The practical clinical role of machine learning models with different algorithms in predicting prostate cancer local recurrence after radical prostatectomy.

BACKGROUND: The detection of local recurrence for prostate cancer (PCa) patients following radical prostatectomy (RP) is challenging and can influence the treatment plan. Our aim was to construct and verify machine learning models with three different algorithms based on post-operative mpMRI for predicting local recurrence of PCa after RP and explore their potential clinical value compared with the Prostate Imaging for Recurrence Reporting (PI-RR) score of expert-level radiologists. METHODS: A total of 176 patients were retrospectively enrolled and randomly divided into training (n = 123) and testing (n = 53) sets. The PI-RR assessments were performed by two expert-level radiologists with access to the operative histopathological and pre-surgical clinical results. The radiomics models to predict local recurrence were built by utilizing three different algorithms (i.e., support vector machine [SVM], linear discriminant analysis [LDA], and logistic regression-least absolute shrinkage and selection operator [LR-LASSO]). The combined model integrating radiomics features and PI-RR score was developed using the most effective classifier. The classification performances of the proposed models were assessed by receiver operating characteristic (ROC) curve analysis. RESULTS: There were no significant differences between the training and testing sets concerning age, prostate-specific antigen (PSA), Gleason score, T-stage, seminal vesicle invasion (SVI), perineural invasion (PNI), and positive surgical margins (PSM). The radiomics model based on LR-LASSO exhibited superior performance than other radiomics models, with an AUC of 0.858 in the testing set; the PI-RR yielded an AUC of 0.833, and there was no significant difference between the best radiomics model and the PI-RR score. The combined model achieved the best predictive performance with an AUC of 0.924, and a significant difference was observed between the combined model and PI-RR score. CONCLUSIONS: Our radiomics model is an effective tool to predict PCa local recurrence after RP. By integrating radiomics features with the PI-RR score, our combined model exhibited significantly better predictive performance of local recurrence than expert-level radiologists' PI-RR assessment.

An Assessment of the Pathological Classification and Postoperative Outcome of Focal Cortical Dysplasia by Simultaneous Hybrid PET/MRI.

OBJECTIVES: The purpose of this research was to investigate whether MRI and Simultaneous Hybrid PET/MRI images were consistent in the histological classification of patients with focal cortical dysplasia. Additionally, this research aimed to evaluate the postoperative outcomes with the MRI and Simultaneous Hybrid PET/MRI images of focal cortical dysplasia. METHODS: A total of 69 cases in this research were evaluated preoperatively for drug-resistant seizures, and then surgical resection procedures of the epileptogenic foci were performed. The postoperative result was histopathologically confirmed as focal cortical dysplasia, and patients then underwent PET and MRI imaging within one month of the seizure. In this study, head MRI was performed using a 3.0 T magnetic resonance scanner (Philips) to obtain 3D T1WI images. The Siemens Biograph 16 scanner was used for a routine scanning of the head to obtain PET images. BrainLAB's iPlan software was used to fuse 3D T1 images with PET images to obtain PET/MRI images. RESULTS: Focal cortical dysplasia was divided into three types according to ILAE: three patients were classified as type I, twenty-five patients as type II, and forty-one patients as type III. Patients age of onset under 18 and age of operation over 18 had a longer duration (p = 0.036, p = 0.021). MRI had a high lesion detection sensitivity of type III focal cortical dysplasia (p = 0.003). Simultaneous Hybrid PET/MRI showed high sensitivity in detecting type II and III focal cortical dysplasia lesions (p = 0.037). The lesions in Simultaneous Hybrid PET/MRI-positive focal cortical dysplasia patients were mostly located in the temporal and multilobar (p = 0.005, 0.040). CONCLUSION: Simultaneous Hybrid PET/MRI has a high accuracy in detecting the classification of focal cortical dysplasia. The results of this study indicate that patients with focal cortical dysplasia with positive Simultaneous Hybrid PET/MRI have better postoperative prognoses.

Exercise modalities for type 2 diabetes: A systematic review and network meta-analysis of randomized trials.

