Big Data in Radiation Oncology

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“Big Data” in Radiation

Oncology

Hao Howard Zhang, PhD and Warren D. D’Souza, PhD, MBA

Department of Radiation Oncology

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Disclosures

Research funding from

NIH/NCI

Varian Medical Systems (Palo Alto, CA)

Philips Healthcare (Cleveland, OH)

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New Economics of Cancer Care

• ASTRO considering to even out variations in care delivery

with a revamped accreditation program

• Essential value-based purchasing components include

physician education, implementation of quality metrics and

deployment of

big data in decision making

(Health Imaging, Lisa Fratt, May 2013)

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What is “Big Data” ?

Big data

is the term for a collection of data sets so large

and complex that it becomes difficult to process using

on-hand database management tools or traditional data

processing applications. The challenges include

capture, curation, storage, search, sharing, transfer,

analysis, and visualization. Big data sizes are a

constantly moving target, as of 2012 ranging from a few

dozen terabytes to many petabytes of data in a single

data set.

“Big Data, for better or worse: 90% of world’s data

generated over last two years”

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Big Data in Medicine

• Quality

• The granddaddy of medical databases is The

Society of Thoracic Surgeons STS National

Database, launched in 1989 as an initiative for

quality improvement and patient safety among

cardiothoracic surgeons.

• Outcomes

• Appropriate use ~ value-based reimbursement.

Currently, fee-for-service reimbursement

incentivizes physicians to treat every patient. In

some cases, the patient can be served by other

options.

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Challenges with Big Data

• Data Provenance

• Source and reliability of data

• Data Aggregation

• Combining data from multiple sources and across

institutions

• Data Interpretation

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National Radiation Oncology

Registry

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RT Data Mining Infrastructure

Oncospace

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IHE-RO

• IHE-RO is an initiative that helps to ensure a safe,

efficient radiation oncology practice by improving

system to system connections.

• Image-based (3-dimensional) radiation therapy

treatment planning.

• Exchanging and storing image registration,

radiation therapy structure sets, radiation therapy

doses.

• Exchange of data required to perform sophisticated

treatment planning.

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National Cancer Informatics

Program

The Old caBIG initiative

• Open development of informatics capabilities for research

• Access to well-described data to facilitate integrative

cancer research

• Provides informatics infrastructure and standards to

improve interoperability between information systems

• Foster collaborative relationships among researchers

across the basic, translational, and clinical continuum

• Training next generation of biomedical investigators

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Radiation Oncology Big Data

• Electronic health records

• Demographics

• Treatment delivery modality

• Diagnostic Imaging

• Anatomical and functional

• Treatment Planning

• Beam/plan parameters, DVH parameters

• Additional Imaging

• kV, Simulation CT

• Outcomes

• Tumor response, Toxicity/Complications

• Blood and tissue samples

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Optimal Treatment Strategy

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Lack of apriori Knowledge

The inverse planning does not quite listen

to me!

Keep trying and …

Hope !!!

I want:

Prostate dose 70Gy Seminal vesicles 50-70 Gy

Bladder max 60Gy Rectum max 60Gy

…….. …..

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Plan Optimality-Practicality

Tradeoff

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Treatment Plan Quality – Overlap

Volume Histogram

Wu et al. Patient driven-geometry information retrieval for IMRT treatment plan quality control. Med Phys 36, 5497 (2009).

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Knowledge-Based Replanning

Wu et al. Patient driven-geometry information retrieval for IMRT treatment plan quality control. Med Phys 36, 5497 (2009).

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Model-Based OAR Sparing

Moore et al. Experience-based quality control of clinical intensity-modulated radiotherapy planning. Int J Radiat Oncol Biol Phys 81, 545 (2011).

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Predicting Treatment Plan Output

Left Parotid

Right Parotid

Zhang et al. Modeling plan-related clinical

complications using machine learning tools in a multi-plan IMRT framework. Int J Radiat Oncol Biol Phys 74, 1617-26 (2009)

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Decision Features

• Decision making in radiotherapy

• Clinical Features such as patient performance status,

organ function, grade and extent of tumor (TNM

system)

• Toxicity measurements and scoring based on

validated scoring systems

• Spatial and temporal distribution of radiotherapy dose

• Additional therapies such as chemotherapy

(sequential or concurrent), targeted agents and

surgery

• Imaging features, including size, volume and more

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Clinical Decision Support System

Lambin et al. Predicting outcomes in

radiation oncology – multifactorial decision support systems.

