ArticleCancer research and treatment2021
A Preoperative Nomogram for Predicting Chemoresistance to Neoadjuvant Chemotherapy in Patients with Locally Advanced Cervical Squamous Carcinoma Treated with Radical Hysterectomy.
Article in Cancer research and treatment, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
2 citing papers in PubMed, 6 citations in OpenAlex.
- A Deep Learning Radiomics Nomogram to Predict Response to Neoadjuvant Chemotherapy for Locally Advanced Cervical Cancer: A Two-Center Study.Diagnostics (Basel, Switzerland) · 2023Article
- Machine Learning-Assisted Ensemble Analysis for the Prediction of Response to Neoadjuvant Chemotherapy in Locally Advanced Cervical Cancer.Frontiers in oncology · 2022Article
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Authors and funding
6 authors at 1 institution in 1 country.
Funding
Abstract
purposeThis 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
methodsThis 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%.
resultsMultivariate 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.
conclusionWe 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.
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