Nomogram model for predicting postoperative survival of patients with stage IB-IIA cervical cancer.
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ABSTRACT:
SUBMITTER: Yang HS
PROVIDER: S-EPMC8640795 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:OBJECTIVES:To investigate the prediction value of a radiomics model based on apparent diffusion coefficient (ADC) maps for pelvic lymph node metastasis (PLNM) in patients with stage?IB-IIA cervical squamous cell carcinoma (CSCC). METHODS:A total of 153 stage?IB-IIA CSCC patients who underwent preoperative MRI including DWI from January 2015 to October 2017 were retrospectively studied and divided into a training cohort ( n = 102) and a validation cohort ( n = 51). Radiomics features were extracted from the ADC maps. The one-way ANOVA method, Mann-Whitney U test and Pearson's correlation analysis were used for selecting radiomics features. Logistic regression analyses were used to develop the model. ROC analyses were used to evaluate the prediction performance of the model. RESULTS:Clinical stage, tumor diameter, and MR-reported lymph node (LN) status were significantly associated with LN status ( p < 0.05 for both the training and validation cohorts). The radiomics model, which incorporated clinical stage, MR-reported LN status, and grey-level non-uniformity, showed good predictive performance in the training group (AUC 0.864; 95%?CI, 0.782 - 0.924) and the validation group (AUC 0.870; 95%?CI, 0.747 - 0.948). The performance of the radiomics model was significantly better than that of each predictive factor alone. CONCLUSION:The presented radiomics model, a non-invasive preoperative prediction tool, has the potential to have more predictive efficacy than clinical and radiological factors for differentiating between metastatic and non-metastatic lymph nodes. ADVANCES IN KNOWLEDGE:A radiomics model derived from the ADC maps of primary lesions demonstrated good performance for predicting PLNM in stage IB-IIA CSCC patients and may help to improve clinical decision-making.
| S-EPMC6580913 | biostudies-literature
Project description:Previous studies have shown that the C-reactive protein/albumin ratio (CAR) is a prognostic indicator in multiple types of carcinomas. This study is the first to evaluate the prognostic significance of CAR in stage IB-IIA cervical cancer patients treated with radical surgery, as well as that of several other inflammation-based factors, including the neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), and prognostic nutritional index (PNI). A total of 235 patients were enrolled in this study. The optimal cut-off values of CAR and other inflammation-based factors were determined by receiver operating characteristic curves. The Kaplan-Meier method and Cox regression model analysis were performed to determine the independent predictors of progression-free survival (PFS) and overall survival (OS). At a cut-off value of 0.15, patients with a high CAR had significantly shorter PFS and OS than those with a lower CAR (P < 0.001). A higher CAR was significantly associated with elevated scores of NLR and PLR and a decreased PNI (P < 0.001). Univariate analyses showed that elevated CAR preoperatively was significantly associated with poor survival; a similar trend was also noted for the NLR, PLR, and PNI. Multivariate analyses demonstrated that only CAR was an independent indicator for PFS (hazard ratio [HR]: 5.164; 95% confidence interval [CI]: 2.495-10.687; P < 0.001) and OS (HR: 4.729; 95% CI: 2.263-9.882; P < 0.001). In conclusion, preoperative CAR is a novel and superior predictor of poor survival in patients with stage IB-IIA cervical cancer.
| S-EPMC5773960 | biostudies-literature
Project description:Objectives: The present study aimed to identify the predictive value of inflammatory indexes stratified according to human papillomavirus (HPV) infection status in women with FIGO 2018 stage IB∼IIA cervical cancer. We also explored the influences of HPV infection status on the survival of cervical cancer patients. Methods: We collected data for 583 women with stage IB∼IIA cervical cancer in Sun Yat-sen University Cancer Center between 2009 and 2017. The t-test, chi-squared (χ2) test and Fisher's exact test were applied to compare the differences of inflammatory indexes and clinicopathological features between HPV-positive and HPV-negative groups. Univariate and multivariate analyses were used to identify clinicopathological factors that were associated with the prognosis of cervical cancer patients. Results: There were no differences in overall survival (OS) and progression-free survival (PFS) between HPV-positive and HPV-negative groups. In HPV-positive group, the maximum tumor size, neoadjuvant chemotherapy and the body mass index (BMI) correlated significantly with C-reactive protein/albumin ratio (CAR). The maximum tumor size and the prognostic nutritional index (PNI) correlated significantly with the platelet-lymphocyte ratio (PLR). The maximum tumor size, neoadjuvant chemotherapy and PLR correlated significantly with PNI. Univariate and multivariate analyses showed that the depth of tumor invasion (HR: 3.651, 95% CI: 1.464-9.103, p = 0.005; HR: 2.478, 95% CI: 1.218-5.043, p = 0.012) and CAR (HR: 5.201, 95% CI: 2.080-13.004, p < 0.0001; HR: 2.769, 95% CI: 1.406-5.455, p = 0.003) were independent predictors of poor OS and PFS. PNI was an independent protective factor of OS (HR: 0.341, 95% CI: 0.156-0.745, p = 0.007). PLR was an independent factor of PFS (HR: 1.991, 95% CI: 1.018-3.894, p = 0.044). In HPV-negative group, BMI correlated significantly with CAR. Only depth of invasion (HR: 9.192, 95% CI: 1.016-83.173, p = 0.048) was the independent predictor of poor OS, and no inflammation indexes were independent predictors of prognosis. Conclusion: In patients with HPV-positive cervical cancer, depth of invasion, PNI and CAR are independent factors of OS, and depth of invasion, PLR and CAR are independent factors for PFS. For patients with HPV-negative disease, no inflammation indexes had predictive value for prognosis. The predictive value of inflammation indexes on prognosis is more significant in patients with HPV-positive cervical cancer. Stratification of HPV infection status promotes a more precise clinical application of inflammation indexes, thus improving their accuracy and feasibility.
