scholarly journals Dynamic Nomogram Predicts Overall Survival in Patients With Occult Breast Cancer: a Population-based Analysis

2021 ◽  
Author(s):  
Xiaomin Yang ◽  
Kunlong Li ◽  
Zhangjun Song ◽  
Huxia Wang ◽  
Sai He ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Overall Survival ◽  
Cox Regression ◽  
Validation Cohort ◽  
Strategic Decision ◽  
Occult Breast Cancer ◽  
Training Cohort ◽  
Cox Regression Analysis ◽  
Ajcc Stage ◽  
Stage System

Abstract Background: Due the rarity of occult breast cancer (OBC), no precise prognostic instruments were available to assess the overall survival (OS) in patients with OBC. The aim of this study is to construct a nomogram for predicting the OS probability in patients with OBC. Methods: Patients who were enrolled in the Surveillance, Epidemiology, and End Results database between 2004 and 2015 were regarded as subjects and studied. We constructed a dynamic nomogram that can predict prognosis in patients with OBC based on crucial independent factors by using univariate and multivariate Cox regression analyses. C-index and calibration plots were chosen for validation. Net reclassification index (NRI), integrated discrimination improvement (IDI) and DCA (Ddecision Curve Analysis) were used to evaluate the nomogram’s clinical pragmatism. Results: Totally, 693 patients with OBC were included in this study. The nomogram integrated six independent prognostic factors through multivariate Cox regression analysis, such as surgical method, radiotherapy status, chemotherapy status, ER status, AJCC-stage and age. The prediction model exhibited robustness with the C-index 0.75 (95%CI: 0.72-0.77) in training cohort and 0.79 (95%CI: 0.76-0.82) in validation group. Moreover, the calibration curves presented favorably. The NRI values of 0.61 (95%CI: 0.28-0.99) for 5-year,0.53 (95%CI: 0.23-0.77) for 8-year OS prediction in the training cohort,0.75 (95%CI: 0.36-1.23) for 5-year and 0.6 (95%CI: 0.15-1.2) for 8-year OS prediction in the validation cohort,and the IDI values of 0.1 (95%CI: 0.04-0.17) for 5-year and 0.11 (95%CI: 0.03-0.19) for 8-year OS prediction in the training cohort, 0.21 (95%CI: 0.09-0.3) for 5-year and 0.22 (95%CI: 0.08-0.32) for 8-year OS prediction in the validation cohort,, indicated that the established nomogram performed significantly better than the AJCC stage system alone. Furthermore, DCA showed that the nomogram in our study was clinically useful and had better discriminative ability than the AJCC stage system. Conclusions: A nomogram was developed and validated to accurately predict the individualized probability OS for patients with occult breast cancer (OBC) and is expected to offer guidance for strategic decision.

scholarly journals An 8-lncRNA Signature Predicts Survival of Triple-Negative Breast Cancer Patients Without Germline BRCA1/2 Mutation

2020 ◽  
Author(s):  
Minling Liu ◽  
Wei Dai ◽  
Mengyuan Zhu ◽  
Xueying Li ◽  
Shan Huang ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Overall Survival ◽  
Triple Negative Breast Cancer ◽  
Triple Negative ◽  
Cox Regression ◽  
Validation Cohort ◽  
Functional Enrichment ◽  
Biological Behavior ◽  
Risk Scores ◽  
Training Cohort

