Radiomics-based machine learning model to predict risk of death within 5-years in clear cell renal cell carcinoma patients
Copyright © 2020 The Author(s). Published by Elsevier Ltd.. All rights reserved..
PURPOSE: The aim of this study was to develop radiomics-based machine learning models based on extracted radiomic features and clinical information to predict the risk of death within 5 years for prognosis of clear cell renal cell carcinoma (ccRCC) patients.
METHODS: According to image quality and clinical data availability, we eventually selected 70 ccRCC patients that underwent CT scans. Manual volume-of-interest (VOI) segmentation of each image was performed by an experienced radiologist using the 3D slicer software package. Prior to feature extraction, image pre-processing was performed on CT images to extract different image features, including wavelet, Laplacian of Gaussian, and resampling of the intensity values to 32, 64 and 128 bin levels. Overall, 2544 3D radiomics features were extracted from each VOI for each patient. Minimum Redundancy Maximum Relevance (MRMR) algorithm was used as feature selector. Four classification algorithms were used, including Generalized Linear Model (GLM), Support Vector Machine (SVM), K-nearest Neighbor (KNN) and XGBoost. We used the Bootstrap resampling method to create validation sets. Area under the receiver operating characteristic (ROC) curve (AUROC), accuracy, sensitivity, and specificity were used to assess the performance of the classification models.
RESULTS: The best single performance among 8 different models was achieved by the XGBoost model using a combination of radiomic features and clinical information (AUROC, accuracy, sensitivity, and specificity with 95% confidence interval were 0.95-0.98, 0.93-0.98, 0.93-0.96 and ~1.0, respectively).
CONCLUSIONS: We developed a robust radiomics-based classifier that is capable of accurately predicting overall survival of RCC patients for prognosis of ccRCC patients. This signature may help identifying high-risk patients who require additional treatment and follow up regimens.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2021 |
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| Erschienen: |
2021 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:129 |
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| Enthalten in: |
Computers in biology and medicine - 129(2021) vom: 04. Feb., Seite 104135 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Nazari, Mostafa [VerfasserIn] |
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| Links: |
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| Themen: |
CT |
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| Anmerkungen: |
Date Completed 01.07.2021 Date Revised 01.07.2021 published: Print-Electronic Citation Status MEDLINE |
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| doi: |
10.1016/j.compbiomed.2020.104135 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM318204150 |
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| 245 | 1 | 0 | |a Radiomics-based machine learning model to predict risk of death within 5-years in clear cell renal cell carcinoma patients |
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| 500 | |a Date Revised 01.07.2021 | ||
| 500 | |a published: Print-Electronic | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a Copyright © 2020 The Author(s). Published by Elsevier Ltd.. All rights reserved. | ||
| 520 | |a PURPOSE: The aim of this study was to develop radiomics-based machine learning models based on extracted radiomic features and clinical information to predict the risk of death within 5 years for prognosis of clear cell renal cell carcinoma (ccRCC) patients | ||
| 520 | |a METHODS: According to image quality and clinical data availability, we eventually selected 70 ccRCC patients that underwent CT scans. Manual volume-of-interest (VOI) segmentation of each image was performed by an experienced radiologist using the 3D slicer software package. Prior to feature extraction, image pre-processing was performed on CT images to extract different image features, including wavelet, Laplacian of Gaussian, and resampling of the intensity values to 32, 64 and 128 bin levels. Overall, 2544 3D radiomics features were extracted from each VOI for each patient. Minimum Redundancy Maximum Relevance (MRMR) algorithm was used as feature selector. Four classification algorithms were used, including Generalized Linear Model (GLM), Support Vector Machine (SVM), K-nearest Neighbor (KNN) and XGBoost. We used the Bootstrap resampling method to create validation sets. Area under the receiver operating characteristic (ROC) curve (AUROC), accuracy, sensitivity, and specificity were used to assess the performance of the classification models | ||
| 520 | |a RESULTS: The best single performance among 8 different models was achieved by the XGBoost model using a combination of radiomic features and clinical information (AUROC, accuracy, sensitivity, and specificity with 95% confidence interval were 0.95-0.98, 0.93-0.98, 0.93-0.96 and ~1.0, respectively) | ||
| 520 | |a CONCLUSIONS: We developed a robust radiomics-based classifier that is capable of accurately predicting overall survival of RCC patients for prognosis of ccRCC patients. This signature may help identifying high-risk patients who require additional treatment and follow up regimens | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Research Support, Non-U.S. Gov't | |
| 650 | 4 | |a CT | |
| 650 | 4 | |a Machine learning | |
| 650 | 4 | |a Radiomics | |
| 650 | 4 | |a Renal cell carcinoma | |
| 650 | 4 | |a Survival prediction | |
| 700 | 1 | |a Shiri, Isaac |e verfasserin |4 aut | |
| 700 | 1 | |a Zaidi, Habib |e verfasserin |4 aut | |
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