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Accuracy and Clinical Utility of a Tumor Grade- and Stage-based Predictive Model in Localized Upper Tract Urothelial Carcinoma

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Accuracy and Clinical Utility of a Tumor Grade- and Stage-based Predictive Model in Localized Upper Tract Urothelial Carcinoma. / Katayama, Satoshi; Mori, Keiichiro; Schuettfort, Victor M; Pradere, Benjamin; Mostafaei, Hadi; Quhal, Fahad; Rajwa, Pawel; Motlagh, Reza Sari; Laukhtina, Ekaterina; Moschini, Marco; Grossmann, Nico C; Araki, Motoo; Teoh, Jeremy Yuen-Chun; Rouprêt, Morgan; Margulis, Vitaly; Enikeev, Dmitry; Karakiewicz, Pierre I; Abufaraj, Mohammad; Compérat, Eva; Nasu, Yasutomo; Shariat, Shahrokh F.

In: EUR UROL FOCUS, Vol. 8, No. 3, 05.2022, p. 761-768.

Research output: SCORING: Contribution to journal › SCORING: Review article › Research

Harvard

Katayama, S, Mori, K, Schuettfort, VM, Pradere, B, Mostafaei, H, Quhal, F, Rajwa, P, Motlagh, RS, Laukhtina, E, Moschini, M, Grossmann, NC, Araki, M, Teoh, JY-C, Rouprêt, M, Margulis, V, Enikeev, D, Karakiewicz, PI, Abufaraj, M, Compérat, E, Nasu, Y & Shariat, SF 2022, 'Accuracy and Clinical Utility of a Tumor Grade- and Stage-based Predictive Model in Localized Upper Tract Urothelial Carcinoma', EUR UROL FOCUS, vol. 8, no. 3, pp. 761-768. https://doi.org/10.1016/j.euf.2021.05.002

APA

Katayama, S., Mori, K., Schuettfort, V. M., Pradere, B., Mostafaei, H., Quhal, F., Rajwa, P., Motlagh, R. S., Laukhtina, E., Moschini, M., Grossmann, N. C., Araki, M., Teoh, J. Y-C., Rouprêt, M., Margulis, V., Enikeev, D., Karakiewicz, P. I., Abufaraj, M., Compérat, E., ... Shariat, S. F. (2022). Accuracy and Clinical Utility of a Tumor Grade- and Stage-based Predictive Model in Localized Upper Tract Urothelial Carcinoma. EUR UROL FOCUS, 8(3), 761-768. https://doi.org/10.1016/j.euf.2021.05.002

Vancouver

Katayama S, Mori K, Schuettfort VM, Pradere B, Mostafaei H, Quhal F et al. Accuracy and Clinical Utility of a Tumor Grade- and Stage-based Predictive Model in Localized Upper Tract Urothelial Carcinoma. EUR UROL FOCUS. 2022 May;8(3):761-768. https://doi.org/10.1016/j.euf.2021.05.002

