Radiomics Model For Predicting The Aggressive Of Pancreatic Solid Pseudopapillary Neoplasm

Part?:the theoretical studySolid Pseudopapillary Neoplasm(SPN)is a rare and low-grade malignant tumor of the pancreas,but its clinical incidence has increased significantly in recent years.At present,surgical resection is the main method for SPN.Traditional pancreatic malignant radical surgery is not only traumatic,high risk,but also easy to cause postoperative pancreatic endocrine and exocrine insufficiency,and most of the pancreatic SPN inert biological behavior,so surgeons tend to take minimally invasive surgery.However,due to the heterogeneity between SPNs,about 14.4%?33.3%of the tumors are invasive and can invade adjacent tissues or distant metastases.The minimally invasive surgery may not completely remove the tumor tissue,which may cause recurrence and metastasis.Therefore,accurately determining the invasiveness of the tumor before surgery is a key factor in determining the surgical plan.SPN occurs mostly in young women,and it is socially harmful.The lack of specific clinical symptoms and laboratory markers in the tumor,needle biopsy cannot fully and accurately reflect the heterogeneity of the tumor.CT is the main means in clinical diagnosis,but the current value of CT signs in the invasive and non-invasive pancreatic SPN is still controversial.Therefore,it is of great clinical value to explore the preoperative CT signs to predict the invasiveness of SPN.In recent years,with the rapid development of precision medicine and individualized treatment,radiomics has become a hot spot in current clinical research.It mainly extracts high-throughput quantitative features from a large number of medical image images(CT,MRI or PET)by machine learning and converts them into high-dimensional data that can be collected.The essence of radiomics is a non-invasive quantitative analysis,which is different from the biopsy to assess the heterogeneity of tumors by histopathology.Radiomics reflects the microenvironment of tumor growth and the tumor itself by depicting the heterogeneity in the image.Heterogeneity,non-invasive,economical,reproducible,and does not impose additional burdens and risks on patients,and the quantitative data obtained from the analysis can make clinical decisions more stable and consistent,so radiomics has been widely carried out in clinical practice,covering diseases diagnosis and differential diagnosis,biological behavior assessment,pathological and grading.tumor staging,efficacy prediction and evaluation,disease prognosis and survival prediction,and all of them show high clinical ValuePart?:Prediction of invasion of pancreatic solid-pseudopapillary tumor using CT findingsPurposes:Based on the pathological gold criteria,the multi-phase enhanced CT findings of invasive and non-invasive pancreatic SPN were analyzed to investigate the diagnostic value of conventional MSCT imaging features in predicting SPN invasiveness.and a predictive model of SPN invasiveness was constructed by combining clinical features.Materials and Methods:The clinical and imaging data of 127 patients with pancreatic SPN confirmed by surgical pathology were retrospectively analyzed.According to the postoperative pathology,they were divided into invasive pancreatic SPN or non-invasive pancreatic SPN.The age,sex and clinical symptoms,surgical methods,imaging signs(location,size,morphology.texture,boundary,exogenous,envelope,hemorrhage,calcification,pancreaticobiliary dilatation,maximum tumor diameter,CT value,CT ratio,enhancement model)and surrounding tumors indirect signs of the two groups were respectively compared.The difference of continuous variables between the two groups was compared with the independent sample t test or Mann-Whitney U test,and the difference of qualitative variables between the two groups was compared by x2 test or Fisher exact test method.Then the parameters of single factor analysis with statistical difference were incorporated into binary logistic regression analysis,and the independent risk factors for predicting the invasion of pancreatic SPN were screened by stepwise backward LR,and the predictive model of combined CT features and clinical information was constructed.The diagnostic efficiency of the model was calculated by ROC curve.Results:Finally,127 patients with pancreatic SPN were included,including 32 in the invasive group and 95 in the non-invasive group.Univariate analysis revealed that patients'age,gender,tumor maximum diameter,capsule,border,and intratumoral hemorrhage were statistically different between the t,^wo group(p=0.017,0.022,0.006,<0.001,0.026,0.017,respectively).Compared with the non-invasive pancreatic SPN group,the average age has increased,the proportion of male patients has increased,and the median diameter of the tumor has decreased,and the proportion of tumor lacking intact capsule and unclear border has increased,and the proportion of bleeding has decreased in the invasive pancreatic SPN group.After the multi-factor logistic regression of conventional CT features with stepwise,it was found that the lack of complete capsule of tumor was an independent risk factor to predict the invasion of SPN[OR=6.259,96%CI(2.221?17.637),p=0.001],The model AUC value was 0.69,95%CI was 0.59?0.79,sensitivity was 84.4%,specificity was 53.7%,and correctness was 61.4%.The combined model had an AUC value of 0.739 and 95%CI of 0.639?0.84.The sensitivity was 81.3%,the specificity was 57.9%,and the correctness was 76.4%.Conclusions:1.Patients' gender,age,tumor maximum diameter,capsule,border,and intratumoral hemorrhage were statistically different between invasive and non-invasive pancreatic SPN.2.The lack of a complete capsule of the tumor is an independent risk factor for predicting the invasiveness of SPN.3.The combined model of conventional CT image features and t age has certain value for predicting the invasiveness of pancreatic SPN.Part ?:CT radiomics for predicting the invasiveness of