Search

Experiencing food craving is nearly ubiquitous and has several negative patho- logical impacts prompting an increase in recent craving-related research. Food cue-reactivity tasks are often used to study craving, but most paradigms ignore the individual food preferences of participants, which could confound the findings. We explored the neuropsychological correlates of food craving preference using psychophysical tasks on human participants considering their individual food pref- erences in a multisensory food exposure set-up. Participants were grouped into Liked Food Exposure (LFE), Disliked Food Exposure (DFE), and Neutral Control (NEC) based on their preference for sweet and savory items. Participants reported their momentary craving for the displayed food stimuli through the desire scale and bidding scale (willingness to pay) pre and post multisensory exposure. Par- ticipants were exposed to food items they either liked or disliked. Our results asserted the effect of the multisensory food exposure showing a statistically signif- icant increase in food craving for DFE participants postexposure to disliked food items. Using computational models and statistical methods, we also show that the desire for food does not necessarily translate to a willingness to pay every time, and instantaneous subjective valuation of food craving is an important parameter for subsequent action. Our results further demonstrate the role of parietal N200 and centro-parietal P300 in reversing food preference and possibly point to the decrease of inhibitory control in up-regulating craving for disliked food.

We conducted a systematic survey of COVID-19 endpoint prediction literature to: (a) identify publications that include data that adhere to FAIR (findability, accessibility, interoperability, and reusability) principles and (b) develop and reuse mortality prediction models that best generalize to these datasets. The largest such cohort data we knew of was used for model development. The associated published prediction model was subjected to recursive feature elimination to find a minimal logistic regression model which had statistically and clinically indistinguishable predictive performance. This model could still not be applied to the four external validation sets that were identified, due to complete absence of needed model features in some external sets. Thus, a generalizable model (GM) was built which could be applied to all four external validation sets. An age-only model was used as a benchmark, as it is the simplest, effective, and robust predictor of mortality currently known in COVID-19 literature. While the GM surpassed the age-only model in three external cohorts, for the fourth external cohort, there was no statistically significant difference. This study underscores: (1) the paucity of FAIR data being shared by researchers despite the glut of COVID-19 prediction models and (2) the difficulty of creating any model that consistently outperforms an age-only model due to the cohort diversity of available datasets.

Purpose Radiomic texture calculation requires discretizing image intensities within the region-of-interest. FBN (fixed-bin-number), FBS (fixed-bin-size) and FBN and FBS with intensity equalization (FBNequal, FBSequal) are four discretization approaches. A crucial choice is the voxel intensity (Hounsfield units, or HU) binning range. We assessed the effect of this choice on radiomic features. Methods The dataset comprised 95 patients with head-and-neck squamous-cell-carcinoma. Dual energy CT data was reconstructed at 21 electron energies (40, 45,… 140 keV). Each of 94 texture features were calculated with 64 extraction parameters. All features were calculated five times: original choice, left shift (-10/-20 HU), right shift (+10/+20 HU). For each feature, Spearman correlation between nominal and four variants were calculated to determine feature stability. This was done for six texture feature types (GLCM, GLRLM, GLSZM, GLDZM, NGTDM, and NGLDM) separately. This analysis was repeated for the four binning algorithms. Effect of feature instability on predictive ability was studied for lymphadenopathy as endpoint. Results FBN and FBNequal algorithms showed good stability (correlation values consistently > 0.9). For FBS and FBSequal algorithms, while median values exceeded 0.9, the 95% lower bound decreased as a function of energy, with poor performance over the entire spectrum. FBNequal was the most stable algorithm, and FBS the least. Conclusions We believe this is the first multi-energy systematic study of the impact of CT HU range used during intensity discretization for radiomic feature extraction. Future analyses should account for this source of uncertainty when evaluating the robustness of their radiomic signature.

