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Śledzińska-Bebyn P, Furtak J, Bebyn M, Serafin Z. Beyond conventional imaging: Advancements in MRI for glioma malignancy prediction and molecular profiling. Magn Reson Imaging 2024; 112:63-81. [PMID: 38914147 DOI: 10.1016/j.mri.2024.06.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2024] [Revised: 05/20/2024] [Accepted: 06/20/2024] [Indexed: 06/26/2024]
Abstract
This review examines the advancements in magnetic resonance imaging (MRI) techniques and their pivotal role in diagnosing and managing gliomas, the most prevalent primary brain tumors. The paper underscores the importance of integrating modern MRI modalities, such as diffusion-weighted imaging and perfusion MRI, which are essential for assessing glioma malignancy and predicting tumor behavior. Special attention is given to the 2021 WHO Classification of Tumors of the Central Nervous System, emphasizing the integration of molecular diagnostics in glioma classification, significantly impacting treatment decisions. The review also explores radiogenomics, which correlates imaging features with molecular markers to tailor personalized treatment strategies. Despite technological progress, MRI protocol standardization and result interpretation challenges persist, affecting diagnostic consistency across different settings. Furthermore, the review addresses MRI's capacity to distinguish between tumor recurrence and pseudoprogression, which is vital for patient management. The necessity for greater standardization and collaborative research to harness MRI's full potential in glioma diagnosis and personalized therapy is highlighted, advocating for an enhanced understanding of glioma biology and more effective treatment approaches.
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Affiliation(s)
- Paulina Śledzińska-Bebyn
- Department of Radiology, 10th Military Research Hospital and Polyclinic, 85-681 Bydgoszcz, Poland.
| | - Jacek Furtak
- Department of Clinical Medicine, Faculty of Medicine, University of Science and Technology, Bydgoszcz, Poland; Department of Neurosurgery, 10th Military Research Hospital and Polyclinic, 85-681 Bydgoszcz, Poland
| | - Marek Bebyn
- Department of Internal Diseases, 10th Military Clinical Hospital and Polyclinic, 85-681 Bydgoszcz, Poland
| | - Zbigniew Serafin
- Department of Radiology and Diagnostic Imaging, Nicolaus Copernicus University, Collegium Medicum, Bydgoszcz, Poland
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Che F, Wei Y, Xu Q, Li Q, Zhang T, Wang LY, Li M, Yuan F, Song B. Noninvasive identification of SOX9 status using radiomics signatures may help construct personalized treatment strategy in hepatocellular carcinoma. Abdom Radiol (NY) 2024; 49:3024-3035. [PMID: 38446180 DOI: 10.1007/s00261-024-04190-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2023] [Revised: 12/31/2023] [Accepted: 01/16/2024] [Indexed: 03/07/2024]
Abstract
OBJECTIVES To develop and validate a radiomics-based model for predicting SOX9-positive hepatocellular carcinoma (HCC) using preoperative contrast-enhanced computed tomography (CT) images. METHODS From January 2013 to April 2017, patients with histologically proven HCC who received systemic sorafenib treatment after curative resection were retrospectively enrolled. Radiomic features were extracted from portal venous phase CT images and selected to build a radiomics score using logistic regression analysis. The factors associated with SOX9 expression were selected and combined by univariate and multivariate analyses to establish clinico-liver imaging (CL) model and clinico-liver imaging-radiomics (CLR) model. Diagnostic performance was measured by area under curve (AUC). Overall survival (OS) and recurrence-free survival (RFS) rates were compared using Kaplan-Meier method. RESULTS A total of 108 patients (training cohort: n = 80; validation cohort: n = 28) were enrolled. Multivariate analyses revealed that the albumin-bilirubin grade and tumor size were significant independent factors for predicting SOX9-positive HCCs and were included in the CL model. The CLR model integrating the radiomics score with albumin-bilirubin grade and tumor size showed better discriminative performance than the CL model with AUCs of 0.912 and 0.790 in the training and validation cohorts. Survival curves for RFS and OS showed that SOX9 expression was closely related to the prognosis of HCC patients. RFS and OS rates were significantly lower in patients with SOX9-positive than SOX9-negative (51.02% vs. 75.00% at 1-year RFS rates; 76.92% vs. 94.94% at 2-year OS rates). CONCLUSION Radiomics signatures may serve as noninvasive predictors for SOX9 status evaluation in patients with HCC and may aid in constructing individualized treatment strategies.
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Affiliation(s)
- Feng Che
- Department of Radiology, West China Hospital, Sichuan University, No 37, Guoxue Alley, Chengdu, 610041, Sichuan, China
| | - Yi Wei
- Department of Radiology, West China Hospital, Sichuan University, No 37, Guoxue Alley, Chengdu, 610041, Sichuan, China
| | - Qing Xu
- Institute of Clinical Pathology, Key Laboratory of Transplant Engineering and Immunology, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Qian Li
- Department of Radiology, West China Hospital, Sichuan University, No 37, Guoxue Alley, Chengdu, 610041, Sichuan, China
| | - Tong Zhang
- Department of Radiology, West China Hospital, Sichuan University, No 37, Guoxue Alley, Chengdu, 610041, Sichuan, China
| | - Li-Ye Wang
- Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China
| | - Man Li
- Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China
| | - Fang Yuan
- Department of Radiology, West China Hospital, Sichuan University, No 37, Guoxue Alley, Chengdu, 610041, Sichuan, China.
| | - Bin Song
- Department of Radiology, West China Hospital, Sichuan University, No 37, Guoxue Alley, Chengdu, 610041, Sichuan, China.
- Department of Radiology, Sanya People's Hospital, Sanya, Hainan, China.
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Foffano L, Vida R, Piacentini A, Molteni E, Cucciniello L, Da Ros L, Silvia B, Cereser L, Roncato R, Gerratana L, Puglisi F. Is ctDNA ready to outpace imaging in monitoring early and advanced breast cancer? Expert Rev Anticancer Ther 2024; 24:679-691. [PMID: 38855809 DOI: 10.1080/14737140.2024.2362173] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2024] [Accepted: 05/28/2024] [Indexed: 06/11/2024]
Abstract
INTRODUCTION Circulating tumor DNA (ctDNA) and radiological imaging are increasingly recognized as crucial elements in breast cancer management. While radiology remains the cornerstone for screening and monitoring, ctDNA holds distinctive advantages in anticipating diagnosis, recurrence, or progression, providing concurrent biological insights complementary to imaging results. AREAS COVERED This review delves into the current evidence on the synergistic relationship between ctDNA and imaging in breast cancer. It presents data on the clinical validity and utility of ctDNA in both early and advanced settings, providing insights into emerging liquid biopsy techniques like epigenetics and fragmentomics. Simultaneously, it explores the present and future landscape of imaging methodologies, particularly focusing on radiomics. EXPERT OPINION Numerous are the current technical, strategic, and economic challenges preventing the clinical integration of ctDNA analysis in the breast cancer monitoring. Understanding these complexities and devising targeted strategies is pivotal to effectively embedding this methodology into personalized patient care.
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Affiliation(s)
- Lorenzo Foffano
- Department of Medicine, University of Udine, Udine, Italy
- Department of Medical Oncology, CRO Aviano, National Cancer Institute, IRCCS, Aviano, Italy
| | - Riccardo Vida
- Department of Medicine, University of Udine, Udine, Italy
- Department of Medical Oncology, CRO Aviano, National Cancer Institute, IRCCS, Aviano, Italy
| | | | - Elisabetta Molteni
- Department of Medicine, University of Udine, Udine, Italy
- Weill Cornell Medicine, Department of Medicine, Division of Hematology-Oncology, New York, NY, USA
| | - Linda Cucciniello
- Department of Medicine, University of Udine, Udine, Italy
- Department of Medical Oncology, CRO Aviano, National Cancer Institute, IRCCS, Aviano, Italy
| | - Lucia Da Ros
- Department of Medical Oncology, CRO Aviano, National Cancer Institute, IRCCS, Aviano, Italy
| | - Buriolla Silvia
- Department of Oncology, Santa Maria della Misericordia University Hospital, Azienda Sanitaria Universitaria Friuli Centrale (ASUFC), Udine, Italy
| | - Lorenzo Cereser
- Department of Medicine, University of Udine, Udine, Italy
- Azienda Sanitaria-Universitaria Friuli Centrale (ASUFC), University Hospital S. Maria della Misericordia, Udine, Italy
| | | | - Lorenzo Gerratana
- Department of Medicine, University of Udine, Udine, Italy
- Department of Medical Oncology, CRO Aviano, National Cancer Institute, IRCCS, Aviano, Italy
| | - Fabio Puglisi
- Department of Medicine, University of Udine, Udine, Italy
- Department of Medical Oncology, CRO Aviano, National Cancer Institute, IRCCS, Aviano, Italy
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Yuan G, Cai L, Qu W, Zhou Z, Liang P, Chen J, Xu C, Zhang J, Wang S, Chu Q, Li Z. Identification of Calculous Pyonephrosis by CT-Based Radiomics and Deep Learning. Bioengineering (Basel) 2024; 11:662. [PMID: 39061744 PMCID: PMC11274102 DOI: 10.3390/bioengineering11070662] [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: 06/06/2024] [Revised: 06/18/2024] [Accepted: 06/22/2024] [Indexed: 07/28/2024] Open
Abstract
Urgent detection of calculous pyonephrosis is crucial for surgical planning and preventing severe outcomes. This study aims to evaluate the performance of computed tomography (CT)-based radiomics and a three-dimensional convolutional neural network (3D-CNN) model, integrated with independent clinical factors, to identify patients with calculous pyonephrosis. We recruited 182 patients receiving either percutaneous nephrostomy tube placement or percutaneous nephrolithotomy for calculous hydronephrosis or pyonephrosis. The regions of interest were manually delineated on plain CT images and the CT attenuation value (HU) was measured. Radiomics analysis was performed using least absolute shrinkage and selection operator (LASSO). A 3D-CNN model was also developed. The better-performing machine-learning model was combined with independent clinical factors to build a comprehensive clinical machine-learning model. The performance of these models was assessed using receiver operating characteristic analysis and decision curve analysis. Fever, blood neutrophils, and urine leukocytes were independent risk factors for pyonephrosis. The radiomics model showed higher area under the curve (AUC) than the 3D-CNN model and HU (0.876 vs. 0.599, 0.578; p = 0.003, 0.002) in the testing cohort. The clinical machine-learning model surpassed the clinical model in both the training (0.975 vs. 0.904, p = 0.019) and testing (0.967 vs. 0.889, p = 0.045) cohorts.
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Affiliation(s)
- Guanjie Yuan
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China; (G.Y.); (L.C.); (W.Q.); (Z.Z.); (P.L.); (Z.L.)
| | - Lingli Cai
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China; (G.Y.); (L.C.); (W.Q.); (Z.Z.); (P.L.); (Z.L.)
| | - Weinuo Qu
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China; (G.Y.); (L.C.); (W.Q.); (Z.Z.); (P.L.); (Z.L.)
| | - Ziling Zhou
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China; (G.Y.); (L.C.); (W.Q.); (Z.Z.); (P.L.); (Z.L.)
| | - Ping Liang
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China; (G.Y.); (L.C.); (W.Q.); (Z.Z.); (P.L.); (Z.L.)
| | - Jun Chen
- Bayer Healthcare, Wuhan 430000, China;
| | - Chuou Xu
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China; (G.Y.); (L.C.); (W.Q.); (Z.Z.); (P.L.); (Z.L.)
| | - Jiaqiao Zhang
- Department of Urology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China;
| | - Shaogang Wang
- Department of Urology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China;
| | - Qian Chu
- Department of Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China;
| | - Zhen Li
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China; (G.Y.); (L.C.); (W.Q.); (Z.Z.); (P.L.); (Z.L.)
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Singh S, Mohajer B, Wells SA, Garg T, Hanneman K, Takahashi T, AlDandan O, McBee MP, Jawahar A. Imaging Genomics and Multiomics: A Guide for Beginners Starting Radiomics-Based Research. Acad Radiol 2024; 31:2281-2291. [PMID: 38286723 DOI: 10.1016/j.acra.2024.01.024] [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: 10/30/2023] [Revised: 01/08/2024] [Accepted: 01/12/2024] [Indexed: 01/31/2024]
Abstract
Radiomics uses advanced mathematical analysis of pixel-level information from radiologic images to extract existing information in traditional imaging algorithms. It is intended to find imaging biomarkers related to the genomics of tumors or disease patterns that improve medical care by advanced detection of tumor response patterns in tumors and to assess prognosis. Radiomics expands the paradigm of medical imaging to help with diagnosis, management of diseases and prognostication, leveraging image features by extracting information that can be used as imaging biomarkers to predict prognosis and response to treatment. Radiogenomics is an emerging area in radiomics that investigates the association between imaging characteristics and gene expression profiles. There are an increasing number of research publications using different radiomics approaches without a clear consensus on which method works best. We aim to describe the workflow of radiomics along with a guide of what to expect when starting a radiomics-based research project.
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Affiliation(s)
- Shiva Singh
- Radiology and Imaging Sciences, National Institutes of Health, Bethesda, Maryland
| | - Bahram Mohajer
- Radiology and Radiological Sciences, Johns Hopkins Medicine, Baltimore, Maryland
| | - Shane A Wells
- Radiology, University of Michigan, Ann Arbor, Michigan
| | - Tushar Garg
- Radiology and Radiological Sciences, Johns Hopkins Medicine, Baltimore, Maryland
| | - Kate Hanneman
- Medical Imaging, University of Toronto, Toronto, ON, Canada
| | | | - Omran AlDandan
- Department of Radiology, Imam Abdulrahman Bin Faisal University, College of Medicine: Dammam, Eastern, Saudi Arabia
| | - Morgan P McBee
- Radiology and Radiological Sciences, Medical University of South Carolina, Charleston, South Carolina
| | - Anugayathri Jawahar
- Radiology, Northwestern University-Feinberg School of Medicine, 800, Arkes Pavilion, 676 N St. Clair St, Chicago, IL 60611.
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6
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Zhu N, Meng X, Wang Z, Hu Y, Zhao T, Fan H, Niu F, Han J. Radiomics in Diagnosis, Grading, and Treatment Response Assessment of Soft Tissue Sarcomas: A Systematic Review and Meta-analysis. Acad Radiol 2024:S1076-6332(24)00193-4. [PMID: 38772802 DOI: 10.1016/j.acra.2024.03.029] [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: 01/20/2024] [Revised: 03/12/2024] [Accepted: 03/22/2024] [Indexed: 05/23/2024]
Abstract
RATIONALE AND OBJECTIVES To evaluate radiomics in soft tissue sarcomas (STSs) for diagnostic accuracy, grading, and treatment response assessment, with a focus on clinical relevance. METHODS In this diagnostic accuracy study, radiomics was applied using multiple MRI sequences and AI classifiers, with histopathological diagnosis as the reference standard. Statistical analysis involved meta-analysis, random-effects model, and Deeks' funnel plot asymmetry test. RESULTS Among 579 unique titles and abstracts, 24 articles were included in the systematic review, with 21 used for meta-analysis. Radiomics demonstrated a pooled sensitivity of 84% (95% CI: 80-87) and specificity of 63% (95% CI: 56-70), AUC of 0.93 for diagnosis, sensitivity of 84% (95% CI: 82-87) and specificity of 73% (95% CI: 68-77), AUC of 0.91 for grading, and sensitivity of 83% (95% CI: 67-94) and specificity of 67% (95% CI: 59-74), AUC of 0.87 for treatment response assessment. CONCLUSION Radiomics exhibits potential for accurate diagnosis, grading, and treatment response assessment in STSs, emphasizing the need for standardization and prospective trials. CLINICAL RELEVANCE STATEMENT Radiomics offers precise tools for STS diagnosis, grading, and treatment response assessment, with implications for optimizing patient care and treatment strategies in this complex malignancy.
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Affiliation(s)
- Nana Zhu
- The Department of Radiology, Tianjin Hospital, 406 Jiefang Southern Road, Tianjin, China; Graduate School, Tianjin Medical University, Tianjin, China
| | - Xianghong Meng
- The Department of Radiology, Tianjin Hospital, 406 Jiefang Southern Road, Tianjin, China
| | - Zhi Wang
- The Department of Radiology, Tianjin Hospital, 406 Jiefang Southern Road, Tianjin, China; Graduate School, Tianjin Medical University, Tianjin, China.
| | - Yongcheng Hu
- The Department of Radiology, Tianjin Hospital, 406 Jiefang Southern Road, Tianjin, China; Graduate School, Tianjin Medical University, Tianjin, China
| | - Tingting Zhao
- The Department of Radiology, Tianjin Hospital, 406 Jiefang Southern Road, Tianjin, China; Graduate School, Tianjin Medical University, Tianjin, China
| | - Hongxing Fan
- The Department of Radiology, Tianjin Hospital, 406 Jiefang Southern Road, Tianjin, China; Graduate School, Tianjin Medical University, Tianjin, China
| | - Feige Niu
- The Department of Radiology, Tianjin Hospital, 406 Jiefang Southern Road, Tianjin, China; Graduate School, Tianjin Medical University, Tianjin, China
| | - Jun Han
- The Department of Radiology, Tianjin Hospital, 406 Jiefang Southern Road, Tianjin, China; Graduate School, Tianjin University, Tianjin, China
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7
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Coroller T, Sahiner B, Amatya A, Gossmann A, Karagiannis K, Moloney C, Samala RK, Santana-Quintero L, Solovieff N, Wang C, Amiri-Kordestani L, Cao Q, Cha KH, Charlab R, Cross FH, Hu T, Huang R, Kraft J, Krusche P, Li Y, Li Z, Mazo I, Paul R, Schnakenberg S, Serra P, Smith S, Song C, Su F, Tiwari M, Vechery C, Xiong X, Zarate JP, Zhu H, Chakravartty A, Liu Q, Ohlssen D, Petrick N, Schneider JA, Walderhaug M, Zuber E. Methodology for Good Machine Learning with Multi-Omics Data. Clin Pharmacol Ther 2024; 115:745-757. [PMID: 37965805 DOI: 10.1002/cpt.3105] [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: 08/18/2023] [Accepted: 10/20/2023] [Indexed: 11/16/2023]
Abstract
In 2020, Novartis Pharmaceuticals Corporation and the U.S. Food and Drug Administration (FDA) started a 4-year scientific collaboration to approach complex new data modalities and advanced analytics. The scientific question was to find novel radio-genomics-based prognostic and predictive factors for HR+/HER- metastatic breast cancer under a Research Collaboration Agreement. This collaboration has been providing valuable insights to help successfully implement future scientific projects, particularly using artificial intelligence and machine learning. This tutorial aims to provide tangible guidelines for a multi-omics project that includes multidisciplinary expert teams, spanning across different institutions. We cover key ideas, such as "maintaining effective communication" and "following good data science practices," followed by the four steps of exploratory projects, namely (1) plan, (2) design, (3) develop, and (4) disseminate. We break each step into smaller concepts with strategies for implementation and provide illustrations from our collaboration to further give the readers actionable guidance.
