Construction and Validation of a Nomogram for Predicting Progression-Free Survival in Patients with Early-Stage Testicular Germ Cell Tumor

2021 ◽  
Vol 16 ◽  
Author(s):  
Jin-Guo Chen ◽  
Jing-Quan Wang ◽  
Tian-Wen Peng ◽  
Zhe-Sheng Chen ◽  
Shan-Chao Zhao
Keyword(s):  
Prognostic Factors ◽  
Validation Cohort ◽  
Early Stage ◽  
Predictive Ability ◽  
Testicular Germ Cell Tumor ◽  
Progression Free Survival ◽  
Cell Tumor ◽  
Testicular Germ Cell ◽  
Free Survival ◽  
Training Cohort

Background: Testicular Germ Cell Tumor (TGCT) is the most common malignant tumor in young men, but there is a lack of prediction model to evaluate prognosis of patients with TGCT. Objective: To explore the prognostic factors for Progression-Free Survival (PFS) and construct a nomogram model for patients with early-stage TGCT after radical orchiectomy. Methods: Patients with TGCT from The Cancer Genome Atlas (TCGA) database were used as the training cohort; univariate and multivariate cox analysis were performed. A nomogram was constructed based on the independent prognostic factors. Patients from the Nanfang Hospital affiliated with Southern Medical University were used as the cohort to validate the predictive ability using the nomogram model. Harrell's concordance index (C-index) and calibration plots were used to evaluate the nomogram. Results: A total of 110 and 62 patients with TGCT were included in training cohort and validation cohort, respectively. Lymphatic Vascular Invasion (LVI), American Joint Committee on Cancer (AJCC) stage and adjuvant therapy were independent prognostic factors in multivariate regression analyses and were included to establish a nomogram. The C-index in the training cohort for 1-, 3-, and 5-year PFS were 0.768, 0.74 and 0.689, respectively. While the C-index for 1-, 3-, and 5-year PFS in the external validation cohort were 0.853, 0.663 and 0.609, respectively. The calibration plots for 1-, 3-, and 5-year PFS in the training and validation cohort showed satisfactory consistency between predicted and actual outcomes. The nomogram revealed a better predictive ability for PFS than AJCC staging system. Conclusion: The nomogram as a simple and visual tool to predict individual PFS in patients with TGCT could guide clinicians and clinical pharmacists in therapeutic strategy.

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 MRI Radiomic Features: A Potential Biomarker for Progression-Free Survival Prediction of Patients With Locally Advanced Cervical Cancer Undergoing Surgery

2021 ◽  
Vol 11 ◽  
Author(s):  
Mengting Cai ◽  
Fei Yao ◽  
Jie Ding ◽  
Ruru Zheng ◽  
Xiaowan Huang ◽  
...  
Keyword(s):  
Cervical Cancer ◽  
Validation Cohort ◽  
Locally Advanced ◽  
Progression Free Survival ◽  
Risk Groups ◽  
Advanced Cervical Cancer ◽  
Free Survival ◽  
Training Cohort ◽  
Clinical Model ◽  
Locally Advanced Cervical Cancer

ObjectivesTo investigate the prognostic role of radiomic features based on pretreatment MRI in predicting progression-free survival (PFS) of locally advanced cervical cancer (LACC).MethodsAll 181 women with histologically confirmed LACC were randomly divided into the training cohort (n = 126) and the validation cohort (n = 55). For each patient, we extracted radiomic features from whole tumors on sagittal T2WI and axial DWI. The least absolute shrinkage and selection operator (LASSO) algorithm combined with the Cox survival analysis was applied to select features and construct a radiomic score (Rad-score) model. The cutoff value of the Rad-score was used to divide the patients into high- and low-risk groups by the X-tile. Kaplan–Meier analysis and log-rank test were used to assess the prognostic value of the Rad-score. In addition, we totally developed three models, the clinical model, the Rad-score, and the combined nomogram.ResultsThe Rad-score demonstrated good performance in stratifying patients into high- and low-risk groups of progression in the training (HR = 3.279, 95% CI: 2.865–3.693, p &lt; 0.0001) and validation cohorts (HR = 2.247, 95% CI: 1.735–2.759, p &lt; 0.0001). Otherwise, the combined nomogram, integrating the Rad-score and patient’s age, hemoglobin, white blood cell, and lymph vascular space invasion, demonstrated prominent discrimination, yielding an AUC of 0.879 (95% CI, 0.811–0.947) in the training cohort and 0.820 (95% CI, 0.668–0.971) in the validation cohort. The Delong test verified that the combined nomogram showed better performance in estimating PFS than the clinical model and Rad-score in the training cohort (p = 0.038, p = 0.043).ConclusionThe radiomics nomogram performed well in individualized PFS estimation for the patients with LACC, which might guide individual treatment decisions.

