Radiomics for the Discrimination of Infiltrative vs In Situ Breast Cancer

Breast cancer is the most common malignant tumor in women worldwide. Its early diagnosis relies on radiology and clinical evaluation, supplemented by biopsy confirmation. Technological advances in medical imaging, especially in the field of artificial intelligence, allow to address clinical challenges in cancer detection and classification, as well as in the assessment of treatment response, and in monitoring disease progression. Radiomics allows to extract features from images, related to tumor size, shape, intensity, and texture, providing comprehensive tumor characterization. In this paper, we briefly review some Radiomics approaches in breast cancer, focusing on the non-invasive distinction between in-situ and infiltrating breast tumors, and present a preliminary test using Radiomics signatures in DCE-MRI and machine learning, aimed to investigate the feasibility of distinguishing infiltrating cancer from ductal carcinoma in situ (DCIS) diagnosed by preoperative core needle biopsy

Artificial Neural Networks (ANN) for automatic detection of dendritic-shaped cancer cells of cutaneous melanoma in Reflectance Confocal Microscopy (RCM) images

Melanoma (MM) is one of the tumors with the highest incidence. In Italy, MM affected about 13,700 patients out of 373,000 new cases of cancer in 2018, with prognosis dependent on the degree of tumor invasion and presence of metastasis at diagnosis: only an early detection can lead to a better prognosis. Recent evidence suggests that MM is a family of different tumors with varying abilities to grow and metastasize: dendritic-shaped tumor cells were typically found in thin MM in situ. Reflectance Confocal Microscopy (RCM) is a non-invasive imaging tool that enables in vivo observation of the skin at a quasi-histological resolution, providing transverse-section grayscale images related to refractive index of different tissues. In this work, a dataset of RCM images, from 13 healthy subjects and 22 patients affected by MM in situ, were used to train a Multi-Layer Perceptron (MLP) artificial neural network. Each image was subdivided into sub-blocks, labeled as positive if containing significant clusters of dendritic-shaped tumour cells. In each block, various standard features were calculated, e.g. Haralick's and features from the run-length matrices. The MLP was trained to recognize the presence of clusters of dendritic-shaped cancer cells. The preliminary results are encouraging, giving AUC=0.81 with about 73% accuracy. Tests are currently underway to improve quality

Altered structural brain networks in linguistic variants of frontotemporal dementia

none7Semantic (svPPA) and nonfluent (nfvPPA) variants of primary progressive aphasia (PPA) have recently been associated with distinct patterns of white matter and functional network alterations in left frontoinsular and anterior temporal regions, respectively. Little information exists, however, about the topological characteristics of gray matter covariance networks in these two PPA variants. In the present study, we used a graph theory approach to describe the structural covariance network organization in 34 patients with svPPA, 34 patients with nfvPPA and 110 healthy controls. All participants underwent a 3 T structural MRI. Next, we used cortical thickness values and subcortical volumes to define subject-specific connectivity networks. Patients with svPPA and nfvPPA were characterized by higher values of normalized characteristic path length compared with controls. Moreover, svPPA patients had lower values of normalized clustering coefficient relative to healthy controls. At a regional level, patients with svPPA showed a reduced connectivity and impaired information processing in temporal and limbic brain areas relative to controls and nfvPPA patients. By contrast, local network changes in patients with nfvPPA were focused on frontal brain regions such as the pars opercularis and the middle frontal cortex. Of note, a predominance of local metric changes was observed in the left hemisphere in both nfvPPA and svPPA brain networks. Taken together, these findings provide new evidences of a suboptimal topological organization of the structural covariance networks in svPPA and nfvPPA patients. Moreover, we further confirm that distinct patterns of structural network alterations are related to neurodegenerative mechanisms underlying each PPA variant.Nigro, Salvatore; Tafuri, Benedetta; Urso, Daniele; De Blasi, Roberto; Cedola, Alessia; Gigli, Giuseppe; Logroscino, GiancarloNigro, Salvatore; Tafuri, Benedetta; Urso, Daniele; De Blasi, Roberto; Cedola, Alessia; Gigli, Giuseppe; Logroscino, Giancarl

Grey-matter correlates of empathy in 4-Repeat Tauopathies

Abstract Loss of empathy is an early and central symptom of frontotemporal lobar degeneration spectrum diseases. We aimed to investigate the topographical distribution of morphometric brain changes associated with empathy in Progressive Supranuclear Palsy (PSP) and Corticobasal Syndrome (CBS) patients. Twenty-seven participants with CBS and 31 with PSP were evaluated using Interpersonal Reactivity Index scales in correlation with gray matter atrophy using a voxel-based morphometry approach. Lower levels of empathy were associated with an increased atrophy in fronto-temporal cortical structures. At subcortical level, empathy scores were positively correlated with gray matter volume in the amygdala, hippocampus and the cerebellum. These findings allow to extend the traditional cortico-centric view of cognitive empathy to the cerebellar regions in patients with neurodegenerative disorders and suggest that the cerebellum may play a more prominent role in social cognition than previously appreciated

Behavioral variant frontotemporal dementia in patients with primary psychiatric disorder: A magnetic resonance imaging study

