A Machine Learning Model for Prostate Cancer Prediction in Korean Men

Development and Internal Validation of a Prostate Health Index Based Nomogram for Predicting Prostate Cancer at Extended Biopsy

12074 Background: Skeletal-related events (SREs) are detrimental clinical events in bone metastasis from advanced cancer, defined by pathologic fracture, spinal cord compression, and inevitable surgical or radiational intervention to the bone. Given their negative impact on quality of life and prognosis and interpatient heterogeneity in the SRE risk, accurate identification of patients with high SRE risk is critical. Methods: The patient-level data from three randomized clinical trials that administered zoledronic acid (ZA) to patients with bone-metastatic breast cancer, castration-resistant prostate cancer (CRPC), and other types of cancer were analyzed. (N = 460, 452, and 315, respectively) Machine learning (ML) models to predict SREs within 18 months (breast cancer), 12 months (CRPC), and 9 months (other cancers) were developed based on more than 40 baseline clinical and laboratory data. Seven ML algorithms and five feature selection methods were utilized to develop multiple models. The ML model with the best performance was identified based on the F1 score and the area under the receiver operating characteristic curve (AUC-ROC) for each cancer type and interrogated for important features with Shapley additive explanations. Lastly, the ML models’ ability to stratify patients by the cumulative SRE risk was evaluated by calculating hazard ratio (HR) with Cox-proportional hazards models. Results: Among the multiple ML models developed with different algorithms and feature selection methods, the model developed utilizing the random forests algorithm and the Boruta method for selecting features demonstrated the best performance in all types of cancer (F1 0.70, 0.67, and 0.67, and AUC-ROC 0.72, 0.68, and 0.73 for breast cancer, CRPC, and other cancers, respectively). In the ML model for breast cancer, performance status (PS), history of SRE, serum alkaline phosphatase (ALP), history of anti-neoplastic surgery, radiation therapy, and pathologic fracture were included as important features. Serum ALP, albumin, sodium, Gleason scores, and geographic regions were shown to be relevant in the CRPC model. For other cancers, serum ALP, albumin, total protein, phosphorus, red blood cell count, white blood cell count, visceral metastases, and history of arthritis were incorporated in the ML model. The ML model prediction successfully stratified the patients for cumulative SRE risk in all three cohorts. (HR with 95% confidence interval: 2.43 [1.86 – 3.18], 1.92 [1.51 – 2.45], and 3.06 [2.29 – 4.09] for breast cancer, CRPC, and other types of cancer, respectively). Conclusions: ML models incorporating baseline clinical and laboratory data can identify patients with bone metastasis on ZA harboring a high SRE risk.

Background and purpose — External validation of machine learning (ML) prediction models is an essential step before clinical application. We assessed the proportion, performance, and transparent reporting of externally validated ML prediction models in orthopedic surgery, using the Transparent Reporting for Individual Prognosis or Diagnosis (TRIPOD) guidelines. Material and methods — We performed a systematic search using synonyms for every orthopedic specialty, ML, and external validation. The proportion was determined by using 59 ML prediction models with only internal validation in orthopedic surgical outcome published up until June 18, 2020, previously identified by our group. Model performance was evaluated using discrimination, calibration, and decision-curve analysis. The TRIPOD guidelines assessed transparent reporting. Results — We included 18 studies externally validating 10 different ML prediction models of the 59 available ML models after screening 4,682 studies. All external validations identified in this review retained good discrimination. Other key performance measures were provided in only 3 studies, rendering overall performance evaluation difficult. The overall median TRIPOD completeness was 61% (IQR 43–89), with 6 items being reported in less than 4/18 of the studies. Interpretation — Most current predictive ML models are not externally validated. The 18 available external validation studies were characterized by incomplete reporting of performance measures, limiting a transparent examination of model performance. Further prospective studies are needed to validate or refute the myriad of predictive ML models in orthopedics while adhering to existing guidelines. This ensures clinicians can take full advantage of validated and clinically implementable ML decision tools.

