Predicting lymph node metastasis in pancreatobiliary cancer with magnetic resonance imaging: A prospective analysis

Reconsideration of the clinical impact of neoadjuvant therapy in resectable and borderline resectable pancreatic cancer: A dual-institution collaborative clinical study

PURPOSE

We investigated true indication of neoadjuvant therapy (NAT) in resectable pancreatic cancer and the optimal surgical timing in borderline resectable pancreatic cancer.

METHODS

A total of 687 patients with resectable or borderline resectable pancreatic cancer were enrolled. Survival analysis was performed by intention-to-treat analysis and propensity score matching (PSM) was conducted.

RESULTS

In resectable disease, the NAT group showed better overall survival (OS) compared with the upfront group. Multivariate analysis identified CA19-9 level (≥100 U/mL) and lymph node metastasis to be prognostic factors, and a tumor size of 25 mm was the optimal cut-off value to predict lymph node metastasis. There was no significant survival difference between patients with a tumor size ≤25 mm and CA19-9 < 100 U/mL and those in the NAT group. In borderline resectable disease, OS in the NAT group was significantly better than that in the upfront group. CEA (≥5 ng/mL) and CA19-9 (≥100 U/mL) were identified as prognostic factors; however, the OS of patients fulfilling these factors was worse than that of the NAT group.

CONCLUSIONS

NAT could be unnecessary in patients with tumor size ≤25 mm and CA19-9 < 100 U/mL in resectable disease. In borderline resectable disease, surgery should be delayed until tumor marker levels are well controlled.

Dual-energy computed tomography in a multiparametric regression model for diagnosing lymph node metastases in pancreatic ductal adenocarcinoma

OBJECTIVE

To investigate the diagnostic value of dual-energy computed tomography (DECT) quantitative parameters in the identification of regional lymph node metastasis in pancreatic ductal adenocarcinoma (PDAC).

METHODS

This retrospective diagnostic study assessed 145 patients with pathologically confirmed pancreatic ductal adenocarcinoma from August 2016-October 2020. Quantitative parameters for targeted lymph nodes were measured using DECT, and all parameters were compared between benign and metastatic lymph nodes to determine their diagnostic value. A logistic regression model was constructed; the receiver operator characteristics curve was plotted; the area under the curve (AUC) was calculated to evaluate the diagnostic efficacy of each energy DECT parameter; and the DeLong test was used to compare AUC differences. Model evaluation was used for correlation analysis of each DECT parameter.

RESULTS

Statistical differences in benign and metastatic lymph nodes were found for several parameters. Venous phase iodine density had the highest diagnostic efficacy as a single parameter, with AUC 0.949 [95% confidence interval (CI):0.915-0.972, threshold: 3.95], sensitivity 79.80%, specificity 96.00%, and accuracy 87.44%. Regression models with multiple parameters had the highest diagnostic efficacy, with AUC 0.992 (95% CI: 0.967-0.999), sensitivity 95.96%, specificity 96%, and accuracy 94.97%, which was higher than that for a single DECT parameter, and the difference was statistically significant.

CONCLUSION

Among all DECT parameters for regional lymph node metastasis in PDAC, venous phase iodine density has the highest diagnostic efficacy as a single parameter, which is convenient for use in clinical settings, whereas a multiparametric regression model has higher diagnostic value compared with the single-parameter model.

Pancreatic ductal adenocarcinoma (PDAC) regional nodal disease at standard lymphadenectomy: is MRI accurate for identifying node-positive patients?

OBJECTIVE

To determine the accuracy of qualitative and quantitative MRI features for the diagnosis of pathologic regional lymph nodes at standard lymphadenectomy in patients with pancreatic ductal adenocarcinoma (PDAC).

METHODS

All adult patients with pancreatic MRI performed from 2011 to 2021 within 3 months of a pancreaticoduodenectomy were eligible for inclusion in this single-center retrospective cohort study. Regional nodes at standard lymphadenectomy were independently reviewed by two fellowship-trained abdominal radiologists for the following qualitative features: heterogeneous T2 signal, round shape, indistinct margin, peri-nodal fat stranding, and restricted diffusion greater than the spleen. Quantitative characteristics including primary tumor size, largest node short- and long-axes length, number of regional nodes, absolute apparent diffusion coefficient (ADC) values, and ADC node-to-spleen signal index were assessed. Analysis was at the patient-level with surgical pathology as the reference standard.