| S-EPMC8724028 | biostudies-literature
Project description:OBJECTIVE:To evaluate the association of sociodemographic and hospital characteristics with adherence to National Comprehensive Cancer Network treatment guidelines for stage IB-IIA cervical cancer and to analyze the relationship between adherent care and survival. METHODS:This is a retrospective population-based cohort study of patients with stage IB-IIA invasive cervical cancer reported to the California Cancer Registry from January 1, 1995, through December 31, 2009. Adherence to National Comprehensive Cancer Network guideline care was defined by year- and stage-appropriate surgical procedures, radiation, and chemotherapy. Multivariate logistic regression, Kaplan-Meier estimate, and Cox proportional hazard models were used to examine associations between patient, tumor, and treatment characteristics and National Comprehensive Cancer Network guideline adherence and cervical cancer-specific 5-year survival. RESULTS:A total of 6,063 patients were identified. Forty-seven percent received National Comprehensive Cancer Network guideline-adherent care, and 18.8% were treated in high-volume centers (20 or more patients/year). On multivariate analysis, lowest socioeconomic status (adjusted odds ratio [OR] 0.69, 95% CI 0.57-0.84), low-middle socioeconomic status (adjusted OR 0.76, 95% CI 0.64-0.92), and Charlson-Deyo comorbidity score 1 or higher (adjusted OR 0.78, 95% CI 0.69-0.89) were patient characteristics associated with receipt of nonguideline care. Receiving adherent care was less common in low-volume centers (45.9%) than in high-volume centers (50.9%) (effect size 0.90, 95% CI 0.84-0.96). Death from cervical cancer was more common in the nonadherent group (13.3%) than in the adherent group (8.6%) (effect size 1.55, 95% CI 1.34-1.80). Black race (adjusted hazard ratio 1.56, 95% CI 1.08-2.27), Medicaid payer status (adjusted hazard ratio 1.47, 95% CI 1.15-1.87), and Charlson-Deyo comorbidity score 1 or higher (adjusted hazard ratio 2.07, 95% CI 1.68-2.56) were all associated with increased risk of dying from cervical cancer. CONCLUSION:Among patients with early-stage cervical cancer, National Comprehensive Cancer Network guideline-nonadherent care was independently associated with increased cervical cancer-specific mortality along with black race and Medicaid payer status. Nonadherence was more prevalent in patients with older age, lower socioeconomic status, and receipt of care in low-volume centers. Attention should be paid to increase guideline adherence.
| S-EPMC5912990 | biostudies-literature
Project description:BackgroundVarious studies reported that the prognosis of patients with cervical cancer (CC) was significantly associated with immunity, whereas limited studies have explored whether immune-associated genes could be classifiers for recurrence-free survival (RFS) of stage I CC. Thus, an improved immune-related gene signature for stage I CC patients' prognosis is urgently required.Materials and methodsWe retrospectively analyzed the gene expression profiles of stage I CC patients in the GSE44001 set from the Gene Expression Omnibus (GEO) database. The stage I CC patients were randomly divided into the training group and the internal validation group. The training patients were adopted to develop a prognostic immune gene-based signature; meanwhile, the internal validation patients were used to validate the power of the selected immune gene-related signature using univariate Cox proportional hazard analysis, least absolute shrinkage and selection operator (LASSO), and multivariate Cox regression analysis. The accuracy and reliability of the immune gene-related signature were evaluated based on Kaplan-Meier analysis and time-dependent receiver operating characteristic (ROC) curves.ResultsHigh power of the 8-immune gene signature was found on the basis of ROC analysis (AUC at 1, 3, and 5 years were exhibited in the internal validation group (0.702, 0.715, and 0.728, respectively), external validation group (0.702, 0.825, and 0.842, respectively), and entire GEO dataset (0.840, 0.894, and 0.852, respectively)). Besides, C-index, ROC, calibration plots, and decision curve analysis (DCA) also acted well in our nomogram, suggestive of a high ability of the nomogram to elevate the prognostic prediction of stage I CC patients.ConclusionsIn this study, we successfully constructed an integrated 8-immune gene-based signature which could accurately identify patients with low prognostic risk from those with high prognostic risk. In addition, we developed an immune-related nomogram which can elevate the prognostic prediction of stage I CC patients.
