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Hatt M, Krizsan AK, Rahmim A, Bradshaw TJ, Costa PF, Forgacs A, Seifert R, Zwanenburg A, El Naqa I, Kinahan PE, Tixier F, Jha AK, Visvikis D. Joint EANM/SNMMI guideline on radiomics in nuclear medicine : Jointly supported by the EANM Physics Committee and the SNMMI Physics, Instrumentation and Data Sciences Council. Eur J Nucl Med Mol Imaging 2023; 50:352-375. [PMID: 36326868 PMCID: PMC9816255 DOI: 10.1007/s00259-022-06001-6] [Citation(s) in RCA: 32] [Impact Index Per Article: 32.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2022] [Accepted: 10/09/2022] [Indexed: 11/05/2022]
Abstract
PURPOSE The purpose of this guideline is to provide comprehensive information on best practices for robust radiomics analyses for both hand-crafted and deep learning-based approaches. METHODS In a cooperative effort between the EANM and SNMMI, we agreed upon current best practices and recommendations for relevant aspects of radiomics analyses, including study design, quality assurance, data collection, impact of acquisition and reconstruction, detection and segmentation, feature standardization and implementation, as well as appropriate modelling schemes, model evaluation, and interpretation. We also offer an outlook for future perspectives. CONCLUSION Radiomics is a very quickly evolving field of research. The present guideline focused on established findings as well as recommendations based on the state of the art. Though this guideline recognizes both hand-crafted and deep learning-based radiomics approaches, it primarily focuses on the former as this field is more mature. This guideline will be updated once more studies and results have contributed to improved consensus regarding the application of deep learning methods for radiomics. Although methodological recommendations in the present document are valid for most medical image modalities, we focus here on nuclear medicine, and specific recommendations when necessary are made for PET/CT, PET/MR, and quantitative SPECT.
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Affiliation(s)
- M Hatt
- LaTIM, INSERM, UMR 1101, Univ Brest, Brest, France
| | | | - A Rahmim
- Departments of Radiology and Physics, University of British Columbia, Vancouver, BC, Canada
| | - T J Bradshaw
- Department of Radiology, University of Wisconsin, Madison, WI, USA
| | - P F Costa
- Department of Nuclear Medicine, West German Cancer Center, University of Duisburg-Essen and German Cancer Consortium (DKTK)-University Hospital Essen, Essen, Germany
| | | | - R Seifert
- Department of Nuclear Medicine, West German Cancer Center, University of Duisburg-Essen and German Cancer Consortium (DKTK)-University Hospital Essen, Essen, Germany.
- Department of Nuclear Medicine, Münster University Hospital, Münster, Germany.
| | - A Zwanenburg
- OncoRay-National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Helmholtz-Zentrum Dresden-Rossendorf, Dresden, Germany
- National Center for Tumor Diseases (NCT/UCC), Dresden, Germany
- German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - I El Naqa
- Department of Machine Learning, Moffitt Cancer Center, Tampa, FL, 33626, USA
| | - P E Kinahan
- Imaging Research Laboratory, PET/CT Physics, Department of Radiology, UW Medical Center, University of Washington, Seattle, WA, USA
| | - F Tixier
- LaTIM, INSERM, UMR 1101, Univ Brest, Brest, France
| | - A K Jha
- McKelvey School of Engineering and Mallinckrodt Institute of Radiology, Washington University in St. Louis, Saint Louis, MO, USA
| | - D Visvikis
- LaTIM, INSERM, UMR 1101, Univ Brest, Brest, France
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Castillo-Flores S, Gonzalez MR, Bryce-Alberti M, de Souza F, Subhawong TK, Kuker R, Pretell-Mazzini J. PET-CT in the Evaluation of Neoadjuvant/Adjuvant Treatment Response of Soft-tissue Sarcomas: A Comprehensive Review of the Literature. JBJS Rev 2022; 10:01874474-202212000-00003. [PMID: 36639875 DOI: 10.2106/jbjs.rvw.22.00131] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/15/2023]
Abstract
➢ In soft-tissue sarcomas (STSs), the use of positron emission tomography-computed tomography (PET-CT) through a standardized uptake value reduction rate correlates well with histopathological response to neoadjuvant treatment and survival. ➢ PET-CT has shown a better sensitivity to diagnose systemic involvement compared with magnetic resonance imaging and CT; therefore, it has an important role in detecting recurrent systemic disease. However, delaying the use of PET-CT scan, to differentiate tumor recurrence from benign fluorodeoxyglucose uptake changes after surgical treatment and radiotherapy, is essential. ➢ PET-CT limitations such as difficult differentiation between benign inflammatory and malignant processes, inefficient discrimination between benign soft-tissue tumors and STSs, and low sensitivity when evaluating small pulmonary metastases must be of special consideration.
