Highlights of Day 2 Breakout Sessions:

Radiation Oncology Room

2nd Interdisciplinary ConferenceProstate Cancer: Predictive Models for Decision Making

April 9, 2011

How the Radiation Oncologist and the Radiation Technique

Affect Outcome – P. Hoskin

• Technique does not matter if done well in appropriately selected patients– IMRT, IGRT, HDR, LDR, Protons, SBRT

• Modern techniques have < 5 % late toxicity rates• The skill of the radiation oncologist is highly

predictive for toxicity and clinical outcomes• Learning curve for brachytherapy procedures• Beware low doses to greater amounts of normal

tissue with modern EBRT techniques: second malignancies

• There is a great variability in contouring between experts

• The entire radiation therapy team affects outcome– Physicists, dosimetrists, therapists, etc.

Predicting Toxicity After External Radiotherapy: Sexual and Urinary Dysfunction – M. Roach III

• The bladder is hard to nail down– Plastic/elastic/mobile organ– Modeled as a static structure – Incompletely defined D-V relationships with toxicity– Other factors matter: prior TRUP, hormones, consequential

late effects• ED is dose and volume dependent

– Impacted by baseline function– Interobserver variation in OAR definitions– Host genetic factors play a role (SNPs)

• Blood supply to OARs may be affected by newer ablative doses/techniques

• Brachytherapy – beware trauma/number of needle sticks

• Be observant! Hypertension is emerging as protective against some toxicities (GI > GU)

Predicting Toxicity After External Radiotherapy: GI Dysfunction – R. Valdagni

• The future is multifactorial predictive models– Dosimetric (cannot be de-emphasized as we explore others)– Clinical – Genetic/biomolecular

• We mix GI toxicity endpoints which likely have different DV constraints and etiologies– Bleeding, abdominal pain, fecal incontinence

• Genetic markers confer a radiosentitive or radioresistant phenotype for GI toxicity– ATM predicts for toxicity in brachytherapy– SNPs are associated with toxicity

• Several nomograms predict for GI toxicity• Can we include clinical and genetic factors into the

inverse treatment planning process along with dose-volume factors?

Predicting Toxicity After Brachytherapy – A. Martinez

• Few predictive models for brachytherapy• Tumor control is equal with HDR and LDR• HDR allows more dosimetric control – inverse

optimization • Dose rate predicts for toxicity - HDR offers lower:

– Urinary toxicity– Rectal toxicity– Sexual toxicity

• Published parameters for brachytherapy toxicity not uniform – Day of LDR dosimetry CT scan– Reported dose-volume endpoints

• Real time planning is the way of the future• Dose painting is safely delivered without increased

toxicity• Can decrease toxicity with tracking OARs like NVBs

Predicting Radio-Induced Toxicity: The Challenge of Moving from Qualitative Events to Quantitative Scoring – T. Rancati

• Radiation effects produce a continuous response which is recorded as graded response data which is analyzed in a YES/NO form by considering the proportion of subjects whose reaction exceeds a certain threshold

• Challenges in developing toxicity models:– Large databases are needed– Prospective collection– Capture subjective, objective, medical, and analytic data

(SOMA)– Capturing timing and severity of acute and late reactions– Recording proper baseline scores before treatment– Need for long term follow-up– Need different models for different endpoints– Need to reduce a continuous scale of symptoms to a

graded/dichotomized endpoint for modeling purposes– Need to choose which aspect of toxicity is relevant to the

patient’s quality of life (Peak toxicity? Persistent mild toxicity? Toxicity involving social impairment?)

Volume Effects and Dose Responses in Rectal Complications of External Beam Radiotherapy – A. Jackson

• Quantec: reviewed and synthesized data on dose-volume response of complications from external beam radiotherapy

• Volumes exposed high dose significantly associated with complications

• Wide range of threshold volumes at intermediate doses associated with complications

• Regional sensitivities exist within organs at risk• Dose atlases are the future of predictive models• Models need to be updated as new techniques

mature

How to Design the Next Generation of Predictive Models in Radiation Oncology

• Dose-Volume Relationships• Clinical Parameters• Molecular and Genetic Markers

How to Design the Next Generation of Predictive Models in Radiation Oncology

• Dosimetry: Optimize Dose-Volume Constraints– Clearly identify the OARs that predict for toxicity– Properly and homogeneously contour target volumes and

OARs– Gain better understanding of sub-regions within OARs and

their relationship to toxicity– Incorporate daily imaging into our understanding of dose-

volume relationships (right now we rely on a single point in time: the simulation scan BUT we get daily imaging, role of adaptive planning)

• Standardize Toxicity Reporting and Definitions

• Integration of Patient Reported Outcomes with D-V Data

How to Design the Next Generation of Predictive Models in Radiation Oncology

• Clinical Parameters– Identify parameters that are best added to new models– Appropriate integration of these parameters into new

predictive models– Look for new parameters – careful observation and meticulous

data collection is necessary to recognize new patterns (hypertension being protective for GU/GI toxicities)

• Integrate molecular and genetic information– Get tissue and blood

• “Intelligent Planning”– Incorporate nomograms into modern inverse iterative

planning algorithms