Diagnostic accuracy of CT in assessing extra-regional lymphadenopathy in pancreatic and peri-ampullary cancer: a systematic review and meta-analysis

The preoperative prediction of lymph node metastasis of resectable pancreatic ductal adenocarcinoma using dual-layer spectral computed tomography

OBJECTIVES

To investigate the value of dual-layer spectral computed tomography (DLCT) parameters derived from primary tumors in predicting lymph node metastasis (LNM) of resectable pancreatic ductal adenocarcinoma (PDAC).

MATERIALS AND METHODS

In this retrospective study, patients with resectable PDAC who underwent DLCT within 2-week intervals before surgery were enrolled and randomly divided into training and validation sets at a 7:3 ratio. The patients' clinical data, CT morphological features, and DLCT parameters were analyzed. Univariate and multivariate logistic analyses were used to identify the predictors and construct a predictive model, and receiver operator characteristic (ROC) curves were programmed to evaluate the predictive efficacy.

RESULTS

We enrolled 107 patients (44 patients with LNM and 63 patients without LNM). Among all variables, iodine concentration in the venous phase, extracellular volume, and tumor size were identified as independent predictors of LNM. The nomogram model, incorporating the two DLCT parameters and the morphological feature, achieved an area under the curve (AUC) of 0.877 (95% confidence interval [CI]: 0.803-0.952) and 0.842 (95% CI: 0.707-0.977) for predicting LNM in the training and validation sets, respectively. Furthermore, the AUC of the nomogram model was greater than that of morphological features of lymph nodes in the training (AUC = 0.877 vs. 0.570) and validation (AUC = 0.842 vs. 0.583) sets.

CONCLUSIONS

DLCT has the potential to predict LNM in patients with resectable PDAC and show a better predictive value than morphological features of lymph nodes.

KEY POINTS

Question Morphological features of lymph nodes are of limited value in detecting metastatic lymph nodes in pancreatic ductal adenocarcinoma (PDAC). Findings Dual-layer spectral computed tomography (DLCT) parameters and morphological features derived from PDAC lesions show good preoperatively predictive efficacy for lymph node metastasis. Clinical relevance The proposed DLCT-based nomogram model may serve as an effective and convenient tool for preoperatively predicting lymph node metastasis of resectable PDAC.

Tumor-stroma ratio combined with PD-L1 identifies pancreatic ductal adenocarcinoma patients at risk for lymph node metastases

BACKGROUND

Pathological examination of lymph node metastasis (LNM) is crucial for treating pancreatic ductal adenocarcinoma (PDAC). Although the tumour stroma is correlated with prognosis in multiple solid tumors, its role in detecting LNM in PDAC is unclear. Thus, this study aimed to investigate the relationship of tumor-stroma ratio (TSR) with LNM, survival and mutational profile in PDAC.

METHODS

In this multicenter retrospective study, we examined molecular and clinicopathologic features of 737 PDAC patients from 5 independent cohorts, including surgically resected and endoscopic ultrasound fine-needle aspiration (EUS-FNA) biopsy specimens. TSR was evaluated on hematoxylin and eosin-stained slides and classified as stroma-low (<50% stroma) or stroma-high (≥50% stroma).

RESULTS

Compared to TSR-high cases, TSR-low cases were significantly associated with LNM (P < 0.001). TSR could accurately distinguish patients with and without LNM with an area under curve (AUC) of 0.749, with the sensitivity and specificity of 76.5% and 71.6%, respectively. This accuracy of TSR for identifying LNM was further increased by adding other factors including PD-L1 expression or pretreatment serum CA19-9 levels. TSR showed similar levels of accuracy in analysis of resected tumor specimens and EUS-FNA biopsies. Moreover, we found that TSR could also identify residual nodal involvement after neoadjuvant therapy (NAT) using pretreatment EUS-FNA biopsy samples. Heterogeneous genetic alterations were observed between TSR-low and TSR-high subgroups. TSR was identified as an independent predictor of LNM and worse disease-free survival. Major findings were all reproducible in validation, EUS-FNA biopsy, and pre-treatment NAT EUS-FNA biopsy cohorts.

CONCLUSIONS

TSR served as a robust and reproducible biomarker that identifies patients at risk for LNM. TSR might be used to select treatment and management strategies for PDAC patients.

Surgical outcomes of histopathological stage I pancreatic cancer with special reference to oncological differences between pStage I and ypStage I cases

OBJECTIVES

Histopathological stage I pancreatic cancer (PC) consists of four entities according to the Union for International Cancer Control (UICC) 8th staging system: pStage IA (cases underwent up-front surgery, pT1 [tumor size ≤20 mm] pN0), pStage IB (cases underwent up-front surgery, pT2 [20 mm < tumor size ≤40 mm] pN0), ypStage IA and IB (i.e., underwent neoadjuvant treatment [NAT]). This study aimed to evaluate surgical outcomes in patients with histopathological stage I PC ((y)pStage I).

METHODS

Patients with PC who underwent resection and were diagnosed with (y)pStage I were included in this study (n = 121). We evaluated and compared recurrence-free survival (RFS) and overall survival (OS) among these four categories: pStage I vs. ypStage I, (y)pStage IA vs. IB, ypStage IA vs. IB, and pStage IA vs. IB.

RESULTS

There was no difference in the prognosis between pStage I and ypStage I cases, even though ypStage I included more advanced cases at NAT initiation. For pStage I (n = 25), no differences in prognosis were noted between pStage IA and IB (median RFS, 24 vs. 21 months, p = 0.871; median OS, 49.5 vs. 35.5 months, p = 0.213). However, in the ypStage I cohort (n = 96), there was significantly better RFS in ypStage IA (median RFS, 42 vs. 14 months, p = 0.0114; median OS, 52 vs. 36 months, p = 0.250).

CONCLUSIONS

Survival was comparable between pStage I and ypStage I cohorts, although the subcategories within each cohort (IA vs. IB) had different clinicopathological characteristics and outcomes.

Neoadjuvant gemcitabine and S-1 in pancreatic ductal adenocarcinoma: Effects on nutritional status and pancreaticoduodenectomy outcomes

BACKGROUND

With the advent of improved chemotherapy options, neoadjuvant chemotherapy has gained acceptance as a multidisciplinary treatment approach for localized pancreatic ductal adenocarcinoma. This study aimed to clarify whether neoadjuvant chemotherapy with gemcitabine and S-1 influences preoperative nutritional status and postoperative outcomes, particularly in patients undergoing highly invasive pancreatic resection.

METHODS

Patients with resectable pancreatic ductal adenocarcinoma who underwent pancreaticoduodenectomy as upfront surgery or after neoadjuvant chemotherapy with gemcitabine and S-1 between January 2015 and December 2022 were assessed. In addition to perioperative surgical outcomes, preoperative nutritional status was evaluated using serum albumin, controlling nutritional status, and prognostic nutritional index.

RESULTS

A total of 158 patients who underwent upfront pancreaticoduodenectomy and 119 who received neoadjuvant chemotherapy with gemcitabine and S-1 before pancreaticoduodenectomy were evaluated. Preoperative nutritional assessments (serum albumin, controlling nutritional status score, and prognostic nutritional index) showed no significant differences between groups, either at the initial consultation or immediately before surgery. No significant differences were observed in postoperative outcomes, including blood loss, operation time, and morbidity. The neoadjuvant chemotherapy with gemcitabine and S-1 group had a significantly greater rate of negative tumor margins (R0 resection rate 86% vs 74%, P = .018), and improved overall survival (hazard ratio, 0.41; 95% confidence interval, 0.25-0.67, P < .001) compared with the upfront pancreaticoduodenectomy group.

CONCLUSIONS

Neoadjuvant chemotherapy with gemcitabine and S-1 does not adversely impact preoperative nutritional status and enhances the effectiveness of pancreaticoduodenectomy for resectable pancreatic ductal adenocarcinoma, leading to improved pathologically curative resection rates and overall survival.

Contrast-enhanced MRI-based intratumoral heterogeneity assessment for predicting lymph node metastasis in resectable pancreatic ductal adenocarcinoma

OBJECTIVES

To develop and validate a contrast-enhanced MRI-based intratumoral heterogeneity (ITH) model for predicting lymph node (LN) metastasis in resectable pancreatic ductal adenocarcinoma (PDAC).

