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 ◽  
Author(s):  
He-San Luo ◽  
Ying-Ying Chen ◽  
Sheng-Xi Wu ◽  
Shao-Fu Huang ◽  
Hong-Yao Xu ◽  
...  
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 chemoradiotherapy. 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 radiomics features calculating Rad-score for predicting LPFS in the training cohort. The univariate and multivariate analysis 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. After LASSO COX regression analysis, seventeen radiomics features were selected 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 radiomics features and clinical factors, which can be used to predict LPFS of patients after CCRT. This model is conducive to making individualized chemoradiotherapy strategy and providing scientific basis for subsequent intensive adjuvant therapy for ESCC patients.

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.

scholarly journals Prognostic Nutritional Index as an independent prognostic factor in locoregionally advanced squamous cell head and neck cancer

ESMO Open ◽  
2018 ◽  
Vol 3 (6) ◽  
pp. e000425 ◽  
Author(s):  
Gema Bruixola ◽  
Javier Caballero ◽  
Federica Papaccio ◽  
Angelica Petrillo ◽  
Aina Iranzo ◽  
...  
Keyword(s):  
Multivariate Analysis ◽  
Prognostic Factors ◽  
Prognostic Factor ◽  
Head And Neck ◽  
Squamous Cell ◽  
Validation Cohort ◽  
Prognostic Nutritional Index ◽  
Training Cohort ◽  
Lymphocyte Ratio ◽  
Nutritional Index

BackgroundLocally advanced head and neck squamous cell carcinoma (LAHNSCC) is a heterogeneous disease in which better predictive and prognostic factors are needed. Apart from TNM stage, both systemic inflammation and poor nutritional status have a negative impact on survival.MethodsWe retrospectively analysed two independent cohorts of a total of 145 patients with LAHNSCC treated with induction chemotherapy followed by concurrent chemoradiotherapy at two different academic institutions. Full clinical data, including the Prognostic Nutritional Index (PNI), neutrophil to lymphocyte ratio and derived neutrophil to lymphocyte ratio, were analysed in a training cohort of 50 patients. Receiver operating characteristic curve analysis was used to establish optimal cut-off. Univariate and multivariate analyses of prognostic factors for overall survival (OS) were performed. Independent predictors of OS identified in multivariate analysis were confirmed in a validation cohort of 95 patients.ResultsIn the univariate analysis, low PNI (PNI<45) (p=0.001), large primary tumour (T4) (p=0.044) and advanced lymph node disease (N2b-N3) (p=0.025) were significantly associated with poorer OS in the validation cohort. The independent prognostic factors in the multivariate analysis for OS identified in the training cohort were dRNL (p=0.030) and PNI (p=0.042). In the validation cohort, only the PNI remained as independent prognostic factor (p=0.007).ConclusionsPNI is a readily available, independent prognostic biomarker for OS in LAHNSCC. Adding PNI to tumour staging could improve individual risk stratification of patients with LAHNSCC in future clinical trials.

scholarly journals Expression of EEF1A1 Is Associated with Prognosis of Patients with Colon Adenocarcinoma

2019 ◽  
Vol 8 (11) ◽  
pp. 1903 ◽  
Author(s):  
Eun kyo Joung ◽  
Jiyoung Kim ◽  
Nara Yoon ◽  
Lee-so Maeng ◽  
Ji Hoon Kim ◽  
...  
Keyword(s):  
Colon Cancer ◽  
Multivariate Analysis ◽  
Prognostic Factor ◽  
Cox Regression ◽  
Disease Free Survival ◽  
Colon Adenocarcinoma ◽  
Independent Prognostic Factor ◽  
Free Survival ◽  
Cox Regression Analysis ◽  
Disease Free

