BigData in Urology | The urology of the futur

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BIGDATA
(BIGDATA ANALYTICS)
IN UROLOGY
@HUPERTAN
Abbaye Royale de Fontevraud
Samedi 7 décembre 2019
Rencontres d’Urologie de Fontevraud
#Fontevraud2019.

What is the history of health
data?
What is the BigData?
We talk about artificial
intelligence, machine learning
and deep learning: what's the
difference?
What applications today in
medicine.
What will be the urology of
tomorrow?
BIGDATA is a vast subject,
inseparable from BIGDATA
ANALYTICS. I will share my
experience in the field of data
analysis and BigData!
The presentation was done
remotely using ZOOM.

CONFLICT OF INTEREST STATEMENT
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BIGDATA (BIGDATA ANALYTICS) in UROLOGY ©2019 @hupertan

@hupertan by Hupertan,MD
• 2019 – urologist – sexologist
previously
• 1993 – Assistant «IT & Biostatistics» - UMF Cluj
• 1999 – 2007 SPSS consultant – «Data Mining»
• 2001 – DEA– «Data Mining»  PhD
• 2003 – Hupertan StatisticsBIGDATA (BIGDATA ANALYTICS) in UROLOGY ©2019 @hupertan

IBM PC AT 286
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1990
BIGDATA (BIGDATA ANALYTICS) in UROLOGY ©2019 @hupertan

Health Data levels
1. «Lab» level
2. Warehouse (entreprise, hospital)
3. eHealth - BigdataBIGDATA (BIGDATA ANALYTICS) in UROLOGY ©2019

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BIGDATA (BIGDATA ANALYTICS) in UROLOGY ©2019

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Univariate analysis
P value= 0,05
𝒑𝒂𝒕𝒊𝒆𝒏𝒕 ∈ 𝒔𝒂𝒎𝒑𝒍𝒆 (disease Y/N)

Concentration of serum prostate specific antigen in men who developed clinical prostate cancer
according to interval between blood collection and date of diagnosis (observation time).
Carol Parkes et al. BMJ 1995;311:1340-1343
©1995 by British Medical Journal Publishing Group
Fig 1Concentration of serum prostate specific
antigen in men who developed clinical prostate
cancer according to interval between blood collection
and date of diagnosis (observation time). Median of
the cases is shown (−---). Concentrations are
expressed in multiples of median for controls of
same age from same centre. Nine cases and four
controls from Washington County study 12 and
one case from the Social Insurance Institution
study 13 were previously published. Results are
shown separately for men who died of prostate
cancer (solid dots) and for those who presented
clinically with prostate cancer and were still alive or
had died from other causes at end of follow up (open
dots). Numbers to the right of the vertical axis are

«It is much more important to
know what sort of a
patient has a disease
than what sort
a disease a patient has»
WilliamOsler
Sir William Osler

Health Data levels
1. Lab level
2. Warehouse (hospital)
3. eHealth - BigdataBIGDATA (BIGDATA ANALYTICS) in UROLOGY ©2019

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est soumise à la licence CC BY-
SA-NC
Multivariate analysis
predictive models
𝒑𝒂𝒕𝒊𝒆𝒏𝒕 ∈ 𝒓𝒊𝒔𝒌 𝒈𝒓𝒐𝒖𝒑
Machine learning

ALBERTSEN PC, HANLEY JA, GLEASON DF, BARRY MJ.
Competing risk analysis of men aged 55 to 74 years at diagnosis managed
conservatively for clinically localized prostate cancer.
JAMA. 1998 ; 280:975-80

D'AMICO AV, WHITTINGTON R, MALKOWICZ SB, SHULTZ D,
BLANK K, BRODERICK GA, TOMASZEWSKI JE, RENSHAW AA,
KAPLAN I, BEARD CJ, WEIN Biocheminical outcome after radical
prostatectomy, external beam radiation therapy, or interstitial radiation therapy for
clinicaly localized prostate cancer,
JAMA 1998 ;280 :969-74

