Information technology (IT) can enhance or change many scenarios in cancer research for the better. In this paper, we introduce several examples, starting with clinical data reuse and collaboration including data sharing in research networks. Key challenges are semantic interoperability and data access (including data privacy). We deal with gathering and analyzing genomic information, where cloud computing, uncertainties and reproducibility challenge researchers. Also, new sources for additional phenotypical data are shown in patient-reported outcome and machine learning in imaging. Last, we focus on therapy assistance, introducing tools used in molecular tumor boards and techniques for computer-assisted surgery. We discuss the need for metadata to aggregate and analyze data sets reliably. We conclude with an outlook towards a learning health care system in oncology, which connects bench and bedside by employing modern IT solutions.

1.
Gilbert
RJ
.
E-books: A tale of digital disruption
.
J Econ Perspect
.
2015
;
29
(
3
):
165
84
. 0895-3309
2.
Lambin
P
,
van Stiphout
RG
,
Starmans
MH
,
Rios-Velazquez
E
,
Nalbantov
G
,
Aerts
HJ
, et al.
Predicting outcomes in radiation oncology—multifactorial decision support systems
.
Nat Rev Clin Oncol
.
2013
Jan
;
10
(
1
):
27
40
.
[PubMed]
1759-4774
3.
Kibbe
W
. Cancer Clinical Research.
Oncology Informatics
.
Elsevier
;
2016
. pp.
41
53
.
4.
Riley
WT
. A New Era of Clinical Research Methods in a Data-Rich Environment.
Oncology Informatics
.
Elsevier
;
2016
. pp.
343
55
.
5.
Miriovsky
BJ
,
Shulman
LN
,
Abernethy
AP
.
Importance of health information technology, electronic health records, and continuously aggregating data to comparative effectiveness research and learning health care
.
J Clin Oncol
.
2012
Dec
;
30
(
34
):
4243
8
.
[PubMed]
0732-183X
6.
Hesse
BW
,
Ahern
D
,
Beckjord
E
. Oncology Informatics: Using Health Information Technology to Improve Processes and Outcomes in Cancer. San Diego, UNITED STATES, Elsevier Science & Technology,
2016
.Available from: http://ebookcentral.proquest.com/lib/dkfz/detail.action?docID=4454380
7.
Koppel
R
,
Metlay
JP
,
Cohen
A
,
Abaluck
B
,
Localio
AR
,
Kimmel
SE
, et al.
Role of computerized physician order entry systems in facilitating medication errors
.
JAMA
.
2005
Mar
;
293
(
10
):
1197
203
.
[PubMed]
0098-7484
8.
Abdiwahab
E
,
Taplin
SH
,
Coronado
G
,
Dacus
H
,
Leypoldt
M
,
Skinner
C
. Early Detection in the Age of Information Technology.
Oncology Informatics
.
Elsevier
;
2016
. pp.
123
43
.
9.
Rea
S
,
Pathak
J
,
Savova
G
,
Oniki
TA
,
Westberg
L
,
Beebe
CE
, et al.
Building a robust, scalable and standards-driven infrastructure for secondary use of EHR data: the SHARPn project
.
J Biomed Inform
.
2012
Aug
;
45
(
4
):
763
71
.
[PubMed]
1532-0464
10.
Safran
C
,
Bloomrosen
M
,
Hammond
WE
,
Labkoff
S
,
Markel-Fox
S
,
Tang
PC
, et al.;
Expert Panel
.
Toward a national framework for the secondary use of health data: an American Medical Informatics Association White Paper
.
J Am Med Inform Assoc
.
2007
Jan-Feb
;
14
(
1
):
1
9
.
[PubMed]
1067-5027
11.
Linder
JA
,
Haas
JS
,
Iyer
A
,
Labuzetta
MA
,
Ibara
M
,
Celeste
M
, et al.
Secondary use of electronic health record data: spontaneous triggered adverse drug event reporting
.
Pharmacoepidemiol Drug Saf
.
2010
Dec
;
19
(
12
):
1211
5
.
[PubMed]
1053-8569
12.
Botsis
T
,
Hartvigsen
G
,
Chen
F
,
Weng
C
.