Background: The prognostic role of the translational factor, elongation factor-1 alpha 1 (EEF1A1), in colon cancer is unclear. Objectives: The present study aimed to investigate the expression of EEF1A in tissues obtained from patients with stage II and III colon cancer and analyze its association with patient prognosis. Methods: A total of 281 patients with colon cancer who underwent curative resection were analyzed according to EEF1A1 expression. Results: The five-year overall survival in the high-EEF1A1 group was 87.7%, whereas it was 65.6% in the low-EEF1A1 expression group (hazard ratio (HR) 2.47, 95% confidence interval (CI) 1.38–4.44, p = 0.002). The five-year disease-free survival of patients with high EEF1A1 expression was 82.5%, which was longer than the rate of 55.4% observed for patients with low EEF1A1 expression (HR 2.94, 95% CI 1.72–5.04, p < 0.001). Univariate Cox regression analysis indicated that age, preoperative carcinoembryonic antigen level, adjuvant treatment, total number of metastatic lymph nodes, and EEF1A1 expression level were significant prognostic factors for death. In multivariate analysis, expression of EEF1A1 was an independent prognostic factor associated with death (HR 3.01, 95% CI 1.636–5.543, p < 0.001). EEF1A1 expression was also an independent prognostic factor for disease-free survival in multivariate analysis (HR 2.54, 95% CI 1.459–4.434, p < 0.001). Conclusions: Our study demonstrated that high expression of EEF1A1 has a favorable prognostic effect on patients with colon adenocarcinoma.

A prognostic nomogram for prediction of recurrence following surgical resection of desmoid tumors.

2012 ◽  
Vol 30 (15_suppl) ◽  
pp. 10015-10015
Author(s):  
Aimee Marie Crago ◽  
Brian Denton ◽  
James J. Mezhir ◽  
Meera Hameed ◽  
Mithat Gonen ◽  
...  
Keyword(s):  
Local Recurrence ◽  
Abdominal Wall ◽  
Cox Regression ◽  
Young Patients ◽  
Desmoid Tumors ◽  
Free Survival ◽  
Cox Regression Analysis ◽  
Prospective Database ◽  
Systemic Therapies

10015 Background: Desmoid tumors can respond to novel chemotherapeutics (e.g., sorafenib). We sought to construct a postoperative nomogram identifying desmoid patients who are at high-risk for local recurrence and potential candidates for systemic therapy. Methods: Desmoid patients undergoing resection from 1982-2011 were identified from a single-institution prospective database. Cox regression analysis was used to create a desmoid-specific recurrence nomogram integrating clinical risk factors. Results: Desmoids were treated surgically in 495 patients (median follow-up 60 months). Of 439 patients undergoing complete gross resection, 100 recurred (92 within 5 years of operation). Five-year recurrence-free survival (RFS) was 71%. Only 8 patients died of disease, all after R2 resection (6 with intraabdominal desmoids). Radiation was associated with worse RFS (p<0.001). Multivariate analysis suggested associations between recurrence and extremity location, young age, and large tumors, but not margin (Table). Abdominal wall tumors had the best outcome (5-year RFS 92% vs. 34% in patients <25y.o. with large, extremity tumors). Age, site and size were used to construct an internally-validated nomogram (concordance index 0.703). Integration of margin, gender, depth, and presentation status (primary vs. recurrent disease) did not improve concordance significantly (0.707). Conclusions: A postoperative nomogram including only size, site and age predicts local recurrence and aids in counseling patients. Systemic therapies may be tested in young patients with large, extremity desmoids, but surgery alone is curative for most abdominal wall lesions. [Table: see text]

scholarly journals Neutrophil to Lymphocyte Ratio as Prognostic Indicator for Patients with Esophageal Squamous Cell Cancer

2017 ◽  
Vol 32 (4) ◽  
pp. 409-414 ◽  
Author(s):  
Guo-Dong Gao ◽  
Bo Sun ◽  
Xian-Bin Wang ◽  
Shi-Meng Wang
Keyword(s):  
Overall Survival ◽  
Squamous Cell ◽  
Cox Regression ◽  
Cell Cancer ◽  
Prognostic Indicator ◽  
Neutrophil To Lymphocyte Ratio ◽  
Cox Regression Analysis ◽  
Lymphocyte Ratio ◽  
Leukocyte Counts ◽  
Clinical Records