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MAASTRO Clinic Larynx Data

Egelmeer 2011

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Local Control Nomogram

• Prognostic factors for overall survival were low hemoglobin level, male sex,

high T-status, presence of nodal involvement, older age, lower EQD_2T, and non-glottic tumor

• Unfavorable prognostic factors for local control were low hemoglobin level,

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Overall Survival

Egelmeer et al. Development and

Validation of a nomogram for prediction And local control in laryngeal

Carcinoma patients treated with Radiotherapy alone: a cohort study

Based on 994 patients. Radiother Oncol 100, 108-15 (2011)

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RTOG 9311

• 163 patients entered in a Phase I/II 3-D radiation therapy dose escalation trial.

• Patients stratified for different radiation treatment levels depending on their V20 (the percentage of their total lung volume that would receive in excess of 20 Gy with the treatment plan).

• Patients with a V20<25% (Group 1) received 70.9 Gy/33 fractions, 77.4Gy/36 fractions, 83.8 Gy/39 fractions and 90.3 Gy/42 fractions successively.

• Patients with a V20 of 25-37% (Group 2) received 70.9 Gy and 77.4 Gy successively.

• The treatment arm for patients with a V20>37% (Group 3) closed early secondary to poor accrual(2 patients) and the perception of excessive risk for the development of pneumonitis.

• Patients were allowed to receive neoadjuvant chemotherapy before radiation therapy only, but not concurrently.

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Radiation Pneumonitis Events

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Netherlands Cancer Institute Data

• Patients

– 81 patients (41 with malignant lymphoma and 40 breast cancer); – Mean age = 41 years (range = 18-74 years); 25 men and 56

women; 26 smokers

• Malignant lymphoma

– Radiation alone (n = 18) – Chemotherapy + radiation (n = 23)

• Breast

– Radiation alone (n = 5) – Radiation + Chemotherapy (n = 24) – Radiation + tamoxifen (n = 11)

Theuws et al. Prediction of overall pulmonary function loss in relation to the 3-D dose distribution for patient with breast cancer and malignant lymphoma. Radiother Oncol 1998;49:233-43.

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Treatment Modality Influence

Theuws et al. Prediction of overall pulmonary function loss in relation to the 3-D

dose distribution for patient with breast cancer and malignant lymphoma. Radiother Oncol 1998;49:233-43.

circles – Breast; squares – Lymphoma

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Smoking Status

circles – Breast; squares – Lymphoma

open symbols – radiation alone; closed symbols – radiation + chemotherapy

Theuws et al. Prediction of overall pulmonary function loss in relation to the 3-D dose distribution for patients with breast cancer and malignant lymphoma. Radiother Oncol 1998;49:233-43.

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Duke Radiation Pneumonitis

234 patients; 34 patients with

Grade 2+ pneumonitis;

70% of patients treated at 1.8–2.0 Gy/fraction,

once daily; remaining treated at 1.25 Gy/fraction

To CTV and 1.6 Gy/fraction to GTV, twice daily

27 non-dose factors (biological, clinical and

other factors)

Chen et al. Using patient data similarities to predict radiation

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Radiation Pneumonitis Models

Dose factors

Dose + Clinical factors

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Wisdom of Crowds

Das et al. Combining multiple models to generate consensus: Application to radiation-induced pneumonitis prediction. Med Phys 35, 5098-109 (2008).

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Probability of Radiation

Pneumonitis

Das et al. Combining multiple models to generate consensus: Application to radiation-induced pneumonitis prediction. Med Phys 35, 5098-109 (2008).

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Challenges with Outcomes

Analysis

• Quantitative Analysis of Normal Tissue Effects in the

Clinic (QUANTEC)

• Current state of dose response knowledge

• Challenge in generalizing recommendations

• Difficulty in determining actual dose to patients, consistency

of structure delineation, outcome scoring, heterogeneity in institutional treatment delivery practices

• Inferences about treatment plan quality

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Tan et al. 2013. Int J Radiat Oncol Biol Phys 85: 1375-82.

FDG-PET Features & Pathologic

Tumor Response

A new SUV intensity feature - Skewness pre-CRT

• Top: responder, more skewed (fewer

higher SUVs)

• Bottom: non-responder, less skewed

(more higher SUVs)

Three texture features post-CRT – Inertia, Correlation, and Cluster Prominence

• Top: responder, homogeneous FDG

uptake post-CRT

• Bottom: non-responder, heterogeneous

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Tan et al. 2013. Int J Radiat Oncol Biol Phys 85: 1375-82.

Spatial-Temporal FDG-PET

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Zhang et al. 2014. Int J Radiat Oncol Biol Phys 88: 195-203

Therapy Response Prediction

• Response of 20 patients with esophageal cancer to chemoradiotherapy (CRT)

• SVM model with selected features from all feature groups: AUC = 1.0, sensitivity = 100%, specificity = 100%

• Models with conventional PET/CT response measures or clinical parameters: AUCs < 0.75

• Multi-institution data (Univ of Maryland, Wake Forest Univ, Oregon Health & Science Univ)