Bibtex

@article{28a401852e3e42b6b6b090cb7f3a5cc6,
title = "Accuracy and Clinical Utility of a Tumor Grade- and Stage-based Predictive Model in Localized Upper Tract Urothelial Carcinoma",
abstract = "BACKGROUND: Among various clinicopathologic factors used to identify low-risk upper tract urothelial carcinoma (UTUC), tumor grade and stage are of utmost importance. The clinical value added by inclusion of other risk factors remains unproven.OBJECTIVE: To assess the performance of a tumor grade- and stage-based (GS) model to identify patients with UTUC for whom kidney-sparing surgery (KSS) could be attempted.DESIGN, SETTING, AND PARTICIPANTS: In this international study, we reviewed the medical records of 1240 patients with UTUC who underwent radical nephroureterectomy. Complete data needed for risk stratification according to the European Association of Urology (EAU) and National Comprehensive Cancer Network (NCCN) guidelines were available for 560 patients.OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: Univariable and multivariable logistic regression analyses were performed to determine if risk factors were associated with the presence of localized UTUC. The sensitivity, specificity, positive predictive value, negative predictive value, and accuracy of the GS, EAU, and NCCN models in predicting pathologic stage were calculated.RESULTS AND LIMITATIONS: Overall, 198 patients (35%) had clinically low-grade, noninvasive tumors, and 283 (51%) had ≤pT1disease. On multivariable analyses, none of the EAU and NCCN risk factors were associated with the presence of non-muscle-invasive UTUC among patients with low-grade and low-stage UTUC. The GS model exhibited the highest accuracy, sensitivity, and negative predictive value among all three models. According to the GS, EAU, and NCCN models, the proportion of patients eligible for KSS was 35%, 6%, and 4%, respectively. Decision curve analysis revealed that the net benefit of the three models was similar within the clinically reasonable range of probability thresholds.CONCLUSIONS: The GS model showed favorable predictive accuracy and identified a greater number of KSS-eligible patients than the EAU and NCCN models. A decision-making algorithm that weighs the benefits of avoiding unnecessary kidney loss against the risk of undertreatment in case of advanced carcinoma is necessary for individualized treatment for UTUC patients.PATIENT SUMMARY: We assessed the ability of three models to predict low-grade, low-stage disease in patients with cancer of the upper urinary tract. No risk factors other than grade assessed on biopsy and stage assessed from scans were associated with better prediction of localized cancer. A model based on grade and stage may help to identify patients who could benefit from kidney-sparing treatment of their cancer.",
keywords = "Carcinoma in Situ, Carcinoma, Transitional Cell/pathology, Humans, Kidney Neoplasms/surgery, Nephroureterectomy/methods, Urinary Bladder Neoplasms/surgery, Urothelium/pathology",
author = "Satoshi Katayama and Keiichiro Mori and Schuettfort, {Victor M} and Benjamin Pradere and Hadi Mostafaei and Fahad Quhal and Pawel Rajwa and Motlagh, {Reza Sari} and Ekaterina Laukhtina and Marco Moschini and Grossmann, {Nico C} and Motoo Araki and Teoh, {Jeremy Yuen-Chun} and Morgan Roupr{\^e}t and Vitaly Margulis and Dmitry Enikeev and Karakiewicz, {Pierre I} and Mohammad Abufaraj and Eva Comp{\'e}rat and Yasutomo Nasu and Shariat, {Shahrokh F}",
note = "Copyright {\textcopyright} 2021 The Authors. Published by Elsevier B.V. All rights reserved.",
year = "2022",
month = may,
doi = "10.1016/j.euf.2021.05.002",
language = "English",
volume = "8",
pages = "761--768",
journal = "EUR UROL FOCUS",
issn = "2405-4569",
publisher = "Elsevier BV",
number = "3",

}

RIS

TY - JOUR

T1 - Accuracy and Clinical Utility of a Tumor Grade- and Stage-based Predictive Model in Localized Upper Tract Urothelial Carcinoma

AU - Katayama, Satoshi

AU - Mori, Keiichiro

AU - Schuettfort, Victor M

AU - Pradere, Benjamin

AU - Mostafaei, Hadi

AU - Quhal, Fahad

AU - Rajwa, Pawel

AU - Motlagh, Reza Sari

AU - Laukhtina, Ekaterina

AU - Moschini, Marco

AU - Grossmann, Nico C

AU - Araki, Motoo

AU - Teoh, Jeremy Yuen-Chun

AU - Rouprêt, Morgan

AU - Margulis, Vitaly

AU - Enikeev, Dmitry

AU - Karakiewicz, Pierre I

AU - Abufaraj, Mohammad

AU - Compérat, Eva

AU - Nasu, Yasutomo

AU - Shariat, Shahrokh F

N1 - Copyright © 2021 The Authors. Published by Elsevier B.V. All rights reserved.