Abstract Background: Triple-negative breast cancer (TNBC) is a particular breast cancer subtype with poor prognosis due to its aggressive biological behavior and strong heterogeneity. TNBC with germline BRCA1/2 mutation (gBRCAm) have higher sensitivity to DNA damaging agents including platinum-based chemotherapy and PARP inhibitors. But the treatment of TNBC without gBRCAm remains challenging. This study aimed to develop a long non-coding RNA (lncRNA) signature of TNBC patients without gBRCAm to improve risk stratification and optimize individualized treatment.Methods: 98 TNBC patients without gBRCAm were acquired from The Cancer Genome Atlas (TCGA) database. The univariable Cox regression analysis and LASSO Cox regression model were applied to establish an lncRNA signature in the training cohort (N = 59). Then Kaplan–Meier survival curve and time-dependent ROC curve were used to validate the prognostic ability of the signature. The signature related mRNAs were identified using the Pearson correlation. Functional enrichment analysis of related mRNA was performed using the Metascape. The qPCR assay was performed to confirm the expressions and clinicopathological correlationsof two potential lncRNAs HAGLROS and TONSL-AS1 in 30 paired clinical triple-negative breast cancer samples without gBRCAm.Results:We developed an 8-lncRNA signature in the training cohort including HAGLROS, AL139002.1, AL391244.2, AP000696.1, AL391056.1, AL513304.1, TONSL-AS1 and AL031008.1. In both the training and validation cohort, patients with higher risk scores showed significantly worse overall survival compared to those with lower risk scores(P=0.00018 and P =0.0068 respectively). 1, 5, 8-year AUC in the training cohort were 1.000, 1.000 and 0.908 respectively, in the validation cohort were 0.785, 0.790 and 0.892 respectively indicating that our signature has a good prognostic capacity. Signature related mRNA mainly enriched in terms include RNA metabolic process, DNA repair pathways, and so on. Two potential lncRNAs HAGLROS and TONSL-AS1 were found frequently overexpressed in TNBC without gBRCAm, and significantly associated with tumor grade and invasion.Conclusions: We constructed a novel 8-lncRNA signaturewhich significantly associated with the overall survival of TNBC patients without gBRCAm. Among those 8lncRNAs, HAGLROS and TONSL-AS1 may be potential therapeutic targetswhich function needed further exploration.

scholarly journals An 8-lncRNA signature predicts the survival of triple-negative breast cancer patients without germline BRCA1/2 mutation

2021 ◽  
Vol 3 (3) ◽  
pp. 15-32
Author(s):  
Minling LIU ◽  
Wei DAI ◽  
Mengyuan ZHU ◽  
Xueying LI ◽  
Min WEI ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Overall Survival ◽  
Triple Negative Breast Cancer ◽  
Triple Negative ◽  
Cox Regression ◽  
The Cancer Genome Atlas ◽  
Risk Scores ◽  
Training Cohort ◽  
Cox Regression Analysis ◽  
Platinum Based Chemotherapy

Purpose: TNBC with germline BRCA1/2 mutation (gBRCAm) have higher sensitivity to DNA damaging agents including platinum-based chemotherapy and PARP inhibitors. But the treatment of TNBC without gBRCAm remains challenging. This study aimed to develop a long non-coding RNA (lncRNA) signature of TNBC patients without gBRCAm to improve risk stratification and optimize individualized treatment. Methods: 98 TNBC patients without gBRCAm were acquired from The Cancer Genome Atlas database. The univariable Cox regression analysis and LASSO Cox regression model were applied to establish an lncRNA signature in the training cohort. Then Kaplan–Meier survival curve and time-dependent ROC curve were used to validate the prognostic ability of the signature. The qPCR assay was performed to confirm the expressions and clinicopathological correlations of two potential lncRNAs HAGLROS and TONSL-AS1 in 30 paired clinical triple-negative breast cancer samples without gBRCAm. Results: We developed an 8-lncRNA signature in the training cohort including HAGLROS, AL139002.1, AL391244.2, AP000696.1, AL391056.1, AL513304.1, TONSL-AS1 and AL031008.1. Patients with higher risk scores showed significantly worse overall survival compared to those with lower risk scores (P=0.00018 and P =0.0068 respectively). 30 paired specimens of TNBC without gBRCAm in our center showed that two potential lncRNAs HAGLROS and TONSL-AS1 were found frequently overexpressed, and significantly associated with tumor grade and invasion. Conclusion: We constructed a novel 8-lncRNA signature which significantly associated with the overall survival of TNBC patients without gBRCAm. Among those 8 lncRNAs, HAGLROS and TONSL-AS1 may be potential therapeutic targets which function needed further exploration.

