Purpose To use CT-arterial phase images of pre-treatment ovarian cancer patients,combined with deep learning algorithms and machine learning to build a model to predict the efficacy of neoadjuvant chemotherapy in ovarian cancer.Materials and Methods A total of 302 consecutive patients who underwent surgery and were pathologically diagnosed with ovarian cancer from March 2013 to August 2019 in Tianjin Medical University were retrospectively collected.All patients were partitioned into training and test sets according to the ratio of 7∶3.In the python environment,VGG13 model was integrated via combining deep learning network and machine learning,and features were filtered via least absolute shrinkage and selection operator algorithm to build a prediction model for classification and prediction of CT images.The area under the curve(AUC),accuracy,sensitivity,specificity,and Fl-Score were calculated,respectively.Results The AUC,accuracy,sensitivity,specificity,and Fl-Score of the model in the training set were 0.87,0.81,0.80,0.82 and 0.79,and 0.90,0.84,0.93,0.77 and 0.83 in the test set,respectively.The AUC of five-fold cross-validation were 0.86,0.88,0.88,0.90 and 0.87,respectively.Conclusion Predictive model based on CT images combined with deep learning and machine learning methods can provide a new clinical perspective for developing chemotherapy regimens for ovarian cancer.
Deep learningTomography,X-ray computedNeoadjuvant chemotherapyOvarian cancerPredict healing efficacy
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