Introduction: Accurate tools to inform individual prognosis in patients with autosomal dominant polycystic kidney disease (ADPKD) are lacking. Here, we report an artificial intelligence (AI)-generated method for routinely measuring total kidney volume (TKV).
Methods: An ensemble U-net algorithm was created using the nnUNet approach. The training and internal cross-validation cohort consisted of all 1.5T magnetic resonance imaging (MRI) data acquired using 5 different MRI scanners (454 kidneys, 227 scans) in the CYSTic consortium, which was first manually segmented by a single human operator. As an independent validation cohort, we utilized 48 sequential clinical MRI scans with reference results of manual segmentation acquired by 6 individual analysts at a single center. The tool was then implemented for clinical use and its performance analyzed.
Results: The training or internal validation cohort was younger (mean age 44.0 vs. 51.5 years) and the female-to-male ratio higher (1.2 vs. 0.94) compared to the clinical validation cohort. The majority of CYSTic patients had mutations (79%) and typical disease (Mayo Imaging class 1, 86%). The median DICE score on the clinical validation data set between the algorithm and human analysts was 0.96 for left and right kidneys with a median TKV error of -1.8%. The time taken to manually segment kidneys in the CYSTic data set was 56 (±28) minutes, whereas manual corrections of the algorithm output took 8.5 (±9.2) minutes per scan.
Conclusion: Our AI-based algorithm demonstrates performance comparable to manual segmentation. Its rapidity and precision in real-world clinical cases demonstrate its suitability for clinical application.
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http://dx.doi.org/10.1016/j.ekir.2023.10.029 | DOI Listing |
Lung Cancer
December 2024
Department of Medical and Surgical Sciences, University of Bologna, Bologna, Italy; Medical Oncology, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Bologna, Italy.
Background: The upfront treatment of non-oncogene-addicted NSCLC relies on immunotherapy alone (ICI) or in combination with chemotherapy (CT-ICI). Genomic aberrations such as KRAS, TP53, KEAP1, SMARCA4, or STK11 may impact survival outcomes.
Methods: We performed an observational study of 145 patients treated with first-line IO or CT-ICI for advanced non-squamous (nsq) NSCLC at our institution tested with an extensive lab-developed NGS panel.
Cardiovasc Diabetol
December 2024
Saw Swee Hock School of Public Heath, National University of Singapore, Singapore, 117549, Republic of Singapore.
Background: Data on the relationship between potassium intake and major cardiovascular events (MACE) in patients with diabetes are scarce. We aim to study the association between estimated potassium intake and risk of MACE in individuals with type 2 diabetes.
Methods: The discovery cohort consisted of 1572 participants with type 2 diabetes from a secondary hospital.
BMC Urol
December 2024
The Department of Urology, Guangdong Second Provincial General Hospital, Guangzhou, 510317, China.
Background: Here, we aim to develop and validate a viable prognostic nomogram model for predicting a stone-free rate of kidney stones patients based on retrospective cohort analysis.
Methods: This is a retrospective study that obtained a continuous cohort from the databases of two hospitals (General Hospital of Southern Theater Command, and Guangdong Second Provincial General Hospital), including 522 patients with kidney stones who underwent Endoscopic Combined Intrarenal Surgery (ECIRS) from January 2015 to December 2022.The characteristics of the primary cohort between the SF (stone-free) and SR (stone residue) groups were identified using single factor and multivariate logistic regression analyses.
BMC Pulm Med
December 2024
Department of Endocrinology and Metabolism, The Eighth Affiliated Hospital, Sun Yat-Sen University, Shenzhen, 518033, China.
Background: The association between glycemic control and short-, and long-term lung health remains controversial. This study aimed to investigate the relationship between glucose control and overall lung health in a national cohort.
Methods: The analysis included 5610 subjects from NHANES 2007-2012.
Clin Gastroenterol Hepatol
December 2024
Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore; Division of Gastroenterology and Hepatology, Department of Medicine, National University Health System, Singapore.
Background And Aims: Primary sclerosing cholangitis (PSC) is a known risk factor for hepatobiliary malignancies. We conducted a systematic review and meta-analysis of published studies to determine the incidence and risk factors for hepatobiliary malignancies in people with PSC.
Methods: Pubmed and Embase databases were searched from inception to April 10, 2024 for cohort studies reporting data on the incidence of cholangiocarcinoma (CCA), hepatocellular carcinoma (HCC), or gallbladder cancer (GBC) in PSC.
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