METHODS

Lesions were encoded into different habitats based on enhancement ratios at arterial, venous, and delayed phases of contrast-enhanced MRI. Habitat models on enhanced ratio mapping and single sequences, radiomic models, and clinical models were developed for evaluating LN metastasis. The performance of the models was evaluated via different metrics. Additionally, patients were stratified into high-risk and low-risk groups based on an ensembled model to assess prognosis after adjuvant therapy.

RESULTS

We developed an ensembled radiomics-habitat-clinical (RHC) model that integrates radiomics, habitat, and clinical data for precise prediction of LN metastasis in PDAC. The RHC model showed strong predictive performance, with area under the curve (AUC) values of 0.805, 0.779, and 0.615 in the derivation, internal validation, and external validation cohorts, respectively. Using an optimal threshold of 0.46, the model effectively stratified patients, revealing significant differences in recurrence-free survival and overall survival (OS) (p = 0.004 and p < 0.001). Adjuvant therapy improved OS in the high-risk group (p = 0.004), but no significant benefit was observed in the low-risk group (p = 0.069).

CONCLUSION

We developed an MRI-based ITH model that provides reliable estimates of LN metastasis for resectable PDAC and may offer additional value in guiding clinical decision-making.

CRITICAL RELEVANCE STATEMENT

This ensemble RHC model facilitates preoperative prediction of LN metastasis in resectable PDAC using contrast-enhanced MRI. This offers a foundation for enhanced prognostic assessment and supports the management of personalized adjuvant treatment strategies.

KEY POINTS

MRI-based habitat models can predict LN metastasis in PDAC. Both the radiomics model and clinical characteristics were useful for predicting LN metastasis in PDAC. The RHC models have the potential to enhance predictive accuracy and inform personalized therapeutic decisions.

Preoperative assessment of pancreatic cancer with [68Ga]Ga-DOTA-FAPI-04 PET/MR versus [18F]-FDG PET/CT plus contrast-enhanced CT: a prospective preliminary study

PURPOSE

To assess the diagnostic performance of [68Ga]Ga-DOTA-FAPI-04 PET/MR imaging in the preoperative evaluation of pancreatic cancer and compare it with that of [18F]-FDG PET/CT plus contrast-enhanced CT (CECT).

METHODS

Thirty-one patients with pancreatic cancer underwent preoperative [68Ga]Ga-DOTA-FAPI-04 PET/MR, [18F]-FDG PET/CT, and CECT imaging. Two nuclear medicine physicians independently reviewed two sets of images (set 1, [68Ga]Ga-DOTA-FAPI-04 PET/MR; set 2, [18F]-FDG PET/CT plus CECT) and reached a consensus on tumour resectability, N staging (N0 or N positive) and M staging (M0 or M1). Based on the above indices, the resectability of the tumour was determined according to a five-point scale. Clinical, operative, and pathological findings were used as a reference standard to compare the diagnostic performance of the two imaging sets via the McNemar test.

RESULTS

The diagnostic performance of [68Ga]Ga-DOTA-FAPI-04 PET/MR imaging was not significantly different from that of [18F]-FDG PET/CT plus CECT imaging in the assessment of tumour resectability (area under the receiver operating characteristic curve: 0.854 vs. 0.775, p = 0.192), N staging [accuracy: 82.4% (14 of 17 patients) vs. 58.8% (10 of 17 patients), p = 0.125] and M staging [accuracy: 100% (31 of 31 patients) vs. 90.3% (28 of 31 patients), p = 0.250]. However, compared with [18F]-FDG PET/CT plus CECT imaging, [68Ga]Ga-DOTA-FAPI-04 PET/MR imaging changed the M stage in three patients by upstaging from M0 to M1 in 2 patients and downstaging from M1 to M0 in 2 patients. In 13 patients with liver metastases, the number of liver metastases detected via [68Ga]Ga-DOTA-FAPI-04 PET/MR imaging was greater than that detected via [18F]-FDG PET/CT plus CECT imaging (324 vs. 240). In 3 patients with peritoneal metastases, [68Ga]Ga-DOTA-FAPI-04 PET/MR imaging detected more peritoneal metastases than did [18F]-FDG PET/CT plus CECT imaging.

CONCLUSIONS

[68Ga]Ga-DOTA-FAPI-04 PET/MR imaging has diagnostic accuracy comparable to [18F]-FDG PET/CT plus CECT in terms of preoperative staging and assessment of resectability in pancreatic cancer; additionally, it exhibits superior capability in detecting liver and peritoneal metastases. Consequently, [68Ga]Ga-DOTA-FAPI-04 PET/MR has the potential to become a one-stop imaging tool for the preoperative evaluation of pancreatic cancer.

Novel machine-learning model for predicting lymph node metastasis in resectable pancreatic ductal adenocarcinoma

Aim

Lymph node metastasis is an adverse prognostic factor in pancreatic ductal adenocarcinoma. However, it remains a challenge to predict lymph node metastasis using preoperative imaging alone. We used machine learning (combining preoperative imaging findings, tumor markers, and clinical information) to create a novel prediction model for lymph node metastasis in resectable pancreatic ductal adenocarcinoma.

Methods

The data of patients with resectable pancreatic ductal adenocarcinoma who underwent surgery between September 1991 and October 2022 were retrospectively examined. Machine-learning software (Statistical Package for the Social Sciences Modeler) was used to create a prediction model, and parameter tuning was performed to improve the model's accuracy. We also analyzed the contribution of each feature to prediction using individual conditional expectation and partial dependence plots.

Results

Of the 331 cases included in the study, 241 comprised the training cohort and 90 comprised the test cohort. After parameter tuning, the areas under the receiver operating characteristic curves for the training and test cohorts were 0.780 and 0.795, respectively. Individual conditional expectation and partial dependence plots showed that larger tumor size and carbohydrate antigen 19-9 and Duke pancreatic monoclonal antigen type 2 levels were associated with positive lymph node metastasis prediction in this model; neoadjuvant treatment was associated with negative lymph node metastasis prediction.

Conclusion

Machine learning may contribute to the creation of an effective predictive model of lymph node metastasis in pancreatic ductal adenocarcinoma. Prediction models using machine learning may contribute to the development of new treatment strategies in resectable pancreatic ductal adenocarcinoma.

Ferumoxtran-10-enhanced MRI for pre-operative metastatic lymph node detection in pancreatic, duodenal, or periampullary adenocarcinoma

OBJECTIVES

To assess 3-Tesla (3-T) ultra-small superparamagnetic iron oxide (USPIO)-enhanced MRI in detecting lymph node (LN) metastases for resectable adenocarcinomas of the pancreas, duodenum, or periampullary region in a node-to-node validation against histopathology.

METHODS

Twenty-seven consecutive patients with a resectable pancreatic, duodenal, or periampullary adenocarcinoma were enrolled in this prospective single expert centre study. Ferumoxtran-10-enhanced 3-T MRI was performed pre-surgery. LNs found on MRI were scored for suspicion of metastasis by two expert radiologists using a dedicated scoring system. Node-to-node matching from in vivo MRI to histopathology was performed using a post-operative ex vivo 7-T MRI of the resection specimen. Sensitivity and specificity were calculated using crosstabs.

RESULTS

Eighteen out of 27 patients (median age 65 years, 11 men) were included in the final analysis (pre-surgery withdrawal n = 4, not resected because of unexpected metastases peroperatively n = 2, and excluded because of inadequate contrast-agent uptake n = 3). On MRI 453 LNs with a median size of 4.0 mm were detected, of which 58 (13%) were classified as suspicious. At histopathology 385 LNs with a median size of 5.0 mm were found, of which 45 (12%) were metastatic. For 55 LNs node-to-node matching was possible. Analysis of these 55 matched LNs, resulted in a sensitivity and specificity of 83% (95% CI: 36-100%) and 92% (95% CI: 80-98%), respectively.

CONCLUSION

USPIO-enhanced MRI is a promising technique to preoperatively detect and localise LN metastases in patients with pancreatic, duodenal, or periampullary adenocarcinoma.

