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Background Although deep learning has brought revolutionary changes in health care, reliance on manually selected cross-sectional images and segmentation remain methodological barriers. Purpose To develop and validate an automated preoperative artificial intelligence (AI) algorithm for tumor and lymph node (LN) segmentation with CT imaging for prediction of LN metastasis in patients with pancreatic ductal adenocarcinoma (PDAC). Materials and Methods In this retrospective study, patients with surgically resected, pathologically confirmed PDAC underwent multidetector CT from January 2015 to April 2020. Three models were developed, including an AI model, a clinical model, and a radiomics model. CT-determined LN metastasis was diagnosed by radiologists. Multivariable logistic regression analysis was conducted to develop the clinical and radiomics models. The performance of the models was determined on the basis of their discrimination and clinical utility. Kaplan-Meier curves, the log-rank test, or Cox regression were used for survival analysis. Results Overall, 734 patients (mean age, 62 years ± 9 [SD]; 453 men) were evaluated. All patients were split into training (

To develop and validate a radiomics model to predict fibroblast activation protein (FAP) expression in patients with pancreatic ductal adenocarcinoma (PDAC).This retrospective study included consecutive 152 patients with PDAC who underwent MDCT scan and surgical resection from January 2017 to December 2017 (training set) and from January 2018 to April 2018 (validation set). In the training set, 1409 portal radiomic features were extracted from each patient's preoperative imaging. Optimal features were selected using the least absolute shrinkage and selection operator (LASSO) logistic regression algorithm, whereupon the extreme gradient boosting (XGBoost) was developed using the radiomics features. The performance of the XGBoost classifier performance was assessed by its calibration, discrimination, and clinical usefulness.The patients were divided into FAP-low (n = 91; 59.87%) and FAP-high (n = 61; 40.13%) groups according to the optimal FAP cutoff (45.71%). Patients in the FAP-low group showed longer survival. The XGBoost classifier comprised 13 selected radiomics features and showed good discrimination in the training set [area under the curve (AUC), 0.97] and the validation set (AUC, 0.75). It also performed well in the calibration test and decision-curve analysis, demonstrating its potential clinical value.The XGBoost classifier based on CT radiomics in the portal venous phase can non-invasively predict FAP expression and may help to improve clinical decision-making in patients with PDAC.

Objective To develop and validate a magnetic resonance imaging (MRI)-based machine learning classifier for evaluating the tumor-stroma ratio (TSR) in patients with pancreatic ductal adenocarcinoma (PDAC). Materials and Methods In this retrospective study, 148 patients with PDAC underwent an MR scan and surgical resection. We used hematoxylin and eosin to quantify the TSR. For each patient, we extracted 1,409 radiomics features and reduced them using the least absolute shrinkage and selection operator logistic regression algorithm. The extreme gradient boosting (XGBoost) classifier was developed using a training set comprising 110 consecutive patients, admitted between December 2016 and December 2017. The model was validated in 38 consecutive patients, admitted between January 2018 and April 2018. We determined the performance of the XGBoost classifier based on its discriminative ability, calibration, and clinical utility. Results A log-rank test revealed significantly longer survival in the TSR-low group. The prediction model displayed good discrimination in the training (area under the curve [AUC], 0.82) and validation set (AUC, 0.78). While the sensitivity, specificity, accuracy, positive predictive value, and negative predictive value for the training set were 77.14%, 75.00%, 0.76%, 0.84%, and 0.65%, respectively, those for the validation set were 58.33%, 92.86%, 0.71%, 0.93%, and 0.57%, respectively. Conclusion We developed an XGBoost classifier based on MRI radiomics features, a non-invasive prediction tool that can evaluate the TSR of patients with PDAC. Moreover, it will provide a basis for interstitial targeted therapy selection and monitoring.

