Fundamentals of Radiation Oncology for Treatment of Vertebral                     Metastases

Gottumukkala, Sujana; Srivastava, Udayan; Brocklehurst, Samantha; Mendel, Jameson T.; Kumar, Kiran A; Yu, Fang; Agarwal, Amit Kumar; Shah, Bhavya; Vira, Shaleen; Raj, Karuna M.

doi:10.1148/rg.2021210052

Cited by 11 publications

(7 citation statements)

References 52 publications

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“…Individuals can present with high-grade Bilsky scores and not be suitable surgical candidates. Further work using our Bilsky prediction model could involve combining imaging data with clinical information (e.g., age, cancer subtype, and degree of neurological impairment) to improve the selection of patients for more aggressive management including surgery and/or SBRT ( 21 , 36 ). Our DL model is focused on Bilsky classification and currently does not have the ability to segment or outline tumors.…”

Section: Discussionmentioning

confidence: 99%

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

et al. 2022

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BackgroundMetastatic 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.PurposeTo develop a DL model for automated classification of MESCC on MRI.Materials and MethodsPatients 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.ResultsOverall, 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.ConclusionA 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.

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Section: Discussionmentioning

confidence: 99%

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

et al. 2022

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“…The deep learning model had a superior interobserver agreement for the detection of trichotomous Bilsky grading (normal, low, and high-grade) compared to general and specialist radiologists. The deep learning model could be used to triage patients for an urgent MRI if there is high-grade disease or reduce the need for an MRI when there is no evidence of high-grade disease on CT. Future work using our CT MESCC deep learning model would involve adding clinical data (e.g., cancer subtype, neurological impairment, and pre-existing medical conditions) to improve the selection of patients for more aggressive therapy, including stereotactic body radiotherapy (SBRT) and surgery [40]. Additional work would also involve the prospective deployment of the deep learning model onto the institutional radiology reporting system.…”

Section: Discussionmentioning

confidence: 99%

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

Hallinan

Zhu

Zhang

et al. 2022

Cancers

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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.

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“…The treatment of spinal metastases has become a hot topic to reduce pain-related neurological symptoms and surgery-related complications (11), along with individualized precision radiotherapy (12). Multi-treatment methods have been applied to treat spinal metastasis, like operation, radiotherapy, bisphosphonates and denosumab, embolization, and radiofrequency thermal ablation, electrochemotherapy and high-intensity focused ultrasound (13).…”

Section: Spinal Metastasis Is a Very Common Disease; Bones Arementioning

confidence: 99%

Study on the clinical efficacy of bone-filled mesh vertebroplasty combined with posterior screw and rod internal fixation in the treatment of thoracolumbar metastases: a retrospective cohort study

Zhu¹,

Qiao²,

Li³

et al. 2022

Ann Palliat Med

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Background: Thoracolumbar metastases is a difficult disease to deal with in spinal surgery. The aim of this study is to investigate the clinical efficacy of bone-filled mesh vertebroplasty combined with posterior spinal internal fixation in the treatment of thoracolumbar metastases. Methods:The clinical data of 68 patients with thoracolumbar vertebral metastases from January 2018 to April 2020 were retrospectively analyzed. A total of 37 cases underwent bone filling mesh pocket vertebroplasty combined with posterior spinal internal fixation as the observation group, and 31 cases underwent routine vertebroplasty combined with posterior spinal internal fixation as the control group. The visual analogue scale (VAS) scores, Oswestry disability index (ODI) scores, Karnofsky performance status (KPS) scores, and the heights of the anterior margin and middle of the diseased vertebra were compared between the 2 groups before and 1 week, 3 months, 6 months, and 1 year after surgery.Results: All cases successfully completed the operation, and there was no pulmonary embolism, paraplegia, or perioperative death in follow-up reported. Intraoperative bone cement leakage occurred in 4 cases with a total of 6 vertebrae in the observation group (leakage rate: 14.29%), and in 8 cases with a total of 11 vertebrae in control group (leakage rate: 31.43%). The differences in VAS scores, ODI scores, KPS scores, and the heights of the anterior margin and middle of the diseased vertebra between preoperative and postoperative periods at 1 week, 3 months, 6 months, and 1 year in both groups were statistically significant (P<0.05), while the differences between the 2 groups were not statistically significant (P<0.05). Conclusions:The application of bone-filled mesh vertebroplasty combined with posterior internal pedicle screws fixation for the treatment of thoracolumbar metastases can not only reduce the injury of the operation, but also achieve the purpose of relieving pain, controlling local tumor growth to a certain extent, restoring neural function, and rebuilding the stability of the spine, which has important clinical value.

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Fundamentals of Radiation Oncology for Treatment of Vertebral Metastases

Cited by 11 publications

References 52 publications

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

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

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

Study on the clinical efficacy of bone-filled mesh vertebroplasty combined with posterior screw and rod internal fixation in the treatment of thoracolumbar metastases: a retrospective cohort study

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