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

2020 ◽  
Vol 28 (6) ◽  
pp. 1113-1121
Author(s):  
Peng Liu ◽  
Qianbiao Gu ◽  
Xiaoli Hu ◽  
Xianzheng Tan ◽  
Jianbin Liu ◽  
...  
Keyword(s):  
Lymph Node ◽  
Standard Deviation ◽  
Pancreatic Ductal Adenocarcinoma ◽  
Cross Validation ◽  
Evaluation Method ◽  
Ductal Adenocarcinoma ◽  
Ct Evaluation ◽  
Leave One Out ◽  
Conventional Ct ◽  
Predictive Efficiency

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.

Prognostic Relevance of Extracapsular Lymph Node Involvement in Pancreatic Ductal Adenocarcinoma

2009 ◽  
Vol 16 (11) ◽  
pp. 3070-3079 ◽  
Author(s):  
Gregory Sergeant ◽  
Nadine Ectors ◽  
Steffen Fieuws ◽  
Raymond Aerts ◽  
Baki Topal
Keyword(s):  
Lymph Node ◽  
Pancreatic Ductal Adenocarcinoma ◽  
Lymph Node Involvement ◽  
Ductal Adenocarcinoma ◽  
Prognostic Relevance ◽  
Node Involvement

scholarly journals Imaging-Based Subtypes of Pancreatic Ductal Adenocarcinoma Exhibit Differential Growth and Metabolic Patterns in the Pre-Diagnostic Period: Implications for Early Detection

2020 ◽  
Vol 10 ◽  
Author(s):  
Mohamed Zaid ◽  
Dalia Elganainy ◽  
Prashant Dogra ◽  
Annie Dai ◽  
Lauren Widmann ◽  
...  
Keyword(s):  
Growth Rate ◽  
Early Detection ◽  
Pancreatic Ductal Adenocarcinoma ◽  
Cross Validation ◽  
Abdominal Fat ◽  
Metabolic Effects ◽  
Ct Scans ◽  
Ductal Adenocarcinoma ◽  
Tumor Growth Rate ◽  
Average Growth

BackgroundPreviously, we characterized subtypes of pancreatic ductal adenocarcinoma (PDAC) on computed-tomography (CT) scans, whereby conspicuous (high delta) PDAC tumors are more likely to have aggressive biology and poorer clinical outcomes compared to inconspicuous (low delta) tumors. Here, we hypothesized that these imaging-based subtypes would exhibit different growth-rates and distinctive metabolic effects in the period prior to PDAC diagnosis.Materials and methodsRetrospectively, we evaluated 55 patients who developed PDAC as a second primary cancer and underwent serial pre-diagnostic (T0) and diagnostic (T1) CT-scans. We scored the PDAC tumors into high and low delta on T1 and, serially, obtained the biaxial measurements of the pancreatic lesions (T0-T1). We used the Gompertz-function to model the growth-kinetics and estimate the tumor growth-rate constant (α) which was used for tumor binary classification, followed by cross-validation of the classifier accuracy. We used maximum-likelihood estimation to estimate initiation-time from a single cell (10-6 mm3) to a 10 mm3 tumor mass. Finally, we serially quantified the subcutaneous-abdominal-fat (SAF), visceral-abdominal-fat (VAF), and muscles volumes (cm3) on CT-scans, and recorded the change in blood glucose (BG) levels. T-test, likelihood-ratio, Cox proportional-hazards, and Kaplan-Meier were used for statistical analysis and p-value &lt;0.05 was considered significant.ResultsCompared to high delta tumors, low delta tumors had significantly slower average growth-rate constants (0.024 month−1 vs. 0.088 month−1, p&lt;0.0001) and longer average initiation-times (14 years vs. 5 years, p&lt;0.0001). α demonstrated high accuracy (area under the curve (AUC)=0.85) in classifying the tumors into high and low delta, with an optimal cut-off of 0.034 month−1. Leave-one-out-cross-validation showed 80% accuracy in predicting the delta-class (AUC=0.84). High delta tumors exhibited accelerated SAF, VAF, and muscle wasting (p &lt;0.001), and BG disturbance (p&lt;0.01) compared to low delta tumors. Patients with low delta tumors had better PDAC-specific progression-free survival (log-rank, p&lt;0.0001), earlier stage tumors (p=0.005), and higher likelihood to receive resection after PDAC diagnosis (p=0.008), compared to those with high delta tumors.ConclusionImaging-based subtypes of PDAC exhibit distinct growth, metabolic, and clinical profiles during the pre-diagnostic period. Our results suggest that heterogeneous disease biology may be an important consideration in early detection strategies for PDAC.

