RESUMEN
RATIONALE & OBJECTIVE: Body mass index (BMI) is an independent predictor of kidney disease progression in individuals with autosomal dominant polycystic kidney disease (ADPKD). Adipocytes do not simply act as a fat reservoir but are active endocrine organs. We hypothesized that greater visceral abdominal adiposity would associate with more rapid kidney growth in ADPKD and influence the efficacy of tolvaptan. STUDY DESIGN: A retrospective cohort study. SETTING & PARTICIPANTS: 1,053 patients enrolled in the TEMPO 3:4 tolvaptan trial with ADPKD and at high risk of rapid disease progression. PREDICTOR: Estimates of visceral adiposity extracted from coronal plane magnetic resonance imaging (MRI) scans using deep learning. OUTCOME: Annual change in total kidney volume (TKV) and effect of tolvaptan on kidney growth. ANALYTICAL APPROACH: Multinomial logistic regression and linear mixed models. RESULTS: In fully adjusted models, the highest tertile of visceral adiposity was associated with greater odds of annual change in TKV of≥7% versus<5% (odds ratio [OR], 4.78 [95% CI, 3.03-7.47]). The association was stronger in women than men (interaction P<0.01). In linear mixed models with an outcome of percent change in TKV per year, tolvaptan efficacy (% change in TKV) was reduced with higher visceral adiposity (3-way interaction of treatment ∗ time ∗ visceral adiposity, P=0.002). Visceral adiposity significantly improved classification performance of predicting rapid annual percent change in TKV for individuals with a normal BMI (DeLong's test z score: -2.03; P=0.04). Greater visceral adiposity was not associated with estimated glomerular filtration rate (eGFR) slope in the overall cohort; however, visceral adiposity was associated with more rapid decline in eGFR slope (below the median) in women (fully adjusted OR, 1.06 [95% CI, 1.01-1.11] per 10 unit increase in visceral adiposity) but not men (OR, 0.98 [95% CI, 0.95-1.02]). LIMITATIONS: Retrospective; rapid progressors; computational demand of deep learning. CONCLUSIONS: Visceral adiposity that can be quantified by MRI in the coronal plane using a deep learning segmentation model independently associates with more rapid kidney growth and improves classification of rapid progression in individuals with a normal BMI. Tolvaptan efficacy decreases with increasing visceral adiposity. PLAIN-LANGUAGE SUMMARY: We analyzed images from a previous study with the drug tolvaptan conducted in patients with autosomal dominant polycystic kidney disease (ADPKD) to measure the amount of fat tissue surrounding the kidneys (visceral fat). We had previously shown body mass index can predict kidney growth in this population; now we determined whether visceral fat was an important factor associated with kidney growth. Using a machine learning tool to automate measurement of fat in images, we observed that visceral fat was independently associated with kidney growth, that it was a better predictor of faster kidney growth in lean patients than body mass index, and that having more visceral fat made treatment of ADPKD with tolvaptan less effective.
RESUMEN
Automatic abnormality identification of brachial plexus (BP) from normal magnetic resonance imaging to localize and identify a neurologic injury in clinical practice (MRI) is still a novel topic in brachial plexopathy. This study developed and evaluated an approach to differentiate abnormal BP with artificial intelligence (AI) over three commonly used MRI sequences, i.e. T1, FLUID sensitive and post-gadolinium sequences. A BP dataset was collected by radiological experts and a semi-supervised artificial intelligence method was used to segment the BP (based on nnU-net). Hereafter, a radiomics method was utilized to extract 107 shape and texture features from these ROIs. From various machine learning methods, we selected six widely recognized classifiers for training our Brachial plexus (BP) models and assessing their efficacy. To optimize these models, we introduced a dynamic feature selection approach aimed at discarding redundant and less informative features. Our experimental findings demonstrated that, in the context of identifying abnormal BP cases, shape features displayed heightened sensitivity compared to texture features. Notably, both the Logistic classifier and Bagging classifier outperformed other methods in our study. These evaluations illuminated the exceptional performance of our model trained on FLUID-sensitive sequences, which notably exceeded the results of both T1 and post-gadolinium sequences. Crucially, our analysis highlighted that both its classification accuracies and AUC score (area under the curve of receiver operating characteristics) over FLUID-sensitive sequence exceeded 90%. This outcome served as a robust experimental validation, affirming the substantial potential and strong feasibility of integrating AI into clinical practice.