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Affiliation(s)
- Samy Castillo-Flores
- Medical Student at Facultad de Medicina Alberto Hurtado, Universidad Peruana Cayetano Heredia, Lima, Peru
| | - Marcos R Gonzalez
- Medical Student at Facultad de Medicina Alberto Hurtado, Universidad Peruana Cayetano Heredia, Lima, Peru
| | - Mayte Bryce-Alberti
- Medical Student at Facultad de Medicina Alberto Hurtado, Universidad Peruana Cayetano Heredia, Lima, Peru
| | - Felipe de Souza
- Division of Musculoskeletal Radiology, Department of Radiology, University of Miami Miller School of Medicine, Miami, Florida
| | - Ty K Subhawong
- Division of Musculoskeletal Radiology, Department of Radiology, University of Miami Miller School of Medicine, Miami, Florida
| | - Russ Kuker
- Division of Musculoskeletal Radiology, Department of Radiology, University of Miami Miller School of Medicine, Miami, Florida
| | - Juan Pretell-Mazzini
- Division of Orthopedic Oncology, Miami Cancer Institute, Baptist Health System South Florida, Plantation, Florida
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Wolsztynski E, O'Sullivan F, Eary JF. Spatially coherent modeling of 3D FDG-PET data for assessment of intratumoral heterogeneity and uptake gradients. J Med Imaging (Bellingham) 2022; 9:045003. [PMID: 35915767 PMCID: PMC9334646 DOI: 10.1117/1.jmi.9.4.045003] [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: 09/10/2021] [Accepted: 06/28/2022] [Indexed: 11/14/2022] Open
Abstract
Purpose: Radiomics have become invaluable for non-invasive cancer patient risk prediction, and the community now turns to exogenous assessment, e.g., from genomics, for interpretability of these agnostic analyses. Yet, some opportunities for clinically interpretable modeling of positron emission tomography (PET) imaging data remain unexplored, that could facilitate insightful characterization at voxel level. Approach: Here, we present a novel deformable tubular representation of the distribution of tracer uptake within a volume of interest, and derive interpretable prognostic summaries from it. This data-adaptive strategy yields a 3D-coherent and smooth model fit, and a profile curve describing tracer uptake as a function of voxel location within the volume. Local trends in uptake rates are assessed at each voxel via the calculation of gradients derived from this curve. Intratumoral heterogeneity can also be assessed directly from it. Results: We illustrate the added value of this approach over previous strategies, in terms of volume rendering and coherence of the structural representation of the data. We further demonstrate consistency of the implementation via simulations, and prognostic potential of heterogeneity and statistical summaries of the uptake gradients derived from the model on a clinical cohort of 158 sarcoma patients imaged with F 18 -fluorodeoxyglucose-PET, in multivariate prognostic models of patient survival. Conclusions: The proposed approach captures uptake characteristics consistently at any location, and yields a description of variations in uptake that holds prognostic value complementarily to structural heterogeneity. This creates opportunities for monitoring of local areas of greater interest within a tumor, e.g., to assess therapeutic response in avid locations.
