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Nanni C, Deroose CM, Balogova S, Lapa C, Withofs N, Subesinghe M, Jamet B, Zamagni E, Ippolito D, Delforge M, Kraeber-Bodéré F. EANM guidelines on the use of [ 18F]FDG PET/CT in diagnosis, staging, prognostication, therapy assessment, and restaging of plasma cell disorders. Eur J Nucl Med Mol Imaging 2024:10.1007/s00259-024-06858-9. [PMID: 39207486 DOI: 10.1007/s00259-024-06858-9] [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: 04/24/2024] [Accepted: 07/21/2024] [Indexed: 09/04/2024]
Abstract
We provide updated guidance and standards for the indication, acquisition, and interpretation of [18F]FDG PET/CT for plasma cell disorders. Procedures and characteristics are reported and different scenarios for the clinical use of [18F]FDG PET/CT are discussed. This document provides clinicians and technicians with the best available evidence to support the implementation of [18F]FDG PET/CT imaging in routine practice and future research.
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Affiliation(s)
- Cristina Nanni
- Nuclear Medicine, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Bologna, Italy
| | - Christophe M Deroose
- Nuclear Medicine, University Hospitals (UZ) Leuven, 3000, Leuven, Belgium
- Nuclear Medicine and Molecular Imaging, Department of Imaging and Pathology, KU Leuven, Leuven, Belgium
| | - Sona Balogova
- Nuclear Medicine, Comenius University, Bratislava, Slovakia
- Médecine Nucléaire, Hôpital Tenon, GH AP.SU, Paris, France
| | - Constantin Lapa
- Nuclear Medicine, Faculty of Medicine, University of Augsburg, Augsburg, Germany
| | - Nadia Withofs
- Division of Nuclear Medicine and Oncological Imaging, Department of Medical Physics, CHU of Liege, Liege, Belgium
- GIGA-CRC in Vivo Imaging, University of Liege, Liege, Belgium
| | - Manil Subesinghe
- Department of Cancer Imaging, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Bastien Jamet
- Médecine Nucléaire, CHU Nantes, F-44000, Nantes, France
| | - Elena Zamagni
- IRCCS Azienda Ospedaliero-Universitaria di Bologna, Istituto di Ematologia "Seràgnoli", Bologna, Italy.
- Dipartimento di Scienze Mediche e Chirurgiche, Università di Bologna, Bologna, Italy.
| | - Davide Ippolito
- Department of Diagnostic Radiology, Fondazione IRCCS San Gerardo dei Tintori, Via Pergolesi 33, 20900, Monza, Italy
- University of Milano-Bicocca, School of Medicine, Via Cadore 33, 20090, Monza, Italy
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Manco L, Albano D, Urso L, Arnaboldi M, Castellani M, Florimonte L, Guidi G, Turra A, Castello A, Panareo S. Positron Emission Tomography-Derived Radiomics and Artificial Intelligence in Multiple Myeloma: State-of-the-Art. J Clin Med 2023; 12:7669. [PMID: 38137738 PMCID: PMC10743775 DOI: 10.3390/jcm12247669] [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: 11/01/2023] [Revised: 12/02/2023] [Accepted: 12/09/2023] [Indexed: 12/24/2023] Open
Abstract
Multiple myeloma (MM) is a heterogeneous neoplasm accounting for the second most prevalent hematologic disorder. The identification of noninvasive, valuable biomarkers is of utmost importance for the best patient treatment selection, especially in heterogeneous diseases like MM. Despite molecular imaging with positron emission tomography (PET) has achieved a primary role in the characterization of MM, it is not free from shortcomings. In recent years, radiomics and artificial intelligence (AI), which includes machine learning (ML) and deep learning (DL) algorithms, have played an important role in mining additional information from medical images beyond human eyes' resolving power. Our review provides a summary of the current status of radiomics and AI in different clinical contexts of MM. A systematic search of PubMed, Web of Science, and Scopus was conducted, including all the articles published in English that explored radiomics and AI analyses of PET/CT images in MM. The initial results have highlighted the potential role of such new features in order to improve the clinical stratification of MM patients, as well as to increase their clinical benefits. However, more studies are warranted before these approaches can be implemented in clinical routines.
