scholarly journals A Panel of Cancer Testis Antigens and Clinical Risk Factors to Predict Metastasis in Colorectal Cancer

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Ramyar Molania ◽  
Frouzandeh Mahjoubi ◽  
Rezvan Mirzaei ◽  
Saeed-Reza Khatami ◽  
Bahar Mahjoubi
Keyword(s):  
Colorectal Cancer ◽  
Risk Factors ◽  
Lymph Node ◽  
Liver Metastasis ◽  
Clinical Risk Factors ◽  
Cancer Testis Antigen ◽  
Node Metastasis ◽  
Clinical Risk ◽  
Expression Frequency ◽  
Cancer Testis

Colorectal cancer (CRC) is the third common carcinoma with a high rate of mortality worldwide and several studies have investigated some molecular and clinicopathological markers for diagnosis and prognosis of its malignant phenotypes. The aim of this study is to evaluate expression frequency of PAGE4, SCP-1, and SPANXA/D cancer testis antigen (CTA) genes as well as some clinical risk markers to predict liver metastasis of colorectal cancer patients. The expression frequency of PAGE4, SCP-1, and SPANXA/D cancer/testis antigen (CTA) genes was obtained using reverse transcription polymerase chain reaction (RT-PCR) assay in 90 colorectal tumor samples including both negative and positive liver metastasis tumors. Statistical analysis was performed to assess the association of three studied genes and clinical risk factors with CRC liver metastasis. The frequency of PAGE4 and SCP-1 genes expression was significantly higher in the primary tumours with liver metastasis when statistically compared with primary tumors with no liver metastasis (P<0.05). Among all clinical risk factors studied, the lymph node metastasis and the depth of invasion were statistically correlated with liver metastasis of CRC patients. In addition, using multiple logistic regression, we constructed a model based on PAGE4 and lymph node metastasis to predict liver metastasis of CRC.

scholarly journals Development and Validation a Nomogram for Predicting the Third Station Lymph Node Metastasis in Early Gastric Cancer

2021 ◽  
Author(s):  
Xiaoxiao Wang ◽  
Cong Li ◽  
Mengjie Fang ◽  
Liwen Zhang ◽  
Lianzhen Zhong ◽  
...  
Keyword(s):  
Risk Factors ◽  
Gastric Cancer ◽  
Lymph Node ◽  
Lymph Node Metastasis ◽  
Lymph Nodes ◽  
Validation Cohort ◽  
Predictive Accuracy ◽  
Clinical Risk Factors ◽  
Node Metastasis ◽  
Clinical Risk

Abstract Background:This study aimed to evaluate the value of radiomic nomogram in predicting lymph node metastasis in T1-2 gastric cancer according to the No. 3 station lymph nodes.Methods:A total of 159 T1-2 gastric cancer (GC) patients who had undergone surgery with lymphadenectomy between March 2012 and November 2017 were retrospectively collected and divided into a primary cohort (n = 80) and a validation cohort (n = 79). Radiomic features were extracted from both tumor region and No. 3 station lymph nodes (LN) based on computed tomography (CT) images per patient. Then, key features were selected using minimum redundancy maximum relevance algorithm and fed into two radiomic signatures, respectively. Meanwhile, the predictive performance of clinical risk factors was studied. Finally, a nomogram was built by merging radiomic signatures and clinical risk factors and evaluated by the area under the receiver operator characteristic curve (AUC) as well as decision curve.Results: Two radiomic signatures, reflecting phenotypes of the tumor and LN respectively, were significantly associated with LN metastasis. A nomogram incorporating two radiomic signatures and CT-reported LN metastasis status showed good discrimination of LN metastasis in both the primary cohort (AUC: 0.915; 95% confidence interval [CI]: 0.832-0.998) and validation cohort (AUC: 0.908; 95%CI: 0.814-1.000). The decision curve also indicated its potential clinical usefulness.Conclusions:The nomogram received favorable predictive accuracy in predicting No.3 station LN metastasis in T1-2 GC, and could assist the choice of therapy.

scholarly journals Integrating No.3 lymph nodes and primary tumor radiomics to predict lymph node metastasis in T1-2 gastric cancer