Mo1658 Prognostic Impact of Para-Aortic Lymph Node Micrometastasis in Pancreatic Ductal Adenocarcinoma

2015 ◽  
Vol 148 (4) ◽  
pp. S-1164
Author(s):  
Toshiaki Komo ◽  
Yoshiaki Murakami ◽  
Kenichiro Uemura ◽  
Yasushi Hashimoto ◽  
Naru Kondo ◽  
...  
Keyword(s):  
Lymph Node ◽  
Pancreatic Ductal Adenocarcinoma ◽  
Ductal Adenocarcinoma ◽  
Prognostic Impact ◽  
Lymph Node Micrometastasis ◽  
Aortic Lymph Node

scholarly journals Para-aortic Lymph Node Involvement Should Not Be a Contraindication to Resection of Pancreatic Ductal Adenocarcinoma

HPB ◽  
2021 ◽  
Vol 23 ◽  
pp. S723
Author(s):  
S.A. Chughtai ◽  
R. Pande ◽  
M. Ahuja ◽  
K. Roberts ◽  
B. Dasari ◽  
...  
Keyword(s):  
Lymph Node ◽  
Pancreatic Ductal Adenocarcinoma ◽  
Lymph Node Involvement ◽  
Ductal Adenocarcinoma ◽  
Aortic Lymph Node ◽  
Node Involvement

Prognostic Implication of pN Stage Subdivision Using Metastatic Lymph Node Ratio in Resected Pancreatic Ductal Adenocarcinoma

2019 ◽  
Vol 28 (3) ◽  
pp. 245-251
Author(s):  
Jung-Soo Pyo ◽  
Nae Yu Kim ◽  
Byoung Kwan Son ◽  
Kwang Hyun Chung
Keyword(s):  
Lymph Node ◽  
Pancreatic Ductal Adenocarcinoma ◽  
Lymph Node Ratio ◽  
Metastatic Lymph Node ◽  
Meta Analysis ◽  
Ductal Adenocarcinoma ◽  
Prognostic Implication ◽  
Metastatic Lymph ◽  
Optimal Criterion ◽  
Node Ratio

In this meta-analysis, we aimed to evaluate the prognostic implication of the metastatic lymph node ratio (mLNR) and its optimal criterion in pancreatic ductal adenocarcinoma (PDAC) with lymph node metastasis (LNM). The present study included 3735 patients with PDAC who had LNM, from 11 eligible studies. We carried out a meta-analysis to determine the correlation between a high mLNR and PDAC prognosis. The estimated mean numbers of examined and metastatic lymph nodes were 22.396 (95% confidence interval [CI] = 19.681-25.111) and 6.496 (95% CI = 4.646-8.345), respectively. A high mLNR was significantly correlated with worse overall survival (hazard ratio = 1.344, 95% CI = 1.276-1.416). In 3 subgroups based on high mLNR criteria (>0 and <0.2, ≥0.2 and <0.4, and ≥0.4), there were significant correlations between a high mLNR and worse survival. A cutoff of 0.200 showed the highest hazard ratio (1.391, 95% CI = 1.268-1.525), which was statistically significant. Our results showed that mLNR is a useful prognostic factor for PDAC with LNM. Although the optimal criterion of high mLNR may be 0.200, further cumulative studies are required before this can be applied in daily practice.

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