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Mirza-Aghazadeh-Attari M, Srinivas T, Kamireddy A, Kim A, Weiss CR. Radiomics Features Extracted From Pre- and Postprocedural Imaging in Early Prediction of Treatment Response in Patients Undergoing Transarterial Radioembolization of Hepatic Lesions: A Systematic Review, Meta-Analysis, and Quality Appraisal Study. J Am Coll Radiol 2024; 21:740-751. [PMID: 38220040 DOI: 10.1016/j.jacr.2023.12.029] [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: 10/23/2023] [Revised: 12/20/2023] [Accepted: 12/21/2023] [Indexed: 01/16/2024]
Abstract
INTRODUCTION Transarterial radioembolization (TARE) is one of the most promising therapeutic options for hepatic masses. Radiomics features, which are quantitative numeric features extracted from medical images, are considered to have potential in predicting treatment response in TARE. This article aims to provide meta-analytic evidence and critically appraise the methodology of radiomics studies published in this regard. METHODS A systematic search was performed on PubMed, Scopus, Embase, and Web of Science. All relevant articles were retrieved, and the characteristics of the studies were extracted. The Radiomics Quality Score and Checklist for Evaluation of Radiomics Research were used to assess the methodologic quality of the studies. Pooled sensitivity, specificity, and area under the receiver operating characteristic curve in predicting objective response were determined. RESULTS The systematic review included 15 studies. The average Radiomics Quality Score of these studies was 11.4 ± 2.1, and the average Checklist for Evaluation of Radiomics Research score was 33± 6.7. There was a notable correlation (correlation coefficient = 0.73) between the two metrics. Adherence to quality measures differed considerably among the studies and even within different components of the same studies. The pooled sensitivity and specificity of the radiomics models in predicting complete or partial response were 83.5% (95% confidence interval 76%-88.9%) and 86.7% (95% confidence interval 78%-92%), respectively. CONCLUSION Radiomics models show great potential in predicting treatment response in TARE of hepatic lesions. However, the heterogeneity seen between the methodologic quality of studies may limit the generalizability of the results. Future initiatives should aim to develop radiomics signatures using multiple external datasets and adhere to quality measures in radiomics methodology.
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Affiliation(s)
- Mohammad Mirza-Aghazadeh-Attari
- Division of Vascular and Interventional Radiology, Russell H. Morgan Department of Radiology and Radiological Sciences, Johns Hopkins University, Baltimore, Maryland
| | - Tara Srinivas
- Division of Vascular and Interventional Radiology, Russell H. Morgan Department of Radiology and Radiological Sciences, Johns Hopkins University, Baltimore, Maryland
| | - Arun Kamireddy
- Division of Vascular and Interventional Radiology, Russell H. Morgan Department of Radiology and Radiological Sciences, Johns Hopkins University, Baltimore, Maryland
| | - Alan Kim
- Division of Vascular and Interventional Radiology, Russell H. Morgan Department of Radiology and Radiological Sciences, Johns Hopkins University, Baltimore, Maryland
| | - Clifford R Weiss
- Division of Vascular and Interventional Radiology, Russell H. Morgan Department of Radiology and Radiological Sciences, Johns Hopkins University, Baltimore, Maryland.
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Triggiani S, Contaldo MT, Mastellone G, Cè M, Ierardi AM, Carrafiello G, Cellina M. The Role of Artificial Intelligence and Texture Analysis in Interventional Radiological Treatments of Liver Masses: A Narrative Review. Crit Rev Oncog 2024; 29:37-52. [PMID: 38505880 DOI: 10.1615/critrevoncog.2023049855] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/21/2024]
Abstract
Liver lesions, including both benign and malignant tumors, pose significant challenges in interventional radiological treatment planning and prognostication. The emerging field of artificial intelligence (AI) and its integration with texture analysis techniques have shown promising potential in predicting treatment outcomes, enhancing precision, and aiding clinical decision-making. This comprehensive review aims to summarize the current state-of-the-art research on the application of AI and texture analysis in determining treatment response, recurrence rates, and overall survival outcomes for patients undergoing interventional radiological treatment for liver lesions. Furthermore, the review addresses the challenges associated with the implementation of AI and texture analysis in clinical practice, including data acquisition, standardization of imaging protocols, and model validation. Future directions and potential advancements in this field are discussed. Integration of multi-modal imaging data, incorporation of genomics and clinical data, and the development of predictive models with enhanced interpretability are proposed as potential avenues for further research. In conclusion, the application of AI and texture analysis in predicting outcomes of interventional radiological treatment for liver lesions shows great promise in augmenting clinical decision-making and improving patient care. By leveraging these technologies, clinicians can potentially enhance treatment planning, optimize intervention strategies, and ultimately improve patient outcomes in the management of liver lesions.
