Development of a CT radiomics nomogram for preoperative prediction of Ki-67 index in pancreatic ductal adenocarcinoma: a two-center retrospective study.

To develop and validate a contrast-enhanced computed tomography (CECT)-based radiomics nomogram for the preoperative evaluation of Ki-67 proliferation status in pancreatic ductal adenocarcinoma (PDAC). In this two-center retrospective study, a total of 181 patients (95 in the training cohort; 42 in the testing cohort, and 44 in the external validation cohort) with PDAC who underwent CECT examination were included. Radiomic features were extracted from portal venous phase images. The radiomics signatures were built by using two feature-selecting methods (relief and recursive feature elimination) and four classifiers (support vector machine, naive Bayes, linear discriminant analysis (LDA), and logistic regression (LR)). Multivariate LR was used to build a clinical model and radiomics-clinical nomogram. The predictive performances of the models were evaluated using area under receiver operating characteristic curve (AUC) and decision curve analysis (DCA). The relief selector and LDA classifier using twelve features built the optimal radiomics signature, with AUCs of 0.948, 0.927, and 0.824 in the training, testing, and external validation cohorts, respectively. The radiomics-clinical nomogram incorporating the optimal radiomics signature, CT-reported lymph node status, and CA19-9 showed better predictive performance with AUCs of 0.976, 0.955, and 0.882 in the training, testing, and external validation cohorts, respectively. The calibration curve and DCA demonstrated goodness-of-fit and improved benefits in clinical practice of the nomogram. The radiomics-clinical nomogram is an effective and non-invasive computer-aided tool to predict the Ki-67 expression status in patients with PDAC. The radiomics-clinical nomogram is an effective and non-invasive computer-aided tool to predict the Ki-67 expression status in patients with pancreatic ductal adenocarcinoma. The radiomics analysis could be helpful to predict Ki-67 expression status in patients with pancreatic ductal adenocarcinoma (PDAC). The radiomics-clinical nomogram integrated with the radiomics signature, clinical data, and CT radiological features could significantly improve the differential diagnosis of Ki-67 expression status. The radiomics-clinical nomogram showed satisfactory calibration and net benefit for discriminating high and low Ki-67 expression status in PDAC.

Li Q ，Song Z ，Li X ，Zhang D ，Yu J ，Li Z ，Huang J ，Su K ，Liu Q ，Zhang X ，Tang Z ... -  《-》

A clinical-radiomics nomogram based on dual-layer spectral detector CT to predict cancer stage in pancreatic ductal adenocarcinoma.

This study aimed to evaluate the efficacy of radiomics signatures derived from polyenergetic images (PEIs) and virtual monoenergetic images (VMIs) obtained through dual-layer spectral detector CT (DLCT). Moreover, it sought to develop a clinical-radiomics nomogram based on DLCT for predicting cancer stage (early stage: stage I-II, advanced stage: stage III-IV) in pancreatic ductal adenocarcinoma (PDAC). A total of 173 patients histopathologically diagnosed with PDAC and who underwent contrast-enhanced DLCT were enrolled in this study. Among them, 49 were in the early stage, and 124 were in the advanced stage. Patients were randomly categorized into training (n = 122) and test (n = 51) cohorts at a 7:3 ratio. Radiomics features were extracted from PEIs and 40-keV VMIs were reconstructed at both arterial and portal venous phases. Radiomics signatures were constructed based on both PEIs and 40-keV VMIs. A radiomics nomogram was developed by integrating the 40-keV VMI-based radiomics signature with selected clinical predictors. The performance of the nomogram was assessed using receiver operating characteristic (ROC) curves, calibration curves, and decision curves analysis (DCA). The PEI-based radiomics signature demonstrated satisfactory diagnostic efficacy, with the areas under the ROC curves (AUCs) of 0.92 in both the training and test cohorts. The optimal radiomics signature was based on 40-keV VMIs, with AUCs of 0.96 and 0.94 in the training and test cohorts. The nomogram, which integrated a 40-keV VMI-based radiomics signature with two clinical parameters (tumour diameter and normalized iodine density at the portal venous phase), demonstrated promising calibration and discrimination in both the training and test cohorts (0.97 and 0.91, respectively). DCA indicated that the clinical-radiomics nomogram provided the most significant clinical benefit. The radiomics signature derived from 40-keV VMI and the clinical-radiomics nomogram based on DLCT both exhibited exceptional performance in distinguishing early from advanced stages in PDAC, aiding clinical decision-making for patients with this condition.

