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Li X, Shi X, Wang Y, Pang J, Zhao X, Xu Y, Li Q, Wang N, Duan F, Nie P. A CT-based radiomics nomogram for predicting histologic grade and outcome in chondrosarcoma. Cancer Imaging 2024; 24:50. [PMID: 38605380 PMCID: PMC11007871 DOI: 10.1186/s40644-024-00695-7] [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: 07/04/2023] [Accepted: 03/29/2024] [Indexed: 04/13/2024] Open
Abstract
OBJECTIVE The preoperative identification of tumor grade in chondrosarcoma (CS) is crucial for devising effective treatment strategies and predicting outcomes. The study aims to build and validate a CT-based radiomics nomogram (RN) for the preoperative identification of tumor grade in CS, and to evaluate the correlation between the RN-predicted tumor grade and postoperative outcome. METHODS A total of 196 patients (139 in the training cohort and 57 in the external validation cohort) were derived from three different centers. A clinical model, radiomics signature (RS) and RN (which combines significant clinical factors and RS) were developed and validated to assess their ability to distinguish low-grade from high-grade CS with area under the curve (AUC). Additionally, Kaplan-Meier survival analysis was applied to examine the association between RN-predicted tumor grade and recurrence-free survival (RFS) of CS. The predictive accuracy of the RN was evaluated using Harrell's concordance index (C-index), hazard ratio (HR) and AUC. RESULTS Size, endosteal scalloping and active periostitis were selected to build the clinical model. Three radiomics features, based on CT images, were selected to construct the RS. Both the RN (AUC, 0.842) and RS (AUC, 0.835) were superior to the clinical model (AUC, 0.776) in the validation set (P = 0.003, 0.040, respectively). A correlation between Nomogram score (Nomo-score, derived from RN) and RFS was observed through Kaplan-Meier survival analysis in the training and test cohorts (log-rank P < 0.050). Patients with high Nomo-score tumors were 2.669 times more likely to suffer recurrence than those with low Nomo-score tumors (HR, 2.669, P < 0.001). CONCLUSIONS The CT-based RN performed well in predicting both the histologic grade and outcome of CS.
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Affiliation(s)
- Xiaoli Li
- Department of Radiology, The Affiliated Hospital of Qingdao University, No. 369, Shanghai Road, 266000, Qingdao, Qingdao, Shandong, China
| | - Xianglong Shi
- Department of Radiology, The Affiliated Hospital of Qingdao University, No. 369, Shanghai Road, 266000, Qingdao, Qingdao, Shandong, China
| | - Yanmei Wang
- GE Healthcare China, Pudong New Town, Shanghai, China
| | - Jing Pang
- Department of Radiology, The Affiliated Hospital of Qingdao University, No. 369, Shanghai Road, 266000, Qingdao, Qingdao, Shandong, China
| | - Xia Zhao
- Department of Radiology, The Affiliated Hospital of Shandong University of Traditional Chinese Medicine, Jinan, Shandong, China
| | - Yuchao Xu
- School of Nuclear Science and Technology, University of South China, Hengyang, Hunan, China
| | - Qiyuan Li
- Department of Radiology, The Affiliated Hospital of Qingdao University, No. 369, Shanghai Road, 266000, Qingdao, Qingdao, Shandong, China
| | - Ning Wang
- Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, No. 324, Jingwu Road, 250021, Jinan, Shandong, China.
| | - Feng Duan
- Department of Radiology, The Affiliated Hospital of Qingdao University, No. 369, Shanghai Road, 266000, Qingdao, Qingdao, Shandong, China.
| | - Pei Nie
- Department of Radiology, The Affiliated Hospital of Qingdao University, No. 369, Shanghai Road, 266000, Qingdao, Qingdao, Shandong, China.