CLINICAL RELEVANCE STATEMENT

Detection of (distant) LN metastases with USPIO-enhanced MRI could be used to determine a personalised treatment strategy that could involve neoadjuvant or palliative chemotherapy, guided resection of distant LNs, or targeted radiotherapy.

REGISTRATION

The study was registered on clinicaltrials.gov NCT04311047. https://clinicaltrials.gov/ct2/show/NCT04311047?term=lymph+node&cond=Pancreatic+Cancer&cntry=NL&draw=2&rank=1 .

KEY POINTS

LN metastases of pancreatic, duodenal, or periampullary adenocarcinoma cannot be reliably detected with current imaging. This technique detected LN metastases with a sensitivity and specificity of 83% and 92%, respectively. MRI with ferumoxtran-10 is a promising technique to improve preoperative staging in these cancers.

Preoperative Prediction of Lymph Node Metastases in Nonfunctional Pancreatic Neuroendocrine Tumors Using a Combined CT Radiomics-Clinical Model

BACKGROUND

PanNETs are a rare group of pancreatic tumors that display heterogeneous histopathological and clinical behavior. Nodal disease has been established as one of the strongest predictors of patient outcomes in PanNETs. Lack of accurate preoperative assessment of nodal disease is a major limitation in the management of these patients, in particular those with small (< 2 cm) low-grade tumors. The aim of the study was to evaluate the ability of radiomic features (RF) to preoperatively predict the presence of nodal disease in pancreatic neuroendocrine tumors (PanNETs).

PATIENTS AND METHODS

An institutional database was used to identify patients with nonfunctional PanNETs undergoing resection. Pancreas protocol computed tomography was obtained, manually segmented, and RF were extracted. These were analyzed using the minimum redundancy maximum relevance analysis for hierarchical feature selection. Youden index was used to identify the optimal cutoff for predicting nodal disease. A random forest prediction model was trained using RF and clinicopathological characteristics and validated internally.

RESULTS

Of the 320 patients included in the study, 92 (28.8%) had nodal disease based on histopathological assessment of the surgical specimen. A radiomic signature based on ten selected RF was developed. Clinicopathological characteristics predictive of nodal disease included tumor grade and size. Upon internal validation the combined radiomics and clinical feature model demonstrated adequate performance (AUC 0.80) in identifying nodal disease. The model accurately identified nodal disease in 85% of patients with small tumors (< 2 cm).

CONCLUSIONS

Non-invasive preoperative assessment of nodal disease using RF and clinicopathological characteristics is feasible.

Development and validation of a model for predicting the expression of Ki-67 in pancreatic ductal adenocarcinoma with radiological features and dual-energy computed tomography quantitative parameters

OBJECTIVE

To construct and validate a model based on the dual-energy computed tomography (DECT) quantitative parameters and radiological features to predict Ki-67 expression levels in pancreatic ductal adenocarcinoma (PDAC).

MATERIALS AND METHODS

Data from 143 PDAC patients were analysed. The variables of clinic, radiology and DECT were evaluated. In the arterial phase and portal venous phase (PVP), the normalized iodine concentration (NIC), normalized effective atomic number and slope of the spectral attenuation curves were measured. The extracellular volume fraction (ECVf) was measured in the equilibrium phase. Univariate analysis was used to screen independent risk factors to predict Ki-67 expression. The Radiology, DECT and DECT-Radiology models were constructed, and their diagnostic effectiveness and clinical applicability were obtained through area under the curve (AUC) and decision curve analysis, respectively. The nomogram was established based on the optimal model, and its goodness-of-fit was assessed by a calibration curve.

RESULTS

Computed tomography reported regional lymph node status, NIC of PVP, and ECVf were independent predictors for Ki-67 expression prediction. The AUCs of the Radiology, DECT, and DECT-Radiology models were 0.705, 0.884, and 0.905, respectively, in the training cohort, and 0.669, 0.835, and 0.865, respectively, in the validation cohort. The DECT-Radiology nomogram was established based on the DECT-Radiology model, which showed the highest net benefit and satisfactory consistency.

CONCLUSIONS

The DECT-Radiology model shows favourable predictive efficacy for Ki-67 expression, which may be of value for clinical decision-making in PDAC patients.

CRITICAL RELEVANCE STATEMENT

The DECT-Radiology model could contribute to the preoperative and non-invasive assessment of Ki-67 expression of PDAC, which may help clinicians to screen out PDAC patients with high Ki-67 expression.

KEY POINTS

• Dual-energy computed tomography (DECT) can predict Ki-67 in pancreatic ductal adenocarcinoma (PDAC). • The DECT-Radiology model facilitates preoperative and non-invasive assessment of PDAC Ki-67 expression. • The nomogram may help screen out PDAC patients with high Ki-67 expression.

Preoperative prediction of disease-free survival in pancreatic ductal adenocarcinoma patients after R0 resection using contrast-enhanced CT and CA19-9

OBJECTIVES

To investigate the efficiency of a combination of preoperative contrast-enhanced computed tomography (CECT) and carbohydrate antigen 19-9 (CA19-9) in predicting disease-free survival (DFS) after R0 resection of pancreatic ductal adenocarcinoma (PDAC).

METHODS

A total of 138 PDAC patients who underwent curative R0 resection were retrospectively enrolled and allocated chronologically to training (n = 91, January 2014-July 2019) and validation cohorts (n = 47, August 2019-December 2020). Using univariable and multivariable Cox regression analyses, we constructed a preoperative clinicoradiographic model based on the combination of CECT features and serum CA19-9 concentrations, and validated it in the validation cohort. The prognostic performance was evaluated and compared with that of postoperative clinicopathological and tumor-node-metastasis (TNM) models. Kaplan-Meier analysis was conducted to verify the preoperative prognostic stratification performance of the proposed model.

RESULTS

The preoperative clinicoradiographic model included five independent prognostic factors (tumor diameter on CECT > 4 cm, extrapancreatic organ infiltration, CECT-reported lymph node metastasis, peripheral enhancement, and preoperative CA19-9 levels > 180 U/mL). It better predicted DFS than did the postoperative clinicopathological (C-index, 0.802 vs. 0.787; p < 0.05) and TNM (C-index, 0.802 vs. 0.711; p < 0.001) models in the validation cohort. Low-risk patients had significantly better DFS than patients at the high-risk, defined by the model preoperatively (p < 0.001, training cohort; p < 0.01, validation cohort).

CONCLUSIONS

The clinicoradiographic model, integrating preoperative CECT features and serum CA19-9 levels, helped preoperatively predict postsurgical DFS for PDAC and could facilitate clinical decision-making.

CLINICAL RELEVANCE STATEMENT

We constructed a simple model integrating clinical and radiological features for the prediction of disease-free survival after curative R0 resection in patients with pancreatic ductal adenocarcinoma; this novel model may facilitate preoperative identification of patients at high risk of recurrence and metastasis that may benefit from neoadjuvant treatments.

KEY POINTS

• Existing clinicopathological predictors for prognosis in pancreatic ductal adenocarcinoma (PDAC) patients who underwent R0 resection can only be ascertained postoperatively and do not allow preoperative prediction. • We constructed a clinicoradiographic model, using preoperative contrast-enhanced computed tomography (CECT) features and preoperative carbohydrate antigen 19-9 (CA19-9) levels, and presented it as a nomogram. • The presented model can predict disease-free survival (DFS) in patients with PDAC better than can postoperative clinicopathological or tumor-node-metastasis (TNM) models.

Intraoperative paraaortic lymph node sampling during resection for pancreatic cancer: evolving role in the modern chemotherapy era

BACKGROUND

The role of paraaortic lymph node (PALN) sampling for pancreatic cancer (PC) remains controversial. This study aimed to assess the yield of PALN sampling and its impact on long-term outcomes.

METHODS

Patients treated from 2005 to 2018 were included. Since 2015, patients with borderline resectable or unresectable (BR/UR) tumours received standardized neoadjuvant therapy (NAT), whereas patients with resectable tumours underwent upfront resection.

