Overall survival nomogram for patients with spinal bone metastases (SBM).
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ABSTRACT:
SUBMITTER: Osong B
PROVIDER: S-EPMC7985219 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Study designRetrospective analysis of a registered cohort of patients treated and irradiated for metastases in the spinal column in a single institute.ObjectiveThis is the first study to develop and internally validate radiomics features for predicting six-month survival probability for patients with spinal bone metastases (SBM).Background dataExtracted radiomics features from routine clinical CT images can be used to identify textural and intensity-based features unperceivable to human observers and associate them with a patient survival probability or disease progression.MethodsA study was conducted on 250 patients treated for metastases in the spinal column irradiated for the first time between 2014 and 2016, at the MAASTRO clinic in Maastricht, the Netherlands. The first 150 available patients were used to develop the model and the subsequent 100 patient were considered as a test set for the model. A bootstrap (B = 400) stepwise model selection, which combines both the forward and backward variable elimination procedure, was used to select the most useful predictive features from the training data based on the Akaike information criterion (AIC). The stepwise selection procedure was applied to the 400 bootstrap samples, and the results were plotted as a histogram to visualize how often each variable was selected. Only variables selected more than 90 % of the time over the bootstrap runs were used to build the final model.A prognostic index (PI) called radiomics score (radscore) and clinical score (clinscore) was calculated for each patient. The prognostic index was not scaled, the original values were used which can be extracted from the model directly or calculated as a linear combination of the variables in the model multiplied by the respective beta value for each patient.ResultsThe clinical model had a good discrimination power. The radiomics model, on the other hand, had an inferior performance with no added predictive power to the clinical model. The internal imaging characteristics do not seem to have a value in the prediction of survival. However, the Shape features were excluded from further analyses in our study since all biopsies had a standard shape hence no variability.
| S-EPMC8777154 | 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
Project description:PurposeTo develop and validate a radiomics nomogram for predicting overall survival (OS) in multiple myeloma (MM) patients.Material and methodsA total of 121 MM patients was enrolled and divided into training (n=84) and validation (n=37) sets. The radiomics signature was established by the selected radiomics features from lumbar MRI. The radiomics signature and clinical risk factors were integrated in multivariate Cox regression model for constructing radiomics nomogram to predict MM OS. The predictive ability and accuracy of the nomogram were evaluated by the index of concordance (C-index) and calibration curves, and compared with other four models including the clinical model, radiomics signature model, the Durie-Salmon staging system (D-S) and the International Staging System (ISS). The potential association between the radiomics signature and progression-free survival (PFS) was also explored.ResultsThe radiomics signature, 1q21 gain, del (17p), and β2-MG≥5.5 mg/L showed significant association with MM OS. The predictive ability of radiomics nomogram was better than the clinical model, radiomics signature model, the D-S and the ISS (C-index: 0.793 vs. 0.733 vs. 0.742 vs. 0.554 vs. 0.671 in training set, and 0.812 vs. 0.799 vs.0.717 vs. 0.512 vs. 0.761 in validation set). The radiomics signature lacked the predictive ability for PFS (log-rank P=0.001 in training set and log-rank P=0.103 in validation set), whereas the 1-, 2- and 3-year PFS rates all showed significant difference between the high and low risk groups (P ≤ 0.05).ConclusionThe MRI-based bone marrow radiomics may be an additional useful tool for MM OS prediction.
