Objective:To evaluate the predictive value of 18F-FDG PET-based radiomics models for lymphovascular invasion (LVI) and visceral pleural invasion (VPI) in lung adenocarcinoma (LAC) prior to surgery. Methods:Eighty-seven patients with LAC (42 males, 45 females, age: (64.6±9.0) years; 90 lesions) pathologically confirmed in the Affiliated Taizhou People′s Hospital of Nanjing Medical University between August 2018 and August 2022 were retrospectively included. Based on the radiomics features extracted from PET images, the machine learning models were constructed by using the support vector machine (SVM), logical regression (LR), decision tree (DT), and K-nearest neighbor (KNN) algorithm. Stratified sampling (Python′s StratifiedkFold function) was employed to divide the data into training set and test set at a ratio of 8∶2. The model stability was assessed using the 50% discount cross-validation. The ROC curve was drawn, and the AUC was calculated to evaluate the value of radiomics models in predicting LVI and VPI in LAC. Delong test was used to compare AUCs of different models.Results:The radiomics models (SVM, LR, DT, KNN) based on PET images showed good predictive value for LVI and VPI in patients with LAC. For LVI, the AUCs were 0.91, 0.90, 0.91, 0.91 in the training set, and were 0.85, 0.87, 0.77, 0.78 in the test set; for VPI, the AUCs were 0.86, 0.86, 0.84, 0.81 in the training set, and were 0.82, 0.80, 0.69, 0.78 in the test set. The F1 scores of the SVM model were the best (0.59 and 0.66 for predicting LVI and VPI respectively). The Delong test showed that there were no significant differences in AUCs among the four models ( z values: from -1.46 to 1.71, all P>0.05). Conclusions:The machine learning models based on 18F-FDG PET radiomics features are effective in predicting LVI and VPI in patients with LAC prior to surgery. These models can assist clinicians in stratifying the risk of LAC and making informed clinical decisions. The SVM model has the best performance in predicting LVI and VPI.

Although somatic cells can be reprogrammed to pluripotent stem cells (PSCs) with pure chemicals, authentic pluripotency of chemically induced pluripotent stem cells (CiPSCs) has never been achieved through tetraploid complementation assay. Spontaneous reprogramming of spermatogonial stem cells (SSCs) was another non-transgenic way to obtain PSCs, but this process lacks mechanistic explanation. Here, we reconstructed the trajectory of mouse SSC reprogramming and developed a five-chemical combination, boosting the reprogramming efficiency by nearly 80- to 100-folds. More importantly, chemical induced germline-derived PSCs (5C-gPSCs), but not gPSCs and chemical induced pluripotent stem cells, had authentic pluripotency, as determined by tetraploid complementation. Mechanistically, SSCs traversed through an inverted pathway of in vivo germ cell development, exhibiting the expression signatures and DNA methylation dynamics from spermatogonia to primordial germ cells and further to epiblasts. Besides, SSC-specific imprinting control regions switched from biallelic methylated states to monoallelic methylated states by imprinting demethylation and then re-methylation on one of the two alleles in 5C-gPSCs, which was apparently distinct with the imprinting reprogramming in vivo as DNA methylation simultaneously occurred on both alleles. Our work sheds light on the unique regulatory network underpinning SSC reprogramming, providing insights to understand generic mechanisms for cell-fate decision and epigenetic-related disorders in regenerative medicine.
Male ; Mice ; Animals ; Cellular Reprogramming/genetics* ; Tetraploidy ; Pluripotent Stem Cells/metabolism* ; Induced Pluripotent Stem Cells/metabolism* ; DNA Methylation ; Spermatogonia/metabolism* ; Germ Cells/metabolism*