CT-based Potential Predictor for CKD-free Survival after Partial Nephrectomy in Patients with Small RCC

CT-based Potential Predictor for CKD-free Survival after Partial Nephrectomy in Patients with Small RCC | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article CT-based Potential Predictor for CKD-free Survival after Partial Nephrectomy in Patients with Small RCC Seong Min Ahn, Dae Chul Jung, Min Hoan Moon, Jung Wook Lee, Kyunghwa Han, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4609411/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 01 Oct, 2025 Read the published version in BMC Urology → Version 1 posted 16 You are reading this latest preprint version Abstract Background To determine whether postoperative renal parenchymal volume from first post-operative computed tomography (CT) is a significant prognostic factor for chronic kidney disease (CKD) on the long-term follow up after partial nephrectomy (PN). Methods This retrospective study included 319 patients who underwent PN for T1 localized renal cell carcinoma (RCC) between September 2006 and December 2020. Kidney volume data of first postoperative CT and preoperative CT was made with a three-dimensional rendering software. Time-dependent cox proportional-hazards regression analysis was used to find important risk factors that indicate the development of new-onset CKD following PN, adding kidney volume data to various clinical parameters. Results Of the 319 patients who underwent PN for T1 localized RCC, a total of 13 patients (4.0%) had new-onset CKD at last follow up and developed it at a median follow up of 46 months. Univariable analyses of the Cox proportional hazards model showed that age, hypertension, preoperative/postoperative eGFR, and total kidney volume/kilogram body weight were potential risk factors associated with new-onset CKD development. In multivariable cox proportional models, the likelihood-ratio test confirmed that overall performance of models was improved by including total kidney volume (p = 0.008). Conclusions Renal parenchymal volume of first postoperative CT was a significant risk factor of CKD development on long-term follow up in patients with T1 RCC after PN. Therefore, first postoperative imaging studies will be able to help predict CKD development, as well as to assess the success of the surgery and to monitor recurrence or complications. Renal cell carcinoma Partial nephrectomy Chronic kidney disease Computed tomography Prognosis Figures Figure 1 Figure 2 Figure 3 Background Partial nephrectomy (PN) has become the standard surgical treatment for renal cell carcinoma (RCC) with a tumor diameter < 7 cm (T1a/b) [1]. PN has advantage of preserving renal function, thus delaying progression to chronic kidney disease (CKD) or reducing the risk of developing metabolic or cardiovascular disorders compared with radical nephrectomy (RN) [2, 3]. Nevertheless, about one-third of patients with preoperative estimated glomerular filtration rate (eGFR) ≥ 60 mL/min/1.73 m 2 develop CKD stage III or greater after PN [4, 5]. Multiple studies have tried to find predictive variables of long-term renal functional outcome after PN. To date, clinical variables such as age, DM, preoperative eGFR, and tumor size have been proven to be significant predictors of CKD progression in some studies. Also, using these major predictive variables, many nomograms for predicting long-term renal function have been established [6-9]. Recently, computed tomography (CT) is a widely available imaging modality for preoperative staging of RCC. Preoperative CT-based prediction models have also been suggested using various imaging features such as renal kidney volume, tumor size, location, and margin [10-13]. In addition, most guidelines suggest first post-operative imaging studies for abdominal surveillance from 6 months to 2 years in patients with localized T1 stage disease [14]. Furthermore, postoperative CT scans have revealed that the parenchymal volume of the operated kidney is reduced to varying degrees by approximately 20% or more and that compensatory hypertrophy is mostly observed in the contralateral kidney [15]. Our hypothesis was that a change in renal parenchymal volume before and after surgery could be a predictor and even prognostic factor of postoperative renal function. From first post-operative CT, we wanted to obtain more information using CT volumetry reflected on postoperative parenchymal volume changes in both kidneys. The present study aimed to determine whether post-operative renal parenchymal volume could be a significant prognostic factor for CKD with a long-term follow up. Methods 1. Study populations This retrospective study was approved by the Institutional Review Board (IRB) of our institution. The requirement for informed consent was waived by the IRB due to the retrospective nature of this study. Between September 2006 and December 2020, a total of 338 patients who underwent partial nephrectomy performed by a single surgeon for renal tumors were identified through a review of electronic medical records (Figure 1) . Inclusion criteria were: patients with T1 localized renal cell carcinoma (size < 7 cm, confirmed by pathologist), normal contralateral kidney, and available CT taken in 6 – 24 months of follow-up after surgery, including corticomedullary or nephrogenic phase. Among these patients, 19 patients were excluded if one of the following exclusion criteria was met: (1) patients who were converted to RN (n = 4), (2) those who had preoperative eGFR < 60 mL/min/1.73 m 2 (n = 8), (3) those who had pathologically confirmed benign lesions (e.g., oncocytoma, angiomyolipoma) (n = 1), (4) those who had recurrence of renal cell carcinoma after partial nephrectomy, (5) those who had developed CKD before first follow-up postoperative CT (n = 5), and (6) those who did not have available eGFR value before the first follow-up postoperative CT (n = 1). Finally, 319 patients were included in the evaluation. Figure 1. Flow diagram for selecting study patients. PN, partial nephrectomy; CT, computed tomography; eGFR, estimated glomerular filtration rate; CKD, chronic kidney disease. The following patient demographic details and perioperative information were taken: age, sex, height, weight, BMI, ASA score, DM, Hypertension, Dyslipidemia, pathologic tumor size, tumor histopathology, clamp type (total clamping, n = 2; selective clamping, n = 1; off clamp, n = 0), total operative time, preoperative eGFR, postoperative eGFR (measured on the first postoperative day), preoperative creatinine, postoperative creatinine, and RENAL score. Regarding renal function evaluation, eGFR was calculated using the Modification of Diet in Renal Disease formula. eGFR was measured in the same laboratory preoperatively and after surgery, on the first day, and first, third, sixth and the 12th months, then yearly until 7 years. The latest eGFR was defined as the value of eGFR at the last follow-up. Patients who had new-onset CKD upgrading into stage III or greater (i.e., eGFR < 60 mL/min/1.73 m 2 for at least two measurements) were analyzed. 2. CT image acquisition and Segmentation of renal parenchyma Multiphase kidney CT was performed with one of three helical CT scanners (Discovery CT 750 HD, GE Healthcare; iCT256, Philips Healthcare; or Somatom Definition Flash, Siemens Healthcare). CT protocols were as follows: 3.0-mm slice thickness, 100-kVp tube voltage, and variable tube current. Abdominal scans were carried out in the craniocaudal direction with coverage of both kidneys. After pre-contrast imaging, a nonionic contrast agent (IOBRIX® inj.240; Taejoon Pharm Co., Ltd., Seoul, Korea) was injected intravenously through a high-pressure syringe at a rate of 3-4 mL/s with a bolus tracking method. The corticomedullary phase (NP) and nephrographic phase (CMP) were scanned for 20-45 sec and 120 sec after contrast agent injection, respectively. Additionally, CT images included coronal and sagittal reformatted images. Among a total of 319 patients having first postoperative CT, 170 patients had available preoperative CT. Kidney volume data of first postoperative CT and available preoperative CT were made with a three-dimensional (3D) rendering software (3D-slicer, NIH, version 4.13.0). All CT scans from included patients were manually segmented by a technologist using this software. The software semi-automatically identified and marked pixels that constituted the remaining functional renal parenchyma. If necessary, editing was continued to completely separate the renal parenchyma from the renal pelvicalyceal system and renal sinus (Figure 2) . Figure 2. 3D segmentation process images obtained from 3D-slicer are shown. The renal parenchyma (A; axial, B; coronal, C; sagittal image, D; 3-dimmentional image) were segmented semi-automatically and the volume were measured afterward. To extract topological information from various structures of the kidney, the volume of each segmented structure was calculated. The volume was determined by multiplying the number of segmented voxels with the pixel spacing (x,y) and the slice thickness (z). Finally, volumetric data were derived from the operated ipsilateral kidney, contralateral kidney, and total kidney volume (operated ipsilateral volume plus contralateral). Using the above-mentioned method similarly, the kidney volume in the preoperative CT was measured by excluding tumor from total kidney parenchyma. 