scholarly journals A Novel Risk Model Based on Lipid Metabolism-Associated Genes Predicts Prognosis and Indicates Immune Microenvironment in Breast Cancer

2021 ◽  
Vol 9 ◽  
Author(s):  
Zhimin Ye ◽  
Shengmei Zou ◽  
Zhiyuan Niu ◽  
Zhijie Xu ◽  
Yongbin Hu
Keyword(s):  
Breast Cancer ◽  
Lipid Metabolism ◽  
Risk Model ◽  
Cox Regression ◽  
Validation Cohort ◽  
Consensus Clustering ◽  
Immune Microenvironment ◽  
Training Cohort ◽  
Cox Regression Analysis ◽  
Model Based

BackgroundBreast cancer (BRCA) is the most common tumor in women, and lipid metabolism involvement has been demonstrated in its tumorigenesis and development. However, the role of lipid metabolism-associated genes (LMAGs) in the immune microenvironment and prognosis of BRCA remains unclear.MethodsA total of 1076 patients with BRCA were extracted from The Cancer Genome Atlas database and randomly assigned to the training cohort (n = 760) or validation cohort (n = 316). Kaplan–Meier analysis was used to assess differences in survival. Consensus clustering was performed to categorize the patients with BRCA into subtypes. Using multivariate Cox regression analysis, an LMAG-based prognostic risk model was constructed from the training cohort and validated using the validation cohort. The immune microenvironment was evaluated using the ESTIMATE and tumor immune estimation resource algorithms, CIBERSORT, and single sample gene set enrichment analyses.ResultsConsensus clustering classified the patients with BRCA into two subgroups with significantly different overall survival rates and immune microenvironments. Better prognosis was associated with high immune infiltration. The prognostic risk model, based on four LMAGs (MED10, PLA2G2D, CYP4F11, and GPS2), successfully stratified the patients into high- and low-risk groups in both the training and validation sets. High risk scores predicted poor prognosis and indicated low immune status. Subgroup analysis suggested that the risk model was an independent predictor of prognosis in BRCA.ConclusionThis study demonstrated, for the first time, that LMAG expression plays a crucial role in BRCA. The LMAG-based risk model successfully predicted the prognosis and indicated the immune microenvironment of patients with BRCA. Our study may provide inspiration for further research on BRCA pathomechanisms.

scholarly journals An 8-lncRNA signature predicts survival of triple-negative breast cancer patients without germline BRCA1/2 mutation

2020 ◽  
Author(s):  
Minling Liu ◽  
Wei Dai ◽  
Mengyuan Zhu ◽  
Xueying Li ◽  
Shan Huang ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Overall Survival ◽  
Triple Negative Breast Cancer ◽  
Triple Negative ◽  
Cox Regression ◽  
Validation Cohort ◽  
Functional Enrichment ◽  
Biological Behavior ◽  
Risk Scores ◽  
Training Cohort

