ArticleJournal of cancer research and clinical oncology2023
Nomograms constructed for predicting diagnosis and prognosis in cervical cancer patients with second primary malignancies: a SEER database analysis.
Article in Journal of cancer research and clinical oncology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Cervical cancer prediction using deformable kernel darknet-53 and depth wise separable convolutional neural networks.Scientific reports · 2025Article
- A nomogram for predicting prognosis for young cervical neuroendocrine carcinoma: A SEER-based study and external validation.Frontiers in oncology · 2025Article
- Causes of death analysis and the prognostic model construction in neuroendocrine carcinoma of the cervix: A SEER-based study.Cancer medicine · 2024Article
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6 authors.
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Abstract
purposeCervical cancer (CC) patients are more likely to develop second primary malignancies (SPMs) than general population. With the advancement in cancer therapy, CC patients are achieving long-term survival, leading SPMs to our attention. Our study aims to establish diagnostic and prognostic nomograms for CC patients with second primary malignancies (CCSPMs) to help make personalized follow-up plans and treatments.
methodsData of CCSPMs between 2000 and 2019 was extracted from SEER. The proportions and the median interval time of CCSPM onset were calculated. 11 related clinical characteristics, including age, race, marital status, grade, FIGO stage, radiotherapy, chemotherapy, and surgery, were further explore. Logistic and Cox regressions were employed to predict risk factors for CCSPMs diagnosis. Finally, two nomograms were developed to predict the probability occurrence and prognosis of CCSPMs, respectively.
resultsFor diagnostic nomogram construction, 59,178 CC patients were randomly divided into training (n = 41,426) and validation cohorts (n = 17,752). For prognostic nomogram construction, 3527 CCSPMs patients were randomly divided into training (n = 2469) and validation cohorts (n = 1058). The diagnostic nomogram consisting of above 11 independent risk factors (all P < 0.05), had high accuracy (AUCtraining = 0.851 and AUCvalidating = 0.845). The prognostic nomogram integrated with eight independent prognostic factors such as treatments, FIGO stage and TNM stage performed well in predicting 5-year OS (AUCtraining = 0.835 and AUCvalidating = 0.837).
conclusionOur diagnostic and prognostic nomograms could facilitate clinicians to quantify individual SPMs risk and survival probabilities and optimize the surveillance recommendations and personalized clinical decision-making.
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