Development and Validation of Nomograms to Predict Local, Regional, and Distant Recurrence in Patients With Thin (T1) Melanomas

2021 ◽  
Vol 39 (11) ◽  
pp. 1243-1252
Author(s):  
Mary-Ann El Sharouni ◽  
Tasnia Ahmed ◽  
Alexander H. R. Varey ◽  
Sjoerd G. Elias ◽  
Arjen J. Witkamp ◽  
...  
Keyword(s):  
High Risk ◽  
Sentinel Node ◽  
External Validation ◽  
Distant Recurrence ◽  
Treatment Center ◽  
Cox Models ◽  
T Stage ◽  
Node Status ◽  
C Statistic ◽  
Sentinel Node Status

PURPOSE Although the prognosis of patients with thin primary cutaneous melanomas (T1, ≤ 1.0 mm) is generally excellent, some develop recurrence. We sought to develop and validate a model predicting recurrences in patients with thin melanomas. METHODS A Dutch population-based cohort (n = 25,930, development set) and a cohort from an Australian melanoma treatment center (n = 2,968, validation set) were analyzed (median follow-up 6.7 and 12.0 years, respectively). Multivariable Cox models were generated for local, regional, and distant recurrence-free survival (RFS). Discrimination was assessed using Harrell's C-statistic for each outcome. Each nomogram performance was evaluated using calibration plots defining low-risk and high-risk groups as the lowest and top 5% of the nomogram risk score, respectively. The nomograms' C-statistics were compared with those of a model including the current American Joint Committee on Cancer staging parameters (T-stage and sentinel node status). RESULTS Local, regional, and distant recurrences were found in 209 (0.8%), 503 (1.9%), and 203 (0.8%) Dutch patients, respectively, and 23 (0.8%), 61 (2.1%), and 75 (2.5%) Australian patients, respectively. C-statistics of 0.79 (95% CI, 0.75 to 0.82) for local RFS, 0.77 (95% CI, 0.75 to 0.78) for regional RFS, and 0.80 (95% CI, 0.77 to 0.83) for distant RFS were obtained for the development model. External validation showed C-statistics of 0.80 (95% CI, 0.69 to 0.90), 0.76 (95% CI, 0.70 to 0.82), and 0.74 (95% CI, 0.69 to 0.80), respectively. Calibration plots showed a good match between predicted and observed rates. Using the nomogram, the C-statistic was increased by 9%-12% for the development cohort and by 11%-15% for the validation cohort, compared with a model including only T-stage and sentinel node status. CONCLUSION Most patients with thin melanomas have an excellent prognosis, but some develop recurrence. The presented nomograms can accurately identify a subgroup at high risk. An online calculator is available at www.melanomarisk.org.au .

Molecular markers to complement sentinel node status in predicting survival in patients with high-risk locally invasive melanoma

2016 ◽  
Vol 139 (3) ◽  
pp. 664-672 ◽  
Author(s):  
Casey J. Rowe ◽  
Fiona Tang ◽  
Maria Celia B. Hughes ◽  
Mathieu P. Rodero ◽  
Maryrose Malt ◽  
...  
Keyword(s):  
Molecular Markers ◽  
High Risk ◽  
Sentinel Node ◽  
Node Status ◽  
Sentinel Node Status

Sentinel node total tumour load as a predictive factor for non-sentinel node status in early breast cancer patients – The porttle study

2020 ◽  
Vol 32 ◽  
pp. 108-114 ◽  
Author(s):  
José Luis Fougo ◽  
Isabel Amendoeira ◽  
Maria José Brito ◽  
Ana Paula Correia ◽  
Ana Gonçalves ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Sentinel Node ◽  
Cancer Patients ◽  
Early Breast Cancer ◽  
Predictive Factor ◽  
Breast Cancer Patients ◽  
Node Status ◽  
Tumour Load ◽  
Non Sentinel Node Status ◽  
Sentinel Node Status

scholarly journals Identification of the high-risk patient in primary percutaneous coronary intervention: development and validation of a novel predictive index

2020 ◽  
Vol 41 (Supplement_2) ◽  
Author(s):  
S Blake ◽  
C Proscia ◽  
A Eleuteri ◽  
D Groves ◽  
R.H Stables
Keyword(s):  
High Risk ◽  
Risk Model ◽  
External Validation ◽  
Coronary Intervention ◽  
Lv Function ◽  
Composite Outcome ◽  
C Statistic ◽  
Percutaneous Coronary ◽  
Risk Patients ◽  
Myocardial Blush Grade

