1
|
Wang FA, Li Y, Zeng T. Deep Learning of radiology-genomics integration for computational oncology: A mini review. Comput Struct Biotechnol J 2024; 23:2708-2716. [PMID: 39035833 PMCID: PMC11260400 DOI: 10.1016/j.csbj.2024.06.019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2024] [Revised: 06/18/2024] [Accepted: 06/18/2024] [Indexed: 07/23/2024] Open
Abstract
In the field of computational oncology, patient status is often assessed using radiology-genomics, which includes two key technologies and data, such as radiology and genomics. Recent advances in deep learning have facilitated the integration of radiology-genomics data, and even new omics data, significantly improving the robustness and accuracy of clinical predictions. These factors are driving artificial intelligence (AI) closer to practical clinical applications. In particular, deep learning models are crucial in identifying new radiology-genomics biomarkers and therapeutic targets, supported by explainable AI (xAI) methods. This review focuses on recent developments in deep learning for radiology-genomics integration, highlights current challenges, and outlines some research directions for multimodal integration and biomarker discovery of radiology-genomics or radiology-omics that are urgently needed in computational oncology.
Collapse
Affiliation(s)
- Feng-ao Wang
- Key Laboratory of Systems Health Science of Zhejiang Province, School of Life Science, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou 310024, China
| | - Yixue Li
- Key Laboratory of Systems Health Science of Zhejiang Province, School of Life Science, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou 310024, China
- Guangzhou National Laboratory, Guangzhou, China
- GMU-GIBH Joint School of Life Sciences, The Guangdong-Hong Kong-Macau Joint Laboratory for Cell Fate Regulation and Diseases, Guangzhou Laboratory, Guangzhou Medical University, Guangzhou, China
| | - Tao Zeng
- Guangzhou National Laboratory, Guangzhou, China
- GMU-GIBH Joint School of Life Sciences, The Guangdong-Hong Kong-Macau Joint Laboratory for Cell Fate Regulation and Diseases, Guangzhou Laboratory, Guangzhou Medical University, Guangzhou, China
| |
Collapse
|
2
|
Hinterwimmer F, Serena RS, Wilhelm N, Breden S, Consalvo S, Seidl F, Juestel D, Burgkart RHH, Woertler K, von Eisenhart-Rothe R, Neumann J, Rueckert D. Recommender-based bone tumour classification with radiographs-a link to the past. Eur Radiol 2024; 34:6629-6638. [PMID: 38488971 DOI: 10.1007/s00330-024-10672-0] [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: 10/20/2023] [Revised: 01/16/2024] [Accepted: 02/05/2024] [Indexed: 03/17/2024]
Abstract
OBJECTIVES To develop an algorithm to link undiagnosed patients to previous patient histories based on radiographs, and simultaneous classification of multiple bone tumours to enable early and specific diagnosis. MATERIALS AND METHODS For this retrospective study, data from 2000 to 2021 were curated from our database by two orthopaedic surgeons, a radiologist and a data scientist. Patients with complete clinical and pre-therapy radiographic data were eligible. To ensure feasibility, the ten most frequent primary tumour entities, confirmed histologically or by tumour board decision, were included. We implemented a ResNet and transformer model to establish baseline results. Our method extracts image features using deep learning and then clusters the k most similar images to the target image using a hash-based nearest-neighbour recommender approach that performs simultaneous classification by majority voting. The results were evaluated with precision-at-k, accuracy, precision and recall. Discrete parameters were described by incidence and percentage ratios. For continuous parameters, based on a normality test, respective statistical measures were calculated. RESULTS Included were data from 809 patients (1792 radiographs; mean age 33.73 ± 18.65, range 3-89 years; 443 men), with Osteochondroma (28.31%) and Ewing sarcoma (1.11%) as the most and least common entities, respectively. The dataset was split into training (80%) and test subsets (20%). For k = 3, our model achieved the highest mean accuracy, precision and recall (92.86%, 92.86% and 34.08%), significantly outperforming state-of-the-art models (54.10%, 55.57%, 19.85% and 62.80%, 61.33%, 23.05%). CONCLUSION Our novel approach surpasses current models in tumour classification and links to past patient data, leveraging expert insights. CLINICAL RELEVANCE STATEMENT The proposed algorithm could serve as a vital support tool for clinicians and general practitioners with limited experience in bone tumour classification by identifying similar cases and classifying bone tumour entities. KEY POINTS • Addressed accurate bone tumour classification using radiographic features. • Model achieved 92.86%, 92.86% and 34.08% mean accuracy, precision and recall, respectively, significantly surpassing state-of-the-art models. • Enhanced diagnosis by integrating prior expert patient assessments.
