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PubMed2004

Preoperative neural network using combined magnetic resonance imaging variables, prostate-specific antigen, and gleason score for predicting prostate cancer biochemical recurrence after radical prostatectomy.

OBJECTIVES: To develop and test an artificial neural network (ANN) for predicting biochemical recurrence based on the combined use of pelvic coil magnetic resonance imaging (pMRI), prostate-specific antigen (PSA) measurement, and biopsy Gleason score, after radical prostatectomy and to investigate whether it is more accurate than logistic regression analysis (LRA) in men with clinically localized prostate cancer. METHODS: We evaluated 191 consecutive men who had undergone retropubic radical prostatectomy for clinically localized prostate cancer. None of the men had lymph node metastasis as determined by adequate follow-up and pathologic criteria. The preoperative predictive variables included clinical TNM stage, serum PSA level, biopsy Gleason score, and pMRI findings. The predicted result was biochemical failure (PSA level of 0.1 ng/mL or greater). The patient data were randomly split into four cross-validation sets and used to develop and validate the LRA and ANN models. The predictive ability of the ANN was compared with that of LRA, Han tables, and the Kattan nomogram using area under the receiver operating characteristic curve (AUROC) analysis. RESULTS: Of the 191 patients, 57 (30%) developed disease progression at a median follow-up of 64 months (mean 61, range 2 to 86). Using all the input variables, the AUROC of the ANN was significantly greater (P <0.05) than the AUROC of LRA, Han tables, or the Kattan nomogram for the prediction of PSA recurrence 5 years after radical prostatectomy (0.897 +/- 0.063 versus 0.785 +/- 0.060, 0.733 +/- 0.061, and 0.737 +/- 0.071, respectively). Removing the pMRI findings from the previous models, the AUROC of the ANN decreased statistically significantly (P <0.05) and was comparable to the AUROC of conventional predictive tools (P >0.05). CONCLUSIONS: Using the pMRI findings, the ANN was superior to LRA, predictive tables, and nomograms to predict biochemical recurrence accurately. Confirmatory studies are warranted.

Adult↗
PubMed2001

Evaluation of artificial neural networks for the prediction of pathologic stage in prostate carcinoma.

BACKGROUND: Currently, the standard for predicting pathologic stage from information available at the time of prostate biopsy is the "Partin nomograms" that were derived using logistic regression analysis. The authors retrospectively reviewed a large series of men with clinically localized prostate carcinoma who underwent staging pelvic lymphadenectomy and radical retropubic prostatectomy. They then utilized pathologic and clinical data at the time of prostate biopsy to develop and test an artificial neural network (ANN) to predict the final pathologic stage for this group of men. They then compared the results of ANN with the previous nomograms. METHODS: Five thousand seven hundred forty-four men were treated at the authors' institution from 1985 to 1998. An ANN was developed using two randomly selected training and validation sets for predicting pathologic stage. Input variables included age, preoperative serum prostate specific antigen level, clinical TNM (tumor, lymph node, and metastasis) classification, and Gleason score from the biopsy specimen. Outcomes included organ confinement and lymph node involvement status. RESULTS: The ANN was slightly superior to the nomograms in predicting pathologic stage, such as organ confinement and lymph node involvement status. CONCLUSIONS: In predicting organ confinement and lymph node involvement status, ANN was more accurate and had a larger area under ROC than the nomograms based on the logistic regression method. Artificial neural network models can be developed and used to better predict final pathologic stage when preoperative pathologic and clinical features are known.

Adult↗
PubMed1991

A method for prediction of phenytoin levels in the acute clinical setting.

Phenytoin (PHT) administration is complicated by saturation kinetics within the therapeutic range, causing marked changes in drug concentration with small changes in dose. The "half-life" increases with concentration, varying from 8-24 hr up to weeks, making it difficult to obtain the steady state levels needed by most prediction algorithms and nomograms. A Bayesian prediction program (Epidose) is presented which explicitly models PHT absorption and elimination kinetics in the non-steady state. The algorithm accounts for the interdependency of closely spaced sequential samples. Estimates of future PHT concentration were made on 20 hospital inpatients, most of whom were acutely ill and received other medications. Future (mean = 4 day) PHT concentrations were predicted over a range from 4 to 22 micrograms/ml (mean 13.9 micrograms/ml) with a median absolute error of 1.0 microgram/ml. These data demonstrate that the program can be used for accurate PHT concentration predictions in sick patients.

