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Bontempi D, Nuernberg L, Pai S, Krishnaswamy D, Thiriveedhi V, Hosny A, Mak RH, Farahani K, Kikinis R, Fedorov A, Aerts HJWL. End-to-end reproducible AI pipelines in radiology using the cloud. Nat Commun 2024; 15:6931. [PMID: 39138215 PMCID: PMC11322541 DOI: 10.1038/s41467-024-51202-2] [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: 07/05/2023] [Accepted: 07/30/2024] [Indexed: 08/15/2024] Open
Abstract
Artificial intelligence (AI) algorithms hold the potential to revolutionize radiology. However, a significant portion of the published literature lacks transparency and reproducibility, which hampers sustained progress toward clinical translation. Although several reporting guidelines have been proposed, identifying practical means to address these issues remains challenging. Here, we show the potential of cloud-based infrastructure for implementing and sharing transparent and reproducible AI-based radiology pipelines. We demonstrate end-to-end reproducibility from retrieving cloud-hosted data, through data pre-processing, deep learning inference, and post-processing, to the analysis and reporting of the final results. We successfully implement two distinct use cases, starting from recent literature on AI-based biomarkers for cancer imaging. Using cloud-hosted data and computing, we confirm the findings of these studies and extend the validation to previously unseen data for one of the use cases. Furthermore, we provide the community with transparent and easy-to-extend examples of pipelines impactful for the broader oncology field. Our approach demonstrates the potential of cloud resources for implementing, sharing, and using reproducible and transparent AI pipelines, which can accelerate the translation into clinical solutions.
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Affiliation(s)
- Dennis Bontempi
- Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Boston, MA, USA
- Radiology and Nuclear Medicine, CARIM & GROW, Maastricht University, Maastricht, The Netherlands
- Department of Radiation Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA
| | - Leonard Nuernberg
- Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Boston, MA, USA
- Radiology and Nuclear Medicine, CARIM & GROW, Maastricht University, Maastricht, The Netherlands
- Department of Radiation Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA
| | - Suraj Pai
- Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Boston, MA, USA
- Radiology and Nuclear Medicine, CARIM & GROW, Maastricht University, Maastricht, The Netherlands
- Department of Radiation Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA
| | - Deepa Krishnaswamy
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Vamsi Thiriveedhi
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Ahmed Hosny
- Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Boston, MA, USA
- Department of Radiation Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA
| | - Raymond H Mak
- Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Boston, MA, USA
- Department of Radiation Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA
| | - Keyvan Farahani
- National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, USA
| | - Ron Kikinis
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Andrey Fedorov
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Hugo J W L Aerts
- Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Boston, MA, USA.
- Radiology and Nuclear Medicine, CARIM & GROW, Maastricht University, Maastricht, The Netherlands.
- Department of Radiation Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA.
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2
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Jacob AM, Lindemann AF, Wagenpfeil J, Geiger S, Layer YC, Salam B, Panahabadi S, Kurt D, Wintergerst MWM, Schildberg FA, Kuetting D, Attenberger UI, Abdullah Z, Böhner AMC. Autofluorescence-based tissue characterization enhances clinical prospects of light-sheet-microscopy. Sci Rep 2024; 14:18033. [PMID: 39098935 PMCID: PMC11298517 DOI: 10.1038/s41598-024-67366-2] [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: 11/28/2023] [Accepted: 07/10/2024] [Indexed: 08/06/2024] Open
Abstract
Light sheet fluorescence microscopy (LSFM) is a transformative imaging method that enables the visualization of non-dissected specimen in real-time 3D. Optical clearing of tissues is essential for LSFM, typically employing toxic solvents. Here, we test the applicability of a non-hazardous alternative, ethyl cinnamate (ECi). We comprehensively characterized autofluorescence (AF) spectra in diverse murine tissues-ocular globe, knee, and liver-employing LSFM under various excitation wavelengths (405-785 nm) to test the feasibility of unstained samples for diagnostic purposes, in particular regarding percutaneous biopsies, as they constitute to most harvested type of tissue sample in clinical routine. Ocular globe structures were best discerned with 640 nm excitation. Knee tissue showed complex variation in AF spectra variation influenced by tissue depth and structure. Liver exhibited a unique AF pattern, likely linked to vasculature. Hepatic tissue samples were used to demonstrate the compatibility of our protocol for antibody staining. Furthermore, we employed machine learning to augment raw images and segment liver structures based on AF spectra. Radiologists rated representative samples transferred to the clinical assessment software. Learning-generated images scored highest in quality. Additionally, we investigated an actual murine biopsy. Our study pioneers the application of AF spectra for tissue characterization and diagnostic potential of optically cleared unstained percutaneous biopsies, contributing to the clinical translation of LSFM.