3. Statistical analysis Descriptive statistics were used to summarize clinical characteristics and outcomes. Data are expressed as mean ± SD for continuous variables and frequency (percentage) for categorical variables. Time-dependent cox models were constructed because the kidney volume changed over time during the follow-up period ( Supplementary Tables 1 and 2) . Kaplan–Meier curve analysis was performed to show CKD-free survival probability after PN surgery. CKD-free survival (CFS) time was defined as the duration between the date of the first postoperative CT scan and the date of CKD progression. Univariate Cox proportional hazards regression analysis was performed to find important risk factors that could indicate the development of new-onset CKD following PN. Variables with P-values below 0.5 in univariate analysis were included in the multivariable Cox proportional hazards regression analyses to develop predictive models. The C-index was used to evaluate the performance of the Cox proportional hazard model. The C-index indicates the proportion of samples that are correctly ranked when samples are listed in the order of predicted survival time. A value of 0.5 indicates that the model is no better at predicting an outcome than random chance and a value of 1 means that the model perfectly predicts an outcome [16]. The comparison of C-index in different cox proportional hazard models was based on the method developed by Kang et al. [17]. Likelihood ratio test (LRT) was used to compare values of the goodness of fit between different cox proportional hazard models in predicting CKD development. P -values of less than 0.05 were considered statistically significant. All statistical analyses were carried out using R software version 4.3.1 (R Foundation for Statistical Computing, http://www.r-project.org ). Results A total of 319 patients with RCC who received PN at Yonsei Severance Hospital from September 2006 to September 2020 were enrolled, including 218 (68.3 %) males and 101 (31.7 %) females. The median follow-up duration was 58 months (IQR: 37-82 months). A total of 13 (4.0 %) patients had new-onset CKD at the last follow-up and developed new-onset CKD at a median follow-up of 46 months. Other descriptive characteristics for the overall population are listed in Table 1 . Table 1 . Demographics and clinical characteristics Characteristics Mean ± SD or n (%) Age, mean±SD 52.9 ± 11.8 Sex, male, n (%) 218 (68.3%) CKD development, n (%) 13 (4.1%) Median follow-up length, months 57.5 ± 22.8 Ht, cm, mean±SD 166.4 ± 12.7 Wt, kg, mean±SD 70.3 ± 12.3 BMI, kg/m 2 , mean±SD 25.2 ± 3.3 ASA score 1 121 (38.1%) 2 160 (50.3%) 3 37 (11.6%) HTN, yes, n (%) 95 (29.8%) DM, yes, n (%) 38 (11.9%) Dyslipidemia, yes, n (%) 51 (16.0%) Preop eGFR, mean±SD 87.4 ± 11.7 Tumor size, cm, mean±SD 2.8 ± 1.4 Total OP time, min 140.8 ± 62.6 CLAMP_TYPE 0 73 (22.9%) 1 98 (30.7%) 2 148 (46.4%) Preoperative eGFR 87.4 ± 11.7 Preoperative creatinine 0.8 ± 0.2 Postoperative creatinine 0.9 ± 0.2 RENAL_SCORE 6.9 ± 2.0 Postoperative total kidney volume (PostTKV), ml 277.7 ± 80.3 Postoperative operated kidney volume (PostOKV), ml 141.3 ± 50.2 Preoperative KV (PreTKV), ml (n=170) 318.18 ± 63.9 PostTKV/BW * 10, 10 * ml/kg 39.9 ± 10.5 PostOKV/BW * 10, 10 * ml/kg 20.2 ± 6.6 PreTKV/BW * 10, 10 * ml/kg (n=170) 46.3 ± 7.7 Kaplan–Meier analysis demonstrated that CKD-free survival rates at 1-, 3-, 5- and 7-year were 99.4%, 98.7%, 96.7% and 93.3%, respectively (Figure 3). Figure 3. A, Kaplan–Meier curve showing CKD-free survival probability after PN surgery. B, Magnification of the curve with overall CKD-free survival probability of more than 0.9 which clearly demonstrate CKD-free survival rates over time. CKD, chronic kidney disease. Univariable analyses using the Cox proportional hazards model showed that age at surgery ( P = 0.001), HTN ( P = 0.005), preoperative eGFR ( P < 0.001), postoperative eGFR ( P = 0.003), and total kidney volume/kilogram body weight ( P = 0.037) were potential risk factors associated with new-onset CKD development (Table 2). Table 2. Univariable analysis of factors associated with CKD Variables HR (95% CI) p-value Age 1.1 (1.04-1.16) 0.001 Sex F Ref. Ref. M 0.84 (0.28-2.59) 0.727 BMI 1.08 (0.93-1.25) 0.286 ASA 1 Ref. Ref. 2 2.24 (0.65-7.76) 0.202 3 3.72 (0.65-21.33) 0.141 HTN (yes) 5.5 (1.69-17.88) 0.005 DM (yes) 1.6 (0.35-7.28) 0.551 Dyslipidemia (yes) 0.9 (0.2-4.07) 0.875 Preop eGFR 0.92 (0.88-0.97) 0.002 Postop eGFR 0.95 (0.91-0.99) 0.008 Tumor size 1.15 (0.83-1.6) 0.264 CLAMP_TYPE 0 Ref. 1 1.43 (0.26-7.8) 0.68 2 1.15 (0.24-5.57) 0.866 TOTAL OP TIME 1 (0.99-1.01) 0.827 Preop creatinine 1.84 (0.33-10.27) 0.524 Postop creatinine 1.74 (0.34-8.78) 0.521 RENAL SCORE 1.21 (0.92-1.6) 0.148 PostTKV 0.99 (0.99-1.0) 0.07 PostOKV 0.99 (0.98-1.0) 0.173 PreTKV (n=170) 0.98 (0.96-1.0) 0.013 TotalTKV/BW * 10 0.95 (0.93-0.97) 0.005 Two different Cox proportional hazards models were fit for CKD-free survival (Table 3). The first model (Model A) included age, HTN, and preoperative eGFR. The second model (Model B) included three predictors from Model A, plus total kidney volume/kilogram body weight. In Model B, presence of HTN at time of surgery (HR: 4.21, 95% CI: 1.20 – 14.81, P = 0.025) and lower preoperative eGFR (HR: 0.94, 95% CI: 0.89 – 0.98, P = 0.007), and smaller total kidney volume/kilogram body weight (HR: 0.95, 95% CI: 0.91 – 0.99, P = 0.020) were significant predictors. The discrimination power of the two Cox proportional hazard models was evaluated based on the C-index value. The C-index of model B (0.892) was slightly higher than that of model A (0.875). Table 3. Multivariable analysis of factors associated with CKD Model A Model B Predictors Estimates 95% CI p-value Estimates 95% CI p-value AGE 1.07 1.01 – 1.14 0.024 1.06 1.00 – 1.12 0.058 HTN 3.19 0.96 – 10.57 0.058 4.21 1.20 – 14.81 0.025 preop eGFR 0.94 0.90 – 0.99 0.016 0.94 0.89 – 0.98 0.007 Kidney volume/BW * 10 0.95 0.91 – 0.99 0.020 C-index 0.875 (se = 0.044) 0.892 (se = 0.026) The likelihood-ratio test also confirmed that the overall performance of model B was improved by including total kidney volume (Table 4) . Table 4. Comparisons of Model A and Model B in the values of prognosis prediction Model Log likelihood Degrees of Freedom χ 2 p-value Model A -55.069 3 - - Model B -51.553 4 7.0318 0.008 Discussion The present study showed that kidney volume estimated by the first post-operative CT was a significant predictor of postoperative CKD development. We also found that HTN and lower preoperative eGFR were predictive variables of postoperative renal function on a long-term follow up, which considerably aligned with results of Ali et al. [ 6 ]. They developed a nomogram to predict 5-year CKD-free survival after on-clamp PN using variables including age, tumor size, presence of diabetes mellitus, sex, and preoperative eGFR in Korea. One of the primary goals of PN is to preserve postoperative renal function, while ensuring patient’s safety and obtaining an oncological cure. Previous analyses showed that about one-third of patients with preoperative eGFR greater than 60 mL/min/1.73 m 2 progressed to CKD stage III or greater after PN [ 4 , 5 ]. Progression of CKD was found to increase the risk of cardiovascular heart disease and all-cause mortality [ 18 – 20 ]. Thus, if alternative treatment options are feasible, surgical approach should be determined with sufficient consideration of high-risk patients for CKD. Recently, many studies have used CT or Magnetic Resonance Imaging (MRI) volumetry for predicting renal function after nephrectomy. Some studies have demonstrated that preoperative parenchymal or cortex volume on CT/MR renal volumetry estimated by three-dimensional image reconstruction can predict postoperative renal function in patients with RCC after PN [ 10 , 13 ]. While volume and function are separate characteristics, CT volumetry has also been reported to be a dependable method for estimating split kidney function (SKF) and predicting the post-donation function of the remaining kidney. It has been found to be superior to nuclear renography [ 21 – 23 ]. Renal volume by CT/MR volumetry has become increasingly important for predicting renal function. However, studies using renal volume calculated by postoperative images are insufficient. After PN, the parenchymal volume of the operated ipsilateral kidney is decreased while that of the contralateral kidney is increased. Median parenchymal volume changes of the total kidney were approximately − 5% within 1 year after PN [ 15 ]. In the present study, operated ipsilateral or contralateral kidney volume was not a significant predictor of new-onset CKD development in univariate cox regression analysis, contrary to total kidney volume. This result might be explained because overall renal function was more reflected in the volume of the entire kidneys, not just one of the kidneys as shown in previous human autopsy studies, which reported that the number and size of functioning nephrons were strongly correlated with renal volume [ 24 ]. In some volumetric CT studies, parenchymal volume loss after PN has been proven to be a significant predictor of renal function loss, showing a strong correlation [ 25 , 26 ]. However, most of such studies were focused on renal volume changes after surgery. In addition, long-term prediction was not well defined. Our study proved that postoperative volume of the entire kidney itself was a significant predictor of CKD development. The purpose of obtaining the first post-operative images was to assess the success of the surgery and to monitor recurrence or complications. In patients with pT1 tumors, the rate of recurrence was found to be around 2% following PN, with a 3-year recurrence-free survival rate close to 99% and the median time to recurrence of 19 months [ 27 , 28 ]. Postoperative complications include genitourinary (e.g., urinary leak, urinary fistula, and hematuria), wound-related, and infectious problems (e.g., urinary tract infection, sepsis, and perirenal abscess). Reifsnyder et al. have reported that overall rate of complications is 17% in patients undergoing laparoscopic PN, with the majority of the complications occurring within 30 days [ 29 ]. Therefore, more information can be obtained from the first postoperative CT taken six months later other than recurrence or post-op complications by focusing on CT volumetry reflecting changes in postoperative parenchymal volume of both kidneys. This allows post-op CT to provide functional and structural information at the same time. Our final cox proportional hazards model contained four variables, including first postoperative CT volumetry data. By examining the C-index and likelihood ratio test, it was found that the model had a better performance to predict CKD-free survival outcomes. Considering this, patients with smaller postoperative total kidney volume/kilogram body weight might need a more rigorous postoperative assessment and surveillance of renal function. The present study has several limitations. First, since our study was retrospective in nature, confounding variables or selection bias might have an impact on the results. Second, this study had a single-center design, suggesting that results need to be validated by multi-center study with larger numbers of subjects. Third, among a total of 319 study patients, only 170 preoperative CT scans were available. Of patients included in our study, nearly half of these patients were referred to our hospital for a small RCC. Because CT images taken from outside hospitals were different from ours and from each other in protocols such as dynamic phase, slice thickness, and multiplanar reformation, making a segmentation using preoperative CT scan