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Woltsche JN, Smolle M, Szolar D, Bergovec M, Leithner A. Prevalence and characteristics of benign cartilaginous tumours of the shoulder joint. An MRI-based study. Skeletal Radiol 2024; 53:59-66. [PMID: 37269383 PMCID: PMC10661778 DOI: 10.1007/s00256-023-04375-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/08/2023] [Revised: 05/28/2023] [Accepted: 05/28/2023] [Indexed: 06/05/2023]
Abstract
OBJECTIVE Enchondromas (EC) of the shoulder joint are benign intraosseous cartilage neoplasms, with atypical cartilaginous tumours (ACT) representing their intermediate counterpart. They are usually found incidentally on clinical imaging performed for other reasons. Thus far the prevalence of ECs of the shoulder has been analysed in only one study reaching a figure of 2.1%. MATERIALS AND METHODS The aim of the current study was to validate this number via retrospective analysis of a 45 times larger, uniform cohort consisting of 21.550 patients who had received an MRI of the shoulder at a single radiologic centre over a time span of 13.2 years. RESULTS Ninety-three of 21.550 patients presented with at least one cartilaginous tumour. Four patients showed two lesions at the same time resulting in a total number of 97 cartilage tumours (89 ECs [91.8%], 8 ACTs [8.2%]). Based on the 93 patients, the overall prevalence was 0.39% for ECs and 0.04% for ACTs. Mean size of the 97 ECs/ACTs was 2.3 ± 1.5 cm; most neoplasms were located in the proximal humerus (96.9%), in the metaphysis (60.8%) and peripherally (56.7%). Of all lesions, 94 tumours (96.9%) were located in the humerus and 3 (3.1%) in the scapula. CONCLUSION Frequency of EC/ACT of the shoulder joint appears to have been overestimated, with the current study revealing a prevalence of 0.43%.
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Affiliation(s)
- Johannes Nikolaus Woltsche
- Department of Orthopaedics and Trauma, Medical University of Graz, Auenbruggerplatz 5, 8036, Graz, Austria
| | - Maria Smolle
- Department of Orthopaedics and Trauma, Medical University of Graz, Auenbruggerplatz 5, 8036, Graz, Austria.
| | | | - Marko Bergovec
- Department of Orthopaedics and Trauma, Medical University of Graz, Auenbruggerplatz 5, 8036, Graz, Austria
| | - Andreas Leithner
- Department of Orthopaedics and Trauma, Medical University of Graz, Auenbruggerplatz 5, 8036, Graz, Austria
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Wei J, Lu S, Liu W, Liu H, Feng L, Tao Y, Pu Z, Liu Q, Hu Z, Wang H, Li W, Kang W, Yin C, Feng Z. A machine learning-based model for clinical prediction of distal metastasis in chondrosarcoma: a multicenter, retrospective study. PeerJ 2023; 11:e16485. [PMID: 38130920 PMCID: PMC10734410 DOI: 10.7717/peerj.16485] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2023] [Accepted: 10/27/2023] [Indexed: 12/23/2023] Open
Abstract
Background The occurrence of distant metastases (DM) limits the overall survival (OS) of patients with chondrosarcoma (CS). Early diagnosis and treatment of CS remains a great challenge in clinical practice. The aim of this study was to investigate metastatic factors and develop a risk stratification model for clinicians' decision-making. Methods Six machine learning (ML) algorithms, including logistic regression (LR), plain Bayesian classifier (NBC), decision tree (DT), random forest (RF), gradient boosting machine (GBM) and extreme gradient boosting (XGBoost). A 10-fold cross-validation was performed for each model separately, multicenter data was used as external validation, and the best (highest AUC) model was selected to build the network calculator. Results A total of 1,385 patients met the inclusion criteria, including 82 (5.9%) patients with metastatic CS. Multivariate logistic regression analysis showed that the risk of DM was significantly higher in patients with higher pathologic grades, T-stage, N-stage, and non-left primary lesions, as well as those who did not receive surgery and chemotherapy. The AUC of the six ML algorithms for predicting DM ranged from 0.911-0.985, with the extreme gradient enhancement algorithm (XGBoost) having the highest AUC. Therefore, we used the XGB model and uploaded the results to an online risk calculator for estimating DM risk. Conclusions In this study, combined with adequate SEER case database and external validation with data from multicenter institutions in different geographic regions, we confirmed that CS, T, N, laterality, and grading of surgery and chemotherapy were independent risk factors for DM. Based on the easily available clinical risk factors, machine learning algorithms built the XGB model that predicts the best outcome for DM. An online risk calculator helps simplify the patient assessment process and provides decision guidance for precision medicine and long-term cancer surveillance, which contributes to the interpretability of the model.
