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Zheng W, Hou G, Ju D, Yan F, Liu K, Niu Z, Huang L, Xing Z, Kong L, Liu P, Zhang G, Wei D, Yuan J. Predicting estimated glomerular filtration rate after partial and radical nephrectomy based on split renal function measured by radionuclide: a large-scale retrospective study. World J Urol 2023; 41:3567-3573. [PMID: 37906264 PMCID: PMC10693500 DOI: 10.1007/s00345-023-04686-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2023] [Accepted: 10/08/2023] [Indexed: 11/02/2023] Open
Abstract
PURPOSE The purpose of this study was to develop predictive models for postoperative estimated glomerular filtration rate (eGFR) based on the split glomerular filtration rate measured by radionuclide (rGFR), as choosing radical nephrectomy (RN) or partial nephrectomy (PN) for complex renal masses requires accurate prediction of postoperative eGFR. METHODS Patients who underwent RN or PN for a single renal mass at Xijing Hospital between 2008 and 2022 were retrospectively included. Preoperative split rGFR was evaluated using technetium-99 m-diethylenetriaminepentaacetic acid (Tc-99 m DTPA) renal dynamic imaging, and the postoperative short-term (< 7 days) and long-term (3 months to 5 years) eGFRs were assessed. Linear mixed-effect models were used to predict eGFRs, with marginal R2 reflecting predictive ability. RESULTS After excluding patients with missing follow-up eGFRs, the data of 2251 (RN: 1286, PN: 965) and 2447 (RN: 1417, PN: 1030) patients were respectively included in the long-term and short-term models. Two models were established to predict long-term eGFRs after RN (marginal R2 = 0.554) and PN (marginal R2 = 0.630), respectively. Two other models were established to predict short-term eGFRs after RN (marginal R2 = 0.692) and PN (marginal R2 = 0.656), respectively. In terms of long-term eGFRs, laparoscopic and robotic surgery were superior to open surgery in both PN and RN. CONCLUSIONS We developed novel tools for predicting short-term and long-term eGFRs after RN and PN based on split rGFR that can help in preoperative decision-making.
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Affiliation(s)
- Wanxiang Zheng
- Department of Urology, Xijing Hospital, Fourth Military Medical University, Xi'an, China
| | - Guangdong Hou
- Department of Urology, Xijing Hospital, Fourth Military Medical University, Xi'an, China
| | - Dongen Ju
- Department of Urology, Xijing Hospital, Fourth Military Medical University, Xi'an, China
| | - Fei Yan
- Department of Urology, Xijing Hospital, Fourth Military Medical University, Xi'an, China
- School of Pharmacy, Health Science Center, Xi'an Jiaotong University, Xi'an, China
| | - Kepu Liu
- Department of Urology, Xijing Hospital, Fourth Military Medical University, Xi'an, China
| | - Zhiping Niu
- Department of Urology, Xijing Hospital, Fourth Military Medical University, Xi'an, China
| | - Luguang Huang
- Information Center, Xijing Hospital, Fourth Military Medical University, Xi'an, China
| | - Zibao Xing
- Department of Urology, Xijing Hospital, Fourth Military Medical University, Xi'an, China
- Department of Urology, The 73rd Army Group Hospital, Xiamen, China
| | - Lingchen Kong
- Department of Urology, Xijing Hospital, Fourth Military Medical University, Xi'an, China
| | - Pengfei Liu
- Department of Urology, Xijing Hospital, Fourth Military Medical University, Xi'an, China
- Air Force Hospital of Western Theater Command, PLA, Chengdu, China
| | - Geng Zhang
- Department of Urology, Xijing Hospital, Fourth Military Medical University, Xi'an, China
| | - Di Wei
- Department of Urology, Xijing Hospital, Fourth Military Medical University, Xi'an, China.
| | - Jianlin Yuan
- Department of Urology, Xijing Hospital, Fourth Military Medical University, Xi'an, China.
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Li S, Li Z, Huang X, Zhang P, Deng J, Liu X, Xue C, Zhang W, Zhou J. CT, MRI, and radiomics studies of liver metastasis histopathological growth patterns: an up-to-date review. ABDOMINAL RADIOLOGY (NEW YORK) 2022; 47:3494-3506. [PMID: 35895118 DOI: 10.1007/s00261-022-03616-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/06/2022] [Revised: 07/05/2022] [Accepted: 07/07/2022] [Indexed: 02/07/2023]
Abstract
The histopathological growth patterns (HGPs) of liver metastases (LMs) are independently associated with the long-term prognosis of the primary tumor, with different HGPs predicting different patient outcomes and clinical treatment decisions. Non-invasive imaging biomarkers for stratification of HGPs are beneficial for treatment monitoring, evaluation of efficacy, and prognosis prediction of LMs. This review describes the state of research regarding computed tomography (CT), magnetic resonance imaging (MRI), and radiomics imaging biomarkers for LM-HGPs; discusses the advantages of CT, MRI, and radiomics for classification of LM-HGPs; and provides a reference for the stratification of LM-HGPs. Finally, the difficulties and deficiencies of CT, MRI, and radiomics in LM-HGP research are summarized along with the proposed directions for future research.
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Affiliation(s)
- Shenglin Li
- Department of Radiology, Lanzhou University Second Hospital, Cuiyingmen No.82, Chengguan District, Lanzhou, 730030, China.,Second Clinical School, Lanzhou University, Lanzhou, China.,Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, China.,Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, China
| | - Zhengxiao Li
- Department of Radiology, Lanzhou University Second Hospital, Cuiyingmen No.82, Chengguan District, Lanzhou, 730030, China.,Second Clinical School, Lanzhou University, Lanzhou, China.,Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, China.,Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, China
| | - Xiaoyu Huang
- Department of Radiology, Lanzhou University Second Hospital, Cuiyingmen No.82, Chengguan District, Lanzhou, 730030, China.,Second Clinical School, Lanzhou University, Lanzhou, China.,Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, China.,Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, China
| | - Peng Zhang
- Department of Pathology, Lanzhou University Second Hospital, Cuiyingmen No.82, Chengguan District, Lanzhou, 730030, China
| | - Juan Deng
- Department of Radiology, Lanzhou University Second Hospital, Cuiyingmen No.82, Chengguan District, Lanzhou, 730030, China.,Second Clinical School, Lanzhou University, Lanzhou, China.,Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, China.,Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, China
| | - Xianwang Liu
- Department of Radiology, Lanzhou University Second Hospital, Cuiyingmen No.82, Chengguan District, Lanzhou, 730030, China.,Second Clinical School, Lanzhou University, Lanzhou, China.,Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, China.,Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, China
| | - Caiqiang Xue
- Department of Radiology, Lanzhou University Second Hospital, Cuiyingmen No.82, Chengguan District, Lanzhou, 730030, China.,Second Clinical School, Lanzhou University, Lanzhou, China.,Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, China.,Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, China
| | - Wenjuan Zhang
- Department of Radiology, Lanzhou University Second Hospital, Cuiyingmen No.82, Chengguan District, Lanzhou, 730030, China.,Second Clinical School, Lanzhou University, Lanzhou, China.,Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, China.,Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, China
| | - Junlin Zhou
- Department of Radiology, Lanzhou University Second Hospital, Cuiyingmen No.82, Chengguan District, Lanzhou, 730030, China. .,Second Clinical School, Lanzhou University, Lanzhou, China. .,Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, China. .,Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, China.
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