CT影像组学预测局部晚期宫颈癌同步放化疗后生存期

doi:10.3760/cma.j.cn113030-20230222-00033

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Abstract Objective To construct machine learning models based on CT imaging and clinical parameters for predicting progression-free survival (PFS) of locally advanced cervical cancer (LACC) patients after concurrent chemoradiotherapy (CCRT). Methods Clinical data of 167 LACC patients treated with CCRT at Shandong Cancer Hospital from September 2015 to October 2021 were retrospectively analyzed. All patients were randomly divided into the training and validation cohorts according to the ratio of 7 vs. 3. Clinical features were selected by univariate and multivariate Cox proportional hazards model (P<0.1). Radiomics models and nomograms were constructed by radiomics features which were selected by least absolute shrinkage and selection operator (LASSO) Cox regression model to predict the 1-, 3- and 5-year PFS. Combined models and nomogram models were developed by selected clinical and radiomics features. The Kaplan Meier-curve, receiver operating characteristic (ROC) curve, C-index and calibration curve were used to evaluate the model performance. Results A total of 1 409 radiomics features were extracted based on the region of interest (ROI) in CT images. CT radiomics models showed better performance for predicting 1-, 3-and 5-year PFS than the clinical model in the training and validation cohorts. The combined model displayed the optimal performance in predicting 1-, 3-and 5-year PFS in the training cohort [area under the curve (AUC): 0.760, 0.648, 0.661, C-index: 0.740, 0.667, 0.709] and verification cohort (AUC: 0.763, 0.677, 0.648, C-index: 0.748, 0.668, 0.678). Conclusions Combined model constructed based on CT radiomics and clinical features yield better prediction performance than that based on radiomics or clinical features alone. As an objective image analysis approach, it possesses high prediction efficiency for PFS of LACC patients after CCRT, which can provide reference for clinical decision-making.

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	Liu Huiling
	Cui Yongbin
	Chang Cheng
	Qiu Qingtao
	Yin Yong
	Wang Ruozheng

Key words： Uterine cervical neoplasms Concurrent chemoradiotherapy Computed tomography Radiomics Survival prediction

Received: 22 February 2023

Fund:Special Funds Project of Central Guidance on Local Science and Technology Development (ZYYD2022B18); Key Research and Development Program of Xinjiang Uygur Autonomous Region of China (2022B03019-X); State Key Laboratory of Pathogenesis, Prevention and Treatment of High Incidence Diseases in Central Asian Fund (SKL-HIDCA-2020-GJ4)

Corresponding Authors: Wang Ruozheng, Email: wrz8526@vip.163.com; Yin Yong, Email: yinyongsd@126.com

Cite this article:

Liu Huiling,Cui Yongbin,Chang Cheng et al. CT radiomics model for predicting progression-free survival of locally advanced cervical cancer after concurrent chemoradiotherapy[J]. Chinese Journal of Radiation Oncology, 2023, 32(8): 697-703.

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http://journal12.magtechjournal.com/Jweb_fszlx/EN/10.3760/cma.j.cn113030-20230222-00033 OR http://journal12.magtechjournal.com/Jweb_fszlx/EN/Y2023/V32/I8/697

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