首页|Selcuk University School of Medicine Reports Findings in Machine Learning (Navig ating the gray zone: Machine learning can differentiate malignancy in PI-RADS 3 lesions)
Selcuk University School of Medicine Reports Findings in Machine Learning (Navig ating the gray zone: Machine learning can differentiate malignancy in PI-RADS 3 lesions)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Machine Learning is th e subject of a report. According to news originating from Konya, Turkey, by News Rx correspondents, research stated, “The objective of this study is to predict t he probability of prostate cancer in PI-RADS 3 lesions using machine learning me thods that incorporate clinical and mpMRI parameters. The study included patient s who had PI-RADS 3 lesions detected on mpMRI and underwent fusion biopsy betwee n January 2020 and January 2024.” Our news journalists obtained a quote from the research from the Selcuk Universi ty School of Medicine, “Radiological parameters (Apparent diffusion coefficient (ADC), tumour ADC/contralateral ADC ratio, Ktrans value, periprostatic adipose t issue thickness, lesion size, prostate volume) and clinical parameters (age, bod y mass index, total prostate specific antigen, free PSA, PSA density, systemic i nflammatory index, neutrophil-lymphocyte ratio [NLR] , platelet lymphocyte ratio, lymphocyte monocyte ratio) were documented. The pro bability of prostate cancer prediction in PI-RADS 3 lesions was calculated using 6 different machine-learning models, with the input parameters being the aforem entioned variables. Of the 235 participants in the trial, 61 had malignant fusio n biopsy pathology and 174 had benign pathology. Among 6 different machine learn ing algorithms, the random forest model had the highest accuracy (0.86±0.04; 95% CI 0.85-0.87), F1 score (0.91±0.03; 95% CI 0.91-0.92) and AUC valu e (0.92±0.06; 95% CI 0.88-0.90). In SHAP analysis based on random forest model, tumour ADC, tumour ADC/contralateral ADC ratio and PSA density wer e the 3 most successful parameters in predicting malignancy. On the other hand, systemic inflammatory index and neutrophil lymphocyte ratio showed higher accura cy in predicting malignancy than total PSA, age, free PSA/total PSA and lesion s ize in SHAP analysis. Among the machine learning models we developed, especially the random forest model can predict malignancy in PI-RADS 3 lesions and prevent unnecessary biopsy.”
KonyaTurkeyEurasiaBlood CellsCyb orgsEmerging TechnologiesHealth and MedicineHemic and Immune SystemsImmu nologyLymphocytesMachine LearningMononuclear Leukocytes
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