Background This study aimed to evaluate the correlation between neutrophil to lymphocyte ratio (NLR) with overall survival (OS) of esophageal squamous cell carcinoma (ESCC) patients. Method Records of patients with diagnosed ESCC were reviewed. Leukocyte counts and patients' characteristics were extracted from their clinical records to calculate NLR. Correlation between NLR and baseline characteristics with overall survival (OS) was then analyzed using Cox regression. The patients were then separated into higher and lower NLR groups according to median NLR. OS was further compared between the 2 groups. Results A total of 1281 patients were included in the study. Cox regression analysis showed a significant correlation of NLR with OS of ESCC patients. The median pretreatment NLR was identified as 2.86. Higher NLR was associated with worse prognosis in terms of OS. Conclusions Pretreatment NLR is independently associated with OS of ESCC patients. Therefore, NLR may be used as a predictive indicator for pretreatment evaluation and adjustment of treatment regimen.

scholarly journals A Radiomics Nomogram for Non-Invasive Prediction of Progression-Free Survival in Esophageal Squamous Cell Carcinoma

2021 ◽  
Author(s):  
Ting Yan ◽  
lili liu ◽  
Meilan Peng ◽  
Zhenpeng Yan ◽  
Qingyu Wang ◽  
...  
Keyword(s):  
Squamous Cell Carcinoma ◽  
Esophageal Squamous Cell Carcinoma ◽  
Cell Carcinoma ◽  
Squamous Cell ◽  
Validation Cohort ◽  
Progression Free Survival ◽  
Tnm Staging ◽  
Free Survival ◽  
Training Cohort ◽  
Radiomics Signature

Abstract Objectives: To construct a prognostic model for preoperative prediction based on computed tomography (CT) images of esophageal squamous cell carcinoma (ESCC). Methods: Radiomics signature was constructed using the least absolute shrinkage and selection operator (LASSO) with high throughput radiomics features that extracted from the CT images of 272 patients (204 in training and 68 in validation cohort), who were pathologically confirmed ESCC. Multivariable logistic regression was adopted to build the radiomics signature and another predictive nomogram model, which was composed with radiomics signature, traditional TNM stage and clinical features. Then its performance was assessed by the calibration and decision curve analysis (DCA). Results: 16 radiomics features were selected from 954 to build a radiomics signature，which were significantly associated with progression-free survival (PFS) (p<0.001). The area under the curve (AUC) of performance was 0.891 (95% CI: 0.845-0.936) for training cohort and 0.706 (95% CI: 0.583-0.829) for validation cohort. The radscore of signatures’ combination showed significant discrimination for survival status in both two cohort. Kaplan-Meier survival curve further confirmed the radscore has a better prognostic performance in training cohort. Radiomics nomogram combined radscore with TNM staging showed significant improvement over TNM staging alone in training cohort (C-index, 0.802 vs 0.628; p<0.05), and it is the same with clinical data (C-index, 0.798 vs 0.660; p<0.05). Findings were confirmed in the validation cohort. DCA showed CT-based radiomics model will receive benefit when the threshold probability was between 0 and 0.9. Heat maps revealed associations between radiomics features and tumor stages.Conclusions: Multiparametric CT-based radiomics nomograms provided improved prognostic ability in ESCC.

scholarly journals Prognostic Nomogram for Postoperative Patients With Gastroesophageal Junction Cancer of No Distant Metastasis

2021 ◽  
Vol 11 ◽  
Author(s):  
Qiang Guo ◽  
YuanYuan Peng ◽  
Heng Yang ◽  
JiaLong Guo
Keyword(s):  
Regression Analysis ◽  
Surgical Resection ◽  
Distant Metastasis ◽  
Calibration Curve ◽  
Cox Regression ◽  
Validation Cohort ◽  
Predictive Accuracy ◽  
Gastroesophageal Junction ◽  
Training Cohort ◽  
Cox Regression Analysis

