scholarly journals Nomogram for Predicting Postoperative Cancer-specific Early Death in Patients with Epithelial Ovarian Cancer based on the SEER Database: A Large Cohort Study

2021 ◽  
Author(s):  
Tingting Zhang ◽  
Liancheng Zhu
Keyword(s):  
Ovarian Cancer ◽  
Cancer Patients ◽  
Early Death ◽  
Roc Curves ◽  
Lesion Size ◽  
Seer Database ◽  
Training Set ◽  
Internal Validation ◽  
Residual Lesion ◽  
Validation Set

Abstract Background: Ovarian cancer is a common gynecological malignant tumor. Poor prognosis is strongly associated with early death, but there is no effective tool to predict this. This study aimed to construct a nomogram for predicting cancer-specific early death in ovarian cancer patients.Methods: Our study used data from the Surveillance, Epidemiology, and End Results (SEER) database of ovarian cancer patients registered from 1988 to 2016. Important independent prognostic factors were determined by univariate and multivariate logistic regression and LASSO Cox regression. Several risk factors were considered in constructing the nomogram. Nomogram discrimination and calibration were evaluated using C-index, internal validation, and receiver operating characteristic (ROC) curves.Results: A total of 4769 patients were included. Patients were assigned to the training set (n = 3340; 70%) and validation set (n = 1429; 30%). Based on the training set, eight variables were shown to be significant factors for early death and were incorporated in the nomogram: AJCC (American Joint Committee on Cancer) stage, residual lesion size, chemotherapy, serum CA125 level, tumor size, number of lymph nodes examined, surgery of primary site, and age. The C-indices and ROC curves showed that the nomogram had better predictive ability than the AJCC staging system and good clinical practicability. Internal validation based on validation set showed good consistency between predicted and observed values for early death. Conclusions: Compared with predictions made using AJCC stage or residual lesion size, the nomogram was able to provide more robust predictions for early death in ovarian cancer patients.

scholarly journals Nomogram for predicting postoperative cancer-specific early death in patients with epithelial ovarian cancer based on the SEER database: a large cohort study

2021 ◽  
Author(s):  
Tingting Zhang ◽  
Liancheng Zhu
Keyword(s):  
Ovarian Cancer ◽  
Cox Regression ◽  
Early Death ◽  
Roc Curves ◽  
Lesion Size ◽  
Training Set ◽  
Internal Validation ◽  
Residual Lesion ◽  
Ajcc Stage ◽  
Validation Set

Abstract Purpose Ovarian cancer is a common gynecological malignant tumor. Poor prognosis is strongly associated with early death, but there is no effective tool to predict this. This study aimed to construct a nomogram for predicting cancer-specific early death in patients with ovarian cancer. Methods We used data from the Surveillance, Epidemiology, and End Results database of patients with ovarian cancer registered from 1988 to 2016. Important independent prognostic factors were determined by univariate and multivariate logistic regression and LASSO Cox regression. Several risk factors were considered in constructing the nomogram. Nomogram discrimination and calibration were evaluated using C-index, internal validation, and receiver operating characteristic (ROC) curves. Results A total of 4769 patients were included. Patients were assigned to the training set (n = 3340; 70%) and validation set (n = 1429; 30%). Based on the training set, eight variables were shown to be significant factors for early death and were incorporated in the nomogram: American Joint Committee on Cancer (AJCC) stage, residual lesion size, chemotherapy, serum CA125 level, tumor size, number of lymph nodes examined, surgery of primary site, and age. The concordance indices and ROC curves showed that the nomogram had better predictive ability than the AJCC staging system and good clinical practicability. Internal validation based on validation set showed good consistency between predicted and observed values for early death. Conclusion Compared with predictions made based on AJCC stage or residual lesion size, the nomogram could provide more robust predictions for early death in patients with ovarian cancer.

scholarly journals Evaluation of Prognostic and Predictive Significance of Circulating MicroRNAs in Ovarian Cancer Patients

