{"paper_id":"4597dd08-1a50-4dc9-bc5c-dc59cbe14782","body_text":"A Contemporary Comparison of Laparoscopic versus Open Partial Nephrectomy for Renal Cell Carcinoma | 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 A Contemporary Comparison of Laparoscopic versus Open Partial Nephrectomy for Renal Cell Carcinoma Edouard Nicaise, Adam S. Feldman, Andrew Gusev, Alice Yu, Naren Nimmagadda, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3258719/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 12 Mar, 2024 Read the published version in BMC Urology → Version 1 posted 4 You are reading this latest preprint version Abstract Purpose: To analyze surgical and oncologic outcomes of patients undergoing open partial nephrectomy (OPN) versus laparoscopic partial nephrectomy (LPN) for treatment of renal cell carcinoma (RCC). Methods: We retrospectively investigated our institutional RCC database for patients who underwent PN for RCC from 1997-2018. Decision for technique was at the discretion of the operating urologist, following practice patterns and training history. Outcomes analyzed included pre/peri/post-operative parameters, pathologic outcomes, and disease recurrence rates. Results: 1088 patients underwent PN from 1997-2018. After exclusionary criteria, 631 patients who underwent 647 unique PNs for a total of 162 OPN and 485 LPN remained. Baseline, pre-op, and pathologic characteristics were not different. Surgical time was lower in laparoscopic cases [185 vs 205 minutes] (p = 0.013). Margin involvement was not different; LPN had lower estimated blood loss (EBL) [150 vs 250 mL] (p < 0.001) and longer ischemia time [21 vs 19 min] (p = 0.005). LPN had shorter length of stay [2 vs 4 days] (p < 0.001), fewer overall complications (p < 0.001), and no difference in high-grade complications [2.89 vs 4.32%] (p = .379). Fewer LPN patients developed metastases [1.65 vs 4.94%] (p = 0.0499). Local recurrence rates were not different [1.24 vs 3.09%] (p = 0.193). Renal function was equivalent between cohorts post-operatively. Conclusion: Our results show that LPN has equivalent oncologic outcomes to OPN, with no difference in patient and tumor characteristics. LPN was associated with lower EBL, shorter length of stay, and lower overall complication risk. Renal function was equally maintained. Laparoscopy Open surgery Partial Nephrectomy Renal Cell Carcinoma Renal Function Outcomes Figures Figure 1 Figure 2 Introduction Over the past 20 years the overall incidence of renal masses has notably increased in the USA [ 1 ]. Nephron-sparing surgery (NSS) has become the main approach for the treatment of small to mid-sized masses (≤ 7cm) given the impact radical nephrectomy (RN) can have on long-term renal function [ 2 – 5 ]. Greater usage of contemporary abdominal imaging has helped identify these masses amenable to partial nephrectomy (PN) [ 6 ]. Evidence supports the advantage PN has over RN when reducing the risk of surgically induced chronic kidney disease (CKD) [ 4 ]. PN also has equal oncological, post-operative, and overall survival outcomes compared to RN for the treatment of small to midsize renal masses [ 7 ]. The development of both laparoscopic and robot-assisted approaches to PN has made these techniques more appealing and increasingly used in comparison to an open approach [ 8 ]. Minimally invasive techniques for PN from earlier reports demonstrated more complications and longer operative times than open approaches requiring attention to patient selection, however, newer series have now shown evidence of decreasing complication rates, shorter ischemia time and shorter hospital stays in comparison to open surgeries [ 9 – 12 ]. The primary barrier to widespread adoption of minimally invasive approach has been technical difficulty, but advancements in laparoscopic techniques and training have helped promote its popularity and bridge this gap. Contemporary data comparing robot-assisted to laparoscopic approaches to PN exist, whereas reviews of laparoscopic compared to open approaches are often older and from outdated series [ 13 , 14 ]. Some of these reviews still question the efficacy of laparoscopic approaches compared to open approaches for partial nephrectomy [ 9 , 14 ]. We analyzed peri-operative and postoperative outcomes of pure laparoscopic vs open techniques for partial nephrectomy. We assessed the frequency and severity of postoperative complications, length of hospital stays, impact on renal function as measured by estimated glomerular filtration rate (GFR) and the CKD Stage, and the rates of locally recurrent and metastatic disease. Materials and Methods The institutional review board approved this retrospective study. We investigated our institutional renal cell carcinoma (RCC) database for patients who underwent partial nephrectomy from 1997 to 2018. Only clinical stage T1 tumors were included (cT2 n = 6). Exclusion criteria were patients who had undergone robot-assisted laparoscopic partial nephrectomy, benign surgical pathology, diagnosis of hereditary/genetic RCC syndrome and multiple tumors at initial presentation. Operations converted from laparoscopic to open were excluded from the final analysis (n = 25). 3 urologic surgeons, with a range of 7 to 20 years in practice, primarily performed the laparoscopic cases, whereas 2 urologic surgeons, with a range of 30 to 40 years of experience performed the open cases. There were 13 open surgeries performed by primarily laparoscopic surgeons that were excluded given tumor complexity (n = 13). This provided a total of 631 patients who underwent 647 PN: 485 LPN and 162 OPN. Preoperative variables included age at time of surgery, sex, BMI, tumor size according to most recent imaging prior to surgery, clinical stage, RENAL score, American Society of Anesthesiologists Physical Status (ASA Score), estimated GFR, and CKD Stage. Tumor characteristics were obtained from preoperative abdominopelvic computerized tomography and/or magnetic resonance imaging. Size was recorded as the longest single dimension of the lesion as measured by the radiologist. Clinical staging was performed according to the TNM RCC staging system. RENAL Nephrometry scoring was retrospectively determined based on review of the imaging and identified tumor features according to previously published criteria [ 15 ]. Scores were classified as Low (sum 4–6), Intermediate (sum 7–9) or High (sum 10–12) grade. The ASA Score was classified according to the documented definitions. Scores were classified as low risk (ASA Score I-II) or high risk (ASA Score III-IV). GFR was estimated using the CKD-EPI Creatinine equation with race optional. CKD Staging was based on the guidelines introduced by the National Kidney Foundation (NKF) Kidney Disease Outcomes Quality Initiative (KDOQI). Stages were then classified as low grade (CKD Stages G1-G2) and high grade (CKD Stages G3a-G5) using the cutoff GFR of 60 mL/min/1.73m2. The decision for OPN vs LPN was determined based on surgeon experience. Surgical characteristics included operative time, type of arterial clamping, ischemic time, estimated blood loss, presence of positive surgical margins and intra-operative complications. Disease outcomes included rates of local recurrence and rates of distant metastatic disease. Local recurrence was defined as tumor recurrence in the ipsilateral site of prior partial nephrectomy or adjacent perinephric tissue. Distant metastatic disease was evidenced by extrarenal imaging and occasionally confirmed with extrarenal biopsies. Abdominopelvic cross-sectional imaging and chest CT or XR were obtained at 6–12-month intervals following surgery. Postoperative complications were defined as having occurred within 30 days status post PN. Complications were also analyzed with the Clavien-Dindo grade system and separated into low-grade (grade ≤ 2) vs high-grade (grade ≥ 3a) complications. Post-operative CKD Staging and estimated GFR were determined from serum creatinine levels measured at 3 distinct time ranges: within 1 year from surgery, within 1–5 years and then 5 + years out from surgery if available based on compliance with follow-ups and laboratory appointments. Continuous data were compared using the Two-Sample Wilcoxon Rank Sum Test. Ordinal and categorical variables were compared with Pearson Chi-Square test or Fischer’s Exact Test. Kaplan-Meier analysis with log-rank comparison was performed to compare 10-year local recurrence-free and metastasis-free survival rates. All analysis was performed using Statistical/Data Analysis Special Edition v.15.1 (StataSE, College Station, Texas, USA) with a two-sided p < 0.05 considered to indicate statistical significance. Results There were 1088 patients who underwent PN from 1997–2018. Following exclusion criteria, a total of 631 patients with 647 PN cases, 162 OPN, and 485 LPN, underwent partial nephrectomy for confirmed RCC with a median follow-up of 3.4 (IQR: 1.6–6.8) years after surgery (LPN: 3.2 [1.5–6.3]; OPN: 4.0 [1.8–8.2]). Tables 1 and 2 list the demographic, tumor, operative, perioperative, and post-operative data for the patients who underwent LPN or OPN for confirmed RCC from 1997–2018 at our institution. Patient baseline characteristics of age, BMI, sex, ASA PS, tumor sizes, clinical stage, and RENAL Nephrometry Score were not statistically different between the two cohorts. There was a higher percentage of entirely endophytic tumors in the OPN group (16 [10.53%] vs 19 [4.24%], p = 0.017), although no difference was observed with nearness to collecting system or location relative to polar lines. Table 1 Baseline Characteristics Laparoscopic Open P value N = 485 N = 162 Data are presented as median (IQR) or n (%) Age at surgery (years) 59 (51–68) 57 (49–67) 0.183 Sex Male 346 (71.34%) 106 (65.43%) 0.156 Female 139 (28.66%) 56 (34.57%) BMI (kg/m^2) 29 (26–33) 29 (27–33) 0.166 ASA Score Low Grade (1–2) 365 (75.88%) 102 (70.83%) 0.221 High Grade (3–4) 116 (24.12%) 42 (29.17%) Pre-Op GFR (mL/min/1.73 m2) 81.30 (66.26–92.16) 78.08 (62.69–93.38) 0.560 Pre-Op CKD Staging LG (Stages 1–2) HG (Stages 3–5) 414 71 (85.36%) (14.64%) 130 31 (80.75%) (19.25%) 0.164 Tumor Size (cm) 2.6 (1.9–3.7) 2.8 (2.1-4.0) 0.142 Clinical Stage cT1a 398 (82.06%) 124 (76.54%) 0.123 cT1b 87 (17.94%) 38 (23.46%) RENAL Nephrometry Score Low (4–6) 252 (56.25%) 83 (54.61%) 0.229 Moderate (7–9) 187 (41.74%) 62 (40.79%) High (10–12) 9 (2.01%) 7 (4.61%) Exophytic versus Endophytic ≥ 50% exophytic < 50% exophytic Entirely endophytic 311 118 19 (69.42%) (26.34%) (4.24%) 94 42 16 (61.84%) (27.63%) (10.53) 0.017 Nearness to Collecting System (mm) 10 (2–20) 10 (2–20) 0.304 Location