Enhancing precision medicine : a nomogram for predicting platinum resistance in epithelial ovarian cancer

© 2024. The Author(s)..

BACKGROUND: This study aimed to develop a novel nomogram that can accurately estimate platinum resistance to enhance precision medicine in epithelial ovarian cancer(EOC).

METHODS: EOC patients who received primary therapy at the General Hospital of Ningxia Medical University between January 31, 2019, and June 30, 2021 were included. The LASSO analysis was utilized to screen the variables which contained clinical features and platinum-resistance gene immunohistochemistry scores. A nomogram was created after the logistic regression analysis to develop the prediction model. The consistency index (C-index), calibration curve, receiver operating characteristic (ROC) curve, and decision curve analysis (DCA) were used to assess the nomogram's performance.

RESULTS: The logistic regression analysis created a prediction model based on 11 factors filtered down by LASSO regression. As predictors, the immunohistochemical scores of CXLC1, CXCL2, IL6, ABCC1, LRP, BCL2, vascular tumor thrombus, ascites cancer cells, maximum tumor diameter, neoadjuvant chemotherapy, and HE4 were employed. The C-index of the nomogram was found to be 0.975. The nomogram's specificity is 95.35% and its sensitivity, with a cut-off value of 165.6, is 92.59%, as seen by the ROC curve. After the nomogram was externally validated in the test cohort, the coincidence rate was determined to be 84%, and the ROC curve indicated that the nomogram's AUC was 0.949.

CONCLUSION: A nomogram containing clinical characteristics and platinum gene IHC scores was developed and validated to predict the risk of EOC platinum resistance.

Medienart:

E-Artikel

Erscheinungsjahr:

2024

Erschienen:

2024

Enthalten in:

Zur Gesamtaufnahme - volume:22

Enthalten in:

World journal of surgical oncology - 22(2024), 1 vom: 21. März, Seite 81

Sprache:

Englisch

Beteiligte Personen:

Li, Ruyue [VerfasserIn]
Xiong, Zhuo [VerfasserIn]
Ma, Yuan [VerfasserIn]
Li, Yongmei [VerfasserIn]
Yang, Yu'e [VerfasserIn]
Ma, Shaohan [VerfasserIn]
Ha, Chunfang [VerfasserIn]

Links:

Volltext

Themen:

49DFR088MY
Clinical features
Epithelial ovarian cancer
Journal Article
Nomogram
Platinum
Platinum resistance genes
Predictive modeling

Anmerkungen:

Date Completed 22.03.2024

Date Revised 23.03.2024

published: Electronic

Citation Status MEDLINE

doi:

10.1186/s12957-024-03359-9

funding:

Förderinstitution / Projekttitel:

PPN (Katalog-ID):

NLM369991990