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Objective: To report a prospectively planned analysis of two randomised controlled trials with embedded comparisons of prednisolone versus tetracosactide depot for the treatment of infantile epileptic spasms syndrome (IESS)., Methods: Individual patient data from patients randomly allocated to prednisolone or tetracosactide depot were analysed from two trials (UKISS, ICISS). The comparison was embedded within trials in which some patients also received vigabatrin but only patients receiving monotherapy with randomly allocated hormonal treatments are included in this analysis. The main outcome was cessation of spasms (Days 13-14 after randomisation). Lead time to treatment and underlying aetiology were taken into account. Cessation of spasms on Days 14-42 inclusive, electroclinical response (EEG Day 14), plus developmental and epilepsy outcomes (at 14 months in UKISS and 18 months in ICISS) are also reported. Minimum treatment was prednisolone 40 mg per day for two weeks or tetracosactide depot 0·5 mg IM on alternate days for two weeks, all followed by a reducing dose of prednisolone over two weeks., Results: 126 infants were included in this study. On tetracosactide depot, 47 of 62 (76%) were free of spasms on Days 13-14 compared to 43 of 64 (67%) on prednisolone (difference 9%, 95% CI -7·2% to +25·2%, chi square 1·15, p = 0·28). For Day 14-42 cessation of spasms, on tetracosactide depot, 41 of 61 (67%) were free of spasms compared to 35 of 62 (56%) on prednisolone (difference 11%, 95% CI -6·4% to +28·4%, chi square 1·51, p = 0·22). There was no significant difference in mean VABS score between infants who received prednisolone compared with those who received tetracosactide depot (74·8 (SD 18·3) versus 78·0 (SD 20·2) t = -0·91 p = 0·36). The proportion with ongoing epilepsy at the time of developmental assessment was 20 of 61 (33%) in the tetracosactide group compared with 26 out of 63 (41%) in the prednisolone group (difference 8%, 95% CI -9·2% to +25·2%, Chi [2] 0·95, p = 0·33)., Significance: With hormone monotherapy, either prednisolone or tetracosactide depot may be recommended for infantile epileptic spasms syndrome., Competing Interests: Declaration of competing interest JPO unsuccessfully approached Aventis for funding of a follow up study to look at visual field defects: he appeared in a promotional video for Aventis: he received income from UCB Pharma. The study sponsor for UKISS and ICISS received funding from Marathon and from UCB Pharma which was used in part to fund the research reported including salaries to JPO and SE. JPO, SE, FJKOC, EH and AL all received IP payments from the sponsor relating to funding from Marathon. AL received funding from Hoechst-Marion-Roussel to attend a conference. No other authors declared a conflict of interest., (© 2022 Published by Elsevier Ltd on behalf of European Paediatric Neurology Society.)

One outstanding challenge for machine learning in diagnostic biomedical imaging is algorithm interpretability. A key application is the identification of subtle epileptogenic focal cortical dysplasias (FCDs) from structural MRI. FCDs are difficult to visualize on structural MRI but are often amenable to surgical resection. We aimed to develop an open-source, interpretable, surface-based machine-learning algorithm to automatically identify FCDs on heterogeneous structural MRI data from epilepsy surgery centres worldwide. The Multi-centre Epilepsy Lesion Detection (MELD) Project collated and harmonized a retrospective MRI cohort of 1015 participants, 618 patients with focal FCD-related epilepsy and 397 controls, from 22 epilepsy centres worldwide. We created a neural network for FCD detection based on 33 surface-based features. The network was trained and cross-validated on 50% of the total cohort and tested on the remaining 50% as well as on 2 independent test sites. Multidimensional feature analysis and integrated gradient saliencies were used to interrogate network performance. Our pipeline outputs individual patient reports, which identify the location of predicted lesions, alongside their imaging features and relative saliency to the classifier. On a restricted 'gold-standard' subcohort of seizure-free patients with FCD type IIB who had T1 and fluid-attenuated inversion recovery MRI data, the MELD FCD surface-based algorithm had a sensitivity of 85%. Across the entire withheld test cohort the sensitivity was 59% and specificity was 54%. After including a border zone around lesions, to account for uncertainty around the borders of manually delineated lesion masks, the sensitivity was 67%. This multicentre, multinational study with open access protocols and code has developed a robust and interpretable machine-learning algorithm for automated detection of focal cortical dysplasias, giving physicians greater confidence in the identification of subtle MRI lesions in individuals with epilepsy., (© The Author(s) 2022. Published by Oxford University Press on behalf of the Guarantors of Brain.)