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Affiliation(s)
- Alice M Jacob
- Institute of Molecular Medicine and Experimental Immunology, Medical Faculty, University Hospital Bonn, University of Bonn, Venusberg-Campus 1, 53127, Bonn, Germany
| | - Anna F Lindemann
- Institute of Molecular Medicine and Experimental Immunology, Medical Faculty, University Hospital Bonn, University of Bonn, Venusberg-Campus 1, 53127, Bonn, Germany
| | - Julia Wagenpfeil
- Clinics for Diagnostic and Interventional Radiology, University Hospital Bonn, Venusberg-Campus 1, 53127, Bonn, Germany
| | - Sergej Geiger
- Clinics for Diagnostic and Interventional Radiology, University Hospital Bonn, Venusberg-Campus 1, 53127, Bonn, Germany
| | - Yannik C Layer
- Clinics for Diagnostic and Interventional Radiology, University Hospital Bonn, Venusberg-Campus 1, 53127, Bonn, Germany
| | - Babak Salam
- Clinics for Diagnostic and Interventional Radiology, University Hospital Bonn, Venusberg-Campus 1, 53127, Bonn, Germany
| | - Sarah Panahabadi
- Clinic for Diagnostic and Interventional Neuroradiology, University Hospital Bonn, Venusberg-Campus 1, 53127, Bonn, Germany
| | - Darius Kurt
- Clinics for Diagnostic and Interventional Radiology, University Hospital Bonn, Venusberg-Campus 1, 53127, Bonn, Germany
| | | | - Frank A Schildberg
- Clinic for Orthopedics and Trauma Surgery, University Hospital Bonn, Venusberg-Campus 1, 53127, Bonn, Germany
| | - Daniel Kuetting
- Clinics for Diagnostic and Interventional Radiology, University Hospital Bonn, Venusberg-Campus 1, 53127, Bonn, Germany
| | - Ulrike I Attenberger
- Clinics for Diagnostic and Interventional Radiology, University Hospital Bonn, Venusberg-Campus 1, 53127, Bonn, Germany
- Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna and General Hospital, Waehringer Guertel 18-20, 1090, Vienna, Austria
| | - Zeinab Abdullah
- Institute of Molecular Medicine and Experimental Immunology, Medical Faculty, University Hospital Bonn, University of Bonn, Venusberg-Campus 1, 53127, Bonn, Germany
| | - Alexander M C Böhner
- Clinics for Diagnostic and Interventional Radiology, University Hospital Bonn, Venusberg-Campus 1, 53127, Bonn, Germany.
