Development, comparison, and validation of four intelligent, practical machine learning models for patients with prostate-specific antigen in the gray zone

Abstract

PurposeMachine learning prediction models based on LogisticRegression, XGBoost, GaussianNB, and LGBMClassifier for patients in the prostate-specific antigen gray zone are to be developed and compared, identifying valuable predictors. Predictive models are to be integrated into actual clinical decisions.MethodsPatient information was collected from December 01, 2014 to December 01, 2022 from the Department of Urology, The First Affiliated Hospital of Nanchang University. Patients with a pathological diagnosis of prostate hyperplasia or prostate cancer (any PCa) and having a prostate-specific antigen (PSA) level of 4–10 ng/mL before prostate puncture were included in the initial information collection. Eventually, 756 patients were selected. Age, total prostate-specific antigen (tPSA), free prostate-specific antigen (fPSA), fPSA/tPSA, prostate volume (PV), prostate-specific antigen density (PSAD), (fPSA/tPSA)/PSAD, and the prostate MRI results of these patients were recorded. After univariate and multivariate logistic analyses, statistically significant predictors were screened to build and compare machine learning models based on LogisticRegression, XGBoost, GaussianNB, and LGBMClassifier to determine more valuable predictors.ResultsMachine learning prediction models based on LogisticRegression, XGBoost, GaussianNB, and LGBMClassifier exhibit higher predictive power than individual metrics. The area under the curve (AUC) (95% CI), accuracy, sensitivity, specificity, positive predictive value, negative predictive value, and F1 score of the LogisticRegression machine learning prediction model were 0.932 (0.881–0.983), 0.792, 0.824, 0.919, 0.652, 0.920, and 0.728, respectively; of the XGBoost machine learning prediction model were 0.813 (0.723–0.904), 0.771, 0.800, 0.768, 0.737, 0.793 and 0.767, respectively; of the GaussianNB machine learning prediction model were 0.902 (0.843–0.962), 0.813, 0.875, 0.819, 0.600, 0.909, and 0.712, respectively; and of the LGBMClassifier machine learning prediction model were 0.886 (0.809–0.963), 0.833, 0.882, 0.806, 0.725, 0.911, and 0.796, respectively. The LogisticRegression machine learning prediction model has the highest AUC among all prediction models, and the difference between the AUC of the LogisticRegression prediction model and those of XGBoost, GaussianNB, and LGBMClassifier is statistically significant (p < 0.001).ConclusionMachine learning prediction models based on LogisticRegression, XGBoost, GaussianNB, and LGBMClassifier algorithms exhibit superior predictability for patients in the PSA gray area, with the LogisticRegression model yielding the best prediction. The aforementioned predictive models can be used for actual clinical decision-making.​

Diagnostic performance of MRI in detecting prostate cancer in patients with prostate-specific antigen levels of 4–10 ng/mL: a systematic review and meta-analysis

“…The MRI characteristics are summarized in Table 3 . Eleven studies entailed the use of 3-T scanners [ 17 – 19 , 21 – 23 , 28 , 29 , 31 , 32 , 34 ], six studies used 1.5-T scanners [ 20 , 24 – 27 , 30 ], one study used both [ 16 ]; one article did not provide relevant explanations [ 33 ]. The relevant elements are described in detail in the Supplemental Materials (S-4) .…”

GeneAI 3.0: powerful, novel, generalized hybrid and ensemble deep learning frameworks for miRNA species classification of stationary patterns from nucleotides

“…For simplicity and availability, we selected the following ML models: LR 115,116 , Linear SVM [117][118][119] , Decision Tree (DT) 120 , RF [121][122][123] , Extra Trees (ET) 124,125 , Extreme Gradient Boost (XGBoost) 88,126 , K-Nearest Neighbors (KNN) 127,128 , Linear Discriminant Analysis (LDA) 129,130 , Light Gradient Boosting Machine (LGBM) 131,132 , and Naive Bayes (NB) 133 . We specifically chose six nonlinear models (DT, RF, ET, XGBoost, KNN, LGBM) as they are suitable for nonlinear classification tasks, which is crucial for effectively classifying binary-encoded miRNA species.…”