Malignant hypertension (mHTN) is an acute form of hypertension with multiorgan damage, including renal impairment. Renal cortical thickness (RCT) by ultrasonography is associated with renal function in kidney disease. However, the relationship between RCT and renal dysfunction in mHTN is not yet fully understood.

A cohort study.

This was a study conducted between January 2008 and June 2023 at the First Affiliated Hospital of Sun Yat-sen University.

A total of 292 mHTN patients with thrombotic microangiopathy (TMA) who underwent renal biopsy were included in the study.

Patient characteristics for RCT were examined using linear regression. The association between RCT and an increase in estimated glomerular filtration rate (eGFR) of ≥15% was analysed by Cox regression. RCT was analysed in two groups classified by median RCT.

Overall, patients with larger RCT exhibited a lower global sclerosis ratio and a lower proportion of intravascular thrombosis. RCT was strongly positively correlated with mean kidney length (coefficient B 1.018; 95% CI 0.638 to 1.397; ppp=0.001) and tubular atrophy/interstitial fibrosis ratio (coefficient B –0.880; 95% CI –1.569 to –0.192; p=0.012). Moreover, larger RCTs had a better renal function improvement of ≥15% increase in eGFR in patients with mHTN with TMA (HR 1.745; 95% CI 1.012 to 3.009; p=0.045).

Smaller RCTs were correlated with a decline in renal function and deterioration of renal pathology. Furthermore, larger RCTs showed a better renal function improvement of ≥15% increase in eGFR in mHTN patients with TMA. RCT may be a predictive factor for renal damage in mHTN.

Histopathological evaluation of prostate biopsies using the Gleason scoring system is critical for prostate cancer diagnosis and treatment selection. However, grading variability among pathologists can lead to inconsistent assessments, risking inappropriate treatment. Similar challenges complicate the assessment of other prognostic features like cribriform cancer morphology and perineural invasion. Many pathology departments are also facing an increasingly unsustainable workload due to rising prostate cancer incidence and a decreasing pathologist workforce coinciding with increasing requirements for more complex assessments and reporting. Digital pathology and artificial intelligence (AI) algorithms for analysing whole slide images show promise in improving the accuracy and efficiency of histopathological assessments. Studies have demonstrated AI’s capability to diagnose and grade prostate cancer comparably to expert pathologists. However, external validations on diverse data sets have been limited and often show reduced performance. Historically, there have been no well-established guidelines for AI study designs and validation methods. Diagnostic assessments of AI systems often lack preregistered protocols and rigorous external cohort sampling, essential for reliable evidence of their safety and accuracy.

This study protocol covers the retrospective validation of an AI system for prostate biopsy assessment. The primary objective of the study is to develop a high-performing and robust AI model for diagnosis and Gleason scoring of prostate cancer in core needle biopsies, and at scale evaluate whether it can generalise to fully external data from independent patients, pathology laboratories and digitalisation platforms. The secondary objectives cover AI performance in estimating cancer extent and detecting cribriform prostate cancer and perineural invasion. This protocol outlines the steps for data collection, predefined partitioning of data cohorts for AI model training and validation, model development and predetermined statistical analyses, ensuring systematic development and comprehensive validation of the system. The protocol adheres to Transparent Reporting of a multivariable prediction model of Individual Prognosis Or Diagnosis+AI (TRIPOD+AI), Protocol Items for External Cohort Evaluation of a Deep Learning System in Cancer Diagnostics (PIECES), Checklist for AI in Medical Imaging (CLAIM) and other relevant best practices.

Data collection and usage were approved by the respective ethical review boards of each participating clinical laboratory, and centralised anonymised data handling was approved by the Swedish Ethical Review Authority. The study will be conducted in agreement with the Helsinki Declaration. The findings will be disseminated in peer-reviewed publications (open access).