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Affiliation(s)
| | - Berkman Sahiner
- Center for Devices and Radiological Health, U.S. Food and Drug Administration, Silver Spring, Maryland, USA
| | - Anup Amatya
- Center for Drug Evaluation and Research, U.S. Food and Drug Administration, Silver Spring, Maryland, USA
| | - Alexej Gossmann
- Center for Devices and Radiological Health, U.S. Food and Drug Administration, Silver Spring, Maryland, USA
| | - Konstantinos Karagiannis
- Center for Biologics Evaluation and Research, U.S. Food and Drug Administration, Silver Spring, Maryland, USA
| | | | - Ravi K Samala
- Center for Devices and Radiological Health, U.S. Food and Drug Administration, Silver Spring, Maryland, USA
| | - Luis Santana-Quintero
- Center for Biologics Evaluation and Research, U.S. Food and Drug Administration, Silver Spring, Maryland, USA
| | - Nadia Solovieff
- Novartis Pharmaceutical Company, East Hanover, New Jersey, USA
| | - Craig Wang
- Novartis Pharma AG, Rotkreuz, Switzerland
| | - Laleh Amiri-Kordestani
- Center for Drug Evaluation and Research, U.S. Food and Drug Administration, Silver Spring, Maryland, USA
| | - Qian Cao
- Center for Devices and Radiological Health, U.S. Food and Drug Administration, Silver Spring, Maryland, USA
| | - Kenny H Cha
- Center for Devices and Radiological Health, U.S. Food and Drug Administration, Silver Spring, Maryland, USA
| | - Rosane Charlab
- Center for Drug Evaluation and Research, U.S. Food and Drug Administration, Silver Spring, Maryland, USA
| | - Frank H Cross
- Center for Drug Evaluation and Research, U.S. Food and Drug Administration, Silver Spring, Maryland, USA
| | - Tingting Hu
- Center for Devices and Radiological Health, U.S. Food and Drug Administration, Silver Spring, Maryland, USA
| | - Ruihao Huang
- Center for Drug Evaluation and Research, U.S. Food and Drug Administration, Silver Spring, Maryland, USA
| | - Jeffrey Kraft
- Center for Drug Evaluation and Research, U.S. Food and Drug Administration, Silver Spring, Maryland, USA
| | | | - Yutong Li
- Novartis Pharmaceutical Company, East Hanover, New Jersey, USA
| | - Zheng Li
- Novartis Pharmaceutical Company, East Hanover, New Jersey, USA
| | - Ilya Mazo
- Center for Biologics Evaluation and Research, U.S. Food and Drug Administration, Silver Spring, Maryland, USA
| | - Rahul Paul
- Center for Biologics Evaluation and Research, U.S. Food and Drug Administration, Silver Spring, Maryland, USA
| | | | - Paolo Serra
- Novartis Pharmaceutical Company, East Hanover, New Jersey, USA
| | - Sean Smith
- Center for Biologics Evaluation and Research, U.S. Food and Drug Administration, Silver Spring, Maryland, USA
| | - Chi Song
- Center for Drug Evaluation and Research, U.S. Food and Drug Administration, Silver Spring, Maryland, USA
| | - Fei Su
- Novartis Pharmaceutical Company, East Hanover, New Jersey, USA
| | - Mohit Tiwari
- Center for Drug Evaluation and Research, U.S. Food and Drug Administration, Silver Spring, Maryland, USA
| | - Colin Vechery
- Novartis Pharmaceutical Company, East Hanover, New Jersey, USA
| | - Xin Xiong
- Center for Devices and Radiological Health, U.S. Food and Drug Administration, Silver Spring, Maryland, USA
| | | | - Hao Zhu
- Center for Drug Evaluation and Research, U.S. Food and Drug Administration, Silver Spring, Maryland, USA
| | | | - Qi Liu
- Center for Drug Evaluation and Research, U.S. Food and Drug Administration, Silver Spring, Maryland, USA
| | - David Ohlssen
- Novartis Pharmaceutical Company, East Hanover, New Jersey, USA
| | - Nicholas Petrick
- Center for Devices and Radiological Health, U.S. Food and Drug Administration, Silver Spring, Maryland, USA
| | - Julie A Schneider
- Oncology Center of Excellence, U.S. Food and Drug Administration, Silver Spring, Maryland, USA
| | - Mark Walderhaug
- Center for Biologics Evaluation and Research, U.S. Food and Drug Administration, Silver Spring, Maryland, USA
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Gu P, Mendonca O, Carter D, Dube S, Wang P, Huang X, Li D, Moore JH, McGovern DPB. AI-luminating Artificial Intelligence in Inflammatory Bowel Diseases: A Narrative Review on the Role of AI in Endoscopy, Histology, and Imaging for IBD. Inflamm Bowel Dis 2024:izae030. [PMID: 38452040 DOI: 10.1093/ibd/izae030] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/17/2023] [Indexed: 03/09/2024]
Abstract
Endoscopy, histology, and cross-sectional imaging serve as fundamental pillars in the detection, monitoring, and prognostication of inflammatory bowel disease (IBD). However, interpretation of these studies often relies on subjective human judgment, which can lead to delays, intra- and interobserver variability, and potential diagnostic discrepancies. With the rising incidence of IBD globally coupled with the exponential digitization of these data, there is a growing demand for innovative approaches to streamline diagnosis and elevate clinical decision-making. In this context, artificial intelligence (AI) technologies emerge as a timely solution to address the evolving challenges in IBD. Early studies using deep learning and radiomics approaches for endoscopy, histology, and imaging in IBD have demonstrated promising results for using AI to detect, diagnose, characterize, phenotype, and prognosticate IBD. Nonetheless, the available literature has inherent limitations and knowledge gaps that need to be addressed before AI can transition into a mainstream clinical tool for IBD. To better understand the potential value of integrating AI in IBD, we review the available literature to summarize our current understanding and identify gaps in knowledge to inform future investigations.
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Affiliation(s)
- Phillip Gu
- F. Widjaja Inflammatory Bowel Disease Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | | | - Dan Carter
- Department of Gastroenterology, Sheba Medical Center, Tel Aviv, Israel
| | - Shishir Dube
- F. Widjaja Inflammatory Bowel Disease Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Paul Wang
- Department of Computational Biomedicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Xiuzhen Huang
- Department of Computational Biomedicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Debiao Li
- Biomedical Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Jason H Moore
- Department of Computational Biomedicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Dermot P B McGovern
- F. Widjaja Inflammatory Bowel Disease Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
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Demetriou D, Lockhat Z, Brzozowski L, Saini KS, Dlamini Z, Hull R. The Convergence of Radiology and Genomics: Advancing Breast Cancer Diagnosis with Radiogenomics. Cancers (Basel) 2024; 16:1076. [PMID: 38473432 DOI: 10.3390/cancers16051076] [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: 01/12/2024] [Revised: 02/09/2024] [Accepted: 02/22/2024] [Indexed: 03/14/2024] Open
Abstract
Despite significant progress in the prevention, screening, diagnosis, prognosis, and therapy of breast cancer (BC), it remains a highly prevalent and life-threatening disease affecting millions worldwide. Molecular subtyping of BC is crucial for predictive and prognostic purposes due to the diverse clinical behaviors observed across various types. The molecular heterogeneity of BC poses uncertainties in its impact on diagnosis, prognosis, and treatment. Numerous studies have highlighted genetic and environmental differences between patients from different geographic regions, emphasizing the need for localized research. International studies have revealed that patients with African heritage are often diagnosed at a more advanced stage and exhibit poorer responses to treatment and lower survival rates. Despite these global findings, there is a dearth of in-depth studies focusing on communities in the African region. Early diagnosis and timely treatment are paramount to improving survival rates. In this context, radiogenomics emerges as a promising field within precision medicine. By associating genetic patterns with image attributes or features, radiogenomics has the potential to significantly improve early detection, prognosis, and diagnosis. It can provide valuable insights into potential treatment options and predict the likelihood of survival, progression, and relapse. Radiogenomics allows for visual features and genetic marker linkage that promises to eliminate the need for biopsy and sequencing. The application of radiogenomics not only contributes to advancing precision oncology and individualized patient treatment but also streamlines clinical workflows. This review aims to delve into the theoretical underpinnings of radiogenomics and explore its practical applications in the diagnosis, management, and treatment of BC and to put radiogenomics on a path towards fully integrated diagnostics.
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Affiliation(s)
- Demetra Demetriou
- SAMRC Precision Oncology Research Unit (PORU), DSI/NRF SARChI Chair in Precision Oncology and Cancer Prevention (POCP), Pan African Cancer Research Institute (PACRI), University of Pretoria, Hatfield, Pretoria 0028, South Africa
| | - Zarina Lockhat
- Department of Radiology, Faculty of Health Sciences, Steve Biko Academic Hospital, University of Pretoria, Hatfield, Pretoria 0028, South Africa
| | - Luke Brzozowski
- Translational Research and Core Facilities, University Health Network, Toronto, ON M5G 1L7, Canada
| | - Kamal S Saini
- Fortrea Inc., 8 Moore Drive, Durham, NC 27709, USA
- Addenbrooke's Hospital, Cambridge University Hospitals NHS Foundation Trust, Cambridge CB2 0QQ, UK
| | - Zodwa Dlamini
- SAMRC Precision Oncology Research Unit (PORU), DSI/NRF SARChI Chair in Precision Oncology and Cancer Prevention (POCP), Pan African Cancer Research Institute (PACRI), University of Pretoria, Hatfield, Pretoria 0028, South Africa
| | - Rodney Hull
- SAMRC Precision Oncology Research Unit (PORU), DSI/NRF SARChI Chair in Precision Oncology and Cancer Prevention (POCP), Pan African Cancer Research Institute (PACRI), University of Pretoria, Hatfield, Pretoria 0028, South Africa
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Santinha J, Katsaros V, Stranjalis G, Liouta E, Boskos C, Matos C, Viegas C, Papanikolaou N. Development of End-to-End AI-Based MRI Image Analysis System for Predicting IDH Mutation Status of Patients with Gliomas: Multicentric Validation. JOURNAL OF IMAGING INFORMATICS IN MEDICINE 2024; 37:31-44. [PMID: 38343254 DOI: 10.1007/s10278-023-00918-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/05/2023] [Revised: 08/08/2023] [Accepted: 08/23/2023] [Indexed: 03/02/2024]
Abstract
Radiogenomics has shown potential to predict genomic phenotypes from medical images. The development of models using standard-of-care pre-operative MRI images, as opposed to advanced MRI images, enables a broader reach of such models. In this work, a radiogenomics model for IDH mutation status prediction from standard-of-care MRIs in patients with glioma was developed and validated using multicentric data. A cohort of 142 (wild-type: 32.4%) patients with glioma retrieved from the TCIA/TCGA was used to train a logistic regression model to predict the IDH mutation status. The model was evaluated using retrospective data collected in two distinct hospitals, comprising 36 (wild-type: 63.9%) and 53 (wild-type: 75.5%) patients. Model development utilized ROC analysis. Model discrimination and calibration were used for validation. The model yielded an AUC of 0.741 vs. 0.716 vs. 0.938, a sensitivity of 0.784 vs. 0.739 vs. 0.875, and a specificity of 0.657 vs. 0.692 vs. 1.000 on the training, test cohort 1, and test cohort 2, respectively. The assessment of model fairness suggested an unbiased model for age and sex, and calibration tests showed a p < 0.05. These results indicate that the developed model allows the prediction of the IDH mutation status in gliomas using standard-of-care MRI images and does not appear to hold sex and age biases.
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Affiliation(s)
- João Santinha
- Computational Clinical Imaging Group, Champalimaud Research , Champalimaud Foundation, Av. Brasília, 1400-038, Lisbon, Portugal.
- Instituto Superior Técnico, Universidade de Lisboa, Av. Rovisco Pais 1, 1049-001, Lisbon, Portugal.
| | - Vasileios Katsaros
- Department of Radiology, General Anti-Cancer and Oncological Hospital of Athens, St. Savvas, Athens, Greece
| | - George Stranjalis
- Department of Neurosurgery, National and Kapodistrian University of Athens, Evangelismos Hospital, Athens, Greece
- Hellenic Center for Neurosurgical Research "Prof. Petros Kokkalis", Athens, Greece
- Athens Microneurosurgery Laboratory, Athens, Greece
| | - Evangelia Liouta
- Department of Neurosurgery, National and Kapodistrian University of Athens, Evangelismos Hospital, Athens, Greece
- Hellenic Center for Neurosurgical Research "Prof. Petros Kokkalis", Athens, Greece
| | - Christos Boskos
- Athens Microneurosurgery Laboratory, Athens, Greece
- IATROPOLIS CyberKnife Center, Hellenic Neuro-Oncology Society, Chalandri, Greece
| | - Celso Matos
- Radiology Department, Champalimaud Clinical Centre, Champalimaud Foundation, Av. Brasília, 1400-038, Lisbon, Portugal
| | - Catarina Viegas
- Department of Neurosurgery, Hospital Garcia de Orta, Almada, Portugal
| | - Nickolas Papanikolaou
- Computational Clinical Imaging Group, Champalimaud Research , Champalimaud Foundation, Av. Brasília, 1400-038, Lisbon, Portugal
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11
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Sun R, Zhang M, Yang L, Yang S, Li N, Huang Y, Song H, Wang B, Huang C, Hou F, Wang H. Preoperative CT-based deep learning radiomics model to predict lymph node metastasis and patient prognosis in bladder cancer: a two-center study. Insights Imaging 2024; 15:21. [PMID: 38270647 PMCID: PMC10811316 DOI: 10.1186/s13244-023-01569-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2023] [Accepted: 11/09/2023] [Indexed: 01/26/2024] Open
Abstract
OBJECTIVE To establish a model for predicting lymph node metastasis in bladder cancer (BCa) patients. METHODS We retroactively enrolled 239 patients who underwent three-phase CT and resection for BCa in two centers (training set, n = 185; external test set, n = 54). We reviewed the clinical characteristics and CT features to identify significant predictors to construct a clinical model. We extracted the hand-crafted radiomics features and deep learning features of the lesions. We used the Minimum Redundancy Maximum Relevance algorithm and the least absolute shrinkage and selection operator logistic regression algorithm to screen features. We used nine classifiers to establish the radiomics machine learning signatures. To compensate for the uneven distribution of the data, we used the synthetic minority over-sampling technique to retrain each machine-learning classifier. We constructed the combined model using the top-performing radiomics signature and clinical model, and finally presented as a nomogram. We evaluated the combined model's performance using the area under the receiver operating characteristic, accuracy, calibration curves, and decision curve analysis. We used the Kaplan-Meier survival curve to analyze the prognosis of BCa patients. RESULTS The combined model incorporating radiomics signature and clinical model achieved an area under the receiver operating characteristic of 0.834 (95% CI: 0.659-1.000) for the external test set. The calibration curves and decision curve analysis demonstrated exceptional calibration and promising clinical use. The combined model showed good risk stratification performance for progression-free survival. CONCLUSION The proposed CT-based combined model is effective and reliable for predicting lymph node status of BCa patients preoperatively. CRITICAL RELEVANCE STATEMENT Bladder cancer is a type of urogenital cancer that has a high morbidity and mortality rate. Lymph node metastasis is an independent risk factor for death in bladder cancer patients. This study aimed to investigate the performance of a deep learning radiomics model for preoperatively predicting lymph node metastasis in bladder cancer patients. KEY POINTS • Conventional imaging is not sufficiently accurate to determine lymph node status. • Deep learning radiomics model accurately predicted bladder cancer lymph node metastasis. • The proposed method showed satisfactory patient risk stratification for progression-free survival.
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Affiliation(s)
- Rui Sun
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, 266003, Shandong, China
| | - Meng Zhang
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, 266003, Shandong, China
| | - Lei Yang
- Department of Radiology, Qingdao Center Hospital, Qingdao, 266042, Shandong, China
| | - Shifeng Yang
- Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, 250000, Shandong, China
| | - Na Li
- Department of Radiology, The People's Hospital of Zhangqiu Area, Jinan, 250200, Shandong, China
| | - Yonghua Huang
- Department of Radiology, The Puyang Oilfield General Hospital, Puyang, 457001, Henan, China
| | - Hongzheng Song
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, 266003, Shandong, China
| | - Bo Wang
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, 266003, Shandong, China
| | - Chencui Huang
- Department of Research Collaboration, R&D Center, Beijing Deepwise & League of PHD Technology Co., Ltd., Beijing, 100080, China
| | - Feng Hou
- Department of Pathology, The Affiliated Hospital of Qingdao University, Qingdao, 266003, Shandong, China.
| | - Hexiang Wang
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, 266003, Shandong, China.
