Machine learning for differentiation of lipid-poor adrenal adenoma and subclinical pheochromocytoma based on multiphase CT imaging radiomics.

Background: There is a paucity of research investigating the application of machine learning techniques for distinguishing between lipid-poor adrenal adenoma (LPA) and subclinical pheochromocytoma (sPHEO) based on radiomic features extracted from non-contrast and dynamic contrast-enhanced computed tomography (CT) scans of the abdomen.

Methods: We conducted a retrospective analysis of multiphase spiral CT scans, including non-contrast, arterial, venous, and delayed phases, as well as thin- and thick-thickness images from 134 patients with surgically and pathologically confirmed. A total of 52 patients with LPA and 44 patients with sPHEO were randomly assigned to training/testing sets in a 7:3 ratio.

Thermal Properties and Key Groups Evolution of Low-Temperature Oxidation for Bituminous Coal under Lean-Oxygen Environment.

To deeply explore the spontaneous combustion disaster of coal caused by air leakage and oxygen supply, low-temperature coal oxidation experiments under different oxygen concentrations (DOC) were carried out. Within the coal spontaneous combustion characteristic measurement system, a synchronous thermal analyzer (STA) and a Fourier transform infrared spectrometer (FTIR), the macro laws of gas and heat generation under DOC are analyzed, and the mechanism of the development of coal spontaneous combustion restricted by the lean-oxygen environment is also revealed. The results show that the change of oxygen concentration (OC) does not affect the critical temperature value and gas index change trend, but the lean-oxygen environment reduces the gas concentration and heat production rate very obviously.