deep segmentation networks are trained and tested both dependently (based on the proposed training scheme) and independently. Upon comparison, the models trained with the proposed training scheme enable the highly accurate 2D and 3D segmentation of kidneys and kidney stones. We believe this work is a fundamental step toward AI-driven diagnostic strategies, which can be an essential component of personalized patient care and improved decision-making in treating kidney diseases.
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Matsumoto S, Arita Y, Yoshida S, Fukushima H, Kimura K, Yamada I, Tanaka H, Yagi F, Yokoyama M, Matsuoka Y, Oya M, Tateishi U, Jinzaki M, Fujii Y. Utility of radiomics features of diffusion-weighted magnetic resonance imaging for differentiation of fat-poor angiomyolipoma from clear cell renal cell carcinoma: model development and external validation. Abdom Radiol (NY) 2022; 47:2178-2186. [PMID: 35426498 DOI: 10.1007/s00261-022-03486-5] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2021] [Revised: 03/02/2022] [Accepted: 03/04/2022] [Indexed: 11/29/2022]
Abstract
PURPOSE To investigate the utility of radiomics features of diffusion-weighted magnetic resonance imaging (DW-MRI) to differentiate fat-poor angiomyolipoma (fpAML) from clear cell renal cell carcinoma (ccRCC). MATERIALS AND METHODS This multi-institutional study included two cohorts with pathologically confirmed renal tumors: 65 patients with ccRCC and 18 with fpAML in the model development cohort, and 17 with ccRCC and 13 with fpAML in the external validation cohort. All patients underwent magnetic resonance imaging (MRI) including DW-MRI. Radiomics analysis was used to extract 39 imaging features from the apparent diffusion coefficient (ADC) map. The radiomics features were analyzed with unsupervised hierarchical cluster analysis. A random forest (RF) model was used to identify radiomics features important for differentiating fpAML from ccRCC in the development cohort. The diagnostic performance of the RF model was evaluated in the development and validation cohorts. RESULTS The cases in the developmental cohort were classified into three groups with different frequencies of fpAML by cluster analysis of radiomics features. RF analysis of the development cohort showed that the mean ADC value was important for differentiating fpAML from ccRCC, as well as higher-texture features including gray-level run length matrix (GLRLM)_long-run low gray-level enhancement (LRLGE), and GLRLM_low gray-level run emphasis (LGRE). The area under the curve values of the development [0.90, 95% confidence interval (CI) 0.80-1.00] and validation cohorts (0.87, 95% CI 0.74-1.00) were similar (P = 0.91). CONCLUSION The radiomics features of ADC maps are useful for differentiating fpAML from ccRCC.
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Affiliation(s)
- Shunya Matsumoto
- Department of Urology, Tokyo Medical and Dental University, 1-5-45 Yushima, Bunkyo-Ku, Tokyo, 113-8510, Japan
| | - Yuki Arita
- Department of Radiology, Keio University School of Medicine, Tokyo, Japan
| | - Soichiro Yoshida
- Department of Urology, Tokyo Medical and Dental University, 1-5-45 Yushima, Bunkyo-Ku, Tokyo, 113-8510, Japan.