BackgroundGastroesophageal junction (GEJ) was one of the most common malignant tumors. However, the value of clinicopathological features in predicting the prognosis of postoperative patients with GEJ cancer and without distant metastasis was still unclear.MethodsThe 3425 GEJ patients diagnosed and underwent surgical resection without distant metastasis in the Surveillance, Epidemiology and End Results (SEER) database from 2010 to 2015 were enrolled,and they were randomly divided into training and validation cohorts with 7:3 ratio. Univariate and multivariate Cox regression analysis were used to determine the predictive factors that constituted the nomogram. The predictive accuracy and discriminability of Nomogram were determined by the area under the curve (AUC), C index, and calibration curve, and the influence of various factors on prognosis was explored.Results2,400 patients were designed as training cohort and 1025 patients were designed as validation cohort. The percentages of the distribution of demographic and clinicopathological characteristics in the training and validation cohorts tended to be the same. In the training cohort, multivariate Cox regression analysis revealed that the age, tumor grade, T stage and N stage were independent prognostic risk factors for patients with GEJ cancer without distant metastasis. The C index of nomogram model was 0.667. The AUC of the receiver operating characteristic (ROC) analysis for 3- and 5-year overall survival (OS) were 0.704 and 0.71, respectively. The calibration curve of 3- and 5-year OS after operation showed that there was the best consistency between nomogram prediction and actual observation. In the validation cohort, the C index of nomogram model, the AUC of 3- and 5-year OS, and the calibration curve were similar to the training cohort.ConclusionsNomogram could evaluate the prognosis of patients with GEJ cancer who underwent surgical resection without distant metastasis.

scholarly journals Prognostic factors affecting survival and recurrence in patients with early cervical squamous cell cancer following radical hysterectomy

2019 ◽  
Vol 48 (4) ◽  
pp. 030006051988974
Author(s):  
Dan Li ◽  
Xiaoxian Xu ◽  
Dingding Yan ◽  
Shuhui Yuan ◽  
Juan Ni ◽  
...  
Keyword(s):  
Risk Factors ◽  
Lymph Node ◽  
Squamous Cell ◽  
Radical Hysterectomy ◽  
Tumor Size ◽  
Cox Regression ◽  
Squamous Cell Cancer ◽  
Postoperative Radiotherapy ◽  
Cell Cancer ◽  
Cox Regression Analysis

Objective This study aimed to investigate the clinical and histological features affecting the survival of patients with early cervical squamous cell cancer treated with radical hysterectomy. Methods We retrospectively analyzed clinical and histological data for patients with stage IB-IIA cervical cancer treated by radical hysterectomy at Zhejiang Cancer Hospital from August 2008 to January 2013. Results A total of 1435 patients were included in the study. Cox regression analysis identified tumor size >4 cm, lymphovascular space involvement (LVSI), lymph node ratio (LNR), and squamous cell carcinoma antigen (SCC-Ag) >2.65 ng/mL as independent prognostic risk factors. Among 1096 patients without high pathological risk factors, the 5-year local recurrence rates for SCC-Ag ≤2.65 and >2.65 ng/mL were 6.6% and 25.7%, respectively. Among 332 patients with lymph node positivity, the overall survival rates for LNR ≤0.19 and >0.19 were 87.8% and 55.6%, respectively. Conclusions LVSI, tumor size >4 cm, LNR >0.19, and SCC-Ag >2.65 ng/mL may predict a poor prognosis in patients with early cervical squamous cell cancer treated with radical hysterectomy. SCC-Ag >2.65 ng/mL may be a useful prognostic factor guiding the use of postoperative radiotherapy in patients without pathologic risk factors.

scholarly journals Long Intergenic Non Coding RNA and MicroRNA Profiles of Pheochromocytoma and Paraganglioma as Prognostic Biomarkers

2021 ◽  
Vol 5 (Supplement_1) ◽  
pp. A1024-A1024
Author(s):  
Suman Ghosal
Keyword(s):  
Multivariate Analysis ◽  
Risk Score ◽  
Cox Regression ◽  
Expression Profiles ◽  
Differential Expression Analysis ◽  
Hormone Secretion ◽  
Tumor Location ◽  
The Cancer Genome Atlas ◽  
Free Survival ◽  
Cox Regression Analysis