Wu L ，Cen C ，Yue X ，Chen L ，Wu H ，Yang M ，Lu Y ，Ma L ，Li X ，Wu H ，Zheng C ，Han P ... -  《-》

Radiomics nomogram for the preoperative prediction of lymph node metastasis in pancreatic ductal adenocarcinoma.

To develop and validate a radiomics nomogram for the preoperative prediction of lymph node (LN) metastasis in pancreatic ductal adenocarcinoma (PDAC). In this retrospective study, 225 patients with surgically resected, pathologically confirmed PDAC underwent multislice computed tomography (MSCT) between January 2014 and January 2017. Radiomics features were extracted from arterial CT scans. The least absolute shrinkage and selection operator method was used to select the features. Multivariable logistic regression analysis was used to develop the predictive model, and a radiomics nomogram was built and internally validated in 45 consecutive patients with PDAC between February 2017 and December 2017. The performance of the nomogram was assessed in the training and validation cohort. Finally, the clinical usefulness of the nomogram was estimated using decision curve analysis (DCA). The radiomics signature, which consisted of 13 selected features of the arterial phase, was significantly associated with LN status (p < 0.05) in both the training and validation cohorts. The multivariable logistic regression model included the radiomics signature and CT-reported LN status. The individualized prediction nomogram showed good discrimination in the training cohort [area under the curve (AUC), 0.75; 95% confidence interval (CI), 0.68-0.82] and in the validation cohort (AUC, 0.81; 95% CI, 0.69-0.94) and good calibration. DCA demonstrated that the radiomics nomogram was clinically useful. The presented radiomics nomogram that incorporates the radiomics signature and CT-reported LN status is a noninvasive, preoperative prediction tool with favorable predictive accuracy for LN metastasis in patients with PDAC.

Bian Y ，Guo S ，Jiang H ，Gao S ，Shao C ，Cao K ，Fang X ，Li J ，Wang L ，Ma C ，Zheng J ，Jin G ，Lu J ... -  《-》

Feasibility of a CT-based lymph node radiomics nomogram in detecting lymph node metastasis in PDAC patients.

To investigate the potential value of a contrast enhanced computed tomography (CECT)-based radiological-radiomics nomogram combining a lymph node (LN) radiomics signature and LNs' radiological features for preoperative detection of LN metastasis in patients with pancreatic ductal adenocarcinoma (PDAC). In this retrospective study, 196 LNs in 61 PDAC patients were enrolled and divided into the training (137 LNs) and validation (59 LNs) cohorts. Radiomic features were extracted from portal venous phase images of LNs. The least absolute shrinkage and selection operator (LASSO) regression algorithm with 10-fold cross-validation was used to select optimal features to determine the radiomics score (Rad-score). The radiological-radiomics nomogram was developed by using significant predictors of LN metastasis by multivariate logistic regression (LR) analysis in the training cohort and validated in the validation cohort independently. Its diagnostic performance was assessed by receiver operating characteristic curve (ROC), decision curve (DCA) and calibration curve analyses. The radiological model, including LN size, and margin and enhancement pattern (three significant predictors), exhibited areas under the curves (AUCs) of 0.831 and 0.756 in the training and validation cohorts, respectively. Nine radiomic features were used to construct a radiomics model, which showed AUCs of 0.879 and 0.804 in the training and validation cohorts, respectively. The radiological-radiomics nomogram, which incorporated the LN Rad-score and the three LNs' radiological features, performed better than the Rad-score and radiological models individually, with AUCs of 0.937 and 0.851 in the training and validation cohorts, respectively. Calibration curve analysis and DCA revealed that the radiological-radiomics nomogram showed satisfactory consistency and the highest net benefit for preoperative diagnosis of LN metastasis. The CT-based LN radiological-radiomics nomogram may serve as a valid and convenient computer-aided tool for personalized risk assessment of LN metastasis and help clinicians make appropriate clinical decisions for PADC patients.