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Affiliation(s)
- Sonia Triggiani
- Postgraduation School in Radiodiagnostics, Università degli Studi di Milano, Via Festa del Perdono, 7, 20122 Milan, Italy
| | - Maria T Contaldo
- Postgraduation School in Radiodiagnostics, Università degli Studi di Milano, 20122 Milan, Italy
| | - Giulia Mastellone
- Postgraduation School in Radiodiagnostics, Università degli Studi di Milano, 20122 Milan, Italy
| | - Maurizio Cè
- Postgraduation School in Radiodiagnostics, Università degli Studi di Milano, Via Festa del Perdono, 7, 20122 Milan, Italy
| | - Anna M Ierardi
- Radiology Department, Fondazione IRCCS Cà Granda, Policlinico di Milano Ospedale Maggiore, 20122 Milan, Italy
| | - Gianpaolo Carrafiello
- Postgraduation School in Radiodiagnostics, Università degli Studi di Milano, Via Festa del Perdono, 7, 20122 Milan, Italy; Radiology Department, Fondazione IRCCS Cà Granda, Policlinico di Milano Ospedale Maggiore, Università di Milano, 20122 Milan, Italy
| | - Michaela Cellina
- Radiology Department, Fatebenefratelli Hospital, ASST Fatebenefratelli Sacco, Milano, Piazza Principessa Clotilde 3, 20121, Milan, Italy
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Rogasch JMM, Shi K, Kersting D, Seifert R. Methodological evaluation of original articles on radiomics and machine learning for outcome prediction based on positron emission tomography (PET). Nuklearmedizin 2023; 62:361-369. [PMID: 37995708 PMCID: PMC10667066 DOI: 10.1055/a-2198-0545] [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: 09/15/2023] [Accepted: 10/25/2023] [Indexed: 11/25/2023]
Abstract
AIM Despite a vast number of articles on radiomics and machine learning in positron emission tomography (PET) imaging, clinical applicability remains limited, partly owing to poor methodological quality. We therefore systematically investigated the methodology described in publications on radiomics and machine learning for PET-based outcome prediction. METHODS A systematic search for original articles was run on PubMed. All articles were rated according to 17 criteria proposed by the authors. Criteria with >2 rating categories were binarized into "adequate" or "inadequate". The association between the number of "adequate" criteria per article and the date of publication was examined. RESULTS One hundred articles were identified (published between 07/2017 and 09/2023). The median proportion of articles per criterion that were rated "adequate" was 65% (range: 23-98%). Nineteen articles (19%) mentioned neither a test cohort nor cross-validation to separate training from testing. The median number of criteria with an "adequate" rating per article was 12.5 out of 17 (range, 4-17), and this did not increase with later dates of publication (Spearman's rho, 0.094; p = 0.35). In 22 articles (22%), less than half of the items were rated "adequate". Only 8% of articles published the source code, and 10% made the dataset openly available. CONCLUSION Among the articles investigated, methodological weaknesses have been identified, and the degree of compliance with recommendations on methodological quality and reporting shows potential for improvement. Better adherence to established guidelines could increase the clinical significance of radiomics and machine learning for PET-based outcome prediction and finally lead to the widespread use in routine clinical practice.
