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At least 163 records · Page 9Linked to original sources
PubMed2006

Defining high risk prostate cancer with risk groups and nomograms: implications for designing clinical trials.

PURPOSE: Death from prostate cancer is usually preceded by metastases and it usually occurs in men with high risk disease who experienced biochemical failure with a short prostate specific antigen doubling time. We developed a model for determining disease specific survival in prostate cancer. MATERIALS AND METHODS: We used the model for defining high risk prostate cancer that was developed by the Radiation Therapy Oncology Group and combined it with the Kattan nomogram for predicting the risk of metastases. We selected 414 Radiation Therapy Oncology Group intermediate and high risk patients who were treated with external beam radiotherapy alone. Excluded were patients with low risk disease. The Kaplan-Meier product limit method was used to estimate the probability of freedom from biochemical failure, overall survival and disease specific survival. RESULTS: A significant difference was observed in freedom from biochemical failure, disease specific survival and overall survival among the 3 tertiles created by the nomogram using the cutoff points less than 8.5%, 8.5% to 15% and greater than 15% (p <0.001, 0.0002 and 0.0003, respectively). Only the risk of metastases using the categorized nomogram score (less than 8.5% and 8.5% to 15% vs greater than 15%), not preradiotherapy prostate specific antigen or Radiation Therapy Oncology Group risk (Radiation Therapy Oncology Group 2 vs 3), was a significant predictor of disease specific and overall survival for intermediate/high risk patients and intermediate/high risk with 15% or less risk for metastases. CONCLUSIONS: We combined a risk group stratification scheme for disease specific survival with a nomogram predicting the risk of metastases and created a model that may be useful for designing phase III trials with metastases and disease specific survival as study end points.

Clinical Trials as Topic↗
PubMed2005

A postoperative prognostic nomogram predicting recurrence for patients with conventional clear cell renal cell carcinoma.

PURPOSE: Few published studies have simultaneously analyzed multiple prognostic factors to predict recurrence after surgery for conventional clear cell renal cortical carcinomas. We developed and performed external validation of a postoperative nomogram for this purpose. We used a prospectively updated database of more than 1,400 patients treated at a single institution. MATERIALS AND METHODS: From January 1989 to August 2002, 833 nephrectomies (partial and radical) for renal cell carcinoma of conventional clear cell histology performed at Memorial Sloan-Kettering Cancer Center were reviewed from the center's kidney database. Patients with von Hippel-Lindau disease or familial syndromes, as well as patients presenting with synchronous bilateral renal masses, or distant metastases or metastatic regional lymph nodes before or at surgery were excluded from study. We modeled clinicopathological data and disease followup for 701 patients with conventional clear cell renal cell carcinoma. Prognostic variables for the nomogram included pathological stage, Fuhrman grade, tumor size, necrosis, vascular invasion and clinical presentation (ie incidental asymptomatic, locally symptomatic or systemically symptomatic). RESULTS: Disease recurrence was noted in 72 of 701 patients. Those patients without evidence of disease had a median and maximum followup of 32 and 120 months, respectively. The 5-year probability of freedom from recurrence for the patient cohort was 80.9% (95% confidence interval 75.7% to 85.1%). A nomogram was designed based on a Cox proportional hazards regression model. Following external validation predictions by the nomogram appeared accurate and discriminating, and the concordance index was 0.82. CONCLUSIONS: A nomogram has been developed that can be used to predict the 5-year probability of freedom from recurrence for patients with conventional clear cell renal cell carcinoma. This nomogram may be useful for patient counseling, clinical trial design and effective patient followup strategies.

Carcinoma, Renal Cell↗
PubMed2006

Development and validation of a nomogram for predicting outcome of patients with vulvar cancer.

