Computed Tomography-Based Radiomics for Long-Term Prognostication of High-Risk Localized Prostate Cancer Patients Received Whole Pelvic Radiotherapy
Given the high death rate caused by high-risk prostate cancer (PCa) (>40%) and the reliability issues associated with traditional prognostic markers, the purpose of this study is to investigate planning computed tomography (pCT)-based radiomics for the long-term prognostication of high-risk local...
| Main Authors: | , , , , , , , , , , , , , |
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| Format: | Journal Article |
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MDPI AG
2023
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| Online Access: | Government of Hong Kong Special Administrative Region Health and Medical Research Fund Research Fellowship Scheme 2021, grant number 06200137; and The Hong Kong Polytechnic University Project of Strategic Importance Fund 2021, grant number P0035421 http://hdl.handle.net/20.500.11937/93832 |
| _version_ | 1848765795903995904 |
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| author | Leung, Vincent WS Ng, Curtise Lam, Sai-Kit Wong, Po-Tsz Ng, Ka-Yan Tam, Cheuk-Hong Lee, Tsz-Ching Chow, Kin-Chun Chow, Yan-Kate Tam, Victor CW Lee, Shara WY Lim, Fiona MY Wu, Jackie Q Cai, Jing |
| author_facet | Leung, Vincent WS Ng, Curtise Lam, Sai-Kit Wong, Po-Tsz Ng, Ka-Yan Tam, Cheuk-Hong Lee, Tsz-Ching Chow, Kin-Chun Chow, Yan-Kate Tam, Victor CW Lee, Shara WY Lim, Fiona MY Wu, Jackie Q Cai, Jing |
| author_sort | Leung, Vincent WS |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | Given the high death rate caused by high-risk prostate cancer (PCa) (>40%) and the reliability issues associated with traditional prognostic markers, the purpose of this study is to investigate planning computed tomography (pCT)-based radiomics for the long-term prognostication of high-risk localized PCa patients who received whole pelvic radiotherapy (WPRT). This is a retrospective study with methods based on best practice procedures for radiomics research. Sixty-four patients were selected and randomly assigned to training (n = 45) and testing (n = 19) cohorts for radiomics model development with five major steps: pCT image acquisition using a Philips Big Bore CT simulator; multiple manual segmentations of clinical target volume for the prostate (CTVprostate) on the pCT images; feature extraction from the CTVprostate using PyRadiomics; feature selection for overfitting avoidance; and model development with three-fold cross-validation. The radiomics model and signature performances were evaluated based on the area under the receiver operating characteristic curve (AUC) as well as accuracy, sensitivity and specificity. This study’s results show that our pCT-based radiomics model was able to predict the six-year progression-free survival of the high-risk localized PCa patients who received the WPRT with highly consistent performances (mean AUC: 0.76 (training) and 0.71 (testing)). These are comparable to findings of other similar studies including those using magnetic resonance imaging (MRI)-based radiomics. The accuracy, sensitivity and specificity of our radiomics signature that consisted of two texture features were 0.778, 0.833 and 0.556 (training) and 0.842, 0.867 and 0.750 (testing), respectively. Since CT is more readily available than MRI and is the standard-of-care modality for PCa WPRT planning, pCT-based radiomics could be used as a routine non-invasive approach to the prognostic prediction of WPRT treatment outcomes in high-risk localized PCa. |
| first_indexed | 2025-11-14T11:40:56Z |
| format | Journal Article |
| id | curtin-20.500.11937-93832 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T11:40:56Z |
| publishDate | 2023 |