Objectives We aimed to develop and validate a multimodality radiomics model for the preoperative prediction of nonfunctional pancreatic neuroendocrine tumor (NF-pNET) grade (G). Methods This retrospective study assessed 123 patients with surgically resected, pathologically confirmed NF-pNETs who underwent multidetector computed tomography and MRI scans between December 2012 and May 2020. Radiomic features were extracted from multidetector computed tomography and MRI. Wilcoxon rank-sum test and Max-Relevance and Min-Redundancy tests were used to select the features. The linear discriminative analysis (LDA) was used to construct the four models including a clinical model, MRI radiomics model, computed tomography radiomics model, and mixed radiomics model. The performance of the models was assessed using a training cohort (82 patients) and a validation cohort (41 patients), and decision curve analysis was applied for clinical use. Results We successfully constructed 4 models to predict the tumor grade of NF- pNETs. Model 4 combined 6 features of T2-weighted imaging radiomics features and 1 arterial-phase computed tomography radiomics feature, and showed better discrimination in the training cohort (AUC = 0.92) and validation cohort (AUC = 0.85) relative to the other models. In the decision curves, if the threshold probability was 0.07–0.87, the use of the radiomics score to distinguish NF-pNET G1 and G2/3 offered more benefit than did the use of a “treat all patients” or a “treat none” scheme in the training cohort of the MRI radiomics model. Conclusion The LDA classifier combining multimodality images may be a valuable noninvasive tool for distinguishing NF-pNET grades and avoid unnecessary surgery.

Purpose To evaluate the diagnostic performance of the delayed-phase difference between tumor and pancreas for differentiating solid pseudopapillary tumors (SPTs) from non-functional neuroendocrine tumors (NF-NETs) of the pancreas. Methods This retrospective review included 148 consecutive patients with SPT and 98 consecutive patients with NF-NET confirmed by pathology. Patients with SPT and NF-NET were matched via propensity score matching (PSM). All patients underwent multidetector computed tomography (MDCT). For each patient, the delayed-phase difference between the tumor and pancreas was measured, and the performance of this variable was assessed based on its discriminative ability and clinical utility. Results After PSM, 27 patients with SPT and 27 patients with NF-NET were included in the matched analysis. There were no statistically significant differences in clinical and CT characteristics between the resulting two groups (p > 0.05). The delayed-phase difference values between the tumor and pancreas were significantly lower in patients with SPT (median: -0.45; range: -2.05 to 0.73) than in patients with NF-NET (median: 0.71; range: -1.39 to 2.38). The delayed-phase difference between tumor and pancreas had a high diagnostic accuracy (area under the curve=0.88). The best cutoff point based on maximizing the sum of the sensitivity and specificity was -0.23 (sensitivity = 88.89%; specificity = 88.89%; accuracy = 0.89). Conclusions The delayed-phase difference between tumor and pancreas can accurately and noninvasively differentiate SPT from NF-NET.

To develop and validate a machine-learning classifier based on contrast-enhanced computed tomography (CT) for the preoperative prediction of CD20+ B lymphocyte expression in patients with pancreatic ductal adenocarcinoma (PDAC). Overall, 189 patients with PDAC (n = 132 and n = 57 in the training and validation sets, respectively) underwent immunohistochemistry and radiomics feature extraction. The X-tile software was used to stratify them into groups with ‘high’ and ‘low’ CD20+ B lymphocyte expression levels. For each patient, 1409 radiomic features were extracted from volumes of interest and reduced using variance analysis and Spearman correlation analysis. A multilayer perceptron (MLP) network classifier was developed using the training and validation set. Model performance was determined by its discriminative ability, calibration, and clinical utility. A log-rank test showed that the patients with high CD20+ B expression had significantly longer survival than those with low CD20+ B expression. The prediction model showed good discrimination in both the training and validation sets. For the training set, the area under the curve (AUC), sensitivity, specificity, accuracy, positive predictive value, and negative predictive value were 0.82 (95% CI 0.74–0.89), 92.42%, 57.58%, 0.75, 0.69, and 0.88, respectively; whereas these values for the validation set were 0.84 (95% CI 0.72–0.93), 86.21%, 78.57%, 0.83, 0.81, and 0.85, respectively. The MLP network classifier based on contrast-enhanced CT can accurately predict CD20+ B expression in patients with PDAC.