Abstract Background Triple-negative breast cancer (TNBC) is a particular breast cancer subtype with poor prognosis due to its aggressive biological behavior and strong heterogeneity. TNBC with germline BRCA1/2 mutation (gBRCAm) have higher sensitivity to DNA damaging agents including platinum-based chemotherapy and PARP inhibitors. But the treatment of TNBC without gBRCAm remains challenging. This study aimed to develop a long non-coding RNA (lncRNA) signature of TNBC patients without gBRCAm to improve risk stratification and optimize individualized treatment. Methods 98 TNBC patients without gBRCAm were acquired from The Cancer Genome Atlas (TCGA) database. The univariable Cox regression analysis and LASSO Cox regression model were applied to establish an lncRNA signature in the training cohort (N = 59). Then Kaplan–Meier survival curve and time-dependent ROC curve were used to validate the prognostic ability of the signature. The signature related mRNAs were identified using the Pearson correlation. Functional enrichment analysis of related mRNA was performed using the Metascape. The qPCR assay was performed to confirm the expressions and clinicopathological correlationsof two potential lncRNAs HAGLROS and TONSL-AS1 in 30 paired clinical triple-negative breast cancer samples without gBRCAm. Results We developed an 8-lncRNA signature in the training cohort including HAGLROS, AL139002.1, AL391244.2, AP000696.1, AL391056.1, AL513304.1, TONSL-AS1 and AL031008.1. In both the training and validation cohort, patients with higher risk scores showed significantly worse overall survival compared to those with lower risk scores(P = 0.00018 and P = 0.0068 respectively). 1, 5, 8-year AUC in the training cohort were 1.000, 1.000 and 0.908 respectively, in the validation cohort were 0.785, 0.790 and 0.892 respectively indicating that our signature has a good prognostic capacity. Signature related mRNA mainly enriched in terms include RNA metabolic process, DNA repair pathways, and so on. Two potential lncRNAs HAGLROS and TONSL-AS1 were found frequently overexpressed in TNBC without gBRCAm, and significantly associated with tumor grade and invasion. Conclusions We constructed a novel 8-lncRNA signaturewhich significantly associated with the overall survival of TNBC patients without gBRCAm. Among those 8lncRNAs, HAGLROS and TONSL-AS1 may be potential therapeutic targetswhich function needed further exploration.

scholarly journals Trends of incidence and prognosis of primary adenocarcinoma of the bladder

2021 ◽  
Vol 13 ◽  
pp. 175628722110180
Author(s):  
Haowen Lu ◽  
Weidong Zhu ◽  
Weipu Mao ◽  
Feng Zu ◽  
Yali Wang ◽  
...  
Keyword(s):  
Prediction Model ◽  
Marital Status ◽  
Cox Regression ◽  
Tnm Staging ◽  
Primary Site ◽  
Primary Adenocarcinoma ◽  
Cancer Specific Survival ◽  
Training Cohort ◽  
Cox Regression Analysis ◽  
Ajcc Stage

Background: Primary adenocarcinoma of the bladder (ACB) is a rare malignant tumor of the bladder with limited understanding of its incidence and prognosis. Methods: Patients diagnosed with ACB between 2004 and 2015 were obtained from the SEER database. The incidence changes of ACB patients between 1975 and 2016 were detected by Joinpoint software. Nomograms were constructed based on the results of multivariate Cox regression analysis to predict overall survival (OS) and cancer-specific survival (CSS) in patients with ACB, and the constructed nomograms were validated. Results: The incidence of ACB was trending down from 1991 to 2016. A total of 1039 patients were included in the study and randomly assigned to the training cohort (727) and validation cohort (312). In the training cohort, multivariate Cox regression showed that age, marital status, primary site, histology type, grade, AJCC stage, T stage, SEER stage, surgery, radiotherapy, and chemotherapy were independent prognostic factors for OS, whereas these were age, marital status, primary site, histology type, grade, AJCC stage, T/N stage, SEER stage, surgery, and radiotherapy for CSS. Based on the above Cox regression results, we constructed prognostic nomograms for OS and CSS in ACB patients. The C-index of the nomogram OS was 0.773 and the C-index of CSS was 0.785, which was significantly better than the C-index of the TNM staging prediction model. The area under the curve (AUC) and net benefit of the prediction model were higher than those of the TNM staging system. In addition, the calibration curves were very close to the ideal curve, suggesting appreciable reliability of the nomograms. Conclusion: The incidence of ACB patients showed a decreasing trend in the past 25 years. We constructed a clinically useful prognostic nomogram for calculating OS and CSS of ACB patients, which can provide a personalized risk assessment for ACB patient survival.

scholarly journals Identification of a Novel Gene Model-Based Homologous Recombination Deficiency Score to Improve Survival Prediction of TNBC.