might not be accurate or consistent. Conclusions Renal parenchymal volume of the first post-operative CT was a significant risk factor of postoperative CKD development on long-term follow-up in patients with T1 RCC and normal preoperative renal function after PN. Consequently, first post-operative imaging studies will help predict CKD development for long-term follow-up, assess the success of surgery, and monitor recurrence or complications. Based on these results, patients with smaller kidney volume should have a more rigorous postoperative assessment and surveillance of renal function. Abbreviations BMI: Body mass index CKD: Chronic kidney disease CT: Computed tomography DM: Diabetes mellitus eGFR: Estimated glomerular filtration rate HTN: Hypertension MRI: Magnetic Resonance Imaging PN: Partial nephrectomy RCC: Renal cell carcinoma RN: Radical nephrectomy Declarations Data availability The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request. Acknowledgements Not applicable. Funding This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. NRF-2022R1F1A1065543). This study was supported by a grant from TAEJOON Pharmaceutical Co., Ltd. Ethics declarations Ethics approval and consent to participate The study has been approved by the institutional review board of the Severance Hospital, South Korea (IRB No. 4-2020-0533) and it conforms to the provisions of the Declaration of Helsinki. The informed consent was waived due to the retrospective design of the study by the institutional review board of the Severance Hospital, South Korea. Consent for publication Not applicable. Competing interests The authors declare no competing interests. Author information Authors and affiliations Department of Radiology, Severance Hospital, Yonsei University College of Medicine, 50 Yonsei-ro, Seodaemun-gu, Seoul 120-752, South Korea Seong Min Ahn, Dae Chul Jung Department of Radiology, Seoul Metropolitan Government Seoul National University (SMG-SNU) Boramae Medical Center, Seoul National University College of Medicine, Seoul, South Korea Min Hoan Moon Department of Biomedical Engineering and Center for Biotechnology and Interdisciplinary Studies, Rensselaer Polytechnic Institute, Troy, NY, 12180, USA Jung Wook Lee Department of Radiology, Yonsei Biomedical Research Institute, Research Institute of Radiological Science, Seoul, South Korea Kyunghwa Han, Yonghan Kwon Contributions S.M.A.: Study conception, Data analysis/interpretation, Visualization, Writing - Original Draft. D.C.J.: Study conception, Methodology, Data acquisition, Writing - Review & Editing, Funding acquisition. 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Supplementary Files Supplementarymaterials.docx Cite Share Download PDF Status: Published Journal Publication published 01 Oct, 2025 Read the published version in BMC Urology → Version 1 posted Editorial decision: Revision requested 18 Jun, 2025 Reviews received at journal 10 Jun, 2025 Reviews received at journal 04 Jun, 2025 Reviews received at journal 24 May, 2025 Reviewers agreed at journal 23 May, 2025 Reviewers agreed at journal 21 May, 2025 Reviewers agreed at journal 21 May, 2025 Reviewers agreed at journal 18 Apr, 2025 Reviews received at journal 04 Sep, 2024 Reviewers agreed at journal 31 Aug, 2024 Reviewers agreed at journal 14 Aug, 2024 Reviewers invited by journal 12 Aug, 2024 Editor invited by journal 24 Jun, 2024 Editor assigned by journal 21 Jun, 2024 Submission checks completed at journal 21 Jun, 2024 First submitted to journal 20 Jun, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4609411","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":324560941,"identity":"49542c8a-2eaa-49d0-9a30-07f7b02a60ae","order_by":0,"name":"Seong Min Ahn","email":"","orcid":"","institution":"Yonsei University College of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Seong","middleName":"Min","lastName":"Ahn","suffix":""},{"id":324560943,"identity":"4840d245-6d4f-4842-94f7-09c13e9e23ba","order_by":1,"name":"Dae Chul Jung","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAuklEQVRIiWNgGAWjYDCCAwwMzAwMFnIMDDykaZEwJl1LYgPRWviOHz74ubBNIn3DjdzDHxhq7AhrkTyTliw9s00id8ONvDQJhmPJhLUYHMgxY+YFa8kxY2BgO0CElvPvv4G0pBvcyDH+wPCPGC03cthAWhKADAMJxjYitEjeeGYszXNOwnDmmTdmEol9RPiF73zyw888ZTbyfMeBDvvwjYgQgwMFkJMSSNDAwCDfQJLyUTAKRsEoGEkAAPrEOT3JHVqVAAAAAElFTkSuQmCC","orcid":"","institution":"Yonsei University College of Medicine","correspondingAuthor":true,"prefix":"","firstName":"Dae","middleName":"Chul","lastName":"Jung","suffix":""},{"id":324560947,"identity":"ab5e1bdf-7989-43d0-8687-66f2cc7bb8f1","order_by":2,"name":"Min Hoan Moon","email":"","orcid":"","institution":"Seoul Metropolitan Government Seoul National University (SMG-SNU), Seoul National University College of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Min","middleName":"Hoan","lastName":"Moon","suffix":""},{"id":324560948,"identity":"1c94fc02-7032-413d-a712-b2358a5705fe","order_by":3,"name":"Jung Wook Lee","email":"","orcid":"","institution":"Rensselaer Polytechnic Institute","correspondingAuthor":false,"prefix":"","firstName":"Jung","middleName":"Wook","lastName":"Lee","suffix":""},{"id":324560950,"identity":"ebd7aca1-cbb9-4ecd-b1f3-9951db647b7e","order_by":4,"name":"Kyunghwa Han","email":"","orcid":"","institution":"Department of Radiology, Yonsei Biomedical Research Institute, Research Institute of Radiological Science, Seoul, South Korea","correspondingAuthor":false,"prefix":"","firstName":"Kyunghwa","middleName":"","lastName":"Han","suffix":""},{"id":324560952,"identity":"9c0d1b26-6ed2-4002-9750-c6cf3fd56f27","order_by":5,"name":"Yonghan Kwon","email":"","orcid":"","institution":"Department of Radiology, Yonsei Biomedical Research Institute, Research Institute of Radiological Science, Seoul, South Korea","correspondingAuthor":false,"prefix":"","firstName":"Yonghan","middleName":"","lastName":"Kwon","suffix":""}],"badges":[],"createdAt":"2024-06-20 06:00:07","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4609411/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4609411/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12894-025-01907-3","type":"published","date":"2025-10-01T15:56:53+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":60600971,"identity":"990bc8e4-c462-4c40-9f8f-a2621e34a150","added_by":"auto","created_at":"2024-07-18 16:03:43","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":695364,"visible":true,"origin":"","legend":"\u003cp\u003eFlow diagram for selecting study patients. PN, partial nephrectomy; CT, computed tomography; eGFR, estimated glomerular filtration rate; CKD, chronic kidney disease.\u003c/p\u003e","description":"","filename":"figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-4609411/v1/8d58017b85dd4e92e4c1ea5c.png"},{"id":60600972,"identity":"722c5f2a-9e70-4a70-aa26-8ccea1dd0ccc","added_by":"auto","created_at":"2024-07-18 16:03:43","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":2656142,"visible":true,"origin":"","legend":"\u003cp\u003e3D segmentation process images obtained from 3D-slicer are shown. The renal parenchyma (A; axial, B; coronal, C; sagittal image, D; 3-dimmentional image) were segmented semi-automatically and the volume were measured afterward.\u003c/p\u003e","description":"","filename":"figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-4609411/v1/f2918f263720872abccfbc55.png"},{"id":60600970,"identity":"5b96d049-5788-4ff6-b553-8751f82c0af1","added_by":"auto","created_at":"2024-07-18 16:03:42","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":497887,"visible":true,"origin":"","legend":"\u003cp\u003eA, Kaplan–Meier curve showing CKD-free survival probability after PN surgery. B, Magnification of the curve with overall CKD-free survival probability of more than 0.9 which clearly demonstrate CKD-free survival rates over time. CKD, chronic kidney disease.\u003c/p\u003e","description":"","filename":"figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-4609411/v1/0ca5181fe40f7265c2f19a8e.png"},{"id":92883588,"identity":"7b36d0c6-93c2-4086-8197-82cc03c91a1c","added_by":"auto","created_at":"2025-10-06 16:02:51","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":6905351,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4609411/v1/dbf8feb1-d793-4add-b661-d84b2865b374.pdf"},{"id":60600969,"identity":"a8e3ba51-3279-4ce7-a682-a17fd916fc36","added_by":"auto","created_at":"2024-07-18 16:03:42","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":33313,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarymaterials.docx","url":"https://assets-eu.researchsquare.com/files/rs-4609411/v1/05a22afbd734a87e7ebfbe14.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"CT-based Potential Predictor for CKD-free Survival after Partial Nephrectomy in Patients with Small RCC","fulltext":[{"header":"Background","content":"\u003cp\u003ePartial nephrectomy (PN) has become the standard surgical treatment for renal cell carcinoma (RCC) with a tumor diameter \u0026lt; 7 cm (T1a/b)\u0026nbsp;[1]. PN has advantage of preserving renal function, thus delaying progression to chronic kidney disease (CKD) or reducing the risk of developing metabolic or cardiovascular disorders compared with radical nephrectomy (RN)\u0026nbsp;[2, 3]. Nevertheless, about one-third of patients with preoperative estimated glomerular filtration rate (eGFR)\u0026nbsp;\u0026ge; 60 mL/min/1.73 m\u003csup\u003e2\u0026nbsp;\u003c/sup\u003edevelop CKD stage III or greater after PN\u0026nbsp;[4, 5]. Multiple studies have tried to find predictive variables of long-term renal functional outcome after PN. To date, clinical variables such as age, DM, preoperative eGFR, and tumor size have been proven to be significant predictors of CKD progression in some studies. Also, using these major predictive variables, many nomograms for predicting long-term renal function have been established\u0026nbsp;[6-9].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eRecently, computed tomography (CT) is a widely available imaging modality for preoperative staging of RCC. Preoperative CT-based prediction models have also been suggested using various imaging features such as renal kidney volume, tumor size, location, and margin\u0026nbsp;[10-13]. In addition, most guidelines suggest first post-operative imaging studies for abdominal surveillance from 6 months to 2 years in patients with localized T1 stage disease\u0026nbsp;[14]. Furthermore, postoperative CT scans have revealed that the parenchymal volume of the operated kidney is reduced to varying degrees by approximately 20% or more and that compensatory hypertrophy is mostly observed in the contralateral kidney\u0026nbsp;[15].