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12
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Javed AA, Zhu Z, Kinny-Köster B, Habib JR, Kawamoto S, Hruban RH, Fishman EK, Wolfgang CL, He J, Chu LC. Accurate non-invasive grading of nonfunctional pancreatic neuroendocrine tumors with a CT derived radiomics signature. Diagn Interv Imaging 2024; 105:33-39. [PMID: 37598013 PMCID: PMC10873069 DOI: 10.1016/j.diii.2023.08.002] [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: 02/22/2023] [Revised: 08/02/2023] [Accepted: 08/03/2023] [Indexed: 08/21/2023]
Abstract
PURPOSE The purpose of this study was to develop a radiomics-signature using computed tomography (CT) data for the preoperative prediction of grade of nonfunctional pancreatic neuroendocrine tumors (NF-PNETs). MATERIALS AND METHODS A retrospective study was performed on patients undergoing resection for NF-PNETs between 2010 and 2019. A total of 2436 radiomic features were extracted from arterial and venous phases of pancreas-protocol CT examinations. Radiomic features that were associated with final pathologic grade observed in the surgical specimens were subjected to joint mutual information maximization for hierarchical feature selection and the development of the radiomic-signature. Youden-index was used to identify optimal cutoff for determining tumor grade. A random forest prediction model was trained and validated internally. The performance of this tool in predicting tumor grade was compared to that of EUS-FNA sampling that was used as the standard of reference. RESULTS A total of 270 patients were included and a fusion radiomic-signature based on 10 selected features was developed using the development cohort (n = 201). There were 149 men and 121 women with a mean age of 59.4 ± 12.3 (standard deviation) years (range: 23.3-85.0 years). Upon internal validation in a new set of 69 patients, a strong discrimination was observed with an area under the curve (AUC) of 0.80 (95% confidence interval [CI]: 0.71-0.90) with corresponding sensitivity and specificity of 87.5% (95% CI: 79.7-95.3) and 73.3% (95% CI: 62.9-83.8) respectively. Of the study population, 143 patients (52.9%) underwent EUS-FNA. Biopsies were non-diagnostic in 26 patients (18.2%) and could not be graded due to insufficient sample in 42 patients (29.4%). In the cohort of 75 patients (52.4%) in whom biopsies were graded the radiomic-signature demonstrated not different AUC as compared to EUS-FNA (AUC: 0.69 vs. 0.67; P = 0.723), however greater sensitivity (i.e., ability to accurately identify G2/3 lesion was observed (80.8% vs. 42.3%; P < 0.001). CONCLUSION Non-invasive assessment of tumor grade in patients with PNETs using the proposed radiomic-signature demonstrated high accuracy. Prospective validation and optimization could overcome the commonly experienced diagnostic uncertainty in the assessment of tumor grade in patients with PNETs and could facilitate clinical decision-making.
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Affiliation(s)
- Ammar A Javed
- Department of Surgery, The Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA; Department of Surgery, New York University Langone Hospital, New York City, New York 10016, USA
| | - Zhuotun Zhu
- Department of Radiology, The Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA
| | - Benedict Kinny-Köster
- Department of Surgery, New York University Langone Hospital, New York City, New York 10016, USA
| | - Joseph R Habib
- Department of Surgery, The Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA
| | - Satomi Kawamoto
- Department of Radiology, The Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA
| | - Ralph H Hruban
- Department of Pathology, The Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA; Sol Goldman Pancreatic Cancer Research Center, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA
| | - Elliot K Fishman
- Department of Radiology, The Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA
| | - Christopher L Wolfgang
- Department of Surgery, New York University Langone Hospital, New York City, New York 10016, USA
| | - Jin He
- Department of Surgery, The Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA
| | - Linda C Chu
- Department of Radiology, The Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA.
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Miranda J, Horvat N, Araujo-Filho JAB, Albuquerque KS, Charbel C, Trindade BMC, Cardoso DL, de Padua Gomes de Farias L, Chakraborty J, Nomura CH. The Role of Radiomics in Rectal Cancer. J Gastrointest Cancer 2023; 54:1158-1180. [PMID: 37155130 PMCID: PMC11301614 DOI: 10.1007/s12029-022-00909-w] [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] [Accepted: 12/26/2022] [Indexed: 05/10/2023]
Abstract
PURPOSE Radiomics is a promising method for advancing imaging assessment in rectal cancer. This review aims to describe the emerging role of radiomics in the imaging assessment of rectal cancer, including various applications of radiomics based on CT, MRI, or PET/CT. METHODS We conducted a literature review to highlight the progress of radiomic research to date and the challenges that need to be addressed before radiomics can be implemented clinically. RESULTS The results suggest that radiomics has the potential to provide valuable information for clinical decision-making in rectal cancer. However, there are still challenges in terms of standardization of imaging protocols, feature extraction, and validation of radiomic models. Despite these challenges, radiomics holds great promise for personalized medicine in rectal cancer, with the potential to improve diagnosis, prognosis, and treatment planning. Further research is needed to validate the clinical utility of radiomics and to establish its role in routine clinical practice. CONCLUSION Overall, radiomics has emerged as a powerful tool for improving the imaging assessment of rectal cancer, and its potential benefits should not be underestimated.
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Affiliation(s)
- Joao Miranda
- Department of Radiology, Memorial Sloan Kettering Cancer Center, 1275 York Ave, Box 29, New York, NY, 10065, USA
| | - Natally Horvat
- Department of Radiology, Memorial Sloan Kettering Cancer Center, 1275 York Ave, Box 29, New York, NY, 10065, USA.
| | - Jose A B Araujo-Filho
- Department of Radiology, Hospital Sirio-Libanes, 91 Adma Jafet, Sao Paulo, SP, 01308-050, Brazil
| | - Kamila S Albuquerque
- Department of Radiology, Hospital Beneficência Portuguesa, 637 Maestro Cardim, Sao Paulo, SP, 01323-001, Brazil
| | - Charlotte Charbel
- Department of Radiology, Memorial Sloan Kettering Cancer Center, 1275 York Ave, Box 29, New York, NY, 10065, USA
| | - Bruno M C Trindade
- Department of Radiology, University of Sao Paulo, 75 Dr. Ovídio Pires de Campos, Sao Paulo, SP, 05403-010, Brazil
| | - Daniel L Cardoso
- Department of Radiology, Hospital Sirio-Libanes, 91 Adma Jafet, Sao Paulo, SP, 01308-050, Brazil
| | | | - Jayasree Chakraborty
- Department of Surgery, Memorial Sloan Kettering Cancer Center, 1275 York Ave, New York, NY, 10065, USA
| | - Cesar Higa Nomura
- Department of Radiology, University of Sao Paulo, 75 Dr. Ovídio Pires de Campos, Sao Paulo, SP, 05403-010, Brazil
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Cao X, Yang H, Luo X, Zou L, Zhang Q, Li Q, Zhang J, Li X, Shi Y, Jin C. A Cox Nomogram for Assessing Recurrence Free Survival in Hepatocellular Carcinoma Following Surgical Resection Using Dynamic Contrast-Enhanced MRI Radiomics. J Magn Reson Imaging 2023; 58:1930-1941. [PMID: 37177868 DOI: 10.1002/jmri.28725] [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: 12/12/2022] [Revised: 03/28/2023] [Accepted: 03/28/2023] [Indexed: 05/15/2023] Open
Abstract
BACKGROUND The prognosis of hepatocellular carcinoma (HCC) is difficult to predict and carries high mortality. This study utilized radiomic techniques with clinical examinations to assess recurrence in HCC. PURPOSE To develop a Cox nomogram to assess the risk of postoperative recurrence in HCC using radiomic features of three volumes of interest (VOIs) in preoperative dynamic contrast-enhanced MRI (DCE-MRI), along with clinical findings. STUDY TYPE Retrospective. SUBJECTS 249 patients with pathologically proven HCCs undergoing surgical resection at three institutions were selected. FIELD STRENGTH/SEQUENCE Fat saturated T2-weighted, Fat saturated T1-weighted, and DCE-MRI performed at 1.5 T and 3.0 T. ASSESSMENT Three VOIs were generated; the tumor VOI corresponds to the area from the tumor core to the outer perimeter of the tumor, the tumor +10 mm VOI represents the area from the tumor perimeter to 10 mm distal to the tumor in all directions, finally, the background liver parenchyma VOI represents the hepatic tissue outside the tumor. Three models were generated. The total radiomic model combined information from the three listed VOI's above. The clinical-radiological model combines physical examination findings with imaging characteristics such as tumor size, margin features, and metastasis. The combined radiomic model includes features from both models listed above and showed the highest reliability for assessing 24-month survival for HCC. STATISTICAL TESTS The least absolute shrinkage and selection operator (LASSO) Cox regression, univariable, and multivariable Cox regression, Kmeans clustering, and Kaplan-Meier analysis. The discrimination performance of each model was quantified by the C-index. A P value <0.05 was considered statistically significant. RESULTS The combined radiomic model, which included features from the radiomic VOI's and clinical imaging provided the highest performance (C-index: training cohort = 0.893, test cohort = 0.851, external cohort = 0.797) in assessing the survival of HCC. CONCLUSION The combined radiomic model provides superior ability to discern the possibility of recurrence-free survival in HCC over the total radiomic and the clinical-radiological models. EVIDENCE LEVEL 4. TECHNICAL EFFICACY Stage 2.
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Affiliation(s)
- Xinshan Cao
- Department of Radiology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
- Department of Radiology, Affiliated Hospital of Binzhou Medical College, Binzhou, China
| | - Haoran Yang
- Department of Radiology, Affiliated Hospital of Binzhou Medical College, Binzhou, China
| | - Xin Luo
- Department of Radiology, Zibo Central Hospital, Zibo, China
| | - Linxuan Zou
- Department of Radiology, Affiliated Hospital of Binzhou Medical College, Binzhou, China
| | - Qiang Zhang
- Department of Radiology, Affiliated Hospital of Binzhou Medical College, Binzhou, China
| | - Qilin Li
- Department of Radiology, Zibo Central Hospital, Zibo, China
| | - Juntao Zhang
- GE Healthcare Precision Health Institution, Shanghai, China
| | - Xiangfeng Li
- Department of Radiology, The Fourth People Hospital of Zibo, Zibo, China
| | - Yan Shi
- Department of Medical Ultrasonics, Affiliated Hospital of Binzhou Medical College, Binzhou, China
| | - Chenwang Jin
- Department of Radiology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
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Formentin C, Joaquim AF, Ghizoni E. Posterior fossa tumors in children: current insights. Eur J Pediatr 2023; 182:4833-4850. [PMID: 37679511 DOI: 10.1007/s00431-023-05189-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/18/2023] [Revised: 08/29/2023] [Accepted: 09/02/2023] [Indexed: 09/09/2023]
Abstract
While in adults most intracranial tumors develop around the cerebral hemispheres, 45 to 60% of pediatric lesions are found in the posterior fossa, although this anatomical region represents only 10% of the intracranial volume. The latest edition of the WHO classification for CNS tumors presented some fundamental paradigm shifts that particularly affected the classification of pediatric tumors, also influencing those that affect posterior fossa. Molecular biomarkers play an important role in the diagnosis, prognosis, and treatment of childhood posterior fossa tumors and can be used to predict patient outcomes and response to treatment and monitor its effectiveness. Although genetic studies have identified several posterior fossa tumor types, differing in terms of their location, cell of origin, genetic mechanisms, and clinical behavior, recent management strategies still depend on uniform approaches, mainly based on the extent of resection. However, significant progress has been made in guiding therapy decisions with biological or molecular stratification criteria and utilizing molecularly targeted treatments that address specific tumor biological characteristics. The primary focus of this review is on the latest advances in the diagnosis and treatment of common subtypes of posterior fossa tumors in children, as well as potential therapeutic approaches in the future. Conclusion: Molecular biomarkers play a central role, not only in the diagnosis and prognosis of posterior fossa tumors in children but also in customizing treatment plans. They anticipate patient outcomes, measure treatment responses, and assess therapeutic effectiveness. Advances in neuroimaging and treatment have significantly enhanced outcomes for children with these tumors. What is Known: • Central nervous system tumors are the most common solid neoplasms in children and adolescents, with approximately 45 to 60% of them located in the posterior fossa. • Multimodal approaches that include neurosurgery, radiation therapy, and chemotherapy are typically used to manage childhood posterior fossa tumors What is New: • Notable progress has been achieved in the diagnosis, categorization and management of posterior fossa tumors in children, leading to improvement in survival and quality of life.
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Affiliation(s)
- Cleiton Formentin
- Division of Neurosurgery, Department of Neurology, University of Campinas, Tessalia Vieira de Camargo St., 126. 13083-887, Campinas, SP, Brazil.
- Centro Infantil Boldrini, Campinas, SP, Brazil.
| | - Andrei Fernandes Joaquim
- Division of Neurosurgery, Department of Neurology, University of Campinas, Tessalia Vieira de Camargo St., 126. 13083-887, Campinas, SP, Brazil
- Centro Infantil Boldrini, Campinas, SP, Brazil
| | - Enrico Ghizoni
- Division of Neurosurgery, Department of Neurology, University of Campinas, Tessalia Vieira de Camargo St., 126. 13083-887, Campinas, SP, Brazil
- Centro Infantil Boldrini, Campinas, SP, Brazil
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Zheng J, Du PZ, Yang C, Tao YY, Li L, Li ZM, Yang L. DCE-MRI-based radiomics in predicting angiopoietin-2 expression in hepatocellular carcinoma. Abdom Radiol (NY) 2023; 48:3343-3352. [PMID: 37495746 PMCID: PMC10556176 DOI: 10.1007/s00261-023-04007-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2023] [Revised: 07/11/2023] [Accepted: 07/13/2023] [Indexed: 07/28/2023]
Abstract
BACKGROUND Hepatocellular carcinoma (HCC) is the sixth most common cancer, and the third leading cause of cancer death worldwide. Studies have shown that increased angiopoietin-2 (Ang-2) expression relative to Ang-1 expression in tumors is associated with a poor prognosis.The purpose of this study was to investigate the efficacy of predicting Ang-2 expression in HCC by preoperative dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI)-based radiomics. METHODS The data of 52 patients with HCC who underwent surgical resection in our hospital were retrospectively analyzed. Ang-2 expression in HCC was analyzed by immunohistochemistry. All patients underwent preoperative upper abdominal DCE-MRI and intravoxel incoherent motion diffusion-weighted imaging scans. Radiomics features were extracted from the early and late arterial and portal phases of axial DCE-MRI. Univariate analysis and least absolute shrinkage and selection operator (LASSO) was performed to select the optimal radiomics features for analysis. A logistic regression analysis was performed to establish a DCE-MRI radiomics model, clinic-radiologic (CR) model and combined model integrating the radiomics score with CR factors. The stability of each model was verified by 10-fold cross-validation. Receiver operating characteristic (ROC) curve analysis, calibration curve analysis and decision curve analysis (DCA) were employed to evaluate these models. RESULTS Among the 52 HCC patients, high Ang-2 expression was found in 30, and low Ang-2 expression was found in 22. The areas under the ROC curve (AUCs) for the radiomics model, CR model and combined model for predicting Ang-2 expression were 0.800, 0.874, and 0.933, respectively. The DeLong test showed that there was no significant difference in the AUC between the radiomics model and the CR model (p > 0.05) but that the AUC for the combined model was significantly greater than those for the other 2 models (p < 0.05). The DCA results showed that the combined model outperformed the other 2 models and had the highest net benefit. CONCLUSION The DCE-MRI-based radiomics model has the potential to predict Ang-2 expression in HCC patients; the combined model integrating the radiomics score with CR factors can further improve the prediction performance.
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Affiliation(s)
- Jing Zheng
- Medical Imaging Key Laboratory of Sichuan Province, Department of Radiology, Interventional Medical Center, Medical Research Center, Affiliated Hospital of North Sichuan Medical College, Nanchong, 637000, China
| | - Pei-Zhuo Du
- Department of Radiology, The Second Clinical Medical College of North Sichuan Medical College, Nanchong, 637000, China
| | - Cui Yang
- Medical Imaging Key Laboratory of Sichuan Province, Department of Radiology, Interventional Medical Center, Medical Research Center, Affiliated Hospital of North Sichuan Medical College, Nanchong, 637000, China
- Department of Radiology, Panzhihua Central Hospital, Panzhihua, 617000, China
| | - Yun-Yun Tao
- Medical Imaging Key Laboratory of Sichuan Province, Department of Radiology, Interventional Medical Center, Medical Research Center, Affiliated Hospital of North Sichuan Medical College, Nanchong, 637000, China
| | - Li Li
- Department of Pathology, Affiliated Hospital of North Sichuan Medical College, Nanchong, 637000, China
| | - Zu-Mao Li
- Department of Pathology, Affiliated Hospital of North Sichuan Medical College, Nanchong, 637000, China
| | - Lin Yang
- Medical Imaging Key Laboratory of Sichuan Province, Department of Radiology, Interventional Medical Center, Medical Research Center, Affiliated Hospital of North Sichuan Medical College, Nanchong, 637000, China.
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Jin N, Qiao B, Zhao M, Li L, Zhu L, Zang X, Gu B, Zhang H. Predicting cervical lymph node metastasis in OSCC based on computed tomography imaging genomics. Cancer Med 2023; 12:19260-19271. [PMID: 37635388 PMCID: PMC10557859 DOI: 10.1002/cam4.6474] [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: 05/12/2023] [Revised: 08/01/2023] [Accepted: 08/15/2023] [Indexed: 08/29/2023] Open
Abstract
BACKGROUND To investigate the correlation between computed tomography (CT) radiomic characteristics and key genes for cervical lymph node metastasis (LNM) in oral squamous cell carcinoma (OSCC). METHODS The region of interest was annotated at the edge of the primary tumor on enhanced CT images from 140 patients with OSCC and obtained radiomic features. Ribonucleic acid (RNA) sequencing was performed on pathological sections from 20 patients. the DESeq software package was used to compare differential gene expression between groups. Weighted gene co-expression network analysis was used to construct co-expressed gene modules, and the KEGG and GO databases were used for pathway enrichment analysis of key gene modules. Finally, Pearson correlation coefficients were calculated between key genes of enriched pathways and radiomic features. RESULTS Four hundred and eighty radiomic features were extracted from enhanced CT images of 140 patients; seven of these correlated significantly with cervical LNM in OSCC (p < 0.01). A total of 3527 differentially expressed RNAs were screened from RNA sequencing data of 20 cases. original_glrlm_RunVariance showed significant positive correlation with most long noncoding RNAs. CONCLUSIONS OSCC cervical LNM is related to the salivary hair bump signaling pathway and biological process. Original_glrlm_RunVariance correlated with LNM and most differentially expressed long noncoding RNAs.