Collapse
Affiliation(s)
- Florian Hinterwimmer
- Department of Orthopaedics and Sports Orthopaedics, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany.
- Institute for AI and Informatics in Medicine, Technical University of Munich, Munich, Germany.
| | - Ricardo Smits Serena
- Department of Orthopaedics and Sports Orthopaedics, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
- Institute for AI and Informatics in Medicine, Technical University of Munich, Munich, Germany
| | - Nikolas Wilhelm
- Department of Orthopaedics and Sports Orthopaedics, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Sebastian Breden
- Department of Orthopaedics and Sports Orthopaedics, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Sarah Consalvo
- Department of Orthopaedics and Sports Orthopaedics, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Fritz Seidl
- Institute of Biological and Medical Imaging, Helmholtz Zentrum München, Neuherberg, Germany
| | - Dominik Juestel
- Institute of Biological and Medical Imaging, Helmholtz Zentrum München, Neuherberg, Germany
- Institute at Helmholtz: Institute of Computational Biology, Oberschleißheim, Germany
- Chair of Biological Imaging at the Central Institute for Translational Cancer Research (TranslaTUM), School of Medicine, Technical University of Munich, Munich, Germany
| | - Rainer H H Burgkart
- Department of Orthopaedics and Sports Orthopaedics, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Klaus Woertler
- Musculoskeletal Radiology Section, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Ruediger von Eisenhart-Rothe
- Department of Orthopaedics and Sports Orthopaedics, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Jan Neumann
- Musculoskeletal Radiology Section, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Daniel Rueckert
- Institute for AI and Informatics in Medicine, Technical University of Munich, Munich, Germany
| |
Collapse
|
3
|
Usuff R, Kothandapani S, Rangan R, Dhatchnamurthy S. Enhancing radiographic image interpretation: WARES-PRS model for knee bone tumour detection. NETWORK (BRISTOL, ENGLAND) 2024:1-31. [PMID: 38932464 DOI: 10.1080/0954898x.2024.2357660] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/05/2024] [Accepted: 05/15/2024] [Indexed: 06/28/2024]
Abstract
The early diagnosis of tumour is significant in biomedical research field to lower the severity level and restrict the process extension from cancer. Moreover, the detection of early sign of cancer is undertaken with extensive research efforts that dedicated to the disclosure and recognition of tumours. However, the limited data size as well as diverse appearance of images lowered the detection performance and failed to detect complex stage of tumour. So to solve these issues, a Weighted Adaptive Random Ensemble Support Vector-based Partial Reinforcement Search (WARES-PRS) algorithm is proposed that detected bone lesions accurately and also predicted the severity level stage efficiently. Further, the detection is performed with varied stages to diminish the presence of noise and undertaken effective classification. The performance is validated with CNUH dataset that enhanced image pre-processing tasks. Despite the proposed method uncover the mutual relationships between each pixel's local texture and the overall image's global context. The detection and classification efficiency is validated with various measures and the experimental results revealed that the detection accuracy is enhanced for the proposed approach by 98.5%. The outcomes of our study have exhibited a substantial contribution to assisting physicians in the detection of knee bone tumours.