2021 ◽  
Author(s):  
Wenxiang Zhang ◽  
Bolun Ai ◽  
Xiangyi Kong ◽  
Xiangyu Wang ◽  
Jie Zhai ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Homologous Recombination ◽  
Risk Score ◽  
Cox Regression ◽  
Roc Curves ◽  
Cox Regression Analysis ◽  
Homologous Recombination Deficiency ◽  
Ajcc Stage ◽  
Kaplan Meier

Abstract Background Triple-negative breast cancer (TNBC) is a specific histological type of breast cancer with a poor prognosis, early recurrence, which lacks durable chemotherapy responses and effective targeted therapies. We aimed to construct an accurate prognostic risk model based on homologous recombination deficiency (HRD) - gene expression profiles for improving prognosis prediction of TNBC. Methods Triple-negative breast cancer RNA sequencing data and sample clinical information were downloaded from the breast invasive carcinoma (BRCA) cohort in the Cancer Genome Atlas (TCGA) database. Combined with the HRD database, tumor samples were divided into two sets. We screened differentially expressed genes (DEGs) and then identified HRD-related prognostic genes using weighted gene co-expression network analysis (WGCNA) and Cox regression analysis. The least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression analysis were used to identifying key prognostic genes. Risk scores were calculated and compared with HRD score, Kaplan–Meier (KM) survival analysis were used to assess its prognostic power. GSE103091 dataset from GEO (Gene Expression Omnibus) database was used to validate the signature. Univariate and multivariate Cox regression were performed to independently verify the prognosis of the risk score. A nomogram was constructed and revealed by time-dependent ROC curves to guide clinical practice. Results We found that HRD tumor samples (HRD score > = 42) in TNBC patients were associated with poor overall survival (p = 0.027). We identified a total of 147 differential genes including 203 up-regulated and 213 down-regulated genes, among which 29 were prognosis-related genes. Through the LASSO method, 6 key prognostic genes ((MUCL1, IVL, FAM46C, CHI3L1, PRR15L, and CLEC3A) were selected and a 6-gene risk score was constructed. We found risk score was negatively associated with homologous recombination deficiency (HRD) scores (r = -0.22, p = 0.019). Compared with the low-risk group, Kaplan-Meier survival analysis shows that the high-risk group has an obvious poorer prognosis (P < 0.0001). Finally, we integrated the risk score model and clinical factors of TNBC (AJCC-stage, HRD score, T stage, and N stage) to construct a compound nomogram. Time-dependent ROC curves showed the risk score performed better in 1-, 3- and 5-year survival predictions compared with AJCC-stage. Conclusions Based on HRD gene expression data, our six HRD-related gene signature and nomogram could be practical and reliable tools for predicting OS in patients with TNBC.

Prognostic Nomogram Incorporating Immunohistochemical Results for the Overall Survival of Patients with Bladder Cancer.

2020 ◽  
Author(s):  
Tianwei Wang ◽  
Yunyan Wang
Keyword(s):  
Risk Factors ◽  
Overall Survival ◽  
Bladder Cancer ◽  
Risk Model ◽  
Cox Regression ◽  
Validation Cohort ◽  
Multivariable Analysis ◽  
Clinical Information ◽  
Internal Validation ◽  
Cox Regression Analysis

Abstract Objectives: In this study, we want to combine GATA3, VEGF, EGFR and Ki67 with clinical information to develop and validate a prognostic nomogram for bladder cancer.Methods: A total of 188 patients with clinical information and immunohistochemistry were enrolled in this study, from 1996 to 2018. Univariable and multivariable cox regression analysis was applied to identify risk factors for nomogram of overall survival (OS). The calibration of the nomogram was performed and the Area Under Curve (AUC) was calculated to assess the performance of the nomogram. Internal validation was performed with the validation cohort., the calibration curve and the AUC were calculated simultaneously.Results: Univariable and multivariable analysis showed that age (HR: 2.229; 95% CI: 1.162-4.274; P=0.016), histology (HR: 0.320; 95% CI: 0.136-0.751; P=0.009), GATA3 (HR: 0.348; 95% CI: 0.171-0.709; P=0.004), VEGF (HR: 2.295; 95% CI: 1.225-4.301; P=0.010) and grade (HR: 4.938; 95% CI: 1.339-18.207; P=0.016) remained as independent risk factors for OS. The age, histology, grade, GATA3 and VEGF were included to build the nomogram. The accuracy of the risk model was further verified with the C-index. The C-index were 0.65 (95% CI, 0.58-0.72) and 0.58 (95% CI, 0.46-0.70) in the training and validation cohort respectively. Conclusions: A combination of clinical variables with immunohistochemical results based nomogram would predict the overall survival of patients with bladder cancer.