| | - Hiroshi Fukushima
- Department of Urology, Tokyo Medical and Dental University, 1-5-45 Yushima, Bunkyo-Ku, Tokyo, 113-8510, Japan
| | - Koichiro Kimura
- Department of Diagnostic Radiology and Nuclear Medicine, Tokyo Medical and Dental University, Tokyo, Japan
| | - Ichiro Yamada
- Department of Diagnostic Radiology and Nuclear Medicine, Tokyo Medical and Dental University, Tokyo, Japan
| | - Hajime Tanaka
- Department of Urology, Tokyo Medical and Dental University, 1-5-45 Yushima, Bunkyo-Ku, Tokyo, 113-8510, Japan
| | - Fumiko Yagi
- Department of Radiology, Keio University School of Medicine, Tokyo, Japan
| | - Minato Yokoyama
- Department of Urology, Tokyo Medical and Dental University, 1-5-45 Yushima, Bunkyo-Ku, Tokyo, 113-8510, Japan
| | - Yoh Matsuoka
- Department of Urology, Tokyo Medical and Dental University, 1-5-45 Yushima, Bunkyo-Ku, Tokyo, 113-8510, Japan
| | - Mototsugu Oya
- Department of Urology, Keio University School of Medicine, Tokyo, Japan
| | - Ukihide Tateishi
- Department of Diagnostic Radiology and Nuclear Medicine, Tokyo Medical and Dental University, Tokyo, Japan
| | - Masahiro Jinzaki
- Department of Radiology, Keio University School of Medicine, Tokyo, Japan
| | - Yasuhisa Fujii
- Department of Urology, Tokyo Medical and Dental University, 1-5-45 Yushima, Bunkyo-Ku, Tokyo, 113-8510, Japan
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Ma Y, Guan Z, Liang H, Cao H. Predicting the WHO/ISUP Grade of Clear Cell Renal Cell Carcinoma Through CT-Based Tumoral and Peritumoral Radiomics. Front Oncol 2022; 12:831112. [PMID: 35237524 PMCID: PMC8884273 DOI: 10.3389/fonc.2022.831112] [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: 12/08/2021] [Accepted: 01/17/2022] [Indexed: 12/20/2022] Open
Abstract
Objectives This study aims to establish predictive logistic models for the World Health Organization/International Society of Urological Pathology (WHO/ISUP) grades of clear cell renal cell carcinoma (ccRCC) based on tumoral and peritumoral radiomics. Methods A cohort of 370 patients with pathologically confirmed ccRCCs were included in this retrospective study between January 2014 and December 2020 according to the WHO/ISUP grading system. The volume of interests of triphasic computed tomography images were depicted manually using the “itk-SNAP” software, and the radiomics features were calculated. The cohort was segmented into the training cohort and validation cohort with a random proportion of 7:3. After extraction of radiomics features by analysis of variance (ANOVA) or Mann-Whitney U test, correlation analysis, and the least absolute shrinkage and selection operator (LASSO) method, the logistic models of tumoral radiomics (LR-tumor) and peritumoral radiomics (LR-peritumor) were developed. The LR-peritumor was subdivided into LR-peritumor-2mm, LR-peritumor-5mm, and LR-peritumor-10mm, and the LR-peritumor-2mm was subdivided into LR-peritumor-kid and LR-peritumor-fat based on the neighboring tissues of ccRCCs. Finally, an integrative model of tumoral and peritumoral radiomics (LR-tumor/peritumor) was built. The value of areas under the receiver operator characteristics curve (AUCs) was calculated to assess the efficacy of the models. Results There were 209 low-grade and 161 high-grade ccRCCs enrolled. The AUCs of LR-tumor in CT images of venous phase were 0.802 in the training cohort and 0.796 in the validation cohort. The AUCs were higher in the LR-peritumor-2mm than those in LR-peritumor-5mm and LR-peritumor-10mm (training cohort: 0.788 vs. 0.788 and 0.759; validation cohort: 0.787 vs. 0.785 and 0.758). Moreover, the AUCs of LR-peritumor-fat were higher compared with those of LR-peritumor-kid. The LR-tumor/peritumor displayed the highest AUCs of 0.812 in the training cohort and 0.804 in the validation cohort. Conclusions The tumoral and peritumoral radiomics helped to predict the WHO/ISUP grades of ccRCCs. On the diagnostic performance of peritumoral radiomics, better results were seen for the LR-peritumor-2mm and LR-peritumor-fat.