RESULTS

Of 75 patients, 85% (64/75) were positive for regional nodal disease on histopathology. None of the qualitative variables evaluated on MRI was associated with pathologic nodes. Median primary tumor maximum diameter was slightly larger for patients with pathologic nodes compared to those without (18 mm (10-42 mm) vs 16 mm (9-22 mm), p = 0.027). None of the other quantitative features was associated with pathologic nodes. Radiologist opinion was not associated with pathologic nodes (p = 0.520). Interobserver agreement was fair (kappa = 0.257).

CONCLUSIONS

Lymph node morphologic features and radiologist opinion using MRI are of limited value for diagnosing PDAC regional nodal disease. Improved diagnostic techniques are needed given the prognostic implications of pathologic lymph nodes in these patients.

KEY POINTS

• Multiple lymph node morphologic features routinely assessed on MRI for malignancies elsewhere in the body are likely not applicable when assessing for pancreatic ductal adenocarcinoma nodal disease. • Interobserver agreement for the presence or absence of pancreatic ductal adenocarcinoma lymph node morphologic features on MRI is fair (kappa = 0.257). • Many more lymph nodes are resected at PDAC standard lymphadenectomy than are detectable on MRI, median 25 vs 5 (p < 0.001), suggesting improved diagnostic techniques are needed to identify PDAC nodal disease.

Diagnosis of Metastatic Lymph Nodes in Patients With Hepatocellular Carcinoma Using Dual-Energy Computed Tomography

OBJECTIVE

Our study aimed to investigate the role of quantitative parameters derived from dual-energy computed tomography (DECT) in discriminating metastatic from nonmetastatic lymph nodes in hepatocellular carcinoma (HCC).

METHODS

Forty-two patients (34 males; mean age, 53.7 years) with HCC underwent unenhanced computed tomography scans and triple-phase DECT scans of the upper abdomen. A total of 72 suspected lymph nodes were resected, including 43 nonmetastatic and 29 metastatic lymph nodes. The maximum short-axis diameter of the lymph nodes, iodine concentration, normalized iodine concentration (NIC), and slope of the spectral curve were analyzed for the HCC primary lesions and the suspected lymph nodes. Lymph node metastasis was confirmed by pathologic examination.

RESULTS

A maximum short-axis diameter of >10 mm had a sensitivity and a specificity of 75.9% (22/29) and 53.5% (23/43) in diagnosing metastatic lymph nodes. The iodine concentration, NIC, and slope of the spectral curve of the nonmetastatic lymph nodes were significantly higher than those of the primary HCC lesions and the metastatic lymph nodes (all P < 0.05). Among all the analyzed spectral parameters, the NIC in the arterial phase had the highest sensitivity and specificity of 88.4% and 86.2% in diagnosing metastatic lymph nodes.

CONCLUSIONS

The arterial phase NIC of DECT has superior diagnostic performance than the traditional lymph node size in diagnosing metastatic lymph nodes in HCC.

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

Objectives

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).

Materials and methods

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.

Results

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.

Conclusions

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.

Classifying primary central nervous system lymphoma from glioblastoma using deep learning and radiomics based machine learning approach - a systematic review and meta-analysis

Background

Glioblastoma (GBM) and primary central nervous system lymphoma (PCNSL) are common in elderly yet difficult to differentiate on MRI. Their management and prognosis are quite different. Recent surge of interest in predictive analytics, using machine learning (ML) from radiomic features and deep learning (DL) for diagnosing, predicting response and prognosticating disease has evinced interest among radiologists and clinicians. The objective of this systematic review and meta-analysis was to evaluate the deep learning &amp; ML algorithms in classifying PCNSL from GBM.

