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Rowe SP, Islam MZ, Viglianti B, Solnes LB, Baraban E, Gorin MA, Oldan JD. Molecular imaging for non-invasive risk stratification of renal masses. Diagn Interv Imaging 2024; 105:305-310. [PMID: 39054210 DOI: 10.1016/j.diii.2024.07.003] [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: 07/02/2024] [Accepted: 07/04/2024] [Indexed: 07/27/2024]
Abstract
Anatomic imaging with contrast-enhanced computed tomography (CT) and magnetic resonance imaging (MRI) has long been the mainstay of renal mass characterization. However, those modalities are often unable to adequately characterize indeterminate, solid, enhancing renal masses - with some exceptions, such as the development of the clear-cell likelihood score on multi-parametric MRI. As such, molecular imaging approaches have gained traction as an alternative to anatomic imaging. Mitochondrial imaging with 99mTc-sestamibi single-photon emission computed tomography/CT is a cost-effective means of non-invasively identifying oncocytomas and other indolent renal masses. On the other end of the spectrum, carbonic anhydrase IX agents, most notably the monoclonal antibody girentuximab - which can be labeled with positron emission tomography radionuclides such as zirconium-89 - are effective at identifying renal masses that are likely to be aggressive clear cell renal cell carcinomas. Renal mass biopsy, which has a relatively high non-diagnostic rate and does not definitively characterize many oncocytic neoplasms, nonetheless may play an important role in any algorithm targeted to renal mass risk stratification. The combination of molecular imaging and biopsy in selected patients with other advanced imaging methods, such as artificial intelligence/machine learning and the abstraction of radiomics features, offers the optimal way forward for maximization of the information to be gained from risk stratification of indeterminate renal masses. With the proper application of those methods, inappropriately aggressive therapy for benign and indolent renal masses may be curtailed.
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Affiliation(s)
- Steven P Rowe
- Molecular Imaging and Therapeutics, University of North Carolina, Chapel Hill, NC 27516, USA.
| | - Md Zobaer Islam
- Molecular Imaging and Therapeutics, University of North Carolina, Chapel Hill, NC 27516, USA
| | - Benjamin Viglianti
- Division of Nuclear Medicine and Molecular Imaging, Department of Radiology, University of Michigan, Ann Arbor, MI 48109, USA
| | - Lilja B Solnes
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA
| | - Ezra Baraban
- Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA
| | - Michael A Gorin
- Milton and Carroll Petrie Department of Urology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Jorge D Oldan
- Molecular Imaging and Therapeutics, University of North Carolina, Chapel Hill, NC 27516, USA
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2
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Yang Z, Liu C. Research on the application of radiomics in breast cancer: A bibliometrics and visualization analysis. Medicine (Baltimore) 2024; 103:e39463. [PMID: 39213225 PMCID: PMC11365679 DOI: 10.1097/md.0000000000039463] [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] [Received: 03/26/2024] [Revised: 04/24/2024] [Accepted: 08/06/2024] [Indexed: 09/04/2024] Open
Abstract
Breast cancer is the most prevalent form of cancer worldwide. Therefore, improved disease detection has emerged as a focal point in clinical studies. At the forefront of innovation, radiomics has the capability to extract comprehensive insights from medical images, ultimately enhancing the accuracy of diagnostic procedures. There has been rapid growth in the field of radiomics research on breast cancer in the past few years. We explored pertinent research articles in the Web of Science Core Collection database to gain a thorough understanding of breast cancer radiomics. We used CiteSpace to conduct a bibliometric analysis of the annual distribution of different nations, institutions, journals, authors, keywords, and references in the field of breast cancer radiomics. GraphPad Prism software was used to examine and graph yearly and country-specific trends and the proportions of publications. The tools utilized for the visualization of science mapping included CiteSpace and VOSviewer. Of the 891 publications, most were original articles (731, 91.09%) and a few were reviews (160, 8.91%). Most academic research has been published in China and the United States. The study centers predominantly consisted of major academic institutions, such as Fudan University and the Chinese Academy of Sciences, with some of their members being prominent figures in the field. Pinker, Katja has published the largest number of research papers. The majority of these studies have been published in medical journals focusing on radiology and oncology in recent years. In the realm of cutting-edge medical research, the top two keywords, magnetic resonance imaging and machine learning stand at the forefront as current areas of intense focus. Breast cancer radiomics is advancing rapidly, presenting numerous opportunities and obstacles. Our study of the literature in this academic area aimed to pinpoint the primary themes addressed in the studies and anticipate prospective avenues for research.
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Affiliation(s)
- Zhe Yang
- Department of Radiology, the Second Affiliated Hospital of Shandong First Medical University, Tai’an, Shandong, China
| | - Chenglong Liu
- Department of Radiology, the Second Affiliated Hospital of Shandong First Medical University, Tai’an, Shandong, China
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Llorián-Salvador Ó, Windeler N, Martin N, Etzel L, Andrade-Navarro MA, Bernhardt D, Rost B, Borm KJ, Combs SE, Duma MN, Peeken JC. CT-based radiomics for predicting breast cancer radiotherapy side effects. Sci Rep 2024; 14:20051. [PMID: 39209947 PMCID: PMC11362146 DOI: 10.1038/s41598-024-70723-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2024] [Accepted: 08/20/2024] [Indexed: 09/04/2024] Open
Abstract
Skin inflammation with the potential sequel of moist epitheliolysis and edema constitute the most frequent breast radiotherapy (RT) acute side effects. The aim of this study was to compare the predictive value of tissue-derived radiomics features to the total breast volume (TBV) for the moist cells epitheliolysis as a surrogate for skin inflammation, and edema. Radiomics features were extracted from computed tomography (CT) scans of 252 breast cancer patients from two volumes of interest: TBV and glandular tissue (GT). Machine learning classifiers were trained on radiomics and clinical features, which were evaluated for both side effects. The best radiomics model was a least absolute shrinkage and selection operator (LASSO) classifier, using TBV features, predicting moist cells epitheliolysis, achieving an area under the receiver operating characteristic (AUROC) of 0.74. This was comparable to TBV breast volume (AUROC of 0.75). Combined models of radiomics and clinical features did not improve performance. Exclusion of volume-correlated features slightly reduced the predictive performance (AUROC 0.71). We could demonstrate the general propensity of planning CT-based radiomics models to predict breast RT-dependent side effects. Mammary tissue was more predictive than glandular tissue. The radiomics features performance was influenced by their high correlation to TBV volume.
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Affiliation(s)
- Óscar Llorián-Salvador
- Department of Radiation Oncology, Klinikum Rechts der Isar, Technische Universität München, Ismaninger Straße 22, 81675, Munich, Germany.
- Department of Informatics, Bioinformatics and Computational Biology-i12, Technische Universität München, Boltzmannstr. 3, 85748, Munich, Germany.
- Institute of Organismic and Molecular Evolution, Johannes Gutenberg University Mainz, Hanns-Dieter-Hüsch- Weg 15, 55128, Mainz, Germany.
| | - Nora Windeler
- Department of Radiation Oncology, Klinikum Rechts der Isar, Technische Universität München, Ismaninger Straße 22, 81675, Munich, Germany
| | - Nicole Martin
- Department of Informatics, Bioinformatics and Computational Biology-i12, Technische Universität München, Boltzmannstr. 3, 85748, Munich, Germany
| | - Lucas Etzel
- Department of Radiation Oncology, Klinikum Rechts der Isar, Technische Universität München, Ismaninger Straße 22, 81675, Munich, Germany
- Deutsches Konsortium für Translationale Krebsforschung (DKTK), Partner Site Munich, 69120, Heidelberg, Germany
| | - Miguel A Andrade-Navarro
- Institute of Organismic and Molecular Evolution, Johannes Gutenberg University Mainz, Hanns-Dieter-Hüsch- Weg 15, 55128, Mainz, Germany
| | - Denise Bernhardt
- Department of Radiation Oncology, Klinikum Rechts der Isar, Technische Universität München, Ismaninger Straße 22, 81675, Munich, Germany
- Deutsches Konsortium für Translationale Krebsforschung (DKTK), Partner Site Munich, 69120, Heidelberg, Germany
- Department of Radiation Sciences (DRS), Institute of Radiation Medicine (IRM), Helmholtz Zentrum, 85764, München, Germany
| | - Burkhard Rost
- Department of Informatics, Bioinformatics and Computational Biology-i12, Technische Universität München, Boltzmannstr. 3, 85748, Munich, Germany
| | - Kai J Borm
- Department of Radiation Oncology, Klinikum Rechts der Isar, Technische Universität München, Ismaninger Straße 22, 81675, Munich, Germany
| | - Stephanie E Combs
- Department of Radiation Oncology, Klinikum Rechts der Isar, Technische Universität München, Ismaninger Straße 22, 81675, Munich, Germany
- Deutsches Konsortium für Translationale Krebsforschung (DKTK), Partner Site Munich, 69120, Heidelberg, Germany
- Department of Radiation Sciences (DRS), Institute of Radiation Medicine (IRM), Helmholtz Zentrum, 85764, München, Germany
| | - Marciana N Duma
- Department of Radiation Oncology, Klinikum Rechts der Isar, Technische Universität München, Ismaninger Straße 22, 81675, Munich, Germany
- Department of Radiation Oncology, Helios Clinics of Schwerin - University Campus of MSH Medical School Hamburg, Schwerin, Germany
- Department for Human Medicine, MSH Medical School Hamburg, Hamburg, Germany
| | - Jan C Peeken
- Department of Radiation Oncology, Klinikum Rechts der Isar, Technische Universität München, Ismaninger Straße 22, 81675, Munich, Germany
- Deutsches Konsortium für Translationale Krebsforschung (DKTK), Partner Site Munich, 69120, Heidelberg, Germany
- Department of Radiation Sciences (DRS), Institute of Radiation Medicine (IRM), Helmholtz Zentrum, 85764, München, Germany
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Song H, Wang X, Wu R, Liu W. The influence of manual segmentation strategies and different phases selection on machine learning-based computed tomography in renal tumors: a systematic review and meta-analysis. LA RADIOLOGIA MEDICA 2024; 129:1025-1037. [PMID: 38740709 DOI: 10.1007/s11547-024-01825-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/04/2023] [Accepted: 04/29/2024] [Indexed: 05/16/2024]
Abstract
BACKGROUND Delineating the region/volume of interest (ROI/VOI) and selecting the phases are of importance in developing machine learning (ML). The results will change when choosing different methods of drawing the ROI/VOI and selecting different phases. However, there is no related standard for delineating the ROI/VOI and selecting the phases in renal tumors to develop ML based on computed tomography (CT). METHODS The PubMed and Web of Science were searched for related studies published until March 1, 2023. Inclusion criteria were studies that developed ML models in renal tumors from CT images. And the binary diagnostic accuracy data were extracted to obtain the outcomes, such as sensitivity (SE), specificity (SP), accuracy (ACC), and area under the curve (AUC). RESULTS Twenty-three papers were included in the meta-analysis with a pooled SE of 87% (95% CI 85-88%), SP of 82% (95% CI 79-85%), and AUC of 91% (95% CI 89-93%) in phases; a pooled SE of 82% (95% CI 80-84%), SP of 85% (95% CI 83-86%), and AUC of 90% (95% CI 88-93%) in phases combined with delineating strategies, respectively. In all different combinations, the contour-focused and single phase produce the highest AUC of 93% (95% CI 90-95%). In subgroup analyses (sample size, year of publication, and geographical distribution), the performance was acceptable on phases and phases combined strategies. CONCLUSIONS To explore the effect of manual segmentation strategies and different phases selection on ML-based CT, we find that the method of single phase (CMP or NP) combined with contour-focused was considered a better strategy compared to the other strategies.
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Affiliation(s)
- Honghao Song
- Department of Pediatric Surgery, Shandong Provincial Hospital, Shandong University, Jinan, 250021, Shandong, People's Republic of China
| | - Xiaoqing Wang
- Department of Pediatric Surgery, Shandong Provincial Hospital Affiliated to Shandong First Medical University, 324 Jingwu Street, Jinan, 250021, Shandong, People's Republic of China
- Post-doctoral Research Station of Clinical Medicine, Liaocheng People's Hospital, Liaocheng, 252004, Shandong, People's Republic of China
| | - Rongde Wu
- Department of Pediatric Surgery, Shandong Provincial Hospital Affiliated to Shandong First Medical University, 324 Jingwu Street, Jinan, 250021, Shandong, People's Republic of China
| | - Wei Liu
- Department of Pediatric Surgery, Shandong Provincial Hospital Affiliated to Shandong First Medical University, 324 Jingwu Street, Jinan, 250021, Shandong, People's Republic of China.
