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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

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hupertan.stat@me.com

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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

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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.

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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

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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

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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?

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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

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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 (?)

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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)

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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?

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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
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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
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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
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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!»
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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
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50y, Gleason score= 4+5, PSA= 8 ng/ml,T1c

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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.
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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%
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