Ghanem Ahmad, Munairdjy Debeh Fadi George, Borghol Abdul Hamid, Zagorec Nikola, Tapia Amanda L, Smith Byron, Paul Stefan, Basit Abdul, AlKhatib Bassel, Nader Nay, Bou Antoun Marie Therese, Gregory Adriana V, Yang Hana, Schauer Rachel S, Dahl Neera K, Hanna Christian, Torres Vicente E, Kline Timothy L, Harris Peter C, Cornec-Le Gall Emilie, Chebib Fouad T
Division of Nephrology and Hypertension, Mayo Clinic, Jacksonville, Florida, USA.
University Brest, Inserm, UMR 1078, GGB, CHU Brest, Centre de Références Maladies Rénales Héréditaires de L'enfant et de L'adulte MARHEA, F-29200 Brest, France.
Kidney Int Rep. 2025 May 7;10(8):2690-2707. doi: 10.1016/j.ekir.2025.04.062. eCollection 2025 Aug.
ADPKD is the third most common disease-causing variant in autosomal dominant polycystic kidney disease (ADPKD) after ADPKD- and ADPKD-. This study aimed to characterize the clinical presentation, progression, and distinctive imaging phenotype of ADPKD-
This retrospective cohort study included patients with disease-causing variants in , nontruncating (), or . Patients were matched by sex (48.1% male), age (mean [SD]: 57.7 ± 13.3 years), and height-adjusted total kidney volume (TKV; htTKV) (median [Q1-Q3]: 572.9 [314.1-1137.9] ml/m). Two predictive models were developed in the development cohort 81): a deep-learning model incorporating cyst-parenchymal surface area (CPSA) and cystic index, and a practical model using percentage of TKV occupied by the 2 largest cysts, with cyst volumes estimated from cyst diameters using the formula . Models were validated in an internal specificity cohort ( = 569) and an external sensitivity cohort ( = 36).
Patients with ADPKD- exhibited fewer (median cyst number: 42) but larger cysts (average cyst volume: 12.1 ml), with 88.9% having no liver cysts, compared with ADPKD- and ADPKD-. The estimated glomerular filtration rate (eGFR) of decline was slower in ADPKD- (-0.69 ml/min per 1.73 m/yr) than in ADPKD- (-1.62, = 0.006) and in ADPKD- (-0.90, = 0.737). The deep-learning model demonstrated an area-under-the-curve (AUC) of 0.949 for distinguishing ADPKD- patients in the development cohort, and 88.9% specificity in the internal cohort. A volume-to-TKV ratio ≥ 18.6% identified ADPKD- with an AUC of 0.814 and demonstrated 72.2% sensitivity in the external cohort.
We provide a detailed characterization of the ADPKD phenotype that can be distinguished using a practical or deep-learning segmentation model applicable in diverse clinical settings.
在常染色体显性多囊肾病（ADPKD）中，ADPKD-是继ADPKD-和ADPKD-之后第三常见的致病变异。本研究旨在描述ADPKD-的临床表现、疾病进展及独特的影像学表型。
这项回顾性队列研究纳入了携带致病变异、非截短型（）或的患者。患者按照性别（48.1%为男性）、年龄（平均[标准差]：57.7±13.3岁）和身高校正后的总肾体积（TKV；htTKV）（中位数[四分位数间距]：572.9[314.1 - 1137.9]ml/m）进行匹配。在开发队列（n = 81）中构建了两个预测模型：一个是结合囊肿-实质表面积（CPSA）和囊肿指数的深度学习模型，另一个是使用最大的2个囊肿占TKV的百分比的实用模型，囊肿体积根据囊肿直径使用公式估算。模型在内部特异性队列（n = 569）和外部敏感性队列（n = 36）中进行验证。
与ADPKD-和ADPKD-相比，ADPKD-患者的囊肿数量较少（中位数囊肿数：42个）但囊肿较大（平均囊肿体积：12.1ml），88.9%的患者无肝囊肿。ADPKD-患者的估计肾小球滤过率（eGFR）下降速度较慢（-0.69ml/min per 1.73m²/年），低于ADPKD-（-1.62，P = 0.006）和ADPKD-（-0.90，P = 0.737）。深度学习模型在开发队列中区分ADPKD-患者的曲线下面积（AUC）为0.949，在内部队列中的特异性为88.9%。体积与TKV比值≥18.6%可识别ADPKD-，AUC为0.814，在外部队列中的敏感性为72.2%。
我们提供了ADPKD表型的详细特征，可使用适用于不同临床环境的实用或深度学习分割模型进行区分。
