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Zhang YF, Meng X. A clinical and CT-based radiomics prediction model study on residual back pain after vertebral augmentation for osteoporotic vertebral compression fractures. Asian J Surg 2024:S1015-9584(24)01496-9. [PMID: 39019758 DOI: 10.1016/j.asjsur.2024.07.125] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2024] [Accepted: 07/05/2024] [Indexed: 07/19/2024] Open
Affiliation(s)
| | - Xin Meng
- The People's Hospital of Tongjiang, Sichuan, China
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Zheng J, Liu W, Chen J, Sun Y, Chen C, Li J, Yi C, Zeng G, Chen Y, Song W. Differential diagnostic value of radiomics models in benign versus malignant vertebral compression fractures: A systematic review and meta-analysis. Eur J Radiol 2024; 178:111621. [PMID: 39018646 DOI: 10.1016/j.ejrad.2024.111621] [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: 12/08/2023] [Revised: 06/29/2024] [Accepted: 07/11/2024] [Indexed: 07/19/2024]
Abstract
PURPOSE Early diagnosis of benign and malignant vertebral compression fractures by analyzing imaging data is crucial to guide treatment and assess prognosis, and the development of radiomics made it an alternative option to biopsy examination. This systematic review and meta-analysis was conducted with the purpose of quantifying the diagnostic efficacy of radiomics models in distinguishing between benign and malignant vertebral compression fractures. METHODS Searching on PubMed, Embase, Web of Science and Cochrane Library was conducted to identify eligible studies published before September 23, 2023. After evaluating for methodological quality and risk of bias using the Radiomics Quality Score (RQS) and the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2), we selected studies providing confusion matrix results to be included in random-effects meta-analysis. RESULTS A total of sixteen articles, involving 1,519 vertebrae with pathological-diagnosed tumor infiltration, were included in our meta-analysis. The combined sensitivity and specificity of the top-performing models were 0.92 (95 % CI: 0.87-0.96) and 0.93 (95 % CI: 0.88-0.96), respectively. Their AUC was 0.97 (95 % CI: 0.96-0.99). By contrast, radiologists' combined sensitivity was 0.90 (95 %CI: 0.75-0.97) and specificity was 0.92 (95 %CI: 0.67-0.98). The AUC was 0.96 (95 %CI: 0.94-0.97). Subsequent subgroup analysis and sensitivity test suggested that part of the heterogeneity might be explained by differences in imaging modality, segmentation, deep learning and cross-validation. CONCLUSION We found remarkable diagnosis potential in correctly distinguishing vertebral compression fractures in complex clinical contexts. However, the published radiomics models still have a great heterogeneity, and more large-scale clinical trials are essential to validate their generalizability.
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Affiliation(s)
- Jiayuan Zheng
- Department of Orthopedic Surgery, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou 510120, China.
| | - Wenzhou Liu
- Department of Orthopedic Surgery, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou 510120, China.
| | - Jianan Chen
- Department of Orthopedic Surgery, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou 510120, China.
| | - Yujun Sun
- Department of Orthopedic Surgery, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou 510120, China.
| | - Chen Chen
- Department of Orthopedic Surgery, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou 510120, China.
| | - Jiajie Li
- Department of Orthopedic Surgery, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou 510120, China.
| | - Chunyan Yi
- Department of Orthopedic Surgery, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou 510120, China.
| | - Gang Zeng
- Department of Orthopedic Surgery, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou 510120, China.
| | - Yanbo Chen
- Department of Orthopedic Surgery, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou 510120, China.
| | - Weidong Song
- Department of Orthopedic Surgery, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou 510120, China.
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Geng W, Zhu J, Li M, Pi B, Wang X, Xing J, Xu H, Yang H. Radiomics Based on Multimodal magnetic resonance imaging for the Differential Diagnosis of Benign and Malignant Vertebral Compression Fractures. Orthop Surg 2024. [PMID: 38982652 DOI: 10.1111/os.14148] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/17/2024] [Revised: 06/06/2024] [Accepted: 06/09/2024] [Indexed: 07/11/2024] Open
Abstract
OBJECTIVES Recent studies have indicated that radiomics may have excellent performance and clinical application prospects in the differential diagnosis of benign and malignant vertebral compression fractures (VCFs). However, multimodal magnetic resonance imaging (MRI)-based radiomics model is rarely used in the differential diagnosis of benign and malignant VCFs, and is limited to lumbar. Herein, this study intends to develop and validate MRI radiomics models for differential diagnoses of benign and malignant VCFs in patients. METHODS This cross-sectional study involved 151 adult patients diagnosed with VCF in The First Affiliated Hospital of Soochow University in 2016-2021. The study was conducted in three steps: (i) the original MRI images were segmented, and the region of interest (ROI) was marked out; (ii) among the extracted features, those features with Pearson's correlation coefficient lower than 0.9 and the top 15 with the highest variance and Lasso regression coefficient less than and more than 0 were selected; (iii) MRI images and combined data were studied by logistic regression, decision tree, random forest and extreme gradient boosting (XGBoost) models in training set and the test set (ratio of 8:2), respectively; and the models were further verified and evaluated for the differential diagnosis performance. The evaluated indexes included area under receiver (AUC) of operating characteristic curve, accuracy, sensitivity, specificity, negative predictive value (NPV), positive predictive value (PPV), and 95% confidence intervals (CIs). The AUCs were used to assess the predictive performance of different machine learning modes for benign and malignant VCFs. RESULTS A total of 1144 radiomics features, and 14 clinical features were extracted. Finally, 12 radiomics features were included in the radiomics model, and 12 radiomics features with 14 clinical features were included in the combined model. In the radiomics model, the differential diagnosis performance in the logistic regression model with the AUC of 0.905 ± 0.026, accuracy of 0.817 ± 0.057, sensitivity of 0.831 ± 0.065, and negative predictive value of 0.813 ± 0.042, was superior to the other three. In the combined model, XGBoost model had the superior differential diagnosis performance with specificity (0.979 ± 0.026) and positive predictive value (0.971 ± 0.035). CONCLUSION The multimodal MRI-based radiomics model performed well in the differential diagnosis of benign and malignant VCFs, which may provide a tool for clinicians to differentially diagnose VCFs.
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Affiliation(s)
- Wei Geng
- Department of Orthopedics, The First Affiliated Hospital of Soochow University, Suzhou, China
| | - Jingfen Zhu
- Department of Radiology, The First Affiliated Hospital of Soochow University, Suzhou, China
| | - Mao Li
- Department of Orthopedics, The First Affiliated Hospital of Soochow University, Suzhou, China
| | - Bin Pi
- Department of Orthopedics, The First Affiliated Hospital of Soochow University, Suzhou, China
| | - Xiantao Wang
- Department of Orthopedics, Ruihua Affiliated of Soochow University, Suzhou, China
| | - Junhui Xing
- Department of Orthopedics, Dushu Lake Hospital Affiliated to Soochow University, Suzhou, China
| | - Haibo Xu
- Department of Orthopedics, Dushu Lake Hospital Affiliated to Soochow University, Suzhou, China
| | - Huilin Yang
- Department of Orthopedics, The First Affiliated Hospital of Soochow University, Suzhou, China
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Park JY, Lee SH, Kim YJ, Kim KG, Lee GJ. Machine learning model based on radiomics features for AO/OTA classification of pelvic fractures on pelvic radiographs. PLoS One 2024; 19:e0304350. [PMID: 38814948 PMCID: PMC11139281 DOI: 10.1371/journal.pone.0304350] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2024] [Accepted: 05/10/2024] [Indexed: 06/01/2024] Open
Abstract
Depending on the degree of fracture, pelvic fracture can be accompanied by vascular damage, and in severe cases, it may progress to hemorrhagic shock. Pelvic radiography can quickly diagnose pelvic fractures, and the Association for Osteosynthesis Foundation and Orthopedic Trauma Association (AO/OTA) classification system is useful for evaluating pelvic fracture instability. This study aimed to develop a radiomics-based machine-learning algorithm to quickly diagnose fractures on pelvic X-ray and classify their instability. data used were pelvic anteroposterior radiographs of 990 adults over 18 years of age diagnosed with pelvic fractures, and 200 normal subjects. A total of 93 features were extracted based on radiomics:18 first-order, 24 GLCM, 16 GLRLM, 16 GLSZM, 5 NGTDM, and 14 GLDM features. To improve the performance of machine learning, the feature selection methods RFE, SFS, LASSO, and Ridge were used, and the machine learning models used LR, SVM, RF, XGB, MLP, KNN, and LGBM. Performance measurement was evaluated by area under the curve (AUC) by analyzing the receiver operating characteristic curve. The machine learning model was trained based on the selected features using four feature-selection methods. When the RFE feature selection method was used, the average AUC was higher than that of the other methods. Among them, the combination with the machine learning model SVM showed the best performance, with an average AUC of 0.75±0.06. By obtaining a feature-importance graph for the combination of RFE and SVM, it is possible to identify features with high importance. The AO/OTA classification of normal pelvic rings and pelvic fractures on pelvic AP radiographs using a radiomics-based machine learning model showed the highest AUC when using the SVM classification combination. Further research on the radiomic features of each part of the pelvic bone constituting the pelvic ring is needed.
