Development and Validation of a CT-Based Signature for the Prediction of Distant Metastasis Before Treatment of Non-Small Cell Lung Cancer.

RATIONALE AND OBJECTIVES: To develop and validate a radiomics model, a clinical-semantic model and a combined model by using standard methods for the pretreatment prediction of distant metastasis (DM) in patients with non-small-cell lung cancer (NSCLC) and to explore whether the combined model provides added value compared to the individual models. MATERIALS AND METHODS: This retrospective study involved 356 patients with NSCLC. According to the image biomarker standardization initiative reference manual, we standardized the image processing and feature extraction using in-house software. Finally, 6692 radiomics features were extracted from each lesion based on contrast-enhanced chest CT images. The least absolute shrinkage selection operator and the recursive feature elimination algorithm were used to select features. The logistic regression classifier was used to build the model. Three models (radiomics model, clinical-semantic model and combined model) were constructed to predict DM in NSCLC. Area under the receiver operating characteristic curves were used to validate the ability of the three models to predict DM. A visual nomogram based on the combined model was developed for DM risk assessment in each patient. RESULTS: The receiver operating characteristic curve showed predictive performance for DM of the radiomics model (area under the curve [AUC] values for training and validation were 0.76 [95% CI, 0.704 - 0.820] and 0.76 [95% CI, 0.653 - 0.858], respectively). The combined model had AUCs of 0.78 (95% CI, 0.723 - 0.835) and 0.77 (95% CI, 0.673 - 0.870) in the training and validation cohorts, respectively. Both the radiomics model and combined model performed better than the clinical-semantic model (0.70 [95% CI, 0.634 - 0.760] and 0.67 [95% CI, 0.554 - 0.787] in the training and validation cohorts, respectively). CONCLUSION: The radiomics model and combined model may be useful for the prediction of DM in patients with NSCLC.

Predicting Progression-Free Survival Using MRI-Based Radiomics for Patients With Nonmetastatic Nasopharyngeal Carcinoma.

Objectives: This study aimed to explore the predictive value of MRI-based radiomic model for progression-free survival (PFS) in nonmetastatic nasopharyngeal carcinoma (NPC). Methods: A total of 327 nonmetastatic NPC patients [training cohort (n = 230) and validation cohort (n = 97)] were enrolled. The clinical and MRI data were collected. The least absolute shrinkage selection operator (LASSO) and recursive feature elimination (RFE) were used to select radiomic features. Five models [Model 1: clinical data, Model 2: overall stage, Model 3: radiomics, Model 4: radiomics + overall stage, Model 5: radiomics + overall stage + Epstein-Barr virus (EBV) DNA] were constructed. The prognostic performances of these models were evaluated by Harrell's concordance index (C-index). The Kaplan-Meier method was applied for the survival analysis. Results: Model 5 incorporating radiomics, overall stage, and EBV DNA yielded the highest C-indices for predicting PFS in comparison with Model 1, Model 2, Model 3, and Model 4 (training cohorts: 0.805 vs. 0.766 vs. 0.749 vs. 0.641 vs. 0.563, validation cohorts: 0.874 vs. 0.839 vs. 836 vs. 0.689 vs. 0.456). The survival curve showed that the high-risk group yielded a lower PFS than the low-risk group. Conclusions: The model incorporating radiomics, overall stage, and EBV DNA showed better performance for predicting PFS in nonmetastatic NPC patients.

Phytophthora capsici homologue of the cell cycle regulator SDA1 is required for sporangial morphology, mycelial growth and plant infection.

SDA1 encodes a highly conserved protein that is widely distributed in eukaryotic organisms. SDA1 is essential for cell cycle progression and organization of the actin cytoskeleton in yeasts and humans. In this study, we identified a Phytophthora capsici orthologue of yeast SDA1, named PcSDA1. In P. capsici, PcSDA1 is strongly expressed in three asexual developmental states (mycelium, sporangia and germinating cysts), as well as late in infection. Silencing or overexpression of PcSDA1 in P. capsici transformants affected the growth of hyphae and sporangiophores, sporangial development, cyst germination and zoospore release. Phalloidin staining confirmed that PcSDA1 is required for organization of the actin cytoskeleton. Moreover, 4',6-diamidino-2-phenylindole (DAPI) staining and PcSDA1-green fluorescent protein (GFP) fusions revealed that PcSDA1 is involved in the regulation of nuclear distribution in hyphae and sporangia. Both silenced and overexpression transformants showed severely diminished virulence. Thus, our results suggest that PcSDA1 plays a similar role in the regulation of the actin cytoskeleton and nuclear division in this filamentous organism as in non-filamentous yeasts and human cells.

Characterization of necrosis-inducing NLP proteins in Phytophthora capsici.

BACKGROUND: Effector proteins function not only as toxins to induce plant cell death, but also enable pathogens to suppress or evade plant defense responses. NLP-like proteins are considered to be effector proteins, and they have been isolated from bacteria, fungi, and oomycete plant pathogens. There is increasing evidence that NLPs have the ability to induce cell death and ethylene accumulation in plants. RESULTS: We evaluated the expression patterns of 11 targeted PcNLP genes by qRT-PCR at different time points after infection by P. capsici. Several PcNLP genes were strongly expressed at the early stages in the infection process, but the expression of other PcNLP genes gradually increased to a maximum at late stages of infection. The genes PcNLP2, PcNLP6 and PcNLP14 showed the highest expression levels during infection by P. capsici. The necrosis-inducing activity of all targeted PcNLP genes was evaluated using heterologous expression by PVX agroinfection of Capsicum annuum and Nicotiana benthamiana and by Western blot analysis. The members of the PcNLP family can induce chlorosis or necrosis during infection of pepper and tobacco leaves, but the chlorotic or necrotic response caused by PcNLP genes was stronger in pepper leaves than in tobacco leaves. Moreover, PcNLP2, PcNLP6, and PcNLP14 caused the largest chlorotic or necrotic areas in both host plants, indicating that these three genes contribute to strong virulence during infection by P. capsici. This was confirmed through functional evaluation of their silenced transformants. In addition, we further verified that four conserved residues are putatively active sites in PcNLP1 by site-directed mutagenesis. CONCLUSIONS: Each targeted PcNLP gene affects cells or tissues differently depending upon the stage of infection. Most PcNLP genes could trigger necrotic or chlorotic responses when expressed in the host C. annuum and the non-host N. benthamiana. Individual PcNLP genes have different phytotoxic effects, and PcNLP2, PcNLP6, and PcNLP14 may play important roles in symptom development and may be crucial for virulence, necrosis-inducing activity, or cell death during infection by P. capsici.