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Affiliation(s)
- Luigi Manco
- Medical Physics Unit, Azienda USL of Ferrara, 45100 Ferrara, Italy; (L.M.); (A.T.)
| | - Domenico Albano
- Nuclear Medicine Department, University of Brescia and ASST Spedali Civili di Brescia, 25123 Brescia, Italy;
| | - Luca Urso
- Department of Translational Medicine, University of Ferrara, 44121 Ferrara, Italy;
| | - Mattia Arnaboldi
- Nuclear Medicine Unit, Fondazione IRCCS Ca’ Granda, Ospedale Maggiore Policlinico, 20122 Milan, Italy; (M.A.); (M.C.); (L.F.)
| | - Massimo Castellani
- Nuclear Medicine Unit, Fondazione IRCCS Ca’ Granda, Ospedale Maggiore Policlinico, 20122 Milan, Italy; (M.A.); (M.C.); (L.F.)
| | - Luigia Florimonte
- Nuclear Medicine Unit, Fondazione IRCCS Ca’ Granda, Ospedale Maggiore Policlinico, 20122 Milan, Italy; (M.A.); (M.C.); (L.F.)
| | - Gabriele Guidi
- Medical Physics Unit, University Hospital of Modena, 41125 Modena, Italy;
| | - Alessandro Turra
- Medical Physics Unit, Azienda USL of Ferrara, 45100 Ferrara, Italy; (L.M.); (A.T.)
| | - Angelo Castello
- Nuclear Medicine Unit, Fondazione IRCCS Ca’ Granda, Ospedale Maggiore Policlinico, 20122 Milan, Italy; (M.A.); (M.C.); (L.F.)
| | - Stefano Panareo
- Nuclear Medicine Unit, Department of Oncology and Hematology, University Hospital of Modena, Via del Pozzo 71, 41124 Modena, Italy;
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Zhang X, Peng J, Ji G, Li T, Li B, Xiong H. Research status and progress of radiomics in bone and soft tissue tumors: A review. Medicine (Baltimore) 2023; 102:e36196. [PMID: 38013345 PMCID: PMC10681559 DOI: 10.1097/md.0000000000036198] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/03/2023] [Accepted: 10/27/2023] [Indexed: 11/29/2023] Open
Abstract
Bone and soft tissue tumors are diverse, accompanying by complex histological components and significantly divergent biological behaviors. It is a challenge to address the demand for qualitative imaging as traditional imaging is restricted to the detection of anatomical structures and aberrant signals. With the improvement of digitalization in hospitals and medical centers, the introduction of electronic medical records and easier access to large amounts of information coupled with the improved computational power, traditional medicine has evolved into the combination of human brain, minimal data, and artificial intelligence. Scholars are committed to mining deeper levels of imaging data, and radiomics is worthy of promotion. Radiomics extracts subvisual quantitative features, analyzes them based on medical images, and quantifies tumor heterogeneity by outlining the region of interest and modeling. Two observers separately examined PubMed, Web of Science and CNKI to find existing studies, case reports, and clinical guidelines about research status and progress of radiomics in bone and soft tissue tumors from January 2010 to February 2023. When evaluating the literature, factors such as patient age, medical history, and severity of the condition will be considered. This narrative review summarizes the application and progress of radiomics in bone and soft tissue tumors.
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Affiliation(s)
- Xiaohan Zhang
- Department of Radiology, The First Affiliated Hospital of Yangtze University, Jingzhou, China
| | - Jie Peng
- Department of Radiology, The First Affiliated Hospital of Yangtze University, Jingzhou, China
| | - Guanghai Ji
- Department of Radiology, The First Affiliated Hospital of Yangtze University, Jingzhou, China
| | - Tian Li
- Department of Radiology, The First Affiliated Hospital of Yangtze University, Jingzhou, China
| | - Bo Li
- Department of Radiology, The First Affiliated Hospital of Yangtze University, Jingzhou, China
| | - Hao Xiong
- Department of Radiology, The First Affiliated Hospital of Yangtze University, Jingzhou, China
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Kraeber-Bodéré F, Jamet B, Bezzi D, Zamagni E, Moreau P, Nanni C. New Developments in Myeloma Treatment and Response Assessment. J Nucl Med 2023; 64:1331-1343. [PMID: 37591548 PMCID: PMC10478822 DOI: 10.2967/jnumed.122.264972] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2023] [Revised: 07/06/2023] [Indexed: 08/19/2023] Open
Abstract
Recent innovative strategies have dramatically redefined the therapeutic landscape for treating multiple myeloma patients. In particular, the development and application of immunotherapy and high-dose therapy have demonstrated high response rates and have prolonged remission duration. Over the past decade, new morphologic or hybrid imaging techniques have gradually replaced conventional skeletal surveys. PET/CT using 18F-FDG is a powerful imaging tool for the workup at diagnosis and for therapeutic evaluation allowing medullary and extramedullary assessment. The independent negative prognostic value for progression-free and overall survival derived from baseline PET-derived parameters such as the presence of extramedullary disease or paramedullary disease, as well as the number of focal bone lesions and SUVmax, has been reported in several large prospective studies. During therapeutic evaluation, 18F-FDG PET/CT is considered the reference imaging technique because it can be performed much earlier than MRI, which lacks specificity. Persistence of significant abnormal 18F-FDG uptake after therapy is an independent negative prognostic factor, and 18F-FDG PET/CT and medullary flow cytometry are complementary tools for detecting minimal residual disease before maintenance therapy. The definition of a PET metabolic complete response has recently been standardized and the interpretation criteria harmonized. The development of advanced PET analysis and radiomics using machine learning, as well as hybrid imaging with PET/MRI, offers new perspectives for multiple myeloma imaging. Most recently, innovative radiopharmaceuticals such as C-X-C chemokine receptor type 4-targeted small molecules and anti-CD38 radiolabeled antibodies have shown promising results for tumor phenotype imaging and as potential theranostics.