\u003c/p\u003e\n\u003cp\u003eOur hypothesis was that a change in renal parenchymal volume before and after surgery could be a predictor and even prognostic factor of postoperative renal function. From first post-operative CT, we wanted to obtain more information using CT volumetry reflected on postoperative parenchymal volume changes in both kidneys. The present study aimed to determine whether post-operative renal parenchymal volume could be a significant prognostic factor for CKD with a long-term follow up.\u0026nbsp;\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e1. Study populations\u003c/p\u003e\n\u003cp\u003eThis retrospective study was approved by the Institutional Review Board (IRB) of our institution. The requirement for informed consent was waived by the IRB due to the retrospective nature of this study. Between September 2006 and December 2020, a total of 338 patients who underwent partial nephrectomy performed by a single surgeon for renal tumors were identified through a review of electronic medical records \u003cstrong\u003e(Figure 1)\u003c/strong\u003e. Inclusion criteria were: patients with T1 localized renal cell carcinoma (size \u0026lt; 7 cm, confirmed by pathologist), normal contralateral kidney, and available CT taken in 6 \u0026ndash; 24 months of follow-up after surgery, including corticomedullary or nephrogenic phase. Among these patients, 19 patients were excluded if one of the following exclusion criteria was met: (1) patients who were converted to RN (n = 4), (2) those who had preoperative eGFR \u0026lt; 60 mL/min/1.73 m\u003csup\u003e2\u003c/sup\u003e (n = 8), (3) those who had pathologically confirmed benign lesions (e.g., oncocytoma, angiomyolipoma) (n = 1), (4) those who had recurrence of renal cell carcinoma after partial nephrectomy, (5) those who had developed CKD before first follow-up postoperative CT (n = 5), and (6) those who did not have available eGFR value before the first follow-up postoperative CT (n = 1). Finally, 319 patients were included in the evaluation. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFigure 1. Flow diagram for selecting study patients. PN, partial nephrectomy; CT, computed tomography; eGFR, estimated glomerular filtration rate; CKD, chronic kidney disease.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe following patient demographic details and perioperative information were taken: age, sex, height, weight, BMI, ASA score, DM, Hypertension, Dyslipidemia, pathologic tumor size, tumor histopathology, clamp type\u0026nbsp;(total clamping, n = 2; selective clamping, n = 1; off clamp, n = 0), total operative time, preoperative eGFR, postoperative eGFR (measured on the first postoperative day), preoperative creatinine, postoperative creatinine, and RENAL score.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eRegarding renal function evaluation, eGFR was calculated using the Modification of Diet in Renal Disease formula. eGFR was measured in the same laboratory preoperatively and after surgery, on the first day, and first, third, sixth and the 12th months, then yearly until 7 years. The latest eGFR was defined as the value of eGFR at the last follow-up. Patients who had new-onset CKD upgrading into stage III or greater (i.e., eGFR \u0026lt; 60 mL/min/1.73 m\u003csup\u003e2\u003c/sup\u003e for at least two measurements) were analyzed.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e2. CT image acquisition and Segmentation of renal parenchyma\u003c/p\u003e\n\u003cp\u003eMultiphase kidney CT was performed with one of three helical CT scanners (Discovery CT 750 HD, GE Healthcare; iCT256, Philips Healthcare; or Somatom Definition Flash, Siemens Healthcare). CT protocols were as follows: 3.0-mm slice thickness, 100-kVp tube voltage, and variable tube current. \u0026nbsp;Abdominal scans were carried out in the craniocaudal direction with coverage of both kidneys. After pre-contrast imaging, a nonionic contrast agent (IOBRIX\u0026reg; inj.240; Taejoon Pharm Co., Ltd., Seoul, Korea) was injected intravenously through a high-pressure syringe at a rate of 3-4 mL/s with a bolus tracking method. The corticomedullary phase (NP) and nephrographic phase (CMP) were scanned for 20-45 sec and 120 sec after contrast agent injection, respectively. Additionally, CT images included coronal and sagittal reformatted images.\u003c/p\u003e\n\u003cp\u003eAmong a total of 319 patients having first postoperative CT, 170 patients had available preoperative CT. Kidney volume data of first postoperative CT and available preoperative CT were made with a three-dimensional (3D) rendering software (3D-slicer, NIH, version 4.13.0). All CT scans from included patients were manually segmented by a technologist using this software. The software semi-automatically identified and marked pixels that constituted the remaining functional renal parenchyma. If necessary, editing was continued to completely separate the renal parenchyma from the renal pelvicalyceal system and renal sinus \u003cstrong\u003e(Figure 2)\u003c/strong\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFigure 2. 3D segmentation process images obtained from 3D-slicer are shown. The renal parenchyma (A; axial, B; coronal, C; sagittal image, D; 3-dimmentional image) were segmented semi-automatically and the volume were measured afterward.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTo extract topological information from various structures of the kidney, the volume of each segmented structure was calculated. The volume was determined by multiplying the number of segmented voxels with the pixel spacing (x,y) and the slice thickness (z). Finally, volumetric data were derived from the operated ipsilateral kidney, contralateral kidney, and total kidney volume (operated ipsilateral volume plus contralateral). Using the above-mentioned method similarly, the kidney volume in the preoperative CT was measured by excluding tumor from total kidney parenchyma.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e3. Statistical analysis\u003c/p\u003e\n\u003cp\u003eDescriptive statistics were used to summarize clinical characteristics and outcomes. Data are expressed as mean\u0026nbsp;\u0026plusmn;\u0026nbsp;SD for continuous variables and frequency (percentage) for categorical variables. Time-dependent cox models were constructed because the kidney volume changed over time during the follow-up period (\u003cstrong\u003eSupplementary\u003c/strong\u003e \u003cstrong\u003eTables 1 and 2)\u003c/strong\u003e. Kaplan\u0026ndash;Meier curve analysis was performed to show CKD-free survival probability after PN surgery. CKD-free survival (CFS) time was defined as the duration between the date of the first postoperative CT scan and the date of CKD progression.\u003c/p\u003e\n\u003cp\u003eUnivariate Cox proportional hazards regression analysis was performed to find important risk factors that could indicate the development of new-onset CKD following PN. Variables with P-values below 0.5 in univariate analysis were included in the multivariable Cox proportional hazards regression analyses to develop predictive models.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe C-index was used to evaluate the performance of the Cox proportional hazard model. The C-index indicates the proportion of samples that are correctly ranked when samples are listed in the order of predicted survival time. A value of 0.5 indicates that the model is no better at predicting an outcome than random chance and a value of 1 means that the model perfectly predicts an outcome\u0026nbsp;[16].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe comparison of C-index in different cox proportional hazard models was based on the method developed by Kang et al.\u0026nbsp;[17]. Likelihood ratio test (LRT) was used to compare values of the goodness of fit between different cox proportional hazard models in predicting CKD development. \u003cem\u003eP\u003c/em\u003e-values of less than 0.05 were considered statistically significant. All statistical analyses were carried out using R software version 4.3.1 (R Foundation for Statistical Computing,\u0026nbsp;\u003ca href=\"http://www.r-project.org\"\u003ehttp://www.r-project.org\u003c/a\u003e).\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eA total of 319 patients with RCC who received PN at Yonsei Severance Hospital from September 2006 to September 2020 were enrolled, including 218 (68.3 %) males and 101 (31.7 %) females. The median follow-up duration was 58 months (IQR: 37-82 months). A total of 13 (4.0 %) patients had new-onset CKD at the last follow-up and developed new-onset CKD at a median follow-up of 46 months. Other descriptive characteristics for the overall population are listed in \u003cstrong\u003eTable 1\u003c/strong\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1\u003c/strong\u003e. Demographics and clinical characteristics\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"601\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"55.074875207986686%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCharacteristics\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"44.925124792013314%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean \u0026plusmn; SD or n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"55.074875207986686%\"\u003e\n \u003cp\u003eAge, mean\u0026plusmn;SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"44.925124792013314%\"\u003e\n \u003cp\u003e52.9 \u0026plusmn; 11.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"55.074875207986686%\"\u003e\n \u003cp\u003eSex, male, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"44.925124792013314%\"\u003e\n \u003cp\u003e218 (68.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"55.074875207986686%\"\u003e\n \u003cp\u003eCKD development, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"44.925124792013314%\"\u003e\n \u003cp\u003e13 (4.1%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"55.074875207986686%\"\u003e\n \u003cp\u003eMedian follow-up length, months\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"44.925124792013314%\"\u003e\n \u003cp\u003e57.5 \u0026plusmn; 22.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"55.074875207986686%\"\u003e\n \u003cp\u003eHt, cm, mean\u0026plusmn;SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"44.925124792013314%\"\u003e\n \u003cp\u003e166.4 \u0026plusmn; 12.7\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"55.074875207986686%\"\u003e\n \u003cp\u003eWt, kg, mean\u0026plusmn;SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"44.925124792013314%\"\u003e\n \u003cp\u003e70.3 \u0026plusmn; 12.