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Affiliation(s)
- Nenghao Jin
- Medical School of Chinese PLABeijingChina
- Department of Stomatology, The First Medical CentreChinese PLA General HospitalBeijingChina
| | - Bo Qiao
- Medical School of Chinese PLABeijingChina
- Department of Stomatology, The First Medical CentreChinese PLA General HospitalBeijingChina
| | - Min Zhao
- Pharmaceutical Diagnostics, GE HealthcareBeijingChina
- Research Center of Medical Big Data, Chinese PLA General HospitalBeijingChina
| | - Liangbo Li
- Medical School of Chinese PLABeijingChina
- Department of Stomatology, The First Medical CentreChinese PLA General HospitalBeijingChina
| | - Liang Zhu
- Medical School of Chinese PLABeijingChina
- Department of Stomatology, The First Medical CentreChinese PLA General HospitalBeijingChina
| | - Xiaoyi Zang
- Medical School of Chinese PLABeijingChina
- Department of Stomatology, The First Medical CentreChinese PLA General HospitalBeijingChina
| | - Bin Gu
- Department of Stomatology, The First Medical CentreChinese PLA General HospitalBeijingChina
| | - Haizhong Zhang
- Department of Stomatology, The First Medical CentreChinese PLA General HospitalBeijingChina
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Hajianfar G, Kalayinia S, Hosseinzadeh M, Samanian S, Maleki M, Sossi V, Rahmim A, Salmanpour MR. Prediction of Parkinson's disease pathogenic variants using hybrid Machine learning systems and radiomic features. Phys Med 2023; 113:102647. [PMID: 37579523 DOI: 10.1016/j.ejmp.2023.102647] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/11/2022] [Revised: 05/08/2023] [Accepted: 07/29/2023] [Indexed: 08/16/2023] Open
Abstract
PURPOSE In Parkinson's disease (PD), 5-10% of cases are of genetic origin with mutations identified in several genes such as leucine-rich repeat kinase 2 (LRRK2) and glucocerebrosidase (GBA). We aim to predict these two gene mutations using hybrid machine learning systems (HMLS), via imaging and non-imaging data, with the long-term goal to predict conversion to active disease. METHODS We studied 264 and 129 patients with known LRRK2 and GBA mutations status from PPMI database. Each dataset includes 513 features such as clinical features (CFs), conventional imaging features (CIFs) and radiomic features (RFs) extracted from DAT-SPECT images. Features, normalized by Z-score, were univariately analyzed for statistical significance by the t-test and chi-square test, adjusted by Benjamini-Hochberg correction. Multiple HMLSs, including 11 features extraction (FEA) or 10 features selection algorithms (FSA) linked with 21 classifiers were utilized. We also employed Ensemble Voting (EV) to classify the genes. RESULTS For prediction of LRRK2 mutation status, a number of HMLSs resulted in accuracies of 0.98 ± 0.02 and 1.00 in 5-fold cross-validation (80% out of total data points) and external testing (remaining 20%), respectively. For predicting GBA mutation status, multiple HMLSs resulted in high accuracies of 0.90 ± 0.08 and 0.96 in 5-fold cross-validation and external testing, respectively. We additionally showed that SPECT-based RFs added value to the specific prediction of of GBA mutation status. CONCLUSION We demonstrated that combining medical information with SPECT-based imaging features, and optimal utilization of HMLS can produce excellent prediction of the mutations status in PD patients.
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Affiliation(s)
- Ghasem Hajianfar
- Rajaie Cardiovascular Medical and Research Center, Iran University of Medical Sciences, Tehran, Iran; Technological Virtual Collaboration (TECVICO Corp.), Vancouver BC, Canada
| | - Samira Kalayinia
- Cardiogenetic Research Center, Rajaie Cardiovascular Medical and Research Center, Iran University of Medical Sciences, Tehran, Iran
| | - Mahdi Hosseinzadeh
- Technological Virtual Collaboration (TECVICO Corp.), Vancouver BC, Canada; Department of Electrical and Computer Engineering, Tarbiat Modares University, Tehran, Iran
| | - Sara Samanian
- Firoozgar Hospital Medical Genetics Laboratory, Iran University of Medical Sciences, Tehran, Iran
| | - Majid Maleki
- Rajaie Cardiovascular Medical and Research Center, Iran University of Medical Sciences, Tehran, Iran
| | - Vesna Sossi
- Department of Physics and Astronomy, University of British Columbia, Vancouver, BC, Canada
| | - Arman Rahmim
- Department of Physics and Astronomy, University of British Columbia, Vancouver, BC, Canada; Department of Integrative Oncology, BC Cancer Research Institute, Vancouver, BC, Canada
| | - Mohammad R Salmanpour
- Technological Virtual Collaboration (TECVICO Corp.), Vancouver BC, Canada; Department of Integrative Oncology, BC Cancer Research Institute, Vancouver, BC, Canada.
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Zheng D, Zhang Y, Huang D, Wang M, Guo N, Zhu S, Zhang J, Ying T. Incremental predictive utility of a radiomics signature in a nomogram for the recurrence of atrial fibrillation. Front Cardiovasc Med 2023; 10:1203009. [PMID: 37636308 PMCID: PMC10451088 DOI: 10.3389/fcvm.2023.1203009] [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: 04/10/2023] [Accepted: 07/19/2023] [Indexed: 08/29/2023] Open
Abstract
Background Recurrence of atrial fibrillation (AF) after catheter ablation (CA) remains a challenge today. Although it is believed that evaluating the structural and functional remodeling of the left atrium (LA) may be helpful in predicting AF recurrence, there is a lack of consensus on prediction accuracy. Ultrasound-based radiomics is currently receiving increasing attention because it might aid in the diagnosis and prognosis prediction of AF recurrence. However, research on LA ultrasound radiomics is limited. Objective We aim to investigate the incremental predictive utility of LA radiomics and construct a radiomics nomogram to preoperatively predict AF recurrence following CA. Methods A training cohort of 232 AF patients was designed for nomogram construction, while a validation cohort (n = 100) served as the model performance test. AF recurrence during a follow-up period of 3-12 months was defined as the endpoint. The radiomics features related to AF recurrence were extracted and selected to create the radiomics score (rad score). These rad scores, along with other morphological and functional indicators for AF recurrence, were included in the multivariate Cox analysis to establish a nomogram for the prediction of the likelihood of AF recurrence within 1 year following CA. Results In the training and validation cohorts, AF recurrence rates accounted for 32.3% (75/232) and 25.0% (25/100), respectively. We extracted seven types of radiomics features associated with AF recurrence from apical four-chamber view echocardiography images and established a rad score for each patient. The radiomics nomogram was built with the rad score, AF type, left atrial appendage emptying flow velocity, and peak atrial longitudinal strain. It outperformed the nomogram building without the rad score in terms of the predictive efficacy of CA outcome and showed favorable performance in both cohorts. Conclusion We revealed the incremental utility of a radiomics signature in the prediction of AF recurrence and preliminarily developed and validated a radiomics nomogram for identifying patients who were at high risk of post-CA recurrence, which contributed to an appropriate management strategy for AF.
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Affiliation(s)
- Dongyan Zheng
- Department of Ultrasound, Shanghai Jiao Tong University Affiliated Sixth People’s Hospital, Shanghai, China
| | - Yueli Zhang
- Department of Ultrasound, Shanghai Jiao Tong University Affiliated Sixth People’s Hospital, Shanghai, China
| | - Dong Huang
- Department of Cardiology, Shanghai Jiao Tong University Affiliated Sixth People’s Hospital, Shanghai, China
| | - Man Wang
- Department of Ultrasound, Shanghai Jiao Tong University Affiliated Sixth People’s Hospital, Shanghai, China
| | - Ning Guo
- Department of Ultrasound, Shanghai Jiao Tong University Affiliated Sixth People’s Hospital, Shanghai, China
| | - Shu Zhu
- Department of Ultrasound, Shanghai Jiao Tong University Affiliated Sixth People’s Hospital, Shanghai, China
| | - Juanjuan Zhang
- Department of Cardiology, Shanghai Jiao Tong University Affiliated Sixth People’s Hospital, Shanghai, China
| | - Tao Ying
- Department of Ultrasound, Shanghai Jiao Tong University Affiliated Sixth People’s Hospital, Shanghai, China
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Huang W, Jin Y, Jiang L, Liang M. Radiomics optimizing the evaluation of endometrial receptivity for women with unexplained recurrent pregnancy loss. Front Endocrinol (Lausanne) 2023; 14:1181058. [PMID: 37795355 PMCID: PMC10545880 DOI: 10.3389/fendo.2023.1181058] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/07/2023] [Accepted: 07/24/2023] [Indexed: 10/06/2023] Open
Abstract
Background The optimization of endometrial receptivity (ER) through individualized therapies has been shown to enhance the likelihood of successful gestation. However, current practice lacks comprehensive methods for evaluating the ER of patients with recurrent pregnancy loss (RPL). Radiomics, an emerging AI-based technique that enables the extraction of mineable information from medical images, holds potential to offer a more objective and quantitative approach to ER assessment. This innovative tool may facilitate a deeper understanding of the endometrial environment and enable clinicians to optimize ER evaluation in RPL patients. Objective This study aimed to identify ultrasound radiomics features associated with ER, with the purpose of predicting successful ongoing pregnancies in RPL patients, and to assess the predictive accuracy of these features against regular ER parameters. Methods This retrospective, controlled study involved 262 patients with unexplained RPL and 273 controls with a history of uncomplicated full-term pregnancies. Radiomics features were extracted from ultrasound endometrial segmentation images to derive a radiomics score (rad-score) for each participant. Associations between rad-scores, baseline clinical variables, and sonographic data were evaluated using univariate and multivariate logistic regression analyses to identify potential indicators of RPL. Receiver operating characteristic (ROC) curve analysis was performed to evaluate the predictive accuracy of the rad-score and other identified indicators in discriminating RPL cases. Furthermore, the relationships between age and these identified indicators were assessed via Pearson correlation analysis. Results From the 1312 extracted radiomics features, five non-zero coefficient radiomics signatures were identified as significantly associated with RPL, forming the basis of the rad-score. Following multivariate logistic regression analysis, age, spiral artery pulsatility index (SA-PI), vascularisation index (VI), and rad-score emerged as independent correlates of RPL (all P<0.05). ROC curve analyses revealed the superior discriminative capability of the rad-score (AUC=0.882) over age (AUC=0.778), SA-PI (AUC=0.771), and VI (AUC=0.595). There were notable correlations between age and rad-score (r=0.275), VI (r=-0.224), and SA-PI (r=0.211), indicating age-related variations in RPL predictors. Conclusion This study revealed a significant association between unexplained RPL and elevated endometrial rad-scores during the WOI. Furthermore, it demonstrated the potential of rad-scores as a promising predictive tool for successful ongoing pregnancies in RPL patients.
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Yang ZH, Han YJ, Cheng M, Wang R, Li J, Zhao HP, Gao JB. Prognostic value of computed tomography radiomics features in patients with gastric neuroendocrine neoplasm. Front Oncol 2023; 13:1143291. [PMID: 37409252 PMCID: PMC10319063 DOI: 10.3389/fonc.2023.1143291] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2023] [Accepted: 06/05/2023] [Indexed: 07/07/2023] Open
Abstract
Purpose The present study aimed to investigate the clinical prognostic significance of radiomics signature (R-signature) in patients with gastric neuroendocrine neoplasm (GNEN). Methods and Materials A retrospective study of 182 patients with GNEN who underwent dual-phase enhanced computed tomography (CT) scanning was conducted. LASSO-Cox regression analysis was used to screen the features and establish the arterial, venous and the arteriovenous phase combined R-signature, respectively. The association between the optimal R-signature with the best prognostic performance and overall survival (OS) was assessed in the training cohort and verified in the validation cohort. Univariate and multivariate Cox regression analysis were used to identify the significant factors of clinicopathological characteristics for OS. Furthermore, the performance of a combined radiomics-clinical nomogram integrating the R-signature and independent clinicopathological risk factors was evaluated. Results The arteriovenous phase combined R-signature had the best performance in predicting OS, and its C-index value was better than the independent arterial and venous phase R-signature (0.803 vs 0.784 and 0.803 vs 0.756, P<0.001, respectively). The optimal R-signature was significantly associated with OS in the training cohort and validation cohort. GNEN patients could be successfully divided into high and low prognostic risk groups with radiomics score median. The combined radiomics-clinical nomogram combining this R-signature and independent clinicopathological risk factors (sex, age, treatment methods, T stage, N stage, M stage, tumor boundary, Ki67, CD56) exhibited significant prognostic superiority over clinical nomogram, R-signature alone, and traditional TNM staging system (C-index, 0.882 vs 0.861, 882 vs 0.803, and 0.882 vs 0.870 respectively, P<0.001). All calibration curves showed remarkable consistency between predicted and actual survival, and decision curve analysis verified the usefulness of the combined radiomics-clinical nomogram for clinical practice. Conclusions The R-signature could be used to stratify patients with GNEN into high and low risk groups. Furthermore, the combined radiomics-clinical nomogram provided better predictive accuracy than other predictive models and might aid clinicians with therapeutic decision-making and patient counseling.
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Affiliation(s)
- Zhi-hao Yang
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Henan Key Laboratory of Image Diagnosis and Treatment for Digestive System Tumor, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Yi-jing Han
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Henan Key Laboratory of Image Diagnosis and Treatment for Digestive System Tumor, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Ming Cheng
- Henan Key Laboratory of Image Diagnosis and Treatment for Digestive System Tumor, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Department of Medical Information, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Rui Wang
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Department of Medical Information, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Jing Li
- Department of Radiology, Affiliated Tumor Hospital of Zhengzhou University, Zhengzhou, China
| | - Hui-ping Zhao
- Department of Radiology, Shanxi Provincial People’s Hospital, Xi’an, China
| | - Jian-bo Gao
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Henan Key Laboratory of Image Diagnosis and Treatment for Digestive System Tumor, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
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Demirel E, Dilek O. Relationship between body composition and PBRM1 mutations in clear cell renal cell carcinoma: a propensity score matching analysis. REVISTA DA ASSOCIACAO MEDICA BRASILEIRA (1992) 2023; 69:e20220415. [PMID: 37222312 DOI: 10.1590/1806-9282.20220415] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/22/2022] [Accepted: 02/20/2023] [Indexed: 05/25/2023]
Abstract
OBJECTIVE This study aimed to examine the relationship between body muscle and adipose tissue composition in clear cell renal cell carcinoma patients with PBRM1 gene mutation. METHODS Cancer Genome Atlas Kidney clear cell renal cell carcinoma and Clinical Proteomic Tumor Analysis Consortium clear cell renal cell carcinoma collections were retrieved from the Cancer Imaging Archive. A total of 291 clear cell renal cell carcinoma patients were included in the study retrospectively. Patients' characteristics were obtained from Cancer Imaging Archive. Body composition was assessed with abdominal computed tomography using the automated artificial intelligence software (AID-U™, iAID Inc., Seoul, Korea). Body composition parameters of the patients were calculated. To investigate the net effect of body composition, the propensity score matching procedure was applied over age, gender, and T-stage parameters. RESULTS Of the patients, 184 were males and 107 were females. Mutations in the PBRM1 gene were detected in 77 of the patients. While there was no difference in adipose tissue areas between the PBRM1 mutation group and those without PBRM1 mutation, statistically significant differences were found in normal attenuated muscle area parameters. CONCLUSION This study shows that there was no difference between adipose tissue areas in patients with PBMR1 mutation, but normal attenuated muscle area was found to be higher in PBRM1 patients.
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Affiliation(s)
- Emin Demirel
- Emirdag City of Hospital, Department of Radiology - Afyonkarahisar, Turkey
| | - Okan Dilek
- University of Health Sciences, Adana City Training and Research Hospital, Department of Radiology - Adana, Turkey
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Duan J, Bernard ME, Castle JR, Feng X, Wang C, Kenamond MC, Chen Q. Contouring quality assurance methodology based on multiple geometric features against deep learning auto-segmentation. Med Phys 2023; 50:2715-2732. [PMID: 36788735 PMCID: PMC10175153 DOI: 10.1002/mp.16299] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2022] [Revised: 01/06/2023] [Accepted: 01/26/2023] [Indexed: 02/16/2023] Open
Abstract
BACKGROUND Contouring error is one of the top failure modes in radiation treatment. Multiple efforts have been made to develop tools to automatically detect segmentation errors. Deep learning-based auto-segmentation (DLAS) has been used as a baseline for flagging manual segmentation errors, but those efforts are limited to using only one or two contour comparison metrics. PURPOSE The purpose of this research is to develop an improved contouring quality assurance system to identify and flag manual contouring errors. METHODS AND MATERIALS DLAS contours were used as a reference to compare with manually segmented contours. A total of 27 geometric agreement metrics were determined from the comparisons between the two segmentation approaches. Feature selection was performed to optimize the training of a machine learning classification model to identify potential contouring errors. A public dataset with 339 cases was used to train and test the classifier. Four independent classifiers were trained using five-fold cross validation, and the predictions from each classifier were ensembled using soft voting. The trained model was validated on a held-out testing dataset. An additional independent clinical dataset with 60 cases was used to test the generalizability of the model. Model predictions were reviewed by an expert to confirm or reject the findings. RESULTS The proposed machine learning multiple features (ML-MF) approach outperformed traditional nonmachine-learning-based approaches that are based on only one or two geometric agreement metrics. The machine learning model achieved recall (precision) values of 0.842 (0.899), 0.762 (0.762), 0.727 (0.842), and 0.773 (0.773) for Brainstem, Parotid_L, Parotid_R, and mandible contours, respectively compared to 0.526 (0.909), 0.619 (0.765), 0.682 (0.882), 0.773 (0.568) for an approach based solely on Dice similarity coefficient values. In the external validation dataset, 66.7, 93.3, 94.1, and 58.8% of flagged cases were confirmed to have contouring errors by an expert for Brainstem, Parotid_L, Parotid_R, and mandible contours, respectively. CONCLUSIONS The proposed ML-MF approach, which includes multiple geometric agreement metrics to flag manual contouring errors, demonstrated superior performance in comparison to traditional methods. This method is easy to implement in clinical practice and can help to reduce the significant time and labor costs associated with manual segmentation and review.
