On July 25. Dr. Ying Xiao delivered a presentation titled "RTOG - Radiation Therapy Oncology Group Bioinformatics."

1. RTOG Bioinformatics
Y. Xiao, Ph.D.
RTOG, ACR
Radiation Oncology, Jefferson
Medical College
1

2. Evidence Based
Radiation Oncology
Radiation Therapy Oncology Group (RTOG)
 Improve The Survival Outcome And Quality Of Life
 Evaluate New Forms Of Radiotherapy Delivery
 Test New Systemic Therapies In Conjunction With
Radiotherapy
 Employ Translational Research Strategies
2

3. RTOG Bioinformatics Mission
To facilitate the development and to
develop personalized predictive models
for radiation therapy guidance from
specific characteristics of patients and
treatments with integrated clinical trial
databases, bridging clinical science,
physics, biology, information technology
and mathematics
3

4. BIOINFORMATICS ELEMENTS AND
PROCEDURES Available to BioWG
Database
RT Dose/Images/Clinical Data
Genomic/Proteomic
Biomarker
Data Analysis
Protocol development/Protocol operation
support/Trial Outcome-Secondary Analysis
Validation/Development/Research
4

5. DATA/
DATA Integration
5

6. RTOG DATA for BioWG Investigation
Protocol N Endpoints
0022 oropharyngeal cancer 60 Salivary function
0117 lung 73 Pneumonitis and esophagitis
0126 prostate ~1500 Erectile dysfunction; rectal bleeding
Fecal incontinence vs dose
0225 nasopharyngeal 60 Salivary function
0232 prostate brachytherapy
0234 head and neck 230 TCP? Ongoing, not recruiting
0236 lung SBRT 52 Ongoing: TCP, toxicity
0321 prostate HDR brachy 110 Late/Acute GU/GI
0522 head and neck Local control
0529 IMRT anal canal cancer 59 GI/GU acute
9311 lung ~150 NIH R01 (Deasy). Toxicity: esophagitis; pneumonitis
9406 EBRT prostate 800 NIH R01 (Tucker) toxicity
9803 3D CRT GBM 40 Brain toxicity
6

7. Rapid Learning
CAT
(Computer Assisted Theragnostics)
MAASTRO/RTOG Collaboration
Andre Dekker, PhD
MAASTRO Knowledge Engineering
7

8. Why Rapid Learning/CAT?
Personalized medicine improves survival and quality of life.
Personalized medicine
• Explosion of data
• Explosion of
decisions
• Decision support
• Evidence base
[..] rapid learning [..] where we can learn from each patient to guide
practice, is [..] crucial to guide rational health policy and to contain costs
[..].
Lancet Oncol 2011;12:933
8

9. Prediction by MDs?
• Non Small Cell Lung
Cancer
• 2 year survival
• 30 patients
• 8 MDs
• AUC: 0.57
• Retrospective
9

10. How to get
data for rapid learning
10

11. Challenges to share data
[..] the problem is not really technical […]. Rather, the problems are
ethical, political, and administrative. Lancet Oncol 2011;12:933
1.Administrative (time)
2.Political (value, authorship)
3.Ethical (privacy)
4.Technical
11

12. CAT approach
CAT is a research project in which
we develop an IT infrastructure -> technical
to make radiotherapy centers
semantic interoperable (SIOp*) -> administrative
while the data stays inside your hospital -> ethical
under your full control -> political
* SIOp level 3 = Machine Readable ->Data in common syntax and with common meaning
12

13. Components
CTMS
PACS ETL
Export
Distributed Deident.
Learning & Filter
Application
Ontology
Sharing
XML Query
DICOM
13

14. Key features
• No sharing of data, truly federated
• Machine learning (retro.) & clinical trials (prosp.)
• NCI Thesaurus with formal additions
• 5 languages, 5 countries & 5 legal systems
• Focus on radiotherapy
• Inclusion of non-academic centers
• Industry involvement
14

15. Network 11/2011
5
2
Active or funded CAT partners (10)
Prospective centers (4)
Map from cgadvertising.com
15

16. Laryngeal carcinoma model
• 994 MAASTRO patients
• 1990-2005
• www.predictcancer.org
• Input parameters
– Age
– Hemoglobin
– T-stage
– EDQ2T (Gy)
– Gender
– N+
– Tumor location
• Output parameters
– Overall survival
www.predictcancer.org, Egelmeer et al., Radiother Oncol. 2011 Jul;100(1):108 16

17. Larynx Query
17

18. Validation - Result
18

19. Final Model Created
Central Server
Distributed Update
Learning Model
Architecture
Send Average Send Average
Send Average Consensus Model
Consensus Model
Consensus Model
Send Model
Parameters
Send Model
Model Server Parameters
RTOG Send Model
Parameters
Model Server
Learn Model Model Server Roma
MAASTRO
from Local Data
Learn Model
Learn Model from Local Data
from Local Data
Only aggregate data is exchanged between the Central Server and the local Servers 19

