Nomograms why when what Congres CURy 2009
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The document discusses the role of nomograms in improving decision-making in oncology by providing graphical representations of mathematical relationships related to cancer treatment outcomes. It emphasizes the necessity for accurate predictions of treatment efficacy and patient quality of life, while suggesting that these tools cannot replace clinical judgment. Additionally, it addresses the significance of updating nomograms with new data to enhance patient involvement in treatment decisions.
![hupertan.stat@me.com
… yes, BUT:
1. Maximize cancer control/ minimize treatment morbidity OK, BUT:
Does exist nomogram able to predict in same time cancer control
and treatment morbidity?
How to predict cancer control: survival? surrogate end points?
What means treatment morbidity in a statistical point of view: QoL
score? Erection function IIEF?
2. Accuracy of the prediction of the PM >> CJ OK, BUT:
Y=f(X1, X2, X3, ..,Xi)
Y=[ Y1, Y2, Y3, ..,Yn ]=f(X1, X2, X3, ..,Xm)
m inputs => n outputs (social, familial, sexual....)
Þ Imply more actively patient in the medical decision making process OK BUT:
well informed patient = associate probability to each
possible outcome ? Let himself on the new to compute the
risk hazard? What probability he will choice you?](https://image.slidesharecdn.com/nomogramswhywhenwhatcongrescury2009-140604123543-phpapp02/85/Nomograms-why-when-what-Congres-CURy-2009-7-320.jpg)
Nomograms why when what Congres CURy 2009
- 1. hupertan.stat@me.com Nomograms Why, When, What, How use?.. ….but The 2nd World Congress on Controversies in Urology (CURy) Lisbon, Portugal, February 5- 8, 2009 Vincent HUPERTAN, M.D., MR Lyon University - E.R.I.C. Knowledge engineering
- 3. hupertan.stat@me.com Nomogram ≠ predictive model(PM) is the graphical representation of mathematical relationships or laws (Etymology: Greek nomos = law) or a graphical calculating device, a two-dimensional diagram designed to allow the approximate graphical computation of a function. Fahrenheitvs.Celsiusscale
- 4. hupertan.stat@me.com • This slideshow is a visual support for interventions Dr. Hupertan as expert or trainer during training seminars , courses for medical students, conferences or congresses . • This slideshow created by Dr. Hupertan , MD , is intended primarily for health professionals in training ( medical students , interns and clinical leaders ) or not (doctors,... ) . • This slide contains links to other sites. • Conflict of interest : "no declared conflicts of interest " • Using Slideshow : this slideshow can be downloaded , used while mentioning the author.
- 5. hupertan.stat@me.com Predictive nomogram ★ A device that suppose two elements: 1. equation of an event probability 2. specific functional representation in a graphic form
- 6. hupertan.stat@me.com Why? 1. The necessity to improve the decision making process in oncology 1. Clinical heterogeneity of cancers 2. Importance quantity / quality of life ratio 3. Perfect treatment = utopia maximize cancer control/ minimize treatment morbidity 2. Lack of performance in prediction of the clinical judgment (CJ) 1. The clinician (experts) out-perform prediction classifiers= too much weight on their own judgment 2. Human mental process prove difficulties to use numbers 3. Emotional considerations: particular cases are more “weighted” Accuracy of the prediction of the PM >> CJ 3. Paucity of RCT data implying a lack of the “evidence”*)=> we should use data to improve the medical decision making process, implying more actively patient in that *)Evidence Based Medicine
- 7. hupertan.stat@me.com … yes, BUT: 1. Maximize cancer control/ minimize treatment morbidity OK, BUT: Does exist nomogram able to predict in same time cancer control and treatment morbidity? How to predict cancer control: survival? surrogate end points? What means treatment morbidity in a statistical point of view: QoL score? Erection function IIEF? 2. Accuracy of the prediction of the PM >> CJ OK, BUT: Y=f(X1, X2, X3, ..,Xi) Y=[ Y1, Y2, Y3, ..,Yn ]=f(X1, X2, X3, ..,Xm) m inputs => n outputs (social, familial, sexual....) Þ Imply more actively patient in the medical decision making process OK BUT: well informed patient = associate probability to each possible outcome ? Let himself on the new to compute the risk hazard? What probability he will choice you?