OBJECTIVE: To construct and validate a nomogram to predict relapse-free survival of patients treated for vulvar cancer. METHODS: Data from 244 patients treated for vulvar cancer at a single institution (Creteil, France) were used as a training set to develop and calibrate a nomogram for predicting relapse-free survival and local relapse-free survival. We used bootstrap resampling for the internal validation and we tested the nomogram on an independent validation set of patients (Torino, Italy) for the external validation. RESULTS: The nomograms were based on a Cox proportional hazards regression model. Covariates for the relapse-free survival model included age, T stage, number of metastatic nodes, bilateral lymph node involvement, omission of the lymphadenectomy, margin status, lymphovascular space invasion, and depth of invasion. The concordance indices were 0.85 and 0.83 in the training set before and after bootstrapping, respectively, and 0.83 in the validation set. The predictions of our nomogram discriminated better than did the International Federation of Gynecology and Obstetrics stage (0.83 compared with 0.78, P = .01). The calibration of our nomogram was good. In the validation set, 2-year and 5-year relapse-free survival were well predicted with less than 5% difference between the predicted and observed survivals for each quartile. A nomogram for predicting local relapse was also developed. CONCLUSION: We have developed nomograms for predicting distant and local relapse of vulvar cancer at 2 and 5 years and validated them both internally and externally. These nomograms will be freely available on the International Society for the Study of Vulvovaginal Disease Web site. LEVEL OF EVIDENCE: III.

Adult↗
PubMed2005

Estimation of pelvic tilt on anteroposterior X-rays--a comparison of six parameters.

OBJECTIVE: To compare six different parameters described in literature for estimation of pelvic tilt on an anteroposterior pelvic radiograph and to create a simple nomogram for tilt correction of prosthetic cup version in total hip arthroplasty. DESIGN: Simultaneous anteroposterior and lateral pelvic radiographs are taken routinely in our institution and were analyzed prospectively. The different parameters (including three distances and three ratios) were measured and compared to the actual pelvic tilt on the lateral radiograph using simple linear regression analysis. PATIENTS: One hundred and four consecutive patients (41 men, 63 women with a mean age of 31.7 years, SD 9.2 years, range 15.7-59.1 years) were studied. RESULTS: The strongest correlation between pelvic tilt and one of the six parameters for both men and women was the distance between the upper border of the symphysis and the sacrococcygeal joint. The correlation coefficient was 0.68 for men (P<0.001) and 0.61 for women (P<0.001). Based on this linear correlation, a nomogram was created that enables fast, tilt-corrected cup version measurements in clinical routine use. CONCLUSION: This simple method for correcting variations in pelvic tilt on plain radiographs can potentially improve the radiologist's ability to diagnose and interpret malformations of the acetabulum (particularly acetabular retroversion and excessive acetabular overcoverage) and post-operative orientation of the prosthetic acetabulum.

Adolescent↗
PubMed1999

Naive Bayesian-based nomogram for prediction of prostate cancer recurrence.

This paper introduces a schema with naive-Bayesian classifier and patient weighting technique to develop a prostate cancer recurrence prediction model from patient data. We propose the graphical presentation of naive-Bayesian classifier with a nomogram, which can be used both for prediction or can provide means to data analysis. The resulting model was experimentally evaluated; the results were favorable both in terms of interpretability and predictive accuracy.

Bayes Theorem↗
PubMed2003

Predictive modeling for the presence of prostate carcinoma using clinical, laboratory, and ultrasound parameters in patients with prostate specific antigen levels < or = 10 ng/mL.

BACKGROUND: The objective of the current study was to develop a model for predicting the presence of prostate carcinoma using clinical, laboratory, and transrectal ultrasound (TRUS) data. METHODS: Data were collected on 1237 referred men with serum prostate specific antigen (PSA) levels < or = 10 ng/mL who underwent an initial prostate biopsy. Variables analyzed included age, race, family history, referral indication(s), prior vasectomy, digital rectal examination (DRE), PSA level, PSA density (PSAD), and TRUS findings. Twenty percent of the data were reserved randomly for study validation. Logistic regression analysis was performed to estimate the relative risk, 95% confidence interval, and P values. RESULTS: Independent predictors of a positive biopsy result included elevated PSAD, abnormal DRE, hypoechoic TRUS finding, and age 75 years or older. Based on these variables, a predictive nomogram was developed. The sensitivity and specificity of the model were 92% and 24%, respectively, in the validation study for which the predictive probability > or = 10% was used to indicate the presence of prostate carcinoma. The area under the receiver operating characteristic curve (AUC) for the model was 73%, which was significantly higher compared with the prediction based on PSA alone (AUC, 62%). If it was validated externally, then application of this model to the biopsy decision could result in a 24% reduction in unnecessary biopsy procedures, with an overall reduction of 20%. CONCLUSIONS: Incorporation of clinical, laboratory, and TRUS data into a prebiopsy nomogram significantly improved the prediction of prostate carcinoma over the use of individual factors alone. Predictive nomograms may serve as an aid to patient counseling regarding prostate biopsy outcome and to reduce the number of unnecessary biopsy procedures.

