Cancer Res Treat.  2021 Jan;53(1):233-242. 10.4143/crt.2020.159.

A Preoperative Nomogram for Predicting Chemoresistance to Neoadjuvant Chemotherapy in Patients with Locally Advanced Cervical Squamous Carcinoma Treated with Radical Hysterectomy

• Ou Z
• Zhao D
• Li B
• Wang Y
• Liu S
• Zhang Y

• KMID: 2510665
• DOI: http://doi.org/10.4143/crt.2020.159

Abstract

Purpose
This study aimed to investigate the factors associated with chemoresistance to neoadjuvant chemotherapy (NACT) followed by radical hysterectomy (RH) and construct a nomogram to predict the chemoresistance in patients with locally advanced cervical squamous carcinoma (LACSC).
Materials and Methods
This retrospective study included 516 patients with International Federation of Gynecology and Obstetrics (2003) stage IB2 and IIA2 cervical cancer treated with NACT and RH between 2007 and 2017. Clinicopathologic data were collected, and patients were assigned to training (n=381) and validation (n=135) sets. Univariate and multivariate analyses were performed to analyze factors associated with chemoresistance to NACT. A nomogram was built using the multivariate logistic regression analysis results. We evaluated the discriminative ability and accuracy of the model using a concordance index and a calibration curve. The predictive probability of chemoresistance to NACT was defined as > 34%.
Results
Multivariate analysis confirmed menopausal status, clinical tumor diameter, serum squamous cell carcinoma antigen level, and parametrial invasion on magnetic resonance imaging before treatment as independent prognostic factors associated with chemoresistance to NACT. The concordance indices of the nomogram for training and validation sets were 0.861 (95% confidence interval [CI], 0.822 to 0.900) and 0.807 (95% CI, 0.807 to 0.888), respectively. Calibration plots revealed a good fit between the modelpredicted probabilities and actual probabilities (Hosmer-Lemeshow test, p=0.597). Furthermore, grouping based on the nomogram was associated with progression-free survival.
Conclusion
We developed a nomogram for predicting chemoresistance in LACSC patients treated with RH. This nomogram can help physicians make clinical decisions regarding primary management and postoperative follow-up of the patients.

Keyword

Uterine cervical neoplasms; Neoadjuvant chemotherapy; Chemoresistance; Nomograms

Figure

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  Fig. 1 Inclusion criteria. MRI, magnetic resonance imaging; NACT, neoadjuvant chemotherapy; SCC-Ag, squamous cell carcinoma antigen.

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  Fig. 2 Nomogram predicting chemoresistance in patients with locally advanced cervical squamous carcinoma treated with neoadjuvant chemotherapy and radical hysterectomy. MRI, magnetic resonance imaging; SCC-Ag, squamous cell carcinoma antigen.

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  Fig. 3 Receiver operating characteristic curve for prediction of chemoresistance based on nomogram model in training set (A) and validation set (B). Calibration plot for nomogram model in training set (C) and validation set (D). Green line, the ideal reference line, indicated the perfect prediction of ideal model; Blue line, the apparent line, represented the entire cohort in training set or validation set; Red line, the bias-corrected line, is bias-corrected by bootstrapping (B=1,000 repetitions), suggesting observed performance of current nomogram. AUC, area under curve; CI, confidence interval.

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  Fig. 4 Progression-free survival plot based on grouping of nomogram model for chemoresistance.

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  Fig. 5 Treatment-flow chart based on chemoresistance. NACT, neoadjuvant chemotherapy.

Reference

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