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Nguyen TT, Thai MS, Chau QT, Dobbs RW, Tiong HY, Pham DM, Truong HTT, Thai KL, Nguyen HDK, Huynh TT, Le HP, Ngo XT. Three-dimensional reconstruction of renal tumor anatomy for preoperative planning of robotic partial nephrectomy in renal cell carcinoma cases with duplex kidney: a case report. J Med Case Rep 2024; 18:262. [PMID: 38802967 PMCID: PMC11131312 DOI: 10.1186/s13256-024-04582-4] [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: 03/12/2024] [Accepted: 05/02/2024] [Indexed: 05/29/2024] Open
Abstract
BACKGROUND The duplex kidney is one of the common congenital anomalies of the kidney and urinary tract. We present two cases of renal tumor accompanied with ipsilateral duplex kidney. The image of the tumor, renal artery system and collecting system were rendered by AI software (Fujifilm's Synapse® AI Platform) to support the diagnosis and surgical planning. CASE PRESENTATION Two Vietnamese patients (a 45-year-old man and a 54-year-old woman) with incidental cT1 renal cell carcinoma (RCC) were confirmed to have ipsilateral duplex kidneys by 3D reconstruction AI technique. One patient had a Renal score 9ah tumor of left kidney while the other had a Renal score 9 × tumor of right kidney in which a preoperative CT scan failed to identify a diagnosis of duplex kidney. Using the Da Vinci platform, we successfully performed robotic partial nephrectomy without any damage to the collecting system in both cases. CONCLUSION RCC with duplex kidneys is a rare condition. By utilizing a novel AI reconstruction technique with adequate information, two patients with RCC in duplex kidneys were successfully performed robotic partial nephrectomy without complication.
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Affiliation(s)
- Tuan Thanh Nguyen
- University of Medicine and Pharmacy at Ho Chi Minh City, 217 Hong Bang Street, Ward 11, District 5, Ho Chi Minh City, Viet Nam
- Cho Ray Hospital, Ho Chi Minh City, Viet Nam
- University of California Irvine, Irvine, USA
| | - Minh Sam Thai
- University of Medicine and Pharmacy at Ho Chi Minh City, 217 Hong Bang Street, Ward 11, District 5, Ho Chi Minh City, Viet Nam
- Cho Ray Hospital, Ho Chi Minh City, Viet Nam
| | | | - Ryan W Dobbs
- Cook County Health and Hospitals System, Chicago, IL, USA
| | - Ho Yee Tiong
- National University Hospital, Singapore, Singapore
| | - Duc Minh Pham
- University of Medicine and Pharmacy at Ho Chi Minh City, 217 Hong Bang Street, Ward 11, District 5, Ho Chi Minh City, Viet Nam
- Cho Ray Hospital, Ho Chi Minh City, Viet Nam
| | | | - Kinh Luan Thai
- University of Medicine and Pharmacy at Ho Chi Minh City, 217 Hong Bang Street, Ward 11, District 5, Ho Chi Minh City, Viet Nam
- Cho Ray Hospital, Ho Chi Minh City, Viet Nam
| | - Huynh Dang Khoa Nguyen
- University of Medicine and Pharmacy at Ho Chi Minh City, 217 Hong Bang Street, Ward 11, District 5, Ho Chi Minh City, Viet Nam
| | - Thanh Thien Huynh
- University of Medicine and Pharmacy at Ho Chi Minh City, 217 Hong Bang Street, Ward 11, District 5, Ho Chi Minh City, Viet Nam
| | - Huu Phuoc Le
- University of Medicine and Pharmacy at Ho Chi Minh City, 217 Hong Bang Street, Ward 11, District 5, Ho Chi Minh City, Viet Nam
| | - Xuan Thai Ngo
- University of Medicine and Pharmacy at Ho Chi Minh City, 217 Hong Bang Street, Ward 11, District 5, Ho Chi Minh City, Viet Nam.
- Cho Ray Hospital, Ho Chi Minh City, Viet Nam.
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Dou J, Jiang N, Zeng J, Wang S, Tian S, Shan S, Li Y, Xu Z, Lin X, Jin S, Dong J, Chen H. Novel 3D morphological characteristics for congenital biliary dilatation diagnosis: a case-control study. Int J Surg 2024; 110:2614-2624. [PMID: 38376858 DOI: 10.1097/js9.0000000000001204] [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: 12/12/2023] [Accepted: 02/04/2024] [Indexed: 02/21/2024]
Abstract
BACKGROUND Congenital biliary dilatation (CBD) necessitates the timely removal of dilated bile ducts. Accurate differentiation between CBD and secondary biliary dilatation (SBD) is crucial for treatment decisions, and identification of CBD with intrahepatic involvement is vital for surgical planning and supportive care. This study aimed to develop quantitative models based on bile duct morphology to distinguish CBD from SBD and further identify CBD with intrahepatic involvement. MATERIALS AND METHODS The retrospective study included 131 CBD and 209 SBD patients between December 2014 and December 2021 for model development, internal validation, and testing. A separate cohort of 15 CBD and 34 SBD patients between January 2022 and December 2022 was recruited for temporally-independent validation. Quantitative shape-based (Shape) and diameter-based (Diam) morphological characteristics of bile ducts were extracted to build a CBD diagnosis model to distinguish CBD from SBD and an intrahepatic involvement identification model to classify CBD with/without intrahepatic involvement. The diagnostic performance of the models was compared with that of experienced hepatobiliary surgeons. RESULTS The CBD diagnosis model using clinical, Shape, and Diam characteristics showed good performance with an AUROC of 0.942 (95% CI: 0.890-0.994), AUPRC of 0.917 (0.855-0.979), accuracy of 0.891, sensitivity of 0.950, and F1-score of 0.864. The model outperformed two experienced surgeons in accuracy, sensitivity, and F1-score. The intrahepatic involvement identification model using clinical, Shape, and Diam characteristics yielded outstanding performance with an AUROC of 0.944 (0.879-1.000), AUPRC of 0.982 (0.947-1.000), accuracy of 0.932, sensitivity of 0.971, and F1-score of 0.957. The models demonstrated generalizable performance on the temporally-independent validation cohort. CONCLUSIONS This study developed two robust quantitative models for distinguishing CBD from SBD and identifying CBD with intrahepatic involvement, respectively, based on morphological characteristics of the bile ducts, showing great potential in risk stratification and surgical planning of CBD.
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Affiliation(s)
- Jiaqi Dou
- Department of Biomedical Engineering, Center for Biomedical Imaging Research, School of Medicine
| | - Nan Jiang
- Hepatopancreatobiliary Center, Beijing Tsinghua Changgung Hospital, Key Laboratory of Digital Intelligence Hepatology, Ministry of Education, School of Clinical Medicine
- Institute for Precision Medicine, Tsinghua University
- Research Unit of Precision Hepatobiliary Surgery Paradigm, Chinese Academy of Medical Sciences
| | - Jianping Zeng
- Hepatopancreatobiliary Center, Beijing Tsinghua Changgung Hospital, Key Laboratory of Digital Intelligence Hepatology, Ministry of Education, School of Clinical Medicine
- Institute for Precision Medicine, Tsinghua University
- Research Unit of Precision Hepatobiliary Surgery Paradigm, Chinese Academy of Medical Sciences
| | - Siyuan Wang
- Hepatopancreatobiliary Center, Beijing Tsinghua Changgung Hospital, Key Laboratory of Digital Intelligence Hepatology, Ministry of Education, School of Clinical Medicine
- Institute for Precision Medicine, Tsinghua University
- Research Unit of Precision Hepatobiliary Surgery Paradigm, Chinese Academy of Medical Sciences
| | - Song Tian
- Philips Healthcare, Beijing, People's Republic of China
| | - Siqiao Shan
- Hepatopancreatobiliary Center, Beijing Tsinghua Changgung Hospital, Key Laboratory of Digital Intelligence Hepatology, Ministry of Education, School of Clinical Medicine
- Institute for Precision Medicine, Tsinghua University
- Research Unit of Precision Hepatobiliary Surgery Paradigm, Chinese Academy of Medical Sciences
| | - Yuze Li
- Department of Biomedical Engineering, Center for Biomedical Imaging Research, School of Medicine
| | - Ziming Xu
- Department of Biomedical Engineering, Center for Biomedical Imaging Research, School of Medicine
| | - Xiaoqi Lin
- Department of Biomedical Engineering, Center for Biomedical Imaging Research, School of Medicine
| | - Shuo Jin
- Hepatopancreatobiliary Center, Beijing Tsinghua Changgung Hospital, Key Laboratory of Digital Intelligence Hepatology, Ministry of Education, School of Clinical Medicine
- Institute for Precision Medicine, Tsinghua University
- Research Unit of Precision Hepatobiliary Surgery Paradigm, Chinese Academy of Medical Sciences
| | - Jiahong Dong
- Hepatopancreatobiliary Center, Beijing Tsinghua Changgung Hospital, Key Laboratory of Digital Intelligence Hepatology, Ministry of Education, School of Clinical Medicine
- Institute for Precision Medicine, Tsinghua University
- Research Unit of Precision Hepatobiliary Surgery Paradigm, Chinese Academy of Medical Sciences
| | - Huijun Chen
- Department of Biomedical Engineering, Center for Biomedical Imaging Research, School of Medicine
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Raman AG, Fisher D, Yap F, Oberai A, Duddalwar VA. Radiomics and Artificial Intelligence: Renal Cell Carcinoma. Urol Clin North Am 2024; 51:35-45. [PMID: 37945101 DOI: 10.1016/j.ucl.2023.06.007] [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: 11/12/2023]
Abstract
There is a clinical need for accurate diagnosis and prognostication of kidney cancer using imaging. Radiomics and deep learning methods applied to imaging have shown promise in tasks such as tumor segmentation, classification, staging, and grading, as well as assessment of preoperative scores and correlation with tumor biomarkers. Artificial intelligence is also expected to play a significant role in advancing personalized medicine for the treatment of renal cell carcinoma.
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Affiliation(s)
- Alex G Raman
- Department of Radiology, University of Southern California, 1500 San Pablo Street, 2nd Floor, Los Angeles, CA 90033, USA; Western University of Health Sciences, 309 East Second Street, Pomona, CA 91766-1854, USA
| | - David Fisher
- Department of Radiology, University of Southern California, 1500 San Pablo Street, 2nd Floor, Los Angeles, CA 90033, USA
| | - Felix Yap
- Radiology Associates, San Luis Obispo, 1310 Las Tablas Road, Templeton, CA 93465, USA
| | - Assad Oberai
- Viterbi School of Engineering, University of Southern California, 3650 McClintock Avenue, Los Angeles, CA 90089, USA
| | - Vinay A Duddalwar
- Department of Radiology, University of Southern California, 1500 San Pablo Street, 2nd Floor, Los Angeles, CA 90033, USA; Viterbi School of Engineering, University of Southern California, 3650 McClintock Avenue, Los Angeles, CA 90089, USA.
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8
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Trovato P, Simonetti I, Morrone A, Fusco R, Setola SV, Giacobbe G, Brunese MC, Pecchi A, Triggiani S, Pellegrino G, Petralia G, Sica G, Petrillo A, Granata V. Scientific Status Quo of Small Renal Lesions: Diagnostic Assessment and Radiomics. J Clin Med 2024; 13:547. [PMID: 38256682 PMCID: PMC10816509 DOI: 10.3390/jcm13020547] [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: 11/01/2023] [Revised: 01/05/2024] [Accepted: 01/16/2024] [Indexed: 01/24/2024] Open
Abstract
Background: Small renal masses (SRMs) are defined as contrast-enhanced renal lesions less than or equal to 4 cm in maximal diameter, which can be compatible with stage T1a renal cell carcinomas (RCCs). Currently, 50-61% of all renal tumors are found incidentally. Methods: The characteristics of the lesion influence the choice of the type of management, which include several methods SRM of management, including nephrectomy, partial nephrectomy, ablation, observation, and also stereotactic body radiotherapy. Typical imaging methods available for differentiating benign from malignant renal lesions include ultrasound (US), contrast-enhanced ultrasound (CEUS), computed tomography (CT), and magnetic resonance imaging (MRI). Results: Although ultrasound is the first imaging technique used to detect small renal lesions, it has several limitations. CT is the main and most widely used imaging technique for SRM characterization. The main advantages of MRI compared to CT are the better contrast resolution and tissue characterization, the use of functional imaging sequences, the possibility of performing the examination in patients allergic to iodine-containing contrast medium, and the absence of exposure to ionizing radiation. For a correct evaluation during imaging follow-up, it is necessary to use a reliable method for the assessment of renal lesions, represented by the Bosniak classification system. This classification was initially developed based on contrast-enhanced CT imaging findings, and the 2019 revision proposed the inclusion of MRI features; however, the latest classification has not yet received widespread validation. Conclusions: The use of radiomics in the evaluation of renal masses is an emerging and increasingly central field with several applications such as characterizing renal masses, distinguishing RCC subtypes, monitoring response to targeted therapeutic agents, and prognosis in a metastatic context.