Collapse
Affiliation(s)
- Rahamathunnisa Usuff
- School of Computer Science Engineering and Information Systems, Vellore Institute of Technology, Vellore, Tamil Nadu, India
| | - Sudhakar Kothandapani
- Department of Computer Science & Engineering, Madanapalle Institute of Technology & Science, Madanapalle, Andhra Pradesh, India
| | - Rajesh Rangan
- Department of Computer Science and Engineering, School of Computing, Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology, Avadi, Chennai, India
| | - Saravanan Dhatchnamurthy
- Department of Computer Science and Engineering, School of Computing, Sathyabama Institute of Science and Technology, Chennai, India
| |
Collapse
|
4
|
Giraud P, Bibault JE. Artificial intelligence in radiotherapy: Current applications and future trends. Diagn Interv Imaging 2024:S2211-5684(24)00137-2. [PMID: 38918124 DOI: 10.1016/j.diii.2024.06.001] [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: 05/22/2024] [Revised: 05/31/2024] [Accepted: 06/01/2024] [Indexed: 06/27/2024]
Abstract
Radiation therapy has dramatically changed with the advent of computed tomography and intensity modulation. This added complexity to the workflow but allowed for more precise and reproducible treatment. As a result, these advances required the accurate delineation of many more volumes, raising questions about how to delineate them, in a uniform manner across centers. Then, as computing power improved, reverse planning became possible and three-dimensional dose distributions could be generated. Artificial intelligence offers the opportunity to make such workflow more efficient while increasing practice homogeneity. Many artificial intelligence-based tools are being implemented in routine practice to increase efficiency, reduce workload and improve homogeneity of treatments. Data retrieved from this workflow could be combined with clinical data and omic data to develop predictive tools to support clinical decision-making process. Such predictive tools are at the stage of proof-of-concept and need to be explainatory, prospectively validated, and based on large and multicenter cohorts. Nevertheless, they could bridge the gap to personalized radiation oncology, by personalizing oncologic strategies, dose prescriptions to tumor volumes and dose constraints to organs at risk.
Collapse
Affiliation(s)
- Paul Giraud
- INSERM UMR 1138, Centre de Recherche des Cordeliers, 75006 Paris, France; Department of Radiotherapy, Hôpital Européen Georges Pompidou, AP-HP, 75015 Paris, France; Université Paris Cité, Faculté de Médecine, 75006, Paris, France.
| | - Jean-Emmanuel Bibault
- Department of Radiotherapy, Hôpital Européen Georges Pompidou, AP-HP, 75015 Paris, France; Université Paris Cité, Faculté de Médecine, 75006, Paris, France
| |
Collapse
|
5
|
Wang S, Sun M, Sun J, Wang Q, Wang G, Wang X, Meng X, Wang Z, Yu H. Advancing musculoskeletal tumor diagnosis: Automated segmentation and predictive classification using deep learning and radiomics. Comput Biol Med 2024; 175:108502. [PMID: 38678943 DOI: 10.1016/j.compbiomed.2024.108502] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2024] [Revised: 03/18/2024] [Accepted: 04/21/2024] [Indexed: 05/01/2024]
Abstract
OBJECTIVES Musculoskeletal (MSK) tumors, given their high mortality rate and heterogeneity, necessitate precise examination and diagnosis to guide clinical treatment effectively. Magnetic resonance imaging (MRI) is pivotal in detecting MSK tumors, as it offers exceptional image contrast between bone and soft tissue. This study aims to enhance the speed of detection and the diagnostic accuracy of MSK tumors through automated segmentation and grading utilizing MRI. MATERIALS AND METHODS The research included 170 patients (mean age, 58 years ±12 (standard deviation), 84 men) with MSK lesions, who underwent MRI scans from April 2021 to May 2023. We proposed a deep learning (DL) segmentation model MSAPN based on multi-scale attention and pixel-level reconstruction, and compared it with existing algorithms. Using MSAPN-segmented lesions to extract their radiomic features for the benign and malignant classification of tumors. RESULTS Compared to the most advanced segmentation algorithms, MSAPN demonstrates better performance. The Dice similarity coefficients (DSC) are 0.871 and 0.815 in the testing set and independent validation set, respectively. The radiomics model for classifying benign and malignant lesions achieves an accuracy of 0.890. Moreover, there is no statistically significant difference between the radiomics model based on manual segmentation and MSAPN segmentation. CONCLUSION This research contributes to the advancement of MSK tumor diagnosis through automated segmentation and predictive classification. The integration of DL algorithms and radiomics shows promising results, and the visualization analysis of feature maps enhances clinical interpretability.