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Affiliation(s)
- Eric Wolsztynski
- University College Cork, Statistics Department, Cork, Ireland.,Insight SFI Research Centre for Data Analytics, Cork, Ireland
| | - Finbarr O'Sullivan
- University College Cork, Statistics Department, Cork, Ireland.,Insight SFI Research Centre for Data Analytics, Cork, Ireland
| | - Janet F Eary
- National Cancer Institute, Bethesda, Maryland, United States
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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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A Systematic Review of PET Textural Analysis and Radiomics in Cancer. Diagnostics (Basel) 2021; 11:diagnostics11020380. [PMID: 33672285 PMCID: PMC7926413 DOI: 10.3390/diagnostics11020380] [Citation(s) in RCA: 30] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2021] [Revised: 02/10/2021] [Accepted: 02/19/2021] [Indexed: 12/12/2022] Open
Abstract
Background: Although many works have supported the utility of PET radiomics, several authors have raised concerns over the robustness and replicability of the results. This study aimed to perform a systematic review on the topic of PET radiomics and the used methodologies. Methods: PubMed was searched up to 15 October 2020. Original research articles based on human data specifying at least one tumor type and PET image were included, excluding those that apply only first-order statistics and those including fewer than 20 patients. Each publication, cancer type, objective and several methodological parameters (number of patients and features, validation approach, among other things) were extracted. Results: A total of 290 studies were included. Lung (28%) and head and neck (24%) were the most studied cancers. The most common objective was prognosis/treatment response (46%), followed by diagnosis/staging (21%), tumor characterization (18%) and technical evaluations (15%). The average number of patients included was 114 (median = 71; range 20–1419), and the average number of high-order features calculated per study was 31 (median = 26, range 1–286). Conclusions: PET radiomics is a promising field, but the number of patients in most publications is insufficient, and very few papers perform in-depth validations. The role of standardization initiatives will be crucial in the upcoming years.
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Shirvani SM, Huntzinger CJ, Melcher T, Olcott PD, Voronenko Y, Bartlett-Roberto J, Mazin S. Biology-guided radiotherapy: redefining the role of radiotherapy in metastatic cancer. Br J Radiol 2021; 94:20200873. [PMID: 33112685 PMCID: PMC7774706 DOI: 10.1259/bjr.20200873] [Citation(s) in RCA: 33] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2020] [Revised: 09/30/2020] [Accepted: 10/05/2020] [Indexed: 12/14/2022] Open
Abstract
The emerging biological understanding of metastatic cancer and proof-of-concept clinical trials suggest that debulking all gross disease holds great promise for improving patient outcomes. However, ablation of multiple targets with conventional external beam radiotherapy systems is burdensome, which limits investigation and utilization of complete metastatic ablation in the majority of patients with advanced disease. To overcome this logistical hurdle, technical innovation is necessary. Biology-guided radiotherapy (BgRT) is a new external beam radiotherapy delivery modality combining positron emission tomography-computed tomography (PET-CT) with a 6 MV linear accelerator. The key innovation is continuous response of the linear accelerator to outgoing tumor PET emissions with beamlets of radiotherapy at subsecond latency. This allows the deposited dose to track tumors in real time. Multiple new hardware and algorithmic advances further facilitate this low-latency feedback process. By transforming tumors into their own fiducials after intravenous injection of a radiotracer, BgRT has the potential to enable complete metastatic ablation in a manner efficient for a single patient and scalable to entire populations with metastatic disease. Future trends may further enhance the utility of BgRT in the clinic as this technology dovetails with other innovations in radiotherapy, including novel dose painting and fractionation schemes, radiomics, and new radiotracers.
