In:
PLOS ONE, Public Library of Science (PLoS), Vol. 16, No. 10 ( 2021-10-19), p. e0258783-
Abstract:
The aim of this study was to develop a new diagnostic tool to predict lymph node metastasis (LNM) in patients with advanced epithelial ovarian cancer undergoing primary cytoreductive surgery. Materials and method The FRANCOGYN group’s multicenter retrospective ovarian cancer cohort furnished the patient population on which we developed a logistic regression model. The prediction model equation enabled us to create LNM risk groups with simple lymphadenectomy decision rules associated with a user-friendly free interactive web application called shinyLNM. Results 277 patients from the FRANCOGYN cohort were included; 115 with no LNM and 162 with LNM. Three variables were independently and significantly (p 〈 0.05) associated with LNM in multivariate analysis: pelvic and/or para-aortic LNM on CT and/or PET/CT (p 〈 0.00), initial PCI ≥ 10 and/or diaphragmatic carcinosis (p = 0.02), and initial CA125 ≥ 500 (p = 0.02). The ROC-AUC of this prediction model after leave-one-out cross-validation was 0.72. There was no difference between the predicted and the observed probabilities of LNM (p = 0.09). Specificity for the group at high risk of LNM was 83.5%, the LR+ was 2.73, and the observed probability of LNM was 79.3%; sensitivity for the group at low-risk of LNM was 92.0%, the LR- was 0.24, and the observed probability of LNM was 25.0%. Conclusion This new tool may prove useful for improving surgical planning and provide useful information for patients.
Type of Medium:
Online Resource
ISSN:
1932-6203
DOI:
10.1371/journal.pone.0258783
DOI:
10.1371/journal.pone.0258783.g001
DOI:
10.1371/journal.pone.0258783.g002
DOI:
10.1371/journal.pone.0258783.g003
DOI:
10.1371/journal.pone.0258783.g004
DOI:
10.1371/journal.pone.0258783.g005
DOI:
10.1371/journal.pone.0258783.t001
DOI:
10.1371/journal.pone.0258783.t002
DOI:
10.1371/journal.pone.0258783.t003
DOI:
10.1371/journal.pone.0258783.s001
Language:
English
Publisher:
Public Library of Science (PLoS)
Publication Date:
2021
detail.hit.zdb_id:
2267670-3
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