RESULTS

Among 771 patients with PALN excision, metastasis was confirmed in 76 patients (10%) and 59 proceeded to planned resection. PALN metastasis was associated with early recurrence and shorter survival after upfront resection (1-year recurrence-free rate: 23% vs 57% for resectable tumours, P < 0.001, 9% vs 52% for BR/UR tumours, P = 0.006; median overall survival (OS): 19 vs 31 months for resectable tumours, P < 0.001, 17 vs 23 months for BR/UR tumours, P = 0.057). In contrast, they were both similar between patients with/without PALN metastasis following NAT for BR tumours (1-year recurrence-free rate, 50% vs 58%, P = 0.88; median OS, 28 vs 35 months, P = 0.45, respectively).

CONCLUSIONS

Occult metastasis to PALNs is not uncommon in patients with potentially resectable PC. Its prognostic impact depends on the treatment strategy, and the intraoperative decision for resection should be individualized.

Predictive role of radiomics features extracted from preoperative cross-sectional imaging of pancreatic ductal adenocarcinoma in detecting lymph node metastasis: a systemic review and meta-analysis

Lymph node metastases are associated with poor clinical outcomes in pancreatic ductal adenocarcinoma (PDAC). In preoperative imaging, conventional diagnostic modalities do not provide the desired accuracy in diagnosing lymph node metastasis. The current review aims to determine the pooled diagnostic profile of studies examining the role of radiomics features in detecting lymph node metastasis in PDAC. PubMed, Google Scholar, and Embase databases were searched for relevant articles. The quality of the studies was examined using the Radiomics Quality Score and Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) tools. Pooled results for sensitivity, specificity, likelihood, and odds ratios with the corresponding 95% confidence intervals (CIs) were calculated using a random-effect model (DerSimonian-Liard method). No significant publication bias was detected among the studies included in this meta-analysis. The pooled sensitivity of the validation datasets included in the study was 77.4% (72.7%, 81.5%) and pooled specificity was 72.4% (63.8, 79.6%). The diagnostic odds ratio of the validation datasets was 9.6 (6.0, 15.2). No statistically significant heterogeneity was detected for sensitivity and odds ratio (P values of 0.3 and 0.08, respectively). However, there was significant heterogeneity concerning specificity (P = 0.003). The pretest probability of having lymph node metastasis in the pooled databases was 52% and a positive post-test probability was 76% after the radiomics features were used, showing a net benefit of 24%. Classifiers trained on radiomics features extracted from preoperative images can improve the sensitivity and specificity of conventional cross-sectional imaging in detecting lymph node metastasis in PDAC.

Artificial Intelligence to Predict Lymph Node Metastasis at CT in Pancreatic Ductal Adenocarcinoma

Background Although deep learning has brought revolutionary changes in health care, reliance on manually selected cross-sectional images and segmentation remain methodological barriers. Purpose To develop and validate an automated preoperative artificial intelligence (AI) algorithm for tumor and lymph node (LN) segmentation with CT imaging for prediction of LN metastasis in patients with pancreatic ductal adenocarcinoma (PDAC). Materials and Methods In this retrospective study, patients with surgically resected, pathologically confirmed PDAC underwent multidetector CT from January 2015 to April 2020. Three models were developed, including an AI model, a clinical model, and a radiomics model. CT-determined LN metastasis was diagnosed by radiologists. Multivariable logistic regression analysis was conducted to develop the clinical and radiomics models. The performance of the models was determined on the basis of their discrimination and clinical utility. Kaplan-Meier curves, the log-rank test, or Cox regression were used for survival analysis. Results Overall, 734 patients (mean age, 62 years ± 9 [SD]; 453 men) were evaluated. All patients were split into training (n = 545) and validation (n = 189) sets. Patients who had LN metastasis (LN-positive group) accounted for 340 of 734 (46%) patients. In the training set, the AI model showed the highest performance (area under the receiver operating characteristic curve [AUC], 0.91) in the prediction of LN metastasis, whereas the radiologists and the clinical and radiomics models had AUCs of 0.58, 0.76, and 0.71, respectively. In the validation set, the AI model showed the highest performance (AUC, 0.92) in the prediction of LN metastasis, whereas the radiologists and the clinical and radiomics models had AUCs of 0.65, 0.77, and 0.68, respectively (P < .001). AI model-predicted positive LN metastasis was associated with worse survival (hazard ratio, 1.46; 95% CI: 1.13, 1.89; P = .004). Conclusion An artificial intelligence model outperformed radiologists and clinical and radiomics models for prediction of lymph node metastasis at CT in patients with pancreatic ductal adenocarcinoma. © RSNA, 2022 Online supplemental material is available for this article. See also the editorial by Chu and Fishman in this issue.

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.

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.

Value of Clinical Information on Radiology Reports in Oncological Imaging

Radiological reporting errors have a direct negative impact on patient treatment. The purpose of this study was to investigate the contribution of clinical information (CI) in radiological reporting of oncological imaging and the dependence on the radiologists’ experience level (EL). Sixty-four patients with several types of carcinomas and twenty patients without tumors were enrolled. Computed tomography datasets acquired in primary or follow-up staging were independently analyzed by three radiologists (R) with different EL (R1: 15 years; R2: 10 years, R3: 1 year). Reading was initially performed without and 3 months later with CI. Overall, diagnostic accuracy and sensitivity for primary tumor detection increased significantly when receiving CI from 77% to 87%; p = 0.01 and 73% to 83%; p = 0.01, respectively. All radiologists benefitted from CI; R1: 85% vs. 92%, p = 0.15; R2: 77% vs. 83%, p = 0.33; R3: 70% vs. 86%, p = 0.02. Overall, diagnostic accuracy and sensitivity for detecting lymphogenous metastases increased from 80% to 85% (p = 0.13) and 42% to 56% (p = 0.13), for detection of hematogenous metastases from 85% to 86% (p = 0.61) and 46% to 60% (p = 0.15). Specificity remained stable (>90%). Thus, CI in oncological imaging seems to be essential for correct radiological reporting, especially for residents, and should be available for the radiologist whenever possible.

Radiomics nomogram for the preoperative prediction of lymph node metastasis in pancreatic ductal adenocarcinoma

PURPOSE

To develop and validate a radiomics nomogram for the preoperative prediction of lymph node (LN) metastasis in pancreatic ductal adenocarcinoma (PDAC).

MATERIALS AND METHODS

In this retrospective study, 225 patients with surgically resected, pathologically confirmed PDAC underwent multislice computed tomography (MSCT) between January 2014 and January 2017. Radiomics features were extracted from arterial CT scans. The least absolute shrinkage and selection operator method was used to select the features. Multivariable logistic regression analysis was used to develop the predictive model, and a radiomics nomogram was built and internally validated in 45 consecutive patients with PDAC between February 2017 and December 2017. The performance of the nomogram was assessed in the training and validation cohort. Finally, the clinical usefulness of the nomogram was estimated using decision curve analysis (DCA).

RESULTS

The radiomics signature, which consisted of 13 selected features of the arterial phase, was significantly associated with LN status (p < 0.05) in both the training and validation cohorts. The multivariable logistic regression model included the radiomics signature and CT-reported LN status. The individualized prediction nomogram showed good discrimination in the training cohort [area under the curve (AUC), 0.75; 95% confidence interval (CI), 0.68-0.82] and in the validation cohort (AUC, 0.81; 95% CI, 0.69-0.94) and good calibration. DCA demonstrated that the radiomics nomogram was clinically useful.

CONCLUSIONS

The presented radiomics nomogram that incorporates the radiomics signature and CT-reported LN status is a noninvasive, preoperative prediction tool with favorable predictive accuracy for LN metastasis in patients with PDAC.

Implementation of a regional reference center in pancreatic surgery. Experience after 631 procedures

INTRODUCTION

The main objective of this study is to determine whether our unit meets the quality standards required by the scientific community from the reference centers for pancreatic surgery in terms of peri-operative results. The secondary objectives are to compare the different pancreatic surgery techniques performed in terms of early post-operative morbidity and mortality and to analyze the impact of the resections added in these terms.

