Radiation therapy with phenotypic medicine: towards N-of-1 personalization.

In current clinical practice, radiotherapy (RT) is prescribed as a pre-determined total dose divided over daily doses (fractions) given over several weeks. The treatment response is typically assessed months after the end of RT. However, the conventional one-dose-fits-all strategy may not achieve the desired outcome, owing to patient and tumor heterogeneity. Therefore, a treatment strategy that allows for RT dose personalization based on each individual response is preferred. Multiple strategies have been adopted to address this challenge. As an alternative to current known strategies, artificial intelligence (AI)-derived mechanism-independent small data phenotypic medicine (PM) platforms may be utilized for N-of-1 RT personalization. Unlike existing big data approaches, PM does not engage in model refining, training, and validation, and guides treatment by utilizing prospectively collected patient's own small datasets. With PM, clinicians may guide patients' RT dose recommendations using their responses in real-time and potentially avoid over-treatment in good responders and under-treatment in poor responders. In this paper, we discuss the potential of engaging PM to guide clinicians on upfront dose selections and ongoing adaptations during RT, as well as considerations and limitations for implementation. For practicing oncologists, clinical trialists, and researchers, PM can either be implemented as a standalone strategy or in complement with other existing RT personalizations. In addition, PM can either be used for monotherapeutic RT personalization, or in combination with other therapeutics (e.g. chemotherapy, targeted therapy). The potential of N-of-1 RT personalization with drugs will also be presented.

Is two-staged gamma knife surgery a reasonable management option for very large cerebellar metastases? A case series of three patients.

OBJECTIVE: Two-staged gamma knife surgery (GKS) is a method that may extend the upper tumor volume limit for using GKS in the management of brain metastases. However, the safety of treating very large posterior fossa lesions with this technique has not been well demonstrated. Therefore, we analyzed our experience in treating cerebellar metastases larger than 12 cm3 with two-staged GKS. METHODS: Four consecutive patients harboring 12 to 30 cm3 cerebellar metastases scheduled two-staged GKS were included in the study, and all but one patient completed the treatment. The treatment doses were 10-13 Gy. All patients were followed with regular MR imaging and clinical assessments, and the tumor volumes were measured on all treatment and follow-up images. RESULTS: Tumor progression was not demonstrated in any of the patients. Tumor volumes decreased by, on average, more than half between the two stages. The median survival was 22 months, and no patient died due to intracranial tumor progression. Peritumoral edema at the first GKS resolved in all patients, replaced by asymptomatic mild T2 changes in two of them not requiring any treatment. No radiation-induced complication has developed thus far. CONCLUSION: Staged GKS seems to be a feasible management option for very large cerebellar metastases.

CURATE.AI COR-Tx platform as a digital therapy and digital diagnostic for cognitive function in patients with brain tumour postradiotherapy treatment: protocol for a prospective mixed-methods feasibility clinical trial.

INTRODUCTION: Conventional interventional modalities for preserving or improving cognitive function in patients with brain tumour undergoing radiotherapy usually involve pharmacological and/or cognitive rehabilitation therapy administered at fixed doses or intensities, often resulting in suboptimal or no response, due to the dynamically evolving patient state over the course of disease. The personalisation of interventions may result in more effective results for this population. We have developed the CURATE.AI COR-Tx platform, which combines a previously validated, artificial intelligence-derived personalised dosing technology with digital cognitive training. METHODS AND ANALYSIS: This is a prospective, single-centre, single-arm, mixed-methods feasibility clinical trial with the primary objective of testing the feasibility of the CURATE.AI COR-Tx platform intervention as both a digital intervention and digital diagnostic for cognitive function. Fifteen patient participants diagnosed with a brain tumour requiring radiotherapy will be recruited. Participants will undergo a remote, home-based 10-week personalised digital intervention using the CURATE.AI COR-Tx platform three times a week. Cognitive function will be assessed via a combined non-digital cognitive evaluation and a digital diagnostic session at five time points: preradiotherapy, preintervention and postintervention and 16-weeks and 32-weeks postintervention. Feasibility outcomes relating to acceptability, demand, implementation, practicality and limited efficacy testing as well as usability and user experience will be assessed at the end of the intervention through semistructured patient interviews and a study team focus group discussion at study completion. All outcomes will be analysed quantitatively and qualitatively. ETHICS AND DISSEMINATION: This study has been approved by the National Healthcare Group (NHG) DSRB (DSRB2020/00249). We will report our findings at scientific conferences and/or in peer-reviewed journals. TRIAL REGISTRATION NUMBER: NCT04848935.

