In the present study, we evaluated the reproducibility and validity of dietary patterns among Chinese adult populations. A random subsample of 203 participants (aged 31-80 years) from a community-based nutrition and health survey was enrolled. An eighty-seven-item FFQ was administered twice (FFQ1 and FFQ2) 1 year apart; four 3 consecutive day, 24-h dietary recalls (24-HDR, as a reference method) were performed between the administrations of the two FFQ every 3 months. Dietary patterns from three separate dietary sources were derived using factor analysis based on twenty-eight predefined food groups. Comparisons between dietary pattern scores were made by using Pearson's or intraclass correlation coefficients (ICC), cross-classification analysis, weighted κ statistic and Bland-Altman plots; the four major dietary patterns identified from FFQ1, FFQ2 and 24-HDR were similar. Regarding reproducibility, ICC for z-scores between FFQ1 and FFQ2 were all >0·6 for dietary patterns. The 'animal and plant protein' pattern had the highest ICC of 0·870. For validity, the adjusted Pearson's correlation coefficients for dietary pattern z-scores between two FFQ and the mean of four 3 consecutive day 24-HDR ranged from 0·387 for the 'Chinese traditional' pattern to 0·838 for the 'animal and plant protein' pattern. More than 75 % of the participants were classified into the same or adjacent quartile, and <5 % were misclassified into opposite quartiles. The weighted κ ranged from 0·259 to 0·680. Bland-Altman plots indicated that no significant deviation was found between two dietary assessment methods. Our findings indicate a good reasonable reproducibility and a reasonable validity of dietary patterns derived by factor analysis in China.

BackgroundThe aim of this study was to evaluate the clinical usefulness of radiomics signature-derived 18F-fluorodeoxyglucose (18F-FDG) positron emission tomography–computed tomography (PET-CT) for the early prediction of neoadjuvant chemotherapy (NAC) outcomes in patients with (BC).MethodsA total of 124 patients with BC who underwent pretreatment PET-CT scanning and received NAC between December 2016 and August 2019 were studied. The dataset was randomly assigned in a 7:3 ratio to either the training or validation cohort. Primary tumor segmentation was performed, and radiomics signatures were extracted from each PET-derived volume of interest (VOI) and CT-derived VOI. Radiomics signatures associated with pathological treatment response were selected from within a training cohort (n = 85), which were then applied to generate different classifiers to predict the probability of pathological complete response (pCR). Different models were then independently tested in the validation cohort (n = 39) regarding their accuracy, sensitivity, specificity, and area under the curve (AUC).ResultsThirty-five patients (28.2%) had pCR to NAC. Twelve features consisting of five PET-derived signatures, four CT-derived signatures, and three clinicopathological variables were candidates for the model’s development. The random forest (RF), k-nearest neighbors (KNN), and decision tree (DT) classifiers were established, which could be utilized to predict pCR to NAC with AUC ranging from 0.819 to 0.849 in the validation cohort.ConclusionsThe PET/CT-based radiomics analysis might provide efficient predictors of pCR in patients with BC, which could potentially be applied in clinical practice for individualized treatment strategy formulation.

Most of the existing methods for debaising in click-through rate (CTR) prediction depend on an oversimplified assumption, i.e., the click probability is the product of observation probability and relevance probability. However, since there is a complicated interplay between these two probabilities, these methods cannot be applied to other scenarios, e.g. query auto completion (QAC) and route recommendation. We propose a general debiasing framework without simplifying the relationships between variables, which can handle all scenarios in CTR prediction. Simulation experiments show that: under the simplest scenario, our method maintains a similar AUC with the state-of-the-art methods; in other scenarios, our method achieves considerable improvements compared with existing methods. Meanwhile, in online experiments, the framework also gains significant improvements consistently.

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