Interpretable Deep-learning Model Based on Superb Microvascular Imaging for Noninvasive Diagnosis of Interstitial Fibrosis in Chronic Kidney Disease.

Rationale And Objectives: To develop an interpretable deep learning (XDL) model based on superb microvascular imaging (SMI) for the noninvasive diagnosis of the degree of interstitial fibrosis (IF) in chronic kidney disease (CKD).

Methods: We included CKD patients who underwent renal biopsy, two-dimensional ultrasound, and SMI examinations between May 2022 and October 2023. Based on the pathological IF score, they were divided into two groups: minimal-mild IF (≤25%) and moderate-severe IF (>25%).

Multimodal ultrasound deep learning to detect fibrosis in early chronic kidney disease.

We developed a multimodal ultrasound (US) deep learning (DL) fusion model to automatically classify early fibrosis in patients with chronic kidney disease (CKD). This prospective study included patients with CKD who underwent continuous gray-scale US, superb microvascular imaging, and strain elastography from May to November 2022. According to the pathological tubular atrophy and interstitial fibrosis score, patients were divided into minimal and mild groups (affected area ≤10% and 11 - 25% of the total cortical volume, respectively).

Interpretable Machine Learning Model Based on Superb Microvascular Imaging for Non-Invasive Determination of Crescent Status of IgAN.

Purpose: To assess the crescentic status of IgA nephropathy (IgAN) non-invasively using a superb microvascular imaging (SMI)-based radiomics machine learning (ML) model.

Patients And Methods: IgAN patients who underwent renal biopsy from June 2022 to October 2023, with two-dimensional ultrasound (US) and SMI examinations conducted one day prior to the renal biopsy. The patients selected were divided randomly into a training group and a test group in a 7:3 ratio.

Is Ablation Suitable For Small Renal Masses (SRMs)? A Meta-Analysis.

Rationale And Objectives: To compare perioperative and oncology outcomes of ablation and partial nephrectomy in small renal masses (SRMs).

Methods: We conduct this meta-analysis strictly according to the PRISMA standard, and the quality evaluation follows the AMSTAR standard. Four databases, Embase, PubMed, Cochrane Library, and Web of Science, were systematically searched.

Update on comparison of laparoscopic sleeve gastrectomy and laparoscopic Roux-en-Y gastric bypass: a systematic review and meta-analysis of weight loss, comorbidities, and quality of life at 5 years.

Background: Laparoscopic Sleeve Gastrectomy (LSG) and Laparoscopic Roux-en-Y Gastric Bypass (LRYGB) are the two most commonly performed bariatric surgeries for the treatment of obesity. This meta-analysis was performed with the aim of summarizing the available evidence on weight loss, remission of comorbidities, and quality of life in LRYGB and LSG, complementing the current literature.

Methods: We searched PubMed, EMBASE and the Cochrane Library from January 2012 to June 2023 for randomized controlled trials and non-randomized interventional studies.

Comparing the safety and effectiveness of minimally invasive surgery and open inguinal lymph node dissection in penile cancer: A systematic review and meta-analysis.

Objective: To compare the safety and effectiveness of minimally invasive surgery (MIS) with open inguinal lymph node dissection (O-ILND) in penile cancer.

Methods: We performed a systematic reviews and cumulative meta-analyses of primary results of interest according to PRISMA criteria, and quality assessment followed AMSTAR. The system searched five databases, including Zhiwang, Embase, PubMed, Cochrane Library and Web of Science.

Dihydroartemisinin inhibits HNSCC invasion and migration by controlling miR-195-5p expression.

Objectives: Dihydroartemisinin (DHA), an artemisinin derivative extracted from the traditional Chinese medicinal herb , has the potential to suppress head and neck squamous cell carcinoma (HNSCC) progression. However, the mechanisms underlying these effects remain unclear. Therefore, we aimed to examine the mechanisms underlying the effects of DHA on tumor invasion and migration.

Amplifying Chinese physicians' emphasis on patients' psychological states beyond urologic diagnoses with ChatGPT - a multicenter cross-sectional study.

Background: Artificial intelligence (AI) technologies, particularly large language models (LLMs), have been widely employed by the medical community. In addressing the intricacies of urology, ChatGPT offers a novel possibility to aid in clinical decision-making. This study aimed to investigate the decision-making ability of LLMs in solving complex urology-related problems and assess their effectiveness in providing psychological support to patients with urological disorders.

Comparing efficacy and safety of mirabegron, tamsulosin, and solifenacin in ureteral stent-related symptoms: outcomes from a network meta-analysis.

Background: Although ureteral stents are a well-established and commonly used method for renal drainage, the ureteral stent-related symptoms (SRSs) they cause in patients cannot be ignored. It is currently unclear whether mirabegron has a place in the treatment of SRSs. Our study aims to systematically evaluate the efficacy and safety of mirabegron in treating SRSs in adult patients.

An interpretable clinical ultrasound-radiomics combined model for diagnosis of stage I cervical cancer.