For esophageal cancer patients, detection of residual tumor after neoadjuvant chemoradiotherapy (nCRT) is important for guiding treatment decisions regarding standard esophagectomy or active surveillance. Prediction models incorporating clinical variables and FDG-PET quantitative radiomic features have been previously developed in a single-center setting (‘development cohort’, Beukinga et al. 2018) to identify patients with residual tumor after nCRT. The aim was to improve generalizability by redeveloping the models (i.e. ‘model extension’) using a multicenter prospectively acquired cohort. The development cohort was combined with a patient cohort collected from a prospective multicenter study in four Dutch institutes. Patients underwent nCRT according to the CROSS regimen followed by esophagectomy 6–14 weeks after nCRT between 2013–2019. Outcome was tumor regression grade (TRG) 1–2 (0–10% tumor) vs TRG 3–4 (>10% tumor). FDG-PET/CT was performed 6–12 weeks after nCRT. Post-treatment FDG-PET-radiomic features were calculated from gross tumor volumes. Features were standardized per scanner. A bootstrapped least absolute shrinkage and selection (LASSO) model using clinical variables and radiomic features was developed and a model incorporating clinical variables was developed for reference. Some 262 patients were included. Median age was 65 years (IQR 60–70), 158 of 262 were male (82%) and outcomes were 129/262 TRG 1–2 (49%) and 133/262 TRG 3–4 (51%). The extended LASSO model included seven radiomic features and the clinical variables cT stage and histology (internally validated AUC, 0.59). At a probability threshold to obtain 90% sensitivity for detection of TRG 3–4 (benchmark with bite-on-bite biopsies + fine-needle aspiration of suspected lymph nodes), specificity was 36% and accuracy 63%. A LASSO clinical reference model (including cT, sex, histology) achieved an internally validated AUC of 0.58 (Fig. 1). The extended internally validated LASSO model incorporating post-treatment radiomic features and clinical variables yielded moderate discriminative ability. The improvement in predictive performance with this model compared with a model using only clinical variables was marginal. The application of radiomics to post-treatment FDG-PET/CT scans is of no help in decision-making in individual patients regarding the choice for active surveillance after nCRT.

Background Stratifying high-risk histopathologic features in endometrial carcinoma is important for treatment planning. Radiomics analysis at preoperative MRI holds potential to identify high-risk phenotypes. Purpose To evaluate the performance of multiparametric MRI three-dimensional radiomics-based machine learning models for differentiating low- from high-risk histopathologic markers-deep myometrial invasion (MI), lymphovascular space invasion (LVSI), and high-grade status-and advanced-stage endometrial carcinoma. Materials and Methods This dual-center retrospective study included women with histologically proven endometrial carcinoma who underwent 1.5-T MRI before hysterectomy between January 2011 and July 2015. Exclusion criteria were tumor diameter less than 1 cm, missing MRI sequences or histopathology reports, neoadjuvant therapy, and malignant neoplasms other than endometrial carcinoma. Three-dimensional radiomics features were extracted after tumor segmentation at MRI (T2-weighted, diffusion-weighted, and dynamic contrast-enhanced MRI). Predictive features were selected in the training set with use of random forest (RF) models for each end point, and trained RF models were applied to the external test set. Five board-certified radiologists conducted MRI-based staging and deep MI assessment in the training set. Areas under the receiver operating characteristic curve (AUCs) were reported with balanced accuracies, and radiologists' readings were compared with radiomics with use of McNemar tests. Results In total, 157 women were included: 94 at the first institution (training set; mean age, 66 years ± 11 [SD]) and 63 at the second institution (test set; 67 years ± 12). RF models dichotomizing deep MI, LVSI, high grade, and International Federation of Gynecology and Obstetrics (FIGO) stage led to AUCs of 0.81 (95% CI: 0.68, 0.88), 0.80 (95% CI: 0.67, 0.93), 0.74 (95% CI: 0.61, 0.86), and 0.84 (95% CI: 0.72, 0.92), respectively, in the test set. In the training set, radiomics provided increased performance compared with radiologists' readings for identifying deep MI (balanced accuracy, 86% vs 79%; P = .03), while no evidence of a difference was observed in performance for advanced FIGO stage (80% vs 78%; P = .27). Conclusion Three-dimensional radiomics can stratify patients by using preoperative MRI according to high-risk histopathologic end points in endometrial carcinoma and provide nonsignificantly different or higher performance than radiologists in identifying advanced stage and deep myometrial invasion, respectively. © RSNA, 2022 Online supplemental material is available for this article. See also the editorial by Kido and Nishio in this issue.

