scholarly journals Pancreatic Ductal Adenocarcinoma at CT: A Combined Nomogram Model to Preoperatively Predict Cancer Stage and Survival Outcome

2021 ◽  
Vol 11 ◽  
Author(s):  
Chunyuan Cen ◽  
Liying Liu ◽  
Xin Li ◽  
Ailan Wu ◽  
Huan Liu ◽  
...  
Keyword(s):  
Overall Survival ◽  
Pancreatic Ductal Adenocarcinoma ◽  
Clinical Characteristics ◽  
Validation Cohort ◽  
Stage I ◽  
Ductal Adenocarcinoma ◽  
Training Cohort ◽  
Calibration Curves ◽  
Kaplan Meier ◽  
Radiomics Signature

ObjectivesTo construct a nomogram model that combines clinical characteristics and radiomics signatures to preoperatively discriminate pancreatic ductal adenocarcinoma (PDAC) in stage I-II and III-IV and predict overall survival.MethodsA total of 135 patients with histopathologically confirmed PDAC who underwent contrast-enhanced CT were included. A total of 384 radiomics features were extracted from arterial phase (AP) or portal venous phase (PVP) images. Four steps were used for feature selection, and multivariable logistic regression analysis were used to build radiomics signatures and combined nomogram model. Performance of the proposed model was assessed by using receiver operating characteristic (ROC) curves, calibration curves and decision curve analysis (DCA). Kaplan-Meier analysis was applied to analyze overall survival in the stage I-II and III-IV PDAC groups.ResultsThe AP+PVP radiomics signature showed the best performance among the three radiomics signatures [training cohort: area under the curve (AUC) = 0.919; validation cohort: AUC = 0.831]. The combined nomogram model integrating AP+PVP radiomics signature with clinical characteristics (tumor location, carcinoembryonic antigen level, and tumor maximum diameter) demonstrated the best discrimination performance (training cohort: AUC = 0.940; validation cohort: AUC = 0.912). Calibration curves and DCA verified the clinical usefulness of the combined nomogram model. Kaplan-Meier analysis showed that overall survival of patients in the predicted stage I-II PDAC group was longer than patients in stage III-IV PDAC group (p<0.0001).ConclusionsWe propose a combined model with excellent performance for the preoperative, individualized, noninvasive discrimination of stage I-II and III-IV PDAC and prediction of overall survival.

Clinical Characteristics and Overall Survival in Patients with Anaplastic Pancreatic Cancer

2014 ◽  
Vol 80 (2) ◽  
pp. 117-123 ◽  
Author(s):  
Clancy J. Clark ◽  
Janani S. Arun ◽  
Rondell P. Graham ◽  
Lizhi Zhang ◽  
Michael Farnell ◽  
...  
Keyword(s):  
Pancreatic Cancer ◽  
Overall Survival ◽  
Pancreatic Ductal Adenocarcinoma ◽  
Tumor Stage ◽  
Ductal Adenocarcinoma ◽  
Rank Test ◽  
Poor Overall Survival ◽  
Perioperative Morbidity ◽  
Log Rank Test ◽  
Kaplan Meier

Anaplastic pancreatic cancer (APC) is a rare undifferentiated variant of pancreatic ductal adenocarcinoma with poor overall survival (OS). The aim of this study was to evaluate the clinical outcomes of APC compared with differentiated pancreatic ductal adenocarcinoma. We conducted a retrospective review of all patients treated at the Mayo Clinic with pathologically confirmed APC from 1987 to 2011. After matching with control subjects with pancreatic ductal adenocarcinoma, OS was evaluated using Kaplan-Meier estimates and log-rank test. Sixteen patients were identified with APC (56.3% male, median age 57 years). Ten patients underwent exploration of whom eight underwent pancreatectomy. Perioperative morbidity was 60 per cent with no mortality. The median OS was 12.8 months. However, patients with APC who underwent resection had longer OS compared with those who were not resected, 34.1 versus 3.3 months ( P = 0.001). After matching age, sex, tumor stage, and year of operation, the median OS was similar between patients with APC and those with ductal adenocarcinoma treated with pancreatic resection, 44.1 versus 39.9 months, ( P = 0.763). Overall survival for APC is poor; however, when resected, survival is similar to differentiated pancreatic ductal adenocarcinoma.

scholarly journals Development and Validation of a Nomogram for Predicting Overall Survival In Synchronous Peritoneal Metastasis of Colorectal Cance