Collapse
Affiliation(s)
- Shuo Wang
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, 300072, China; State Key Laboratory of Advanced Medical Materials and Devices, Tianjin University, Tianjin, 300072, China.
| | - Man Sun
- Radiology Department, Tianjin University Tianjin Hospital, Tianjin, 300299, China.
| | - Jinglai Sun
- The School of Precision Instrument and Opto-Electronics Engineering, Tianjin University, Tianjin, 300072, China.
| | - Qingsong Wang
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, 300072, China.
| | - Guangpu Wang
- The School of Precision Instrument and Opto-Electronics Engineering, Tianjin University, Tianjin, 300072, China.
| | - Xiaolin Wang
- The School of Precision Instrument and Opto-Electronics Engineering, Tianjin University, Tianjin, 300072, China.
| | - Xianghong Meng
- Radiology Department, Tianjin University Tianjin Hospital, Tianjin, 300299, China.
| | - Zhi Wang
- Radiology Department, Tianjin University Tianjin Hospital, Tianjin, 300299, China.
| | - Hui Yu
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, 300072, China; State Key Laboratory of Advanced Medical Materials and Devices, Tianjin University, Tianjin, 300072, China; The School of Precision Instrument and Opto-Electronics Engineering, Tianjin University, Tianjin, 300072, China.
| |
Collapse
|
6
|
Caloro E, Gnocchi G, Quarrella C, Ce M, Carrafiello G, Cellina M. Artificial Intelligence in Bone Metastasis Imaging: Recent Progresses from Diagnosis to Treatment - A Narrative Review. Crit Rev Oncog 2024; 29:77-90. [PMID: 38505883 DOI: 10.1615/critrevoncog.2023050470] [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
The introduction of artificial intelligence (AI) represents an actual revolution in the radiological field, including bone lesion imaging. Bone lesions are often detected both in healthy and oncological patients and the differential diagnosis can be challenging but decisive, because it affects the diagnostic and therapeutic process, especially in case of metastases. Several studies have already demonstrated how the integration of AI-based tools in the current clinical workflow could bring benefits to patients and to healthcare workers. AI technologies could help radiologists in early bone metastases detection, increasing the diagnostic accuracy and reducing the overdiagnosis and the number of unnecessary deeper investigations. In addition, radiomics and radiogenomics approaches could go beyond the qualitative features, visible to the human eyes, extrapolating cancer genomic and behavior information from imaging, in order to plan a targeted and personalized treatment. In this article, we want to provide a comprehensive summary of the most promising AI applications in bone metastasis imaging and their role from diagnosis to treatment and prognosis, including the analysis of future challenges and new perspectives.