Relative to Polar Lines Entirely above/below < 50% across Majority between/interpolar 196 146 106 (43.75%) (32.59%) (23.66%) 61 60 31 (40.13%) (39.47%) (20.39%) 0.304 Pathologic Stage pT1a 383 (78.97%) 123 (75.93%) 0.736 pT1b 75 (15.46%) 29 (17.90%) pT2a 1 (0.21%) 0 (0%) pT2b 0 (0%) 0 (0%) pT3a 26 (5.36%) 10 (6.17%) Tumor Grade Low Grade (1–2) High Grade (3–4) Not Reported 39 3 443 (8.04%) (0.62%) (91.34%) 19 3 140 (11.73%) (1.85%) (86.42%) 0.107 RCC Subtype Clear Cell 293 (60.41%) 99 (61.11%) 0.985 Papillary 24 (4.95%) 9 (5.56%) Papillary Type 1 71 (14.64%) 21 (12.96%) Papillary Type 2 15 (3.09%) 6 (3.70%) Chromophobe 44 (9.07%) 14 (8.64%) Other Unclassified 34 4 (7.01%) (0.82%) 11 2 (6.79%) (1.23%) Table 2 Operative and Post-Operative Results Laparoscopic Open P value N = 485 N = 162 Data are presented as median (IQR) or n (%) Surgical time (mins) 185 (150–235) 205 (168–250) 0.013 Clamping Type Main artery only 353 (72.78%) 60 (37.04%) < 0.001 Artery and Vein 29 (5.98%) 39 (24.07%) Selective arterial 73 (15.05%) 4 (2.47%) None 30 (6.19%) 56 (34.57%) Unknown 0 (0%) 3 (1.85%) Ischemic Time (mins) 21 (17–27) 19 (14–25) 0.005 EBL (mL) 150 (100–300) 250 (150–425) < 0.001 Margins 0.704 Involved 15 (3.09%) 6 (3.70%) Uninvolved 470 (96.91%) 156 (96.30%) Length of Hospital Stay (days) 2 (2–3) 4 (3–5) < 0.001 Complications None 414 (85.36%) 118 (72.84%) < 0.001 Any 71 (14.64%) 44 (27.16%) Clavien-Dindo Grade HG (≥ 3) Pneumothorax Hemorrhage Urine Leak 14 1 6 7 (2.89%) 7 2 3 2 (4.32%) 0.372 LG/None 471 (97.11%) 155 (95.68%) Local Recurrence 6 (1.24%) 5 (3.09%) 0.193 Metastasis 8 (1.65%) 8 (4.94%) 0.0499 LPN was associated with shorter operative time (p = 0.013), lower EBL (p < 0.001), fewer overall postoperative complications (14.64 vs 27.16%, p < 0.001), and shorter length of stay (p < 0.001). Minor complications not requiring intervention included postoperative nausea and/or vomiting, ileus, urinary retention, minor renal hematoma, atelectasis, and thromboembolic problems. OPN had less vascular clamping at the time of tumor resection (p < 0.001) along with shorter ischemic time if clamping was performed (p = 0.005). There were no significant differences in RCC Subtype (p = 0.985) or pathological staging (p = 0.736). There was no difference in the number of Clavien-Dindo Classified ≥ 3a complications between LPN and OPN (2.89 vs 4.32%, p = 0.498). Primary urological complications noted were acute renal failure, urinary leakage, ureteral obstruction, hemorrhage, and urinary tract infection. Non-urological complications represented cardiac, hematological, gastrointestinal, pulmonary, and thromboembolic problems. Table 2 summarizes the types of high-grade postoperative complications encountered. The three primarily reported complications requiring re-intervention were pneumothorax, urine leak, and hemorrhage. No difference was observed in regard to positive margins on final pathology: 15 LPN patients compared to 6 OPN patients (3.09 vs 3.70%, p = 0.704). Local recurrence occurred in 6 LPN (1.24%) and 5 OPN (3.09%) cases, with no statistical significance (p = 0.193). Figure 1 demonstrates the no difference in 10-year recurrence-free survival rate of LPN compared to OPN. There was a difference in the 10-year rate of metastasis between LPN vs OPN (1.65 vs 4.94%, p = 0.0499). Table 3 summarizes the renal function of both cohorts pre and post-partial nephrectomy. Pre-operative GFR and CKD staging were not significantly different between LPN vs OPN. There were no differences in postoperative GFR between LPN vs OPN within the 1-year mark (73.94 vs 75.01 mL/min/1.73m2, p = 0.815), between 1–5 years post-operatively (75.81 vs 77.85, p = 0.822), or after 5 + years post-operatively (73.11 vs 67.64, p = 0.406) (Fig. 2). There was also no difference in post-operative renal function according to CKD staging. During the period of follow-up, 3 patients (2 LPN, 1 OPN) progressed to stage 5 CKD or end-stage renal disease requiring dialysis. Discussion The incidence of small renal masses has increased in the USA over the past 20 years [ 1 ]. Partial nephrectomy has proven to be an effective and equivalent oncologic treatment method in comparison to radical nephrectomy, without significant long-term impact on renal function [ 3 ]. Minimally invasive alternatives to OPN, including laparoscopic and robot-assisted PN, have become accepted. Recent studies have demonstrated improvements in overall complication rates, ischemic time, EBL, and length of stay, but there had been concerns regarding positive margin rates, local recurrence, and metastasis rates [ 16 – 19 ]. Contemporary studies have compared these long-term oncological outcomes between robot-assisted and laparoscopic PN, with favorable results for both minimally invasive techniques. However, there are fewer studies featuring comparisons of laparoscopic to open technique. Our data demonstrate that LPN has improved perioperative outcomes as measured by the shorter operative time, lower EBL, decreased postoperative complication rate, and shorter length of stay. This could be explained by the size and complexity of the tumors selected for open resection, but our data showed no significant difference in tumor size, clinical staging, or RENAL Nephrometry Score between the two cohorts. However, there was a statistically higher percentage of entirely endophytic tumors in the OPN cohort, which may have contributed to the difference in perioperative outcomes. Although remaining tumor-related factors, including nearness to collecting system and polar line location were not different. The ASA Score was not different between both cohorts as well. Although LPN is associated with lower overall postoperative complications, there was no difference in the rate of higher-grade complications with the most common being hemorrhage and urine leak. Prior studies have often demonstrated LPN is associated with longer warm ischemic time [ 10 ], [ 12 , 20 ], which was also identified in our cohort (21 vs 19 minutes). Although this was statistically significant, this difference is still relatively small and may not, in effect, be clinically significant. There was no difference in GFR between our open and laparoscopic cohorts following PN. The overall percent decline in GFR was 7–11% compared to the initial preoperative value. Only 3 patients total [2 LPN (0.4%) vs 1 OPN (0.6%)] progressed to end-stage renal disease (ESRD) during follow-up, defined as CKD Stage 5, or need for dialysis. There were early concerns for higher risk of local recurrence and distant metastasis rates in LPN vs OPN, but more recent studies have demonstrated no statistical differences in these oncologic measures [ 9 – 14 , 20 – 22 ]. Our data also supports the noninferiority of LPN with no difference in positive surgical margin, local recurrence, or metastasis rates. OPN displayed higher rates of distant metastasis and overall time to metastasis, despite statistically significant matched tumor sizes, clinical staging, and RENAL Scores. Although tumor size and stage were statistically insignificant, there were slight differences in tumor size and clinical stage which we may have been underpowered to statistically demonstrate but may partially explain the differences in metastasis. Limitations to this study included the retrospective nature of the analysis and a wide range in follow-up time for both cohorts, but our OPN and LPN cohorts were well matched in terms of patient and preoperative tumor characteristics. Tumor grading was missing from the majority of final surgical pathology reports, which could impact survival outcomes. This study will strengthen the current research on the efficacy of LPN in treating renal masses with improved perioperative and equivalent oncological outcomes. It can help demonstrate the noninferiority of LPN, particularly at sites where robot-assisted PN is unavailable given limitations in access or training. Conclusions Our results show that pure LPN has equivalent oncologic outcomes to OPN. Laparoscopic technique was associated with shorter operative time, lower EBL, shorter length of stay, and lower overall complication risk. Slight differences in tumor size, ischemia time, and rate of metastasis are of unlikely clinical significance. Renal function was equally maintained in both cohorts. Declarations Ethics Approval and Consent to Participate: All methods were carried out in accordance with the Massachusetts General Hospital Institutional Review Board (IRB). The MGH IRB ethical approval was received to retrospectively review patient data from the institutional kidney cancer database. Informed consent was not required for our retrospective review according to the MGH IRB given the retrospective nature of the study protocol. In addition, no protected health information was reported, and the exported dataset from which we worked, was entirely de-identified. Consent for Publication: Not applicable. Availability of Data and Materials: Data regarding any of the subjects in the study has not been previously published unless specified. Data will be made available to the editors of the journal for review or query upon request. Raw data and statistical code can be provided upon request. Competing Interests: All authors do not have any competing interests to disclose Statements and Declarations: No relevant financial or personal relationships to be disclosed Funding : Not applicable Authors’ Contributions: All authors participated in project planning, development, and data collection. E.N. was responsible for the data analysis. E.N., A.F., and D.D. wrote the main manuscript text. E.N. and A.G. prepared tables 1-3. E.N. prepared figures 1-2. All authors reviewed the manuscript and participated in final comments and revisions. Acknowledgments: Not applicable References A. M. Saad, M. M. Gad, M. J. Al-Husseini, I. A. Ruhban, M. B. Sonbol, and T. H. 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Urol. , vol. 190, no. 1, pp. 44–49, Jul. 2013, doi: 10.1016/j.juro.2012.12.102. Lane Brian R., Novick Andrew C., Babineau Denise, Fergany Amr F., Kaouk Jihad H., and Gill Inderbir S., “Comparison of Laparoscopic and Open Partial Nephrectomy for Tumor in a Solitary Kidney,” J. Urol. , vol. 179, no. 3, pp. 847–852, Mar. 2008, doi: 10.1016/j.juro.2007.10.050. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 12 Mar, 2024 Read the published version in BMC Urology → Version 1 posted Editorial decision: Major revision 31 Aug, 2023 Editor assigned by journal 31 Aug, 2023 Submission checks completed at journal 14 Aug, 2023 First submitted to journal 12 Aug, 2023 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-3258719\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":false,\"archivedVersions\":[],\"articleType\":\"Research Article\",\"associatedPublications\":[],\"authors\":[{\"id\":226356865,\"identity\":\"ca2261f4-f04a-4fe0-b028-fe2b0d9c409c\",\"order_by\":0,\"name\":\"Edouard Nicaise\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Massachusetts General Hospital\",\"correspondingAuthor\":false,\"submittingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Edouard\",\"middleName\":\"\",\"lastName\":\"Nicaise\",\"suffix\":\"\"},{\"id\":226356867,\"identity\":\"99b1bb00-2df5-46ad-988e-be3f1aab8511\",\"order_by\":1,\"name\":\"Adam S. Feldman\",\"email\":\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA9ElEQVRIiWNgGAWjYDAC5gOMB3iAND/xWtgSGMBaJBuAxAGStBgcIFYLfxvzgQNvc+zyjG83P/v8cYdNvnwD87GPX/BokTjGlnBw7rbkYrM7x4xnHDyTZrnhAFvybBl81tzvMTjMu405cduNBGOGg22HDQwYeIyZJfDokD/G/wGopT5x84z0z0At/w3kGwhoMTjGwwDUcjhxg0QOyJYDBsDQMGb8gEeL4TE2A6BfjifOuHOmmOFsW7KBwWG2ZGZ8XpE7xvzwwdtt1Yn9s9s3M1S22RnItzcfZvyBTw8cwJ0PtIKZhzQtQECkLaNgFIyCUTBCAAC4HFM9AvE4OAAAAABJRU5ErkJggg==\",\"orcid\":\"\",\"institution\":\"Massachusetts General Hospital\",\"correspondingAuthor\":true,\"submittingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Adam\",\"middleName\":\"S.\",\"lastName\":\"Feldman\",\"suffix\":\"\"},{\"id\":226356868,\"identity\":\"068325b7-9f4f-4648-8e73-be99b9c7293c\",\"order_by\":2,\"name\":\"Andrew Gusev\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Massachusetts General Hospital\",\"correspondingAuthor\":false,\"submittingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Andrew\",\"middleName\":\"\",\"lastName\":\"Gusev\",\"suffix\":\"\"},{\"id\":226356869,\"identity\":\"89d9c231-571f-4e6a-a75e-d8fd9a178723\",\"order_by\":3,\"name\":\"Alice Yu\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"H. Lee Moffitt Cancer Center\",\"correspondingAuthor\":false,\"submittingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Alice\",\"middleName\":\"\",\"lastName\":\"Yu\",\"suffix\":\"\"},{\"id\":226356870,\"identity\":\"bc7d438a-54be-448a-b6bc-7eddb32f0fc2\",\"order_by\":4,\"name\":\"Naren Nimmagadda\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Johns Hopkins University School of Medicine\",\"correspondingAuthor\":false,\"submittingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Naren\",\"middleName\":\"\",\"lastName\":\"Nimmagadda\",\"suffix\":\"\"},{\"id\":226356871,\"identity\":\"ebea5d6b-192e-445e-bc25-2916f75fb8f0\",\"order_by\":5,\"name\":\"Matthew F. Wszolek\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Massachusetts General Hospital\",\"correspondingAuthor\":false,\"submittingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Matthew\",\"middleName\":\"F.\",\"lastName\":\"Wszolek\",\"suffix\":\"\"},{\"id\":226356872,\"identity\":\"0aa3b971-bf40-453e-b2ab-82565b0971bf\",\"order_by\":6,\"name\":\"Francis McGovern\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Massachusetts General Hospital\",\"correspondingAuthor\":false,\"submittingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Francis\",\"middleName\":\"\",\"lastName\":\"McGovern\",\"suffix\":\"\"},{\"id\":226356873,\"identity\":\"84b13380-cbd0-4750-a39d-17f4ecb219a3\",\"order_by\":7,\"name\":\"Michael L. Blute\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Massachusetts General Hospital\",\"correspondingAuthor\":false,\"submittingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Michael\",\"middleName\":\"L.\",\"lastName\":\"Blute\",\"suffix\":\"\"},{\"id\":226356874,\"identity\":\"98266c73-e9ac-42b9-ba36-031945d0802b\",\"order_by\":8,\"name\":\"Douglas M. Dahl\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Massachusetts General Hospital\",\"correspondingAuthor\":false,\"submittingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Douglas\",\"middleName\":\"M.\",\"lastName\":\"Dahl\",\"suffix\":\"\"}],\"badges\":[],\"createdAt\":\"2023-08-12 17:44:16\",\"currentVersionCode\":1,\"declarations\":\"\",\"doi\":\"10.21203/rs.3.rs-3258719/v1\",\"doiUrl\":\"https://doi.org/10.21203/rs.3.rs-3258719/v1\",\"draftVersion\":[],\"editorialEvents\":[{\"content\":\"https://doi.org/10.1186/s12894-024-01423-w\",\"type\":\"published\",\"date\":\"2024-03-12T15:00:39+00:00\"}],\"editorialNote\":\"\",\"failedWorkflow\":false,\"files\":[{\"id\":41868067,\"identity\":\"59eadc97-b39b-4565-8e0c-7cffbdb99a31\",\"added_by\":\"auto\",\"created_at\":\"2023-08-21 13:18:18\",\"extension\":\"png\",\"order_by\":1,\"title\":\"Figure 1\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":141599,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eRecurrence-free (A) and Metastasis-Free (B) Survival Estimates for LPN vs OPN\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Figure1AB.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-3258719/v1/fb7d7581983e3038c5c5d87d.png\"},{\"id\":41868068,\"identity\":\"94cadaf2-e0af-4955-acd5-72dbeafc3013\",\"added_by\":\"auto\",\"created_at\":\"2023-08-21 13:18:18\",\"extension\":\"png\",\"order_by\":2,\"title\":\"Figure 2\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":108303,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eMedian GFR Change over time after LPN vs OPN\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Figure2GFR.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-3258719/v1/3b69d52dea48b873e432e1ca.png\"},{\"id\":52906977,\"identity\":\"655b7598-6e28-42e8-873a-1fed206ae095\",\"added_by\":\"auto\",\"created_at\":\"2024-03-18 15:08:41\",\"extension\":\"pdf\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":718661,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"manuscript.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-3258719/v1/752c2a3b-84f9-4c72-bb02-ecce632e073a.pdf\"}],\"financialInterests\":\"No competing interests reported.\",\"formattedTitle\":\"A Contemporary Comparison of Laparoscopic versus Open Partial Nephrectomy for Renal Cell Carcinoma\",\"fulltext\":[{\"header\":\"Introduction\",\"content\":\"\\u003cp\\u003eOver the past 20 years the overall incidence of renal masses has notably increased in the USA [\\u003cspan citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e1\\u003c/span\\u003e]. Nephron-sparing surgery (NSS) has become the main approach for the treatment of small to mid-sized\\u0026nbsp;masses (\\u0026le;\\u0026thinsp;7cm)\\u0026nbsp;given the impact radical nephrectomy (RN) can have on long-term renal function [\\u003cspan additionalcitationids=\\\"CR3 CR4\\\" citationid=\\\"CR2\\\" class=\\\"CitationRef\\\"\\u003e2\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR5\\\" class=\\\"CitationRef\\\"\\u003e5\\u003c/span\\u003e]. Greater usage of contemporary abdominal imaging has helped\\u0026nbsp;identify these masses amenable to partial nephrectomy (PN) [\\u003cspan citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e6\\u003c/span\\u003e].\\u0026nbsp;Evidence supports the advantage PN has over\\u0026nbsp;RN\\u0026nbsp;when reducing the risk of\\u0026nbsp;surgically induced\\u0026nbsp;chronic kidney disease (CKD) [\\u003cspan citationid=\\\"CR4\\\" class=\\\"CitationRef\\\"\\u003e4\\u003c/span\\u003e].\\u0026nbsp;PN also has equal oncological, post-operative, and overall survival outcomes compared to RN for the treatment of small to midsize renal masses [\\u003cspan citationid=\\\"CR7\\\" class=\\\"CitationRef\\\"\\u003e7\\u003c/span\\u003e]. The development of both laparoscopic and robot-assisted approaches to PN has made these techniques more appealing and increasingly used in comparison to an open approach [\\u003cspan citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e8\\u003c/span\\u003e]. Minimally invasive techniques for PN from earlier reports demonstrated more complications and longer operative times than open approaches requiring attention to patient selection, however, newer series have now shown evidence of decreasing complication rates, shorter ischemia time and shorter hospital stays in comparison to open surgeries [\\u003cspan additionalcitationids=\\\"CR10 CR11\\\" citationid=\\\"CR9\\\" class=\\\"CitationRef\\\"\\u003e9\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR12\\\" class=\\\"CitationRef\\\"\\u003e12\\u003c/span\\u003e]. The primary barrier to widespread adoption of minimally invasive approach has been technical difficulty, but advancements in laparoscopic techniques and training have helped promote its popularity and bridge this gap.\\u003c/p\\u003e \\u003cp\\u003eContemporary data comparing robot-assisted to laparoscopic approaches to PN exist, whereas reviews of laparoscopic compared to open approaches are often older and from outdated series [\\u003cspan citationid=\\\"CR13\\\" class=\\\"CitationRef\\\"\\u003e13\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR14\\\" class=\\\"CitationRef\\\"\\u003e14\\u003c/span\\u003e]. Some of these reviews still question the efficacy of laparoscopic approaches compared to open approaches for partial nephrectomy [\\u003cspan citationid=\\\"CR9\\\" class=\\\"CitationRef\\\"\\u003e9\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR14\\\" class=\\\"CitationRef\\\"\\u003e14\\u003c/span\\u003e]. We analyzed peri-operative and postoperative outcomes of pure laparoscopic vs open techniques for partial nephrectomy. We assessed the frequency and severity of postoperative complications, length of hospital stays, impact on renal function as measured by estimated glomerular filtration rate (GFR) and the CKD Stage, and the rates of locally recurrent and metastatic disease.\\u003c/p\\u003e\"},{\"header\":\"Materials and Methods\",\"content\":\"\\u003cp\\u003e The institutional review board approved this retrospective study. We investigated our institutional renal cell carcinoma (RCC) database for patients who underwent partial nephrectomy from 1997 to 2018. Only clinical stage T1 tumors were included (cT2 n\\u0026thinsp;=\\u0026thinsp;6). Exclusion criteria were patients who had undergone robot-assisted laparoscopic partial nephrectomy, benign surgical pathology, diagnosis of hereditary/genetic RCC syndrome and multiple tumors at initial presentation. Operations converted from laparoscopic to open were excluded from the final analysis (n\\u0026thinsp;=\\u0026thinsp;25). 3 urologic surgeons, with a range of 7 to 20 years in practice, primarily performed the laparoscopic cases, whereas 2 urologic surgeons, with a range of 30 to 40 years of experience performed the open cases. There were 13 open surgeries performed by primarily laparoscopic surgeons that were excluded given tumor complexity (n\\u0026thinsp;=\\u0026thinsp;13). This provided a total of 631 patients who underwent 647 PN: 485 LPN and 162 OPN.