METHOD

Descriptive, retrospective and single-center study, corresponding to the period 2006-2019. The results obtained were compared with the proposed quality standards, by Bassi et al. and Sabater et al., required from the reference centers in pancreatic surgery. The sample was divided according to surgical technique and compared in terms of early post-operative morbidity and mortality, studying the impact of extended vascular and visceral resections. All patients undergoing pancreatic surgery in our unit due to pancreatic, malignant and benign pathology were included, since it was implemented as a reference center. Emergency procedures were excluded.

RESULTS

631 patients were analyzed. The values ​​obtained in the quality standards are in range. The most frequent surgery was pancreaticoduodenectomy, which associated higher peri-operative morbidity and mortality rates (P ≤ .05). The extended vascular resections impacted the pancreaticoduodenectomy group, associating a longer mean stay (P = .01) and a higher rate of re-interventions (P = .02).

CONCLUSIONS

The experience accumulated allows to meet the required quality standards, as well as perform extended resections to pancreatectomy with good results in terms of post-operative morbidity and mortality.

A CT-Based Radiomics Nomogram for Preoperative Prediction of Lymph Node Metastasis in Periampullary Carcinomas

Purpose

To establish and validate a radiomics nomogram for preoperatively predicting lymph node (LN) metastasis in periampullary carcinomas.

Materials and Methods

A total of 122 patients with periampullary carcinoma were assigned into a training set (n = 85) and a validation set (n = 37). The preoperative CT radiomics of all patients were retrospectively assessed and the radiomic features were extracted from portal venous-phase images. The one-way analysis of variance test and the least absolute shrinkage and selection operator regression were used for feature selection. A radiomics signature was constructed with logistic regression algorithm, and the radiomics score was calculated. Multivariate logistic regression model integrating independent risk factors was adopted to develop a radiomics nomogram. The performance of the radiomics nomogram was assessed by its calibration, discrimination, and clinical utility with independent validation.

Results

The radiomics signature, constructed by seven selected features, was closely related to LN metastasis in the training set (p < 0.001) and validation set (p = 0.017). The radiomics nomogram that incorporated radiomics signature and CT-reported LN status demonstrated favorable calibration and discrimination in the training set [area under the curve (AUC), 0.853] and validation set (AUC, 0.853). The decision curve indicated the clinical utility of our nomogram.

Conclusion

Our CT-based radiomics nomogram, incorporating radiomics signature and CT-reported LN status, could be an individualized and non-invasive tool for preoperative prediction of LN metastasis in periampullary carcinomas, which might assist clinical decision making.

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.

Clinical Implication of Node-negative Resectable Pancreatic Cancer

BACKGROUND

Lymph node metastasis is one of the strongest prognostic factors of pancreatic cancer. However, the clinical implication of pathologically node-negative pancreatic cancer (pN0-PC) has not been fully investigated.

METHODS

Patients who underwent surgical resection for radiologically resectable pancreatic cancer between 2002 and 2018 were included in this study. A clinicopathological examination focusing on pN0-PC was performed.

RESULTS

Of all 533 patients, 155 (29.1%) were diagnosed with pN0-PC and 378 (70.9%) were diagnosed with node-positive pancreatic cancer (pN1/2-PC). The 5-year survival rates of patients with pN0-PC and pN1/2-PC were 57.1% and 25.0%, respectively (p < 0.001). A multivariate analysis revealed six prognostic factors in pN0-PC: age ≥ 70 years, nonadministration of adjuvant chemotherapy, anterior serosal invasion, nerve plexus invasion, and microscopic lymphatic and venous invasions. The 5-year survival rates of patients who had pN0-PC with 0-1 risk factor, with 2-3 risk factors, and with 4-6 risk factors were 87.6%, 47.9%, and 16.4%, respectively. Survival of patients who had pN0-PC with 4-6 risk factors was comparable to that of pN1/2 patients. The diagnostic capability of metastasis-negative lymph node was unsatisfactory, with a predictive value of < 43%.

CONCLUSIONS

Although the prognosis of patients with pN0-PC was better than that of patients with pN1/2-PC, it is not satisfactory. Survival of patients who had pN0-PC with 0-1 risk factors was extremely favorable; however, survival of patients who had pN0-PC with 4-6 risk factors was similar to those with pN1/2-PC.

Age-Related Differences in the Prognosis of Pancreatic Cancer According to Perioperative Systemic Therapy

OBJECTIVES

In this study, we retrospectively assessed the feasibility and prognostic efficacy of perioperative chemo(radio)therapy for pancreatic cancer (PC) patients according to age.

METHODS

A total of 556 consecutive patients who underwent curative-intent pancreatectomy for PC between 2000 and 2018 were enrolled.

RESULTS

Of the 556 patients who underwent resection, 95 (17%) were elderly (age, ≥75 years). Postoperative complications did not significantly differ between the 2 age groups, and postoperative prognoses were also similar (recurrence-free survival [RFS], P = 0.68; overall survival [OS], P = 0.28). In this cohort, 103 patients (19%) underwent preoperative chemo(radio)therapy, and 417 (77%) underwent postoperative chemotherapy. Perioperative therapy was found to be significantly beneficial for younger patients (preoperative therapy: RFS, P = 0.006; OS, P < 0.001; postoperative therapy: RFS, P < 0.001; OS, P < 0.001). Conversely, no significant survival benefit of perioperative therapy was found for the elderly (preoperative therapy: RFS, P = 0.28; OS, P = 0.44; postoperative therapy: RFS, P = 0.77; OS, P = 0.08).

CONCLUSIONS

This study demonstrated that, although perioperative therapy is feasible for selected elderly patients with PC, this approach might not be as beneficial as it is for younger PC patients.

Implementation of a regional reference center in pancreatic surgery. Experience after 631 procedures

INTRODUCTION

The main objective of this study is to determine if our unit meets the quality standards required by the scientific community from the reference centers for pancreatic surgery in terms of peri-operative results. The secondary objectives are to compare the different pancreatic surgery techniques performed in terms of early post-operative morbidity and mortality and to analyze the impact of the resections added in these terms.

METHOD

Descriptive, retrospective and single-center study, corresponding to the period 2006-2019. The results obtained were compared with the proposed quality standards, by Bassi et al. and Sabater et al., required from the reference centers in pancreatic surgery. The sample was divided according to surgical technique and compared in terms of early post-operative morbidity and mortality, studying the impact of extended vascular and visceral resections. All patients undergoing pancreatic surgery in our unit due to pancreatic, malignant and benign pathology were included, since it was implemented as a reference center. Emergency procedures were excluded.

RESULTS

631 patients were analyzed. The values ??obtained in the quality standards are in range. The most frequent surgery was cephalic duodenopancreatectomy, which associated higher peri-operative morbidity and mortality rates (p ≤ 0.05). The extended vascular resections impacted the cephalic duodenopancreatectomy group, associating a longer mean stay (p = 0.01) and a higher rate of re-interventions (p = 0.02).

CONCLUSIONS

The experience accumulated allows to meet the required quality standards, as well as perform extended resections to pancreatectomy with good results in terms of post-operative morbi-mortality.

Fucosylated C4b-binding protein α-chain, a novel serum biomarker that predicts lymph node metastasis in pancreatic ductal adenocarcinoma

C4b-binding protein α-chain (C4BPA) was previously identified as a novel serum biomarker for pancreatic ductal adenocarcinoma (PDAC). To apply this biomarker for clinical diagnosis, a lectin ELISA was established to measure serum fucosylated (Fuc)-C4BPA levels in 45 patients with PDAC, 20 patients with chronic pancreatitis (CP) and 50 healthy volunteers (HVs) in one training and three validation sets. The lecithin ELISA developed in the current study exhibited satisfactory within-run (2.6–6.7%) and between-day (1.8–3.6%) coefficient of variations. Serum Fuc-C4BPA levels in patients with PDAC (0.54±0.27 AU/ml) was significantly higher than that in HVs (0.21±0.06 AU/ml; P<0.0001) and patients with CP (0.25±0.03 AU/ml; P<0.0001). Additionally, serum Fuc-C4BPA levels in preoperative patients were significantly decreased compared with postoperative patient sera (P<0.0003). The receiver operating characteristic (ROC) curve analyses revealed that the area under the curve (AUC) of Fuc-C4BPA (0.985) was higher than that of carbohydrate antigen (CA)19-9 (0.843), carcinoembryonic antigen (0.548) and total C4BPA (0.875) (P<0.001). To analyze the clinical significance of Fuc-C4BPA, the ability of Fuc-C4BPA to predict lymph node metastasis was compared with that of CA19-9. The AUC of serum Fuc-C4BPA levels (0.703) was significantly higher than that of serum CA19-9 levels (0.500) in patients with PDAC (P<0.001). The current study established a novel lectin ELISA for measuring serum Fuc-C4BPA levels. Thus, Fuc-C4BPA has potential clinical applications owing to its high diagnostic value in PDAC.

