Efficacy of MRI data harmonization in the age of machine learning. A multicenter study across 36 datasets

Pooling publicly-available MRI data from multiple sites allows to assemble extensive groups of subjects, increase statistical power, and promote data reuse with machine learning techniques. The harmonization of multicenter data is necessary to reduce the confounding effect associated with non-biological sources of variability in the data. However, when applied to the entire dataset before machine learning, the harmonization leads to data leakage, because information outside the training set may affect model building, and potentially falsely overestimate performance. We propose a 1) measurement of the efficacy of data harmonization; 2) harmonizer transformer, i.e., an implementation of the ComBat harmonization allowing its encapsulation among the preprocessing steps of a machine learning pipeline, avoiding data leakage. We tested these tools using brain T1-weighted MRI data from 1740 healthy subjects acquired at 36 sites. After harmonization, the site effect was removed or reduced, and we measured the data leakage effect in predicting individual age from MRI data, highlighting that introducing the harmonizer transformer into a machine learning pipeline allows for avoiding data leakage

Improvement prostate cancer detection rate of suspicious lesions through MRI/TRUS fusion guided biopsy by a multiteam of radiologists

Abstract Background The objective of our study was to analyze the data of our biopsies, determine a detection rate (DR), compare it with the data in the literature and draw possible deductions, so as to offer the patient the possibility of not having other biopsies in the future. Methods We have enrolled 189 biopsy-naive patients in the period between September 2018 and December 2020. Each patient underwent multiparametric (mp)-MRI which was reviewed by our team of radiologists. In our center, each examination is examined by 4 radiologists separately with an overall final result. Through the t student test, any statistically significant differences between the DRs and the concordance rate between the positive cores and the suspected area on MRI were analyzed for each urologist who performed the procedure. Results The absolute (DR) was 69.3% (131/189 patients). The relative DR for each PIRADS score was 41% for PIRADS 3, 70.2% for PIRADS 4, 89.3% for PIRADS 5. We found a high percentage of agreement between the positive biopsy samples and the suspicious area identified on MRI: 90.8% (119/131 patients). There were no statistically significant differences between the DRs of the urologists who performed the procedure (p = 0.89), nor for the percentage of agreement between the positivity of the core and the suspected area on MRI (p = 0.92). Conclusions MRI in the future could become the gold standard for performing MRI fusion-guided biopsies to have a better diagnostic result and avoid rebiopsies. A team MRI reading allows greater accuracy in identifying the suspected lesion, which is demonstrated by a high rate of agreement with the positivity of the cores (90.8%). There is a cost problem due to the need to carry out the mpMRI but it could have less impact in the future. In addition, the MRI provides useful information on the extent of the disease (e.g., cT3a/b) which allows you to better plan the surgical strategy or other therapies

The tumor-associated antigen RHAMM (HMMR/CD168) is expressed by monocyte-derived dendritic cells and presented to T cells

We formerly demonstrated that vaccination with Wilms' tumor 1 (WT1)-loaded autologous monocyte-derived dendritic cells (mo-DCs) can be a well-tolerated effective treatment in acute myeloid leukemia (AML) patients. Here, we investigated whether we could introduce the receptor for hyaluronic acid-mediated motility (RHAMM/HMMR/CD168), another clinically relevant tumor-associated antigen, into these mo-DCs through mRNA electroporation and elicit RHAMM-specific immune responses. While RHAMM mRNA electroporation significantly increased RHAMM protein expression by mo-DCs, our data indicate that classical mo-DCs already express and present RHAMM at sufficient levels to activate RHAMM-specific T cells, regardless of electroporation. Moreover, we found that RHAMM-specific T cells are present at vaccination sites in AML patients. Our findings implicate that we and others who are using classical mo-DCs for cancer immunotherapy are already vaccinating against RHAMM

Cerebellar Atrophy in Congenital Fibrosis of the Extraocular Muscles Type 1

We described a family with a molecularly confirmed form of CFEOM1 and a late-onset cerebellar syndrome. Brain MRI showed vermis atrophy in two older family members, who also manifested gait impairment, whereas both neurological examination and neuroimaging findings were normal in a younger relative who harbored the same mutation

A spatial Bayesian network model to assess the benefits of early warning for urban flood risk to people

This article presents a novel methodology to assess flood risk to people by integrating people's vulnerability and ability to cushion hazards through coping and adapting. The proposed approach extends traditional risk assessments beyond material damages; complements quantitative and semi-quantitative data with subjective and local knowledge, improving the use of commonly available information; and produces estimates of model uncertainty by providing probability distributions for all of its outputs. Flood risk to people is modeled using a spatially explicit Bayesian network model calibrated on expert opinion. Risk is assessed in terms of (1) likelihood of non-fatal physical injury, (2) likelihood of post-traumatic stress disorder and (3) likelihood of death. The study area covers the lower part of the Sihl valley (Switzerland) including the city of Zurich. The model is used to estimate the effect of improving an existing early warning system, taking into account the reliability, lead time and scope (i.e., coverage of people reached by the warning). Model results indicate that the potential benefits of an improved early warning in terms of avoided human impacts are particularly relevant in case of a major flood event