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Affiliation(s)
- Piero Trovato
- Radiology Division, Istituto Nazionale Tumori-IRCCS-Fondazione G. Pascale, 80131 Naples, Italy; (P.T.); (I.S.); (S.V.S.); (A.P.); (V.G.)
| | - Igino Simonetti
- Radiology Division, Istituto Nazionale Tumori-IRCCS-Fondazione G. Pascale, 80131 Naples, Italy; (P.T.); (I.S.); (S.V.S.); (A.P.); (V.G.)
| | - Alessio Morrone
- Division of Radiology, Università degli Studi della Campania Luigi Vanvitelli, 80138 Naples, Italy;
| | - Roberta Fusco
- Medical Oncology Division, Igea SpA, 80013 Naples, Italy
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, Via della Signora 2, 20122 Milan, Italy
| | - Sergio Venanzio Setola
- Radiology Division, Istituto Nazionale Tumori-IRCCS-Fondazione G. Pascale, 80131 Naples, Italy; (P.T.); (I.S.); (S.V.S.); (A.P.); (V.G.)
| | - Giuliana Giacobbe
- General and Emergency Radiology Department, “Antonio Cardarelli” Hospital, 80131 Naples, Italy;
| | - Maria Chiara Brunese
- Diagnostic Imaging Section, Department of Medical and Surgical Sciences & Neurosciences, University of Molise, 86100 Campobasso, Italy;
| | - Annarita Pecchi
- Department of Radiology, University of Modena and Reggio Emilia, 41121 Modena, Italy;
| | - Sonia Triggiani
- Postgraduate School of Radiodiagnostics, University of Milan, 20122 Milan, Italy; (S.T.); (G.P.)
| | - Giuseppe Pellegrino
- Postgraduate School of Radiodiagnostics, University of Milan, 20122 Milan, Italy; (S.T.); (G.P.)
| | - Giuseppe Petralia
- Department of Medical Imaging and Radiation Sciences, IEO European Institute of Oncology IRCCS, Via Ripamonti 435, 20141 Milan, Italy;
| | - Giacomo Sica
- Radiology Unit, Monaldi Hospital, Azienda Ospedaliera dei Colli, 80131 Naples, Italy;
| | - Antonella Petrillo
- Radiology Division, Istituto Nazionale Tumori-IRCCS-Fondazione G. Pascale, 80131 Naples, Italy; (P.T.); (I.S.); (S.V.S.); (A.P.); (V.G.)
| | - Vincenza Granata
- Radiology Division, Istituto Nazionale Tumori-IRCCS-Fondazione G. Pascale, 80131 Naples, Italy; (P.T.); (I.S.); (S.V.S.); (A.P.); (V.G.)
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Yuan Z, Shu Z, Peng J, Wang W, Hou J, Han L, Zheng G, Wei Y, Zhong J. Prediction of postoperative liver metastasis in pancreatic ductal adenocarcinoma based on multiparametric magnetic resonance radiomics combined with serological markers: a cohort study of machine learning. Abdom Radiol (NY) 2024; 49:117-130. [PMID: 37819438 DOI: 10.1007/s00261-023-04047-0] [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: 07/19/2023] [Revised: 08/31/2023] [Accepted: 09/03/2023] [Indexed: 10/13/2023]
Abstract
OBJECTIVE To construct and validate a multi-dimensional model based on multiple machine leaning algorithms to predict PCLM using multi-parameter magnetic resonance (MRI) sequences with clinical and imaging parameters. METHODS A total of 148 PDAC retrospectively examined patients were classified as metastatic or non-metastatic based on results at 3 months after surgery. The radiomics features of the primary tumor were extracted from T2WI images, followed by dimension reduction. Then, multiple machine learning methods were used to construct models. Independent predictors were also screened using multifactor logistic regression and a nomogram was constructed in combination with the radiomics model. Area under the receiver operating characteristic curve (AUC) and decision curve analysis (DCA) were used to assess the accuracy and reliability of the nomogram. RESULTS The diagnostic efficacy of the radiomics model in the training and test set was 0.822 and 0.803, sensitivity was 0.742 and 0.692, and specificity was 0.792 and 0.875, respectively. The diagnostic efficacy of the nomogram in the training and test set was 0.866 and 0.832. CONCLUSION A radiomics nomogram based on machine learning improved the accuracy of predicting PCLM and may be useful for early preoperative diagnosis.
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Affiliation(s)
- Zhongyu Yuan
- Jinzhou Medical University, Jinzhou, Liaoning Province, China
| | - Zhenyu Shu
- Cancer Center, Department of Radiology, Zhejiang Provincial Hospital, Affiliated People's Hospital, Hangzhou Medical College, Hanzhou, Zhejiang, China
| | - Jiaxuan Peng
- Jinzhou Medical University, Jinzhou, Liaoning Province, China
| | - Wei Wang
- Jinzhou Medical University, Jinzhou, Liaoning Province, China
| | - Jie Hou
- Jinzhou Medical University, Jinzhou, Liaoning Province, China
| | - Lu Han
- Jinzhou Medical University, Jinzhou, Liaoning Province, China
| | - Guangying Zheng
- Jinzhou Medical University, Jinzhou, Liaoning Province, China
| | - Yuguo Wei
- Advanced Analytics, Global Medical Service, GE Healthcare, China, Xihu District, Hangzhou, 310000, China
| | - Jianguo Zhong
- Cancer Center, Department of Radiology, Zhejiang Provincial Hospital, Affiliated People's Hospital, Hangzhou Medical College, Hanzhou, Zhejiang, China.
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10
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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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11
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Liu H, Wei Z, Xv Y, Tan H, Liao F, Lv F, Jiang Q, Chen T, Xiao M. Validity of a multiphase CT-based radiomics model in predicting the Leibovich risk groups for localized clear cell renal cell carcinoma: an exploratory study. Insights Imaging 2023; 14:167. [PMID: 37816901 PMCID: PMC10564697 DOI: 10.1186/s13244-023-01526-2] [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: 04/05/2023] [Accepted: 09/10/2023] [Indexed: 10/12/2023] Open
Abstract
OBJECTIVE To develop and validate a multiphase CT-based radiomics model for preoperative risk stratification of patients with localized clear cell renal cell carcinoma (ccRCC). METHODS A total of 425 patients with localized ccRCC were enrolled and divided into training, validation, and external testing cohorts. Radiomics features were extracted from three-phase CT images (unenhanced, arterial, and venous), and radiomics signatures were constructed by the least absolute shrinkage and selection operator (LASSO) regression algorithm. The radiomics score (Rad-score) for each patient was calculated. The radiomics model was established and visualized as a nomogram by incorporating significant clinical factors and Rad-score. The predictive performance of the radiomics model was evaluated by the receiver operating characteristic curve, calibration curve, and decision curve analysis (DCA). RESULTS The AUC of the triphasic radiomics signature reached 0.862 (95% CI: 0.809-0.914), 0.853 (95% CI: 0.785-0.921), and 0.837 (95% CI: 0.714-0.959) in three cohorts, respectively, which were higher than arterial, venous, and unenhanced radiomics signatures. Multivariate logistic regression analysis showed that Rad-score (OR: 4.066, 95% CI: 3.495-8.790) and renal vein invasion (OR: 12.914, 95% CI: 1.118-149.112) were independent predictors and used to develop the radiomics model. The radiomics model showed good calibration and discrimination and yielded an AUC of 0.872 (95% CI: 0.821-0.923), 0.865 (95% CI: 0.800-0.930), and 0.848 (95% CI: 0.728-0.967) in three cohorts, respectively. DCA showed the clinical usefulness of the radiomics model in predicting the Leibovich risk groups. CONCLUSIONS The radiomics model can be used as a non-invasive and useful tool to predict the Leibovich risk groups for localized ccRCC patients. CRITICAL RELEVANCE STATEMENT The triphasic CT-based radiomics model achieved favorable performance in preoperatively predicting the Leibovich risk groups in patients with localized ccRCC. Therefore, it can be used as a non-invasive and effective tool for preoperative risk stratification of patients with localized ccRCC. KEY POINTS • The triphasic CT-based radiomics signature achieves better performance than the single-phase radiomics signature. • Radiomics holds prospects in preoperatively predicting the Leibovich risk groups for ccRCC. • This study provides a non-invasive method to stratify patients with localized ccRCC.
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Affiliation(s)
- Huayun Liu
- Department of Urology, The First Affiliated Hospital of Chongqing Medical University, No. 1 Youyi Road, Yuzhong District, Chongqing, 400016, China
| | - Zongjie Wei
- Department of Urology, The First Affiliated Hospital of Chongqing Medical University, No. 1 Youyi Road, Yuzhong District, Chongqing, 400016, China
| | - Yingjie Xv
- Department of Urology, The First Affiliated Hospital of Chongqing Medical University, No. 1 Youyi Road, Yuzhong District, Chongqing, 400016, China
| | - Hao Tan
- Department of Urology, The First Affiliated Hospital of Chongqing Medical University, No. 1 Youyi Road, Yuzhong District, Chongqing, 400016, China
| | - Fangtong Liao
- Department of Urology, The First Affiliated Hospital of Chongqing Medical University, No. 1 Youyi Road, Yuzhong District, Chongqing, 400016, China
| | - Fajin Lv
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Qing Jiang
- Department of Urology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Tao Chen
- Department of Pathology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Mingzhao Xiao
- Department of Urology, The First Affiliated Hospital of Chongqing Medical University, No. 1 Youyi Road, Yuzhong District, Chongqing, 400016, China.
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12
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Shieh A, Cen SY, Varghese BA, Hwang D, Lei X, Setayesh A, Siddiqi I, Aron M, Dsouza A, Gill IS, Wallace W, Duddalwar V. Radiomics Correlation to CD68+ Tumor-Associated Macrophages in Clear Cell Renal Cell Carcinoma. Oncology 2023; 102:260-270. [PMID: 37699367 DOI: 10.1159/000534078] [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: 07/11/2023] [Accepted: 09/04/2023] [Indexed: 09/14/2023]
Abstract
INTRODUCTION Renal cell carcinoma (RCC) is the ninth most common cancer worldwide, with clear cell RCC (ccRCC) being the most frequent histological subtype. The tumor immune microenvironment (TIME) of ccRCC is an important factor to guide treatment, but current assessments are tissue-based, which can be time-consuming and resource-intensive. In this study, we used radiomics extracted from clinically performed computed tomography (CT) as a noninvasive surrogate for CD68 tumor-associated macrophages (TAMs), a significant component of ccRCC TIME. METHODS TAM population was measured by CD68+/PanCK+ ratio and tumor-TAM clustering was measured by normalized K function calculated from multiplex immunofluorescence (mIF). A total of 1,076 regions on mIF slides from 78 patients were included. Radiomic features were extracted from multiphase CT of the ccRCC tumor. Statistical machine learning models, including random forest, Adaptive Boosting, and ElasticNet, were used to predict TAM population and tumor-TAM clustering. RESULTS The best models achieved an area under the ROC curve of 0.81 (95% CI: [0.69, 0.92]) for TAM population and 0.77 (95% CI: [0.66, 0.88]) for tumor-TAM clustering, respectively. CONCLUSION Our study demonstrates the potential of using CT radiomics-derived imaging markers as a surrogate for assessment of TAM in ccRCC for real-time treatment response monitoring and patient selection for targeted therapies and immunotherapies.
