Accuracy of various criteria for lymph node staging in ductal adenocarcinoma of the pancreatic head by computed tomography and magnetic resonance imaging

Loch, Florian N.; Asbach, Patrick; Haas, Matthias; Seeliger, Hendrik; Beyer, Katharina; Schineis, Christian; Degro, Claudius E.; Margonis, Georgios Antonios; Kreis, Martin E.; Kamphues, Carsten

doi:10.1186/s12957-020-01951-3

Cited by 25 publications

(23 citation statements)

References 34 publications

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“…The pathological tumor grade was significantly higher in the ER group as well as the clinical staging. The concordance rate between preoperative diagnosis (cTS, cLNM) and pathological diagnosis (pTS, pLNM) is 52.7% and 56.4% respectively, which is consistent with the previous reports [ 28 , 29 ]. The proportion of patients who received adjuvant therapy and R0 resection, which may affect the outcomes, was not different between the two groups.…”

Section: Resultssupporting

confidence: 91%

Prediction of early recurrence of pancreatic ductal adenocarcinoma after resection

et al. 2021

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Background Even after curative resection, pancreatic ductal adenocarcinoma (PDAC) patients suffer a high rate of recurrence. There is an unmet need to predict which patients will experience early recurrence after resection in order to adjust treatment strategies. Methods Data of patients with resectable PDAC undergoing surgical resection between January 2005 and September 2018 were reviewed to stratify for early recurrence defined as occurring within 6 months of resection. Preoperative data including demographics, tumor markers, blood immune-inflammatory factors and clinicopathological data were examined. We employed Elastic Net, a sparse modeling method, to construct models predicting early recurrence using these multiple preoperative factors. As a result, seven preoperative factors were selected: age, duke pancreatic monoclonal antigen type 2 value, neutrophil:lymphocyte ratio, systemic immune-inflammation index, tumor size, lymph node metastasis and is peripancreatic invasion. Repeated 10-fold cross-validations were performed, and area under the receiver operating characteristic curve (AUC) and decision curve analysis were used to evaluate the usefulness of the models. Results A total of 136 patients was included in the final analysis, of which 35 (34%) experienced early recurrence. Using Elastic Net, we found that 7 of 14 preoperative factors were useful for the predictive model. The mean AUC of all models constructed in the repeated validation was superior to the standard marker CA 19–9 (0.718 vs 0.657), whereas the AUC of the model constructed from the entire patient cohort was 0.767. Decision curve analysis showed that the models had a higher mean net benefit across the majority of the range of reasonable threshold probabilities. Conclusion A model using multiple preoperative factors can improve prediction of early resectable PDAC recurrence.

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Section: Resultssupporting

confidence: 91%

Prediction of early recurrence of pancreatic ductal adenocarcinoma after resection

et al. 2021

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“…However, neither EUS nor PET/CT yield sufficient diagnostic accuracy and reliability for the evaluation of the nodal status. 8,10 Radiomics has recently emerged, a technique that can potentially transform original monochrome images into quantitative features using high-throughput extraction methods. Previous studies have indicated that radiomic features are closely linked to patients' prognosis and underlying genomic phenotyping across a wide range of cancer types.…”

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confidence: 99%

Magnetic Resonance Imaging Radiomics‐Based Nomogram From Primary Tumor for Pretreatment Prediction of Peripancreatic Lymph Node Metastasis in Pancreatic Ductal Adenocarcinoma: A Multicenter Study

