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1.
1.B. Movsas et al., “Who enrolls onto clinical oncology trials? A radiation patterns of care study analysis,” Int. J. Radiat. Oncol., Biol., Phys. 68(4), 11451150 (2007).
http://dx.doi.org/10.1016/j.ijrobp.2007.01.051
2.
2.J. R. Palta et al., “Developing a national radiation oncology registry: From acorns to oaks,” Pract. Radiat. Oncol. 2(1), 1017 (2012).
http://dx.doi.org/10.1016/j.prro.2011.06.002
3.
3.E. Roelofs, L. Persoon, S. Nijsten, W. Wiessler, A. Dekker, and P. Lambin, “Benefits of a clinical data warehouse with data mining tools to collect data for a radiotherapy trial,” Radiother. Oncol. 108(1), 174179 (2013).
http://dx.doi.org/10.1016/j.radonc.2012.09.019
4.
4.National Cancer Informatics Program, Center for Biomedical Informatics and Information Technology, 2014, available at http://cbiit.nci.nih.gov/ncip.
5.
5.T. McNutt, J. Wong, J. Purdy, R. Valicenti, and T. DeWeese, “OncoSpace: A new paradigm for clinical research and decision support in radiation oncology,” in Proceedings of the XVIth International Conference on Computers in Radiotherapy (Amsterdam, Netherlands, 2010).
6.
6.J. A. Moore, K. Evans, W. Yang, J. Herman, and T. McNutt, “Automatic treatment planning implementation using a database of previously treated patients,” J. Phys.: Conf. Ser. 489(1), 012054 (2014).
http://dx.doi.org/10.1088/1742-6596/489/1/012054
7.
7.J. O. Deasy et al., “Improving normal tissue complication probability models: The need to adopt a ‘data-pooling’ culture,” Int. J. Radiat. Oncol., Biol., Phys. 76, S151S154 (2010).
http://dx.doi.org/10.1016/j.ijrobp.2009.06.094
8.
8.I. El Naqa et al., “Dose response explorer: An integrated open-source tool for exploring and modelling radiotherapy dose–volume outcome relationships,” Phys. Med. Biol. 51(22), 57195735 (2006).
http://dx.doi.org/10.1088/0031-9155/51/22/001
9.
9.H. A. Gay and A. Niemierko, “A free program for calculating EUD-based NTCP and TCP in external beam radiotherapy,” Phys. Med. 23, 115125 (2007).
http://dx.doi.org/10.1016/j.ejmp.2007.07.001
10.
10.O. Gayou, D. S. Parda, and M. Miften, “EUCLID: An outcome analysis tool for high-dimensional clinical studies,” Phys. Med. Biol. 52(6), 17051719 (2007).
http://dx.doi.org/10.1088/0031-9155/52/6/011
11.
11.I. Tsougos, I. Grout, K. Theodorou, and C. Kappas, “A free software for the evaluation and comparison of dose response models in clinical radiotherapy (DORES),” Int. J. Radiat. Biol. 85(3), 227237 (2009).
http://dx.doi.org/10.1080/09553000902748567
12.
12.I. El Naqa et al., “Datamining approaches for modeling tumor control probability,” Acta Oncol. 49(8), 13631373 (2010).
http://dx.doi.org/10.3109/02841861003649224
13.
13.L. C. Holloway, J.-A. Miller, S. Kumar, B. M. Whelan, and S. K. Vinod, “Comp plan: A computer program to generate dose and radiobiological metrics from dose-volume histogram files,” Med. Dosim. 37(3), 305309 (2012).
http://dx.doi.org/10.1016/j.meddos.2011.11.004
14.
14.M. Kazhdan et al., “A shape relationship descriptor for radiation therapy planning,” in Medical Image Computing and Computer-Assisted Intervention—MICCAI (Springer Berlin Heidelberg, London, UK, 2009), pp. 100108.
15.
15.P. Simari et al., “A statistical approach for achievable dose querying in IMRT planning,” in Medical Image Computing and Computer-Assisted Intervention—MICCAI (Springer Berlin Heidelberg, Beijing, China, 2010), pp. 521528.
16.
16.B. Wu et al., “Data-driven approach to generating achievable dose–volume histogram objectives in intensity-modulated radiotherapy planning,” Int. J. Radiat. Oncol., Biol., Phys. 79(4), 12411247 (2011).
http://dx.doi.org/10.1016/j.ijrobp.2010.05.026
17.
17.S. F. Petit et al., “Increased organ sparing using shape-based treatment plan optimization for intensity modulated radiation therapy of pancreatic adenocarcinoma,” Radiother. Oncol. 102(1), 3844 (2012).
http://dx.doi.org/10.1016/j.radonc.2011.05.025
18.
18.Y. Yang et al., “An overlap-volume-histogram based method for rectal dose prediction and automated treatment planning in the external beam prostate radiotherapy following hydrogel injection,” Med. Phys. 40(1), 011709(10pp.) (2013).
http://dx.doi.org/10.1118/1.4769424
19.
19.L. B. Marks et al., “Radiation dose–volume effects in the lung,” Int. J. Radiat. Oncol., Biol., Phys. 76(Suppl. 3), S70S76 (2010).
http://dx.doi.org/10.1016/j.ijrobp.2009.06.091
20.