Abstract microRNAs (miRNAs) and long intergenic noncoding RNAs (lincRNAs) have been reported as important markers for many cancers. In search of new markers for the metastatic or aggressive phenotypes in the neuroendocrine tumor pheochromocytomas and paragangliomas (PCPG), we analyzed the non-coding transcriptome from patient gene expression data in The Cancer Genome Atlas. We used differential expression analysis and an elastic-net machine-learning model to identify miRNA and lincRNA transcriptomic signature specific to PCPG molecular subtypes. Similarly, miRNAs and lincRNAs specific to aggressive PCPGs were identified, and univariate and multivariate analysis were performed for identifying factors associated with metastasis-free survival. Upregulation of 13 lincRNAs and 4 miRNAs was found to be associated with aggressive/metastatic PCPGs. RT-PCR validation in tumor samples from PCPG patients confirmed the overexpression of 4 miRNAs and 4 lincRNAs in metastatic compared to non-metastatic PCPGs. Kaplan-Meier analysis identified 3 miRNAs and 5 lincRNAs as prognostic markers for metastasis-free survival of patients in PCPGs. In a multivariate Cox regression analysis combining these miRNA and lincRNA expression signatures with the previously identified clinically relevant parameters like SDHB germline mutation, ATRX somatic mutation, tumor location and hormone secretion phenotypes, we identified the miRNA miR-182 and lincRNA HIF1A-AS2 as independent predictors of poor metastasis-free survival. We formulated a risk-score model using multivariate analysis of lincRNA and miRNA expression profiles, presence of SDHB and ATRX mutations, tumor location, and hormone secretion phenotypes. Stratification of PCPG patients with this risk-score showed significant differences in metastasis-free survival.

scholarly journals The value of complete blood count for the prognosis analysis of preoperative esophageal squamous cell carcinoma

BMC Cancer ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Xiang Lv ◽  
Songtao Han ◽  
Bin Xu ◽  
Yuqin Deng ◽  
Yangchun Feng
Keyword(s):  
Squamous Cell Carcinoma ◽  
Esophageal Squamous Cell Carcinoma ◽  
Cell Carcinoma ◽  
Squamous Cell ◽  
Prognostic Model ◽  
Blood Count ◽  
Validation Cohort ◽  
Complete Blood Count ◽  
Training Cohort ◽  
Cox Regression Analysis

Abstract Objective To investigate the predictive value of preoperative complete blood count for the survival of patients with esophageal squamous cell carcinoma. Methods A total of 1587 patients with pathologically confirmed esophageal squamous cell carcinoma who underwent esophagectomy in the Cancer Hospital Affiliated to Xinjiang Medical University from January 2010 to December 2019 were collected by retrospective study. A total of 359 patients were as the validation cohort from January 2015 to December 2016, and the remaining 1228 patients were as the training cohort. The relevant clinical data were collected by the medical record system, and the patients were followed up by the hospital medical record follow-up system. The follow-up outcome was patient death. The survival time of all patients was obtained. The Cox proportional hazards regression model and nomogram were established to predict the survival prognosis of esophageal squamous cell carcinoma by the index, their cut-off values obtained the training cohort by the ROC curve. The Kaplan-Meier survival curve was established to express the overall survival rate. The 3-year and 5-year calibration curves and C-index were used to determine the accuracy and discrimination of the prognostic model. The decision curve analysis was used to predict the potential of clinical application. Finally, the validation cohort was used to verify the results of the training cohort. Results The cut-off values of NLR, NMR, LMR, RDW and PDW in complete blood count of the training cohort were 3.29, 12.77, 2.95, 15.05 and 13.65%, respectively. All indicators were divided into high and low groups according to cut-off values. Univariate Cox regression analysis model showed that age (≥ 60), NLR (≥3.29), LMR (< 2.95), RDW (≥15.05%) and PDW (≥13.65%) were risk factors for the prognosis of esophageal squamous cell carcinoma; multivariate Cox regression analysis model showed that age (≥ 60), NLR (≥3.29) and LMR (< 2.95) were independent risk factors for esophageal squamous cell carcinoma. Kaplan-Meier curve indicated that age <  60, NLR < 3.52 and LMR ≥ 2.95 groups had higher overall survival (p <  0.05). The 3-year calibration curve indicated that its predictive probability overestimate the actual probability. 5-year calibration curve indicated that its predictive probability was consistent with the actual probability. 5 c-index was 0.730 and 0.737, respectively, indicating that the prognostic model had high accuracy and discrimination. The decision curve analysis indicated good potential for clinical application. The validation cohort also proved the validity of the prognostic model. Conclusion NLR and LMR results in complete blood count results can be used to predict the survival prognosis of patients with preoperative esophageal squamous cell carcinoma.

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