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BackgroundPositron Emission Tomography (PET) is increasingly utilized in radiomics studies for treatment evaluation purposes. Nevertheless, lesion volume identification in PET images is a critical and still challenging step in the process of radiomics, due to the low spatial resolution and high noise level of PET images. Currently, the biological target volume (BTV) is manually contoured by nuclear physicians, with a time expensive and operator-dependent procedure.This study aims to obtain BTVs from cerebral metastases in patients who underwent L-[11C]methionine (11C-MET) PET, using a fully automatic procedure and to use these BTVs to extract radiomics features to stratify between patients who respond to treatment or not. For these purposes, 31 brain metastases, for predictive evaluation, and 25 ones, for follow-up evaluation after treatment, were delineated using the proposed method. Successively, 11C-MET PET studies and related volumetric segmentations were used to extract 108 features to investigate the potential application of radiomics analysis in patients with brain metastases. A novel statistical system has been implemented for feature reduction and selection, while discriminant analysis was used as a method for feature classification.ResultsFor predictive evaluation, 3 features (asphericity, low-intensity run emphasis, and complexity) were able to discriminate between responder and non-responder patients, after feature reduction and selection. Best performance in patient discrimination was obtained using the combination of the three selected features (sensitivity 81.23%, specificity 73.97%, and accuracy 78.27%) compared to the use of all features. Secondly, for follow-up evaluation, 8 features (SUVmean, SULpeak, SUVmin, SULpeakprod-surface-area, SUVmeanprod-sphericity, surface mean SUV 3, SULpeakprod-sphericity, and second angular moment) were selected with optimal performance in discriminant analysis classification (sensitivity 86.28%, specificity 87.75%, and accuracy 86.57%) outperforming the use of all features.ConclusionsThe proposed system is able i) to extract 108 features for each automatically segmented lesion and ii) to select a sub-panel of 11C-MET PET features (3 and 8 in the case of predictive and follow-up evaluation), with valuable association with patient outcome. We believe that our model can be useful to improve treatment response and prognosis evaluation, potentially allowing the personalization of cancer treatment plans.

Background: Osteoarthritis (OA) is a degenerative joints disorder influenced by genetic predisposition. We reported that rs11718863 DVWA SNP was represented in Sicilian with a more severe Kellgren and Lawrence (KL) radiographic grade, displaying its predictive role as OA marker progression. Here, we describe the DVWA SNPs: rs11718863, rs7639618, rs7651842, rs7639807 and rs17040821 probably able to induce protein functional changes. Findings: Sixty-one Sicilian patients with knee OA and 100 healthy subjects were enrolled. Clinical and radiographic evaluation was performed using AKSS scores and KL. Linkage Disequilibrium (LD) analyses were performed in order to verify whether the SNPs segregate as haplotype. All DVWA SNPs'MinorAllele Frequencies (MAF) were greater than in the European. The rs7639618 SNP showed a statistical association with KL. Our analyses show that a LD exists among rs11718863 and rs7639618, as well as between rs7651842, rs7639807 and rs17040821 SNPs. We also observed that three out of the 161 individuals investigated were simultaneously homozygous carriers of the rs7651842, rs7639807 and rs17040821 MAF alleles. Conclusions: In summary, the purpose of this preliminary research was to highlight possible associations between DVWA SNPs and OA clinical and radiographic data. This work represents a multidisciplinary medicine approach to study OA where clinical, radiological and genetic evaluation could contribute to better define OA grading.