Oncologic Applications of Artificial Intelligence and Deep Learning Methods in CT Spine Imaging—A Systematic Review
Abstract
:Simple Summary
Abstract
1. Introduction
2. Technical Aspects
2.1. AI, ML, and DL
2.2. Radiomics
3. Materials and Methods
3.1. Literature Search Strategy
3.2. Study Screening and Selection Criteria
3.3. Data Extraction and Reporting
- Details of the research article: authors, publication date, journal name;
- Primary clinical application: detection, classification, segmentation, treatment, or prognosis prediction;
- Study specifics: study type, patient characteristics or imaging modalities, body parts scanned, and specific bone areas segmented for analysis (e.g., internal or external datasets);
- Machine learning methodologies utilized: radiomics, artificial neural networks, convolutional neural networks, etc.
4. Results
4.1. Search Results
4.2. Performance Assessment
- Sensitivity (Recall) and Specificity: sensitivity (recall) measures the proportion of true positives (spinal tumors correctly identified by the AI system) out of all actual positives (all spinal tumors present in the CT images) [57]. Specificity, on the other hand, quantifies the ability of the AI system to correctly identify true negatives (normal spinal conditions) out of all actual negatives (all non-pathological conditions). These metrics are essential to assessing how well AI algorithms detect both positive and negative cases in spinal oncology;
- Accuracy and Precision: accuracy indicates the overall correctness of the AI system’s predictions, measuring the ratio of correctly predicted cases (both true positives and true negatives) to the total number of cases evaluated. Precision, meanwhile, focuses on the AI system’s ability to accurately identify positive cases among all predicted positive instances, minimizing false positives. These metrics provide a comprehensive view of the AI algorithm’s reliability and correctness in clinical diagnosis;
- Area Under the Curve (AUC): AUC evaluates the performance of AI models in binary classification tasks [58,59], such as distinguishing between diseased and healthy spinal conditions based on CT imaging features. A high AUC value indicates that the AI model effectively ranks diseased cases higher than healthy ones, demonstrating its discriminatory power in spinal oncology diagnostics;
- Figure of Merit (FOM) and F1-score [60]: Figure of Merit encompasses a range of metrics including sensitivity, specificity, accuracy, and precision, tailored to the specific diagnostic challenges presented by spinal tumors. F1-score, a harmonic mean of precision and recall, balances the trade-off between these metrics and is particularly useful in scenarios where there is an imbalance between positive and negative cases in the dataset [61]. A model with a high F1 score indicates both good precision and recall, reflecting a robust model;
- The kappa statistic [62], often denoted as κ, is a measure used to assess the level of agreement between two or more raters or classifiers beyond what would be expected by chance alone. It is particularly useful in the context of classification tasks, where it evaluates how well the AI system’s predictions align with the true classifications compared to random chance [63]. Kappa values range from −1 to 1. A kappa value of 1 indicates perfect agreement between the raters, 0 indicates agreement no better than chance, and negative values suggest worse-than-chance agreement. High kappa values indicate that the AI system’s predictions are consistently aligned with expert assessments or ground truth, which is vital for the system’s clinical applicability and trustworthiness.