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Affiliation(s)
- Jingwei Duan
- Department of Radiation Medicine, University of Kentucky, Lexington, KY 40506
| | - Mark E. Bernard
- Department of Radiation Medicine, University of Kentucky, Lexington, KY 40506
| | - James R. Castle
- Carina Medical LLC, 145 Graham Ave, A168, Lexington, KY 40506
| | - Xue Feng
- Carina Medical LLC, 145 Graham Ave, A168, Lexington, KY 40506
| | - Chi Wang
- Department of Internal Medicine, University of Kentucky, Lexington, KY 40506
| | - Mark C. Kenamond
- Department of Radiation Medicine, University of Kentucky, Lexington, KY 40506
| | - Quan Chen
- Department of Radiation Medicine, University of Kentucky, Lexington, KY 40506
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Mei N, Lu Y, Yang S, Jiang S, Ruan Z, Wang D, Liu X, Ying Y, Li X, Yin B. Oligodendrocyte Transcription Factor 2 as a Potential Prognostic Biomarker of Glioblastoma: Kaplan-Meier Analysis and the Development of a Binary Predictive Model Based on Visually Accessible Rembrandt Image and Magnetic Resonance Imaging Radiomic Features. J Comput Assist Tomogr 2023; Publish Ahead of Print:00004728-990000000-00157. [PMID: 37380154 DOI: 10.1097/rct.0000000000001454] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/30/2023]
Abstract
OBJECTIVE Oligodendrocyte transcription factor 2 (OLIG2) is universally expressed in human glioblastoma (GB). Our study explores whether OLIG2 expression impacts GB patients' overall survival and establishes a machine learning model for OLIG2 level prediction in patients with GB based on clinical, semantic, and magnetic resonance imaging radiomic features. METHODS Kaplan-Meier analysis was used to determine the optimal cutoff value of the OLIG2 in 168 GB patients. Three hundred thirteen patients enrolled in the OLIG2 prediction model were randomly divided into training and testing sets in a ratio of 7:3. The radiomic, semantic, and clinical features were collected for each patient. Recursive feature elimination (RFE) was used for feature selection. The random forest (RF) model was built and fine-tuned, and the area under the curve was calculated to evaluate the performance. Finally, a new testing set excluding IDH-mutant patients was built and tested in a predictive model using the fifth edition of the central nervous system tumor classification criteria. RESULTS One hundred nineteen patients were included in the survival analysis. Oligodendrocyte transcription factor 2 was positively associated with GB survival, with an optimal cutoff of 10% (P = 0.00093). One hundred thirty-four patients were eligible for the OLIG2 prediction model. An RFE-RF model based on 2 semantic and 21 radiomic signatures achieved areas under the curve of 0.854 in the training set, 0.819 in the testing set, and 0.825 in the new testing set. CONCLUSIONS Glioblastoma patients with ≤10% OLIG2 expression tended to have worse overall survival. An RFE-RF model integrating 23 features can predict the OLIG2 level of GB patients preoperatively, irrespective of the central nervous system classification criteria, further guiding individualized treatment.
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Affiliation(s)
- Nan Mei
- From the Departments of Radiology
| | | | | | | | | | | | - Xiujuan Liu
- Pathology, Huashan Hospital, Fudan University, Shanghai, People's Republic of China
| | | | | | - Bo Yin
- From the Departments of Radiology
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Majithia J, Mahajan A, Vaish R, Prakash G, Patwardhan S, Sarin R. Imaging Recommendations for Diagnosis, Staging, and Management of Hereditary Malignancies. Indian J Med Paediatr Oncol 2023. [DOI: 10.1055/s-0042-1760325] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/05/2023] Open
Abstract
AbstractHereditary cancer syndromes, characterized by genetically distinct neoplasms developing in specific organs in more than one family members, predispose an individual to early onset of distinct site-specific tumors. Early age of onset, multiorgan involvement, multiple and bilateral tumors, advanced disease at presentation, and aggressive tumor histology are few characteristic features of hereditary cancer syndromes. A multidisciplinary approach to hereditary cancers has led to a paradigm shift in the field of preventive oncology and precision medicine. Imaging plays a pivotal role in the screening, testing, and follow-up of individuals and their first- and second-degree relatives with hereditary cancers. In fact, a radiologist is often the first to apprise the clinician about the possibility of an underlying hereditary cancer syndrome based on pathognomonic imaging findings. This article focuses on the imaging spectrum of few common hereditary cancer syndromes with specific mention of the imaging features of associated common and uncommon tumors in each syndrome. The screening and surveillance recommendations for each condition with specific management approaches, in contrast to sporadic cases, have also been described.
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Affiliation(s)
- Jinita Majithia
- Department of Radiodiagnosis, Tata Memorial Hospital, Mumbai, Maharashtra, India
| | - Abhishek Mahajan
- Department of Radiology, The Clatterbridge Cancer Centre NHS Foundation Trust, Liverpool, United Kingdom
| | - Richa Vaish
- Department of Head and Neck Oncology, Tata Memorial Hospital, Mumbai, Maharashtra, India
| | - Gagan Prakash
- Department of Uro-Oncology, Tata Memorial Hospital, Mumbai, Maharashtra, India
| | - Saket Patwardhan
- Department of Radiodiagnosis, Tata Memorial Hospital, Mumbai, Maharashtra, India
| | - Rajiv Sarin
- Department of Radiation Oncology and In-Charge Cancer Genetics, Tata Memorial Hospital and Advanced Centre for Treatment Research and Education in Cancer (ACTREC), Mumbai, Maharashtra, India
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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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Martucci M, Russo R, Schimperna F, D’Apolito G, Panfili M, Grimaldi A, Perna A, Ferranti AM, Varcasia G, Giordano C, Gaudino S. Magnetic Resonance Imaging of Primary Adult Brain Tumors: State of the Art and Future Perspectives. Biomedicines 2023; 11:biomedicines11020364. [PMID: 36830900 PMCID: PMC9953338 DOI: 10.3390/biomedicines11020364] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2022] [Revised: 01/20/2023] [Accepted: 01/22/2023] [Indexed: 01/28/2023] Open
Abstract
MRI is undoubtedly the cornerstone of brain tumor imaging, playing a key role in all phases of patient management, starting from diagnosis, through therapy planning, to treatment response and/or recurrence assessment. Currently, neuroimaging can describe morphologic and non-morphologic (functional, hemodynamic, metabolic, cellular, microstructural, and sometimes even genetic) characteristics of brain tumors, greatly contributing to diagnosis and follow-up. Knowing the technical aspects, strength and limits of each MR technique is crucial to correctly interpret MR brain studies and to address clinicians to the best treatment strategy. This article aimed to provide an overview of neuroimaging in the assessment of adult primary brain tumors. We started from the basilar role of conventional/morphological MR sequences, then analyzed, one by one, the non-morphological techniques, and finally highlighted future perspectives, such as radiomics and artificial intelligence.
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Affiliation(s)
- Matia Martucci
- Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico “A. Gemelli” IRCCS, 00168 Rome, Italy
- Correspondence:
| | - Rosellina Russo
- Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico “A. Gemelli” IRCCS, 00168 Rome, Italy
| | | | - Gabriella D’Apolito
- Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico “A. Gemelli” IRCCS, 00168 Rome, Italy
| | - Marco Panfili
- Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico “A. Gemelli” IRCCS, 00168 Rome, Italy
| | - Alessandro Grimaldi
- Istituto di Radiologia, Università Cattolica del Sacro Cuore, 00168 Rome, Italy
| | - Alessandro Perna
- Istituto di Radiologia, Università Cattolica del Sacro Cuore, 00168 Rome, Italy
| | | | - Giuseppe Varcasia
- Istituto di Radiologia, Università Cattolica del Sacro Cuore, 00168 Rome, Italy
| | - Carolina Giordano
- Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico “A. Gemelli” IRCCS, 00168 Rome, Italy
| | - Simona Gaudino
- Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico “A. Gemelli” IRCCS, 00168 Rome, Italy
- Istituto di Radiologia, Università Cattolica del Sacro Cuore, 00168 Rome, Italy
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Kalapala R, Rughwani H, Reddy DN. Artificial Intelligence in Hepatology- Ready for the Primetime. J Clin Exp Hepatol 2023; 13:149-161. [PMID: 36647407 PMCID: PMC9840075 DOI: 10.1016/j.jceh.2022.06.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/19/2022] [Accepted: 06/23/2022] [Indexed: 02/07/2023] Open
Abstract
Artificial Intelligence (AI) is a mathematical process of computer mediating designing of algorithms to support human intelligence. AI in hepatology has shown tremendous promise to plan appropriate management and hence improve treatment outcomes. The field of AI is in a very early phase with limited clinical use. AI tools such as machine learning, deep learning, and 'big data' are in a continuous phase of evolution, presently being applied for clinical and basic research. In this review, we have summarized various AI applications in hepatology, the pitfalls and AI's future implications. Different AI models and algorithms are under study using clinical, laboratory, endoscopic and imaging parameters to diagnose and manage liver diseases and mass lesions. AI has helped to reduce human errors and improve treatment protocols. Further research and validation are required for future use of AI in hepatology.
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Key Words
- ACLF, acute on chronic liver failure
- AI, artificial intelligence
- ALD, alcoholic liver disease
- ALT, alanine transaminase
- ANN, artificial neural network
- AST, aspartate aminotransferase
- AUD, alcohol use disorder
- CHB, chronic hepatitis B
- CHC, chronic hepatitis C
- CLD, chronic liver disease
- CNN, convolutional neural network
- DL, deep learning
- FIB-4, fibrosis-4 score
- GGTP, gamma glutamyl transferase
- HCC, hepatocellular carcinoma
- HDL, high density lipoprotein
- ML, machine learning
- MLR, multi-nomial logistic regressions
- NAFLD
- NAFLD, non-alcoholic fatty liver disease
- NASH, non-alcoholic steatohepatitis
- NLP, natural language processing
- RF, random forest
- RTE, real-time tissue elastography
- SOLs, space-occupying lesions
- SVM, support vector machine
- artificial intelligence
- deep learning
- hepatology
- machine learning
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Affiliation(s)
- Rakesh Kalapala
- Department of Gastroenterology, Asian Institute of Gastroenterology and AIG Hospitals, Hyderabad, India
| | - Hardik Rughwani
- Department of Gastroenterology, Asian Institute of Gastroenterology and AIG Hospitals, Hyderabad, India
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Liu Y, Wei X, Zhang X, Pang C, Xia M, Du Y. CT radiomics combined with clinical variables for predicting the overall survival of hepatocellular carcinoma patients after hepatectomy. Transl Oncol 2022; 26:101536. [PMID: 36115077 PMCID: PMC9483805 DOI: 10.1016/j.tranon.2022.101536] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2022] [Revised: 08/29/2022] [Accepted: 09/04/2022] [Indexed: 11/18/2022] Open
Abstract
PURPOSE To establish a model for assessing the overall survival (OS) of the hepatocellular carcinoma (HCC) patients after hepatectomy based on the clinical and radiomics features. METHODS This study recruited a total of 267 patients with HCC, which were randomly divided into the training (N = 188) and validation (N = 79) cohorts. In the training cohort, radiomic features were selected with the intra-reader and inter-reader correlation coefficient (ICC), Spearman's correlation coefficient, and the least absolute shrinkage and selection operator (LASSO). The radiomics signatures were built by COX regression analysis and compared the predictive potential in the different phases (arterial, portal, and double-phase) and regions of interest (tumor, peritumor 3 mm, peritumor 5 mm). A clinical-radiomics model (CR model) was established by combining the radiomics signatures and clinical risk factors. The validation cohort was used to validate the proposed models. RESULTS A total of 267 patients 86 (45.74%) and 37 (46.84%) patients died in the training and validation cohorts, respectively. Among all the radiomics signatures, those based on the tumor and peritumor (5 mm) (AP-TP5-Signature) showed the best prognostic potential (training cohort 1-3 years AUC:0.774-0.837; validation cohort 1-3 years AUC:0.754-0.810). The CR model showed better discrimination, calibration, and clinical applicability as compared to the clinical model and radiomics features. In addition, the CR model could perform risk-stratification and also allowed for significant discrimination between the Kaplan-Meier curves in most of the subgroups. CONCLUSIONS The CR model could predict the OS of the HCC patients after hepatectomy.
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Affiliation(s)
- Ying Liu
- School of Medical Imaging, North Sichuan Medical College, Nanchong City 637000, Sichuan Province, China
| | - Xiaoqin Wei
- School of Medical Imaging, North Sichuan Medical College, Nanchong City 637000, Sichuan Province, China
| | - Xinrui Zhang
- School of Medical Imaging, North Sichuan Medical College, Nanchong City 637000, Sichuan Province, China
| | - Caifeng Pang
- School of Medical Imaging, North Sichuan Medical College, Nanchong City 637000, Sichuan Province, China
| | - Mingkai Xia
- School of Medical Imaging, North Sichuan Medical College, Nanchong City 637000, Sichuan Province, China
| | - Yong Du
- Department of Radiology, the Affiliated Hospital of North Sichuan Medical College, Nanchong City 637000, Sichuan Province, China.
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Zhang X, Wang C, Zheng D, Liao Y, Wang X, Huang Z, Zhong Q. Radiomics nomogram based on multi-parametric magnetic resonance imaging for predicting early recurrence in small hepatocellular carcinoma after radiofrequency ablation. Front Oncol 2022; 12:1013770. [PMID: 36439458 PMCID: PMC9686343 DOI: 10.3389/fonc.2022.1013770] [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: 08/07/2022] [Accepted: 10/24/2022] [Indexed: 08/10/2023] Open
Abstract
BACKGROUND There are few studies on the application of radiomics in the risk prediction of early recurrence (ER) after radiofrequency ablation (RFA). This study evaluated the value of a multi-parametric magnetic resonance imaging (MRI, mpMRI)-based radiomics nomogram in predicting ER of small hepatocellular carcinoma (HCC) after RFA. MATERIALS AND METHODS A retrospective analysis was performed on 90 patients with small HCC who were treated with RFA. Patients were divided into two groups according to recurrence within 2 years: the ER group (n=38) and the non-ER group (n=52). Preoperative T1WI, T2WI, and contrast-enhanced MRI (CE-MRI) were used for radiomic analysis. Tumor segmentation was performed on the images and applied to extract 1316 radiomics features. The most predictive features were selected using analysis of variance + Mann-Whitney, Spearman's rank correlation test, random forest (importance), and least absolute shrinkage and selection operator analysis. Radiomics models based on each sequence or combined sequences were established using logistic regression analysis. A predictive nomogram was constructed based on the radiomics score (rad-score) and clinical predictors. The predictive efficiency of the nomogram was evaluated using the area under the receiver operating characteristic curve (AUC). Decision curve analysis (DCA) was used to evaluate the clinical efficacy of the nomogram. RESULTS The radiomics model mpMRI, which is based on T1WI, T2WI, and CE-MRI sequences, showed the best predictive performance, with an AUC of 0.812 for the validation cohort. Combined with the clinical risk factors of albumin level, number of tumors, and rad-score of mpMRI, the AUC of the preoperative predictive nomogram in the training and validation cohorts were 0.869 and 0.812, respectively. DCA demonstrated that the combined nomogram is clinically useful. CONCLUSIONS The multi-parametric MRI-based radiomics nomogram has a high predictive value for ER of small HCC after RFA, which could be helpful for personalized risk stratification and further treatment decision-making for patients with small HCC.
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Affiliation(s)
- Xiaojuan Zhang
- Department of Radiology, Fujian Medical University Xiamen Humanity Hospital, Xiamen, China
- Fuzong Clinical Medical College of Fujian Medical University, Fuzhou, China
| | - Chuandong Wang
- Department of Thyroid and Breast Surgery, Fujian Medical University Xiamen Humanity Hospital, Xiamen, China
- Shengli Clinical Medical College of Fujian Medical University, Fuzhou, China
| | - Dan Zheng
- Fuzong Clinical Medical College of Fujian Medical University, Fuzhou, China
- Department of Radiology, 900th Hospital of Joint Logistics Support Force, Fuzhou, China
| | - Yuting Liao
- Institute of Precision Medicine, GE Healthcare, Shanghai, China
| | - Xiaoyang Wang
- Department of Radiology, 900th Hospital of Joint Logistics Support Force, Fuzhou, China
| | - Zhifeng Huang
- Department of Radiology, 900th Hospital of Joint Logistics Support Force, Fuzhou, China
| | - Qun Zhong
- Department of Radiology, The Affiliated People’s Hospital of Fujian University of Traditional Chinese Medicine, Fuzhou, China
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31
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Sussman L, Garcia-Robledo JE, Ordóñez-Reyes C, Forero Y, Mosquera AF, Ruíz-Patiño A, Chamorro DF, Cardona AF. Integration of artificial intelligence and precision oncology in Latin America. FRONTIERS IN MEDICAL TECHNOLOGY 2022; 4:1007822. [PMID: 36311461 PMCID: PMC9608820 DOI: 10.3389/fmedt.2022.1007822] [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: 07/31/2022] [Accepted: 09/21/2022] [Indexed: 11/07/2022] Open
Abstract
Next-generation medicine encompasses different concepts related to healthcare models and technological developments. In Latin America and the Caribbean, healthcare systems are quite different between countries, and cancer control is known to be insufficient and inefficient considering socioeconomically discrepancies. Despite advancements in knowledge about the biology of different oncological diseases, the disease remains a challenge in terms of diagnosis, treatment, and prognosis for clinicians and researchers. With the development of molecular biology, better diagnosis methods, and therapeutic tools in the last years, artificial intelligence (AI) has become important, because it could improve different clinical scenarios: predicting clinically relevant parameters, cancer diagnosis, cancer research, and accelerating the growth of personalized medicine. The incorporation of AI represents an important challenge in terms of diagnosis, treatment, and prognosis for clinicians and researchers in cancer care. Therefore, some studies about AI in Latin America and the Caribbean are being conducted with the aim to improve the performance of AI in those countries. This review introduces AI in cancer care in Latin America and the Caribbean, and the advantages and promising results that it has shown in this socio-demographic context.