Novel 3D printable PEEK-HA-Mg2SiO4 composite material for spine implants: biocompatibility and imaging compatibility assessments.

PURPOSE: To develop a novel 3D printable polyether ether ketone (PEEK)-hydroxyapatite (HA)-magnesium orthosilicate (Mg2SiO4) composite material with enhanced properties for potential use in tumour, osteoporosis and other spinal conditions. We aim to evaluate biocompatibility and imaging compatibility of the material. METHODS: Materials were prepared in three different compositions, namely composite A: 75 weight % PEEK, 20 weight % HA, 5 weight % Mg2SiO4; composite B: 70 weight% PEEK, 25 weight % HA, 5 weight % Mg2SiO4; and composite C: 65 weight % PEEK, 30 weight % HA, 5 weight % Mg2SiO4. The materials were processed to obtain 3D printable filament. Biomechanical properties were analysed as per ASTM standards and biocompatibility of the novel material was evaluated using indirect and direct cell cytotoxicity tests. Cell viability of the novel material was compared to PEEK and PEEK-HA materials. The novel material was used to 3D print a standard spine cage. Furthermore, the CT and MR imaging compatibility of the novel material cage vs PEEK and PEEK-HA cages were evaluated using a phantom setup. RESULTS: Composite A resulted in optimal material processing to obtain a 3D printable filament, while composite B and C resulted in non-optimal processing. Composite A enhanced cell viability up to ~ 20% compared to PEEK and PEEK-HA materials. Composite A cage generated minimal/no artefacts on CT and MR imaging and the images were comparable to that of PEEK and PEEK-HA cages. CONCLUSION: Composite A demonstrated superior bioactivity vs PEEK and PEEK-HA materials and comparable imaging compatibility vs PEEK and PEEK-HA. Therefore, our material displays an excellent potential to manufacture spine implants with enhanced mechanical and bioactive property.

Deep learning assessment compared to radiologist reporting for metastatic spinal cord compression on CT.

Introduction: Metastatic spinal cord compression (MSCC) is a disastrous complication of advanced malignancy. A deep learning (DL) algorithm for MSCC classification on CT could expedite timely diagnosis. In this study, we externally test a DL algorithm for MSCC classification on CT and compare with radiologist assessment. Methods: Retrospective collection of CT and corresponding MRI from patients with suspected MSCC was conducted from September 2007 to September 2020. Exclusion criteria were scans with instrumentation, no intravenous contrast, motion artefacts and non-thoracic coverage. Internal CT dataset split was 84% for training/validation and 16% for testing. An external test set was also utilised. Internal training/validation sets were labelled by radiologists with spine imaging specialization (6 and 11-years post-board certification) and were used to further develop a DL algorithm for MSCC classification. The spine imaging specialist (11-years expertise) labelled the test sets (reference standard). For evaluation of DL algorithm performance, internal and external test data were independently reviewed by four radiologists: two spine specialists (Rad1 and Rad2, 7 and 5-years post-board certification, respectively) and two oncological imaging specialists (Rad3 and Rad4, 3 and 5-years post-board certification, respectively). DL model performance was also compared against the CT report issued by the radiologist in a real clinical setting. Inter-rater agreement (Gwet's kappa) and sensitivity/specificity/AUCs were calculated. Results: Overall, 420 CT scans were evaluated (225 patients, mean age=60 ± 11.9[SD]); 354(84%) CTs for training/validation and 66(16%) CTs for internal testing. The DL algorithm showed high inter-rater agreement for three-class MSCC grading with kappas of 0.872 (p<0.001) and 0.844 (p<0.001) on internal and external testing, respectively. On internal testing DL algorithm inter-rater agreement (κ=0.872) was superior to Rad 2 (κ=0.795) and Rad 3 (κ=0.724) (both p<0.001). DL algorithm kappa of 0.844 on external testing was superior to Rad 3 (κ=0.721) (p<0.001). CT report classification of high-grade MSCC disease was poor with only slight inter-rater agreement (κ=0.027) and low sensitivity (44.0), relative to the DL algorithm with almost-perfect inter-rater agreement (κ=0.813) and high sensitivity (94.0) (p<0.001). Conclusion: Deep learning algorithm for metastatic spinal cord compression on CT showed superior performance to the CT report issued by experienced radiologists and could aid earlier diagnosis.