Authors | AI Method | Publication Year | Main Objectives | Journal | Main Task | Sample Size (No. of CTs/Patients) | Performance of AI Model |
---|---|---|---|---|---|---|---|
Tatjana W. et al. [64] | SVM classifier | 2012 | Using supervised learning methods to detect sclerotic bone metastases in CT imaging. | SPIE Medical Imaging | Detection | 22 | Sensitivity: 71.2–87.0% |
Hammon M. et al. [65] | CADe (RF classifier) | 2013 | Automated detection of osteolytic and osteoblastic spine metastases on CT. | European Radiology | Detection | 134 | Sensitivity 83.0% (lytic), 88.0% (blastic) |
Burns J. et al. [66] | CADe (SVM classifier) | 2013 | Automated detection of sclerotic metastases in the thoracolumbar spine on CT | Radiology | Detection | 59 | Sensitivity 79.0–90.0% |
Roth. H. et al. [67] | CNN (DropConnect) | 2014 | Using Deep CNN methods to detect sclerotic spinal metastases on CT imaging. | Recent Advances in Computational Methods and Clinical Applications for Spine Imaging | Detection | 59 | Sensitivity: 79.0%; AUC 0.834 |
Masoudi S et al. [68] | ResNet-50 with DC-GAN augmentation | 2020 | Detect and classify bone lesions in CT images of prostate cancer patients. | Journal of Clinical Oncology | Detection and Classification (Benign vs. malignant) | 56 | Sensitivity: 81.0%; accuracy 89.0% |
Fan X et al. [69] | AlexNet | 2021 | Using Deep Learning Methods to Identify Spinal Metas in Lung Cancer using CT Images. | Hindawi | Detection | 36 | Sensitivity: 66.0–81.4% |
Noguchi S. et al. [70] | 2D U-Net, 3D U-Net, and ResNet | 2022 | Deep learning to improve radiologists’ performance in spinal metastases detection on CT | European Radiology | Detection | 732 | Sensitivity: 82.4–89.6%; improved radiologist’s sensitivity by 15.3%; reduced mean interpretation time by 83 s (p < 0.05). |
Hoshiai S. et al. [71] | DL Model | 2022 | Detecting vertebral bone metastases on CT | European Journal of Radiology | Detection | 130 | FOM from 0.848 to 0.876 (radiologist) and from 0.752 to 0.799 (resident), (p < 0.05 for both) |
Musa A. et al. [72] | PyRadiomics | 2022 | Detecting prostate cancer bone/spinal metastases invisible in CT | Current Medical Imaging | Detection | 53 | Accuracy 85.0%; sensitivity 78.0–91.0%; specificity: 88.0–93.0% |
Gilberg L. et al. [73] | DLA (U-Net) | 2023 | Deep learning to improve Radiologist’s detection of spinal malignancies in CT imaging. | Applied Sciences | Detection | 32 | Sensitivity: 75.0%; improved radiologist’s sensitivity by 20.8% |
Huo T. et al. [74] | 3D U-Net (DCNN) | 2023 | Deep learning to improve radiologists’ performance in lung cancer spinal metastases detection on CT | Frontiers in Oncology | Detection | 126 | Sensitivity: 89.4%; improved radiologist’s sensitivity by 22.2% and accuracy of 26.2% |
Koike Y. et al. [75] | YOLOv5m and InceptionV3 | 2023 | AI-aided lytic spinal bone metastasis detection on CT scans | International Journal of Computer Assisted Radiology and Surgery | Detection | 2125 | Accuracy 87.2%; precision 94.8%; recall: 74.1%; F1-score 74.1%; AUC 0.940 |
Motohasi M. et al. [7] | U-Net (DeepLabv3+) | 2024 | Using Deep Learning Algorithm to Detect Spinal Metastases on CT Images. | Spine | Detection | 435 | Sensitivity: 75.0–78.0%; precision: 36.0–68.0%; F1 score: 0.48–0.72 |
Chmelik J. et al. [76] | CNN | 2018 | Segmentation and classification of metastatic spinal lesions in 3D CT data | Medical Image Analysis | Classification (Benign vs. malignant) | 31 | AUC 0.780–0.800; sensitivity: 71.0–74.0%; specificity: 82.0–88.0% |
