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Van Den Berghe T, Delbare F, Candries E, Lejoly M, Algoet C, Chen M, Laloo F, Huysse WCJ, Creytens D, Verstraete KL. A retrospective external validation study of the Birmingham Atypical Cartilage Tumour Imaging Protocol (BACTIP) for the management of solitary central cartilage tumours of the proximal humerus and around the knee. Eur Radiol 2024; 34:4988-5006. [PMID: 38319428 DOI: 10.1007/s00330-024-10604-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2023] [Revised: 12/01/2023] [Accepted: 12/20/2023] [Indexed: 02/07/2024]
Abstract
OBJECTIVES This study aimed to externally validate the Birmingham Atypical Cartilage Tumour Imaging Protocol (BACTIP) recommendations for differentiation/follow-up of central cartilage tumours (CCTs) of the proximal humerus, distal femur, and proximal tibia and to propose BACTIP adaptations if the results provide new insights. METHODS MRIs of 123 patients (45 ± 11 years, 37 men) with an untreated CCT with MRI follow-up (n = 62) or histopathological confirmation (n = 61) were retrospectively/consecutively included and categorised following the BACTIP (2003-2020 / Ghent University Hospital/Belgium). Tumour length and endosteal scalloping differences between enchondroma, atypical cartilaginous tumour (ACT), and high-grade chondrosarcoma (CS II/III/dedifferentiated) were evaluated. ROC-curve analysis for differentiating benign from malignant CCTs and for evaluating the BACTIP was performed. RESULTS For lesion length and endosteal scalloping, ROC-AUCs were poor and fair-excellent, respectively, for differentiating different CCT groups (0.59-0.69 versus 0.73-0.91). The diagnostic performance of endosteal scalloping and the BACTIP was higher than that of lesion length. A 1° endosteal scalloping cut-off differentiated enchondroma from ACT + high-grade chondrosarcoma with a sensitivity of 90%, reducing the potential diagnostic delay. However, the specificity was 29%, inducing overmedicalisation (excessive follow-up). ROC-AUC of the BACTIP was poor for differentiating enchondroma from ACT (ROC-AUC = 0.69; 95%CI = 0.51-0.87; p = 0.041) and fair-good for differentiation between other CCT groups (ROC-AUC = 0.72-0.81). BACTIP recommendations were incorrect/unsafe in five ACTs and one CSII, potentially inducing diagnostic delay. Eleven enchondromas received unnecessary referrals/follow-up. CONCLUSION Although promising as a useful tool for management/follow-up of CCTs of the proximal humerus, distal femur, and proximal tibia, five ACTs and one chondrosarcoma grade II were discharged, potentially inducing diagnostic delay, which could be reduced by adapting BACTIP cut-off values. CLINICAL RELEVANCE STATEMENT Mostly, Birmingham Atypical Cartilage Tumour Imaging Protocol (BACTIP) assesses central cartilage tumours of the proximal humerus and the knee correctly. Both when using the BACTIP and when adapting cut-offs, caution should be taken for the trade-off between underdiagnosis/potential diagnostic delay in chondrosarcomas and overmedicalisation in enchondromas. KEY POINTS • This retrospective external validation confirms the Birmingham Atypical Cartilage Tumour Imaging Protocol as a useful tool for initial assessment and follow-up recommendation of central cartilage tumours in the proximal humerus and around the knee in the majority of cases. • Using only the Birmingham Atypical Cartilage Tumour Imaging Protocol, both atypical cartilaginous tumours and high-grade chondrosarcomas (grade II, grade III, and dedifferentiated chondrosarcomas) can be misdiagnosed, excluding them from specialist referral and further follow-up, thus creating a potential risk of delayed diagnosis and worse prognosis. • Adapted cut-offs to maximise detection of atypical cartilaginous tumours and high-grade chondrosarcomas, minimise underdiagnosis and reduce potential diagnostic delay in malignant tumours but increase unnecessary referral and follow-up of benign tumours.
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Affiliation(s)
- Thomas Van Den Berghe
- Department of Radiology and Medical Imaging, Ghent University Hospital, Corneel Heymanslaan 10, 9000, Ghent, Belgium.