A deep learning-based technique for the diagnosis of epidural spinal cord compression on thoracolumbar CT.

PURPOSE: To develop a deep learning (DL) model for epidural spinal cord compression (ESCC) on CT, which will aid earlier ESCC diagnosis for less experienced clinicians. METHODS: We retrospectively collected CT and MRI data from adult patients with suspected ESCC at a tertiary referral institute from 2007 till 2020. A total of 183 patients were used for training/validation of the DL model. A separate test set of 40 patients was used for DL model evaluation and comprised 60 staging CT and matched MRI scans performed with an interval of up to 2 months. DL model performance was compared to eight readers: one musculoskeletal radiologist, two body radiologists, one spine surgeon, and four trainee spine surgeons. Diagnostic performance was evaluated using inter-rater agreement, sensitivity, specificity and AUC. RESULTS: Overall, 3115 axial CT slices were assessed. The DL model showed high kappa of 0.872 for normal, low and high-grade ESCC (trichotomous), which was superior compared to a body radiologist (R4, κ = 0.667) and all four trainee spine surgeons (κ range = 0.625-0.838)(all p < 0.001). In addition, for dichotomous normal versus any grade of ESCC detection, the DL model showed high kappa (κ = 0.879), sensitivity (91.82), specificity (92.01) and AUC (0.919), with the latter AUC superior to all readers (AUC range = 0.732-0.859, all p < 0.001). CONCLUSION: A deep learning model for the objective assessment of ESCC on CT had comparable or superior performance to radiologists and spine surgeons. Earlier diagnosis of ESCC on CT could reduce treatment delays, which are associated with poor outcomes, increased costs, and reduced survival.

Design and 3D printing of novel titanium spine rods with lower flexural modulus and stiffness profile with optimised imaging compatibility.

PURPOSE: To manufacture and test 3D printed novel design titanium spine rods with lower flexural modulus and stiffness compared to standard solid titanium rods for use in metastatic spine tumour surgery (MSTS) and osteoporosis. METHODS: Novel design titanium spine rods were designed and 3D printed. Three-point bending test was performed to assess mechanical performance of rods, while a French bender was used to assess intraoperative rod contourability. Furthermore, 3D printed spine rods were tested for CT & MR imaging compatibility using phantom setup. RESULTS: Different spine rod designs generated includes shell, voronoi, gyroid, diamond, weaire-phelan, kelvin, and star. Tests showed 3D printed rods had lower flexural modulus with reduction ranging from 2 to 25% versus standard rod. Shell rods exhibited highest reduction in flexural modulus of 25% (~ 77.4 GPa) and star rod exhibited lowest reduction in flexural modulus of 2% (100.8GPa). 3D printed rod showed reduction in stiffness ranging from 40 to 59%. Shell rod displayed highest reduction in stiffness of 59% (179.9 N/mm) and gyroid had least reduction in stiffness of 40% (~ 259.2 N/mm). Rod bending test showed that except gyroid, other rod designs demonstrated lesser bending difficulty versus standard rod. All 3D printed rods demonstrated improved CT/MR imaging compatibility with reduced artefacts versus standard rod. CONCLUSION: By utilising novel design approach, we successfully generated a spine rod design portfolio with lower flexural modulus/stiffness profile and better CT/MR imaging compatibility for potential use in MSTS/other conditions such as osteoporosis. Thus, exploration of new rod designs in surgical application could enhance treatment outcome and improve quality of life for patients.

Application of Machine Learning for Differentiating Bone Malignancy on Imaging: A Systematic Review.