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Affiliation(s)
- Jihu Wei
- Faculty of Postgraduate, Guangxi University of Chinese Medicine, Nanning, Guangxi, China
| | - Shijin Lu
- Centre for Translational Medical Research in Integrative Chinese and Western Medicine, Ruikang Hospital Affiliated to Guangxi University of Chinese Medicine, Nanning, Guangxi, China
| | - Wencai Liu
- Department of Orthopaedics, Shanghai Jiao Tong University Affiliated Sixth People’s Hospital, Shanghai, China
| | - He Liu
- Faculty of Postgraduate, Guangxi University of Chinese Medicine, Nanning, Guangxi, China
| | - Lin Feng
- Faculty of Postgraduate, Guangxi University of Chinese Medicine, Nanning, Guangxi, China
| | - Yizi Tao
- Faculty of Postgraduate, Guangxi University of Chinese Medicine, Nanning, Guangxi, China
| | - Zhanglin Pu
- Faculty of Postgraduate, Guangxi University of Chinese Medicine, Nanning, Guangxi, China
| | - Qiang Liu
- Orthopedic Department, Xianyang Central Hospital, Xianyang, Shannxi, China
| | - Zhaohui Hu
- Department of Spine Surgery, Liuzhou People’s Hospital, Liuzhou, Guangxi, China
| | - Haosheng Wang
- Department of Orthopaedics, The Second Hospital of Jilin University, Changchun, China
| | - Wenle Li
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics & Center for Molecular Imaging and Translational Medicine, School of Public Health, Xiamen University, Xianmen, Fujian, China
| | - Wei Kang
- Faculty of Medicine, Macau University of Science and Technology, Macau, China
- Department of Mathematics, Physics and Interdisciplinary Studies, Guangzhou Laboratory (Bioland Laboratory, Guangzhou Regenerative Medicine and Health Guangdong Laboratory), Guangzhou, China
| | - Chengliang Yin
- Faculty of Medicine, Macau University of Science and Technology, Macau, China
| | - Zhe Feng
- Joint & Sports Medicine Surgery Division, Ruikang Hospital Affiliated to Guangxi University of Chinese Medicine, Nanning, Guangxi, China
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Gondim Teixeira PA, Lombard C, Moustache-Espinola P, Germain E, Gillet R, Hossu G, Jaquet Ribeiro G, Blum A. Initial Characterization of Focal Bone Lesions with Conventional Radiographs or Computed Tomography: Diagnostic Performance and Interobserver Agreement Assessment. Can Assoc Radiol J 2022; 74:404-414. [PMID: 36207066 DOI: 10.1177/08465371221131755] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022] Open
Abstract
Objectives: To ascertain the role of CT and conventional radiographs for the initial characterization of focal bone lesions.Methods: Images from 184 patients with confirmed bone tumors included in an ethics committee-approved study were retrospectively evaluated. The reference for benign-malignant distribution was based on histological analysis and long-term follow-up. Radiographs and CT features were analyzed by 2 independent musculoskeletal radiologists blinded to the final diagnosis. Lesion margins, periosteal reaction, cortical lysis, endosteal scalloping, presence of pathologic fracture, and lesion mineralization were evaluated. Results: The benign-malignant distribution in the study population was 68.5-31.5% (126 benign and 58 malignant). In the lesions that could be seen in both radiographs and CT, the performance of these methods for the benign-malignant differentiation was similar (accuracy varying from 72.8% to 76.5%). The interobserver agreement for the overall evaluation of lesion aggressiveness was considerably increased on CT compared to radiographs (Kappa of .63 vs .22). With conventional radiographs, 18 (9.7%) and 20 (10.8%) of the lesions evaluated were not seen respectively by readers 1 and 2. Among these unseen lesions, 50%-61.1% were located in the axial skeleton. Compared to radiographs, the number of lesions with cortical lysis and endosteal scalloping was 26-34% higher with CT. Conclusion: Although radiographs remain the primary imaging tool for lesions in the peripheral skeleton, CT should be performed for axial lesions. CT imaging can assess the extent of perilesional bone lysis more precisely than radiographs with a better evaluation of lesion fracture risk.
