Fundamentals of Radiation Oncology for Treatment of Vertebral Metastases

A critical appraisal of clinical guidelines on radiotherapy treatments for spinal metastasis

This review systematically reviewed the current guidelines related to radiotherapy for spinal metastases, summarized the relevant recommendations, and assessed the quality of their supporting evidence. The guidelines on radiotherapy for spinal metastases were searched by the keyword "guidelines" and "spinal metastasis". The most updated guidelines on radiotherapy for spinal metastasis were selected based on pre-defined inclusion and exclusion criteria. AGREE II was used to evaluate the quality of these guidelines. In addition, the related recommendations were extracted, and their quality was assessed using an evidence-grading scale. Nine guidelines established between 2013 and 2024 were included in this study. Three of the guidelines had an applicability rating of less than 50%. The difference in scores was the largest in rigor of development (range 48.50-88.03%). A total of 44 recommendations based on indications, re-irradiation, radiation dose and regimen, and emergency radiotherapy, were extracted and evaluated for SM. In conclusion, this study summarizes nine guidelines on radiotherapy for SM and provides useful information for improving treatment outcomes in patients with SM. All nine guidelines scored low in terms of adaptability, and most recommendations were based on a moderate-to-high LOE. The timing of re-radiotherapy varies across guidelines and fractionated radiotherapy regimens are available for specific SM patients. SBRT is more suitable than RBRT for patients with oligo-metastases, but more high-quality evidence is needed to confirm the more advantages of SBRT compared with conventional radiotherapy.

Percutaneous cryoablation in the management of spinal metastases: a comprehensive systematic review and meta-analysis

BACKGROUND

Minimally invasive techniques such as vertebroplasty, kyphoplasty, radiofrequency ablation, and stereotactic body radiotherapy have been widely used to manage spinal metastases. Among these, percutaneous cryoablation (PCA) has emerged as a promising option for local tumor control and pain management, offering targeted treatment with minimal damage to surrounding tissues. This systematic review and meta-analysis aimed to evaluate the efficacy and safety of PCA for spinal metastases.

METHODS

A systematic review was conducted using PubMed and Embase databases to identify studies that reported outcomes of PCA for spinal metastases. The reported radiologic, clinical, and complication outcomes were then combined and analyzed using meta-analytic methods including the calculation of pooled means and proportions, subgroup analysis, and meta-regression.

RESULTS

Eleven studies, including 229 patients, met inclusion criteria and were analyzed. Patients had a mean age of 61.8 years, with 60.6% being female. Breast (18.6%), lung (16.0%), and thyroid (8.0%) were the most common primary cancer sites. PCA was primarily conducted under general anesthesia (47.5%) and with CT/MRI guidance (93.9%). Local tumor control was achieved in 70.5% of cases over a mean follow-up of 12.6 months. Pain severity significantly decreased postoperatively, with a mean reduction of 4.5 points (P < 0.0001). Major and minor complication rates were 2.0% and 4.8%, respectively.

CONCLUSIONS

PCA is an effective alternative treatment for spinal metastases, offering pain relief and local tumor control with low complication rates in appropriately selected patients. However, tumor location and patient age may influence treatment outcomes, underscoring the need for individualized treatment planning.

Radiotherapy and surgery: can this combination be further optimized for patients with metastatic spine disease?

This narrative review provides a comprehensive overview of the current status, recent advancements, and future directions in the management of metastatic spine disease using both radiotherapy and surgery. Emphasis has been put on the integrated use of radiotherapy and surgery, incorporating recent developments such as separation surgery, active dose sparing of the surgical field, and the implementation of carbon fiber-reinforced polymer implants. Future studies should explore the effects of minimizing the time between radiotherapy and surgery and investigate the potential of vertebral re-ossification after radiotherapy to obviate the need for stabilization surgery. Concerted efforts should be directed toward fostering multidisciplinary collaboration among radiation oncologists, spine surgeons, and medical oncologists.

A prospectively deployed deep learning-enabled automated quality assurance tool for oncological palliative spine radiation therapy

BACKGROUND

Palliative spine radiation therapy is prone to treatment at the wrong anatomic level. We developed a fully automated deep learning-based spine-targeting quality assurance system (DL-SpiQA) for detecting treatment at the wrong anatomic level. DL-SpiQA was evaluated based on retrospective testing of spine radiation therapy treatments and prospective clinical deployment.