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3
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Peeken JC, Etzel L, Tomov T, Münch S, Schüttrumpf L, Shaktour JH, Kiechle J, Knebel C, Schaub SK, Mayr NA, Woodruff HC, Lambin P, Gersing AS, Bernhardt D, Nyflot MJ, Menze B, Combs SE, Navarro F. Development and benchmarking of a Deep Learning-based MRI-guided gross tumor segmentation algorithm for Radiomics analyses in extremity soft tissue sarcomas. Radiother Oncol 2024; 197:110338. [PMID: 38782301 DOI: 10.1016/j.radonc.2024.110338] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2023] [Revised: 05/05/2024] [Accepted: 05/10/2024] [Indexed: 05/25/2024]
Abstract
BACKGROUND Volume of interest (VOI) segmentation is a crucial step for Radiomics analyses and radiotherapy (RT) treatment planning. Because it can be time-consuming and subject to inter-observer variability, we developed and tested a Deep Learning-based automatic segmentation (DLBAS) algorithm to reproducibly predict the primary gross tumor as VOI for Radiomics analyses in extremity soft tissue sarcomas (STS). METHODS A DLBAS algorithm was trained on a cohort of 157 patients and externally tested on an independent cohort of 87 patients using contrast-enhanced MRI. Manual tumor delineations by a radiation oncologist served as ground truths (GTs). A benchmark study with 20 cases from the test cohort compared the DLBAS predictions against manual VOI segmentations of two residents (ERs) and clinical delineations of two radiation oncologists (ROs). The ROs rated DLBAS predictions regarding their direct applicability. RESULTS The DLBAS achieved a median dice similarity coefficient (DSC) of 0.88 against the GTs in the entire test cohort (interquartile range (IQR): 0.11) and a median DSC of 0.89 (IQR 0.07) and 0.82 (IQR 0.10) in comparison to ERs and ROs, respectively. Radiomics feature stability was high with a median intraclass correlation coefficient of 0.97, 0.95 and 0.94 for GTs, ERs, and ROs, respectively. DLBAS predictions were deemed clinically suitable by the two ROs in 35% and 20% of cases, respectively. CONCLUSION The results demonstrate that the DLBAS algorithm provides reproducible VOI predictions for radiomics feature extraction. Variability remains regarding direct clinical applicability of predictions for RT treatment planning.
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Affiliation(s)
- Jan C Peeken
- Department of Radiation Oncology, Klinikum rechts der Isar, Technical University of Munich (TUM), Munich, Germany; German Consortium for Translational Cancer Research (DKTK), Partner Site Munich, Munich, Germany; Institute of Radiation Medicine (IRM), Helmholtz Zentrum München (HMGU), German Research Center for Environmental Health GmbH, Neuherberg, Germany; Department of Precision Medicine, GROW - School for Oncology and Developmental Biology, Maastricht University, Maastricht, Netherlands
| | - Lucas Etzel
- Department of Radiation Oncology, Klinikum rechts der Isar, Technical University of Munich (TUM), Munich, Germany; German Consortium for Translational Cancer Research (DKTK), Partner Site Munich, Munich, Germany.
| | - Tim Tomov
- Department of Informatics, Technical University of Munich (TUM), Garching, Germany
| | - Stefan Münch
- Department of Radiation Oncology, Klinikum rechts der Isar, Technical University of Munich (TUM), Munich, Germany
| | - Lars Schüttrumpf
- Department of Radiation Oncology, Klinikum rechts der Isar, Technical University of Munich (TUM), Munich, Germany
| | - Julius H Shaktour
- Department of Radiation Oncology, Klinikum rechts der Isar, Technical University of Munich (TUM), Munich, Germany
| | - Johannes Kiechle
- Department of Informatics, Technical University of Munich (TUM), Garching, Germany
| | - Carolin Knebel
- Department of Orthopaedics and Sports Orthopaedics, Klinikum rechts der Isar, Technical University of Munich (TUM), Munich, Germany
| | - Stephanie K Schaub
- Department of Radiation Oncology, University of Washington, Seattle, USA
| | - Nina A Mayr
- College of Human Medicine, Michigan State University, East Lansing, MI, USA
| | - Henry C Woodruff