| S-EPMC8671997 | biostudies-literature
Project description:BackgroundBone metastases in thyroid cancer impair the patient's quality of life and prognosis. Interestingly, wide margins resection as the surgical treatment of bone metastases might improve the overall survival (OS). Nonetheless, data are lacking regarding the potential benefits of this strategy.MethodsIn order to assess the OS of patients with thyroid cancer after a bone metastases carcinologic resection, a retrospective multicentric study was performed, evaluating the 1, 5, 10 and 15 years-OS along with the potential prognosis associated factors.Results40 patients have been included in this multicentric study, with a mean follow-up after surgery of 46.6 ± 58 months. We observed 25 (62.5%) unimestastatic patients and 15 multimetastatic patients (37.5%). The median overall survival after resection was 48 ± 57.3 months. OS at 1, 5, 10, and 15 years was respectively 76.2%, 63.6%, 63.6%, and 31.8%. Survival for patients with a single bone metastasis at 15 year was 82.3%, compared with 0.0% (Log Rank, p = 0.022) for multi-metastatic bone patients.ConclusionsThis study advocates for an increased long term 10-year OS in patients with thyroid cancer, after resection of a single bone metastasis, suggesting the benefits of this strategy in this population.
| S-EPMC9314764 | biostudies-literature
Project description:BackgroundProstate adenocarcinoma (PRAD)-related bone metastases are a leading source of morbidity and mortality; however, good diagnostic biomarkers are not known yet. The aim of this study was to identify biomarkers and prognostic indicators for the diagnosis and treatment of PRAD-associated bone metastases.MethodsBy combining the data from The Cancer Genome Atlas(TCGA) and PRAD SU2C 2019, We performed a comprehensive analysis of the expression differences, biological functions, and interactions of genes associated with PRAD bone metastasis. Annotation, visualization, and integrated discovery were accomplished through the use of gene ontology enrichment and gene set enrichment analysis. The protein-protein interaction network was constructed using the STRING database, and the diagnostic value of prognostic genes was validated using receiver-operating-characteristic and Kaplan-Meier curves.ResultsSix genes (DDX47, PRL17, AS3MT, KLRK1, ISLR, and S100A8) associated with PRAD bone metastases were identified; these had prognostic value as well. Among them, enrichment was observed for the biological processes extracellular matrix tissue, extracellular structural tissue, steroid hormone response, and cell oxidative detoxification. KEGG analysis revealed enrichment in interactions with extracellular matrix receptors, diseases including Parkinson's disease and dilated cardiomyopathy, and estrogen signaling pathways. The area under the curve values of 0.8938, 0.9885, and 0.979, obtained from time-dependent receiver-operating-characteristic curve analysis for 1, 3, and 5-year overall survival confirmed the good performance of the model under consideration. S100A8 expression was not detected in the normal prostate tissue but was detected in PRAD.ConclusionsWe identified ISLR as a potential biomarker for PRAD bone metastasis. Moreover, the genes identified to have prognostic value may act as therapeutic targets for PRAD bone metastasis.
| S-EPMC9243502 | biostudies-literature
Project description:ImportanceA survival prediction model for patients with bone metastases arising from lung cancer would be highly valuable.ObjectiveTo develop and validate a nomogram for assessing the survival probability of patients with metastatic lung cancer receiving radiotherapy for osseous metastases.Design, setting, participantsIn this prognostic study, the putative prognostic indicators for constructing the nomogram were identified using multivariable Cox regression analysis with backward elimination and model selection based on the Akaike information criterion. The nomogram was subjected to internal (bootstrap) and external validation; its calibration and discriminative ability were evaluated with calibration plots and the Uno C statistic, respectively. The