Background and Purpose Previous literature suggests that the dose proximally outside the PTV could have an impact on the incidence of distant metastasis (DM) after SBRT in stage I NSCLC patients. We investigated this observation (along with local failure) in deliveries made by different treatment modalities: robotic mounted linac SBRT (CyberKnife) vs conventional SBRT (VMAT/CRT). Materials and methods This study included 422 stage I NSCLC patients from 2 institutions who received SBRT: 217 treated conventionally and 205 with CyberKnife. The dose behavior outside the PTV of both sub-cohorts were compared by analyzing the mean dose in continuous shells extending 1, 2, 3, …, 100 mm from the PTV. Kaplan-Meier analysis was performed between the two sub-cohorts with respect to DM-free survival and local progression-free survival. A multivariable Cox proportional hazards model was fitted to the combined cohort (n = 422) with respect to DM incidence and local failure. Results The shell-averaged dose fall-off beyond the PTV was found to be significantly more modest in CyberKnife plans than in conventional SBRT plans. In a 30 mm shell around the PTV, the mean dose delivered with CyberKnife (38.1 Gy) is significantly larger than with VMAT/CRT (22.8 Gy, p 10 - 8 ). For 95% of CyberKnife plans, this region receives a mean dose larger than the 21 Gy threshold dose discovered in our previous study. In contrast, this occurs for only 75% of VMAT/CRT plans. The DM-free survival of the entire CyberKnife cohort is superior to that of the 25% of VMAT/CRT patients receiving less than the threshold dose ( VMAT / CRT 21 Gy ), with a hazard ratio of 5.3 (95% CI: 3.0–9.3, p 10 - 8 ). The 2 year DM-free survival rates were 87% (95% CI: 81%–91%) and 44% (95% CI: 28%–58%) for CyberKnife and the below-threshold dose conventional cohorts, respectively. A multivariable analysis of the combined cohort resulted in the confirmation that threshold dose was a significant predictor of DM(HR = 0.28, 95% CI: 0.15–0.55, p 10 - 3 ) when adjusted for other clinical factors. CyberKnife was also found to be superior to the entire VMAT/CRT with respect to local control (HR = 3.44, CI: 1.6–7.3). The 2-year local progression-free survival rates for the CyberKnife cohort and the VMAT/CRT cohort were 96% (95% CI: 92%–98%) and 88% (95% CI: 82%–92%) respectively. Conclusions In standard-of-care CyberKnife treatments, dose distributions that aid distant control are achieved 95% of the time. Although similar doses could be physically achieved by conventional SBRT, this is not always the case with current prescription practices, resulting in worse DM outcomes for 25% of conventional SBRT patients. Furthermore, CyberKnife was found to provide superior local control compared to VMAT/CRT.

In radiation oncology, Machine Learning classification publications are typically related to two outcome classes, e.g. the presence or absence of distant metastasis. However, multi-class classification problems also have great clinical relevance, e.g., predicting the grade of a treatment complication following lung irradiation. This work comprised two studies aimed at making work in this domain less prone to statistical blindsides. In multi-class classification, AUC is not defined, whereas correlation coefficients are. It may seem like solely quoting the correlation coefficient value (in lieu of the AUC value) is a suitable choice. In the first study, we illustrated using Monte Carlo (MC) models why this choice is misleading. We also considered the special case where the multiple classes are not ordinal, but nominal, and explained why Pearson or Spearman correlation coefficients are not only providing incomplete information but are actually meaningless. The second study concerned surrogate biomarkers for a clinical endpoint, which have purported benefits including potential for early assessment, being inexpensive, and being non-invasive. Using a MC experiment, we showed how conclusions derived from surrogate markers can be misleading. The simulated endpoint was radiation toxicity (scale of 0-5). The surrogate marker was the true toxicity grade plus a noise term. Five patient cohorts were simulated, including one control. Two of the cohorts were designed to have a statistically significant difference in toxicity. Under 1000 repeated experiments using the biomarker, these two cohorts were often found to be statistically indistinguishable, with the fraction of such occurrences rising with the level of noise.