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Affiliation(s)
- Julian Manuel Michael Rogasch
- Department of Nuclear Medicine, Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
- Berlin Institute of Health at Charité – Universitätsmedizin Berlin, Berlin
| | - Kuangyu Shi
- Department of Nuclear Medicine, Inselspital University Hospital Bern, Bern, Switzerland
| | - David Kersting
- Department of Nuclear Medicine, University Hospital Essen, Essen, Germany
| | - Robert Seifert
- Department of Nuclear Medicine, University Hospital Essen, Essen, Germany
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Hsieh C, Laguna A, Ikeda I, Maxwell AWP, Chapiro J, Nadolski G, Jiao Z, Bai HX. Using Machine Learning to Predict Response to Image-guided Therapies for Hepatocellular Carcinoma. Radiology 2023; 309:e222891. [PMID: 37934098 DOI: 10.1148/radiol.222891] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2023]
Abstract
Interventional oncology is a rapidly growing field with advances in minimally invasive image-guided local-regional treatments for hepatocellular carcinoma (HCC), including transarterial chemoembolization, transarterial radioembolization, and thermal ablation. However, current standardized clinical staging systems for HCC are limited in their ability to optimize patient selection for treatment as they rely primarily on serum markers and radiologist-defined imaging features. Given the variation in treatment responses, an updated scoring system that includes multidimensional aspects of the disease, including quantitative imaging features, serum markers, and functional biomarkers, is needed to optimally triage patients. With the vast amounts of numerical medical record data and imaging features, researchers have turned to image-based methods, such as radiomics and artificial intelligence (AI), to automatically extract and process multidimensional data from images. The synthesis of these data can provide clinically relevant results to guide personalized treatment plans and optimize resource utilization. Machine learning (ML) is a branch of AI in which a model learns from training data and makes effective predictions by teaching itself. This review article outlines the basics of ML and provides a comprehensive overview of its potential value in the prediction of treatment response in patients with HCC after minimally invasive image-guided therapy.
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Affiliation(s)
- Celina Hsieh
- From the Department of Diagnostic Imaging (C.H., A.W.P.M., Z.J.) and Warren Alpert Medical School (A.L.), Brown University, Providence, RI; Department of Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, Conn (I.I., J.C.); Department of Radiology, Hospital of the University of Pennsylvania, Philadelphia, Pa (G.N.); and Department of Radiology and Radiological Sciences, Johns Hopkins University School of Medicine, 601 N Caroline St, Baltimore, MD 21205 (H.X.B.)
| | - Amanda Laguna
- From the Department of Diagnostic Imaging (C.H., A.W.P.M., Z.J.) and Warren Alpert Medical School (A.L.), Brown University, Providence, RI; Department of Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, Conn (I.I., J.C.); Department of Radiology, Hospital of the University of Pennsylvania, Philadelphia, Pa (G.N.); and Department of Radiology and Radiological Sciences, Johns Hopkins University School of Medicine, 601 N Caroline St, Baltimore, MD 21205 (H.X.B.)
| | - Ian Ikeda
- From the Department of Diagnostic Imaging (C.H., A.W.P.M., Z.J.) and Warren Alpert Medical School (A.L.), Brown University, Providence, RI; Department of Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, Conn (I.I., J.C.); Department of Radiology, Hospital of the University of Pennsylvania, Philadelphia, Pa (G.N.); and Department of Radiology and Radiological Sciences, Johns Hopkins University School of Medicine, 601 N Caroline St, Baltimore, MD 21205 (H.X.B.)
| | - Aaron W P Maxwell
- From the Department of Diagnostic Imaging (C.H., A.W.P.M., Z.J.) and Warren Alpert Medical School (A.L.), Brown University, Providence, RI; Department of Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, Conn (I.I., J.C.); Department of Radiology, Hospital of the University of Pennsylvania, Philadelphia, Pa (G.N.); and Department of Radiology and Radiological Sciences, Johns Hopkins University School of Medicine, 601 N Caroline St, Baltimore, MD 21205 (H.X.B.)