Adult↗
PubMed2005

A nomogram that predicts the presence of sentinel node metastasis in melanoma with better discrimination than the American Joint Committee on Cancer staging system.

BACKGROUND: The threshold and indications for sentinel lymph node (SLN) biopsy in patients with melanoma remain somewhat arbitrary. Many variables associated with SLN positivity have previously been identified, including a significant association between the American Joint Committee on Cancer (AJCC) staging system and SLN status. We developed a user-friendly nomogram that takes several characteristics into account simultaneously to more accurately predict the presence of SLN metastasis for an individual patient. METHODS: A total of 979 patients who underwent successful SLN biopsy for cutaneous melanoma at a single institution between February 1991 and November 2003 were included in the analysis. Predictors were used to develop a nomogram, based on logistic regression analysis, to predict the probability of SLN positivity. A large multi-institutional trial with 3108 patients was used to validate the predictive accuracy of the nomogram compared with the AJCC staging system. RESULTS: The nomogram was developed and found to be accurate and discriminating. The concordance index of the nomogram, a measure of predictive ability, was .694 when evaluated with the validation dataset. In contrast, the concordance index of the AJCC staging system was lower (.663; P < .001). CONCLUSIONS: Using commonly available clinicopathologic information, we developed a nomogram to accurately predict the probability of a positive SLN in patients with melanoma. This tool takes several characteristics into account simultaneously. This model should enable improved patient counseling and treatment selection.

Female↗
PubMed2025

Blood-based DNA methylation markers for autism spectrum disorder identification using machine learning.

BACKGROUND: Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder lacking objective biomarkers for early diagnosis. DNA methylation is a promising epigenetic marker, and machine learning offers a data-driven classification approach. However, few studies have examined whole-blood, genome-wide DNA methylation profiles for ASD diagnosis in school-aged children. METHODS: We analyzed genome-wide DNA methylation data from GEO dataset GSE113967, including 52 children with ASD and 48 typically developing (TD) controls. Differentially methylated positions (DMPs) were identified, and feature selection was performed using support vector machine-recursive feature elimination with cross-validation (SVM-RFECV). Classification models were developed using random forest (RF), extreme gradient boosting (XGBoost), and decision tree (DT) classifiers. A nomogram visualized feature contributions. RESULTS: A total of 138 DMPs differentiated ASD from TD children. Eleven CpG sites selected by SVM-RFECV formed the basis for model construction. RF and XGBoost achieved the highest accuracy (75%), with DT reaching 70%. Functional annotation indicated enrichment in cell adhesion and immune-related pathways. CONCLUSIONS: This exploratory study demonstrates the feasibility of integrating peripheral blood DNA methylation data with machine learning to distinguish children with ASD. While limited by sample size and moderate accuracy, this study provides methodological insights into the feasibility of integrating epigenetic and computational approaches for ASD-related biomarker exploration.

Humans↗
PubMed2005

The Vienna nomogram: validation of a novel biopsy strategy defining the optimal number of cores based on patient age and total prostate volume.

PURPOSE: We conducted a trial in patients with prostate specific antigen (PSA) levels from 2 to 10 ng/ml to validate a newly developed nomogram that defines the optimal number of biopsy cores required for prostate cancer (PCa) detection based on patient age and total prostate volume (Vienna nomogram). MATERIALS AND METHODS: A total of 502 patients underwent transrectal ultrasound guided prostate biopsy using the Vienna nomogram. These results were compared with those of a previous group of 1,051 patients who had standard octant biopsies followed by systematic repeat biopsies after 6 to 8 weeks if the initial biopsy result was negative for PCa. RESULTS: The overall PCa detection rate using the Vienna nomogram was 36.7% compared with 22% on first and 10% on repeat biopsy in the control group. The PCa detection rate using the Vienna nomogram was superior (p=0.002) to the octant biopsy technique, and comparable to a combination of first and repeat biopsy in the control group. Multivariate analysis of the Vienna nomogram showed that only PSA and the number of cores were independent predictors of PCa detection (chi-square = 49, p <0.001). Total prostate volume, transition zone volume and age were not independent predictors of PCa detection. CONCLUSIONS: The Vienna nomogram offers an easy tool to select the optimal number of prostate biopsy cores based on patient age and total prostate volume in PSA range 2 to 10 ng/ml. Cancer detection is significantly improved (66.4%) compared to the control group. The bias factor of larger prostate volume is eliminated by using the Vienna nomogram. Moreover, the Vienna nomogram is advantageous not only in terms of the improved PCa detection rate but also economically makes systematic repeat biopsies unnecessary.