An accurate diagnosis of bone tumours on imaging is crucial for appropriate and successful treatment. The advent of Artificial intelligence (AI) and machine learning methods to characterize and assess bone tumours on various imaging modalities may assist in the diagnostic workflow. The purpose of this review article is to summarise the most recent evidence for AI techniques using imaging for differentiating benign from malignant lesions, the characterization of various malignant bone lesions, and their potential clinical application. A systematic search through electronic databases (PubMed, MEDLINE, Web of Science, and clinicaltrials.gov) was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. A total of 34 articles were retrieved from the databases and the key findings were compiled and summarised. A total of 34 articles reported the use of AI techniques to distinguish between benign vs. malignant bone lesions, of which 12 (35.3%) focused on radiographs, 12 (35.3%) on MRI, 5 (14.7%) on CT and 5 (14.7%) on PET/CT. The overall reported accuracy, sensitivity, and specificity of AI in distinguishing between benign vs. malignant bone lesions ranges from 0.44-0.99, 0.63-1.00, and 0.73-0.96, respectively, with AUCs of 0.73-0.96. In conclusion, the use of AI to discriminate bone lesions on imaging has achieved a relatively good performance in various imaging modalities, with high sensitivity, specificity, and accuracy for distinguishing between benign vs. malignant lesions in several cohort studies. However, further research is necessary to test the clinical performance of these algorithms before they can be facilitated and integrated into routine clinical practice.

Neoadjuvant short-course radiotherapy or chemoradiation plus consolidative chemotherapy followed by radical operation for locally advanced rectal cancer.

Introduction: Limited evidence compares short-course radiotherapy (SCRT) and long-course chemoradiotherapy (LCCRT), both of which are followed by consolidative chemotherapy before radical rectal surgery. We conducted a retrospective cohort study to assess treatment response, survival outcomes, and toxicity in patients with locally advanced rectal cancer. Materials and methods: Patients (cT3-4 and/or N+) treated with SCRT or LCCRT, consolidative chemotherapy, or total mesorectal excision between 2013 and 2021 were identified. the cause-specific cumulative incidence of disease-related treatment failure, locoregional recurrence, distant metastases, and overall survival were evaluated using flexible parametric competing risk analysis and Kaplan-Meier methods, adjusted for treatment regimens and clinicopathological factors. A pathological complete response (pCR), tumor downstaging, and toxicity have been reported. Results: Among the 144 patients, 115 (80%) underwent curative rectal surgery. The LCCRT and SCRT groups achieved pCR in 10 (18%) and seven (12%) patients, respectively (odds ratio, 1.68; 95% confidence interval [CI], 0.59-4.78). The adjusted cause-specific hazard ratio for disease-related treatment failure with LCCRT versus SCRT was 0.26 (95% CI, 0.08-0.87). Three-year cumulative probability of disease-related treatment failure was 10.0% and 25.6% for LCCRT and SCRT, respectively. No significant differences in T-downstaging, N-downstaging, significant pathologic downstaging (ypT0-2N0), locoregional failure, distant metastasis, or overall survival were found. Late rectal toxicity occurred in 10 (15%) LCCRT and two (3%) SCRT patients, respectively. Conclusion: LCCRT with consolidative chemotherapy demonstrated improved disease-related treatment failure compared with SCRT, despite higher late rectal toxicity. Further research is needed to assess the long-term oncologic outcomes and toxicity.

Incident Cardiovascular Diseases Among Survivors of High-Risk Stage II-III Colorectal Cancer: A Cluster-Wide Cohort Study.

BACKGROUND: The incidence and survival of colorectal cancer (CRC) are increasing. There is an increasing number of long-term survivors, many of whom are elderly and have comorbidities. We conducted a population-based study in Hong Kong to assess the long-term cardiovascular disease (CVD) incidence associated with adjuvant fluoropyrimidine-based chemotherapy among CRC survivors. PATIENTS AND METHODS: Using the population-based electronic medical database of Hong Kong, we identified adults who were diagnosed with high-risk stage II-III CRC and treated with radical surgery followed by adjuvant fluoropyrimidine-based chemotherapy between 2010 and 2019. We evaluated the cause-specific cumulative incidence of CVD (including ischemic heart disease, heart failure, cardiomyopathy, and stroke) using the flexible parametric competing risk modeling framework. The control group without a history of CVD was selected from among a noncancer random sample from primary care clinics in the same geographic area. RESULTS: We analyzed 1,037 treated patients with CRC and 5,078 noncancer controls. The adjusted cause-specific hazard ratio (HR) for CVD in the cancer cohort compared with the control group was 2.11 (95% CI, 1.39-3.20). The 1-, 5-, and 10-year cause-specific cumulative incidences were 2.0%, 4.5%, and 5.4% in the cancer cohort versus 1.2%, 3.0%, and 3.8% in the control group, respectively. Age at cancer diagnosis (HR per 5-year increase, 1.16; 95% CI, 1.08-1.24), male sex (HR, 1.40; 95% CI, 1.06-1.86), comorbidity (HR, 1.88; 95% CI, 1.36-2.61 for 1 comorbidity vs none, and HR, 6.61; 95% CI, 4.55-9.60 for ≥2 comorbidities vs none), diabetes (HR, 1.38; 95% CI, 1.04-1.84), hypertension (HR, 3.27; 95% CI, 2.39-4.50), and dyslipidemia/hyperlipidemia (HR, 2.53; 95% CI, 1.68-3.81) were associated with incident CVD. CONCLUSIONS: Exposure to adjuvant fluoropyrimidine-based chemotherapy was associated with an increased risk of CVD among survivors of high-risk stage II-III CRC. Cardiovascular risk monitoring of this group throughout cancer survivorship is advisable.