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Affiliation(s)
- Françoise Kraeber-Bodéré
- Médecine nucléaire, CHU Nantes, Nantes Université, Université Angers, INSERM, CNRS, CRCI2NA, F-44000, Nantes, France
| | - Bastien Jamet
- Médecine nucléaire, CHU Nantes, F-44000, Nantes, France
| | - Davide Bezzi
- Department of Nuclear Medicine, Alma Mater Studiorum, University of Bologna, Bologna. Italy
| | - Elena Zamagni
- IRCCS Azienda Ospedaliero-Universitaria di Bologna, Istituto di Ematologia "Seràgnoli," Bologna, Italy
- Dipartimento di Scienze Mediche e Chirurgiche, Università di Bologna, Bologna, Italy
| | - Philippe Moreau
- Hématologie, CHU Nantes, Nantes Université, Université Angers, INSERM, CNRS, CRCI2NA, F-44000, Nantes, France; and
| | - Cristina Nanni
- Nuclear Medicine, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Bologna, Italy
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Özgül HA, Akin IB, Mutlu U, Balci A. Diagnostic value of machine learning-based computed tomography texture analysis for differentiating multiple myeloma from osteolytic metastatic bone lesions in the peripheral skeleton. Skeletal Radiol 2023; 52:1703-1711. [PMID: 37014470 DOI: 10.1007/s00256-023-04333-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/28/2022] [Revised: 03/25/2023] [Accepted: 03/26/2023] [Indexed: 04/05/2023]
Abstract
OBJECTIVES To report the diagnostic performance of machine learning-based CT texture analysis for differentiating multiple myeloma from osteolytic metastatic bone lesions in the peripheral skeleton. METHODS We retrospectively evaluated 172 patients with multiple myeloma (n = 70) and osteolytic metastatic bone lesions (n = 102) in the peripheral skeleton. Two radiologists individually used two-dimensional manual segmentation to extract texture features from non-contrast CT. In total, 762 radiomic features were extracted. Dimension reduction was performed in three stages: inter-observer agreement analysis, collinearity analysis, and feature selection. Data were randomly divided into training (n = 120) and test (n = 52) groups. Eight machine learning algorithms were used for model development. The primary performance metrics were the area under the receiver operating characteristic curve and accuracy. RESULTS In total, 476 of the 762 texture features demonstrated excellent interobserver agreement. The number of features was reduced to 22 after excluding those with strong collinearity. Of these features, six were included in the machine learning algorithms using the wrapper-based classifier-specific technique. When all eight machine learning algorithms were considered for differentiating multiple myeloma from osteolytic metastatic bone lesions in the peripheral skeleton, the area under the receiver operating characteristic curve and accuracy were 0.776-0.932 and 78.8-92.3%, respectively. The k-nearest neighbors model performed the best, with the area under the receiver operating characteristic curve and accuracy values of 0.902 and 92.3%, respectively. CONCLUSION Machine learning-based CT texture analysis is a promising method for discriminating multiple myeloma from osteolytic metastatic bone lesions.