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7
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Hatt M, Cheze Le Rest C, Antonorsi N, Tixier F, Tankyevych O, Jaouen V, Lucia F, Bourbonne V, Schick U, Badic B, Visvikis D. Radiomics in PET/CT: Current Status and Future AI-Based Evolutions. Semin Nucl Med 2020; 51:126-133. [PMID: 33509369 DOI: 10.1053/j.semnuclmed.2020.09.002] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
This short review aims at providing the readers with an update on the current status, as well as future perspectives in the quickly evolving field of radiomics applied to the field of PET/CT imaging. Numerous pitfalls have been identified in study design, data acquisition, segmentation, features calculation and modeling by the radiomics community, and these are often the same issues across all image modalities and clinical applications, however some of these are specific to PET/CT (and SPECT/CT) imaging and therefore the present paper focuses on those. In most cases, recommendations and potential methodological solutions do exist and should therefore be followed to improve the overall quality and reproducibility of published studies. In terms of future evolutions, the techniques from the larger field of artificial intelligence (AI), including those relying on deep neural networks (also known as deep learning) have already shown impressive potential to provide solutions, especially in terms of automation, but also to maybe fully replace the tools the radiomics community has been using until now in order to build the usual radiomics workflow. Some important challenges remain to be addressed before the full impact of AI may be realized but overall the field has made striking advances over the last few years and it is expected advances will continue at a rapid pace.
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Affiliation(s)
- Mathieu Hatt
- LaTIM, INSERM, UMR 1101, University of Brest, CHRU Brest, France
| | - Catherine Cheze Le Rest
- LaTIM, INSERM, UMR 1101, University of Brest, CHRU Brest, France; Nuclear Medicine Department, CHU Milétrie, Poitiers, France
| | - Nils Antonorsi
- Nuclear Medicine Department, CHU Milétrie, Poitiers, France
| | - Florent Tixier
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY 10065, United States of America
| | | | - Vincent Jaouen
- LaTIM, INSERM, UMR 1101, University of Brest, CHRU Brest, France; IMT-Atlantique, Plouzané, France
| | - Francois Lucia
- LaTIM, INSERM, UMR 1101, University of Brest, CHRU Brest, France
| | | | - Ulrike Schick
- LaTIM, INSERM, UMR 1101, University of Brest, CHRU Brest, France
| | - Bogdan Badic
- LaTIM, INSERM, UMR 1101, University of Brest, CHRU Brest, France
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Bogowicz M, Vuong D, Huellner MW, Pavic M, Andratschke N, Gabrys HS, Guckenberger M, Tanadini-Lang S. CT radiomics and PET radiomics: ready for clinical implementation? THE QUARTERLY JOURNAL OF NUCLEAR MEDICINE AND MOLECULAR IMAGING : OFFICIAL PUBLICATION OF THE ITALIAN ASSOCIATION OF NUCLEAR MEDICINE (AIMN) [AND] THE INTERNATIONAL ASSOCIATION OF RADIOPHARMACOLOGY (IAR), [AND] SECTION OF THE SOCIETY OF RADIOPHARMACEUTICAL CHEMISTRY AND BIOLOGY 2019; 63:355-370. [PMID: 31527578 DOI: 10.23736/s1824-4785.19.03192-3] [Citation(s) in RCA: 45] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
INTRODUCTION Today, rapid technical and clinical developments result in an increasing number of treatment options for oncological diseases. Thus, decision support systems are needed to offer the right treatment to the right patient. Imaging biomarkers hold great promise in patient-individual treatment guidance. Routinely performed for diagnosis and staging, imaging datasets are expected to hold more information than used in the clinical practice. Radiomics describes the extraction of a large number of meaningful quantitative features from medical images, such as computed tomography (CT) and positron emission tomography (PET). Due to the non-invasive nature and ability to capture 3D image-based heterogeneity, radiomic features are potential surrogate markers of the cancer phenotype. Several radiomic studies are published per day, owing to encouraging results of many radiomics-based patient outcome models. Despite this comparably large number of studies, radiomics is mainly studied in proof of principle concept. Hence, a translation of radiomics from a hot topic research field into an essential clinical decision-making tool is lacking, but of high clinical interest. EVIDENCE ACQUISITION Herein, we present a literature review addressing the clinical evidence of CT and PET radiomics. An extensive literature review was conducted in PubMed, including papers on robustness and clinical applications. EVIDENCE SYNTHESIS We summarize image-modality related influences on the robustness of radiomic features and provide an overview of clinical evidence reported in the literature. Today, more evidence has been provided for CT imaging, however, PET imaging offers the promise of direct imaging of biological processes and functions. We provide a summary of future research directions, which needs to be addressed in order to successfully introduce radiomics into clinical medicine. In comparison to CT, more focus should be directed towards harmonization of PET acquisition and reconstruction protocols, which is important for transferable modelling. CONCLUSIONS Both CT and PET radiomics are promising pre-treatment and intra-treatment biomarkers for outcome prediction. Most studies are performed in retrospective setting, however their validation in prospective data collections is ongoing.