- Department of Precision Medicine, GROW - School for Oncology and Developmental Biology, Maastricht University, Maastricht, Netherlands; Department of Radiology and Nuclear Imaging, GROW - School for Oncology and Developmental Biology, Maastricht University Medical Centre, the Netherlands
| | - Philippe Lambin
- Department of Precision Medicine, GROW - School for Oncology and Developmental Biology, Maastricht University, Maastricht, Netherlands; Department of Radiology and Nuclear Imaging, GROW - School for Oncology and Developmental Biology, Maastricht University Medical Centre, the Netherlands
| | | | - Denise Bernhardt
- Department of Radiation Oncology, Klinikum rechts der Isar, Technical University of Munich (TUM), Munich, Germany
| | - Matthew J Nyflot
- Department of Radiation Oncology, University of Washington, Seattle, USA; Department of Radiology, University of Washington, Seattle, USA
| | - Bjoern Menze
- Department of Informatics, Technical University of Munich (TUM), Garching, Germany
| | - Stephanie E Combs
- Department of Radiation Oncology, Klinikum rechts der Isar, Technical University of Munich (TUM), Munich, Germany; German Consortium for Translational Cancer Research (DKTK), Partner Site Munich, Munich, Germany; Institute of Radiation Medicine (IRM), Helmholtz Zentrum München (HMGU), German Research Center for Environmental Health GmbH, Neuherberg, Germany
| | - Fernando Navarro
- Department of Informatics, Technical University of Munich (TUM), Garching, Germany
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Camastra C, Pasini G, Stefano A, Russo G, Vescio B, Bini F, Marinozzi F, Augimeri A. Development and Implementation of an Innovative Framework for Automated Radiomics Analysis in Neuroimaging. J Imaging 2024; 10:96. [PMID: 38667994 PMCID: PMC11051015 DOI: 10.3390/jimaging10040096] [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: 04/01/2024] [Revised: 04/11/2024] [Accepted: 04/16/2024] [Indexed: 04/28/2024] Open
Abstract
Radiomics represents an innovative approach to medical image analysis, enabling comprehensive quantitative evaluation of radiological images through advanced image processing and Machine or Deep Learning algorithms. This technique uncovers intricate data patterns beyond human visual detection. Traditionally, executing a radiomic pipeline involves multiple standardized phases across several software platforms. This could represent a limit that was overcome thanks to the development of the matRadiomics application. MatRadiomics, a freely available, IBSI-compliant tool, features its intuitive Graphical User Interface (GUI), facilitating the entire radiomics workflow from DICOM image importation to segmentation, feature selection and extraction, and Machine Learning model construction. In this project, an extension of matRadiomics was developed to support the importation of brain MRI images and segmentations in NIfTI format, thus extending its applicability to neuroimaging. This enhancement allows for the seamless execution of radiomic pipelines within matRadiomics, offering substantial advantages to the realm of neuroimaging.
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Affiliation(s)
- Chiara Camastra
- Department of Mechanical and Aerospace Engineering, Sapienza University of Rome, Eudossiana 18, 00184 Rome, Italy; (G.P.); (F.B.); (F.M.)
| | - Giovanni Pasini
- Department of Mechanical and Aerospace Engineering, Sapienza University of Rome, Eudossiana 18, 00184 Rome, Italy; (G.P.); (F.B.); (F.M.)
- Institute of Molecular Bioimaging and Physiology, National Research Council (IBFM-CNR), 90015 Cefalù and 88100 Catanzaro, Italy; (A.S.); (G.R.); or (B.V.)
| | - Alessandro Stefano
- Institute of Molecular Bioimaging and Physiology, National Research Council (IBFM-CNR), 90015 Cefalù and 88100 Catanzaro, Italy; (A.S.); (G.R.); or (B.V.)
| | - Giorgio Russo
- Institute of Molecular Bioimaging and Physiology, National Research Council (IBFM-CNR), 90015 Cefalù and 88100 Catanzaro, Italy; (A.S.); (G.R.); or (B.V.)
| | - Basilio Vescio
- Institute of Molecular Bioimaging and Physiology, National Research Council (IBFM-CNR), 90015 Cefalù and 88100 Catanzaro, Italy; (A.S.); (G.R.); or (B.V.)