| publisher | MDPI AG |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-938322024-01-15T03:26:03Z Computed Tomography-Based Radiomics for Long-Term Prognostication of High-Risk Localized Prostate Cancer Patients Received Whole Pelvic Radiotherapy Leung, Vincent WS Ng, Curtise Lam, Sai-Kit Wong, Po-Tsz Ng, Ka-Yan Tam, Cheuk-Hong Lee, Tsz-Ching Chow, Kin-Chun Chow, Yan-Kate Tam, Victor CW Lee, Shara WY Lim, Fiona MY Wu, Jackie Q Cai, Jing Artificial Intelligence Biomarker Machine Learning Malignancy Medical Imaging Prognosis Progression-Free Survival Radiation Therapy Recurrence Tumor Given the high death rate caused by high-risk prostate cancer (PCa) (>40%) and the reliability issues associated with traditional prognostic markers, the purpose of this study is to investigate planning computed tomography (pCT)-based radiomics for the long-term prognostication of high-risk localized PCa patients who received whole pelvic radiotherapy (WPRT). This is a retrospective study with methods based on best practice procedures for radiomics research. Sixty-four patients were selected and randomly assigned to training (n = 45) and testing (n = 19) cohorts for radiomics model development with five major steps: pCT image acquisition using a Philips Big Bore CT simulator; multiple manual segmentations of clinical target volume for the prostate (CTVprostate) on the pCT images; feature extraction from the CTVprostate using PyRadiomics; feature selection for overfitting avoidance; and model development with three-fold cross-validation. The radiomics model and signature performances were evaluated based on the area under the receiver operating characteristic curve (AUC) as well as accuracy, sensitivity and specificity. This study’s results show that our pCT-based radiomics model was able to predict the six-year progression-free survival of the high-risk localized PCa patients who received the WPRT with highly consistent performances (mean AUC: 0.76 (training) and 0.71 (testing)). These are comparable to findings of other similar studies including those using magnetic resonance imaging (MRI)-based radiomics. The accuracy, sensitivity and specificity of our radiomics signature that consisted of two texture features were 0.778, 0.833 and 0.556 (training) and 0.842, 0.867 and 0.750 (testing), respectively. Since CT is more readily available than MRI and is the standard-of-care modality for PCa WPRT planning, pCT-based radiomics could be used as a routine non-invasive approach to the prognostic prediction of WPRT treatment outcomes in high-risk localized PCa. 2023 Journal Article http://hdl.handle.net/20.500.11937/93832 10.3390/jpm13121643 Government of Hong Kong Special Administrative Region Health and Medical Research Fund Research Fellowship Scheme 2021, grant number 06200137; and The Hong Kong Polytechnic University Project of Strategic Importance Fund 2021, grant number P0035421 http://creativecommons.org/licenses/by/4.0/ MDPI AG fulltext |
| spellingShingle | Artificial Intelligence Biomarker Machine Learning Malignancy Medical Imaging Prognosis Progression-Free Survival Radiation Therapy Recurrence Tumor Leung, Vincent WS Ng, Curtise Lam, Sai-Kit Wong, Po-Tsz Ng, Ka-Yan Tam, Cheuk-Hong Lee, Tsz-Ching Chow, Kin-Chun Chow, Yan-Kate Tam, Victor CW Lee, Shara WY Lim, Fiona MY Wu, Jackie Q Cai, Jing Computed Tomography-Based Radiomics for Long-Term Prognostication of High-Risk Localized Prostate Cancer Patients Received Whole Pelvic Radiotherapy |
| title | Computed Tomography-Based Radiomics for Long-Term Prognostication of High-Risk Localized Prostate Cancer Patients Received Whole Pelvic Radiotherapy |
| title_full | Computed Tomography-Based Radiomics for Long-Term Prognostication of High-Risk Localized Prostate Cancer Patients Received Whole Pelvic Radiotherapy |
| title_fullStr | Computed Tomography-Based Radiomics for Long-Term Prognostication of High-Risk Localized Prostate Cancer Patients Received Whole Pelvic Radiotherapy |
| title_full_unstemmed | Computed Tomography-Based Radiomics for Long-Term Prognostication of High-Risk Localized Prostate Cancer Patients Received Whole Pelvic Radiotherapy |
| title_short | Computed Tomography-Based Radiomics for Long-Term Prognostication of High-Risk Localized Prostate Cancer Patients Received Whole Pelvic Radiotherapy |
| title_sort | computed tomography-based radiomics for long-term prognostication of high-risk localized prostate cancer patients received whole pelvic radiotherapy |
| topic | Artificial Intelligence Biomarker Machine Learning Malignancy Medical Imaging Prognosis Progression-Free Survival Radiation Therapy Recurrence Tumor |
| url | Government of Hong Kong Special Administrative Region Health and Medical Research Fund Research Fellowship Scheme 2021, grant number 06200137; and The Hong Kong Polytechnic University Project of Strategic Importance Fund 2021, grant number P0035421 http://hdl.handle.net/20.500.11937/93832 |