A MicroRNA Signature Identifies Pancreatic Ductal Adenocarcinoma Patients at Risk for Lymph Node Metastases

BACKGROUND & AIMS

Pancreatic ductal adenocarcinomas (PDACs) frequently metastasize to the lymph nodes; strategies are needed to identify patients at highest risk for lymph node metastases. We performed genome-wide expression profile analyses of PDAC specimens, collected during surgery or endoscopic ultrasound-guided fine-need aspiration (EUS-FNA), to identify a microRNA (miRNA) signature associated with metastasis to lymph nodes.

METHODS

For biomarker discovery, we analyzed miRNA expression profiles of primary pancreatic tumors from 3 public data sets (The Cancer Genome Atlas, GSE24279, and GSE32688). We then analyzed 157 PDAC specimens (83 from patients with lymph node metastases and 74 without) from Japan, collected from 2001 through 2017, for the training cohort and 107 PDAC specimens (63 from patients with lymph node metastases and 44 without) from a different medical center in Japan, from 2002 through 2016, for the validation cohort. We also analyzed samples collected by EUS-FNA before surgery from 47 patients (22 patients with lymph node metastases and 25 without; 17 for the training cohort and 30 from the validation cohort) and 62 specimens before any treatment from patients who received neoadjuvant chemotherapy (9 patients with lymph node metastasis and 53 without) for additional validation. Multivariate logistic regression analyses were used to evaluate the statistical differences in miRNA expression between patients with vs without metastases.

RESULTS

We identified an miRNA expression pattern associated with diagnosis of PDAC metastasis to lymph nodes. Using logistic regression analysis, we optimized and trained a 6-miRNA risk prediction model for the training cohort; this model discriminated patients with vs without lymph node metastases with an area under the curve (AUC) of 0.84 (95% confidence interval [CI], 0.77-0.89). In the validation cohort, the model identified patients with vs without lymph node metastases with an AUC of 0.73 (95% CI, 0.64-0.81). In EUS-FNA biopsy samples, the model identified patients with vs without lymph node metastases with an AUC of 0.78 (95% CI, 0.63-0.89). The miRNA expression pattern was an independent predictor of PDAC metastasis to lymph nodes in the validation cohort (odds ratio, 17.05; 95% CI, 2.43-119.57) and in the EUS-FNA cohort (95% CI, 0.65-0.87).

CONCLUSIONS

Using data and tumor samples from 3 independent cohorts, we identified an miRNA signature that identifies patients at risk for PDAC metastasis to lymph nodes. The signature has similar levels of accuracy in the analysis of resected tumor specimens and EUS-FNA biopsy specimens. This model might be used to select treatment and management strategies for patients with PDAC.

How to Approach Para-Aortic Lymph Node Metastases During Exploration for Suspected Periampullary Carcinoma: Resection or Bypass?

Background

Intraoperative para-aortic lymph node (PALN) sampling during surgical exploration in patients with suspected pancreatic head cancer remains controversial.

Objective

The aim of this study was to assess the value of routine PALN sampling and the consequences of different treatment strategies on overall patient survival.

Methods

A retrospective, multicenter cohort study was performed in patients who underwent surgical exploration for suspected pancreatic head cancer. In cohort A, the treatment strategy was to avoid pancreatoduodenectomy and to perform a double bypass procedure when PALN metastases were found during exploration. In cohort B, routinely harvested PALNs were not examined intraoperatively and pancreatoduodenectomy was performed regardless. PALNs were examined with the final resection specimen. Clinicopathological data, survival data and complication data were compared between study groups.

Results

Median overall survival for patients with PALN metastases who underwent a double bypass procedure was 7.0 months (95% confidence interval [CI] 5.5–8.5), versus 11 months (95% CI 8.8–13) in the pancreatoduodenectomy group (p = 0.049). Patients with PALN metastases who underwent pancreatoduodenectomy had significantly increased postoperative morbidity compared with patients who underwent a double bypass procedure (p < 0.001). In multivariable analysis, severe comorbidity (ASA grade 2 or higher) was an independent predictor for decreased survival in patients with PALN involvement (hazard ratio 3.607, 95% CI 1.678–7.751; p = 0.001).

Conclusion

In patients with PALN metastases, pancreatoduodenectomy was associated with significant survival benefit compared with a double bypass procedure, but with increased risk of complications. It is important to weigh the advantages of resection versus bypass against factors such as comorbidities and clinical performance when positive intraoperative PALNs are found.

Electronic supplementary material

The online version of this article (10.1245/s10434-020-08304-0) contains supplementary material, which is available to authorized users.

Contrast-enhanced CT radiomics for predicting lymph node metastasis in pancreatic ductal adenocarcinoma: a pilot study

BACKGROUND

We developed a computational model integrating clinical data and imaging features extracted from contrast-enhanced computed tomography (CECT) images, to predict lymph node (LN) metastasis in patients with pancreatic ductal adenocarcinoma (PDAC).

METHODS

This retrospective study included 159 patients with PDAC (118 in the primary cohort and 41 in the validation cohort) who underwent preoperative contrast-enhanced computed tomography examination between 2012 and 2015. All patients underwent surgery and lymph node status was determined. A total of 2041 radiomics features were extracted from venous phase images in the primary cohort, and optimal features were extracted to construct a radiomics signature. A combined prediction model was built by incorporating the radiomics signature and clinical characteristics selected by using multivariable logistic regression. Clinical prediction models were generated and used to evaluate both cohorts.

RESULTS

Fifteen features were selected for constructing the radiomics signature based on the primary cohort. The combined prediction model for identifying preoperative lymph node metastasis reached a better discrimination power than the clinical prediction model, with an area under the curve of 0.944 vs. 0.666 in the primary cohort, and 0.912 vs. 0.713 in the validation cohort.

CONCLUSIONS

This pilot study demonstrated that a noninvasive radiomics signature extracted from contrast-enhanced computed tomography imaging can be conveniently used for preoperative prediction of lymph node metastasis in patients with PDAC.

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

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.

Relationship Between Radiomics and Risk of Lymph Node Metastasis in Pancreatic Ductal Adenocarcinoma

OBJECTIVE

The objective of this study was to explore the exact relationship between the arterial radiomics score (rad-score) and lymph node (LN) metastasis in pancreatic ductal adenocarcinoma (PDAC).

METHODS

A total of 225 patients with pathologically confirmed PDAC who underwent multislice computed tomography within 1 month of resection from December 2016 to August 2017 were retrospectively studied. For each patient, 1029 radiomics features of arterial phase were extracted, which were reduced using the least absolute shrinkage and selection operator logistic regression algorithm. Multivariate logistic regression models were used to analyze the association between the arterial rad-score and LN metastasis.

RESULTS

Lymph node-negative and LN-positive patients accounted for 107 (47.56%) and 118 (52.44%) of the cohort, respectively. The rad-score, which consisted of 12 selected features of the arterial phase, was significantly associated with LN status (P < 0.05). Univariate analysis revealed that the arterial rad-score and T stage were independently and positively associated with risk of LN metastasis (P < 0.05). Multivariate analyses revealed a significant association between the arterial rad-score and the LN metastasis (P < 0.0001). Higher arterial rad-score was associated with LN metastasis (P for trend <0.0001).

CONCLUSIONS

The arterial rad-score is independently and positively associated with the risk of LN metastasis in PDAC.