Collapse
Affiliation(s)
- Elena Caloro
- Università degli studi di Milano, via Festa del Perdono, 7, 20122 Milan, Italy
| | - Giulia Gnocchi
- Postgraduation School in Radiodiagnostics, Università degli Studi di Milano, Via Festa del Perdono, 7, 20122 Milan, Italy
| | - Cettina Quarrella
- Postgraduation School in Radiodiagnostics, Università degli Studi di Milano, Via Festa del Perdono, 7, 20122 Milan, Italy
| | - Maurizio Ce
- Postgraduation School in Radiodiagnostics, Università degli Studi di Milano, 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
| |
Collapse
|
7
|
Crombé A, Spinnato P, Italiano A, Brisse HJ, Feydy A, Fadli D, Kind M. Radiomics and artificial intelligence for soft-tissue sarcomas: Current status and perspectives. Diagn Interv Imaging 2023; 104:567-583. [PMID: 37802753 DOI: 10.1016/j.diii.2023.09.005] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2023] [Revised: 09/18/2023] [Accepted: 09/19/2023] [Indexed: 10/08/2023]
Abstract
This article proposes a summary of the current status of the research regarding the use of radiomics and artificial intelligence to improve the radiological assessment of patients with soft tissue sarcomas (STS), a heterogeneous group of rare and ubiquitous mesenchymal malignancies. After a first part explaining the principle of radiomics approaches, from raw image post-processing to extraction of radiomics features mined with unsupervised and supervised machine-learning algorithms, and the current research involving deep learning algorithms in STS, especially convolutional neural networks, this review details their main research developments since the formalisation of 'radiomics' in oncologic imaging in 2010. This review focuses on CT and MRI and does not involve ultrasonography. Radiomics and deep radiomics have been successfully applied to develop predictive models to discriminate between benign soft-tissue tumors and STS, to predict the histologic grade (i.e., the most important prognostic marker of STS), the response to neoadjuvant chemotherapy and/or radiotherapy, and the patients' survivals and probability for presenting distant metastases. The main findings, limitations and expectations are discussed for each of these outcomes. Overall, after a first decade of publications emphasizing the potential of radiomics through retrospective proof-of-concept studies, almost all positive but with heterogeneous and often non-replicable methods, radiomics is now at a turning point in order to provide robust demonstrations of its clinical impact through open-science, independent databases, and application of good and standardized practices in radiomics such as those provided by the Image Biomarker Standardization Initiative, without forgetting innovative research paths involving other '-omics' data to better understand the relationships between imaging of STS, gene-expression profiles and tumor microenvironment.
Collapse
Affiliation(s)
- Amandine Crombé
- Department of Radiology, Pellegrin University Hospital, 33000 Bordeaux, France; Department of Oncologic Imaging, Bergonié Institute, 33076 Bordeaux, France; 'Sarcotarget' team, BRIC INSERM U1312 and Bordeaux University, 33000 Bordeaux France.
| | - Paolo Spinnato
- Diagnostic and Interventional Radiology, IRCCS Istituto Ortopedico Rizzoli, Bologna 40136, Italy
| | | | | | - Antoine Feydy
- Department of Radiology, Hopital Cochin-AP-HP, 75014 Paris, France; Université Paris Cité, Faculté de Médecine, 75006 Paris, France
| | - David Fadli
- Department of Radiology, Pellegrin University Hospital, 33000 Bordeaux, France
| | - Michèle Kind
- Department of Oncologic Imaging, Bergonié Institute, 33076 Bordeaux, France
| |
Collapse
|
8
|
Gondim Teixeira PA, Lemore A, Vogt N, Oster J, Hossu G, Gillet R, Blum A. Initial Evaluation of Focal Bone Lesions: How Do We Do It? Semin Musculoskelet Radiol 2023; 27:471-479. [PMID: 37748471 DOI: 10.1055/s-0043-1769775] [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: 09/27/2023]
Abstract
Focal bone lesions are frequent, and management greatly depends on the characteristics of their images. After briefly discussing the required work-up, we analyze the most relevant imaging signs for assessing potential aggressiveness. We also describe the imaging aspects of the various types of lesion matrices and their clinical implications.
