首页|Shandong Provincial Hospital Reports Findings in Neuroendocrine Cancer (Identifi cation of Prolactinoma in Pituitary Neuroendocrine Tumors Using Radiomics Analys is Based on Multiparameter MRI)
Shandong Provincial Hospital Reports Findings in Neuroendocrine Cancer (Identifi cation of Prolactinoma in Pituitary Neuroendocrine Tumors Using Radiomics Analys is Based on Multiparameter MRI)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Oncology - Neuroendocr ine Cancer is the subject of a report. According to news originating from Jinan, People's Republic of China, by NewsRx correspondents, research stated, "This st udy aims to investigate the feasibility of preoperatively predicting histologica l subtypes of pituitary neuroendocrine tumors (PitNETs) using machine learning a nd radiomics based on multiparameter MRI. Patients with PitNETs from January 201 6 to May 2022 were retrospectively enrolled from four medical centers." Our news journalists obtained a quote from the research from Shandong Provincial Hospital, "A cfVBNet network was used to automatically segment PitNET multipar ameter MRI. Radiomics features were extracted from the MRI, and the radiomics sc ore (Radscore) of each patient was calculated. To predict histological subtypes, the Gaussian process (GP) machine learning classifier based on radiomics featur es was performed. Multi-classification (six-class histological subtype) and bina ry classification (PRL vs. non-PRL) GP model was constructed. Then, a clinical-r adiomics nomogram combining clinical factors and Radscores was constructed using the multivariate logistic regression analysis. The performance of the models wa s evaluated using receiver operating characteristic (ROC) curves. The PitNET aut osegmentation model eventually achieved the mean Dice similarity coefficient of 0.888 in 1206 patients (mean age 49.3 ± SD years, 52% female). In the multi-classification model, the GP of T2WI got the best area under the ROC curve (AUC), with 0.791, 0.801, and 0.711 in the training, validation, and exter nal testing set, respectively. In the binary classification model, the GP of T2W I combined with CE T1WI demonstrated good performance, with AUC of 0.936, 0.882, and 0.791 in training, validation, and external testing sets, respectively. In the clinical-radiomics nomogram, Radscores and Hardy' grade were identified as p redictors for PRL expression."
JinanPeople's Republic of ChinaAsiaBrain Diseases and ConditionsCancerCentral Nervous System Diseases and Cond itionsCyborgsEmerging TechnologiesEndocrine Gland NeoplasmsEndocrine Sys tem Diseases and ConditionsEndocrinologyHealth and MedicineHypothalamic Di seases and ConditionsMachine LearningNeuroendocrine CancerNeuroendocrine T umorsOncologyPituitary Diseases and ConditionsPituitary NeoplasmsProlact inoma
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