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Affiliation(s)
- Liliana Sussman
- Department of Neurology, Fundación Universitaria de Ciencias de la Salud, Bogotá, Colombia,Foundation for Clinical and Applied Cancer Research – FICMAC, Bogotá, Colombia
| | - Juan Esteban Garcia-Robledo
- Foundation for Clinical and Applied Cancer Research – FICMAC, Bogotá, Colombia,Division of Hematology/Oncology, Mayo Clinic, Scottsdale, AZ, United States
| | - Camila Ordóñez-Reyes
- Foundation for Clinical and Applied Cancer Research – FICMAC, Bogotá, Colombia,MolecularOncology and Biology Systems Research Group (Fox-G), Universidad el Bosque, Bogotá, Colombia
| | - Yency Forero
- Foundation for Clinical and Applied Cancer Research – FICMAC, Bogotá, Colombia,MolecularOncology and Biology Systems Research Group (Fox-G), Universidad el Bosque, Bogotá, Colombia
| | - Andrés F. Mosquera
- Foundation for Clinical and Applied Cancer Research – FICMAC, Bogotá, Colombia,MolecularOncology and Biology Systems Research Group (Fox-G), Universidad el Bosque, Bogotá, Colombia
| | - Alejandro Ruíz-Patiño
- Foundation for Clinical and Applied Cancer Research – FICMAC, Bogotá, Colombia,MolecularOncology and Biology Systems Research Group (Fox-G), Universidad el Bosque, Bogotá, Colombia
| | - Diego F. Chamorro
- Foundation for Clinical and Applied Cancer Research – FICMAC, Bogotá, Colombia,MolecularOncology and Biology Systems Research Group (Fox-G), Universidad el Bosque, Bogotá, Colombia
| | - Andrés F. Cardona
- Foundation for Clinical and Applied Cancer Research – FICMAC, Bogotá, Colombia,MolecularOncology and Biology Systems Research Group (Fox-G), Universidad el Bosque, Bogotá, Colombia,Direction of Research, Science and Education, Luis Carlos Sarmiento Angulo Cancer Treatment and Research Center (CTIC), Bogotá, Colombia,Correspondence: Andrés F. Cardona
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Li J, Zhang C, Guo H, Li S, You Y, Zheng P, Zhang H, Wang H, Bai J. Non-invasive measurement of tumor immune microenvironment and prediction of survival and chemotherapeutic benefits from 18F fluorodeoxyglucose PET/CT images in gastric cancer. Front Immunol 2022; 13:1019386. [PMID: 36311742 PMCID: PMC9606753 DOI: 10.3389/fimmu.2022.1019386] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2022] [Accepted: 09/23/2022] [Indexed: 02/11/2024] Open
Abstract
BACKGROUND The tumor immune microenvironment could provide prognostic and predictive information. It is necessary to develop a noninvasive radiomics-based biomarker of a previously validated tumor immune microenvironment signature of gastric cancer (GC) with immunohistochemistry staining. METHODS A total of 230 patients (training (n = 153) or validation (n = 77) cohort) with gastric cancer were subjected to (Positron Emission Tomography-Computed Tomography) radiomics feature extraction (80 features). A radiomics tumor immune microenvironment score (RTIMS) was developed to predict the tumor immune microenvironment signature with LASSO logistic regression. Furthermore, we evaluated its relation with prognosis and chemotherapy benefits. RESULTS A 8-feature radiomics signature was established and validated (area under the curve=0.692 and 0.713). The RTIMS signature was significantly associated with disease-free survival and overall survival both in the training and validation cohort (all P<0.001). RTIMS was an independent prognostic factor in the Multivariate analysis. Further analysis revealed that high RTIMS patients benefitted from adjuvant chemotherapy (for DFS, stage II: HR 0.208(95% CI 0.061-0.711), p=0.012; stage III: HR 0.321(0.180-0.570), p<0.001, respectively); while there were no benefits from chemotherapy in a low RTIMS patients. CONCLUSION This PET/CT radiomics model provided a promising way to assess the tumor immune microenvironment and to predict clinical outcomes and chemotherapy response. The RTIMS signature could be useful in estimating tumor immune microenvironment and predicting survival and chemotherapy benefit for patients with gastric cancer, when validated by further prospective randomized trials.
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Affiliation(s)
- Junmeng Li
- Department of Gastrointestinal Surgery, Henan Provincial People’s Hospital, Zhengzhou University People’s Hospital, Henan University People’s Hospital, Zhengzhou, China
| | - Chao Zhang
- Department of Gastrointestinal Surgery, Henan Provincial People’s Hospital, Zhengzhou University People’s Hospital, Henan University People’s Hospital, Zhengzhou, China
| | - Huihui Guo
- Department of Radiology, Henan Provincial People’s Hospital, Zhengzhou University People’s Hospital, Henan University People’s Hospital, Zhengzhou, Henan, China
| | - Shuang Li
- Department of Pathology, Henan Provincial People’s Hospital, Zhengzhou University People’s Hospital, Henan University People’s Hospital, Zhengzhou, China
| | - Yang You
- Department of Nuclear Medicine, Henan Provincial People’s Hospital, Zhengzhou University People’s Hospital, Henan University People’s Hospital, Zhengzhou, Henan, China
| | - Peiming Zheng
- Department of Clinical Laboratory, Henan Provincial People’s Hospital, Zhengzhou University People’s Hospital, Henan University People’s Hospital, Zhengzhou, China
| | - Hongquan Zhang
- Department of Thoracic Surgery, The First Hospital Affiliated of Xinxiang Medical University, Xinxiang, China
| | - Huanan Wang
- Department of Gastrointestinal Surgery, The First Hospital Affiliated of Zhengzhou University, Zhengzhou, China
| | - Junwei Bai
- Department of Gastrointestinal Surgery, Henan Provincial People’s Hospital, Zhengzhou University People’s Hospital, Henan University People’s Hospital, Zhengzhou, China
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Hong ZL, Chen S, Peng XR, Li JW, Yang JC, Wu SS. Nomograms for prediction of breast cancer in breast imaging reporting and data system (BI-RADS) ultrasound category 4 or 5 lesions: A single-center retrospective study based on radiomics features. Front Oncol 2022; 12:894476. [PMID: 36212503 PMCID: PMC9538156 DOI: 10.3389/fonc.2022.894476] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2022] [Accepted: 08/29/2022] [Indexed: 11/24/2022] Open
Abstract
Purpose To develop nomograms for predicting breast malignancy in BI-RADS ultrasound (US) category 4 or 5 lesions based on radiomics features. Methods Between January 2020 and January 2022, we prospectively collected and retrospectively analyzed the medical records of 496 patients pathologically proven breast lesions in our hospital. The data set was divided into model training group and validation testing group with a 75/25 split. Radiomics features were obtained using the PyRadiomics package, and the radiomics score was established by least absolute shrinkage and selection operator regression. A nomogram was developed for BI-RADS US category 4 or 5 lesions according to the results of multivariate regression analysis from the training group. Result The AUCs of radiomics score consisting of 31 US features was 0.886. The AUC of the model constructed with radiomics score, patient age, lesion diameter identified by US and BI-RADS category involved was 0.956 (95% CI, 0.910–0.972) for the training group and 0.937 (95% CI, 0.893–0.965) for the validation cohort. The calibration curves showed good agreement between the predictions and observations. Conclusions Both nomogram and radiomics score can be used as methods to assist radiologists and clinicians in predicting breast malignancy in BI-RADS US category 4 or 5 lesions.
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Affiliation(s)
- Zhi-Liang Hong
- Shengli Clinical Medical College of Fujian Medical University, Fuzhou, China
- Department of Ultrasound, Fujian Provincial Hospital, Fuzhou, China
| | - Sheng Chen
- Shengli Clinical Medical College of Fujian Medical University, Fuzhou, China
- Department of Ultrasound, Fujian Provincial Hospital, Fuzhou, China
| | - Xiao-Rui Peng
- Clinical Skills Teaching Center, Fujian Medical University, Fuzhou, China
| | - Jian-Wei Li
- Shengli Clinical Medical College of Fujian Medical University, Fuzhou, China
- Department of Ultrasound, Fujian Provincial Hospital, Fuzhou, China
| | - Jian-Chuan Yang
- Shengli Clinical Medical College of Fujian Medical University, Fuzhou, China
- Department of Ultrasound, Fujian Provincial Hospital, Fuzhou, China
| | - Song-Song Wu
- Shengli Clinical Medical College of Fujian Medical University, Fuzhou, China
- Department of Ultrasound, Fujian Provincial Hospital, Fuzhou, China
- *Correspondence: Song-Song Wu,
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Wu J, Mayer AT, Li R. Integrated imaging and molecular analysis to decipher tumor microenvironment in the era of immunotherapy. Semin Cancer Biol 2022; 84:310-328. [PMID: 33290844 PMCID: PMC8319834 DOI: 10.1016/j.semcancer.2020.12.005] [Citation(s) in RCA: 31] [Impact Index Per Article: 15.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2019] [Revised: 11/29/2020] [Accepted: 12/02/2020] [Indexed: 02/07/2023]
Abstract
Radiological imaging is an integral component of cancer care, including diagnosis, staging, and treatment response monitoring. It contains rich information about tumor phenotypes that are governed not only by cancer cellintrinsic biological processes but also by the tumor microenvironment, such as the composition and function of tumor-infiltrating immune cells. By analyzing the radiological scans using a quantitative radiomics approach, robust relations between specific imaging and molecular phenotypes can be established. Indeed, a number of studies have demonstrated the feasibility of radiogenomics for predicting intrinsic molecular subtypes and gene expression signatures in breast cancer based on MRI. In parallel, promising results have been shown for inferring the amount of tumor-infiltrating lymphocytes, a key factor for the efficacy of cancer immunotherapy, from standard-of-care radiological images. Compared with the biopsy-based approach, radiogenomics offers a unique avenue to profile the molecular makeup of the tumor and immune microenvironment as well as its evolution in a noninvasive and holistic manner through longitudinal imaging scans. Here, we provide a systematic review of the state of the art radiogenomics studies in the era of immunotherapy and discuss emerging paradigms and opportunities in AI and deep learning approaches. These technical advances are expected to transform the radiogenomics field, leading to the discovery of reliable imaging biomarkers. This will pave the way for their clinical translation to guide precision cancer therapy.
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Affiliation(s)
- Jia Wu
- Department of Imaging Physics, MD Anderson Cancer Center, Texas, 77030, USA; Department of Thoracic/Head & Neck Medical Oncology, MD Anderson Cancer Center, Texas, 77030, USA.
| | - Aaron T Mayer
- Department of Bioengineering, Stanford University, Stanford, California, 94305, USA; Department of Radiology, Stanford University, Stanford, California, 94305, USA; Molecular Imaging Program at Stanford, Stanford University, Stanford, California, 94305, USA; BioX Program at Stanford, Stanford University, Stanford, California, 94305, USA
| | - Ruijiang Li
- Department of Radiation Oncology, Stanford University, Stanford, California, 94305, USA
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Rohde S, Münnich N. [Artificial intelligence in orthopaedic and trauma surgery imaging]. ORTHOPADIE (HEIDELBERG, GERMANY) 2022; 51:748-756. [PMID: 35980460 DOI: 10.1007/s00132-022-04293-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 07/25/2022] [Indexed: 06/15/2023]
Abstract
Artificial intelligence (AI) is playing an increasing role in radiological imaging in orthopaedics and trauma surgery. The algorithms available to date are predominantly used in the detection of (occult) fractures and in length and angle measurements in conventional X‑ray images. However, current AI solutions also enable the analysis and pattern recognition of CT datasets, e.g. in the detection of rib or vertebral body fractures. A special application is EOS™ (ATEC Spine Group, Paris, France), which enables a 3‑D simulation of the axial skeleton and semi-automatic length and angle calculations based on a digital 2‑D X‑ray image. In this paper, the current spectrum of AI applications for orthopaedics and trauma surgery is presented and discussed.
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Affiliation(s)
- Stefan Rohde
- Klinik für Radiologie und Neuroradiologie, Klinikum Dortmund gGmbH, Beurhausstr. 40, 44137, Dortmund, Deutschland.
- Fakultät für Gesundheit, Universität Witten-Herdecke, Witten, Deutschland.
| | - Nico Münnich
- Klinik für Radiologie und Neuroradiologie, Klinikum Dortmund gGmbH, Beurhausstr. 40, 44137, Dortmund, Deutschland
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Azadikhah A, Varghese BA, Lei X, Martin-King C, Cen SY, Duddalwar VA. Radiomics quality score in renal masses: a systematic assessment on current literature. Br J Radiol 2022; 95:20211211. [PMID: 35671097 PMCID: PMC10996962 DOI: 10.1259/bjr.20211211] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2021] [Revised: 05/24/2022] [Accepted: 05/31/2022] [Indexed: 11/05/2022] Open
Abstract
OBJECTIVE To perform a systematic assessment and analyze the quality of radiomics methodology in current literature in the evaluation of renal masses using the Radiomics Quality Score (RQS) approach. METHODS We systematically reviewed recent radiomics literature in renal masses published in PubMed, EMBASE, Elsevier, and Web of Science. Two reviewers blinded by each other's scores evaluated the quality of radiomics methodology in studies published from 2015 to August 2021 using the RQS approach. Owing to the diversity in the imaging modalities and radiomics applications, a meta-analysis could not be performed. RESULTS Based on our inclusion/exclusion criteria, a total of 87 published studies were included in our study. The highest RQS was noted in three categories: reporting of clinical utility, gold standard, and feature reduction. The average RQS of the two reviewers ranged from 5 ≤ RQS≤19, with the maximum attainable RQS being 36. Very few (7/87 i.e., 8%) studies received an average RQS that ranged from 17 < RQS≤19, which represents studies with the highest RQS in our study. Many (39/87 i.e., 45%) studies received an average RQS that ranged from 13 < RQS≤15. No significant interreviewer scoring differences were observed. CONCLUSIONS We report that the overall scientific quality and reporting of radiomics studies in renal masses is suboptimal, and subsequent studies should bolster current deficiencies to improve reporting of radiomics methodologies. ADVANCES IN KNOWLEDGE The RQS approach is a meaningful quantitative scoring system to assess radiomics methodology quality and supports a comprehensive evaluation of the radiomics approach before its incorporation into clinical practice.
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Affiliation(s)
- Afshin Azadikhah
- USC Radiomics Laboratory, Department of Radiology, Keck School
of Medicine, University of Southern California,
Los Angeles, United States
| | - Bino Abel Varghese
- USC Radiomics Laboratory, Department of Radiology, Keck School
of Medicine, University of Southern California,
Los Angeles, United States
| | - Xiaomeng Lei
- USC Radiomics Laboratory, Department of Radiology, Keck School
of Medicine, University of Southern California,
Los Angeles, United States
| | - Chloe Martin-King
- USC Radiomics Laboratory, Department of Radiology, Keck School
of Medicine, University of Southern California,
Los Angeles, United States
| | - Steven Yong Cen
- USC Radiomics Laboratory, Department of Radiology, Keck School
of Medicine, University of Southern California,
Los Angeles, United States
| | - Vinay Anant Duddalwar
- USC Radiomics Laboratory, Department of Radiology, Keck School
of Medicine, University of Southern California,
Los Angeles, United States
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Zhang L, Lv L, Li L, Wang YM, Zhao S, Miao L, Gao YN, Li M, Wu N. Radiomics Signature to Predict Prognosis in Early-Stage Lung Adenocarcinoma (≤3 cm) Patients with No Lymph Node Metastasis. Diagnostics (Basel) 2022; 12:diagnostics12081907. [PMID: 36010257 PMCID: PMC9406362 DOI: 10.3390/diagnostics12081907] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2022] [Revised: 08/02/2022] [Accepted: 08/04/2022] [Indexed: 11/19/2022] Open
Abstract
Objectives: To investigate the predictive ability of radiomics signature to predict the prognosis of early-stage primary lung adenocarcinoma (≤3 cm) with no lymph node metastasis (pathological stage I). Materials and Methods: This study included consecutive patients with lung adenocarcinoma (≤3 cm) with no lymph node metastasis (pathological stage I) and divided them into two groups: good prognosis group and poor prognosis group. The association between the radiomics signature and prognosis was explored. An integrative radiomics model was constructed to demonstrate the value of the radiomics signature for individualized prognostic prediction. Results: Six radiomics features were significantly different between the two prognosis groups and were used to construct a radiomics model. On the training and test sets, the area under the receiver operating characteristic curve value of the radiomics model in discriminating between the two groups were 0.946 and 0.888, respectively, and those of the pathological model were 0.761 and 0.798, respectively. A radiomics nomogram combining sex, tumor size and rad-score was built. Conclusion: The radiomics signature has potential utility in estimating the prognosis of patients with pathological stage I lung adenocarcinoma (≤3 cm), potentially enabling a step forward in precision medicine.
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Affiliation(s)
- Li Zhang
- 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, No. 17 Panjiayuan Nanli, Chaoyang District, Beijing 100021, China
| | - Lv Lv
- Department of Nuclear Medicine, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 17 Panjiayuan Nanli, Chaoyang District, Beijing 100021, China
| | - Lin Li
- Department of Pathology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 17 Panjiayuan Nanli, Chaoyang District, Beijing 100021, China
| | - Yan-Mei Wang
- GE Healthcare China, Pudong New Town, Shanghai 201200, China
| | - Shuang Zhao
- Pediatric Translational Medicine Institute, Shanghai Children’s Medical Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200127, China
| | - Lei Miao
- 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, No. 17 Panjiayuan Nanli, Chaoyang District, Beijing 100021, China
| | - Yan-Ning Gao
- State Key Laboratory of Molecular Oncology, Department of Etiology and Carcinogenesis, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 17 Panjiayuan Nanli, Chaoyang District, Beijing 100021, China
| | - Meng 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, No. 17 Panjiayuan Nanli, Chaoyang District, Beijing 100021, China
- Correspondence: (N.W.); (M.L.)
| | - Ning Wu
- 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, No. 17 Panjiayuan Nanli, Chaoyang District, Beijing 100021, China
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Hebei Cancer Hospital, Chinese Academy of Medical Sciences, Langfang 065001, China
- Correspondence: (N.W.); (M.L.)