Collapse
Affiliation(s)
- Pedro Augusto Gondim Teixeira
- Guilloz Imaging Department, University Hospital Center of Nancy, Central Hospital, Nancy Cedex, France
- Université de Lorraine, INSERM, Laboratoire d'Imagerie Diagnostic et Interventionnelle - IADI, Nancy, France
| | - Astrée Lemore
- Guilloz Imaging Department, University Hospital Center of Nancy, Central Hospital, Nancy Cedex, France
- Université de Lorraine, INSERM, Laboratoire d'Imagerie Diagnostic et Interventionnelle - IADI, Nancy, France
| | - Nora Vogt
- Université de Lorraine, INSERM, Laboratoire d'Imagerie Diagnostic et Interventionnelle - IADI, Nancy, France
| | - Julien Oster
- Université de Lorraine, INSERM, Laboratoire d'Imagerie Diagnostic et Interventionnelle - IADI, Nancy, France
| | - Gabriela Hossu
- Université de Lorraine, INSERM, Laboratoire d'Imagerie Diagnostic et Interventionnelle - IADI, Nancy, France
| | - Romain Gillet
- Guilloz Imaging Department, University Hospital Center of Nancy, Central Hospital, Nancy Cedex, France
- Université de Lorraine, INSERM, Laboratoire d'Imagerie Diagnostic et Interventionnelle - IADI, Nancy, France
| | - Alain Blum
- Guilloz Imaging Department, University Hospital Center of Nancy, Central Hospital, Nancy Cedex, France
| |
Collapse
|
9
|
Bordner A, Aouad T, Medina CL, Yang S, Molto A, Talbot H, Dougados M, Feydy A. A deep learning model for the diagnosis of sacroiliitis according to Assessment of SpondyloArthritis International Society classification criteria with magnetic resonance imaging. Diagn Interv Imaging 2023:S2211-5684(23)00056-6. [PMID: 37012131 DOI: 10.1016/j.diii.2023.03.008] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2022] [Revised: 02/28/2023] [Accepted: 03/17/2023] [Indexed: 04/05/2023]
Abstract
PURPOSE The purpose of this study was to develop and evaluate a deep learning model to detect bone marrow edema (BME) in sacroiliac joints and predict the MRI Assessment of SpondyloArthritis International Society (ASAS) definition of active sacroiliitis in patients with chronic inflammatory back pain. MATERIALS AND METHODS MRI examinations of patients from the French prospective multicenter DESIR cohort (DEvenir des Spondyloarthropathies Indifférenciées Récentes) were used for training, validation and testing. Patients with inflammatory back pain lasting three months to three years were recruited. Test datasets were from MRI follow-ups at five years and ten years. The model was evaluated using an external test dataset from the ASAS cohort. A neuronal network classifier (mask-RCNN) was trained and evaluated for sacroiliac joints detection and BME classification. Diagnostic capabilities of the model to predict ASAS MRI active sacroiliitis (BME in at least two half-slices) were assessed using Matthews correlation coefficient (MCC), sensitivity, specificity, accuracy and AUC. The gold standard was experts' majority decision. RESULTS A total of 256 patients with 362 MRI examinations from the DESIR cohort were included, with 27% meeting the ASAS definition for experts. A total of 178 MRI examinations were used for the training set, 25 for the validation set and 159 for the evaluation set. MCCs for DESIR baseline, 5-years, and 10-years follow-up were 0.90 (n = 53), 0.64 (n = 70), and 0.61 (n = 36), respectively. AUCs for predicting ASAS MRI were 0.98 (95% CI: 0.93-1), 0.90 (95% CI: 0.79-1), and 0.80 (95% CI: 0.62-1), respectively. The ASAS external validation cohort included 47 patients (mean age 36 ± 10 [SD] years; women, 51%) with 19% meeting the ASAS definition. MCC was 0.62, sensitivity 56% (95% CI: 42-70), specificity 100% (95% CI: 100-100) and AUC 0.76 (95% CI: 0.57-0.95). CONCLUSION The deep learning model achieves performance close to those of experts for BME detection in sacroiliac joints and determination of active sacroiliitis according to the ASAS definition.
