論文

査読有り 国際誌
2020年1月22日

Multistate Markov Model to Predict the Prognosis of High-Risk Human Papillomavirus-Related Cervical Lesions.

Cancers
  • Ayumi Taguchi
  • Konan Hara
  • Jun Tomio
  • Kei Kawana
  • Tomoki Tanaka
  • Satoshi Baba
  • Akira Kawata
  • Satoko Eguchi
  • Tetsushi Tsuruga
  • Mayuyo Mori
  • Katsuyuki Adachi
  • Takeshi Nagamatsu
  • Katsutoshi Oda
  • Toshiharu Yasugi
  • Yutaka Osuga
  • Tomoyuki Fujii
  • 全て表示

12
2
記述言語
英語
掲載種別
研究論文(学術雑誌)
DOI
10.3390/cancers12020270

Cervical intraepithelial neoplasia (CIN) has a natural history of bidirectional transition between different states. Therefore, conventional statistical models assuming a unidirectional disease progression may oversimplify CIN fate. We applied a continuous-time multistate Markov model to predict this CIN fate by addressing the probability of transitions between multiple states according to the genotypes of high-risk human papillomavirus (HPV). This retrospective cohort comprised 6022 observations in 737 patients (195 normal, 259 CIN1, and 283 CIN2 patients at the time of entry in the cohort). Patients were followed up or treated at the University of Tokyo Hospital between 2008 and 2015. Our model captured the prevalence trend satisfactory, particularly for up to two years. The estimated probabilities for 2-year transition to CIN3 or more were the highest in HPV 16-positive patients (13%, 30%, and 42% from normal, CIN1, and CIN2, respectively) compared with those in the other genotype-positive patients (3.1%-9.6%, 7.6%-16%, and 21%-32% from normal, CIN1, and CIN2, respectively). Approximately 40% of HPV 52- or 58-related CINs remained at CIN1 and CIN2. The Markov model highlights the differences in transition and progression patterns between high-risk HPV-related CINs. HPV genotype-based management may be desirable for patients with cervical lesions.

リンク情報
DOI
https://doi.org/10.3390/cancers12020270
PubMed
https://www.ncbi.nlm.nih.gov/pubmed/31979115
PubMed Central
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7072567
ID情報
  • DOI : 10.3390/cancers12020270
  • PubMed ID : 31979115
  • PubMed Central 記事ID : PMC7072567

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