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Bakas S, Sako C, Akbari H, Bilello M, Sotiras A, Shukla G, Rudie JD, Santamaría NF, Kazerooni AF, Pati S, Rathore S, Mamourian E, Ha SM, Parker W, Doshi J, Baid U, Bergman M, Binder ZA, Verma R, Lustig RA, Desai AS, Bagley SJ, Mourelatos Z, Morrissette J, Watt CD, Brem S, Wolf RL, Melhem ER, Nasrallah MP, Mohan S, O'Rourke DM, Davatzikos C. The University of Pennsylvania glioblastoma (UPenn-GBM) cohort: advanced MRI, clinical, genomics, & radiomics. Sci Data 2022; 9:453. [PMID: 35906241 PMCID: PMC9338035 DOI: 10.1038/s41597-022-01560-7] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2022] [Accepted: 07/12/2022] [Indexed: 02/05/2023] Open
Abstract
Glioblastoma is the most common aggressive adult brain tumor. Numerous studies have reported results from either private institutional data or publicly available datasets. However, current public datasets are limited in terms of: a) number of subjects, b) lack of consistent acquisition protocol, c) data quality, or d) accompanying clinical, demographic, and molecular information. Toward alleviating these limitations, we contribute the "University of Pennsylvania Glioblastoma Imaging, Genomics, and Radiomics" (UPenn-GBM) dataset, which describes the currently largest publicly available comprehensive collection of 630 patients diagnosed with de novo glioblastoma. The UPenn-GBM dataset includes (a) advanced multi-parametric magnetic resonance imaging scans acquired during routine clinical practice, at the University of Pennsylvania Health System, (b) accompanying clinical, demographic, and molecular information, (d) perfusion and diffusion derivative volumes, (e) computationally-derived and manually-revised expert annotations of tumor sub-regions, as well as (f) quantitative imaging (also known as radiomic) features corresponding to each of these regions. This collection describes our contribution towards repeatable, reproducible, and comparative quantitative studies leading to new predictive, prognostic, and diagnostic assessments.
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Affiliation(s)
- Spyridon Bakas
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Chiharu Sako
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Hamed Akbari
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Michel Bilello
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Aristeidis Sotiras
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology and Institute for Informatics, Washington University, School of Medicine, St. Louis, MO, USA
| | - Gaurav Shukla
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiation Oncology, Christiana Care Health System, Philadelphia, PA, USA
| | - Jeffrey D Rudie
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology & Biomedical Imaging, University of California, San Francisco, San Francisco, CA, USA
| | - Natali Flores Santamaría
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Anahita Fathi Kazerooni
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Sarthak Pati
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Saima Rathore
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Elizabeth Mamourian
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Sung Min Ha
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology and Institute for Informatics, Washington University, School of Medicine, St. Louis, MO, USA
| | - William Parker
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Jimit Doshi
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Ujjwal Baid
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Mark Bergman
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
| | - Zev A Binder
- Department of Neurosurgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Ragini Verma
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Robert A Lustig
- Department of Radiation Oncology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Arati S Desai
- Division of Hematology Oncology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Stephen J Bagley
- Division of Hematology Oncology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Zissimos Mourelatos
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Jennifer Morrissette
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Christopher D Watt
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Steven Brem
- Department of Neurosurgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Ronald L Wolf
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Neurosurgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Elias R Melhem
- Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, Baltimore, MD, USA
| | - MacLean P Nasrallah
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Suyash Mohan
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Donald M O'Rourke
- Department of Neurosurgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Christos Davatzikos
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA.
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
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Arthur A, Johnston EW, Winfield JM, Blackledge MD, Jones RL, Huang PH, Messiou C. Virtual Biopsy in Soft Tissue Sarcoma. How Close Are We? Front Oncol 2022; 12:892620. [PMID: 35847882 PMCID: PMC9286756 DOI: 10.3389/fonc.2022.892620] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2022] [Accepted: 05/31/2022] [Indexed: 12/13/2022] Open
Abstract
A shift in radiology to a data-driven specialty has been unlocked by synergistic developments in imaging biomarkers (IB) and computational science. This is advancing the capability to deliver “virtual biopsies” within oncology. The ability to non-invasively probe tumour biology both spatially and temporally would fulfil the potential of imaging to inform management of complex tumours; improving diagnostic accuracy, providing new insights into inter- and intra-tumoral heterogeneity and individualised treatment planning and monitoring. Soft tissue sarcomas (STS) are rare tumours of mesenchymal origin with over 150 histological subtypes and notorious heterogeneity. The combination of inter- and intra-tumoural heterogeneity and the rarity of the disease remain major barriers to effective treatments. We provide an overview of the process of successful IB development, the key imaging and computational advancements in STS including quantitative magnetic resonance imaging, radiomics and artificial intelligence, and the studies to date that have explored the potential biological surrogates to imaging metrics. We discuss the promising future directions of IBs in STS and illustrate how the routine clinical implementation of a virtual biopsy has the potential to revolutionise the management of this group of complex cancers and improve clinical outcomes.
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Affiliation(s)
- Amani Arthur
- Division of Radiotherapy and Imaging, The Institute of Cancer Research, Sutton, United Kingdom
| | - Edward W. Johnston
- Sarcoma Unit, The Royal Marsden National Health Service (NHS) Foundation Trust, London, United Kingdom
| | - Jessica M. Winfield
- Division of Radiotherapy and Imaging, The Institute of Cancer Research, Sutton, United Kingdom
- Sarcoma Unit, The Royal Marsden National Health Service (NHS) Foundation Trust, London, United Kingdom
| | - Matthew D. Blackledge
- Division of Radiotherapy and Imaging, The Institute of Cancer Research, Sutton, United Kingdom
| | - Robin L. Jones
- Sarcoma Unit, The Royal Marsden National Health Service (NHS) Foundation Trust, London, United Kingdom
- Division of Clinical Studies, The Institute of Cancer Research, London, United Kingdom
| | - Paul H. Huang
- Division of Molecular Pathology, The Institute of Cancer Research, Sutton, United Kingdom
- *Correspondence: Paul H. Huang, ; Christina Messiou,
| | - Christina Messiou
- Division of Radiotherapy and Imaging, The Institute of Cancer Research, Sutton, United Kingdom
- Sarcoma Unit, The Royal Marsden National Health Service (NHS) Foundation Trust, London, United Kingdom
- *Correspondence: Paul H. Huang, ; Christina Messiou,
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Role of Artificial Intelligence in Radiogenomics for Cancers in the Era of Precision Medicine. Cancers (Basel) 2022; 14:cancers14122860. [PMID: 35740526 PMCID: PMC9220825 DOI: 10.3390/cancers14122860] [Citation(s) in RCA: 26] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2022] [Revised: 06/03/2022] [Accepted: 06/07/2022] [Indexed: 12/12/2022] Open
Abstract
Simple Summary Recently, radiogenomics has played a significant role and offered a new understanding of cancer’s biology and behavior in response to standard therapy. It also provides a more precise prognosis, investigation, and analysis of the patient’s cancer. Over the years, Artificial Intelligence (AI) has provided a significant strength in radiogenomics. In this paper, we offer computational and oncological prospects of the role of AI in radiogenomics, as well as its offers, achievements, opportunities, and limitations in the current clinical practices. Abstract Radiogenomics, a combination of “Radiomics” and “Genomics,” using Artificial Intelligence (AI) has recently emerged as the state-of-the-art science in precision medicine, especially in oncology care. Radiogenomics syndicates large-scale quantifiable data extracted from radiological medical images enveloped with personalized genomic phenotypes. It fabricates a prediction model through various AI methods to stratify the risk of patients, monitor therapeutic approaches, and assess clinical outcomes. It has recently shown tremendous achievements in prognosis, treatment planning, survival prediction, heterogeneity analysis, reoccurrence, and progression-free survival for human cancer study. Although AI has shown immense performance in oncology care in various clinical aspects, it has several challenges and limitations. The proposed review provides an overview of radiogenomics with the viewpoints on the role of AI in terms of its promises for computational as well as oncological aspects and offers achievements and opportunities in the era of precision medicine. The review also presents various recommendations to diminish these obstacles.
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Liu Q, Hu P. A novel integrative computational framework for breast cancer radiogenomic biomarker discovery. Comput Struct Biotechnol J 2022; 20:2484-2494. [PMID: 35664228 PMCID: PMC9136270 DOI: 10.1016/j.csbj.2022.05.031] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2022] [Revised: 05/14/2022] [Accepted: 05/15/2022] [Indexed: 12/22/2022] Open
Abstract
Bayesian tensor factorization is used to integrate multiomics data for radiogenomics analysis. A regression framework is proposed to handle the unmatched data issue in radiogenomics analysis. Deep learning is used to identify prognostic meaningful radiogenomic biomarkers for cancer.
In precise medicine, it is with great value to develop computational frameworks for identifying prognostic biomarkers which can capture both multi-genomic and phenotypic heterogeneity of breast cancer (BC). Radiogenomics is a field where medical images and genomic measurements are integrated and mined to solve challenging clinical problems. Previous radiogenomic studies suffered from data incompleteness, feature subjectivity and low interpretability. For example, the majority of the radiogenomic studies miss one or two of medical imaging data, genomic data, and clinical outcome data, which results in the data incomplete issue. Feature subjectivity issue comes from the extraction of imaging features with significant human involvement. Thus, there is an urgent need to address above-mentioned limitations so that fully automatic and transparent radiogenomic prognostic biomarkers could be identified for BC. We proposed a novel framework for BC prognostic radiogenomic biomarker identification. This framework involves an explainable DL model for image feature extraction, a Bayesian tensor factorization (BTF) processing for multi-genomic feature extraction, a leverage strategy to utilize unpaired imaging, genomic, and survival outcome data, and a mediation analysis to provide further interpretation for identified biomarkers. This work provided a new perspective for conducting a comprehensive radiogenomic study when only limited resources are given. Compared with baseline traditional radiogenomic biomarkers, the 23 biomarkers identified by the proposed framework performed better in indicating patients’ survival outcome. And their interpretability is guaranteed by different levels of build-in and follow-up analyses.
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Affiliation(s)
- Qian Liu
- Department of Biochemistry and Medical Genetics, University of Manitoba, Winnipeg, Manitoba R3E 0W3, Canada
- Department of Computer Science, University of Manitoba, Winnipeg, Manitoba R3E 0W3, Canada
- Department of Statistics, University of Manitoba, Winnipeg, Manitoba R3E 0W3, Canada
| | - Pingzhao Hu
- Department of Biochemistry and Medical Genetics, University of Manitoba, Winnipeg, Manitoba R3E 0W3, Canada
- Department of Computer Science, University of Manitoba, Winnipeg, Manitoba R3E 0W3, Canada
- Corresponding author at: Department of Biochemistry and Medical Genetics, Room 308 - Basic Medical Sciences Building, 745 Bannatyne Avenue, University of Manitoba, Winnipeg, Manitoba R3E 0J9, Canada.
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Dlamini Z, Skepu A, Kim N, Mkhabele M, Khanyile R, Molefi T, Mbatha S, Setlai B, Mulaudzi T, Mabongo M, Bida M, Kgoebane-Maseko M, Mathabe K, Lockhat Z, Kgokolo M, Chauke-Malinga N, Ramagaga S, Hull R. AI and precision oncology in clinical cancer genomics: From prevention to targeted cancer therapies-an outcomes based patient care. INFORMATICS IN MEDICINE UNLOCKED 2022. [DOI: 10.1016/j.imu.2022.100965] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022] Open
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Khaleel S, Katims A, Cumarasamy S, Rosenzweig S, Attalla K, Hakimi AA, Mehrazin R. Radiogenomics in Clear Cell Renal Cell Carcinoma: A Review of the Current Status and Future Directions. Cancers (Basel) 2022; 14:2085. [PMID: 35565216 PMCID: PMC9100795 DOI: 10.3390/cancers14092085] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2021] [Revised: 11/17/2021] [Accepted: 11/21/2021] [Indexed: 12/30/2022] Open
Abstract
Radiogenomics is a field of translational radiology that aims to associate a disease's radiologic phenotype with its underlying genotype, thus offering a novel class of non-invasive biomarkers with diagnostic, prognostic, and therapeutic potential. We herein review current radiogenomics literature in clear cell renal cell carcinoma (ccRCC), the most common renal malignancy. A literature review was performed by querying PubMed, Medline, Cochrane Library, Google Scholar, and Web of Science databases, identifying all relevant articles using the following search terms: "radiogenomics", "renal cell carcinoma", and "clear cell renal cell carcinoma". Articles included were limited to the English language and published between 2009-2021. Of 141 retrieved articles, 16 fit our inclusion criteria. Most studies used computed tomography (CT) images from open-source and institutional databases to extract radiomic features that were then modeled against common genomic mutations in ccRCC using a variety of machine learning algorithms. In more recent studies, we noted a shift towards the prediction of transcriptomic and/or epigenetic disease profiles, as well as downstream clinical outcomes. Radiogenomics offers a platform for the development of non-invasive biomarkers for ccRCC, with promising results in small-scale retrospective studies. However, more research is needed to identify and validate robust radiogenomic biomarkers before integration into clinical practice.
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Affiliation(s)
- Sari Khaleel
- Memorial Sloan Kettering Cancer Center, Department of Urology, New York, NY 10065, USA; (S.K.); (A.A.H.)
| | - Andrew Katims
- Department of Urology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; (A.K.); (S.C.); (S.R.); (K.A.)
| | - Shivaram Cumarasamy
- Department of Urology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; (A.K.); (S.C.); (S.R.); (K.A.)
| | - Shoshana Rosenzweig
- Department of Urology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; (A.K.); (S.C.); (S.R.); (K.A.)
| | - Kyrollis Attalla
- Department of Urology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; (A.K.); (S.C.); (S.R.); (K.A.)
| | - A Ari Hakimi
- Memorial Sloan Kettering Cancer Center, Department of Urology, New York, NY 10065, USA; (S.K.); (A.A.H.)
| | - Reza Mehrazin
- Department of Urology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; (A.K.); (S.C.); (S.R.); (K.A.)
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Jiang Y, Wang Y, Fu S, Chen T, Zhou Y, Zhang X, Chen C, He LN, Du W, Li H, Lin Z, Zhao Y, Yang Y, Zhao H, Fang W, Huang Y, Hong S, Zhang L. A CT-based radiomics model to predict subsequent brain metastasis in patients with ALK-rearranged non-small cell lung cancer undergoing crizotinib treatment. Thorac Cancer 2022; 13:1558-1569. [PMID: 35437945 PMCID: PMC9161316 DOI: 10.1111/1759-7714.14386] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2022] [Revised: 02/26/2022] [Accepted: 02/28/2022] [Indexed: 11/27/2022] Open
Abstract
Background Brain metastasis (BM) comprises the most common reason for crizotinib failure in patients with anaplastic lymphoma kinase (ALK)‐rearranged non–small cell lung cancer (NSCLC). We hypothesize that its occurrence could be predicted by a computed tomography (CT)‐based radiomics model, therefore, allowing for selection of enriched patient populations for prevention therapies. Methods A total of 75 eligible patients were enrolled from Sun Yat‐sen University Cancer Center between June 2014 and September 2019. The primary endpoint was brain metastasis‐free survival (BMFS), estimated from the initiation of crizotinib to the date of the occurrence of BM. Patients were randomly divided into two cohorts for model training (n = 51) and validation (n = 24), respectively. A radiomics signature was constructed based on features extracted from chest CT before crizotinib treatment. Clinical model was developed using the Cox proportional hazards model. Log‐rank test was performed to describe the difference of BMFS risk. Results Patients with low radiomics score had significantly longer BMFS than those with higher, both in the training cohort (p = 0.019) and validation cohort (p = 0.048). The nomogram combining smoking history and the radiomics signature showed good performance for the estimation of BMFS, both in the training (concordance index [C‐index], 0.762; 95% confidence interval [CI], 0.663–0.861) and validation cohort (C‐index, 0.724; 95% CI, 0.601–0.847). Conclusion We have developed a CT‐based radiomics model to predict subsequent BM in patients with non‐brain metastatic NSCLC undergoing crizotinib treatment. Selection of an enriched patient population at high BM risk will facilitate the design of clinical trials or strategies to prevent BM.
