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. Additionally, a validation set was comprised of 22 LPA cases and 16 sPHEO cases from two other hospitals. We used 3D Slicer and PyRadiomics to segment tumors and extract radiomic features, respectively. We then applied T-test and least absolute shrinkage and selection operator (LASSO) to select features. Six binary classifiers, including K-nearest neighbor (KNN), logistic regression (LR), decision tree (DT), random forest (RF), support vector machine (SVM), and multi-layer perceptron (MLP), were employed to differentiate LPA from sPHEO. Receiver operating characteristic (ROC) curves and area under the curve (AUC) values were compared using DeLong's method. RESULTS: All six classifiers showed good diagnostic performance for each phase and slice thickness, as well as for the entire CT data, with AUC values ranging from 0.706 to 1. Non-contrast CT densities of LPA were significantly lower than those of sPHEO (P < 0.001). However, using the optimal threshold for non-contrast CT density, sensitivity was only 0.743, specificity 0.744, and AUC 0.828. Delayed phase CT density yielded a sensitivity of 0.971, specificity of 0.641, and AUC of 0.814. In radiomics, AUC values for the testing set using non-contrast CT images were: KNN 0.919, LR 0.979, DT 0.835, RF 0.967, SVM 0.979, and MLP 0.981. In the validation set, AUC values were: KNN 0.891, LR 0.974, DT 0.891, RF 0.964, SVM 0.949, and MLP 0.979. CONCLUSIONS: The machine learning model based on CT radiomics can accurately differentiate LPA from sPHEO, even using non-contrast CT data alone, making contrast-enhanced CT unnecessary for diagnosing LPA and sPHEO.

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. According to the temperature of the intersection, OC needs to be lowered to less than 5% when preventing spontaneous combustion of coal. The chain thermal reaction lags in the lean-oxygen environment, and the pyrolysis activity is significantly reduced. Meanwhile, the temperature points at T 6 and T 7 show significant differences. Furthermore, with increasing OC and temperature, the content of the aliphatic hydrocarbon presents an overall trend of first increasing, then decreasing, and continuously increasing after stage IV. It is concluded that •OH, aliphatic hydrocarbons, aromatic hydrocarbons, and carboxyl groups are the key groups for the coal spontaneous combustion evolution under DOC. To combine the spontaneous combustion reaction of coal in the DOC environment, the reaction path of the index gas in the macroscopic phenomenon and the reason for the concentration differences are revealed, the mechanism for exotherm varies caused by OC is clarified, and the microscopic inhibition affection on the chain reaction within the lean-oxygen environment is also explored. The results put forward the key groups evolution mechanism under the DOC for coal oxidation, which could provide the technical guidance for the fire prevention and control on coal mines.