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Affiliation(s)
- Marta Bogowicz
- Department of Radiation Oncology, University Hospital Zurich and University of Zurich, Zurich, Switzerland -
| | - Diem Vuong
- Department of Radiation Oncology, University Hospital Zurich and University of Zurich, Zurich, Switzerland
| | - Martin W Huellner
- Department of Nuclear Medicine, University Hospital Zurich and University of Zurich, Zurich, Switzerland
| | - Matea Pavic
- Department of Radiation Oncology, University Hospital Zurich and University of Zurich, Zurich, Switzerland
| | - Nicolaus Andratschke
- Department of Radiation Oncology, University Hospital Zurich and University of Zurich, Zurich, Switzerland
| | - Hubert S Gabrys
- Department of Radiation Oncology, University Hospital Zurich and University of Zurich, Zurich, Switzerland
| | - Matthias Guckenberger
- Department of Radiation Oncology, University Hospital Zurich and University of Zurich, Zurich, Switzerland
| | - Stephanie Tanadini-Lang
- Department of Radiation Oncology, University Hospital Zurich and University of Zurich, Zurich, Switzerland
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Wolsztynski E, O'Sullivan J, Hughes NM, Mou T, Murphy P, O'Sullivan F, O'Regan K. Combining structural and textural assessments of volumetric FDG-PET uptake in NSCLC. IEEE TRANSACTIONS ON RADIATION AND PLASMA MEDICAL SCIENCES 2019; 3:421-433. [PMID: 33134652 PMCID: PMC7597463 DOI: 10.1109/trpms.2019.2912433] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Numerous studies have reported the prognostic utility of texture analyses and the effectiveness of radiomics in PET and PET/CT assessment of non-small cell lung cancer (NSCLC). Here we explore the potential, relative to this methodology, of an alternative model-based approach to tumour characterization, which was successfully applied to sarcoma in previous works. The spatial distribution of 3D FDG-PET uptake is evaluated in the spatial referential determined by the best-fitting ellipsoidal pattern, which provides a univariate uptake profile function of the radial position of intratumoral voxels. A group of structural features is extracted from this fit that include two heterogeneity variables and statistical summaries of local metabolic gradients. We demonstrate that these variables capture aspects of tumour metabolism that are separate to those described by conventional texture features. Prognostic model selection is performed in terms of a number of classifiers, including stepwise selection of logistic models, LASSO, random forests and neural networks with respect to two-year survival status. Our results for a cohort of 93 NSCLC patients show that structural variables have significant prognostic potential, and that they may be used in conjunction with texture features in a traditional radiomics sense, towards improved baseline multivariate models of patient overall survival. The statistical significance of these models also demonstrates the relevance of these machine learning classifiers for prognostic variable selection.
