Adaptive Machine Learning Approach for Importance Evaluation of Multimodal Breast Cancer Radiomic Features.

Breast cancer holds the highest diagnosis rate among female tumors and is the leading cause of death among women. Quantitative analysis of radiological images shows the potential to address several medical challenges, including the early detection and classification of breast tumors. In the P.I.N.K study, 66 women were enrolled. Their paired Automated Breast Volume Scanner (ABVS) and Digital Breast Tomosynthesis (DBT) images, annotated with cancerous lesions, populated the first ABVS+DBT dataset. This enabled not only a radiomic analysis for the malignant vs. benign breast cancer classification, but also the comparison of the two modalities. For this purpose, the models were trained using a leave-one-out nested cross-validation strategy combined with a proper threshold selection approach. This approach provides statistically significant results even with medium-sized data sets. Additionally it provides distributional variables of importance, thus identifying the most informative radiomic features. The analysis proved the predictive capacity of radiomic models even using a reduced number of features. Indeed, from tomography we achieved AUC-ROC 89.9 % using 19 features and 92.1 % using 7 of them; while from ABVS we attained an AUC-ROC of 72.3 % using 22 features and 85.8 % using only 3 features. Although the predictive power of DBT outperforms ABVS, when comparing the predictions at the patient level, only 8.7% of lesions are misclassified by both methods, suggesting a partial complementarity. Notably, promising results (AUC-ROC ABVS-DBT 71.8 % - 74.1 % ) were achieved using non-geometric features, thus opening the way to the integration of virtual biopsy in medical routine.

Raman spectroscopy and topological machine learning for cancer grading.

In the last decade, Raman Spectroscopy is establishing itself as a highly promising technique for the classification of tumour tissues as it allows to obtain the biochemical maps of the tissues under investigation, making it possible to observe changes among different tissues in terms of biochemical constituents (proteins, lipid structures, DNA, vitamins, and so on). In this paper, we aim to show that techniques emerging from the cross-fertilization of persistent homology and machine learning can support the classification of Raman spectra extracted from cancerous tissues for tumour grading. In more detail, topological features of Raman spectra and machine learning classifiers are trained in combination as an automatic classification pipeline in order to select the best-performing pair. The case study is the grading of chondrosarcoma in four classes: cross and leave-one-patient-out validations have been used to assess the classification accuracy of the method. The binary classification achieves a validation accuracy of 81% and a test accuracy of 90%. Moreover, the test dataset has been collected at a different time and with different equipment. Such results are achieved by a support vector classifier trained with the Betti Curve representation of the topological features extracted from the Raman spectra, and are excellent compared with the existing literature. The added value of such results is that the model for the prediction of the chondrosarcoma grading could easily be implemented in clinical practice, possibly integrated into the acquisition system.

Horticultural Therapy May Reduce Psychological and Physiological Stress in Adolescents with Anorexia Nervosa: A Pilot Study.

Studies in psychiatric populations have found a positive effect of Horticultural therapy (HCT) on reductions in stress levels. The main objective of the present pilot study was to evaluate the impact of the addition of HCT to conventional clinical treatment (Treatment as Usual, TaU) in a sample of six female adolescents with anorexia nervosa restricting type (AN-R), as compared to six AN-R patients, matched for sex and age, under TaU only. This is a prospective, non-profit, pilot study on patients with a previous diagnosis of AN-R and BMI < 16, recruited in 2020 in clinical settings. At enrolment (T0) and after treatment completion (TF), psychiatric assessment was performed. At T0, all the patients underwent: baseline electrocardiogram acquisition with a wearable chest strap for recording heart rate and its variability; skin conductance registration and thermal mapping of the individual's face. An olfactory identification test was administered both to evaluate the olfactory sensoriality and to assess the induced stress. One-way analyses of variance (ANOVAs) were performed to analyze modifications in clinical and physiological variables, considering time (T0, TF) as a within-subjects factor and group (experimental vs. control) as between-subjects factors. When the ANOVA was significant, post hoc analysis was performed by Paired Sample T-tests. Only in the HCT group, stress response levels, as measured by the biological parameters, improved over time. The body uneasiness level and the affective problem measures displayed a significant improvement in the HCT subjects. HCT seems to have a positive influence on stress levels in AN-R.

NAVIGATOR: an Italian regional imaging biobank to promote precision medicine for oncologic patients.