| | - Julius Chapiro
- From the Department of Diagnostic Imaging (C.H., A.W.P.M., Z.J.) and Warren Alpert Medical School (A.L.), Brown University, Providence, RI; Department of Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, Conn (I.I., J.C.); Department of Radiology, Hospital of the University of Pennsylvania, Philadelphia, Pa (G.N.); and Department of Radiology and Radiological Sciences, Johns Hopkins University School of Medicine, 601 N Caroline St, Baltimore, MD 21205 (H.X.B.)
| | - Gregory Nadolski
- From the Department of Diagnostic Imaging (C.H., A.W.P.M., Z.J.) and Warren Alpert Medical School (A.L.), Brown University, Providence, RI; Department of Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, Conn (I.I., J.C.); Department of Radiology, Hospital of the University of Pennsylvania, Philadelphia, Pa (G.N.); and Department of Radiology and Radiological Sciences, Johns Hopkins University School of Medicine, 601 N Caroline St, Baltimore, MD 21205 (H.X.B.)
| | - Zhicheng Jiao
- From the Department of Diagnostic Imaging (C.H., A.W.P.M., Z.J.) and Warren Alpert Medical School (A.L.), Brown University, Providence, RI; Department of Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, Conn (I.I., J.C.); Department of Radiology, Hospital of the University of Pennsylvania, Philadelphia, Pa (G.N.); and Department of Radiology and Radiological Sciences, Johns Hopkins University School of Medicine, 601 N Caroline St, Baltimore, MD 21205 (H.X.B.)
| | - Harrison X Bai
- From the Department of Diagnostic Imaging (C.H., A.W.P.M., Z.J.) and Warren Alpert Medical School (A.L.), Brown University, Providence, RI; Department of Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, Conn (I.I., J.C.); Department of Radiology, Hospital of the University of Pennsylvania, Philadelphia, Pa (G.N.); and Department of Radiology and Radiological Sciences, Johns Hopkins University School of Medicine, 601 N Caroline St, Baltimore, MD 21205 (H.X.B.)
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Wei L, Niraula D, Gates EDH, Fu J, Luo Y, Nyflot MJ, Bowen SR, El Naqa IM, Cui S. Artificial intelligence (AI) and machine learning (ML) in precision oncology: a review on enhancing discoverability through multiomics integration. Br J Radiol 2023; 96:20230211. [PMID: 37660402 PMCID: PMC10546458 DOI: 10.1259/bjr.20230211] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Revised: 06/15/2023] [Accepted: 06/27/2023] [Indexed: 09/05/2023] Open
Abstract
Multiomics data including imaging radiomics and various types of molecular biomarkers have been increasingly investigated for better diagnosis and therapy in the era of precision oncology. Artificial intelligence (AI) including machine learning (ML) and deep learning (DL) techniques combined with the exponential growth of multiomics data may have great potential to revolutionize cancer subtyping, risk stratification, prognostication, prediction and clinical decision-making. In this article, we first present different categories of multiomics data and their roles in diagnosis and therapy. Second, AI-based data fusion methods and modeling methods as well as different validation schemes are illustrated. Third, the applications and examples of multiomics research in oncology are demonstrated. Finally, the challenges regarding the heterogeneity data set, availability of omics data, and validation of the research are discussed. The transition of multiomics research to real clinics still requires consistent efforts in standardizing omics data collection and analysis, building computational infrastructure for data sharing and storing, developing advanced methods to improve data fusion and interpretability, and ultimately, conducting large-scale prospective clinical trials to fill the gap between study findings and clinical benefits.