Purpose To demonstrate the strength of an innovative knowledge-based model-building method for radiotherapy planning using hypofractionated, multi-target prostate patients. Material and methods An initial RapidPlan model was trained using 48 patients who received 60 Gy to prostate (PTV60) and 44 Gy to pelvic nodes (PTV44) in 20 fractions. To improve the model's goodness-of-fit, an intermediate model was generated using the dose-volume histograms of best-spared organs-at-risk (OARs) of the initial model. Using the intermediate model and manual tweaking, all 48 cases were re-planned. The final model, trained using these re-plans, was validated on 50 additional patients. The validated final model was used to determine any planning advantage of using three arcs instead of two on 16 VMAT cases and tested on 25 additional cases to determine efficacy for single-PTV (PTV60-only) treatment planning. Results For model validation, PTV V95% of 99.9% was obtained by both clinical and knowledge-based planning. D1% was lower for model plans: by 1.23 Gy (PTV60, CI = [1.00, 1.45]), and by 2.44 Gy (PTV44, CI = [1.72, 3.16]). OAR sparing was superior for knowledge-based planning: ΔDmean = 3.70 Gy (bladder, CI = [2.83, 4.57]), and 3.22 Gy (rectum, CI = [2.48, 3.95]); ΔD2% = 1.17 Gy (bowel bag, CI = [0.64, 1.69]), and 4.78 Gy (femoral heads, CI = [3.90, 5.66]). Using three arcs instead of two, improvements in OAR sparing and PTV coverage were statistically significant, but of magnitudes Conclusions Our knowledge-based model delivers efficient, consistent plans with excellent PTV coverage and improved OAR sparing compared to clinical plans.

This study was supported in part by the European Research Council Innovative Medicines Initiative (DRAGON#, H2020-JTI-IMI2 101005122), the AI for Health Imaging Award (CHAIMELEON##, H2020-SC1-FA-DTS-2019-1 952172), the UK Research and Innovation Future Leaders Fellowship (MR/V023799/1), the British Heart Foundation (Project Number: TG/18/5/34111, PG/16/78/32402), the SABRE project supported by Boehringer Ingelheim Ltd, the European Union's Horizon 2020 research and innovation programme (ICOVID, 101016131), the Euskampus Foundation (COVID19 Resilience, Ref. COnfVID19), and the Basque Government (consolidated research group MATHMODE, Ref. IT1294-19, and 3KIA project from the ELKARTEK funding program, Ref. KK-2020/00049)., Removing the bias and variance of multicentre data has always been a challenge in large scale digital healthcare studies, which requires the ability to integrate clinical features extracted from data acquired by different scanners and protocols to improve stability and robustness. Previous studies have described various computational approaches to fuse single modality multicentre datasets. However, these surveys rarely focused on evaluation metrics and lacked a checklist for computational data harmonisation studies. In this systematic review, we summarise the computational data harmonisation approaches for multi-modality data in the digital healthcare field, including harmonisation strategies and evaluation metrics based on different theories. In addition, a comprehensive checklist that summarises common practices for data harmonisation studies is proposed to guide researchers to report their research findings more effectively. Last but not least, flowcharts presenting possible ways for methodology and metric selection are proposed and the limitations of different methods have been surveyed for future research., European Research Council Innovative Medicines Initiative H2020-JTI-IMI2 101005122, AI for Health Imaging Award H2020-SC1-FA-DTS-2019-1 952172, UK Research & Innovation (UKRI) MR/V023799/1, British Heart Foundation TG/18/5/34111 PG/16/78/32402, Boehringer Ingelheim, European Commission 101016131, Euskampus Foundation COnfVID19, Basque Government IT1294-19, Basque Government (3KIA project from the ELKARTEK funding program) KK-2020/00049