NAVIGATOR is an Italian regional project boosting precision medicine in oncology with the aim of making it more predictive, preventive, and personalised by advancing translational research based on quantitative imaging and integrative omics analyses. The project's goal is to develop an open imaging biobank for the collection and preservation of a large amount of standardised imaging multimodal datasets, including computed tomography, magnetic resonance imaging, and positron emission tomography data, together with the corresponding patient-related and omics-related relevant information extracted from regional healthcare services using an adapted privacy-preserving model. The project is based on an open-source imaging biobank and an open-science oriented virtual research environment (VRE). Available integrative omics and multi-imaging data of three use cases (prostate cancer, rectal cancer, and gastric cancer) will be collected. All data confined in NAVIGATOR (i.e., standard and novel imaging biomarkers, non-imaging data, health agency data) will be used to create a digital patient model, to support the reliable prediction of the disease phenotype and risk stratification. The VRE that relies on a well-established infrastructure, called D4Science.org, will further provide a multiset infrastructure for processing the integrative omics data, extracting specific radiomic signatures, and for identification and testing of novel imaging biomarkers through big data analytics and artificial intelligence.

Cardio-metabolic risk modeling and assessment through sensor-based measurements.

OBJECTIVE: Cardio-metabolic risk assessment in the general population is of paramount importance to reduce diseases burdened by high morbility and mortality. The present paper defines a strategy for out-of-hospital cardio-metabolic risk assessment, based on data acquired from contact-less sensors. METHODS: We employ Structural Equation Modeling to identify latent clinical variables of cardio-metabolic risk, related to anthropometric, glycolipidic and vascular function factors. Then, we define a set of sensor-based measurements that correlate with the clinical latent variables. RESULTS: Our measurements identify subjects with one or more risk factors in a population of 68 healthy volunteers from the EU-funded SEMEOTICONS project with accuracy 82.4%, sensitivity 82.5%, and specificity 82.1%. CONCLUSIONS: Our preliminary results strengthen the role of self-monitoring systems for cardio-metabolic risk prevention.

A novel evidence-based algorithm to predict thromboembolism in patients with COVID-19: preliminary data from a single-center cohort.

BACKGROUND: Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2)-related disease (COVID-19) is an infectious disease characterized by systemic inflammation, which might enhance baseline thrombotic risk, especially in hospitalized patients. Little is, however, known about predictors of thrombotic complications in patients with COVID-19. METHODS: We prospectively followed up 180 hospitalized COVID-19 patients. Demographics, clinical and laboratory features at presentation and past medical history were tested as predictors of the first thrombotic complication through multivariate Cox regression analysis and a categorical score generated based on the results. RESULTS: Sixty-four thromboses were recorded in 54 patients, of whom seven with thrombosis on admission and 47 with thrombosis during hospitalization. Patients with thrombosis were mainly Caucasian and diabetic, had marked baseline signs of inflammation and organ damage, lower PaO2/FiO2 ratio, higher D-dimer levels and history of major hemorrhages. The latter three variables were independently associated to thrombotic complications and concurred to a 0-5 score, which accounted for 80% of the total sample variability. Patients with three or more points of the newly generated score were at higher risk for thrombotic complications (HR=4.9, P<0.001). Patients with thrombotic complications were more likely to be admitted to intensive care and/or to die (HR=1.9, P=0.036). Five of 180 patients were diagnosed with disseminated intravascular coagulation and three of them died. Eleven minor and no major bleeding events were observed. CONCLUSIONS: Patients with COVID-19 are at increased risk for thrombosis and might be stratified on admission based on lower Pao2/FiO2 ratio, higher D-dimer levels and history of major hemorrhages.

Discriminating Stress From Cognitive Load Using Contactless Thermal Imaging Devices.

This study proposes long wave infrared technology as a contactless alternative to wearable devices for stress detection. To this aim, we studied the change in facial thermal distribution of 17 healthy subjects in response to different stressors (Stroop Test, Mental Arithmetic Test). During the experimental sessions the electrodermal activity (EDA) and the facial thermal response were simultaneously recorded from each subject. It is well known from the literature that EDA can be considered a reliable marker for the psychological state variation, therefore we used it as a reference signal to validate the thermal results. Statistical analysis was performed to evaluate significant differences in the thermal features between stress and non-stress conditions, as well as between stress and cognitive load. Our results are in line with the outcomes of previous studies and show significant differences in the temperature trends over time between stress and resting conditions. As a new result, we found that the mean temperature changes of some less studied facial regions, e.g., the right cheek, are able not only to significantly discriminate between resting and stressful conditions, but also allow to recognize the typology of stressors. This outcome not only directs future studies to consider the thermal patterns of less explored facial regions as possible correlates of mental states, but more importantly it suggests that different psychological states could potentially be discriminated in a contactless manner.