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Affiliation(s)
- Lise Wei
- Department of Radiation Oncology, University of Michigan, Michigan, United States
| | - Dipesh Niraula
- Department of Radiation Oncology, Moffitt Cancer Center, Tampa, United States
| | - Evan D. H. Gates
- Department of Radiation Oncology, University of Washington, Washington, United States
| | - Jie Fu
- Department of Radiation Oncology, Stanford University, Stanford, California, United States
| | - Yi Luo
- Department of Radiation Oncology, Moffitt Cancer Center, Tampa, United States
| | - Matthew J. Nyflot
- Department of Radiation Oncology, University of Washington, Washington, United States
| | - Stephen R. Bowen
- Department of Radiation Oncology, University of Washington, Washington, United States
| | - Issam M El Naqa
- Department of Radiation Oncology, Moffitt Cancer Center, Tampa, United States
| | - Sunan Cui
- Department of Radiation Oncology, University of Washington, Washington, United States
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Abstract
During the past 30 years, several advances have been made allowing for safer and more effective treatment of patients with liver cancer. This report reviews recent advances in radiation therapy for primary liver cancers including hepatocellular carcinoma and intrahepatic cholangiocarcinoma. First, studies focusing on liver stereotactic body radiation therapy (SBRT) are reviewed focusing on lessons learned and knowledge gained from early pioneering trials. Then, new technologies to enhance SBRT treatments are explored including adaptive therapy and MRI-guided and biology-guided radiation therapy. Finally, treatment with Y-90 transarterial radioembolization is reviewed with a focus on novel approaches focused on personalized therapy.
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Dai J, Wang H, Xu Y, Chen X, Tian R. Clinical application of AI-based PET images in oncological patients. Semin Cancer Biol 2023; 91:124-142. [PMID: 36906112 DOI: 10.1016/j.semcancer.2023.03.005] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2022] [Revised: 02/28/2023] [Accepted: 03/07/2023] [Indexed: 03/11/2023]
Abstract
Based on the advantages of revealing the functional status and molecular expression of tumor cells, positron emission tomography (PET) imaging has been performed in numerous types of malignant diseases for diagnosis and monitoring. However, insufficient image quality, the lack of a convincing evaluation tool and intra- and interobserver variation in human work are well-known limitations of nuclear medicine imaging and restrict its clinical application. Artificial intelligence (AI) has gained increasing interest in the field of medical imaging due to its powerful information collection and interpretation ability. The combination of AI and PET imaging potentially provides great assistance to physicians managing patients. Radiomics, an important branch of AI applied in medical imaging, can extract hundreds of abstract mathematical features of images for further analysis. In this review, an overview of the applications of AI in PET imaging is provided, focusing on image enhancement, tumor detection, response and prognosis prediction and correlation analyses with pathology or specific gene mutations in several types of tumors. Our aim is to describe recent clinical applications of AI-based PET imaging in malignant diseases and to focus on the description of possible future developments.
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Affiliation(s)
- Jiaona Dai
- Department of Nuclear Medicine, West China Hospital, Sichuan University, Chengdu 610041, China
| | - Hui Wang
- Department of Nuclear Medicine, West China Hospital, Sichuan University, Chengdu 610041, China
| | - Yuchao Xu
- School of Nuclear Science and Technology, University of South China, Hengyang City 421001, China
| | - Xiyang Chen
- Division of Vascular Surgery, Department of General Surgery, West China Hospital, Sichuan University, Chengdu 610041, China.
| | - Rong Tian
- Department of Nuclear Medicine, West China Hospital, Sichuan University, Chengdu 610041, China.