Abstract Background The clinical diagnosis of behavioral variant frontotemporal dementia (bvFTD) in patients with a history of primary psychiatric disorder (PPD) is challenging. PPD shows the typical cognitive impairments observed in patients with bvFTD. Therefore, the correct identification of bvFTD onset in patients with a lifetime history of PPD is pivotal for an optimal management. Methods Twenty‐nine patients with PPD were included in this study. After clinical and neuropsychological evaluations, 16 patients with PPD were clinically classified as bvFTD (PPD‐bvFTD+), while in 13 cases clinical symptoms were associated with the typical course of the psychiatric disorder itself (PPD‐bvFTD–). Voxel‐ and surface‐based investigations were used to characterize gray matter changes. Volumetric and cortical thickness measures were used to predict the clinical diagnosis at a single‐subject level using a support vector machine (SVM) classification framework. Finally, we compared classification performances of magnetic resonance imaging (MRI) data with automatic visual rating scale of frontal and temporal atrophy. Results PPD‐bvFTD+ showed a gray matter decrease in thalamus, hippocampus, temporal pole, lingual, occipital, and superior frontal gyri compared to PPD‐bvFTD– (p < .05, family‐wise error‐corrected). SVM classifier showed a discrimination accuracy of 86.2% in differentiating PPD patients with bvFTD from those without bvFTD. Conclusions Our study highlights the utility of machine learning applied to structural MRI data to support the clinician in the diagnosis of bvFTD in patients with a history of PPD. Gray matter atrophy in temporal, frontal, and occipital brain regions may represent a useful hallmark for a correct identification of dementia in PPD at a single‐subject level

Magnetic Resonance Parkinsonism Index Is Associated with {REM} Sleep Behavior Disorder in Parkinson's Disease

We investigated the association between the Magnetic Resonance Parkinsonism Index (MRPI) and REM sleep behavior disorder (RBD). We included 226 de novo PD patients (82 PD-RBD and 144 PD-noRBD) and 19 idiopathic RBD patients. Furthermore, 3T T1-weighted MR images were used for automated brainstem calculations. MRPI values were higher in the PD-RBD (p = 0.004) compared to PD-noRBD patients. Moreover, MRPI proved to be a significant predictor of REM Behavior Disorder Screening Questionnaire scores in PD (beta = 0.195, p = 0.007) and iRBD patients (beta = 0.582, p = 0.003). MRPI can be used as an imaging marker of RBD in patients with de novo PD and iRBD

The Relationship Between Muscle Strength and Cognitive Performance Across Alzheimer{\textquotesingle}s Disease Clinical Continuum

Alzheimer's disease (AD) is a neurodegenerative disorder characterized by a progressive cognitive decline, mostly prominent in the domain of memory, but also associated with other cognitive deficits and non-cognitive symptoms. Reduced muscle strength is common in AD. However, the current understanding of its relationship with cognitive decline is limited. This study investigates the relationship between muscle strength and cognition in patients with AD and mild cognitive impairment (MCI). We enrolled 148 consecutive subjects, including 74 patients with probable AD dementia, 37 MCI, and 37 controls. Participants underwent neuropsychological evaluation focused on attention, working memory, declarative memory and learning. Muscle strength and muscle mass were measured through hand dynamometer and bio-electrical impedance analysis, respectively. Patients with AD dementia were divided with respect to the severity of cognitive impairment into mild and moderate-to-severe patients. Moderate-to-severe patients with AD presented lower handgrip strength than MCI and controls. No differences were observed in muscle mass. In MCI and AD dementia, handgrip strength was associated with overall cognitive functioning, attentional and memory performance. The routine implementation of handgrip strength assessment in the clinical work-up of patients with MCI and AD could potentially represent a simple method to monitor functional and cognitive decline along the disease course

Semiautomatic segmentation of glioblastoma (GB) for radiotherapy (RT) treatment planning

During the radiation therapy (RT) process, the treatment is planned and simulated with a treatment planning system (TPS). Contouring identifies the Planning Treatment Volume (PTV), that is the physical RT treatment volume. PTV of Glioblastoma (GB) includes, after expansion, Gross Tumor Volume (GTV, the tumor) and Clinical Target Volume (CTV, tumor plus edema). GlioCAD, a Computer-Assisted Detection software for contouring gliomas in MRI/DTI, was used to delineate GTV. The dataset included the images of 21 patients undergoing RT for GB. For each patient, we co-registered CT-planning images and diagnostic MRI (16 T1-gad, 6 T2 Flair, 13 Flair Fat Sat), which were used for GlioCAD training and validation. CAD outlined the tumor with good accuracy, after ruling out in post-processing some false positives. We identified reliable GTVs, suitable for RT requirements. An evolution of GlioCAD will take into account edema for outlining CTV. The method is promising. Together with a further automatic system for the delineation of organs at risk (OAR) in the brain, the procedure may be helpful for standardization of RT-treatment planning

Artificial Intelligence for the Objective Evaluation of Acne Investigator Global Assessment

The evaluation of Acne using ordinal scales reflects the clinical perception of severity but has shown low reproducibility both intra- and inter-rater. In this study, we investigated if Artificial Intelligence trained on images of Acne patients could perform acne grading with high accuracy and reliabilities superior to those of expert physicians