Thromboembolism risk among patients with diabetes/stress hyperglycemia and COVID-19.

PURPOSE: Individuals with diabetes/stress hyperglycemia carry an increased risk for adverse clinical outcome in case of SARS-CoV-2 infection. The purpose of this study was to evaluate whether this risk is, at least in part, modulated by an increase of thromboembolic complications. METHODS: We prospectively followed 180 hospitalized patients with confirmed COVID-19 pneumonia admitted to the Internal Medicine Units of San Raffaele Hospital. Data from 11 out of 180 patients were considered incomplete and excluded from the analysis. We analysed inflammation, tissue damage biomarkers, hemostatic parameters, thrombotic events (TEs) and clinical outcome according to the presence of diabetes/stress hyperglycemia. RESULTS: Among 169 patients, 51 (30.2%) had diabetes/stress hyperglycemia. Diabetes/stress hyperglycemia and fasting blood glucose (FBG) were associated with increased inflammation and tissue damage circulating markers, higher D-dimer levels, increased prothrombin time and lower antithrombin III activity. Forty-eight venous and 10 arterial TEs were identified in 49 (29%) patients. Diabetes/stress hyperglycemia (HR 2.71, p = 0.001), fasting blood glucose (HR 4.32, p < 0.001) and glucose variability (HR 1.6, p < 0.009) were all associated with an increased risk of thromboembolic complication. TEs significantly increased the risk for an adverse clinical outcome only in the presence of diabetes/stress hyperglycemia (HR 3.05, p = 0.010) or fasting blood glucose ≥7 mmol/L (HR 3.07, p = 0.015). CONCLUSIONS: Thromboembolism risk is higher among patients with diabetes/stress hyperglycemia and COVID-19 pneumonia and is associated to poor clinical outcome. In case of SARS-Cov-2 infection patients with diabetes/stress hyperglycemia could be considered for a more intensive prophylactic anticoagulation regimen.

Machine and Deep Learning Prediction Of Prostate Cancer Aggressiveness Using Multiparametric MRI.

Prostate cancer (PCa) is the most frequent male malignancy and the assessment of PCa aggressiveness, for which a biopsy is required, is fundamental for patient management. Currently, multiparametric (mp) MRI is strongly recommended before biopsy. Quantitative assessment of mpMRI might provide the radiologist with an objective and noninvasive tool for supporting the decision-making in clinical practice and decreasing intra- and inter-reader variability. In this view, high dimensional radiomics features and Machine Learning (ML) techniques, along with Deep Learning (DL) methods working on raw images directly, could assist the radiologist in the clinical workflow. The aim of this study was to develop and validate ML/DL frameworks on mpMRI data to characterize PCas according to their aggressiveness. We optimized several ML/DL frameworks on T2w, ADC and T2w+ADC data, using a patient-based nested validation scheme. The dataset was composed of 112 patients (132 peripheral lesions with Prostate Imaging Reporting and Data System (PI-RADS) score ≥ 3) acquired following both PI-RADS 2.0 and 2.1 guidelines. Firstly, ML/DL frameworks trained and validated on PI-RADS 2.0 data were tested on both PI-RADS 2.0 and 2.1 data. Then, we trained, validated and tested ML/DL frameworks on a multi PI-RADS dataset. We reported the performances in terms of Area Under the Receiver Operating curve (AUROC), specificity and sensitivity. The ML/DL frameworks trained on T2w data achieved the overall best performance. Notably, ML and DL frameworks trained and validated on PI-RADS 2.0 data obtained median AUROC values equal to 0.750 and 0.875, respectively, on unseen PI-RADS 2.0 test set. Similarly, ML/DL frameworks trained and validated on multi PI-RADS T2w data showed median AUROC values equal to 0.795 and 0.750, respectively, on unseen multi PI-RADS test set. Conversely, all the ML/DL frameworks trained and validated on PI-RADS 2.0 data, achieved AUROC values no better than the chance level when tested on PI-RADS 2.1 data. Both ML/DL techniques applied on mpMRI seem to be a valid aid in predicting PCa aggressiveness. In particular, ML/DL frameworks fed with T2w images data (objective, fast and non-invasive) show good performances and might support decision-making in patient diagnostic and therapeutic management, reducing intra- and inter-reader variability.

