Development of a nomogram to predict progression-free survival in patients with locally advanced renal cell carcinoma.
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ABSTRACT:
SUBMITTER: Xiao RT
PROVIDER: S-EPMC8631405 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BackgroundThe management of locally advanced pancreatic cancer (LAPC) patients remains controversial. Better discrimination for overall survival (OS) at diagnosis is needed. We address this issue by developing and validating a prognostic nomogram and a score for OS in LAPC (PROLAP).MethodsAnalyses were derived from 442 LAPC patients enrolled in the LAP07 trial. The prognostic ability of 30 baseline parameters was evaluated using univariate and multivariate Cox regression analyses. Performance assessment and internal validation of the final model were done with Harrell's C-index, calibration plot and bootstrap sample procedures. On the basis of the final model, a prognostic nomogram and a score were developed, and externally validated in 106 consecutive LAPC patients treated in Besançon Hospital, France.ResultsAge, pain, tumour size, albumin and CA 19-9 were independent prognostic factors for OS. The final model had good calibration, acceptable discrimination (C-index=0.60) and robust internal validity. The PROLAP score has the potential to delineate three different prognosis groups with median OS of 15.4, 11.7 and 8.5 months (log-rank P<0.0001). The score ability to discriminate OS was externally confirmed in 63 (59%) patients with complete clinical data derived from a data set of 106 consecutive LAPC patients; median OS of 18.3, 14.1 and 7.6 months for the three groups (log-rank P<0.0001).ConclusionsThe PROLAP nomogram and score can accurately predict OS before initiation of induction chemotherapy in LAPC-untreated patients. They may help to optimise clinical trials design and might offer the opportunity to define risk-adapted strategies for LAPC management in the future.
| S-EPMC4973163 | biostudies-literature
Project description:The prognosis of advanced biliary tract cancer (BTC) patients remains poor due to limited efficacy of chemotherapy and difficulties in management. Thus, prediction of survival is crucial for the clinical management of advanced BTC. The aim was to develop and validate a nomogram to predict 6-month and 12-month survival in advanced BTC patients treated with chemotherapy. A multivariable Cox regression model was used to construct a nomogram in a training set (JCOG1113, a phase III trial comparing gemcitabine plus S-1 [GS] and gemcitabine plus cisplatin, n = 351). External validity of the nomogram was assessed using a test set (JCOG0805, a randomized, phase II trial comparing GS and S-1 alone, n = 100). Predictive performance was assessed in terms of discrimination and calibration. The constructed nomogram included lymph node metastasis, liver metastasis, carbohydrate antigen 19-9, carcinoembryonic antigen, albumin, and C-reactive protein. Uno's concordance index was 0.661 (95% confidence interval [CI] 0.629-0.696) in the training set and 0.640 (95% CI 0.566-0.715) in the test set. The calibration plots for 6-month and 12-month survival showed good agreement in the two analysis sets. The present nomogram can facilitate prediction of the prognosis of advanced BTC patients treated with chemotherapy and help clinicians' prognosis-based decision-making.
| S-EPMC10700490 | biostudies-literature
Project description:BackgroundThe existing staging system cannot meet the needs of accurate survival prediction. Accurate survival prediction for locally advanced cervical cancer (LACC) patients who have undergone concurrent radiochemotherapy (CCRT) can improve their treatment management. Thus, this present study aimed to develop and validate radiomics models based on pretreatment 18Fluorine-fluorodeoxyglucose (18F-FDG) positron emission tomography (PET)-computed tomography (CT) images to accurately predict the prognosis in patients.MethodsThe data from 190 consecutive patients with LACC who underwent pretreatment 18F-FDG PET-CT and CCRT at two cancer hospitals were retrospectively analyzed; 176 patients from the same hospital were randomly divided into training (n = 117) and internal validation (n = 50) cohorts. Clinical features were selected from the training cohort using univariate and multivariate Cox proportional hazards models; radiomic features were extracted from PET and CT images and filtered using least absolute shrinkage and selection operator and Cox proportional hazard regression. Three prediction models and a nomogram were then constructed using the previously selected clinical, CT and PET radiomics features. The external validation cohort that was used to validate the models included 23 patients with LACC from another cancer hospital. The predictive performance of the constructed models was evaluated using receiver operator characteristic curves, Kaplan Meier curves, and a nomogram.ResultsIn total, one clinical, one PET radiomics, and three CT radiomics features were significantly associated with progression-free survival in the training cohort. Across all three cohorts, the combined model displayed better efficacy and clinical utility than any of these parameters alone in predicting 3-year progression-free survival (area under curve: 0.661, 0.718, and 0.775; C-index: 0.698, 0.724, and 0.705, respectively) and 5-year progression-free survival (area under curve: 0.661, 0.711, and 0.767; C-index, 0.698, 0.722, and 0.676, respectively). On subsequent construction of a nomogram, the calibration curve demonstrated good agreement between actually observed and nomogram-predicted values.ConclusionsIn this study, a clinico-radiomics prediction model was developed and successfully validated using an independent external validation cohort. The nomogram incorporating radiomics and clinical features could be a useful clinical tool for the early and accurate assessment of long-term prognosis in patients with LACC patients who undergo concurrent chemoradiotherapy.