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Affiliation(s)
- Jun Young Park
- Department of Health Sciences and Technology, Gachon Advanced Institute for Health Sciences and Technology (GAIHST), Gachon University, Incheon, Republic of Korea
| | - Seung Hwan Lee
- Department of Trauma Surgery, Gachon University Gil Medical Center, Gachon University, Incheon, Republic of Korea
- Department of Traumatology, Gachon University College of Medicine, Gachon University, Incheon, Republic of Korea
| | - Young Jae Kim
- Department of Health Sciences and Technology, Gachon Advanced Institute for Health Sciences and Technology (GAIHST), Gachon University, Incheon, Republic of Korea
- Department of Medical Devices R&D Center, Gachon University Gil Medical Center, Gachon University, Incheon, Republic of Korea
- Department of Biomedical Engineering, Pre-medical Course, College of Medicine, Gachon University, Incheon, Republic of Korea
| | - Kwang Gi Kim
- Department of Health Sciences and Technology, Gachon Advanced Institute for Health Sciences and Technology (GAIHST), Gachon University, Incheon, Republic of Korea
- Department of Medical Devices R&D Center, Gachon University Gil Medical Center, Gachon University, Incheon, Republic of Korea
- Department of Biomedical Engineering, Pre-medical Course, College of Medicine, Gachon University, Incheon, Republic of Korea
| | - Gil Jae Lee
- Department of Trauma Surgery, Gachon University Gil Medical Center, Gachon University, Incheon, Republic of Korea
- Department of Traumatology, Gachon University College of Medicine, Gachon University, Incheon, Republic of Korea
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Liu J, Wang H, Shan X, Zhang L, Cui S, Shi Z, Liu Y, Zhang Y, Wang L. Hybrid transformer convolutional neural network-based radiomics models for osteoporosis screening in routine CT. BMC Med Imaging 2024; 24:62. [PMID: 38486185 PMCID: PMC10938662 DOI: 10.1186/s12880-024-01240-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2023] [Accepted: 03/06/2024] [Indexed: 03/18/2024] Open
Abstract
OBJECTIVE Early diagnosis of osteoporosis is crucial to prevent osteoporotic vertebral fracture and complications of spine surgery. We aimed to conduct a hybrid transformer convolutional neural network (HTCNN)-based radiomics model for osteoporosis screening in routine CT. METHODS To investigate the HTCNN algorithm for vertebrae and trabecular segmentation, 92 training subjects and 45 test subjects were employed. Furthermore, we included 283 vertebral bodies and randomly divided them into the training cohort (n = 204) and test cohort (n = 79) for radiomics analysis. Area receiver operating characteristic curves (AUCs) and decision curve analysis (DCA) were applied to compare the performance and clinical value between radiomics models and Hounsfield Unit (HU) values to detect dual-energy X-ray absorptiometry (DXA) based osteoporosis. RESULTS HTCNN algorithm revealed high precision for the segmentation of the vertebral body and trabecular compartment. In test sets, the mean dice scores reach 0.968 and 0.961. 12 features from the trabecular compartment and 15 features from the entire vertebral body were used to calculate the radiomics score (rad score). Compared with HU values and trabecular rad-score, the vertebrae rad-score suggested the best efficacy for osteoporosis and non-osteoporosis discrimination (training group: AUC = 0.95, 95%CI 0.91-0.99; test group: AUC = 0.97, 95%CI 0.93-1.00) and the differences were significant in test group according to the DeLong test (p < 0.05). CONCLUSIONS This retrospective study demonstrated the superiority of the HTCNN-based vertebrae radiomics model for osteoporosis discrimination in routine CT.
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Affiliation(s)
- Jiachen Liu
- Department of Orthopedics, Shengjing Hospital of China Medical University, 110004, Shenyang, People's Republic of China
| | - Huan Wang
- Department of Orthopedics, Shengjing Hospital of China Medical University, 110004, Shenyang, People's Republic of China
| | - Xiuqi Shan
- Department of Orthopedics, Shengjing Hospital of China Medical University, 110004, Shenyang, People's Republic of China
| | - Lei Zhang
- Department of Orthopedics, Shengjing Hospital of China Medical University, 110004, Shenyang, People's Republic of China
| | - Shaoqian Cui
- Department of Orthopedics, Shengjing Hospital of China Medical University, 110004, Shenyang, People's Republic of China
| | - Zelin Shi
- Shenyang Institute of Automation, Chinese Academy of Sciences, 110016, Shenyang, People's Republic of China
| | - Yunpeng Liu
- Shenyang Institute of Automation, Chinese Academy of Sciences, 110016, Shenyang, People's Republic of China
| | - Yingdi Zhang
- Shenyang Institute of Automation, Chinese Academy of Sciences, 110016, Shenyang, People's Republic of China
| | - Lanbo Wang
- Department of Radiology, Shengjing Hospital of China Medical University, 110004, Shenyang, People's Republic of China.
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Goller SS, Foreman SC, Rischewski JF, Weißinger J, Dietrich AS, Schinz D, Stahl R, Luitjens J, Siller S, Schmidt VF, Erber B, Ricke J, Liebig T, Kirschke JS, Dieckmeyer M, Gersing AS. Differentiation of benign and malignant vertebral fractures using a convolutional neural network to extract CT-based texture features. EUROPEAN SPINE JOURNAL : OFFICIAL PUBLICATION OF THE EUROPEAN SPINE SOCIETY, THE EUROPEAN SPINAL DEFORMITY SOCIETY, AND THE EUROPEAN SECTION OF THE CERVICAL SPINE RESEARCH SOCIETY 2023; 32:4314-4320. [PMID: 37401945 DOI: 10.1007/s00586-023-07838-7] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/14/2023] [Revised: 05/25/2023] [Accepted: 06/20/2023] [Indexed: 07/05/2023]
Abstract
PURPOSE To assess the diagnostic performance of three-dimensional (3D) CT-based texture features (TFs) using a convolutional neural network (CNN)-based framework to differentiate benign (osteoporotic) and malignant vertebral fractures (VFs). METHODS A total of 409 patients who underwent routine thoracolumbar spine CT at two institutions were included. VFs were categorized as benign or malignant using either biopsy or imaging follow-up of at least three months as standard of reference. Automated detection, labelling, and segmentation of the vertebrae were performed using a CNN-based framework ( https://anduin.bonescreen.de ). Eight TFs were extracted: Varianceglobal, Skewnessglobal, energy, entropy, short-run emphasis (SRE), long-run emphasis (LRE), run-length non-uniformity (RLN), and run percentage (RP). Multivariate regression models adjusted for age and sex were used to compare TFs between benign and malignant VFs. RESULTS Skewnessglobal showed a significant difference between the two groups when analyzing fractured vertebrae from T1 to L6 (benign fracture group: 0.70 [0.64-0.76]; malignant fracture group: 0.59 [0.56-0.63]; and p = 0.017), suggesting a higher skewness in benign VFs compared to malignant VFs. CONCLUSION Three-dimensional CT-based global TF skewness assessed using a CNN-based framework showed significant difference between benign and malignant thoracolumbar VFs and may therefore contribute to the clinical diagnostic work-up of patients with VFs.
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Affiliation(s)
- Sophia S Goller
- Department of Radiology, University Hospital, LMU Munich, Marchioninistraße 15, 81377, Munich, Germany.
| | - Sarah C Foreman
- Department of Diagnostic and Interventional Radiology, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Jon F Rischewski
- Institute of Neuroradiology, University Hospital, LMU Munich, Munich, Germany
| | - Jürgen Weißinger
- Institute of Neuroradiology, University Hospital, LMU Munich, Munich, Germany
| | - Anna-Sophia Dietrich
- Department of Diagnostic and Interventional Radiology, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - David Schinz
- Department of Diagnostic and Interventional Neuroradiology, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Robert Stahl
- Institute of Neuroradiology, University Hospital, LMU Munich, Munich, Germany
| | - Johanna Luitjens
- Department of Radiology, University Hospital, LMU Munich, Marchioninistraße 15, 81377, Munich, Germany
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA, USA
| | - Sebastian Siller
- Department of Neurosurgery, University Hospital, LMU Munich, Munich, Germany
| | - Vanessa F Schmidt
- Department of Radiology, University Hospital, LMU Munich, Marchioninistraße 15, 81377, Munich, Germany
| | - Bernd Erber
- Department of Radiology, University Hospital, LMU Munich, Marchioninistraße 15, 81377, Munich, Germany
| | - Jens Ricke
- Department of Radiology, University Hospital, LMU Munich, Marchioninistraße 15, 81377, Munich, Germany
| | - Thomas Liebig
- Institute of Neuroradiology, University Hospital, LMU Munich, Munich, Germany
| | - Jan S Kirschke
- Department of Diagnostic and Interventional Neuroradiology, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
- TUM-Neuroimaging Center, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Michael Dieckmeyer
- Department of Diagnostic and Interventional Neuroradiology, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
- Department of Diagnostic, Interventional and Pediatric Radiology, Inselspital, University Hospital, University of Bern, Bern, Switzerland
| | - Alexandra S Gersing
- Institute of Neuroradiology, University Hospital, LMU Munich, Munich, Germany
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Jiang Y, Zhang W, Huang S, Huang Q, Ye H, Zeng Y, Hua X, Cai J, Liu Z, Liu Q. Preoperative Prediction of New Vertebral Fractures after Vertebral Augmentation with a Radiomics Nomogram. Diagnostics (Basel) 2023; 13:3459. [PMID: 37998595 PMCID: PMC10670105 DOI: 10.3390/diagnostics13223459] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2023] [Revised: 11/11/2023] [Accepted: 11/14/2023] [Indexed: 11/25/2023] Open
Abstract
The occurrence of new vertebral fractures (NVFs) after vertebral augmentation (VA) procedures is common in patients with osteoporotic vertebral compression fractures (OVCFs), leading to painful experiences and financial burdens. We aim to develop a radiomics nomogram for the preoperative prediction of NVFs after VA. Data from center 1 (training set: n = 153; internal validation set: n = 66) and center 2 (external validation set: n = 44) were retrospectively collected. Radiomics features were extracted from MRI images and radiomics scores (radscores) were constructed for each level-specific vertebra based on least absolute shrinkage and selection operator (LASSO). The radiomics nomogram, integrating radiomics signature with presence of intravertebral cleft and number of previous vertebral fractures, was developed by multivariable logistic regression analysis. The predictive performance of the vertebrae was level-specific based on radscores and was generally superior to clinical variables. RadscoreL2 had the optimal discrimination (AUC ≥ 0.751). The nomogram provided good predictive performance (AUC ≥ 0.834), favorable calibration, and large clinical net benefits in each set. It was used successfully to categorize patients into high- or low-risk subgroups. As a noninvasive preoperative prediction tool, the MRI-based radiomics nomogram holds great promise for individualized prediction of NVFs following VA.