scholarly journals Development and validation of Nomograms for predicting overall survival and Cancer-specific survival in patients with ovarian clear cell carcinoma

2020 ◽  
Vol 13 (1) ◽  
Author(s):  
Qian Chen ◽  
Shu Wang ◽  
Jing-He Lang
Keyword(s):  
Overall Survival ◽  
Cell Carcinoma ◽  
Cox Regression ◽  
Validation Cohort ◽  
Clear Cell ◽  
Clear Cell Carcinoma ◽  
Ovarian Clear Cell Carcinoma ◽  
Cancer Specific Survival ◽  
Training Cohort ◽  
Organ Metastasis

Abstract Background Ovarian clear cell carcinoma (OCCC) is a rare histologic type of ovarian cancer. There is a lack of an efficient prognostic predictive tool for OCCC in clinical work. This study aimed to construct and validate nomograms for predicting the overall survival (OS) and cancer-specific survival (CSS) in patients with OCCC. Methods Data of patients with primary diagnosed OCCC in the Surveillance, Epidemiology, and End Results (SEER) database between 2010 and 2016 was extracted. Prognostic factors were evaluated with LASSO Cox regression and multivariate Cox regression analysis, which were applied to construct nomograms. The performance of the nomogram models was assessed by the concordance index (C-index), calibration plots, decision curve analysis (DCA) and risk subgroup classification. The Kaplan-Meier curves were plotted to compare survival outcomes between subgroups. Results A total of 1541 patients from SEER registries were randomly divided into a training cohort (n = 1079) and a validation cohort (n = 462). Age, laterality, stage, lymph node (LN) dissected, organ metastasis and chemotherapy were independently and significantly associated with OS, while laterality, stage, LN dissected, organ metastasis and chemotherapy were independent risk factors for CSS. Nomograms were developed for the prediction of 3- and 5-year OS and CSS. The C-indexes for OS and CSS were 0.802[95% confidence interval (CI) 0.773–0.831] and 0.802 (0.769–0.835), respectively, in the training cohort, while 0.746 (0.691–0.801) and 0.770 (0.721–0.819), respectively, in the validation cohort. Calibration plots illustrated favorable consistency between the nomogram predicted and actual survival. C-index and DCA curves also indicated better performance of nomogram than the AJCC staging system. Significant differences were observed in the survival curves of different risk subgroups. Conclusions We have constructed predictive nomograms and a risk classification system to evaluate the OS and CSS of OCCC patients. They were validated to be of satisfactory predictive value, and could aid in future clinical practice.

Prediction of radiation therapy response in breast cancer patients.

2017 ◽  
Vol 35 (15_suppl) ◽  
pp. 565-565
Author(s):  
Divya Arora ◽  
Salman Hasan ◽  
Deborah Jebakumar ◽  
Yolanda Munoz ◽  
John Ford ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Overall Survival ◽  
Local Recurrence ◽  
Cox Regression ◽  
Tumor Biology ◽  
Radiation Response ◽  
Cox Regression Analysis ◽  
Breast Cancer Radiotherapy ◽  
Unit Increase ◽  
Selection Of