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Affiliation(s)
- Alexander Shieh
- Department of Radiology, Keck School of Medicine, University of Southern California, Los Angeles, California, USA,
| | - Steven Y Cen
- Department of Radiology, Keck School of Medicine, University of Southern California, Los Angeles, California, USA
- Department of Neurology, Keck School of Medicine, University of Southern California, Los Angeles, California, USA
| | - Bino A Varghese
- Department of Radiology, Keck School of Medicine, University of Southern California, Los Angeles, California, USA
| | - Darryl Hwang
- Department of Radiology, Keck School of Medicine, University of Southern California, Los Angeles, California, USA
| | - Xiaomeng Lei
- Department of Radiology, Keck School of Medicine, University of Southern California, Los Angeles, California, USA
| | - Ali Setayesh
- Department of Radiology, Keck School of Medicine, University of Southern California, Los Angeles, California, USA
| | - Imran Siddiqi
- Department of Pathology, University of Southern California, Los Angeles, California, USA
| | - Manju Aron
- Department of Pathology, University of Southern California, Los Angeles, California, USA
| | - Anishka Dsouza
- Division of Medical Oncology, Department of Medicine, University of Southern California, Los Angeles, California, USA
| | - Inderbir S Gill
- Institute of Urology, University of Southern California, Los Angeles, California, USA
| | - William Wallace
- Department of Pathology, University of Southern California, Los Angeles, California, USA
| | - Vinay Duddalwar
- Department of Radiology, Keck School of Medicine, University of Southern California, Los Angeles, California, USA
- Institute of Urology, University of Southern California, Los Angeles, California, USA
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Xu Y, Ye F, Li L, Yang Y, Ouyang J, Zhou Y, Wang S, Xie L, Zhou J, Zhao H, Zhao X. MRI-Based Radiomics Nomogram for Preoperatively Differentiating Intrahepatic Mass-Forming Cholangiocarcinoma From Resectable Colorectal Liver Metastases. Acad Radiol 2023; 30:2010-2020. [PMID: 37414635 DOI: 10.1016/j.acra.2023.04.030] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2023] [Revised: 04/16/2023] [Accepted: 04/22/2023] [Indexed: 07/08/2023]
Abstract
RATIONALE AND OBJECTIVES To establish a radiomics nomogram based on multiparameter magnetic resonance (MR) images for preoperatively differentiating intrahepatic mass-forming cholangiocarcinoma (IMCC) from colorectal cancer liver metastasis (CRLM). MATERIALS AND METHODS A total of 133 patients in training cohort (64 IMCC and 69 CRLM), 57 patients in internal validation cohort (29 IMCC and 28 CRLM), and 51 patients (23 IMCC and 28 CRLM) in external validation cohort were included. Radiomics features were extracted from the multiparameter MR images and selected by the least absolute shrinkage and selection operator algorithm to establish the radiomics model. Clinical variables and magnetic resonance imaging (MRI) findings were selected by univariate and multivariate analyses to construct a clinical model. The radiomics nomogram was combined with radiomics model and clinical model. RESULTS Six features were selected to construct the radiomics model. The radiomics signature showed better discrimination than the clinical model in the training cohort (Area Under the Curve (AUC), 0.92; 95% confidence interval (CI), 0.87-0.96 vs. AUC, 0.74; 95% CI, 0.66-0.83) and the external validation cohort (AUC, 0.90; 95% CI, 0.82-0.98 vs. AUC, 0.81; 95% CI, 0.69-0.93). The radiomics nomogram showed the best discrimination performance with favorable calibration in the training cohort (AUC, 0.94; 95% CI, 0.90-0.97) and the external validation cohort (AUC, 0.92; 95% CI, 0.84-1.00). CONCLUSION The radiomics nomogram combining radiomics signatures based on multiparameter MRI with clinical factors (serum carcinoembryonic antigen level and tumor diameter) may provide a reliable and noninvasive tool to discriminate IMCC from CRLM, which could help guide treatment strategies and prognosis preoperatively prediction.
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Affiliation(s)
- Ying Xu
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China (Y.X., F.Y., L.L., X.Z.)
| | - Feng Ye
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China (Y.X., F.Y., L.L., X.Z.)
| | - Lu Li
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China (Y.X., F.Y., L.L., X.Z.)
| | - Yi Yang
- Department of Hepatobiliary Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China (Y.Y., H.Z.); Key Laboratory of Gene Editing Screening and Research and Development (R&D) of Digestive System Tumor Drugs, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China (Y.Y., H.Z.)
| | - Jingzhong Ouyang
- Department of Hepatobiliary and Pancreatic Surgery, Affiliated Cancer Hospital of Zhengzhou University, Zhengzhou, Henan, China (J.O., Y.Z., J.Z.)
| | - Yanzhao Zhou
- Department of Hepatobiliary and Pancreatic Surgery, Affiliated Cancer Hospital of Zhengzhou University, Zhengzhou, Henan, China (J.O., Y.Z., J.Z.); Department of Hepatobiliary Cancer, Liver Cancer Research Center, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Tianjin, China (Y.Z.)
| | - Sicong Wang
- Magnetic Resonance Imaging Research, General Electric Healthcare, Beijing, China (S.W., L.X.)
| | - Lizhi Xie
- Magnetic Resonance Imaging Research, General Electric Healthcare, Beijing, China (S.W., L.X.)
| | - Jinxue Zhou
- Department of Hepatobiliary and Pancreatic Surgery, Affiliated Cancer Hospital of Zhengzhou University, Zhengzhou, Henan, China (J.O., Y.Z., J.Z.)
| | - Hong Zhao
- Department of Hepatobiliary Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China (Y.Y., H.Z.); Key Laboratory of Gene Editing Screening and Research and Development (R&D) of Digestive System Tumor Drugs, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China (Y.Y., H.Z.)
| | - Xinming Zhao
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China (Y.X., F.Y., L.L., X.Z.).
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14
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Özdemir H, Azamat S, Sam Özdemir M. Can Only the Shape Feature in Radiomics Help Machine Learning Show That Bladder Cancer Has Invaded Muscles? Cureus 2023; 15:e45488. [PMID: 37859896 PMCID: PMC10584356 DOI: 10.7759/cureus.45488] [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] [Accepted: 09/18/2023] [Indexed: 10/21/2023] Open
Abstract
OBJECTIVES The presence of muscle invasion is an important factor in establishing a treatment strategy for bladder cancer (BCa). The aim of this study is to reveal the diagnostic performance of radiomic shape features in predicting muscle-invasive BCa. METHODS In this study, 60 patients with histologically proven BCa who underwent a preoperative MRI were retrospectively recruited. The whole tumor volume was segmented on apparent diffusion coefficient (ADC) maps and T2W images. Afterward, the shape features of the volume of interest were extracted using PyRadiomics. Machine learning classification was performed using statistically different shape features in MATLAB® (The MathWorks, Inc., Natick, Massachusetts, United States). RESULTS The findings revealed that 27 bladder cancer patients had muscle invasion, while 33 had superficial bladder cancer (53 men and seven women; mean age: 62±14). Surface area, volume, and relevant features were significantly greater in the invasive group than in the non-invasive group based on the ADC maps (P<0.05). Superficial bladder cancer had a more spherical form compared to invasive bladder cancer (P=0.05) with both imaging modalities. Flatness and elongation did not differ significantly between groups with either modality (P>0.05). Logistic regression had the highest accuracy of 83.3% (sensitivity 82.8%, specificity 84%) in assessing invasion based on the shape features of ADC maps, while K-nearest neighbors had the highest accuracy of 78.2% (sensitivity 79.1%, specificity 69.4%) in assessing invasion based on T2W images. CONCLUSIONS Shape features can be helpful in predicting muscle invasion in bladder cancer using machine learning methods.
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Affiliation(s)
- Harun Özdemir
- Department of Urology, Başakşehir Çam and Sakura City Hospital, Istanbul, TUR
| | - Sena Azamat
- Department of Radiology, Başakşehir Çam and Sakura City Hospital, Istanbul, TUR
| | - Merve Sam Özdemir
- Department of Radiology, Başakşehir Çam and Sakura City Hospital, Istanbul, TUR
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Varghese BA, Fields BKK, Hwang DH, Duddalwar VA, Matcuk GR, Cen SY. Spatial assessments in texture analysis: what the radiologist needs to know. FRONTIERS IN RADIOLOGY 2023; 3:1240544. [PMID: 37693924 PMCID: PMC10484588 DOI: 10.3389/fradi.2023.1240544] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/15/2023] [Accepted: 08/10/2023] [Indexed: 09/12/2023]
Abstract
To date, studies investigating radiomics-based predictive models have tended to err on the side of data-driven or exploratory analysis of many thousands of extracted features. In particular, spatial assessments of texture have proven to be especially adept at assessing for features of intratumoral heterogeneity in oncologic imaging, which likewise may correspond with tumor biology and behavior. These spatial assessments can be generally classified as spatial filters, which detect areas of rapid change within the grayscale in order to enhance edges and/or textures within an image, or neighborhood-based methods, which quantify gray-level differences of neighboring pixels/voxels within a set distance. Given the high dimensionality of radiomics datasets, data dimensionality reduction methods have been proposed in an attempt to optimize model performance in machine learning studies; however, it should be noted that these approaches should only be applied to training data in order to avoid information leakage and model overfitting. While area under the curve of the receiver operating characteristic is perhaps the most commonly reported assessment of model performance, it is prone to overestimation when output classifications are unbalanced. In such cases, confusion matrices may be additionally reported, whereby diagnostic cut points for model predicted probability may hold more clinical significance to clinical colleagues with respect to related forms of diagnostic testing.
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Affiliation(s)
- Bino A. Varghese
- Department of Radiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
| | - Brandon K. K. Fields
- Department of Radiology & Biomedical Imaging, University of California, San Francisco, San Francisco, CA, United States
| | - Darryl H. Hwang
- Department of Radiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
| | - Vinay A. Duddalwar
- Department of Radiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
| | - George R. Matcuk
- Department of Radiology, Cedars-Sinai Medical Center, Los Angeles, CA, United States
| | - Steven Y. Cen
- Department of Radiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
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Garnier C, Ferrer L, Vargas J, Gallinato O, Jambon E, Le Bras Y, Bernhard JC, Colin T, Grenier N, Marcelin C. A CT-Based Clinical, Radiological and Radiomic Machine Learning Model for Predicting Malignancy of Solid Renal Tumors (UroCCR-75). Diagnostics (Basel) 2023; 13:2548. [PMID: 37568911 PMCID: PMC10417436 DOI: 10.3390/diagnostics13152548] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2023] [Revised: 07/05/2023] [Accepted: 07/25/2023] [Indexed: 08/13/2023] Open
Abstract
BACKGROUND Differentiating benign from malignant renal tumors is important for patient management, and it may be improved by quantitative CT features analysis including radiomic. PURPOSE This study aimed to compare performances of machine learning models using bio-clinical, conventional radiologic and 3D-radiomic features for the differentiation of benign and malignant solid renal tumors using pre-operative multiphasic contrast-enhanced CT examinations. MATERIALS AND METHODS A unicentric retrospective analysis of prospectively acquired data from a national kidney cancer database was conducted between January 2016 and December 2020. Histologic findings were obtained by robotic-assisted partial nephrectomy. Lesion images were semi-automatically segmented, allowing for a 3D-radiomic features extraction in the nephrographic phase. Conventional radiologic parameters such as shape, content and enhancement were combined in the analysis. Biological and clinical features were obtained from the national database. Eight machine learning (ML) models were trained and validated using a ten-fold cross-validation. Predictive performances were evaluated comparing sensitivity, specificity, accuracy and AUC. RESULTS A total of 122 patients with 132 renal lesions, including 111 renal cell carcinomas (RCCs) (111/132, 84%) and 21 benign tumors (21/132, 16%), were evaluated (58 +/- 14 years, men 74%). Unilaterality (100/111, 90% vs. 13/21, 62%; p = 0.02), necrosis (81/111, 73% vs. 8/21, 38%; p = 0.02), lower values of tumor/cortex ratio at portal time (0.61 vs. 0.74, p = 0.01) and higher variation of tumor/cortex ratio between arterial and portal times (0.22 vs. 0.05, p = 0.008) were associated with malignancy. A total of 35 radiomics features were selected, and "intensity mean value" was associated with RCCs in multivariate analysis (OR = 0.99). After ten-fold cross-validation, a C5.0Tree model was retained for its predictive performances, yielding a sensitivity of 95%, specificity of 42%, accuracy of 87% and AUC of 0.74. CONCLUSION Our machine learning-based model combining clinical, radiologic and radiomics features from multiphasic contrast-enhanced CT scans may help differentiate benign from malignant solid renal tumors.