20. Distributed Learning Results
20

21. Web-based Documentation
System
with Exchange of DICOM RT for
Multicenter Clinical Studies
in Particle Therapy
Priv.-Doz. Dr. med. Stephanie E. Combs, DEGRO 2012
21

22. PARTICLE THERAPY
• particle therapy is a new and promising technique in radiation
oncology for cancer treatment
• compared to standard radiation therapy (RT) with photons, it is with
either
– ions (e.g. Carbon C12, Helium He2, Oxygen O16) or
– protons (H1)
• advantages:
− more precise dose delivery sparing of normal tissue and

to the target organs at risk
− enhanced radiobiological
 increase in local tumor control
effectiveness (carbon ions)
22

23. What we need to do.....
• First results of clinical phase I and II trials performed at NIRS support
the assumption that carbon ions provide an enhanced biological
effectiveness in adenoid cystic carcinomas, H/N melanomas, lung and
liver tumors, large soft tissue sarcomas, chordomas / chondrosarcomas
and prostate cancer
• Randomized trials proving the superiority of heavy charged particles in
comparison to photon IMRT and protons required
– Randomized trial of proton vs. Carbon ion RT for chordomas and
chondrosarcomas of the skull base
• Radiobiologic research will enable better exploitation of the advantages
of carbon ion RT in future trials
• Physics Research: e.g.High Precision Treatment of Moving Targets
23

24. HIT
HEIDELBERG ION-BEAM THERAPY
CENTER
• began patient treatment in Nov. 2009
• main focus:
– clinical studies to evaluate the
benefits of ion therapy for several
indications
• ULICE project
(Union of Light Ions Centers in Europe)
– development of a database with
transnational access
– platform for international clinical
multicenter studies
– accessible by external/internal
oncologists, physicists,
researchers
24

25. Establish a common database for hadrontherapy.
- few centers have treated patients with particle therapy
- heterogeneous concepts of dose prescription, delivery
- hetereogeneous studies and treatment protocols
ULICE DATABASE
Harmonized
ULICE DATABASE
Exchange of
study and
clinical
treatment
experience
concept
Standards and
Transfer of
basic (biologic)
know-how
data
25

26. METHODS
• Approach:
– central, web-based system
– interfaces to the mandatory existing information systems of the hospital
– avoidance of redundant entry of any patient/clinical data
– study-specific modules, which can easily be adapted
– implementation of security and data protection measures to fulfill legal
requirements
26
Kessel K., ..., Combs SE, Radiat Oncol, 7, 115 (2012).

27. GENERAL PRINCIPLES AND ARCHITECTURE
• database with standard interfaces of the PACS world
– storage of all data in an SQL database (DICOM data model)
– possibility to dynamically extend with additional data structures
– interfaces to process and import information from
• DICOM data
• HL7 messages
– Java applet for
• manual import of data by web-upload
• receiving and sending data with DICOM C-Store
– the underlying components are compliant with the IHE Framework
• telemedicine record
– combines the functionality of
• a medical record with
• a professional DICOM RT viewer (Class IIb)
– allows us to exchange, store, process and visualize text data, all types of
DICOM images, and any other multimedia data
– with extensions for study documentation
27
Kessel K., ..., Combs SE, Radiat Oncol, 7, 115 (2012).

28. INTEGRATION OF OTHER INFORMATION
SYSTEMS
28
Kessel K., ..., Combs SE, Radiat Oncol

29. SECURITY CONCEPT
• the system uses the encrypted https protocol to exchange data between
the central server and other systems
• users need an account and password to access the system
• a roles-and-rights concept enables a very detailed configuration for each
study or client
• (basic patient) data can be pseudonymized
– the original patient information is kept in a separate database
– when a user has the right to view all data of a patient, the data is de-
pseudonymized “on the fly”
• external users
– communication via application gateway in the demilitarized zone (DMZ)
– client certificates to verify authorized browsers
29
Kessel K., ..., Combs SE, Radiat Oncol, 7, 115 (2012).

30. RESULTS
• documentation began May 2011 and we documented 900 patients
• all access to the system takes place over the Internet
– no software needs to be installed (except a JRE)
• exchange and storage of various DICOM RT data
• visualization with the DICOM RT ion viewer
– examination and review of radiation plans from every computer in the
hospital without having to go to a PACS or a radiation therapy planning
station
• with our web-based approach, we have succeeded in developing a
database for multicenter studies within the ULICE project
• future prospects: extend automatic and electronic study analyses
30
Kessel K., ..., Combs SE, Radiat Oncol, 7, 115 (2012).