Methods

The authors performed a systematic review of the literature from MEDLINE, EMBASE and the Cochrane central trials register for the search strategy in accordance with PRISMA guidelines to select and evaluate studies that included themes of ML, DL, AI, GBM, PCNSL. All studies reporting on ML algorithms or DL that for differentiating PCNSL from GBM on MR imaging were included. These studies were further narrowed down to focus on works published between 2018 and 2021. Two researchers independently conducted the literature screening, database extraction and risk bias assessment. The extracted data was synthesised and analysed by forest plots. Outcomes assessed were test characteristics such as accuracy, sensitivity, specificity and balanced accuracy.

Results

Ten articles meeting the eligibility criteria were identified addressing use of ML and DL in training and validation classifiers to distinguish PCNSL from GBM on MR imaging. The total sample size was 1311 in the included studies. ML approach was used in 6 studies while DL in 4 studies. The lowest reported sensitivity was 80%, while the highest reported sensitivity was 99% in studies in which ML and DL was directly compared with the gold standard histopathology. The lowest reported specificity was 87% while the highest reported specificity was 100%. The highest reported balanced accuracy was 100% and the lowest was 84%.

Conclusions

Extensive search of the database revealed a limited number of studies that have applied ML or DL to differentiate PCNSL from GBM. Of the currently published studies, Both DL &amp; ML algorithms have demonstrated encouraging results and certainly have the potential to aid neurooncologists in taking preoperative decisions in the future leading to not only reduction in morbidities but also be cost effective.

Establishment of a multi-parameters MRI model for predicting small lymph nodes metastases (<10 mm) in patients with resected pancreatic ductal adenocarcinoma

PURPOSE

To evaluate the potential role of MR findings and DWI parameters in predicting small regional lymph nodes metastases (with short-axis diameter < 10 mm) in pancreatic ductal adenocarcinomas (PDACs).

METHODS

A total of 127 patients, 82 in training group and 45 in testing group, with histopathologically diagnosed PDACs who underwent pancreatectomy were retrospectively analyzed. PDACs were divided into two groups of positive and negative lymph node metastases (LNM) based on the pathological results. Pancreatic cancer characteristics, short axis of largest lymph node, and DWI parameters of PDACs were evaluated.

RESULTS

Univariate and multivariate analyses showed that extrapancreatic distance of tumor invasion, short-axis diameter of the largest lymph node, and mean diffusivity of tumor were independently associated with small LNM in patients with PDACs. The combining MRI diagnostic model yielded AUCs of 0.836 and 0.873, and accuracies of 81.7% and 80% in the training and testing groups. The AUC of the MRI model for predicting LNM was higher than that of subjective MRI diagnosis in the training group (rater 1, P = 0.01; rater 2, 0.008) and in a testing group (rater 1, P = 0.036; rater 2, 0.024). Comparing the subjective diagnosis, the error rate of the MRI model was decreased. The defined LNM-positive group by the MRI model showed significantly inferior overall survival compared to the negative group (P = 0.006).

CONCLUSIONS

The MRI model showed excellent performance for individualized and noninvasive prediction of small regional LNM in PDACs. It may be used to identify PDACs with small LNM and contribute to determining an appropriate treatment strategy for PDACs.

Preoperative Prediction of Lymph Node Metastasis of Pancreatic Ductal Adenocarcinoma Based on a Radiomics Nomogram of Dual-Parametric MRI Imaging

Purpose

This study aims to uncover and validate an MRI-based radiomics nomogram for detecting lymph node metastasis (LNM) in pancreatic ductal adenocarcinoma (PDAC) patients prior to surgery.

Materials and Methods

We retrospectively collected 141 patients with pathologically confirmed PDAC who underwent preoperative T2-weighted imaging (T2WI) and portal venous phase (PVP) contrast-enhanced T1-weighted imaging (T1WI) scans between January 2017 and December 2021. The patients were randomly divided into training (n = 98) and validation (n = 43) cohorts at a ratio of 7:3. For each sequence, 1037 radiomics features were extracted and analyzed. After applying the gradient-boosting decision tree (GBDT), the key MRI radiomics features were selected. Three radiomics scores (rad-score 1 for PVP, rad-score 2 for T2WI, and rad-score 3 for T2WI combined with PVP) were calculated. Rad-score 3 and clinical independent risk factors were combined to construct a nomogram for the prediction of LNM of PDAC by multivariable logistic regression analysis. The predictive performances of the rad-scores and the nomogram were assessed by the area under the operating characteristic curve (AUC), and the clinical utility of the radiomics nomogram was assessed by decision curve analysis (DCA).