Adult↗
PubMed2004

Assessment of the enhancement in predictive accuracy provided by systematic biopsy in predicting outcome for clinically localized prostate cancer.

PURPOSE: Current localized prostate cancer treatment outcome nomograms rely on prostate specific antigen (PSA), tumor stage and grade. We investigated whether the addition of prostate biopsy features may enhance the accuracy of a nomogram predicting recurrence after radical prostatectomy (RP). MATERIALS AND METHODS: Clinical data from 1,152 patients who underwent RP were used and included PSA, clinical stage, biopsy Gleason grade and systematic biopsy information that quantified the amount of cancer and high grade cancer. Predictive accuracy for freedom from recurrence after RP was assessed with and without tumor quantification in the biopsy by the area under the receiver operating characteristics curve (AUC). RESULTS: Percentage and number of cores with cancer, and percentage and number of cores with high grade cancer were predictors of outcome when added to models that included PSA, Gleason grade and clinical stage (all p <0.0001). Nomogram accuracy with 3 traditional variables (AUC 0.790) was minimally enhanced with the addition of percentage or number of positive cores (AUC 0.804 and 0.800, respectively), or percentage or number of cores with high grade cancer (AUC 0.802 and 0.800, respectively). Maximum predictive accuracy of 0.811 was achieved after supplementing the traditional 3-variable nomogram with various combinations of additional pathological predictors. CONCLUSIONS: The information provided by systematic biopsies substantially improves the ability to predict outcome following RP. However, some incremental predictive accuracy was achieved by adding systematic biopsy features.

Adult↗
PubMed2004

A clinicobiological model predicting survival in medulloblastoma.

PURPOSE: The purpose of this study was to determine the relative contributions of biological and clinical predictors of survival in patients with medulloblastoma (MB). EXPERIMENTAL DESIGN: Clinical presentation and survival information were obtained for 119 patients who had undergone surgery for MB at the Hospital for Sick Children (Toronto, Ontario, Canada) between 1985 and 2001. A tissue microarray was constructed from the tumor samples. The arrays were assayed for immunohistochemical expression of MYC, p53, platelet-derived growth factor receptor-alpha, ErbB2, MIB-1, and TrkC and for apoptosis (terminal deoxynucleotidyl transferase-mediated nick end labeling). Both univariable and multivariable analyses were conducted to characterize the association between survival and both clinical and biological markers. For the strongest predictors of survival, a weighted predictive score was calculated based on their hazard ratios (HRs). The sum of these scores was then used to give an overall prediction of survival using a nomogram. RESULTS: The four strongest predictors of survival in the final multivariable model were the presence of metastatic disease at presentation (HR, 2.02; P=0.01) and p53 (HR, 2.29; P=0.02), TrkC (HR, 0.65; P=0.14), and ErbB2 (HR, 1.51; P=0.21) immunopositivity. A linear prognostic index was derived, with coefficients equal to the logarithm of these HRs. The 5-year survival rate for patients at the 10th, 50th, and 90th percentiles of the score distribution was 80.0%, 71.0%, and 35.7%, respectively, with radiation therapy and 70.5%, 58.5%, and 20.0%, respectively, without radiation therapy. CONCLUSIONS: In this study, we demonstrate an approach to combining both clinical and biological markers to quantify risk in MB patients. This provides further prognostic information than can be obtained when either clinical factors or biological markers are studied separately and establishes a framework for comparing prognostic markers in future clinical studies.

Adolescent↗
PubMed2006

Development of a nomogram to predict probability of positive initial prostate biopsy among Japanese patients.

OBJECTIVES: Several nomograms for prostate cancer detection have recently been developed. Because the incidence of prostate cancer is lower among Asian men, nomograms based on Western populations cannot be directly applied to Japanese men. We, therefore, developed a model for predicting the probability of a positive initial prostate biopsy using clinical and laboratory data from a Japanese male population. METHODS: Data were collected from 834 Japanese male referrals who underwent initial prostate biopsies as individual screening. We analyzed age, total prostate-specific antigen (PSA) level, free/total PSA (f/t PSA) ratio, prostate volume, and digital rectal examination findings. Of these data, we randomly reserved 20% for study validation. Logistic regression analysis estimated relative risk, 95% confidence intervals, and P values. RESULTS: Independent predictors of a positive biopsy result included elevated PSA levels, decreased f/T PSA ratio, advanced age, small prostate volume, and abnormal digital rectal examination findings. We developed a predictive nomogram for an initial positive biopsy using these variables. The area under the receiver operating characteristic curve for the model was 81.8%, which was significantly greater than that of the prediction based on PSA alone (area under the receiver operating characteristic curve 67.8%). If externally validated, applying this model could reduce unnecessary biopsy procedures by 32% and reduce the overall need for prostate biopsies by 26%. CONCLUSIONS: In this study of a Japanese population, incorporating clinical and laboratory data into a prebiopsy nomogram significantly improved the prediction of prostate cancer compared with predictions based solely on the individual factors.