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

V. Hupertan 1,2, M. Rouprêt2, J.-F. Poisson2, Y. Chrétien2, B. Dufour2, N. Thiounn2, A. Méjean2
From the E.R.I.C "Equipe de Recherche en Ingéniérie des Connaissances", University Lyon 2, France
Department of Urology,a Necker Hospital, GHU Ouest, University René Descartes, Paris V .
INTRODUCTION
DISCUSSION
A postoperative nomogram proposed by Kattan et al, based on
the analysis of a Memorial Sloan-Kettering database. The
postoperative nomogram developed by Kattan et al. was used
to calculate the probability that a patient would be free from
recurrence at 5 years of follow-up. The four variables
included in the nomogram were clinical symptoms,
histology, tumor size, and 1997 TNM stage. The
generalization of the Kattan nomogram to external cohorts of
patients with characteristics different from the original dataset
has therefore yet to be validated.
The aim of this study was to establish the accuracy of the
Kattan nomogram in predicting RCC recurrence in a
representative patient population who underwent surgery in a
large single center.
• some variables included in the nomogram
were not significant prognostic factors for
recurrence(overtraining of the model?)
• selection bias : patient recruitment and
management.
MATERIALAND METHODS
LOW PREDICTIVE ACCURACY OF KATTAN'S POSTOPERATIVE NOMOGRAM FOR
RENAL CELL CARCINOMA RECURRENCE IN A POPULATION OF FRENCH PATIENTS
Abstract
Nr: 550
Nomogram and statistics. The predictive accuracy of the
nomogram was measured by the area under the receiver
operating characteristic (ROC) under the receiver operating
characteristic (ROC) curve as given by Harrell et al's
concordance index (c-index) for censored data and CI by
1000*bootstrap.
Survival analysis. Survival analysis was provides by the Kaplan-
Meier method, the log-rank test (a p-value of <0.05 was
considered significant) and Cox model. The primary endpoint
was recurrence-free survival defined as either the time from
surgery to detection of the first local recurrence or of distant
metastases, or the time from surgery to the close of the study.
Kattan's nomogram accuracy: the c-index
= 0.607 ,95% CI (0.576: 0.635)
CONCLUSIONS
• New dynamic models ?
• More patients?
• Worldwide?
• Specific models?
Characteristics Current study N (%) Kattan et al. N (%)
Sex
Men 402 (71.1) 363 (60.4)
Women 163 (28.9) 238 (39.6)
Median age at diagnosis, y 62 63.5
Symptoms
Incidental 307 (54.3) 384 (63.9)
Local 220 (39) 204 (33.9)
Systemic 38 (6.7) 13 (2.2)
Nephrectomy type
Radical 477 (84.4) NA
Tumorectomy 60 (10.6) NA
Nephron-sparing 28 (5.0) NA
Histology
Clear cell 470 (83.2) 430 (71.5)
Papillary 74 (13.1) 108 (18)
Chromophobe 21 (3.7) 63 (10.5)
Tumor size, cm
Minimum 0.6 0.5
Median 5.1 4.5
Maximum 22 20
Stage, TNM 1997
1 363 (64.2) NA
2 118 (20.9) NA
3a 40 (7.1) NA
3b/c 44 (7.8) 50 (8.3)
Follow-up status
Alive 484 (85.7) 559 (93)
Dead, all causes 81 (14.3) 42 (7)
Dead, disease-related 78 (13.8) NA
Patient Characteristics From the Current Study (N:565)
and for the Patients Included in the Reference Study (N:601)
RESULTS
Survival analysis. The 5-year recurrence-free and disease-
free survival rates were 81.7% and 84.7%, respectively

«we tend to be inconsistent when
processing our mental databse»
2
1
21

http://penseeartificielle.fr/difference-intelligence-artificielle-machine-learning-deep-learning/

The construction of nomograms is the typical
example of machine learning - from the
structured data, a model
(«black box» «white box»)
is used to calculate an output value
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https://www.slideshare.net/SebastianRaschka/nextgen-talk-022015/8
@hupertan

Apprentissage non supervisée
Nuées dynamiques - Kmeans
K=5
Cluster 1
Cluster 2
Cluster 3
Cluster 4
Cluster 5
Centre final classe 2
Variable 1
Variable2
Stage de recherche
DEA Extraction des
connaissances à partir
des données
IRIS Division Biométrie
Université Lyon 2,
Laboratoire E.R.I.C.