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"55.074875207986686%\"\u003e\n \u003cp\u003eBMI, kg/m\u003csup\u003e2\u003c/sup\u003e, mean\u0026plusmn;SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"44.925124792013314%\"\u003e\n \u003cp\u003e25.2 \u0026plusmn; 3.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"55.074875207986686%\"\u003e\n \u003cp\u003eASA score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"44.925124792013314%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"55.074875207986686%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"44.925124792013314%\"\u003e\n \u003cp\u003e121 (38.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"55.074875207986686%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"44.925124792013314%\"\u003e\n \u003cp\u003e160 (50.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"55.074875207986686%\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"44.925124792013314%\"\u003e\n \u003cp\u003e37 (11.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"55.074875207986686%\"\u003e\n \u003cp\u003eHTN, yes, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"44.925124792013314%\"\u003e\n \u003cp\u003e95 (29.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"55.074875207986686%\"\u003e\n \u003cp\u003eDM, yes, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"44.925124792013314%\"\u003e\n \u003cp\u003e38 (11.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"55.074875207986686%\"\u003e\n \u003cp\u003eDyslipidemia, yes, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"44.925124792013314%\"\u003e\n \u003cp\u003e51 (16.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"55.074875207986686%\"\u003e\n \u003cp\u003ePreop\u0026nbsp;eGFR, mean\u0026plusmn;SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"44.925124792013314%\"\u003e\n \u003cp\u003e87.4 \u0026plusmn; 11.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"55.074875207986686%\"\u003e\n \u003cp\u003eTumor size, cm, mean\u0026plusmn;SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"44.925124792013314%\"\u003e\n \u003cp\u003e2.8 \u0026plusmn; 1.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"55.074875207986686%\"\u003e\n \u003cp\u003eTotal OP time, min\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"44.925124792013314%\"\u003e\n \u003cp\u003e140.8 \u0026plusmn; 62.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"55.074875207986686%\"\u003e\n \u003cp\u003eCLAMP_TYPE \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"44.925124792013314%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"55.074875207986686%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"44.925124792013314%\"\u003e\n \u003cp\u003e73 (22.9%) \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"55.074875207986686%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"44.925124792013314%\"\u003e\n \u003cp\u003e98 (30.7%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"55.074875207986686%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"44.925124792013314%\"\u003e\n \u003cp\u003e148 (46.4%) \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"55.074875207986686%\"\u003e\n \u003cp\u003ePreoperative eGFR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"44.925124792013314%\"\u003e\n \u003cp\u003e87.4 \u0026plusmn; 11.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"55.074875207986686%\"\u003e\n \u003cp\u003ePreoperative creatinine \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"44.925124792013314%\"\u003e\n \u003cp\u003e0.8 \u0026plusmn; 0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"55.074875207986686%\"\u003e\n \u003cp\u003ePostoperative creatinine\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"44.925124792013314%\"\u003e\n \u003cp\u003e0.9 \u0026plusmn; 0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"55.074875207986686%\"\u003e\n \u003cp\u003eRENAL_SCORE \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"44.925124792013314%\"\u003e\n \u003cp\u003e6.9 \u0026plusmn; 2.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"55.074875207986686%\"\u003e\n \u003cp\u003ePostoperative total kidney volume (PostTKV), ml\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"44.925124792013314%\"\u003e\n \u003cp\u003e277.7 \u0026plusmn; 80.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"55.074875207986686%\"\u003e\n \u003cp\u003ePostoperative operated kidney volume (PostOKV), ml\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"44.925124792013314%\"\u003e\n \u003cp\u003e141.3 \u0026plusmn; 50.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"55.074875207986686%\"\u003e\n \u003cp\u003ePreoperative KV (PreTKV), ml (n=170)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"44.925124792013314%\"\u003e\n \u003cp\u003e318.18 \u0026plusmn; 63.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"55.074875207986686%\"\u003e\n \u003cp\u003ePostTKV/BW * 10, 10 * ml/kg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"44.925124792013314%\"\u003e\n \u003cp\u003e39.9 \u0026plusmn; 10.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"55.074875207986686%\"\u003e\n \u003cp\u003ePostOKV/BW * 10, 10 * ml/kg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"44.925124792013314%\"\u003e\n \u003cp\u003e20.2 \u0026plusmn; 6.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"55.074875207986686%\"\u003e\n \u003cp\u003ePreTKV/BW * 10, 10 * ml/kg (n=170)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"44.925124792013314%\"\u003e\n \u003cp\u003e46.3 \u0026plusmn; 7.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eKaplan\u0026ndash;Meier analysis demonstrated that CKD-free survival rates at 1-, 3-, 5- and 7-year were 99.4%, 98.7%, 96.7% and 93.3%, respectively \u003cstrong\u003e(Figure 3).\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFigure 3. A, Kaplan\u0026ndash;Meier curve showing CKD-free survival probability after PN surgery. B, Magnification of the curve with overall CKD-free survival probability of more than 0.9 which clearly demonstrate CKD-free survival rates over time. CKD, chronic kidney disease.\u003c/p\u003e\n\u003cp\u003eUnivariable analyses using the Cox proportional hazards model showed that age at surgery (\u003cem\u003eP\u003c/em\u003e = 0.001), HTN (\u003cem\u003eP\u003c/em\u003e = 0.005), preoperative eGFR (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001), postoperative eGFR (\u003cem\u003eP\u003c/em\u003e = 0.003), and total kidney volume/kilogram body weight (\u003cem\u003eP\u003c/em\u003e = 0.037) were potential risk factors associated with new-onset CKD development \u003cstrong\u003e(Table 2).\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2.\u003c/strong\u003e Univariable analysis of factors associated with CKD\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"520\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"40.077071290944126%\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariables\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.15028901734104%\"\u003e\n \u003cp\u003e\u003cstrong\u003eHR (95% CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.772639691714836%\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"40.077071290944126%\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.15028901734104%\"\u003e\n \u003cp\u003e1.1 (1.04-1.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.772639691714836%\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"40.077071290944126%\"\u003e\n \u003cp\u003eSex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.15028901734104%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.772639691714836%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"40.077071290944126%\"\u003e\n \u003cp\u003eF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.15028901734104%\"\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.772639691714836%\"\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"40.077071290944126%\"\u003e\n \u003cp\u003eM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.15028901734104%\"\u003e\n \u003cp\u003e0.84 (0.28-2.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.772639691714836%\"\u003e\n \u003cp\u003e0.727\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"40.077071290944126%\"\u003e\n \u003cp\u003eBMI\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.15028901734104%\"\u003e\n \u003cp\u003e1.08 (0.93-1.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.772639691714836%\"\u003e\n \u003cp\u003e0.286\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"40.077071290944126%\"\u003e\n \u003cp\u003eASA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.15028901734104%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.772639691714836%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"40.077071290944126%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.15028901734104%\"\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.772639691714836%\"\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"40.077071290944126%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.15028901734104%\"\u003e\n \u003cp\u003e2.24 (0.65-7.76)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.772639691714836%\"\u003e\n \u003cp\u003e0.202\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"40.077071290944126%\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.15028901734104%\"\u003e\n \u003cp\u003e3.72 (0.65-21.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.772639691714836%\"\u003e\n \u003cp\u003e0.141\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"40.077071290944126%\"\u003e\n \u003cp\u003eHTN (yes)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.15028901734104%\"\u003e\n \u003cp\u003e5.5 (1.69-17.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.772639691714836%\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"40.077071290944126%\"\u003e\n \u003cp\u003eDM (yes)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.15028901734104%\"\u003e\n \u003cp\u003e1.6 (0.35-7.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.772639691714836%\"\u003e\n \u003cp\u003e0.551\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"40.077071290944126%\"\u003e\n \u003cp\u003eDyslipidemia (yes)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.15028901734104%\"\u003e\n \u003cp\u003e0.9 (0.2-4.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.772639691714836%\"\u003e\n \u003cp\u003e0.875\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"40.077071290944126%\"\u003e\n \u003cp\u003ePreop eGFR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.15028901734104%\"\u003e\n \u003cp\u003e0.92 (0.88-0.