Secondary Use of EHR: Data Quality Issues and Informatics Opportunities
.
AMIA Jt Summits Transl Sci Proc
.
2010
Mar
;
2010
:
1
5
.
[PubMed]
2153-4063
13.
Feder
SL
.
Data Quality in Electronic Health Records Research: Quality Domains and Assessment Methods
.
West J Nurs Res
.
2018
May
;
40
(
5
):
753
66
.
[PubMed]
0193-9459
14.
Savova
GK
,
Tseytlin
E
,
Finan
S
,
Castine
M
,
Miller
T
,
Medvedeva
O
, et al.
DeepPhe: A Natural Language Processing System for Extracting Cancer Phenotypes from Clinical Records
.
Cancer Res
.
2017
Nov
;
77
(
21
):
e115
8
.
[PubMed]
0008-5472
15.
Ford
E
,
Carroll
JA
,
Smith
HE
,
Scott
D
,
Cassell
JA
.
Extracting information from the text of electronic medical records to improve case detection: a systematic review
.
J Am Med Inform Assoc
.
2016
Sep
;
23
(
5
):
1007
15
.
[PubMed]
1067-5027
16.
Lablans
M
,
Schmidt
EE
,
Ückert
F
;
An Architecture for Translational Cancer Research As Exemplified by the German Cancer Consortium
.
JCO Clin
.
Cancer Inform
.
2018
;
•••
:
1
8
.1176-9351
17.
Kahn
MG
,
Callahan
TJ
,
Barnard
J
,
Bauck
AE
,
Brown
J
,
Davidson
BN
, et al.
A Harmonized Data Quality Assessment Terminology and Framework for the Secondary Use of Electronic Health Record Data
.
EGEMS (Wash DC)
.
2016
Sep
;
4
(
1
):
1244
.
[PubMed]
2327-9214
18.
Lablans
M
,
Kadioglu
D
,
Muscholl
M
,
Ückert
F
.
Exploiting Distributed, Heterogeneous and Sensitive Data Stocks while Maintaining the Owner’s Data Sovereignty
.
Methods Inf Med
.
2015
;
54
(
4
):
346
52
.
[PubMed]
0026-1270
19.
Lablans
M
,
Borg
A
,
Ückert
F
.
A RESTful interface to pseudonymization services in modern web applications
.
BMC Med Inform Decis Mak
.
2015
Feb
;
15
(
1
):
2
.
[PubMed]
1472-6947
20.
Regulation (EU)
2016
/679 of the European Parliament and of the Council of 27 April 2016 on the protection of natural persons with regard to the processing of personal data and on the free movement of such data, and repealing Directive 95/46/EC (General Data Protection Regulation) (Text with EEA relevance). 2016, [cited 2018 May 13].Available from: http://data.europa.eu/eli/reg/2016/679/oj/eng
21.
Chen
D
,
Zhao
H
.
Data Security and Privacy Protection Issues in Cloud Computing
; in :
2012 International Conference on Computer Science and Electronics Engineering
.
2012
, pp
647
651
.
22.
Stein
LD
.
The case for cloud computing in genome informatics
.
Genome Biol
.
2010
;
11
(
5
):
207
.
[PubMed]
1474-7596
23.
Lau
JW
,
Lehnert
E
,
Sethi
A
,
Malhotra
R
,
Kaushik
G
,
Onder
Z
, et al.;
Seven Bridges CGC Team
.
The Cancer Genomics Cloud: Collaborative, Reproducible, and Democratized-A New Paradigm in Large-Scale Computational Research
.
Cancer Res
.
2017
Nov
;
77
(
21
):
e3
6
.
[PubMed]
0008-5472
24.
Chen
Y
,
Elenee Argentinis
JD
,
Weber
G
.
IBM Watson: How Cognitive Computing Can Be Applied to Big Data Challenges in Life Sciences Research
.
Clin Ther
.
2016
Apr
;
38
(
4
):
688
701
.
[PubMed]
0149-2918
25.
Afgan
E
,
Baker
D
,
van den Beek
M
,
Blankenberg
D
,
Bouvier
D
,
Čech
M
, et al.
The Galaxy platform for accessible, reproducible and collaborative biomedical analyses: 2016 update
.