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Affiliation(s)
- Hakan Abdullah Özgül
- Department of Radiology, Kemalpaşa State Hospital, Kırovası Küme Street, Kemalpaşa, 35730, Izmir, Turkey.
| | - Işıl Başara Akin
- Department of Radiology, Dokuz Eylul University, Faculty of Medicine, Izmir, Turkey
| | - Uygar Mutlu
- Department of Radiology, Yozgat State Hospital, Yozgat, Turkey
| | - Ali Balci
- Department of Radiology, Dokuz Eylul University, Faculty of Medicine, Izmir, Turkey
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Zhong H, Huang D, Wu J, Chen X, Chen Y, Huang C. 18F‑FDG PET/CT based radiomics features improve prediction of prognosis: multiple machine learning algorithms and multimodality applications for multiple myeloma. BMC Med Imaging 2023; 23:87. [PMID: 37370013 DOI: 10.1186/s12880-023-01033-2] [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: 12/29/2022] [Accepted: 05/23/2023] [Indexed: 06/29/2023] Open
Abstract
PURPOSE Multiple myeloma (MM), the second most hematological malignancy, have been studied extensively in the prognosis of the clinical parameters, however there are only a few studies have discussed the role of dual modalities and multiple algorithms of 18F-FDG (18F-fluorodeoxyglucose) PET/CT based radiomics signatures for prognosis in MM patients. We hope to deeply mine the utility of raiomics data in the prognosis of MM. METHODS We extensively explored the predictive ability and clinical decision-making ability of different combination image data of PET, CT, clinical parameters and six machine learning algorithms, Cox proportional hazards model (Cox), linear gradient boosting models based on Cox's partial likelihood (GB-Cox), Cox model by likelihood based boosting (CoxBoost), generalized boosted regression modelling (GBM), random forests for survival model (RFS) and support vector regression for censored data model (SVCR). And the model evaluation methods include Harrell concordance index, time dependent receiver operating characteristic (ROC) curve, and decision curve analysis (DCA). RESULTS We finally confirmed 5 PET based features, and 4 CT based features, as well as 6 clinical derived features significantly related to progression free survival (PFS) and we included them in the model construction. In various modalities combinations, RSF and GBM algorithms significantly improved the accuracy and clinical net benefit of predicting prognosis compared with other algorithms. For all combinations of various modalities based models, single-modality PET based prognostic models' performance was outperformed baseline clinical parameters based models, while the performance of models of PET and CT combined with clinical parameters was significantly improved in various algorithms. CONCLUSION 18F‑FDG PET/CT based radiomics models implemented with machine learning algorithms can significantly improve the clinical prediction of progress and increased clinical benefits providing prospects for clinical prognostic stratification for precision treatment as well as new research areas.
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Affiliation(s)
- Haoshu Zhong
- Department of Hematology, the Affiliated Hospital of Southwest Medical University, Luzhou City, Sichuan, China
- Stem Cell Laboratory, The Clinical Research Institute, Affiliated Hospital of Southwest Medical University, Luzhou City, Sichuan, China
| | - Delong Huang
- Southwest Medical University, Luzhou City, Sichuan, China
| | - Junhao Wu
- Department of Nuclear Medicine & PET Center, Huashan Hospital, Fudan University, Shanghai, 200040, China
| | - Xiaomin Chen
- Department of Hematology, the Affiliated Hospital of Southwest Medical University, Luzhou City, Sichuan, China
- Stem Cell Laboratory, The Clinical Research Institute, Affiliated Hospital of Southwest Medical University, Luzhou City, Sichuan, China
| | - Yue Chen
- Department of Nuclear Medicine, the Affiliated Hospital of Southwest Medical University, Luzhou City, Sichuan, China
| | - Chunlan Huang
- Department of Hematology, the Affiliated Hospital of Southwest Medical University, Luzhou City, Sichuan, China.
- Stem Cell Laboratory, The Clinical Research Institute, Affiliated Hospital of Southwest Medical University, Luzhou City, Sichuan, China.