The use of serum [-2]proPSA (p2PSA) and its derivative, the prostate health index (PHI), in detecting prostate cancer (PCa) have been consistently shown to have better performance than total prostate-specific antigen (tPSA) in discriminating biopsy outcomes in western countries. However, little is known about their performance in Chinese men. Our objective is to test the performance of p2PSA and PHI and their added value to tPSA in discriminating biopsy outcomes in Chinese men. Consecutive patients who underwent prostate biopsy in three tertiary hospitals in Shanghai, China during 2012-2013 were recruited. Serum tPSA, free PSA (fPSA), and p2PSA were measured centrally using Beckman Coulter's DxI 800 Immunoassay System. The primary outcome is PCa and the secondary outcome is high-grade PCa (Gleason Score of 4 + 3 or worse). Discriminative performance was assessed using the area under the receiver operating characteristic curve (AUC), detection rate and Decision Curve Analysis (DCA). Among 636 patients who underwent prostate biopsy, PHI was a significant predictor of biopsy outcomes, independent of other clinical variables. The AUC in discriminating PCa from non-PCa was consistently higher for PHI than tPSA in the entire cohort (0.88 vs. 0.81) as well as in patients with tPSA at 2-10 ng/ml (0.73 vs. 0.53), at 10.1-20 ng/ml (0.81 vs. 0.58), and at tPSA >20 ng/ml (0.90 vs. 0.80). The differences were statistically significant in all comparisons, P < 0.01. To detect 90% of all PCa in the cohort, 362 and 457 patients would need to be biopsied based on PHI and tPSA cutoff, respectively, a 21% reduction for PHI. Similar results were found for discriminating high-grade PCa. PHI provides added value over tPSA in discriminating PCa and high-grade PCa in patients who underwent prostate biopsy in China.

At present, increased early detection of prostate cancer appears to be the most feasible way to reduce cancer-related mortality. As a result significant efforts have been made to identify more men with curable cancer. This article reviews the role of serum prostate-specific antigen in an early detection or screening strategy and describes efforts to enhance the specificity of prostate-specific antigen testing.

Machine learning (ML) models provide more choices to patients with diabetes mellitus (DM) to more properly manage blood glucose (BG) levels. However, because of numerous types of ML algorithms, choosing an appropriate model is vitally important. In a systematic review and network meta-analysis, this study aimed to comprehensively assess the performance of ML models in predicting BG levels. In addition, we assessed ML models used to detect and predict adverse BG (hypoglycemia) events by calculating pooled estimates of sensitivity and specificity. PubMed, Embase, Web of Science, and Institute of Electrical and Electronics Engineers Explore databases were systematically searched for studies on predicting BG levels and predicting or detecting adverse BG events using ML models, from inception to November 2022. Studies that assessed the performance of different ML models in predicting or detecting BG levels or adverse BG events of patients with DM were included. Studies with no derivation or performance metrics of ML models were excluded. The Quality Assessment of Diagnostic Accuracy Studies tool was applied to assess the quality of included studies. Primary outcomes were the relative ranking of ML models for predicting BG levels in different prediction horizons (PHs) and pooled estimates of the sensitivity and specificity of ML models in detecting or predicting adverse BG events. In total, 46 eligible studies were included for meta-analysis. Regarding ML models for predicting BG levels, the means of the absolute root mean square error (RMSE) in a PH of 15, 30, 45, and 60 minutes were 18.88 (SD 19.71), 21.40 (SD 12.56), 21.27 (SD 5.17), and 30.01 (SD 7.23) mg/dL, respectively. The neural network model (NNM) showed the highest relative performance in different PHs. Furthermore, the pooled estimates of the positive likelihood ratio and the negative likelihood ratio of ML models were 8.3 (95% CI 5.7-12.0) and 0.31 (95% CI 0.22-0.44), respectively, for predicting hypoglycemia and 2.4 (95% CI 1.6-3.7) and 0.37 (95% CI 0.29-0.46), respectively, for detecting hypoglycemia. Statistically significant high heterogeneity was detected in all subgroups, with different sources of heterogeneity. For predicting precise BG levels, the RMSE increases with a rise in the PH, and the NNM shows the highest relative performance among all the ML models. Meanwhile, current ML models have sufficient ability to predict adverse BG events, while their ability to detect adverse BG events needs to be enhanced. PROSPERO CRD42022375250; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=375250.

Comparative Performance of Machine Learning Models in Reducing Unnecessary Targeted Prostate Biopsies.

