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Background: Multiple sclerosis (MS) has two main phenotypes: relapse-remitting MS (RRMS) and progressive MS (PMS), distinguished by disability profiles and treatment response. Differentiating them using conventional MRI is challenging. Objective: This study explores the use of scaled subprofile modelling using principal component analysis (SSM/PCA) on MRI data to distinguish between MS phenotypes. Methods: MRI scans were performed on patients with RRMS (n = 30) and patients with PMS (n = 20), using the standard sequences T1w, T2w, T2w-FLAIR, and the myelin-sensitive sequences magnetisation transfer (MT) ratio (MTR), quantitative MT (qMT), inhomogeneous MT ratio (ihMTR), and quantitative inhomogeneous MT (qihMT). Results: SSM/PCA analysis of qihMT images best differentiated PMS from RRMS, with the highest specificity (87%) and positive predictive value (PPV) (83%), but a lower sensitivity (67%) and negative predictive value (NPV) (72%). Conversely, T1w data analysis showed the highest sensitivity (93%) and NPV (89%), with a lower PPV (67%) and specificity (53%). Phenotype classification agreement between T1w and qihMT was observed in 57% of patients. In the subset with concordant classifications, the sensitivity, specificity, PPV, and NPV were 100%, 88%, 90%, and 100%, respectively. Conclusions: SSM/PCA on MRI data revealed distinctive patterns for MS phenotypes. Optimal discrimination occurred with qihMT and T1w sequences, with qihMT identifying PMS and T1w identifying RRMS. When qihMT and T1w analyses align, MS phenotype prediction improves. [ABSTRACT FROM AUTHOR]

Background: Total metabolic active tumour volume (TMATV) and total tumour burden (TTB) are increasingly studied as prognostic and predictive factors in non-small cell lung cancer (NSCLC) patients. In this study, we investigated the repeatability of TMATV and TTB as function of uptake interval, positron emission tomography/computed tomography (PET/CT) image reconstruction settings, and lesion delineation method. We used six lesion delineation methods, four direct PET image-derived delineations and two based on a majority vote approach, i.e. intersection between two or more delineations (MV2) and between three or more delineations (MV3). To evaluate the accuracy of those methods, they were compared with a reference delineation obtained from the consensus of the segmentations performed by three experienced observers. Ten NSCLC patients underwent two baseline whole-body [18F]2-Fluoro-2-deoxy-2-D-glucose ([18F]FDG) PET/CT studies on separate days, within 3 days. Two scans were obtained on each day at 60 and 90 min post-injection to assess the influence of tracer uptake interval. PET/CT images were reconstructed following the European Association of Nuclear Medicine Research Ltd. (EARL) compliant settings and with point-spread-function (PSF) modelling. Repeatability between the measurements of each day was determined and the influence of uptake interval, reconstruction settings, and lesion delineation method was assessed using the generalized estimating equations model.Results: Based on the Jaccard index with the reference delineation, the MV2 lesion delineation method was the most successful method for automated lesion segmentation. The best overall repeatability (lowest repeatability coefficient, RC) was found for TTB from 90 min of tracer uptake scans reconstructed with EARL compliant settings and delineated with 41% of lesion's maximum SUV method (RC = 11%). In most cases, TMATV and TTB repeatability were not significantly affected by changes in tracer uptake time or reconstruction settings. However, some lesion delineation methods had significantly different repeatability when applied to the same images.Conclusions: This study suggests that under some circumstances TMATV and TTB repeatability are significantly affected by the lesion delineation method used. Performing the delineation with a majority vote approach improves reliability and does not hamper repeatability, regardless of acquisition and reconstruction settings. It is therefore concluded that by using a majority vote based tumour segmentation approach, TMATV and TTB in NSCLC patients can be measured with high reliability and precision. [ABSTRACT FROM AUTHOR]

PET radiomics applied to oncology allow the measurement of intratumoral heterogeneity. This quantification can be affected by image protocols; hence, there is an increased interest in understanding how radiomic expression on PET images is affected by different imaging conditions. To address that interest, this study explored how radiomic features are affected by changes in 18 F-FDG uptake time, image reconstruction, lesion delineation, and radiomic binning settings. Methods: Ten non-small cell lung cancer patients underwent 18 F-FDG PET on 2 consecutive days. On each day, scans were obtained at 60 and 90 min after injection and reconstructed following EARL version 1 and with point-spread-function resolution modeling (PSF-EARL2). Lesions were delineated with an SUV threshold of 4.0, with 40% of SUV max , and with a contrast-based isocontour. PET image intensity was discretized with both a fixed bin width (FBW) and a fixed bin number before the calculation of the radiomic features. Repeatability of features was measured with the intraclass correlation coefficient, and the change in feature value over time was calculated as a function of its repeatability. Features were then classified into use-case scenarios based on their repeatability and susceptibility to tracer uptake time. Results: With PSF-EARL2 reconstruction, 40% of SUV max lesion delineation, and FBW intensity discretization, most features (94%) were repeatable at both uptake times (intraclass correlation coefficient > 0.9), 35% being classified for dual-time-point use cases as being sensitive to changes in uptake time, 39% were classified for cross-sectional studies with an unclear dependency on time, 20% were classified for cross-sectional use while being robust to uptake time changes, and 6% were discarded for poor repeatability. EARL version 1 images had 1 fewer repeatable feature (neighborhood gray-level different matrix coarseness) than PSF-EARL2; the contrast-based delineation had the poorest repeatability of the delineation methods, with 45% of features being discarded; and fixed bin number resulted in lower repeatability than FBW (45% and 6% of features were discarded, respectively). Conclusion: Repeatability was maximized with PSF-EARL2 reconstruction, lesion delineation at 40% of SUV max , and FBW intensity discretization. On the basis of their susceptibility to uptake time, radiomic features were classified into specific non-small cell lung cancer PET radiomics use cases., (© 2022 by the Society of Nuclear Medicine and Molecular Imaging.)