2020 ◽  
Author(s):  
Wenle Chen ◽  
Zixu Yuan ◽  
Aiwen Wu ◽  
Ming Cui ◽  
Zhongyi Yue ◽  
...  
Keyword(s):  
Overall Survival ◽  
Peritoneal Metastasis ◽  
Cox Regression ◽  
Validation Cohort ◽  
Peritoneal Cancer Index ◽  
Training Cohort ◽  
Peritoneal Cancer ◽  
Kaplan Meier ◽  
Development And Validation ◽  
Actual Survival

Abstract Background: Synchronous peritoneal metastases (PM) is a difficult issue to tackle and the prognosis is poor. The aim of this study is to construct a nomogram to predict the overall survival (OS) for synchronous colorectal peritoneal metastasis.Method: In this retrospective study, 332 patients with synchronous PM were included. The training cohort consisting of 251 patients underwent abdominal surgery from February 2007 to February 2018. The risk factors related to prognosis were analyzed by Kaplan-Meier curve and Cox regression model. 81 patients from other two hospitals were enrolled as validation cohort. The prediction effect of this nomogram was evaluated by C-index and the calibration curve. Result: Five predictors were enrolled into this nomogram after multivariate analysis, including age, peritoneal cancer index (PCI), completeness of cytoreductive surgery (CRS), CA19-9, and albumin. The nomogram showed the accuracy to predict the OS at 0.5, 1, 2, and 3 years. The C-index of the nomogram in the training cohort and validation cohort were 0.713 (95% CI, 0.674–0.752) and 0.642 (95% CI, 0.563-0.720) separately. Both training and validation cohorts showed good discrimination of the nomogram for OS. Calibration curves have shown the predicted OS of nomogram are consistent with actual survival.Conclusion: This novel nomogram, combined with age, PCI, CRS, CA19-9, and albumin, has shown good accuracy to predict OS in patients with synchronous PM, which could be used as an easy-to-use tool for clinicians and surgeons to make decisions.

scholarly journals A radiomics nomogram for the prediction of overall survival in patients with hepatocellular carcinoma after hepatectomy

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Qinqin Liu ◽  
Jing Li ◽  
Fei Liu ◽  
Weilin Yang ◽  
Jingjing Ding ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Overall Survival ◽  
Cox Regression ◽  
Validation Cohort ◽  
Cox Model ◽  
Accurate Estimation ◽  
Training Cohort ◽  
Dismal Prognosis ◽  
Texture Parameters ◽  
Radiomics Signature

Abstract Background Hepatocellular carcinoma (HCC) is associated with a dismal prognosis, and prediction of the prognosis of HCC can assist in therapeutic decision-makings. An increasing number of studies have shown that the texture parameters of images can reflect the heterogeneity of tumors, and may have the potential to predict the prognosis of patients with HCC after surgical resection. The aim of this study was to investigate the prognostic value of computed tomography (CT) texture parameters in patients with HCC after hepatectomy and to develop a radiomics nomogram by combining clinicopathological factors and the radiomics signature. Methods In all, 544 eligible patients were enrolled in this retrospective study and were randomly divided into the training cohort (n = 381) and the validation cohort (n = 163). The tumor regions of interest (ROIs) were delineated, and the corresponding texture parameters were extracted. The texture parameters were selected by using the least absolute shrinkage and selection operator (LASSO) Cox model in the training cohort, and a radiomics signature was established. Then, the radiomics signature was further validated as an independent risk factor for overall survival (OS). The radiomics nomogram was established based on the Cox regression model. The concordance index (C-index), calibration plot and decision curve analysis (DCA) were used to evaluate the performance of the radiomics nomogram. Results The radiomics signature was formulated based on 7 OS-related texture parameters, which were selected in the training cohort. In addition, the radiomics nomogram was developed based on the following five variables: α-fetoprotein (AFP), platelet-to-lymphocyte ratio (PLR), largest tumor size, microvascular invasion (MVI) and radiomics score (Rad-score). The nomogram displayed good accuracy in predicting OS (C-index = 0.747) in the training cohort and was confirmed in the validation cohort (C-index = 0.777). The calibration plots also showed excellent agreement between the actual and predicted survival probabilities. The DCA indicated that the radiomics nomogram showed better clinical utility than the clinicopathologic nomogram. Conclusion The radiomics signature is a potential prognostic biomarker of HCC after hepatectomy. The radiomics nomogram that integrated the radiomics signature can provide a more accurate estimation of OS than the clinicopathologic nomogram for HCC patients after hepatectomy.