Background CD8+ T cell in pancreatic ductal adenocarcinoma (PDAC) is closely related to the prognosis and treatment response of patients. Accurate preoperative CD8+ T-cell expression can better identify the population benefitting from immunotherapy. Purpose To develop and validate a machine learning classifier based on noncontrast magnetic resonance imaging (MRI) for the preoperative prediction of CD8+ T-cell expression in patients with PDAC. Study type Retrospective cohort study. Population Overall, 114 patients with PDAC undergoing MR scan and surgical resection; 97 and 47 patients in the training and validation cohorts. FIELD STRENGTH/SEQUENCE/3 T: Breath-hold single-shot fast-spin echo T2-weighted sequence and noncontrast T1-weighted fat-suppressed sequences. Assessment CD8+ T-cell expression was quantified using immunohistochemistry. For each patient, 2232 radiomics features were extracted from noncontrast T1- and T2-weighted images and reduced using the Wilcoxon rank-sum test and least absolute shrinkage and selection operator method. Linear discriminative analysis was used to construct radiomics and mixed models. Model performance was determined by its discriminative ability, calibration, and clinical utility. Statistical tests Kaplan-Meier estimates, Student's t-test, the Kruskal-Wallis H test, and the chi-square test, receiver operating characteristic curve, and decision curve analysis. Results A log-rank test showed that the survival duration in the CD8-high group (25.51 months) was significantly longer than that in the CD8-low group (22.92 months). The mixed model included all MRI characteristics and 13 selected radiomics features, and the area under the curve (AUC) was 0.89 (95% confidence interval [CI], 0.77-0.92) and 0.69 (95% CI, 0.53-0.82) in the training and validation cohorts. The radiomics model included 13 radiomics features, which showed good discrimination in the training cohort (AUC, 0.85; 95% CI, 0.77-0.92) and the validation cohort (AUC, 0.76; 95% CI, 0.61-0.87). Data conclusions This study developed a noncontrast MRI-based radiomics model that can preoperatively determine CD8+ T-cell expression in patients with PDAC and potentially immunotherapy planning. Evidence level 5 TECHNICAL EFFICACY: Stage 2.

To develop and validate a machine learning classifier based on magnetic resonance imaging (MRI), for the preoperative prediction of tumor-infiltrating lymphocytes (TILs) in patients with pancreatic ductal adenocarcinoma (PDAC). In this retrospective study, 156 patients with PDAC underwent MR scan and surgical resection. The expression of CD4, CD8 and CD20 was detected and quantified using immunohistochemistry, and TILs score was achieved by Cox regression model. All patients were divided into TILs score-low and TILs score-high groups. The least absolute shrinkage and selection operator method and the extreme gradient boosting (XGBoost) were used to select the features and to construct a prediction model. The performance of the models was assessed using the training cohort (116 patients) and the validation cohort (40 patients), and decision curve analysis (DCA) was applied for clinical use. The XGBoost prediction model showed good discrimination in the training (AUC 0.86; 95% CI 0.79–0.93) and validation sets (AUC 0.79; 95% CI 0.64–0.93). The sensitivity, specificity, and accuracy for the training set were 86.67%, 75.00%, and 0.81, respectively, whereas those for the validation set were 84.21%, 66.67%, and 0.75, respectively. Decision curve analysis indicated the clinical usefulness of the XGBoost classifier. The model constructed by XGBoost could predict PDAC TILs and may aid clinical decision making for immune therapy.