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Klontzas ME, Triantafyllou M, Leventis D, Koltsakis E, Kalarakis G, Tzortzakakis A, Karantanas AH. Radiomics Analysis for Multiple Myeloma: A Systematic Review with Radiomics Quality Scoring. Diagnostics (Basel) 2023; 13:2021. [PMID: 37370916 DOI: 10.3390/diagnostics13122021] [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: 04/20/2023] [Revised: 06/06/2023] [Accepted: 06/08/2023] [Indexed: 06/29/2023] Open
Abstract
Multiple myeloma (MM) is one of the most common hematological malignancies affecting the bone marrow. Radiomics analysis has been employed in the literature in an attempt to evaluate the bone marrow of MM patients. This manuscript aimed to systematically review radiomics research on MM while employing a radiomics quality score (RQS) to accurately assess research quality in the field. A systematic search was performed on Web of Science, PubMed, and Scopus. The selected manuscripts were evaluated (data extraction and RQS scoring) by three independent readers (R1, R2, and R3) with experience in radiomics analysis. A total of 23 studies with 2682 patients were included, and the median RQS was 10 for R1 (IQR 5.5-12) and R3 (IQR 8.3-12) and 11 (IQR 7.5-12.5) for R2. RQS was not significantly correlated with any of the assessed bibliometric data (impact factor, quartile, year of publication, and imaging modality) (p > 0.05). Our results demonstrated the low quality of published radiomics research in MM, similarly to other fields of radiomics research, highlighting the need to tighten publication standards.
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Affiliation(s)
- Michail E Klontzas
- Department of Medical Imaging, University Hospital of Heraklion, 71110 Heraklion, Greece
- Department of Radiology, School of Medicine, University of Crete, Voutes Campus, 71003 Heraklion, Greece
| | | | - Dimitrios Leventis
- Department of Medical Imaging, University Hospital of Heraklion, 71110 Heraklion, Greece
| | - Emmanouil Koltsakis
- Department of Radiology, Karolinska University Hospital, 14152 Stockholm, Sweden
| | - Georgios Kalarakis
- Department of Radiology, Karolinska University Hospital, 14152 Stockholm, Sweden
- Division of Radiology, Department for Clinical Science, Intervention and Technology (CLINTEC), Karolinska Institutet, 14152 Stockholm, Sweden
| | - Antonios Tzortzakakis
- Division of Radiology, Department for Clinical Science, Intervention and Technology (CLINTEC), Karolinska Institutet, 14152 Stockholm, Sweden
- Medical Radiation Physics and Nuclear Medicine, Section for Nuclear Medicine, Karolinska University Hospital, 14186 Huddinge, Stockholm, Sweden
| | - Apostolos H Karantanas
- Department of Medical Imaging, University Hospital of Heraklion, 71110 Heraklion, Greece
- Department of Radiology, School of Medicine, University of Crete, Voutes Campus, 71003 Heraklion, Greece
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Differentiating Multiple Myeloma and Osteolytic Bone Metastases on Contrast-Enhanced Computed Tomography Scans: The Feasibility of Radiomics Analysis. Diagnostics (Basel) 2023; 13:diagnostics13040755. [PMID: 36832243 PMCID: PMC9955828 DOI: 10.3390/diagnostics13040755] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2022] [Revised: 02/14/2023] [Accepted: 02/15/2023] [Indexed: 02/19/2023] Open
Abstract
Osteolytic lesions can be seen in both multiple myeloma (MM), and osteolytic bone metastasis on computed tomography (CT) scans. We sought to assess the feasibility of a CT-based radiomics model to distinguish MM from metastasis. This study retrospectively included patients with pre-treatment thoracic or abdominal contrast-enhanced CT from institution 1 (training set: 175 patients with 425 lesions) and institution 2 (external test set: 50 patients with 85 lesions). After segmenting osteolytic lesions on CT images, 1218 radiomics features were extracted. A random forest (RF) classifier was used to build the radiomics model with 10-fold cross-validation. Three radiologists distinguished MM from metastasis using a five-point scale, both with and without the assistance of RF model results. Diagnostic performance was evaluated using the area under the curve (AUC). The AUC of the RF model was 0.807 and 0.762 for the training and test set, respectively. The AUC of the RF model and the radiologists (0.653-0.778) was not significantly different for the test set (p ≥ 0.179). The AUC of all radiologists was significantly increased (0.833-0.900) when they were assisted by RF model results (p < 0.001). In conclusion, the CT-based radiomics model can differentiate MM from osteolytic bone metastasis and improve radiologists' diagnostic performance.
