ArticleCancer management and research2021
Development and Validation of a Nomogram for the Estimation of Response to Platinum-Based Neoadjuvant Chemotherapy in Patients with Locally Advanced Cervical Cancer.
Article in Cancer management and research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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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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Who cites it
1 citing paper in PubMed, 3 citations in OpenAlex.
- 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
4 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeNon-response to platinum-based neoadjuvant chemotherapy (non-rNACT) reduces the surgical outcomes of patients with locally advanced cervical cancer (LACC). The development of an accurate preoperative method to predict a patient's response to NACT (rNACT) could help surgeons to manage therapeutic intervention in a more appropriate manner. PATIENTS AND
methodsWe recruited a total of 341 consecutive patients who underwent platinum-based NACT followed by radical surgery (RS) at the Hubei Cancer Hospital between January 1, 2010 and April 1, 2020. All patients had been diagnosed with stage Ib2-IIa2 cervical cancer in accordance with the 2009 International Federation of Gynecology and Obstetrics (FIGO) classification system. First, we created a training cohort of patients who underwent NACT+RS (n=239) to develop a nomogram. We then validated the performance of the nomogram in a validation cohort of patients who underwent NACT+RS (n=102). Data analysis was conducted from October 1, 2020. First, we determined overall survival (OS) and progression-free survival (PFS) after NACT+RS. Multivariate logistic regression was then used to identify independent risk factors that were associated with the response to rNACT; these were then incorporated into the nomogram.
resultsThe analysis identified several significant differences between the rNACT and non-rNACT groups, including neutrophil-lymphocyte ratio (NLR), platelet lymphocyte ratio (PLR), lymphocyte monocyte ratio (LMR), platelet count, and FIGO stage. The performance of the rNACT nomogram score exhibited a robust C-index of 0.76 (95% confidence interval [CI]: 0.65 to 0.87) in the training cohort and high C-index of 0.71 (95% CI: 0.62 to 0.78) in the validation cohort. Clinical impact curves showed that the nomogram had good predictive ability.
conclusionWe successfully established an accurate and optimized nomogram that could be used preoperatively to predict rNACT in patients with LACC. This model can be used to evaluate the risk of an individual patient experiencing rNACT and therefore facilitate the choice of treatment.
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