PD27-01 DEVELOPMENT OF A MACHINE LEARNING (ML) MODEL TO AUTOMATICALLY AND PRECISELY IDENTIFY KIDNEY STONES FROM URETEROSCOPY VIDEO RECORDINGS

Improve clinical feature-based bladder cancer survival prediction models through integration with gene expression profiles and machine learning techniques

Prostate cancer (PCa) diagnosis is often hindered by the need to detect clinically significant disease (csPCa) while minimizing unnecessary biopsies. The Prostate Health Index (PHI) and multiparametric magnetic resonance imaging (mpMRI) are promising tools to address these challenges. To develop and internally validate a predictive model for PCa and csPCa by combining PHI and mpMRI in a high-risk population. This retrospective study included 179 patients who underwent prostate biopsy between 2019 and 2023. Inclusion criteria comprised elevated PSA (> 3 ng/mL), suspicious digital rectal examination and/or family history, PHI values, and pre-biopsy mpMRI. Logistic regression models were developed, and model performance was assessed using C-statistics, calibration plots, and decision curve analysis (DCA). PCa was diagnosed in 40.2% of patients, and csPCa in 34.7% of them. A multivariate model including PHI, prostate volume, and mpMRI achieved an AUC of 0.81 for PCa. For csPCa, the best model combined PHI and prostate volume (AUC 0.76). In the PI-RADS 3 subgroup, PHI showed high discriminatory performance (AUC 0.81), surpassing PSA density (PSA-D). The DCA showed a superior net benefit of the multivariable models compared to single-parameter strategies. Integrating PHI and mpMRI improves PCa diagnostic accuracy and clinical decision-making, especially in ambiguous cases such as PI-RADS 3 lesions, and reduces unnecessary biopsies in clinical practice.

BackgroundAcute coronary syndromes (ACS) are the leading cause of global death. Optimizing mortality risk prediction and early identification of high-risk patients is essential for developing targeted prevention strategies. Many researchers have built machine learning (ML) models to predict the mortality risk in ACS patients. Our meta-analysis aimed to evaluate the predictive value of various ML models in predicting death in ACS patients at different times.MethodsPubMed, Embase, Web of Science, and Cochrane Library were searched systematically from database establishment to March 12, 2022 for studies developing or validating at least one ML predictive model for death in ACS patients. We used PROBAST to assess the risk of bias in the reported predictive models and a random-effects model to assess the pooled C-index and accuracy of these models.ResultsFifty papers were included, involving 216 ML prediction models, 119 of which were externally validated. The combined C-index of the ML models in the validation cohort predicting the in-hospital mortality, 30-day mortality, 3- or 6-month mortality, and 1 year or above mortality in ACS patients were 0.8633 (95% CI 0.8467–0.8802), 0.8296 (95% CI 0.8134–0.8462), 0.8205 (95% CI 0.7881–0.8541), and 0.8197 (95% CI 0.8042–0.8354), respectively, with the corresponding combined accuracy of 0.8569 (95% CI 0.8411–0.8715), 0.8282 (95% CI 0.7922–0.8591), 0.7303 (95% CI 0.7184–0.7418), and 0.7837 (95% CI 0.7455–0.8175), indicating that the ML models were relatively excellent in predicting ACS mortality at different times. Furthermore, common predictors of death in ML models included age, sex, systolic blood pressure, serum creatinine, Killip class, heart rate, diastolic blood pressure, blood glucose, and hemoglobin.ConclusionsThe ML models had excellent predictive power for mortality in ACS, and the methodologies may need to be addressed before they can be used in clinical practice.

Objective To investigate interactions between prostate volume (PV) and the detection of prostate cancer in prostate biopsy. Methods We performed a review of a cohort of 145 biopsy patients, because of increased prostate-specific antigen (PSA) serum levels (≥10 ng/mL, or 4 ng/mL≤tPSA<10 ng/mL and f/t≤0.16), and suspicious findings on digital rectal examination , or/and suspected MRI signals on prostate in the years between Jan 2014 and July 2017. Clinic data was summarized including ages, tPSA, PV and PSAD. Results The cancer positive rates of prostate biopsy were 53%, 33% and 17% in PV groups of <25 mL (Ⅰdegree), 25~55 mL (Ⅱ degree)and ≥55 mL (Ⅲ degree), which showed statistic different (χ2= 9.1653, P= 0.010) by chi-square test. There was a trend that positive rates of prostate biopsy were decreased with increasing prostate volume proven by Cochran-Armitage test (Z=2.9948, P=0.003). Multipule logistic analysis that PV was an independent risk factor for prostate cancer in prostate biopsy that Odds ratios were 6.268(95%CI, 1.802-21.805, P=0.004)and 2.444(95%CI: 0.962-6.205, P=0.060)on Ⅰ/Ⅲ and Ⅱ/Ⅲ degree prostate. Conclusions The positive rate of prostate biopsy is elevated with smaller prostate volume which is a disk factor for prostate cancer in patients with tPSA<100 ng/mL. Key words: Prostatic Neoplasms; Punctures; Risk Factors