AIMS: We aimed to determine the effects of different exercise modalities in patients with type 2 diabetes mellitus (T2DM). METHODS: We searched PubMed, Embase, and the Cochrane Library from their inception until July 2020 to identify randomised controlled trials (RCTs) on exercise in adults with T2DM. Paired reviewers independently performed study selection, data extraction, and risk of bias assessment. The certainty of the evidence was assessed using the Confidence in Network Meta-Analysis (CINeMA) framework. RESULTS: A total of 106 RCTs that enroled eight exercise modalities with 7438 patients were included. Six exercise modalities, except unsupervised aerobic/resistance exercise, significantly reduced glycosylated haemoglobin (HbA1c), with mean differences (MDs) ranging from 0.71 (95% confidence interval [CI]: 0.34-1.08) to 0.34 (95% CI: 0.17-0.52), low to high certainty, in comparison with no exercise. The evidence of low to moderate certainty showed that supervised aerobic/resistance exercise improved glycaemic control, body weight, blood pressure, and blood lipid profiles compared with no exercise. Flexibility exercise may be associated with glycaemic control (HbA1c: MD = 0.71, 95% CI: 0.34-1.08); fasting plasma glucose (MD = 1.48, 95% CI: 0.78-2.17), and weight loss (MD = 1.80, 95% CI: 0.85-2.75) compared with controls, but not blood pressure and lipid profiles. Balance exercise showed the largest benefit in improving total cholesterol (MD = 52.81, 95% CI: 28.47-77.16) and low certainty. We found no significant differences between exercises and the triacylglycerol (TG) level. CONCLUSIONS: Overall, our network meta-analyses support the recommendation for exercise in patients with T2DM, especially supervised exercises. Limited evidence supports the benefits of flexibility and balance exercises. The effectiveness of exercise modalities for TG reduction remains unclear.

Nutritional Recommendations for Type 2 Diabetes: An International Review of 15 Guidelines.

OBJECTIVES: Recommendations from clinical practice guidelines (CPGs) for individuals with type 2 diabetes mellitus (T2DM) may be inconsistent, and little is known about their quality. Our aim in this study was to systematically review the consistency of globally available CPGs containing nutritional recommendations for T2DM and to assess the quality of their methodology and reporting. METHODS: PubMed, China Biology Medicine and 4 main guideline websites were searched. Four researchers independently assessed quality of the methodology and reporting using the Appraisal of Guidelines for Research and Evaluation, second edition (AGREE II) instrument and the Reporting Items for Practice Guidelines in HealThcare (RIGHT) checklist. RESULTS: Fifteen CPGs include 65 nutritional recommendations with 6 sections: 1) body weight and energy balance; 2) dietary eating patterns; 3) macronutrients; 4) micronutrients and supplements; 5) alcohol; and 6) specific, functional foods. Current nutritional recommendations for individuals with T2DM on specific elements and amounts are not completely consistent in different CPGs and fail to assign the specific supporting evidence and strength of recommendations. To use nutritional recommendations to guide and manage individuals with T2DM, it is important to address the current challenges by establishing a solid evidence base and indicating the strength of recommendations. Overall, 8 CPGs classified as recommended for clinical practice used AGREE II. Fifteen CPGs adhere to <60% of RIGHT checklist items. CONCLUSIONS: High-quality evidence is needed to potentially close knowledge gaps and strengthen the recommendation. The AGREE II instrument, along with the RIGHT checklist, should be endorsed and used by CPG developers to ensure higher quality and adequate use of their products.

Physical activity recommendations for patients with type 2 diabetes: a cross-sectional survey.

AIMS: We aim to systematically review the existing guidelines on physical activity for T2DM and assess the consistency of their recommendations, methodological quality, and reporting quality. METHODS: We performed a literature search on PubMed, China Biology Medicine disc, and four main guideline databases in order to identify existing T2DM guidelines. We then evaluated the methodological and reporting qualities of the guidelines using the AGREE II instrument and the RIGHT checklist. Fifteen guidelines were included in total, with eight of these (53.33%) assessed being "recommended" and five graded as having good reporting quality. RESULT: A total of 30 physical activity recommendations were extracted. Fifteen recommendations reported the levels of evidence, and of these, five were found to be based on a high quality of evidence. Aerobic exercise at least 3 days per week was recommended by most guidelines (10/15, 66.67%). Eight guidelines (53.33%) recommended resistance exercise, while combined aerobic and resistance exercise was recommended in three of the guidelines (20.0%). Only two guidelines recommended supervised exercise, and one recommended flexibility exercise. Four of the guidelines (26.67%) solely recommended physical activity or exercise, but with no added details. CONCLUSIONS: The quality of the guidelines concerning physical activity for T2DM was found to be moderate to low and varied substantially. Recommendations regarding physical activity for T2DM are not very specific or clear and remain incompletely consistent, while the level of evidence and the strength of the recommendations were seldom reported. Our findings suggest a need for guidelines for diabetes based on high levels of evidence.