Comparison of quantitative parameters and radiomic features as inputs into machine learning models to predict the Gleason score of prostate cancer lesions.

INTRODUCTION: The classification of prostate cancer (PCa) lesions using Prostate Imaging Reporting and Data System (PI-RADS) suffers from poor inter-reader agreement. This study compared quantitative parameters or radiomic features from multiparametric magnetic resonance imaging (mpMRI) or positron emission tomography (PET), as inputs into machine learning (ML) to predict the Gleason scores (GS) of detected lesions for improved PCa lesion classification. METHODS: 20 biopsy-confirmed PCa subjects underwent imaging before radical prostatectomy. A pathologist assigned GS from tumour tissue. Two radiologists and one nuclear medicine physician delineated the lesions on the mpMR and PET images, yielding 45 lesion inputs. Seven quantitative parameters were extracted from the lesions, namely T2-weighted (T2w) image intensity, apparent diffusion coefficient (ADC), transfer constant (KTRANS), efflux rate constant (Kep), and extracellular volume ratio (Ve) from mpMR images, and SUVmean and SUVmax from PET images. Eight radiomic features were selected out of 109 radiomic features from T2w, ADC and PET images. Quantitative parameters or radiomic features, with risk factors of age, prostate-specific antigen (PSA), PSA density and volume, of 45 different lesion inputs were input in different combinations into four ML models - Decision Tree (DT), Support Vector Machine (SVM), k-Nearest-Neighbour (kNN), Ensembles model (EM). RESULTS: SUVmax yielded the highest accuracy in discriminating detected lesions. Among the 4 ML models, kNN yielded the highest accuracies of 0.929 using either quantitative parameters or radiomic features with risk factors as input. CONCLUSIONS: ML models' performance is dependent on the input combinations and risk factors further improve ML classification accuracy.

Apathy is associated with white matter network disruption and specific cognitive deficits in Parkinson's disease.

BACKGROUND: Apathy is common in Parkinson's disease (PD) but its underlying white matter (WM) architecture is not well understood. Moreover, how apathy affects cognitive functions in PD remains unclear. We investigated apathy-related WM network alterations and the impact of apathy on cognition in the context of PD. METHODS: Apathetic PD patients (aPD), non-apathetic PD patients (naPD), and matched healthy controls (HCs) underwent brain scans and clinical assessment. Graph-theoretical and network-based analyses were used for group comparisons of WM features derived from diffusion spectrum imaging (DSI). Path analysis was used to determine the direct and indirect effects of apathy and other correlates on different cognitive functions. RESULTS: The aPD group was impaired on neural integration measured by global efficiency (p = 0.009) and characteristic path length (p = 0.04), executive function (p < 0.001), episodic memory (p < 0.001) and visuospatial ability (p = 0.02), and had reduced connectivity between the bilateral parietal lobes and between the putamen and temporal regions (p < 0.05). In PD, executive function was directly impacted by apathy and motor severity and indirectly influenced by depression; episodic memory was directly and indirectly impacted by apathy and depression, respectively; conversely, visuospatial ability was not related to any of these factors. Neural integration, though being marginally correlated with apathy, was not associated with cognition. CONCLUSIONS: Our results suggest compromised neural integration and reduced structural connectivity in aPD. Apathy, depression, and motor severity showed distinct impacts on different cognitive functions with apathy being the most influential determinant of cognition in PD.

Lipid suppression in CSI with spatial priors and highly undersampled peripheral k-space.

Mapping 1H brain metabolites using chemical shift imaging is hampered by the presence of subcutaneous lipid signals, which contaminate the metabolites by ringing due to limited spatial resolution. Even though chemical shift imaging at spatial resolution high enough to mitigate the lipid artifacts is infeasible due to signal-to-noise constraints on the metabolites, the lipid signals have orders of magnitude of higher concentration, which enables the collection of high-resolution lipid maps with adequate signal-to-noise. The previously proposed dual-density approach exploits this high signal-to-noise property of the lipid layer to suppress truncation artifacts using high-resolution lipid maps. Another recent approach for lipid suppression makes use of the fact that metabolite and lipid spectra are approximately orthogonal, and seeks sparse metabolite spectra when projected onto lipid-basis functions. This work combines and extends the dual-density approach and the lipid-basis penalty, while estimating the high-resolution lipid image from 2-average k-space data to incur minimal increase on the scan time. Further, we exploit the spectral-spatial sparsity of the lipid ring and propose to estimate it from substantially undersampled (acceleration R=10 in the peripheral k-space) 2-average in vivo data using compressed sensing and still obtain improved lipid suppression relative to using dual-density or lipid-basis penalty alone.

Seven-Tesla proton magnetic resonance spectroscopic imaging in adult X-linked adrenoleukodystrophy.

BACKGROUND: Adults with X-linked adrenoleukodystrophy (X-ALD) remain at risk for progressive neurological deterioration. Phenotypes vary in their pathology, ranging from axonal degeneration to inflammatory demyelination. The severity of symptoms is poorly explained by conventional imaging. OBJECTIVE: To test the hypothesis that neurochemistry in normal-appearing brains differs in adult phenotypes of X-ALD and that neurochemical changes correlate with the severity of symptoms. PATIENTS AND METHODS: Using a 7-Tesla scanner, we performed structural and proton magnetic resonance spectroscopic imaging in 13 adult patients with X-ALD: 4 patients with adult cerebral ALD, 5 patients with adrenomyeloneuropathy (AMN), and 4 female heterozygotes. Nine healthy controls were included. RESULTS: Among adult X-ALD phenotypes, the myo-inositol to creatine ratio was 46% higher and the choline to creatine ratio was 21% higher in normal-appearing white matter of those with adult cerebral ALD compared with those with AMN (P < .05). Both N-acetylaspartate to creatine (P = .03) and glutamate to creatine (P = .04) ratios were lower in AMN patients than in controls. There were no significant differences between patients with AMN and female heterozygotes. In the cortex, patients with adult cerebral ALD had lower N-acetylaspartate to creatine ratios compared with female heterozygotes and controls (P = .02). The global myo-inositol to creatine ratio demonstrated a significant association with Expanded Disability Status Scale score (Spearman rho = 0.66, P = .04). CONCLUSIONS: Seven-Tesla proton magnetic resonance spectroscopic imaging reveals differences in the neurochemistry of adult cerebral ALD but cannot distinguish AMN patients from female heterozygotes. Myo-inositol to creatine ratio correlates with the severity of the symptoms and may be a meaningful biomarker in adult X-ALD.