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Affiliation(s)
| | - Charles Lombard
- Guilloz imaging Department, Central Hospital, 26920University Hospital Center of Nancy, Nancy, France
| | | | - Edouard Germain
- Guilloz imaging Department, Central Hospital, 26920University Hospital Center of Nancy, Nancy, France
| | - Romain Gillet
- Guilloz imaging Department, Central Hospital, 26920University Hospital Center of Nancy, Nancy, France
| | - Gabriela Hossu
- Guilloz imaging Department, Central Hospital, 26920University Hospital Center of Nancy, Nancy, France
| | | | - Alain Blum
- Guilloz imaging Department, Central Hospital, 26920University Hospital Center of Nancy, Nancy, France
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Deng XY, Chen HY, Yu JN, Zhu XL, Chen JY, Shao GL, Yu RS. Diagnostic Value of CT- and MRI-Based Texture Analysis and Imaging Findings for Grading Cartilaginous Tumors in Long Bones. Front Oncol 2021; 11:700204. [PMID: 34722248 PMCID: PMC8551673 DOI: 10.3389/fonc.2021.700204] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2021] [Accepted: 09/28/2021] [Indexed: 01/12/2023] Open
Abstract
Objective To confirm the diagnostic performance of computed tomography (CT)-based texture analysis (CTTA) and magnetic resonance imaging (MRI)-based texture analysis for grading cartilaginous tumors in long bones and to compare these findings to radiological features. Materials and Methods Twenty-nine patients with enchondromas, 20 with low-grade chondrosarcomas and 16 with high-grade chondrosarcomas were included retrospectively. Clinical and radiological information and 9 histogram features extracted from CT, T1WI, and T2WI were evaluated. Binary logistic regression analysis was performed to determine predictive factors for grading cartilaginous tumors and to establish diagnostic models. Another 26 patients were included to validate each model. Receiver operating characteristic (ROC) curves were generated, and accuracy rate, sensitivity, specificity and positive/negative predictive values (PPV/NPV) were calculated. Results On imaging, endosteal scalloping, cortical destruction and calcification shape were predictive for grading cartilaginous tumors. For texture analysis, variance, mean, perc.01%, perc.10%, perc.99% and kurtosis were extracted after multivariate analysis. To differentiate benign cartilaginous tumors from low-grade chondrosarcomas, the imaging features model reached the highest accuracy rate (83.7%) and AUC (0.841), with a sensitivity of 75% and specificity of 93.1%. The CTTA feature model best distinguished low-grade and high-grade chondrosarcomas, with accuracies of 71.9%, and 80% in the training and validation groups, respectively; T1-TA and T2-TA could not distinguish them well. We found that the imaging feature model best differentiated benign and malignant cartilaginous tumors, with an accuracy rate of 89.2%, followed by the T1-TA feature model (80.4%). Conclusions The imaging feature model and CTTA- or MRI-based texture analysis have the potential to differentiate cartilaginous tumors in long bones by grade. MRI-based texture analysis failed to grade chondrosarcomas.
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Affiliation(s)
- Xue-Ying Deng
- Department of Radiology, Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Hangzhou, China.,Institue of Cancer and Basic Medicine (ICBM), Chinese Academy of Sciences, Hangzhou, China
| | - Hai-Yan Chen
- Department of Radiology, Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Hangzhou, China.,Institue of Cancer and Basic Medicine (ICBM), Chinese Academy of Sciences, Hangzhou, China
| | - Jie-Ni Yu
- Department of Radiology, Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Xiu-Liang Zhu
- Department of Radiology, Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Jie-Yu Chen
- Department of Radiology, Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Hangzhou, China.,Institue of Cancer and Basic Medicine (ICBM), Chinese Academy of Sciences, Hangzhou, China
| | - Guo-Liang Shao
- Department of Radiology, Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Hangzhou, China.,Institue of Cancer and Basic Medicine (ICBM), Chinese Academy of Sciences, Hangzhou, China
| | - Ri-Sheng Yu
- Department of Radiology, Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
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