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Xiaoxiao Wang ◽  
Cong Li ◽  
Mengjie Fang ◽  
Liwen Zhang ◽  
Lianzhen Zhong ◽  
...  
Keyword(s):  
Risk Factors ◽  
Gastric Cancer ◽  
Lymph Node ◽  
Lymph Node Metastasis ◽  
Lymph Nodes ◽  
Clinical Risk Factors ◽  
Primary Tumors ◽  
Training Cohort ◽  
Node Metastasis ◽  
Clinical Risk

Abstract Background This study aimed to develope and validate a radiomics nomogram by integrating the quantitative radiomics characteristics of No.3 lymph nodes (LNs) and primary tumors to better predict preoperative lymph node metastasis (LNM) in T1-2 gastric cancer (GC) patients. Methods A total of 159 T1-2 GC patients who had undergone surgery with lymphadenectomy between March 2012 and November 2017 were retrospectively collected and divided into a training cohort (n = 80) and a testing cohort (n = 79). Radiomic features were extracted from both tumor region and No. 3 station LNs based on computed tomography (CT) images per patient. Then, key features were selected using minimum redundancy maximum relevance algorithm and fed into two radiomic signatures, respectively. Meanwhile, the predictive performance of clinical risk factors was studied. Finally, a nomogram was built by merging radiomic signatures and clinical risk factors and evaluated by the area under the receiver operator characteristic curve (AUC) as well as decision curve. Results Two radiomic signatures, reflecting phenotypes of the tumor and LNs respectively, were significantly associated with LN metastasis. A nomogram incorporating two radiomic signatures and CT-reported LN metastasis status showed good discrimination of LN metastasis in both the training cohort (AUC 0.915; 95% confidence interval [CI] 0.832–0.998) and testing cohort (AUC 0.908; 95% CI 0.814–1.000). The decision curve also indicated its potential clinical usefulness. Conclusions The nomogram received favorable predictive accuracy in predicting No.3 LNM in T1-2 GC, and the nomogram showed positive role in predicting LNM in No.4 LNs. The nomogram may be used to predict LNM in T1-2 GC and could assist the choice of therapy.

scholarly journals Identification of an Immune-Related Gene Signature to Improve Prognosis Prediction in Colorectal Cancer Patients

2020 ◽  
Vol 11 ◽  
Author(s):  
Siqi Dai ◽  
Shuang Xu ◽  
Yao Ye ◽  
Kefeng Ding
Keyword(s):  
Colorectal Cancer ◽  
Risk Factors ◽  
Expression Profiles ◽  
Gene Signature ◽  
Clinical Risk Factors ◽  
Survival Prediction ◽  
Related Gene ◽  
Cancer Management ◽  
Clinical Risk ◽  
Immune Related Gene

BackgroundDespite recent advance in immune therapy, great heterogeneity exists in the outcomes of colorectal cancer (CRC) patients. In this study, we aimed to analyze the immune-related gene (IRG) expression profiles from three independent public databases and develop an effective signature to forecast patient’s prognosis.MethodsIRGs were collected from the ImmPort database. The CRC dataset from The Cancer Genome Atlas (TCGA) database was used to identify a prognostic gene signature, which was verified in another two CRC datasets from the Gene Expression Omnibus (GEO). Gene function enrichment analysis was conducted. A prognostic nomogram was built incorporating the IRG signature with clinical risk factors.ResultsThe three datasets had 487, 579, and 224 patients, respectively. A prognostic six-gene-signature (CCL22, LIMK1, MAPKAPK3, FLOT1, GPRC5B, and IL20RB) was developed through feature selection that showed good differentiation between the low- and high-risk groups in the training set (p &lt; 0.001), which was later confirmed in the two validation groups (log-rank p &lt; 0.05). The signature outperformed tumor TNM staging for survival prediction. GO and KEGG functional annotation analysis suggested that the signature was significantly enriched in metabolic processes and regulation of immunity (p &lt; 0.05). When combined with clinical risk factors, the model showed robust prediction capability.ConclusionThe immune-related six-gene signature is a reliable prognostic indicator for CRC patients and could provide insight for personalized cancer management.

scholarly journals Clinical risk factors of colorectal cancer in patients with serrated polyposis syndrome: a multicentre cohort analysis

Gut ◽  
2015 ◽  
Vol 66 (2) ◽  
pp. 278-284 ◽  
Author(s):  
J E G IJspeert ◽  
S A Q Rana ◽  
N S S Atkinson ◽  
Y J van Herwaarden ◽  
B A J Bastiaansen ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Risk Factors ◽  
Cohort Analysis ◽  
Clinical Risk Factors ◽  
Polyposis Syndrome ◽  
Clinical Risk ◽  
Multicentre Cohort