2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
Ann Rita Halvorsen ◽  
Gunnar Kristensen ◽  
Andy Embleton ◽  
Cybil Adusei ◽  
Maria Pilar Barretina-Ginesta ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Cancer Patients ◽  
Prognostic Significance ◽  
Response To Treatment ◽  
Rank Test ◽  
Specific Marker ◽  
High Grade ◽  
Standard Chemotherapy ◽  
Validation Set ◽  
Serous Tumors

Ovarian cancer patients are recognized with poor prognosis. This study aimed to identify microRNAs in plasma for predicting response to treatment and outcome. We have investigated microRNAs in plasma from ovarian cancer patients enrolled in a large multicenter study (ICON7), investigating the effect of adding bevacizumab to standard chemotherapy in patients diagnosed with epithelial ovarian cancer. Patients with different histology, grade, and FIGO stages were included (n=207) in this study. Screening of 754 unique microRNAs was performed in the discovery phase (n=91) using TaqMan Low Density Arrays. The results were validated using single assays and RT-qPCR. Low levels of miR-200b, miR-1274A (tRNALys5), and miR-141 were significantly associated with better survival, confirmed with log-rank test in the validation set. The level of miR-1274A (tRNALys5) correlated with outcome was especially pronounced in the high-grade serous tumors. Interestingly, low level of miR-200c was associated with 5-month prolongation of PFS when treated with bevacizumab compared to standard chemotherapy. We found prognostic significance of miR-200b, miR-141, and miR-1274A (tRNALys5) in all histological types, where miR-1274A (tRNALys5) may be a specific marker in high-grade serous tumors. The level of miR-200c may be predictive of effect of treatment with bevacizumab. However, this needs further validation.

scholarly journals Use of serum biomarkers in staging of canine hepatic fibrosis

2019 ◽  
Vol 31 (5) ◽  
pp. 665-673 ◽  
Author(s):  
Maud Menard ◽  
Alexis Lecoindre ◽  
Jean-Luc Cadoré ◽  
Michèle Chevallier ◽  
Aurélie Pagnon ◽  
...  
Keyword(s):  
Liver Biopsy ◽  
Hepatic Fibrosis ◽  
External Validation ◽  
Model Performance ◽  
Area Under The Curve ◽  
Gamma Glutamyl Transferase ◽  
Training Set ◽  
Internal Validation ◽  
Validation Set ◽  
Glutamyl Transferase

Accurate staging of hepatic fibrosis (HF) is important for treatment and prognosis of canine chronic hepatitis. HF scores are used in human medicine to indirectly stage and monitor HF, decreasing the need for liver biopsy. We developed a canine HF score to screen for moderate or greater HF. We included 96 dogs in our study, including 5 healthy dogs. A liver biopsy for histologic examination and a biochemistry profile were performed on all dogs. The dogs were randomly split into a training set of 58 dogs and a validation set of 38 dogs. A HF score that included alanine aminotransferase, alkaline phosphatase, total bilirubin, potassium, and gamma-glutamyl transferase was developed in the training set. Model performance was confirmed using the internal validation set, and was similar to the performance in the training set. The overall sensitivity and specificity for the study group were 80% and 70% respectively, with an area under the curve of 0.80 (0.71–0.90). This HF score could be used for indirect diagnosis of canine HF when biochemistry panels are performed on the Konelab 30i (Thermo Scientific), using reagents as in our study. External validation is required to determine if the score is sufficiently robust to utilize biochemical results measured in other laboratories with different instruments and methodologies.

Training and validation study for sequential monitoring of CAMLs in circulation to predict ongoing progression in lung cancer patients undergoing definitive radiotherapy.