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Affiliation(s)
- Yang Jiang
- Department of Radiology, The Seventh Affiliated Hospital, Sun Yat-sen University, Shenzhen 518000, China; (Y.J.); (W.Z.); (J.C.)
| | - Wei Zhang
- Department of Radiology, The Seventh Affiliated Hospital, Sun Yat-sen University, Shenzhen 518000, China; (Y.J.); (W.Z.); (J.C.)
| | - Shihao Huang
- Department of Radiology, The Fifth Affiliated Hospital, Sun Yat-sen University, Zhuhai 519000, China;
| | - Qing Huang
- Department of Endocrinology, The Seventh Affiliated Hospital, Sun Yat-sen University, Shenzhen 518000, China;
| | - Haoyi Ye
- Department of Radiology, The Fourth Affiliated Hospital, Guangzhou Medical University, Guangzhou 511300, China;
| | - Yurong Zeng
- Department of Radiology, Huizhou Central People’s Hospital, Huizhou 516000, China;
| | - Xin Hua
- Department of Neurology, The First Affiliated Hospital, Wenzhou Medical University, Wenzhou 325000, China;
| | - Jinhui Cai
- Department of Radiology, The Seventh Affiliated Hospital, Sun Yat-sen University, Shenzhen 518000, China; (Y.J.); (W.Z.); (J.C.)
| | - Zhifeng Liu
- Department of Radiology, The Fourth Affiliated Hospital, Guangzhou Medical University, Guangzhou 511300, China;
| | - Qingyu Liu
- Department of Radiology, The Seventh Affiliated Hospital, Sun Yat-sen University, Shenzhen 518000, China; (Y.J.); (W.Z.); (J.C.)
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Wang X, Zhou D, Kong Y, Cheng N, Gao M, Zhang G, Ma J, Chen Y, Ge S. Value of 18F-FDG-PET/CT radiomics combined with clinical variables in the differential diagnosis of malignant and benign vertebral compression fractures. EJNMMI Res 2023; 13:89. [PMID: 37819414 PMCID: PMC10567613 DOI: 10.1186/s13550-023-01038-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2023] [Accepted: 09/20/2023] [Indexed: 10/13/2023] Open
Abstract
BACKGROUND Vertebral compression fractures (VCFs) are common clinical problems that arise from various reasons. The differential diagnosis of benign and malignant VCFs is challenging. This study was designed to develop and validate a radiomics model to predict benign and malignant VCFs with 18F-fluorodeoxyglucose-positron emission tomography/computed tomography (18F-FDG-PET/CT). RESULTS Twenty-six features (9 PET features and 17 CT features) and eight clinical variables (age, SUVmax, SUVpeak, SULmax, SULpeak, osteolytic destruction, fracture line, and appendices/posterior vertebrae involvement) were ultimately selected. The area under the curve (AUCs) of the radiomics and clinical-radiomics models were significantly different from that of the clinical model in both the training group (0.986, 0.987 vs. 0.884, p < 0.05) and test group (0.962, 0.948 vs. 0.858, p < 0.05), while there was no significant difference between the radiomics model and clinical-radiomics model (p > 0.05). The accuracies of the radiomics and clinical-radiomics models were 94.0% and 95.0% in the training group and 93.2% and 93.2% in the test group, respectively. The three models all showed good calibration (Hosmer-Lemeshow test, p > 0.05). According to the decision curve analysis (DCA), the radiomics model and clinical-radiomics model exhibited higher overall net benefit than the clinical model. CONCLUSIONS The PET/CT-based radiomics and clinical-radiomics models showed good performance in distinguishing between malignant and benign VCFs. The radiomics method may be valuable for treatment decision-making.
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Affiliation(s)
- Xun Wang
- Department of Medical Imaging, Affiliated Hospital of Jining Medical University, Guhuai Road, Jining, Shandong, China
| | - Dandan Zhou
- Big Data and Artificial Intelligence, Jining Polytechnic, Jinyu Road, Jining, Shandong, China
| | - Yu Kong
- Department of Medical Imaging, Affiliated Hospital of Jining Medical University, Guhuai Road, Jining, Shandong, China
| | - Nan Cheng
- Department of Medical Imaging, Affiliated Hospital of Jining Medical University, Guhuai Road, Jining, Shandong, China
| | - Ming Gao
- Department of Medical Imaging, Affiliated Hospital of Jining Medical University, Guhuai Road, Jining, Shandong, China
| | - Guqing Zhang
- Department of Medical Imaging, Affiliated Hospital of Jining Medical University, Guhuai Road, Jining, Shandong, China
| | - Junli Ma
- Department of Radiation Oncology, Affiliated Hospital of Jining Medical University, Guhuai Road, Jining, Shandong, China
| | - Yueqin Chen
- Department of Medical Imaging, Affiliated Hospital of Jining Medical University, Guhuai Road, Jining, Shandong, China.
| | - Shuang Ge
- Department of Radiation Oncology, Affiliated Hospital of Jining Medical University, Guhuai Road, Jining, Shandong, China.
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Chiari-Correia NS, Nogueira-Barbosa MH, Chiari-Correia RD, Azevedo-Marques PM. A 3D Radiomics-Based Artificial Neural Network Model for Benign Versus Malignant Vertebral Compression Fracture Classification in MRI. J Digit Imaging 2023; 36:1565-1577. [PMID: 37253895 PMCID: PMC10406770 DOI: 10.1007/s10278-023-00847-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2023] [Revised: 05/06/2023] [Accepted: 05/08/2023] [Indexed: 06/01/2023] Open
Abstract
To train an artificial neural network model using 3D radiomic features to differentiate benign from malignant vertebral compression fractures (VCFs) on MRI. This retrospective study analyzed sagittal T1-weighted lumbar spine MRIs from 91 patients (average age of 64.24 ± 11.75 years) diagnosed with benign or malignant VCFs from 2010 to 2019, of them 47 (51.6%) had benign VCFs and 44 (48.4%) had malignant VCFs. The lumbar fractures were three-dimensionally segmented and had their radiomic features extracted and selected with the wrapper method. The training set consisted of 100 fractured vertebral bodies from 61 patients (average age of 63.2 ± 12.5 years), and the test set was comprised of 30 fractured vertebral bodies from 30 patients (average age of 66.4 ± 9.9 years). Classification was performed with the multilayer perceptron neural network with a back-propagation algorithm. To validate the model, the tenfold cross-validation technique and an independent test set (holdout) were used. The performance of the model was evaluated using the average with a 95% confidence interval for the ROC AUC, accuracy, sensitivity, and specificity (considering the threshold = 0.5). In the internal validation test, the best model reached a ROC AUC of 0.98, an accuracy of 95% (95/100), a sensitivity of 93.5% (43/46), and specificity of 96.3% (52/54). In the validation with independent test set, the model achieved a ROC AUC of 0.97, an accuracy of 93.3% (28/30), a sensitivity of 93.3% (14/15), and a specificity of 93.3% (14/15). The model proposed in this study using radiomic features could differentiate benign from malignant vertebral compression fractures with excellent performance and is promising as an aid to radiologists in the characterization of VCFs.
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Affiliation(s)
- Natália S Chiari-Correia
- Medical Artificial Intelligence Laboratory of the Ribeirão, Preto Medical School, University of São Paulo, 3900 Bandeirantes Avenue, Ribeirão Preto, SP, 14049-900, Brazil.
| | - Marcello H Nogueira-Barbosa
- Medical Artificial Intelligence Laboratory of the Ribeirão, Preto Medical School, University of São Paulo, 3900 Bandeirantes Avenue, Ribeirão Preto, SP, 14049-900, Brazil
- Department of Medical Imaging, Hematology and Oncology of the Ribeirão Preto Medical School, University of São Paulo, Ribeirão Preto, SP, Brazil
- Department of Orthopedic Surgery, University of Missouri Health Care, Columbia, MO, USA
| | - Rodolfo Dias Chiari-Correia
- Department of Physics, Faculty of Philosophy, Sciences and Letters, University of São Paulo, Ribeirão Preto, SP, Brazil
| | - Paulo M Azevedo-Marques
- Medical Artificial Intelligence Laboratory of the Ribeirão, Preto Medical School, University of São Paulo, 3900 Bandeirantes Avenue, Ribeirão Preto, SP, 14049-900, Brazil
- Department of Medical Imaging, Hematology and Oncology of the Ribeirão Preto Medical School, University of São Paulo, Ribeirão Preto, SP, Brazil
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Duan S, Cao G, Hua Y, Hu J, Zheng Y, Wu F, Xu S, Rong T, Liu B. Identification of Origin for Spinal Metastases from MR Images: Comparison Between Radiomics and Deep Learning Methods. World Neurosurg 2023; 175:e823-e831. [PMID: 37059360 DOI: 10.1016/j.wneu.2023.04.029] [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: 02/16/2023] [Revised: 04/06/2023] [Accepted: 04/07/2023] [Indexed: 04/16/2023]
Abstract
OBJECTIVE To determine whether spinal metastatic lesions originated from lung cancer or from other cancers based on spinal contrast-enhanced T1 (CET1) magnetic resonance (MR) images analyzed using radiomics (RAD) and deep learning (DL) methods. METHODS We recruited and retrospectively reviewed 173 patients diagnosed with spinal metastases at two different centers between July 2018 and June 2021. Of these, 68 involved lung cancer and 105 were other types of cancer. They were assigned to an internal cohort of 149 patients, randomly divided into a training set and a validation set, and to an external cohort of 24 patients. All patients underwent CET1-MR imaging before surgery or biopsy. We developed two predictive algorithms: a DL model and a RAD model. We compared performance between models, and against human radiological assessment, via accuracy (ACC) and receiver operating characteristic (ROC) analyses. Furthermore, we analyzed the correlation between RAD and DL features. RESULTS The DL model outperformed RAD model across the board, with ACC/ area under the receiver operating characteristic curve (AUC) values of 0.93/0.94 (DL) versus 0.84/0.93 (RAD) when applied to the training set from the internal cohort, 0.74/0.76 versus 0.72/0.75 when applied to the validation set, and 0.72/0.76 versus 0.69/0.72 when applied to the external test cohort. For the validation set, it also outperformed expert radiological assessment (ACC: 0.65, AUC: 0.68). We only found weak correlations between DL and RAD features. CONCLUSION The DL algorithm successfully identified the origin of spinal metastases from pre-operative CET1-MR images, outperforming both RAD models and expert assessment by trained radiologists.
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Affiliation(s)
- Shuo Duan
- Department of Orthopaedic Surgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Guanmei Cao
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Yichun Hua
- Department of Medical Oncology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Junnan Hu
- Department of Orthopaedic Surgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Yali Zheng
- Department of Respiratory, Critical Care, and Sleep Medicine, Xiang'an Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China
| | - Fangfang Wu
- Department of Respiratory, Critical Care, and Sleep Medicine, Xiang'an Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China
| | - Shuai Xu
- Department of Spinal Surgery, Peking University People's Hospital, Peking University, Beijing, China
| | - Tianhua Rong
- Department of Orthopaedic Surgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Baoge Liu
- Department of Orthopaedic Surgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China; China National Clinical Research Center for Neurological Diseases, Beijing, China.