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.772639691714836%\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"40.077071290944126%\"\u003e\n \u003cp\u003ePostop eGFR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.15028901734104%\"\u003e\n \u003cp\u003e0.95 (0.91-0.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.772639691714836%\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"40.077071290944126%\"\u003e\n \u003cp\u003eTumor size\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.15028901734104%\"\u003e\n \u003cp\u003e1.15 (0.83-1.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.772639691714836%\"\u003e\n \u003cp\u003e0.264\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"40.077071290944126%\"\u003e\n \u003cp\u003eCLAMP_TYPE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.15028901734104%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.772639691714836%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"40.077071290944126%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.15028901734104%\"\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.772639691714836%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"40.077071290944126%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.15028901734104%\"\u003e\n \u003cp\u003e1.43 (0.26-7.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.772639691714836%\"\u003e\n \u003cp\u003e0.68\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"40.077071290944126%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.15028901734104%\"\u003e\n \u003cp\u003e1.15 (0.24-5.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.772639691714836%\"\u003e\n \u003cp\u003e0.866\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"40.077071290944126%\"\u003e\n \u003cp\u003eTOTAL OP TIME\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.15028901734104%\"\u003e\n \u003cp\u003e1 (0.99-1.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.772639691714836%\"\u003e\n \u003cp\u003e0.827\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"40.077071290944126%\"\u003e\n \u003cp\u003ePreop creatinine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.15028901734104%\"\u003e\n \u003cp\u003e1.84 (0.33-10.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.772639691714836%\"\u003e\n \u003cp\u003e0.524\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"40.077071290944126%\"\u003e\n \u003cp\u003ePostop creatinine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.15028901734104%\"\u003e\n \u003cp\u003e1.74 (0.34-8.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.772639691714836%\"\u003e\n \u003cp\u003e0.521\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"40.077071290944126%\"\u003e\n \u003cp\u003eRENAL SCORE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.15028901734104%\"\u003e\n \u003cp\u003e1.21 (0.92-1.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.772639691714836%\"\u003e\n \u003cp\u003e0.148\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"40.077071290944126%\"\u003e\n \u003cp\u003ePostTKV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.15028901734104%\"\u003e\n \u003cp\u003e0.99 (0.99-1.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.772639691714836%\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"40.077071290944126%\"\u003e\n \u003cp\u003ePostOKV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.15028901734104%\"\u003e\n \u003cp\u003e0.99 (0.98-1.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.772639691714836%\"\u003e\n \u003cp\u003e0.173\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"40.077071290944126%\"\u003e\n \u003cp\u003ePreTKV (n=170)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.15028901734104%\"\u003e\n \u003cp\u003e0.98 (0.96-1.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.772639691714836%\"\u003e\n \u003cp\u003e0.013\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"40.077071290944126%\"\u003e\n \u003cp\u003eTotalTKV/BW * 10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.15028901734104%\"\u003e\n \u003cp\u003e0.95 (0.93-0.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.772639691714836%\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eTwo different Cox proportional hazards models were fit for CKD-free survival \u003cstrong\u003e(Table 3).\u0026nbsp;\u003c/strong\u003eThe first model (Model A) included age, HTN, and preoperative eGFR. The second model (Model B) included three predictors from Model A, plus total kidney volume/kilogram body weight. In Model B, presence of HTN at time of surgery (HR: 4.21, 95% CI: 1.20 \u0026ndash; 14.81, \u003cem\u003eP\u003c/em\u003e = 0.025) and lower preoperative eGFR (HR: 0.94, 95% CI: 0.89 \u0026ndash; 0.98, \u003cem\u003eP\u003c/em\u003e = 0.007), and smaller total kidney volume/kilogram body weight (HR: 0.95, 95% CI: 0.91 \u0026ndash; 0.99, \u003cem\u003eP\u003c/em\u003e = 0.020) were significant predictors. The discrimination power of the two Cox proportional hazard models was evaluated based on the C-index value. The C-index of model B (0.892) was slightly higher than that of model A (0.875).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3.\u003c/strong\u003e Multivariable analysis of factors associated with CKD\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"629\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.119236883942765%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.65818759936407%\" colspan=\"3\"\u003e\n \u003cp\u003eModel A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.15580286168522%\" colspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003eModel B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.066772655007949%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.159235668789808%\"\u003e\n \u003cp\u003ePredictors\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.305732484076433%\"\u003e\n \u003cp\u003eEstimates\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.331210191082803%\"\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.261146496815286%\" colspan=\"2\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.464968152866241%\"\u003e\n \u003cp\u003eEstimates\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.331210191082803%\"\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.146496815286625%\" colspan=\"2\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.159235668789808%\"\u003e\n \u003cp\u003eAGE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.305732484076433%\"\u003e\n \u003cp\u003e1.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.331210191082803%\"\u003e\n \u003cp\u003e1.01\u0026nbsp;\u0026ndash;\u0026nbsp;1.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.261146496815286%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.024\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.464968152866241%\"\u003e\n \u003cp\u003e1.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.331210191082803%\"\u003e\n \u003cp\u003e1.00\u0026nbsp;\u0026ndash;\u0026nbsp;1.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.146496815286625%\" colspan=\"2\"\u003e\n \u003cp\u003e0.058\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.159235668789808%\"\u003e\n \u003cp\u003eHTN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.305732484076433%\"\u003e\n \u003cp\u003e3.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.331210191082803%\"\u003e\n \u003cp\u003e0.96\u0026nbsp;\u0026ndash;\u0026nbsp;10.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.261146496815286%\" colspan=\"2\"\u003e\n \u003cp\u003e0.058\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.464968152866241%\"\u003e\n \u003cp\u003e4.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.331210191082803%\"\u003e\n \u003cp\u003e1.20\u0026nbsp;\u0026ndash;\u0026nbsp;14.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.146496815286625%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.025\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.159235668789808%\"\u003e\n \u003cp\u003epreop eGFR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.305732484076433%\"\u003e\n \u003cp\u003e0.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.331210191082803%\"\u003e\n \u003cp\u003e0.90\u0026nbsp;\u0026ndash;\u0026nbsp;0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.261146496815286%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.016\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.464968152866241%\"\u003e\n \u003cp\u003e0.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.331210191082803%\"\u003e\n \u003cp\u003e0.89\u0026nbsp;\u0026ndash;\u0026nbsp;0.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.146496815286625%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.007\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.159235668789808%\"\u003e\n \u003cp\u003eKidney volume/BW * 10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.305732484076433%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.331210191082803%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.261146496815286%\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.464968152866241%\"\u003e\n \u003cp\u003e0.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.331210191082803%\"\u003e\n \u003cp\u003e0.91\u0026nbsp;\u0026ndash;\u0026nbsp;0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.146496815286625%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.020\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.119236883942765%\"\u003e\n \u003cp\u003eC-index\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.65818759936407%\" colspan=\"3\"\u003e\n \u003cp\u003e0.875 (se = 0.044)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.15580286168522%\" colspan=\"4\"\u003e\n \u003cp\u003e0.892 (se = 0.026)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.066772655007949%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eThe likelihood-ratio test also confirmed that the overall performance of model B was improved by including total kidney volume \u003cstrong\u003e(Table 4)\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4.