Relationship Between Radiomics and Risk of Lymph Node Metastasis in Pancreatic Ductal Adenocarcinoma.
Ontology highlight
ABSTRACT: 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.
SUBMITTER: Bian Y
PROVIDER: S-EPMC6830946 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:PurposeTo develop and validate a radiomics nomogram for the preoperative prediction of lymph node (LN) metastasis in pancreatic ductal adenocarcinoma (PDAC).Materials and methodsIn 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).ResultsThe 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.ConclusionsThe 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.
| S-EPMC8734356 | biostudies-literature
Project description:PurposeTo construct and verify a CT-based multidimensional nomogram for the evaluation of lymph node (LN) status in pancreatic ductal adenocarcinoma (PDAC).Materials and methodsWe retrospectively assessed data from 172 patients with clinicopathologically confirmed PDAC surgically resected between February 2014 and November 2016. Patients were assigned to either a training cohort (n = 121) or a validation cohort (n = 51). We acquired radiomics features from the preoperative venous phase (VP) CT images. The maximum relevance-minimum redundancy (mRMR) algorithm and the least absolute shrinkage and selection operator (LASSO) methods were used to select the optimal features. We used multivariable logistic regression to construct a combined radiomics model for visualization in the form of a nomogram. Performance of the nomogram was evaluated by the receiver operating characteristic (ROC) curve approach, calibration testing, and analysis of clinical usefulness.ResultsA Rad score consisting of 10 LN status-related radiomics features was found to be significantly associated with the actual LN status (P < 0.01). A nomogram that consisted of Rad scores, CT-reported parenchymal atrophy, and CT-reported LN status performed well in terms of predictive power in the training cohort (area under the curve, 0.92), and this was confirmed in the validation cohort (area under the curve, 0.95). The nomogram also performed well in the calibration test and decision curve analysis, demonstrating its potential clinical value.ConclusionA multidimensional radiomics nomogram consisting of Rad scores, CT-reported parenchymal atrophy, and CT-reported LN status may contribute to the non-invasive evaluation of LN status in PDAC patients.
| S-EPMC7482654 | biostudies-literature
Project description:Pancreatic ductal adenocarcinoma (PDAC) patients develop lymph node metastases early and have a particularly poor prognosis. The poor prognosis has been shown to be associated with the physicochemical microenvironment of the tumor tissue, which is characterized by desmoplasia, abnormal microvasculature, extensive hypoxia, and highly elevated interstitial fluid pressure (IFP). In this study, we searched for associations between lymph node metastasis and features of the physicochemical microenvironment in an attempt to identify mechanisms leading to metastatic dissemination and growth. BxPC-3 and Capan-2 PDAC xenografts were used as preclinical models of human PDAC. In both models, lymph node metastasis was associated with high IFP rather than high fraction of hypoxic tissue or high microvascular density. Seven angiogenesis-related genes associated with high IFP-associated lymph node metastasis were detected by quantitative PCR in each of the models, and these genes were all up-regulated in high IFP/highly metastatic tumors. Three genes were mutual for the BxPC-3 and Capan-2 models: transforming growth factor beta, angiogenin, and insulin-like growth factor 1. Further comprehensive studies are needed to determine whether there is a causal relationship between the up-regulation of these genes and high IFP and/or high propensity for lymph node metastasis in PDAC.
| S-EPMC5564626 | biostudies-other
Project description:Pancreatic ductal adenocarcinoma is one of the most aggressive types of cancer. Certain proteins in the tumor microenvironment have attracted considerable attention owing to their association with tumor invasion and metastasis. Here, we used proteomics to identify proteins associated with lymph-node metastasis, which is one of the prognostic factors. We selected lymph node metastasis-positive and -negative patients (n = 5 each) who underwent pancreatectomy between 2005 and 2015 and subjected to comprehensive proteomic profiling of tumor stroma. A total of 490 proteins were detected by mass spectrometry. Software analysis revealed that nine of these proteins were differentially expressed between the two patient groups. We focused on hemopexin and ferritin light chain based on immunohistochemistry results. We assessed the clinicopathological data of 163 patients and found that hemopexin expression was associated with UICC N2 (p = 0.0399), lymph node ratio (p = 0.0252), venous invasion (p = 0.0096), and lymphatic invasion (p = 0.0232). Notably, in vitro assays showed that hemopexin promotes invasion of the pancreatic cancer cells. Our findings suggest that hemopexin is a lymph node metastasis-associated protein that could potentially serve as a useful therapeutic target or biomarker of pancreatic ductal adenocarcinoma.