Aged↗
PubMed2001

An analytically solvable model for biomechanical response of the cornea to refractive surgery.

An analttically solvable model that considers the elasticity of the cornea is developed for use in the current and novel corneal refractive surgery procedures. The model assumes that the cornea is a thin spheroid shell with an elastic response to intraocular pressure. The value of the Young's modulus of the post-operative cornea and its dependence on the geometric parameters of the ablation zone are estimated employing "best-fit" approach to nomograms currently used in corneal refractive surgery. These elasticity parameters are applied for quantitative modeling of different types of refractive surgery for myopia.

Biomechanical Phenomena↗
PubMed2025

A machine learning model and identification of immune infiltration for chronic obstructive pulmonary disease based on disulfidptosis-related genes.

BACKGROUND: Chronic obstructive pulmonary disease (COPD) is a chronic and progressive lung disease. Disulfidptosis-related genes (DRGs) may be involved in the pathogenesis of COPD. From the perspective of predictive, preventive, and personalized medicine (PPPM), clarifying the role of disulfidptosis in the development of COPD could provide a opportunity for primary prediction, targeted prevention, and personalized treatment of the disease. METHODS: We analyzed the expression profiles of DRGs and immune cell infiltration in COPD patients by using the GSE38974 dataset. According to the DRGs, molecular clusters and related immune cell infiltration levels were explored in individuals with COPD. Next, co-expression modules and cluster-specific differentially expressed genes were identified by the Weighted Gene Co-expression Network Analysis (WGCNA). Comparing the performance of the random forest (RF), support vector machine (SVM), generalized linear model (GLM), and eXtreme Gradient Boosting (XGB), we constructed the ptimal machine learning model. RESULTS: DE-DRGs, differential immune cells and two clusters were identified. Notable difference in DRGs, immune cell populations, biological processes, and pathway behaviors were noted among the two clusters. Besides, significant differences in DRGs, immune cells, biological functions, and pathway activities were observed between the two clusters.A nomogram was created to aid in the practical application of clinical procedures. The SVM model achieved the best results in differentiating COPD patients across various clusters. Following that, we identified the top five genes as predictor genes via SVM model. These five genes related to the model were strongly linked to traits of the individuals with COPD. CONCLUSION: Our study demonstrated the relationship between disulfidptosis and COPD and established an optimal machine-learning model to evaluate the subtypes and traits of COPD. DRGs serve as a target for future predictive diagnostics, targeted prevention, and individualized therapy in COPD, facilitating the transition from reactive medical services to PPPM in the management of the disease.

Pulmonary Disease, Chronic Obstructive↗
PubMed1993

Delivery of constant air-oxygen mixtures using a closed circle absorber system.

The aim of this study was to devise and validate a technique to deliver constant air-oxygen mixtures from a standard anaesthetic machine using only oxygen as the compressed gas source. The common gas outlet was modified to allow measured quantities of ambient air to be insufflated via a three-way attachment into a closed circle absorber system with a double-circuit collapsible bellows ventilator. During positive pressure ventilation, leakages of between 50-150 ml.min-1 occur from the circuit and nomograms of the minimal air and oxygen flow rates needed to maintain constant oxygen concentrations in the presence of the leaks were then mathematically derived. The accuracy of the nomograms was tested on three different anaesthetic machines using test lung models. There were no differences observed among the mean oxygen concentrations using the three machines. Pooled mean values (SD) of 30.65% (0.77), 51.07% (1.04) and 70.4% (0.73) were obtained for predicted inspired concentrations of 30, 50 and 70% respectively. Next, the technique was studied on 18 patients who underwent isoflurane or propofol anaesthesia (duration 40-210 min) for various surgical procedures. Pooled mean values (SD) obtained were 29.3% (1.86), 40.95% (1.65) and 50.06% (1.41) respectively for predicted oxygen concentrations of 30, 40 and 50% respectively. We conclude that this technique can be used to deliver constant air-oxygen mixtures accurately during inhalational or total intravenous anaesthesia when N2O is contraindicated but a source of compressed air is not readily available.