Li Y. et al. [77] | ResNet50 | 2021 | Differentiate benign and malignant vertebral fracture on CT using deep learning | European Radiology | Classification (Benign vs. malignant) | 433 | Sensitivity: 95.0%; specificity: 80.0%; accuracy: 88.0% |
Masoudi S. et al. [78] | 2D ResNet-50, ResNeXt-50, 3D ResNet-18, 3D ResNet-50 | 2021 | Differentiate benign versus malignant spinal lesions on CT. | IEEE Access | Classification (Benign vs. malignant) | 114 | Accuracy: 79.4–92.2%; F1-Score: 0.755–0.923 |
Hallinan J. et al. [79] | R-CNN (ResNet50) | 2022 | Deep learning algorithm for grading cord compression secondary to spinal metastasis/epidural disease on CT | Cancers | Classification (Stage/Grade) | 444 | kappas (κ: 0.873–0.911); AUC: 0.953–0.971; sensitivity: 92.6–98.0%; specificity: 94.8–99.8% |
Naseri H. et al. [80] | PyRadiomics | 2022 | Radiomics-based machine learning models to distinguish between metastatic and healthy bone. | Scientific Report | Classification (Benign vs. malignant) | 170 | AUC 0.640–0.950 |
Park, T. et al. [81] | U-Net (CNN) | 2022 | Automated segmentation of the fractured vertebrae on CT and using radiomics to predict benign versus malignant. | Nature | Classification (Benign vs. malignant) | 158 | Dice similarity coefficient: 0.930–0.940; AUC 0.800–0.930 |
Wang, Q. et al. [82] | Research Portal V1.1 | 2022 | Using Machine learning techniques to predict RANKL expression of Spinal GCTB. | Cancers | Classification (Predict biomarkers) | 107 | AUC: 0.658–0.880 sensitivity: 65.7–97.9%; specificity: 23.3–71.9%; accuracy: 64.6–80.2% |
Wang, Q. et al. [83] | PyRadiomics | 2022 | Clinical and CT-Based Radiomics based techniques to predict p53 and VEGF expression in Spinal GCBT. | Frontiers in Oncology | Classification (Predict biomarkers) | 80 | AUC 0.790–0.880 |
Hallinan J. et al. [84] | R-CNN (ResNet50) | 2023 | Deep learning method to diagnose epidural spinal cord compression using thoracolumbar CT. | European Spine Journal | Classification (Stage/Grade) | 223 | kappa (κ = 0.879); sensitivity: 91.8%; specificity: 92.0%; AUC: 0.919 |
Hallinan J. et al. [85] | R-CNN (ResNeXt50) | 2023 | Assess for metastatic spinal cord compression (mainly epidural extension) on CT imaging with external validation | Frontiers in Oncology | Classification (Stage/Grade) | 420 | Almost-perfect inter-rater agreement (κ = 0.813); sensitivity: 94.0% |
Duan S. et al. [86] | Inception_V3 | 2023 | Differentiating benign and malignant vertebral compression fracture on spinal CT imaging. | European Journal of Radiology | Classification (Benign vs. malignant) | 280 | AUC: 0.890–0.990; accuracy: 88.0–99.0% |
Gui C. et al. [87] | PyRadiomics | 2021 | Radiomic modeling to predict risk of vertebral compression fracture after SBRT for spinal metastases | Journal of Neurosurgery | Prognosis (Predicting complications) | 74 | Sensitivity: 84.4%; specificity 80.0%, AUC 0.844–0.878 of 0.844, specificity of 0.800 |
Wang, Q. et al. [88] | PyRadiomics | 2021 | Using Radiomics-based technique to predict recurrence in spinal GCBT from pre-operative CT imaging. | Journal of Bone Oncology | Prognosis (Predict treatment outcome) | 62 | Accuracy: 89.0%; AUC 0.780 (predicting recurrence) |
Massaad E. et al. [89] | CNN (Densenet and U-Net) | 2022 | Using machine learning methods to derive body composition analysis to predict complications in spine tumor surgery. | Journal of Neurosurgery Spine | Prognosis (Predict treatment outcome) | 484 | Body composition analysis using machine learning help predict risk for inferior outcomes. |