\\u003c/p\\u003e \\u003cp\\u003ePreoperative variables included age at time of surgery, sex, BMI, tumor size according to most recent imaging prior to surgery, clinical stage, RENAL score, American Society of Anesthesiologists Physical Status (ASA Score), estimated GFR, and CKD Stage. Tumor characteristics were obtained from preoperative abdominopelvic computerized tomography and/or magnetic resonance imaging. Size was recorded as the longest single dimension of the lesion as measured by the radiologist. Clinical staging was performed according to the TNM RCC staging system. RENAL Nephrometry scoring was retrospectively determined based on review of the imaging and identified tumor features according to previously published criteria [\\u003cspan citationid=\\\"CR15\\\" class=\\\"CitationRef\\\"\\u003e15\\u003c/span\\u003e]. Scores were classified as Low (sum 4\\u0026ndash;6), Intermediate (sum 7\\u0026ndash;9) or High (sum 10\\u0026ndash;12) grade. The ASA Score was classified according to the documented definitions. Scores were classified as low risk (ASA Score I-II) or high risk (ASA Score III-IV). GFR was estimated using the CKD-EPI Creatinine equation with race optional. CKD Staging was based on the guidelines introduced by the National Kidney Foundation (NKF) Kidney Disease Outcomes Quality Initiative (KDOQI). Stages were then classified as low grade (CKD Stages G1-G2) and high grade (CKD Stages G3a-G5) using the cutoff GFR of 60 mL/min/1.73m2.\\u003c/p\\u003e \\u003cp\\u003eThe decision for OPN vs LPN was determined based on surgeon experience. Surgical characteristics included operative time, type of arterial clamping, ischemic time, estimated blood loss, presence of positive surgical margins and intra-operative complications. Disease outcomes included rates of local recurrence and rates of distant metastatic disease. Local recurrence was defined as tumor recurrence in the ipsilateral site of prior partial nephrectomy or adjacent perinephric tissue. Distant metastatic disease was evidenced by extrarenal imaging and occasionally confirmed with extrarenal biopsies. Abdominopelvic cross-sectional imaging and chest CT or XR were obtained at 6\\u0026ndash;12-month intervals following surgery.\\u003c/p\\u003e \\u003cp\\u003ePostoperative complications were defined as having occurred within 30 days status post PN.\\u0026nbsp;Complications were also analyzed with the\\u0026nbsp;Clavien-Dindo grade system and separated into low-grade (grade\\u0026thinsp;\\u0026le;\\u0026thinsp;2) vs high-grade (grade\\u0026thinsp;\\u0026ge;\\u0026thinsp;3a)\\u0026nbsp;complications.\\u003c/p\\u003e \\u003cp\\u003ePost-operative CKD Staging and estimated GFR were determined from serum creatinine levels measured at 3 distinct time ranges: within 1 year from surgery, within 1\\u0026ndash;5 years and then 5\\u0026thinsp;+\\u0026thinsp;years out from surgery if available based on compliance with follow-ups and laboratory appointments.\\u003c/p\\u003e \\u003cp\\u003eContinuous data were compared using the Two-Sample Wilcoxon Rank Sum Test. Ordinal and categorical variables were compared with Pearson Chi-Square test or Fischer\\u0026rsquo;s Exact Test. Kaplan-Meier analysis with log-rank comparison was performed to compare 10-year local recurrence-free and metastasis-free survival rates. All analysis was performed using Statistical/Data Analysis Special Edition v.15.1 (StataSE, College Station, Texas, USA) with a two-sided p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05 considered to indicate statistical significance.\\u003c/p\\u003e\"},{\"header\":\"Results\",\"content\":\"\\u003cp\\u003eThere were 1088 patients who underwent PN from 1997\\u0026ndash;2018. Following exclusion criteria, a total of 631 patients with 647 PN cases, 162 OPN, and 485 LPN, underwent partial nephrectomy for confirmed RCC with a median follow-up of 3.4 (IQR: 1.6\\u0026ndash;6.8) years after surgery (LPN: 3.2 [1.5\\u0026ndash;6.3]; OPN: 4.0 [1.8\\u0026ndash;8.2]).\\u003c/p\\u003e \\u003cp\\u003eTables\\u0026nbsp;\\u003cspan refid=\\\"Tab1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e and \\u003cspan refid=\\\"Tab2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e list the demographic, tumor, operative, perioperative, and post-operative data for the patients who underwent LPN or OPN for confirmed RCC from 1997\\u0026ndash;2018 at our institution. Patient baseline characteristics of age, BMI, sex, ASA PS, tumor sizes, clinical stage, and RENAL Nephrometry Score were not statistically different between the two cohorts. There was a higher percentage of entirely endophytic tumors in the OPN group (16 [10.53%] vs 19 [4.24%], p\\u0026thinsp;=\\u0026thinsp;0.017), although no difference was observed with nearness to collecting system or location relative to polar lines.\\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab1\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 1\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003eBaseline Characteristics\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"8\\\"\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c5\\\" colnum=\\\"5\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c6\\\" colnum=\\\"6\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c7\\\" colnum=\\\"7\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c8\\\" colnum=\\\"8\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"2\\\" rowspan=\\\"3\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e \\u003cth align=\\\"left\\\" colspan=\\\"3\\\" nameend=\\\"c4\\\" namest=\\\"c2\\\"\\u003e \\u003cp\\u003eLaparoscopic\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colspan=\\\"3\\\" nameend=\\\"c7\\\" namest=\\\"c5\\\"\\u003e \\u003cp\\u003eOpen\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c8\\\" morerows=\\\"2\\\" rowspan=\\\"3\\\"\\u003e \\u003cp\\u003e\\u003cem\\u003eP\\u003c/em\\u003e\\u0026nbsp;value\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colspan=\\\"3\\\" nameend=\\\"c4\\\" namest=\\\"c2\\\"\\u003e \\u003cp\\u003eN\\u0026thinsp;=\\u0026thinsp;485\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colspan=\\\"3\\\" nameend=\\\"c7\\\" namest=\\\"c5\\\"\\u003e \\u003cp\\u003eN\\u0026thinsp;=\\u0026thinsp;162\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colspan=\\\"6\\\" nameend=\\\"c7\\\" namest=\\\"c2\\\"\\u003e \\u003cp\\u003eData are presented as median (IQR) or n (%)\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eAge at surgery (years)\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c3\\\" namest=\\\"c2\\\"\\u003e \\u003cp\\u003e59\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e(51\\u0026ndash;68)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c6\\\" namest=\\\"c5\\\"\\u003e \\u003cp\\u003e57\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e(49\\u0026ndash;67)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e0.183\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eSex\\u003c/b\\u003e\\u003c/p\\u003e \\u003cp\\u003eMale\\u003c/p\\u003e\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c3\\\" namest=\\\"c2\\\"\\u003e \\u003cp\\u003e346\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e(71.34%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c6\\\" namest=\\\"c5\\\"\\u003e \\u003cp\\u003e106\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e(65.43%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003e0.156\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eFemale\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c3\\\" namest=\\\"c2\\\"\\u003e \\u003cp\\u003e139\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e(28.66%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c6\\\" namest=\\\"c5\\\"\\u003e \\u003cp\\u003e56\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e(34.57%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eBMI (kg/m^2)\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c3\\\" namest=\\\"c2\\\"\\u003e \\u003cp\\u003e29\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e(26\\u0026ndash;33)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c6\\\" namest=\\\"c5\\\"\\u003e \\u003cp\\u003e29\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e(27\\u0026ndash;33)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e0.166\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eASA Score\\u003c/b\\u003e\\u003c/p\\u003e \\u003cp\\u003eLow Grade\\u0026nbsp;(1\\u0026ndash;2)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c3\\\" namest=\\\"c2\\\"\\u003e \\u003cp\\u003e365\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e(75.88%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c6\\\" namest=\\\"c5\\\"\\u003e \\u003cp\\u003e102\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e(70.83%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003e0.221\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eHigh Grade (3\\u0026ndash;4)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c3\\\" namest=\\\"c2\\\"\\u003e \\u003cp\\u003e116\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e(24.12%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c6\\\" namest=\\\"c5\\\"\\u003e \\u003cp\\u003e42\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e(29.17%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003ePre-Op GFR (mL/min/1.73 m2)\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c3\\\" namest=\\\"c2\\\"\\u003e \\u003cp\\u003e81.30\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e(66.26\\u0026ndash;92.16)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c6\\\" namest=\\\"c5\\\"\\u003e \\u003cp\\u003e78.08\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e(62.69\\u0026ndash;93.38)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e0.560\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003ePre-Op CKD Staging\\u003c/b\\u003e\\u003c/p\\u003e \\u003cp\\u003eLG (Stages 1\\u0026ndash;2)\\u003c/p\\u003e \\u003cp\\u003eHG (Stages 3\\u0026ndash;5)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c3\\\" namest=\\\"c2\\\"\\u003e \\u003cp\\u003e414\\u003c/p\\u003e \\u003cp\\u003e71\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e(85.36%)\\u003c/p\\u003e \\u003cp\\u003e(14.64%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c6\\\" namest=\\\"c5\\\"\\u003e \\u003cp\\u003e130\\u003c/p\\u003e \\u003cp\\u003e31\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e(80.75%)\\u003c/p\\u003e \\u003cp\\u003e(19.25%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e0.164\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eTumor Size (cm)\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c3\\\" namest=\\\"c2\\\"\\u003e \\u003cp\\u003e2.6\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e(1.9\\u0026ndash;3.7)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c6\\\" namest=\\\"c5\\\"\\u003e \\u003cp\\u003e2.8\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e(2.1-4.0)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e0.142\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eClinical Stage\\u003c/b\\u003e\\u003c/p\\u003e \\u003cp\\u003ecT1a\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c3\\\" namest=\\\"c2\\\"\\u003e \\u003cp\\u003e398\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e(82.06%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c6\\\" namest=\\\"c5\\\"\\u003e \\u003cp\\u003e124\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e(76.54%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003e0.123\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003ecT1b\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c3\\\" namest=\\\"c2\\\"\\u003e \\u003cp\\u003e87\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e(17.94%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c6\\\" namest=\\\"c5\\\"\\u003e \\u003cp\\u003e38\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e(23.46%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eRENAL\\u0026nbsp;Nephrometry\\u0026nbsp;Score\\u003c/b\\u003e\\u003c/p\\u003e \\u003cp\\u003eLow (4\\u0026ndash;6)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c3\\\" namest=\\\"c2\\\"\\u003e \\u003cp\\u003e252\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e(56.25%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c6\\\" namest=\\\"c5\\\"\\u003e \\u003cp\\u003e83\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e(54.61%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\" morerows=\\\"2\\\" rowspan=\\\"3\\\"\\u003e \\u003cp\\u003e0.229\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eModerate (7\\u0026ndash;9)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c3\\\" namest=\\\"c2\\\"\\u003e \\u003cp\\u003e187\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e(41.74%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c6\\\" namest=\\\"c5\\\"\\u003e \\u003cp\\u003e62\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e(40.79%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eHigh (10\\u0026ndash;12)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c3\\\" namest=\\\"c2\\\"\\u003e \\u003cp\\u003e9\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e(2.01%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c6\\\" namest=\\\"c5\\\"\\u003e \\u003cp\\u003e7\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e(4.61%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eExophytic versus Endophytic\\u003c/b\\u003e\\u003c/p\\u003e \\u003cp\\u003e\\u0026ge;\\u0026thinsp;50% exophytic\\u003c/p\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;50% exophytic\\u003c/p\\u003e \\u003cp\\u003eEntirely endophytic\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c3\\\" namest=\\\"c2\\\"\\u003e \\u003cp\\u003e311\\u003c/p\\u003e \\u003cp\\u003e118\\u003c/p\\u003e \\u003cp\\u003e19\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e(69.42%)\\u003c/p\\u003e \\u003cp\\u003e(26.34%)\\u003c/p\\u003e \\u003cp\\u003e(4.24%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c6\\\" namest=\\\"c5\\\"\\u003e \\u003cp\\u003e94\\u003c/p\\u003e \\u003cp\\u003e42\\u003c/p\\u003e \\u003cp\\u003e16\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e(61.84%)\\u003c/p\\u003e \\u003cp\\u003e(27.63%)\\u003c/p\\u003e \\u003cp\\u003e(10.53)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e0.017\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eNearness to Collecting System (mm)\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c3\\\" namest=\\\"c2\\\"\\u003e \\u003cp\\u003e10\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e(2\\u0026ndash;20)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c6\\\" namest=\\\"c5\\\"\\u003e \\u003cp\\u003e10\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e(2\\u0026ndash;20)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e0.304\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eLocation Relative to Polar Lines\\u003c/b\\u003e\\u003c/p\\u003e \\u003cp\\u003eEntirely above/below\\u003c/p\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;50% across\\u003c/p\\u003e \\u003cp\\u003eMajority between/interpolar\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c3\\\" namest=\\\"c2\\\"\\u003e \\u003cp\\u003e196\\u003c/p\\u003e \\u003cp\\u003e146\\u003c/p\\u003e \\u003cp\\u003e106\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e(43.75%)\\u003c/p\\u003e \\u003cp\\u003e(32.59%)\\u003c/p\\u003e \\u003cp\\u003e(23.66%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c6\\\" namest=\\\"c5\\\"\\u003e \\u003cp\\u003e61\\u003c/p\\u003e \\u003cp\\u003e60\\u003c/p\\u003e \\u003cp\\u003e31\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e(40.13%)\\u003c/p\\u003e \\u003cp\\u003e(39.47%)\\u003c/p\\u003e \\u003cp\\u003e(20.39%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e0.304\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003ePathologic Stage\\u003c/b\\u003e\\u003c/p\\u003e \\u003cp\\u003epT1a\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e383\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c4\\\" namest=\\\"c3\\\"\\u003e \\u003cp\\u003e(78.97%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e123\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c7\\\" namest=\\\"c6\\\"\\u003e \\u003cp\\u003e(75.93%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\" morerows=\\\"4\\\" rowspan=\\\"5\\\"\\u003e \\u003cp\\u003e0.736\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003epT1b\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e75\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c4\\\" namest=\\\"c3\\\"\\u003e \\u003cp\\u003e(15.46%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e29\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c7\\\" namest=\\\"c6\\\"\\u003e \\u003cp\\u003e(17.90%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003epT2a\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c4\\\" namest=\\\"c3\\\"\\u003e \\u003cp\\u003e(0.21%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c7\\\" namest=\\\"c6\\\"\\u003e \\u003cp\\u003e(0%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003epT2b\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c4\\\" namest=\\\"c3\\\"\\u003e \\u003cp\\u003e(0%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c7\\\" namest=\\\"c6\\\"\\u003e \\u003cp\\u003e(0%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003epT3a\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e26\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c4\\\" namest=\\\"c3\\\"\\u003e \\u003cp\\u003e(5.36%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e10\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c7\\\" namest=\\\"c6\\\"\\u003e \\u003cp\\u003e(6.17%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eTumor Grade\\u003c/b\\u003e\\u003c/p\\u003e \\u003cp\\u003eLow Grade\\u0026nbsp;(1\\u0026ndash;2)\\u003c/p\\u003e \\u003cp\\u003eHigh Grade (3\\u0026ndash;4)\\u003c/p\\u003e \\u003cp\\u003eNot Reported\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e39\\u003c/p\\u003e \\u003cp\\u003e3\\u003c/p\\u003e \\u003cp\\u003e443\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c4\\\" namest=\\\"c3\\\"\\u003e \\u003cp\\u003e(8.04%)\\u003c/p\\u003e \\u003cp\\u003e(0.62%)\\u003c/p\\u003e \\u003cp\\u003e(91.34%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e19\\u003c/p\\u003e \\u003cp\\u003e3\\u003c/p\\u003e \\u003cp\\u003e140\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c7\\\" namest=\\\"c6\\\"\\u003e \\u003cp\\u003e(11.73%)\\u003c/p\\u003e \\u003cp\\u003e(1.85%)\\u003c/p\\u003e \\u003cp\\u003e(86.42%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e0.107\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eRCC Subtype\\u003c/b\\u003e\\u003c/p\\u003e \\u003cp\\u003eClear\\u0026nbsp;Cell\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e293\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c4\\\" namest=\\\"c3\\\"\\u003e \\u003cp\\u003e(60.41%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e99\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c7\\\" namest=\\\"c6\\\"\\u003e \\u003cp\\u003e(61.11%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\" morerows=\\\"5\\\" rowspan=\\\"6\\\"\\u003e \\u003cp\\u003e0.985\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003ePapillary\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e24\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c4\\\" namest=\\\"c3\\\"\\u003e \\u003cp\\u003e(4.95%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e9\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c7\\\" namest=\\\"c6\\\"\\u003e \\u003cp\\u003e(5.56%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003ePapillary Type 1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e71\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c4\\\" namest=\\\"c3\\\"\\u003e \\u003cp\\u003e(14.64%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e21\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c7\\\" namest=\\\"c6\\\"\\u003e \\u003cp\\u003e(12.96%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003ePapillary Type 2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e15\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c4\\\" namest=\\\"c3\\\"\\u003e \\u003cp\\u003e(3.09%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e6\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c7\\\" namest=\\\"c6\\\"\\u003e \\u003cp\\u003e(3.70%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eChromophobe\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e44\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c4\\\" namest=\\\"c3\\\"\\u003e \\u003cp\\u003e(9.07%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e14\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c7\\\" namest=\\\"c6\\\"\\u003e \\u003cp\\u003e(8.64%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eOther\\u003c/p\\u003e \\u003cp\\u003eUnclassified\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e34\\u003c/p\\u003e \\u003cp\\u003e4\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c4\\\" namest=\\\"c3\\\"\\u003e \\u003cp\\u003e(7.01%)\\u003c/p\\u003e \\u003cp\\u003e(0.82%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e11\\u003c/p\\u003e \\u003cp\\u003e2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c7\\\" namest=\\\"c6\\\"\\u003e \\u003cp\\u003e(6.79%)\\u003c/p\\u003e \\u003cp\\u003e(1.23%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab2\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 2\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003eOperative and Post-Operative Results\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"6\\\"\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c5\\\" colnum=\\\"5\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c6\\\" colnum=\\\"6\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"2\\\" rowspan=\\\"3\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e \\u003cth align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c3\\\" namest=\\\"c2\\\"\\u003e \\u003cp\\u003eLaparoscopic\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c5\\\" namest=\\\"c4\\\"\\u003e \\u003cp\\u003eOpen\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c6\\\" morerows=\\\"2\\\" rowspan=\\\"3\\\"\\u003e \\u003cp\\u003e\\u003cem\\u003eP\\u003c/em\\u003e\\u0026nbsp;value\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c3\\\" namest=\\\"c2\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eN\\u0026thinsp;=\\u0026thinsp;485\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c5\\\" namest=\\\"c4\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eN\\u0026thinsp;=\\u0026thinsp;162\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colspan=\\\"4\\\" nameend=\\\"c5\\\" namest=\\\"c2\\\"\\u003e \\u003cp\\u003eData are presented as median (IQR) or n (%)\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eSurgical time (mins)\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e185\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e(150\\u0026ndash;235)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e205\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e(168\\u0026ndash;250)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e0.013\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eClamping Type\\u003c/b\\u003e\\u003c/p\\u003e \\u003cp\\u003eMain artery only\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e353\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e(72.78%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e60\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e(37.04%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\" morerows=\\\"4\\\" rowspan=\\\"5\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eArtery and Vein\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e29\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e(5.98%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e39\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e(24.07%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eSelective arterial\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e73\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e(15.05%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e4\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e(2.47%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eNone\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e30\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e(6.19%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e56\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e(34.57%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eUnknown\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e(0%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e3\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e(1.85%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eIschemic Time (mins)\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e21\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e(17\\u0026ndash;27)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e19\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e(14\\u0026ndash;25)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e0.005\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eEBL (mL)\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e150\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e(100\\u0026ndash;300)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e250\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e(150\\u0026ndash;425)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eMargins\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\" morerows=\\\"2\\\" rowspan=\\\"3\\\"\\u003e \\u003cp\\u003e0.704\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eInvolved\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e15\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e(3.09%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e6\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e(3.70%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eUninvolved\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e470\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e(96.91%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e156\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e(96.30%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eLength of Hospital Stay (days)\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e(2\\u0026ndash;3)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e4\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e(3\\u0026ndash;5)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eComplications\\u003c/b\\u003e\\u003c/p\\u003e \\u003cp\\u003eNone\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e414\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e(85.36%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e118\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e(72.84%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eAny\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e71\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e(14.64%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e44\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e(27.16%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eClavien-Dindo Grade\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eHG (\\u0026ge;\\u0026thinsp;3)\\u003c/p\\u003e \\u003cp\\u003e\\u003cem\\u003ePneumothorax\\u003c/em\\u003e\\u003c/p\\u003e \\u003cp\\u003e\\u003cem\\u003eHemorrhage\\u003c/em\\u003e\\u003c/p\\u003e \\u003cp\\u003e\\u003cem\\u003eUrine Leak\\u003c/em\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e14\\u003c/p\\u003e \\u003cp\\u003e\\u003cem\\u003e1\\u003c/em\\u003e\\u003c/p\\u003e \\u003cp\\u003e\\u003cem\\u003e6\\u003c/em\\u003e\\u003c/p\\u003e \\u003cp\\u003e7\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e(2.89%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e7\\u003c/p\\u003e \\u003cp\\u003e\\u003cem\\u003e2\\u003c/em\\u003e\\u003c/p\\u003e \\u003cp\\u003e\\u003cem\\u003e3\\u003c/em\\u003e\\u003c/p\\u003e \\u003cp\\u003e\\u003cem\\u003e2\\u003c/em\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e(4.32%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.372\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eLG/None\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e471\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e(97.11%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e155\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e(95.68%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eLocal Recurrence\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e6\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e(1.24%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e5\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e(3.09%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.193\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eMetastasis\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e8\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e(1.65%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e8\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e(4.94%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e0.0499\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003cp\\u003eLPN was associated with shorter operative time (p\\u0026thinsp;=\\u0026thinsp;0.013), lower EBL (p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001), fewer overall postoperative complications (14.64 vs 27.16%, p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001), and shorter length of stay (p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001). Minor complications not requiring intervention included postoperative nausea and/or vomiting, ileus, urinary retention, minor renal hematoma, atelectasis, and thromboembolic problems.\\u003c/p\\u003e \\u003cp\\u003eOPN had less vascular clamping at the time of tumor resection (p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001) along with shorter ischemic time if clamping was performed (p\\u0026thinsp;=\\u0026thinsp;0.005). There were no significant differences in RCC Subtype (p\\u0026thinsp;=\\u0026thinsp;0.985) or pathological staging (p\\u0026thinsp;=\\u0026thinsp;0.736). There was no difference in the number of Clavien-Dindo Classified\\u0026thinsp;\\u0026ge;\\u0026thinsp;3a complications between LPN and OPN (2.89 vs 4.32%, p\\u0026thinsp;=\\u0026thinsp;0.498). Primary urological complications noted were\\u0026nbsp;acute renal failure, urinary leakage, ureteral obstruction,\\u0026nbsp;hemorrhage, and urinary tract infection.\\u0026nbsp;Non-urological complications represented cardiac, hematological, gastrointestinal, pulmonary, and thromboembolic problems.\\u0026nbsp;Table\\u0026nbsp;\\u003cspan refid=\\\"Tab2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e summarizes the types of high-grade postoperative complications encountered. The three primarily reported complications requiring re-intervention were pneumothorax, urine leak, and hemorrhage. No difference was observed in regard to positive margins on final pathology: 15 LPN patients compared to 6 OPN patients (3.09 vs 3.70%, p\\u0026thinsp;=\\u0026thinsp;0.704). Local recurrence occurred in 6 LPN (1.24%) and 5 OPN (3.09%) cases, with no statistical significance (p\\u0026thinsp;=\\u0026thinsp;0.193). Figure\\u0026nbsp;1 demonstrates the no difference in 10-year recurrence-free survival rate of LPN compared to OPN. There was a difference in the 10-year rate of metastasis between LPN vs OPN (1.65 vs 4.94%, p\\u0026thinsp;=\\u0026thinsp;0.0499).\\u003c/p\\u003e \\u003cp\\u003eTable\\u0026nbsp;\\u003cspan refid=\\\"Tab3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e summarizes the renal function of both cohorts pre and post-partial nephrectomy. Pre-operative GFR and CKD staging were not significantly different between LPN vs OPN. There were no differences in postoperative GFR between LPN vs OPN within the 1-year mark (73.94 vs 75.01 mL/min/1.73m2, p\\u0026thinsp;=\\u0026thinsp;0.815), between 1\\u0026ndash;5 years post-operatively (75.81 vs 77.85, p\\u0026thinsp;=\\u0026thinsp;0.822), or after 5\\u0026thinsp;+\\u0026thinsp;years post-operatively (73.11 vs 67.64, p\\u0026thinsp;=\\u0026thinsp;0.406) (Fig.