Prediction of out-of-field recurrence after chemoradiotherapy for cervical cancer using a combination model of clinical parameters and magnetic resonance imaging radiomics : a multi-institutional study of the Japanese Radiation Oncology Study Group
| Autor: | Hitoshi Ikushima, Akihiro Haga, Ken Ando, Shingo Kato, Yuko Kaneyasu, Takashi Uno, Noriyuki Okonogi, Kenji Yoshida, Takuro Ariga, Fumiaki Isohashi, Yoko Harima, Ayae Kanemoto, Noriko Ii, Masaru Wakatsuki, Tatsuya Ohno |
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| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Radiation
cervical cancer out-of-field recurrence (OFR) Health Toxicology and Mutagenesis Uterine Cervical Neoplasms prediction Magnetic Resonance Imaging Oncology/Medicine chemoradiotherapy Japan radiomics Radiation Oncology AcademicSubjects/SCI00960 Humans Female Radiology Nuclear Medicine and imaging AcademicSubjects/MED00870 Retrospective Studies MRI |
| Zdroj: | Journal of Radiation Research |
| ISSN: | 1349-9157 |
| Popis: | We retrospectively assessed whether magnetic resonance imaging (MRI) radiomics combined with clinical parameters can improve the predictability of out-of-field recurrence (OFR) of cervical cancer after chemoradiotherapy. The data set was collected from 204 patients with stage IIB (FIGO: International Federation of Gynecology and Obstetrics 2008) cervical cancer who underwent chemoradiotherapy at 14 Japanese institutes. Of these, 180 patients were finally included for analysis. OFR-free survival was calculated using the Kaplan–Meier method, and the statistical significance of clinicopathological parameters for the OFR-free survival was evaluated using the log-rank test and Cox proportional-hazards model. Prediction of OFR from the analysis of diffusion-weighted images (DWI) and T2-weighted images of pretreatment MRI was done using the least absolute shrinkage and selection operator (LASSO) model for engineering image feature extraction. The accuracy of prediction was evaluated by 5-fold cross-validation of the receiver operating characteristic (ROC) analysis. Para-aortic lymph node metastasis (p = 0.003) was a significant prognostic factor in univariate and multivariate analyses. ROC analysis showed an area under the curve (AUC) of 0.709 in predicting OFR using the pretreatment status of para-aortic lymph node metastasis, 0.667 using the LASSO model for DWIs and 0.602 using T2 weighted images. The AUC improved to 0.734 upon combining the pretreatment status of para-aortic lymph node metastasis with that from the LASSO model for DWIs. Combining MRI radiomics with clinical parameters improved the accuracy of predicting OFR after chemoradiotherapy for locally advanced cervical cancer. |
| Databáze: | OpenAIRE |
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