Machine learning approaches for imaging-based prognostication of the outcome of surgery for mesial temporal lobe epilepsy.

OBJECTIVES: Around 30% of patients undergoing surgical resection for drug-resistant mesial temporal lobe epilepsy (MTLE) do not obtain seizure freedom. Success of anterior temporal lobe resection (ATLR) critically depends on the careful selection of surgical candidates, aiming at optimizing seizure freedom while minimizing postoperative morbidity. Structural MRI and FDG-PET neuroimaging are routinely used in presurgical assessment and guide the decision to proceed to surgery. In this study, we evaluate the potential of machine learning techniques applied to standard presurgical MRI and PET imaging features to provide enhanced prognostic value relative to current practice. METHODS: Eighty two patients with drug resistant MTLE were scanned with FDG-PET pre-surgery and T1-weighted MRI pre- and postsurgery. From these images the following features of interest were derived: volume of temporal lobe (TL) hypometabolism, % of extratemporal hypometabolism, presence of contralateral TL hypometabolism, presence of hippocampal sclerosis, laterality of seizure onset volume of tissue resected and % of temporal lobe hypometabolism resected. These measures were used as predictor variables in logistic regression, support vector machines, random forests and artificial neural networks. RESULTS: In the study cohort, 24 of 82 (28.3%) who underwent an ATLR for drug-resistant MTLE did not achieve Engel Class I (i.e., free of disabling seizures) outcome at a minimum of 2 years of postoperative follow-up. We found that machine learning approaches were able to predict up to 73% of the 24 ATLR surgical patients who did not achieve a Class I outcome, at the expense of incorrect prediction for up to 31% of patients who did achieve a Class I outcome. Overall accuracies ranged from 70% to 80%, with an area under the receiver operating characteristic curve (AUC) of .75-.81. We additionally found that information regarding overall extent of both total and significantly hypometabolic tissue resected was crucial to predictive performance, with AUC dropping to .59-.62 using presurgical information alone. Incorporating the laterality of seizure onset and the choice of machine learning algorithm did not significantly change predictive performance. SIGNIFICANCE: Collectively, these results indicate that "acceptable" to "good" patient-specific prognostication for drug-resistant MTLE surgery is feasible with machine learning approaches utilizing commonly collected imaging modalities, but that information on the surgical resection region is critical for optimal prognostication.

Treatment of symptomatic fibroid disease using uterine fibroid embolisation: An Australian perspective.

Uterine leiomyomata (fibroids) are symptomatic in up to 35% of women and treatment can be a costly burden to the individual and society. Options for treatment range from non-hormonal, hormonal, minimally invasive, to surgery. While symptoms from smaller fibroids may respond to simple treatment, those with larger fibroids or with a large volume of disease require a more definitive option. Surgery (hysterectomy or myomectomy) are both well-established treatment modalities with good clinical outcomes. Since the 1990s, uterine fibroid embolisation has emerged as a less invasive option for women than for surgical techniques, while level 1 evidence shows that in the short to mid-term, there is a similar improvement in symptom-related quality of life outcomes to surgery, but with reduced hospital stay and reduced cost. However, in the longer term there may be a need for further treatment or retreatment in some patients compared with surgery. Since its introduction, uptake of this procedure in Australia has been low relative to surgical options. This manuscript reviews the current literature surrounding treatment, along with the trends in uptake of embolisation by Australian women, places this in context of current guidelines from major societies, and encourages gynaecologists and interventional radiologists to be aware of the advantages and limitations of embolisation.

Technique, radiation safety and image quality for chest X-ray imaging through glass and in mobile settings during the COVID-19 pandemic.

The COVID-19 pandemic in 2020 has led to preparations within our hospital for an expected surge of patients. This included developing a technique to perform mobile chest X-ray imaging through glass, allowing the X-ray unit to remain outside of the patient's room, effectively reducing the cleaning time associated with disinfecting equipment. The technique also reduced the infection risk of radiographers. We assessed the attenuation of different types of glass in the hospital and the technique parameters required to account for the glass filtration and additional source to image distance (SID). Radiation measurements were undertaken in a simulated set-up to determine the appropriate position for staff inside and outside the room to ensure occupational doses were kept as low as reasonably achievable. Image quality was scored and technical parameter information collated. The alternative to imaging through glass is the standard portable chest X-ray within the room. The radiation safety requirements for this standard technique were also assessed. Image quality was found to be acceptable or borderline in 90% of the images taken through glass and the average patient dose was 0.02 millisieverts (mSv) per image. The majority (67%) of images were acquired at 110 kV, with an average 5.5 mAs and with SID ranging from 180 to 300 cm. With staff positioned at greater than 1 m from the patient and at more than 1 m laterally from the tube head outside the room to minimise scatter exposure, air kerma values did not exceed 0.5 microgray (µGy) per image. This method has been implemented successfully.

Conventional and Advanced Imaging of Spine Oncologic Disease, Nonoperative Post-treatment Effects, and Unique Spinal Conditions.

In this review, we discuss the imaging features of diseases and conditions ranging from neoplastic to nonoperative post-treatment effects to unique conditions of the spine. Additionally, advanced imaging may increase diagnostic certainty in cases where conventional imaging characteristics of benign lesions and malignant pathology are variable.