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Background: Epilepsy surgery is an established treatment for drug-resistant focal epilepsy (DRFE) that results in seizure freedom in about 60% of patients. Correctly identifying an epileptogenic lesion in magnetic resonance imaging (MRI) is challenging but highly relevant since it improves the likelihood of being referred for presurgical diagnosis. The epileptogenic lesion’s etiology directly relates to the surgical intervention’s indication and outcome. Therefore, it is vital to correctly identify epileptogenic lesions and their etiology presurgically.
Methods: We compared the final histopathological diagnoses of all patients with DRFE undergoing epilepsy surgery at our center between 2015 and 2021 with their MRI diagnoses before and after presurgical diagnosis at our epilepsy center, including MRI evaluations by expert epilepsy neuroradiologists. Additionally, we analyzed the outcome of different subgroups.
Results: This study included 132 patients. The discordance between histopathology and MRI diagnoses significantly decreased from 61.3% for non-expert MRI evaluations (NEMRIs) to 22.1% for epilepsy center MRI evaluations (ECMRIs; p < 0.0001). The MRI-sensitivity improved significantly from 68.6% for NEMRIs to 97.7% for ECMRIs (p < 0.0001). Identifying focal cortical dysplasia (FCD) and amygdala dysplasia was the most challenging for both subgroups. 65.5% of patients with negative NEMRI were seizure-free 12 months postoperatively, no patient with negative ECMRI achieved seizure-freedom. The mean duration of epilepsy until surgical intervention was 13.6 years in patients with an initial negative NEMRI and 9.5 years in patients with a recognized lesion in NEMRI.
Conclusions: This study provides evidence that for patients with DRFE—especially those with initial negative findings in a non-expert MRI—an early consultation at an epilepsy center, including an ECMRI, is important for identifying candidates for epilepsy surgery. NEMRI-negative findings preoperatively do not preclude seizure freedom postoperatively. Therefore, patients with DRFE that remain MRI-negative after initial NEMRI should be referred to an epilepsy center for presurgical evaluation. Nonreferral based on NEMRI negativity may harm such patients and delay surgical intervention. However, ECMRI-negative patients have a reduced chance of becoming seizure-free after epilepsy surgery. Further improvements in MRI technique and evaluation are needed and should be directed towards improving sensitivity for FCDs and amygdala dysplasias.
Objectives: To analyze the performance of radiological assessment categories and quantitative computational analysis of apparent diffusion coefficient (ADC) maps using variant machine learning algorithms to differentiate clinically significant versus insignificant prostate cancer (PCa). Methods: Retrospectively, 73 patients were included in the study. The patients (mean age, 66.3 ± 7.6 years) were examined with multiparametric MRI (mpMRI) prior to radical prostatectomy (n = 33) or targeted biopsy (n = 40). The index lesion was annotated in MRI ADC and the equivalent histologic slides according to the highest Gleason Grade Group (GrG). Volumes of interest (VOIs) were determined for each lesion and normal-appearing peripheral zone. VOIs were processed by radiomic analysis. For the classification of lesions according to their clinical significance (GrG ≥ 3), principal component (PC) analysis, univariate analysis (UA) with consecutive support vector machines, neural networks, and random forest analysis were performed. Results: PC analysis discriminated between benign and malignant prostate tissue. PC evaluation yielded no stratification of PCa lesions according to their clinical significance, but UA revealed differences in clinical assessment categories and radiomic features. We trained three classification models with fifteen feature subsets. We identified a subset of shape features which improved the diagnostic accuracy of the clinical assessment categories (maximum increase in diagnostic accuracy ΔAUC = + 0.05, p < 0.001) while also identifying combinations of features and models which reduced overall accuracy. Conclusions: The impact of radiomic features to differentiate PCa lesions according to their clinical significance remains controversial. It depends on feature selection and the employed machine learning algorithms. It can result in improvement or reduction of diagnostic performance.