Adenocarcinoma↗
PubMed2001

Prognostic significance of pathologic features in localized prostate cancer treated with radical prostatectomy: implications for staging systems and predictive models.

PURPOSE: Although predicting outcome for men with clinically localized prostate cancer (PC) has improved, the staging system and nomograms used to do this are based on results from the North American health system. To be internationally applicable, these models require testing in cohorts from a variety of different health systems based on the predominant PC case identification methods used. PATIENTS AND METHODS: We studied 732 men with localized PC treated with radical prostatectomy and no preoperative therapy between 1986 and 1999 at one Australian institution to determine the effect of clinicopathologic features on disease-free survival. RESULTS: Preoperative serum prostate-specific antigen (PSA) concentration, Gleason score, pathologic stage, and year of surgery were independent predictors of outcome. Although margin status demonstrated only a trend toward significance in multivariate modeling overall, it proved to be independent in subgroups based on later year of surgery (1986 to 1994 v 1995 to 1998), preoperative PSA of less than 10 ng/mL, and Gleason score > or = 7. Adjuvant radiation therapy improved disease-free survival rates in patients with multiple surgical margin involvement. CONCLUSION: This work confirms the prognostic significance of pathologic stage, Gleason score, and preoperative serum PSA. In the context of a contemporaneous screening effect in Australia, these findings may have implications for methods that predict outcome following surgery as screening becomes more prevalent in a population. The independent prognostic effect of margin status may alter with an increase in the proportion of screening-identified PCs. Staging systems and nomograms that predict outcome following surgery require validation in cohorts with different health practices before being universally applied.

Adenocarcinoma↗
PubMed1976

Calculations for pH during CO2 and O2 exchange with blood.

New mathematical formulas are presented to calculate directly the change in blood pH during CO2, and O2 exchange with blood. pH changes are calculated from changes in blood PCO2 and saturation values or from changes in blood PCO2 and blood PO2 values. Computational results agree well with information obtained from a Dill nomogram. These new formulas have applicability to the modelling of gas exchange in blood oxygenators as well as to estimating blood acid-base parameters following the mixing of blood samples.

Blood↗
PubMed1991

[Multifactorial experiment on the combined effect of rifampicin and microbial polysaccharide in experimental plague infection].

Multifactorial analysis was applied to the study of the combined effect of rifampicin and a microbial polysaccharide in experimental plague infection. The effect of the antibiotic and immunomodulator was shown to be synergistic. On the basis of the study results polynomial statistic models of the second order were designed and nomograms or equal level lines were plotted which provided optimization of the combined chemo- and immunotherapy.

Animals↗
PubMed2026

Screening of molecular biomarkers ASPN and LBH and construction of a prediction nomogram for the progression of esophagogastric junction adenocarcinoma.

BACKGROUND: Esophagogastric junction adenocarcinoma (EGJA) is an aggressive malignancy of the digestive system with poor prognosis. Early diagnosis and accurate prediction of tumor progression remain major clinical challenges. This study aimed to identify and validate molecular biomarkers and construct a precise diagnostic model, providing a scientific basis for individualized treatment. METHODS: Differentially expressed genes (DEGs) associated with EGJA were identified using The Cancer Genome Atlas (TCGA) database. Quantitative real-time polymerase chain reaction (qRT-PCR) was then performed for further screening. The protein expression levels of ASPN and LBH were validated by immunohistochemistry in both tumor and adjacent non-tumor tissues. A nomogram was constructed by integrating clinical and pathological features, and its performance and clinical utility were assessed using receiver operating characteristic (ROC) curves and decision curve analysis (DCA). RESULTS: Immunohistochemistry demonstrated that the protein expression of ASPN was significantly upregulated in tumor tissues, with expression levels increasing with tumor stage. Conversely, LBH was downregulated in tumor tissues and decreased with advancing stages. The predictive model achieved an area under the curve (AUC) value of 0.977, indicating excellent diagnostic and prognostic performance. DCA confirmed the clinical net benefit of the model. CONCLUSIONS: ASPN and LBH are critical molecular biomarkers for EGJA. The nomogram combining these two markers enables accurate distinction between early and advanced-stage tumors, offering significant support for early diagnosis of EGJA.