METHODS

The DL-SpiQA workflow involves auto-segmentation and labelling of all vertebral volumes on CT imaging using TotalSegmentator, an open-source deep learning algorithm based on nnU-Net, calculation of the radiation dose to each vertebra, and flagging and categorisation of potential treatments at the wrong anatomic level with automated email reports sent to involved radiation therapy personnel. We developed the DL-SpiQA tool based on retrospective clinical data from patients treated with palliative spine radiation therapy from sites included in the multicentre hospital network between Feb 12, 2014, and Nov 15, 2022. We used historic cases of patients who had a near-miss (ie, wrong-anatomic-level errors caught before the patient was treated) or had received wrong-anatomic-level treatment to test whether the tool could identify known errors successfully. We then used the tool prospectively over 15 months (April 24, 2023, to July 22, 2024) to evaluate any new spine radiation therapy treatment plan created for a patient, looking for any targeting errors, and dose and volume discrepancies. An email report was circulated with all the radiation therapy personnel; if any errors were found, these were highlighted and each error was defined. The tool was internally validated. All cases flagged by DL-SpiQA for both the retrospective and prospective studies were manually reviewed for dosimetric targeting, variant spine anatomy or spinal anomalies, and artificial intelligence (AI) segmentation errors. DL-SpiQA was further validated based on false positive and negative rates estimated from the retrospective results.

FINDINGS

DL-SpiQA was first tested retrospectively on 513 patients with segmentation of 10 106 vertebrae. The system raised flags for ten dose discrepancies, 49 normal anatomic variants, 49 cases with implants or other anomalies, and 20 segmentation errors (4% false positive rate). DL-SpiQA caught one historic treatment at the wrong anatomic level and three near-misses. DL-SpiQA was then prospectively deployed, reviewing 520 cases and identifying six documentation errors, which triggered detailed review by clinicians, and 43 additional cases, which confirmed clinical knowledge of variant anatomy. In all detected cases (ie, 49 of 520 cases in total), the appropriate personnel were alerted. A false negative rate of 0·03% is estimated based on the 4% AI segmentation error rate and the frequency of reported spine radiation therapy errors.

INTERPRETATION

The low false positive rate, the low false negative rate, and the high accuracy in flagging errors show that DL-SpiQA is an effective, AI-driven, automated quality assurance tool that could be used to identify anatomic spine variants and errors in targeting at the anatomic level. The tool could therefore help improve the safety of spine radiotherapy. Further external validation and tailoring is needed.

FUNDING

None.

Primary Stereotactic Body Radiotherapy for Spinal Bone Metastases From Lung Adenocarcinoma

OBJECTIVE

This study aimed to assess the results of primary stereotactic body radiotherapy (SBRT) for spinal bone metastases (SBM) originating from lung adenocarcinoma (ADC). We considered the revised Tokuhashi score (rTS), Spinal Instability Neoplastic Score (SINS), and genetic characteristics.

METHODS

We examined adult patients with lung ADC who underwent primary SBRT (using the CyberKnife System) for SBM between March 2012 and January 2023.

RESULTS

We analyzed data from 99 patients, covering 152 SBM across 194 vertebrae. The overall local control (LC) rate was 77.6% for SBM from lung ADC, with a LC rate of 90.7% at 1 year. The median period for local progression (LP) occurrence was recorded at 10.0 (3-52) months. Additionally, Asian patients demonstrated higher LC rates than White patients. Utilizing the rTS and SINS as predictive tools, we revealed that a poor survival prognosis and an unstable spinal structure were associated with increased rates of LP. Furthermore, the presence of osteolytic bone destructions and pain complaints were significantly correlated with the occurrence of LP. In the cohort of this study, 108 SBM underwent analysis to determine the expression levels of programmed cell death ligand 1 (PD-L1). Additionally, within this group, 60 showed mutations in the epidermal growth factor receptor (EGFR) alongside PD-L1 expression. Nevertheless, these genetic differences did not result in statistically significant differences in the LC rate.

CONCLUSION

The one-year LC rate for primary SBRT targeting SBM from lung ADC stood at 90.7%, particularly with the use of the CyberKnife System. Patients achieving LC exhibited significantly longer survival times compared to those with LP.

Vascular Pathologic Conditions in and around the Spinal Cord

Diagnosing and differentiating spinal vascular pathologic conditions is challenging. Small structures, lengthy imaging examinations, and overlapping imaging features increase the difficulty. Yet, subtle findings and helpful protocols can narrow the differential diagnosis. The authors aim to help radiologists make accurate and timely diagnoses of spinal vascular pathologic conditions in and around the spinal cord by highlighting spinal vascular anatomy, imaging findings, and three broad categories of abnormalities: infarcts, anomalies, and tumors. ©RSNA, 2024.

