Premium
Knowledge‐based isocenter selection in radiosurgery planning
Author(s) -
Berdyshev A.,
Cevik M.,
Aleman D.,
Nordstrom H.,
Riad S.,
Lee Y.,
Sahgal A.,
Ruschin M.
Publication year - 2020
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.14305
Subject(s) - isocenter , radiosurgery , computer science , artificial intelligence , leverage (statistics) , artificial neural network , jackknife resampling , machine learning , residual , radiation treatment planning , feature selection , set (abstract data type) , mathematics , algorithm , radiation therapy , medicine , statistics , radiology , estimator , programming language
Purpose We present a new method for knowledge‐based isocenter selection for treatment planning in radiosurgery. Our objective is to develop a prediction model that can learn from past manually designed treatment plans. We leverage recent advances in deep learning to predict isocenter locations in treatment plans in order to provide a decision support tool. Methods The proposed method adapts a geometric approach using orthogonal moment expansions as a feature vector for describing the shape of the tumor. Our approach accounts primarily for tumor shape and OAR proximity, the two factors that are known to greatly affect the isocenter placement. We solve the prediction problem by training a residual neural network with skip connections on the formed shape descriptors. Our network was trained on 533 patient cases and was validated on a set of out‐of‐sample cases. Results Our method generates heatmap predictions for isocenter locations that are in most cases comparable to the experienced human planners, which shows that the method can be used in treatment planning to guide the users for determining the isocenters. Conclusions Our numerical experiments indicate a positive predictive value on an independent validation set when compared against a test dataset that was not seen by the model during training.