- Biotecnomed SCARL, Campus Universitario di Germaneto, Viale Europa, 88100 Catanzaro, Italy;
| | - Fabiano Bini
- Department of Mechanical and Aerospace Engineering, Sapienza University of Rome, Eudossiana 18, 00184 Rome, Italy; (G.P.); (F.B.); (F.M.)
| | - Franco Marinozzi
- Department of Mechanical and Aerospace Engineering, Sapienza University of Rome, Eudossiana 18, 00184 Rome, Italy; (G.P.); (F.B.); (F.M.)
| | - Antonio Augimeri
- Biotecnomed SCARL, Campus Universitario di Germaneto, Viale Europa, 88100 Catanzaro, Italy;
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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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Pasini G, Russo G, Mantarro C, Bini F, Richiusa S, Morgante L, Comelli A, Russo GI, Sabini MG, Cosentino S, Marinozzi F, Ippolito M, Stefano A. A Critical Analysis of the Robustness of Radiomics to Variations in Segmentation Methods in 18F-PSMA-1007 PET Images of Patients Affected by Prostate Cancer. Diagnostics (Basel) 2023; 13:3640. [PMID: 38132224 PMCID: PMC10743045 DOI: 10.3390/diagnostics13243640] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2023] [Revised: 11/29/2023] [Accepted: 12/08/2023] [Indexed: 12/23/2023] Open
Abstract
BACKGROUND Radiomics shows promising results in supporting the clinical decision process, and much effort has been put into its standardization, thus leading to the Imaging Biomarker Standardization Initiative (IBSI), that established how radiomics features should be computed. However, radiomics still lacks standardization and many factors, such as segmentation methods, limit study reproducibility and robustness. AIM We investigated the impact that three different segmentation methods (manual, thresholding and region growing) have on radiomics features extracted from 18F-PSMA-1007 Positron Emission Tomography (PET) images of 78 patients (43 Low Risk, 35 High Risk). Segmentation was repeated for each patient, thus leading to three datasets of segmentations. Then, feature extraction was performed for each dataset, and 1781 features (107 original, 930 Laplacian of Gaussian (LoG) features, 744 wavelet features) were extracted. Feature robustness and reproducibility were assessed through the intra class correlation coefficient (ICC) to measure agreement between the three segmentation methods. To assess the impact that the three methods had on machine learning models, feature selection was performed through a hybrid descriptive-inferential method, and selected features were given as input to three classifiers, K-Nearest Neighbors (KNN), Support Vector Machines (SVM), Linear Discriminant Analysis (LDA), Random Forest (RF), AdaBoost and Neural Networks (NN), whose performance in discriminating between low-risk and high-risk patients have been validated through 30 times repeated five-fold cross validation. CONCLUSIONS Our study showed that segmentation methods influence radiomics features and that Shape features were the least reproducible (average ICC: 0.27), while GLCM features the most reproducible. Moreover, feature reproducibility changed depending on segmentation type, resulting in 51.18% of LoG features exhibiting excellent reproducibility (range average ICC: 0.68-0.87) and 47.85% of wavelet features exhibiting poor reproducibility that varied between wavelet sub-bands (range average ICC: 0.34-0.80) and resulted in the LLL band showing the highest average ICC (0.80). Finally, model performance showed that region growing led to the highest accuracy (74.49%), improved sensitivity (84.38%) and AUC (79.20%) in contrast with manual segmentation.
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Affiliation(s)
- Giovanni Pasini
- Department of Mechanical and Aerospace Engineering, Sapienza University of Rome, Eudossiana 18, 00184 Rome, Italy; (G.P.); (L.M.); (F.M.)
- Institute of Molecular Bioimaging and Physiology, National Research Council (IBFM-CNR), Contrada, Pietrapollastra-Pisciotto, 90015 Cefalù, Italy; (G.R.); (S.R.); (A.C.); (A.S.)
| | - Giorgio Russo
- Institute of Molecular Bioimaging and Physiology, National Research Council (IBFM-CNR), Contrada, Pietrapollastra-Pisciotto, 90015 Cefalù, Italy; (G.R.); (S.R.); (A.C.); (A.S.)