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Affiliation(s)
- Cassandre Garnier
- Department of Imaging and Interventional Radiology, Hôpital Pellegrin, Place Amélie-Raba-Léon, 33076 Bordeaux, France
| | - Loïc Ferrer
- SOPHiA GENETICS, Multimodal Research, Cité de la Photonique—Bâtiment GIENAH, 11 Avenue de Canteranne, 33600 Pessac, France; (L.F.); (J.V.); (O.G.); (T.C.)
| | - Jennifer Vargas
- SOPHiA GENETICS, Multimodal Research, Cité de la Photonique—Bâtiment GIENAH, 11 Avenue de Canteranne, 33600 Pessac, France; (L.F.); (J.V.); (O.G.); (T.C.)
| | - Olivier Gallinato
- SOPHiA GENETICS, Multimodal Research, Cité de la Photonique—Bâtiment GIENAH, 11 Avenue de Canteranne, 33600 Pessac, France; (L.F.); (J.V.); (O.G.); (T.C.)
| | - Eva Jambon
- Department of Imaging and Interventional Radiology, Hôpital Pellegrin, Place Amélie-Raba-Léon, 33076 Bordeaux, France
| | - Yann Le Bras
- Department of Imaging and Interventional Radiology, Hôpital Pellegrin, Place Amélie-Raba-Léon, 33076 Bordeaux, France
| | | | - Thierry Colin
- SOPHiA GENETICS, Multimodal Research, Cité de la Photonique—Bâtiment GIENAH, 11 Avenue de Canteranne, 33600 Pessac, France; (L.F.); (J.V.); (O.G.); (T.C.)
| | - Nicolas Grenier
- Department of Imaging and Interventional Radiology, Hôpital Pellegrin, Place Amélie-Raba-Léon, 33076 Bordeaux, France
| | - Clément Marcelin
- Department of Imaging and Interventional Radiology, Hôpital Pellegrin, Place Amélie-Raba-Léon, 33076 Bordeaux, France
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Klontzas ME, Koltsakis E, Kalarakis G, Trpkov K, Papathomas T, Karantanas AH, Tzortzakakis A. Machine Learning Integrating 99mTc Sestamibi SPECT/CT and Radiomics Data Achieves Optimal Characterization of Renal Oncocytic Tumors. Cancers (Basel) 2023; 15:3553. [PMID: 37509214 PMCID: PMC10377512 DOI: 10.3390/cancers15143553] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2023] [Revised: 07/04/2023] [Accepted: 07/06/2023] [Indexed: 07/30/2023] Open
Abstract
The increasing evidence of oncocytic renal tumors positive in 99mTc Sestamibi Single Photon Emission Tomography/Computed Tomography (SPECT/CT) examination calls for the development of diagnostic tools to differentiate these tumors from more aggressive forms. This study combined radiomics analysis with the uptake of 99mTc Sestamibi on SPECT/CT to differentiate benign renal oncocytic neoplasms from renal cell carcinoma. A total of 57 renal tumors were prospectively collected. Histopathological analysis and radiomics data extraction were performed. XGBoost classifiers were trained using the radiomics features alone and combined with the results from the visual evaluation of 99mTc Sestamibi SPECT/CT examination. The combined SPECT/radiomics model achieved higher accuracy (95%) with an area under the curve (AUC) of 98.3% (95% CI 93.7-100%) than the radiomics-only model (71.67%) with an AUC of 75% (95% CI 49.7-100%) and visual evaluation of 99mTc Sestamibi SPECT/CT alone (90.8%) with an AUC of 90.8% (95%CI 82.5-99.1%). The positive predictive values of SPECT/radiomics, radiomics-only, and 99mTc Sestamibi SPECT/CT-only models were 100%, 85.71%, and 85%, respectively, whereas the negative predictive values were 85.71%, 55.56%, and 94.6%, respectively. Feature importance analysis revealed that 99mTc Sestamibi uptake was the most influential attribute in the combined model. This study highlights the potential of combining radiomics analysis with 99mTc Sestamibi SPECT/CT to improve the preoperative characterization of benign renal oncocytic neoplasms. The proposed SPECT/radiomics classifier outperformed the visual evaluation of 99mTc Sestamibii SPECT/CT and the radiomics-only model, demonstrating that the integration of 99mTc Sestamibi SPECT/CT and radiomics data provides improved diagnostic performance, with minimal false positive and false negative results.
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Affiliation(s)
- Michail E Klontzas
- Department of Medical Imaging, University Hospital of Heraklion, Heraklion 71110, Greece
- Computational BioMedicine Laboratory, Institute of Computer Science, Foundation for Research and Technology (FORTH), Heraklion 70013, Greece
- Department of Radiology, School of Medicine, University of Crete, Voutes Campus, Heraklion 71110, Greece
| | - Emmanouil Koltsakis
- Department of Diagnostic Radiology, Karolinska University Hospital, Stockholm 17177, Sweden
| | - Georgios Kalarakis
- Department of Diagnostic Radiology, Karolinska University Hospital, Stockholm 17177, Sweden
- Division of Radiology, Department for Clinical Science, Intervention and Technology (CLINTEC), Karolinska Institutet, Stockholm 14152, Sweden
| | - Kiril Trpkov
- Alberta Precision Labs, Department of Pathology and Laboratory Medicine, Cumming School of Medicine, University of Calgary, Calgary, AB T2L 2K5, Canada
| | - Thomas Papathomas
- Institute of Metabolism and Systems Research, University of Birmingham, Birmingham B15 2TT, UK
- Department of Clinical Pathology, Vestre Viken Hospital Trust, Drammen 3004, Norway
| | - Apostolos H Karantanas
- Department of Medical Imaging, University Hospital of Heraklion, Heraklion 71110, Greece
- Computational BioMedicine Laboratory, Institute of Computer Science, Foundation for Research and Technology (FORTH), Heraklion 70013, Greece
- Department of Radiology, School of Medicine, University of Crete, Voutes Campus, Heraklion 71110, Greece
| | - Antonios Tzortzakakis
- Division of Radiology, Department for Clinical Science, Intervention and Technology (CLINTEC), Karolinska Institutet, Stockholm 14152, Sweden
- Medical Radiation Physics and Nuclear Medicine, Section for Nuclear Medicine, Karolinska University Hospital, Huddinge, Stockholm 14186, Sweden
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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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19
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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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20
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Ren Y, Bo L, Shen B, Yang J, Xu S, Shen W, Chen H, Wang X, Chen H, Cai X. Development and validation of a clinical-radiomics model to predict recurrence for patients with hepatocellular carcinoma after curative resection. Med Phys 2023; 50:778-790. [PMID: 36269204 DOI: 10.1002/mp.16061] [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: 03/11/2022] [Revised: 09/15/2022] [Accepted: 09/23/2022] [Indexed: 11/08/2022] Open
Abstract
PURPOSE Recurrence is the leading cause of death in hepatocellular carcinoma (HCC) patients with curative resection. In this study, we aimed to develop a preoperative predictive model based on high-throughput radiomics features and clinical factors for prediction of long- and short-term recurrence for these patients. METHODS A total of 270 patients with HCC who were followed up for at least 5 years after curative hepatectomy between June 2014 and December 2017 were enrolled in this retrospective study. Regions of interest were manually delineated in preoperative T2-weighted images using ITK-SNAP software on each HCC tumor slice. A total of 1197 radiomics features were extracted, and the recursive feature elimination method based on logistic regression was used for radiomics signature building. Tenfold cross-validation was applied for model development. Nomograms were constructed and assessed by calibration plot, which compares nomogram-predicated probability with observed outcome. Receiver-operating characteristic was then generated to evaluate the predictive performance of the model in the development and test cohorts. RESULTS The 10 most recurrence-free survival-related radiomics features were selected for the radiomics signatures. A multiparametric clinical-radiomics model combining albumin and radiomics score for recurrence prediction was further established. The integrated model demonstrated good calibration and satisfactory discrimination, with the area under the curve (AUC) of 0.864, 95% CI 0.842-0.903, sensitivity of 0.889, and specificity of 0.644 in the test set. Calibration curve showed good agreement concerning 5-year recurrence risk predicted by the nomogram. In addition, the AUC of 1-, 2-, 3-, and 4-year recurrence was 0.935 (95% CI 0.836-1.000), 0.861 (95% CI 0.723-0.999), 0.878 (95% CI 0.762-0.994), and 0.878 (95% CI 0.762-0.994) in the test set, respectively. CONCLUSIONS The clinical-radiomics model integrating radiomics features and clinical factors can improve recurrence predictions beyond predictions made using clinical factors or radiomics features alone. Our clinical-radiomics model is a valid method to predict recurrence that should improve preoperative prognostic performance and allow more individualized treatment decisions.
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Affiliation(s)
- Yiyue Ren
- Department of General Surgery, Department of Head and Neck Surgery, Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, Hangzhou, Zhejiang, China
| | - Linlin Bo
- Shandong Key Laboratory of Medical Physics and Image Processing, Shandong Institute of Industrial Technology for Health Sciences and Precision Medicine, School of Physics and Electronics, Shandong Normal University, Jinan, Shandong, China
| | - Bo Shen
- Department of Radiology, Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, Hangzhou, Zhejiang, China.,Department of Radiology, Huzhou Central Hospital, Affiliated Central Hospital of Huzhou University; Affiliated Huzhou Hospital, Zhejiang University School of Medicine, Huzhou, Zhejiang, China
| | - Jing Yang
- Institute of Translational Medicine, Zhejiang University, Hangzhou, Zhejiang, China
| | - Shufeng Xu
- Department of Radiology, People's Hospital of Quzhou, Quzhou Hospital Affiliated to Wenzhou Medical University, Quzhou, Zhejiang, China
| | - Weiqiang Shen
- Department of Radiology, Huzhou Central Hospital, Affiliated Central Hospital of Huzhou University; Affiliated Huzhou Hospital, Zhejiang University School of Medicine, Huzhou, Zhejiang, China
| | - Hao Chen
- Department of Radiology, Huzhou Central Hospital, Affiliated Central Hospital of Huzhou University; Affiliated Huzhou Hospital, Zhejiang University School of Medicine, Huzhou, Zhejiang, China
| | - Xiaoyan Wang
- Department of Medical Imaging, Bengbu Medical College, Bengbu, Anhui, China
| | - Haipeng Chen
- Deepwise Artificial Intelligence Laboratory, Beijing, China
| | - Xiujun Cai
- Department of General Surgery, Department of Head and Neck Surgery, Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, Hangzhou, Zhejiang, China
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Feng S, Gong M, Zhou D, Yuan R, Kong J, Jiang F, Zhang L, Chen W, Li Y. A CT-based radiomics nomogram for differentiation of benign and malignant small renal masses (≤4 cm). Transl Oncol 2023; 29:101627. [PMID: 36731307 PMCID: PMC9937807 DOI: 10.1016/j.tranon.2023.101627] [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: 11/03/2022] [Revised: 12/26/2022] [Accepted: 01/15/2023] [Indexed: 02/04/2023] Open
Abstract
RATIONALE AND OBJECTIVES Based on radiomics signature and clinical data, to develop and verify a radiomics nomogram for preoperative distinguish between benign and malignant of small renal masses (SRM). MATERIALS AND METHODS One hundred and fifty-six patients with malignant (n = 92) and benign (n = 64) SRM were divided into the following three categories: category A, typical angiomyolipoma (AML) with visible fat; category B, benign SRM without visible fat, including fat-poor angiomyolipoma (fp-AML), and other rare benign renal tumors; category C, malignant renal tumors. At the same time, one hundred and fifty-six patients included in the study were divided into the training set (n = 108) and test set (n = 48). Respectively from corticomedullary phase (CP), nephrogram phase (NP) and excretory phase (EP) CT images to extract the radiomics features, and the optimal features were screened to establish the logistic regression model and decision tree model, and computed the radiomics score (Rad-score). Demographics and CT findings were evaluated and statistically significant factors were selected to construct a clinical factors model. The radiomics nomogram was established by merging Rad-score and selected clinical factors. The Akaike information criterion (AIC) values and the area under the curve (AUC) were used to compare model discriminant performance, and decision curve analysis (DCA) was used to assess clinical usefulness. RESULTS Seven, fifteen, nineteen, and seventeen distinguishing features were obtained in the CP, NP, EP, and three-phase joint, respectively, and the logistic regression and decision tree models were built based on this features. In the training set, the logistic regression model works better than the decision tree model for distinguishing categories A and B from category C, with the AUC of CP, NP, EP and three-phase joint were 0.868, 0.906, 0.937 and 0.975, respectively. The radiomics nomogram constructed based on the three-phase joint Rad-score and selected clinical factor performed well on the training set (AUC, 0.988; 95% CI, 0.974-1.000) for differentiation of categories A and B from category C. In the test set, the AUC of clinical factors model, radiomics signature and radiomics nomogram for discriminating categories A and B from category C were 0.814, 0.954 and 0.968, respectively; for the identification of category A from category C, the AUC of the three models were 0.789, 0.979, 0.985, respectively; for discriminating category B from category C, the AUC of the three models were 0.853, 0.915, 0.946, respectively. The radiomics nomogram had better discriminative than the clinical factors model in both training and test sets (P < 0.05). The radiomics nomogram (AIC = 40.222) with the lowest AIC value was considered the best model compared with that of the clinical factors model (AIC = 106.814) and the radiomics signature (AIC = 44.224). The DCA showed that the radiomics nomogram have better clinical utility than the clinical factors model and radiomics signature. CONCLUSIONS The logistic regression model has better discriminative performance than the decision tree model, and the radiomics nomogram based on Rad-score of three-phase joint and clinical factors has a good predictive effect in differentiating benign from malignant of SRM, which may help clinicians develop accurate and individualized treatment strategies.