31. More to Integrate
Andre Dekker, PhD
MAASTRO Knowledge Engineering
31

32. How to learn better?
• AUC of 0.85 is needed
• Survival model AUC 0.75
• More patients
• More variables
• Diversity
32

33. More variables from a simple CT
33

34. Biomarkers
LOO-AUC 0.76
Selected biomarkers: IL6, IL8, CEA
General
LOO-AUC = 0.72:
Gender, WHO, FEV1, Positive lymph
nodes,
Radiomics Tumor Volume
LOO-AUC 0.75
Selected image features: CT Range,
Cluster Shade, Run Percentage
(n = 150)
34

35. Biomarkers General Radiomics
AUC 0.87
Biomarker: IL6, IL8, CEA
General: Gender, WHO-PS, FEV1, Positive lymph nodes, Tumor Volume n = 131
35
Radiomics: Range, Run Length, Run Percentage

36. DATA ANALYSIS
36

37. DATA ANALYSIS
- Evidence Based Radiation
Therapy Quality Assurance
37

38. DATA ANALYSIS
- Evidence Based Radiation
Therapy Quality Assurance
(Structure Definition)
38

39. Critical Impact of Radiotherapy
Protocol Compliance and
Quality TROG 02.02
L. J. Peters et al, Journal of Clinical Oncology, vol. 28, Number 18, June 2010 39

40. Failure To Adhere To Protocol
Associated With Decreased
Survival: RTOG 9704
40
R. Abrams et al, Int. J. Radiation Oncology Biol. Phys., Vol. 82, No. 2, pp. 809–816, 2012

41. Target Defined from Multiple
Institutions
S. Kong, Y. Xiao, M. Machtay, et. Al., A “Dry-Run” Study for RTOG1106/ACRIN6697: A Randomized Phase
II Trial of Using During-Treatment FDG-PET and Modern Technology to Individualize Adaptive Radiation 41
Therapy in Stage III NSCLC ,IASLC, 2011

42. Target Inter-Observer
Variability
Pre-GTV Statistics
42

43. 43
The maximum volume of
brach (brachial plexus) is up
to 4-5 fold of the minimum.
OAR Variation

44. Dry Run for Segmentation and
Plan Evaluation – 1106 Example
GTV contours from different institutions. Red thick line represents the consensus contour.
(a) Case1, (b) Case2.
44

45. Oral Presentation AAPM 2012 45

46. RTOG 0617, NCCTG N0628,CALGB 30609
Conventional vs. High Dose RT
R RT: 60 Gy
A Paclitaxel
N Carboplatin +/-
D Cetuximab Paclitaxel
O Carboplatin X 2
M +/- Cetuximab
RT: 74 Gy
I Paclitaxel
Z Carboplatin +/-
E Cetuximab
J. Bradley et al, ASTRO 2011 46

47. Overall Survival
100
Overall Survival (%)
75
50
Dead Total
25
60 Gy 58 213
74 Gy 70 204
0 HR= 1.45 (1.02, 2.05) p*=0.02
0 3 6 9 12
Patients at Risk Months since Randomization
60 Gy 213 190 149 124 104
74 Gy 204 175 137 116 93
*One-sided p-value, left tail
47

48. DATA ANALYSIS
- Evidence Based Radiation
Therapy Quality Assurance
(Image Guided Radiotherapy)
48

49. IGRT Data Submission
Components
49

50. Variations Between Systems
Y. Cui (Xiao) et al, Int. J. Radiation Oncology Biol. Phys., Vol. 81, No. 1, pp. 305–312, 2011
50

51. IGRT Credentialing for RTOG
Protocols
51

52. IGRT Variations
Y. Cui (Xiao) et al, Implementation of Remote 3D IGRT QA for RTOG Clinical Trials,
Int. J. Radiation Oncology Biol. Phys., In Press 52

53. DATA ANALYSIS
- Evidence Based Radiation
Therapy Quality Assurance
(Intensity Modulated
Radiotherapy Planning)
53

54. Establishing knowledge-based
models for IMRT planning
quality evaluation
Q. Jackie Wu
Yaorong Ge
Jan 2012
DUKE University Radiation Oncology

55. Case1 Right Parotid Case 2 Left Parotid
100
Case 1 R Parotid
80
Case 2 L Parotid
Percent volume (%)
60
40
20
Parotid DVH
0
0 20 40 60 80 100
55
Percent dose (%)

56. Knowledge Models For IMRT
Planning QA
• Understand the patient anatomical, physiological
factors that influence dose coverage
• Quantify their individual influence via mathematical
modeling and machine learning
• Codify treatment planning experience and guidelines
using knowledge engineering
• Model Use these factors to model the inter-patient
variation and to provide “peer-review” type guidance
56

