Machine Learning Approach for the Outcome Prediction of Temporal Lobe Epilepsy Surgery

<div><p>Epilepsy surgery is effective in reducing both the number and frequency of seizures, particularly in temporal lobe epilepsy (TLE). Nevertheless, a significant proportion of these patients continue suffering seizures after surgery. Here we used a machine learning approach to predict the outcome of epilepsy surgery based on supervised classification data mining taking into account not only the common clinical variables, but also pathological and neuropsychological evaluations. We have generated models capable of predicting whether a patient with TLE secondary to hippocampal sclerosis will fully recover from epilepsy or not. The machine learning analysis revealed that outcome could be predicted with an estimated accuracy of almost 90% using some clinical and neuropsychological features. Importantly, not all the features were needed to perform the prediction; some of them proved to be irrelevant to the prognosis. Personality style was found to be one of the key features to predict the outcome. Although we examined relatively few cases, findings were verified across all data, showing that the machine learning approach described in the present study may be a powerful method. Since neuropsychological assessment of epileptic patients is a standard protocol in the pre-surgical evaluation, we propose to include these specific psychological tests and machine learning tools to improve the selection of candidates for epilepsy surgery.</p></div

Features and associated p-values obtained from a Wilcoxon signed-rank test comparing the values of each feature with the Engel output. in order of increasing p-value.

†<p>indicates statistical significance at a 95% confidence level. Features are listed.</p

Summary of the clinical data from the epileptic patients and the surgical outcome.

<p>f: female, gen: secondarily generalized, L: Left, m: male, PC: partial complex seizures, PS: partial simple seizures, R: right, Engel scale for surgical outcome: class I seizure-free, class II rare seizures and class III worthwhile improvement.</p

Variables ordered according to their frequency of selection during resampling and variable subset selection (occ: occurrence). Three different rankings are displayed - one for each of the classifiers used during the search.

<p>Variables ordered according to their frequency of selection during resampling and variable subset selection (occ: occurrence). Three different rankings are displayed - one for each of the classifiers used during the search.</p

Estimated classification performance using LOOCV validation.

<p>Only features available before surgery were included in this performance analysis. The x-axis reflects the size of the subset of features retained. A) The upper chart shows the estimated accuracy; whereas, B) the lower chart shows the associated area under the ROC curve. Note that the features for a given point on the x-axis can differ depending on the classifier used (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0062819#pone-0062819-t003" target="_blank">Table 3</a> for the respective feature subsets).</p

Set of predictive variables and their associated values. Cardinalities of each value are shown between brackets. Missing values are also indicated.

<p>Set of predictive variables and their associated values. Cardinalities of each value are shown between brackets. Missing values are also indicated.</p

Number of times that features were included in the different intermediate subsets selected by the race search feature selection for over 1,000 dataset resamplings.

<p>Number of times that features were included in the different intermediate subsets selected by the race search feature selection for over 1,000 dataset resamplings.</p

Data_Sheet_1_Neuroanatomical and psychological considerations in temporal lobe epilepsy.zip

Temporal lobe epilepsy (TLE) is the most common form of focal epilepsy and is associated with a variety of structural and psychological alterations. Recently, there has been renewed interest in using brain tissue resected during epilepsy surgery, in particular ‘non-epileptic’ brain samples with normal histology that can be found alongside epileptic tissue in the same epileptic patients — with the aim being to study the normal human brain organization using a variety of methods. An important limitation is that different medical characteristics of the patients may modify the brain tissue. Thus, to better determine how ‘normal’ the resected tissue is, it is fundamental to know certain clinical, anatomical and psychological characteristics of the patients. Unfortunately, this information is frequently not fully available for the patient from which the resected tissue has been obtained — or is not fully appreciated by the neuroscientists analyzing the brain samples, who are not necessarily experts in epilepsy. In order to present the full picture of TLE in a way that would be accessible to multiple communities (e.g., basic researchers in neuroscience, neurologists, neurosurgeons and psychologists), we have reviewed 34 TLE patients, who were selected due to the availability of detailed clinical, anatomical, and psychological information for each of the patients. Our aim was to convey the full complexity of the disorder, its putative anatomical substrates, and the wide range of individual variability, with a view toward: (1) emphasizing the importance of considering critical patient information when using brain samples for basic research and (2) gaining a better understanding of normal and abnormal brain functioning. In agreement with a large number of previous reports, this study (1) reinforces the notion of substantial individual variability among epileptic patients, and (2) highlights the common but overlooked psychopathological alterations that occur even in patients who become “seizure-free” after surgery. The first point is based on pre- and post-surgical comparisons of patients with hippocampal sclerosis and patients with normal-looking hippocampus in neuropsychological evaluations. The second emerges from our extensive battery of personality and projective tests, in a two-way comparison of these two types of patients with regard to pre- and post-surgical performance.</p

FGL enhances spatial learning.

<p>(A) Mean distances swam to find the hidden platform in the Morris water maze are represented for control rats (white symbols) and FGL-treated rats (black symbols) over 2 training days (four trials each). <i>N</i>, number of animals. Statistical significance was analyzed with repeated-measures ANOVA. (B) Cumulative frequency distributions of the distances swam by individual rats. Each data point represents the distance swam by one rat in the last trial of each day.</p

FGL triggers hippocampal FGFR1 phosphorylation in vitro and in vivo.

<p>(A) Cartoon structure of the double fibronectin module (FN1+FN2) of human NCAM (Protein Data Bank number 2VKW). The FGL sequence is shown in red with the two glutamine residues critical for the binding to the FGF-receptor highlighted in magenta. (B) Top: Representative immunoblot showing the in vitro phosphorylation of FGFR1 after stimulation of Trex293 cells that express Strep-tagged human FGFR1 with different concentrations of FGL and 10 ng/ml FGF1 (positive control) for 20 min. Bottom: Quantification of FGFR1 phosphorylation by FGL was performed by densitometric analysis of band intensity from four independent experiments similar to the one shown in the upper panel. (C) Phosphorylation of FGFR1 and TrkB was examined from hippocampal homogenates with an enzyme-linked immunosorbent assay (ELISA) 1 h after FGL subcutaneous injection. <i>N</i>, number of animals. Results are expressed as percentage ± SEM, with untreated controls set at 0%. (D–F) Phosphorylation of PLCγ (D), Shc (E), and FRS2 (F) in vitro was examined by Western blot, as described in <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.1001262#pbio-1001262-g001" target="_blank">Figure 1B</a>. Treatment with FGF1 served as the positive control. Results from four independent experiments are expressed as a percentage ± SEM, with untreated controls set at 100%. *<i>p</i><0.05, **<i>p</i><0.01, ***<i>p</i><0.001 compared with controls. Statistics were carried out according to the <i>t</i> test.</p