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Affiliation(s)
- Eric Wolsztynski
- Department of Statistics, School of Mathematical Sciences, University College Cork, T12 XY86, Ireland
| | - Janet O'Sullivan
- Department of Statistics, School of Mathematical Sciences, University College Cork, T12 XY86, Ireland
| | | | - Tian Mou
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Solna, Sweden
| | - Peter Murphy
- PET/CT Unit (Alliance Medical), Cork University Hospital, Cork, Ireland
| | - Finbarr O'Sullivan
- Department of Statistics, School of Mathematical Sciences, University College Cork, T12 XY86, Ireland
| | - Kevin O'Regan
- Department of Radiology, Cork University Hospital, Cork, Ireland
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10
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Spraker MB, Wootton LS, Hippe DS, Ball KC, Peeken JC, Macomber MW, Chapman TR, Hoff MN, Kim EY, Pollack SM, Combs SE, Nyflot MJ. MRI Radiomic Features Are Independently Associated With Overall Survival in Soft Tissue Sarcoma. Adv Radiat Oncol 2019; 4:413-421. [PMID: 31011687 PMCID: PMC6460235 DOI: 10.1016/j.adro.2019.02.003] [Citation(s) in RCA: 44] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2018] [Accepted: 02/12/2019] [Indexed: 11/21/2022] Open
Abstract
Purpose Soft tissue sarcomas (STS) represent a heterogeneous group of diseases, and selection of individualized treatments remains a challenge. The goal of this study was to determine whether radiomic features extracted from magnetic resonance (MR) images are independently associated with overall survival (OS) in STS. Methods and Materials This study analyzed 2 independent cohorts of adult patients with stage II-III STS treated at center 1 (N = 165) and center 2 (N = 61). Thirty radiomic features were extracted from pretreatment T1-weighted contrast-enhanced MR images. Prognostic models for OS were derived on the center 1 cohort and validated on the center 2 cohort. Clinical-only (C), radiomics-only (R), and clinical and radiomics (C+R) penalized Cox models were constructed. Model performance was assessed using Harrell's concordance index. Results In the R model, tumor volume (hazard ratio [HR], 1.5) and 4 texture features (HR, 1.1-1.5) were selected. In the C+R model, both age (HR, 1.4) and grade (HR, 1.7) were selected along with 5 radiomic features. The adjusted c-indices of the 3 models ranged from 0.68 (C) to 0.74 (C+R) in the derivation cohort and 0.68 (R) to 0.78 (C+R) in the validation cohort. The radiomic features were independently associated with OS in the validation cohort after accounting for age and grade (HR, 2.4; P = .009). Conclusions This study found that radiomic features extracted from MR images are independently associated with OS when accounting for age and tumor grade. The overall predictive performance of 3-year OS using a model based on clinical and radiomic features was replicated in an independent cohort. Optimal models using clinical and radiomic features could improve personalized selection of therapy in patients with STS.
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Affiliation(s)
- Matthew B. Spraker
- Department of Radiation Oncology, Washington University in St. Louis, St. Louis, Missouri
- Corresponding author. 4921 Parkview Place, CAM LL, CB 8224, St. Louis, MO 63110.
| | - Landon S. Wootton
- Department of Radiation Oncology, University of Washington, Seattle, Washington
| | - Daniel S. Hippe
- Department of Radiology, University of Washington, Seattle, Washington
| | - Kevin C. Ball
- Aurora St. Luke's Medical Center, Department of Diagnostic Radiology, Milwaukee, Wisconsin
| | - Jan C. Peeken
- Department of Radiation Oncology, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
- Institute of Innovative Radiation therapy, Department of Radiation Sciences, Helmholtz Zentrum München, Neuherberg, Germany
- Deutsches Konsortium für Translationale Krebsforschung, Munich, Germany
| | - Meghan W. Macomber
- Department of Radiation Oncology, University of Washington, Seattle, Washington
| | - Tobias R. Chapman
- Beth Israel Deaconess Medical Center, Department of Radiation Oncology, Harvard Medical School, Boston, Massachusetts
| | - Michael N. Hoff
- Department of Radiology, University of Washington, Seattle, Washington
| | - Edward Y. Kim
- Department of Radiation Oncology, University of Washington, Seattle, Washington
| | - Seth M. Pollack
- Clinical Research Division, Fred Hutchinson Cancer Research Center, Seattle, Washington
- Division of Medical Oncology, University of Washington, Seattle, Washington
| | - Stephanie E. Combs
- Department of Radiation Oncology, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Matthew J. Nyflot
- Department of Radiation Oncology, University of Washington, Seattle, Washington
- Department of Radiology, University of Washington, Seattle, Washington
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