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Duan S, Hua Y, Cao G, Hu J, Cui W, Zhang D, Xu S, Rong T, Liu B. Differential diagnosis of benign and malignant vertebral compression fractures: Comparison and correlation of radiomics and deep learning frameworks based on spinal CT and clinical characteristics. Eur J Radiol 2023; 165:110899. [PMID: 37300935 DOI: 10.1016/j.ejrad.2023.110899] [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: 10/29/2022] [Revised: 04/28/2023] [Accepted: 05/26/2023] [Indexed: 06/12/2023]
Abstract
PURPOSE Differentiating benign from malignant vertebral compression fractures (VCFs) is a diagnostic dilemma in clinical practice. To improve the accuracy and efficiency of diagnosis, we evaluated the performance of deep learning and radiomics methods based on computed tomography (CT) and clinical characteristics in differentiating between Osteoporosis VCFs (OVCFs) and malignant VCFs (MVCFs). METHODS We enrolled a total of 280 patients (155 with OVCFs and 125 with MVCFs) and randomly divided them into a training set (80%, n = 224) and a validation set (20%, n = 56). We developed three predictive models: a deep learning (DL) model, a radiomics (Rad) model, and a combined DL_Rad model, using CT and clinical characteristics data. The Inception_V3 served as the backbone of the DL model. The input data for the DL_Rad model consisted of the combined features of Rad and DCNN features. We calculated the receiver operating characteristic curve, area under the curve (AUC), and accuracy (ACC) to assess the performance of the models. Additionally, we calculated the correlation between Rad features and DCNN features. RESULTS For the training set, the DL_Rad model achieved the best results, with an AUC of 0.99 and ACC of 0.99, followed by the Rad model (AUC: 0.99, ACC: 0.97) and DL model (AUC: 0.99, ACC: 0.94). For the validation set, the DL_Rad model (with an AUC of 0.97 and ACC of 0.93) outperformed the Rad model (with an AUC: 0.93 and ACC: 0.91) and the DL model (with an AUC: 0.89 and ACC: 0.88). Rad features achieved better classifier performance than the DCNN features, and their general correlations were weak. CONCLUSIONS The Deep learnig model, Radiomics model, and Deep learning Radiomics model achieved promising results in discriminating MVCFs from OVCFs, and the DL_Rad model performed the best.
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Affiliation(s)
- Shuo Duan
- Department of Orthopaedic Surgery, Beijing Tiantan Hospital, Capital Medical University, China
| | - Yichun Hua
- Department of Medical Oncology, Beijing Tiantan Hospital, Capital Medical University, China
| | - Guanmei Cao
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, China
| | - Junnan Hu
- Department of Orthopaedic Surgery, Beijing Tiantan Hospital, Capital Medical University, China
| | - Wei Cui
- Department of Orthopaedic Surgery, Beijing Tiantan Hospital, Capital Medical University, China
| | - Duo Zhang
- Department of Orthopaedic Surgery, Beijing Tiantan Hospital, Capital Medical University, China
| | - Shuai Xu
- Department of Spinal Surgery, Peking University People's Hospital, Peking University, China
| | - Tianhua Rong
- Department of Orthopaedic Surgery, Beijing Tiantan Hospital, Capital Medical University, China
| | - Baoge Liu
- Department of Orthopaedic Surgery, Beijing Tiantan Hospital, Capital Medical University, China; China National Clinical Research Center for Neurological Diseases, Beijing, China.
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12
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Zhang H, Cao W, Liu L, Meng Z, Sun N, Meng Y, Fei J. Noninvasive prediction of node-positive breast cancer response to presurgical neoadjuvant chemotherapy therapy based on machine learning of axillary lymph node ultrasound. J Transl Med 2023; 21:337. [PMID: 37211604 DOI: 10.1186/s12967-023-04201-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2023] [Accepted: 05/14/2023] [Indexed: 05/23/2023] Open
Abstract
OBJECTIVES To explore an optimal model to predict the response of patients with axillary lymph node (ALN) positive breast cancer to neoadjuvant chemotherapy (NAC) with machine learning using clinical and ultrasound-based radiomic features. METHODS In this study, 1014 patients with ALN-positive breast cancer confirmed by histological examination and received preoperative NAC in the Affiliated Hospital of Qingdao University (QUH) and Qingdao Municipal Hospital (QMH) were included. Finally, 444 participants from QUH were divided into the training cohort (n = 310) and validation cohort (n = 134) based on the date of ultrasound examination. 81 participants from QMH were used to evaluate the external generalizability of our prediction models. A total of 1032 radiomic features of each ALN ultrasound image were extracted and used to establish the prediction models. The clinical model, radiomics model, and radiomics nomogram with clinical factors (RNWCF) were built. The performance of the models was assessed with respect to discrimination and clinical usefulness. RESULTS Although the radiomics model did not show better predictive efficacy than the clinical model, the RNWCF showed favorable predictive efficacy in the training cohort (AUC, 0.855; 95% CI 0.817-0.893), the validation cohort (AUC, 0.882; 95% CI 0.834-0.928), and the external test cohort (AUC, 0.858; 95% CI 0.782-0.921) compared with the clinical factor model and radiomics model. CONCLUSIONS The RNWCF, a noninvasive, preoperative prediction tool that incorporates a combination of clinical and radiomics features, showed favorable predictive efficacy for the response of node-positive breast cancer to NAC. Therefore, the RNWCF could serve as a potential noninvasive approach to assist personalized treatment strategies, guide ALN management, avoiding unnecessary ALND.
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Affiliation(s)
- Hao Zhang
- Department of Spinal Surgery, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, China
| | - Wen Cao
- Department of Medical Record Management, The Affiliated Hospital of Qingdao University, Pingdu District, Qingdao, Shandong, China
| | - Lianjuan Liu
- Department of Ultrasound, Qingdao Hospital, University of Health and Rehabilitation Sciences (Qingdao Municipal Hospital), Qingdao, Shandong, China
| | - Zifan Meng
- Department of Blood Transfusion, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, China
| | - Ningning Sun
- Department of Breast Disease Center, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, China
| | - Yuanyuan Meng
- Department of Cardiac Ultrasound, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, China
| | - Jie Fei
- Department of Breast Imaging, The Affiliated Hospital of Qingdao University, No. 59 Haier Road, Qingdao, 266000, Shandong, China.
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13
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Zhang H, Yuan G, Wang C, Zhao H, Zhu K, Guo J, Chen M, Liu H, Yang G, Wang Y, Ma X. Differentiation of benign versus malignant indistinguishable vertebral compression fractures by different machine learning with MRI-based radiomic features. Eur Radiol 2023:10.1007/s00330-023-09678-x. [PMID: 37099176 DOI: 10.1007/s00330-023-09678-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2022] [Revised: 02/06/2023] [Accepted: 02/22/2023] [Indexed: 04/27/2023]
Abstract
OBJECTIVES To explore an optimal machine learning (ML) model trained on MRI-based radiomic features to differentiate benign from malignant indistinguishable vertebral compression fractures (VCFs). METHODS This retrospective study included patients within 6 weeks of back pain (non-traumatic) who underwent MRI and were diagnosed with benign and malignant indistinguishable VCFs. The two cohorts were retrospectively recruited from the Affiliated Hospital of Qingdao University (QUH) and Qinghai Red Cross Hospital (QRCH). Three hundred seventy-six participants from QUH were divided into the training (n = 263) and validation (n = 113) cohort based on the date of MRI examination. One hundred three participants from QRCH were used to evaluate the external generalizability of our prediction models. A total of 1045 radiomic features were extracted from each region of interest (ROI) and used to establish the models. The prediction models were established based on 7 different classifiers. RESULTS These models showed favorable efficacy in differentiating benign from malignant indistinguishable VCFs. However, our Gaussian naïve Bayes (GNB) model attained higher AUC and accuracy (0.86, 87.61%) than the other classifiers in validation cohort. It also remains the high accuracy and sensitivity for the external test cohort. CONCLUSIONS Our GNB model performed better than the other models in the present study, suggesting that it may be more useful for differentiating indistinguishable benign form malignant VCFs. KEY POINTS • The differential diagnosis of benign and malignant indistinguishable VCFs based on MRI is rather difficult for spine surgeons or radiologists. • Our ML models facilitate the differential diagnosis of benign and malignant indistinguishable VCFs with improved diagnostic efficacy. • Our GNB model had the high accuracy and sensitivity for clinical application.
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Affiliation(s)
- Hao Zhang
- Department of Spinal Surgery, The Affiliated Hospital of Qingdao University, No. 59 Haier Road, Qingdao, 266000, Shandong, China
| | - Genji Yuan
- College of Computer Science and Technology, Qingdao University, Qingdao, Shandong, China
| | - Chao Wang
- Department of Spinal Surgery, The Affiliated Hospital of Qingdao University, No. 59 Haier Road, Qingdao, 266000, Shandong, China
| | - Hongshun Zhao
- Department of Spinal Surgery, Qinghai Red Cross Hospital, Xining, Qinghai, China
| | - Kai Zhu
- Department of Spinal Surgery, The Affiliated Hospital of Qingdao University, No. 59 Haier Road, Qingdao, 266000, Shandong, China
| | - Jianwei Guo
- Department of Spinal Surgery, The Affiliated Hospital of Qingdao University, No. 59 Haier Road, Qingdao, 266000, Shandong, China
| | - Mingrui Chen
- Department of Spinal Surgery, The Affiliated Hospital of Qingdao University, No. 59 Haier Road, Qingdao, 266000, Shandong, China
| | - Houchen Liu
- Department of Spinal Surgery, The Affiliated Hospital of Qingdao University, No. 59 Haier Road, Qingdao, 266000, Shandong, China
| | - Guangjie Yang
- Department of Nuclear Medicine, The Affiliated Hospital of Qingdao University, No. 59 Haier Road, Qingdao, 266000, Shandong, China.
| | - Yan Wang
- Department of Spinal Surgery, The Affiliated Hospital of Qingdao University, No. 59 Haier Road, Qingdao, 266000, Shandong, China.
| | - Xuexiao Ma
- Department of Spinal Surgery, The Affiliated Hospital of Qingdao University, No. 59 Haier Road, Qingdao, 266000, Shandong, China.