Results

Six radiomics features of T2WI, eight radiomics features of PVP and ten radiomics features of T2WI combined with PVP were found to be associated with LNM. Multivariate logistic regression analysis showed that rad-score 3 and MRI-reported LN status were independent predictors. In the training and validation cohorts, the AUCs of rad-score 1, rad-score 2 and rad-score 3 were 0.769 and 0.751, 0.807 and 0.784, and 0.834 and 0.807, respectively. The predictive value of rad-score 3 was similar to that of rad-score 1 and rad-score 2 in both the training and validation cohorts (P &gt; 0.05). The radiomics nomogram constructed by rad-score 3 and MRI-reported LN status showed encouraging clinical benefit, with an AUC of 0.845 for the training cohort and 0.816 for the validation cohort.

Conclusions

The radiomics nomogram derived from the rad-score based on MRI features and MRI-reported lymph status showed outstanding performance for the preoperative prediction of LNM of PDAC.

The Role of CT in Assessment of Extraregional Lymph Node Involvement in Pancreatic and Periampullary Cancer: A Diagnostic Accuracy Study

Purpose

To investigate the diagnostic accuracy of CT in assessing extraregional lymph node metastases in pancreatic head and periampullary cancer.

Materials and Methods

This prospective observational cohort study was performed at two tertiary hepatopancreatobiliary (HPB) referral centers between March 2013 and December 2014. Patients undergoing pancreatoduodenectomy or bypass surgery with or without palliative radiofrequency ablation were included. Extraregional lymph node involvement was defined as positive lymph nodes in the aortocaval window. Two expert HPB radiologists assessed aortocaval lymph nodes at preoperative CT according to a standardized protocol. All tissue from the aortocaval window was collected intraoperatively. Positive histopathologic finding was the reference standard. Analysis of predictive values and diagnostic accuracy was performed.

Results

A total of 198 consecutive patients (mean age, 66 years; range, 39-86 years; 105 men) with pancreatic head or periampullary carcinoma were included. In 70% of patients, a pancreatoduodenectomy was performed, 4% underwent total pancreatectomy, 4% underwent radiofrequency ablation, and 22% underwent bypass surgery. Forty-four patients (22%) had histologically positive aortocaval lymph nodes. Negative predictive value of CT in assessing aortocaval lymph nodes was 80% for both observers, and positive predictive value was 31%-33%. Overall diagnostic accuracy was 69%-70%.

Conclusion

CT has a low diagnostic accuracy in assessing extraregional lymph node metastases in patients suspected of having pancreatic or periampullary cancer.Keywords: CT, Abdomen/GI, Pancreas, Oncology© RSNA, 2021.

Value of apparent diffusion coefficient for differentiating peripancreatic tuberculous lymphadenopathy from metastatic lymphadenopathy

PURPOSE

To evaluate effectiveness of the apparent diffusion coefficient (ADC) values of the peripancreatic lymphadenopathy to differentiate tuberculous lymphadenopathy from metastatic lymphadenopathy.

MATERIALS AND METHODS

Twenty-nine patients with 65 peripancreatic necrotic tuberculous lymphadenopathy and 31 patients with 47 peripancreatic necrotic metastatic lymphadenopathy from pancreatic ductal adenocarcinoma, who underwent magnetic resonance imaging (MRI), were included in this study. MRI features in the T1-weighted image (WI), T2WI, and diffusion-weighted image were analyzed. The ADC values of necrotic and non-necrotic portions of the lymph nodes were measured and compared using t test. Receiver operating characteristic analysis was performed to obtain the optimal ADC threshold value and diagnostic accuracy for differentiating tuberculous lymphadenopathy from metastatic lymphadenopathy.