2019 ◽  
Vol 37 (15_suppl) ◽  
pp. 3053-3053
Author(s):  
Daniel Adams ◽  
Jianzhong He ◽  
Yawei Qiao ◽  
Ting Xu ◽  
Hui Gao ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Radiation Therapy ◽  
Cancer Patients ◽  
Stage Iv ◽  
Progression Free Survival ◽  
Training Set ◽  
Lung Cancer Patients ◽  
Therapeutic Decisions ◽  
Validation Set ◽  
Nsclc Patients

3053 Background: Cancer Associated Macrophage-Like cells (CAMLs) are a recently described circulating stromal cell common in the peripheral blood of cancer patients that are prognostic for progressive disease. Further, it has been shown that changes in CAML size (i.e. enlargement above 50µm) can predict progression free survival (PFS) in thoracic cancers (e.g. lung). We enrolled 104 unresectable non-small cell lung cancer (NSCLC) patients, with an initial training set review of 54 patients, to determine if change in CAML size after radiation therapy was predictive PFS. Methods: A 2 year single blind prospective study was undertaken to test the relationship of ≥50µm CAMLs to PFS based on imaging in lung patients before and after induction of chemo radiation, or radiation therapy. To achieve a 2-tailed 90% power (α = 0.05) we recruited a training set of 54 patients and validation set of 50 patients all with pathologically confirmed unresectable NSCLC: Stage I (n = 14), Stage II (n = 16), Stage III (n = 61) & Stage IV (n = 13). Baseline (BL) blood samples were taken prior to start of therapy & a 2nd blood sample (T1) was taken after completion of radiotherapy (~30 days). Blood was filtered by CellSieve filtration and CAMLs quantified. Analysis by CAML size of < 49 µm or ≥50 µm was used to evaluate PFS hazard ratios (HRs) by censored univariate & multivariate analysis. Results: CAMLs were found in 95% of samples averaging 2.7 CAMLs/7.5mL sample at BL, with CAMLs ≥50 µm having reduced PFS (HR = 2.2, 95%CI1.3-3.8, p = 0.003). At T1, 18 patients had increased CAML size ≥50 µm with PFS (HR = 4.6, 95%CI2.5-8.3, p < 0.001). In total, ≥50 µm CAMLs at BL was 76% accurate at predicting progression within 24 months while ≥50 µm CAMLs at T1 was 83% accurate at predicting progression. Conclusions: In unresectable NSCLC patients, enlargement of CAMLs during treatment is an indicator active progression. We identify that a single ≥50 µm CAML after induction of radiotherapy, in our training set and confirmed in our validation set, is an indicator of poor prognosis. We suggest that changes in CAML size during therapy may indicate the efficacy of therapy and could potentially help shape subsequent therapeutic decisions.

scholarly journals A Novel Systematic Oxidative Stress Score Predicts the Prognosis of Patients with Operable Breast Cancer

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Kaiming Zhang ◽  
Liqin Ping ◽  
Tian Du ◽  
Yan Wang ◽  
Ya Sun ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Oxidative Stress ◽  
Cancer Patients ◽  
Operable Breast Cancer ◽  
Breast Cancer Patients ◽  
Training Set ◽  
Oxidative Stress Biomarkers ◽  
Stress Biomarkers ◽  
Validation Set ◽  
The Relationship

Background. Breast cancer was associated with imbalance between oxidation and antioxidation. Local oxidative stress in tumors is closely related to the occurrence and development of breast cancer. However, the relationship between systematic oxidative stress and breast cancer remains unclear. This study is aimed at exploring the prognostic value of systematic oxidative stress in patients with operable breast cancer. Methods. A total of 1583 operable female breast cancer patients were randomly assigned into the training set and validation set. The relationship between systematic oxidative stress biomarkers and prognosis were analyzed in the training and validation sets. Results. The systematic oxidative stress score (SOS) was established based on five systematic oxidative stress biomarkers including serum creatinine (CRE), serum albumin (ALB), total bilirubin (TBIL), lactate dehydrogenase (LDH), and blood urea nitrogen (BUN). SOS was an independent prognostic factor for operable breast cancer patients. A nomogram based on SOS and clinical characteristics could accurately predict the prognosis of operable breast cancer patients, and the area under the curve (AUC) of the nomogram was 0.823 in the training set and 0.872 in the validation set, which was much higher than the traditional prognostic indicators. Conclusions. SOS is an independent prognostic indicator for operable breast cancer patients. A prediction model based on SOS could accurately predict the outcome of operable breast cancer patients.

