Nomograms why when what Congres CURy 2009

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

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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mentioning the author.

Predictive nomogram
★ A device that suppose two
elements:
1. equation of an
event probability
2. specific functional
representation in a
graphic form

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

… 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?

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

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 (?)

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)

• 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?

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

«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

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

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

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

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

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

Nomograms why when what Congres CURy 2009

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