Objective: Drug-resistant focal epilepsy is often caused by focal cortical dysplasias (FCDs). The distribution of these lesions across the cerebral cortex and the impact of lesion location on clinical presentation and surgical outcome are largely unknown. We created a neuroimaging cohort of patients with individually mapped FCDs to determine factors associated with lesion location and predictors of postsurgical outcome., Methods: The MELD (Multi-centre Epilepsy Lesion Detection) project collated a retrospective cohort of 580 patients with epilepsy attributed to FCD from 20 epilepsy centers worldwide. Magnetic resonance imaging-based maps of individual FCDs with accompanying demographic, clinical, and surgical information were collected. We mapped the distribution of FCDs, examined for associations between clinical factors and lesion location, and developed a predictive model of postsurgical seizure freedom., Results: FCDs were nonuniformly distributed, concentrating in the superior frontal sulcus, frontal pole, and temporal pole. Epilepsy onset was typically before the age of 10 years. Earlier epilepsy onset was associated with lesions in primary sensory areas, whereas later epilepsy onset was associated with lesions in association cortices. Lesions in temporal and occipital lobes tended to be larger than frontal lobe lesions. Seizure freedom rates varied with FCD location, from around 30% in visual, motor, and premotor areas to 75% in superior temporal and frontal gyri. The predictive model of postsurgical seizure freedom had a positive predictive value of 70% and negative predictive value of 61%., Significance: FCD location is an important determinant of its size, the age at epilepsy onset, and the likelihood of seizure freedom postsurgery. Our atlas of lesion locations can be used to guide the radiological search for subtle lesions in individual patients. Our atlas of regional seizure freedom rates and associated predictive model can be used to estimate individual likelihoods of postsurgical seizure freedom. Data-driven atlases and predictive models are essential for evidence-based, precision medicine and risk counseling in epilepsy., (© 2021 The Authors. Epilepsia published by Wiley Periodicals LLC on behalf of International League Against Epilepsy.)

This is a review of the use of neuroimaging in epilepsy outlining the imaging strategy for new onset seizures and also those with intractable focal seizures being investigated as potential epilepsy surgery candidates. The review describes the radiological features of conditions which may be encountered in each clinical context, together with advice on the optimum imaging protocols to detect epileptogenic lesions. The review also presents the role of imaging in presurgical planning, the demonstration of eloquent function, grid placement and postoperative assessment.

One outstanding challenge for machine learning in diagnostic biomedical imaging is algorithm interpretability. A key application is the identification of subtle epileptogenic focal cortical dysplasias (FCDs) from structural MRI. FCDs are difficult to visualize on structural MRI but are often amenable to surgical resection. We aimed to develop an open-source, interpretable, surface-based machine-learning algorithm to automatically identify FCDs on heterogeneous structural MRI data from epilepsy surgery centres worldwide. The Multi-centre Epilepsy Lesion Detection (MELD) Project collated and harmonized a retrospective MRI cohort of 1015 participants, 618 patients with focal FCD-related epilepsy and 397 controls, from 22 epilepsy centres worldwide. We created a neural network for FCD detection based on 33 surface-based features. The network was trained and cross-validated on 50% of the total cohort and tested on the remaining 50% as well as on 2 independent test sites. Multidimensional feature analysis and integrated gradient saliencies were used to interrogate network performance. Our pipeline outputs individual patient reports, which identify the location of predicted lesions, alongside their imaging features and relative saliency to the classifier. On a restricted 'gold-standard' subcohort of seizure-free patients with FCD type IIB who had T1 and fluid-attenuated inversion recovery MRI data, the MELD FCD surface-based algorithm had a sensitivity of 85%. Across the entire withheld test cohort the sensitivity was 59% and specificity was 54%. After including a border zone around lesions, to account for uncertainty around the borders of manually delineated lesion masks, the sensitivity was 67%. This multicentre, multinational study with open access protocols and code has developed a robust and interpretable machine-learning algorithm for automated detection of focal cortical dysplasias, giving physicians greater confidence in the identification of subtle MRI lesions in individuals with epilepsy. [ABSTRACT FROM AUTHOR]