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Tohidinezhad F, Bontempi D, Zhang Z, Dingemans AM, Aerts J, Bootsma G, Vansteenkiste J, Hashemi S, Smit E, Gietema H, Aerts HJ, Dekker A, Hendriks LEL, Traverso A, De Ruysscher D. Computed tomography-based radiomics for the differential diagnosis of pneumonitis in stage IV non-small cell lung cancer patients treated with immune checkpoint inhibitors. Eur J Cancer 2023; 183:142-151. [PMID: 36857819 DOI: 10.1016/j.ejca.2023.01.027] [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: 12/15/2022] [Revised: 01/29/2023] [Accepted: 01/29/2023] [Indexed: 02/11/2023]
Abstract
INTRODUCTION Immunotherapy-induced pneumonitis (IIP) is a serious side-effect which requires accurate diagnosis and management with high-dose corticosteroids. The differential diagnosis between IIP and other types of pneumonitis (OTP) remains challenging due to similar radiological patterns. This study was aimed to develop a prediction model to differentiate IIP from OTP in patients with stage IV non-small cell lung cancer (NSCLC) who developed pneumonitis during immunotherapy. METHODS Consecutive patients with metastatic NSCLC treated with immunotherapy in six centres in the Netherlands and Belgium from 2017 to 2020 were reviewed and cause-specific pneumonitis events were identified. Seven regions of interest (segmented lungs and spheroidal/cubical regions surrounding the inflammation) were examined to extract the most predictive radiomic features from the chest computed tomography images obtained at pneumonitis manifestation. Models were internally tested regarding discrimination, calibration and decisional benefit. To evaluate the clinical application of the models, predicted labels were compared with the separate clinical and radiological judgements. RESULTS A total of 556 patients were reviewed; 31 patients (5.6%) developed IIP and 41 patients developed OTP (7.4%). The line of immunotherapy was the only predictive factor in the clinical model (2nd versus 1st odds ratio = 0.08, 95% confidence interval:0.01-0.77). The best radiomic model was achieved using a 75-mm spheroidal region of interest which showed an optimism-corrected area under the receiver operating characteristic curve of 0.83 (95% confidence interval:0.77-0.95) with negative and positive predictive values of 80% and 79%, respectively. Good calibration and net benefits were achieved for the radiomic model across the entire range of probabilities. A correct diagnosis was provided by the radiomic model in 10 out of 12 cases with non-conclusive radiological judgements. CONCLUSION Radiomic biomarkers applied to computed tomography imaging may support clinicians making the differential diagnosis of pneumonitis in patients with NSCLC receiving immunotherapy, especially when the radiologic assessment is non-conclusive.
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Affiliation(s)
- Fariba Tohidinezhad
- Department of Radiation Oncology (Maastro Clinic), School for Oncology and Reproduction (GROW), Maastricht University Medical Center, Maastricht, the Netherlands
| | - Dennis Bontempi
- Department of Radiation Oncology (Maastro Clinic), School for Oncology and Reproduction (GROW), Maastricht University Medical Center, Maastricht, the Netherlands; Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Boston, MA, USA; Department of Radiology and Nuclear Medicine, Maastricht University Medical Center, Maastricht, the Netherlands
| | - Zhen Zhang
- Department of Radiation Oncology (Maastro Clinic), School for Oncology and Reproduction (GROW), Maastricht University Medical Center, Maastricht, the Netherlands
| | - Anne-Marie Dingemans
- Department of Pulmonary Diseases, School for Oncology and Reproduction (GROW), Maastricht University Medical Center, Maastricht, the Netherlands
| | - Joachim Aerts
- Department of Pulmonary Medicine, School of Medicine, Erasmus University Medical Center, Rotterdam, the Netherlands
| | - Gerben Bootsma
- Department of Pulmonary Diseases, Zuyderland Hospital, Heerlen, the Netherlands
| | - Johan Vansteenkiste
- Department of Respiratory Oncology, University Hospital KU Leuven, Leuven, Belgium
| | - Sayed Hashemi
- Department of Pulmonary Medicine, Amsterdam UMC, VU University Medical Center, Amsterdam, the Netherlands
| | - Egbert Smit
- Netherlands Cancer Institute, Amsterdam, the Netherlands
| | - Hester Gietema
- Department of Radiology and Nuclear Medicine, Maastricht University Medical Center, Maastricht, the Netherlands
| | - Hugo Jwl Aerts
- Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Boston, MA, USA; Department of Radiology and Nuclear Medicine, Maastricht University Medical Center, Maastricht, the Netherlands; Departments of Radiation Oncology and Radiology, Brigham and Women's Hospital, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA
| | - Andre Dekker
- Department of Radiation Oncology (Maastro Clinic), School for Oncology and Reproduction (GROW), Maastricht University Medical Center, Maastricht, the Netherlands
| | - Lizza E L Hendriks
- Department of Pulmonary Diseases, School for Oncology and Reproduction (GROW), Maastricht University Medical Center, Maastricht, the Netherlands
| | - Alberto Traverso
- Department of Radiation Oncology (Maastro Clinic), School for Oncology and Reproduction (GROW), Maastricht University Medical Center, Maastricht, the Netherlands
| | - Dirk De Ruysscher
- Department of Radiation Oncology (Maastro Clinic), School for Oncology and Reproduction (GROW), Maastricht University Medical Center, Maastricht, the Netherlands.
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Zhang J, Liu J, Liang Z, Xia L, Zhang W, Xing Y, Zhang X, Tang G. Differentiation of acute and chronic vertebral compression fractures using conventional CT based on deep transfer learning features and hand-crafted radiomics features. BMC Musculoskelet Disord 2023; 24:165. [PMID: 36879285 PMCID: PMC9987077 DOI: 10.1186/s12891-023-06281-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/21/2022] [Accepted: 02/28/2023] [Indexed: 03/08/2023] Open
Abstract
BACKGROUND We evaluated the diagnostic efficacy of deep learning radiomics (DLR) and hand-crafted radiomics (HCR) features in differentiating acute and chronic vertebral compression fractures (VCFs). METHODS A total of 365 patients with VCFs were retrospectively analysed based on their computed tomography (CT) scan data. All patients completed MRI examination within 2 weeks. There were 315 acute VCFs and 205 chronic VCFs. Deep transfer learning (DTL) features and HCR features were extracted from CT images of patients with VCFs using DLR and traditional radiomics, respectively, and feature fusion was performed to establish the least absolute shrinkage and selection operator. The MRI display of vertebral bone marrow oedema was used as the gold standard for acute VCF, and the model performance was evaluated using the receiver operating characteristic (ROC).To separately evaluate the effectiveness of DLR, traditional radiomics and feature fusion in the differential diagnosis of acute and chronic VCFs, we constructed a nomogram based on the clinical baseline data to visualize the classification evaluation. The predictive power of each model was compared using the Delong test, and the clinical value of the nomogram was evaluated using decision curve analysis (DCA). RESULTS Fifty DTL features were obtained from DLR, 41 HCR features were obtained from traditional radiomics, and 77 features fusion were obtained after feature screening and fusion of the two. The area under the curve (AUC) of the DLR model in the training cohort and test cohort were 0.992 (95% confidence interval (CI), 0.983-0.999) and 0.871 (95% CI, 0.805-0.938), respectively. While the AUCs of the conventional radiomics model in the training cohort and test cohort were 0.973 (95% CI, 0.955-0.990) and 0.854 (95% CI, 0.773-0.934), respectively. The AUCs of the features fusion model in the training cohort and test cohort were 0.997 (95% CI, 0.994-0.999) and 0.915 (95% CI, 0.855-0.974), respectively. The AUCs of nomogram constructed by the features fusion in combination with clinical baseline data were 0.998 (95% CI, 0.996-0.999) and 0.946 (95% CI, 0.906-0.987) in the training cohort and test cohort, respectively. The Delong test showed that the differences between the features fusion model and the nomogram in the training cohort and the test cohort were not statistically significant (P values were 0.794 and 0.668, respectively), and the differences in the other prediction models in the training cohort and the test cohort were statistically significant (P < 0.05). DCA showed that the nomogram had high clinical value. CONCLUSION The features fusion model can be used for the differential diagnosis of acute and chronic VCFs, and its differential diagnosis ability is improved when compared with that when either radiomics is used alone. At the same time, the nomogram has a high predictive value for acute and chronic VCFs and can be a potential decision-making tool to assist clinicians, especially when a patient is unable to undergo spinal MRI examination.
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Affiliation(s)
- Jun Zhang
- Department of Radiology, Clinical Medical College of Shanghai Tenth People's Hospital of Nanjing Medical University, 301 Middle Yanchang Road, Shanghai, 200072, P.R. China.,Department of Radiology, Sir RunRun Hospital affiliated to Nanjing Medical University, 109 Longmian Road, Nanjing, Jiangsu, 211002, P.R. China
| | - Jiayi Liu
- Department of Radiology, Sir RunRun Hospital affiliated to Nanjing Medical University, 109 Longmian Road, Nanjing, Jiangsu, 211002, P.R. China
| | - Zhipeng Liang
- Department of Radiology, Sir RunRun Hospital affiliated to Nanjing Medical University, 109 Longmian Road, Nanjing, Jiangsu, 211002, P.R. China
| | - Liang Xia
- Department of Radiology, Sir RunRun Hospital affiliated to Nanjing Medical University, 109 Longmian Road, Nanjing, Jiangsu, 211002, P.R. China
| | - Weixiao Zhang
- Department of Radiology, Sir RunRun Hospital affiliated to Nanjing Medical University, 109 Longmian Road, Nanjing, Jiangsu, 211002, P.R. China
| | - Yanfen Xing
- Department of Radiology, Sir RunRun Hospital affiliated to Nanjing Medical University, 109 Longmian Road, Nanjing, Jiangsu, 211002, P.R. China
| | - Xueli Zhang
- Department of Radiology, Shanghai TenthPeople's Hospital, Tongji University School of Medicine, 301 Middle Yanchang Road, Shanghai, 200072, P.R. China
| | - Guangyu Tang
- Department of Radiology, Clinical Medical College of Shanghai Tenth People's Hospital of Nanjing Medical University, 301 Middle Yanchang Road, Shanghai, 200072, P.R. China. .,Department of Radiology, Shanghai TenthPeople's Hospital, Tongji University School of Medicine, 301 Middle Yanchang Road, Shanghai, 200072, P.R. China.