Couche d’entrées
Carte topologique
Apprentissage non supervisée
Réseaux de neurones de Kohonen (SOM)
Structures
topologiques
MAPA
Stage de recherche
DEA Extraction des
connaissances à partir
des données
Université Lyon 2,
Laboratoire E.R.I.C.

Variabilité de la TAs et TAd
Typologie Kmeans à 12 classes
60
80
100
120
140
160
180
00:00 03:00 06:00 09:00 12:00 15:00 18:00 21:00 00:00
Heure
TAd-KM12cl:type-1(40)
TAd-TOTAL
TAs-KM12cl:type-1(40)
TAs-TOTAL
Stage de recherche
DEA Extraction des
connaissances à partir des
données
Université Lyon 2, Laboratoire
E.R.I.C.

Predictive Care Analysis
Solution in Healthcare
Solution using
IBM SPSS MODELER
By Vincent Hupertan
Update 2015 Vincent Hupertan© 2007 SPSS Inc.
SPSS 2007 : carte d identité
§ Éditeur de logiciels international fondé en 1968, coté au NASDAQ depuis
1993
§ Leader dans l’édition de logiciels de Datamining et de CRM analytique
§ Leader incontesté des logiciels d’enquêtes et d’analyse
comportementale
§ Pionnier de l’Analyse Prédictive
§ Chiffre d’affaires mondial en 2006 : 261 M$
§ Plus de 1300 personnes dans le monde
§ 41 collaborateurs en France (ventes, marketing, services, R&D)
§ 4 millions d’utilisateurs et 250 000 clients dans le monde, 4 500 clients
actifs en France
Décisionnel
Data mining
Text mining
Statistiques Enquêtes Applications
Analyse prédictive
Pilotez et améliorez l’activité de votre structure hospitalière grâce à
l’analyse prédictive :
- Grâce à des enquêtes, validez la Certification V2, l’Evaluation des
Pratiques Professionnelles (EPP) et la Gestion des Risques (GDR)
- Grâce à un portail unique disposant d’un accès par métier à
disposition des acteurs de l’hôpital, diffusez l’information nécessaire
à la bonne gouvernance, au pilotage et à la contractualisation interne
- Grâce aux modèles prédictifs, anticipez l’EPRD (état prévisionnel des
recettes et des dépenses) en intégrant la T2A, anticipez les risques
médicaux (infections nosocomiales)

Health Data levels
1. Lab level
2. Warehouse (entreprise, hospital)
3. eHealth  Bigdata
“datafication” of everything

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VOLUME
VARIET
Y
VELOCI
TY
EHRs, PACS, medical devices,
patient-reported outcomes,
financial transaction,
social media posts, oline patients foru
BIGDATA (BIGDATA ANALYTICS) in UROLOGY ©
@hup

Big Data and the analytics bot

https://www.lemagit.fr/conseil/Machine-Learning-vs-Deep-Learning-un-avion-a-helices-et-un-avion-a-reaction
@hupertan

https://ai.googleblog.com/2017/03/assisting-pathologists-in-detecting.html
Assisting Pathologists in Detecting Cancer with
Deep Learning

Nagpal, K., Foote, D., Liu, Y. et al.
Development and
validation of a deep
learning algorithm for
improving Gleason
scoring of prostate
cancer.
npj Digit. Med. 2, 48 (2019) doi:10.1038/s41746-
019-0112-2

The urologist of the future
• AI
• Collaborative medicine
• Telemedicine
• Machine learning
• Personalisation
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Bigdatawillallow
«It is much more important to
know what sort of a
PATIENT has a disease
than what sort
a disease a patient has»
WilliamOslerSir William Osler

«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
4
6
46
?

The best thinga docto
can do for a patient is
sharehis humanity,
packed with his
knowledgeof the
disease, all witha good
dose of empathy.

I thank Thibaud for having invited
me to this wonderful day of
exchanges around the urology of
the future.

Le livre est la concrétisation de mon
blog UROBLOG, crée en 2014. Avec
plus de 70 000 visiteurs, il m’a permis
5 ans après de publier ce livre chez
Editions Leduc.s, écrit dans la même
logique que le livre.
Je suis toujours à la recherche des
auteurs qui souhaiteraient publier des
articles destinés aux patients.

BigData in Urology | The urology of the futur

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