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Affiliation(s)
- Shengxing Feng
- The First Clinical School of Medicine, Guangdong Medical University, Zhanjiang, China,Department of Urology, The People's Hospital of Zhongshan, Zhongshan, China
| | - Mancheng Gong
- Department of Urology, The People's Hospital of Zhongshan, Zhongshan, China.
| | - Dongsheng Zhou
- The First Clinical School of Medicine, Guangdong Medical University, Zhanjiang, China,Department of Urology, The People's Hospital of Zhongshan, Zhongshan, China
| | - Runqiang Yuan
- Department of Urology, The People's Hospital of Zhongshan, Zhongshan, China
| | - Jie Kong
- The First Clinical School of Medicine, Guangdong Medical University, Zhanjiang, China,Department of Urology, The People's Hospital of Zhongshan, Zhongshan, China
| | - Feng Jiang
- The First Clinical School of Medicine, Guangdong Medical University, Zhanjiang, China,Department of Urology, The People's Hospital of Zhongshan, Zhongshan, China
| | - Lijie Zhang
- The First Clinical School of Medicine, Guangdong Medical University, Zhanjiang, China,Department of Urology, The People's Hospital of Zhongshan, Zhongshan, China
| | - Weitian Chen
- The First Clinical School of Medicine, Guangdong Medical University, Zhanjiang, China,Department of Urology, The People's Hospital of Zhongshan, Zhongshan, China
| | - Yueming Li
- The First Clinical School of Medicine, Guangdong Medical University, Zhanjiang, China,Department of Urology, The People's Hospital of Zhongshan, Zhongshan, China
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22
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Qu J, Zhang Q, Song X, Jiang H, Ma H, Li W, Wang X. CT differentiation of the oncocytoma and renal cell carcinoma based on peripheral tumor parenchyma and central hypodense area characterisation. BMC Med Imaging 2023; 23:16. [PMID: 36707788 PMCID: PMC9881251 DOI: 10.1186/s12880-023-00972-0] [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: 09/21/2022] [Accepted: 01/18/2023] [Indexed: 01/28/2023] Open
Abstract
BACKGROUND Although the central scar is an essential imaging characteristic of renal oncocytoma (RO), its utility in distinguishing RO from renal cell carcinoma (RCC) has not been well explored. The study aimed to evaluate whether the combination of CT characteristics of the peripheral tumor parenchyma (PTP) and central hypodense area (CHA) can differentiate typical RO with CHA from RCC. METHODS A total of 132 tumors on the initial dataset were retrospectively evaluated using four-phase CT. The excretory phases were performed more than 20 min after the contrast injection. In corticomedullary phase (CMP) images, all tumors had CHAs. These tumors were categorized into RO (n = 23), clear cell RCC (ccRCC) (n = 85), and non-ccRCC (n = 24) groups. The differences in these qualitative and quantitative CT features of CHA and PTP between ROs and ccRCCs/non-ccRCCs were statistically examined. Logistic regression filters the main factors for separating ROs from ccRCCs/non-ccRCCs. The prediction models omitting and incorporating CHA features were constructed and evaluated, respectively. The effectiveness of the prediction models including CHA characteristics was then confirmed through a validation dataset (8 ROs, 35 ccRCCs, and 10 non-ccRCCs). RESULTS The findings indicate that for differentiating ROs from ccRCCs and non-ccRCCs, prediction models with CHA characteristics surpassed models without CHA, with the corresponding areas under the curve (AUC) being 0.962 and 0.914 versus 0.952 and 0.839 respectively. In the prediction models that included CHA parameters, the relative enhancement ratio (RER) in CMP and enhancement inversion, as well as RER in nephrographic phase and enhancement inversion were the primary drivers for differentiating ROs from ccRCCs and non-ccRCCs, respectively. The prediction models with CHA characteristics had the comparable diagnostic ability on the validation dataset, with respective AUC values of 0.936 and 0.938 for differentiating ROs from ccRCCs and non-ccRCCs. CONCLUSION The prediction models with CHA characteristics can help better differentiate typical ROs from RCCs. When a mass with CHA is discovered, particularly if RO is suspected, EP images with longer delay scanning periods should be acquired to evaluate the enhancement inversion characteristics of CHA.
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Affiliation(s)
- Jianyi Qu
- grid.410645.20000 0001 0455 0905Yuhuangding Hospital, Qingdao University School of Medicine, Shandong Yantai, China
| | - Qianqian Zhang
- grid.410645.20000 0001 0455 0905Yuhuangding Hospital, Qingdao University School of Medicine, Shandong Yantai, China
| | - Xinhong Song
- grid.410645.20000 0001 0455 0905Yuhuangding Hospital, Qingdao University School of Medicine, Shandong Yantai, China
| | - Hong Jiang
- grid.410645.20000 0001 0455 0905Yuhuangding Hospital, Qingdao University School of Medicine, Shandong Yantai, China
| | - Heng Ma
- grid.410645.20000 0001 0455 0905Yuhuangding Hospital, Qingdao University School of Medicine, Shandong Yantai, China
| | - Wenhua Li
- grid.16821.3c0000 0004 0368 8293Xinhua Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China
| | - Xiaofei Wang
- grid.440653.00000 0000 9588 091XYantaishan Hospital, Binzhou Medical University, Shandong Yantai, China
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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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24
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Alzu'bi D, Abdullah M, Hmeidi I, AlAzab R, Gharaibeh M, El-Heis M, Almotairi KH, Forestiero A, Hussein AM, Abualigah L. Kidney Tumor Detection and Classification Based on Deep Learning Approaches: A New Dataset in CT Scans. JOURNAL OF HEALTHCARE ENGINEERING 2022; 2022:3861161. [PMID: 37323471 PMCID: PMC10266909 DOI: 10.1155/2022/3861161] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/17/2022] [Accepted: 09/15/2022] [Indexed: 09/01/2023]
Abstract
Kidney tumor (KT) is one of the diseases that have affected our society and is the seventh most common tumor in both men and women worldwide. The early detection of KT has significant benefits in reducing death rates, producing preventive measures that reduce effects, and overcoming the tumor. Compared to the tedious and time-consuming traditional diagnosis, automatic detection algorithms of deep learning (DL) can save diagnosis time, improve test accuracy, reduce costs, and reduce the radiologist's workload. In this paper, we present detection models for diagnosing the presence of KTs in computed tomography (CT) scans. Toward detecting and classifying KT, we proposed 2D-CNN models; three models are concerning KT detection such as a 2D convolutional neural network with six layers (CNN-6), a ResNet50 with 50 layers, and a VGG16 with 16 layers. The last model is for KT classification as a 2D convolutional neural network with four layers (CNN-4). In addition, a novel dataset from the King Abdullah University Hospital (KAUH) has been collected that consists of 8,400 images of 120 adult patients who have performed CT scans for suspected kidney masses. The dataset was divided into 80% for the training set and 20% for the testing set. The accuracy results for the detection models of 2D CNN-6 and ResNet50 reached 97%, 96%, and 60%, respectively. At the same time, the accuracy results for the classification model of the 2D CNN-4 reached 92%. Our novel models achieved promising results; they enhance the diagnosis of patient conditions with high accuracy, reducing radiologist's workload and providing them with a tool that can automatically assess the condition of the kidneys, reducing the risk of misdiagnosis. Furthermore, increasing the quality of healthcare service and early detection can change the disease's track and preserve the patient's life.
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Affiliation(s)
- Dalia Alzu'bi
- Department of Computer Information Systems, Jordan University of Science and Technology, Irbid 2210, Jordan
| | - Malak Abdullah
- Department of Computer Information Systems, Jordan University of Science and Technology, Irbid 2210, Jordan
| | - Ismail Hmeidi
- Department of Computer Information Systems, Jordan University of Science and Technology, Irbid 2210, Jordan
| | - Rami AlAzab
- Department of General Surgery and Urology, University of Science and Technology, Irbid 22110, Jordan
| | - Maha Gharaibeh
- Department of Diagnostic and Interventional Radiology, Faculty of Medicine, Jordan University of Science and Technology, Irbid 2210, Jordan
| | - Mwaffaq El-Heis
- Department of Diagnostic and Interventional Radiology, Faculty of Medicine, Jordan University of Science and Technology, Irbid 2210, Jordan
| | - Khaled H. Almotairi
- Computer Engineering Department, Computer and Information Systems College, Umm Al-Qura University, Makkah 21955, Saudi Arabia
| | - Agostino Forestiero
- Institute for High Performance Computing and Networking, CNR, Rende (CS), Italy
| | - Ahmad MohdAziz Hussein
- Deanship of E-Learning and Distance Education, Umm Al-Qura University, Makkah 21955, Saudi Arabia
| | - Laith Abualigah
- Hourani Center for Applied Scientific Research, Al-Ahliyya Amman University, Amman 19328, Jordan
- Faculty of Information Technology, Middle East University, Amman 11831, Jordan
- School of Computer Sciences, Universiti Sains Malaysia, Pulau Pinang 11800, Malaysia
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Kong J, He Y, Zhu X, Shao P, Xu Y, Chen Y, Coatrieux JL, Yang G. BKC-Net: Bi-Knowledge Contrastive Learning for renal tumor diagnosis on 3D CT images. Knowl Based Syst 2022. [DOI: 10.1016/j.knosys.2022.109369] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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Li J, Zhang T, Ma J, Zhang N, Zhang Z, Ye Z. Machine-learning-based contrast-enhanced computed tomography radiomic analysis for categorization of ovarian tumors. Front Oncol 2022; 12:934735. [PMID: 36016613 PMCID: PMC9395674 DOI: 10.3389/fonc.2022.934735] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2022] [Accepted: 06/16/2022] [Indexed: 11/13/2022] Open
Abstract
ObjectivesThis study aims to evaluate the diagnostic performance of machine-learning-based contrast-enhanced CT radiomic analysis for categorizing benign and malignant ovarian tumors.MethodsA total of 1,329 patients with ovarian tumors were randomly divided into a training cohort (N=930) and a validation cohort (N=399). All tumors were resected, and pathological findings were confirmed. Radiomic features were extracted from the portal venous phase images of contrast-enhanced CT. The clinical predictors included age, CA-125, HE-4, ascites, and margin of tumor. Both radiomics model (including selected radiomic features) and mixed model (incorporating selected radiomic features and clinical predictors) were constructed respectively. Six classifiers [k-nearest neighbor (KNN), support vector machines (SVM), random forest (RF), logistic regression (LR), multi-layer perceptron (MLP), and eXtreme Gradient Boosting (XGBoost)] were used for each model. The mean relative standard deviation (RSD) and area under the receiver operating characteristic curve (AUC) were applied to evaluate and select the best classifiers. Then, the performances of the two models with selected classifiers were assessed in the validation cohort.ResultsThe MLP classifier with the least RSD (1.21 and 0.53, respectively) was selected as the best classifier in both radiomics and mixed models. The two models with MLP classifier performed well in the validation cohort, with the AUCs of 0.91 and 0.96 and with accuracies (ACCs) of 0.83 and 0.87, respectively. The Delong test showed that the AUC of mixed model was statistically different from that of radiomics model (p<0.001).ConclusionsMachine-learning-based CT radiomic analysis could categorize ovarian tumors with good performance preoperatively. The mixed model with MLP classifier may be a potential tool in clinical applications.