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16
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Wu S, Wei Y, Li H, Zhou C, Chen T, Zhu J, Liu L, Wu S, Ma F, Ye Z, Deng G, Yao Y, Fan B, Liao S, Huang S, Sun X, Chen L, Guo H, Chen W, Zhan X, Liu C. A Predictive Clinical-Radiomics Nomogram for Differentiating Tuberculous Spondylitis from Pyogenic Spondylitis Using CT and Clinical Risk Factors. Infect Drug Resist 2022; 15:7327-7338. [PMID: 36536861 PMCID: PMC9758984 DOI: 10.2147/idr.s388868] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2022] [Accepted: 12/02/2022] [Indexed: 10/30/2023] Open
Abstract
OBJECTIVE The study aimed to develop and validate a nomogram model with clinical risk factors and radiomic features for differentiating tuberculous spondylitis (TS) from pyogenic spondylitis (PS). METHODS A total of 254 patients with TS (n = 141) or PS (n = 113) were randomly divided into training (n = 180) and validation (n = 74) groups. In addition, 43 patients (TS = 22 and PS = 21) were collected to construct a test cohort. t-test analysis, de-redundancy analysis, and minimum absolute shrinkage and selection operator (lasso) algorithm were utilized on the training set to obtain the optimal radiomics features from computed tomography (CT) for constructing the radiomics model and determine the radiomics score (Rad-score). Eight clinical risk predictors were identified to develop the clinical model. Combined with clinical risk predictors and Rad-scores, a nomogram model was constructed using multivariate logistic regression analysis. RESULTS A total of 1781 features were extracted, and 12 optimal radiomic features were utilized to construct the radiomic model and determine the Rad-score. The combined clinical radiomics model revealed good discrimination performance in both the training cohort and the validation cohort (AUC = 0.891 and 0.830) and was superior to the clinical (AUC = 0.807 and 0.785) and radiomics (AUC = 0.796 and 0.811) models. The calibration curve and DCA also depicted that the nomogram had better clinical efficacy. The discriminative performance of the model is well validated in the test cohort (AUC=0.877). CONCLUSION The clinical radiomic nomogram could serve as a promising predictive tool for differentiating TS from PS, which could be helpful for clinical decision-making.
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Affiliation(s)
- Shaofeng Wu
- Department of Spine and Osteopathy Ward, The First Affiliated Hospital of Guangxi Medical University, Nanning, People’s Republic of China
| | - Yating Wei
- Department of Spine and Osteopathy Ward, The First Affiliated Hospital of Guangxi Medical University, Nanning, People’s Republic of China
| | - Hao Li
- Department of Spine and Osteopathy Ward, The First Affiliated Hospital of Guangxi Medical University, Nanning, People’s Republic of China
| | - Chenxing Zhou
- Department of Spine and Osteopathy Ward, The First Affiliated Hospital of Guangxi Medical University, Nanning, People’s Republic of China
| | - Tianyou Chen
- Department of Spine and Osteopathy Ward, The First Affiliated Hospital of Guangxi Medical University, Nanning, People’s Republic of China
| | - Jichong Zhu
- Department of Spine and Osteopathy Ward, The First Affiliated Hospital of Guangxi Medical University, Nanning, People’s Republic of China
| | - Lu Liu
- Department of Spine and Osteopathy Ward, The First Affiliated Hospital of Guangxi Medical University, Nanning, People’s Republic of China
| | - Siling Wu
- Department of Spine and Osteopathy Ward, The First Affiliated Hospital of Guangxi Medical University, Nanning, People’s Republic of China
| | - Fengzhi Ma
- Department of Spine and Osteopathy Ward, The First Affiliated Hospital of Guangxi Medical University, Nanning, People’s Republic of China
| | - Zhen Ye
- Department of Spine and Osteopathy Ward, The First Affiliated Hospital of Guangxi Medical University, Nanning, People’s Republic of China
| | - Guobing Deng
- Department of Spine and Osteopathy Ward, The First Affiliated Hospital of Guangxi Medical University, Nanning, People’s Republic of China
| | - Yuanlin Yao
- Department of Spine and Osteopathy Ward, The First Affiliated Hospital of Guangxi Medical University, Nanning, People’s Republic of China
| | - Binguang Fan
- Department of Spine and Osteopathy Ward, The First Affiliated Hospital of Guangxi Medical University, Nanning, People’s Republic of China
| | - Shian Liao
- Department of Spine and Osteopathy Ward, The First Affiliated Hospital of Guangxi Medical University, Nanning, People’s Republic of China
| | - Shengsheng Huang
- Department of Spine and Osteopathy Ward, The First Affiliated Hospital of Guangxi Medical University, Nanning, People’s Republic of China
| | - Xuhua Sun
- Department of Spine and Osteopathy Ward, The First Affiliated Hospital of Guangxi Medical University, Nanning, People’s Republic of China
| | - Liyi Chen
- Department of Spine and Osteopathy Ward, The First Affiliated Hospital of Guangxi Medical University, Nanning, People’s Republic of China
| | - Hao Guo
- Department of Spine and Osteopathy Ward, The First Affiliated Hospital of Guangxi Medical University, Nanning, People’s Republic of China
| | - Wuhua Chen
- Department of Spine and Osteopathy Ward, The First Affiliated Hospital of Guangxi Medical University, Nanning, People’s Republic of China
| | - Xinli Zhan
- Department of Spine and Osteopathy Ward, The First Affiliated Hospital of Guangxi Medical University, Nanning, People’s Republic of China
| | - Chong Liu
- Department of Spine and Osteopathy Ward, The First Affiliated Hospital of Guangxi Medical University, Nanning, People’s Republic of China
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Kim AY, Yoon MA, Ham SJ, Cho YC, Ko Y, Park B, Kim S, Lee E, Lee RW, Chee CG, Lee MH, Lee SH, Chung HW. Prediction of the Acuity of Vertebral Compression Fractures on CT Using Radiologic and Radiomic Features. Acad Radiol 2022; 29:1512-1520. [PMID: 34998683 DOI: 10.1016/j.acra.2021.12.008] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2021] [Revised: 12/08/2021] [Accepted: 12/08/2021] [Indexed: 12/14/2022]
Abstract
RATIONALE AND OBJECTIVES To develop and validate prediction models to differentiate acute and chronic vertebral compression fractures based on radiologic and radiomic features on CT. MATERIALS AND METHODS This study included acute and chronic compression fractures in patients who underwent both spine CT and MRI examinations. For each fractured vertebra, three CT findings ([1] cortical disruption, [2] hypoattenuating cleft or sclerotic line, and [3] relative bone marrow attenuation) were assessed by two radiologists. A radiomic score was built from 280 radiomic features extracted from non-contrast-enhanced CT images. Weighted multivariable logistic regression analysis was performed to build a radiologic model based on CT findings and an integrated model combining the radiomic score and CT findings. Model performance was evaluated and compared. Models were externally validated using an independent test cohort. RESULTS A total to 238 fractures (159 acute and 79 chronic) in 122 patients and 58 fractures (39 acute and 19 chronic) in 32 patients were included in the training and test cohorts, respectively. The AUC of the radiomic score was 0.95 in the training and 0.93 in the test cohorts. The AUC of the radiologic model was 0.89 in the training and 0.83 in the test cohorts. The discriminatory performance of the integrated model was significantly higher than the radiologic model in both the training (AUC, 0.97; p<0.01) and the test (AUC, 0.95; p=0.01) cohorts. CONCLUSION Combining radiomics with radiologic findings significantly improved the performance of CT in determining the acuity of vertebral compression fractures.
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Affiliation(s)
- A Yeon Kim
- Department of Radiology and Research Institute of Radiology (A.Y.K., M.A.Y., S.J.H., C.G.C., M.H.L., S.H.L., H.W.C.), University of Ulsan College of Medicine, Asan Medical Center, 88 Olympic-ro 43-gil, Songpa-gu, Seoul 05505, Korea; Biomedical Research Center (Y.C.C., Y.K.), Asan Institute for Life Sciences, Asan Medical Center, Songpa-gu, Seoul, Korea; Health Innovation Big Data Center (B.P.), Asan Institute for Life Sciences, Asan Medical Center, Songpa-gu, Seoul, Korea; Department of Clinical Epidemiology and Biostatistics (S.K.), Asan Medical Center, Songpa-gu, Seoul, Korea; Department of Radiology (E.L.), Seoul National University Bundang Hospital, Seongnam-si, Gyeonggi-do, Korea; Department of Radiology (R.W.L.), Inha University Hospital, Jung-gu, Incheon, Korea
| | - Min A Yoon
- Department of Radiology and Research Institute of Radiology (A.Y.K., M.A.Y., S.J.H., C.G.C., M.H.L., S.H.L., H.W.C.), University of Ulsan College of Medicine, Asan Medical Center, 88 Olympic-ro 43-gil, Songpa-gu, Seoul 05505, Korea; Biomedical Research Center (Y.C.C., Y.K.), Asan Institute for Life Sciences, Asan Medical Center, Songpa-gu, Seoul, Korea; Health Innovation Big Data Center (B.P.), Asan Institute for Life Sciences, Asan Medical Center, Songpa-gu, Seoul, Korea; Department of Clinical Epidemiology and Biostatistics (S.K.), Asan Medical Center, Songpa-gu, Seoul, Korea; Department of Radiology (E.L.), Seoul National University Bundang Hospital, Seongnam-si, Gyeonggi-do, Korea; Department of Radiology (R.W.L.), Inha University Hospital, Jung-gu, Incheon, Korea.