Differential diagnosis and analysis of pancreatic cancer resectability using CT and MRI

The purpose of this publication is to comprehend the modern achievements of radiological diagnosis in surgical oncopancreatology via a critical analysis of recent English-language publications. CT and MRI are essential diagnostic methods in surgical and oncological pancreatology. The possibilities of tomography examination regarding analysis of tumor dimension, CT criteria for pancreatic cancer diagnosis, assessment of vascular invasion, differential diagnosis of tumors, as well as estimation of possible early postoperative complications and radiological features of the region of interest after neoadjuvant and adjuvant therapy for pancreatic tumors are critically analyzed. The role of modern diagnostic methods in improvement of treatment directly related to the early staging of pancreatic tumors is negligible, since the factors affecting the phases of slow and rapid tumor growth are unclear. Most likely, cyclic clinical and radiological evaluation of the pancreas will not give the expected results and is associated with advanced financial and physical costs. Perhaps, one of the approaches for effective treatment of pancreatic cancer will be the recognition of the need for laboratory and instrumental examinations by each patient. Acceptable frequency of examinations and own funds are determined by a patients himself in this case.

Pancreatic ductal adenocarcinoma and paraaortic lymph nodes metastases: The accuracy of intraoperative frozen section

BACKGROUND

Pancreatoduodenectomy for pancreatic ductal adenocarcinoma (PDAC) with paraaortic lymph nodes metastases (PALN +) is associated with poor survival. Still, there are no current guidelines advocating systematic detection of PALN+.

METHODS

All consecutive patients who underwent surgical exploration/resection with concurrent paraaortic (group 16) lymphadenectomy for PDAC between 2009 and 2016 were considered for inclusion. Resection was systematically aborted in case of intraoperative PALN + detection. Diagnostic performance of preoperative imaging upon blind review and intraoperative PALN dissection with frozen section (FS) for PALN detection were evaluated. Additionally, the prognostic significance of PALN + on overall survival (OS) was analyzed.

RESULTS

Over the study period, among 129 patients undergoing surgery for PDAC, 113 had intraoperative PALN dissection with FS analysis. Median number of resected PALN was 3 (range, 1-15). Overall, PALN+ was found in 19 patients (16.8%). Upon blind review, preoperative imaging performed poorly for PALN + detection with a low agreement between imaging and final pathology (Kappa-Cohen index<0.2). In contrast, PALN FS showed high detection performances and strong agreement with final pathology (Kappa-Cohen index = 0.783, 95%CI 0.779-0.867, p < 0.001). Regarding survival outcomes, there was no difference between patients with PALN+ and patients not resected in the setting of liver metastases or locally unresectable disease found at exploration (p = 0.708).

CONCLUSIONS

Before PD for PDAC, intraoperative PALN dissection and FS analysis yields accurate PALN assessment and allows appropriate patient selection. This should be routinely performed and aborting resection should be strongly considered in case of PALN+.

The effects of systemic inflammatory response on prognosis of pancreatic ductal adenocarcinoma

BACKGROUNDS/AIMS

The aim of this study was to investigate the prognostic significance of neutrophyil-lymphocyte ratio (NLR), platelet-lymphocyte ratio (PLR), CRP and CA19-9 in patients were diagnosed with pancreatic ductal adenocarcinoma (PDAC) to better verify pre-operative risk stratification and management.

METHODS

This retrospective study included data from 133 consecutive patients with PDAC, who were treated between 2013 and 2015. PDAC diagnosis was made by cytology or assumed by radiological assessment or surgical resection samples. All clinico-pathological data were retrieved from medical records at our institution. The laboratory data were obtained before any treatment modality. Dates of death were obtained from the central registry.

RESULTS

There was a statistically significant relation between radiological staging and CA19-9 and survival (p=0.001, p=0.005) and there are significant differences in CA19-9 level between stage I and III, I and IV, II and III, and II and IV. Both CRP and CA19-9 levels were statistically significantly higher in patients with radiological lymph node metastasis than patients with N0 disease (p=0.037, p=0.026). NLR and CA19-9 levels were also higher in metastatic disease (p=0.032, p=0.007). According to Spearman's correlation analysis, we found in all patients that there was a negative correlation between the survival time and CRP and neutrophil count (p=0.019, p=0.011).

CONCLUSIONS

Preoperative CRP, CA19-9 and NLR are simple, repeatable, inexpensive and well available marker, can give information on lymph node and solid organ metastasis and survival, give clues to prognosis and be useful in clinical staging of patients with PDAC.

Intravoxel incoherent motion magnetic resonance imaging for differentiating metastatic and non-metastatic lymph nodes in pancreatic ductal adenocarcinoma

OBJECTIVES

To evaluate the diagnostic potential of intravoxel incoherent motion (IVIM) DWI for differentiating metastatic and non-metastatic lymph node stations (LNS) in pancreatic ductal adenocarcinoma (PDAC).

METHODS

59 LNS histologically diagnosed following surgical resection from 15 patients were included. IVIM DWI with 12 b values was added to the standard MRI protocol. Evaluation of parameters was performed pre-operatively and included the apparent diffusion coefficient (ADC), pure diffusion coefficient (D), pseudo-diffusion coefficient (D*) and perfusion fraction (f). Diagnostic performance of ADC, D, D* and f for differentiating between metastatic and non-metastatic LNS was evaluated using ROC analysis.

RESULTS

Metastatic LNS had significantly lower D, D*, f and ADC values than the non-metastatic LNS (p< 0.01). The best diagnostic performance was found in D, with an area under the ROC curve of 0.979, while the area under the ROC curve values of D*, f and ADC were 0.867, 0.855 and 0.940, respectively. The optimal cut-off values for distinguishing metastatic and non-metastatic lymph nodes were D = 1.180 × 10-3 mm2/s; D* = 14.750 × 10-3 mm2/s, f = 20.65 %, and ADC = 1.390 × 10-3 mm2/s.

CONCLUSION

IVIM DWI is useful for differentiating between metastatic and non-metastatic LNS in PDAC.

KEY POINTS

• IVIM DWI is feasible for diagnosing LN metastasis in PDAC. • Metastatic LNS has lower D, D*, f, ADC values than non-metastatic LNS. • D-value from IVIM model has best diagnostic performance, followed by ADC value. • D* has the lowest AUC value.

Pancreatic Adenocarcinoma Staging in the Era of Preoperative Chemotherapy and Radiation Therapy

Pancreatic ductal adenocarcinoma (PDA) remains among the most challenging malignancies to treat. At diagnosis, the tumor often already extends beyond the confines of the pancreas, spreading to an extent such that primary surgery with curative intent is very rarely feasible. Considerable momentum is now being given to a treatment strategy involving neoadjuvant chemotherapy or chemotherapy and radiation therapy in patients with nonmetastatic PDA. The main advantage of this strategy is better selection of patients likely to benefit from curative-intent surgery through the achievement of negative resection margins. Patients with rapidly progressive disease are identified and are spared ineffective surgery with its attendant morbidity. Neoadjuvant therapy can downstage tumors classified as locally advanced at initial imaging studies to resectable tumors. However, the imaging study evaluation of the response to neoadjuvant therapy is extremely complex. Thus, the diagnostic performance of imaging studies is not sufficient to ensure the accurate selection of patients in whom negative-margin resection is likely to be achieved. More specifically, standard criteria for predicting vascular invasion, based on the amount of tumor-vessel contact, are not valid after neoadjuvant therapy. ^©RSNA, 2018.

Preoperative neutrophil-to-lymphocyte ratio and tumor-related factors to predict lymph node metastasis in patients with pancreatic ductal adenocarcinoma (PDAC)

As a poor prognosis indicator in patients with pancreatic ductal adenocarcinoma (PDCA), lymph node (LN) metastasis is of great importance in treatment. Present study was performed to evaluate the predictive value of preoperative neutrophil-to-lymphocyte ratio (NLR), Platelet-to-lymphocyte ratio (PLR) and possible clinical parameters on the LN metastasis in PDCA patients. A total of 159 operable patients with PDCA were enrolled in our study. The clinical utility of NLR and other clinical parameters was evaluated by receiver operating characteristic (ROC) curves. Overall survival analysis indicated that LN metastasis is an independent prognostic factor. The logistic analysis was used to determine the independent parameters associated with LN metastasis. Ideal cutoff values for predicting LN metastasis are 2.12 for NLR and 130.96 for PLR according to the ROC curve. Multivariate analyses indicate that NLR (HR 2.588; 95% CI 1.246-5.376; P = 0.011), CA125 (HR 6.348; 95% CI 2.056-19.594; P = 0.001) and CA19-9 (HR 2.738; 95% CI 1.151-6.515; P = 0.023) are associated significantly with LN metastasis independently. Preoperative NLR, CA125 and CA19-9 are useful biomarkers for the prediction of LN metastasis in PDCA patients.