Thermal vulnerability detection in integrated electronic and photonic circuits using infrared thermography.

Failure prediction of any electrical/optical component is crucial for estimating its operating life. Using high temperature operating life (HTOL) tests, it is possible to model the failure mechanisms for integrated circuits. Conventional HTOL standards are not suitable for operating life prediction of photonic components owing to their functional dependence on the thermo-optic effect. This work presents an infrared (IR)-assisted thermal vulnerability detection technique suitable for photonic as well as electronic components. By accurately mapping the thermal profile of an integrated circuit under a stress condition, it is possible to precisely locate the heat center for predicting the long-term operational failures within the device under test. For the first time, the reliability testing is extended to a fully functional microwave photonic system using conventional IR thermography. By applying image fusion using affine transformation on multimodal acquisition, it was demonstrated that by comparing the IR profile and GDSII layout, it is possible to accurately locate the heat centers along with spatial information on the type of component. Multiple IR profiles of optical as well as electrical components/circuits were acquired and mapped onto the layout files. In order to ascertain the degree of effectiveness of the proposed technique, IR profiles of complementary metal-oxide semiconductor RF and digital circuits were also analyzed. The presented technique offers a reliable automated identification of heat spots within a circuit/system.

Computer-aided diagnostic system for thyroid nodule sonographic evaluation outperforms the specificity of less experienced examiners.

PURPOSE: Computer-aided diagnosis (CAD) may improve interobserver agreement in the risk stratification of thyroid nodules. This study aims to evaluate the performance of the Korean Thyroid Imaging Reporting and Data System (K-TIRADS) classification as estimated by an expert radiologist, a senior resident, a medical student, and a CAD system, as well as the interobserver agreement among them. METHODS: Between July 2016 and 2018, 107 nodules (size 5-40 mm, 27 malignant) were classified according to the K-TIRADS by an expert radiologist and CAD software. A third-year resident and a medical student with basic imaging training, both blinded to previous findings, retrospectively estimated the K-TIRADS classification. The diagnostic performance was calculated, including sensitivity, specificity, positive and negative predictive values, and the area under the receiver operating characteristic curve. RESULTS: The CAD system and the expert achieved a sensitivity of 70.37% (95% CI 49.82-86.25%) and 81.48% (61.92-93.7%) and a specificity of 87.50% (78.21-93.84%) and 88.75% (79.72-94.72%), respectively. The specificity of the student was significantly lower (76.25% [65.42-85.05%], p = 0.02). CONCLUSION: In our opinion, the CAD evaluation of thyroid nodules stratification risk has a potential role in a didactic field and does not play a real and effective role in the clinical field, where not only images but also specialistic medical practice is fundamental to achieve a diagnosis based on family history, genetics, lab tests, and so on. The CAD system may be useful for less experienced operators as its specificity was significantly higher.

Fault Detection in Power Equipment via an Unmanned Aerial System Using Multi Modal Data.

The power transmission lines are the link between power plants and the points of consumption, through substations. Most importantly, the assessment of damaged aerial power lines and rusted conductors is of extreme importance for public safety; hence, power lines and associated components must be periodically inspected to ensure a continuous supply and to identify any fault and defect. To achieve these objectives, recently, Unmanned Aerial Vehicles (UAVs) have been widely used; in fact, they provide a safe way to bring sensors close to the power transmission lines and their associated components without halting the equipment during the inspection, and reducing operational cost and risk. In this work, a drone, equipped with multi-modal sensors, captures images in the visible and infrared domain and transmits them to the ground station. We used state-of-the-art computer vision methods to highlight expected faults (i.e., hot spots) or damaged components of the electrical infrastructure (i.e., damaged insulators). Infrared imaging, which is invariant to large scale and illumination changes in the real operating environment, supported the identification of faults in power transmission lines; while a neural network is adapted and trained to detect and classify insulators from an optical video stream. We demonstrate our approach on data captured by a drone in Parma, Italy.

Cognitive, emotional, and behavioral profile in children and adolescents with chronic pain associated with rheumatic diseases: A case-control study.