\\u0026nbsp;2). There was also no difference in post-operative renal function according to CKD staging. During the period of follow-up, 3 patients (2 LPN, 1 OPN) progressed to stage 5 CKD or end-stage renal disease requiring dialysis.\\u003c/p\\u003e \\n\\u003cp\\u003e\\u003cimg 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\\\" style=\\\"width: 679px; height: 643.397px;\\\" width=\\\"679\\\" height=\\\"643.397\\\"\\u003e\\u003cbr\\u003e\\u003c/p\\u003e\"},{\"header\":\"Discussion\",\"content\":\"\\u003cp\\u003eThe incidence of small renal masses has increased in the USA over the past 20 years [\\u003cspan citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e1\\u003c/span\\u003e]. Partial nephrectomy has proven to be an effective and equivalent oncologic treatment method in comparison to radical nephrectomy, without significant long-term impact on renal function [\\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e3\\u003c/span\\u003e]. Minimally invasive alternatives to OPN, including laparoscopic and robot-assisted PN, have become accepted. Recent studies have demonstrated improvements in overall complication rates, ischemic time, EBL, and length of stay, but there had been concerns regarding positive margin rates, local recurrence, and metastasis rates [\\u003cspan additionalcitationids=\\\"CR17 CR18\\\" citationid=\\\"CR16\\\" class=\\\"CitationRef\\\"\\u003e16\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR19\\\" class=\\\"CitationRef\\\"\\u003e19\\u003c/span\\u003e]. Contemporary studies have compared these long-term oncological outcomes between robot-assisted and laparoscopic PN, with favorable results for both minimally invasive techniques. However, there are fewer studies featuring comparisons of laparoscopic to open technique.\\u003c/p\\u003e \\u003cp\\u003eOur data demonstrate that LPN has improved perioperative outcomes as measured by the shorter operative time, lower EBL, decreased postoperative complication rate, and shorter length of stay. This could be explained by the size and complexity of the tumors selected for open resection, but our data showed no significant difference in tumor size, clinical staging, or RENAL Nephrometry Score between the two cohorts. However, there was a statistically higher percentage of entirely endophytic tumors in the OPN cohort, which may have contributed to the difference in perioperative outcomes. Although remaining tumor-related factors, including nearness to collecting system and polar line location were not different. The ASA Score was not different between both cohorts as well. Although LPN is associated with lower overall postoperative complications, there was no difference in the rate of higher-grade complications with the most common being hemorrhage and urine leak.\\u003c/p\\u003e \\u003cp\\u003ePrior studies have often demonstrated LPN is associated with longer warm ischemic time [\\u003cspan citationid=\\\"CR10\\\" class=\\\"CitationRef\\\"\\u003e10\\u003c/span\\u003e], [\\u003cspan citationid=\\\"CR12\\\" class=\\\"CitationRef\\\"\\u003e12\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR20\\\" class=\\\"CitationRef\\\"\\u003e20\\u003c/span\\u003e], which was also identified in our cohort (21 vs 19 minutes). Although this was statistically significant, this difference is still relatively small and may not, in effect, be clinically significant. There was no difference in GFR between our open and laparoscopic cohorts following PN. The overall percent decline in GFR was 7\\u0026ndash;11% compared to the initial preoperative value. Only 3 patients total [2 LPN (0.4%) vs 1 OPN (0.6%)] progressed to end-stage renal disease (ESRD) during follow-up, defined as CKD Stage 5, or need for dialysis.\\u003c/p\\u003e \\u003cp\\u003eThere were early concerns for higher risk of local recurrence and distant metastasis rates in LPN vs OPN, but more recent studies have demonstrated no statistical differences in these oncologic measures [\\u003cspan additionalcitationids=\\\"CR10 CR11 CR12 CR13\\\" citationid=\\\"CR9\\\" class=\\\"CitationRef\\\"\\u003e9\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR14\\\" class=\\\"CitationRef\\\"\\u003e14\\u003c/span\\u003e, \\u003cspan additionalcitationids=\\\"CR21\\\" citationid=\\\"CR20\\\" class=\\\"CitationRef\\\"\\u003e20\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR22\\\" class=\\\"CitationRef\\\"\\u003e22\\u003c/span\\u003e]. Our data also supports the noninferiority of LPN with no difference in positive surgical margin, local recurrence, or metastasis rates. OPN displayed higher rates of distant metastasis and overall time to metastasis, despite statistically significant matched tumor sizes, clinical staging, and RENAL Scores. Although tumor size and stage were statistically insignificant, there were slight differences in tumor size and clinical stage which we may have been underpowered to statistically demonstrate but may partially explain the differences in metastasis.\\u003c/p\\u003e \\u003cp\\u003eLimitations to this study included the retrospective nature of the analysis and a wide range in follow-up time for both cohorts, but our OPN and LPN cohorts were well matched in terms of patient and preoperative tumor characteristics. Tumor grading was missing from the majority of final surgical pathology reports, which could impact survival outcomes. This study will strengthen the current research on the efficacy of LPN in treating renal masses with improved perioperative and equivalent oncological outcomes. It can help demonstrate the noninferiority of LPN, particularly at sites where robot-assisted PN is unavailable given limitations in access or training.\\u003c/p\\u003e\"},{\"header\":\"Conclusions\",\"content\":\"\\u003cp\\u003eOur results show that pure LPN has equivalent oncologic outcomes to OPN. Laparoscopic technique was associated with shorter operative time, lower EBL, shorter length of stay, and lower overall complication risk. Slight differences in tumor size, ischemia time, and rate of metastasis are of unlikely clinical significance. Renal function was equally maintained in both cohorts.\\u003c/p\\u003e\"},{\"header\":\"Declarations\",\"content\":\"\\u003cp\\u003e\\u003cem\\u003eEthics Approval and Consent to Participate:\\u0026nbsp;\\u003c/em\\u003eAll methods were carried out in accordance with the Massachusetts General Hospital Institutional Review Board (IRB). \\u0026nbsp;The MGH IRB ethical approval was received to retrospectively review patient data from the institutional kidney cancer database. Informed consent was not required for our retrospective review according to the MGH IRB given the retrospective nature of the study protocol. In addition, no protected health information was reported, and the exported dataset from which we worked, was entirely de-identified.\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cem\\u003eConsent for Publication:\\u0026nbsp;\\u003c/em\\u003eNot applicable.\\u003cem\\u003e\\u0026nbsp;\\u003c/em\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cem\\u003eAvailability of Data and Materials:\\u003c/em\\u003e Data regarding any of the subjects in the study has not been previously published unless specified. Data will be made available to the editors of the journal for review or query upon request. Raw data and statistical code can be provided upon request.\\u003cem\\u003e\\u0026nbsp;\\u003c/em\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cem\\u003eCompeting Interests:\\u003c/em\\u003e All authors do not have any competing interests to disclose\\u003cem\\u003e\\u0026nbsp;\\u003c/em\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eStatements and Declarations:\\u003cstrong\\u003e\\u0026nbsp;\\u003c/strong\\u003eNo relevant financial or personal relationships to be disclosed\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cem\\u003eFunding\\u003c/em\\u003e: Not applicable\\u003cem\\u003e\\u0026nbsp;\\u003c/em\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cem\\u003eAuthors\\u0026rsquo; Contributions:\\u0026nbsp;\\u003c/em\\u003eAll authors participated in project planning, development, and data collection. E.N. was responsible for the data analysis. E.N., A.F., and D.D. wrote the main manuscript text. E.N. and A.G. prepared tables 1-3. E.N. prepared figures 1-2. All authors reviewed the manuscript and participated in final comments and revisions.\\u003cem\\u003e\\u0026nbsp;\\u003c/em\\u003e\\u003c/p\\u003e\\n\\n\\u003cp\\u003e\\u003cem\\u003eAcknowledgments:\\u003c/em\\u003e Not applicable\\u003c/p\\u003e\"},{\"header\":\"References\",\"content\":\"\\u003col\\u003e\\n\\u003cli\\u003eA. M. Saad, M. M. Gad, M. J. Al-Husseini, I. A. Ruhban, M. B. Sonbol, and T. H. Ho, \\u0026ldquo;Trends in Renal-Cell Carcinoma Incidence and Mortality in the United States in the Last 2 Decades: A SEER-Based Study,\\u0026rdquo; \\u003cem\\u003eClin. Genitourin. Cancer\\u003c/em\\u003e, vol. 17, no. 1, pp. 46-57.e5, Feb. 2019, doi: 10.1016/j.clgc.2018.10.002.\\u003c/li\\u003e\\n\\u003cli\\u003eR. J. Motzer \\u003cem\\u003eet al.\\u003c/em\\u003e, \\u0026ldquo;Kidney Cancer, Version 3.2022, NCCN Clinical Practice Guidelines in Oncology,\\u0026rdquo; \\u003cem\\u003eJ. Natl. Compr. Canc. Netw.\\u003c/em\\u003e, vol. 20, no. 1, pp. 71\\u0026ndash;90, Jan. 2022, doi: 10.6004/jnccn.2022.0001.\\u003c/li\\u003e\\n\\u003cli\\u003eM. A. Clark \\u003cem\\u003eet al.\\u003c/em\\u003e, \\u0026ldquo;Chronic Kidney Disease Before and After Partial Nephrectomy,\\u0026rdquo; \\u003cem\\u003eJ. Urol.\\u003c/em\\u003e, vol. 185, no. 1, pp. 43\\u0026ndash;48, Jan. 2011, doi: 10.1016/j.juro.2010.09.019.\\u003c/li\\u003e\\n\\u003cli\\u003eE. Streja, K. Kalantar-Zadeh, M. Z. Molnar, J. Landman, O. A. Arah, and C. P. Kovesdy, \\u0026ldquo;Radical versus partial nephrectomy, chronic kidney disease progression and mortality in US veterans,\\u0026rdquo; \\u003cem\\u003eNephrol. Dial. Transplant.\\u003c/em\\u003e, p. gfw358, Oct. 2016, doi: 10.1093/ndt/gfw358.\\u003c/li\\u003e\\n\\u003cli\\u003eS. Campbell \\u003cem\\u003eet al.\\u003c/em\\u003e, \\u0026ldquo;Renal Mass and Localized Renal Cancer: AUA Guideline,\\u0026rdquo; \\u003cem\\u003eJ. Urol.\\u003c/em\\u003e, vol. 198, no. 3, pp. 520\\u0026ndash;529, Sep. 2017, doi: 10.1016/j.juro.2017.04.100.\\u003c/li\\u003e\\n\\u003cli\\u003eJ. H. Kim \\u003cem\\u003eet al.