A Multidisciplinary Update on Treatment Modalities for Metastatic Spinal Tumors with a Surgical Emphasis: A Literature Review and Evaluation of the Role of Artificial Intelligence

Spinal metastases occur in up to 40% of patients with cancer. Of these cases, 10% become symptomatic. The reported incidence of spinal metastases has increased in recent years due to innovations in imaging modalities and oncological treatments. As the incidence of spinal metastases rises, so does the demand for improved treatments and treatment algorithms, which now emphasize greater multidisciplinary collaboration and are increasingly customized per patient. Uniquely, we discuss the potential clinical applications of AI and NGS in the treatment of spinal metastases. Material and Methods: A PubMed search for articles published from 2000 to 2023 regarding spinal metastases and artificial intelligence in healthcare was completed. After screening for relevance, the key findings from each study were summarized in this update. Results: This review summarizes the evidence from studies reporting on treatment modalities for spinal metastases, including minimally invasive surgery (MIS), external beam radiation therapy (EBRT), stereotactic radiosurgery (SRS), CFR-PEEK instrumentation, radiofrequency ablation (RFA), next-generation sequencing (NGS), artificial intelligence, and predictive models.

Management of Metastatic Spinal Disease - A Practical Approach

STUDY DESIGN

Narrative review.

OBJECTIVE

This review presents a comprehensive approach to the management of spinal metastases.

METHODS

N/A.

RESULTS

The wide spectrum of clinical presentation in spinal metastases necessitates a personalized approach to treatment planning. This includes a comprehensive diagnostic workup, oncological management, palliation of symptoms, and surgical intervention if appropriate. A systematic and multidisciplinary approach allows optimal shared decision making to reach an evidence-informed and value-congruent treatment plan for the patient. We highlight how advances in stereotactic body radiotherapy (SBRT) and separation surgery may be incorporated into clinical management from a spine surgeon's perspective.

CONCLUSION

This review summarizes the approach and management of spinal metastases, its outcomes and complications.

Dosimetric Comparison of Conventional Radiotherapy, Volumetric Modulated Arc Therapy, and Proton Beam Therapy for Palliation of Thoracic Spine Metastases Secondary to Breast or Prostate Cancer

The aim of this planning study was to compare the dosimetric outcomes of Volumetric Modulated Arc Therapy (VMAT), Proton Beam Therapy (PBT), and conventional External Beam Radiation Therapy (cEBRT) in the treatment of thoracic spinal metastases originating from breast or prostate cancer. Our study utilized data from 30 different treatment plans and evaluated target coverage and doses to vital organs at risk (OARs), such as the spinal cord, heart, esophagus, and lungs. The results showed that VMAT and PBT achieved superior target coverage and significantly lower doses to the spinal cord compared to cEBRT (target: median PTVD95%: 75.2 for cEBRT vs. 92.9 and 91.7 for VMAT (p < 0.001) and PBT (p < 0.001), respectively; spinal cord: median Dmax%: 105.1 for cEBRT vs. 100.4 and 103.6 for VMAT (p < 0.001) and PBT (p = 0.002), respectively). Specifically, VMAT was notable for its superior target coverage and PBT for significantly lower doses to heart, lungs, and esophagus. However, VMAT resulted in higher lung doses, indicating potential trade-offs among different techniques. The study demonstrated the relative advantages of VMAT and PBT over traditional RT in the palliative treatment of spinal metastases using conventional fractionation. These findings underscore the potential of VMAT and PBT to improve dosimetric outcomes, suggesting that they may be more suitable for certain patient groups for whom the sparing of specific OARs is especially important.

Dynamic contrast-enhanced magnetic resonance imaging parameter changes as an early biomarker of tumor responses following radiation therapy in patients with spinal metastases: a systematic review

PURPOSE

This systematic review aims to assess and summarize the clinical values of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) parameter changes as early biomarkers of tumor responses following radiation therapy (RT) in patients with spinal metastases.

MATERIALS AND METHODS

A systematic search was conducted on five electronic databases: PubMed, Scopus, Science Direct, Cochrane, and Embase. Studies were included if they mentioned DCE-MRI parameter changes before and after RT in patients with spinal metastases with a correlation to tumor responses based on clinical and imaging criteria. The Quality Assessment of Diagnostic Accuracy Studies 2 was used to assess study quality.