training and validation set cohorts were from a tertiary medical center in northern Taiwan and a tertiary institution in southern Taiwan, respectively. The training set comprised 477 patients with metastatic lung cancer who received radiotherapy for osseous metastases between January 2000 and December 2013. The validation set comprised 235 similar patients treated between January 2011 and December 2017. Data analysis was conducted May 2018 to July 2018.Main outcomes and measuresThe nomogram end points were death within 3, 6, and 12 months.ResultsOf 477 patients in the training set, 292 patients (61.2%) were male, and the mean (SD) age was 62.86 (11.66) years. Of 235 patients in the validating set, 113 patients (48.1%) were male, and the mean (SD) age was 62.65 (11.49) years. In the training set, 186 (39%), 291 (61%), and 359 (75%) patients died within 3, 6, and 12 months, respectively, and the median overall survival was 4.21 (95% CI, 3.68-4.90) months. In the validating set, 84 (36%), 120 (51%), and 144 (61%) patients died within 3, 6, and 12 months, respectively, and the median overall survival was 5.20 (95% CI, 4.07-7.17) months. Body mass index (18.5 to <25 vs ≥25: hazard ratio [HR], 1.42; 95% CI, 1.14-1.78 and <18.5 vs ≥25: HR, 2.31; 95% CI, 1.56-3.44), histology (non-small cell vs small cell lung cancer: HR, 0.59; 95% CI, 0.41-0.86), epidermal growth factor receptor mutation (positive vs unknown: HR, 0.66; 95% CI, 0.46-0.93 and negative vs unknown: HR, 0.98; 95% CI, 0.66-1.45), smoking status (ever smoker vs never smoker: HR, 1.50; 95% CI, 1.24-1.83), age, and neutrophil to lymphocyte ratio were incorporated. The HRs of age and neutrophil to lymphocyte ratio were modeled nonlinearly with restricted cubic splines (both P < .001). The nomogram's discriminative ability was good in the training set (C statistic, ≥0.77; P < .001) and was validated using both an internal bootstrap method (C statistic, ≥0.76; P < .001) and an external validating set (C statistic, ≥0.75; P < .001). The calibration plots for the end points showed optimal agreement between the nomogram's assessment and actual observations.Conclusions and relevanceThe nomogram (with web-based tool) can be useful for assessing the probability of survival at 3, 6, and 12 months in patients with metastatic lung cancer referred for radiotherapy to treat bone metastases, and it may guide radiation oncologists in treatment decision making and engaging patients in end-of-life discussions and/or hospice referrals at appropriate times.
| S-EPMC6324455 | biostudies-literature
Project description:PurposeBone metastases (BM) from differentiated thyroid carcinoma (DTC) impact negatively the quality of life and the life expectancy of patients. The aim of the study was (a) to evaluate the overall survival (OS) and prognostic factors of OS and (b) to assess predictive factors of complete BM response (C-BM-R) using radioiodine treatment (RAI) either alone or in association with focal treatment modalities.MethodsA total of 178 consecutive DTC patients harbouring BM, treated between 1989 and 2015, were enrolled in this retrospective study conducted in two tertiary referral centers. OS analysis was performed for the whole cohort, and only the 145 considered non-RAI refractory patients at BM diagnosis were evaluated for C-BM-R following RAI.ResultsThe median OS from BM diagnosis was 57 months (IQR: 24-93). In multivariate analysis, OS was significantly reduced in the case of T4 stage, 18FDG uptake by the BM and RAI refractory status. Among the 145 DTC considered non-RAI refractory patients at BM diagnosis, 46 patients (31.7%) achieved a C-BM-R following RAI treatment, either alone in 32 (18%) patients or in association with focal BM treatment modalities in 14. The absence of extra-skeletal distant metastasis and of 18FDG uptake in BM were predictive for C-BM-R.ConclusionsIn nearly one-third of DTC patients with RAI avid BM, RAI alone or in combination with BM focal treatment can induce C-BM-R. The presence of 18FDG uptake in BM is associated with an absence of C-BM-R and with a poor OS. 18FDG PET-CT should be performed when BM is suspected.