| S-EPMC7360047 | biostudies-literature
Project description:Lymph node (LN) metastasis occurs frequently in pancreatic ductal adenocarcinoma (PDAC), representing an advanced disease stage and independently predicting patient survival. Current nodal staging is inadequate preoperatively and even less so postoperatively, and molecular biomarkers are needed to improve prognostication and selection of therapy. Recent data have suggested important roles of miRNAs in PDAC tumorigenesis and progression. The aim of the present study was to identify miRNA expression signature for nodal spread in PDAC patients. Using PDAC human tissue specimens, we identified 39 miRNAs which were differently expressed in LN positive compared to LN negative PDAC samples. Of them, six miRNAs have been reported to play a role in cancer invasion and metastasis. A high versus low six- miRNA signature score was predictive of LN metastasis in the PDAC validation cohort. We demonstrated a similar expression pattern of four out of the six miRNAs in the plasma of PDAC patients. The results of our in-vitro studies revealed that miR-141 and miR-720 are involved in the process of epithelial to mesenchymal-transition in PDAC. These miRNAs significantly inhibited in vitro proliferation, migration and invasion of PDAC cells as evidence by gain- and loss- of function studies, specifically, via ZEB-1 and TWIST1 transcription factors, as well as through the activation of the MAP4K4/JNK signaling pathway.
| S-EPMC6498999 | biostudies-literature
Project description:Background & aimsPancreatic 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.MethodsFor 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.ResultsWe 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).ConclusionsUsing 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.
| S-EPMC7483849 | biostudies-literature
Project description:BackgroundNegative regional lymph nodes do not indicate a lack of distant metastasis. A considerable number of patients with negative regional lymph node pancreatic cancer will skip the step of regional lymph node metastasis and directly develop distant metastasis.MethodsWe retrospectively analyzed the clinicopathological characteristics of patients with negative regional lymph node pancreatic cancer and distant metastasis in the Surveillance, Epidemiology, and End Results database from 2010 to 2015. Multivariate logistic analysis and Cox analysis were used to determine the independent risk factors that promoted distant metastasis and the 1-, 2-, and 3-year cancer-specific survival in this subgroup.ResultsSex, age, pathological grade, surgery, radiotherapy, race, tumor location, and tumor size were significantly correlated with distant metastasis (P < 0.05). Among these factors, pathological grade II and above, tumor site other than the pancreatic head, and tumor size >40 mm were independent risk factors for distant metastasis; age ≥60 years, tumor size ≤21 mm, surgery, and radiation were protective factors against distant metastasis. Age, pathological grade, surgery, chemotherapy, and metastasis site were identified as predictors of survival. Among them, age ≥40 years, pathological grade II and above, and multiple distant metastasis were considered independent risk factors for cancer-specific survival. Surgery and chemotherapy were considered protective factors for cancer-specific survival. The prediction performance of the nomogram was significantly better than that of the traditional American Joint Committee on Cancer tumor, node, metastasis staging system. We also established an online dynamic nomogram calculator, which can predict the survival rate of patients at different follow-up time points.ConclusionPathological grade, tumor location, and tumor size were independent risk factors for distant metastasis in pancreatic ductal adenocarcinoma with negative regional lymph nodes. Older age, smaller tumor size, surgery, and radiotherapy were protective factors against distant metastasis. A new nomogram that was constructed could effectively predict cancer-specific survival in pancreatic ductal adenocarcinoma with negative regional lymph nodes and distant metastasis. Furthermore, an online dynamic nomogram calculator was established.