Objective: The purpose of this retrospective study was to establish a combined model based on ultrasound (US)-radiomics and clinical factors to predict patients with stage I cervical cancer (CC) before surgery.

Materials And Methods: A total of 209 CC patients who had cervical lesions found by transvaginal sonography (TVS) from the First Affiliated Hospital of Anhui Medical University were retrospectively reviewed, patients were divided into the training set (n = 146) and internal validation set (n = 63), and 52 CC patients from Anhui Provincial Maternity and Child Health Hospital and Nanchong Central Hospital were taken as the external validation set. The clinical independent predictors were selected by univariate and multivariate logistic regression analyses.

Ultrasound contrast-enhanced radiomics model for preoperative prediction of the tumor grade of clear cell renal cell carcinoma: an exploratory study.

Background: This study aims to explore machine learning(ML) methods for non-invasive assessment of WHO/ISUP nuclear grading in clear cell renal cell carcinoma(ccRCC) using contrast-enhanced ultrasound(CEUS) radiomics.

Methods: This retrospective study included 122 patients diagnosed as ccRCC after surgical resection. They were divided into a training set (n = 86) and a testing set(n = 36).

Ultrasound-Based Deep Learning Radiomics Nomogram for the Assessment of Lymphovascular Invasion in Invasive Breast Cancer: A Multicenter Study.

Rationale And Objectives: The aim of this study was to develop a deep learning radiomics nomogram (DLRN) based on B-mode ultrasound (BMUS) and color doppler flow imaging (CDFI) images for preoperative assessment of lymphovascular invasion (LVI) status in invasive breast cancer (IBC).

Materials And Methods: In this multicenter, retrospective study, 832 pathologically confirmed IBC patients were recruited from eight hospitals. The samples were divided into training, internal test, and external test sets.

Perioperative and Oncological Outcomes of Partial Versus Radical Nephrectomy for Complex Renal Tumors (RENAL Score ≥ 7): Systematic Review and Meta-Analysis.

Objective: Which is superior, partial nephrectomy (PN) or radical nephrectomy (RN), for the treatment of complex renal tumours (RENAL or score ≥ 7)?

Methods: This systematic review and meta-analysis was conducted in accordance with the PRISMA statement. A systematic search of the literature published before November 2023 was conducted using Pubmed, Embase, Cochran, and Web of Science libraries. We included studies comparing perioperative and oncologic outcomes of partial nephrectomy and radical nephrectomy for complex renal tumors.

Enhanced Artificial Intelligence Strategies in Renal Oncology: Iterative Optimization and Comparative Analysis of GPT 3.5 Versus 4.0.

Background: The rise of artificial intelligence (AI) in medicine has revealed the potential of ChatGPT as a pivotal tool in medical diagnosis and treatment. This study assesses the efficacy of ChatGPT versions 3.5 and 4.

A comparison of the postoperative outcomes between intraoperative leak testing and no intraoperative leak testing for gastric cancer surgery: a systematic review and meta-analysis.

Background: Postoperative anastomotic leakage (PAL) is a serious complication of gastric cancer surgery. Although perioperative management has made considerable progress, anastomotic leakage (AL) cannot always be avoided. The purpose of this study is to evaluate whether intraoperative leak testing (IOLT) can reduce the incidence of PAL and other postoperative outcomes in gastric cancer surgery.

Comparing the safety and efficacy of single-port versus multi-port robotic-assisted techniques in urological surgeries: a systematic review and meta-analysis.

Objective: Comparing the safety and efficacy of single-port (SP) versus multi-port (MP) robotic-assisted techniques in urological surgeries.

Methods: A systematic review and cumulative meta-analysis was performed using PRISMA criteria for primary outcomes of interest, and quality assessment followed AMSTAR. Four databases were systematically searched: Embase, PubMed, The Cochrane Library, and Web of Science.

Mayo Adhesion Probability Score Predicts Perioperative Outcome of Partial Nephrectomy: A Systematic Review and Meta-Analysis.

Assessing whether Mayo adhesive probability (MAP) levels affect perioperative outcomes after partial nephrectomy (PN). This systematic review and meta-analysis were conducted in accordance with the PRISMA statement. A systematic search of the literature published before February 1, 2023 was conducted using Pubmed, Embase, Cochran, and Web of Science libraries.

A Transvaginal Ultrasound-Based Deep Learning Model for the Noninvasive Diagnosis of Myometrial Invasion in Patients with Endometrial Cancer: Comparison with Radiologists.

Rationale And Objectives: This study aimed to determine the feasibility of using the deep learning (DL) method to determine the degree (whether myometrial invasion [MI] >50%) of MI in patients with endometrial cancer (EC) based on ultrasound (US) images.

Materials And Methods: From September 2017 to April 2023, 1289 US images of 604 patients with EC who underwent surgical resection at center 1, center 2 or center 3 were obtained and divided into a training set and an internal validation set. Ninety-five patients from center 4 and center 5 were randomly selected as the external testing set according to the same criteria as those for the primary cohort.