Nucleic Acids Res
.
2016
Jul
;
44
W1
:
W3
10
.
[PubMed]
0305-1048
26.
Meric-Bernstam
F
,
Johnson
A
,
Holla
V
,
Bailey
AM
,
Brusco
L
,
Chen
K
, et al.
A decision support framework for genomically informed investigational cancer therapy
.
J Natl Cancer Inst
.
2015
Apr
;
107
(
7
):
djv098
.
[PubMed]
0027-8874
27.
Rehm
HL
.
Evolving health care through personal genomics
.
Nat Rev Genet
.
2017
Apr
;
18
(
4
):
259
67
.
[PubMed]
1471-0056
28.
McConnell
MV
,
Shcherbina
A
,
Pavlovic
A
,
Homburger
JR
,
Goldfeder
RL
,
Waggot
D
, et al.
Feasibility of Obtaining Measures of Lifestyle From a Smartphone App: The MyHeart Counts Cardiovascular Health Study
.
JAMA Cardiol
.
2017
Jan
;
2
(
1
):
67
76
.
[PubMed]
2380-6583
29.
Shcherbina
A
,
Mattsson
CM
,
Waggott
D
,
Salisbury
H
,
Christle
JW
,
Hastie
T
, et al.
Accuracy in Wrist-Worn, Sensor-Based Measurements of Heart Rate and Energy Expenditure in a Diverse Cohort
.
J Pers Med
.
2017
May
;
7
(
2
):
3
.
[PubMed]
2075-4426
30.
Krist
AH
,
Nease
DE
,
Kreps
GL
,
Overholser
L
,
McKenzie
M
. Engaging Patients in Primary and Specialty Care.
Oncology Informatics
.
Elsevier
;
2016
. pp.
55
79
.
31.
Mohammadzadeh
N
,
Safdari
R
,
Rahimi
A
.
Cancer care management through a mobile phone health approach: key considerations
.
Asian Pac J Cancer Prev
.
2013
;
14
(
9
):
4961
4
.
[PubMed]
1513-7368
32.
Ware
JE
 Jr
,
Sherbourne
CD
.
The MOS 36-item short-form health survey (SF-36). I. Conceptual framework and item selection
.
Med Care
.
1992
Jun
;
30
(
6
):
473
83
.
[PubMed]
0025-7079
33.
Devlin
NJ
,
Brooks
R
.
EQ-5D and the EuroQol Group: Past, Present and Future
.
Appl Health Econ Health Policy
.
2017
Apr
;
15
(
2
):
127
37
.
[PubMed]
1175-5652
34.
Nixon
A
,
Wild
D
,
Muehlhausen
W
.
Patient Reported Outcomes: an Overview.
Turin, SEEd Medical Publishers,
2016
, [cited 2018 Apr 30].Available from: http://public.eblib.com/choice/publicfullrecord.aspx?p=4712333
35.
Ernst
J
,
Broemer
L
.
Digital unterstützte Modelle in der Patientenbeobachtung
.
Forum (Genova)
.
2018
;
•••
:
1
6
.1121-8142
36.
Basch
E
,
Deal
AM
,
Dueck
AC
,
Scher
HI
,
Kris
MG
,
Hudis
C
, et al.
Overall Survival Results of a Trial Assessing Patient-Reported Outcomes for Symptom Monitoring During Routine Cancer Treatment
.
JAMA
.
2017
Jul
;
318
(
2
):
197
8
.
[PubMed]
0098-7484
37.
Gillies
RJ
,
Kinahan
PE
,
Hricak
H
.
Radiomics: Images Are More than Pictures, They Are Data
.
Radiology
.
2016
Feb
;
278
(
2
):
563
77
.
[PubMed]
0033-8419
38.
Jäger
PF
,
Bickelhaupt
S
,
Laun
FB
,
Lederer
W
,
Heidi
D
,
Kuder
TA
, et al.
 Revealing Hidden Potentials of the q-Space Signal in Breast Cancer. In:
Descoteaux
M
,
Maier-Hein
L
,
Franz
A
,
Jannin
P
,
Collins
DL
,
Duchesne
S
, editors
.