| S-EPMC8289578 | biostudies-literature
Project description:BackgroundThe increased use of computed tomography has brought a corresponding increase in the numbers of early-stage lung cancer patients receiving treatment. However, even for stage IA3 lung adenocarcinoma, many patients experience postoperative recurrence and metastasis. The existing TNM staging system for lung cancer does not take many clinical and pathological factors into consideration, resulting in the failure to detect and intervene as soon as possible in those with high recurrence risk. The purpose of this study was to explore the risk factors for postoperative recurrence-free survival (RFS) in patients with stage IA3 lung adenocarcinoma, and to construct and verify a nomogram model for predicting RFS in patients with the disease.MethodsThis study analyzed patients with stage IA3 lung adenocarcinoma who underwent surgical treatment. Univariate and multivariate analysis were used to analyze the independent risk factors for postoperative RFS and establish a nomogram model. Concordance index (C-index), receiver operating characteristic curve, clinical decision analysis, and calibration curve were used to evaluate the discrimination and calibration of the nomogram model. Data from two other institutions were used for external validation, and the nomogram scores were combined with X-tile software to screen high-risk groups of recurrence.ResultsThe internal cohort included 235 eligible patients with stage IA3 lung adenocarcinoma from 7,235 lung cancer. Multivariate analysis showed smoking, solid nodules, mucinous lung adenocarcinoma, and micropapillary component ≥5% were independent risk factors for RFS. A nomogram model was constructed based on the above results and the bootstrap method was used for internal validation. The internal and external validation C-indexes of the nomogram were 0.822 (95% CI: 0.751-0.891) and 0.812, respectively, indicating the obvious prediction performance was good. The X-tile software combined with nomogram scores showed the low-risk group (5-RFS rate, 0.65-0.99) had better RFS than the high-risk group (5-RFS rate, 0.20-0.65) (P<0.0001).ConclusionsWe constructed a nomogram model for predicting postoperative RFS in patients with stage IA3 lung adenocarcinoma which can individually evaluate the risk of postoperative recurrence, screen high-risk groups, and develop individualized follow-up and intervention strategies to improve the survival rate of the patients.
| S-EPMC9742613 | biostudies-literature
Project description:PurposeThe overall survival (OS) of patients diagnosed with stage II-III colorectal cancer (CRC) can vary greatly, even between patients with the same tumor stage. We aimed to design a nomogram to predict OS in resected, stage II-III CRC and stratify patients with CRC into different risk groups.Patients and methodsBased on data from 873 patients with CRC, we used univariate Cox regression analysis to select the significant prognostic features, which were subjected to the least absolute shrinkage and selection operator (LASSO) regression algorithm for feature selection. Cross-validation was used to confirm suitable tuning parameters (λ) for LASSO logistic regression. Then, the nomogram was used to estimate 3- and 5-year OS based on the multivariable Cox regression model. The survival curves of the two groups were produced using the Kaplan-Meier method. Risk group stratification was performed to assess the predictive capacity of the nomogram.ResultsPreoperative mean platelet volume, preoperative platelet distribution width, monocytes, and postoperative adjuvant chemotherapy were identified as independent prognostic factors by LASSO regression and integrated for the construction of the nomogram. The nomogram provided good discrimination, with C-indices of 0.67 and 0.69 for the training and validation sets, respectively. Calibration plots illustrated excellent agreement between the nomogram predictions and actual observations for 3- and 5-year OS. Moreover, a significant difference in OS was shown between patients stratified into different risk groups (P < .001).ConclusionWe constructed and validated an original predictive nomogram for OS in patients with CRC after surgery, facilitating physicians to appraise the individual survival of postoperative patients accurately and identify high-risk patients who need more aggressive treatment and follow-up strategies.