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İnce O, Önder H, Gençtürk M, Cebeci H, Golzarian J, Young S. Prediction of Response of Hepatocellular Carcinoma to Radioembolization: Machine Learning Using Preprocedural Clinical Factors and MR Imaging Radiomics. J Vasc Interv Radiol 2023; 34:235-243.e3. [PMID: 36384224 DOI: 10.1016/j.jvir.2022.11.004] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2022] [Revised: 10/22/2022] [Accepted: 11/06/2022] [Indexed: 11/14/2022] Open
Abstract
PURPOSE To create and evaluate the ability of machine learning-based models with clinicoradiomic features to predict radiologic response after transarterial radioembolization (TARE). MATERIALS AND METHODS 82 treatment-naïve patients (65 responders and 17 nonresponders; median age: 65 years; interquartile range: 11) who underwent selective TARE were included. Treatment responses were evaluated using the European Association for the Study of the Liver criteria at 3-month follow-up. Laboratory, clinical, and procedural information were collected. Radiomic features were extracted from pretreatment contrast-enhanced T1-weighted magnetic resonance images obtained within 3 months before TARE. Feature selection consisted of intraclass correlation, followed by Pearson correlation analysis and finally, sequential feature selection algorithm. Support vector machine, logistic regression, random forest, and LightGBM models were created with both clinicoradiomic features and clinical features alone. Performance metrics were calculated with a nested 5-fold cross-validation technique. The performances of the models were compared by Wilcoxon signed-rank and Friedman tests. RESULTS In total, 1,128 features were extracted. The feature selection process resulted in 12 features (8 radiomic and 4 clinical features) being included in the final analysis. The area under the receiver operating characteristic curve values from the support vector machine, logistic regression, random forest, and LightGBM models were 0.94, 0.94, 0.88, and 0.92 with clinicoradiomic features and 0.82, 0.83, 0.82, and 0.83 with clinical features alone, respectively. All models exhibited significantly higher performances when radiomic features were included (P = .028, .028, .043, and .028, respectively). CONCLUSIONS Based on clinical and imaging-based information before treatment, machine learning-based clinicoradiomic models demonstrated potential to predict response to TARE.
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Affiliation(s)
- Okan İnce
- Department of Radiology, Medical School, University of Minnesota, Minneapolis, Minnesota.
| | - Hakan Önder
- Department of Radiology, Prof. Dr. Cemil Taşcıoğlu City Hospital, Health Sciences University, Istanbul, Turkey
| | - Mehmet Gençtürk
- Department of Radiology, Medical School, University of Minnesota, Minneapolis, Minnesota
| | - Hakan Cebeci
- Department of Radiology, Medical School, University of Minnesota, Minneapolis, Minnesota
| | - Jafar Golzarian
- Department of Radiology, Medical School, University of Minnesota, Minneapolis, Minnesota
| | - Shamar Young
- Department of Radiology, College of Medicine, University of Arizona, Tucson, Arizona
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Morland D, Triumbari EKA, Boldrini L, Gatta R, Pizzuto D, Annunziata S. Radiomics in Oncological PET Imaging: A Systematic Review-Part 2, Infradiaphragmatic Cancers, Blood Malignancies, Melanoma and Musculoskeletal Cancers. Diagnostics (Basel) 2022; 12:diagnostics12061330. [PMID: 35741139 PMCID: PMC9222024 DOI: 10.3390/diagnostics12061330] [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: 05/04/2022] [Revised: 05/19/2022] [Accepted: 05/20/2022] [Indexed: 12/04/2022] Open
Abstract
The objective of this review was to summarize published radiomics studies dealing with infradiaphragmatic cancers, blood malignancies, melanoma, and musculoskeletal cancers, and assess their quality. PubMed database was searched from January 1990 to February 2022 for articles performing radiomics on PET imaging of at least 1 specified tumor type. Exclusion criteria includd: non-oncological studies; supradiaphragmatic tumors; reviews, comments, cases reports; phantom or animal studies; technical articles without a clinically oriented question; studies including <30 patients in the training cohort. The review database contained PMID, first author, year of publication, cancer type, number of patients, study design, independent validation cohort and objective. This database was completed twice by the same person; discrepant results were resolved by a third reading of the articles. A total of 162 studies met inclusion criteria; 61 (37.7%) studies included >100 patients, 13 (8.0%) were prospective and 61 (37.7%) used an independent validation set. The most represented cancers were esophagus, lymphoma, and cervical cancer (n = 24, n = 24 and n = 19 articles, respectively). Most studies focused on 18F-FDG, and prognostic and response to treatment objectives. Although radiomics and artificial intelligence are technically challenging, new contributions and guidelines help improving research quality over the years and pave the way toward personalized medicine.
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Affiliation(s)
- David Morland
- Unità di Medicina Nucleare, TracerGLab, Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168 Roma, Italy; (E.K.A.T.); (D.P.); (S.A.)