PY - 2022/5

Y1 - 2022/5

N2 - BACKGROUND: Among various clinicopathologic factors used to identify low-risk upper tract urothelial carcinoma (UTUC), tumor grade and stage are of utmost importance. The clinical value added by inclusion of other risk factors remains unproven.OBJECTIVE: To assess the performance of a tumor grade- and stage-based (GS) model to identify patients with UTUC for whom kidney-sparing surgery (KSS) could be attempted.DESIGN, SETTING, AND PARTICIPANTS: In this international study, we reviewed the medical records of 1240 patients with UTUC who underwent radical nephroureterectomy. Complete data needed for risk stratification according to the European Association of Urology (EAU) and National Comprehensive Cancer Network (NCCN) guidelines were available for 560 patients.OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: Univariable and multivariable logistic regression analyses were performed to determine if risk factors were associated with the presence of localized UTUC. The sensitivity, specificity, positive predictive value, negative predictive value, and accuracy of the GS, EAU, and NCCN models in predicting pathologic stage were calculated.RESULTS AND LIMITATIONS: Overall, 198 patients (35%) had clinically low-grade, noninvasive tumors, and 283 (51%) had ≤pT1disease. On multivariable analyses, none of the EAU and NCCN risk factors were associated with the presence of non-muscle-invasive UTUC among patients with low-grade and low-stage UTUC. The GS model exhibited the highest accuracy, sensitivity, and negative predictive value among all three models. According to the GS, EAU, and NCCN models, the proportion of patients eligible for KSS was 35%, 6%, and 4%, respectively. Decision curve analysis revealed that the net benefit of the three models was similar within the clinically reasonable range of probability thresholds.CONCLUSIONS: The GS model showed favorable predictive accuracy and identified a greater number of KSS-eligible patients than the EAU and NCCN models. A decision-making algorithm that weighs the benefits of avoiding unnecessary kidney loss against the risk of undertreatment in case of advanced carcinoma is necessary for individualized treatment for UTUC patients.PATIENT SUMMARY: We assessed the ability of three models to predict low-grade, low-stage disease in patients with cancer of the upper urinary tract. No risk factors other than grade assessed on biopsy and stage assessed from scans were associated with better prediction of localized cancer. A model based on grade and stage may help to identify patients who could benefit from kidney-sparing treatment of their cancer.

AB - BACKGROUND: Among various clinicopathologic factors used to identify low-risk upper tract urothelial carcinoma (UTUC), tumor grade and stage are of utmost importance. The clinical value added by inclusion of other risk factors remains unproven.OBJECTIVE: To assess the performance of a tumor grade- and stage-based (GS) model to identify patients with UTUC for whom kidney-sparing surgery (KSS) could be attempted.DESIGN, SETTING, AND PARTICIPANTS: In this international study, we reviewed the medical records of 1240 patients with UTUC who underwent radical nephroureterectomy. Complete data needed for risk stratification according to the European Association of Urology (EAU) and National Comprehensive Cancer Network (NCCN) guidelines were available for 560 patients.OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: Univariable and multivariable logistic regression analyses were performed to determine if risk factors were associated with the presence of localized UTUC. The sensitivity, specificity, positive predictive value, negative predictive value, and accuracy of the GS, EAU, and NCCN models in predicting pathologic stage were calculated.RESULTS AND LIMITATIONS: Overall, 198 patients (35%) had clinically low-grade, noninvasive tumors, and 283 (51%) had ≤pT1disease. On multivariable analyses, none of the EAU and NCCN risk factors were associated with the presence of non-muscle-invasive UTUC among patients with low-grade and low-stage UTUC. The GS model exhibited the highest accuracy, sensitivity, and negative predictive value among all three models. According to the GS, EAU, and NCCN models, the proportion of patients eligible for KSS was 35%, 6%, and 4%, respectively. Decision curve analysis revealed that the net benefit of the three models was similar within the clinically reasonable range of probability thresholds.CONCLUSIONS: The GS model showed favorable predictive accuracy and identified a greater number of KSS-eligible patients than the EAU and NCCN models. A decision-making algorithm that weighs the benefits of avoiding unnecessary kidney loss against the risk of undertreatment in case of advanced carcinoma is necessary for individualized treatment for UTUC patients.PATIENT SUMMARY: We assessed the ability of three models to predict low-grade, low-stage disease in patients with cancer of the upper urinary tract. No risk factors other than grade assessed on biopsy and stage assessed from scans were associated with better prediction of localized cancer. A model based on grade and stage may help to identify patients who could benefit from kidney-sparing treatment of their cancer.

KW - Carcinoma in Situ

KW - Carcinoma, Transitional Cell/pathology

KW - Humans

KW - Kidney Neoplasms/surgery

KW - Nephroureterectomy/methods

KW - Urinary Bladder Neoplasms/surgery

KW - Urothelium/pathology

U2 - 10.1016/j.euf.2021.05.002

DO - 10.1016/j.euf.2021.05.002

M3 - SCORING: Review article

C2 - 34053904

VL - 8

SP - 761

EP - 768

JO - EUR UROL FOCUS

JF - EUR UROL FOCUS

SN - 2405-4569

IS - 3

ER -