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Affiliation(s)
- Yanqing Ma
- Cancer Center, Department of Radiology, Zhejiang Provincial People's Hospital, Affiliated People's Hospital, Hangzhou Medical College, Hangzhou, China
| | - Zheng Guan
- Cancer Center, Department of Radiology, Zhejiang Provincial People's Hospital, Affiliated People's Hospital, Hangzhou Medical College, Hangzhou, China
| | - Hong Liang
- The Department of Radiology, Hangzhou Medical College, Hangzhou, China
| | - Hanbo Cao
- Cancer Center, Department of Radiology, Zhejiang Provincial People's Hospital, Affiliated People's Hospital, Hangzhou Medical College, Hangzhou, China
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Demirjian NL, Varghese BA, Cen SY, Hwang DH, Aron M, Siddiqui I, Fields BKK, Lei X, Yap FY, Rivas M, Reddy SS, Zahoor H, Liu DH, Desai M, Rhie SK, Gill IS, Duddalwar V. CT-based radiomics stratification of tumor grade and TNM stage of clear cell renal cell carcinoma. Eur Radiol 2021; 32:2552-2563. [PMID: 34757449 DOI: 10.1007/s00330-021-08344-4] [Citation(s) in RCA: 33] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2021] [Revised: 08/26/2021] [Accepted: 09/24/2021] [Indexed: 12/14/2022]
Abstract
OBJECTIVES To evaluate the utility of CT-based radiomics signatures in discriminating low-grade (grades 1-2) clear cell renal cell carcinomas (ccRCC) from high-grade (grades 3-4) and low TNM stage (stages I-II) ccRCC from high TNM stage (stages III-IV). METHODS A total of 587 subjects (mean age 60.2 years ± 12.2; range 22-88.7 years) with ccRCC were included. A total of 255 tumors were high grade and 153 were high stage. For each subject, one dominant tumor was delineated as the region of interest (ROI). Our institutional radiomics pipeline was then used to extract 2824 radiomics features across 12 texture families from the manually segmented volumes of interest. Separate iterations of the machine learning models using all extracted features (full model) as well as only a subset of previously identified robust metrics (robust model) were developed. Variable of importance (VOI) analysis was performed using the out-of-bag Gini index to identify the top 10 radiomics metrics driving each classifier. Model performance was reported using area under the receiver operating curve (AUC). RESULTS The highest AUC to distinguish between low- and high-grade ccRCC was 0.70 (95% CI 0.62-0.78) and the highest AUC to distinguish between low- and high-stage ccRCC was 0.80 (95% CI 0.74-0.86). Comparable AUCs of 0.73 (95% CI 0.65-0.8) and 0.77 (95% CI 0.7-0.84) were reported using the robust model for grade and stage classification, respectively. VOI analysis revealed the importance of neighborhood operation-based methods, including GLCM, GLDM, and GLRLM, in driving the performance of the robust models for both grade and stage classification. CONCLUSION Post-validation, CT-based radiomics signatures may prove to be useful tools to assess ccRCC grade and stage and could potentially add to current prognostic models. Multiphase CT-based radiomics signatures have potential to serve as a non-invasive stratification schema for distinguishing between low- and high-grade as well as low- and high-stage ccRCC. KEY POINTS • Radiomics signatures derived from clinical multiphase CT images were able to stratify low- from high-grade ccRCC, with an AUC of 0.70 (95% CI 0.62-0.78). • Radiomics signatures derived from multiphase CT images yielded discriminative power to stratify low from high TNM stage in ccRCC, with an AUC of 0.80 (95% CI 0.74-0.86). • Models created using only robust radiomics features achieved comparable AUCs of 0.73 (95% CI 0.65-0.80) and 0.77 (95% CI 0.70-0.84) to the model with all radiomics features in classifying ccRCC grade and stage, respectively.
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Affiliation(s)
| | - Bino A Varghese
- Department of Radiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Steven Y Cen
- Department of Radiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Darryl H Hwang
- Department of Radiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Manju Aron
- Department of Pathology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Imran Siddiqui
- Department of Pathology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | | | - Xiaomeng Lei
- Department of Radiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Felix Y Yap
- Radiology Associates of San Luis Obispo, Atascadero, CA, USA
| | - Marielena Rivas
- Department of Radiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Sharath S Reddy
- Department of Urology, Yale New Haven Hospital, New Haven, CT, USA
| | - Haris Zahoor
- Department of Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Derek H Liu
- Department of Radiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Mihir Desai
- Department of Urology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Suhn K Rhie
- Department of Biochemistry and Molecular Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Inderbir S Gill
- Department of Urology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Vinay Duddalwar
- Department of Radiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA. .,Department of Urology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.