scholarly journals The Prognostic Value of the Albumin to Gamma-Glutamyltransferase Ratio in Patients with Hepatocellular Carcinoma Undergoing Radiofrequency Ablation

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Wenfeng Liu ◽  
Feng Zhang ◽  
Bing Quan ◽  
Miao Li ◽  
Shenxin Lu ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Radiofrequency Ablation ◽  
Validation Cohort ◽  
Early Stage ◽  
Roc Curves ◽  
Free Survival ◽  
Training Cohort ◽  
Gamma Glutamyltransferase ◽  
Lemeshow Test ◽  
Early Hcc

Albumin to gamma-glutamyltransferase ratio (AGR) is a newly developed biomarker for the prediction of patients’ prognosis in solid tumors. The purpose of the study was to establish a novel AGR-based nomogram to predict tumor prognosis in patients with early-stage HCC undergoing radiofrequency ablation (RFA). 394 hepatocellular carcinoma (HCC) patients who had received RFA as initial treatment were classified into the training cohort and validation cohort. Independent prognostic factors were identified by univariate and multivariate analyses. The value of AGR was evaluated by the concordance index ( C -index), receiver operating characteristic (ROC) curves, and likelihood ratio tests (LAT). Logistic regression and nomogram were performed to establish the pretreatment scoring model based on the clinical variables. As a result, AGR = 0.63 was identified as the best cutoff value to predict overall survival (OS) in the training cohort. According to the results of multivariate analysis, AGR was an independent indicator for OS and recurrence-free survival (RFS). In both training cohort and validation cohort, the high-AGR group showed better RFS and OS than the low-AGR group. What is more, the C -index, area under the ROC curves, and LAT χ 2 values suggested that AGR outperformed the Child-Pugh (CP) grade and albumin-bilirubin (ALBI) grade in terms of predicting OS. The AGR, AKP, and tumor size were used to establish the OS nomogram. Besides, the results of Hosmer-Lemeshow test and calibration curve analysis displayed that both nomograms in the training and validation cohorts performed well in terms of calibration. Therefore, the AGR-based nomogram can predict the postoperative prognosis of early HCC patients undergoing RFA.

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 To construct a prognostic model for preoperative prediction on computed tomography (CT) images of esophageal squamous cell carcinoma (ESCC), we constructed radiomics signature with high throughput radiomics features extracted from CT images of 272 patients (204 in training and 68 in validation cohort). 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. 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. 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), which were confirmed in validation cohort. DCA showed the model will receive benefit when the threshold probability was between 0 and 0.9. Collectively, multiparametric CT-based radiomics nomograms provided improved prognostic ability in ESCC.

scholarly journals A Radiomics Signature-Based Nomogram to Predict the Progression-Free Survival of Patients With Hepatocellular Carcinoma After Transcatheter Arterial Chemoembolization Plus Radiofrequency Ablation

2021 ◽  
Vol 8 ◽  
Author(s):  
Shiji Fang ◽  
Linqiang Lai ◽  
Jinyu Zhu ◽  
Liyun Zheng ◽  
Yuanyuan Xu ◽  
...  
Keyword(s):  
Validation Cohort ◽  
Progression Free Survival ◽  
Clinical Factors ◽  
Combined Model ◽  
Free Survival ◽  
Training Cohort ◽  
Clinical Model ◽  
Cox Analysis ◽  
Radiomics Signature ◽  
Arterial Chemoembolization