Author(s): Abdallah, MS; Adam, J; Adamczyk, L; Adams, JR; Adkins, JK; Agakishiev, G; Aggarwal, I; Aggarwal, MM; Ahammed, Z; Alekseev, I; Anderson, DM; Aparin, A; Aschenauer, EC; Ashraf, MU; Atetalla, FG; Attri, A; Averichev, GS; Bairathi, V; Baker, W; Ball Cap, JG; Barish, K; Behera, A; Bellwied, R; Bhagat, P; Bhasin, A; Bielcik, J; Bielcikova, J; Bordyuzhin, IG; Brandenburg, JD; Brandin, AV; Bunzarov, I; Butterworth, J; Cai, XZ; Caines, H; De La Barca Sanchez, MC; Cebra, D; Chakaberia, I; Chaloupka, P; Chan, BK; Chang, FH; Chang, Z; Chankova-Bunzarova, N; Chatterjee, A; Chattopadhyay, S; Chen, D; Chen, J; Chen, JH; Chen, X; Chen, Z; Cheng, J; Chevalier, M; Choudhury, S; Christie, W; Chu, X; Crawford, HJ; Csanad, M; Daugherity, M; Dedovich, TG; Deppner, IM; Derevschikov, AA; Dhamija, A; Di Carlo, L; Didenko, L; Dong, X; Drachenberg, JL; Dunlop, JC; Elsey, N; Engelage, J; Eppley, G; Esumi, S; Evdokimov, O; Ewigleben, A; Eyser, O; Fatemi, R; Fawzi, FM; Fazio, S; Federic, P; Fedorisin, J; Feng, CJ; Feng, Y; Filip, P; Finch, E; Fisyak, Y; Francisco, A; Fu, C | Abstract: We present the first inclusive measurements of the invariant and softdrop jet mass in proton-proton collisions at s=200 GeV at STAR. The measurements are fully corrected for detector effects, and reported differentially in both the jet transverse momentum and jet radius parameter. We compare the measurements to established leading-order Monte Carlo event generators and find that STAR-tuned pythia-6 reproduces the data, while LHC tunes of pythia-8 and herwig-7 do not agree with the data, providing further constraints on parameter tuning. Finally, we observe that softdrop grooming, for which the contribution of wide-angle nonperturbative radiation is suppressed, shifts the jet mass distributions into closer agreement with the partonic jet mass as determined by both pythia-8 and a next-to-leading-logarithmic accuracy perturbative QCD calculation. These measurements complement recent LHC measurements in a different kinematic region, as well as establish a baseline for future jet mass measurements in heavy-ion collisions at RHIC.

Author(s): Abdallah, MS; Adam, J; Adamczyk, L; Adams, JR; Adkins, JK; Agakishiev, G; Aggarwal, I; Aggarwal, MM; Ahammed, Z; Alekseev, I; Anderson, DM; Aparin, A; Aschenauer, EC; Ashraf, MU; Atetalla, FG; Attri, A; Averichev, GS; Bairathi, V; Baker, W; Ball Cap, JG; Barish, K; Behera, A; Bellwied, R; Bhagat, P; Bhasin, A; Bielcik, J; Bielcikova, J; Bordyuzhin, IG; Brandenburg, JD; Brandin, AV; Bunzarov, I; Butterworth, J; Cai, XZ; Caines, H; Calderon De La Barca Sanchez, M; Cebra, D; Chakaberia, I; Chaloupka, P; Chan, BK; Chang, FH; Chang, Z; Chankova-Bunzarova, N; Chatterjee, A; Chattopadhyay, S; Chen, D; Chen, J; Chen, JH; Chen, X; Chen, Z; Cheng, J; Chevalier, M; Choudhury, S; Christie, W; Chu, X; Crawford, HJ; Csanad, M; Daugherity, M; Dedovich, TG; Deppner, IM; Derevschikov, AA; Dhamija, A; Di Carlo, L; Didenko, L; Dong, X; Drachenberg, JL; Dunlop, JC; Elsey, N; Engelage, J; Eppley, G; Esumi, S; Evdokimov, O; Ewigleben, A; Eyser, O; Fatemi, R; Fawzi, FM; Fazio, S; Federic, P; Fedorisin, J; Feng, CJ; Feng, Y; Filip, P; Finch, E; Fisyak, Y; Francisco, A; Fu, C | Abstract: We report a systematic measurement of cumulants, Cn, for net-proton, proton, and antiproton multiplicity distributions, and correlation functions, κn, for proton and antiproton multiplicity distributions up to the fourth order in Au+Au collisions at sNN=7.7, 11.5, 14.5, 19.6, 27, 39, 54.4, 62.4, and 200 GeV. The Cn and κn are presented as a function of collision energy, centrality and kinematic acceptance in rapidity, y, and transverse momentum, pT. The data were taken during the first phase of the Beam Energy Scan (BES) program (2010-2017) at the BNL Relativistic Heavy Ion Collider (RHIC) facility. The measurements are carried out at midrapidity (|y|l 0.5) and transverse momentum 0.4