Pseudo-siamese network combined with dosimetric and clinical factors, radiomics features, CT images and 3D dose distribution for the prediction of radiation pneumonitis: A feasibility study

ABSTRACT: Highlights • We firstly proposed a novel method that combined dosimetric and clinical factors, radiomics feature, 3D CT images and dose distribution for predicting RP.• A pseudo-siamese network were developed for combination of 1D and 3D information.• The used deep learning frame showed a good accuracy (81.8%) for the prediction of RP.

Purpose

Radiation pneumonitis (RP)(grade ≥ 2) can have a considerable impact on patient quality-of life. In previous studies, the traditional method commonly used radiomics and clinical factors for RP prediction. This study aims to develop and evaluate a novel pseudo-siamese network (PSN) to assist radiologists predict RP before radiotherapy based on combination of dosimetric and clinical factors, radiomics features, CT (computed tomography) images,

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PROVIDER: S-EPMC9720487 | biostudies-literature | 2022 Nov

REPOSITORIES: biostudies-literature