Fundamentals of Radiation Oncology for Treatment of Vertebral Metastases

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.

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.