Search Results (9)

Early and reliable identification of vertebral metastases on computed tomography remains a major challenge in oncologic imaging due to the morphological complexity of metastatic lesions and the high inter-patient variability of spinal anatomy. In this study, an end-to-end interpretable radiomic-based framework was developed to automatically distinguish healthy from metastatic vertebrae using segmented DICOM data, coupled with an interactive virtual reality (VR) visualization module implemented in Unity 3D. The proposed framework integrates radiomic feature extraction and selection, informed undersampling to address class imbalance, and automatic machine learning-based classification. To facilitate interpretation, patient-specific 3D models with overlapped classifier outputs were integrated into a VR desktop application, enabling advanced exploration of patient-specific spinal models, with color-coded visualization of algorithmic predictions and expert-defined suspicious lesions. The final classification model, trained using a Random Forest algorithm and optimized via stratified 5-fold cross-validation, achieved an overall accuracy of 0.86, an Area Under the Receiver Operating Characteristic Curve of 0.91, and an F1-score of 0.81 for the metastatic class on the independent test set, achieving competitive diagnostic performance while preserving transparency and clinical interpretability. This study represents a foundational step toward intelligent, interactive, and clinically interpretable tools for the diagnosis and follow-up of spinal metastatic disease. Full article

Background/Objectives: Spinal metastases from lung cancer cause substantial pain, instability, and neurologic compromise. Radiotherapy and kyphoplasty are standard treatment modalities, while radiofrequency ablation (RFA) has emerged as a potential adjunct for cytoreduction. The objective of this study was to evaluate whether RFA confers additional benefit when combined with kyphoplasty and radiotherapy in patients with lung cancer spinal metastases. Methods: We conducted a retrospective cohort study of adults with lung cancer and spinal metastases from 2012–2024 using the TriNetX database. Patients were identified using International Classification of Disease, Tenth Revision, Clinical Modification (ICD-10-CM) codes and stratified into two groups: kyphoplasty with radiotherapy alone versus kyphoplasty with radiotherapy and RFA. Propensity score matching was applied to balance demographic and clinical covariates. The primary outcome was 1-year all-cause mortality. Secondary outcomes included tumor recurrence, neurologic complications, and pain burden as assessed by opioid prescription rates. Risk ratios (RR) with 95% confidence intervals (CI) were calculated. Results: A total of 703 patients met inclusion criteria. After matching, no significant differences were observed between groups for 1-year mortality (RR 1.021, 95% CI 0.83–1.256), tumor recurrence (RR 0.989, 95% CI 0.789–1.238), neurologic complications (RR 1.052, 95% CI 0.563–1.967), or opioid use as a pain proxy (RR 0.986, 95% CI 0.76–1.28). Conclusions: The addition of RFA to kyphoplasty and radiotherapy did not significantly impact survival, recurrence, neurologic, or pain outcomes in patients with spinal metastases from lung cancer. These findings suggest that the incremental benefit of RFA in this setting is limited and emphasize the need for prospective studies to refine patient selection and treatment strategies. Full article

Objectives: Spinal diseases are commonly encountered health problems with a wide spectrum. In addition to degenerative changes, other common spinal pathologies include metastases and compression fractures. Benign tumors like hemangiomas and infections such as spondylodiscitis are also frequently observed. Although magnetic resonance imaging (MRI) is considered the gold standard in diagnostic imaging, the morphological similarities of lesions can pose significant challenges in differential diagnoses. In recent years, the use of artificial intelligence applications in medical imaging has become increasingly widespread. In this study, we aim to detect and classify vertebral body lesions using the YOLO-v8 (You Only Look Once, version 8) deep learning architecture. Materials and Methods: This study included MRI data from 235 patients with vertebral body lesions. The dataset comprised sagittal T1- and T2-weighted sequences. The diagnostic categories consisted of acute compression fractures, metastases, hemangiomas, atypical hemangiomas, and spondylodiscitis. For automated detection and classification of vertebral lesions, the YOLOv8 deep learning model was employed. Following image standardization and data augmentation, a total of 4179 images were generated. The dataset was randomly split into training (80%) and validation (20%) subsets. Additionally, an independent test set was constructed using MRI images from 54 patients who were not included in the training or validation phases to evaluate the model’s performance. Results: In the test, the YOLOv8 model achieved classification accuracies of 0.84 and 0.85 for T1- and T2-weighted MRI sequences, respectively. Among the diagnostic categories, spondylodiscitis had the highest accuracy in the T1 dataset (0.94), while acute compression fractures were most accurately detected in the T2 dataset (0.93). Hemangiomas exhibited the lowest classification accuracy in both modalities (0.73). The F1 scores were calculated as 0.83 for T1-weighted and 0.82 for T2-weighted sequences at optimal confidence thresholds. The model’s mean average precision (mAP) 0.5 values were 0.82 for T1 and 0.86 for T2 datasets, indicating high precision in lesion detection. Conclusions: The YOLO-v8 deep learning model we used demonstrates effective performance in distinguishing vertebral body metastases from different groups of benign pathologies. Full article