- Department of Diagnostic Sciences, Ghent University, Sint-Pietersnieuwstraat 25, 9000, Ghent, Belgium.
| | - Felix Delbare
- Department of Radiology and Medical Imaging, Ghent University Hospital, Corneel Heymanslaan 10, 9000, Ghent, Belgium
- Department of Diagnostic Sciences, Ghent University, Sint-Pietersnieuwstraat 25, 9000, Ghent, Belgium
| | - Esther Candries
- Department of Radiology and Medical Imaging, Ghent University Hospital, Corneel Heymanslaan 10, 9000, Ghent, Belgium
- Department of Diagnostic Sciences, Ghent University, Sint-Pietersnieuwstraat 25, 9000, Ghent, Belgium
| | - Maryse Lejoly
- Department of Radiology and Medical Imaging, Ghent University Hospital, Corneel Heymanslaan 10, 9000, Ghent, Belgium
- Department of Diagnostic Sciences, Ghent University, Sint-Pietersnieuwstraat 25, 9000, Ghent, Belgium
| | - Chloé Algoet
- Department of Radiology and Medical Imaging, Ghent University Hospital, Corneel Heymanslaan 10, 9000, Ghent, Belgium
- Department of Diagnostic Sciences, Ghent University, Sint-Pietersnieuwstraat 25, 9000, Ghent, Belgium
| | - Min Chen
- Department of Radiology, Peking University Shenzhen Hospital, Shenzhen, 518036, China
| | - Frederiek Laloo
- Department of Radiology and Medical Imaging, Ghent University Hospital, Corneel Heymanslaan 10, 9000, Ghent, Belgium
- Department of Diagnostic Sciences, Ghent University, Sint-Pietersnieuwstraat 25, 9000, Ghent, Belgium
| | - Wouter C J Huysse
- Department of Radiology and Medical Imaging, Ghent University Hospital, Corneel Heymanslaan 10, 9000, Ghent, Belgium
- Department of Diagnostic Sciences, Ghent University, Sint-Pietersnieuwstraat 25, 9000, Ghent, Belgium
| | - David Creytens
- Department of Pathology, Ghent University Hospital, Corneel Heymanslaan 10, 9000, Ghent, Belgium
| | - Koenraad L Verstraete
- Department of Radiology and Medical Imaging, Ghent University Hospital, Corneel Heymanslaan 10, 9000, Ghent, Belgium
- Department of Diagnostic Sciences, Ghent University, Sint-Pietersnieuwstraat 25, 9000, Ghent, Belgium
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Ghasemi A, Ahlawat S. Bone Reporting and Data System (Bone-RADS) and Other Proposed Practice Guidelines for Reporting Bone Tumors. ROFO-FORTSCHR RONTG 2024. [PMID: 38490222 DOI: 10.1055/a-2262-8411] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/17/2024]
Abstract
BACKGROUND The purpose of this article is to review the different bone tumor radiology reporting systems [Bone Reporting and Data System (Bone-RADS), Osseous Tumor Reporting and Data System (OT-RADS), Solitary Bone Tumor Imaging Reporting and Data System (BTI-RADS), and Radiological Evaluation Score for Bone Tumors (REST)] and summarize their advantages and disadvantages. METHODS A selective search of PubMed was performed for literature regarding the definition and discussion of bone tumor reporting systems. No time frame was selected, but the search was particularly focused on current literature on musculoskeletal radiology lexicon. RESULTS To date, four major reporting systems has been proposed to standardize and systematize the reporting of imaging studies of bone tumors: Bone-RADS, OT-RADS, BTI-RADS, and REST. Both Bone-RADS and OT-RADS aid in the characterization and management of bone lesions on CT and MRI. OT-RADS and REST can be applied to MRI and radiography, respectively. CONCLUSION Radiologists play a central role in the detection and characterization of asymptomatic (or incidentally detected) and symptomatic bone tumors. There are several existing bone tumor reporting systems with various advantages and disadvantages including emphasis on lesion characterization as well as management of incidentally detected bone lesions. KEY POINTS · Four bone tumor reporting systems have been proposed thus far.. · Bone-RADS guides management of incidental bone lesions on CT and MRI.. · OT-RADS guides management of bone lesions on MRI with high accuracy.. · BTI-RADS classifies bone tumors on CT and MRI..