| S-EPMC9974279 | biostudies-literature
Project description:IntroductionThe tumor-draining lymph node (TDLN) plays a role in tumor immunity. Intratumorally administered microspheres (MS) that encapsulate immunomodulatory agents have emerged as a treatment strategy capable of causing profound changes in the tumor microenvironment (TME) and eliciting potent antitumor effects. We hypothesized that local delivery of MS to the TME may also drain to and therefore target the TDLN to initiate antitumor immune responses.MethodsFluorescent MS were injected into orthotopically implanted murine pancreatic tumors, and tissues were examined by whole-mount microscopy and imaging flow cytometry. The role of the TDLN was investigated for mice treated with intratumoral interleukin-12 (IL-12)-encapsulated MS in combination with stereotactic body radiotherapy (SBRT) by cytokine profile and TDLN ablation.ResultsFluorescent AF-594 MS delivered intratumorally were detected in the tumor, peritumoral lymphatics, and the TDLN 2 h after injection. Phagocytic cells were observed with internalized fluorescent MS. SBRT + IL-12 MS-induced upregulation of Th1 and antitumor factors IL-12, IFN-γ, CXCL10, and granzyme B in the TDLN, and excision of the TDLN partially abrogated treatment efficacy.ConclusionsOur results demonstrate that intratumorally administered MS not only target the TME, but also drain to the TDLN. Furthermore, MS encapsulated with a potent antitumor cytokine, IL-12, induce an antitumor cytokine profile in the TDLN, which is essential for treatment efficacy.
| S-EPMC7904097 | biostudies-literature
Project description:BackgroundFew studies have simultaneously assessed the prognostic value of the multiple classification systems for lymph node (LN) metastases in resected pancreatic ductal adenocarcinoma (PDAC).MethodsIn 600 patients with resected PDAC, we examined the association of LN parameters (AJCC 7th and 8th editions, LN ratio (LNR), and log odds of metastatic LN (LODDS)) with pattern of recurrence and patient survival using logistic regression and Cox proportional hazards regression, respectively. Regression models adjusted for age, sex, margin status, tumour grade, and perioperative therapy.ResultsLymph node metastases classified by AJCC 7th and 8th editions, LNR, and LODDS were associated with worse disease free-survival (DFS) and overall survival (OS) (all Ptrend<0.01). American Joint Committee on Cancer 8th edition effectively predicted DFS and OS, while minimising model complexity. Lymph node metastases had weaker prognostic value in patients with positive margins and distal resections (both Pinteraction<0.03). Lymph node metastases by AJCC 7th and 8th editions did not predict the likelihood of local disease as the first site of recurrence.ConclusionsAmerican Joint Committee on Cancer 8th edition LN classification is an effective and practical tool to predict outcomes in patients with resected PDAC. However, the prognostic value of LN metastases is attenuated in patients with positive resection margins and distal pancreatectomies.
| S-EPMC5729468 | biostudies-literature
Project description:ObjectivesPreoperative lymph node (LN) status is essential in formulating the treatment strategy among pancreatic cancer patients. However, it is still challenging to evaluate the preoperative LN status precisely now.MethodsA multivariate model was established based on the multiview-guided two-stream convolution network (MTCN) radiomics algorithms, which focused on primary tumor and peri-tumor features. Regarding discriminative ability, survival fitting, and model accuracy, different models were compared.ResultsThree hundred and sixty-three pancreatic cancer patients were divided in to train and test cohorts by 7:3. The modified MTCN (MTCN+) model was established based on age, CA125, MTCN scores, and radiologist judgement. The MTCN+ model outperformed the MTCN model and the artificial model in discriminative ability and model accuracy. [Train cohort area under curve (AUC): 0.823 vs. 0.793 vs. 0.592; train cohort accuracy (ACC): 76.1 vs. 74.4 vs. 56.7%; test cohort AUC: 0.815 vs. 0.749 vs. 0.640; test cohort ACC: 76.1 vs. 70.6 vs. 63.3%; external validation AUC: 0.854 vs. 0.792 vs. 0.542; external validation ACC: 71.4 vs. 67.9 vs. 53.5%]. The survivorship curves fitted well between actual LN status and predicted LN status regarding disease free survival and overall survival. Nevertheless, the MTCN+ model performed poorly in assessing the LN metastatic burden among the LN positive population. Notably, among the patients with small primary tumors, the MTCN+ model performed steadily as well (AUC: 0.823, ACC: 79.5%).ConclusionsA novel MTCN+ preoperative LN status predictive model was established and outperformed the artificial judgement and deep-learning radiomics judgement. Around 40% misdiagnosed patients judged by radiologists could be corrected. And the model could help precisely predict the survival prognosis.
| S-EPMC10442094 | biostudies-literature