Enhanced CT-Based Radiomics to Predict Micropapillary Pattern Within Lung Invasive Adenocarcinoma.

OBJECTIVE: We aimed to investigate whether enhanced CT-based radiomics can predict micropapillary pattern (MPP) of lung invasive adenocarcinoma (IAC) in the pre-op phase and to develop an individual diagnostic predictive model for MPP in IAC. METHODS: 170 patients who underwent complete resection for pathologically confirmed lung IAC were included in our study. Of these 121 were used as a training cohort and the other 49 as a test cohort. Clinical features and enhanced CT images were collected and assessed. Quantitative CT analysis was performed based on feature types including first order, shape, gray-level co-occurrence matrix-based, gray-level size zone matrix-based, gray-level run length matrix-based, gray-level dependence matrix-based, neighboring gray tone difference matrix-based features and transform types including Log, wavelet and local binary pattern. Receiver operating characteristic (ROC) and area under the curve (AUC) were used to value the ability to identify the lung IAC with MPP using these characteristics. RESULTS: Using quantitative CT analysis, one thousand three hundred and seventeen radiomics features were deciphered from R (https://www.r-project.org/). Then these radiomic features were decreased to 14 features after dimension reduction using the least absolute shrinkage and selection operator (LASSO) method in R. After correlation analysis, 5 key features were obtained and used as signatures for predicting MPP within IAC. The individualized prediction model which included age, smoking, family tumor history and radiomics signature had better identification (AUC=0.739) in comparison with the model consisting only of radiomics features (AUC=0.722). DeLong test showed that the difference in AUC between the two models was statistically significant (P<0.01). Compared with the simple radiomics model, the more comprehensive individual prediction model has better prediction performance. CONCLUSION: The use of radiomics approach is of great value in the diagnosis of tumors by non-invasive means. The individualized prediction model in the study, when incorporated with age, smoking and radiomics signature, had effective predictive performance of lung IAC with MPP lesions. The combination of imaging features and clinical features can provide additional diagnostic value to identify the micropapillary pattern in IAC and can affect clinical diagnosis and treatment.

MRI-Based Radiomics: Nomograms predicting the short-term response after transcatheter arterial chemoembolization (TACE) in hepatocellular carcinoma patients with diameter less than 5 cm.

PURPOSE: To construct MRI radiomics nomograms that can predict short-term response after TACE in HCC patients with diameter less than 5 cm. METHODS: MRI images and clinical data of 153 cases with tumor diameter less than 5 cm before TACE from 3 hospitals were collected retrospectively and divided into 1 internal training set and 1 external validation set. The T2-weighted imaging (T2WI) and dynamic contrast-enhanced MRI arterial phase (DCE-MR AP) images were studied. Multivariable logistic regression was used to construct Radiomics models, Clinics models, and Nomograms based on T2WI and DCE-MR AP, respectively. The receiver characteristic curve (ROC) was used to evaluate the predictive performance of each model. RESULTS: In this study, 113 eligible cases in Hospital 1 were collected as the training set, and 40 eligible cases in other hospitals were used as the verification set. 11 T2WI features and 11 DCE-MRI AP features with the most predictive value were finally screened. 3 models based on T2WI and 3 models based on DCE-MRI AP were established, respectively. The area under curve (AUC) value of Nomogram based on T2WI of training set and validation set was 0.83 and 0.81, respectively. The AUC value of the models based on T2WI and models based on AP was almost equal, and Nomograms were the most effective models among all three types of models. CONCLUSION: MRI-based Nomogram has greater predictive efficacy to predict the response after TACE than Radiomics and Clinics models alone, and the efficacy of T2WI-based models and DCE-MRI AP-based models was almost equal.

Transcriptionomic Study on Apoptosis of SKOV-3 Cells Induced by Phycoerythrin from Gracilaria lemaneiformis.

OBJECTIVE: To investigate the effects of Phycoerythrin (PE) on the human ovarian cancer cell line SKOV-3 and its antitumor mechanisms from a transcriptional point of view. METHODS: SKOV-3 cells were exposed to different concentrations of phycoerythrin. The efficiency of this treatment was evaluated through cell growth inhibition, changes in cell morphology, apoptosis and intracellular ROS levels. High throughput sequencing (RNA-seq) was performed to screen Differentially Expressed Genes (DEGs), which was verified using RT-PCR and Western blotting. RESULTS: PE showed a significant inhibitory effect on the growth of SKOV-3 cells in a time- and dose-dependent manner. H&E staining, electron microscopy and flow cytometry revealed that PE induced apoptosis in SKOV-3 cells. Transcriptome analysis showed that 2963 genes were differentially expressed between untreated or PEtreated cells. GO and KEGG pathway analyses identified 16 classical pathways that were enriched. We verified 8 DEGs including, JNK, GADD45A, EDEM2, RAD23, UBQLN, CAPN1, XBP1, and OS9. These results were consistent with the results from transcriptional sequences. CONCLUSION: The inhibitory effect of PE on SKOV-3 cells was a result of interaction with multiple pathways and signaling molecules. Among these, the ROS/JNK/Bcl-2 signaling pathway, upregulation of JNK, GADD45A and RAD23 as well as downregulation of XBP1 and OS9 played a critical role in the PE -induced apoptosis in human ovarian cancer cells.

Study on PD-L1 Expression in NSCLC Patients and Related Influencing Factors in the Real World.

PD-L1 is one of the current biomarkers for immune checkpoint inhibitor (ICI) therapy in patients with non-small-cell lung cancer. However, the expression of PD-L1 in the real world and its related influencing factors remain unclear. We want to observe the expression of PD-L1 in the real world and study the related influencing factors through the collection and analysis of clinical data. R software (version 4.0) was used to perform data analysis and the "corplot" package for correlation analysis. A total of 296 individuals (mean [SD] age, 67 [9] years; 23%female) were assessed. According to the expression amount of PD-L1, the cohort was divided into low nonexpression group (PD-L1 < 1%, 26.7%), low-expression group (1% ≤ PD-L1 < 50%, 49.3%), and high-expression group (PD-L1 ≥ 50%, 23.5%). Age, gender, underlying diseases, smoking status, and PD-L1 expression level were not statistically significant. We found that the expression of PD-L1 was correlated with serum albumin (P < 0.05) and pathological type (P < 0.05) and had a negative correlation with EGFR mutation but did not correlate with gender, age, smoking status, combined with underlying diseases, tumor stage, whether it was initially treated or not, sampling site, specimen type, specimen storage time, R-IFN, CD4, CD8, NLR, CRP, and LDH. The present findings indicated that serum albumin, pathological type, and EGFR mutations are associated with PD-L1 expression in patients with NSCLC, which may provide a new basis for individualized immunotherapy and need further study to confirm. The results of this study help to further reveal the actual expression of PD-L1 in non-small-cell lung cancer patients with real events.