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Affiliation(s)
- Jiaojiao Li
- Department of Radiology, National Clinical Research Center for Cancer, Tianjin Key Laboratory of Cancer Prevention and Therapy, Tianjin’s Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China
- Department of Radiology, First Affiliated Hospital of Hebei North University, Zhangjiakou, China
| | - Tianzhu Zhang
- Department of Radiology, Tianjin Medical University General Hospital, Tianjin, China
| | - Juanwei Ma
- Department of Radiology, National Clinical Research Center for Cancer, Tianjin Key Laboratory of Cancer Prevention and Therapy, Tianjin’s Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China
| | - Ningnannan Zhang
- Department of Radiology, Tianjin Medical University General Hospital, Tianjin, China
| | - Zhang Zhang
- Department of Radiology, Tianjin Medical University General Hospital, Tianjin, China
- *Correspondence: Zhaoxiang Ye, ; Zhang Zhang,
| | - Zhaoxiang Ye
- Department of Radiology, National Clinical Research Center for Cancer, Tianjin Key Laboratory of Cancer Prevention and Therapy, Tianjin’s Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China
- *Correspondence: Zhaoxiang Ye, ; Zhang Zhang,
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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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Gao Y, Wang X, Wang S, Miao Y, Zhu C, Li C, Huang G, Jiang Y, Li J, Zhao X, Wu X. Differential Diagnosis of Type 1 and Type 2 Papillary Renal Cell Carcinoma Based on Enhanced CT Radiomics Nomogram. Front Oncol 2022; 12:854979. [PMID: 35719928 PMCID: PMC9204229 DOI: 10.3389/fonc.2022.854979] [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: 01/14/2022] [Accepted: 05/11/2022] [Indexed: 11/13/2022] Open
Abstract
Objectives To construct a contrast-enhanced CT-based radiomics nomogram that combines clinical factors and a radiomics signature to distinguish papillary renal cell carcinoma (pRCC) type 1 from pRCC type 2 tumours. Methods A total of 131 patients with 60 in pRCC type 1 and 71 in pRCC type 2 were enrolled and divided into training set (n=91) and testing set (n=40). Patient demographics and enhanced CT imaging characteristics were evaluated to set up a clinical factors model. A radiomics signature was constructed and radiomics score (Rad-score) was calculated by extracting radiomics features from contrast-enhanced CT images in corticomedullary phase (CMP) and nephrographic phase (NP). A radiomics nomogram was then built by incorporating the Rad-score and significant clinical factors according to multivariate logistic regression analysis. The diagnostic performance of the clinical factors model, radiomics signature and radiomics nomogram was evaluated on both the training and testing sets. Results Three validated features were extracted from the CT images and used to construct the radiomics signature. Boundary blurring as an independent risk factor for tumours was used to build clinical factors model. The AUC value of the radiomics nomogram, which was based on the selected clinical factors and Rad-score, were 0.855 and 0.831 in the training and testing sets, respectively. The decision curves of the radiomics nomogram and radiomics signature in the training set indicated an overall net benefit over the clinical factors model. Conclusion Radiomics nomogram combining clinical factors and radiomics signature is a non-invasive prediction method with a good prediction for pRCC type 1 tumours and type 2 tumours preoperatively and has some significance in guiding clinicians selecting subsequent treatment plans.
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Affiliation(s)
- Yankun Gao
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Xingwei Wang
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Shihui Wang
- Department of Radiology, The First Affiliated Hospital of Wannan Medical college, Wuhu, China
| | - Yingying Miao
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Chao Zhu
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Cuiping Li
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Guoquan Huang
- Department of Imaging, Wuhu Second People's Hospital, Wuhu, China
| | - Yan Jiang
- Department of Pathology, The First Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Jianying Li
- CT Research Center, GE Healthcare China, Shanghai, China
| | - Xiaoying Zhao
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Xingwang Wu
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei, China
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Granata V, Fusco R, Belli A, Danti G, Bicci E, Cutolo C, Petrillo A, Izzo F. Diffusion weighted imaging and diffusion kurtosis imaging in abdominal oncological setting: why and when. Infect Agent Cancer 2022; 17:25. [PMID: 35681237 PMCID: PMC9185934 DOI: 10.1186/s13027-022-00441-3] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2022] [Accepted: 05/30/2022] [Indexed: 12/13/2022] Open
Abstract
This article provides an overview of diffusion kurtosis (DKI) imaging in abdominal oncology. DKI allows for more data on tissue structures than the conventional diffusion model (DWI). However, DKI requires high quality images at b-values greater than 1000 s/mm2 and high signal-to-noise ratio (SNR) that traditionally MRI systems are not able to acquire and therefore there are generally amplified anatomical distortions on the images due to less homogeneity of the field. Advances in both hardware and software on modern MRI scanners have currently enabled ultra-high b-value imaging and offered the ability to apply DKI to multiple extracranial sites. Previous studies have evaluated the ability of DKI to characterize and discriminate tumor grade compared to conventional DWI. Additionally, in several studies the DKI sequences used were based on planar echo (EPI) acquisition, which is susceptible to motion, metal and air artefacts and prone to low SNRs and distortions, leading to low quality images for some small lesions, which may affect the accuracy of the results. Another problem is the optimal b-value of DKI, which remains to be explored and not yet standardized, as well as the manual selection of the ROI, which could affect the accuracy of some parameters.
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Affiliation(s)
- Vincenza Granata
- Division of Radiology, "Istituto Nazionale Tumori IRCCS Fondazione Pascale - IRCCS di Napoli", I-80131, Naples, Italy.
| | | | - Andrea Belli
- Division of Hepatobiliary Surgical Oncology, "Istituto Nazionale Tumori IRCCS Fondazione Pascale - IRCCS di Napoli", I-80131, Naples, Italy
| | - Ginevra Danti
- Department of Radiology, Azienda Ospedaliero-Universitaria Careggi, Florence, Italy.,Italian Society of Medical and Interventional Radiology, SIRM Foundation, Milan, Italy
| | - Eleonora Bicci
- Department of Radiology, Azienda Ospedaliero-Universitaria Careggi, Florence, Italy
| | - Carmen Cutolo
- Department of Medicine, Surgery and Dentistry, University of Salerno, Salerno, Italy
| | - Antonella Petrillo
- Division of Radiology, "Istituto Nazionale Tumori IRCCS Fondazione Pascale - IRCCS di Napoli", I-80131, Naples, Italy
| | - Francesco Izzo
- Division of Hepatobiliary Surgical Oncology, "Istituto Nazionale Tumori IRCCS Fondazione Pascale - IRCCS di Napoli", I-80131, Naples, Italy
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Radiology Imaging Scans for Early Diagnosis of Kidney Tumors: A Review of Data Analytics-Based Machine Learning and Deep Learning Approaches. BIG DATA AND COGNITIVE COMPUTING 2022. [DOI: 10.3390/bdcc6010029] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Abstract
Plenty of disease types exist in world communities that can be explained by humans’ lifestyles or the economic, social, genetic, and other factors of the country of residence. Recently, most research has focused on studying common diseases in the population to reduce death risks, take the best procedure for treatment, and enhance the healthcare level of the communities. Kidney Disease is one of the common diseases that have affected our societies. Sectionicularly Kidney Tumors (KT) are the 10th most prevalent tumor for men and women worldwide. Overall, the lifetime likelihood of developing a kidney tumor for males is about 1 in 466 (2.02 percent) and it is around 1 in 80 (1.03 percent) for females. Still, more research is needed on new diagnostic, early, and innovative methods regarding finding an appropriate treatment method for KT. Compared to the tedious and time-consuming traditional diagnosis, automatic detection algorithms of machine learning can save diagnosis time, improve test accuracy, and reduce costs. Previous studies have shown that deep learning can play a role in dealing with complex tasks, diagnosis and segmentation, and classification of Kidney Tumors, one of the most malignant tumors. The goals of this review article on deep learning in radiology imaging are to summarize what has already been accomplished, determine the techniques used by the researchers in previous years in diagnosing Kidney Tumors through medical imaging, and identify some promising future avenues, whether in terms of applications or technological developments, as well as identifying common problems, describing ways to expand the data set, summarizing the knowledge and best practices, and determining remaining challenges and future directions.
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Zhou Y, Zhou G, Zhang J, Xu C, Zhu F, Xu P. DCE-MRI based radiomics nomogram for preoperatively differentiating combined hepatocellular-cholangiocarcinoma from mass-forming intrahepatic cholangiocarcinoma. Eur Radiol 2022; 32:5004-5015. [PMID: 35128572 DOI: 10.1007/s00330-022-08548-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2021] [Revised: 12/19/2021] [Accepted: 12/20/2021] [Indexed: 02/07/2023]
Abstract
OBJECTIVE To establish a radiomics nomogram based on dynamic contrast-enhanced (DCE) MR images to preoperatively differentiate combined hepatocellular-cholangiocarcinoma (cHCC-CC) from mass-forming intrahepatic cholangiocarcinoma (IMCC). METHODS A total of 151 training cohort patients (45 cHCC-CC and 106 IMCC) and 65 validation cohort patients (19 cHCC-CC and 46 IMCC) were enrolled. Findings of clinical characteristics and MR features were analyzed. Radiomics features were extracted from the DCE-MR images. A radiomics signature was built based on radiomics features by the least absolute shrinkage and selection operator algorithm. Univariate and multivariate analyses were used to identify the significant clinicoradiological variables and construct a clinical model. The radiomics signature and significant clinicoradiological variables were then incorporated into the radiomics nomogram by multivariate logistic regression analysis. Performance of the radiomics nomogram, radiomics signature, and clinical model was assessed by receiver operating characteristic and area under the curve (AUC) was compared. RESULTS Eleven radiomics features were selected to develop the radiomics signature. The radiomics nomogram integrating the alpha fetoprotein, background liver disease (cirrhosis or chronic hepatitis), and radiomics signature showed favorable calibration and discrimination performance with an AUC value of 0.945 in training cohort and 0.897 in validation cohort. The AUCs for the radiomics signature and clinical model were 0.848 and 0.856 in training cohort and 0.792 and 0.809 in validation cohort, respectively. The radiomics nomogram outperformed both the radiomics signature and clinical model alone (p < 0.05). CONCLUSION The radiomics nomogram based on DCE-MRI may provide an effective and noninvasive tool to differentiate cHCC-CC from IMCC, which could help guide treatment strategies. KEY POINTS • The radiomics signature based on dynamic contrast-enhanced magnetic resonance imaging is useful to preoperatively differentiate cHCC-CC from IMCC. • The radiomics nomogram showed the best performance in both training and validation cohorts for differentiating cHCC-CC from IMCC.
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Affiliation(s)
- Yang Zhou
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, No 300, Guangzhou Road, Nanjing, 210029, Jiangsu Province, China
| | - Guofeng Zhou
- Department of Radiology, Zhongshan Hospital, Fudan University, No.180 Fenglin Road, Xuhui District, Shanghai, 200032, China.,Shanghai Institute of Medical Imaging, No.180 Fenglin Road, Xuhui District, Shanghai, 200032, China
| | - Jiulou Zhang
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, No 300, Guangzhou Road, Nanjing, 210029, Jiangsu Province, China
| | - Chen Xu
- Department of Pathology, Zhongshan Hospital, Fudan University, Shanghai, 200032, China
| | - Feipeng Zhu
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, No 300, Guangzhou Road, Nanjing, 210029, Jiangsu Province, China.
| | - Pengju Xu
- Department of Radiology, Zhongshan Hospital, Fudan University, No.180 Fenglin Road, Xuhui District, Shanghai, 200032, China. .,Shanghai Institute of Medical Imaging, No.180 Fenglin Road, Xuhui District, Shanghai, 200032, China.