565 Background: While radiation portals are tailored to a patient’s unique anatomy and the selection of systemic agents routinely employs biomarker data, the selection of radiotherapy based on a patient’s tumor biology is not routinely utilized in breast cancer. The purpose of this study was to identify which genetic markers are possible predictors for local recurrence as a surrogate for radiation response. Methods: We identified 200 patients who received radiotherapy for breast cancer. Selected tumor markers included: Androgen receptor (AR), Hypoxia Inducible Factor 1-α (HIF-1), Phosphotidylinosotol-4,5-bisphosphate 3-kinase (PI3K), and Interleukin 13 (IL-13). Biomarkers were analyzed in terms of “extent” and “intensity” on a scale of 0-3 and scored by 2 separate pathologists. The primary endpoint of local recurrence (LR) & secondary endpoint of overall survival were analyzed using Kaplan-Meier survival curves, log-rank test for differences, and Cox regression models. Results: Median follow up was 7.98 years. At 5 years, the rate of LR was 92.6% and overall survival was 89.4%. On multivariate Cox regression analysis, a one unit increase in IL-13 extent increased the hazard of LR by 73%. A one unit decrease in AR extent increased the hazard of LR by 134%. The hazard of death increased 3.2 times for each unit increase in HIF1 extent. The hazard of death increased 1.5 times for each unit increase in PI3K extent. PI3K extent and intensity was increased, and AR extent and intensity was decreased in triple negative breast cancer (TNBC) (n = 68) vs non-TNBC (p < 0.0001). African Americans had a 4.2 times hazard of LR vs Caucasians. Conclusions: Expression of IL-13 was associated with a higher risk of LR; expression of AR was associated with decreased LR. These two markers may be instrumental in predicting radiation response. If this study is validated, cancers that express more IL-13 may require higher doses or targeted therapy. In contrast, those cancers expressing AR may not require as aggressive therapy. Lastly, PI3K and HIF1 α expression were significant predictors of worse overall survival. The clinical implications of these biologic markers are significant as they may help to guide biologically-driven, personalized breast cancer radiotherapy.

scholarly journals Development and validation of a radiomics-based model to predict local progression-free survival after chemo-radiotherapy in patients with esophageal squamous cell cancer

2021 ◽  
Vol 16 (1) ◽  
Author(s):  
He-San Luo ◽  
Ying-Ying Chen ◽  
Wei-Zhen Huang ◽  
Sheng-Xi Wu ◽  
Shao-Fu Huang ◽  
...  
Keyword(s):  
Multivariate Analysis ◽  
Local Recurrence ◽  
Squamous Cell ◽  
Cox Regression ◽  
Validation Cohort ◽  
Cell Cancer ◽  
Clinical Factors ◽  
Free Survival ◽  
Training Cohort ◽  
Cox Regression Analysis

Abstract Purpose To develop a nomogram model for predicting local progress-free survival (LPFS) in esophageal squamous cell carcinoma (ESCC) patients treated with concurrent chemo-radiotherapy (CCRT). Methods We collected the clinical data of ESCC patients treated with CCRT in our hospital. Eligible patients were randomly divided into training cohort and validation cohort. The least absolute shrinkage and selection operator (LASSO) with COX regression was performed to select optimal radiomic features to calculate Rad-score for predicting LPFS in the training cohort. The univariate and multivariate analyses were performed to identify the predictive clinical factors for developing a nomogram model. The C-index was used to assess the performance of the predictive model and calibration curve was used to evaluate the accuracy. Results A total of 221 ESCC patients were included in our study, with 155 patients in training cohort and 66 patients in validation cohort. Seventeen radiomic features were selected by LASSO COX regression analysis to calculate Rad-score for predicting LPFS. The patients with a Rad-score ≥ 0.1411 had high risk of local recurrence, and those with a Rad-score < 0.1411 had low risk of local recurrence. Multivariate analysis showed that N stage, CR status and Rad-score were independent predictive factors for LPFS. A nomogram model was built based on the result of multivariate analysis. The C-index of the nomogram was 0.745 (95% CI 0.7700–0.790) in training cohort and 0.723(95% CI 0.654–0.791) in validation cohort. The 3-year LPFS rate predicted by the nomogram model was highly consistent with the actual 3-year LPFS rate both in the training cohort and the validation cohort. Conclusion We developed and validated a prediction model based on radiomic features and clinical factors, which can be used to predict LPFS of patients after CCRT. This model is conducive to identifying the patients with ESCC benefited more from CCRT.

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