scholarly journals A Combined RNA Signature Predicts Recurrence Risk of Stage I-IIIA Lung Squamous Cell Carcinoma

2021 ◽  
Vol 12 ◽  
Author(s):  
Li Sun ◽  
Juan Li ◽  
Xiaomeng Li ◽  
Xuemei Yang ◽  
Shujun Zhang ◽  
...  
Keyword(s):  
Squamous Cell Carcinoma ◽  
Validation Cohort ◽  
Lung Squamous Cell Carcinoma ◽  
Predictive Performance ◽  
Stage I ◽  
Molecular Signature ◽  
Training Cohort ◽  
Entire Cohort ◽  
Kaplan Meier ◽  
Good Predictive Performance

ObjectiveRecurrence remains the main cause of the poor prognosis in stage I-IIIA lung squamous cell carcinoma (LUSC) after surgical resection. In the present study, we aimed to identify the long non-coding RNAs (lncRNAs), microRNAs (miRNAs), and messenger RNAs (mRNAs) related to the recurrence of stage I-IIIA LUSC. Moreover, we constructed a risk assessment model to predict the recurrence of LUSC patients.MethodsRNA sequencing data (including miRNAs, lncRNAs, and mRNAs) and relevant clinical information were obtained from The Cancer Genome Atlas (TCGA) database. The differentially expressed lncRNAs, miRNAs, and mRNAs were identified using the “DESeq2” package of the R language. Univariate Cox proportional hazards regression analysis and Kaplan-Meier curve were used to identify recurrence-related genes. Stepwise multivariate Cox regression analysis was carried out to establish a risk model for predicting recurrence in the training cohort. Moreover, Kaplan-Meier curves and receiver operating characteristic (ROC) curves were adopted to examine the predictive performance of the signature in the training cohort, validation cohort, and entire cohort.ResultsBased on the TCGA database, we analyzed the differentially expressed genes (DEGs) among 27 patients with recurrent stage I-IIIA LUSC and 134 patients with non-recurrent stage I-IIIA LUSC, and identified 431 lncRNAs, 36 miRNAs, and 746 mRNAs with different expression levels. Out of these DEGs, the optimal combination of DEGs was finally determined, and a nine-joint RNA molecular signature was constructed for clinical prediction of recurrence, including LINC02683, AC244517.5, LINC02418, LINC01322, AC011468.3, hsa-mir-6825, AC020637.1, AC027117.2, and SERPINB12. The ROC curve proved that the model had good predictive performance in predicting recurrence. The area under the curve (AUC) of the prognostic model for recurrence-free survival (RFS) was 0.989 at 3 years and 0.958 at 5 years (in the training set). The combined RNA signature also revealed good predictive performance in predicting the recurrence in the validation cohort and entire cohort.ConclusionsIn the present study, we constructed a nine-joint RNA molecular signature for recurrence prediction of stage I-IIIA LUSC. Collectively, our findings provided new and valuable clinical evidence for predicting the recurrence and targeted treatment of stage I-IIIA LUSC.

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

2022 ◽  
Vol 22 (1) ◽  
Author(s):  
Yun Bian ◽  
Shiwei Guo ◽  
Hui Jiang ◽  
Suizhi Gao ◽  
Chengwei Shao ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Lymph Node ◽  
Pancreatic Ductal Adenocarcinoma ◽  
Validation Cohort ◽  
Ductal Adenocarcinoma ◽  
Arterial Phase ◽  
Multivariable Logistic Regression Analysis ◽  
Preoperative Prediction ◽  
Multivariable Logistic Regression ◽  
Radiomics Signature

Abstract 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.

scholarly journals A prospectively validated machine learning model for the prediction of survival and tumor subtype in pancreatic ductal adenocarcinoma

2019 ◽  
Author(s):  
Georgios Kaissis ◽  
Sebastian Ziegelmayer ◽  
Fabian Lohöfer ◽  
Hana Algül ◽  
Matthias Eiber ◽  
...  
Keyword(s):  
Machine Learning ◽  
Overall Survival ◽  
Median Overall Survival ◽  
Validation Cohort ◽  
Learning Algorithm ◽  
External Validation ◽  
Ductal Adenocarcinoma ◽  
Machine Learning Algorithm ◽  
Training Cohort ◽  
Histopathological Subtype