| | - Su Jung Ham
- Department of Radiology and Research Institute of Radiology (A.Y.K., M.A.Y., S.J.H., C.G.C., M.H.L., S.H.L., H.W.C.), University of Ulsan College of Medicine, Asan Medical Center, 88 Olympic-ro 43-gil, Songpa-gu, Seoul 05505, Korea; Biomedical Research Center (Y.C.C., Y.K.), Asan Institute for Life Sciences, Asan Medical Center, Songpa-gu, Seoul, Korea; Health Innovation Big Data Center (B.P.), Asan Institute for Life Sciences, Asan Medical Center, Songpa-gu, Seoul, Korea; Department of Clinical Epidemiology and Biostatistics (S.K.), Asan Medical Center, Songpa-gu, Seoul, Korea; Department of Radiology (E.L.), Seoul National University Bundang Hospital, Seongnam-si, Gyeonggi-do, Korea; Department of Radiology (R.W.L.), Inha University Hospital, Jung-gu, Incheon, Korea
| | - Young Chul Cho
- Department of Radiology and Research Institute of Radiology (A.Y.K., M.A.Y., S.J.H., C.G.C., M.H.L., S.H.L., H.W.C.), University of Ulsan College of Medicine, Asan Medical Center, 88 Olympic-ro 43-gil, Songpa-gu, Seoul 05505, Korea; Biomedical Research Center (Y.C.C., Y.K.), Asan Institute for Life Sciences, Asan Medical Center, Songpa-gu, Seoul, Korea; Health Innovation Big Data Center (B.P.), Asan Institute for Life Sciences, Asan Medical Center, Songpa-gu, Seoul, Korea; Department of Clinical Epidemiology and Biostatistics (S.K.), Asan Medical Center, Songpa-gu, Seoul, Korea; Department of Radiology (E.L.), Seoul National University Bundang Hospital, Seongnam-si, Gyeonggi-do, Korea; Department of Radiology (R.W.L.), Inha University Hospital, Jung-gu, Incheon, Korea
| | - Yousun Ko
- Department of Radiology and Research Institute of Radiology (A.Y.K., M.A.Y., S.J.H., C.G.C., M.H.L., S.H.L., H.W.C.), University of Ulsan College of Medicine, Asan Medical Center, 88 Olympic-ro 43-gil, Songpa-gu, Seoul 05505, Korea; Biomedical Research Center (Y.C.C., Y.K.), Asan Institute for Life Sciences, Asan Medical Center, Songpa-gu, Seoul, Korea; Health Innovation Big Data Center (B.P.), Asan Institute for Life Sciences, Asan Medical Center, Songpa-gu, Seoul, Korea; Department of Clinical Epidemiology and Biostatistics (S.K.), Asan Medical Center, Songpa-gu, Seoul, Korea; Department of Radiology (E.L.), Seoul National University Bundang Hospital, Seongnam-si, Gyeonggi-do, Korea; Department of Radiology (R.W.L.), Inha University Hospital, Jung-gu, Incheon, Korea
| | - Bumwoo Park
- Department of Radiology and Research Institute of Radiology (A.Y.K., M.A.Y., S.J.H., C.G.C., M.H.L., S.H.L., H.W.C.), University of Ulsan College of Medicine, Asan Medical Center, 88 Olympic-ro 43-gil, Songpa-gu, Seoul 05505, Korea; Biomedical Research Center (Y.C.C., Y.K.), Asan Institute for Life Sciences, Asan Medical Center, Songpa-gu, Seoul, Korea; Health Innovation Big Data Center (B.P.), Asan Institute for Life Sciences, Asan Medical Center, Songpa-gu, Seoul, Korea; Department of Clinical Epidemiology and Biostatistics (S.K.), Asan Medical Center, Songpa-gu, Seoul, Korea; Department of Radiology (E.L.), Seoul National University Bundang Hospital, Seongnam-si, Gyeonggi-do, Korea; Department of Radiology (R.W.L.), Inha University Hospital, Jung-gu, Incheon, Korea
| | - Seonok Kim
- Department of Radiology and Research Institute of Radiology (A.Y.K., M.A.Y., S.J.H., C.G.C., M.H.L., S.H.L., H.W.C.), University of Ulsan College of Medicine, Asan Medical Center, 88 Olympic-ro 43-gil, Songpa-gu, Seoul 05505, Korea; Biomedical Research Center (Y.C.C., Y.K.), Asan Institute for Life Sciences, Asan Medical Center, Songpa-gu, Seoul, Korea; Health Innovation Big Data Center (B.P.), Asan Institute for Life Sciences, Asan Medical Center, Songpa-gu, Seoul, Korea; Department of Clinical Epidemiology and Biostatistics (S.K.), Asan Medical Center, Songpa-gu, Seoul, Korea; Department of Radiology (E.L.), Seoul National University Bundang Hospital, Seongnam-si, Gyeonggi-do, Korea; Department of Radiology (R.W.L.), Inha University Hospital, Jung-gu, Incheon, Korea
| | - Eugene Lee
- Department of Radiology and Research Institute of Radiology (A.Y.K., M.A.Y., S.J.H., C.G.C., M.H.L., S.H.L., H.W.C.), University of Ulsan College of Medicine, Asan Medical Center, 88 Olympic-ro 43-gil, Songpa-gu, Seoul 05505, Korea; Biomedical Research Center (Y.C.C., Y.K.), Asan Institute for Life Sciences, Asan Medical Center, Songpa-gu, Seoul, Korea; Health Innovation Big Data Center (B.P.), Asan Institute for Life Sciences, Asan Medical Center, Songpa-gu, Seoul, Korea; Department of Clinical Epidemiology and Biostatistics (S.K.), Asan Medical Center, Songpa-gu, Seoul, Korea; Department of Radiology (E.L.), Seoul National University Bundang Hospital, Seongnam-si, Gyeonggi-do, Korea; Department of Radiology (R.W.L.), Inha University Hospital, Jung-gu, Incheon, Korea
| | - Ro Woon Lee
- Department of Radiology and Research Institute of Radiology (A.Y.K., M.A.Y., S.J.H., C.G.C., M.H.L., S.H.L., H.W.C.), University of Ulsan College of Medicine, Asan Medical Center, 88 Olympic-ro 43-gil, Songpa-gu, Seoul 05505, Korea; Biomedical Research Center (Y.C.C., Y.K.), Asan Institute for Life Sciences, Asan Medical Center, Songpa-gu, Seoul, Korea; Health Innovation Big Data Center (B.P.), Asan Institute for Life Sciences, Asan Medical Center, Songpa-gu, Seoul, Korea; Department of Clinical Epidemiology and Biostatistics (S.K.), Asan Medical Center, Songpa-gu, Seoul, Korea; Department of Radiology (E.L.), Seoul National University Bundang Hospital, Seongnam-si, Gyeonggi-do, Korea; Department of Radiology (R.W.L.), Inha University Hospital, Jung-gu, Incheon, Korea
| | - Choong Guen Chee
- Department of Radiology and Research Institute of Radiology (A.Y.K., M.A.Y., S.J.H., C.G.C., M.H.L., S.H.L., H.W.C.), University of Ulsan College of Medicine, Asan Medical Center, 88 Olympic-ro 43-gil, Songpa-gu, Seoul 05505, Korea; Biomedical Research Center (Y.C.C., Y.K.), Asan Institute for Life Sciences, Asan Medical Center, Songpa-gu, Seoul, Korea; Health Innovation Big Data Center (B.P.), Asan Institute for Life Sciences, Asan Medical Center, Songpa-gu, Seoul, Korea; Department of Clinical Epidemiology and Biostatistics (S.K.), Asan Medical Center, Songpa-gu, Seoul, Korea; Department of Radiology (E.L.), Seoul National University Bundang Hospital, Seongnam-si, Gyeonggi-do, Korea; Department of Radiology (R.W.L.), Inha University Hospital, Jung-gu, Incheon, Korea
| | - Min Hee Lee
- Department of Radiology and Research Institute of Radiology (A.Y.K., M.A.Y., S.J.H., C.G.C., M.H.L., S.H.L., H.W.C.), University of Ulsan College of Medicine, Asan Medical Center, 88 Olympic-ro 43-gil, Songpa-gu, Seoul 05505, Korea; Biomedical Research Center (Y.C.C., Y.K.), Asan Institute for Life Sciences, Asan Medical Center, Songpa-gu, Seoul, Korea; Health Innovation Big Data Center (B.P.), Asan Institute for Life Sciences, Asan Medical Center, Songpa-gu, Seoul, Korea; Department of Clinical Epidemiology and Biostatistics (S.K.), Asan Medical Center, Songpa-gu, Seoul, Korea; Department of Radiology (E.L.), Seoul National University Bundang Hospital, Seongnam-si, Gyeonggi-do, Korea; Department of Radiology (R.W.L.), Inha University Hospital, Jung-gu, Incheon, Korea
| | - Sang Hoon Lee
- Department of Radiology and Research Institute of Radiology (A.Y.K., M.A.Y., S.J.H., C.G.C., M.H.L., S.H.L., H.W.C.), University of Ulsan College of Medicine, Asan Medical Center, 88 Olympic-ro 43-gil, Songpa-gu, Seoul 05505, Korea; Biomedical Research Center (Y.C.C., Y.K.), Asan Institute for Life Sciences, Asan Medical Center, Songpa-gu, Seoul, Korea; Health Innovation Big Data Center (B.P.), Asan Institute for Life Sciences, Asan Medical Center, Songpa-gu, Seoul, Korea; Department of Clinical Epidemiology and Biostatistics (S.K.), Asan Medical Center, Songpa-gu, Seoul, Korea; Department of Radiology (E.L.), Seoul National University Bundang Hospital, Seongnam-si, Gyeonggi-do, Korea; Department of Radiology (R.W.L.), Inha University Hospital, Jung-gu, Incheon, Korea
| | - Hye Won Chung
- Department of Radiology and Research Institute of Radiology (A.Y.K., M.A.Y., S.J.H., C.G.C., M.H.L., S.H.L., H.W.C.), University of Ulsan College of Medicine, Asan Medical Center, 88 Olympic-ro 43-gil, Songpa-gu, Seoul 05505, Korea; Biomedical Research Center (Y.C.C., Y.K.), Asan Institute for Life Sciences, Asan Medical Center, Songpa-gu, Seoul, Korea; Health Innovation Big Data Center (B.P.), Asan Institute for Life Sciences, Asan Medical Center, Songpa-gu, Seoul, Korea; Department of Clinical Epidemiology and Biostatistics (S.K.), Asan Medical Center, Songpa-gu, Seoul, Korea; Department of Radiology (E.L.), Seoul National University Bundang Hospital, Seongnam-si, Gyeonggi-do, Korea; Department of Radiology (R.W.L.), Inha University Hospital, Jung-gu, Incheon, Korea
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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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Miao L, Ma ST, Jiang X, Zhang HH, Wang YM, Li M. Prediction of the therapeutic efficacy of epirubicin combined with ifosfamide in patients with lung metastases from soft tissue sarcoma based on contrast-enhanced CT radiomics features. BMC Med Imaging 2022; 22:131. [PMID: 35883116 PMCID: PMC9316811 DOI: 10.1186/s12880-022-00859-6] [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: 04/27/2022] [Accepted: 07/18/2022] [Indexed: 11/24/2022] Open
Abstract
Objective To investigate the value of contrast-enhanced computed tomography (CECT) radiomics features in predicting the efficacy of epirubicin combined with ifosfamide in patients with pulmonary metastases from soft tissue sarcoma. Methods A retrospective analysis of 51 patients with pulmonary metastases from soft tissue sarcoma who received the chemotherapy regimen of epirubicin combined with ifosfamide was performed, and efficacy was evaluated by Recist1.1. ROIs (1 or 2) were selected for each patient. Lung metastases were used as target lesions (86 target lesions total), and the patients were divided into a progression group (n = 29) and a non-progressive group (n = 57); the latter included a stable group (n = 34) and a partial response group (n = 23). Information on lung metastases was extracted from CECT images before chemotherapy, and all lesions were delineated by ITK-SNAP software manually or semiautomatically. The decision tree classifier had a better performance in all radiomics models. A receiver operating characteristic curve was plotted to evaluate the predictive performance of the radiomics model. Results In total, 851 CECT radiomics features were extracted for each target lesion and finally reduced to 2 radiomics features, which were then used to construct a radiomics model. Areas under the curves of the model for predicting lesion progression were 0.917 and 0.856 in training and testing groups, respectively. Conclusion The model established based on the radiomics features of CECT before treatment has certain predictive value for assessing the efficacy of chemotherapy for patients with soft tissue sarcoma lung metastases. Supplementary Information The online version contains supplementary material available at 10.1186/s12880-022-00859-6.