Collapse
Affiliation(s)
- Adrien Bordner
- Sorbonne Médecine Université, 75013 Paris, France; Department of Radiology, Hôpital Cochin, APHP, 75014 Paris, France.
| | - Théodore Aouad
- CentraleSupélec, Université Paris-Saclay, Inria, 91190 Gif-sur-Yvette, France
| | - Clementina Lopez Medina
- Department of Rheumatology, Reina Sofia University Hospital, IMIBIC, University of Cordoba, 14004 Cordoba, Spain
| | - Sisi Yang
- Department of Radiology, Hôpital Cochin, APHP, 75014 Paris, France; Université Paris Cité, 75006 Paris, France
| | - Anna Molto
- Department of Rheumatology, Hôpital Cochin, APHP, 75014 Paris, France; INSERM U1153, Clinical Epidemiology and Biostatistics, PRES Sorbonne Paris-Cité, 75004 Paris, France
| | - Hugues Talbot
- CentraleSupélec, Université Paris-Saclay, Inria, 91190 Gif-sur-Yvette, France
| | - Maxime Dougados
- Université Paris Cité, 75006 Paris, France; Department of Rheumatology, Hôpital Cochin, APHP, 75014 Paris, France; INSERM U1153, Clinical Epidemiology and Biostatistics, PRES Sorbonne Paris-Cité, 75004 Paris, France
| | - Antoine Feydy
- Department of Radiology, Hôpital Cochin, APHP, 75014 Paris, France; Université Paris Cité, 75006 Paris, France; INSERM U1153, Clinical Epidemiology and Biostatistics, PRES Sorbonne Paris-Cité, 75004 Paris, France
| |
Collapse
|
10
|
Zerouali M, Parpaleix A, Benbakoura M, Rigault C, Champsaur P, Guenoun D. Automatic deep learning-based assessment of spinopelvic coronal and sagittal alignment. Diagn Interv Imaging 2023:S2211-5684(23)00051-7. [PMID: 36959006 DOI: 10.1016/j.diii.2023.03.003] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2022] [Revised: 02/28/2023] [Accepted: 03/07/2023] [Indexed: 03/25/2023]
Abstract
PURPOSE The purpose of this study was to evaluate an artificial intelligence (AI) solution for estimating coronal and sagittal spinopelvic alignment on conventional uniplanar two-dimensional whole-spine radiograph. MATERIAL AND METHODS This retrospective observational study included 100 patients (35 men, 65 women) with a median age of 14 years (IQR: 13, 15.25; age range: 3-64 years) who underwent conventional uniplanar two-dimensional whole-spine radiograph in standing position between January and July 2022. Ten most commonly used spinopelvic coronal and sagittal parameters were retrospectively measured without AI by a junior radiologist and approved or adjusted by a senior musculoskeletal radiologist to reach final measurements. Final measurements were used as the ground truth to assess AI performance for each parameter. AI performances were estimated using mean absolute errors (MAE), intraclass correlation coefficient (ICCs), and accuracy for selected clinically relevant thresholds. Readers visually classified AI outputs to assess reliability at a patient-level. RESULTS AI solution showed excellent consistency without bias in coronal (ICCs ≥ 0.95; MAE ≤ 2.9° or 1.97 mm) and sagittal (ICCs ≥ 0.85; MAE ≤ 4.4° or 2.7 mm) spinopelvic evaluation, except for kyphosis (ICC = 0.58; MAE = 8.7°). AI accuracy to classify low Cobb angle, severe scoliosis or frontal pelvic asymmetry was 91% (95% CI: 85-96), 99% (95% CI: 97-100) and 94% (95% CI: 89-98), respectively. Overall, AI provided reliable measurements in 72/100 patients (72%). CONCLUSION The AI solution used in this study for combined coronal and sagittal spinopelvic balance assessment provides results consistent with those of a senior musculoskeletal radiologist, and shows potential benefit for reducing workload in future routine implementation.