Assessment of spontaneous cardiovascular oscillations in Parkinson's disease

Parkinson's disease (PD) has been reported to involve postganglionic sympathetic failure and a wide spectrum of autonomic dysfunctions including cardiovascular, sexual, bladder, gastrointestinal and sudo-motor abnormalities. While these symptoms may have a significant impact on daily activities, as well as quality of life, the evaluation of autonomic nervous system (ANS) dysfunctions relies on a large and expensive battery of autonomic tests only accessible in highly specialized laboratories. In this paper we aim to devise a comprehensive computational assessment of disease-related heartbeat dynamics based on instantaneous, time-varying estimates of spontaneous (resting state) cardiovascular oscillations in PD. To this end, we combine standard ANS-related heart rate variability (HRV) metrics with measures of instantaneous complexity (dominant Lyapunov exponent and entropy) and higher-order statistics (bispectra). Such measures are computed over 600-s recordings acquired at rest in 29 healthy subjects and 30 PD patients. The only significant group-wise differences were found in the variability of the dominant Lyapunov exponent. Also, the best PD vs. healthy controls classification performance (balanced accuracy: 73.47%) was achieved only when retaining the time-varying, non-stationary structure of the dynamical features, whereas classification performance dropped significantly (balanced accuracy: 61.91%) when excluding variability-related features. Additionally, both linear and nonlinear model features correlated with both clinical and neuropsychological assessments of the considered patient population. Our results demonstrate the added value and potential of instantaneous measures of heartbeat dynamics and its variability in characterizing PD-related disabilities in motor and cognitive domains

HCV treatment in children and young adults with HIV/HCV co-infection in Europe

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New cellular imaging-based method to distinguish the SPG4 subtype of hereditary spastic paraplegia

Background and purpose: Microtubule defects are a common feature in several neurodegenerative disorders, including hereditary spastic paraplegia. The most frequent form of hereditary spastic paraplegia is caused by mutations in the SPG4/SPAST gene, encoding the microtubule severing enzyme spastin. To date, there is no effective therapy available but spastin-enhancing therapeutic approaches are emerging; thus prognostic and predictive biomarkers are urgently required. Methods: An automated, simple, fast and non-invasive cell imaging-based method was developed to quantify microtubule cytoskeleton organization changes in lymphoblastoid cells and peripheral blood mononuclear cells. Results: It was observed that lymphoblastoid cells and peripheral blood mononuclear cells from individuals affected by SPG4-hereditary spastic paraplegia show a polarized microtubule cytoskeleton organization. In a pilot study on freshly isolated peripheral blood mononuclear cells, our method discriminates SPG4-hereditary spastic paraplegia from healthy donors and other hereditary spastic paraplegia subtypes. In addition, it is shown that our method can detect the effects of spastin protein level changes. Conclusions: These findings open the possibility of a rapid, non-invasive, inexpensive test useful to recognize SPG4-hereditary spastic paraplegia subtype and evaluate the effects of spastin-enhancing drug in non-neuronal cells

Progression of brain atrophy in spinocerebellar ataxia type 2: A longitudinal tensor-based morphometry study

Spinocerebellar ataxia type 2 (SCA2) is the second most frequent autosomal dominant inherited ataxia worldwide. We investigated the capability of magnetic resonance imaging (MRI) to track in vivo progression of brain atrophy in SCA2 by examining twice 10 SCA2 patients (mean interval 3.6 years) and 16 age- and gender-matched healthy controls (mean interval 3.3 years) on the same 1.5 T MRI scanner. We used T1-weighted images and tensor-based morphometry (TBM) to investigate volume changes and the Inherited Ataxia Clinical Rating Scale to assess the clinical deficit. With respect to controls, SCA2 patients showed significant higher atrophy rates in the midbrain, including substantia nigra, basis pontis, middle cerebellar peduncles and posterior medulla corresponding to the gracilis and cuneatus tracts and nuclei, cerebellar white matter (WM) and cortical gray matter (GM) in the inferior portions of the cerebellar hemisphers. No differences in WM or GM volume loss were observed in the supratentorial compartment. TBM findings did not correlate with modifications of the neurological deficit. In conclusion, MRI volumetry using TBM is capable of demonstrating the progression of pontocerebellar atrophy in SCA2, supporting a possible role of MRI as biomarker in future trials

Deep Learning in Neuroimaging: Effect of Data Leakage in Cross-validation Using 2D Convolutional Neural Networks

In recent years, 2D convolutional neural networks (CNNs) have been extensively used to diagnose neurological diseases from magnetic resonance imaging (MRI) data due to their potential to discern subtle and intricate patterns. Despite the high performances reported in numerous studies, developing CNN models with good generalization abilities is still a challenging task due to possible data leakage introduced during cross-validation (CV). In this study, we quantitatively assessed the effect of a data leakage caused by 3D MRI data splitting based on a 2D slice-level using three 2D CNN models to classify patients with Alzheimer’s disease (AD) and Parkinson’s disease (PD). Our experiments showed that slice-level CV erroneously boosted the average slice level accuracy on the test set by 30% on Open Access Series of Imaging Studies (OASIS), 29% on Alzheimer’s Disease Neuroimaging Initiative (ADNI), 48% on Parkinson’s Progression Markers Initiative (PPMI) and 55% on a local de-novo PD Versilia dataset. Further tests on a randomly labeled OASIS-derived dataset produced about 96% of (erroneous) accuracy (slice-level split) and 50% accuracy (subject-level split), as expected from a randomized experiment. Overall, the extent of the effect of an erroneous slice-based CV is severe, especially for small datasets