57. Example Of Parotid Sparing
Modeling Case 1 Case 2 Case 3 Case 4 Case 5 Case 6
Volume (%)
100
50 Actual Plan DVH
0
0 50 100 50 100 50 100 50 100 50 100 50 100
Modeled Lower
Dose (%) and Upper
Case 7 Case 8 Case 9 Case 10 Case 11 Case 12
Bound of DVH
Volume (%)
100
50
0
0 50 100 50 100 50 100 50 100 50 100 50 100
Dose (%)
Case 13 Case 14 Case 15 Case 16 Case 17 Case 18
Volume (%)
100
50
0
0 50 100 50 100 50 100 50 100 50 100 50 100
Dose (%)
Case 19 Case 20 Case 21 Case 22 Case 23 Case 24
Volume (%)
100
50
0
0 50 100 50 100 50 100 50 100 50 100 50 100
Dose (%)
57

58. Moving Forward
• Bigger Database
– Knowledge and experience embedded in the IMRT
cases
– Collaboration with RTOG who has large case pools
• Comprehensive Knowledge Base
– All types of knowledge sources
– Predictive models
– Ontological framework
• Intuitive and Integrated Decision Support
– Minimize cognitive burden
58

59. Intuitive and Integrative Decision
Support
Prediction QUANTEC Guidelines
Gui
59

60. Experience-based dose volume
histogram prediction in IMRT: A new
QC paradigm
Kevin L. Moore, Ph.D., DABR
60

61. Introduction
• Current TPSs cannot guarantee that IMRT plans are fully optimal, and applying quality
control methods to the treatment planning process can have a very large impact
• The degree to which plan quality variations compromise patient care is unknown
• Investigations (WashU, JHU, ROR) have shown that plan quality variations can be
quite sizeable, vastly exceeding other uncertainties and putting patients that should
have had a low risk of complications into a high-risk category
• Quantitative assessment of PTV metrics is easy, normal tissue sparing is not.
Anatomical variations confound the plan evaluation process because expected values
are (as yet) not available.
KL Moore et al, "Experience-based QC of IMRT planning" IJROBP 82, 545 (2011)
61

62. Predicting DVHs with universal basis function evolution
• With universal fitting function and parameter fits pjq(k), which are derived from all N
training patients, we have the desired result:
• This equation takes as input geometric variables and the derived fitting parameters
and outputs a predicted DVH for an organ based only on the input structure set SSij
62

63. Results: rectum and bladder predictions (training pts)
N=20 training patients
63

64. Results: rectum and bladder predictions (“new” pts)
M=20 validation patients
64

65. Quantifying plan quality variations in RTOG trials
• Employ modeling toolkit as
secondary study on closed
RTOG protocol (e.g. 01-26) to
quantify the clinical
importance of IMRT plan
variability
• Plan quality variations in a large-scale, multi-
institutional trial will serve as an excellent
surrogate for the variability in plan quality
across IMRT practitioners
• These data represent an unique and nearly ideal test bed to not only quantify plan
quality variability but, due to the associated outcomes data with the treatment plans, it
can address its clinical importance as well
65

66. DATA ANALYSIS
- Analytical Algorithms
66

67. Statistical inference
Frequentist inference Bayesian inference
Probability: the proportion of Probability describes degree of belief. It
times that an event would reflects one’s strength of belief that
occur in a large number the proposition is true. Bayesian
of similar repeated trials. inference inherently embraces a
subjective notion of probability.
Model parameters are fixed, Start with a prior belief about the likely
use the observed data to values of model parameters, then use
make Inference about observed data to modify these
parameters, e.g. Maximum parameters, i.e., deriving posterior
Likelihood Estimation, probability distribution.
confidence intervals and P-
values The Dempster-Shafer theory is an extension
of the Bayesian inference.
Wenzhou Chen, Yunfeng Cui, Yanyan He, Yan Yu, James Galvin, Yousuff M. Hussaini, Ying Xiao, “Application of Dempster-
67
Shafer Theory in Dose Response Outcome Analysis”, Physics in Medicine and Biology, In Press

68. Probability, Belief & Plausibility of
RP
68

69. LKB parameters from Dempster–
Shafer theory and other references
69

70. FUTURE DIRECTIONS
70

71. Introducing A New Organizational Structure
NCI Clinical Trials Network

72. Data and QA Process Flow
(receipt, QA, storage)
Imaging
Institution
Studies
Patient data
Medidata ACR QA
IROC
Rave Cloud
QA
QA
Study Group Study Groups
Patient data (second analysis, outcomes,
publications etc.)
IROC 72

73. 73