BACKGROUND: The prevalence of chronic pain is about 30% in children and adolescents which suffer from severe emotional distress. The aim of this observational study is to investigate cognitive, emotional and behavioral consequences of benign chronic pain in children and adolescents suffering of reumathologic diseases. MATERIALS AND METHODS: A total of 49 participants, chronic pain participants (CPPs) and controls (CGPs), affected by rheumatic diseases, were enrolled. Assessment included collection of sociodemographic data, pain characteristics, and administration of Visual Analog Scale (VAS), Depression Inventory for Children and Adolescents (CDI), Conners' Parent Rating Scales-Revised (CPRS-R), Child Behavior Checklist (CBCL), and Screen for Child Anxiety-Related Disorders (SCARED). For the statistical analysis, Student's t-test for independent samples and Pearson's correlation were used. The significance value was set at p less than .05. RESULTS: A significant difference of mean scores of CBCL items and of CPRS items between the two groups was found. In CPPs, a significant correlation between VAS and mean scores of several CBCL items and between VAS and mean scores of several CPRS items was found. CONCLUSION: Chronic pain is a real syndrome in which an interdisciplinary treatment should be applied, considering the psychopathological risk, especially in developmental age.

A wize mirror for lifestyle improvement.

This paper discusses the problem of fostering lifestyle changes towards healthier habits via tailored user guidance. We present a novel multisensory device, the Wize Mirror, which will be able to detect semeiotic face signs related to cardio-metabolic risk, and encourage users to reduce their risk by improving their lifestyle. Offering a proper user guidance requires solving three main issues: user profiling, definition of a wellness index based on biophysical data, and personalized guidance by means of coaching and supportive messages. For each of these issues, the solutions proposed in the EU FP7 Project SEMEOTICONS are presented, highlighting their advantages with respect to the state-of-the-art.

A cost-of-illness study of spina bifida in Italy.

INTRODUCTION: Spina bifida (SB) is a congenital malformation of the spinal cord, nerves, and adjacent covering structures, with different levels of severity and functional disability. The economic cost of spina bifida and its prevention using folic acid have never been estimated in Italy. This study was conducted to define the cost of illness of SB in Italy. METHODS: A retrospective multicenter observational study on the social cost of patients with SB was carried out in three SB centers in Italy. Cost data were collected relating to the 12 months preceding the enrollment time (T0), and subsequently 3 months after the T0 time (±20 days) through a case report form designed to collect the relevant information on the costs incurred during the period considered. The data for all patients were analyzed through multivariate analysis on the main parameters. RESULTS: We enrolled 128 patients equally divided between males and females, with a mean age of 13 years (minimum, 0; maximum, 29). Diagnosis was mostly postnatal, with 64 cases diagnosed at birth and 33 cases diagnosed subsequently. The lesion severity levels, as defined in the inclusion criteria, were walking (52 patients); walking with simple orthoses (33 patients); walking with complex orthoses (16 patients); and nonwalking, (25 patients). The anatomic type identified is open SB in most cases (84 patients), followed by closed SB (37 patients) and SB occulta (3 patients). The most significant cost per year was for assistive devices, for a total of 4307.00 €, followed by hospitalization (907.00 €), examinations (592.00 €), and drug therapy (328.00 €). Cost breakdown by age range shows that the highest costs are incurred in the 0-4 age range. The highest cost was for cases of open SB (12,103.00 €). The cost/degree of severity ratio showed that the highest cost was for nonwalking patients (14,323.00 €), followed by patients walking with complex orthoses (13,799.00 €). CONCLUSION: The data from this study show that the mean total cost for a patient with SB was 11,351.00 € per year. Based on data provided by the Italian Institute of Health, we can estimate a total annual social cost of about 60 million Euros per year for SB in Italy. Cost of illness was correlated with age and degree of severity of SB.

Effects of botulinum toxin type a in the bladder wall of children with neurogenic bladder dysfunction: a comparison of histological features before and after injections.