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Affiliation(s)
- Yongluo Jiang
- State Key Laboratory of Oncology in South China, Guangzhou, China.,Collaborative Innovation Center for Cancer Medicine, Guangzhou, China.,Department of Nuclear Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Yixing Wang
- State Key Laboratory of Oncology in South China, Guangzhou, China.,Collaborative Innovation Center for Cancer Medicine, Guangzhou, China.,Department of Medical Oncology, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Sha Fu
- Cellular & Molecular Diagnostics Center, Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
| | - Tao Chen
- State Key Laboratory of Oncology in South China, Guangzhou, China.,Collaborative Innovation Center for Cancer Medicine, Guangzhou, China.,Department of Nuclear Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Yixin Zhou
- State Key Laboratory of Oncology in South China, Guangzhou, China.,Collaborative Innovation Center for Cancer Medicine, Guangzhou, China.,Department of VIP region, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Xuanye Zhang
- State Key Laboratory of Oncology in South China, Guangzhou, China.,Collaborative Innovation Center for Cancer Medicine, Guangzhou, China.,Department of Medical Oncology, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Chen Chen
- State Key Laboratory of Oncology in South China, Guangzhou, China.,Collaborative Innovation Center for Cancer Medicine, Guangzhou, China.,Department of Radiation Oncology, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Li-Na He
- State Key Laboratory of Oncology in South China, Guangzhou, China.,Collaborative Innovation Center for Cancer Medicine, Guangzhou, China.,Department of Medical Oncology, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Wei Du
- State Key Laboratory of Oncology in South China, Guangzhou, China.,Collaborative Innovation Center for Cancer Medicine, Guangzhou, China.,Department of Medical Oncology, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Haifeng Li
- State Key Laboratory of Oncology in South China, Guangzhou, China.,Collaborative Innovation Center for Cancer Medicine, Guangzhou, China.,Department of Medical Oncology, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Zuan Lin
- State Key Laboratory of Oncology in South China, Guangzhou, China.,Collaborative Innovation Center for Cancer Medicine, Guangzhou, China.,Department of Clinical Research, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Yuanyuan Zhao
- State Key Laboratory of Oncology in South China, Guangzhou, China.,Collaborative Innovation Center for Cancer Medicine, Guangzhou, China.,Department of Medical Oncology, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Yunpeng Yang
- State Key Laboratory of Oncology in South China, Guangzhou, China.,Collaborative Innovation Center for Cancer Medicine, Guangzhou, China.,Department of Medical Oncology, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Hongyun Zhao
- State Key Laboratory of Oncology in South China, Guangzhou, China.,Collaborative Innovation Center for Cancer Medicine, Guangzhou, China.,Department of Clinical Research, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Wenfeng Fang
- State Key Laboratory of Oncology in South China, Guangzhou, China.,Collaborative Innovation Center for Cancer Medicine, Guangzhou, China.,Department of Medical Oncology, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Yan Huang
- State Key Laboratory of Oncology in South China, Guangzhou, China.,Collaborative Innovation Center for Cancer Medicine, Guangzhou, China.,Department of Medical Oncology, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Shaodong Hong
- State Key Laboratory of Oncology in South China, Guangzhou, China.,Collaborative Innovation Center for Cancer Medicine, Guangzhou, China.,Department of Medical Oncology, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Li Zhang
- State Key Laboratory of Oncology in South China, Guangzhou, China.,Collaborative Innovation Center for Cancer Medicine, Guangzhou, China.,Department of Medical Oncology, Sun Yat-sen University Cancer Center, Guangzhou, China
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Wang Y, Luo S, Jin G, Fu R, Yu Z, Zhang J. Preoperative clinical-radiomics nomogram for microvascular invasion prediction in hepatocellular carcinoma using [Formula: see text]F-FDG PET/CT. BMC Med Imaging 2022; 22:70. [PMID: 35428272 PMCID: PMC9013080 DOI: 10.1186/s12880-022-00796-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2022] [Accepted: 04/05/2022] [Indexed: 11/10/2022] Open
Abstract
PURPOSE To develop a clinical-radiomics nomogram by incorporating radiomics score and clinical predictors for preoperative prediction of microvascular invasion in hepatocellular carcinoma. METHODS A total of 97 HCC patients were retrospectively enrolled from Shanghai Universal Medical Imaging Diagnostic Center and Changhai Hospital Affiliated to the Second Military Medical University. 909 CT and 909 PET slicers from 97 HCC patients were divided into a training cohort (N = 637) and a validation cohort (N = 272). Radiomics features were extracted from each CT or PET slicer, and features selection was performed with least absolute shrinkage and selection operator regression and radiomics score was also generated. The clinical-radiomics nomogram was established by integrating radiomics score and clinical predictors, and the performance of the models were evaluated from its discrimination ability, calibration ability, and clinical usefulness. RESULTS The radiomics score consisted of 45 selected features, and age, the ratio of maximum to minimum tumor diameter, and [Formula: see text]F-FDG uptake status were independent predictors of microvascular invasion. The clinical-radiomics nomogram showed better performance for MVI detection (0.890 [0.854, 0.927]) than the clinical nomogram (0.849 [0.804, 0.893]) ([Formula: see text]). Both nomograms showed good calibration and the clinical-radiomics nomogram's clinical practicability outperformed the clinical nomogram. CONCLUSIONS With the combination of radiomics score and clinical predictors, the clinical-radiomics nomogram can significantly improve the predictive efficacy of microvascular invasion in hepatocellular carcinoma ([Formula: see text]) compared with clinical nomogram.
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Affiliation(s)
- Yutao Wang
- The Affiliated Hospital of Medical School, Ningbo University, Ningbo, Zhejiang Province 315020 China
- Shanghai Universal Medical Imaging Diagnostic Center, Shanghai University, Building 8, 406 Guilin Road, Xuhui District, Shanghai, 201103 China
| | - Shuying Luo
- Faculty of Electrical Engineering and Computer Science, Ningbo University, Ningbo, Zhejiang Province 315211 China
| | - Gehui Jin
- Medical School, Ningbo University, Ningbo, Zhejiang Province 315211 China
| | - Randi Fu
- Faculty of Electrical Engineering and Computer Science, Ningbo University, Ningbo, Zhejiang Province 315211 China
| | - Zhongfei Yu
- Shanghai Universal Medical Imaging Diagnostic Center, Shanghai University, Building 8, 406 Guilin Road, Xuhui District, Shanghai, 201103 China
| | - Jian Zhang
- Shanghai Universal Medical Imaging Diagnostic Center, Shanghai University, Building 8, 406 Guilin Road, Xuhui District, Shanghai, 201103 China
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Yang G, Nie P, Yan L, Zhang M, Wang Y, Zhao L, Li M, Xie F, Xie H, Li X, Xiang F, Wang N, Cheng N, Zhao X, Wang N, Wang Y, Chen C, Yun C, Cui J, Duan S, Zhang R, Hao D, Wang X, Wang Z, Niu H. The radiomics-based tumor heterogeneity adds incremental value to the existing prognostic models for predicting outcome in localized clear cell renal cell carcinoma: a multicenter study. Eur J Nucl Med Mol Imaging 2022; 49:2949-2959. [PMID: 35344062 DOI: 10.1007/s00259-022-05773-1] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2021] [Accepted: 03/19/2022] [Indexed: 12/28/2022]
Abstract
PURPOSE Tumor heterogeneity, which is associated with poor outcomes, has not been exhibited in the University of California, Los Angeles, Integrated Staging System (UISS), and the Stage, Size, Grade and Necrosis (SSIGN) scores. Radiomics allows an in-depth characterization of heterogeneity across the tumor, but its incremental value to the existing prognostic models for clear cell renal cell carcinoma (ccRCC) outcome is unknown. The purpose of this study was to evaluate the association between the radiomics-based tumor heterogeneity and postoperative risk of recurrence in localized ccRCC, and to assess its incremental value to UISS and SSIGN. METHODS A multicenter 866 ccRCC patients derived from 12 Chinese hospitals were studied. The endpoint was recurrence-free survival (RFS). A CT-based radiomics signature (RS) was developed and assessed in the whole cohort and in the subgroups stratified by UISS and SSIGN. Two combined nomograms, the R-UISS (combining RS and UISS) and R-SSIGN (combining RS and SSIGN), were developed. The incremental value of RS to UISS and SSIGN in RFS prediction was evaluated. R statistical software was used for statistics. RESULTS Patients with low radiomics scores were 4.44 times more likely to experience recurrence than those with high radiomics scores (P<0.001). Stratified analysis suggested the association is significant among low- and intermediate-risk patients identified by UISS and SSIGN. The R-UISS and R-SSIGN showed better predictive capability than UISS and SSIGN did with higher C-indices (R-UISS vs. UISS, 0.74 vs. 0.64; R-SSIGN vs. SSIGN, 0.78 vs. 0.76) and higher clinical net benefit. CONCLUSIONS The radiomics-based tumor heterogeneity can predict outcome and add incremental value to the existing prognostic models in localized ccRCC patients. Incorporating radiomics-based tumor heterogeneity in ccRCC prognostic models may provide the opportunity to better surveillance and adjuvant clinical trial design.
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Affiliation(s)
- Guangjie Yang
- Department of Nuclear Medicine, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, China
| | - Pei Nie
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, China
| | - Lei Yan
- Department of Nuclear Medicine, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, China
| | - Mingxin Zhang
- Department of Urology, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, China
| | - Yangyang Wang
- Department of Nuclear Medicine, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, China
| | - Lianzi Zhao
- Department of Radiation Oncology, Fudan University Shanghai Cancer Center, Shanghai, China
| | - Mingyao Li
- Department of Radiation Oncology, Ningbo Medical Center Lihuili Hospital, Ningbo, Zhejiang, China
| | - Fei Xie
- Department of Urology, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, China
| | - Haizhu Xie
- Department of Radiology, Yantai Yuhuangding Hospital, The Affiliated Hospital of Qingdao University, Yantai, Shandong, China
| | - Xianjun Li
- Department of Radiology, Weifang People's Hospital, Weifang, Shandong, China
| | - Fawei Xiang
- Department of Radiology, Weifang People's Hospital, Weifang, Shandong, China
| | - Nan Wang
- Department of Nuclear Medicine, Liaocheng People's Hospital, Liaocheng, Shandong, China
| | - Nan Cheng
- Department of Medical Imaging, The Affiliated Hospital of Jining Medical College, Jining, Shandong, China
| | - Xia Zhao
- Department of Radiology, The Affiliated Hospital of Shandong University of Traditional Chinese Medicine, Jinan, Shandong, China
| | - Ning Wang
- Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, China
| | - Yicong Wang
- Department of Nuclear Medicine, Binzhou Medical University Hospital, Binzhou, Shandong, China
| | - Chengcheng Chen
- Department of Radiology, Rizhao People's Hospital, Rizhao, Shandong, China
| | - Canhua Yun
- Department of Nuclear Medicine, The Second Hospital, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, China
| | - Jingjing Cui
- Shanghai United Imaging Intelligence, Co., Ltd., Shanghai, China
| | - Shaofeng Duan
- GE Healthcare, Precision Health Institution, Shanghai, China
| | - Ran Zhang
- Huiying Medical Technology Co. Ltd, Beijing, China
| | - Dapeng Hao
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, China.
| | - Ximing Wang
- Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, China.
| | - Zhenguang Wang
- Department of Nuclear Medicine, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, China.
| | - Haitao Niu
- Department of Urology, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, China.
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Ge C, Chen Z, Lin Y, Zheng Y, Cao P, Chen X. Preoperative prediction of residual back pain after vertebral augmentation for osteoporotic vertebral compression fractures: Initial application of a radiomics score based nomogram. Front Endocrinol (Lausanne) 2022; 13:1093508. [PMID: 36619583 PMCID: PMC9816386 DOI: 10.3389/fendo.2022.1093508] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/09/2022] [Accepted: 12/12/2022] [Indexed: 12/24/2022] Open
Abstract
BACKGROUND Most patients with osteoporotic vertebral compression fracture (OVCF) obtain pain relief after vertebral augmentation, but some will experience residual back pain (RBP) after surgery. Although several risk factors of RBP have been reported, it is still difficult to estimate the risk of RBP preoperatively. Radiomics is helpful for disease diagnosis and outcome prediction by establishing complementary relationships between human-recognizable and computer-extracted features. However, musculoskeletal radiomics investigations are less frequently reported. OBJECTIVE This study aims to establish a radiomics score (rad-score) based nomogram for the preoperative prediction of RBP in OVCF patients. METHODS The training cohort of 731 OVCF patients was used for nomogram development, and the validation cohort was utilized for performance test. RBP was determined as the score of visual analogue scale ≥ 4 at both 3 and 30 days following surgery. After normalization, the RBP-related radiomics features were selected to create rad-scores. These rad-scores, along with the RBP predictors initially identified by univariate analyses, were included in the multivariate analysis to establish a nomogram for the assessment of the RBP risk in OVCF patients preoperatively. RESULTS A total of 81 patients (11.2%) developed RBP postoperatively. We finally selected 8 radiomics features from 1316 features extracted from each segmented image to determine the rad-score. Multivariate analysis revealed that the rad-score plus bone mineral density, intravertebral cleft, and thoracolumbar fascia injury were independent factors of RBP. Our nomograms based on these factors demonstrated good discrimination, calibration, and clinical utility in both training and validation cohorts. Furthermore, it achieved better performance than the rad-score itself, as well as the nomogram only incorporating regular features. CONCLUSION We developed and validated a nomogram incorporating the rad-score and regular features for preoperative prediction of the RBP risk in OVCF patients, which contributed to improved surgical outcomes and patient satisfaction.
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Affiliation(s)
- Chen Ge
- Department of Orthopaedics, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- *Correspondence: Chen Ge,
| | - Zhe Chen
- Department of Orthopaedics, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yazhou Lin
- Department of Orthopaedics, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yuehuan Zheng
- Department of Orthopaedics, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Peng Cao
- Department of Orthopaedics, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Xiaoyong Chen
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
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Yi Z, Long L, Zeng Y, Liu Z. Current Advances and Challenges in Radiomics of Brain Tumors. Front Oncol 2021; 11:732196. [PMID: 34722274 PMCID: PMC8551958 DOI: 10.3389/fonc.2021.732196] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2021] [Accepted: 09/23/2021] [Indexed: 12/12/2022] Open
Abstract
Imaging diagnosis is crucial for early detection and monitoring of brain tumors. Radiomics enable the extraction of a large mass of quantitative features from complex clinical imaging arrays, and then transform them into high-dimensional data which can subsequently be mined to find their relevance with the tumor's histological features, which reflect underlying genetic mutations and malignancy, along with grade, progression, therapeutic effect, or even overall survival (OS). Compared to traditional brain imaging, radiomics provides quantitative information linked to meaningful biologic characteristics and application of deep learning which sheds light on the full automation of imaging diagnosis. Recent studies have shown that radiomics' application is broad in identifying primary tumor, differential diagnosis, grading, evaluation of mutation status and aggression, prediction of treatment response and recurrence in pituitary tumors, gliomas, and brain metastases. In this descriptive review, besides establishing a general understanding among protocols, results, and clinical significance of these studies, we further discuss the current limitations along with future development of radiomics.
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Affiliation(s)
- Zhenjie Yi
- Department of Neurosurgery, Xiangya Hospital, Central South University, Changsha, China
- XiangYa School of Medicine, Central South University, Changsha, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China
| | - Lifu Long
- XiangYa School of Medicine, Central South University, Changsha, China
| | - Yu Zeng
- Department of Neurosurgery, Xiangya Hospital, Central South University, Changsha, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China
| | - Zhixiong Liu
- Department of Neurosurgery, Xiangya Hospital, Central South University, Changsha, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China
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49
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Prostate Cancer Radiogenomics-From Imaging to Molecular Characterization. Int J Mol Sci 2021; 22:ijms22189971. [PMID: 34576134 PMCID: PMC8465891 DOI: 10.3390/ijms22189971] [Citation(s) in RCA: 51] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2021] [Revised: 09/06/2021] [Accepted: 09/10/2021] [Indexed: 12/24/2022] Open
Abstract
Radiomics and genomics represent two of the most promising fields of cancer research, designed to improve the risk stratification and disease management of patients with prostate cancer (PCa). Radiomics involves a conversion of imaging derivate quantitative features using manual or automated algorithms, enhancing existing data through mathematical analysis. This could increase the clinical value in PCa management. To extract features from imaging methods such as magnetic resonance imaging (MRI), the empiric nature of the analysis using machine learning and artificial intelligence could help make the best clinical decisions. Genomics information can be explained or decoded by radiomics. The development of methodologies can create more-efficient predictive models and can better characterize the molecular features of PCa. Additionally, the identification of new imaging biomarkers can overcome the known heterogeneity of PCa, by non-invasive radiological assessment of the whole specific organ. In the future, the validation of recent findings, in large, randomized cohorts of PCa patients, can establish the role of radiogenomics. Briefly, we aimed to review the current literature of highly quantitative and qualitative results from well-designed studies for the diagnoses, treatment, and follow-up of prostate cancer, based on radiomics, genomics and radiogenomics research.
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50
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Bansal GJ, Santosh D. Accuracy of MRI for prediction of response to neo-adjuvant chemotherapy in triple negative breast cancer compared to other subtypes of breast cancer. Indian J Radiol Imaging 2021; 26:475-481. [PMID: 28104942 PMCID: PMC5201078 DOI: 10.4103/0971-3026.195793] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022] Open
Abstract
Purpose: The aim of this study was to compare the accuracy of magnetic resonance imaging (MRI) for the prediction of response to neo-adjuvant chemotherapy in triple negative (TN) breast cancer, with respect to other subtypes. Materials and Methods: There were a total of 1610 breast cancers diagnosed between March 2009 and August 2014, out of which 82 patients underwent MRI before and after neo-adjuvant chemotherapy but just before surgery. TN cancers were analyzed with respect to others subtypes. Accuracy of MRI for prediction of pathological complete response was compared between different subtypes by obtaining receiver operating characteristic (ROC) curves. The Statistical Package for the Social Sciences version 21 was used for all data analysis, with P value of 0.05 as statistically significant. Results: Out of 82 patients, 29 were luminal (HR+/HER2−), 23 were TN (HR−, HER2−), 11 were HER2 positive (HR−, HER2+), and 19 were of hybrid subtype (HR+/HER2+). TN cancers presented as masses on the pre-chemotherapy MRI scan, were grade 3 on histopathology, and showed concentric shrinkage following chemotherapy. TN cancers were more likely to have both imaging and pathological complete response following chemotherapy (P = 0.055) in contrast to luminal cancers, which show residual cancer. ROC curves were constructed for the prediction of pathological complete response with MRI. For the TN subgroup, MR had a sensitivity of 0.745 and specificity of 0.700 (P = 0.035), with an area under curve of 0.745 (95% confidence interval: 0.526–0.965), which was significantly better compared to other subtypes. Conclusion: TN breast cancers present as masses and show concentric shrinkage following chemotherapy. MRI is most accurate in predicting response to chemotherapy in the TN group, compared to others subtypes. MRI underestimates residual disease in luminal cancers.
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Affiliation(s)
- Gaurav J Bansal
- The Breast Centre, University Hospital of Llandough, Penarth, United Kingdom
| | - Divya Santosh
- The Breast Centre, University Hospital of Llandough, Penarth, United Kingdom
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