Characterizing the ADPKD-IFT140 Phenotypic Signature With Deep Learning and Advanced Imaging Biomarkers
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: Th...
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Elsevier
2025-08-01
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| Series: | Kidney International Reports |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2468024925002876 |
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| author | 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 Hana Yang Rachel S. Schauer Neera K. Dahl Christian Hanna Vicente E. Torres Timothy L. Kline Peter C. Harris Emilie Cornec-Le Gall Fouad T. Chebib |
| author_facet | 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 Hana Yang Rachel S. Schauer Neera K. Dahl Christian Hanna Vicente E. Torres Timothy L. Kline Peter C. Harris Emilie Cornec-Le Gall Fouad T. Chebib |
| author_sort | Ahmad Ghanem |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-7cc335e18d334b23b22fa4041f60b53f |
| institution | DOAJ |
| issn | 2468-0249 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Elsevier |
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| series | Kidney International Reports |
| spelling | doaj-art-7cc335e18d334b23b22fa4041f60b53f2025-08-20T02:52:56ZengElsevierKidney International Reports2468-02492025-08-011082690270710.1016/j.ekir.2025.04.062Characterizing the ADPKD-IFT140 Phenotypic Signature With Deep Learning and Advanced Imaging BiomarkersAhmad Ghanem0Fadi George Munairdjy Debeh1Abdul Hamid Borghol2Nikola Zagorec3Amanda L. Tapia4Byron Smith5Stefan Paul6Abdul Basit7Bassel AlKhatib8Nay Nader9Marie Therese Bou Antoun10Adriana V. Gregory11Hana Yang12Rachel S. Schauer13Neera K. Dahl14Christian Hanna15Vicente E. Torres16Timothy L. Kline17Peter C. Harris18Emilie Cornec-Le Gall19Fouad T. Chebib20Division of Nephrology and Hypertension, Mayo Clinic, Jacksonville, Florida, USADivision of Nephrology and Hypertension, Mayo Clinic, Jacksonville, Florida, USADivision of Nephrology and Hypertension, Mayo Clinic, Jacksonville, Florida, USAUniversity 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; Department of Nephrology and Dialysis, Dubrava University Hospital, Faculty of Pharmacy and Biochemistry, University of Zagreb, CroatiaDivision of Clinical Trials and Biostatistics, Department of Quantitative Health Sciences, Mayo Clinic, Rochester, Minnesota, USADivision of Clinical Trials and Biostatistics, Department of Quantitative Health Sciences, Mayo Clinic, Rochester, Minnesota, USADivision of Nephrology and Hypertension, Mayo Clinic, Jacksonville, Florida, USADivision of Nephrology and Hypertension, Mayo Clinic, Jacksonville, Florida, USADivision of Nephrology and Hypertension, Mayo Clinic, Jacksonville, Florida, USADivision of Nephrology and Hypertension, Mayo Clinic, Jacksonville, Florida, USADivision of Nephrology and Hypertension, Mayo Clinic, Jacksonville, Florida, USADivision of Nephrology and Hypertension, Mayo Clinic, Rochester, Minnesota, USA; Department of Radiology, Mayo Clinic, Rochester, Minnesota, USA; The Mayo Clinic Robert M. and Billie Kelley Pirnie Translational Polycystic Kidney Disease Center Mayo Clinic, Rochester, Minnesota, USADivision of Nephrology and Hypertension, Mayo Clinic, Rochester, Minnesota, USA; The Mayo Clinic Robert M. and Billie Kelley Pirnie Translational Polycystic Kidney Disease Center Mayo Clinic, Rochester, Minnesota, USADivision of Nephrology and Hypertension, Mayo Clinic, Rochester, Minnesota, USA; The Mayo Clinic Robert M. and Billie Kelley Pirnie Translational Polycystic Kidney Disease Center Mayo Clinic, Rochester, Minnesota, USADivision of Nephrology and Hypertension, Mayo Clinic, Rochester, Minnesota, USA; The Mayo Clinic Robert M. and Billie Kelley Pirnie Translational Polycystic Kidney Disease Center Mayo Clinic, Rochester, Minnesota, USADivision of Nephrology and Hypertension, Mayo Clinic, Rochester, Minnesota, USA; The Mayo Clinic Robert M. and Billie Kelley Pirnie Translational Polycystic Kidney Disease Center Mayo Clinic, Rochester, Minnesota, USA; Division of Pediatric Nephrology and Hypertension, Mayo Clinic, Rochester, Minnesota, USADivision of Nephrology and Hypertension, Mayo Clinic, Rochester, Minnesota, USA; The Mayo Clinic Robert M. and Billie Kelley Pirnie Translational Polycystic Kidney Disease Center Mayo Clinic, Rochester, Minnesota, USADivision of Nephrology and Hypertension, Mayo Clinic, Rochester, Minnesota, USA; Department of Radiology, Mayo Clinic, Rochester, Minnesota, USA; The Mayo Clinic Robert M. and Billie Kelley Pirnie Translational Polycystic Kidney Disease Center Mayo Clinic, Rochester, Minnesota, USADivision of Nephrology and Hypertension, Mayo Clinic, Rochester, Minnesota, USA; The Mayo Clinic Robert M. and Billie Kelley Pirnie Translational Polycystic Kidney Disease Center Mayo Clinic, Rochester, Minnesota, USAUniversity Brest, Inserm, UMR 1078, GGB, CHU Brest, Centre de Références Maladies Rénales Héréditaires de L’enfant et de L’adulte MARHEA, F-29200 Brest, FranceDivision of Nephrology and Hypertension, Mayo Clinic, Jacksonville, Florida, USA; Correspondence: Fouad T. Chebib, Mayo Clinic, 4500 San Pablo Rd S, Jacksonville, Florida 32224, USA.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.http://www.sciencedirect.com/science/article/pii/S2468024925002876ADPKDatypicalCKDcystcyst segmentationIFT140 |
| spellingShingle | 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 Hana Yang Rachel S. Schauer Neera K. Dahl Christian Hanna Vicente E. Torres Timothy L. Kline Peter C. Harris Emilie Cornec-Le Gall Fouad T. Chebib Characterizing the ADPKD-IFT140 Phenotypic Signature With Deep Learning and Advanced Imaging Biomarkers Kidney International Reports ADPKD atypical CKD cyst cyst segmentation IFT140 |
| title | Characterizing the ADPKD-IFT140 Phenotypic Signature With Deep Learning and Advanced Imaging Biomarkers |
| title_full | Characterizing the ADPKD-IFT140 Phenotypic Signature With Deep Learning and Advanced Imaging Biomarkers |
| title_fullStr | Characterizing the ADPKD-IFT140 Phenotypic Signature With Deep Learning and Advanced Imaging Biomarkers |
| title_full_unstemmed | Characterizing the ADPKD-IFT140 Phenotypic Signature With Deep Learning and Advanced Imaging Biomarkers |
| title_short | Characterizing the ADPKD-IFT140 Phenotypic Signature With Deep Learning and Advanced Imaging Biomarkers |
| title_sort | characterizing the adpkd ift140 phenotypic signature with deep learning and advanced imaging biomarkers |
| topic | ADPKD atypical CKD cyst cyst segmentation IFT140 |
| url | http://www.sciencedirect.com/science/article/pii/S2468024925002876 |
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