RESUMEN
Ectropis grisescens (Lepidoptera, Geometridae) is one of the main leaf-eating pests in tea plantations in China. In this study, the complete mitochondrial genome of this species was sequenced and assembled. The total length of the mitochondrial genome of E. grisescens was 15,794 bp (GenBank accession No. MW337302). The base composition was 41.26% for A, 39.49% for T, 7.92% for G, and 11.33% for C. The circular mitogenome contained 13 protein-coding genes, 2 ribosomal RNA genes, and 22 transfer RNA genes. Phylogenetic analysis performed using 13 protein-coding genes of 15 species of Geometridae and an out-group Pieris melete (Lepidoptera, Pieridae) showed that E. grisescens is closely related to species of E. obliqua, and this is consistent with the morphological identification.
RESUMEN
BACKGROUND: Accurate estimation of the recurrence of pancreatic neuroendocrine tumors help with prognosis, guide follow-up, and avoid futile treatments. PURPOSE: To investigate whether MRI features could preoperatively estimate the recurrence of pancreatic neuroendocrine tumors (PNETs) and to refine a novel prognostic model through developing a nomogram incorporating various MRI features. STUDY TYPE: Retrospective. POPULATION: In all, 81 patients with clinicopathologically confirmed nonmetastatic PNETs. FIELD STRENGTH/SEQUENCES: 1.5 T MR, including T1 -weighted, T2 -weighted, and diffusion-weighted imaging sequences. ASSESSMENT: Qualitative and quantitative MRI features of PNET were assessed by three experienced radiologists. STATISTICAL TESTS: Uni- and multivariable analyses for recurrence-free survival (RFS) were evaluated using a Cox proportional hazards model. The MRI-based nomogram was then designed based on multivariable logistic analysis in our study and the performance of the nomogram was validated according to C-index, calibration, and decision curve analyses. RESULTS: MRI features, including tumor size (hazard ratio [HR]: 14.131; P = 0.034), enhancement pattern (HR: 21.821, P = 0.032), and the apparent diffusion coefficient (ADC) values (HR: 0.055, P = 0.038) were significant independent predictors of RFS at multivariable analysis. The performance of the nomogram incorporating various MRI features (with a C-index of 0.910) was improved compared with that based on tumor size, enhancement pattern, and ADC alone (with C-index values of 0.672, 0.851, and 0.809, respectively). The calibration curve of the nomogram exhibited perfect consistency between estimation and observation at 0.5, 1, and 2 years after surgery. The decision curve showed that a nomogram incorporating three features had more favorable clinical predictive usefulness than any single feature. DATA CONCLUSION: MRI features can be considered effective recurrence predictors for PNETs after surgery. The preliminary nomogram incorporating various MRI features could assess the risk of recurrence in PNETs and may be used to optimize individual treatment strategies. LEVEL OF EVIDENCE: 4 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;50:397-409.