Dual-energy CT-based radiomics for predicting invasiveness of lung adenocarcinoma appearing as ground-glass nodules.

Objectives: To explore the value of radiomics based on Dual-energy CT (DECT) for discriminating preinvasive or MIA from IA appearing as GGNs before surgery. Methods: The retrospective study included 92 patients with lung adenocarcinoma comprising 30 IA and 62 preinvasive-MIA, which were further divided into a training (n=64) and a test set (n=28). Clinical and radiographic features along with quantitative parameters were recorded. Radiomics features were derived from virtual monoenergetic images (VMI), including 50kev and 150kev images. Intraclass correlation coefficients (ICCs), Pearson's correlation analysis and least absolute shrinkage and selection operator (LASSO) penalized logistic regression were conducted to eliminate unstable and redundant features. The performance of the models was evaluated by area under the curve (AUC) and the clinical utility was assessed using decision curve analysis (DCA). Results: The DECT-based radiomics model performed well with an AUC of 0.957 and 0.865 in the training and test set. The clinical-DECT model, comprising sex, age, tumor size, density, smoking, alcohol, effective atomic number, and normalized iodine concentration, had an AUC of 0.929 in the training and 0.719 in the test set. In addition, the radiomics model revealed a higher AUC value and a greater net benefit to patients than the clinical-DECT model. Conclusion: DECT-based radiomics features were valuable in predicting the invasiveness of GGNs, yielding a better predictive performance than the clinical-DECT model.

Automatic segmentation of prostate magnetic resonance imaging using generative adversarial networks.

BACKGROUND: Automatic and detailed segmentation of the prostate using magnetic resonance imaging (MRI) plays an essential role in prostate imaging diagnosis. Traditionally, prostate gland was manually delineated by the clinician in a time-consuming process that requires professional experience of the observer. Thus, we proposed an automatic prostate segmentation method, called SegDGAN, which is based on a classic generative adversarial network model. MATERIAL AND METHODS: The proposed method comprises a fully convolutional generation network of densely con- nected blocks and a critic network with multi-scale feature extraction. In these computations, the objective function is optimized using mean absolute error and the Dice coefficient, leading to improved accuracy of segmentation results and correspondence with the ground truth. The common and similar medical image segmentation networks U-Net, FCN, and SegAN were selected for qualitative and quantitative comparisons with SegDGAN using a 220-patient dataset and the public datasets. The commonly used segmentation evaluation metrics DSC, VOE, ASD, and HD were used to compare the accuracy of segmentation between these methods. RESULTS: SegDGAN achieved the highest DSC value of 91.66%, the lowest VOE value of 15.28%, the lowest ASD values of 0.51 mm and the lowest HD value of 11.58 mm with the clinical dataset. In addition, the highest DSC value, and the lowest VOE, ASD and HD values obtained with the public data set PROMISE12 were 86.24%, 23.60%, 1.02 mm and 7.57 mm, respectively. CONCLUSIONS: Our experimental results show that the SegDGAN model have the potential to improve the accuracy of MRI-based prostate gland segmentation. Code has been made available at: https://github.com/w3user/SegDGAN.

Cross-section measurements for the reactions of 14 MeV neutrons on indium isotopes.

The cross sections for the reactions (115)In(n, p)(115g)Cd, (115)In(n, alpha)(112)Ag, (115)In(n, 2n)(114m)In, (113)In(n, 2n)(112m)In, (115)In(n, n')(115m)In, and (113)In(n, n')(113m)In induced by 14 MeV neutrons have been measured by activation relative to the (27)Al(n, alpha)(24)Na. Measurements were carried out by gamma-detection using a coaxial HPGe detector. As samples, natural indium has been used. The fast neutrons were produced by the T(d, n)(4)He reaction. The results obtained are compared with existing data.