- National Laboratory of South, National Institute for Nuclear Physics (LNS-INFN), 95125 Catania, Italy
| | - Cristina Mantarro
- Nuclear Medicine Department, Cannizzaro Hospital, 95125 Catania, Italy; (C.M.); (S.C.); (M.I.)
| | - Fabiano Bini
- Department of Mechanical and Aerospace Engineering, Sapienza University of Rome, Eudossiana 18, 00184 Rome, Italy; (G.P.); (L.M.); (F.M.)
| | - Selene Richiusa
- Institute of Molecular Bioimaging and Physiology, National Research Council (IBFM-CNR), Contrada, Pietrapollastra-Pisciotto, 90015 Cefalù, Italy; (G.R.); (S.R.); (A.C.); (A.S.)
| | - Lucrezia Morgante
- Department of Mechanical and Aerospace Engineering, Sapienza University of Rome, Eudossiana 18, 00184 Rome, Italy; (G.P.); (L.M.); (F.M.)
| | - Albert Comelli
- Institute of Molecular Bioimaging and Physiology, National Research Council (IBFM-CNR), Contrada, Pietrapollastra-Pisciotto, 90015 Cefalù, Italy; (G.R.); (S.R.); (A.C.); (A.S.)
- Ri.MED Foundation, Via Bandiera 11, 90133 Palermo, Italy
| | - Giorgio Ivan Russo
- Department of Surgery, Urology Section, University of Catania, 95125 Catania, Italy;
| | | | - Sebastiano Cosentino
- Nuclear Medicine Department, Cannizzaro Hospital, 95125 Catania, Italy; (C.M.); (S.C.); (M.I.)
| | - Franco Marinozzi
- Department of Mechanical and Aerospace Engineering, Sapienza University of Rome, Eudossiana 18, 00184 Rome, Italy; (G.P.); (L.M.); (F.M.)
| | - Massimo Ippolito
- Nuclear Medicine Department, Cannizzaro Hospital, 95125 Catania, Italy; (C.M.); (S.C.); (M.I.)
| | - Alessandro Stefano
- Institute of Molecular Bioimaging and Physiology, National Research Council (IBFM-CNR), Contrada, Pietrapollastra-Pisciotto, 90015 Cefalù, Italy; (G.R.); (S.R.); (A.C.); (A.S.)
- National Laboratory of South, National Institute for Nuclear Physics (LNS-INFN), 95125 Catania, Italy
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7
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Tay SH, Zhang X, Chua MLK. Radiomics in precision oncology: hype or ludum mutante. BMC Med 2023; 21:465. [PMID: 38017483 PMCID: PMC10683192 DOI: 10.1186/s12916-023-03165-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/06/2023] [Accepted: 11/08/2023] [Indexed: 11/30/2023] Open
Affiliation(s)
- Shi Hui Tay
- Precision Radiotherapeutics Oncology Programme, Division of Medical Sciences, National Cancer Centre Singapore, 30 Hospital Boulevard, Singapore, 168583, Singapore
| | - Xin Zhang
- Precision Radiotherapeutics Oncology Programme, Division of Medical Sciences, National Cancer Centre Singapore, 30 Hospital Boulevard, Singapore, 168583, Singapore
- Radiation Oncology Center, Chongqing University Cancer Hospital, Chongqing, China
| | - Melvin L K Chua
- Precision Radiotherapeutics Oncology Programme, Division of Medical Sciences, National Cancer Centre Singapore, 30 Hospital Boulevard, Singapore, 168583, Singapore.
- Department of Head and Neck and Thoracic Radiation Oncology, National Cancer Centre Singapore, 30 Hospital Boulevard, Singapore, 168583, Singapore.
- Oncology Academic Clinical Programme, Duke-NUS Medical School, Singapore, Singapore.
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