Literature DB >> 23628308

Evaluation of anatomic and morphologic nomogram to predict malignant and high-grade disease in a cohort of patients with small renal masses.

Aditya Bagrodia1, Brian Harrow1, Zhuo-Wei Liu1, Ephrem O Olweny1, Stephen Faddegon1, Gang Yin1, Yung Khan Tan1, Woong Kyu Han1, Yair Lotan1, Vitaly Margulis1, Jeffrey A Cadeddu2.   

Abstract

OBJECTIVE: To evaluate a nomogram using the RENAL Nephrometry Score (RENAL-NS) that was developed to characterize masses as benign vs. malignant and high vs. low grade in our patients with small renal masses treated with partial nephrectomy (PN). The nomogram was previously developed and validated in patients with widely variable tumor sizes.
MATERIALS AND METHODS: Retrospective review of PN performed between 1/2003 and 7/2011. Imaging was reviewed by a urologic surgeon for RENAL-NS. Final pathology was used to classify tumors as benign or malignant and low (I/II) or high (III/IV) Fuhrman grade. Patient age, gender, and RENAL score were entered into the nomogram described by Kutikov et al. to determine probabilities of cancer and high-grade disease. Area under the curve was determined to assess agreement between observed and expected outcomes for prediction of benign vs. malignant disease and for prediction of high- vs. low-grade or benign disease.
RESULTS: A total of 250 patients with 252 masses underwent PN during the study period; 179/250 (71.6%) had preoperative imaging available. RENAL-NS was assigned to 181 masses. Twenty-two percent of tumors were benign. Eighteen percent of tumors were high grade. Area under the curve was 0.648 for predicting benign vs. malignant disease and 0.955 for predicting low-grade or benign vs. high-grade disease.
CONCLUSIONS: The RENAL-NS score nomogram by Kutikov does not discriminate well between benign and malignant disease for small renal masses. The nomogram may potentially be useful in identifying high-grade tumors. Further validation is required where the nomogram probability and final pathologic specimen are available.
Copyright © 2014 Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Kidney cancer; Nephrometry; Nomogram; Prediction; Prognostics; RENAL; Renal cell cancer; Validation

Mesh:

Year:  2013        PMID: 23628308     DOI: 10.1016/j.urolonc.2013.03.003

Source DB:  PubMed          Journal:  Urol Oncol        ISSN: 1078-1439            Impact factor:   3.498


  5 in total

1.  External validation of the RENAL nephrometry score nomogram for predicting high-grade renal cell carcinoma in solid, enhancing, and small renal masses.

Authors:  Kyo Chul Koo; Hanna Yoo; Tae Young Shin; Jongchan Kim; Young Deuk Choi; Koon Ho Rha; Won Sik Ham
Journal:  World J Urol       Date:  2013-09-07       Impact factor: 4.226

2.  External validation of a nomogram including the computed tomography imaging score to predict indolent renal masses.

Authors:  X Chen; B Wan; D Yang; H Zhao; W Tan
Journal:  Int Urol Nephrol       Date:  2017-04-17       Impact factor: 2.370

3.  Patients with anatomically "simple" renal masses are more likely to be placed on active surveillance than those with anatomically "complex" lesions.

Authors:  Jeffrey J Tomaszewski; Robert G Uzzo; Neil Kocher; Tianyu Li; Brandon Manley; Reza Mehrazin; Timothy Ito; Philip Abbosh; Rosalia Viterbo; David Y T Chen; Richard E Greenberg; Daniel Canter; Marc C Smaldone; Alexander Kutikov
Journal:  Urol Oncol       Date:  2014-06-07       Impact factor: 3.498

4.  A comparative analysis of various surgical approaches of nephron-sparing surgery and correlation of histopathological grade with RENAL nephrometry score in renal cell carcinoma.

Authors:  Harshit Garg; Deviprasad Tiwari; Brusabhanu Nayak; Prabhjot Singh; Siddharth Yadav; Rajeev Kumar; Amlesh Seth; Rishi Nayyar; Premnath Dogra
Journal:  J Minim Access Surg       Date:  2019-02-18       Impact factor: 1.407

5.  Pathological diagnostic nomograms for predicting malignant histology and unfavorable pathology in patients with endophytic renal tumor.

Authors:  Xinxi Deng; Xiaoqiang Liu; Bing Hu; Ming Jiang; Ke Zhu; Jianqiang Nie; Taobin Liu; Luyao Chen; Wen Deng; Bin Fu; Situ Xiong
Journal:  Front Oncol       Date:  2022-09-21       Impact factor: 5.738

  5 in total

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