AbstractPurposeTo develop a supervised machine learning algorithm capable of predicting above vs. below-median overall survival from medical imaging-derived radiomic features in a cohort of patients with pancreatic ductal adenocarcinoma (PDAC).Materials and Methods102 patients with histopathologically proven PDAC were retrospectively assessed as the training cohort and 30 prospectively enrolled patients served as the external validation cohort. Tumors were segmented in pre-operative diffusion weighted-(DW)-MRI derived ADC maps and radiomic features were extracted. A Random Forest machine learning algorithm was fit to the training cohort and tested in the external validation cohort. The histopathological subtype of the tumor samples was assessed by immunohistochemistry in 21/30 patients of the external validation cohort. Individual radiomic feature importance was evaluated.ResultsThe machine learning algorithm achieved a sensitivity of 87% and a specificity of 80% (ROC-AUC 90%) for the prediction of above- vs. below-median survival on the unseen data of the external validation cohort. Heterogeneity-related features were highly ranked by the model. Of the 21 patients for whom the histopathological subtype was determined, 8/9 patients predicted by the model to experience below-median overall survival exhibited the quasi-mesenchymal subtype, while 11/12 patients predicted to experience above-median survival exhibited a non-quasi-mesenchymal subtype (Fisher’s exact test P<0.001).ConclusionThe application of machine-learning to the radiomic analysis of DW-MRI-derived ADC maps allowed the prediction of overall survival with high diagnostic accuracy in a prospectively collected cohort. The high overlap of clinically relevant histopathological subtypes with model predictions underlines the potential of quantitative imaging workflows in pre-operative subtyping and risk assessment in PDAC.

Clinical features and outcomes of colloid carcinoma of pancreas compared to pancreatic ductal adenocarcinoma.

2021 ◽  
Vol 39 (15_suppl) ◽  
pp. e16259-e16259
Author(s):  
Lana Khalil ◽  
Katerina Mary Zakka ◽  
Renjian Jiang ◽  
Mckenna Penely ◽  
Olatunji B. Alese ◽  
...  
Keyword(s):  
Overall Survival ◽  
Multivariate Analysis ◽  
Prognostic Factors ◽  
Pancreatic Ductal Adenocarcinoma ◽  
Clinical Features ◽  
Primary Site ◽  
Stage I ◽  
Stage Ii ◽  
Ductal Adenocarcinoma ◽  
Carcinoma Of Pancreas

e16259 Background: Colloid carcinoma (CC) of the pancreas is a rare histopathological subtype of ductal adenocarcinoma (PDAC), with poorly defined prognostic factors and therapeutic outcomes. The aim of this study is to characterize the clinicopathological features and evaluate the overall survival (OS) and prognostic factors of patients with pancreatic CC using National Cancer Database (NCDB). Methods: Patients diagnosed with CC of the pancreas and PDAC between 2004 and 2016 were identified from the NCDB using ICD-O-3 morphology (8480/3 for CC and 8140/3 for PDAC) and topography codes (C25). Univariate and multivariable analyses were conducted and Kaplan-Meier analysis and Cox proportional hazards models were used to perform OS analysis. Results: A total of 56,846 patients met the inclusion criteria for the final analysis. Of the total population included, 2,430 patients (4.3%) had CC and 54,416 patients (95.7%) had PDAC. For both, CC and PDAC, there was a male preponderance (52.0%, 52.5%), Caucasians (85.1%, 84%), occurrence above the age of 70 (39.2%, 38.2%), and the most common primary site was the head of the pancreas (50.5%, 53%). For CC, the percentage of pathologic stage III colloid pancreas cancer appeared the lowest (3.5%, 85 patients), compared to stage I (16.7%), stage II (37.8%), and stage IV (42.1%). While in PDAC, the percentage of pathologic stage I (5.94%) and stage III (4.44%) patients was lower than stage II (37.21%) and IV (52.41%). CC and PDAC more frequently presented with < 5cm tumor, at academic or research cancer centers, and diagnosed between 2009 and 2013 compared to 2004–2008 ( p< 0.001). For both CC and PDAC, the majority underwent surgical resection (58%, 53%), systemic chemotherapy (57.8%, 63%) and did not receive radiotherapy (78.8%, 77.6%). A positive surgical margin on pathologic evaluation was associated with worse outcomes for CC and PDAC in both univariate and multivariate analysis (HR 1.61; 1.56–1.66; p< 0.001 and HR 1.43; 1.38–1.48, p< 0.001). CC had a better 1-year overall survival (OS) in all stages compared to PDAC (p < 0.001). In multivariate analysis, mucinous carcinoma histology, female sex, diagnosis between 2004 and 2009, well/moderately differentiated histology, chemotherapy, age at diagnosis less than 60, radiation therapy after surgery, and local surgical procedure of primary site and pancreatectomy (p < 0.001) were associated with better OS compared to PDAC. Colloid histology was associated with better 1-year overall survival (OS) in all stages compared to PDAC (p < 0.001). Conclusions: Colloid carcinoma of pancreas is associated with a better overall survival as compared to pancreatic ductal adenocarcinoma. This is the largest study to address the clinical features and outcomes of colloid carcinoma of pancreas.