| S-EPMC9165254 | biostudies-literature
Project description:More than 35% of lung adenocarcinoma patients have bone metastases at diagnosis and have a poor survival. Periostin, a carboxylated matrix protein, mediates lung cancer cell dissemination by promoting epithelial-mesenchymal transition, and is involved in bone response to mechanical stress and bone formation regulation. This suggests that periostin may be used as a biomarker to predict survival in lung cancer patients. Serum periostin was assessed at diagnosis in a prospective cohort of 133 patients with lung adenocarcinoma of all stages. Patients were divided into localized and bone metastatic groups. Both groups were matched to healthy controls. Survival analysis and Cox proportional hazards models were conducted in the total population and in bone metastatic group. The median serum periostin level was higher in bone metastatic (n = 67; median: 1752 pmol/L) than in the localized group (n = 66; 861 pmol/L; p < 0.0001). Patients with high periostin (>median) had a poorer overall survival in the whole population (33.3 weeks vs. NR; p < 0.0001) and the bone metastatic group (24.4 vs. 66.1 weeks; p < 0.001). In multivariate analysis, patients with high periostin had increased risk of death (HR = 2.09, 95%CI [1.06-4.13]; p = 0.03). This was also found in the bone metastatic group (HR = 3.62, 95%CI [1.74-7.52]; p = 0.0005). Immunohistochemistry on bone metastasis biopsies showed periostin expression in the bone matrix and nuclear and cytoplasmic staining in cancer cells. Serum periostin was an independent survival biomarker in all-stage and in bone metastatic lung adenocarcinoma patients. IHC data suggest that periostin might be induced in cancer cells in bone metastatic niche in addition to bone microenvironment expression.
| S-EPMC8190464 | biostudies-literature
Project description:Colorectal cancer (CRC) is the third foremost cause of cancer-related death and the fourth most commonly diagnosed cancer globally. The study aimed to evaluate the survival predictors using the Cox Proportional Hazards (CPH) and established a novel nomogram to predict the Overall Survival (OS) of the CRC patients. A historical cohort study, included 1868 patients with CRC, was performed using medical records gathered from Iran's three tertiary colorectal referral centers from 2006 to 2019. Two datasets were considered as train set and one set as the test set. First, the most significant prognostic risk factors on survival were selected using univariable CPH. Then, independent prognostic factors were identified to construct a nomogram using the multivariable CPH regression model. The nomogram performance was assessed by the concordance index (C-index) and the time-dependent area under the ROC curve. The age of patients, body mass index (BMI), family history, tumor grading, tumor stage, primary site, diabetes history, T stage, N stage, and type of treatment were considered as significant predictors of CRC patients in univariable CPH model (p < 0.2). The multivariable CPH model revealed that BMI, family history, grade and tumor stage were significant (p < 0.05). The C-index in the train data was 0.692 (95% CI, 0.650-0.734), as well as 0.627 (0.670, 0.686) in the test data. We improved a novel nomogram diagram according to factors for predicting OS in CRC patients, which could assist clinical decision-making and prognosis predictions in patients with CRC.
| S-EPMC8345484 | biostudies-literature
Project description:BackgroundPatients with metastatic cancer referred to radiation oncology have diverse prognoses and there is significant interest in personalizing treatment. We hypothesized that patients selected for higher biologically equivalent doses have improved overall survival.MethodsThe study population consists of 355 consecutive adult patients with distant metastases treated by a single radiation oncologist from 2014 to 2018. The validated NEAT model was used to prospectively stratify patients into four distinct cohorts. Radiation dose intensity was standardized using the equivalent dose in 2 Gy fractions (EQD2) model with an α/β of 10. Radiation dose intensity on survival was assessed via Cox regression models and propensity score match pairing with Kaplan-Meier analysis.ResultsThe median survival was 9.3 months and the median follow-up for surviving patients was 18.3 months. The NEAT model cohorts indicated median survivals of 29.5, 11.8, 4.9, and 1.8 months. Patients receiving an EQD2 of ≥40 Gy had a median survival of 16.0 months versus 3.8 months for patients receiving an EQD2 of <40 Gy (p < 0.001). On multivariable analysis, performance status, primary tumor site, radiation dose intensity, albumin, liver metastases, and number of active tumors were all independent predictors of survival (p < 0.05 for all). Propensity score matching was performed for performance status, albumin, number of active tumors, primary tumor site, and liver metastasis, finding higher EQD2 to remain significantly associated with improved survival within the matched cohort (p = 0.004).ConclusionHigher radiation dose intensity was used in patients with better prognosis and was associated with improved survival for patients with metastatic disease.
| S-EPMC8607262 | biostudies-literature