Altruistic seagull optimization algorithm enables selection of radiomic features for predicting benign and malignant pulmonary nodules.

Accurately differentiating indeterminate pulmonary nodules remains a significant challenge in clinical practice. This challenge becomes increasingly formidable when dealing with the vast radiomic features obtained from low-dose computed tomography, a lung cancer screening technique being rolling out in many areas of the world. Consequently, this study proposed the Altruistic Seagull Optimization Algorithm (AltSOA) for the selection of radiomic features in predicting the malignancy risk of pulmonary nodules. This innovative approach incorporated altruism into the traditional seagull optimization algorithm to seek a global optimal solution. A multi-objective fitness function was designed for training the pulmonary nodule prediction model, aiming to use fewer radiomic features while ensuring prediction performance. Among global radiomic features, the AltSOA identified 11 interested features, including the gray level co-occurrence matrix. This automatically selected panel of radiomic features enabled precise prediction (area under the curve = 0.8383 (95 % confidence interval 0.7862-0.8863)) of the malignancy risk of pulmonary nodules, surpassing the proficiency of radiologists. Furthermore, the interpretability, clinical utility, and generalizability of the pulmonary nodule prediction model were thoroughly discussed. All results consistently underscore the superiority of the AltSOA in predicting the malignancy risk of pulmonary nodules. And the proposed malignant risk prediction model for pulmonary nodules holds promise for enhancing existing lung cancer screening methods. The supporting source codes of this work can be found at: https://github.com/zzl2022/PBMPN.

The Effect of Precipitate Evolution on Austenite Grain Growth in RAFM Steel.

To study the effects of various types of precipitates and precipitate evolution behavior on austenite (size and phase fraction) in reduced activation ferritic/martensitic (RAFM) steel, RAFM steel was heated to various austenitizing temperatures. The microstructures of specimens were observed using optical microscopy (OM) and transmission electron microscopy (TEM). The results indicate that the M23C6 and MX precipitates gradually coarsen and dissolve into the matrix as the austenitizing temperatures increase. The M23C6 precipitates dissolve completely at 1100 °C, while the MX precipitates dissolve completely at 1200 °C. The evolution of two types of precipitate has a significant effect on the size of austenite. Based on the Zener pinning model, the effect of precipitate evolution on austenite grain size is quantified. It was found that the coarsening and dissolution of M23C6 and MX precipitates leads to a decrease in pinning pressure on grain boundaries, facilitating the rapid growth of austenite grains. The austenite phase fraction is also affected by the coarsening and dissolution of precipitates.