Your browser doesn't support javascript.
loading
Knowledge-based isocenter selection in radiosurgery planning.
Berdyshev, A; Cevik, M; Aleman, D; Nordstrom, H; Riad, S; Lee, Y; Sahgal, A; Ruschin, M.
Afiliación
  • Berdyshev A; Department of Mechanical and Industrial Engineering, University of Toronto, Toronto, ON, Canada.
  • Cevik M; Department of Mechanical and Industrial Engineering, Ryerson University, Toronto, ON, Canada.
  • Aleman D; Department of Mechanical and Industrial Engineering, University of Toronto, Toronto, ON, Canada.
  • Nordstrom H; Elekta Instrument, Stockholm, AB, Sweden.
  • Riad S; Elekta Instrument, Stockholm, AB, Sweden.
  • Lee Y; Department of Radiation Oncology, Sunnybrook Health Sciences Center, Toronto, ON, Canada.
  • Sahgal A; Department of Radiation Oncology, Sunnybrook Health Sciences Center, Toronto, ON, Canada.
  • Ruschin M; Department of Radiation Oncology, Sunnybrook Health Sciences Center, Toronto, ON, Canada.
Med Phys ; 47(9): 3913-3927, 2020 Sep.
Article en En | MEDLINE | ID: mdl-32473064
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.
Asunto(s)
Palabras clave
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Radiocirugia Tipo de estudio: Prognostic_studies Límite: Humans Idioma: En Revista: Med Phys Año: 2020 Tipo del documento: Article País de afiliación: Canadá Pais de publicación: Estados Unidos
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Radiocirugia Tipo de estudio: Prognostic_studies Límite: Humans Idioma: En Revista: Med Phys Año: 2020 Tipo del documento: Article País de afiliación: Canadá Pais de publicación: Estados Unidos