20.M. A. List et al., “The performance status scale for head and neck cancer patients and the functional assessment of cancer therapy-head and neck scale: A study of utility and validity,” Cancer 77(11), 22942301 (1996).
http://dx.doi.org/10.1002/(SICI)1097-0142(19960601)77:11<2294::AID-CNCR17>3.0.CO;2-S
21.
21.A. Y. Chen et al., “The development and validation of a dysphagia-specific quality-of-life questionnaire for patients with head and neck cancer: The M. D. Anderson dysphagia inventory,” Arch. Otolaryngol., Head Neck Surg. 127(7), 870876 (2001).
22.
22.A. Jackson et al., “The lessons of QUANTEC: Recommendations for reporting and gathering data on dose–volume dependencies of treatment outcome,” Int. J. Radiat. Oncol., Biol., Phys. 76, S155S160 (2010).
http://dx.doi.org/10.1016/j.ijrobp.2009.08.074
23.
23.M. Godwin et al., “Pragmatic controlled clinical trials in primary care: The struggle between external and internal validity,” BMC Med. Res. Methodol. 3(1), 2834 (2003).
http://dx.doi.org/10.1186/1471-2288-3-28
24.
24.J. D. Lurie and T. S. Morgan, “Pros and cons of pragmatic clinical trials,” J. Comp. Eff. Res. 2(1), 5358 (2012).
http://dx.doi.org/10.2217/cer.12.74
25.
25.M. Söhn, D. Yan, J. Liang, E. Meldolesi, C. Vargas, and M. Alber, “Incidence of late rectal bleeding in high-dose conformal radiotherapy of prostate cancer using equivalent uniform dose–based and dose–volume–based normal tissue complication probability models,” Int. J. Radiat. Oncol., Biol., Phys. 67(4), 10661073 (2007).
http://dx.doi.org/10.1016/j.ijrobp.2006.10.014
26.
26.M. A. Ebert et al., “Derivation and representation of dose-volume response from large clinical trial data sets: An example from the RADAR prostate radiotherapy trial,” J. Phys.: Conf. Ser. 489(1), 012090 (2014).
http://dx.doi.org/10.1088/1742-6596/489/1/012090
27.
27.L. A. Dawson, M. Biersack, G. Lockwood, A. Eisbruch, T. S. Lawrence, and R. K. Ten Haken, “Use of principal component analysis to evaluate the partial organ tolerance of normal tissues to radiation,” Int. J. Radiat. Oncol., Biol., Phys. 62(3), 829837 (2005).
http://dx.doi.org/10.1016/j.ijrobp.2004.11.013
28.
28.I. El Naqa et al., “Multivariable modeling of radiotherapy outcomes, including dose–volume and clinical factors,” Int. J. Radiat. Oncol., Biol., Phys. 64(4), 12751286 (2006).
http://dx.doi.org/10.1016/j.ijrobp.2005.11.022
29.
29.L. B. Marks et al., “Use of normal tissue complication probability models in the clinic,” Int. J. Radiat. Oncol., Biol., Phys. 76, S10S19 (2010).
http://dx.doi.org/10.1016/j.ijrobp.2009.07.1754
30.
30.A. Niemierko, “A unified model of tissue response to radiation,” Med. Phys. 26, 1100 (1999).
31.
31.J. O. Deasy, V. Moiseenko, L. Marks, K. S. C. Chao, J. Nam, and A. Eisbruch, “Radiotherapy dose–volume effects on salivary gland function,” Int. J. Radiat. Oncol., Biol., Phys. 76, S58S63 (2010).
http://dx.doi.org/10.1016/j.ijrobp.2009.06.090
32.
32.F. Y. Feng et al., “Intensity-modulated radiotherapy of head and neck cancer aiming to reduce dysphagia: Early dose–effect relationships for the swallowing structures,” Int. J. Radiat. Oncol., Biol., Phys. 68(5), 12891298 (2007).
http://dx.doi.org/10.1016/j.ijrobp.2007.02.049
33.
33.P. C. Levendag et al., “Dysphagia disorders in patients with cancer of the oropharynx are significantly affected by the radiation therapy dose to the superior and middle constrictor muscle: A dose–effect relationship,” Radiother. Oncol. 85(1), 6473 (2007).
http://dx.doi.org/10.1016/j.radonc.2007.07.009
34.
34.T. Rancati et al., “Radiation dose–volume effects in the larynx and pharynx,” Int. J. Radiat. Oncol., Biol., Phys. 76, S64S69 (2010).
http://dx.doi.org/10.1016/j.ijrobp.2009.03.079
35.
35.A. Eisbruch et al., “Chemo-IMRT of oropharyngeal cancer aiming to reduce dysphagia: Swallowing organs late complication probabilities and dosimetric correlates,” Int. J. Radiat. Oncol., Biol., Phys. 81(3), e93e99 (2011).
http://dx.doi.org/10.1016/j.ijrobp.2010.12.067
36.
36.D. R. Gomez et al., “Correlation of osteoradionecrosis and dental events with dosimetric parameters in intensity-modulated radiation therapy for head-and-neck cancer,” Int. J. Radiat. Oncol., Biol., Phys. 81(4), e207e213 (2011).
http://dx.doi.org/10.1016/j.ijrobp.2011.02.003
37.
37.J. Hey et al., “The influence of parotid gland sparing on radiation damages of dental hard tissues,” Clin. Oral Invest. 17(6), 16191625 (2013).
http://dx.doi.org/10.1007/s00784-012-0854-6
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/content/aapm/journal/medphys/42/7/10.1118/1.4922686
2015-06-23
2016-09-30