Objective: The study aims to establish an magnetic resonance imaging radiomics signature-based nomogram for predicting the progression-free survival of intermediate and advanced hepatocellular carcinoma (HCC) patients treated with transcatheter arterial chemoembolization (TACE) plus radiofrequency ablationMaterials and Methods: A total of 113 intermediate and advanced HCC patients treated with TACE and RFA were eligible for this study. Patients were classified into a training cohort (n = 78 cases) and a validation cohort (n = 35 cases). Radiomics features were extracted from contrast-enhanced T1W images by analysis kit software. Dimension reduction was conducted to select optimal features using the least absolute shrinkage and selection operator (LASSO). A rad-score was calculated and used to classify the patients into high-risk and low-risk groups and further integrated into multivariate Cox analysis. Two prediction models based on radiomics signature combined with or without clinical factors and a clinical model based on clinical factors were developed. A nomogram comcined radiomics signature and clinical factors were established and the concordance index (C-index) was used for measuring discrimination ability of the model, calibration curve was used for measuring calibration ability, and decision curve and clinical impact curve are used for measuring clinical utility.Results: Eight radiomics features were selected by LASSO, and the cut-off of the Rad-score was 1.62. The C-index of the radiomics signature for PFS was 0.646 (95%: 0.582–0.71) in the training cohort and 0.669 (95% CI:0.572–0.766) in validation cohort. The median PFS of the low-risk group [30.4 (95% CI: 19.41–41.38)] months was higher than that of the high-risk group [8.1 (95% CI: 4.41–11.79)] months in the training cohort (log rank test, z = 16.58, p &lt; 0.001) and was verified in the validation cohort. Multivariate Cox analysis showed that BCLC stage [hazard ratio (HR): 2.52, 95% CI: 1.42–4.47, p = 0.002], AFP level (HR: 2.01, 95% CI: 1.01–3.99 p = 0.046), time interval (HR: 0.48, 95% CI: 0.26–0.87, p = 0.016) and radiomics signature (HR 2.98, 95% CI: 1.60–5.51, p = 0.001) were independent prognostic factors of PFS in the training cohort. The C-index of the combined model in the training cohort was higher than that of clinical model for PFS prediction [0.722 (95% CI: 0.657–0.786) vs. 0.669 (95% CI: 0.657–0.786), p＜0.001]. Similarly, The C-index of the combined model in the validation cohort, was higher than that of clinical model [0.821 (95% CI: 0.726–0.915) vs. 0.76 (95% CI: 0.667–0.851), p = 0.004]. The calibration curve, decision curve and clinical impact curve showed that the nomogram can be used to accurately predict the PFS of patients.Conclusion: The radiomics signature was a prognostic risk factor, and a nomogram combined radiomics and clinical factors acts as a new strategy for predicted the PFS of intermediate and advanced HCC treated with TACE plus RFA.

scholarly journals Impact of Testicular Germ Cell Tumor Laterality on Recurrence-Free Survival in the Mexican Population

2021 ◽  
Vol 12 (5) ◽  
pp. 173-177
Author(s):  
David Davila Dupont ◽  
Daniel Motola Kuba ◽  
Thalia de los Milagros Alcantara Velarde ◽  
Erika Adriana Martinez Castaneda ◽  
Rita Dorantes Heredia ◽  
...  
Keyword(s):  
Germ Cell ◽  
Germ Cell Tumor ◽  
Testicular Germ Cell Tumor ◽  
Cell Tumor ◽  
Mexican Population ◽  
Recurrence Free Survival ◽  
Testicular Germ Cell ◽  
Free Survival ◽  
Tumor Laterality

Six-gene model based on expression predicts distinct disease-free survival in early-stage microsatellite stability (MSS) endometrial, colorectal and stomach tumors.