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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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Sun J, Pan L, Zha T, Xing W, Chen J, Duan S. The role of MRI texture analysis based on susceptibility-weighted imaging in predicting Fuhrman grade of clear cell renal cell carcinoma. Acta Radiol 2021; 62:1104-1111. [PMID: 32867506 DOI: 10.1177/0284185120951964] [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] [Indexed: 12/17/2022]
Abstract
BACKGROUND The Fuhrman nuclear grade system is one of the most important independent indicators in patients with clear cell renal cell carcinoma (ccRCC) for aggressiveness and prognosis. Preoperative assessment of tumor aggressiveness is important for surgical decision-making. PURPOSE To explore the role of magnetic resonance imaging (MRI) texture analysis based on susceptibility-weighted imaging (SWI) in predicting Fuhrman grade of ccRCC. MATERIAL AND METHODS A total of 45 patients with SWI and surgically proven ccRCC were divided into two groups: the low-grade group (Fuhrman I/II, n = 29) and the high-grade group (Fuhrman III/IV, n = 16). Texture features were extracted from SWI images. Feature selection was performed, and multivariable logistic regression analysis was performed to develop the SWI-based texture model for grading ccRCCs. Receiver operating characteristic (ROC) curve analysis and leave-group-out cross-validation (LGOCV) were performed to test the reliability of the model. RESULTS A total of 396 SWI-based texture features were extracted from each SWI image. The SWI-based texture model developed by multivariable logistic regression analysis was: SWIscore = -0.59 + 1.60 * ZonePercentage. The area under the ROC curve of the SWI-based texture model for differentiating high-grade ccRCC from low-grade ccRCC was 0.81 (95% confidence interval 0.67-0.94), with 80% accuracy, 56.25% sensitivity, and 93.10% specificity. After 100 LGOCVs, the mean accuracy, sensitivity, and specificity were 90.91%, 91.83%, and 89.89% for the training sets, and 77.29%, 80.52%, and 71.44% for the test sets, respectively. CONCLUSION SWI-based texture analysis might be a reliable quantitative approach for differentiating high-grade ccRCC from low-grade ccRCC.
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Affiliation(s)
- Jun Sun
- Department of Radiology, Third Affiliated Hospital of Soochow University, Changzhou, Jiangsu, PR China
| | - Liang Pan
- GE Healthcare China, Shanghai, Shanghai, PR China
| | - Tingting Zha
- Department of Radiology, Third Affiliated Hospital of Soochow University, Changzhou, Jiangsu, PR China
| | - Wei Xing
- Department of Radiology, Third Affiliated Hospital of Soochow University, Changzhou, Jiangsu, PR China
| | - Jie Chen
- Department of Radiology, Third Affiliated Hospital of Soochow University, Changzhou, Jiangsu, PR China
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Liu H, Tang K, Chen Z, Li Z, Meng X, Xia D. Comparison and development of preoperative systemic inflammation markers-based models for the prediction of unfavorable pathology in newly diagnosed clinical T1 renal cell carcinoma. Pathol Res Pract 2021; 225:153563. [PMID: 34371466 DOI: 10.1016/j.prp.2021.153563] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/01/2021] [Revised: 07/18/2021] [Accepted: 07/20/2021] [Indexed: 12/16/2022]
Abstract
BACKGROUND We sought to investigate the preoperative risk factors associated with the unfavorable pathology (UP) of clinical T1 (cT1) renal lesions. The aims of this study were to develop and compare several novel models capable of accurately identifying those patients at high risk of harboring occult adverse histopathological characteristics. METHODS The clinical parameters and preoperative laboratory test results from 1281 cT1 renal cell carcinomas (RCCs) patients who underwent partial nephrectomy (PN) or radical nephrectomy (RN) were collected. The data was randomly split into training (70%) and testing (30%) datasets. We performed univariable and multivariable logistic regression analyses for significant predictors and, subsequently, constructed predictive models based on those significant risk factors. Receiver operating characteristic (ROC) analysis was used to determine the model with the highest discrimination power with corresponding area under the curve (AUC). Calibration curves were plotted and decision curve analyses (DCAs) were applied to explore clinical net benefit. RESULTS UP was identified in 21.1% (n = 270), 21.0% (n = 188) and 21.3% (n = 82) patients in the total population, training cohort and validation cohort, respectively. R.E.N.A.L. (radius, exophytic/endophytic properties, nearness of tumor to collecting system or sinus, anterior/posterior, location relative to the polar lines) nephrometry score, tumor size, neutrophil-to-lymphocyte ratio (NLR) and albumin-to-globulin ratio (AGR) were independent predictors of UP. Among those predictive models, the model that consisted of tumor size, hemoglobin, NLR and AGR performs best according to the highest AUC of 0.70 and the highest net benefit. When tumor histology was added to the biomarker-based model, including tumor size, hemoglobin, NLR and AGR, the AUC improved from 0.60 to 0.63 in the validation cohort. CONCLUSIONS In this analytical model study, our findings verified that systemic inflammation response markers showed high potential for identifying UP. Our biomarker-based models well predicted occult aggressive histopathological characteristics among patients with cT1 renal lesions, and the use of models may be greatly beneficial to urologists in tailoring precise management and therapy for patients. Robust validation is warranted prior to adoption into clinical practice.
