Fast estimation of patient‐specific organ doses from abdomen and head CT examinations without segmenting internal organs using machine learning models
Abstract Background Computed Tomography (CT) imaging is essential for disease detection but carries a risk of cancer due to X‐ray exposure. Typically, assessing this risk requires segmentation of the internal organ contours to predict organ doses, which hinders its clinical application. This study i...
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| Format: | Article |
| Language: | English |
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Wiley
2025-06-01
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| Series: | Precision Radiation Oncology |
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| Online Access: | https://doi.org/10.1002/pro6.70016 |
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| author | Wencheng Shao Liangyong Qu Xin Lin Ying Huang Weihai Zhuo Haikuan Liu |
| author_facet | Wencheng Shao Liangyong Qu Xin Lin Ying Huang Weihai Zhuo Haikuan Liu |
| author_sort | Wencheng Shao |
| collection | DOAJ |
| description | Abstract Background Computed Tomography (CT) imaging is essential for disease detection but carries a risk of cancer due to X‐ray exposure. Typically, assessing this risk requires segmentation of the internal organ contours to predict organ doses, which hinders its clinical application. This study introduces a method that uses support vector regression (SVR) models trained on skin outline radiomic features to predict organ doses without organ segmentation, thus streamlining the process for clinical use. Methods CT scans of the head and abdomen were used to extract radiomic features of the skin outline. These features were used as inputs, with organ doses from Monte Carlo simulations as benchmarks to train the SVR models for predicting organ doses. The accuracy of the models was evaluated using the mean absolute percentage error (MAPE) and coefficient of determination (R2). Results The results showed a high precision in dose prediction for various organs, including the brain (MAPE: 1.5%, R2: 0.9), eyes (MAPE: 5%, R2: 0.84), lens (MAPE: 5%, R2: 0.82), bowel (MAPE: 6%, R2: 0.84), kidneys (MAPE: 7.5%, R2: 0.7), and liver (MAPE: 8%, R2: 0.67). Internal organ disturbances had a minimal impact on accuracy. Conclusions The SVR models efficiently predicted patient‐specific organ doses from CT scans, offering a user‐friendly tool for rapid segmentation‐free dose prediction. This innovation can significantly enhance clinical efficiency and accessibility in predicting patient‐specific organ doses using CT. |
| format | Article |
| id | doaj-art-74753c2951ea4b8caca2a6a619f5a71f |
| institution | DOAJ |
| issn | 2398-7324 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Wiley |
| record_format | Article |
| series | Precision Radiation Oncology |
| spelling | doaj-art-74753c2951ea4b8caca2a6a619f5a71f2025-08-20T03:15:46ZengWileyPrecision Radiation Oncology2398-73242025-06-0192778610.1002/pro6.70016Fast estimation of patient‐specific organ doses from abdomen and head CT examinations without segmenting internal organs using machine learning modelsWencheng Shao0Liangyong Qu1Xin Lin2Ying Huang3Weihai Zhuo4Haikuan Liu5Institute of Radiation Medicine Fudan University Shanghai ChinaDepartment of Radiology Shanghai Zhongye Hospital Shanghai ChinaInstitute of Radiation Medicine Fudan University Shanghai ChinaDepartment of Nuclear Science and Technology Institute of Modern Physics Fudan University Shanghai ChinaInstitute of Radiation Medicine Fudan University Shanghai ChinaInstitute of Radiation Medicine Fudan University Shanghai ChinaAbstract Background Computed Tomography (CT) imaging is essential for disease detection but carries a risk of cancer due to X‐ray exposure. Typically, assessing this risk requires segmentation of the internal organ contours to predict organ doses, which hinders its clinical application. This study introduces a method that uses support vector regression (SVR) models trained on skin outline radiomic features to predict organ doses without organ segmentation, thus streamlining the process for clinical use. Methods CT scans of the head and abdomen were used to extract radiomic features of the skin outline. These features were used as inputs, with organ doses from Monte Carlo simulations as benchmarks to train the SVR models for predicting organ doses. The accuracy of the models was evaluated using the mean absolute percentage error (MAPE) and coefficient of determination (R2). Results The results showed a high precision in dose prediction for various organs, including the brain (MAPE: 1.5%, R2: 0.9), eyes (MAPE: 5%, R2: 0.84), lens (MAPE: 5%, R2: 0.82), bowel (MAPE: 6%, R2: 0.84), kidneys (MAPE: 7.5%, R2: 0.7), and liver (MAPE: 8%, R2: 0.67). Internal organ disturbances had a minimal impact on accuracy. Conclusions The SVR models efficiently predicted patient‐specific organ doses from CT scans, offering a user‐friendly tool for rapid segmentation‐free dose prediction. This innovation can significantly enhance clinical efficiency and accessibility in predicting patient‐specific organ doses using CT.https://doi.org/10.1002/pro6.70016computed tomographyorgan doseradiomics featuressegmentation‐freesupport vector regression |
| spellingShingle | Wencheng Shao Liangyong Qu Xin Lin Ying Huang Weihai Zhuo Haikuan Liu Fast estimation of patient‐specific organ doses from abdomen and head CT examinations without segmenting internal organs using machine learning models Precision Radiation Oncology computed tomography organ dose radiomics features segmentation‐free support vector regression |
| title | Fast estimation of patient‐specific organ doses from abdomen and head CT examinations without segmenting internal organs using machine learning models |
| title_full | Fast estimation of patient‐specific organ doses from abdomen and head CT examinations without segmenting internal organs using machine learning models |
| title_fullStr | Fast estimation of patient‐specific organ doses from abdomen and head CT examinations without segmenting internal organs using machine learning models |
| title_full_unstemmed | Fast estimation of patient‐specific organ doses from abdomen and head CT examinations without segmenting internal organs using machine learning models |
| title_short | Fast estimation of patient‐specific organ doses from abdomen and head CT examinations without segmenting internal organs using machine learning models |
| title_sort | fast estimation of patient specific organ doses from abdomen and head ct examinations without segmenting internal organs using machine learning models |
| topic | computed tomography organ dose radiomics features segmentation‐free support vector regression |
| url | https://doi.org/10.1002/pro6.70016 |
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