Kidney international reports · 2025
Characterizing the ADPKD-IFT140 Phenotypic Signature With Deep Learning and Advanced Imaging Biomarkers.
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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
Аннотация
INTRODUCTION: ADPKD-IFT140 is the third most common disease-causing variant in autosomal dominant polycystic kidney disease (ADPKD) after ADPKD-PKD1 and ADPKD-PKD2. This study aimed to characterize the clinical presentation, progression, and distinctive imaging phenotype of ADPKD-IFT140.
METHODS: This retrospective cohort study included patients with disease-causing variants in IFT140, nontruncating PKD1 (PKD1NT), or PKD2. 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 (n = 81): a deep-learning model incorporating cyst-parenchymal surface area (CPSA) and cystic index, and a practical model using percentage of TKVellipsoid occupied by the 2 largest cysts, with cyst volumes estimated from cyst diameters using the formula V = π 6 ( d 1 3 + d 2 3 ) . Models were validated in an internal specificity cohort (n = 569) and an external sensitivity cohort (n = 36).
RESULTS: Patients with ADPKD-IFT140 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-PKD1NT and ADPKD-PKD2. The estimated glomerular filtration rate (eGFR) of decline was slower in ADPKD-IFT140 (-0.69 ml/min per 1.73 m2/yr) than in ADPKD-PKD1NT (-1.62, P = 0.006) and in ADPKD-PKD2 (-0.90, P = 0.737). The deep-learning model demonstrated an area-under-the-curve (AUC) of 0.949 for distinguishing ADPKD-IFT140 patients in the development cohort, and 88.9% specificity in the internal cohort. A volume-to-TKV ratio ≥ 18.6% identified ADPKD-IFT140 with an AUC of 0.814 and demonstrated 72.2% sensitivity in the external cohort.
CONCLUSION: We provide a detailed characterization of the ADPKD-IFT140 phenotype that can be distinguished using a practical or deep-learning segmentation model applicable in diverse clinical settings.
METHODS: This retrospective cohort study included patients with disease-causing variants in IFT140, nontruncating PKD1 (PKD1NT), or PKD2. 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 (n = 81): a deep-learning model incorporating cyst-parenchymal surface area (CPSA) and cystic index, and a practical model using percentage of TKVellipsoid occupied by the 2 largest cysts, with cyst volumes estimated from cyst diameters using the formula V = π 6 ( d 1 3 + d 2 3 ) . Models were validated in an internal specificity cohort (n = 569) and an external sensitivity cohort (n = 36).
RESULTS: Patients with ADPKD-IFT140 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-PKD1NT and ADPKD-PKD2. The estimated glomerular filtration rate (eGFR) of decline was slower in ADPKD-IFT140 (-0.69 ml/min per 1.73 m2/yr) than in ADPKD-PKD1NT (-1.62, P = 0.006) and in ADPKD-PKD2 (-0.90, P = 0.737). The deep-learning model demonstrated an area-under-the-curve (AUC) of 0.949 for distinguishing ADPKD-IFT140 patients in the development cohort, and 88.9% specificity in the internal cohort. A volume-to-TKV ratio ≥ 18.6% identified ADPKD-IFT140 with an AUC of 0.814 and demonstrated 72.2% sensitivity in the external cohort.
CONCLUSION: We provide a detailed characterization of the ADPKD-IFT140 phenotype that can be distinguished using a practical or deep-learning segmentation model applicable in diverse clinical settings.