Shi

Huang

et al. 2022

Magnetic Resonance Imaging

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Background Determining the absence or presence of peripancreatic lymph nodal metastasis (PLNM) is important to the pathologic staging, prognostication, and guidance of treatment in pancreatic ductal adenocarcinoma (PDAC) patients. Computed tomography and MRI had a poor sensitivity and diagnostic accuracy in the assessment of PLNM. Purposes To develop and validate a 3 T MRI primary tumor radiomics‐based nomogram from multicenter datasets for pretreatment prediction of the PLNM in PDAC patients. Study Type Retrospective. Subjects A total of 251 patients (156 men and 95 women; mean age, 60.85 ± 8.23 years) with histologically confirmed pancreatic ductal adenocarcinoma from three hospitals. Field Strength and Sequences A 3.0 T and fat‐suppressed T1‐weighted imaging. Assessment Quantitative imaging features were extracted from fat‐suppressed T1‐weighted (FS T1WI) images at the arterial phase. Statistical Tests Normally distributed data were compared by using t‐tests, while the Mann–Whitney U test was used to evaluate non‐normally distributed data. The diagnostic performances of the preoperative and postoperative nomograms were assessed in the external validation cohort with the area under receiver operating characteristics curve (AUC), calibration curve, and decision curve analysis (DCA). AUCs were compared with the De Long test. A p value below 0.05 was considered to be statistically significant. Results The AUCs of magnetic resonance imaging (MRI) Rad‐score were 0.868 (95% confidence level [CI]: 0.613–0.852) and 0.772 (95% CI: 0.659–0.879) in the training and internal validation cohort, respectively. The preoperative and postoperative nomograms could accurately predict PLNM in the training cohort (AUC = 0.909 and 0.851) and were validated in both the internal and external cohorts (AUC = 0.835 and 0.805, 0.808 and 0.733, respectively). DCA indicated that the two novel nomograms are of similar clinical usefulness. Data Conclusion Pre−/postoperative nomograms and the constructed radiomics signature from primary tumor based on FS T1WI of arterial phase could serve as a potential tool to predict PLNM in patients with PDAC. Evidence Level 3 Technical Efficacy Stage 2

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“…Criteria for lymph node configuration also exist with numerous descriptors, which can sometimes be helpful [ 5 – 8 ]. There have been some promising approaches by combining size and configuration criteria to facilitate and standardize the diagnostic workup for lymph nodes at specific anatomic locations, such as in the mesorectum in a European Society of Gastrointestinal and Abdominal Radiology (ESGAR) consensus statement [ 9 ].…”

Section: Introductionmentioning

confidence: 99%

Introducing the Node Reporting and Data System 1.0 (Node-RADS): a concept for standardized assessment of lymph nodes in cancer

et al. 2021

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“Node-RADS” addresses the lack of consensus in the radiologic assessment of lymph node involvement by cancer and meets the increasing demand for structured reporting on the likelihood of disease involvement. Node Reporting and Data System 1.0 (Node-RADS) systematically classifies the degree of suspicion of lymph node involvement based on the synthesis of established imaging findings. Straightforward definitions of imaging findings for two proposed scoring categories “size” and “configuration” are combined into assessment categories between 1 (“very low likelihood”) and 5 (“very high likelihood”). This scoring system is suitable for assessing likely involvement of lymph nodes on CT and MRI scans. It can be applied at any anatomical site, and to regional and non-regional lymph nodes in relation to a primary tumor location. Node-RADS will improve communication with referring physicians and promote the consistency of reporting for primary staging and in response assessment settings. Key Points • Node-RADS standardizes reporting of possible cancer involvement of regional and distant lymph nodes on CT and MRI. • Node-RADS proposes the scoring categories “size” and “configuration” for assigning the 5-point Node-RADS score from 1 (“very low likelihood”) to 5 (“very high likelihood”). • Node-RADS aims to increase consensus among radiologists for primary staging and in response assessment settings.

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Accuracy of various criteria for lymph node staging in ductal adenocarcinoma of the pancreatic head by computed tomography and magnetic resonance imaging

Cited by 25 publications

References 34 publications

Prediction of early recurrence of pancreatic ductal adenocarcinoma after resection

Prediction of early recurrence of pancreatic ductal adenocarcinoma after resection

Magnetic Resonance Imaging Radiomics‐Based Nomogram From Primary Tumor for Pretreatment Prediction of Peripancreatic Lymph Node Metastasis in Pancreatic Ductal Adenocarcinoma: A Multicenter Study

Introducing the Node Reporting and Data System 1.0 (Node-RADS): a concept for standardized assessment of lymph nodes in cancer

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