\u003c/strong\u003e Comparisons of Model A and Model B in the values of prognosis prediction\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.640599001663894%\" valign=\"top\"\u003e\n \u003cp\u003eModel\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.96339434276206%\" valign=\"top\"\u003e\n \u003cp\u003eLog likelihood\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.62728785357737%\" valign=\"top\"\u003e\n \u003cp\u003eDegrees of Freedom\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.80199667221298%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026chi;\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.966722129783694%\" valign=\"top\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.640599001663894%\" valign=\"top\"\u003e\n \u003cp\u003eModel A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.96339434276206%\" valign=\"top\"\u003e\n \u003cp\u003e-55.069\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.62728785357737%\" valign=\"top\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.80199667221298%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.966722129783694%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.640599001663894%\" valign=\"top\"\u003e\n \u003cp\u003eModel B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.96339434276206%\" valign=\"top\"\u003e\n \u003cp\u003e-51.553\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.62728785357737%\" valign=\"top\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.80199667221298%\" valign=\"top\"\u003e\n \u003cp\u003e7.0318\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.966722129783694%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.008\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe present study showed that kidney volume estimated by the first post-operative CT was a significant predictor of postoperative CKD development. We also found that HTN and lower preoperative eGFR were predictive variables of postoperative renal function on a long-term follow up, which considerably aligned with results of Ali et al. [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. They developed a nomogram to predict 5-year CKD-free survival after on-clamp PN using variables including age, tumor size, presence of diabetes mellitus, sex, and preoperative eGFR in Korea. One of the primary goals of PN is to preserve postoperative renal function, while ensuring patient\u0026rsquo;s safety and obtaining an oncological cure. Previous analyses showed that about one-third of patients with preoperative eGFR greater than 60 mL/min/1.73 m\u003csup\u003e2\u003c/sup\u003e progressed to CKD stage III or greater after PN [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Progression of CKD was found to increase the risk of cardiovascular heart disease and all-cause mortality [\u003cspan additionalcitationids=\"CR19\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Thus, if alternative treatment options are feasible, surgical approach should be determined with sufficient consideration of high-risk patients for CKD.\u003c/p\u003e \u003cp\u003eRecently, many studies have used CT or Magnetic Resonance Imaging (MRI) volumetry for predicting renal function after nephrectomy. Some studies have demonstrated that preoperative parenchymal or cortex volume on CT/MR renal volumetry estimated by three-dimensional image reconstruction can predict postoperative renal function in patients with RCC after PN [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. While volume and function are separate characteristics, CT volumetry has also been reported to be a dependable method for estimating split kidney function (SKF) and predicting the post-donation function of the remaining kidney. It has been found to be superior to nuclear renography [\u003cspan additionalcitationids=\"CR22\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Renal volume by CT/MR volumetry has become increasingly important for predicting renal function. However, studies using renal volume calculated by postoperative images are insufficient. After PN, the parenchymal volume of the operated ipsilateral kidney is decreased while that of the contralateral kidney is increased. Median parenchymal volume changes of the total kidney were approximately \u0026minus;\u0026thinsp;5% within 1 year after PN [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. In the present study, operated ipsilateral or contralateral kidney volume was not a significant predictor of new-onset CKD development in univariate cox regression analysis, contrary to total kidney volume. This result might be explained because overall renal function was more reflected in the volume of the entire kidneys, not just one of the kidneys as shown in previous human autopsy studies, which reported that the number and size of functioning nephrons were strongly correlated with renal volume [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. In some volumetric CT studies, parenchymal volume loss after PN has been proven to be a significant predictor of renal function loss, showing a strong correlation [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. However, most of such studies were focused on renal volume changes after surgery. In addition, long-term prediction was not well defined. Our study proved that postoperative volume of the entire kidney itself was a significant predictor of CKD development.\u003c/p\u003e \u003cp\u003eThe purpose of obtaining the first post-operative images was to assess the success of the surgery and to monitor recurrence or complications. In patients with pT1 tumors, the rate of recurrence was found to be around 2% following PN, with a 3-year recurrence-free survival rate close to 99% and the median time to recurrence of 19 months [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Postoperative complications include genitourinary (e.g., urinary leak, urinary fistula, and hematuria), wound-related, and infectious problems (e.g., urinary tract infection, sepsis, and perirenal abscess). Reifsnyder et al. have reported that overall rate of complications is 17% in patients undergoing laparoscopic PN, with the majority of the complications occurring within 30 days [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Therefore, more information can be obtained from the first postoperative CT taken six months later other than recurrence or post-op complications by focusing on CT volumetry reflecting changes in postoperative parenchymal volume of both kidneys. This allows post-op CT to provide functional and structural information at the same time.\u003c/p\u003e \u003cp\u003eOur final cox proportional hazards model contained four variables, including first postoperative CT volumetry data. By examining the C-index and likelihood ratio test, it was found that the model had a better performance to predict CKD-free survival outcomes. Considering this, patients with smaller postoperative total kidney volume/kilogram body weight might need a more rigorous postoperative assessment and surveillance of renal function.\u003c/p\u003e \u003cp\u003eThe present study has several limitations. First, since our study was retrospective in nature, confounding variables or selection bias might have an impact on the results. Second, this study had a single-center design, suggesting that results need to be validated by multi-center study with larger numbers of subjects. Third, among a total of 319 study patients, only 170 preoperative CT scans were available. Of patients included in our study, nearly half of these patients were referred to our hospital for a small RCC. Because CT images taken from outside hospitals were different from ours and from each other in protocols such as dynamic phase, slice thickness, and multiplanar reformation, making a segmentation using preoperative CT scan might not be accurate or consistent.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eRenal parenchymal volume of the first post-operative CT was a significant risk factor of postoperative CKD development on long-term follow-up in patients with T1 RCC and normal preoperative renal function after PN. Consequently, first post-operative imaging studies will help predict CKD development for long-term follow-up, assess the success of surgery, and monitor recurrence or complications. Based on these results, patients with smaller kidney volume should have a more rigorous postoperative assessment and surveillance of renal function.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003e\u003cstrong\u003eBMI:\u003c/strong\u003e Body mass index\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCKD:\u003c/strong\u003e Chronic kidney disease\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCT:\u0026nbsp;\u003c/strong\u003eComputed tomography\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDM:\u003c/strong\u003e Diabetes mellitus\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eeGFR:\u003c/strong\u003e Estimated glomerular filtration rate\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHTN:\u003c/strong\u003e Hypertension\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMRI:\u003c/strong\u003e Magnetic Resonance Imaging\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePN:\u003c/strong\u003e Partial nephrectomy\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRCC:\u003c/strong\u003e Renal cell carcinoma\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRN:\u003c/strong\u003e Radical nephrectomy\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. NRF-2022R1F1A1065543). This study was supported by a grant from TAEJOON Pharmaceutical Co., Ltd.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics declarations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study has been approved by the institutional review board of the Severance Hospital, South Korea (IRB No. 4-2020-0533) and it conforms to the provisions of the Declaration of Helsinki. The informed consent was waived due to the retrospective design of the study by the institutional review board of the Severance Hospital, South Korea.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors and affiliations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDepartment of Radiology, Severance Hospital, Yonsei University College of Medicine, 50 Yonsei-ro, Seodaemun-gu, Seoul 120-752, South Korea\u003c/p\u003e\n\u003cp\u003eSeong Min Ahn, Dae Chul Jung\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDepartment of Radiology, Seoul Metropolitan Government Seoul National University (SMG-SNU) Boramae Medical Center, Seoul National University College of Medicine, Seoul, South Korea\u003c/p\u003e\n\u003cp\u003eMin Hoan Moon\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDepartment of Biomedical Engineering and Center for Biotechnology and Interdisciplinary Studies, Rensselaer Polytechnic Institute, Troy, NY, 12180, USA\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eJung Wook Lee\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDepartment of Radiology, Yonsei Biomedical Research Institute, Research Institute of Radiological Science, Seoul,\u0026nbsp;South Korea\u003c/p\u003e\n\u003cp\u003eKyunghwa Han, Yonghan Kwon\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eContributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eS.M.A.: Study conception, Data analysis/interpretation, Visualization, Writing - Original Draft.