Big Data in Radiation Oncology

Transcription

1 Big Data in Radiation Oncology Hao Howard Zhang, PhD and Warren D. D Souza, PhD, MBA Department of Radiation Oncology University of Maryland School of Medicine, Baltimore MD

2 Disclosures Research funding from NIH/NCI Varian Medical Systems (Palo Alto, CA) Philips Healthcare (Cleveland, OH)

3 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)

4 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. Source: Wikipedia Big Data, for better or worse: 90% of world s data generated over last two years

5 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.

6 Challenges with Big Data Data Provenance Source and reliability of data Data Aggregation Combining data from multiple sources and across institutions Data Interpretation Putting the data to work..but cautiously!

7 National Radiation Oncology Registry

8 NROR - Prostate

9 RT Data Mining Infrastructure Oncospace T. McNutt et al.

10 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.

11 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

12 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 Genomics, proteomics, metabolomics

13 Courtesy of Howard Zhang, PhD Optimal Treatment Strategy

14 Treatment Plan Quality

15 Lack of apriori Knowledge The inverse planning does not quite listen to me! I want: Prostate dose 70Gy Seminal vesicles Gy Bladder max 60Gy Rectum max 60Gy.... Keep trying and Hope!!! Courtesy of Lei Xing, PhD

16 Plan Optimality-Practicality Tradeoff Moore et al. Quantitative Metrics for Assessing Plan Quality. Sem Rad Oncol 22, 62-9 (2012)

17 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).

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

19 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).

20 Predicting Treatment Plan Output Left 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, (2009) Right Parotid

21 Treatment Outcomes

22 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 advanced functional metrics

23 Clinical Decision Support System Lambin et al. Predicting outcomes in radiation oncology multifactorial decision support systems. Nature Rev Clin Oncol 27-40, 2013

24 MAASTRO Clinic Larynx Data Validation Dataset Training Dataset Egelmeer 2011

25 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, male sex, high T-status, nodal involvement, older age, and lower EQD 2T.

26 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, (2011)

27 RTOG 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.

28 Radiation Pneumonitis Events RTOG Data WUSTL Data

29 Radiation Pneumonitis & Dose

30 Netherlands Cancer Institute Data Patients 81 patients (41 with malignant lymphoma and 40 breast cancer); Mean age = 41 years (range = 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:

31 Treatment Modality Influence 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 patient with breast cancer and malignant lymphoma. Radiother Oncol 1998;49:

32 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:

33 Duke Radiation Pneumonitis 234 patients; 34 patients with Grade 2+ pneumonitis; 70% of patients treated at 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 pneumonitis via a self-organizing map. Phys Med Biol 53, (2008)

34 Radiation Pneumonitis Models Dose + Clinical factors Dose factors

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

36 Probability of Radiation Pneumonitis Das et al. Combining multiple models to generate consensus: Application to radiation-induced pneumonitis prediction. Med Phys 35, (2008).

37 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 Subject to uncertainties in dose response

38 Imaging Features

39 FDG-PET Features & Pathologic Tumor Response Three texture features post-crt Inertia, Correlation, and Cluster Prominence Top: responder, homogeneous FDG uptake post-crt Bottom: non-responder, heterogeneous FDG uptake post-crt A new SUV intensity feature - Skewness pre-crt Top: responder, more skewed (fewer higher SUVs) Bottom: non-responder, less skewed (more higher SUVs) Tan et al Int J Radiat Oncol Biol Phys 85:

40 Spatial-Temporal FDG-PET Features Tan et al Int J Radiat Oncol Biol Phys 85:

41 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) Zhang et al Int J Radiat Oncol Biol Phys 88:

42 Correlation & Causation

43 Beware the Data! GIGO Garbage in Garbage Out

44 Acknowledgements Radiation Oncology William F. Regine, MD Minesh M. Mehta, MB.Ch.B Mohan Suntha, MD, MBA Steven J Feigenberg, MD Medical Physics & Operations Research Nilesh Mistry, PhD Wei Lu, PhD Baoshe Zhang, PhD Shifeng Chen, PhD Diagnostic Radiology & Nuclear Medicine Wengen Chen, MD, PhD Seth Kligerman, MD Computer Sciences & Industrial Engineering Robert R. Meyer, PhD Bruce Golden, PhD Amitabh Varshney, PhD

45 Thank You! University of Maryland