Abstract Background Primary percutaneous coronary intervention (PPCI) is the best treatment for patients with ST elevation myocardial infarction (STEMI). Several risk scores have been created to help risk-stratify these patients but few of these can be calculated in-lab, during the acute event. Development of a score that could be applied during PPCI could aid operators' decisions regarding adjunctive therapies and post-procedural surveillance which could improve patient outcomes. This study aimed to develop a simple, practical risk model that could be applied during PPCI to identify high-risk patients. Methods Demographic, clinical and outcome data were collected for all patients, as part of the HEAT-PPCI trial, who underwent PPCI for suspected STEMI between February 2012 and November 2013 at our hospital. Independent predictors of the composite outcome of 28-day mortality or severe impairment of LV function (ejection fraction ≤35%) were identified using multiple logistic regression. A risk model was fitted and internal validation was performed by bootstrapping. External validation was performed on a separate cohort of patients with STEMI. Results The derivation cohort included 1271 patients, with 131/1271 = 10.3% experiencing the composite outcome of 28-day mortality or poor LV function. Three variables were required to predict the outcome: age (OR:2.07, 95% CI 1.55 to 2.78), location of the culprit artery (OR:6.16, 95% CI 4.00 to 9.47), myocardial blush grade post-PPCI (OR:2.32, 95% CI 1.39 to 3.88). External validation was performed on 324 patients undergoing PPCI from a different centre. The model showed good discrimination on ROC-curve analysis (c statistic 0.79, 95% CI 0.75 to 0.83) and performed well on external validation (c statistic 0.87, 95% CI 0.72 to 0.95). Accuracy of the risk model on the validation data was improved by simple recalibration. The model was used to create a risk prediction chart that can be used in-lab during PPCI (Figure 1). Conclusions We have developed a risk model that accurately predicts 28-day mortality or poor LV function following STEMI using age, culprit location and myocardial blush grade. The model can assist operators in identifying high-risk patients during PPCI. Funding Acknowledgement Type of funding source: Public hospital(s). Main funding source(s): National Health Service, UK

A comprehensive prognostic index for patients with CLL.

2013 ◽  
Vol 31 (15_suppl) ◽  
pp. 7015-7015
Author(s):  
Natali Pflug ◽  
Jasmin Bahlo ◽  
Tait D. Shanafelt ◽  
Barbara Eichhorst ◽  
Manuela Bergmann ◽  
...  
Keyword(s):  
High Risk ◽  
Risk Score ◽  
Mayo Clinic ◽  
External Validation ◽  
Prognostic Index ◽  
Observation Time ◽  
Clinical Staging ◽  
Newly Diagnosed ◽  
C Statistic ◽  
Very High

7015 Background: Besides clinical staging, a number of biomarkers predicting OS in CLL have been identified. The multiplicity of markers, limited information on their independent value, and a lack of understanding of how to interpret discordant markers are major barriers to use in routine clinical practice. We developed an integrated prognostic index using the database of the German CLL Study Group (GCLLSG), which was subsequently validated in a cohort of untreated CLL patients (pts) from the Mayo Clinic. Methods: The analysis was based on a dataset collected between 1997 and 2006 in 3 GCLLSG phase III trials. The external validation was performed on a series of newly diagnosed CLL pts managed at Mayo Clinic. Results: The GCLLSG dataset (1,948 physically fit pts at early and advanced stage; median age: 60 yr (range 30-81); median observation time 63.4 mo) was used as a training dataset. 7 parameters were identified as independent predictors for OS: sex, age, ECOG status, del 17p, del 11q, IGHV mutation status, thymidine kinase and β2-microglobulin. By using a weighted grading a prognostic index was derived separating four different pts groups: low risk (score 0 - 2), intermediate risk (score 3-5), high risk (score 6-10) and very high risk (score 11-14) with significant different OS rates (95.2%, 86.9%, 67.7% and 18.7% OS after 5 yr for the low, intermediate, high and very high risk group respectively (p<0.001). This prognostic index was validated in a cohort of 676 newly diagnosed, untreated pts from the Mayo Clinic (median age 61.5 yr (range 32 - 89); median observation time 47.0 mo). The 4 risk groups were reproduced with 98.3%, 95.4%, 75.4% and 10.8% OS after 5 yr. The prognostic index predicts OS independent of Rai/Binet stage and provides accurate estimations regarding time to first treatment (TTF). C-statistic is 0.75. Conclusions: Using a multi-step process including external validation, we developed a comprehensive prognostic index combining clinical, serum, and molecular information into a single risk score for pts with untreated CLL. The prognostic index provides more accurate prediction of both TTF and OS. To our knowledge it is the first prognostic model in CLL to reach the C-statistic threshold (c > 0.70) necessary to have utility at the level of the individual.

scholarly journals Sentinel Node Status and Immunosuppression: Recurrence Factors in Localized Merkel Cell Carcinoma

2014 ◽  
pp. 0 ◽  
Author(s):  
T Jouary ◽  
E Kubica ◽  
S Dalle ◽  
C Pages ◽  
A Duval-Modeste ◽  
...  
Keyword(s):  
Sentinel Node ◽  
Cell Carcinoma ◽  
Merkel Cell Carcinoma ◽  
Merkel Cell ◽  
Node Status ◽  
Sentinel Node Status

Sentinel Node Status Prediction by Four Statistical Models

2009 ◽  
Vol 250 (6) ◽  
pp. 964-969 ◽  
Author(s):  
Simone Mocellin ◽  
John F. Thompson ◽  
Sandro Pasquali ◽  
Maria C. Montesco ◽  
Pierluigi Pilati ◽  
...  
Keyword(s):  
Sentinel Node ◽  
Statistical Models ◽  
Node Status ◽  
Sentinel Node Status
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