RESULTS

On T2WI, the signal intensity of necrotic portions was variable in tuberculous lymphadenopathy, but was mostly high in metastatic lymphadenopathy. The mean ADCs of necrotic portions of tuberculous lymphadenopathy were significantly lower than those of metastatic lymphadenopathy ([0.919 ± 0.272] × 10^-3 mm²/s vs. [1.553 ± 0.406] × 10^-3 mm²/s, p < 0.001). Receiver operating characteristic analysis for differentiating tuberculous from metastatic lymphadenopathy demonstrated an area under the curve for the ADC values of necrotic portions of 0.929 (95% CI, 0.865-0.969) with an ADC threshold of 1.022. The sensitivity and specificity for the differentiation of tuberculous from metastatic lymphadenopathy were 80.0% and 97.8%, respectively.

CONCLUSION

The ADC values of necrotic portions of peripancreatic lymphadenopathy may be useful for differentiating tuberculous from metastatic lymphadenopathy.

Impact of Borderline Resectability in Pancreatic Head Cancer on Patient Survival: Biology Matters According to the New International Consensus Criteria

Background

International consensus criteria (ICC) have redefined borderline resectability for pancreatic ductal adenocarcinoma (PDAC) according to three dimensions: anatomical (BR-A), biological (BR-B), and conditional (BR-C). The present definition acknowledges that resectability is not just about the anatomic relationship between the tumour and vessels but that biological and conditional dimensions also are important.

Methods

Patients’ tumours were retrospectively defined borderline resectable according to ICC. The study cohort was grouped into either BR-A or BR-B and compared with patients considered primarily resectable (R). Differences in postoperative complications, pathological reports, overall (OS), and disease-free survival were assessed.

Results

A total of 345 patients underwent resection for PDAC. By applying ICC in routine preoperative assessment, 30 patients were classified as stage BR-A and 62 patients as stage BR-B. In total, 253 patients were considered R. The cohort did not contain BR-C patients. No differences in postoperative complications were detected. Median OS was significantly shorter in BR-A (15 months) and BR-B (12 months) compared with R (20 months) patients (BR-A vs. R: p = 0.09 and BR-B vs. R: p < 0.001). CA19-9, as the determining factor of BR-B patients, turned out to be an independent prognostic risk factor for OS.

Conclusions

Preoperative staging defining surgical resectability in PDAC according to ICC is crucial for patient survival. Patients with PDAC BR-B should be considered for multimodal neoadjuvant therapy even if considered anatomically resectable.

Differential and prognostic MRI features of gallbladder neuroendocrine tumors and adenocarcinomas

OBJECTIVES

To identify MRI features that are helpful for the differentiation of gallbladder neuroendocrine tumors (GB-NETs) from gallbladder adenocarcinomas (GB-ADCs) and to evaluate their prognostic values.

METHODS

Between January 2008 and December 2018, we retrospectively enrolled patients who underwent MRI for GB malignancy. Two radiologists independently assessed the MRI findings and reached a consensus. Significant MRI features, which distinguish GB-NETs from GB-ADCs, were identified. Cox regression analyses were performed to find MRI features that were prognostic for overall survival.

RESULTS

There were 63 patients with GB-NETs (n = 21) and GB-ADCs (n = 42). Compared with GB-ADCs, GB-NETs more frequently demonstrated the following MRI features: well-defined margins, intact overlying mucosa, and thick rim contrast enhancement and/or diffusion restriction (ps < 0.001). Liver metastases were more common and demonstrated thick rim contrast enhancement and diffusion restriction in GB-NETs (ps < 0.001). Lymph node (LN) metastasis showed thick rim diffusion restriction more often in GB-NETs than in GB-ADCs (p = 0.009). On quantitative analysis, the sizes of the GB mass and metastatic LNs in GB-NETs were larger than those in GB-ADCs (p = 0.002 and p = 0.010, respectively). The ratio of apparent diffusion coefficient values between the lesion and the spleen was lower in the GB mass, liver metastases, and LN metastases of GB-NETs than those of GB-ADCs (p < 0.001, p = 0.017, and p < 0.001, respectively). Survival analysis revealed that a large metastatic LN (hazard ratio 1.737; 95% confidence interval, 1.112-2.712) was the only poor prognostic factor (p = 0.015).

CONCLUSION

Several MRI features aided in differentiating between GB-NETs and GB-ADCs. A large metastatic LN was associated with poor survival.

