首页|Shandong University Reports Findings in Brain Abscess (Diffusionweighted imagin g-based radiomics model using automatic machine learning to differentiate cerebr al cystic metastases from brain abscesses)
Shandong University Reports Findings in Brain Abscess (Diffusionweighted imagin g-based radiomics model using automatic machine learning to differentiate cerebr al cystic metastases from brain abscesses)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Central Nervous System Diseases and Conditions - Brain Abscess is the subject of a report. According t o news reporting originating in Shandong, People's Republic of China, by NewsRx journalists, research stated, "To develop a radiomics model based on diffusion-w eighted imaging (DWI) utilizing automated machine learning method to differentia te cerebral cystic metastases from brain abscesses. A total of 186 patients with cerebral cystic metastases (n = 98) and brain abscesses (n = 88) from two clini cal institutions were retrospectively included." The news reporters obtained a quote from the research from Shandong University, "The datasets (129 from institution A) were randomly portioned into separate 75% training and 25% internal testing sets. Radiomics features were ex tracted from DWI images using two subregions of the lesion (cystic core and soli d wall). A thorough image preprocessing method was applied to DWI images to ensu re the robustness of radiomics features before feature extraction. Then the Tree -based Pipeline Optimization Tool (TPOT) was utilized to search for the best opt imized machine learning pipeline, using a fivefold cross-validation in the train ing set. The external test set (57 from institution B) was used to evaluate the model's performance. Seven distinct TPOT models were optimized to distinguish be tween cerebral cystic metastases and abscesses either based on different feature s combination or using wavelet transform. The optimal model demonstrated an AUC of 1.00, an accuracy of 0.97, sensitivity of 1.00, and specificity of 0.93 in th e internal test set, based on the combination of cystic core and solid wall radi omics signature using wavelet transform. In the external test set, this model re ached 1.00 AUC, 0.96 accuracy, 1.00 sensitivity, and 0.93 specificity."
ShandongPeople's Republic of ChinaAs iaBrain AbscessBrain Diseases and ConditionsCentral Nervous System Disease s and ConditionsCentral Nervous System InfectionsCyborgsEmerging Technolog iesHealth and MedicineMachine LearningSuppuration
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