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Affiliation(s)
- Hailang Liu
- Department of Urology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, Hubei, China
| | - Kun Tang
- Department of Urology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, Hubei, China
| | - Zhiqiang Chen
- Department of Urology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, Hubei, China
| | - Zhen Li
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, Hubei, China
| | - Xiaoyan Meng
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, Hubei, China.
| | - Ding Xia
- Department of Urology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, Hubei, China.
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Ji W, Wang J, Zhou R, Wang M, Wang W, Pang P, Kong M, Zhou C. Diagnostic Performance of Vascular Permeability and Texture Parameters for Evaluating the Response to Neoadjuvant Chemoradiotherapy in Patients With Esophageal Squamous Cell Carcinoma. Front Oncol 2021; 11:604480. [PMID: 34084740 PMCID: PMC8168434 DOI: 10.3389/fonc.2021.604480] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2020] [Accepted: 04/21/2021] [Indexed: 12/09/2022] Open
Abstract
Background Esophageal squamous cell carcinoma (ESCC) is an aggressive type of cancer, associated with poor prognosis. The development of an accurate and non-invasive method to evaluate the pathologic response of patients with ESCC to chemoradiotherapy remains a critical issue. Therefore, the aim of this study was to assess the importance of vascular permeability and texture parameters in predicting the response to neoadjuvant chemoradiotherapy (NACRT) in patients with ESCC. Methods This prospective analysis included patients with T1–T2 stage of ESCC, without either lymphatic or metastasis, and distant metastasis. All patients underwent surgery having received two rounds of NACRT. All patients underwent dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) twice, i.e., before the first NACRT and after the second NACRT. Patients were assessed for treatment response at 30 days after the second NACRT. Patients were divided into the complete response (CR) and partial response (PR) groups based on their responses to NACRT. Vascular permeability and texture parameters were extracted from the DCE-MRI scans. After assessing the diagnostic performance of individual parameters, a combined model with vascular permeability and texture parameters was generated to predict the response to NACRT. Results In this study, the CR and PR groups included 16 patients each. The volume transfer constant (Ktrans), extracellular extravascular volume fraction (ve), and entropy values, as well as changes to each of these parameters, extracted from the second DCE-MRI scans, showed significant differences between the CR and PR groups. The area under the curve (AUC) of Ktrans, ve, and entropy values showed good diagnostic ability (0.813, 0.789, and 0.707, respectively). A logistic regression model combining Ktrans, ve, and entropy had significant diagnostic ability (AUC=0.977). Conclusions The use of a combined model with vascular permeability and texture parameters can improve post-NACRT prognostication in patients with ESCC.
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Affiliation(s)
- Wenbing Ji
- Department of Radiology, Taizhou Hospital of Zhejiang Province, Taizhou, China
| | - Jian Wang
- Department of Radiology, Taizhou Hospital of Zhejiang Province, Taizhou, China
| | - Rongzhen Zhou
- Department of Radiology, Taizhou Hospital of Zhejiang Province, Taizhou, China
| | - Minke Wang
- Department of Radiology, Taizhou Hospital of Zhejiang Province, Taizhou, China
| | - Weizhen Wang
- Department of Radiology, Taizhou Hospital of Zhejiang Province, Taizhou, China
| | - Peipei Pang
- Advanced Application Team, GE Healthcare, Shanghai, China
| | - Min Kong
- Department of Thoracic Surgery, Taizhou Hospital of Zhejiang Province, Taizhou, China
| | - Chao Zhou
- Department of Radiotherapy, Taizhou Hospital of Zhejiang Province, Taizhou, China
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Hsieh JJ. The 2021 COVID-19 Artificial Intelligence Issue. Clin Genitourin Cancer 2021; 19:1-2. [PMID: 33610279 PMCID: PMC7889475 DOI: 10.1016/j.clgc.2021.01.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
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Campi R, Stewart GD, Staehler M, Dabestani S, Kuczyk MA, Shuch BM, Finelli A, Bex A, Ljungberg B, Capitanio U. Novel Liquid Biomarkers and Innovative Imaging for Kidney Cancer Diagnosis: What Can Be Implemented in Our Practice Today? A Systematic Review of the Literature. Eur Urol Oncol 2021; 4:22-41. [DOI: 10.1016/j.euo.2020.12.011] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2020] [Revised: 11/26/2020] [Accepted: 12/14/2020] [Indexed: 12/12/2022]