MP60-12 GENETIC POLYMORPHISMS AT 19q13.33 ARE ASSOCIATED WITH [-2]proPSA LEVELS AND PROVIDE ADDITIONAL PREDICTIVE VALUE TO PROSTATE HEALTH INDEX FOR PROSTATE CANCER

Background: Beyond prostate-specific antigen (PSA), other biomarkers for prostate cancer (PCa) detection are available and need to be evaluated for clinical routine. Objective: The aim of the study was to evaluate the Prostate Health Index (PHI) density (PHID) in comparison with PHI in a large Caucasian group >1,000 men. Methods: PHID values were used from available patient data with PSA, free PSA, and [−2]pro­PSA and prostate volume from 3 former surveys from 2002 to 2014. Those 1,446 patients from a single-center cohort included 701 men with PCa and 745 with no PCa. All patients received initial or repeat biopsies. The diagnostic accuracy was evaluated by receiver operating characteristic (ROC) curves comparing area under the ROC curves (AUCs), precision-recall approach, and decision curve analysis (DCA). Results: PHID medians differed almost 2-fold between PCa (1.12) and no PCa (0.62) in comparison to PHI (48.6 vs. 33; p always <0.0001). However, PHID and PHI were equal regarding the AUC (0.737 vs. 0.749; p = 0.226), and the curves of the precision-recall analysis also overlapped in the sensitivity range between 70 and 100%. DCA had a maximum net benefit of only ∼5% for PHID versus PHI between 45 and 55% threshold probability. Contrary, in the 689 men with a prostate volume ≤40 cm³, PHI (AUC 0.732) showed a significant larger AUC than PHID (AUC 0.69, p = 0.014). Conclusions: Based on DCA, PHID had only a small advantage in comparison with PHI alone, while ROC analysis and precision-recall analysis showed similar results. In smaller prostates, PHI even outperformed PHID. The increment for PHID in this large Caucasian cohort is too small to justify a routine clinical use.

Objective Developing a follow-up strategy to reduce unnecessary prostate biopsies in patients with prostate specific antigen (PSA) ranging from 4 to 10 ng/ml based on the new model. Methods In the first stage, 436 patients with PSA ranging from 4 to 10 ng/ml who had undergone transrectal ultrasound (TRUS)-guided prostate biopsy were evaluated. PSA, free PSA (fPSA), digital rectal examination (DRE) findings, ultrasonic hypoechoic mass, ultrasonic microcalcifications, prostate volume (PV), PSA density (PSAD) and age, were regarded as predictive factors. In order to select independent predictors, a multiple logistic regression analysis involving a backward elimination selection procedure was applied. Based on the comprehensive analysis of the results, a new model to evaluate the risk of prostate cancer and an effective follow-up strategy were developed. Results Our model included PSA, PV, fPSA, age, rate of abnormal DRE findings and rate of hypoechoic masses detected by TRUS. When compared with using PSA alone, an evidently large area under the receiver-operating characteristic curve was obtained in our model(0.782 vs. 0.566). According to the degree of prostate cancer risk (PCaR), patients were divided into two groups. In the second stage of our study, prostate biopsies were implemented immediately for patients with PCaR>0.52 while the rest of the patients underwent closely follow-up. Compared with the first stage, the detectable rate of PCa in the second stage was significantly increased (33.0% vs. 21.1%, P=0.012). There was no significant difference between the two stages in the distribution of Gleason score (P=0.808). Conclusions A follow-up strategy were developed based on the new model to improve the PCa detection rate, which reduced unnecessary prostate biopsies without delaying the diagnosis and treatment of the patients. Key words: Prostate specific antigen (PSA); Model; Prostate biopsy; Prostate cancer risk (PCaR)