Collapse
Affiliation(s)
- Mohamed Zerouali
- Department of Radiology, Institute for Locomotion, Sainte-Marguerite Hospital, APHM, 13009 Marseille, France
| | | | | | | | - Pierre Champsaur
- Department of Radiology, Institute for Locomotion, Sainte-Marguerite Hospital, APHM, 13009 Marseille, France; Institute of Movement Sciences (ISM), CNRS, Aix Marseille University, 13005 Marseille, France
| | - Daphné Guenoun
- Department of Radiology, Institute for Locomotion, Sainte-Marguerite Hospital, APHM, 13009 Marseille, France; Institute of Movement Sciences (ISM), CNRS, Aix Marseille University, 13005 Marseille, France.
| |
Collapse
|
11
|
Gondim Teixeira PA. The role of the radiologist in the management of soft-tissue masses: With great power comes great responsibility. Diagn Interv Imaging 2023; 104:205-206. [PMID: 36868902 DOI: 10.1016/j.diii.2023.02.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2023] [Accepted: 02/15/2023] [Indexed: 03/05/2023]
|
12
|
Crombé A, Kind M, Fadli D, Miceli M, Linck PA, Bianchi G, Sambri A, Spinnato P. Soft-tissue sarcoma in adults: Imaging appearances, pitfalls and diagnostic algorithms. Diagn Interv Imaging 2022; 104:207-220. [PMID: 36567193 DOI: 10.1016/j.diii.2022.12.001] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2022] [Revised: 12/08/2022] [Accepted: 12/13/2022] [Indexed: 12/24/2022]
Abstract
This article provides an overview of the current knowledge regarding diagnostic imaging of patients with soft-tissue sarcomas, which is a heterogeneous group of rare mesenchymal malignancies. After an initial contextualization, diagnostic flow-chart based on initial radiological findings of soft-tissue masses (with specific focus on adipocytic soft-tissue tumors [STTs], hemorragic STTs and retroperitoneal STTs) are provided considering relevant results from novel researches, guidelines, and experts' viewpoints, with the aim to help radiologists and clinicians in their practice. Particularly, the central place of sarcoma reference centers in the diagnostic and therapeutic management is highlighted, as well as the pivotal role that radiologists should play to correctly identify patients with soft-tissue sarcoma at the initial stage of the disease. Indications and methods for performing imaging-guided biopsies are also discussed, as well as clues to improve soft-tissue sarcoma grading with conventional and quantitative imaging.
Collapse
Affiliation(s)
- Amandine Crombé
- Department of Musculoskeletal Imaging, Pellegrin University Hospital, Bordeaux 33076, France; Department of Diagnostic and Interventional Oncological Imaging, Institut Bergonié, Regional Comprehensive Cancer of Nouvelle-Aquitaine, Bordeaux 33076, France; Models in Oncology (MONC) Team, INRIA Bordeaux Sud-Ouest, CNRS UMR 5251 & Bordeaux University, 33400 Talence, France.
| | - Michèle Kind
- Department of Diagnostic and Interventional Oncological Imaging, Institut Bergonié, Regional Comprehensive Cancer of Nouvelle-Aquitaine, Bordeaux 33076, France
| | - David Fadli
- Department of Musculoskeletal Imaging, Pellegrin University Hospital, Bordeaux 33076, France
| | - Marco Miceli
- Diagnostic and Interventional Radiology, IRCCS Istituto Ortopedico Rizzoli, Bologna 40136, Italy
| | - Pierre-Antoine Linck
- Department of Diagnostic and Interventional Oncological Imaging, Institut Bergonié, Regional Comprehensive Cancer of Nouvelle-Aquitaine, Bordeaux 33076, France
| | - Giuseppe Bianchi
- Orthopedic Musculoskeletal Oncology Unit, IRCCS Istituto Ortopedico Rizzoli, Bologna 40136, Italy
| | - Andrea Sambri
- Orthopedics and Traumatology Department, IRCCS Azienda Ospedaliero Universitaria di Bologna, Via Massarenti 9, Bologna 40138, Italy
| | - Paolo Spinnato
- Diagnostic and Interventional Radiology, IRCCS Istituto Ortopedico Rizzoli, Bologna 40136, Italy
| |
Collapse
|