Geometric and Dosimetric Evaluation of a RayStation Deep Learning Model for Auto-Segmentation of Organs at Risk in a Real-World Head and Neck Cancer Dataset.

Journal: Clinical oncology (Royal College of Radiologists (Great Britain))

PMID: 40120536

Abstract

AIMS: To assess geometric accuracy and dosimetric impact of a deep learning segmentation (DLS) model on a large, diverse dataset of head and neck cancer (HNC) patients treated with intensity-modulated proton therapy (IMPT).

Authors

D Sharma

Department of Medical Physics, Apollo Proton Cancer Center, Chennai, Tamil Nadu, India. Electronic address: drdayananda_s@apollohospitals.com.
G Singh

Indian Council of Agricultural Research-Indian Institute of Wheat and Barley Research, Karnal, India.
N Burela

Department of Radiation Oncology, Apollo Proton Cancer Center, Chennai, Tamil Nadu, India.
S Gayen

Department of Pharmaceutical Sciences, Dr. Harisingh Gour University, Sagar, India.
G Aishwarya

Department of Medical Physics, Apollo Proton Cancer Center, Chennai, Tamil Nadu, India.
S Nangia

Department of Radiation Oncology, Apollo Proton Cancer Center, Chennai, Tamil Nadu, India.

Keywords

Aged Deep Learning Female Head and Neck Neoplasms Humans Male Middle Aged Organs at Risk Proton Therapy Radiometry Radiotherapy Dosage Radiotherapy Planning, Computer-Assisted Radiotherapy, Intensity-Modulated

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View on PubMed Access via DOI PubMed (40120536)

Geometric and Dosimetric Evaluation of a RayStation Deep Learning Model for Auto-Segmentation of Organs at Risk in a Real-World Head and Neck Cancer Dataset.

Abstract

Authors

Keywords

External Resources

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