Applying a radiomics-based strategy to preoperatively predict lymph node metastasis in the resectable pancreatic ductal adenocarcinoma

Abstract

PURPOSE: This retrospective study is designed to develop a Radiomics-based strategy for preoperatively predicting lymph node (LN) status in the resectable pancreatic ductal adenocarcinoma (PDAC) patients. METHODS: Eighty-five patients with histopathological confirmed PDAC are included, of which 35 are LN metastasis positive and 50 are LN metastasis negative. Initially, 1,124 radiomics features are computed from CT images of each patient. After a series of feature selection, a Radiomics logistic regression (LOG) model is developed. Subsequently, the predictive efficiency of the model is validated using a leave-one-out cross-validation method. The model performance is evaluated on discrimination and compared with the conventional CT evaluation method based on subjective CT image features. RESULTS: Radiomics LOG model is developed based on eight most related radiomics features. Remarkable differences are demonstrated between patients with LN metastasis positive and LN metastasis negative in Radiomics LOG scores namely, 0.535±1.307 (mean±standard deviation) vs. −1.514±1.800 (mean±standard deviation) with p < 0.001. Radiomics LOG model shows significantly higher predictive efficiency compared to the conventional evaluation method of LN status in which areas under ROC curves are AUC = 0.841 with 95% confidence interval (CI: 0.758∼0.925) vs. AUC = 0.682 with (95% CI: 0.566∼0.798). Leave-one-out cross validation indicates that the Radiomics LOG model correctly classifies 70.3% cases, while the conventional CT evaluation method only correctly classifies 57.0% cases. CONCLUSION: A radiomics-based strategy provides an individualized LN status evaluation in PDAC patients, which may help clinicians implement an optimal personalized patient treatment.

Basic pancreatic lesions: Radiologic-pathologic correlation

“…[ 62 , 63 ] The concept behind radiomics is that medical images contain much more information than is visible to the eyes of radiologists, which is called “hidden” information. [ 64 ] Radiomics combined artificial intelligence (AI) algorithms, particularly deep learning, has demonstrated remarkable progress in pancreatic image-recognition tasks including tumor classification, [ 65 , 66 , 67 ] grade, [ 68 , 69 , 70 ] survival, [ 71 , 72 ] treatment respond prediction, [ 73 , 74 , 75 , 76 ] lymphatic metastasis, [ 77 , 78 , 79 ] tumor microenvironment, [ 80 , 81 , 82 ] and so on. In the future, the diagnosis of pancreatic diseases will enter an era of precision and individuation.…”

Using Quantitative Imaging for Personalized Medicine in Pancreatic Cancer: A Review of Radiomics and Deep Learning Applications

“…Patients with a high risk of recurrence may be treated more aggressively or with other treatment modalities using the risk stratification proposed by the model developed by Tang et al Bian et al predicted the risk of lymph node metastasis in pancreatic cancer patients using a radiomic model [ 103 ]. Liu et al were able to predict lymph node metastasis with their radiomic model as well [ 104 ]. If a pancreatic cancer patient already has lymph node metastases, a radical pancreatic operation may be futile and not worth the risks.…”

Setting the Research Agenda for Clinical Artificial Intelligence in Pancreatic Adenocarcinoma Imaging

“…Only three articles stratified the results based on tumour size, reporting model performance for the subgroup of lesions with sizes smaller than 2 cm [35][36][37] 2022)) [35,38]. The study by Liu et al (2020) was the only one comparing AI performance to radiologists based on the analysis of radiology reports, but no reader study was conducted [37]. As is shown in Table 1, only three studies externally tested the proposed models, and four articles used internal cross-validation without separate testing set [35][36][37][38][39][40][41].…”