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32
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Chen X, Wang W, Huang J, Wu J, Chen L, Cai C, Cai S, Chen Z. Ultrafast water–fat separation using deep learning–based single‐shot MRI. Magn Reson Med 2022; 87:2811-2825. [DOI: 10.1002/mrm.29172] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2021] [Revised: 12/13/2021] [Accepted: 01/07/2022] [Indexed: 12/16/2022]
Affiliation(s)
- Xinran Chen
- Department of Electronic Science Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance School of Electronic Science and Engineering National Model Microelectronics College Xiamen University Xiamen Fujian People’s Republic of China
| | - Wei Wang
- Department of Electronic Science Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance School of Electronic Science and Engineering National Model Microelectronics College Xiamen University Xiamen Fujian People’s Republic of China
| | - Jianpan Huang
- Department of Biomedical Engineering City University of Hong Kong Hong Kong People’s Republic of China
| | - Jian Wu
- Department of Electronic Science Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance School of Electronic Science and Engineering National Model Microelectronics College Xiamen University Xiamen Fujian People’s Republic of China
| | - Lin Chen
- Department of Electronic Science Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance School of Electronic Science and Engineering National Model Microelectronics College Xiamen University Xiamen Fujian People’s Republic of China
| | - Congbo Cai
- Department of Electronic Science Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance School of Electronic Science and Engineering National Model Microelectronics College Xiamen University Xiamen Fujian People’s Republic of China
| | - Shuhui Cai
- Department of Electronic Science Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance School of Electronic Science and Engineering National Model Microelectronics College Xiamen University Xiamen Fujian People’s Republic of China
| | - Zhong Chen
- Department of Electronic Science Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance School of Electronic Science and Engineering National Model Microelectronics College Xiamen University Xiamen Fujian People’s Republic of China
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Latif G, Yousif Al Anezi F, Iskandar DNFA, Bashar A, Alghazo J. Recent Advances in Classification of Brain Tumor from MR Images - State of the Art Review from 2017 to 2021. Curr Med Imaging 2022; 18:903-918. [PMID: 35040408 DOI: 10.2174/1573405618666220117151726] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2021] [Revised: 09/14/2021] [Accepted: 10/28/2021] [Indexed: 11/22/2022]
Abstract
BACKGROUND The task of identifying a tumor in the brain is a complex problem that requires sophisticated skills and inference mechanisms to accurately locate the tumor region. The complex nature of the brain tissue makes the problem of locating, segmenting, and ultimately classifying Magnetic Resonance (MR) images a complex problem. The aim of this review paper is to consolidate the details of the most relevant and recent approaches proposed in this domain for the binary and multi-class classification of brain tumors using brain MR images. OBJECTIVE In this review paper, a detailed summary of the latest techniques used for brain MR image feature extraction and classification is presented. A lot of research papers have been published recently with various techniques proposed for identifying an efficient method for the correct recognition and diagnosis of brain MR images. The review paper allows researchers in the field to familiarize themselves with the latest developments and be able to propose novel techniques that have not yet been explored in this research domain. In addition, the review paper will facilitate researchers, who are new to machine learning algorithms for brain tumor recognition, to understand the basics of the field and pave the way for them to be able to contribute to this vital field of medical research. RESULTS In this paper, the review is performed for all recently proposed methods for both feature extraction and classification. It also identifies the combination of feature extraction methods and classification methods that when combined would be the most efficient technique for the recognition and diagnosis of brain tumor from MR images. In addition, the paper presents the performance metrics particularly the recognition accuracy, of selected research published between 2017- 2021.
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Affiliation(s)
- Ghazanfar Latif
- College of Computer Engineering and Sciences, Prince Mohammad bin Fahd University, Khobar, Saudi Arabia.
- Université du Québec a Chicoutimi, 555 boulevard de l'Université, Chicoutimi, QC, G7H2B1, Canada
| | - Faisal Yousif Al Anezi
- Management Information Department, Prince Mohammad bin Fahd University, Khobar, Saudi Arabia
| | - D N F Awang Iskandar
- Faculty of Computer Science and Information Technology, Universiti Malaysia Sarawak, Malaysia
| | - Abul Bashar
- College of Computer Engineering and Sciences, Prince Mohammad bin Fahd University, Khobar, Saudi Arabia
| | - Jaafar Alghazo
- Department of Electrical and Computer Engineering, Virginia Military Institute, Lexington, VA Corresponding Author *: Ghazanfar Latif, Department of Computer Science, Prince Mohammad bin Fahd University, Al-Khobar, 31952, Saudi Arabia
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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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Luo S, Wei R, Lu S, Lai S, Wu J, Wu Z, Pang X, Wei X, Jiang X, Zhen X, Yang R. Fuhrman nuclear grade prediction of clear cell renal cell carcinoma: influence of volume of interest delineation strategies on machine learning-based dynamic enhanced CT radiomics analysis. Eur Radiol 2021; 32:2340-2350. [PMID: 34636962 DOI: 10.1007/s00330-021-08322-w] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2021] [Revised: 08/18/2021] [Accepted: 09/09/2021] [Indexed: 12/11/2022]
Abstract
OBJECTIVE To investigate the influence of different volume of interest (VOI) delineation strategies on machine learning-based predictive models for discrimination between low and high nuclear grade clear cell renal cell carcinoma (ccRCC) on dynamic contrast-enhanced CT. METHODS This study retrospectively collected 177 patients with pathologically proven ccRCC (124 low-grade; 53 high-grade). Tumor VOI was manually segmented, followed by artificially introducing uncertainties as: (i) contour-focused VOI, (ii) margin erosion of 2 or 4 mm, and (iii) margin dilation (2, 4, or 6 mm) inclusive of perirenal fat, peritumoral renal parenchyma, or both. Radiomics features were extracted from four-phase CT images (unenhanced phase (UP), corticomedullary phase (CMP), nephrographic phase (NP), excretory phase (EP)). Different combinations of four-phasic features for each VOI delineation strategy were used to build 176 classification models. The best VOI delineation strategy and superior CT phase were identified and the top-ranked features were analyzed. RESULTS Features extracted from UP and EP outperformed features from other single/combined phase(s). Shape features and first-order statistics features exhibited superior discrimination. The best performance (ACC 81%, SEN 67%, SPE 87%, AUC 0.87) was achieved with radiomics features extracted from UP and EP based on contour-focused VOI. CONCLUSION Shape and first-order features extracted from UP + EP images are better feature representations. Contour-focused VOI erosion by 2 mm or dilation by 4 mm within peritumor renal parenchyma exerts limited impact on discriminative performance. It provides a reference for segmentation tolerance in radiomics-based modeling for ccRCC nuclear grading. KEY POINTS • Lesion delineation uncertainties are tolerated within a VOI erosion range of 2 mm or dilation range of 4 mm within peritumor renal parenchyma for radiomics-based ccRCC nuclear grading. • Radiomics features extracted from unenhanced phase and excretory phase are superior to other single/combined phase(s) at differentiating high vs low nuclear grade ccRCC. • Shape features and first-order statistics features showed superior discriminative capability compared to texture features.
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Affiliation(s)
- Shiwei Luo
- Department of Radiology, Guangzhou First People's Hospital, South China University of Technology, Guangzhou, Guangdong, 510180, China
| | - Ruili Wei
- Department of Radiology, Guangzhou First People's Hospital, South China University of Technology, Guangzhou, Guangdong, 510180, China
| | - Songlin Lu
- School of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong, 510515, China
| | - Shengsheng Lai
- School of Medical Equipment, Guangdong Food and Drug Vocational College, Guangzhou, Guangdong, 510520, China
| | - Jialiang Wu
- Department of Radiology, University of Hong Kong-Shenzhen Hospital, Shenzhen, Guangdong, 518000, China
| | - Zhe Wu
- Department of Radiology, Guangzhou First People's Hospital, South China University of Technology, Guangzhou, Guangdong, 510180, China
| | - Xinrui Pang
- Department of Radiology, Guangzhou First People's Hospital, South China University of Technology, Guangzhou, Guangdong, 510180, China
| | - Xinhua Wei
- Department of Radiology, Guangzhou First People's Hospital, South China University of Technology, Guangzhou, Guangdong, 510180, China
| | - Xinqing Jiang
- Department of Radiology, Guangzhou First People's Hospital, South China University of Technology, Guangzhou, Guangdong, 510180, China
| | - Xin Zhen
- School of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong, 510515, China.
| | - Ruimeng Yang
- Department of Radiology, Guangzhou First People's Hospital, South China University of Technology, Guangzhou, Guangdong, 510180, China.
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A Radiomic-based Machine Learning Algorithm to Reliably Differentiate Benign Renal Masses from Renal Cell Carcinoma. Eur Urol Focus 2021; 8:988-994. [PMID: 34538748 DOI: 10.1016/j.euf.2021.09.004] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2021] [Revised: 08/17/2021] [Accepted: 09/07/2021] [Indexed: 01/20/2023]
Abstract
BACKGROUND A substantial proportion of patients undergo treatment for renal masses where active surveillance or observation may be more appropriate. OBJECTIVE To determine whether radiomic-based machine learning platforms can distinguish benign from malignant renal masses. DESIGN, SETTING, AND PARTICIPANTS A prospectively maintained single-institutional renal mass registry was queried to identify patients with a computed tomography-proven clinically localized renal mass who underwent partial or radical nephrectomy. INTERVENTION Radiomic analysis of preoperative scans was performed. Clinical and radiomic variables of importance were identified through decision tree analysis, which were incorporated into Random Forest and REAL Adaboost predictive models. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS The primary outcome was the degree of congruity between the virtual diagnosis and final pathology. Subanalyses were performed for small renal masses and patients who had percutaneous renal mass biopsies as part of their workup. Receiver operating characteristic curves were used to evaluate each model's discriminatory function. RESULTS AND LIMITATIONS A total of 684 patients met the selection criteria. Of them, 76% had renal cell carcinoma; 57% had small renal masses, of which 73% were malignant. Predictive modeling differentiated benign pathology from malignant with an area under the curve (AUC) of 0.84 (95% confidence interval [CI] 0.79-0.9). In small renal masses, radiomic analysis yielded a discriminatory AUC of 0.77 (95% CI 0.69-0.85). When negative and nondiagnostic biopsies were supplemented with radiomic analysis, accuracy increased from 83.3% to 93.4%. CONCLUSIONS Radiomic-based predictive modeling may distinguish benign from malignant renal masses. Clinical factors did not substantially improve the diagnostic accuracy of predictive models. Enhanced diagnostic predictability may improve patient selection before surgery and increase the utilization of active surveillance protocols. PATIENT SUMMARY Not all kidney tumors are cancerous, and some can be watched. We evaluated a new method that uses radiographic features invisible to the naked eye to distinguish benign masses from true cancers and found that it can do so with acceptable accuracy.
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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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Varghese BA, Hwang D, Cen SY, Lei X, Levy J, Desai B, Goodenough DJ, Duddalwar VA. Identification of robust and reproducible CT-texture metrics using a customized 3D-printed texture phantom. J Appl Clin Med Phys 2021; 22:98-107. [PMID: 33434374 PMCID: PMC7882093 DOI: 10.1002/acm2.13162] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2020] [Revised: 12/15/2020] [Accepted: 12/16/2020] [Indexed: 12/11/2022] Open
Abstract
OBJECTIVE The objective of this study was to evaluate the robustness and reproducibility of computed tomography-based texture analysis (CTTA) metrics extracted from CT images of a customized texture phantom built for assessing the association of texture metrics to three-dimensional (3D) printed progressively increasing textural heterogeneity. MATERIALS AND METHODS A custom-built 3D-printed texture phantom comprising of six texture patterns was used to evaluate the robustness and reproducibility of a radiomics panel under a variety of routine abdominal imaging protocols. The phantom was scanned on four CT scanners (Philips, Canon, GE, and Siemens) to assess reproducibility. The robustness assessment was conducted by imaging the texture phantom across different CT imaging parameters such as slice thickness, field of view (FOV), tube voltage, and tube current for each scanner. The texture panel comprised of 387 features belonging to 15 subgroups of texture extraction methods (e.g., Gray-level Co-occurrence Matrix: GLCM). Twelve unique image settings were tested on all the four scanners (e.g., FOV125). Interclass correlation two-way mixed with absolute agreement (ICC3) was used to assess the robustness and reproducibility of radiomic features. Linear regression was used to test the association between change in radiomic features and increased texture heterogeneity. Results were summarized in heat maps. RESULTS A total of 5612 (23.2%) of 24 090 features showed excellent robustness and reproducibility (ICC ≥ 0.9). Intensity, GLCM 3D, and gray-level run length matrix (GLRLM) 3D features showed best performance. Among imaging variables, changes in slice thickness affected all metrics more intensely compared to other imaging variables in reducing the ICC3. From the analysis of linear trend effect of the CTTA metrics, the top three metrics with high linear correlations across all scanners and scanning settings were from the GLRLM 2D/3D and discrete cosine transform (DCT) texture family. CONCLUSION The choice of scanner and imaging protocols affect texture metrics. Furthermore, not all CTTA metrics have a linear association with linearly varying texture patterns.
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Affiliation(s)
- Bino A. Varghese
- Department of RadiologyUniversity of Southern CaliforniaLos AngelesCAUSA
| | - Darryl Hwang
- Department of RadiologyUniversity of Southern CaliforniaLos AngelesCAUSA
| | - Steven Y. Cen
- Department of RadiologyUniversity of Southern CaliforniaLos AngelesCAUSA
| | - Xiaomeng Lei
- Department of RadiologyUniversity of Southern CaliforniaLos AngelesCAUSA
| | | | - Bhushan Desai
- Department of RadiologyUniversity of Southern CaliforniaLos AngelesCAUSA
| | | | - Vinay A. Duddalwar
- Department of RadiologyUniversity of Southern CaliforniaLos AngelesCAUSA
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