Adult↗
PubMed1996

Is continuous infusion of beta-lactam antibiotics worthwhile?--efficacy and pharmacokinetic considerations.

The most important pharmacodynamic parameter for beta-lactam antibiotics has been shown to be the time above the MIC, which is used as an argument to administer beta-lactam antibiotics by continuous infusion. Studies in vitro and in laboratory animals comparing efficacy of continuous and intermittent infusion of beta-lactam antibiotics generally show continuous infusion to be more efficacious. While comparative trials in humans are scarce and a significant difference was only found in subgroup analysis in one study, several case-reports support the use of continuous infusion. Arguments in favour and against continuous infusion are discussed. Although dose-ranging studies have not yet been performed in humans, the results from in-vitro and in-vivo experiments indicate that 4 x MIC for the infecting bacterium would be the target concentration. Pharmacokinetic studies which have been performed in humans during continuous infusion show that serum concentrations can be predicted from total clearance or, using population pharmacokinetic modelling, the elimination rate constant as obtained during intermittent infusion. A nomogram is presented which allows calculation of the daily dose to obtain the target steady state blood concentrations suggested by the susceptibility of the infecting bacterium, usually 4 x MIC. For bacteria with a low MIC, the daily dose may be substantially lower than that used in conventional dosing regimens, while in infections which are difficult to treat as a result of more resistant bacteria, continuous infusion may be more effective than an equivalent bolus dose.

Animals↗
PubMed2003

Biochemical (prostate specific antigen) recurrence probability following radical prostatectomy for clinically localized prostate cancer.

PURPOSE: We retrospectively reviewed the clinical followup for a large series of men with clinically localized prostate cancer who underwent radical retropubic prostatectomy to identify clinical and/or pathological indicators of biochemical (prostate specific antigen [PSA]) recurrence. We then used those indicators to develop multivariate models for determination of recurrence probability following radical retropubic prostatectomy. MATERIALS AND METHODS: From 1982 to 1999, 2,091 consecutive men underwent radical retropubic prostatectomy and pelvic lymphadenectomy for clinically localized adenocarcinoma of the prostate (clinical stage T1c or T2 disease with Gleason score 5 or greater). Actuarial analysis was performed comparing freedom from biochemical recurrence after radical retropubic prostatectomy (PSA 0.2 ng./ml. or greater.) using the Kaplan-Meier method. Event time distributions for the time to recurrence were compared using the log rank statistic or the Cox proportional hazards regression model. The first model was developed using preoperative variables only and the second model using all available variables. Observed and predicted recurrence-free survival curves for different models were compared to select a model for calculation of predicted recurrence-free probabilities and confidence intervals. RESULTS: With a median followup of 5.9 years (range 1 to 17) 360 men (17%) had biochemical recurrence. Overall actuarial 5, 10 and 15-year biochemical recurrence-free survival rates were 84%, 72% and 61%, respectively. The relative risk of biochemical recurrence following surgery decreased with time, even after adjusted for other perioperative parameters. Variables identified for the preoperative model were biopsy Gleason score, clinical TNM stage and PSA. Variables identified for the postoperative model were prostatectomy Gleason score, PSA and pathological organ confinement status. Nomograms were generated and corrected for the decreasing relative risk of biochemical recurrence over time. CONCLUSIONS: Using 3 preoperative or postoperative parameters, these nomograms can easily be used to determine the 3, 5, 7 and 10-year biochemical recurrence-free survival probabilities among men who undergo radical retropubic prostatectomy for clinically localized prostate cancer in the modern era.

Adenocarcinoma↗
PubMed2003

Volume kinetic analysis of fluid shifts accompanying intravenous infusions of glucose solution.

Volume kinetics is a mathematical tool for macroscopic (whole-body) evaluation of the distribution and elimination of fluid given by intravenous infusion. Although the kinetic system has mostly been applied to crystalloid fluids, such as Ringer's solution, it has more recently been extended to glucose solution, which is characterized by interdependence between glucose and fluid kinetics. The elimination of glucose, as estimated by a one-compartment open model, serves as the driving force for cellular uptake of glucose and, by virtue of osmosis, of water. Key findings include the observation that the infused fluid, besides being accumulated in the cells, occupies a central body fluid space (V1), which is no larger than 3-4 L, and that the cellular hydration has a much longer time-course than the hydration of V1. This explains the risk of hypovolemia associated with rapid infusion of 5% glucose; the dilution of V1, which is quite substantial owing to the small size of this space at baseline, stimulates a brisk diuresis while the excess water is being "trapped" in the cells along with the glucose. Model linearity has been demonstrated for 2.5% glucose solution and this allows the construction of nomograms for administration of such fluid during surgery and critical illness.