Diagnostic Accuracy of CT for Metastatic Epidural Spinal Cord Compression.

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.

Application of Artificial Intelligence Methods for Imaging of Spinal Metastasis.

Spinal metastasis is the most common malignant disease of the spine. Recently, major advances in machine learning and artificial intelligence technology have led to their increased use in oncological imaging. The purpose of this study is to review and summarise the present evidence for artificial intelligence applications in the detection, classification and management of spinal metastasis, along with their potential integration into clinical practice. A systematic, detailed search of the main electronic medical databases was undertaken in concordance with the PRISMA guidelines. A total of 30 articles were retrieved from the database and reviewed. Key findings of current AI applications were compiled and summarised. The main clinical applications of AI techniques include image processing, diagnosis, decision support, treatment assistance and prognostic outcomes. In the realm of spinal oncology, artificial intelligence technologies have achieved relatively good performance and hold immense potential to aid clinicians, including enhancing work efficiency and reducing adverse events. Further research is required to validate the clinical performance of the AI tools and facilitate their integration into routine clinical practice.

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.

State-of-the-Art Imaging Techniques in Metastatic Spinal Cord Compression.

Metastatic Spinal Cord Compression (MSCC) is a debilitating complication in oncology patients. This narrative review discusses the strengths and limitations of various imaging modalities in diagnosing MSCC, the role of imaging in stereotactic body radiotherapy (SBRT) for MSCC treatment, and recent advances in deep learning (DL) tools for MSCC diagnosis. PubMed and Google Scholar databases were searched using targeted keywords. Studies were reviewed in consensus among the co-authors for their suitability before inclusion. MRI is the gold standard of imaging to diagnose MSCC with reported sensitivity and specificity of 93% and 97% respectively. CT Myelogram appears to have comparable sensitivity and specificity to contrast-enhanced MRI. Conventional CT has a lower diagnostic accuracy than MRI in MSCC diagnosis, but is helpful in emergent situations with limited access to MRI. Metal artifact reduction techniques for MRI and CT are continually being researched for patients with spinal implants. Imaging is crucial for SBRT treatment planning and three-dimensional positional verification of the treatment isocentre prior to SBRT delivery. Structural and functional MRI may be helpful in post-treatment surveillance. DL tools may improve detection of vertebral metastasis and reduce time to MSCC diagnosis. This enables earlier institution of definitive therapy for better outcomes.

Clinical characteristics and genetic testing outcome of suspected hereditary peripheral nerve sheath tumours in a tertiary cancer institution in Singapore.

