Clinical-radiomic models based on digital breast tomosynthesis images: a preliminary investigation of a predictive tool for cancer diagnosis
ObjectiveThis study aimed to develop a clinical–radiomic model based on radiomic features extracted from digital breast tomosynthesis (DBT) images and clinical factors that may help to discriminate between benign and malignant breast lesions.Materials and methodsA total of 150 patients were included...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-05-01
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| Series: | Frontiers in Oncology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fonc.2023.1152158/full |
| _version_ | 1797828948189511680 |
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| author | Federica Murtas Federica Murtas Valeria Landoni Pedro Ordòñez Laura Greco Francesca Romana Ferranti Andrea Russo Letizia Perracchio Antonello Vidiri |
| author_facet | Federica Murtas Federica Murtas Valeria Landoni Pedro Ordòñez Laura Greco Francesca Romana Ferranti Andrea Russo Letizia Perracchio Antonello Vidiri |
| author_sort | Federica Murtas |
| collection | DOAJ |
| description | ObjectiveThis study aimed to develop a clinical–radiomic model based on radiomic features extracted from digital breast tomosynthesis (DBT) images and clinical factors that may help to discriminate between benign and malignant breast lesions.Materials and methodsA total of 150 patients were included in this study. DBT images acquired in the setting of a screening protocol were used. Lesions were delineated by two expert radiologists. Malignity was always confirmed by histopathological data. The data were randomly divided into training and validation set with an 80:20 ratio. A total of 58 radiomic features were extracted from each lesion using the LIFEx Software. Three different key methods of feature selection were implemented in Python: (1) K best (KB), (2) sequential (S), and (3) Random Forrest (RF). A model was therefore produced for each subset of seven variables using a machine-learning algorithm, which exploits the RF classification based on the Gini index.ResultsAll three clinical–radiomic models show significant differences (p < 0.05) between malignant and benign tumors. The area under the curve (AUC) values of the models obtained with three different feature selection methods were 0.72 [0.64,0.80], 0.72 [0.64,0.80] and 0.74 [0.66,0.82] for KB, SFS, and RF, respectively.ConclusionThe clinical–radiomic models developed by using radiomic features from DBT images showed a good discriminating power and hence may help radiologists in breast cancer tumor diagnoses already at the first screening. |
| first_indexed | 2024-04-09T13:12:35Z |
| format | Article |
| id | doaj.art-953a414bfcd547ee88172906bfa7605d |
| institution | Directory Open Access Journal |
| issn | 2234-943X |
| language | English |
| last_indexed | 2024-04-09T13:12:35Z |
| publishDate | 2023-05-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Oncology |
| spelling | doaj.art-953a414bfcd547ee88172906bfa7605d2023-05-12T06:34:52ZengFrontiers Media S.A.Frontiers in Oncology2234-943X2023-05-011310.3389/fonc.2023.11521581152158Clinical-radiomic models based on digital breast tomosynthesis images: a preliminary investigation of a predictive tool for cancer diagnosisFederica Murtas0Federica Murtas1Valeria Landoni2Pedro Ordòñez3Laura Greco4Francesca Romana Ferranti5Andrea Russo6Letizia Perracchio7Antonello Vidiri8Medical Physics Department, IRCCS Regina Elena National Cancer Institute, Rome, ItalyDepartment of Biomedicine and Prevention, University of Rome "Tor Vergata", Rome, ItalyMedical Physics Department, IRCCS Regina Elena National Cancer Institute, Rome, ItalyMedical Physics Department, IRCCS Regina Elena National Cancer Institute, Rome, ItalyRadiology and Diagnostic Imaging Department, IRCCS Regina Elena National Cancer Institute, Rome, ItalyRadiology and Diagnostic Imaging Department, IRCCS Regina Elena National Cancer Institute, Rome, ItalyPathology Department, IRCCS Regina Elena National Cancer Institute, Rome, ItalyPathology Department, IRCCS Regina Elena National Cancer Institute, Rome, ItalyRadiology and Diagnostic Imaging Department, IRCCS Regina Elena National Cancer Institute, Rome, ItalyObjectiveThis study aimed to develop a clinical–radiomic model based on radiomic features extracted from digital breast tomosynthesis (DBT) images and clinical factors that may help to discriminate between benign and malignant breast lesions.Materials and methodsA total of 150 patients were included in this study. DBT images acquired in the setting of a screening protocol were used. Lesions were delineated by two expert radiologists. Malignity was always confirmed by histopathological data. The data were randomly divided into training and validation set with an 80:20 ratio. A total of 58 radiomic features were extracted from each lesion using the LIFEx Software. Three different key methods of feature selection were implemented in Python: (1) K best (KB), (2) sequential (S), and (3) Random Forrest (RF). A model was therefore produced for each subset of seven variables using a machine-learning algorithm, which exploits the RF classification based on the Gini index.ResultsAll three clinical–radiomic models show significant differences (p < 0.05) between malignant and benign tumors. The area under the curve (AUC) values of the models obtained with three different feature selection methods were 0.72 [0.64,0.80], 0.72 [0.64,0.80] and 0.74 [0.66,0.82] for KB, SFS, and RF, respectively.ConclusionThe clinical–radiomic models developed by using radiomic features from DBT images showed a good discriminating power and hence may help radiologists in breast cancer tumor diagnoses already at the first screening.https://www.frontiersin.org/articles/10.3389/fonc.2023.1152158/fullradiomicpredictive modelbreast cancerAItomosynthesis (DBT) |
| spellingShingle | Federica Murtas Federica Murtas Valeria Landoni Pedro Ordòñez Laura Greco Francesca Romana Ferranti Andrea Russo Letizia Perracchio Antonello Vidiri Clinical-radiomic models based on digital breast tomosynthesis images: a preliminary investigation of a predictive tool for cancer diagnosis Frontiers in Oncology radiomic predictive model breast cancer AI tomosynthesis (DBT) |
| title | Clinical-radiomic models based on digital breast tomosynthesis images: a preliminary investigation of a predictive tool for cancer diagnosis |
| title_full | Clinical-radiomic models based on digital breast tomosynthesis images: a preliminary investigation of a predictive tool for cancer diagnosis |
| title_fullStr | Clinical-radiomic models based on digital breast tomosynthesis images: a preliminary investigation of a predictive tool for cancer diagnosis |
| title_full_unstemmed | Clinical-radiomic models based on digital breast tomosynthesis images: a preliminary investigation of a predictive tool for cancer diagnosis |
| title_short | Clinical-radiomic models based on digital breast tomosynthesis images: a preliminary investigation of a predictive tool for cancer diagnosis |
| title_sort | clinical radiomic models based on digital breast tomosynthesis images a preliminary investigation of a predictive tool for cancer diagnosis |
| topic | radiomic predictive model breast cancer AI tomosynthesis (DBT) |
| url | https://www.frontiersin.org/articles/10.3389/fonc.2023.1152158/full |
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