Few studies have reported the reproducibility and stability of ultrasound (US) images based radiomics features obtained from automatic segmentation in oncology. The purpose of this study is to study the accuracy of automatic segmentation algorithms based on multiple U-net models and their effects on radiomics features from US images for patients with ovarian cancer. A total of 469 US images from 127 patients were collected and randomly divided into three groups: training sets (353 images), validation sets (23 images), and test sets (93 images) for automatic segmentation models building. Manual segmentation of target volumes was delineated as ground truth. Automatic segmentations were conducted with U-net, U-net++, U-net with Resnet as the backbone (U-net with Resnet), and CE-Net. A python 3.7.0 and package Pyradiomics 2.2.0 were used to extract radiomic features from the segmented target volumes. The accuracy of automatic segmentations was evaluated by Jaccard similarity coefficient (JSC), dice similarity coefficient (DSC), and average surface distance (ASD). The reliability of radiomics features were evaluated by Pearson correlation and intraclass correlation coefficients (ICC). CE-Net and U-net with Resnet outperformed U-net and U-net++ in accuracy performance by achieving a DSC, JSC, and ASD of 0.87, 0.79, 8.54, and 0.86, 0.78, 10.00, respectively. A total of 97 features were extracted from the delineated target volumes. The average Pearson correlation was 0.86 (95% CI, 0.83–0.89), 0.87 (95% CI, 0.84–0.90), 0.88 (95% CI, 0.86–0.91), and 0.90 (95% CI, 0.88–0.92) for U-net++, U-net, U-net with Resnet, and CE-Net, respectively. The average ICC was 0.84 (95% CI, 0.81–0.87), 0.85 (95% CI, 0.82–0.88), 0.88 (95% CI, 0.85–0.90), and 0.89 (95% CI, 0.86–0.91) for U-net++, U-net, U-net with Resnet, and CE-Net, respectively. CE-Net based segmentation achieved the best radiomics reliability. In conclusion, U-net based automatic segmentation was accurate enough to delineate the target volumes on US images for patients with ovarian cancer. Radiomics features extracted from automatic segmented targets showed good reproducibility and for reliability further radiomics investigations.

ObjectivesNon-invasive method to predict the histological subtypes preoperatively is essential for the overall management of ovarian cancer (OC). The feasibility of radiomics in the differentiating of epithelial ovarian cancer (EOC) and non-epithelial ovarian cancer (NEOC) based on computed tomography (CT) images was investigated.MethodsRadiomics features were extracted from preoperative CT for 101 patients with pathologically proven OC. Radiomics signature was built using the least absolute shrinkage and selection operator (LASSO) logistic regression. A nomogram was developed with the combination of radiomics features and clinical factors to differentiate EOC and NEOC.ResultsEight radiomics features were selected to build a radiomics signature with an area under curve (AUC) of 0.781 (95% confidence interval (CI), 0.666 -0.897) in the discrimination between EOC and NEOC. The AUC of the combined model integrating clinical factors and radiomics features was 0.869 (95% CI, 0.783 -0.955). The nomogram demonstrated that the combined model provides a better net benefit to predict histological subtypes compared with radiomics signature and clinical factors alone when the threshold probability is within a range from 0.43 to 0.97.ConclusionsNomogram developed with CT radiomics signature and clinical factors is feasible to predict the histological subtypes preoperative for patients with OC.

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The NIDD gene, neuronal NOS (nNOS)-interacting DHHC domain-containing protein with dendritic mRNA, codes a protein that upregulates nNOS enzyme activity by the interaction with the postsynaptic density protein 95/discsslarge/zon occlusens-1 (PDZ) domain of nNOS. Glial cell activation, especially Müller cells, may be an important factor contributing to retinal ganglion cell (RGC) death in glaucoma. The present study was to measure nNOS and NIDD expression in DBA/2J mice, a mouse model of glaucoma, and their correlation with glaucomatous phenotypes. Slit-lamp biomicroscopy, fundus photography, intraocular pressure (IOP) measurement, histology, and optic nerve axon counts were used to examine the ocular phenotypes of DBA/2J mice. Quantitative real-time PCR(RT-PCR) and Western blot analysis were used to analyze mRNA and protein expression of nNOS and NIDD. Their spatial distribution was evaluated by immunohistochemistry. Immunofluorescence was performed to observe the colocalization of nNOS and NIDD and the association of NIDD with Müller cells. The results showed that the prevalence and severity of ocular abnormalities, IOP, optic nerve cupping, and optic nerve atrophy increased with age. The mRNA and protein expression of nNOS reached the peak at 9 months old. The protein of NIDD underwent a similar change, while the mRNA of NIDD significantly increased at 6 months old. The expression of NIDD physically coexisted with nNOS in Müller cells. Administration of NOS inhibitor N(G)-Nitro-L-arginine-methyl-ester (L-NAME) by intraperitoneal injection (i.p.) prevented RGCs from apoptosis as shown in the increase of Brn-3a (RGC marker) expression, which was accompanied by decreased expression of NIDD. The spatiotemporal changes of nNOS/NIDD expression and its interference suggest that NIDD-nNOS axis may play a role in the degenerative process of RGC in glaucoma.