Application of Machine Learning for Differentiating Bone Malignancy on Imaging: A Systematic Review
Abstract
:Simple Summary
Abstract
1. Introduction
2. Materials and Methods
2.1. The Literature Search Strategy
2.2. The Study Screening and Selection Criteria
2.3. Data Extraction and Reporting
- Research article details: Complete authorship, date of journal or publication, Journal name;
- Main clinical use: Differentiating benign vs. malignant bone lesions, characterization and classification of various bone tumours;
- Patient population: Patients with known bone lesions, who have undergone various imaging investigations (X-ray, CT, PET/CT or MRI) and have subsequently undergone histopathological confirmation;
- Research study details: The type of study, patient or imaging modality sample sizes (for example, internal or external data sets), imaging modalities used (CT, MRI, bone scans or PET/CT), treatment or management information and outcome/prognostic measures;
- Machine Learning techniques used: Radiomics and convolutional neural networks, among others.
3. Results
3.1. Search Results
3.2. Machine Learning Techniques
4. Discussion
4.1. Machine Learning on Conventional Radiographs
4.2. Machine Learning on Computed Tomography (CT) Imaging
4.3. Machine Learning on Magnetic Resonance Imaging (MRI)
4.4. Machine Learning on Positiron Emission Tomography with CT (PET/CT) Imaging
4.5. Potential Clinical Impact and Applications
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Authors | Artificial Intelligence Method | Publication Year | Main Objectives | Title of Journal | Main Imaging Modality Used | Performance |
---|---|---|---|---|---|---|
Xiong et al. [71] | Machine learning–based classifiers (ANN) | 2021 | Differentiate Multiple Myeloma from Metastases | Frontiers in Oncology | MRI | MCC = 0.605; Accuracy: 0.815; Sensitivity 0.879; Specificity: 0.790 |
Eweje et al. [72] | EfficientNet-B0 architecture and a logistic regression model (CNN) | 2020 | Differentiate between benign and malignant bone lesion | EBioMedicine | MRI | Accuracy: 0.760; Sensitivity: 0.790; Specificity: 0.750; AUC: 0.820 |
Zhong et al. [73] | Radiomics using MRI machine learning techniques (SVM) | 2020 | Differentiate between osteoradionecrosis from spinal metastases in Nasopharyngeal Carcinoma | BMC Medical Imaging | MRI | Accuracy: 0.737; Sensitivity: 0.843; Specificity 0.614; AUC of 0.725 |
Sun et al. [74] | Radiomics using CT machine learning techniques (SVM) | 2021 | Distinguish between benign and malignant bone tumours | Cancer Imaging | MRI | AUC of 0.892; NRI of 0.238; IDI of 0.163 |
Reinus et al. [75] | Two layer feed-forward neural network (ANN) | 1994 | Diagnosis of focal bone lesion | Investigative Radiology | X-ray | Accuracy 0.835 |
Filograna et al. [76] | Radiomics using MRI machine learning techniques (SVM) | 2019 | Differentiate between metastatic vs. non-metastatic vertebral bodies | La Radiologia Medica | MRI | AUC: 0.841–0.912 |
Yin P. et al. [77] | Radiomics using MRI machine learning techniques | 2019 | Differentiation of lesions in the sacrum, for example, chordoma, giant cell tumour, or metastatic lesions) | Journal of Magnetic Resonance Imaging | MRI | Accuracy: 0.810; AUC: 0.840 |
Acar E. et al. [78] | Texture analysis and PET CT machine learning techniques (Weighted KNN algorithm) | 2019 | Differentiating metastatic and completely responded sclerotic bone lesions in prostate cancer | British Journal of Radiology | PET/CT | Accuracy: 0.735; Sensitivity: 0.735; Specificity: 0.737; AUC: 0.76 |
He et al. [79] | EfficientNet-B0 convolutional neural network architecture | 2020 | Differentiating benign between benign and malignant bone lesion | EbioMedicine | X-ray | Accuracy: 0.734 AUC: 0.877 (benign); 0.916 (malignant) Accuracy: 0.99 Average mean IoU: 0.848 |