KEY POINTS

• Compared with gallbladder adenocarcinomas (GB-ADCs), neuroendocrine tumors (GB-NETs) and their metastases to the liver and lymph nodes more frequently demonstrated a thick rim appearance on contrast-enhanced MRI and diffusion-weighted images. • The ratio of apparent diffusion coefficient values between the lesion and the spleen was significantly lower for the primary mass, liver metastases, and lymph node metastases of GB-NETs than for those of GB-ADCs. • A large metastatic lymph node was the only poor prognostic factor for overall survival in patients with GB-NETs and GB-ADCs.

An application study of CT perfusion imaging in assessing metastatic involvement of perigastric lymph nodes in patients with T1 gastric cancer

OBJECTIVE

To assess metastatic involvement of perigastric lymph nodes (PLNs) in patients with T1 gastric cancer by using CT perfusion imaging (CTPI).

METHODS

A total of 82 annotated PLNs of 33 patients with T1 gastric cancer confirmed by endoscopic ultrasonography underwent CTPI and portal phase CT scan before operation. The scan data were post-processed to acquire perfusion maps and calculate perfusion parameters including blood flow (BF) and permeability surface (PS). A radiologist measured the short axis diameters and perfusion parameters of PLNs. According to the post-operative pathology result, PLNs were divided into two groups: metastatic and inflammatory LNs. Perfusion parameters values and the size of PLNs between two groups were respectively compared statistically by t-test, and a receiver operating characteristic curve analysis was used to determine the optimal diagnostic cut-off value with sensitivity, specificity and area under the curve.

RESULTS

Examined 82 PLNs were metastatic in 45 (54.9%) and inflammatory in 37 (45.1%). The mean values of perfusion parameters and the short axis diameters in metastatic and inflammatory PLNs, respectively, were BF of 97.48 vs 81.21 ml/100 mg /min (p < 0.001), PS of 45.11 vs 36.80 ml/100 mg /min (p < 0.001), and the size of 1.51 cm vs 1.29 cm (p = 0.059). The sensitivity of 84.4%, specificity of 67.6% and area under the curve of 0.826 for BF with cut-off value of 88.89 ml/100 mg /min for differentiating metastatic from inflammatory nodes were higher than those of PS or the size of PLNs (p < 0.001).

CONCLUSION

CT perfusion parameters values were different between metastatic and inflammatory PLNs in T1 gastric cancer. BF value may be the most reliable diagnostic marker of metastatic PLNs, and it is helpful for clinicians to choose treatment modality or management plan in T1 gastric cancer patients.

ADVANCES IN KNOWLEDGE

CTPI gives information on vascularization of LNs.BF value might be a more effective marker than PS or the size of LNs for differentiating metastatic from inflammatory LNs in patients with T1 gastric cancer.

The value of 18F-FDG PET/CT and carbohydrate antigen 19-9 in predicting lymph node micrometastases of pancreatic cancer

PURPOSE

This study aimed to assess the value of ¹⁸F-FDG PET/CT and carbohydrate antigen 19-9 (CA 19-9) levels in predicting lymph node micrometastases in patients with pancreatic cancer.

PATIENTS AND METHODS

A total of 160 patients with pancreatic carcinoma were included in the study from 2012 to 2017. All patients underwent surgical treatment and PET/CT scans as well as tests to measure CA 19-9 levels before surgery. The PET/CT scans were evaluated by 2 nuclear medicine physicians who were blinded to the clinical information and were compared to the postsurgical pathological findings. Logistic regression analysis was performed to determine the variables that could predict lymph node micrometastases. Receiver operating characteristic (ROC) curves were utilized to find the best cutoff value of the variables related to predicting lymph node micrometastases.

RESULTS

The maximum standardized uptake value (SUV_max) of the primary tumor and CA 19-9 level were potent predictors for determining the lymph node status. The best SUV_max and CA 19-9 cutoff values for predicting lymph node micrometastases were 7.05 (sensitivity = 71.2%, specificity = 76.6%) and 240.55 U/ml (sensitivity = 62.1%, specificity = 79.8%), respectively.

CONCLUSION

Patients with pancreatic cancer with a tumor SUV_max ≥ 7.05 or a CA 19-9 value ≥ 240.55 are likely to have lymph node micrometastases.