scholarly journals Prognostic Evaluation of Vimentin Expression in Correlation with Ki67 and CD44 in Surgically Resected Pancreatic Ductal Adenocarcinoma

2017 ◽  
Vol 2017 ◽  
pp. 1-7 ◽  
Author(s):  
Despoina Myoteri ◽  
Dionysios Dellaportas ◽  
Panagis M. Lykoudis ◽  
Alexandros Apostolopoulos ◽  
Athanasios Marinis ◽  
...  
Keyword(s):  
Overall Survival ◽  
Pancreatic Ductal Adenocarcinoma ◽  
Surgical Resection ◽  
Prognostic Significance ◽  
Ductal Adenocarcinoma ◽  
Vimentin Expression ◽  
Term Survival ◽  
Therapeutic Modalities ◽  
Kaplan Meier ◽  
Surgical Resection Margin

Purpose. Radical surgical resection with adjuvant chemotherapy or chemo-radiotherapy is the most effective treatment for pancreatic ductal adenocarcinoma (PDAC). However, relatively few studies investigate the prognostic significance of biological markers in PDAC. This study aims to look into the expressions of vimentin, Ki67, and CD44 in PDAC surgical specimens and their potential prognostic implications in survival. Method. The study was designed as retrospective, and vimentin, Ki67, and CD44 expressions were evaluated by immunohistochemistry in 53 pancreatic ductal adenocarcinoma cases. Overall survival was assessed by the Kaplan–Meier method. Results. Patients’ median age was 68 years. The median survival was 18 months. The tumors were T3-4 in 40/53 (75.5%), and metastases in lymph nodes were found in 42 out of 53 (79.2%) cases. On multivariate analysis, the size of primary tumor (p<0.001), the surgical resection margin status (p=0.042), and vimentin expression (p=0.011) were independently correlated with overall survival. Conclusions. Long-term survival after resection of PDAC is still about 15%. Vimentin expression is a potential independent adverse prognostic molecular marker and should be included in histopathological reports. Also, CD44 expression correlates with high Ki67, vimentin positivity, and N stage and may represent a potential target of novel therapeutic modalities in pancreatic adenocarcinoma patients.

scholarly journals Gastric Cancer Treatments and Survival Trends in the United States

2020 ◽  
Vol 28 (1) ◽  
pp. 138-151
Author(s):  
Kelly A. Stahl ◽  
Elizabeth J. Olecki ◽  
Matthew E. Dixon ◽  
June S. Peng ◽  
Madeline B. Torres ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Overall Survival ◽  
Stage Iv ◽  
The United States ◽  
Current Treatment ◽  
Stage I ◽  
Stage Ii ◽  
Chi Square ◽  
Specific Guideline ◽  
Kaplan Meier

Gastric cancer is the third most common cause of cancer deaths worldwide. Despite evidence-based recommendation for treatment, the current treatment patterns for all stages of gastric cancer remain largely unexplored. This study investigates trends in the treatments and survival of gastric cancer. The National Cancer Database was used to identify gastric adenocarcinoma patients from 2004–2016. Chi-square tests were used to examine subgroup differences between disease stages: Stage I, II/III and IV. Multivariate analyses identified factors associated with the receipt of guideline concordant care. The Kaplan–Meier method was used to assess three-year overall survival. The final cohort included 108,150 patients: 23,584 Stage I, 40,216 Stage II/III, and 44,350 Stage IV. Stage specific guideline concordant care was received in only 73% of patients with Stage I disease and 51% of patients with Stage II/III disease. Patients who received guideline consistent care had significantly improved survival compared to those who did not. Overall, we found only moderate improvement in guideline adherence and three-year overall survival during the 13-year study time period. This study showed underutilization of stage specific guideline concordant care for stage I and II/III disease.

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