| S-EPMC7131840 | biostudies-literature
Project description:BackgroundPrognostic prediction for surgical treatment of gastric cancer remains valuable in clinical practice. This study aimed to develop survival models for postoperative gastric cancer patients.MethodsEleven thousand seventy-five patients from the Surveillance, Epidemiology, and End Results (SEER) database were included, and 122 patients from the Chinese database were used for external validation. The training cohort was created to create three separate models, including Cox regression, RSF, and DeepSurv, using data from the SEER database split into training and test cohorts with a 7:3 ratio. Test cohort was used to evaluate model performance using c-index, Brier scores, calibration, and the area under the curve (AUC). The new risk stratification based on the best model will be compared with the AJCC stage on the test and Chinese cohorts using decision curve analysis (DCA), the net reclassification index (NRI), and integrated discrimination improvement (IDI).ResultsIt was discovered that the DeepSurv model predicted postoperative gastric cancer patients' overall survival (OS) with a c-index of 0.787; the area under the curve reached 0.781, 0.798, 0.868 at 1-, 3- and 5- years, respectively; the Brier score was below 0.25 at different time points; showing an advantage over the Cox and RSF models. The results are also validated in the China cohort. The calibration plots demonstrated good agreement between the DeepSurv model's forecast and actual results. The NRI values (test cohort: 0.399, 0.288, 0.267 for 1-, 3- and 5-year OS prediction; China cohort:0.399, 0.288 for 1- and 3-year OS prediction) and IDI (test cohort: 0.188, 0.169, 0.157 for 1-, 3- and 5-year OS prediction; China cohort: 0.189, 0.169 for 1- and 3-year OS prediction) indicated that the risk score stratification performed significantly better than the AJCC staging alone (P < 0.05). DCA showed that the risk score stratification was clinically useful and had better discriminative ability than the AJCC staging. Finally, an interactive native web-based prediction tool was constructed for the survival prediction of patients with postoperative gastric cancer.ConclusionIn this study, a high-performance prediction model for the postoperative prognosis of gastric cancer was developed using DeepSurv, which offers essential benefits for risk stratification and prognosis prediction for each patient.
| S-EPMC11018873 | biostudies-literature
Project description:This study aimed to develop a nomogram to predict the overall survival (OS) of stage IV breast cancer patients. We searched the National Cancer Database (NCDB) for stage IV breast cancer patients diagnosed between 2010 and 2013. Predictors of OS were identified and a nomogram was developed and validated using concordance index (C-index), calibration plots, and risk group stratifications. A total of 7199 patients from the NCDB were included in the study. With a median follow-up of 25.7 months, the 1-year and 3-year OS rates were 80.6% and 52.5%, respectively. Race, age, comorbidity status, T-stage, grade, ER/PR/Her2 status, the presence of lung/liver/brain metastasis, surgery, radiotherapy, and chemotherapy were significantly associated with OS. The developed nomogram had a C-index of 0.722 (95% CI 0.710-0.734) and 0.725 (95% CI 0.713-0.736) in the training and the validation cohorts, respectively. The predicted survival using the nomogram is well correlated with actual OS. The nomogram was able to stratify patients into different risk groups, among which the survival benefit of local therapy varied. We developed a nomogram to predict the overall survival of stage IV breast cancer patients. Prospectively designed studies with international collaborations are needed to further validate our nomogram.
| S-EPMC5673913 | biostudies-literature
Project description:BACKGROUND: Nomograms are predictive tools that are widely used for estimating cancer prognosis. The aim of this study was to develop a nomogram for the prediction of overall survival (OS) in patients diagnosed with cervical cancer. METHODS: Cervical cancer databases of two large institutions were analysed. Overall survival was defined as the clinical endpoint and OS probabilities were estimated using the Kaplan-Meier method. Based on the results of survival analyses and previous studies, relevant covariates were identified, a nomogram was constructed and validated using bootstrap cross-validation. Discrimination of the nomogram was quantified with the concordance probability. RESULTS: In total, 528 consecutive patients with invasive cervical cancer, who had all nomogram variables available, were identified. Mean 5-year OS rates for patients with International Federation of Gynecologists and Obstetricians (FIGO) stage IA, IB, II, III, and IV were 99.0%, 88.6%, 65.8%, 58.7%, and 41.5%, respectively. Seventy-six cancer-related deaths were observed during the follow-up period. FIGO stage, tumour size, age, histologic subtype, lymph node ratio, and parametrial involvement were selected as nomogram covariates. The prognostic performance of the model exceeded that of FIGO stage alone and the model's estimated optimism-corrected concordance probability was 0.723, indicating accurate prediction of OS. We present the prediction model as nomogram and provide a web-based risk calculator (http://www.ccc.ac.at/gcu). CONCLUSION: Based on six easily available parameters, a novel statistical model to predict OS of patients diagnosed with cervical cancer was constructed and validated. The model was implemented in a nomogram and provides accurate prediction of individual patients' prognosis useful for patient counselling and deciding on follow-up strategies.
| S-EPMC3464766 | biostudies-other