Intraoperative frozen section risk assessment accurately tailors the surgical staging in patients affected by early-stage endometrial cancer; The application of 2 different risk algorithms

Paolo Sala, Matteo Morotti, Mario Valenzano Menada, Elisa Cannavino, Ilaria Maffeo, Luca Abete, Ezio Fulcheri, Stefania Menoni, Pierluigi Venturini, Andrea Papadia

Research output: Contribution to journalArticle

Abstract

Objective: The aim of this study was to investigate the frozen section (FS) accuracy in tailoring the surgical staging of patients affected by endometrial cancer, using 2 different risk classifications. Methods/Materials: A retrospective analysis of 331 women affected by type I endometrial cancer and submitted to FS assessment at the time of surgery. Pathologic features were examined on the frozen and permanent sections according to both the GOG33 and the Mayo Clinic algorithms.We compared the 2 models through the determination of Landis and Koch kappa statistics, concordance rate, sensitivity, specificity, positive predictive value, and negative predictive value for each risk algorithm, to assess whether there are differences in FS accuracy depending on the model used. Results: The observed agreement between the frozen and permanent sections was respectively good (k = 0.790) for the GOG33 and optimal (k = 0.810) for the Mayo classification. Applying the GOG33 algorithm, 20 patients (6.7%) were moved to an upper risk status, and 20 (6.7%)weremoved to a lower risk status on the permanent section; the concordance ratewas 86.5%. With the Mayo Clinic algorithm, discordant cases between frozen and permanent sections were 19 (7.6%), and the risk of lymphatic spread was underestimated only in 1 case (0.4%); the concordance ratewas 92.4%. The sensitivity, specificity, positive predictive value, and negative predictive value for theGOG33were 92%, 94%, 92%, and 93%,whereaswith the Mayo algorithm, these were 98%, 91%, 77%, and 99%, respectively. Conclusions: According to higher correlation rate and observed agreement (92.4% vs 86.5% and k = 0.810 vs 0.790, respectively), the Mayo Clinic algorithm minimizes the number of patients undertreated at the time of surgery than the GOG33 classification and can be adopted as an FS algorithm to tailor the surgical treatment of early-stage endometrial cancer even in different centers.

Original languageEnglish
Pages (from-to)1021-1026
Number of pages6
JournalInternational Journal of Gynecological Cancer
Volume24
Issue number6
DOIs
Publication statusPublished - 2014

Fingerprint

Frozen Sections
Endometrial Neoplasms
Sensitivity and Specificity

Keywords

  • Endometrial cancer
  • Frozen section
  • Intraoperative assessment
  • Lymph node dissection
  • Surgical staging

ASJC Scopus subject areas

  • Obstetrics and Gynaecology
  • Oncology
  • Medicine(all)

Cite this

Intraoperative frozen section risk assessment accurately tailors the surgical staging in patients affected by early-stage endometrial cancer; The application of 2 different risk algorithms. / Sala, Paolo; Morotti, Matteo; Menada, Mario Valenzano; Cannavino, Elisa; Maffeo, Ilaria; Abete, Luca; Fulcheri, Ezio; Menoni, Stefania; Venturini, Pierluigi; Papadia, Andrea.

In: International Journal of Gynecological Cancer, Vol. 24, No. 6, 2014, p. 1021-1026.

Research output: Contribution to journalArticle

Sala, Paolo ; Morotti, Matteo ; Menada, Mario Valenzano ; Cannavino, Elisa ; Maffeo, Ilaria ; Abete, Luca ; Fulcheri, Ezio ; Menoni, Stefania ; Venturini, Pierluigi ; Papadia, Andrea. / Intraoperative frozen section risk assessment accurately tailors the surgical staging in patients affected by early-stage endometrial cancer; The application of 2 different risk algorithms. In: International Journal of Gynecological Cancer. 2014 ; Vol. 24, No. 6. pp. 1021-1026.
@article{0a7b7957ce1e4036afcd568f24197a54,
title = "Intraoperative frozen section risk assessment accurately tailors the surgical staging in patients affected by early-stage endometrial cancer; The application of 2 different risk algorithms",
abstract = "Objective: The aim of this study was to investigate the frozen section (FS) accuracy in tailoring the surgical staging of patients affected by endometrial cancer, using 2 different risk classifications. Methods/Materials: A retrospective analysis of 331 women affected by type I endometrial cancer and submitted to FS assessment at the time of surgery. Pathologic features were examined on the frozen and permanent sections according to both the GOG33 and the Mayo Clinic algorithms.We compared the 2 models through the determination of Landis and Koch kappa statistics, concordance rate, sensitivity, specificity, positive predictive value, and negative predictive value for each risk algorithm, to assess whether there are differences in FS accuracy depending on the model used. Results: The observed agreement between the frozen and permanent sections was respectively good (k = 0.790) for the GOG33 and optimal (k = 0.810) for the Mayo classification. Applying the GOG33 algorithm, 20 patients (6.7{\%}) were moved to an upper risk status, and 20 (6.7{\%})weremoved to a lower risk status on the permanent section; the concordance ratewas 86.5{\%}. With the Mayo Clinic algorithm, discordant cases between frozen and permanent sections were 19 (7.6{\%}), and the risk of lymphatic spread was underestimated only in 1 case (0.4{\%}); the concordance ratewas 92.4{\%}. The sensitivity, specificity, positive predictive value, and negative predictive value for theGOG33were 92{\%}, 94{\%}, 92{\%}, and 93{\%},whereaswith the Mayo algorithm, these were 98{\%}, 91{\%}, 77{\%}, and 99{\%}, respectively. Conclusions: According to higher correlation rate and observed agreement (92.4{\%} vs 86.5{\%} and k = 0.810 vs 0.790, respectively), the Mayo Clinic algorithm minimizes the number of patients undertreated at the time of surgery than the GOG33 classification and can be adopted as an FS algorithm to tailor the surgical treatment of early-stage endometrial cancer even in different centers.",
keywords = "Endometrial cancer, Frozen section, Intraoperative assessment, Lymph node dissection, Surgical staging",
author = "Paolo Sala and Matteo Morotti and Menada, {Mario Valenzano} and Elisa Cannavino and Ilaria Maffeo and Luca Abete and Ezio Fulcheri and Stefania Menoni and Pierluigi Venturini and Andrea Papadia",
year = "2014",
doi = "10.1097/IGC.0000000000000145",
language = "English",
volume = "24",
pages = "1021--1026",
journal = "International Journal of Gynecological Cancer",
issn = "1048-891X",
publisher = "Lippincott Williams and Wilkins",
number = "6",

