The value of radiomics model based on MR hyperresolution reconstructed images in preoperative predic-tion of lymphatic vascular infiltration in early cervical cancer
Objective:To explore the value of a radiomics model based on deep transfer learning super-reconstructed images of MR for preoperative prediction of lymphatic vessel infiltration(LVSI)of early cervical cancer(CC).Methods:A retrospective analysis was conducted on the MR images and clinical data of 100 early CC patients confirmed by postoperative pathology.The original images(OI)of sagittal T2 WI lipid-pressure non enhanced sequence were reconstructed using deep transfer learning(SRI).The entire tumor area was labeled in 3D on both OI and SRI using ITK-SNAP software.Ac-cording to the pathological results,the patients were divided into LVSI positive and LVSI negative groups,and randomly divided into a training set(80 cases)and a validation set(20 cases)in a 8:2 ra-tio.Feature extraction and Least Absolute Shrinkage and Selection Operator(LASSO)regression were performed on the annotated 3D VOI images from OI and SRI to screen for radiomics features.LightG-BM radiomics models were established,and the diagnostic efficacy of the models was evaluated using AUC.The clinical value of the models was evaluated using Decision Curve Analysis(DC A).Results:The diagnostic efficacy of the OI radiomics model in CC was evaluated with a training set AUC of 0.795(95％CI:0.696～0.894),sensitivity of 0.533,and specificity of 0.920.The validation set AUC=0.637(95％CI:0.350～0.924),with a sensitivity of 0.429 and a specificity of 0.923.The diagnostic effi-cacy of SRI radiomics classification in CC was evaluated with training set AUC=0.817(95％CI:0.7 22～0.913),sensitivity of 0.920,specificity of 0.717,validation set AUC=0.815(95％CI:0.625～1.000),sensitivity of 0.667,specificity of 0.786.The two sets of image training sets and validation sets showed good calibration and discrimination abilities,and the diagnostic efficiency of SRI compared to OI's radiomics model was significantly improved.The DCA results showed that the model had high clinical value.Conclusion:The SRI radiomics model based on MR deep transfer learning has good appli-cation value in predicting LVSI of cervical cancer before surgery,which improves the diagnostic effi-ciency compared to OI and can help better guide clinical treatment decisions.
维普
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