- 8. hupertan.stat@me.com When? To inform the patient about the outcome that MIGHT BE! …the fact that predict the issue will change: diagnostic procedures treatment choice (alternative treatments, adjuvant treatment exists) or treatment modalities (extension of the lymph-nodes dissection) follow-up
- 9. hupertan.stat@me.com What “nomogram” to choose? (nomogram specifications) 1. Functional representation of the nomogram: Ergonomy, simplicity 2. Nomogram core(PM): Output: relevant for the clinical practice; Data set used for the learning process: Patients: geographic area, academic centers Predictors: variability(inter rather,within rather),standardization colinearity? significative features?exhaustivity or parsimony? Quality of data set(?), noise (?), missing data (?)
- 10. hupertan.stat@me.com 2. Nomogram core(PM): • Modeling tools: • machine learning: neuronal nets, machine vector, induction graph, bayesian • statistic : regression, Cox model • symbolic learning, rules induction Validation: internal: learning set-test set bootstrap, jackknife external: academic/non-academic centers publication bias (negatives) «invalidating nomogram» What “nomogram” to choose? (nomogram specifications)
- 11. hupertan.stat@me.com • Simplest possible • Linked to an actionable question • Modeling: statistics, significativity of the features • Good performance in prediction: • Accuracy (validation in similar sample data) • Calibration • Discrimination: Harell c index or AUC ∈(0.7-0.8*) • Generalizability • Updating models using my own data (e.g. using bayesian technics and bootstrap) • Estimation by confidence interval What “nomogram” I use? c index >0.8 : memorized data? over-learning?
- 12. hupertan.stat@me.com How to use predictive nomograms • is difficult to use it as well! • after validation in your data or in identical sample • using à confidence interval, and if possible built-in on your data • we should dispose official recommendation (E.A.U., A.F.U.) • for patients to be informed BY doctors • permanent updated with new data: • new patients • new features: genomic and biomolecular data 1 2 12
- 13. hupertan.stat@me.com «no nomogram will ever take the place of good clinical judgement and the well-informed patients.» Robert W. Ross, Philip W. Kantoff Predicting Outcomes in Prostate Cancer: How Many More Nomograms Do Se Nedd? J.CLIN.ONCOL, 25,2077:3563-3564 1 3 13
- 14. hupertan.stat@me.com Thanks to: • Pr Laurent Boccon-Gibod, Bichat Hospital • Pr Jean-Hugues Chauchat, Knowledge engineering Labs, (PhD Thesis Director) The presentation it has been inspired by papers • Michael W.Kattan • Philip W. Kantoff • Frank E.Harell • Rodolfo Montironi • Robert W. Ross • Peter T. Scardino • Ashutosh Tewari • Blaz Zupan and many others 1 4 14
- 15. hupertan.stat@me.com Case Nr 1 50y old, caucasian, Website designer Benign prostatic hyperplasia (BPH) with LUTS : AUA-SI = 8 (moderate) Erectile dysfunction (ED), IIEF15 (Erectile Function-domain)=7 (severe) PSA=8 ng/ml, DRE=T1c Transrectal ultrasound-guided biopsy of the prostate: prostate volume=30; Gleason score= 4+5; 3 positives cores on 12. The patient says: «Using internet I found that the probability to be healed 5 years latter as 87% in the case of the surgery, and only about 73% in the case of the external beam radiation therapy. I choose the surgery!» 1 5 15
- 16. hupertan.stat@me.com Case nr 1 As urologist in a non academic center do you operate him? How to explain the difference? What confidence around the estimate? Progression Free Probability Radical Prostatectomy meant «Healed»? rising PSA after surgery= after radiation therapy 1 6 16 50y, Gleason score= 4+5, PSA= 8 ng/ml,T1c
- 17. hupertan.stat@me.com Case Nr 2 65y old, caucasian, statistician Hemochromatosis Benign prostatic hyperplasia (BPH) with LUTS : AUA-SI = 20 (severe) Erectile dysfunction (ED), IIEF15 (Erectile Function-domain)=26 (mild) PSA=30 ng/ml, DRE=T1c 2 previously biopsy of prostate= negatives Transrectal ultrasound-guided biopsy of the prostate: prostate volume= 65 cc Gleason score= 3+3; 6 positives cores on 12. 1 7 17
- 18. hupertan.stat@me.com Case Nr 2 As urologist in a non academic center you explain that in the case of the prostatectomy the 5 years progression free probability is 93%, and only about 72% in the case of the external beam radiation therapy. The patient (statistician) ask you: «But if YOU are the surgeon, what are the estimation of the same progression free probability?» You have no Idea about it! 93% as the nomogram predict, because the nomogram has been validated 93% ± 5% (α, risque of error) around 93% 1 8 18