To develop and validate a CT nomogram and a radiomics nomogram to differentiate mass-forming chronic pancreatitis (MFCP) from pancreatic ductal adenocarcinoma (PDAC) in patients with chronic pancreatitis (CP).In this retrospective study, the data of 138 patients with histopathologically diagnosed MFCP or PDAC treated at our institution were retrospectively analyzed. Two radiologists analyzed the original cross-sectional CT images based on predefined criteria. Image segmentation, feature extraction, and feature reduction and selection were used to create the radiomics model. The CT and radiomics models were developed using data from a training cohort of 103 consecutive patients. The models were validated in 35 consecutive patients. Multivariable logistic regression analysis was conducted to develop a model for the differential diagnosis of MFCP and PDAC and visualized as a nomogram. The nomograms' performances were determined based on their differentiating ability and clinical utility.The mean age of patients was 53.7 years, 75.4% were male. The CT nomogram showed good differentiation between the two entities in the training (area under the curve [AUC], 0.87) and validation (AUC, 0.94) cohorts. The radiomics nomogram showed good differentiation in the training (AUC, 0.91) and validation (AUC, 0.93) cohorts. Decision curve analysis showed that patients could benefit from the CT and radiomics nomograms, if the threshold probability was 0.05-0.85 and0.05, respectively.The two nomograms reasonably accurately differentiated MFCP from PDAC in patients with CP and hold potential for refining the management of pancreatic masses in CP patients.• A CT nomogram and a computed tomography-based radiomics nomogram reasonably accurately differentiated mass-forming chronic pancreatitis from pancreatic ductal adenocarcinoma in patients with chronic pancreatitis (CP). • The two nomograms can monitor the cancer risk in patients with CP and hold promise to optimize the management of pancreatic masses in patients with CP.

Conventional chemotherapy has limited benefit in pancreatic ductal adenocarcinoma (PDAC), necessitating identification of novel therapeutic targets. Radiomics may enable non-invasive prediction of CD20 expression, a hypothesized therapeutic target in PDAC. To develop a machine learning classifier based on noncontrast magnetic resonance imaging for predicting CD20 expression in PDAC.Retrospective study was conducted on preoperative noncontrast magnetic resonance imaging of 156 patients with pathologically confirmed PDAC from January 2017 to April 2018. For each patient, 1409 radiomics features were selected using minimum absolute contraction and selective operator logistic regression algorithms. CD20 expression was quantified using immunohistochemistry. A multilayer perceptron network classifier was developed using the training and validation set.A log-rank test showed that the CD20-high group (22.37 months, 95% CI: 19.10-25.65) had significantly longer survival than the CD20-low group (14.9 months, 95% CI: 10.96-18.84). The predictive model showed good differentiation in training (area under the curve [AUC], 0.79) and validation (AUC, 0.79) sets. Sensitivity, specificity, accuracy, positive predictive value, and negative predictive value were 73.21%, 75.47%, 0.74, 0.76, and 0.73, respectively, for the training set and 69.23%, 80.95%, 0.74, 0.82, and 0.68, respectively, for the validation set.Multilayer perceptron classifier based on noncontrast magnetic resonance imaging scanning can predict the level of CD20 expression in PDAC patients.

CD8To develop and validate a machine learning classifier based on noncontrast magnetic resonance imaging (MRI) for the preoperative prediction of CD8Retrospective cohort study.Overall, 114 patients with PDAC undergoing MR scan and surgical resection; 97 and 47 patients in the training and validation cohorts. FIELD STRENGTH/SEQUENCE/3 T: Breath-hold single-shot fast-spin echo T2-weighted sequence and noncontrast T1-weighted fat-suppressed sequences.CD8Kaplan-Meier estimates, Student's t-test, the Kruskal-Wallis H test, and the chi-square test, receiver operating characteristic curve, and decision curve analysis.A log-rank test showed that the survival duration in the CD8-high group (25.51 months) was significantly longer than that in the CD8-low group (22.92 months). The mixed model included all MRI characteristics and 13 selected radiomics features, and the area under the curve (AUC) was 0.89 (95% confidence interval [CI], 0.77-0.92) and 0.69 (95% CI, 0.53-0.82) in the training and validation cohorts. The radiomics model included 13 radiomics features, which showed good discrimination in the training cohort (AUC, 0.85; 95% CI, 0.77-0.92) and the validation cohort (AUC, 0.76; 95% CI, 0.61-0.87).This study developed a noncontrast MRI-based radiomics model that can preoperatively determine CD85 TECHNICAL EFFICACY: Stage 2.