| S-EPMC10826285 | biostudies-literature
Project description:ObjectivesThe aim of this study was to establish and validate a MRI-based radiomics nomogram to predict progression-free survival (PFS) of clival chordoma.MethodsA total of 174 patients were enrolled in the study (train cohort: 121 cases, test cohort: 53 cases). Radiomic features were extracted from multiparametric MRIs. Intraclass correlation coefficient analysis and a Lasso and Elastic-Net regularized generalized linear model were used for feature selection. Then, a nomogram was established via univariate and multivariate Cox regression analysis in the train cohort. The performance of this nomogram was assessed by area under curve (AUC) and calibration curve.ResultsA total of 3318 radiomic features were extracted from each patient, of which 2563 radiomic features were stable features. After feature selection, seven radiomic features were selected. Cox regression analysis revealed that 2 clinical factors (degree of resection, and presence or absence of primary chordoma) and 4 radiomic features were independent prognostic factors. The AUC of the established nomogram was 0.747, 0.807, and 0.904 for PFS prediction at 1, 3, and 5 years in the train cohort, respectively, compared with 0.582, 0.852, and 0.914 in the test cohort. Calibration and risk score stratified survival curves were satisfactory in the train and test cohort.ConclusionsThe presented nomogram demonstrated a favorable predictive accuracy of PFS, which provided a novel tool to predict prognosis and risk stratification. Our results suggest that radiomic analysis can effectively help neurosurgeons perform individualized evaluations of patients with clival chordomas.
| S-EPMC9800789 | biostudies-literature
Project description:BACKGROUND: Pelvic lymph node (LN) status after preoperative chemoradiotherapy (CRT) is an important indicator of oncologic outcome in patients with locally advanced rectal cancer. The purpose of this study was to develop a nomogram to predict LN status after preoperative CRT in locally advanced rectal cancer patients. METHODS: The nomogram was developed in a training cohort (n=891) using logistic regression analyses and validated in a validation cohort (n=258) from a prospectively registered tumour registry at Asan Medical Center. The model was internally and externally validated for discrimination and calibration using bootstrap resampling. Model performance was evaluated by the concordance index (c-index) and calibration curve. RESULTS: Pretreatment ypT stage, patient age, preCRT tumour differentiation, cN stage, lymphovascular invasion, and perineural invasion were reliable predictors of LN metastasis after preoperative CRT. The nomogram developed using these parameters had c-indices of 0.81 (training) and 0.77 (validation). The calibration plot suggested good agreement between actual and nomogram-predicted LN status after preoperative CRT. CONCLUSIONS: This nomogram improves prediction of LN status after preoperative CRT in patients with locally advanced rectal cancer. It will be useful for counselling patients as well as for the design and stratification of patients in clinical trials.