A Comprehensive Assessment of Accurate Lymph Node Staging and Preoperative Detection in Resected Pancreatic Cancer

BACKGROUND

The current (seventh edition) American Joint Commission on Cancer (AJCC) Staging System for pancreatic ductal adenocarcinoma (PDAC) dichotomizes pathologic lymph node (LN) involvement into absence (pN0) or presence (pN1) of disease. The recently announced eighth edition also includes stratification on the number of positive nodes. Furthermore, LNs detected on preoperative imaging (CT, MRI, or endoscopic ultrasound-EUS) are considered to be pathologically involved in other gastrointestinal cancers. However, this is less well defined for PDAC. Therefore, the three aims of this study were to determine (1) whether the new AJCC staging system led to more accurate staging, (2) the number of nodes needed to be examined to detect pathologic involvement, and (3) if pN disease could be reliably detected on preoperative imaging in PDAC.

METHODS

A retrospective review of all patients undergoing pancreatectomy at a single US academic center from January 1990 to September 2015. Pathology reports of resected specimens were reviewed to determine the total number of LNs examined and those positive for metastasis. CT, MRI, and/or EUS reports were used to determine the presence or absence of preoperatively detectable LN enlargement.

RESULTS

Of the 490 surgical resections for PDAC, pN1 disease was detected in 59.4% (n = 291) and was positively correlated with the number of LNs pathologically examined (P < 0.001). Patients with pN1 disease had a shorter overall survival (OS) than those without nodal involvement (25.1 vs. 44.0 months; P < 0.001); however, OS was not different when stratifying by the number of nodes as on the eighth AJCC system. Pathologic examination of > 20 LNs in treatment naïve patients was optimal to detect pN1 disease and predict longer OS for those without nodal involvement (median survival > 41.1 months, P = 0.03 when compared to < 15 or 15-19 LNs examined). LNs were detected by CT, MRI, or EUS in 30.7% (103/335) of patients. The positive predictive value (PPV) of preoperative LN detection for pathologic involvement was 77.3% for treatment naïve patients and 84.2% for those without biliary obstruction.

CONCLUSIONS

Although the LN scoring in the seventh PDAC AJCC Staging System was sufficient to predict OS of our patients, more LNs than previously considered (20 vs. 15) were optimal to detect pathologic involvement. Preoperative LN detection was an accurate predictor of pN1 disease for treatment naïve patients without biliary obstruction.

Contrast-enhanced CT in determining resectability in patients with pancreatic carcinoma: a meta-analysis of the positive predictive values of CT

OBJECTIVE

To obtain a summary positive predictive value (sPPV) of contrast-enhanced CT in determining resectability.

METHODS

The MEDLINE and EMBASE databases from JAN2005 to DEC2015 were searched and checked for inclusion criteria. Data on study design, patient characteristics, imaging techniques, image evaluation, reference standard, time interval between CT and reference standard, and data on resectability/unresectablity were extracted by two reviewers. We used a fixed-effects or random-effects approach to obtain sPPV for resectability. Several subgroups were defined: 1) bolus-triggering versus fixed-timing; 2) pancreatic and portal phases versus portal phase alone; 3) all criteria (liver metastases/lymphnode involvement/local advanced/vascular invasion) versus only vascular invasion as criteria for unresectability.

RESULTS

Twenty-nine articles were included (2171 patients). Most studies were performed in multicentre settings, initiated by the department of radiology and retrospectively performed. The I2-value was 68%, indicating heterogeneity of data. The sPPV was 81% (95%CI: 75-86%). False positives were mostly liver, peritoneal, or lymphnode metastases. Bolus-triggering had a slightly higher sPPV compared to fixed-timing, 87% (95%CI: 81-91%) versus 78% (95%CI: 66-86%) (p = 0.077). No differences were observed in other subgroups.

CONCLUSIONS

This meta-analysis showed a sPPV of 81% for predicting resectability by CT, meaning that 19% of patients falsely undergo surgical exploration.

KEY POINTS

• Predicting resectability of pancreatic cancer by CT is 81% (95%CI: 75-86%). • The percentage of patients falsely undergoing surgical exploration is 19%. • The false positives are liver metastases, peritoneal metastases, or lymph node metastases.

Prognostic value of lymph node metastases detected during surgical exploration for pancreatic or periampullary cancer: a systematic review and meta-analysis

BACKGROUND

Hepatic-artery and para-aortic lymph node metastases (LNM) may be detected during surgical exploration for pancreatic (PDAC) or periampullary cancer. Some surgeons will continue the resection while others abort the exploration.

METHODS

A systematic search was performed in PubMed, EMBASE and Cochrane Library for studies investigating survival in patients with intra-operatively detected hepatic-artery or para-aortic LNM. Survival was stratified for node positive (N1) disease.

RESULTS

After screening 3088 studies, 13 studies with 2045 patients undergoing pancreatoduodenectomy were included. No study reported survival data after detection of LNM and aborted surgical exploration. In 110 patients with hepatic-artery LNM, median survival ranged between 7 and 17 months. Estimated pooled mean survival in 84 patients with hepatic-artery LNM was 15 [95%CI 12-18] months (13 months in PDAC), compared to 19 [16-22] months in 270 patients with N1-disease without hepatic-artery LNM (p = 0.020). In 192 patients with para-aortic LNM, median survival ranged between 5 and 32 months. Estimated pooled mean survival in 169 patients with para-aortic LNM was 13 [8-17] months (11 months in PDAC), compared to 17 (6-27) months in 506 patients with N1-disease without para-aortic LNM (p < 0.001). Data on the impact of (neo)adjuvant therapy on survival were lacking.

CONCLUSION

Survival after pancreatoduodenectomy in patients with intra-operatively detected hepatic-artery and especially para-aortic LNM is inferior to patients undergoing pancreatoduodenectomy with other N1 disease. It remains unclear what the consequence of this should be since data on (neo-)adjuvant therapy and survival after aborted exploration are lacking.

Presurgical Evaluation of Pancreatic Cancer: A Comprehensive Imaging Comparison of CT Versus MRI

OBJECTIVE

The purpose of this study was to compare comprehensive CT and MRI in the presurgical evaluation of pancreatic cancer.

MATERIALS AND METHODS

Thirty-eight patients with pathologically proven pancreatic cancer were included in a retrospective study. CT with negative-contrast CT cholangiopancreatography and CT angiography (CTA) (CT image set) versus MRI with MRCP and MR angiography (MRI image set) were analyzed independently by two reviewers for tumor detection, extension, metastasis, vascular invasion, and resectability. These results were compared with the surgical and pathologic findings.

RESULTS

The rate of detection of tumors was higher with MRI than with CT but not significantly so (reviewer 1, p = 1.000; reviewer 2, p = 0.500). In the evaluation of vessel involvement, nodal status, and resectability, although CT had higher ROC AUC values than did MRI (reviewer 1, 0.913 vs 0.858, 0.613 vs 0.503, and 0.866 vs 0.774; reviewer 2, 0.879 vs 0.849, 0.640 vs 0.583, and 0.830 vs 0.815), the differences were not statistically significant (p = 0.189 vs 0.494, 0.328 vs 0.244, and 0.193 vs 0.813 for reviewers 1 and 2). In the evaluation of tumor extension and organ metastases in the 38 patients, correct diagnosis of one of two liver metastases was achieved with both image sets, one case of omental and one case of peritoneal seeding were underestimated, and one case of stomach invasion was overestimated.

CONCLUSION

MRI and CT had similar performance in the presurgical evaluation of pancreatic cancer.