The intraductal papillary mucinous neoplasm (IPMN) of the pancreas is regarded as a precursor to pancreatic cancer; this study aimed to develop and validate a model based on CT characteristics for the non-invasive prediction of the high-risk IPMN of the pancreas. In this retrospective study, all patients underwent multidetector CT and surgical resection. A prediction model was developed based on a training set consisting of 136 patients with low-risk IPMN and 85 patients with high-risk IPMN between October 2012 and April 2019, and a multivariable logistic regression model was adopted to establish a nomogram. The nomogram performance was determined by its discrimination, calibration, and clinical usefulness. The model was validated in 80 consecutive patients between May 2019 and April 2020, of which 47 and 33 patients had low-risk and high-risk IPMNs, respectively. The multivariable logistic regression model of CT characteristics included the enhancing mural nodule size, the main pancreatic duct (MPD) diameter, the abrupt change in caliber of the MPD with distal pancreatic atrophy, cyst size, thickened enhancing cyst wall, and the presence of lymphadenopathy. The individualized prediction nomogram using these predictors of the high-risk IPMN achieved an area under the curve (AUC) of 0.92 (95% CI 0.88–0.95) in the training set and 0.87 (95% CI 0.79–0.95) in the validation set. The decision curve analysis demonstrated that the nomogram was clinically useful. The CT nomogram, which is a non-invasive predictive tool, can preoperatively predict the risk of malignant IPMN and help identify patients who require a surgical procedure.

To develop and validate a machine-learning classifier based on contrast-enhanced computed tomography (CT) for the preoperative prediction of CD20Overall, 189 patients with PDAC (n = 132 and n = 57 in the training and validation sets, respectively) underwent immunohistochemistry and radiomics feature extraction. The X-tile software was used to stratify them into groups with 'high' and 'low' CD20A log-rank test showed that the patients with high CD20The MLP network classifier based on contrast-enhanced CT can accurately predict CD20

ObjectivesThis study constructed and validated a machine learning model to predict CD8+ tumor-infiltrating lymphocyte expression levels in patients with pancreatic ductal adenocarcinoma (PDAC) using computed tomography (CT) radiomic features.Materials and MethodsIn this retrospective study, 184 PDAC patients were randomly assigned to a training dataset (n =137) and validation dataset (n =47). All patients were divided into CD8+ T-high and -low groups using X-tile plots. A total of 1409 radiomics features were extracted from the segmentation of regions of interest, based on preoperative CT images of each patient. The LASSO algorithm was applied to reduce the dimensionality of the data and select features. The extreme gradient boosting classifier (XGBoost) was developed using a training set consisting of 137 consecutive patients admitted between January 2017 and December 2017. The model was validated in 47 consecutive patients admitted between January 2018 and April 2018. The performance of the XGBoost classifier was determined by its discriminative ability, calibration, and clinical usefulness.ResultsThe cut-off value of the CD8+ T-cell level was 18.69%, as determined by the X-tile program. A Kaplan−Meier analysis indicated a correlation between higher CD8+ T-cell levels and better overall survival (p = 0.001). The XGBoost classifier showed good discrimination in the training set (area under curve [AUC], 0.75; 95% confidence interval [CI]: 0.67–0.83) and validation set (AUC, 0.67; 95% CI: 0.51–0.83). Moreover, it showed a good calibration. The sensitivity, specificity, accuracy, positive and negative predictive values were 80.65%, 60.00%, 0.69, 0.63, and 0.79, respectively, for the training set, and 80.95%, 57.69%, 0.68, 0.61, and 0.79, respectively, for the validation set.ConclusionsWe developed a CT-based XGBoost classifier to extrapolate the infiltration levels of CD8+ T-cells in patients with PDAC. This method could be useful in identifying potential patients who can benefit from immunotherapies.