pancreatic SPNPurposes:Because radiomics can quantitatively assess the heterogeneity of tumors,this study based on preoperative routine CT images through radiomics and machine learning methods to construct an radiomics label and nomogram model for predicting the invasiveness of pancreatic SPN,and to explore the clinical value of radiomics in the prediction aggressive of pancreatic SPN.Materials and Methods:The clinical and imaging data of 112 patients with pancreatic SPN confirmed by surgery and pathology from October 2012 to October 2018 were retrospectively collected.77 patients were used as a training group by the ratio of 7:3 between October 2012 and June 2016,35 patients bet.ween July 2016 and October 2018 were used as a validation group,and all patients were divided into invasive or non-invasive pancreatic SPN according to postoperative pathology.The clinical data and routine CT signs of patients with invasive and non-invasive pancreas in the training and validation groups were compared.The statistically significant variables in the training group were included in the multivariate logistic regression,and the LR method was used to screen through the stepwise retreat.Predicting independent risk factors for pancreatic SPN invasiveness and constructing predictive models.In the radiomics analysis,the three-phase CT images of all patients were pre-processed,and then the thr-ee-phase images were segmented layer by layer using ITK-SNAP,and then the AK was used to extract the radiomics f-eatures.Six radiomics labels based on the images of plain scan,pancreatic parenchyma,portal vein,plain scan combined with pancreatic parenchyma,plain scan combined with portal vein and plain scan combined with pancreatic parenchyma and portal vein were established according to different scanned images.Before the screening of radiomics features,the intra-and inter-group correlation coefficients was used to evaluate the reproducibility of image segmentation and the stability of photographic genre features.The data was preprocessed,and then the phenotypic parameters of the six imaging labels were screened by single factor analysis,correlation analysis and LASSO regression with ten-fold cross-validation,and the radiomics labels were calculated and evaluated by ROC.The predictive efficacy of each radiomics label in the training,validation,and all 112 patients were compared,and finally selected the best predictive radiomics label in the validation group,multivariate logistic regression analysis by combining patient age with conventional CT f-eatures and build a nomogram model.ROC curve,AUC value,Bootstrap and HL test were used to evaluate the predictive performance of the nomogram model in the training and validation groups.Delong test was used to compare whether the prediction performance between the models was significantly different.The prediction results of the model are visualized by using the nomogram.and the correction effect and clinical value of the nomogram are evaluated using the calibration curve and the decision curveResults:There were no statistically significant differences in clinical data and routine CT findings between 112 patients in this group and 127 patients in the previous section.There were no statistically significant differences in the clinical data,routine CT signs,and invasive patients between the training group and the validation group.The intra-and inter-group ICC of tumor segmentation was greater than 0.75,so all 396%radiomics features were included in the feature screening.After date analysis,it was found that the radiomics label based on the pancreatic parenchymal phase image had the highest predictive power in the validation group,and the AUC value was 0.88.So the nomogram prediction model consists of patient age,conventional CT features and radiomics label based on the pancreatic parenchymal phase image s.After multi-parameter logistic regression,the radiomics label and tumor capsule are found as an independent risk factor for predicting the invasiveness of pancreatic SPN.The adjusted OR value of tumor capsule was 7.635,95%CI was 1.709 to 34.108,P value was 0.008,and the adjusted OR value of radiomics label was 9.849,95%CI was 2.314?41.912,P value is 0.002.The AUC value of the predictive performance of the nomogram model in the training group is 0.856.Delong test found that the predictive power of the nomogram model is higher than the clinical information model,the conventional CT feature model and the combine model,and the difference is statistically significant(p=0.0177,0.0006,0.0006,respectively).In the validation group,the AUC value of the prediction performance of the nomogram model is 0.932.The Hosmer-Lemeshoe goodness-of-fit test was not statistically significant between the training and validation groups(p=0.794,0.668,respectively).Decision curve analysis shows that the nomogram model has high clinical value.Conclusions:1.Six radiomics label have a good predictive effect on pancreatic SPN invasiveness.The radiomics label based on pancreatic parenchyma phase images was the most predictive in the validation group and all 112 pancreatic SPNs.2.In the conventional CT model of the training group and the combined model with clinical data,the tumor capsule is an independent risk factor for predicting the invasiveness of pancreatic SPN;while in the nomogram model,the radiomics label and tumor capsule are independent risk factor for predicting the invasiveness of pancreatic SPN3.In multi-model comparison,the predictive performance of the nomogram model is higher than the clinical,conventional CT features and the combined model,and the Delong test shows that the difference is statistically significant.The nomogram model has higher prediction performance in both the training group and the verification group,and the Hosmer-Lemeshoe goodness-of-fit test indicates that the model has no deviation from the training group.Decision curve analysis shows that the nomogram model has high clinical value.