Objective To explore the CT and MRI characteristics of pancreatic mucinous cystic neoplasm with associated invasive carcinoma(MCN-AIC)and their clinical application.Methods A retrospective analysis was conducted on the CT and MRI manifes-tations,clinical presentations,and laboratory results of 10 patients with pathologically confirmed MCN-AIC.Results Four of 10 patients presented to the clinic with abdominal pain.Plain CT showed all 10 lesions with hypointensity,and enhanced CT showed 8 lesions with mild delayed enhancement.MRI showed 8 lesions with limited diffusion on diffusion weighted imaging(DWI).2 lesions had cal-cification and 8 lesions had no calcification.6 lesions were located at the pancreatic head,3 at the pancreatic tail,and remaining one at the pancreatic neck.In addition,main pancreatic duct dilatation in 6 leisons,no main pancreatic duct dilatation in 4 lesions,thickened cyst wall in 10 lesions,wall nodules in 4 lesions,no wall nodules in 6 lesions,and intratumoral segregations in 5 lesions,5 lesions without segregations.Conclusion The CT and MRI manifestations of MCN-AIC have certain characteristics and play an important role in imaging diagnosis,which can provide a reference basis for the treatment.

Objective To investigate the classification capability of a deep learning classification model based on biparametric mag-netic resonance imaging(bpMRI)for clinically significant prostate cancer(csPCa)and clinically insignificant prostate cancer(cisPCa).Methods A retrospective analysis was conducted on the data of 565 prostate bpMRI patients.A deep learning classification model was established for csPCa.The patients were randomly divided into training set(452 cases)and internal test set(113 cases)at a ratio of 8︰2.Internal validation was performed,followed by external validation(external validation set)using data from 120 patients across four different hospitals.The area under the curve(AUC)of the receiver operating characteristic(ROC)curve,F1 score,precision,sensi-tivity,specificity,accuracy,and calibration curves were used to evaluate the model.Decision curve analysis(DCA)was also applied to assess the clinical benefit of the model.Results The deep learn-ing classification model for csPCa classification demonstrated the following performance across the training set,internaltest set,and external validation set:sensitivity of 0.986,0.887,and 0.750;specificity of 0.967,0.850,and 0.976;precision of 0.963,0.839,and 0.818;accuracy of 0.974,0.862,and 0.792;F1 score of 0.974,0.862,and 0.783;and AUC of 0.998,0.896,and 0.883,respec-tively.The calibration curves for all three datasets showed high consistency between predicted and actual probabilities.DCA indicated that the highest net benefit threshold probabilities for the training set,internal test set,and external validation set were 0.2-0.7,0.2-0.6,and 0.2-0.5,respectively.Conclusion The deep learning classification model demonstrated excellent performance in classifying csPCa and exhibited good generalizability,which is worhty of clinical application.

Objective To investigate the classification capability of a deep learning classification model based on biparametric mag-netic resonance imaging(bpMRI)for clinically significant prostate cancer(csPCa)and clinically insignificant prostate cancer(cisPCa).Methods A retrospective analysis was conducted on the data of 565 prostate bpMRI patients.A deep learning classification model was established for csPCa.The patients were randomly divided into training set(452 cases)and internal test set(113 cases)at a ratio of 8︰2.Internal validation was performed,followed by external validation(external validation set)using data from 120 patients across four different hospitals.The area under the curve(AUC)of the receiver operating characteristic(ROC)curve,F1 score,precision,sensi-tivity,specificity,accuracy,and calibration curves were used to evaluate the model.Decision curve analysis(DCA)was also applied to assess the clinical benefit of the model.Results The deep learn-ing classification model for csPCa classification demonstrated the following performance across the training set,internaltest set,and external validation set:sensitivity of 0.986,0.887,and 0.750;specificity of 0.967,0.850,and 0.976;precision of 0.963,0.839,and 0.818;accuracy of 0.974,0.862,and 0.792;F1 score of 0.974,0.862,and 0.783;and AUC of 0.998,0.896,and 0.883,respec-tively.The calibration curves for all three datasets showed high consistency between predicted and actual probabilities.DCA indicated that the highest net benefit threshold probabilities for the training set,internal test set,and external validation set were 0.2-0.7,0.2-0.6,and 0.2-0.5,respectively.Conclusion The deep learning classification model demonstrated excellent performance in classifying csPCa and exhibited good generalizability,which is worhty of clinical application.

Objective To explore the CT and MRI characteristics of pancreatic mucinous cystic neoplasm with associated invasive carcinoma(MCN-AIC)and their clinical application.Methods A retrospective analysis was conducted on the CT and MRI manifes-tations,clinical presentations,and laboratory results of 10 patients with pathologically confirmed MCN-AIC.Results Four of 10 patients presented to the clinic with abdominal pain.Plain CT showed all 10 lesions with hypointensity,and enhanced CT showed 8 lesions with mild delayed enhancement.MRI showed 8 lesions with limited diffusion on diffusion weighted imaging(DWI).2 lesions had cal-cification and 8 lesions had no calcification.6 lesions were located at the pancreatic head,3 at the pancreatic tail,and remaining one at the pancreatic neck.In addition,main pancreatic duct dilatation in 6 leisons,no main pancreatic duct dilatation in 4 lesions,thickened cyst wall in 10 lesions,wall nodules in 4 lesions,no wall nodules in 6 lesions,and intratumoral segregations in 5 lesions,5 lesions without segregations.Conclusion The CT and MRI manifestations of MCN-AIC have certain characteristics and play an important role in imaging diagnosis,which can provide a reference basis for the treatment.