Dataset Information

Postoperative Nomogram for Predicting Cancer-Specific Mortality in Medullary Thyroid Cancer.

ABSTRACT:

Background

Medullary thyroid cancer (MTC) is a rare thyroid cancer accounting for 5 % of all thyroid malignancies. The purpose of our study was to design a predictive nomogram for cancer-specific mortality (CSM) utilizing clinical, pathological, and biochemical variables in patients with MTC.

Methods

MTC patients managed entirely at Memorial Sloan-Kettering Cancer Center between 1986 and 2010 were identified. Patient, tumor, and treatment characteristics were recorded, and variables predictive of CSM were identified by univariable analyses. A multivariable competing risk model was then built to predict the 10-year cancer specific mortality of MTC. All predictors of interest were added in the starting full model before selection, including age, gender, pre- and postoperative serum calcitonin, pre- and postoperative CEA, RET mutation status, perivascular invasion, margin status, pathologic T status, pathologic N status, and M status. Stepdown method was used in model selection to choose predictive variables.

Results

Of 249 MTC patients, 22.5 % (56/249) died from MTC, whereas 6.4 % (16/249) died secondary to other causes. Mean follow-up period was 87 ± 67 months. The seven variables with the highest predictive accuracy for cancer specific mortality included age, gender, postoperative calcitonin, perivascular invasion, pathologic T status, pathologic N status, and M status. These variables were used to create the final nomogram. Discrimination from the final nomogram was measured at 0.77 with appropriate calibration.

Conclusions

We describe the first nomogram that estimates cause-specific mortality in individual patients with MTC. This predictive nomogram will facilitate patient counseling in terms of prognosis and subsequent clinical follow up.

SUBMITTER: Ho AS

PROVIDER: S-EPMC4986610 | biostudies-literature | 2015 Aug

REPOSITORIES: biostudies-literature

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Publications

Postoperative Nomogram for Predicting Cancer-Specific Mortality in Medullary Thyroid Cancer.

Ho Allen S AS Wang Lu L Palmer Frank L FL Yu Changhong C Toset Arnbjorn A Patel Snehal S Kattan Michael W MW Tuttle R Michael RM Ganly Ian I

Annals of surgical oncology 20141104 8

<h4>Background</h4>Medullary thyroid cancer (MTC) is a rare thyroid cancer accounting for 5 % of all thyroid malignancies. The purpose of our study was to design a predictive nomogram for cancer-specific mortality (CSM) utilizing clinical, pathological, and biochemical variables in patients with MTC.<h4>Methods</h4>MTC patients managed entirely at Memorial Sloan-Kettering Cancer Center between 1986 and 2010 were identified. Patient, tumor, and treatment characteristics were recorded, and variabl ...[more]

PMID: 25366585

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Project description:BackgroundThe natural history of prostate-specific antigen (PSA)-defined biochemical recurrence (BCR) of prostate cancer (PCa) after definitive local therapy is highly variable. Validated prediction models for PCa-specific mortality (PCSM) in this population are needed for treatment decision-making and clinical trial design.ObjectiveTo develop and validate a nomogram to predict the probability of PCSM from the time of BCR among men with rising PSA levels after radical prostatectomy.Design, setting, and participantsBetween 1987 and 2011, 2254 men treated by radical prostatectomy at one of five high-volume hospitals experienced BCR, defined as three successive PSA rises (final value >0.2 ng/ml), single PSA >0.4 ng/ml, or use of secondary therapy administered for detectable PSA >0.1 ng/ml. Clinical information and follow-up data were modeled using competing-risk regression analysis to predict PCSM from the time of BCR.InterventionRadical prostatectomy for localized prostate cancer and subsequent PCa BCR.Outcome measurements and statistical analysisPCSM.Results and limitationsThe 10-yr PCSM and mortality from competing causes was 19% (95% confidence interval [CI] 16-21%) and 17% (95% CI 14-19%), respectively. A nomogram predicting PCSM for all patients had an internally validated concordance index of 0.774. Inclusion of PSA doubling time (PSADT) in a nomogram based on standard parameters modestly improved predictive accuracy (concordance index 0.763 vs 0.754). Significant parameters in the models were preoperative PSA, pathological Gleason score, extraprostatic extension, seminal vesicle invasion, time to PCa BCR, PSA level at PCa BCR, and PSADT (all p<0.05).ConclusionsWe constructed and validated a nomogram to predict the risk of PCSM at 10 yr among men with PCa BCR after radical prostatectomy. The nomogram may be used for patient counseling and the design of clinical trials for PCa.Patient summaryFor men with biochemical recurrence of prostate cancer after radical prostatectomy, we have developed a model to predict the long-term risk of death from prostate cancer.

Project description:BackgroundThe diagnosis of radioiodine refractory differentiated thyroid cancer (RAIR-DTC) is primarily based on clinical evolution and iodine uptake over the lesions, which is still time-consuming, thus urging a predictive model for timely RAIR-DTC informing. The aim of this study was to develop a nomogram model for RAIR prediction among DTC patients with distant metastases (DM).MethodsData were extracted from the treatment and follow-up databases of Peking Union Medical College Hospital between 2010 and 2021. A total of 124 patients were included and divided into RAIR (n=71) and non-RAIR (n=53) according to 2015 ATA guidelines. All patients underwent total thyroidectomy followed by at least two courses of RAI treatment. Serological markers and various clinical, pathological, genetic status, and imaging factors were integrated into this study. The pre-treatment stimulated Tg and pre- and post-treatment suppressed Tg at the first and second course RAI treatment were defined as s-Tg1, s-Tg2, sup-Tg1, and sup-Tg2, respectively. Δs-Tg denoted s-Tg1/s-Tg2, and Δs-TSH denoted s-TSH1/s-TSH2. Multivariate logistic regression and correlation analysis were utilized to determine the independent predictors of RAIR. The performance of the nomogram was assessed by internal validation and receiver operating characteristic (ROC) curve, and benefit in clinical decision-making was assessed using decision curve.ResultsIn univariate logistic regression, nine possible risk factors were related to RAIR. Correlation analysis showed four of the above factors associated with RAIR. Through multivariate logistic regression, Δs-Tg/Δs-TSH<1.50 and age upon diagnosis were obtained to develop a convenient nomogram model for predicting RAIR. The model was internally validated and had good predictive efficacy with an AUC of 0.830, specificity of 0.830, and sensitivity of 0.755. The decision curve also showed that if the model is used for clinical decision-making when the probability threshold is between 0.23 and 0.97, the net benefit of patients is markedly higher than that of the TreatAll and TreatNone control groups.By using 1.50 as a cut-off ofΔs-Tg/Δs-TSH, differing biochemical progression among the generally so-called RAIR can be further stratified as meaningfully rapidly or slowly progressive patients (P=0.012).ConclusionsA convenient user-friendly nomogram model was developed with good predictive efficacy for RAIR. The progression of RAIR can be further stratified as rapidly or slowly progressive by using 1.50 as a cut-off value of Δs-Tg/Δs-TSH.

Dataset Information

Postoperative Nomogram for Predicting Cancer-Specific Mortality in Medullary Thyroid Cancer.

Background

Methods

Results

Conclusions

Publications

Postoperative Nomogram for Predicting Cancer-Specific Mortality in Medullary Thyroid Cancer.

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