Objective:To develop and assess a three-class machine learning model for predicting wild-type, 19 del, and 21 L858R mutations of the epidermal growth factor receptor (EGFR) in lung adenocarcinoma using 18F-FDG PET/CT radiomic features and clinical features. Methods:The retrospective data was collected from 703 patients (346 males, 357 females; age (64.3±9.0) years) with lung adenocarcinoma at the First People′s Hospital of Changzhou from January 2018 to June 2023. Patients were divided into the training set (563 cases) and test set (140 cases) at the ratio of 8∶2. Clinical features were selected using recursive feature elimination (RFE). Radiomic features were extracted from PET and CT images, and the optimal feature sets were selected using minimum redundancy maximum relevance (mRMR) and least absolute shrinkage and selection operator (LASSO) methods. Base models were constructed by using random forest (RF), logistic regression (LR), support vector machine (SVM), K-nearest neighbors (KNN), and multi-layer perceptron (MLP), and the stacking method was applied to establish the CT and PET ensemble models. Delong test was used to compare the AUC differences between the PET/CT combined model and the clinical + PET/CT integrated model.Results:Among 703 patients, 273 were with EGFR wild-type, 202 were with 19 del mutation, and 228 were with 21 L858R mutation. In the single-modal analysis, the AUCs of CT ensemble model in the training and test sets were 0.893 and 0.667, respectively, while the AUCs of PET ensemble model were 0.692 and 0.660. The AUC of PET/CT combined model were 0.897 in training set and 0.672 in test set. The AUC of clinical + PET/CT integrated model showed further improvement, with AUCs of 0.902 and 0.721 in training and test sets, respectively. Notably, the clinical + PET/CT integrated model outperformed PET/CT combined model in predicting wild-type EGFR (test set AUC: 0.784 vs 0.707; Z=3.28, P=0.001). Conclusion:The three-class model (clinical + PET/CT integrated model) based on 18F-FDG PET/CT radiomics and clinical features effectively predicts EGFR mutation subtypes in lung adenocarcinoma.

Objective:To develop and assess a three-class machine learning model for predicting wild-type, 19 del, and 21 L858R mutations of the epidermal growth factor receptor (EGFR) in lung adenocarcinoma using 18F-FDG PET/CT radiomic features and clinical features. Methods:The retrospective data was collected from 703 patients (346 males, 357 females; age (64.3±9.0) years) with lung adenocarcinoma at the First People′s Hospital of Changzhou from January 2018 to June 2023. Patients were divided into the training set (563 cases) and test set (140 cases) at the ratio of 8∶2. Clinical features were selected using recursive feature elimination (RFE). Radiomic features were extracted from PET and CT images, and the optimal feature sets were selected using minimum redundancy maximum relevance (mRMR) and least absolute shrinkage and selection operator (LASSO) methods. Base models were constructed by using random forest (RF), logistic regression (LR), support vector machine (SVM), K-nearest neighbors (KNN), and multi-layer perceptron (MLP), and the stacking method was applied to establish the CT and PET ensemble models. Delong test was used to compare the AUC differences between the PET/CT combined model and the clinical + PET/CT integrated model.Results:Among 703 patients, 273 were with EGFR wild-type, 202 were with 19 del mutation, and 228 were with 21 L858R mutation. In the single-modal analysis, the AUCs of CT ensemble model in the training and test sets were 0.893 and 0.667, respectively, while the AUCs of PET ensemble model were 0.692 and 0.660. The AUC of PET/CT combined model were 0.897 in training set and 0.672 in test set. The AUC of clinical + PET/CT integrated model showed further improvement, with AUCs of 0.902 and 0.721 in training and test sets, respectively. Notably, the clinical + PET/CT integrated model outperformed PET/CT combined model in predicting wild-type EGFR (test set AUC: 0.784 vs 0.707; Z=3.28, P=0.001). Conclusion:The three-class model (clinical + PET/CT integrated model) based on 18F-FDG PET/CT radiomics and clinical features effectively predicts EGFR mutation subtypes in lung adenocarcinoma.