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Chen K, Cao J, Zhang X, Wang X, Zhao X, Li Q, Chen S, Wang P, Liu T, Du J, Liu S, Zhang L. Differentiation between spinal multiple myeloma and metastases originated from lung using multi-view attention-guided network. Front Oncol 2022; 12:981769. [PMID: 36158659 PMCID: PMC9495278 DOI: 10.3389/fonc.2022.981769] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2022] [Accepted: 08/12/2022] [Indexed: 11/16/2022] Open
Abstract
Purpose Multiple myeloma (MM) and metastasis originated are the two common malignancy diseases in the spine. They usually show similar imaging patterns and are highly demanded to differentiate for precision diagnosis and treatment planning. The objective of this study is therefore to construct a novel deep-learning-based method for effective differentiation of two diseases, with the comparative study of traditional radiomics analysis. Methods We retrospectively enrolled a total of 217 patients with 269 lesions, who were diagnosed with spinal MM (79 cases, 81 lesions) or spinal metastases originated from lung cancer (138 cases, 188 lesions) confirmed by postoperative pathology. Magnetic resonance imaging (MRI) sequences of all patients were collected and reviewed. A novel deep learning model of the Multi-view Attention-Guided Network (MAGN) was constructed based on contrast-enhanced T1WI (CET1) sequences. The constructed model extracts features from three views (sagittal, coronal and axial) and fused them for a more comprehensive differentiation analysis, and the attention guidance strategy is adopted for improving the classification performance, and increasing the interpretability of the method. The diagnostic efficiency among MAGN, radiomics model and the radiologist assessment were compared by the area under the receiver operating characteristic curve (AUC). Results Ablation studies were conducted to demonstrate the validity of multi-view fusion and attention guidance strategies: It has shown that the diagnostic model using multi-view fusion achieved higher diagnostic performance [ACC (0.79), AUC (0.77) and F1-score (0.67)] than those using single-view (sagittal, axial and coronal) images. Besides, MAGN incorporating attention guidance strategy further boosted performance as the ACC, AUC and F1-scores reached 0.81, 0.78 and 0.71, respectively. In addition, the MAGN outperforms the radiomics methods and radiologist assessment. The highest ACC, AUC and F1-score for the latter two methods were 0.71, 0.76 & 0.54, and 0.69, 0.71, & 0.65, respectively. Conclusions The proposed MAGN can achieve satisfactory performance in differentiating spinal MM between metastases originating from lung cancer, which also outperforms the radiomics method and radiologist assessment.
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Affiliation(s)
- Kaili Chen
- Department of Hematology, Myeloma & Lymphoma Center, Shanghai Changzheng Hospital, Changzheng Hospital of the Naval Medical University, Huangpu, China
- *Correspondence: Juan Du, ; Shiyuan Liu, ; Lichi Zhang,
| | - Jiashi Cao
- Department of Orthopedics, No. 455 Hospital of Chinese People’s Liberation Army, Shanghai 455 Hospital, Navy Medical University, Shanghai, China
- Department of Orthopaedic Oncology, Spine Tumor Center, Shanghai Changzheng Hospital, Changzheng Hospital of the Navy Medical University, Huangpu, China
- *Correspondence: Juan Du, ; Shiyuan Liu, ; Lichi Zhang,
| | - Xin Zhang
- Institute for Medical Image Technology, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
- *Correspondence: Juan Du, ; Shiyuan Liu, ; Lichi Zhang,
| | - Xiang Wang
- Department of Radiology, Changzheng Hospital, Shanghai Changzheng Hospital, Navy Medical University, Huangpu, China
- *Correspondence: Juan Du, ; Shiyuan Liu, ; Lichi Zhang,
| | - Xiangyu Zhao
- Institute for Medical Image Technology, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Qingchu Li
- Department of Radiology, Changzheng Hospital, Shanghai Changzheng Hospital, Navy Medical University, Huangpu, China
| | - Song Chen
- Department of Radiology, Changzheng Hospital, Shanghai Changzheng Hospital, Navy Medical University, Huangpu, China
| | - Peng Wang
- Department of Radiology, Changzheng Hospital, Shanghai Changzheng Hospital, Navy Medical University, Huangpu, China
| | - Tielong Liu
- Department of Orthopaedic Oncology, Spine Tumor Center, Shanghai Changzheng Hospital, Changzheng Hospital of the Navy Medical University, Huangpu, China
| | - Juan Du
- Department of Hematology, Myeloma & Lymphoma Center, Shanghai Changzheng Hospital, Changzheng Hospital of the Naval Medical University, Huangpu, China
- *Correspondence: Juan Du, ; Shiyuan Liu, ; Lichi Zhang,
| | - Shiyuan Liu
- Department of Radiology, Changzheng Hospital, Shanghai Changzheng Hospital, Navy Medical University, Huangpu, China
- *Correspondence: Juan Du, ; Shiyuan Liu, ; Lichi Zhang,
| | - Lichi Zhang
- Institute for Medical Image Technology, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
- *Correspondence: Juan Du, ; Shiyuan Liu, ; Lichi Zhang,
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