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Classification of changes occurring in lung patient during radiotherapy using relative γ analysis and hidden Markov models
Author(s) -
Varfalvy Nicolas,
Piron Ophelie,
Cyr Marc François,
Dagnault Anne,
Archambault Louis
Publication year - 2017
Publication title -
medical physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.473
H-Index - 180
eISSN - 2473-4209
pISSN - 0094-2405
DOI - 10.1002/mp.12488
Subject(s) - hidden markov model , pattern recognition (psychology) , standard deviation , artificial intelligence , fraction (chemistry) , computer science , medicine , statistics , nuclear medicine , mathematics , chemistry , organic chemistry
Purpose To present a new automated patient classification method based on relative gamma analysis and hidden Markov models ( HMM ) to identify patients undergoing important anatomical changes during radiation therapy. Methods Daily EPID images of every treatment field were acquired for 52 patients treated for lung cancer. In addition, CBCT were acquired on a regular basis. Gamma analysis was performed relative to the first fraction given that no significant anatomical change was observed on the CBCT of the first fraction compared to the planning CT . Several parameters were extracted from the gamma analysis (e.g., average gamma value, standard deviation, percent above 1). These parameters formed patient‐specific time series. Data from the first 24 patients were used as a training set for the HMM . The trained HMM was then applied to the remaining 28 patients and compared to manual clinical evaluation and fixed thresholds. Results A three‐category system was used for patient classification ranging from minor deviations (category 1) to severe deviations (category 3) from the treatment plan. Patient classified using the HMM lead to the same result as the classification made by a human expert 83% of the time. The HMM overestimate the category 10% of the time and underestimate 7% of the time. Both methods never disagree by more than one category. In addition, the information provided by the HMM is richer than the simple threshold‐based approach. HMM provides information on the likelihood that a patient will improve or deteriorate as well as the expected time the patient will remain in that state. Conclusion We showed a method to classify patients during the course of radiotherapy based on relative changes in EPID images and a hidden Markov model. Information obtained through this automated classification can complement the clinical information collected during treatment and help identify patients in need of a plan adaptation.