MICCAI 2017. Springer International Publishing
.
Medical Image Computing and Computer Assisted Intervention
.
2017
. pp.
664
71
.
39.
Kickingereder
P
,
Neuberger
U
,
Bonekamp
D
,
Piechotta
PL
,
Götz
M
,
Wick
A
, et al.
Radiomic subtyping improves disease stratification beyond key molecular, clinical, and standard imaging characteristics in patients with glioblastoma
.
Neuro-oncol
.
2018
May
;
20
(
6
):
848
57
.
[PubMed]
1522-8517
40.
An Augmented Reality Microscope for Cancer Detection. Res Blog [cited 2018 Apr 27];Available from: https://research.googleblog.com/2018/04/an-augmented-reality-microscope.html
41.
Thomas
JJ
,
Cook
KA
.
A visual analytics agenda
.
IEEE Comput Graph Appl
.
2006
Jan-Feb
;
26
(
1
):
10
3
.
[PubMed]
0272-1716
42.
Shneiderman
B
,
Plaisant
C
,
Hesse
BW
.
Improving Healthcare with Interactive Visualization
.
Computer
.
2013
;
46
(
5
):
58
66
. 0018-9162
43.
Khan
A
,
Mukhtar
H
,
Ahmad
HF
,
Gondal
MA
,
Ilyas
QM
.
Improving Usability through Enhanced Visualization in Healthcare
; in :
2017 IEEE 13th International Symposium on Autonomous Decentralized System (ISADS)
.
2017
, pp
39
44
.
44.
Onukwugha
E
,
Plaisant
C
,
Shneiderman
B
. Data Visualization Tools for Investigating Health Services Utilization Among Cancer Patients.
Oncology Informatics
.
Elsevier
;
2016
. pp.
207
29
.
45.
Horowitz
TS
,
Rensink
RA
. Extended Vision for Oncology.
Oncology Informatics
.
Elsevier
;
2016
. pp.
287
303
.
46.
Dasgupta
A
,
Lee
JY
,
Wilson
R
,
Lafrance
RA
,
Cramer
N
,
Cook
K
, et al.
Familiarity Vs Trust: A Comparative Study of Domain Scientists’ Trust in Visual Analytics and Conventional Analysis Methods
.
IEEE Trans Vis Comput Graph
.
2017
Jan
;
23
(
1
):
271
80
.
[PubMed]
1077-2626
47.
Dancey
JE
,
Bedard
PL
,
Onetto
N
,
Hudson
TJ
.
The genetic basis for cancer treatment decisions
.
Cell
.
2012
Feb
;
148
(
3
):
409
20
.
[PubMed]
0092-8674
48.
Hanahan
D
,
Weinberg
RA
.
Hallmarks of cancer: the next generation
.
Cell
.
2011
Mar
;
144
(
5
):
646
74
.
[PubMed]
0092-8674
49.
Wong
KM
,
Hudson
TJ
,
McPherson
JD
.
Unraveling the genetics of cancer: genome sequencing and beyond
.
Annu Rev Genomics Hum Genet
.
2011
;
12
(
1
):
407
30
.
[PubMed]
1527-8204
50.
Schwaederle
M
,
Parker
BA
,
Schwab
RB
,
Fanta
PT
,
Boles
SG
,
Daniels
GA
, et al.
Molecular tumor board: the University of California-San Diego Moores Cancer Center experience
.
Oncologist
.
2014
Jun
;
19
(
6
):
631
6
.
[PubMed]
1083-7159
51.
Horak
P
,
Klink
B
,
Heining
C
,
Gröschel
S
,
Hutter
B
,
Fröhlich
M
, et al.
Precision oncology based on omics data: the NCT Heidelberg experience
.
Int J Cancer
.
2017
Sep
;
141
(
5
):
877
86
.
[PubMed]
0020-7136
52.
Perera-Bel
J
,
Hutter
B
,
Heining
C
,
Bleckmann
A
,
Fröhlich
M
,
Fröhling
S
, et al.
From somatic variants towards precision oncology: evidence-driven reporting of treatment options in molecular tumor boards
.
Genome Med
.
2018
Mar
;
10
(
1
):
18
.