| S-EPMC4102937 | biostudies-other
Project description:ObjectivesTo develop a prognostic prediction MRI-based nomogram model for locally advanced rectal cancer (LARC) treated with neoadjuvant therapy.MethodsThis was a retrospective analysis of 233 LARC (MRI-T stage 3-4 (mrT) and/or MRI-N stage 1-2 (mrN), M0) patients who had undergone neoadjuvant radiotherapy and total mesorectal excision (TME) surgery with baseline MRI and operative pathology assessments at our institution from March 2015 to March 2018. The patients were sequentially allocated to training and validation cohorts at a ratio of 4:3 based on the image examination date. A nomogram model was developed based on the univariate logistic regression analysis and multivariable Cox regression analysis results of the training cohort for disease-free survival (DFS). To evaluate the clinical usefulness of the nomogram, Harrell's concordance index (C-index), calibration plot, receiver operating characteristic (ROC) curve analysis, and decision curve analysis (DCA) were conducted in both cohorts.ResultsThe median follow-up times were 43.2 months (13.3-61.3 months) and 32.0 months (12.3-39.5 months) in the training and validation cohorts. Multivariate Cox regression analysis identified MRI-detected extramural vascular invasion (mrEMVI), pathological T stage (ypT) and perineural invasion (PNI) as independent predictors. Lymphovascular invasion (LVI) (which almost reached statistical significance in multivariate regression analysis) and three other independent predictors were included in the nomogram model. The nomogram showed the best predictive ability for DFS (C-index: 0.769 (training cohort) and 0.776 (validation cohort)). It had a good 3-year DFS predictive capacity [area under the curve, AUC=0.843 (training cohort) and 0.771 (validation cohort)]. DCA revealed that the use of the nomogram model was associated with benefits for the prediction of 3-year DFS in both cohorts.ConclusionWe developed and validated a novel nomogram model based on MRI factors and pathological factors for predicting DFS in LARC treated with neoadjuvant therapy. This model has good predictive value for prognosis, which could improve the risk stratification and individual treatment of LARC patients.
| S-EPMC8634258 | biostudies-literature
Project description:BACKGROUND:Previous related studies have mainly focused on renal cell carcinoma (RCC) with venous tumor thrombus, specifically inferior vena cava tumor thrombus with renal vein tumor thrombus (RVTT). However, only a few studies have focused on postoperative long-term survival of RCC patients exclusively with RVTT. Our aim was to investigate the independent prognostic factors for locally advanced RCC with RVTT in China. METHODS:Patients with locally advanced RCC with RVTT were enrolled for the study from January 2000 to December 2015. All patients underwent radical nephrectomy. Survival analysis was estimated using Kaplan-Meier. Univariable and multivariable survival analyses were performed using COX. Patients were divided into high-risk, middle-risk, and low-risk groups based on independent prognostic factors and then analyzed for survival. RESULTS:One hundred twenty-eight consecutive patients (103 men & 25 women) were enrolled with a median age of 61 years. Thrombi were all graded 0 using the Mayo system, of which 23 were friable. None of the thrombi detached during surgery. 121 patients were successfully followed up, with a median follow-up period of 47 months. Median overall survival was 127 months (95%CI: 101-153). The 5-year and 10-year cancer-specific survival (CSS) rate was 67.9 and 57.0%. 59 patients had recurrence with median time of 40 months. Friable thrombus, paraneoplastic syndrome (PNS), modified Fuhrman grade 3/4 and perirenal fat invasion were independent prognostic factors (p < 0.05). The 5-year CSS for the Low-risk group (no factors) was 100%, Middle-risk group (1-2 factors) was 68.6%, while the High-risk group (3-4 factors) was 0%. CONCLUSIONS:After radical surgery, RCC patients with RVTT had a relatively fair prognosis except for patients with friable thrombus, PNS, higher modified Fuhrman grade and perirenal fat invasion.
| S-EPMC6373083 | biostudies-other
Project description:BackgroundRisk stratification for localized renal cell carcinoma (RCC) relies heavily on retrospective models, limiting their generalizability to contemporary cohorts.ObjectiveTo introduce a contemporary RCC prognostic model, developed using prospective, highly annotated data from a phase III adjuvant trial.Design, setting, and participantsThe model utilizes outcome data from the ECOG-ACRIN 2805 (ASSURE) RCC trial.Outcome measurements and statistical analysisThe primary outcome for the model is disease-free survival (DFS), with overall survival (OS) and early disease progression (EDP) as secondary outcomes. Model performance was assessed using discrimination and calibration tests.Results and limitationsA total of 1735 patients were included in the analysis, with 887 DFS events occurring over a median follow-up of 9.6 yr. Five common tumor variables (histology, size, grade, tumor necrosis, and nodal involvement) were included in each model. Tumor histology was the single most powerful predictor for each model outcome. The C-statistics at 1 yr were 78.4% and 81.9% for DFS and OS, respectively. Degradation of the DFS, DFS validation set, and OS model's discriminatory ability was seen over time, with a global c-index of 68.0% (95% confidence interval or CI [65.5, 70.4]), 68.6% [65.1%, 72.2%], and 69.4% (95% CI [66.9%, 71.9%], respectively. The EDP model had a c-index of 75.1% (95% CI [71.3, 79.0]).ConclusionsWe introduce a contemporary RCC recurrence model built and internally validated using prospective and highly annotated data from a clinical trial. Performance characteristics of the current model exceed available prognostic models with the added benefit of being histology inclusive and TNM agnostic.Patient summaryImportant decisions, including treatment protocols, clinical trial eligibility, and life planning, rest on our ability to predict cancer outcomes accurately. Here, we introduce a contemporary renal cell carcinoma prognostic model leveraging high-quality data from a clinical trial. The current model predicts three outcome measures commonly utilized in clinical practice and exceeds the predictive ability of available prognostic models.