}

TY - JOUR

T1 - Intraoperative frozen section risk assessment accurately tailors the surgical staging in patients affected by early-stage endometrial cancer; The application of 2 different risk algorithms

AU - Sala, Paolo

AU - Morotti, Matteo

AU - Menada, Mario Valenzano

AU - Cannavino, Elisa

AU - Maffeo, Ilaria

AU - Abete, Luca

AU - Fulcheri, Ezio

AU - Menoni, Stefania

AU - Venturini, Pierluigi

AU - Papadia, Andrea

PY - 2014

Y1 - 2014

N2 - Objective: The aim of this study was to investigate the frozen section (FS) accuracy in tailoring the surgical staging of patients affected by endometrial cancer, using 2 different risk classifications. Methods/Materials: A retrospective analysis of 331 women affected by type I endometrial cancer and submitted to FS assessment at the time of surgery. Pathologic features were examined on the frozen and permanent sections according to both the GOG33 and the Mayo Clinic algorithms.We compared the 2 models through the determination of Landis and Koch kappa statistics, concordance rate, sensitivity, specificity, positive predictive value, and negative predictive value for each risk algorithm, to assess whether there are differences in FS accuracy depending on the model used. Results: The observed agreement between the frozen and permanent sections was respectively good (k = 0.790) for the GOG33 and optimal (k = 0.810) for the Mayo classification. Applying the GOG33 algorithm, 20 patients (6.7%) were moved to an upper risk status, and 20 (6.7%)weremoved to a lower risk status on the permanent section; the concordance ratewas 86.5%. With the Mayo Clinic algorithm, discordant cases between frozen and permanent sections were 19 (7.6%), and the risk of lymphatic spread was underestimated only in 1 case (0.4%); the concordance ratewas 92.4%. The sensitivity, specificity, positive predictive value, and negative predictive value for theGOG33were 92%, 94%, 92%, and 93%,whereaswith the Mayo algorithm, these were 98%, 91%, 77%, and 99%, respectively. Conclusions: According to higher correlation rate and observed agreement (92.4% vs 86.5% and k = 0.810 vs 0.790, respectively), the Mayo Clinic algorithm minimizes the number of patients undertreated at the time of surgery than the GOG33 classification and can be adopted as an FS algorithm to tailor the surgical treatment of early-stage endometrial cancer even in different centers.

AB - Objective: The aim of this study was to investigate the frozen section (FS) accuracy in tailoring the surgical staging of patients affected by endometrial cancer, using 2 different risk classifications. Methods/Materials: A retrospective analysis of 331 women affected by type I endometrial cancer and submitted to FS assessment at the time of surgery. Pathologic features were examined on the frozen and permanent sections according to both the GOG33 and the Mayo Clinic algorithms.We compared the 2 models through the determination of Landis and Koch kappa statistics, concordance rate, sensitivity, specificity, positive predictive value, and negative predictive value for each risk algorithm, to assess whether there are differences in FS accuracy depending on the model used. Results: The observed agreement between the frozen and permanent sections was respectively good (k = 0.790) for the GOG33 and optimal (k = 0.810) for the Mayo classification. Applying the GOG33 algorithm, 20 patients (6.7%) were moved to an upper risk status, and 20 (6.7%)weremoved to a lower risk status on the permanent section; the concordance ratewas 86.5%. With the Mayo Clinic algorithm, discordant cases between frozen and permanent sections were 19 (7.6%), and the risk of lymphatic spread was underestimated only in 1 case (0.4%); the concordance ratewas 92.4%. The sensitivity, specificity, positive predictive value, and negative predictive value for theGOG33were 92%, 94%, 92%, and 93%,whereaswith the Mayo algorithm, these were 98%, 91%, 77%, and 99%, respectively. Conclusions: According to higher correlation rate and observed agreement (92.4% vs 86.5% and k = 0.810 vs 0.790, respectively), the Mayo Clinic algorithm minimizes the number of patients undertreated at the time of surgery than the GOG33 classification and can be adopted as an FS algorithm to tailor the surgical treatment of early-stage endometrial cancer even in different centers.

KW - Endometrial cancer

KW - Frozen section

KW - Intraoperative assessment

KW - Lymph node dissection

KW - Surgical staging

UR - http://www.scopus.com/inward/record.url?scp=84904202066&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84904202066&partnerID=8YFLogxK

U2 - 10.1097/IGC.0000000000000145

DO - 10.1097/IGC.0000000000000145

M3 - Article

C2 - 24905611

AN - SCOPUS:84904202066

VL - 24

SP - 1021

EP - 1026

JO - International Journal of Gynecological Cancer

JF - International Journal of Gynecological Cancer

SN - 1048-891X

IS - 6

ER -