Objective:To investigate the value of intratumoral and peritumoral radiomics models based on 18F-FDG PET-CT in predicting epidermal growth factor receptor (EGFR) mutation status in lung adenocarcinoma and interpret peritumoral radiomics features. Methods:This study was a cross-sectional study. Patients with lung adenocarcinoma who underwent 18F-FDG PET-CT at the Third Affiliated Hospital of Soochow University between January 2018 and April 2022 were retrospectively collected and samplied into a training set (309 cases) and a test set (206 cases) in a 6∶4 ratio randomly. Radiomics features were extracted from the intratumoral and peritumoral regions of interest based on PET and CT images, respectively, and the optimal feature sets were selected. Radiomics models were established using the XGBoost algorithm, and radiomics scores (intratumoral CT label, peritumoral CT label, intratumoral PET label, peritumoral PET label) were calculated. Logistic regression analysis was used to construct a clinical model and a combined model (incorporating PET-CT intratumoral and peritumoral radiomics, clinical features, and CT semantic features). The predictive performance of the models was evaluated using receiver operating characteristic curves and the area under the curve (AUC). Unsupervised clustering, Spearman correlation analysis, and visualization methods were used for the interpretability of peritumoral radiomics features. Results:In both the training and test sets, the AUC value of CT peritumoral labels was greater than that of CT intratumoral labels for predicting EGFR mutation status in lung adenocarcinoma (training set: Z=3.84, P<0.001; test set: Z=1.99, P=0.046). In the test set, the AUC value of PET intratumoral labels (0.684) was slightly higher than that of PET peritumoral labels (0.672) for predicting EGFR mutation status, but the difference was not statistically significant ( P>0.05). The combined model had the highest AUC value for predicting EGFR mutation status of lung adenocarcinoma in both the training and test sets and was significantly better than the clinical model (training set: Z=6.52, P<0.001; test set: Z=2.31, P=0.021). Interpretability analysis revealed that CT peritumoral radiomics features were correlated with CT shape features, and there were significant differences in CT peritumoral features between different EGFR mutation statuses. Conclusions:The value of CT peritumoral labels is superior to that of CT intratumoral labels in predicting EGFR mutation status in lung adenocarcinoma. The predictive performance of the model can be improved by combining PET-CT intratumoral and peritumoral radiomics, clinical features, and CT semantic features.

ObjectiveTo investigate the dietary preference and nutritional knowledge needs of the elderly people who dined at meal service sites. MethodsUsing the form of stratified and convenience sampling method with self-designed questionnaire was used， in November 2021， to select 700 elderly people who dine at meal service sites in 7 jurisdictions in Shanghai were selected， and a self-designed questionnaire was used to investigate the basic information. Results91.64% of the elderly surveyed would eat at relatively fixed meal service sites， and the total Dietary Diversity Score （DDS9） was 3.56±1.46. 41.45% of the elderly with diseases preferred unhealthy cooking methods. Only 8.03% of the surveyed seniors said they were unwilling to accept targeted and personalized nutrition tips and reminders. Multivariate logistic regression analysis showed that the probability reaching the “understanding” level of “Food Guide Pagoda for Chinese Residents” and “Four Principles Recommended by the Core Dietary Guidelines for the Elderly” was different in the elderly with different education levels. The willingness of the elderly to expect to receive different nutrition tips and reminders was related to whether they cared about the corresponding contents. There was a statistically significant difference （P<0.05） among the elderly who were concerned about different health problems in terms of the willingness to receive different nutritional tips. There were significant differences in the proportion of elderly people with different health status for intervention （χ²=5.402， P<0.05）. ConclusionThe elderly who dine at meal service sites are highly dependent on the sites， have a low level of dietary diversification， and do not have a high degree of understanding of nutrition-related knowledge， and have a high demand for targeted nutritional interventions. Nutritional interventions for the sick elderly should be piloted through multiple channels.