scholarly journals A Nomogram From The SEER Database For Predicting The Prognosis of Small Cell Lung Cancer With Different Patterns of Metastases

2021 ◽  
Author(s):  
Hongli Ruan ◽  
Yanmei Liu ◽  
Huali Sun ◽  
Yan Ding ◽  
Shenpeng Ying ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Liver Metastases ◽  
Small Cell Lung Cancer ◽  
Cell Lung Cancer ◽  
Distant Metastases ◽  
Small Cell ◽  
Seer Database ◽  
Small Cell Lung ◽  
Training Set ◽  
Validation Set

Abstract Backgroud: Distant metastases are one of the leading causes of high mortality in small cell lung cancer (SCLC). This research is aimed to investigate the different patterns of metastases in SCLC patients and their impact on prognosis based on the data from the Surveillance, Epidemiology, and End Results (SEER) database. Methods: The 2010-2015 SEER database of 15637 SCLC patients diagnosed from January to August were used as a training set for development of a nomogram. 7310 SCLC patients diagnosed from September to December were used as the validation set. Results: The overall survival (OS) of SCLC patients with no distant metastases, bone metastases, brain metastases, and liver metastases were 16.6, 9.1, 8.8 and 6.0 months, respectively. Patients with solitary liver metastases have the worst prognosis in the cases with single metastatic site. In the patients with multi-site metastases, the clinical outcomes of the cases combined with liver metastases were the worst. Our Cox model indicated that age, gender, American Joint Committee on Cancer (AJCC) stage, metastases, chemotherapy, radiation and surgery were independent predictors for OS in SCLC patients. The c-index value of nomogram was 7.52 in training set and 7.48 in of validation set for predicting OS in SCLC, indicating that the predictive ability of our nomogram model was of great superiority.Conclusion: The prognosis of SCLC patients with liver metastases alone or combined with other metastatic sites were worse than other metastatic models. Our nomogram model that integrated significant factors can aid as an individualized clinical predictive tool in SCLC patients.

scholarly journals Development and Validation of a Scoring System for Early Diagnosis of Malignant Pleural Effusion Based on a Nomogram

2021 ◽  
Vol 11 ◽  
Author(s):  
Aihua Wu ◽  
Zhigang Liang ◽  
Songbo Yuan ◽  
Shanshan Wang ◽  
Weidong Peng ◽  
...  
Keyword(s):  
Pleural Effusion ◽  
Diagnostic Accuracy ◽  
Prediction Model ◽  
Scoring System ◽  
Malignant Pleural Effusion ◽  
Diagnostic Performance ◽  
External Validation ◽  
Training Set ◽  
Internal Validation ◽  
Validation Set