Background. The aim of this study was to assess the surgical outcomes and survival of patients surgically treated for metastatic epidural spinal cord compression (MESCC), with a specific focus on identifying factors that influence overall survival and readmission-free survival. Methods. All patients who underwent surgery for spine metastases at our department in the period 2018–2022 were included in the study. Results. A total of 175 patients (n = 71 females, median age 67.15 years) were included. The most common primary tumors were lung carcinoma (n = 31), prostate carcinoma (n = 31), breast carcinoma (n = 28), multiple myeloma (n = 25), and renal cell carcinoma (n = 11). ECOG performance status was 0 (n = 7), 1 (n = 97), 2 (n = 27), 3 (n = 17), and 4 (n = 27). Pathological fractures were present in n = 108 patients. Decompression only was performed in n = 42, additional instrumentation in n = 133, and vertebral body replacement in n = 23. The most common complications were wound healing deficits and hardware failure. Preoperative motor deficits were present in n = 89 patients. Postoperatively, n = 122 improved, n = 43 was unchanged, and n = 10 deteriorated. Mean overall survival (OS) was 239.2 days, with a 30-day mortality rate of 18.3%. Favorable prognostic factors included Tomita score < 7, Frankel score A–C, ECOG 0–1, and Modified Tokuhashi score > 10 (p < 0.01). Factors affecting OS and readmission-free survival (RFS) included prognostic scores, adjuvant therapy, ASA classification, surgical complications, metastasis number, and postoperative improvement. Better prognostic scores, adjuvant therapy, and clinical improvement were associated with longer OS and RFS, while complications or deterioration resulted in worse outcomes. Conclusions. Patients undergoing decompression and/or stabilization for metastatic spinal tumors showed improved outcomes, with favorable prognosis linked to Tomita score < 7, Frankel score A–C, ECOG 0–1, and Modified Tokuhashi score > 10. Full article

We aim to investigate the feasibility and evaluate the performance of a ResNet-50 convolutional neural network (CNN) based on magnetic resonance imaging (MRI) in predicting primary tumor sites in spinal metastases. Conventional sequences (T1-weighted, T2-weighted, and fat-suppressed T2-weighted sequences) MRIs of spinal metastases patients confirmed by pathology from August 2006 to August 2019 were retrospectively analyzed. Patients were partitioned into non-overlapping sets of 90% for training and 10% for testing. A deep learning model using ResNet-50 CNN was trained to classify primary tumor sites. Top-1 accuracy, precision, sensitivity, area under the curve for the receiver-operating characteristic (AUC-ROC), and F1 score were considered as the evaluation metrics. A total of 295 spinal metastases patients (mean age ± standard deviation, 59.9 years ± 10.9; 154 men) were evaluated. Included metastases originated from lung cancer (n = 142), kidney cancer (n = 50), mammary cancer (n = 41), thyroid cancer (n = 34), and prostate cancer (n = 28). For 5-class classification, AUC-ROC and top-1 accuracy were 0.77 and 52.97%, respectively. Additionally, AUC-ROC for different sequence subsets ranged between 0.70 (for T2-weighted) and 0.74 (for fat-suppressed T2-weighted). Our developed ResNet-50 CNN model for predicting primary tumor sites in spinal metastases at MRI has the potential to help prioritize the examinations and treatments in case of unknown primary for radiologists and oncologists. Full article

