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.

Hypertensive Intracerebral Hemorrhage in Young Patients From a Tertiary Care Center in Saudi Arabia: An Observational Study

Objective: Young patients with intracerebral hemorrhage (ICH) make up a small but important subgroup of patients with ICH. This study investigated the clinical characteristics and outcomes of hypertensive ICH in very young (18-45 years) and young (46-55 years) patients.

Methods: This was a retrospective study of patients aged 18-55 years with hypertensive ICH admitted to a hospital from April 2014 to April 2019. Clinical and radiologic features as well as long-term clinical outcomes were compared between 2 age groups: group 1 (18-45 years) and group 2 (46-55 years). Factors affecting the clinical outcome were investigated as well.

Results: Of 63 patients with hypertensive ICH, 24 (38.1%) were in group 1 (mean ± SD age of 38 ± 4.6 years), and 39 (61.9%) were in group 2 (50 ± 2.5 years). The risk factor profile was similar except for diabetes, which was more prevalent in group 1 (odds ratio [OR] = 4.65; 95% CI, 1.4-15.2). Patients in group 1 had higher mean ± SD NIH Stroke Scale scores (15.7 ± 4.6, P = .044), had lower Glasgow Coma Scale (GCS) scores (OR = 3.33; 95% CI, 1.0-10.8), were at higher risk of intubation (OR = 2.79; 95% CI, 1.1-9.9), and had higher ICH volume (21 ± 18, P = .034). Worse clinical outcome was higher in group 1 (OR = 5.14; 95% CI, 1.0-26.1). Low GCS score, mean hematoma volume, and intraventricular extension were independently associated with worse outcome.

Conclusions: Relatively young patients with hypertensive ICH have higher prevalence of diabetes and worse clinical outcome in comparison to older patients with hypertensive ICH. Such patients should be monitored and treated more aggressively.

Sciatic nerve excursion during neural mobilization with ankle movement using dynamic ultrasound imaging: a cross-sectional study

PURPOSE

Ankle movement is used as a sensitizing maneuver for sciatica during neurodynamic techniques. In vivo studies on the sciatic nerve biomechanics associated with ankle movement during different positions of neighboring joints are scarce. The aim of this study was to investigate sciatic nerve excursion during ankle dorsiflexion in different positions in a healthy population.

METHODS

This is a cross-sectional study. High-resolution dynamic ultrasound imaging was used to measure longitudinal excursion of the sciatic nerve in the posterior thigh of 27 healthy participants during ankle dorsiflexion in six positions of the neck, hip, and knee. Both the long and short distance of the nerve excursion were measured. Wilcoxon signed-rank tests were used for data analysis, and Eta squared (r) was used to quantify the effect size.

RESULTS

Ankle dorsiflexion resulted in distal sciatic nerve excursion that was significantly higher in positions in which the knee was extended (median 0.7-1.6 mm) than in positions in which the knee was flexed (median 0.5-1.4 mm) (P ≤ 0.049, r ≥ 0.379). There were no significant differences in nerve excursion between positions where the neck was neutral compared with positions where the neck was flexed (P ≥ 0.710, r ≤ 0.072) or between positions where the hip was neutral compared with positions where the hip was flexed (P ≥ 0.456, r ≤ 0.143).

CONCLUSION

The positions of adjacent joints, particularly the knee, had an impact on the excursion of the sciatic nerve in the thigh during ankle movement.