Risk Factors for Lymph Node Metastasis in Submucosal Invasive Colorectal Cancer

2008 ◽  
Vol 32 (9) ◽  
pp. 2089-2094 ◽  
Author(s):  
Pyong W. Choi ◽  
Chang Sik Yu ◽  
Se J. Jang ◽  
Sang H. Jung ◽  
Hee C. Kim ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Risk Factors ◽  
Lymph Node ◽  
Lymph Node Metastasis ◽  
Node Metastasis

Novel risk factors for lymph node metastasis in early invasive colorectal cancer: a multi-institution pathology review

2013 ◽  
Vol 49 (9) ◽  
pp. 1314-1323 ◽  
Author(s):  
Hideki Ueno ◽  
Kazuo Hase ◽  
Yojiro Hashiguchi ◽  
Hideyuki Shimazaki ◽  
Shinji Yoshii ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Risk Factors ◽  
Lymph Node ◽  
Lymph Node Metastasis ◽  
Node Metastasis ◽  
Novel Risk Factors ◽  
Pathology Review

scholarly journals Computed Tomography-Based Radiomics Model to Predict Central Cervical Lymph Node Metastases in Papillary Thyroid Carcinoma: A Multicenter Study

2021 ◽  
Vol 12 ◽  
Author(s):  
Jingjing Li ◽  
Xinxin Wu ◽  
Ning Mao ◽  
Guibin Zheng ◽  
Haicheng Zhang ◽  
...  
Keyword(s):  
Risk Factors ◽  
Lymph Node ◽  
Clinical Risk Factors ◽  
Papillary Thyroid ◽  
Test Set ◽  
Clinical Risk ◽  
Node Metastases ◽  
Test Sets ◽  
Linear Svm ◽  
External Test

ObjectivesThis study aimed to develop a computed tomography (CT)-based radiomics model to predict central lymph node metastases (CLNM) preoperatively in patients with papillary thyroid carcinoma (PTC).MethodsIn this retrospective study, 678 patients with PTC were enrolled from Yantai Yuhuangding Hot3spital (n=605) and the Affiliated Hospital of Binzhou Medical University (n=73) within August 2010 to December 2020. The patients were randomly divided into a training set (n=423), an internal test set (n=182), and an external test set (n=73). Radiomics features of each patient were extracted from preoperative plain scan and contrast-enhanced CT images (arterial and venous phases). One-way analysis of variance (ANOVA) and least absolute shrinkage and selection operator algorithm were used for feature selection. The K-nearest neighbor, logistics regression, decision tree, linear-support vector machine (linear-SVM), Gaussian-SVM, and polynomial-SVM algorithms were used to establish radiomics models for CLNM prediction. The clinical risk factors were selected by ANOVA and multivariate logistic regression. Incorporated with clinical risk factors, a combined radiomics model was established for the preoperative prediction of CLNM in patients with PTCs. The performance of the combined radiomics model was evaluated using the receiver operating characteristic (ROC) and calibration curves in the training and test sets. The clinical usefulness was evaluated through decision curve analysis (DCA).ResultsA total of 4227 radiomic features were extracted from the CT images of each patient, and 14 non-zero coefficient features associated with CLNM were selected. Four clinical variables (sex, age, tumor diameter, and CT-reported lymph node status) were significantly associated with CLNM. Linear-SVM led to the best prediction model, which incorporated radiomic features and clinical risk factors. Areas under the ROC curves of 0.747 (95% confidence interval [CI] 0.706–0.782), 0.710 (95% CI 0.634–0.786), and 0.764 (95% CI 0.654–0.875) were obtained in the training, internal, and external test sets, respectively. The linear-SVM algorithm also showed better sensitivity (0.702 [95% CI 0.600–0.790] vs. 0.477 [95% CI 0.409–0.545]) and accuracy (0.670 [95% CI 0.600–0.738] vs. 0.642 [95% CI 0.569–0.712]) than an experienced radiologist in the internal test set in the combined radiomics model. The calibration plot reflected a favorable agreement between the actual and estimated probabilities of CLNM. The DCA indicated the clinical usefulness of the combined radiomics model.ConclusionThe combined radiomics model is a non-invasive preoperative tool that incorporates radiomic features and clinical risk factors to predict CLNM in patients with PTC.

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