Seou Y. et al. [90] | PyRadiomics | 2023 | Radiomics-based prediction of vertebral compression fracture prior to spinal SBRT from planning CT. | European Spine Journal | Prognosis (Predicting complications) | 85 | Accuracy: 78.8–82.9%; precision: 60.0–62.5%; F1 score: 0.573–0.650 |
Delrieu L. et al. [91] | U-Net | 2024 | Automated body composition analysis using L3 as reference on CT scans to predict treatment outcome in cancer patients. | Frontiers in Nuclear Medicine | Prognosis (Treatment Outcome) | 352 | DICE similarity coefficient: 0.850–0.940 |
Khalid S. et al. [92] | DL Model | 2024 | Detection of sarcopenic obesity and association with adverse outcomes in patients undergoing surgical treatment for spinal metastases | Journal of Neurosurgery Spine | Prognosis (Predicting complications) | 62 | DL detected sarcopenia patients increased odds of non-home discharge, readmission, and postoperative mortality. |
Sebastiaan et al. [93] | DeepMedic | 2022 | Clinical Utility of CNN for treatment planning in spinal metastases. | Physics and Imaging in Radiation Oncology | Treatment Planning | 782 | DSC 96.7% HD: 3.6 mm. Acceptable: 77.0% |
Netherton T. et al. [94] | CNN (X-Net) | 2022 | Automating Treatment Planning for Spinal Radiation Therapy using CT Imaging | International Journal of Oncology, Biology and Physics | Treatment Planning | 220 | Dice-similarity coefficient: 0.850 (cervical), 0.903 (thoracic), 93.7 (lumbar); AUC: 0.820; end-to-end treatment planning time <8 min |
Hernandez S. et al. [95] | nn-UNet | 2023 | Automating Treatment Planning for Paediatric Craniospinal Radiation Therapy using CT. | Paediatric Blood Cancer | Treatment Planning | 143 | Dice similarity coefficient: 0.650–0.980; end-to-end treatment planning time: 3.5 ± 0.4 min |
4.3. Applications
4.3.1. Detection of Spinal Lesions
4.3.2. Classification of Spinal Lesions
4.3.3. Prognosis and Predicting Complications
4.3.4. Treatment Planning
5. Discussion
5.1. Interpretation and Implications of Findings
5.2. Integration into Clinical Practice
5.3. Other Potential Applications
5.3.1. Improving Image Quality
5.3.2. Predicting Primary Malignancy from Spinal Metastases
5.3.3. Quantifying Tumor Burden to Predict Treatment Response
5.4. Study Limitations
6. Conclusions
Author Contributions
Funding
Conflicts of Interest
References
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Ong, W.; Lee, A.; Tan, W.C.; Fong, K.T.D.; Lai, D.D.; Tan, Y.L.; Low, X.Z.; Ge, S.; Makmur, A.; Ong, S.J.; et al. Oncologic Applications of Artificial Intelligence and Deep Learning Methods in CT Spine Imaging—A Systematic Review. Cancers 2024, 16, 2988. https://doi.org/10.3390/cancers16172988
Ong W, Lee A, Tan WC, Fong KTD, Lai DD, Tan YL, Low XZ, Ge S, Makmur A, Ong SJ, et al. Oncologic Applications of Artificial Intelligence and Deep Learning Methods in CT Spine Imaging—A Systematic Review. Cancers. 2024; 16(17):2988. https://doi.org/10.3390/cancers16172988
Chicago/Turabian StyleOng, Wilson, Aric Lee, Wei Chuan Tan, Kuan Ting Dominic Fong, Daoyong David Lai, Yi Liang Tan, Xi Zhen Low, Shuliang Ge, Andrew Makmur, Shao Jin Ong, and et al. 2024. "Oncologic Applications of Artificial Intelligence and Deep Learning Methods in CT Spine Imaging—A Systematic Review" Cancers 16, no. 17: 2988. https://doi.org/10.3390/cancers16172988