PURPOSE: Botulinum toxin type A has gained popularity in urology. Most reported studies have been in adults at urology centers and most have addressed long-term safety. Since botulinum toxin type A treatment for neurogenic bladder dysfunction requires repeat injections, verifying that such treatment does not induce fibrosis in children seems essential. MATERIALS AND METHODS: The study was approved by the institutional review board and patients were enrolled after we obtained written consent. Patients with neurogenic bladder dysfunction not responding to conventional treatment (anticholinergics and clean intermittent catheterization) were treated with 10 IU/kg botulinum toxin type A up to a maximum of 300 IU. Endoscopic cold cup biopsies were obtained from the posterolateral bladder wall 1.5 to 2 cm above the ureteral orifice. Bladder wall findings were categorized into 3 groups, including inflammatory infiltration, edema and fibrosis. Each criterion was then graded as mild or severe and analyzed by Fisher's exact test (p <0.05). RESULTS: A total of 46 bladder wall biopsies were obtained from 40 patients 2 to 18 years old. Biopsies were evaluated in groups 1 and 2, including group 1-20 from patients with no botulinum toxin type A injection and group 2-20 after botulinum toxin type A injection. Group 2 was subdivided into group 3-10 biopsies after 1 injection and group 4-10 after multiple injections. Six patients underwent biopsy twice, that is before the first and second treatments. Histological changes were present in all biopsies. When comparing groups 1 and 2, there was no statistically significant difference in inflammation and edema. However, there was a significant difference in fibrosis between groups 1 and 4 (p <0.05) with apparently decreased fibrosis after multiple injections. CONCLUSIONS: In our experience repeat botulinum toxin type A injections into the detrusor in children do not lead to increased fibrosis in the bladder wall. This study confirms the long-term safety of botulinum toxin type A in the pediatric population.

A simplified technique for botulinum toxin injections in children with neurogenic bladder.

PURPOSE: Botulinum toxin type A has revolutionized the treatment of neurogenic bladder dysfunction. The original injection technique used a rigid cystoscope and a flexible collagen needle. To date botulinum toxin type A injection techniques have not been standardized. We present our experience in pediatric patients using a new flexible injection system. MATERIALS AND METHODS: We treated 24 patients 3.8 to 17.5 years old who had neurogenic bladder dysfunction with botulinum toxin type A bladder and/or sphincter injection using a rigid cystoscope and the new N-DO™ endo-injector needle system. Another 24 patients 3.6 to 17.8 years old were treated with a 3.7Fr standard flexible needle and served as controls. Operative time, hospital stay, complications and efficacy were considered. Selection criteria and treatment were the same in the 2 groups. The 10 IU/kg dose was determined according to European Association of Urology guidelines. RESULTS: All patients received botulinum toxin type A bladder injection while 11 patients in the endo-injector group and 5 controls also received urethral injection. In the endo-injector needle and control groups average operative time was 12.4 and 17.3 minutes for the bladder, and 5.1 and 10.1 minutes for the urethra, respectively (each p <0.05). All patients were discharged home the day after the procedure. No complications were observed. Urodynamics revealed an average maximum detrusor pressure decrease of 25 and 21 cm H(2)O, and an average bladder capacity increase of 75 and 80 ml in the endo-injector and control groups, respectively (p not significant). CONCLUSIONS: While retaining efficacy, the endo-injector needle technique appears to be more rapid than the standard procedure for botulinum toxin type A injection for neurogenic bladder dysfunction. Whether patients may be treated with sedation only remains to be clarified.

Periventricular nodular heterotopia: report of a pediatric series.

Periventricular nodular heterotopia is a malformation that occurs in both males and females and is associated with a variety of clinical and neuroradiologic signs. A gene called filamin-1 (FLN-1) has recently been identified. We review the clinical and imaging findings from a series of pediatric patients with periventricular nodular heterotopia. Five patients (three males and two females; age range = 4-18 years) were investigated. In our series, periventricular nodular heterotopia can be the common denominator in different conditions. Periventricular nodular heterotopia can occur alone or be associated with cortical malformations. Epilepsy was present in three of the five patients and was resistant to drugs in one female. Mental retardation was present in three of the five patients. Two male patients had normal intelligence, with no cortical anomalies; patient 3 had unilateral periventricular nodular heterotopia. The associated malformations were more severe in the female patients and slight only in patient 1. The two females showed anomalies rarely reported in association with bilateral periventricular nodular heterotopia. We believe that other genes can be involved in children with atypical neuroradiologic periventricular nodular heterotopia. No mutations were detected in 6 of the 48 exons of the FLN-1 gene, although this does not allow any definitive conclusions to be reached. We conclude that our series of patients with periventricular nodular heterotopia clearly highlights the complexity of the clinical, neurologic, and neuroradiologic characteristics associated with this malformation.