Deep learning and radiomics for gastric cancer serosal invasion: automated segmentation and multi-machine learning from two centers.

Objective: The objective of this study is to develop an automated method for segmenting spleen computed tomography (CT) images using a deep learning model. This approach is intended to address the limitations of manual segmentation, which is known to be susceptible to inter-observer variability. Subsequently, a prediction model of gastric cancer (GC) serosal invasion was constructed in conjunction with radiomics and deep learning features, and a nomogram was generated to explore the clinical guiding significance.

Deep Learning-based U-Mamba Model to Predict Differentiated Gastric Cancer using Radiomics Features from Spleen Segmentation.

Objective: This study aimed to develop an automated method for segmenting spleen computed tomography (CT) images using a deep learning model to address the limitations of manual segmentation, which is known to be susceptible to inter-observer variability. Subsequently, a prediction model for gastric cancer (GC) differentiation was constructed alongside radiomics, and a nomogram was generated to investigate its clinical guiding significance.

Methods: This study enrolled 262 patients with pathologically confirmed GC.

Biological treatment of mixtures of toluene and n-hexane vapours in a hollow fibre membrane bioreactor.

Membrane bioreactors are gaining interest for the control of contaminated air streams. In this study, the removal of toluene and n-hexane vapours in a hollow fibre membrane bioreactor (HFMB) was investigated. The focus was on quantifying the possible interactions occurring during the simultaneous biotreatment of the two volatile pollutants.