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Iacobas DA, Mgbemena VE, Iacobas S, Menezes KM, Wang H, Saganti PB. Genomic Fabric Remodeling in Metastatic Clear Cell Renal Cell Carcinoma (ccRCC): A New Paradigm and Proposal for a Personalized Gene Therapy Approach. Cancers (Basel) 2020; 12:cancers12123678. [PMID: 33302383 PMCID: PMC7762545 DOI: 10.3390/cancers12123678] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2020] [Accepted: 12/05/2020] [Indexed: 12/30/2022] Open
Abstract
Simple Summary We applied the genomic fabric principles for personalized gene therapy to a case of clear cell renal cell carcinoma (ccRCC). Despite decades of research, the process of finding the molecular mechanisms responsible for the disease and, more importantly, the therapeutic solution is still a work in progress. We analyzed the transcriptomes of the chest wall metastasis, two distinct cancer nodules, and the cancer-free surrounding tissue in the surgically removed right kidney of a Fuhrman grade 3 metastatic ccRCC patient. The studies revealed that even histopathologically equally classified cancer nodules from the same kidney have different transcriptomic topologies, requiring tailored therapeutic solutions not only for each patient but even for each cancer nodule. We identified death-associated protein kinase 3 (DAPK3); transcription activation suppressor (TASOR); family with sequence similarity 27, member C, long non-coding RNA (FAM27C); and UDP-N-acetylglucosaminyltransferase subunit (ALG13) as the gene master regulators of the four profiled regions and proposed molecular mechanisms by which expression manipulation of TASOR and ALG13 may selectively destroy the cancer cells without affecting many of the normal cells. Abstract Published transcriptomic data from surgically removed metastatic clear cell renal cell carcinoma samples were analyzed from the genomic fabric paradigm (GFP) perspective to identify the best targets for gene therapy. GFP considers the transcriptome as a multi-dimensional mathematical object constrained by a dynamic set of expression controls and correlations among genes. Every gene in the chest wall metastasis, two distinct cancer nodules, and the surrounding normal tissue of the right kidney was characterized by three independent measures: average expression level, relative expression variation, and expression correlation with each other gene. The analyses determined the cancer-induced regulation, control, and remodeling of the chemokine and vascular endothelial growth factor (VEGF) signaling, apoptosis, basal transcription factors, cell cycle, oxidative phosphorylation, renal cell carcinoma, and RNA polymerase pathways. Interestingly, the three cancer regions exhibited different transcriptomic organization, suggesting that the gene therapy should not be personalized only for every patient but also for each major cancer nodule. The gene hierarchy was established on the basis of gene commanding height, and the gene master regulators DAPK3,TASOR, FAM27C and ALG13 were identified in each profiled region. We delineated the molecular mechanisms by which TASOR overexpression and ALG13 silencing would selectively affect the cancer cells with little consequences for the normal cells.
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Affiliation(s)
- Dumitru A. Iacobas
- Personalized Genomics Laboratory, CRI Center for Computational Systems Biology, Roy G Perry College of Engineering, Prairie View A&M University, Prairie View, TX 77446, USA
- Correspondence: (D.A.I.); (P.B.S.); Tel.: +1-(936)-261-9626 (D.A.I.)
| | - Victoria E. Mgbemena
- Department of Biology, MD and S Brailsford College of Arts and Sciences, Prairie View A&M University, Prairie View, TX 77446, USA;
| | - Sanda Iacobas
- Department of Pathology, New York Medical College, Valhalla, NY 10595, USA;
| | - Kareena M. Menezes
- CRI Radiation Institute for Science & Engineering, MD and S Brailsford College of Arts and Sciences, Prairie View A&M University, Prairie View, TX 77446, USA; (K.M.M.); (H.W.)
| | - Huichen Wang
- CRI Radiation Institute for Science & Engineering, MD and S Brailsford College of Arts and Sciences, Prairie View A&M University, Prairie View, TX 77446, USA; (K.M.M.); (H.W.)
| | - Premkumar B. Saganti
- CRI Radiation Institute for Science & Engineering, MD and S Brailsford College of Arts and Sciences, Prairie View A&M University, Prairie View, TX 77446, USA; (K.M.M.); (H.W.)
- Department of Physics, MD and S Brailsford College of Arts and Sciences, Prairie View A&M University, Prairie View, TX 77446, USA
- Correspondence: (D.A.I.); (P.B.S.); Tel.: +1-(936)-261-9626 (D.A.I.)
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