BackgroundThe diagnostic value of clinical and laboratory features to differentiate between malignant pleural effusion (MPE) and benign pleural effusion (BPE) has not yet been established.ObjectivesThe present study aimed to develop and validate the diagnostic accuracy of a scoring system based on a nomogram to distinguish MPE from BPE.MethodsA total of 1,239 eligible patients with PE were recruited in this study and randomly divided into a training set and an internal validation set at a ratio of 7:3. Logistic regression analysis was performed in the training set, and a nomogram was developed using selected predictors. The diagnostic accuracy of an innovative scoring system based on the nomogram was established and validated in the training, internal validation, and external validation sets (n = 217). The discriminatory power and the calibration and clinical values of the prediction model were evaluated.ResultsSeven variables [effusion carcinoembryonic antigen (CEA), effusion adenosine deaminase (ADA), erythrocyte sedimentation rate (ESR), PE/serum CEA ratio (CEA ratio), effusion carbohydrate antigen 19-9 (CA19-9), effusion cytokeratin 19 fragment (CYFRA 21-1), and serum lactate dehydrogenase (LDH)/effusion ADA ratio (cancer ratio, CR)] were validated and used to develop a nomogram. The prediction model showed both good discrimination and calibration capabilities for all sets. A scoring system was established based on the nomogram scores to distinguish MPE from BPE. The scoring system showed favorable diagnostic performance in the training set [area under the curve (AUC) = 0.955, 95% confidence interval (CI) = 0.942–0.968], the internal validation set (AUC = 0.952, 95% CI = 0.932–0.973), and the external validation set (AUC = 0.973, 95% CI = 0.956–0.990). In addition, the scoring system achieved satisfactory discriminative abilities at separating lung cancer-associated MPE from tuberculous pleurisy effusion (TPE) in the combined training and validation sets.ConclusionsThe present study developed and validated a scoring system based on seven parameters. The scoring system exhibited a reliable diagnostic performance in distinguishing MPE from BPE and might guide clinical decision-making.

scholarly journals A Practical Nomogram to Predict Early Death in Advanced Epithelial Ovarian Cancer

2021 ◽  
Vol 11 ◽  
Author(s):  
Zixuan Song ◽  
Yangzi Zhou ◽  
Xue Bai ◽  
Dandan Zhang
Keyword(s):  
Ovarian Cancer ◽  
Epithelial Ovarian Cancer ◽  
Stage Iv ◽  
Early Death ◽  
Premature Death ◽  
Figo Stage ◽  
Stage Iii ◽  
Internal Validation ◽  
Gynecological Malignancy ◽  
Clinical Pragmatism

Background: Ovarian cancer is a common gynecological malignancy, most of which is epithelial ovarian cancer (EOC). Advanced EOC is linked with a higher incidence of premature death. To date, no effective prognostic tools are available to evaluate the possibility of early death in patients with advanced EOC.Methods: Advanced (FIGO stage III and IV) EOC patients who were enrolled in the Surveillance, Epidemiology, and End Results database between 2004 and 2015 were regarded as subjects and studied. We aimed to construct a nomogram that can deliver early death prognosis in patients with advanced EOC by identifying crucial independent factors using univariate and multivariate logistic regression analyses to help deliver accurate prognoses.Results: In total, 13,403 patients with advanced EOC were included in this study. Three hundred ninety-seven out of a total of 9,379 FIGO stage III patients died early. There were 4,024 patients with FIGO stage IV, 414 of whom died early. Nomograms based on independent prognostic factors have the satisfactory predictive capability and clinical pragmatism. The internal validation feature of the nomogram demonstrated a high level of accuracy of the predicted death.Conclusions: By analyzing data from a large cohort, a clinically convenient nomogram was established to predict premature death in advanced EOC. This tool can aid clinicians in screening patients who are at higher risk for tailoring treatment plans.

scholarly journals Construction and validation of a 6-gene nomogram discriminating lung metastasis risk of breast cancer patients

PLoS ONE ◽  
2020 ◽  
Vol 15 (12) ◽  
pp. e0244693
Author(s):  
Lingchen Wang ◽  
Wenhua Wang ◽  
Shaopeng Zeng ◽  
Huilie Zheng ◽  
Quqin Lu
Keyword(s):  
Breast Cancer ◽  
Cancer Patients ◽  
Lung Metastasis ◽  
Cancer Metastasis ◽  
External Validation ◽  
Breast Cancer Patients ◽  
Training Set ◽  
Validation Set ◽  
Robust Likelihood ◽  
Six Genes