Purpose To evaluate the diagnostic performance of the radiomics score (rad-score) for differentiating focal-type autoimmune pancreatitis (fAIP) from pancreatic ductal adenocarcinoma (PDAC). Methods This retrospective review included 42 consecutive patients with fAIP diagnosed according to the International Consensus Diagnostic Criteria between January 2011 and December 2018. Furthermore, 334 consecutive patients with PDAC confirmed by pathology were also reviewed during the same period. Patients with PDAC and fAIP were matched via propensity score matching (PSM). All patients underwent multidetector computed tomography (MDCT). For each patient, 1409 radiomics features of the portal phase were extracted and reduced using the least absolute shrinkage and selection operator (LASSO) logistic regression algorithm. The portal rad-score performance was assessed based on its discriminative ability. Results After PSM, we matched 55 patients with PDAC to 42 patients with fAIP, based on clinical and CT characteristics (e.g., patient age, sex, body mass index, location, size, enhanced mode). A rad-score for discriminating fAIP from PDAC, which contained four CT derived radiomic features, was developed (area under the curve = 0.97). The sensitivity, specificity, and accuracy of the radiomics model were 95.24%, 92.73% and 0.94, respectively. Conclusion The portal rad-score can accurately and noninvasively differentiate fAIP from PDAC.

To develop and validate a nomogram for the preoperative prediction of pancreatic serous cystic neoplasm (SCN) and mucinous cystic neoplasm (MCN) based on multidetector computed tomography (MDCT). In this retrospective study, the data of 227 patients with SCN and MCN were analyzed. Each patient underwent MDCT and surgical resection. A multivariable logistic regression model was developed using a training set consisting of 129 patients with SCN and 38 patients with MCN who were admitted between October 2012 and April 2019. The model was validated in 60 consecutive patients, 44 of whom had SCN and 16 of whom had MCN, admitted between May 2019 and April 2020. The regression model was adopted to establish a nomogram. Nomogram performance was determined by its discriminative ability and clinical utility. The multivariable logistic regression model included sex, size, location, shape, cyst characteristic, and cystic wall thickening. The individualized prediction nomogram showed good discrimination in the training sample (AUC 0.89; 95% CI 0.83–0.95) and in the validation sample (AUC 0.81; 95% CI 0.70–0.94). If the threshold probability is between 0.03 and 0.9, and > 0.93 in the prediction model, using the nomogram to predict SCN and MCN is more beneficial than the treat-all-patients as SCN scheme or the treat-all-patients as MCN scheme. The prediction model showed better discrimination than the radiologists’ diagnosis (AUC = 0.68). The nomogram could predict SCN and MCN preoperatively and may aid clinical decision-making.

The family of semaphorins has been demonstrated to possess tumor suppressor activity, in which semaphorin 3B (SEMA3B) is differentially expressed in several types of tumors. The relationship between SEMA3B expression and its clinical significance in gastric cancer (GC) is currently unclear. In the present study, the expression and methylation status of the SEMA3B gene were detected by quantitative PCR and bisulfite sequencing PCR (BSP). Data indicated that the levels of SEMA3B mRNA decreased in gastric tumor tissues and the methylation status of SEMA3B in the tumor group was higher than the paired normal tissues. By BSP, the SEMA3B gene showed high methylated status which was detected in all 4 cell lines (AGS, BGC-823, MGC-803 and SGC-7901). Treatment of the cells with 5-Aza-2'-deoxycytidine revealed clearly elevated mRNA levels of SEMA3B. These results were further confirmed by western blot analysis of Sema3b protein expression. At the same time, increased expression of p53 mRNA in BGC-823, MGC-803 was detected and indicated that p53 may be involved in the regulation of SEMA3B expression in specific genetic background. Downregulation of SEMA3B was negatively correlated with tumor size and N staging in GC (p