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Principal Investigator: Aleksandar Denic
Organization: MAYO CLINIC ROCHESTER
Fiscal Year: 2024
Award: $351,377
Funding agency: National Institute of Diabetes and Digestive and Kidney Diseases
PROJECT SUMMARY/ABSTRACT
The prevalence and burden of chronic kidney disease (CKD) is increasing worldwide. Patients who undergo
radical or partial nephrectomy for kidney cancer resemble the general population with comorbidities, but with
the difference that a majority undergo pre- and post-surgery abdominal imaging. Despite successful surgical
tumor removal, there is a concern for future progressive CKD. Following nephrectomy, the unaffected kidney
undergoes compensatory hypertrophy, and the degree of hypertrophy and kidney function decline depend on
the comorbidity burden and the amount of the removed kidney tissue. With the recent advances in artificial
intelligence (AI)--based quantification of kidney volumes from CT scans, there is an opportunity to evaluate
automated imaging biomarkers as prognostic tools. There are also algorithms that quantify the number and
volume of simple parenchymal cysts and many radiomic/texture features. The Co-Principal Investigators in this
program are uniquely equipped for the proposed studies. Dr. Denic has expertise in kidney micro- and macro-
anatomy and advanced biostatistical skills. Dr. Kline has expertise in AI and developing advanced image
processing techniques. The central hypothesis of this proposal is that macrostructural findings on
imaging of the retained (non-operated) kidney after radical or partial nephrectomy are prognostic for
progressive CKD. In Aim 1, we will determine whether the degree of compensatory hypertrophy in the
retained kidney after nephrectomy predicts progressive CKD. Using a recently created deep learning algorithm
we developed, we will quantify the kidney, cortex, and medullary volumes in pre-surgery and follow-up CT
scans (at median 1-year post-surgery). From these volumes, we will calculate the degree of compensatory
changes in kidney volumes and assess their association with baseline comorbidities and microstructural
measures. Finally, we will develop models to predict progressive CKD. In Aim 2, we will first optimize and
finalize training of the model to quantify cysts and their size in CT images and develop postprocessing steps to
separate cortical from medullary cysts. We will then develop models to assess whether the number and size of
cysts (overall, cortical, medullary) in the retained kidney in pre-surgery scans, and changes in number and size
of cysts (overall, cortical, medullary) in the retained kidney over 1-year post-surgery, can predict progressive
CKD. In Aim 3, we will determine whether novel radiological imaging texture features on pre-surgery scans are
reflective of microstructural measures of nephron size and nephrosclerosis and whether kidney texture
features on follow-up CT scans predict progressive CKD. This research program will be facilitated by Mayo
Clinic’s outstanding clinical and research environment at all three sites dedicated to improving patient care.
The goal is to develop a tool that can guide clinical decision-making in everyday practice, and that can help
clinicians in improving their care of patients at an individual level by assessing the future risk of CKD.
Terms: <AI system><Abdomen><Abscission><Active Follow-up><Affect><Algorithms><Anatomic Sites><Anatomic structures><Anatomy><Area><Arizona><Artificial Intelligence><Biological Markers><Biometrics><Biometry><Biopsy><Biostatistics><Blood Serum><Body Tissues><CAT scan><CT X Ray><CT Xray><CT imaging><CT scan><Characteristics><Chronic Kidney Failure><Chronic Renal Disease><Chronic Renal Failure><Clinic><Clinical><Clinical Data><Communities><Compensation><Complication><Computed Tomography><Computer Reasoning><Contralateral><Creatinine><Cyst><Data><Dedications><Development><Diagnosis><Disease Progression><Distant><Environment><Excision><Extirpation><Fibrosis><Florida><Future><General Population><General Public><Generalized Growth><Goals><Growth><Histology><Hypertrophy><Image><Individual><Ischemia><Kidney><Kidney Cancer><Kidney Carcinoma><Kidney Failure><Kidney Insufficiency><Kidney Urinary System><Long-Term Effects><Longterm Effects><MR Imaging><MR Tomography><MRI><MRIs><Machine Intelligence><Magnetic Resonance Imaging><Measures><Medical Imaging, Magnetic Resonance / Nuclear Magnetic Resonance><Methods><Modeling><Modification><NMR Imaging><NMR Tomography><Nephrectomy><Nephrons><Nephrosclerosis><Normal Tissue><Normal tissue morphology><Nuclear Magnetic Resonance Imaging><Operative Procedures><Operative Surgical Procedures><Outcome><Patient Care><Patient Care Delivery><Patients><Prevalence><Principal Investigator><Radiography><Registries><Removal><Renal Cancer><Renal Carcinoma><Renal Failure><Renal Insufficiency><Renal function><Research><Risk><Risk Factors><Roentgenography><Scanning><Serum><Site><Surface><Surgical><Surgical Interventions><Surgical Procedure><Surgical Removal><Techniques><Texture><Tissue Growth><Tissues><Tomodensitometry><Training><Uriniferous Tube><Validation><Work><X-Ray CAT Scan><X-Ray Computed Tomography><X-Ray Computerized Tomography><Xray CAT scan><Xray Computed Tomography><Xray computerized tomography><Zeugmatography><abdominal CT><abdominal computed tomography><active followup><auto-segmentation><automated segmentation><automatic segmentation><autosegmentation><bio-markers><biologic marker><biomarker><care for patients><care of patients><caring for patients><catscan><chronic kidney disease><clinical decision-making><co-morbid><co-morbidity><cohort><comorbidity><computed axial tomography><computer tomography><computerized axial tomography><computerized tomography><cone-beam CT><cone-beam computed tomography><deep learning><deep learning algorithm><deep learning method><deep learning strategy><density><developmental><follow up><follow-up><followed up><followup><glomerular sclerosis><glomerulosclerosis><image processing><imaging><imaging biomarker><imaging marker><imaging-based biological marker><imaging-based biomarker><imaging-based marker><implantation><improved><individualized clinical decision><individualized decision><interstitial><kidney biopsy><kidney cortex><kidney cortical portion><kidney function><kidney medulla><kidney medullary portion><machine learning based model><machine learning model><non-contrast CT><noncontrast CT><noncontrast computed tomography><novel><ontogeny><personalized clinical decision><personalized data-driven decision><personalized decision><preservation><prognostic><prognostic tool><programs><radiologic imaging><radiological imaging><radiomics><renal><renal biopsy><renal cortex><renal medulla><renal sclerosis><resection><skills><surgery><tool><tumor><validations><volume CT><volume computed tomography><volumetric computed tomography>