Preoperative recurrence prediction in pancreatic ductal adenocarcinoma after radical resection using radiomics of diagnostic computed tomography

Abstract

Background The high recurrence rate after radical resection of pancreatic ductal adenocarcinoma (PDAC) leads to its poor prognosis. We aimed to develop a model to preoperatively predict the risk of recurrence based on computed tomography (CT) radiomics and multiple clinical parameters. Methods Datasets were retrospectively collected and analysed of 220 PDAC patients who underwent contrast-enhanced computed tomography (CE-CT) and received radical resection at 3 institutions in China between 2013 and 2017, with 153 from one institution as a training set, the remaining 67 as a validation set. For each patient, CT radiomics features were extracted from intratumoral and peritumoral regions to establish intratumoral, peritumoral and combined radiomics models using artificial neural network (ANN) algorithm. By incorporating clinical factors, radiomics-clinical nomograms were finally built by multivariable logistic regression analysis to predict 1- and 2-year recurrence risk. Findings The developed radiomics model integrating intratumoral and peritumoral radiomics features was superior to the conventionally constructed model merely using intratumoral radiomics features. Further, radiomics-clinical nomograms outperformed other models in predicting 1-year recurrence with an area under the receiver operating characteristic curve (AUROC) of 0.916 (95%CI, 0.860-0.955) in the training set and 0.764 (95%CI, 0.644-0.859) in the validation set, and 2-year recurrence with an AUROC of 0.872 (95%CI: 0.809-0.921) in the training set and 0.773 (95%CI, 0.654-0.866) in the validation set. Interpretation This study has developed and externally validated a radiomics-clinical nomogram integrating intra- and peritumoral CT radiomics signature as well as clinical factors to predict the recurrence risk of PDAC after radical resection, which will facilitate optimized and individualized treatment strategies. Funding This work was supported by the National Key R&D Program of China [grant number: 2018YFE0114800], the General Program of National Natural Science Foundation of China [grant number: 81772562, 2017; 81871351, 2018], the Fundamental Research Funds for the Central Universities [grant number: 2021FZZX005-08], and Zhejiang Provincial Key Projects of Technology Research [grant number: WKJ-ZJ-2033].

Preoperative prediction of early recurrence in resectable pancreatic cancer integrating clinical, radiologic, and CT radiomics features

“…Accordingly, the radiomics model in the present study did not demonstrate sufficient predictive ability for the early recurrence of pancreatic cancer. A previous study attempted to include perilesional information by increasing the segmentation boundary; however, this approach could not take into account broader contextual information, such as adjacent organ invasion and vascular abutment, which were also demonstrated to be significant in the present study [ 42 ]. One promising approach for enhancing radiomics-based models involves providing additional information, such as radiologic features and clinical data, that cannot be obtained from a segmented image alone [ 47 ].…”

Predicting programmed death-ligand 1 expression level in non-small cell lung cancer using a combination of peritumoral and intratumoral radiomic features on computed tomography

“…Although the pathology test kit used in our study was different from their pathology test kits, our results were comparable to their results. The prediction model can be constructed using only CT images without There has been a recent increase in interest in studying the peritumoral region as the immediate environment of the tumor mass, the microenvironment, and tumor habitat can harbor variable diseasespecific factors (Zhang et al 2012, Li et al 2022. Parra et al showed that NSCLC histologic specimens had higher numbers of TAICs in the peritumoral compartment than in the intratumoral region, and PD-L1 expression was correlated with TAICs for adenocarcinoma and squamous cell carcinoma (Parra et al 2016).…”

Artificial intelligence in pancreatic cancer

“…Then, ANN and LR models were employed to develop the ITV model, PTV model, combined model, clinical model, and radiomics-clinical model. Radiomics-clinical model outperformed other models in predicting 1-year recurrence (AUC 0.764 for validation set) and 2-year recurrence (AUC 0.773 for validation set) 224 .…”

“…These facts make it challenging to predict the prognosis of PC. Due to its excellent computational power, AI was used to analyze PC prognoses, including survival time 204 - 221 , recurrence risk 78 , 221 - 224 , metastasis 225 - 230 , therapy response 79 - 81 , 231 - 240 , etc.…”