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Affiliation(s)
- Ali Ghasemi
- Russell H. Morgan Department of Radiology and Radiological Sciences, Johns Hopkins Medical Institutions Campus, Baltimore, United States
| | - Shivani Ahlawat
- Russell H. Morgan Department of Radiology and Radiological Sciences, Johns Hopkins Medical Institutions Campus, Baltimore, United States
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Gitto S, Cuocolo R, Huisman M, Messina C, Albano D, Omoumi P, Kotter E, Maas M, Van Ooijen P, Sconfienza LM. CT and MRI radiomics of bone and soft-tissue sarcomas: an updated systematic review of reproducibility and validation strategies. Insights Imaging 2024; 15:54. [PMID: 38411750 PMCID: PMC10899555 DOI: 10.1186/s13244-024-01614-x] [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: 12/22/2023] [Accepted: 01/09/2024] [Indexed: 02/28/2024] Open
Abstract
OBJECTIVE To systematically review radiomic feature reproducibility and model validation strategies in recent studies dealing with CT and MRI radiomics of bone and soft-tissue sarcomas, thus updating a previous version of this review which included studies published up to 2020. METHODS A literature search was conducted on EMBASE and PubMed databases for papers published between January 2021 and March 2023. Data regarding radiomic feature reproducibility and model validation strategies were extracted and analyzed. RESULTS Out of 201 identified papers, 55 were included. They dealt with radiomics of bone (n = 23) or soft-tissue (n = 32) tumors. Thirty-two (out of 54 employing manual or semiautomatic segmentation, 59%) studies included a feature reproducibility analysis. Reproducibility was assessed based on intra/interobserver segmentation variability in 30 (55%) and geometrical transformations of the region of interest in 2 (4%) studies. At least one machine learning validation technique was used for model development in 34 (62%) papers, and K-fold cross-validation was employed most frequently. A clinical validation of the model was reported in 38 (69%) papers. It was performed using a separate dataset from the primary institution (internal test) in 22 (40%), an independent dataset from another institution (external test) in 14 (25%) and both in 2 (4%) studies. CONCLUSIONS Compared to papers published up to 2020, a clear improvement was noted with almost double publications reporting methodological aspects related to reproducibility and validation. Larger multicenter investigations including external clinical validation and the publication of databases in open-access repositories could further improve methodology and bring radiomics from a research area to the clinical stage. CRITICAL RELEVANCE STATEMENT An improvement in feature reproducibility and model validation strategies has been shown in this updated systematic review on radiomics of bone and soft-tissue sarcomas, highlighting efforts to enhance methodology and bring radiomics from a research area to the clinical stage. KEY POINTS • 2021-2023 radiomic studies on CT and MRI of musculoskeletal sarcomas were reviewed. • Feature reproducibility was assessed in more than half (59%) of the studies. • Model clinical validation was performed in 69% of the studies. • Internal (44%) and/or external (29%) test datasets were employed for clinical validation.
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Affiliation(s)
- Salvatore Gitto
- Dipartimento di Scienze Biomediche per la Salute, Università degli Studi di Milano, Milan, Italy
- IRCCS Istituto Ortopedico Galeazzi, Milan, Italy
| | - Renato Cuocolo
- Department of Medicine, Surgery and Dentistry, University of Salerno, Baronissi, Italy
| | - Merel Huisman
- Radboud University Medical Center, Department of Radiology and Nuclear Medicine, Nijmegen, The Netherlands
| | - Carmelo Messina
- Dipartimento di Scienze Biomediche per la Salute, Università degli Studi di Milano, Milan, Italy
- IRCCS Istituto Ortopedico Galeazzi, Milan, Italy
| | - Domenico Albano
- IRCCS Istituto Ortopedico Galeazzi, Milan, Italy
- Dipartimento di Scienze Biomediche, Chirurgiche ed Odontoiatriche, Università degli Studi di Milano, Milan, Italy
| | - Patrick Omoumi
- Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Elmar Kotter
- Department of Radiology, Freiburg University Medical Center, Freiburg, Germany
| | - Mario Maas
- Department of Radiology and Nuclear Medicine, Amsterdam UMC Location University of Amsterdam, Amsterdam, The Netherlands
| | - Peter Van Ooijen
- Department of Radiation Oncology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Luca Maria Sconfienza
- Dipartimento di Scienze Biomediche per la Salute, Università degli Studi di Milano, Milan, Italy.
- IRCCS Istituto Ortopedico Galeazzi, Milan, Italy.