Li Q ，Song Z ，Zhang D ，Li X ，Liu Q ，Yu J ，Li Z ，Zhang J ，Ren X ，Wen Y ，Tang Z ... -  《Frontiers in Oncology》

Development and validation of a clinicoradiomic nomogram to assess the HER2 status of patients with invasive ductal carcinoma.

The determination of HER2 expression status contributes significantly to HER2-targeted therapy in breast carcinoma. However, an economical, efficient, and non-invasive assessment of HER2 is lacking. We aimed to develop a clinicoradiomic nomogram based on radiomics scores extracted from multiparametric MRI (mpMRI, including ADC-map, T2W1, DCE-T1WI) and clinical risk factors to assess HER2 status. We retrospectively collected 214 patients with pathologically confirmed invasive ductal carcinoma between January 2018 to March 2021 from Fudan University Shanghai Cancer Center, and randomly divided this cohort into training set (n = 128, 42 HER2-positive and 86 HER2-negative cases) and validation set (n = 86, 28 HER2-positive and 58 HER2-negative cases) at a ratio of 6:4. The original and transformed pretherapy mpMRI images were treated by semi-automated segmentation and manual modification on the DeepWise scientific research platform v1.6 ( http://keyan.deepwise.com/ ), then radiomics feature extraction was implemented with PyRadiomics library. Recursive feature elimination (RFE) based on logistic regression (LR) and LASSO regression were adpoted to identify optimal features before modeling. LR, Linear Discriminant Analysis (LDA), support vector machine (SVM), random forest (RF), naive Bayesian (NB) and XGBoost (XGB) algorithms were used to construct the radiomics signatures. Independent clinical predictors were identified through univariate logistic analysis (age, tumor location, ki-67 index, histological grade, and lymph node metastasis). Then, the radiomics signature with the best diagnostic performance (Rad score) was further combined with significant clinical risk factors to develop a clinicoradiomic model (nomogram) using multivariate logistic regression. The discriminative power of the constructed models were evaluated by AUC, DeLong test, calibration curve, and decision curve analysis (DCA). 70 (32.71%) of the enrolled 214 cases were HER2-positive, while 144 (67.29%) were HER2-negative. Eleven best radiomics features were retained to develop 6 radiomcis classifiers in which RF classifier showed the highest AUC of 0.887 (95%CI: 0.827-0.947) in the training set and acheived the AUC of 0.840 (95%CI: 0.758-0.922) in the validation set. A nomogram that incorporated the Rad score with two selected clinical factors (Ki-67 index and histological grade) was constructed and yielded better discrimination compared with Rad score (p = 0.374, Delong test), with an AUC of 0.945 (95%CI: 0.904-0.987) in the training set and 0.868 (95%CI: 0.789-0.948; p = 0.123) in the validation set. Moreover, calibration with the p-value of 0.732 using Hosmer-Lemeshow test demonstrated good agreement, and the DCA verified the benefits of the nomogram. Post largescale validation, the clinicoradiomic nomogram may have the potential to be used as a non-invasive tool for determination of HER2 expression status in clinical HER2-targeted therapy prediction.

Xu A ，Chu X ，Zhang S ，Zheng J ，Shi D ，Lv S ，Li F ，Weng X ... -  《BMC CANCER》