Development of a novel deep learning-based prediction model for the prognosis of operable cervical cancer

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dc.contributor.authorDong, Taotao
dc.contributor.authorWang, Linlin
dc.contributor.authorLi, Ruowen
dc.contributor.authorLiu, Qingqing
dc.contributor.authorXu, Yiyue
dc.contributor.authorWei, Yuan
dc.contributor.authorJiao, Xinlin
dc.contributor.authorLi, Xiaofeng
dc.contributor.authorZhang, Yida
dc.contributor.authorZhang, Youzhong
dc.contributor.authorSong, Kun
dc.contributor.authorYang, Xingsheng
dc.contributor.authorCui, Baoxia
dc.date.accessioned2023-02-27T07:24:25Z
dc.date.available2023-02-27T07:24:25Z
dc.date.issued2022-11-26
dc.description.abstractBackground: Cervical cancer ranks as the 4th most common female cancer worldwide. Early stage cervical cancer patients can be treated with operation, but clinical staging system is not a good predictor of patients' survival. We aimed to develop a novel prognostic model to predict the prognosis for operable cervical cancer patients with better accuracy than clinical staging system. Methods: A total of 13,952 operable cervical cancer patients were retrospectively enrolled in this study. The whole dataset was randomly split into a training set (n = 9,068, 65%), validation set (n = 2,442, 17.5%), and testing set (n = 2,442, 17.5%). Cox proportional hazard (CPH) model and random survival forest (RSF) model were used as baseline models for the prediction of overall survival (OS). Then, a deep survival learning model (DSLM) was developed for OS prediction. Finally, a novel prognostic model was explored based on this DSLM. Results: The C-indexes for the CPH and RSF model were 0.731 and 0.753, respectively. DSLM, which had four layers that had 50 neurons in each layer, achieved a C-index of 0.782 in the validation set and a C-index of 0.758 in the testing set. The novel prognostic model based on DSLM showed better performances than the conventional clinical staging system (area under receiver operating curves were 0.826 and 0.689, respectively). Personalized survival curves for individual patient using this novel model also showed notably different survival slopes. Conclusions: Our study developed a novel, practical, personalized prognostic model for operable cervical cancer patients. This novel prognostic model may have the potential to provide a more prognostic information to oncologists.en_US
dc.identifier.citationDong, T., Wang, L., Li, R., Liu, Q., Xu, Y., Wei, Y., Jiao, X., Li, X., Zhang, Y., Zhang, Y., Song, K., Yang, X., & Cui, B. (2022). Development of a Novel Deep Learning-Based Prediction Model for the Prognosis of Operable Cervical Cancer. Computational and mathematical methods in medicine, 2022, 4364663. https://doi.org/10.1155/2022/4364663en_US
dc.identifier.otherDOI: 10.1155/2022/4364663
dc.identifier.urihttps://hdl.handle.net/20.500.14041/5838
dc.language.isoen_USen_US
dc.publisherComputational and Mathematical Methods in Medicineen_US
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 United States*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/us/*
dc.subjectNovel Deep Learning-Based Prediction Modelen_US
dc.subjectPrognosisen_US
dc.subjectOperable Cervical Canceren_US
dc.titleDevelopment of a novel deep learning-based prediction model for the prognosis of operable cervical canceren_US
dc.typeArticleen_US
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