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Kostopoulos S, Boci N, Cavouras D, Tsagkalis A, Papaioannou M, Tsikrika A, Glotsos D, Asvestas P, Lavdas E. Radiomics Texture Analysis of Bone Marrow Alterations in MRI Knee Examinations. J Imaging 2023; 9:252. [PMID: 37998099 PMCID: PMC10672553 DOI: 10.3390/jimaging9110252] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2023] [Revised: 11/01/2023] [Accepted: 11/16/2023] [Indexed: 11/25/2023] Open
Abstract
Accurate diagnosis and timely intervention are key to addressing common knee conditions effectively. In this work, we aim to identify textural changes in knee lesions based on bone marrow edema (BME), injury (INJ), and osteoarthritis (OST). One hundred and twenty-one MRI knee examinations were selected. Cases were divided into three groups based on radiological findings: forty-one in the BME, thirty-seven in the INJ, and forty-three in the OST groups. From each ROI, eighty-one radiomic descriptors were calculated, encoding texture information. The results suggested differences in the texture characteristics of regions of interest (ROIs) extracted from PD-FSE and STIR sequences. We observed that the ROIs associated with BME exhibited greater local contrast and a wider range of structural diversity compared to the ROIs corresponding to OST. When it comes to STIR sequences, the ROIs related to BME showed higher uniformity in terms of both signal intensity and the variability of local structures compared to the INJ ROIs. A combined radiomic descriptor managed to achieve a high separation ability, with AUC of 0.93 ± 0.02 in the test set. Radiomics analysis may provide a non-invasive and quantitative means to assess the spatial distribution and heterogeneity of bone marrow edema, aiding in its early detection and characterization.
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Affiliation(s)
- Spiros Kostopoulos
- Medical Image and Signal Processing Laboratory, Department of Biomedical Engineering, University of West Attica, 12241 Athens, Greece; (D.C.); (P.A.)
| | - Nada Boci
- Department of Biomedical Sciences, University of West Attica, 12241 Athens, Greece; (N.B.); (E.L.)
- Department of Radiology, Animus Kyanous Stavros, 57014 Larissa, Greece
| | - Dionisis Cavouras
- Medical Image and Signal Processing Laboratory, Department of Biomedical Engineering, University of West Attica, 12241 Athens, Greece; (D.C.); (P.A.)
| | | | - Maria Papaioannou
- Department of Radiology, Animus Kyanous Stavros, 57014 Larissa, Greece
| | - Alexandra Tsikrika
- Department of Radiology, General University Hospital of Larissa, 41334 Larissa, Greece;
| | - Dimitris Glotsos
- Medical Image and Signal Processing Laboratory, Department of Biomedical Engineering, University of West Attica, 12241 Athens, Greece; (D.C.); (P.A.)
| | - Pantelis Asvestas
- Medical Image and Signal Processing Laboratory, Department of Biomedical Engineering, University of West Attica, 12241 Athens, Greece; (D.C.); (P.A.)
| | - Eleftherios Lavdas
- Department of Biomedical Sciences, University of West Attica, 12241 Athens, Greece; (N.B.); (E.L.)
- Department of Radiology, Animus Kyanous Stavros, 57014 Larissa, Greece
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Yoon H, Choi WH, Joo MW, Ha S, Chung YA. SPECT/CT Radiomics for Differentiating between Enchondroma and Grade I Chondrosarcoma. Tomography 2023; 9:1868-1875. [PMID: 37888740 PMCID: PMC10610631 DOI: 10.3390/tomography9050148] [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: 08/30/2023] [Revised: 10/11/2023] [Accepted: 10/14/2023] [Indexed: 10/28/2023] Open
Abstract
This study was performed to assess the value of SPECT/CT radiomics parameters in differentiating enchondroma and atypical cartilaginous tumors (ACTs) located in the long bones. Quantitative HDP SPECT/CT data of 49 patients with enchondromas or ACTs in the long bones were retrospectively reviewed. Patients were randomly split into training (n = 32) and test (n = 17) data, and SPECT/CT radiomics parameters were extracted. In training data, LASSO was employed for feature reduction. Selected parameters were compared with classic quantitative parameters for the prediction of diagnosis. Significant parameters from training data were again tested in the test data. A total of 12 (37.5%) and 6 (35.2%) patients were diagnosed as ACTs in training and test data, respectively. LASSO regression selected two radiomics features, zone-length non-uniformity for zone (ZLNUGLZLM) and coarseness for neighborhood grey-level difference (CoarsenessNGLDM). Multivariate analysis revealed higher ZLNUGLZLM as the only significant independent factor for the prediction of ACTs, with sensitivity and specificity of 85.0% and 58.3%, respectively, with a cut-off value of 191.26. In test data, higher ZLNUGLZLM was again associated with the diagnosis of ACTs, with sensitivity and specificity of 83.3% and 90.9%, respectively. HDP SPECT/CT radiomics may provide added value for differentiating between enchondromas and ACTs.