2021 ◽  
Vol 39 (15_suppl) ◽  
pp. e14578-e14578
Author(s):  
Tufeng Chen ◽  
Jianpei Liu ◽  
Xinyi Liu ◽  
Mengli Huang
Keyword(s):  
Immune Cells ◽  
Validation Cohort ◽  
Early Stage ◽  
Disease Free Survival ◽  
Low Risk ◽  
Rna Seq ◽  
Free Survival ◽  
Training Cohort ◽  
Microsatellite Stability ◽  
Disease Free

e14578 Background: Microsatellite stability (MSS) tumors hardly benefit from immunotherapies and are more probable to occur postoperative recurrence. However, some studies have revealed that a subset of MSS patients harbor “hot immune microenvironment” tumors, indicating high heterogeneity in such wide range of patient population. On the other hand, researches of mechanism of MSI formation found potential similarities in endometrial and gastrointestinal tumors. We hypothesized the transcriptomic features in these cancers correlated with immune-related signatures and patients’ prognosis. Methods: Early stage (I-III stage) MSS tumors, including endometrial, colorectal, and gastric from TCGA project were analyzed as training cohort (n=170). A combined cohort consisting of 604 colorectal and stomach cancers from GEO datasets (GSE39582，GSE62254) was validation cohort. The RNA-Seq profiling data and disease-free survival (DFS) data of patients were collected. Cibersort tool was used to evaluate twenty-two immune cells’ enrichment. The prediction model was developed by three steps: Univariate cox regression of DFS was conducted to select 9 immune cells. Then the train cohort was divided into two groups based on non-negative matrix factorization (NMF) method using this 9 immune cell features. Differentially expressed genes of these two groups were identified and screened further by lasso regression. Log-rank test was used to evaluate the difference of DFS. Results: A six-gene lasso-cox model was developed. The genes were LYZ, WFDC2, CAPS, RHPN1, TFF2 and TGFBR2. Based on the score evaluated by this model, patients in training cohort were divided into high-risk and low-risk groups. Low-risk population had much longer DFS (HR 0.07, 95%CI 0.03-0.18, p<0.001). In validation cohort, lower risk score was also verified to be associated with a lower likelihood of recurrence (HR 0.66, 95%CI 0.5-0.88, p=0.0047). Conclusions: We developed a model of six-genes predicting disease-free survival based on RNA-Seq data in early stage MSS patients. Further validation was needed to implement in larger clinical cohorts.

scholarly journals Efficacy of lenvatinib for unresectable hepatocellular carcinoma based on background liver disease etiology: multi-center retrospective study

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Atsushi Hiraoka ◽  
Takashi Kumada ◽  
Toshifumi Tada ◽  
Joji Tani ◽  
Kazuya Kariyama ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Prognostic Factors ◽  
Liver Disease ◽  
Hazard Analysis ◽  
Progression Free Survival ◽  
Significant Prognostic Factor ◽  
Disease Etiology ◽  
Alcoholic Fatty Liver ◽  
Free Survival ◽  
Significant Difference

AbstractIt was recently reported that hepatocellular carcinoma (HCC) patients with non-alcoholic steatohepatitis (NASH) are not responsive to immune-checkpoint inhibitor (ICI) treatment. The present study aimed to evaluate the therapeutic efficacy of lenvatinib in patients with non-alcoholic fatty liver disease (NAFLD)/NASH-related unresectable-HCC (u-HCC). Five hundred thirty u-HCC patients with Child–Pugh A were enrolled, and divided into the NAFLD/NASH (n = 103) and Viral/Alcohol (n = 427) groups. Clinical features were compared in a retrospective manner. Progression-free survival (PFS) was better in the NAFLD/NASH than the Viral/Alcohol group (median 9.3 vs. 7.5 months, P = 0.012), while there was no significant difference in overall survival (OS) (20.5 vs. 16.9 months, P = 0.057). In Cox-hazard analysis of prognostic factors for PFS, elevated ALT (≥ 30 U/L) (HR 1.247, P = 0.029), modified ALBI grade 2b (HR 1.236, P = 0.047), elevated AFP (≥ 400 ng/mL) (HR 1.294, P = 0.014), and NAFLD/NASH etiology (HR 0.763, P = 0.036) were significant prognostic factors. NAFLD/NASH etiology was not a significant prognostic factor in Cox-hazard analysis for OS (HR0.758, P = 0.092), whereas AFP (≥ 400 ng/mL) (HR 1.402, P = 0.009), BCLC C stage (HR 1.297, P = 0.035), later line use (HR 0.737, P = 0.014), and modified ALBI grade 2b (HR 1.875, P < 0.001) were significant. Lenvatinib can improve the prognosis of patients affected by u-HCC irrespective of HCC etiology or its line of treatment.

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