| S-EPMC8627688 | biostudies-literature
Project description:PurposeTo assess the impact of comorbidity on treatment outcomes in patients with locally recurrent nasopharyngeal carcinoma (lrNPC) using intensity-modulated radiotherapy (IMRT) and to develop a nomogram that combines prognostic factors to predict clinical outcome and guide individual treatment.MethodsThis was a retrospective analysis of patients with lrNPC who were reirradiated with IMRT between 2003 and 2014. Comorbidity was evaluated by Adult Comorbidity Evaluation-27 grading (ACE-27). The significant prognostic factors (P < 0.05) by multivariate analysis using the Cox regression model were adopted into the nomogram model. Harrell concordance index (C-index) calibration curves were applied to assess this model.ResultsBetween 2003 and 2014, 469 lrNPC patients treated in our institution were enrolled. Significant comorbidity (moderate or severe grade) was present in 17.1% of patients by ACE-27. Patients with no or mild comorbidity had a 5-year overall survival (OS) rate of 36.2 versus 20.0% among those with comorbidity of moderate or severe grade (P < 0.0001). The chemotherapy used was not significantly different in patients with lrNPC (P > 0.05). For the rT3-4 patients, the 5-year OS rate in the chemotherapy + radiation therapy (RT) group was 30.0 versus 16.7% for RT only (P = 0.005). The rT3-4 patients with no or mild comorbidity were associated with a higher 5-year OS rate in the chemotherapy + RT group than in the RT only group (32.1 and 17.1%, respectively; P=0.003). However, for the rT3-4 patients with a comorbidity (moderate or severe grade), the 5-year OS rate in the chemotherapy + RT group vs. RT alone was not significantly different (15.7 vs. 15.0%, respectively; p > 0.05). Eight independent prognostic factors identified from multivariable analysis were fitted into a nomogram, including comorbidity. The C-index of the nomogram was 0.715. The area under curves (AUCs) for the prediction of 1-, 3-, and 5-year overall survival were 0.770, 0.764, and 0.780, respectively.ConclusionComorbidity is among eight important prognostic factors for patients undergoing reirradiation. We developed a nomogram for lrNPC patients to predict the probability of death after reirradiation and guide individualized management.
| S-EPMC7855849 | biostudies-literature
Project description:In locally advanced rectal cancer a preoperative predictive biomarker is necessary to adjust treatment specifically for those patients expected to suffer relapse. We applied whole genome methylation CpG island array analyses to an initial set of patients (n=11) to identify differentially methylated regions (DMRs) that separate a good from a bad prognosis group. Using a quantitative high-resolution approach, candidate DMRs were first validated in a set of 61 patients (test set) and then confirmed DMRs were further validated in additional independent patient cohorts (n=71, n=42). We identified twenty highly discriminative DMRs and validated them in the test set using the MassARRAY technique. Ten DMRs could be confirmed which allowed separation into prognosis groups (p=0.0207, HR=4.09). The classifier was validated in two additional cohorts (n=71, p=0.0345, HR=3.57 and n=42, p=0.0113, HR=3.78). Interestingly, six of the ten DMRs represented regions close to the transcriptional start sites of genes which are also marked by the Polycomb Repressor Complex component EZH2. In conclusion we present a classifier comprising 10 DMRs which predicts patient prognosis with a high degree of accuracy. These data may now help to discriminate between patients that may respond better to standard treatments from those that may require alternative modalities.
| S-EPMC4226671 | biostudies-literature