Machine-learning-based approach for nonunion prediction following osteoporotic vertebral fractures

Assessment of Hidden Blood Loss in Spinal Metastasis Surgery: A Comprehensive Approach with MRI-Based Radiomics Models

BACKGROUND

Patients undergoing surgery for spinal metastasis are predisposed to hidden blood loss (HBL), which is associated with poor surgical outcomes but unpredictable.

PURPOSE

To evaluate the role of MRI-based radiomics models for assess the risk of HBL in patients undergoing spinal metastasis surgery.

STUDY TYPE

Retrospective.

SUBJECTS

202 patients (42.6% female) operated on for spinal metastasis with a mean age of 58 ± 11 years were divided into a training (n = 162) and a validation cohort (n = 40).

FIELD STRENGTH/SEQUENCE

1.5T or 3.0T scanners. Sagittal T1-weighted and fat-suppressed T2-weighted imaging sequences.

ASSESSMENT

HBL was calculated using the Gross formula. Patients were classified as low and high HBL group, with 1000 mL as the threshold. Radiomics models were constructed with radiomics features. The radiomics score (Radscore) was obtained from the optimal radiomics model. Clinical variables were accessed using univariate and multivariate logistic regression analyses. Independent risk variables were used to build a clinical model. Clinical variables combined with Radscore were used to establish a combined model.

STATISTICAL TESTS

Predictive performance was evaluated using area under the curve (AUC), accuracy, sensitivity, specificity, and F1 score. Calibration curves and decision curves analyses were produced to evaluate the accuracy and clinical utility.

RESULTS

Among the radiomics models, the fusion (T1WI + FS-T2WI) model demonstrated the highest predictive efficacy (AUC: 0.744, 95% confidence interval [CI]: 0.576-0.914). The Radscore model (AUC: 0.809, 95% CI: 0.664-0.954) performs slightly better than the clinical model (AUC: 0.721, 95% CI: 0.524-0.918; P = 0.418) and the combined model (AUC: 0.752, 95% CI: 0.593-0.911; P = 0.178).

DATA CONCLUSION

A radiomics model may serve as a promising assessment tool for the risk of HBL in patients undergoing spinal metastasis surgery, and guide perioperative planning to improve surgical outcomes.

LEVEL OF EVIDENCE

3 TECHNICAL EFFICACY: Stage 2.

Machine Learning and Deep Learning in Spinal Injury: A Narrative Review of Algorithms in Diagnosis and Prognosis

Spinal injuries, including cervical and thoracolumbar fractures, continue to be a major public health concern. Recent advancements in machine learning and deep learning technologies offer exciting prospects for improving both diagnostic and prognostic approaches in spinal injury care. This narrative review systematically explores the practical utility of these computational methods, with a focus on their application in imaging techniques such as computed tomography (CT) and magnetic resonance imaging (MRI), as well as in structured clinical data. Of the 39 studies included, 34 were focused on diagnostic applications, chiefly using deep learning to carry out tasks like vertebral fracture identification, differentiation between benign and malignant fractures, and AO fracture classification. The remaining five were prognostic, using machine learning to analyze parameters for predicting outcomes such as vertebral collapse and future fracture risk. This review highlights the potential benefit of machine learning and deep learning in spinal injury care, especially their roles in enhancing diagnostic capabilities, detailed fracture characterization, risk assessments, and individualized treatment planning.

Management of Osteoporotic Vertebral Fracture: Review Update 2022

A vertebral fracture is the most common type of osteoporotic fracture. Osteoporotic vertebral fractures (OVFs) cause a variety of morbidities and deaths. There are currently few "gold standard treatments" outlined for the management of OVFs in terms of quantity and quality. Conservative treatment is the primary treatment option for OVFs. The treatment of pain includes short-term bed rest, analgesic medication, anti-osteoporotic medications, exercise, and a brace. Numerous reports have been made on studies for vertebral augmentation (VA), including vertebroplasty and kyphoplasty. There is still debate and controversy about the effectiveness of VA in comparison with conservative treatment. Until more robust data are available, current evidence does not support the routine use of VA for OVF. Despite the fact that the majority of OVFs heal without surgery, 15%-35% of patients with an unstable fracture, persistent intractable back pain, or severely collapsed vertebra that causes a neurologic deficit, kyphosis, or chronic pseudarthrosis frequently require surgery. Because no single approach can guarantee the best surgical outcomes, customized surgical techniques are required. Surgeons must stay current on developments in the osteoporotic spine field and be open to new treatment options. Osteoporosis management and prevention are critical to lowering the risk of future OVFs. Clinical studies on bisphosphonate's effects on fracture healing are lacking. Teriparatide was intermittently administered, which dramatically improved spinal fusion and fracture healing while lowering mortality risk. According to the available literature, there are no standard management methods for OVFs. More multimodal approaches, including conservative and surgical treatment, VA, and medications that treat osteoporosis and promote fracture healing, are required to improve the quality of the majority of guidelines.