\u003c/p\u003e\n\u003cp\u003eD.C.J.: Study conception, Methodology, Data acquisition, Writing - Review \u0026amp; Editing, Funding acquisition.\u003c/p\u003e\n\u003cp\u003eM.H.M.: Data analysis/interpretation, Writing - Review \u0026amp; Editing.\u003c/p\u003e\n\u003cp\u003eJ.W.L.: Software, Data curation.\u003c/p\u003e\n\u003cp\u003eK.H., Y.K.: Statistical analysis.\u003c/p\u003e\n\u003cp\u003eAll authors read and approved the final manuscript.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eLjungberg B, Albiges L, Abu-Ghanem Y, et al. European Association of Urology Guidelines on Renal Cell Carcinoma: The 2022 Update. Eur Urol. 2022;82(4):399\u0026ndash;410.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMacLennan S, Imamura M, Lapitan MC, et al. Systematic review of perioperative and quality-of-life outcomes following surgical management of localised renal cancer. Eur Urol. 2012;62(6):1097\u0026ndash;117.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCapitanio U, Terrone C, Antonelli A, et al. Nephron-sparing techniques independently decrease the risk of cardiovascular events relative to radical nephrectomy in patients with a T1a-T1b renal mass and normal preoperative renal function. Eur Urol. 2015;67(4):683\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eClark MA, Shikanov S, Raman JD, et al. Chronic kidney disease before and after partial nephrectomy. 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J Urol. 2014;192(6):1612\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHarrell FE Jr, Lee KL, Mark DB. Multivariable prognostic models: issues in developing models, evaluating assumptions and adequacy, and measuring and reducing errors. Stat Med. 1996;15(4):361\u0026ndash;87.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKang L, Chen W, Petrick NA, Gallas BD. Comparing two correlated C indices with right-censored survival outcome: a one-shot nonparametric approach. Stat Med. 2015;34(4):685\u0026ndash;703.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDi Angelantonio E, Danesh J, Eiriksdottir G, Gudnason V. Renal function and risk of coronary heart disease in general populations: new prospective study and systematic review. PLoS Med. 2007;4(9):e270.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGo AS, Bansal N, Chandra M, et al. Chronic kidney disease and risk for presenting with acute myocardial infarction versus stable exertional angina in adults with coronary heart disease. J Am Coll Cardiol. 2011;58(15):1600\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTonelli M, Wiebe N, Culleton B, et al. Chronic kidney disease and mortality risk: a systematic review. J Am Soc Nephrol. 2006;17(7):2034\u0026ndash;47.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHori S, Tanaka N, Yoneda T, et al. Remnant renal volume can predict prognosis of remnant renal function in kidney transplantation donors: a prospective observational study. BMC Nephrol. 2021;22(1):367.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSummerlin AL, Lockhart ME, Strang AM, Kolettis PN, Fineberg NS, Smith JK. Determination of split renal function by 3D reconstruction of CT angiograms: a comparison with gamma camera renography. AJR Am J Roentgenol. 2008;191(5):1552\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBarbas AS, Li Y, Zair M, et al. CT volumetry is superior to nuclear renography for prediction of residual kidney function in living donors. Clin Transpl. 2016;30(9):1028\u0026ndash;35.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNyengaard JR, Bendtsen TF. Glomerular number and size in relation to age, kidney weight, and body surface in normal man. Anat Rec. 1992;232(2):194\u0026ndash;201.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSharma N, O'Hara J, Novick AC, Lieber M, Remer EM, Herts BR. Correlation between loss of renal function and loss of renal volume after partial nephrectomy for tumor in a solitary kidney. J Urol. 2008;179(4):1284\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMir MC, Campbell RA, Sharma N et al. Parenchymal volume preservation and ischemia during partial nephrectomy: functional and volumetric analysis [published correction appears in Urology. 2013;82(5):1195. Simmons, Matt N [added]]. \u003cem\u003eUrology\u003c/em\u003e. 2013;82(2):263\u0026ndash;268.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTakagi T, Yoshida K, Wada A, et al. Predictive factors for recurrence after partial nephrectomy for clinical T1 renal cell carcinoma: a retrospective study of 1227 cases from a single institution. Int J Clin Oncol. 2020;25(5):892\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAcar \u0026Ouml;, Şanlı \u0026Ouml;. Surgical Management of Local Recurrences of Renal Cell Carcinoma. Surg Res Pract. 2016;2016:2394942.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eReifsnyder JE, Ramasamy R, Ng CK, et al. Laparoscopic and open partial nephrectomy: complication comparison using the Clavien system. JSLS. 2012;16(1):38\u0026ndash;44.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-urology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"buro","sideBox":"Learn more about [BMC Urology](http://bmcurol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/buro/default.aspx","title":"BMC Urology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Renal cell carcinoma, Partial nephrectomy, Chronic kidney disease, Computed tomography, Prognosis","lastPublishedDoi":"10.21203/rs.3.rs-4609411/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4609411/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eTo determine whether postoperative renal parenchymal volume from first post-operative computed tomography (CT) is a significant prognostic factor for chronic kidney disease (CKD) on the long-term follow up after partial nephrectomy (PN).\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003e This retrospective study included 319 patients who underwent PN for T1 localized renal cell carcinoma (RCC) between September 2006 and December 2020. Kidney volume data of first postoperative CT and preoperative CT was made with a three-dimensional rendering software. Time-dependent cox proportional-hazards regression analysis was used to find important risk factors that indicate the development of new-onset CKD following PN, adding kidney volume data to various clinical parameters.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eOf the 319 patients who underwent PN for T1 localized RCC, a total of 13 patients (4.0%) had new-onset CKD at last follow up and developed it at a median follow up of 46 months. Univariable analyses of the Cox proportional hazards model showed that age, hypertension, preoperative/postoperative eGFR, and total kidney volume/kilogram body weight were potential risk factors associated with new-onset CKD development. In multivariable cox proportional models, the likelihood-ratio test confirmed that overall performance of models was improved by including total kidney volume (p\u0026thinsp;=\u0026thinsp;0.008).\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eRenal parenchymal volume of first postoperative CT was a significant risk factor of CKD development on long-term follow up in patients with T1 RCC after PN. Therefore, first postoperative imaging studies will be able to help predict CKD development, as well as to assess the success of the surgery and to monitor recurrence or complications.\u003c/p\u003e","manuscriptTitle":"CT-based Potential Predictor for CKD-free Survival after Partial Nephrectomy in Patients with Small RCC","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-07-18 16:03:37","doi":"10.21203/rs.3.rs-4609411/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-06-18T07:32:25+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-06-10T20:22:19+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-06-05T00:00:43+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-05-24T08:08:37+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"49824902928232682967891517150915826502","date":"2025-05-23T19:49:15+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"317211163651888147803241180331218617043","date":"2025-05-21T18:30:06+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"146715969200885124681818974995521342855","date":"2025-05-21T12:27:31+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"180587170332839588641944006782516036976","date":"2025-04-18T07:45:19+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-09-04T14:51:23+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"99527828719444632357472551236741191807","date":"2024-08-31T13:07:32+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"328019055533146308529958187454450571459","date":"2024-08-14T17:26:44+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-08-12T15:35:25+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-06-24T09:15:44+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-06-21T09:23:37+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-06-21T09:23:22+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Urology","date":"2024-06-20T05:58:48+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-urology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"buro","sideBox":"Learn more about [BMC Urology](http://bmcurol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/buro/default.aspx","title":"BMC Urology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"2711b67a-def2-4828-ba19-7cd191b03a6a","owner":[],"postedDate":"July 18th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-10-06T15:58:39+00:00","versionOfRecord":{"articleIdentity":"rs-4609411","link":"https://doi.org/10.1186/s12894-025-01907-3","journal":{"identity":"bmc-urology","isVorOnly":false,"title":"BMC Urology"},"publishedOn":"2025-10-01 15:56:53","publishedOnDateReadable":"October 1st, 2025"},"versionCreatedAt":"2024-07-18 16:03:37","video":"","vorDoi":"10.1186/s12894-025-01907-3","vorDoiUrl":"https://doi.org/10.1186/s12894-025-01907-3","workflowStages":[]},"version":"v1","identity":"rs-4609411","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4609411","identity":"rs-4609411","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}