- Service de Médecine Nucléaire, Institut Godinot, 51100 Reims, France
- Laboratoire de Biophysique, UFR de Médecine, Université de Reims Champagne-Ardenne, 51100 Reims, France
- CReSTIC (Centre de Recherche en Sciences et Technologies de l’Information et de la Communication), EA 3804, Université de Reims Champagne-Ardenne, 51100 Reims, France
- Correspondence:
| | - Elizabeth Katherine Anna Triumbari
- Unità di Medicina Nucleare, TracerGLab, Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168 Roma, Italy; (E.K.A.T.); (D.P.); (S.A.)
| | - Luca Boldrini
- Unità di Radioterapia Oncologica, Radiomics, Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168 Roma, Italy; (L.B.); (R.G.)
| | - Roberto Gatta
- Unità di Radioterapia Oncologica, Radiomics, Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168 Roma, Italy; (L.B.); (R.G.)
- Department of Clinical and Experimental Sciences, University of Brescia, 25121 Brescia, Italy
- Department of Oncology, Lausanne University Hospital, 1011 Lausanne, Switzerland
| | - Daniele Pizzuto
- Unità di Medicina Nucleare, TracerGLab, Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168 Roma, Italy; (E.K.A.T.); (D.P.); (S.A.)
| | - Salvatore Annunziata
- Unità di Medicina Nucleare, TracerGLab, Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168 Roma, Italy; (E.K.A.T.); (D.P.); (S.A.)
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Waddell JJ, Townsend PH, Collins ZS, Walter C. Liver-Directed Therapy for Metastatic Colon Cancer: Update. CURRENT COLORECTAL CANCER REPORTS 2022. [DOI: 10.1007/s11888-022-00474-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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11
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Knešaurek K, Martinez RB, Ghesani M. Tumour-to-normal tissue (T/N) dosimetry ratios role in assessment of 90Y selective internal radiation therapy (SIRT). Br J Radiol 2022; 95:20210294. [PMID: 34762514 PMCID: PMC8722260 DOI: 10.1259/bjr.20210294] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023] Open
Abstract
OBJECTIVE The purpose of our work is to assess the role of tumour-to-normal tissue (T/N) dosimetry ratios for predicting response in patients undergoing locoregional therapy to the liver with 90Y microspheres. METHODS A total of 39 patients (7 female:32 male, mean age 68.3 ± 7.6 years), underwent positron emission tomography (PET)/CT imaging after treatment with 90Y microspheres. For attenuation correction and localization of the 90Y microspheres, the low-dose, non-diagnostic CT images from PET/CT were used. The acquisition took 15 min and the reconstruction matrix size was 200 × 200 × 75 mm and voxel size of 4.07 × 4.07 × 3.00 mm. For dosimetry calculations, the local deposition method with known activity of 90Y was used. For each patient, regions of interest for tumour(s) and whole liver were manually created; the normal tissue region of interest was created automatically. mRECIST criteria on MRI done at 1 month post-treatment and subsequently every 3 months after 90Y treatment, were used to assess response. RESULTS For 39 patients, the mean liver, tumour and normal tissue doses (mean ± SD) were, 55.17 ± 26.04 Gy, 911.87 ± 866.54 Gy and 47.79 ± 20.47 Gy, respectively. Among these patients, 31 (79%) showed complete response (CR) and 8 (21%) showed progression of disease (PD). For patients with CR, the mean T/N dose ratio obtained was 24.91 (range 3.09-80.12) and for patients with PD, the mean T/N dose ratio was significantly lower, at 6.69 (range 0.36-14.75). CONCLUSION Our data show that patients with CR have a statistically higher T/N dose ratio than those with PD. Because, the number of PD cases was limited and partial volume effect was not considered, further investigation is warranted. ADVANCES IN KNOWLEDGE T/N dosimetry ratios can be used for assessing response in patients undergoing locoregional therapy to the liver with 90Y microspheres.