Spinal bone metastases from uterine leiomyosarcoma (LMS) are relatively uncommon and few data are present in the literature. In this study, cases of nine consecutive patients who underwent spinal surgery for metastatic uterine LMS between 2012 and 2022 at a single institution were retrospectively reviewed. The recorded demographic, operative, and postoperative factors were reviewed, and the functional outcomes were determined by changes in Frankel grade classification during follow-up. A systematic review of the literature was also performed to evaluate operative and postoperative factors and outcomes for patients with the same gynecological metastases to the spine. For our cases, the mean time between primary tumors to bone metastases diagnosis was 5.2 years, and the thoracic vertebrae were the most affected segment. Overall, median survival after diagnosis of metastatic spine lesions was 46 months. For the systematic review, the mean time between primary tumors to bone metastases was 4.9 years, with the lumbar spine as the most involved site of metastasis. Overall, median survival after diagnosis was 102 months. Once a spinal bone lesion from LMS is identified, surgical treatment can be beneficial and successful in alleviating symptoms. Further efforts will be crucial to identify prognostic markers as well as therapeutic targets to improve survival in these patients. Full article

Background: Early diagnosis of metastatic epidural spinal cord compression (MESCC) is vital to expedite therapy and prevent paralysis. Staging CT is performed routinely in cancer patients and presents an opportunity for earlier diagnosis. Methods: This retrospective study included 123 CT scans from 101 patients who underwent spine MRI within 30 days, excluding 549 CT scans from 216 patients due to CT performed post-MRI, non-contrast CT, or a gap greater than 30 days between modalities. Reference standard MESCC gradings on CT were provided in consensus via two spine radiologists (11 and 7 years of experience) analyzing the MRI scans. CT scans were labeled using the original reports and by three radiologists (3, 13, and 14 years of experience) using dedicated CT windowing. Results: For normal/none versus low/high-grade MESCC per CT scan, all radiologists demonstrated almost perfect agreement with kappa values ranging from 0.866 (95% CI 0.787–0.945) to 0.947 (95% CI 0.899–0.995), compared to slight agreement for the reports (kappa = 0.095, 95%CI −0.098–0.287). Radiologists also showed high sensitivities ranging from 91.51 (95% CI 84.49–96.04) to 98.11 (95% CI 93.35–99.77), compared to 44.34 (95% CI 34.69–54.31) for the reports. Conclusion: Dedicated radiologist review for MESCC on CT showed high interobserver agreement and sensitivity compared to the current standard of care. Full article

Background: Metastatic epidural spinal cord compression (MESCC) is a disastrous complication of advanced malignancy. Deep learning (DL) models for automatic MESCC classification on staging CT were developed to aid earlier diagnosis. Methods: This retrospective study included 444 CT staging studies from 185 patients with suspected MESCC who underwent MRI spine studies within 60 days of the CT studies. The DL model training/validation dataset consisted of 316/358 (88%) and the test set of 42/358 (12%) CT studies. Training/validation and test datasets were labeled in consensus by two subspecialized radiologists (6 and 11-years-experience) using the MRI studies as the reference standard. Test sets were labeled by the developed DL models and four radiologists (2–7 years of experience) for comparison. Results: DL models showed almost-perfect interobserver agreement for classification of CT spine images into normal, low, and high-grade MESCC, with kappas ranging from 0.873–0.911 (p < 0.001). The DL models (lowest κ = 0.873, 95% CI 0.858–0.887) also showed superior interobserver agreement compared to two of the four radiologists for three-class classification, including a specialist (κ = 0.820, 95% CI 0.803–0.837) and general radiologist (κ = 0.726, 95% CI 0.706–0.747), both p < 0.001. Conclusion: DL models for the MESCC classification on a CT showed comparable to superior interobserver agreement to radiologists and could be used to aid earlier diagnosis. Full article

The 2020 World Health Organization classification defined giant cell tumors of bone (GCTBs) as intermediate malignant tumors. Since the mutated H3F3A was found to be a specific marker for GCTB, it has become very useful in diagnosing GCTB. Curettage is the most common treatment for GCTBs. Preoperative administration of denosumab makes curettage difficult and increases the risk of local recurrence. Curettage is recommended to achieve good functional outcomes, even for local recurrence. For pathological fractures, joints should be preserved as much as possible and curettage should be attempted. Preoperative administration of denosumab for pelvic and spinal GCTBs reduces extraosseous lesions, hardens the tumor, and facilitates en bloc resection. Nerve-sparing surgery after embolization is a possible treatment for sacral GCTBS. Denosumab therapy with or without embolization is indicated for inoperable pelvic, spinal, and sacral GCTBs. It is recommended to first observe lung metastases, then administer denosumab for growing lesions. Radiotherapy is associated with a risk of malignant transformation and should be limited to cases where surgery is impossible and denosumab, zoledronic acid, or embolization is not available. Local recurrence after 2 years or more should be indicative of malignant transformation. This review summarizes the treatment approaches for non-malignant and malignant GCTBs. Full article