A Tale of Two Organ Systems: Imaging review of diseases affecting the thoracic and neurological systems. Part 1

Kee, Tze Phei; Venkatanarasimha, Nanda; Mohideen, Salahudeen Mohamed Haja; Chan, Ling Ling; Gogna, Apoorva; Schaefer, Pamela W.; Chia, Ghim Song; Choi, Yoon Seong; Chen, Robert Chun

doi:10.1067/j.cpradiol.2021.06.008

Current Problems in Diagnostic Radiology

2022

DOI: 10.1067/j.cpradiol.2021.06.008

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A Tale of Two Organ Systems: Imaging review of diseases affecting the thoracic and neurological systems. Part 1

Tze Phei Kee

Nanda Venkatanarasimha

Salahudeen Mohamed Haja Mohideen

et al.

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2024

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Predicting patient-specific organ doses from thoracic CT examinations using support vector regression algorithm

Shao,

Lin,

Huang

et al. 2024

XST

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PURPOSE: This study aims to propose and develop a fast, accurate, and robust prediction method of patient-specific organ doses from CT examinations using minimized computational resources. MATERIALS AND METHODS: We randomly selected the image data of 723 patients who underwent thoracic CT examinations. We performed auto-segmentation based on the selected data to generate the regions of interest (ROIs) of thoracic organs using the DeepViewer software. For each patient, radiomics features of the thoracic ROIs were extracted via the Pyradiomics package. The support vector regression (SVR) model was trained based on the radiomics features and reference organ dose obtained by Monte Carlo (MC) simulation. The root mean squared error (RMSE), mean absolute percentage error (MAPE), and coefficient of determination (R-squared) were evaluated. The robustness was verified by randomly assigning patients to the train and test sets of data and comparing regression metrics of different patient assignments. RESULTS: For the right lung, left lung, lungs, esophagus, heart, and trachea, results showed that the trained SVR model achieved the RMSEs of 2 mGy to 2.8 mGy on the test sets, 1.5 mGy to 2.5 mGy on the train sets. The calculated MAPE ranged from 0.1 to 0.18 on the test sets, and 0.08 to 0.15 on the train sets. The calculated R-squared was 0.75 to 0.89 on test sets. CONCLUSIONS: By combined utilization of the SVR algorithm and thoracic radiomics features, patient-specific thoracic organ doses could be predicted accurately, fast, and robustly in one second even using one single CPU core.

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Predicting patient-specific organ doses from thoracic CT examinations using support vector regression algorithm

Shao,

Lin,

Huang

et al. 2024

XST

View full text Add to dashboard Cite

show abstract

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A Tale of Two Organ Systems: Imaging review of diseases affecting the thoracic and neurological systems. Part 1

Cited by 1 publication

References 69 publications

Predicting patient-specific organ doses from thoracic CT examinations using support vector regression algorithm

Predicting patient-specific organ doses from thoracic CT examinations using support vector regression algorithm

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