ASPN↗
PubMed2006

Use of nomograms as predictive tools in bladder cancer.

Bladder cancer is a common genitourinary malignancy that demonstrates a great variation in risk of tumor recurrence and progression following treatment. The dramatic differences in clinical behavior dictate vastly differing treatments, which may range from simple surveillance to combination radical surgery with systemic chemotherapy. For non-muscle invasive bladder cancer prediction of the risk of recurrence and progression is necessary to assess the need for intravesical therapy and possible early cystectomy. In contrast, prediction of advanced disease response to primary treatment such as cystectomy and the response to systemic chemotherapy plays an important role in treatment assignment for patients with muscle invasive disease. To estimate these risk traditional risk grouping schemes such as the present TNM staging system has been used to guide patient treatment. More recently, improved prognostic tools such as nomograms have been developed to provide a more accurate assessment of outcomes. Clinicians are enthusiastically working to utilize these statistical methods in bladder cancer. We summarize the current status of outcome predictive models for bladder cancer; and focus particularly on the ability of nomograms to predict disease recurrence, progression, and patient survival.

Algorithms↗
PubMed2026

An individualized nomogram for predicting progression-free survival in systemic anaplastic large cell lymphoma: a multicenter, retrospective, and internally validated study.

OBJECTIVES: To develop an individualized nomogram for predicting disease progression risk in systemic anaplastic large cell lymphoma (sALCL). METHODS: Independent predictors of progression-free survival (PFS) were identified using Cox regression in a multicenter retrospective cohort of 109 sALCL patients (2010-2022). These were incorporated into a three-factor nomogram, evaluated via bootstrapped internal validation (1000 resamples), ROC analysis, C-index, decision curve analysis (DCA), and clinical impact curve (CIC). RESULTS: A total of 29 PFS events occurred during a median follow-up of 31 months. Multivariable modelling selected serum &#x3b2;2-microglobulin elevation, extranodal disease, and front-line chemotherapy choice (CHOP versus CHOPE or BV+CHP) as autonomous progression drivers. Upon internal bootstrap validation, the nomogram yielded strong prognostic accuracy, achieving AUCs of 0.81, 0.85 and 0.87 for 1-, 3- and 5-year progression-free survival, alongside a corrected C-index of 0.779 (95% CI: 0.699 - 0.861). Calibration plots showed close agreement between predicted and observed outcomes, while DCA confirmed superior net clinical benefit versus conventional IPI or Ann Arbor stratification across multiple decision thresholds. CONCLUSION: This first sALCL-specific nomogram integrates clinical and treatment variables to provide personalized PFS risk estimation. While internally validated, this exploratory, observation-based tool requires external validation and recalibration in prospective cohorts before clinical implementation.

Humans↗
PubMed2000

Assessing radiographic status of rheumatoid arthritis: introduction of a short erosion scale.

OBJECTIVE: Although radiographs are an important marker of rheumatoid arthritis severity, no valid simple scoring method exists. Current scoring systems are cumbersome, difficult to learn, time consuming, and suitable only for experts. In addition, there is no "gold standard" for radiographic severity, so that it is impossible for the clinician, trialist, or researcher to place patients' scores along a continuum of radiographic severity. We investigated the scoring and scaling properties of radiographs read by the Larsen method, and we developed a shortened scale that can be placed in the perspective of a linear severity continuum. METHODS: A total of 3,538 paired hand radiographs obtained over a 24 year period were read by Larsen method and evaluated by Rasch analysis. By iterative methods the number of joints was reduced so that proper fitting, scaling, and dimensionality were obtained. The shortened scale was then tested against the full Larsen scale to determine its ability to detect radiographic progression. RESULTS: A scale consisting of 6 joints on each hand (3 wrist areas and 3 metacarpophalangeal joints) performed as well as the full 28 joint Larsen score, and had improved fit and scaling properties. This Short Erosion Scale (SES) also performed as well as the Larsen score in measuring radiographic progression as measured by comparative effect sizes. In addition, a nomogram was developed to translate SES scores into radiographic severity scores according to a Rasch logit scale. CONCLUSION: We developed a Short Erosion Scale by reducing the number of joints evaluated in the Larsen method from 28 to 12. The scale is at least as sensitive in detecting change as the full Larsen scale. SES fits the Rasch model and is hierarchical and generally well spaced. Graphs and nomograms are available so that intrinsic radiographic severity can be documented. This scale is suitable for use in clinical practice and observational studies, and may be appropriate for clinical trials as well.