Reicher et al. [80] | TensorFlow Inception-v3 recurrent convolutional neural network (CNN) trained to the ImageNet model | 2018 | Classifying bone tumour matrix | SIIM (Society for Imaging Informatics in Medicine) | X-ray | Accuracy 0.93 |
Y. Li et al. [81] | Super label guided convolutional neural network | 2018 | Distinguish between benign and malignant bone tumours and classifies bone tumour matrix | Artificial Neural Networks and Machine Learning | CT | Accuracy: 0.740 |
Park et al. [82] | ResNet 50, GoogleNet Inception v3, and EfficientNet-b1, b2, and b3 | 2022 | Distinguish between benign, malignant or no tumour over the femur | PLoS ONE | X-ray | Accuracy: 0.853; Sensitivity: 0.822; Specificity: 0.912; Precision: 0.821; AUC: 0.953 |
Liu et al. [83] | PyTorch 1.2.0 with Python 3.7.3, XGBoost and SHapley ad- ditive exPlanations value | 2021 | Classification of benign, intermediate and malignant bone lesions | European Radiology | X-ray | AUC: 0.898 (benign); 0.894 (malignant); 0.865 (intermediate) Macroaverage AUC: 0.872 |
Pan D. et al. [84] | Radiomics using X-ray machine learning techniques, SHapley Additive exPlanations (SHAP) | 2021 | Classification of benign, intermediate and malignant bone lesions | Biomed Research International | X-ray | AUC: 0.970 (binary), 0.940 (tertiary); Accuracy: 0.947 (binary); 0.828 (tertiary) |
Hong J. H. et al. [85] | Radiomics using CT machine learning techniques | 2021 | Distinguish between benign bone island and osteoblastic metastasis | Radiology | CT | AUC: 0.960; Sensitivity: 0.800; Specificity: 0.960; Accuracy: 0.860 |
von Schacky et al. [86] | Radiomics using X-ray machine learning techniques, Python 3.7.7, scikit- learn 0.22.2 andfastai library | 2022 | Distinguish between benign and malignant bone tumours | European Radiology | X-ray | Accuracy: 0.80 (internal), 0.75 (external); Sensitivity: 0.75, 0.90; AUC: 0.79, 0.90 |
T. Perk et al. [87] | Radiomics using PET/CT machine learning techniques | 2018 | Distinguish between benign and malignant bone tumours | Physics in Medicine and Biology | PET/CT | AUC: 0.950; Sensitivity: 0.880; Specificity: 0.890 |
Bao H. D. et al. [88] | Radiomics using X-ray machine learning techniques | 2017 | Classification of different bone tumours | Journal of Digital Imaging | X-ray | Primary accuracy: 0.440–0.620; Differential accuracy: 0.620–0.800 |
Kahn et al. [89] | Bayesian network using X-ray machine learning techniques | 2001 | Classification of different bone tumours | Journal of Digital Imaging | X-ray | Accuracy: 0.890 (binary), 0.680 (tertiary) |
Gitto et al. [90] | Radiomics using MRI machine learning techniques | 2022 | Distinguish between benign vs. malignant cartilaginous lesions | EBioMedicine | MRI | Accuracy: 0.98 (ACT), 0.80 (CS2); AUC: 0.94 (ACT), 0.90 (CS2) |
Gitto et al. [91] | Radiomics using MRI machine learning techniques | 2020 | Distinguish between low-grade vs. high-grade cartilaginous lesions | European Journal of Radiology | MRI | Accuracy: 0.857 (training), 0.750 (test); AUC: 0.850 (training), 0.78 (test) |
Yin et al. [92] | Radiomics using CT machine learning techniques, Pyradiomics python package, | 2020 | Distinguish between benign vs. malignant sacral tumour | Frontier Oncology | CT | Accuracy: 0.81 (Clinical-LR), 0.81 (Clinical-DNN); AUC: 0.84 (Clinical-LR), 0.83 (Clinical-DNN) |
Georgeanu et al. [93] | Radiomics using MRI machine learning techniques, ResNet50 image classifiers | 2022 | Distinguish between benign and malignant bone tumours | Medicina (Kaunas) | MRI | Accuracy: 0.808 (training), 0.805 (test); AUC: 0.885 (training), 0.879 (test) |
Fan et al. [34] | Radiomics using PET/CT machine learning techniques | 2021 | Distinguish between benign and metastatic vertebral lesions | Frontiers in Medicine | PET/CT | Accuracy: 0.875 (LR), 0.834 (SVM), 0.750 (Decision Tree) |