BACKGROUND: Peripheral Nerve Sheath Tumors (PNST) are a diverse group of mostly benign tumours uncommon in the general population. About 5-10% of PNSTs are hereditary, predominantly arising from germline variants in NF1, NF2, SMARCB1, or LZTR1 gene. METHODS: We reviewed the clinical characteristics and genetic testing results of patients referred to the NCIS Adult Cancer Genetics Clinic for suspected hereditary PNST. RESULTS: 3,001 patients suspected to have various hereditary cancer syndromes were evaluated between year 2000 to March 2021. 13 (0.4%) were clinically diagnosed to have hereditary PNSTs. The majority were male (54%), with a median age at presentation to the genetics clinic of 29 years (range 19-48). 11/13 (85%) patients had multiple PNSTs, 12/13 (92%) had young onset PNSTs, 5/13 (38.5%) had personal and family history of PNST. 11/13 patients (85%) had clinical features of neurofibromatosis type 1 (NF1) including one patient who also fulfilled clinical criteria of neurofibromatosis type 2 (NF2); 2/13 (14%) had multiple schwannomas. Four patients underwent multi-gene panel testing, including one patient with clinical NF1, one patient who met both clinical NF1 and NF2 criteria, and two patients with multiple schwannomas. The patient with clinical features of NF1 was heterozygous for a pathogenic c. 2033dup variant in the NF1 gene. The patient with both NF1/NF2 features was heterozygous for a novel c.732 T > A nonsense variant in the NF2 gene. The two patients with multiple schwannomas were heterozygous for a pathogenic/likely pathogenic variant in the LZTR1 gene and are the first LZTR1-positive schwannomatosis patients reported in Asia. CONCLUSION: Hereditary PNSTs are rare referrals to an adult cancer genetics clinic. NF1 is the most common PNST seen. LZTR1 variants may be the underlying cause in Asian patients with multiple schwannomatosis.

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.

Cytokine Release Syndrome in Cancer Patients Receiving Immune Checkpoint Inhibitors: A Case Series of 25 Patients and Review of the Literature.

Cytokine release syndrome (CRS) is a phenomenon of immune hyperactivation described in the setting of immunotherapy. Unlike other immune-related adverse events, CRS triggered by immune checkpoint inhibitors (ICIs) is not well described. The clinical characteristics and course of 25 patients with ICI-induced CRS from 2 tertiary hospitals were abstracted retrospectively from the medical records and analyzed. CRS events were confirmed by 2 independent reviewers and graded using the Lee et al. scale. The median duration of CRS was 15.0 days (Q1; Q3 6.3; 29.8) and 10 (40.0%) had multiple episodes of CRS flares. Comparing the clinical factors and biomarkers in Grades 1-2 and 3-5 CRS, we found that patients with Grades 3-5 CRS had following: (i) had longer time to fever onset [25.0 days (Q1; Q3 13.0; 136.5) vs. 3.0 days (Q1; Q3 0.0; 18.0), p=0.027]; (ii) more cardiovascular (p=0.002), neurologic (p=0.001), pulmonary (p=0.044) and rheumatic (p=0.037) involvement; (iii) lower platelet count (p=0.041) and higher urea (p=0.041) at presentation compared to patients with Grades 1-2 CRS. 7 patients (28.0%) with Grades 1-2 CRS were rechallenged using ICIs without event. 9 patients (36.0%) were treated with pulse methylprednisolone and 6 patients (24.0%) were treated with tocilizumab. Despite this, 3 patients (50%) who received tocilizumab had fatal (Grade 5) outcomes from ICI-induced CRS. Longer time to fever onset, lower platelet count and higher urea at presentation were associated with Grade 3-5 CRS. These parameters may be used to predict which patients are likely to develop severe CRS.

Stereotactic radiosurgery in alveolar soft part sarcoma brain metastases: Case series and literature review.

Alveolar soft part sarcoma (ASPS) has the highest incidence of brain metastasis amongst sarcomas. There is a paucity of literature published focusing on radiation therapy for this condition. This is a single centre retrospective review of the treatment of three patients with 12 ASPS brain metastasis using single dose stereotactic radiosurgery (SRS). Five lesions were treated with low (<25 Gy) and seven with high (≥25 Gy) dose. Four lesions had a volume of >1.5 cm3 and were defined as large, while seven had a volume of ≤0.5 cm3 and were defined as small. The local tumor control as well as the clinical complication rates were studied. There was a statistically significant relation between treatment dose and tumor control rate. All large tumors treated with low dose recurred and required surgical removal within two months following SRS, while the large lesion treated with high dose recurred after 11 months. Five of the six small tumors treated with high doses were controlled, while the sixth required retreatment and was stable thereafter. No patient suffered from undue symptomatic radiation effects. The success rate following SRS for small ASPS metastases treated with high doses seems to be sufficient to justify the treatment. The short time for large tumor to recur, significant increase in tumor size requiring surgical removal of the tumors, makes low dose SRS unattractive. Based on this limited patient population, it seems that high dose SRS should be used for all ASPS brain metastases except for large tumors deemed surgically accessible.