Objective:To explore the strategy of preimplantation genetic testing for monogenic disorders (PGT-M) with variants of uncertain significance (VUS).Methods:Monogenic disorder couples who carried VUS and sought fertility counseling between 2018 and 2020 in Reproductive and Genetic Hospital of CITIC-Xiangya were recruited in this study. The pathogenicity of VUS was reanalyzed according to the Standards and Guidelines for the Interpretation of Sequence Variants released by the American College of Medical Genetics and Genomics (ACMG) and the Bayesian Classification. Those VUSs were reclassified as "pathogenic/likely pathogenic variants (P/LP)", "likely pathogenic VUS", "variants of uncertain significance", or "likely benign VUS". PGT-M was applied to families with VUS upgraded as "P/LP" or "likely pathogenic VUS" under the principle of couples fully voluntary and understanding the risks. We also followed up the developmental status of fetuses and the health condition of the born children.Results:1) A total of 25 variants were detected in 16 families with monogenic disorders, including 1 P, 3 LP, and 21 VUS. After reanalysis, 11 VUS and 7 VUS were upgraded as LP (52.4%) and "likely pathogenic VUS" (33.3%), respectively. Two VUS were still reclassified as "variants of uncertain significance"(9.5%), and 1 VUS was reclassified as "likely benign VUS" (4.8%). 2) PGT-M was implemented for 14 families with monogenic disorders, including 9 families with VUS upgraded as LP, 2 families with one LP/P and one "likely pathogenic VUS", and 3 families with only "likely pathogenic VUS". 3) Twelve healthy babies were born after PGT-M. Following up was done according to the onset age of diseases: 8 offsprings did not show the symptoms as probands, and 4 offsprings had not yet reached the age of onset and need continuous follow-up.Conclusion:It is necessary to actively search for new evidence and reanalyze the pathogenicity of VUS according to ACMG guidelines before PGT-M. Under fully informed consent of the patients, PGT-M can be carried out for VUS reclassified as "P/LP" and "likely pathogenic VUS", to reduce the risk of recurrence.

Objective:To explore the strategy of preimplantation genetic testing for monogenic disorders (PGT-M) with variants of uncertain significance (VUS).Methods:Monogenic disorder couples who carried VUS and sought fertility counseling between 2018 and 2020 in Reproductive and Genetic Hospital of CITIC-Xiangya were recruited in this study. The pathogenicity of VUS was reanalyzed according to the Standards and Guidelines for the Interpretation of Sequence Variants released by the American College of Medical Genetics and Genomics (ACMG) and the Bayesian Classification. Those VUSs were reclassified as "pathogenic/likely pathogenic variants (P/LP)", "likely pathogenic VUS", "variants of uncertain significance", or "likely benign VUS". PGT-M was applied to families with VUS upgraded as "P/LP" or "likely pathogenic VUS" under the principle of couples fully voluntary and understanding the risks. We also followed up the developmental status of fetuses and the health condition of the born children.Results:1) A total of 25 variants were detected in 16 families with monogenic disorders, including 1 P, 3 LP, and 21 VUS. After reanalysis, 11 VUS and 7 VUS were upgraded as LP (52.4%) and "likely pathogenic VUS" (33.3%), respectively. Two VUS were still reclassified as "variants of uncertain significance"(9.5%), and 1 VUS was reclassified as "likely benign VUS" (4.8%). 2) PGT-M was implemented for 14 families with monogenic disorders, including 9 families with VUS upgraded as LP, 2 families with one LP/P and one "likely pathogenic VUS", and 3 families with only "likely pathogenic VUS". 3) Twelve healthy babies were born after PGT-M. Following up was done according to the onset age of diseases: 8 offsprings did not show the symptoms as probands, and 4 offsprings had not yet reached the age of onset and need continuous follow-up.Conclusion:It is necessary to actively search for new evidence and reanalyze the pathogenicity of VUS according to ACMG guidelines before PGT-M. Under fully informed consent of the patients, PGT-M can be carried out for VUS reclassified as "P/LP" and "likely pathogenic VUS", to reduce the risk of recurrence.