RESULTS

This systematic review included seven studies involving 107 patients. All seven studies evaluated the transfer constant (Ktrans), six studies evaluated the plasma volume fraction (Vp), three studies evaluated the extravascular extracellular space volume fraction, and two studies evaluated the rate constant. There were variations in the type of primary cancer, RT techniques used, post-treatment scan time, and median follow-up time. Despite the variations, however, the collected evidence generally suggested that significant differences could be detected in DCE-MRI parameters between before and after RT, which might reflect treatment success or failures in long-term follow-up. Responders showed higher reduction and lower values of Ktrans and Vp after RT. DCE-MRI parameters showed changes and detectable recurrences significantly earlier (up to 6 months) than conventional MRI with favorable diagnostic values.

CONCLUSION

The results of this systematic review suggested that DCE-MRI parameter changes in patients with spinal metastases could be a promising tool for treatment-response assessment following RT. Lower values and higher reduction of Ktrans and Vp after treatment demonstrated good prediction of local control. Compared to conventional MRI, DCE-MRI showed more rapid changes and earlier prediction of treatment failure.

New insights on clinical perspectives of FLASH radiotherapy: from low- to very high electron energy

Radiotherapy (RT) is performed in approximately 75% of patients with cancer, and its efficacy is often hampered by the low tolerance of the surrounding normal tissues. Recent advancements have demonstrated the potential to widen the therapeutic window using "very short" radiation treatment delivery (from a conventional dose rate between 0.5 Gy/min and 2 Gy/min to more than 40 Gy/s) causing a significant increase of normal tissue tolerance without varying the tumor effect. This phenomenon is called "FLASH Effect (FE)" and has been discovered by using electrons. Although several physical, dosimetric, and radiobiological aspects need to be clarified, current preclinical "in vivo" studies have reported a significant protective effect of FLASH RT on neurocognitive function, skin toxicity, lung fibrosis, and bowel injury. Therefore, the current radiobiological premises lay the foundation for groundbreaking potentials in clinical translation, which could be addressed to an initial application of Low Energy Electron FLASH (LEE) for the treatment of superficial tumors to a subsequent Very High Energy Electron FLASH (VHEE) for the treatment of deep tumors. Herein, we report a clinical investigational scenario that, if supported by preclinical studies, could be drawn in the near future.

Deep Learning Model for Grading Metastatic Epidural Spinal Cord Compression on Staging CT

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.

Deep Learning Model for Classifying Metastatic Epidural Spinal Cord Compression on MRI

Background

Metastatic epidural spinal cord compression (MESCC) is a devastating complication of advanced cancer. A deep learning (DL) model for automated MESCC classification on MRI could aid earlier diagnosis and referral.

Purpose

To develop a DL model for automated classification of MESCC on MRI.

Materials and Methods

Patients with known MESCC diagnosed on MRI between September 2007 and September 2017 were eligible. MRI studies with instrumentation, suboptimal image quality, and non-thoracic regions were excluded. Axial T2-weighted images were utilized. The internal dataset split was 82% and 18% for training/validation and test sets, respectively. External testing was also performed. Internal training/validation data were labeled using the Bilsky MESCC classification by a musculoskeletal radiologist (10-year experience) and a neuroradiologist (5-year experience). These labels were used to train a DL model utilizing a prototypical convolutional neural network. Internal and external test sets were labeled by the musculoskeletal radiologist as the reference standard. For assessment of DL model performance and interobserver variability, test sets were labeled independently by the neuroradiologist (5-year experience), a spine surgeon (5-year experience), and a radiation oncologist (11-year experience). Inter-rater agreement (Gwet’s kappa) and sensitivity/specificity were calculated.

Results

Overall, 215 MRI spine studies were analyzed [164 patients, mean age = 62 ± 12(SD)] with 177 (82%) for training/validation and 38 (18%) for internal testing. For internal testing, the DL model and specialists all showed almost perfect agreement (kappas = 0.92–0.98, p < 0.001) for dichotomous Bilsky classification (low versus high grade) compared to the reference standard. Similar performance was seen for external testing on a set of 32 MRI spines with the DL model and specialists all showing almost perfect agreement (kappas = 0.94–0.95, p < 0.001) compared to the reference standard.

Conclusion

A DL model showed comparable agreement to a subspecialist radiologist and clinical specialists for the classification of malignant epidural spinal cord compression and could optimize earlier diagnosis and surgical referral.