Xu et al. [94] | Radiomics using PET/CT machine learning techniques | 2014 | Distinguish between malignant and benign bone and soft-tissue lesions | Annals of Nuclear Medicine | PET/CT | Accuracy 0.825; Sensitivity 0.864; Specificity 0.772 |
Chianca et al. [95] | Radiomics using MRI machine learning techniques, 3D Slicer heterogeneityCAD module (hCAD) and PyRadiomics | 2021 | Distinguish between different benign, primary malignant vs. metastatic vertebral lesions | European Journal of Radiology | MRI | 2-Label Classification—Accuracy: 0.94 (training), 0.86 (test); 3-Label Classification—Accuracy: 0.80 (training), 0.69 (test). |
Gitto et al. [96] | Radiomics using MRI machine learning techniques | 2022 | Distinguish between benign and metastatic vertebral lesions | La Radiologica Medica | MRI | Accuracy—0.76; Sensitivity: 0.78; Specificity: 0.68; AUC 0.78 |
Consalvo et al. [97] | PyTorch 1.9.0 and cuda toolkit 11.1 & ResNet18 architecture | 2022 | Distinguish between Ewing Sarcoma (ES) vs. acute osteomyelitis (OM) | Anticancer Research | X-ray | Accuracy: 0.867 (ES), 0.903 (OM); Sensitivity: 1.00 (ES), 0.930 (OM); Specificity: 0.76 (ES), 0.844 |
Zhao et al. [98] | Radiomics using MRI machine learning techniques | 2022 | Distinguish between benign vs. malignant bone lesions | Journal of Magnetic Resonance Imaging | MRI | Improved Sensitivities 0.12 to 0.36 as compared to manual. |
Bradshaw et al. [99] | Deep convolutional neural network via VGG19 architecture | 2018 | Classifying benign and malignant bone lesion | Journal of Nuclear Medicine | PET/CT | Accuracy: 0.88; Sensitivity: 0.90; Specificity 0.85. |
Do et al. [100] | Deep convolutional neural network with combination of global and patch-based models | 2021 | Classifying bone tumours in the knee into benign vs. malignant | Diagnostics | X-ray | Accuracy: 0.99 Average mean IoU: 0.848 |
Masoudi et al. [101] | Deep convolutional neural network with 2D ResNet- 50 & 3D ResNet-18 | 2021 | Classify benign or malignant bone lesions in prostate cancer | IEEE Access | CT | Accuracy: 0.922; F1: 92.3% |
Gitto et al. [102] | Machine-learning classifier (LogitBoost) | 2021 | Classification of atypical cartilaginous tumours and higher-grade chondrosarcoma, of long bones. | EBioMedicine | CT | Accuracy 0.750, AUC 0.78 (Validation set) |
von Schacky et al. [103] | Mask region–based convolutional neural network (Mask-RCNN-X101) | 2021 | Classify benign or malignant bone lesions | Radiology | X-ray | Accuracy: 80.2%, Sensitivity: 62.9%, Specificity: 88.2% |
Imaging Modality | Accuracy | Sensitivity | Specificity | Area under Curve (AUC) |
---|---|---|---|---|
X-ray | 0.44–0.99 | 0.75–1.00 | 0.78–0.91 | 0.79–0.95 |
Computed Tomography (CT) | 0.74–0.92 | 0.80 | 0.96 | 0.78–0.96 |
Magnetic Resonance Imaging (MRI) | 0.74–0.98 | 0.78–0.88 | 0.61–0.79 | 0.73–0.94 |
Positron Emission Tomography with CT (PET/CT) | 0.74–0.88 | 0.84–0.90 | 0.74–0.85 | 0.76–0.95 |
Overall | 0.44–0.99 | 0.63–1.00 | 0.73–0.96 | 0.73–0.96 |
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Ong, W.; Zhu, L.; Tan, Y.L.; Teo, E.C.; Tan, J.H.; Kumar, N.; Vellayappan, B.A.; Ooi, B.C.; Quek, S.T.; Makmur, A.; et al. Application of Machine Learning for Differentiating Bone Malignancy on Imaging: A Systematic Review. Cancers 2023, 15, 1837. https://doi.org/10.3390/cancers15061837
Ong W, Zhu L, Tan YL, Teo EC, Tan JH, Kumar N, Vellayappan BA, Ooi BC, Quek ST, Makmur A, et al. Application of Machine Learning for Differentiating Bone Malignancy on Imaging: A Systematic Review. Cancers. 2023; 15(6):1837. https://doi.org/10.3390/cancers15061837
Chicago/Turabian StyleOng, Wilson, Lei Zhu, Yi Liang Tan, Ee Chin Teo, Jiong Hao Tan, Naresh Kumar, Balamurugan A. Vellayappan, Beng Chin Ooi, Swee Tian Quek, Andrew Makmur, and et al. 2023. "Application of Machine Learning for Differentiating Bone Malignancy on Imaging: A Systematic Review" Cancers 15, no. 6: 1837. https://doi.org/10.3390/cancers15061837