Body Fluid Compartments↗
PubMed2026

A per- and polyfluoroalkyl substances-based gene signature links prognosis to immune landscapes in thyroid cancer.

BACKGROUND: Thyroid cancer (THCA) is the most common endocrine malignancy with a rising global incidence and significant heterogeneity. Although per- and polyfluoroalkyl substances (PFAS) exposure is linked to thyroid dysfunction, the prognostic value of per- and polyfluoroalkyl substances-related genes (PFASRGs) and their role in the tumor immune microenvironment (TME) remain poorly understood. This study aims to systematically screen key PFASRGs and evaluate their prognostic value as biomarkers for THCA. METHODS: Utilizing The Cancer Genome Atlas (TCGA)-THCA transcriptomic data and PFASRGs, we constructed a prognostic model through differential expression analysis, univariate and multivariate Cox regression analyses, and the least absolute shrinkage and selection operator (LASSO). The model's robustness was validated using receiver operating characteristic (ROC) curves, Kaplan-Meier analysis, and clinical nomograms. Furthermore, the TME, immunotherapy response, and drug sensitivities were systematically evaluated. Distinct molecular landscapes were characterized by stratifying the cohort via unsupervised consensus clustering analysis. RESULTS: The eight-gene prognostic model demonstrated robust performance, with area under the curve (AUC) values exceeding 0.85 across all validation cohorts. High-risk patients exhibited significantly shorter overall survival and an "inflamed" TME characterized by high immune scores and checkpoint expression. In contrast, the therapeutic efficacy of anti-programmed death-ligand 1 (PD-L1) agents was more pronounced in the low-risk category, as evidenced by a superior objective response. Furthermore, distinct molecular subtypes and risk-specific sensitivities to targeted agents, such as sorafenib and sunitinib, were identified, highlighting the model's clinical utility for personalized treatment. CONCLUSIONS: We established a novel THCA prognostic framework based on eight PFASRGs. This model exhibits superior performance in risk stratification, effectively distinguishing cohorts with divergent clinical trajectories, unique immune microenvironment features, and varied therapeutic responses. Our findings provide a powerful predictive tool for refining prognostic evaluation and facilitating the implementation of personalized management strategies for THCA patients.

Per- and polyfluoroalkyl substances-related genes ↗
PubMed2005

Clinical model to predict survival in chemonaive patients with advanced non-small-cell lung cancer treated with third-generation chemotherapy regimens based on eastern cooperative oncology group data.

PURPOSE: (1) Identify clinical factors that can be used to predict survival in chemotherapy-naive patients with advanced non-small-cell lung cancer (NSCLC) treated with third-generation chemotherapy regimens, and (2) build a clinical model to predict survival in this patient population. PATIENTS AND METHODS: Using data from two randomized, phase III Eastern Cooperative Oncology Group (ECOG) trials (E5592/E1594), we performed univariate and multivariate stepwise Cox regression analyses to identify survival prognostic factors. We used 75% of randomly sampled data to build a prediction model for survival, and the remaining 25% of data to validate the model. RESULTS: From 1993 to 1999, 1,436 patients with stage IV or IIIB NSCLC with effusion were treated with platinum-based doublets (involving either paclitaxel, docetaxel, or gemcitabine). The response rate and median survival time were 20% and 8.2 months, respectively. One- and 2-year survivals were 33% and 11%, respectively. In multivariate analysis, six independent poor prognostic factors were identified: skin metastasis (hazard ratio [HR], 1.88), lower performance status (ECOG 1 or 2; HR, 1.46), loss of appetite (HR, 1.62), liver metastasis (HR, 1.32), >/= four metastatic sites (HR, 1.20), and no prior surgery (HR, 1.16). A nomogram using six pretreatment prognostic factors was built to predict 1- and 2-year survival. CONCLUSION: Six pretreatment factors can be used to predict survival in chemotherapy-naive NSCLC patients treated with standard chemotherapy. Using our prognostic nomogram, 1- and 2-year survival probability of NSCLC patients can be estimated before treatment. This prognostic model may help clinicians and patients in clinical decision making, as well as investigators in research planning.

Aged↗