Prediction of Prostate Cancer Risk Stratification Based on A Nonlinear Transformation Stacking Learning Strategy
2
조회
0
다운로드
1
인용
0
참고문헌
키워드
초록
Purpose: Prostate cancer (PCa) is an epithelial malignancy that originates in the prostate gland and is generally categorized into low, intermediate, and high-risk groups. The primary diagnostic indicator for PCa is the measurement of serum prostate-specific antigen (PSA) values. However, reliance on PSA levels can result in false positives, leading to unnecessary biopsies and an increased risk of invasive injuries. Therefore, it is imperative to develop an efficient and accurate method for PCa risk stratification. Many recent studies on PCa risk stratification based on clinical data have employed a binary classification, distinguishing between low to intermediate and high risk. In this paper, we propose a novel machine learning (ML) approach utilizing a stacking learning strategy for predicting the tripartite risk stratification of PCa. Methods: Clinical records, featuring attributes selected using the lasso method, were utilized with 5 ML classifiers. The outputs of these classifiers underwent transformation by various nonlinear transformers and were then concatenated with the lasso-selected features, resulting in a set of new features. A stacking learning strategy, integrating different ML classifiers, was developed based on these new features. Results: Our proposed approach demonstrated superior performance, achieving an accuracy of 0.83 and an area under the receiver operating characteristic curve value of 0.88 in a dataset comprising 197 PCa patients with 42 clinical characteristics. Conclusions: This study aimed to improve clinicians’ ability to rapidly assess PCa risk stratification while reducing the burden on patients. This was achieved by using artificial intelligence-related technologies as an auxiliary method for diagnosing PCa.
Abstract
Purpose: Prostate cancer (PCa) is an epithelial malignancy that originates in the prostate gland and is generally categorized into low, intermediate, and high-risk groups. The primary diagnostic indicator for PCa is the measurement of serum prostate-specific antigen (PSA) values. However, reliance on PSA levels can result in false positives, leading to unnecessary biopsies and an increased risk of invasive injuries. Therefore, it is imperative to develop an efficient and accurate method for PCa risk stratification. Many recent studies on PCa risk stratification based on clinical data have employed a binary classification, distinguishing between low to intermediate and high risk. In this paper, we propose a novel machine learning (ML) approach utilizing a stacking learning strategy for predicting the tripartite risk stratification of PCa. Methods: Clinical records, featuring attributes selected using the lasso method, were utilized with 5 ML classifiers. The outputs of these classifiers underwent transformation by various nonlinear transformers and were then concatenated with the lasso-selected features, resulting in a set of new features. A stacking learning strategy, integrating different ML classifiers, was developed based on these new features. Results: Our proposed approach demonstrated superior performance, achieving an accuracy of 0.83 and an area under the receiver operating characteristic curve value of 0.88 in a dataset comprising 197 PCa patients with 42 clinical characteristics. Conclusions: This study aimed to improve clinicians’ ability to rapidly assess PCa risk stratification while reducing the burden on patients. This was achieved by using artificial intelligence-related technologies as an auxiliary method for diagnosing PCa.
참고문헌 (27)
- "Cancer Stat Facts:Prostate Cancer" (2023)[URL]
- Pienta KJ, "Risk factors for prostate cancer", Ann Intern Med, 118, pp.793~803 (1993)
- Bulten W, "Artificial intelligence for diagnosis and Gleason grading of prostate cancer : the PANDA challenge", Nat Med, 28, pp.154~163 (2022)
- Selvadurai ED, "Medium-term outcomes of active surveillance for localised prostate cancer", Eur Urol, 64, pp.981~987 (2013)
- Sanda MG, "Clinically localized prostate cancer : AUA", ASTRO, J Urol, pp.2018, 199[DOI]
- Hood SP, "Identifying prostate cancer and its clinical risk in asymptomatic men using machine learning of high dimensional peripheral blood flow cytometric natural killer cell subset phenotyping data", Elife, 9, pp.e50936~ (2020)
- Liang L, "A nomogram based on a multiparametric ultrasound radiomics model for discrimination between malignant and benign prostate lesions", Front Oncol, 11, pp.610785~ (2021)
- Yang C, "Development and validation of a clinic machine-learning nomogram for the prediction of risk stratifications of prostate cancer based on functional subsets of peripheral lymphocyte", J Transl Med, 21, pp.465~ (2023)
- Sunoqrot MRS, "Artificial intelligence for prostate MRI : open datasets, available applications, and grand challenges", Eur Radiol Exp, 6, pp.35~ (2022)
- Chiu PK, "Enhancement of prostate cancer diagnosis by machine learning techniques : an algorithm development and validation study", Prostate Cancer Prostatic Dis, 25, pp.672~676 (2022)
- Kim C, "Machine-learning model to predict the cause of death using a stacking ensemble method for observational data", J Am Med Inform Assoc, 28, pp.1098~1107 (2021)
- Domínguez-Almendros S, "Logistic regression models", Allergol Immunopathol(Madr), 39, pp.295~305 (2011)
- Mottet N, "EAU-EANM-ESTRO-ESUR-SIOG guidelines on prostate cancer-2020 update. Part 1 : screening, diagnosis, and local treatment with curative intent", Eur Urol, 79, pp.243~262 (2021)
- Liu X, "Normalization methods for the analysis of unbalanced transcriptome data : a review", Front Bioeng Biotechnol, 7, pp.358~ (2019)
- Wang T, "Comprehensive molecular analyses of a macrophage-related gene signature with regard to prognosis, immune features, and biomarkers for immunotherapy in hepatocellular carcinoma based on WGCNA and the LASSO algorithm", Front Immunol, 13, pp.843408~ (2022)
- Pi L, "Combined performance of screening and variable selection methods in ultra-high dimensional data in predicting time-to-event outcomes", Diagn Progn Res, 2, pp.21~ (2018)
- Huang S, "Applications of support vector machine(SVM)learning in cancer genomics", Cancer Genomics Proteomics, 15, pp.41~51 (2018)
- Szeghalmy S, "A comparative study of the use of stratified cross-validation and distribution-balanced stratified cross-validation in imbalanced learning", Sensors(Basel), 23, pp.2333~ (2023)
- Hanko M, "Random forest-based prediction of outcome and mortality in patients with traumatic brain injury undergoing primary decompressive craniectomy", World Neurosurg, 148, pp.e450~e458 (2021)
- Hou N, "Predicting 30-days mortality for MIMIC-III patients with sepsis-3 : a machine learning approach using XGboost", J Transl Med, 18, pp.462~ (2020)
- Hwangbo L, "Stacking ensemble learning model to predict 6-month mortality in ischemic stroke patients", Sci Rep, 12, pp.17389~ (2022)
- He K, "Deep residual learning for image recognition", 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp.770~778 (2016)
- Ahmed B, "DeepAProt : deep learning based abiotic stress protein sequence classification and identification tool in cereals", Front Plant Sci, 13, pp.1008756~ (2023)
- Tang Z, "Automatic sparse connectivity learning for neural networks", IEEE Trans Neural Netw Learn Syst, 34, pp.7350~7364 (2023)
- Hu D, "A likelihood ratio test for the homogeneity of between-study variance in network meta-analysis", Syst Rev, 10, pp.310~ (2021)
- Sherwani RAK, "Analysis of COVID-19 data using neutrosophic Kruskal Wallis H test", BMC Med Res Methodol, 21, pp.215~ (2021)
- Cosma G, "Prostate cancer : early detection and assessing clinical risk using deep machine learning of high dimensional peripheral blood flow cytometric phenotyping data", Front Immunol, 12, pp.786828~ (2021)