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Affiliation(s)
- Karin Knešaurek
- Diagnostic, Molecular & Interventional Radiology, Icahn School of Medicine at Mount Sinai, New York, USA
| | - Ricardo Bello Martinez
- Diagnostic, Molecular & Interventional Radiology, Icahn School of Medicine at Mount Sinai, New York, USA
| | - Munir Ghesani
- Diagnostic, Molecular & Interventional Radiology, Icahn School of Medicine at Mount Sinai, New York, USA
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Labour J, Boissard P, Baudier T, Khayi F, Kryza D, Durebex PV, Martino SPD, Mognetti T, Sarrut D, Badel JN. Yttrium-90 quantitative phantom study using digital photon counting PET. EJNMMI Phys 2021; 8:56. [PMID: 34318383 PMCID: PMC8316557 DOI: 10.1186/s40658-021-00402-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2021] [Accepted: 06/23/2021] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND PET imaging of 90Y-microsphere distribution following radioembolisation is challenging due to the count-starved statistics from the low branching ratio of e+/e- pair production during 90Y decay. PET systems using silicon photo-multipliers have shown better 90Y image quality compared to conventional photo-multiplier tubes. The main goal of the present study was to evaluate reconstruction parameters for different phantom configurations and varying listmode acquisition lengths to improve quantitative accuracy in 90Y dosimetry, using digital photon counting PET/CT. METHODS Quantitative PET and dosimetry accuracy were evaluated using two uniform cylindrical phantoms specific for PET calibration validation. A third body phantom with a 9:1 hot sphere-to-background ratio was scanned at different activity concentrations of 90Y. Reconstructions were performed using OSEM algorithm with varying parameters. Time-of-flight and point-spread function modellings were included in all reconstructions. Absorbed dose calculations were carried out using voxel S-values convolution and were compared to reference Monte Carlo simulations. Dose-volume histograms and root-mean-square deviations were used to evaluate reconstruction parameter sets. Using listmode data, phantom and patient datasets were rebinned into various lengths of time to assess the influence of count statistics on the calculation of absorbed dose. Comparisons between the local energy deposition method and the absorbed dose calculations were performed. RESULTS Using a 2-mm full width at half maximum post-reconstruction Gaussian filter, the dosimetric accuracy was found to be similar to that found with no filter applied but also reduced noise. Larger filter sizes should not be used. An acquisition length of more than 10 min/bed reduces image noise but has no significant impact in the quantification of phantom or patient data for the digital photon counting PET. 3 iterations with 10 subsets were found suitable for large spheres whereas 1 iteration with 30 subsets could improve dosimetry for smaller spheres. CONCLUSION The best choice of the combination of iterations and subsets depends on the size of the spheres. However, one should be careful on this choice, depending on the imaging conditions and setup. This study can be useful in this choice for future studies for more accurate 90Y post-dosimetry using a digital photon counting PET/CT.
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Affiliation(s)
- Joey Labour
- CREATIS; CNRS UMR 5220; INSERM U 1044; Université de Lyon; INSA-Lyon; Université Lyon 1, Lyon, France
- Centre de lutte contre le cancer Léon Bérard, Lyon, France
| | | | - Thomas Baudier
- CREATIS; CNRS UMR 5220; INSERM U 1044; Université de Lyon; INSA-Lyon; Université Lyon 1, Lyon, France
- Centre de lutte contre le cancer Léon Bérard, Lyon, France
| | - Fouzi Khayi
- Centre de lutte contre le cancer Léon Bérard, Lyon, France
| | - David Kryza
- Centre de lutte contre le cancer Léon Bérard, Lyon, France
- Hospices Civils de Lyon; Université de Lyon; Université Claude Bernard Lyon 1; LAGEPP UMR 5007 CNRS, Lyon, France
| | | | | | | | - David Sarrut
- CREATIS; CNRS UMR 5220; INSERM U 1044; Université de Lyon; INSA-Lyon; Université Lyon 1, Lyon, France
- Centre de lutte contre le cancer Léon Bérard, Lyon, France
| | - Jean-Noël Badel
- CREATIS; CNRS UMR 5220; INSERM U 1044; Université de Lyon; INSA-Lyon; Université Lyon 1, Lyon, France
- Centre de lutte contre le cancer Léon Bérard, Lyon, France
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