Breast cancer is the most common malignant disease in women. Metastasis is the foremost cause of death. Breast tumor cells have a proclivity to metastasize to specific organs. The lung is one of the most common sites of breast cancer metastasis. Therefore, we aimed to build a useful and convenient prediction tool based on several genes that may affect lung metastasis-free survival (LMFS). We preliminarily identified 319 genes associated with lung metastasis in the training set GSE5327 (n = 58). Enrichment analysis of GO functions and KEGG pathways was conducted based on these genes. The best genes for modeling were selected using a robust likelihood-based survival modeling approach: GOLGB1, TMEM158, CXCL8, MCM5, HIF1AN, and TSPAN31. A prognostic nomogram for predicting lung metastasis in breast cancer was developed based on these six genes. The effectiveness of the nomogram was evaluated in the training set GSE5327 and the validation set GSE2603. Both the internal validation and the external validation manifested the effectiveness of our 6-gene prognostic nomogram in predicting the lung metastasis risk of breast cancer patients. On the other hand, in the validation set GSE2603, we found that neither the six genes in the nomogram nor the risk predicted by the nomogram were associated with bone metastasis of breast cancer, preliminarily suggesting that these genes and nomogram were specifically associated with lung metastasis of breast cancer. What’s more, five genes in the nomogram were significantly differentially expressed between breast cancer and normal breast tissues in the TIMER database. In conclusion, we constructed a new and convenient prediction model based on 6 genes that showed practical value in predicting the lung metastasis risk for clinical breast cancer patients. In addition, some of these genes could be treated as potential metastasis biomarkers for antimetastatic therapy in breast cancer. The evolution of this nomogram will provide a good reference for the prediction of tumor metastasis to other specific organs.

scholarly journals A Prognostic Model Based on Immune-Related Long Non-Coding RNAs for Patients With Cervical Cancer

2020 ◽  
Vol 11 ◽  
Author(s):  
Peijie Chen ◽  
Yuting Gao ◽  
Si Ouyang ◽  
Li Wei ◽  
Min Zhou ◽  
...  
Keyword(s):  
Cervical Cancer ◽  
Cancer Patients ◽  
Prognostic Model ◽  
Risk Group ◽  
Risk Groups ◽  
Low Risk ◽  
Training Set ◽  
Non Coding Rnas ◽  
Validation Set ◽  
Immune Related Genes

Objectives: The study is performed to analyze the relationship between immune-related long non-coding RNAs (lncRNAs) and the prognosis of cervical cancer patients. We constructed a prognostic model and explored the immune characteristics of different risk groups.Methods: We downloaded the gene expression profiles and clinical data of 227 patients from The Cancer Genome Atlas database and extracted immune-related lncRNAs. Cox regression analysis was used to pick out the predictive lncRNAs. The risk score of each patient was calculated based on the expression level of lncRNAs and regression coefficient (β), and a prognostic model was constructed. The overall survival (OS) of different risk groups was analyzed and compared by the Kaplan–Meier method. To analyze the distribution of immune-related genes in each group, principal component analysis and Gene set enrichment analysis were carried out. Estimation of STromal and Immune cells in MAlignant Tumors using Expression data was performed to explore the immune microenvironment.Results: Patients were divided into training set and validation set. Five immune-related lncRNAs (H1FX-AS1, AL441992.1, USP30-AS1, AP001527.2, and AL031123.2) were selected for the construction of the prognostic model. Patients in the training set were divided into high-risk group with longer OS and low-risk group with shorter OS (p = 0.004); meanwhile, similar result were found in validation set (p = 0.013), combination set (p &lt; 0.001) and patients with different tumor stages. This model was further confirmed in 56 cervical cancer tissues by Q-PCR. The distribution of immune-related genes was significantly different in each group. In addition, the immune score and the programmed death-ligand 1 expression of the low-risk group was higher.Conclusions: The prognostic model based on immune-related lncRNAs could predict the prognosis and immune status of cervical cancer patients which is conducive to clinical prognosis judgment and individual treatment.

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