Predicting xerostomia induced by IMRT treatments: A logistic regression approach

Radiotherapy is one of the main treatments used against cancer. Radiotherapy uses radiation to destroy cancerous cells trying, at the same time, to minimize the damages in healthy tissues. The planning of a radiotherapy treatment is patient dependent, resulting in a lengthy trial and error procedure until a treatment complying as most as possible with the medical prescription is found. Intensity Modulated Radiation Therapy (IMRT) is one technique of radiation treatment that allows the achievement of a high degree of conformity between the area to be treated and the dose absorbed by healthy tissues. Nevertheless, it is still not possible to eliminate completely the potential treatments' side-effects. In this retrospective study we use the clinical data from patients with head-and-neck cancer treated at the Portuguese Institute of Oncology of Coimbra and explore the possibility of classifying new and untreated patients according to the probability of xerostomia 12 months after the beginning of IMRT treatments by using a logistic regression approach. The results obtained show that the classifier presents a high discriminative ability in predicting the binary response “at risk for xerostomia at 12 months”.

[1]  Tom Fawcett,et al.  ROC Graphs: Notes and Practical Considerations for Data Mining Researchers , 2003 .

[2]  T. Bortfeld IMRT: a review and preview , 2006, Physics in medicine and biology.

[3]  Horst W. Hamacher,et al.  Mathematical optimization in intensity modulated radiation therapy , 2008, 4OR.

[4]  H. Romeijn,et al.  A novel linear programming approach to fluence map optimization for intensity modulated radiation therapy treatment planning. , 2003, Physics in medicine and biology.

[5]  Andrew P. Bradley,et al.  The use of the area under the ROC curve in the evaluation of machine learning algorithms , 1997, Pattern Recognit..

[6]  T. Pajak,et al.  Toxicity criteria of the Radiation Therapy Oncology Group (RTOG) and the European Organization for Research and Treatment of Cancer (EORTC) , 1995, International journal of radiation oncology, biology, physics.

[7]  J. Hanley,et al.  The meaning and use of the area under a receiver operating characteristic (ROC) curve. , 1982, Radiology.

[8]  Bill J. Salter,et al.  A Tutorial on Radiation Oncology and Optimization , 2005 .

[9]  Humberto Rocha,et al.  Beam angle optimization for intensity-modulated radiation therapy using a guided pattern search method , 2013, Physics in medicine and biology.

[10]  J. M. Dias,et al.  Discretization of optimal beamlet intensities in IMRT: A binary integer programming approach , 2012, Math. Comput. Model..

[11]  Trevor Hastie,et al.  An Introduction to Statistical Learning , 2013, Springer Texts in Statistics.

[12]  Yuhong Yang CONSISTENCY OF CROSS VALIDATION FOR COMPARING REGRESSION PROCEDURES , 2007, 0803.2963.

[13]  Jerzy W. Grzymala-Busse,et al.  A Comparison of Several Approaches to Missing Attribute Values in Data Mining , 2000, Rough Sets and Current Trends in Computing.

[14]  L. Xing,et al.  Multiobjective evolutionary optimization of the number of beams, their orientations and weights for intensity-modulated radiation therapy , 2004, Physics in Medicine and Biology.

[15]  Nasser M. Nasrabadi,et al.  Pattern Recognition and Machine Learning , 2006, Technometrics.

[16]  B. Ferreira,et al.  RESPONSE, an Electronic Health Patient Information Software for Radiation Therapy , 2015 .