Prognostic model based on magnetic resonance imaging, whole-tumour apparent diffusion coefficient values and HPV genotyping for stage IB-IV cervical cancer patients following chemoradiotherapy
European Radiology, ISSN: 1432-1084, Vol: 29, Issue: 2, Page: 556-565
2019
- 18Citations
- 25Captures
- 1Mentions
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
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- Citations18
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- 18
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- Captures25
- Readers25
- 25
- Mentions1
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- 1
Most Recent News
Identification of Novel Biomarkers MCM2 and GINS2 for Cervical Cancer
Key words: GINS2, MCM2, Cervical cancer, Prognosis, Differentially expressed genes INTRODUCTION Cervical cancer (CC) is a common gynecological malignant tumor, it is the fourth leading
Article Description
Objectives: To develop and validate a prognostic model of integrating whole-tumour apparent diffusion coefficient (ADC) from pretreatment diffusion-weighted (DW) magnetic resonance (MR) imaging with human papillomavirus (HPV) genotyping in predicting the overall survival (OS) and disease-free survival (DFS) for women with stage IB–IV cervical cancer following concurrent chemoradiotherapy (CCRT). Methods: We retrospectively analysed three prospectively collected cohorts comprising 300 patients with stage IB–IV cervical cancer treated with CCRT in 2007–2014 and filtered 134 female patients who underwent MR imaging at 3.0 T for final analysis (age, 24–92 years; median, 54 years). Univariate and multivariate Cox regression analyses were used to evaluate the whole-tumour ADC histogram parameters, HPV genotyping and relevant clinical variables in predicting OS and DFS. The dataset was randomly split into training (n = 88) and testing (n = 46) datasets for construction and independent bootstrap validation of the models. Results: The median follow-up time for surviving patients was 69 months (range, 9–126 months). Non-squamous cell type, ADC <0.77 × 10 mm/s, T3-4, M1 stage and high-risk HPV status were selected to generate a model, in which the OS and DFS for the low, intermediate and high-risk groups were significantly stratified (p < 0.0001). The prognostic model improved the prediction significantly compared with the International Federation of Gynaecology and Obstetrics (FIGO) stage for both the training and independent testing datasets (p < 0.0001). Conclusions: The prognostic model based on integrated clinical and imaging data could be a useful clinical biomarker to predict OS and DFS in patients with stage IB–IV cervical cancer treated with CCRT. Key points: • ADCis the best prognostic factor among ADC parameters in cervical cancer treated with CCRT • A novel prognostic model was built based on histology, ADC, T and M stage and HPV status • The prognostic model outperforms FIGO stage in the survival prediction.
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85050701593&origin=inward; http://dx.doi.org/10.1007/s00330-018-5651-4; http://www.ncbi.nlm.nih.gov/pubmed/30051142; http://link.springer.com/10.1007/s00330-018-5651-4; https://dx.doi.org/10.1007/s00330-018-5651-4; https://link.springer.com/article/10.1007/s00330-018-5651-4
Springer Science and Business Media LLC
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