No abstract

Although the incidence and mortality of prostate cancer have gradually begun to decline in the past few years, it is still one of the leading causes of death from malignant tumors in the world. The occurrence and development of prostate cancer are affected by race, family history, microenvironment, and other factors. In recent decades, more and more studies have confirmed that prostate microflora in the tumor microenvironment may play an important role in the occurrence, development, and prognosis of prostate cancer. Microorganisms or their metabolites may affect the occurrence and metastasis of cancer cells or regulate anti-cancer immune surveillance. In addition, the use of tumor microenvironment bacteria in interventional targeting therapy of tumors also shows a unique advantage. In this review, we introduce the pathway of microbiota into prostate cancer, focusing on the mechanism of microorganisms in tumorigenesis and development, as well as the prospect and significance of microorganisms as tumor biomarkers and tumor prevention and treatment.

Objectives: Multiple meta-analyses which investigated the comparative efficacy and safety of artificial intelligence (AI)-aid colonoscopy (AIC) vs. conventional colonoscopy (CC) in the detection of polyp and adenoma have been published. However, a definitive conclusion has not yet been generated. This systematic review selected from discordant meta-analyses to draw a definitive conclusion about whether AIC is better than CC for the detection of polyp and adenoma.Methods: We comprehensively searched potentially eligible literature in PubMed, Embase, Cochrane library, and China National Knowledgement Infrastructure (CNKI) databases from their inceptions until to April 2021. Assessment of Multiple Systematic Reviews (AMSTAR) instrument was used to assess the methodological quality. Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) checklist was used to assess the reporting quality. Two investigators independently used the Jadad decision algorithm to select high-quality meta-analyses which summarized the best available evidence.Results: Seven meta-analyses met our selection criteria finally. AMSTAR score ranged from 8 to 10, and PRISMA score ranged from 23 to 26. According to the Jadad decision algorithm, two high-quality meta-analyses were selected. These two meta-analyses suggested that AIC was superior to CC for colonoscopy outcomes, especially for polyp detection rate (PDR) and adenoma detection rate (ADR).Conclusion: Based on the best available evidence, we conclude that AIC should be preferentially selected for the route screening of colorectal lesions because it has potential value of increasing the polyp and adenoma detection. However, the continued improvement of AIC in differentiating the shape and pathology of colorectal lesions is needed.

PurposePSA is currently the most commonly used screening indicator for prostate cancer. However, it has limited specificity for the diagnosis of prostate cancer. We aim to construct machine learning-based models and enhance the prediction of prostate cancer.MethodsThe data of 551 patients who underwent prostate biopsy were retrospectively retrieved and divided into training and test datasets in a 3:1 ratio. We constructed five PCa prediction models with four supervised machine learning algorithms, including tPSA univariate logistic regression (LR), multivariate LR, decision tree (DT), random forest (RF), and support vector machine (SVM). The five prediction models were compared based on model performance metrics, such as the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, calibration curve, and clinical decision curve analysis (DCA).ResultsAll five models had good calibration in the training dataset. In the training dataset, the RF, DT, and multivariate LR models showed better discrimination, with AUCs of 1.0, 0.922 and 0.91, respectively, than the tPSA univariate LR and SVM models. In the test dataset, the multivariate LR model exhibited the best discrimination (AUC=0.918). The multivariate LR model and SVM model had better extrapolation and generalizability, with little change in performance between the training and test datasets. Compared with the DCA curves of the tPSA LR model, the other four models exhibited better net clinical benefits.ConclusionThe results of the current retrospective study suggest that machine learning techniques can predict prostate cancer with significantly better AUC, accuracy, and net clinical benefits.