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Affiliation(s)
- Hyukjin Yoon
- Division of Nuclear Medicine, Department of Radiology, St. Vincent’s Hospital, College of Medicine, The Catholic University of Korea, Seoul 06591, Republic of Korea; (H.Y.); (W.H.C.)
| | - Woo Hee Choi
- Division of Nuclear Medicine, Department of Radiology, St. Vincent’s Hospital, College of Medicine, The Catholic University of Korea, Seoul 06591, Republic of Korea; (H.Y.); (W.H.C.)
| | - Min Wook Joo
- Department of Orthopedic Surgery, St. Vincent’s Hospital, College of Medicine, The Catholic University of Korea, Seoul 06591, Republic of Korea;
| | - Seunggyun Ha
- Division of Nuclear Medicine, Department of Radiology, Seoul St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul 06591, Republic of Korea;
| | - Yong-An Chung
- Division of Nuclear Medicine, Department of Radiology, Incheon St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul 06591, Republic of Korea
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Meng Y, Yang Y, Hu M, Zhang Z, Zhou X. Artificial intelligence-based radiomics in bone tumors: Technical advances and clinical application. Semin Cancer Biol 2023; 95:75-87. [PMID: 37499847 DOI: 10.1016/j.semcancer.2023.07.003] [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: 05/22/2023] [Revised: 07/21/2023] [Accepted: 07/22/2023] [Indexed: 07/29/2023]
Abstract
Radiomics is the extraction of predefined mathematic features from medical images for predicting variables of clinical interest. Recent research has demonstrated that radiomics can be processed by artificial intelligence algorithms to reveal complex patterns and trends for diagnosis, and prediction of prognosis and response to treatment modalities in various types of cancer. Artificial intelligence tools can utilize radiological images to solve next-generation issues in clinical decision making. Bone tumors can be classified as primary and secondary (metastatic) tumors. Osteosarcoma, Ewing sarcoma, and chondrosarcoma are the dominating primary tumors of bone. The development of bone tumor model systems and relevant research, and the assessment of novel treatment methods are ongoing to improve clinical outcomes, notably for patients with metastases. Artificial intelligence and radiomics have been utilized in almost full spectrum of clinical care of bone tumors. Radiomics models have achieved excellent performance in the diagnosis and grading of bone tumors. Furthermore, the models enable to predict overall survival, metastases, and recurrence. Radiomics features have exhibited promise in assisting therapeutic planning and evaluation, especially neoadjuvant chemotherapy. This review provides an overview of the evolution and opportunities for artificial intelligence in imaging, with a focus on hand-crafted features and deep learning-based radiomics approaches. We summarize the current application of artificial intelligence-based radiomics both in primary and metastatic bone tumors, and discuss the limitations and future opportunities of artificial intelligence-based radiomics in this field. In the era of personalized medicine, our in-depth understanding of emerging artificial intelligence-based radiomics approaches will bring innovative solutions to bone tumors and achieve clinical application.
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Affiliation(s)
- Yichen Meng
- Department of Orthopedics, Second Affiliated Hospital of Naval Medical University, Shanghai 200003, PR China
| | - Yue Yang
- Department of Orthopedics, Second Affiliated Hospital of Naval Medical University, Shanghai 200003, PR China
| | - Miao Hu
- Department of Orthopedics, Second Affiliated Hospital of Naval Medical University, Shanghai 200003, PR China
| | - Zheng Zhang
- Department of Orthopedics, Second Affiliated Hospital of Naval Medical University, Shanghai 200003, PR China.
| | - Xuhui Zhou
- Department of Orthopedics, Second Affiliated Hospital of Naval Medical University, Shanghai 200003, PR China.
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