Arthritis, Rheumatoid↗
PubMed1989

[A mathematical model for predicting the efficacy of glucocorticoid therapy of glomerulonephritis].

A mathematical model is proposed for prediction of the efficacy of glucocorticoid therapy developed on the basis of the Bayes theorem and successive Wald's analysis. The model uses the retrospective values of the results of radioligand determination of the number of glucocorticoid receptor of lymphocytes and static renal scintigraphy. By means of the blind method the informative value of the mathematical model to predict the inefficacy of glucocorticoid therapy in nephrotic glomerulonephritis was 96%. A nomogram was developed.

Algorithms↗
PubMed2006

Pre-treatment nomogram for disease-specific survival of patients with chemotherapy-naive androgen independent prostate cancer.

OBJECTIVE: Our objective was to develop a nomogram that predicts the probability of cancer-specific survival in men with untreated androgen-independent prostate cancer (AIPC). METHODS: AIPC was diagnosed in 129 consecutive patients between 1989 and 2002. No patient received cytotoxic chemotherapy. Univariate and multivariate Cox regression models were used to test the association between prostate-specific antigen (PSA) level at initiation of androgen deprivation, PSA doubling time (PSADT), PSA nadir on androgen deprivation therapy (ADT), time from ADT to AIPC, and AIPC-specific mortality. Multivariate regression coefficients were then used to develop a nomogram predicting AIPC-specific survival at 12-60 mo after AIPC diagnosis. Two-hundred bootstrap resamples were used to internally validate the nomogram. RESULTS: AIPC-specific mortality was recorded in 74 of 129 patients (57.4%). Other-cause mortality was recorded in 7 men (5.4%). Median overall survival was 52.0 mo (mean, 36.0 mo) and median AIPC-specific survival was 54.0 mo (mean, 35.0 mo). In univariate regression models, all variables were significant predictors of AIPC-specific survival (p < or = 0.02). In multivariate models, PSADT and time from androgen deprivation to AIPC remained statistically significant (p < or = 0.004). Bootstrap-corrected predictive accuracy of the nomogram was 80.9% versus 74.9% for our previous model. CONCLUSIONS: A nomogram predicting AIPC-specific survival is between 13% and 14% more accurate than previous nomograms and 6% more accurate than tree regression-based predictions obtained from the same data. Moreover, a nomogram approach combines several advantages, such as user-friendly interface and precise estimation of individual recurrence probability at several time points after AIPC diagnosis, which all patients deserve to know and all treating physicians need to know.

Aged↗
PubMed2000

A model to predict poor survival in patients undergoing transjugular intrahepatic portosystemic shunts.

Transjugular intrahepatic portosystemic shunts (TIPS) may worsen liver function and decrease survival in some patients. The Child-Pugh classification has several drawbacks when used to determine survival in such patients. The survival of 231 patients at 4 medical centers within the United States who underwent elective TIPS was studied to develop statistical models to (1) predict patient survival and (2) identify those patients whose liver-related mortality post-TIPS would be 3 months or less. Among these elective TIPS patients, 173 had the procedure for prevention of variceal rebleeding and 58 for treatment of refractory ascites. Death related to liver disease occurred in 110 patients, 70 within 3 months. Cox proportional-hazards regression identified serum concentrations of bilirubin and creatinine, international normalized ratio for prothrombin time (INR), and the cause of the underlying liver disease as predictors of survival in patients undergoing elective TIPS, either for prevention of variceal rebleeding or for treatment of refractory ascites. These variables can be used to calculate a risk score (R) for patients undergoing elective TIPS. Patients with R > 1.8 had a median survival of 3 months or less. This model was superior to both the Child-Pugh classification, as well as the Child-Pugh score, in predicting survival. Using logistic regression and the same variables, we also developed a nomogram that indicates which patients survive less than 3 months. Finally, the model was validated among an independent set of 71 patients from the Netherlands. This Mayo TIPS model may predict early death following elective TIPS for either prevention of variceal rebleeding or for treatment of refractory ascites.

Adult↗