\\u003c/em\\u003e, \\u0026ldquo;National trends of preoperative imaging modalities before partial nephrectomy for renal masses in the U.S. from 2007\\u0026ndash;2015,\\u0026rdquo; \\u003cem\\u003eCan. Urol. Assoc. J.\\u003c/em\\u003e, vol. 13, no. 3, Aug. 2018, doi: 10.5489/cuaj.5414.\\u003c/li\\u003e\\n\\u003cli\\u003eE. Scosyrev \\u003cem\\u003eet al.\\u003c/em\\u003e, \\u0026ldquo;Overall Survival after Partial Versus Radical Nephrectomy for a Small Renal Mass: Systematic Review of Observational Studies,\\u0026rdquo; \\u003cem\\u003eUrol. Pract.\\u003c/em\\u003e, vol. 1, no. 1, pp. 27\\u0026ndash;34, May 2014, doi: 10.1016/j.urpr.2014.02.009.\\u003c/li\\u003e\\n\\u003cli\\u003eK. R. Ghani, S. Sukumar, J. D. Sammon, C. G. Rogers, Q.-D. Trinh, and M. Menon, \\u0026ldquo;Practice Patterns and Outcomes of Open and Minimally Invasive Partial Nephrectomy Since the Introduction of Robotic Partial Nephrectomy: Results from the Nationwide Inpatient Sample,\\u0026rdquo; \\u003cem\\u003eJ. Urol.\\u003c/em\\u003e, vol. 191, no. 4, pp. 907\\u0026ndash;913, Apr. 2014, doi: 10.1016/j.juro.2013.10.099.\\u003c/li\\u003e\\n\\u003cli\\u003eG. H. Rezaeetalab, H. Karami, F. Dadkhah, N. Simforoosh, and N. Shakhssalim, \\u0026ldquo;Laparoscopic Versus Open Partial Nephrectomy for Stage T1a of Renal Tumors,\\u0026rdquo; \\u003cem\\u003eUrol. J.\\u003c/em\\u003e, vol. 13, no. 6, pp. 2903\\u0026ndash;2907, Dec. 2016, doi: 10.22037/uj.v13i6.3572.\\u003c/li\\u003e\\n\\u003cli\\u003eF. Porpiglia, A. Volpe, M. Billia, and R. M. Scarpa, \\u0026ldquo;Laparoscopic versus Open Partial Nephrectomy: Analysis of the Current Literature,\\u0026rdquo; \\u003cem\\u003eEur. Urol.\\u003c/em\\u003e, vol. 53, no. 4, pp. 732\\u0026ndash;743, Apr. 2008, doi: 10.1016/j.eururo.2008.01.025.\\u003c/li\\u003e\\n\\u003cli\\u003eI. S. Gill \\u003cem\\u003eet al.\\u003c/em\\u003e, \\u0026ldquo;Comparison of 1,800 Laparoscopic and Open Partial Nephrectomies for Single Renal Tumors,\\u0026rdquo; \\u003cem\\u003eJ. Urol.\\u003c/em\\u003e, vol. 178, no. 1, pp. 41\\u0026ndash;46, Jul. 2007, doi: 10.1016/j.juro.2007.03.038.\\u003c/li\\u003e\\n\\u003cli\\u003eC. You \\u003cem\\u003eet al.\\u003c/em\\u003e, \\u0026ldquo;Laparoscopic Versus Open Partial Nephrectomy: A Systemic Review and Meta-Analysis of Surgical, Oncological, and Functional Outcomes,\\u0026rdquo; \\u003cem\\u003eFront. Oncol.\\u003c/em\\u003e, vol. 10, p. 583979, Oct. 2020, doi: 10.3389/fonc.2020.583979.\\u003c/li\\u003e\\n\\u003cli\\u003eM. Marszalek, H. Meixl, M. Polajnar, M. Rauchenwald, K. Jeschke, and S. Madersbacher, \\u0026ldquo;Laparoscopic and Open Partial Nephrectomy: A Matched-Pair Comparison of 200 Patients,\\u0026rdquo; \\u003cem\\u003eEur. Urol.\\u003c/em\\u003e, vol. 55, no. 5, pp. 1171\\u0026ndash;1178, May 2009, doi: 10.1016/j.eururo.2009.01.042.\\u003c/li\\u003e\\n\\u003cli\\u003eS. Permpongkosol, H. S. Bagga, F. R. Romero, M. Sroka, T. W. Jarrett, and L. R. Kavoussi, \\u0026ldquo;Laparoscopic Versus Open Partial Nephrectomy for the Treatment of Pathological T \\u003csub\\u003e1\\u003c/sub\\u003e N \\u003csub\\u003e0\\u003c/sub\\u003e M \\u003csub\\u003e0\\u003c/sub\\u003e Renal Cell Carcinoma: A 5-Year Survival Rate,\\u0026rdquo; \\u003cem\\u003eJ. Urol.\\u003c/em\\u003e, vol. 176, no. 5, pp. 1984\\u0026ndash;1989, Nov. 2006, doi: 10.1016/j.juro.2006.07.033.\\u003c/li\\u003e\\n\\u003cli\\u003eA. Kutikov and R. G. Uzzo, \\u0026ldquo;The R.E.N.A.L. Nephrometry Score: A Comprehensive Standardized System for Quantitating Renal Tumor Size, Location and Depth,\\u0026rdquo; \\u003cem\\u003eJ. Urol.\\u003c/em\\u003e, vol. 182, no. 3, pp. 844\\u0026ndash;853, Sep. 2009, doi: 10.1016/j.juro.2009.05.035.\\u003c/li\\u003e\\n\\u003cli\\u003eK. D. Chang \\u003cem\\u003eet al.\\u003c/em\\u003e, \\u0026ldquo;Functional and oncological outcomes of open, laparoscopic and robot-assisted partial nephrectomy: a multicentre comparative matched-pair analyses with a median of 5 years\\u0026rsquo; follow-up,\\u0026rdquo; \\u003cem\\u003eBJU Int.\\u003c/em\\u003e, vol. 122, no. 4, pp. 618\\u0026ndash;626, Oct. 2018, doi: 10.1111/bju.14250.\\u003c/li\\u003e\\n\\u003cli\\u003eS. M. Lucas, M. J. Mellon, L. Erntsberger, and C. P. Sundaram, \\u0026ldquo;A Comparison of Robotic, Laparoscopic and Open Partial Nephrectomy,\\u0026rdquo; \\u003cem\\u003eJSLS\\u003c/em\\u003e, vol. 16, no. 4, pp. 581\\u0026ndash;587, 2012, doi: 10.4293/108680812X13462882737177.\\u003c/li\\u003e\\n\\u003cli\\u003eE. L. Wood \\u003cem\\u003eet al.\\u003c/em\\u003e, \\u0026ldquo;Local Tumor Bed Recurrence Following Partial Nephrectomy in Patients with Small Renal Masses,\\u0026rdquo; \\u003cem\\u003eJ. Urol.\\u003c/em\\u003e, vol. 199, no. 2, pp. 393\\u0026ndash;400, Feb. 2018, doi: 10.1016/j.juro.2017.09.072.\\u003c/li\\u003e\\n\\u003cli\\u003eW. Tabayoyong \\u003cem\\u003eet al.\\u003c/em\\u003e, \\u0026ldquo;Variation in Surgical Margin Status by Surgical Approach among Patients Undergoing Partial Nephrectomy for Small Renal Masses,\\u0026rdquo; \\u003cem\\u003eJ. Urol.\\u003c/em\\u003e, vol. 194, no. 6, pp. 1548\\u0026ndash;1553, Dec. 2015, doi: 10.1016/j.juro.2015.06.076.\\u003c/li\\u003e\\n\\u003cli\\u003eM. Abdelhafez, A. Bastian, S. Rausch, A. Stenzl, J. Bedke, and S. Kruck, \\u0026ldquo;Laparoscopic versus Open Partial Nephrectomy: Comparison of Overall and Subgroup Outcomes,\\u0026rdquo; \\u003cem\\u003eAnticancer Res.\\u003c/em\\u003e, vol. 37, no. 1, pp. 261\\u0026ndash;266, Jan. 2017, doi: 10.21873/anticanres.11316.\\u003c/li\\u003e\\n\\u003cli\\u003eB. R. Lane, S. C. Campbell, and I. S. Gill, \\u0026ldquo;10-Year Oncologic Outcomes After Laparoscopic and Open Partial Nephrectomy,\\u0026rdquo; \\u003cem\\u003eJ. Urol.\\u003c/em\\u003e, vol. 190, no. 1, pp. 44\\u0026ndash;49, Jul. 2013, doi: 10.1016/j.juro.2012.12.102.\\u003c/li\\u003e\\n\\u003cli\\u003eLane Brian R., Novick Andrew C., Babineau Denise, Fergany Amr F., Kaouk Jihad H., and Gill Inderbir S., \\u0026ldquo;Comparison of Laparoscopic and Open Partial Nephrectomy for Tumor in a Solitary Kidney,\\u0026rdquo; \\u003cem\\u003eJ. Urol.\\u003c/em\\u003e, vol. 179, no. 3, pp. 847\\u0026ndash;852, Mar. 2008, doi: 10.1016/j.juro.2007.10.050.\\u003c/li\\u003e\\n\\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\":\"info@researchsquare.com\",\"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\":\"Laparoscopy, Open surgery, Partial Nephrectomy, Renal Cell Carcinoma, Renal Function, Outcomes\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-3258719/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-3258719/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003cp\\u003e\\u003cstrong\\u003ePurpose:\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eTo analyze surgical and oncologic outcomes of patients undergoing open partial nephrectomy (OPN) versus laparoscopic partial nephrectomy (LPN) for treatment of renal cell carcinoma (RCC).\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eMethods:\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eWe retrospectively investigated our institutional RCC database for patients who underwent PN for RCC from 1997-2018. Decision for technique was at the discretion of the operating urologist, following practice patterns and training history. Outcomes analyzed included pre/peri/post-operative parameters, pathologic outcomes, and disease recurrence rates.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eResults:\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003e1088 patients underwent PN from 1997-2018. After exclusionary criteria, 631 patients who underwent 647 unique PNs for a total of 162 OPN and 485 LPN remained. Baseline, pre-op, and pathologic characteristics were not different. Surgical time was lower in laparoscopic cases [185 vs 205 minutes] (p = 0.013). Margin involvement was not different; LPN had lower estimated blood loss (EBL) [150 vs 250 mL] (p \\u0026lt; 0.001) and longer ischemia time [21 vs 19 min] (p = 0.005). LPN had shorter length of stay [2 vs 4 days] (p \\u0026lt; 0.001), fewer overall complications (p \\u0026lt; 0.001), and no difference in high-grade complications [2.89 vs 4.32%] (p = .379). Fewer LPN patients developed metastases [1.65 vs 4.94%] (p = 0.0499). Local recurrence rates were not different [1.24 vs 3.09%] (p = 0.193). Renal function was equivalent between cohorts post-operatively.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eConclusion:\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eOur results show that LPN has equivalent oncologic outcomes to OPN, with no difference in patient and tumor characteristics. LPN was associated with lower EBL, shorter length of stay, and lower overall complication risk. \\u0026nbsp;Renal function was equally maintained.\\u003c/p\\u003e\",\"manuscriptTitle\":\"A Contemporary Comparison of Laparoscopic versus Open Partial Nephrectomy for Renal Cell Carcinoma\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2023-08-21 13:18:13\",\"doi\":\"10.21203/rs.3.rs-3258719/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0},{\"type\":\"decision\",\"content\":\"Major revision\",\"date\":\"2023-09-01T03:31:13+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"editorAssigned\",\"content\":\"\",\"date\":\"2023-09-01T03:25:55+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"checksComplete\",\"content\":\"\",\"date\":\"2023-08-14T17:31:09+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"submitted\",\"content\":\"BMC Urology\",\"date\":\"2023-08-12T17:42:31+00:00\",\"index\":\"\",\"fulltext\":\"\"}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"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\":\"4f568643-651a-489a-8287-3dca5ca0c886\",\"owner\":[],\"postedDate\":\"August 21st, 2023\",\"published\":true,\"recentEditorialEvents\":[],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"published-in-journal\",\"subjectAreas\":[],\"tags\":[],\"updatedAt\":\"2024-03-18T15:02:12+00:00\",\"versionOfRecord\":{\"articleIdentity\":\"rs-3258719\",\"link\":\"https://doi.org/10.1186/s12894-024-01423-w\",\"journal\":{\"identity\":\"bmc-urology\",\"isVorOnly\":false,\"title\":\"BMC Urology\"},\"publishedOn\":\"2024-03-12 15:00:39\",\"publishedOnDateReadable\":\"March 12th, 2024\"},\"versionCreatedAt\":\"2023-08-21 13:18:13\",\"video\":\"\",\"vorDoi\":\"10.1186/s12894-024-01423-w\",\"vorDoiUrl\":\"https://doi.org/10.1186/s12894-024-01423-w\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-3258719\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-3258719\",\"identity\":\"rs-3258719\",\"version\":[\"v1\"]},\"buildId\":\"WrCJVZZCHTDjtuVLN7oU0\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}