Characterizing the ADPKD-IFT140 Phenotypic Signature With Deep Learning and Advanced Imaging Biomarkers - Ahmad Ghanem; Fadi George Munairdjy Debeh; Abdul Hamid Borghol; Nikola Zagorec; Amanda L. Tapia; Byron Smith; Stefan Paul; Abdul Basit; Bassel AlKhatib; Nay Nader; Marie Therese Bou Antoun; Adriana V. Gregory; et al. (Kidney International Reports)
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=π6(d13+d23). 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.