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Affiliation(s)
- Lei Miao
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 17 Panjiayuan Nanli, Chaoyang District, Beijing, 100021, China
| | - Shu-Tao Ma
- Department of Medical Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 17, Panjiayuan Nanli, Chaoyang District, Beijing, 100021, China
| | - Xu Jiang
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 17 Panjiayuan Nanli, Chaoyang District, Beijing, 100021, China
| | - Huan-Huan Zhang
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 17 Panjiayuan Nanli, Chaoyang District, Beijing, 100021, China
| | - Yan-Mei Wang
- GE Healthcare China, Pudong New Town, Shanghai, People's Republic of China
| | - Meng Li
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 17 Panjiayuan Nanli, Chaoyang District, Beijing, 100021, China.
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Automated segmentation of the fractured vertebrae on CT and its applicability in a radiomics model to predict fracture malignancy. Sci Rep 2022; 12:6735. [PMID: 35468985 PMCID: PMC9038736 DOI: 10.1038/s41598-022-10807-7] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2021] [Accepted: 04/13/2022] [Indexed: 11/08/2022] Open
Abstract
Although CT radiomics has shown promising results in the evaluation of vertebral fractures, the need for manual segmentation of fractured vertebrae limited the routine clinical implementation of radiomics. Therefore, automated segmentation of fractured vertebrae is needed for successful clinical use of radiomics. In this study, we aimed to develop and validate an automated algorithm for segmentation of fractured vertebral bodies on CT, and to evaluate the applicability of the algorithm in a radiomics prediction model to differentiate benign and malignant fractures. A convolutional neural network was trained to perform automated segmentation of fractured vertebral bodies using 341 vertebrae with benign or malignant fractures from 158 patients, and was validated on independent test sets (internal test, 86 vertebrae [59 patients]; external test, 102 vertebrae [59 patients]). Then, a radiomics model predicting fracture malignancy on CT was constructed, and the prediction performance was compared between automated and human expert segmentations. The algorithm achieved good agreement with human expert segmentation at testing (Dice similarity coefficient, 0.93-0.94; cross-sectional area error, 2.66-2.97%; average surface distance, 0.40-0.54 mm). The radiomics model demonstrated good performance in the training set (AUC, 0.93). In the test sets, automated and human expert segmentations showed comparable prediction performances (AUC, internal test, 0.80 vs 0.87, p = 0.044; external test, 0.83 vs 0.80, p = 0.37). In summary, we developed and validated an automated segmentation algorithm that showed comparable performance to human expert segmentation in a CT radiomics model to predict fracture malignancy, which may enable more practical clinical utilization of radiomics.
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Zheng X, Xiao C, Xie Z, Liu L, Chen Y. Prediction Models for Prognosis of Femoral Neck–Fracture Patients 6 Months after Total Hip Arthroplasty. Int J Gen Med 2022; 15:4339-4356. [PMID: 35480995 PMCID: PMC9037899 DOI: 10.2147/ijgm.s347425] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2021] [Accepted: 03/02/2022] [Indexed: 11/23/2022] Open
Abstract
Purpose To establish prediction models for 6-month prognosis in femoral neck–fracture patients receiving total hip arthroplasty (THA). Patients and Methods In total, 182 computed tomography image pairs from 85 patients were collected and divided into a training set (n=127) and testing set (n=55). Least absolute shrinkage–selection operator regression was used for selecting optimal predictors. A random-forest algorithm was used to establish the prediction models, which were evaluated for accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and area under the curve (AUC). Results The best model in this study was constructed based on demographic data, preoperative laboratory indicators, and three preoperative radiomic features. In the random-forest model, activated partial thromboplastin time, a preoperative radiomic feature (maximum diameter), and fibrinogen were important variables correlating with patient outcomes. The AUC, sensitivity, specificity, PPV, NPV, and accuracy in the training set were 0.986 (95% CI 0.971–1), 0.925 (95% CI 0.862–0.988), 0.983 (95% CI 0.951–1.016), 0.984 (95% CI 0.953–1.014), 0.922 (95% CI 0.856–0.988), and 0.953 (95% CI 0.916–0.990), respectively. The AUC, sensitivity, specificity, PPV, NPV, and accuracy in the testing set were 0.949 (95% CI 0.885–1), 0.767 (95% CI 0.615–0.918), 1 (95% CI 1–1), 1 (95% CI 1–1), 0.781 (95% CI 0.638–0.924), and 0.873 (95% CI 0.785–0.961), respectively. Conclusion The model based on demographic, preoperative clinical, and preoperative radiomic data showed the best predictive ability for 6-month prognosis in the femoral neck–fracture patients receiving THA.
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Affiliation(s)
- Xiaofeng Zheng
- Department of Orthopedics, Third Hospital of Mianyang Sichuan Mental Health Center, Mianyang, Sichuan, 621000, People’s Republic of China
| | - Cong Xiao
- Department of Orthopedics, Third Hospital of Mianyang Sichuan Mental Health Center, Mianyang, Sichuan, 621000, People’s Republic of China
| | - Zhuocheng Xie
- Department of Orthopedics, Sichuan Science City Hospital, Mianyang, Sichuan, 621000, People’s Republic of China
| | - Lijuan Liu
- Department of Orthopedics, Third Hospital of Mianyang Sichuan Mental Health Center, Mianyang, Sichuan, 621000, People’s Republic of China
| | - Yinhua Chen
- Department of Orthopedics, Third Hospital of Mianyang Sichuan Mental Health Center, Mianyang, Sichuan, 621000, People’s Republic of China
- Correspondence: Yinhua Chen, Department of Orthopedics, Third Hospital of Mianyang Sichuan Mental Health Center, 190 East Jiannan Road, Youxian, Mianyang, Sichuan, 621000, People’s Republic of China, Tel +86-139-9014-7729, Email
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22
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Yang H, Yan S, Li J, Zheng X, Yao Q, Duan S, Zhu J, Li C, Qin J. Prediction of acute versus chronic osteoporotic vertebral fracture using radiomics-clinical model on CT. Eur J Radiol 2022; 149:110197. [DOI: 10.1016/j.ejrad.2022.110197] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2021] [Revised: 12/26/2021] [Accepted: 01/31/2022] [Indexed: 11/28/2022]
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Dai H, Wang Y, Fu R, Ye S, He X, Luo S, Jin W. Radiomics and stacking regression model for measuring bone mineral density using abdominal computed tomography. Acta Radiol 2021; 64:228-236. [PMID: 34964365 DOI: 10.1177/02841851211068149] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
BACKGROUND Measurement of bone mineral density (BMD) is the most important method to diagnose osteoporosis. However, current BMD measurement is always performed after a fracture has occurred. PURPOSE To explore whether a radiomic model based on abdominal computed tomography (CT) can predict the BMD of lumbar vertebrae. MATERIAL AND METHODS A total of 245 patients who underwent both dual-energy X-ray absorptiometry (DXA) and abdominal CT examination (training cohort, n = 196; validation cohort, n = 49) were included in our retrospective study. In total, 1218 image features were extracted from abdominal CT images for each patient. Combined with clinical information, three steps including least absolute shrinkage and selection operator (LASSO) regression were used to select key features. A two-tier stacking regression model with multi-algorithm fusion was used for BMD prediction, which can integrate the advantages of linear model and non-linear model. The prediction results of this model were compared with those using a single regressor. The degree-of-freedom adjusted coefficient of determination (Adjusted-R2), root mean square error (RMSE), and mean absolute error (MAE) were used to evaluate the regression performance. RESULTS Compared with other regression methods, the two-tier stacking regression model has a higher regression performance, with Adjusted-R2, RMSE, and MAE of 0.830, 0.077, and 0.06, respectively. Pearson correlation analysis and Bland-Altman analysis showed that the BMD predicted by the model had a high correlation with the DXA results (r = 0.932, difference = -0.01 ± 0.1412 mg/cm2). CONCLUSION Using radiomics, the BMD of lumbar vertebrae could be predicted from abdominal CT images.
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Affiliation(s)
- Hong Dai
- Faculty of Electrical Engineering and Computer Science, Ningbo University, Ningbo, Zhejiang, PR China
| | - Yutao Wang
- Department of Medical imaging, The Affiliated Hospital of Medical School, Ningbo University, Ningbo, Zhejiang, PR China
| | - Randi Fu
- Faculty of Electrical Engineering and Computer Science, Ningbo University, Ningbo, Zhejiang, PR China
| | - Sijia Ye
- Department of Medical imaging, The Affiliated Hospital of Medical School, Ningbo University, Ningbo, Zhejiang, PR China
| | - Xiuchao He
- Department of Medical imaging, The Affiliated Hospital of Medical School, Ningbo University, Ningbo, Zhejiang, PR China
| | - Shuying Luo
- Faculty of Electrical Engineering and Computer Science, Ningbo University, Ningbo, Zhejiang, PR China
| | - Wei Jin
- Faculty of Electrical Engineering and Computer Science, Ningbo University, Ningbo, Zhejiang, PR China
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