[PubMed]
1756-994X
53.
Gao
J
,
Aksoy
BA
,
Dogrusoz
U
,
Dresdner
G
,
Gross
B
,
Sumer
SO
, et al.
Integrative analysis of complex cancer genomics and clinical profiles using the cBioPortal
.
Sci Signal
.
2013
Apr
;
6
(
269
):
pl1
1
.
[PubMed]
1945-0877
54.
Kurnit
KC
,
Bailey
AM
,
Zeng
J
,
Johnson
AM
,
Shufean
MA
,
Brusco
L
, et al.
“Personalized Cancer Therapy”: A Publicly Available Precision Oncology Resource
.
Cancer Res
.
2017
Nov
;
77
(
21
):
e123
6
.
[PubMed]
0008-5472
55.
Lipscomb
CE
.
Medical Subject Headings (MeSH)
.
Bull Med Libr Assoc
.
2000
Jul
;
88
(
3
):
265
6
.
[PubMed]
0025-7338
56.
Patel
C
,
Cimino
J
,
Dolby
J
,
Fokoue
A
,
Kalyanpur
A
,
Kershenbaum
A
, et al.
 Matching Patient Records to Clinical Trials Using Ontologies.
The Semantic Web
.
Berlin, Heidelberg
:
Springer
;
2007
. pp.
816
29
.
57.
Wicks
P
,
Vaughan
TE
,
Massagli
MP
,
Heywood
J
.
Accelerated clinical discovery using self-reported patient data collected online and a patient-matching algorithm
.
Nat Biotechnol
.
2011
May
;
29
(
5
):
411
4
.
[PubMed]
1087-0156
58.
Maier-Hein
L
,
Vedula
SS
,
Speidel
S
,
Navab
N
,
Kikinis
R
,
Park
A
, et al.
Surgical data science for next-generation interventions
.
Nat Biomed Eng
.
2017
;
1
(
9
):
691
6
. 2157-846X
59.
Maier-Hein
L
,
Mersmann
S
,
Kondermann
D
,
Bodenstedt
S
,
Sanchez
A
,
Stock
C
, et al.
 Can Masses of Non-Experts Train Highly Accurate Image Classifiers? in : Medical Image Computing and Computer-Assisted Intervention – MICCAI 2014. Springer, Cham,
2014
, pp 438–445.
60.
Maier-Hein
L
,
Kondermann
D
,
Roß
T
,
Mersmann
S
,
Heim
E
,
Bodenstedt
S
, et al.
Crowdtruth validation: a new paradigm for validating algorithms that rely on image correspondences
.
Int J CARS
.
2015
Aug
;
10
(
8
):
1201
12
.
[PubMed]
1861-6410
61.
Heim
E
,
Seitel
A
,
Isensee
F
,
Andrulis
J
,
Stock
C
,
Ross
T
, et al.
Clickstream analysis for crowd-based object segmentation with confidence
.
IEEE Trans Pattern Anal Mach Intell
.
2017
Nov
;
•••
:
1
1
.
[PubMed]
0162-8828
62.
Maier-Hein
L
,
Ross
T
,
Gröhl
J
,
Glocker
B
,
Bodenstedt
S
,
Stock
C
, et al.
 Crowd-Algorithm Collaboration for Large-Scale Endoscopic Image Annotation with Confidence. In:
Ourselin
S
,
Joskowicz
L
,
Sabuncu
MR
,
Unal
G
,
Wells
W
, editors
.
MICCAI 2016
.
Medical Image Computing and Computer-Assisted Intervention
.
Cham
:
Springer International Publishing
;
2016
. pp.
616
23
.
63.
Ross
T
,
Zimmerer
D
,
Vemuri
A
,
Isensee
F
,
Wiesenfarth
M
,
Bodenstedt
S
, et al.
Exploiting the potential of unlabeled endoscopic video data with self-supervised learning.
ArXiv171109726 Cs
2017
[cited 2018 Apr 24];Available from: http://arxiv.org/abs/1711.09726
64.
Schilsky
RL
,
Miller
RS
. Creating a Learning Health Care System in Oncology.
Oncology Informatics
.
Elsevier
;
2016
. pp.
3
21
.
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