Abstract

To develop a hypothesis-generating framework for automatic extraction of dose-outcome relationships from an in-house, analytic oncology database.

Dose–volume histograms (DVH) and clinical outcomes have been routinely stored to the authors’ database for 684 head and neck cancer patients treated from 2007 to 2014. Database queries were developed to extract outcomes that had been assessed for at least 100 patients, as well as DVH curves for organs-at-risk (OAR) that were contoured for at least 100 patients. DVH curves for paired OAR (e.g., left and right parotids) were automatically combined and included as additional structures for analysis. For each OAR-outcome combination, only patients with both OAR and outcome records were analyzed. DVH dose points, , at a given normalized volume threshold were stratified into two groups based on severity of toxicity outcomes after treatment completion. The probability of an outcome was modeled at each = [0%, 1%, …, 100%] by logistic regression. Notable OAR-outcome combinations were defined as having statistically significant regression parameters ( < 0.05) and an odds ratio of at least 1.05 (5% increase in odds per Gy).

A total of 57 individual and combined structures and 97 outcomes were queried from the database. Of all possible OAR-outcome combinations, 17% resulted in significant logistic regression fits ( < 0.05) having an odds ratio of at least 1.05. Further manual inspection revealed a number of reasonable models based on either reported literature or proximity between neighboring OARs. The data-mining algorithm confirmed the following well-known OAR-dose/outcome relationships: dysphagia/larynx, voice changes/larynx, esophagitis/esophagus, xerostomia/parotid glands, and mucositis/oral mucosa. Several surrogate relationships, defined as OAR not directly attributed to an outcome, were also observed, including esophagitis/larynx, mucositis/mandible, and xerostomia/mandible.

Prospective collection of clinical data has enabled large-scale analysis of dose-outcome relationships. The current data-mining framework revealed both known and novel dosimetric and clinical relationships, underscoring the potential utility of this analytic approach in hypothesis generation. Multivariate models and advanced, 3D dosimetric features may be necessary to further evaluate the complex relationship between neighboring OAR and observed outcomes.

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