Impact of deep learning on radiologists and radiology residents in detecting breast cancer on CT: a cross-vendor test study.
Clin Radiol
; 79(1): e41-e47, 2024 Jan.
Article
en En
| MEDLINE
| ID: mdl-37872026
ABSTRACT
AIM:
To investigate the effect of deep learning on the diagnostic performance of radiologists and radiology residents in detecting breast cancers on computed tomography (CT). MATERIALS ANDMETHODS:
In this retrospective study, patients undergoing contrast-enhanced chest CT between January 2010 and December 2020 using equipment from two vendors were included. Patients with confirmed breast cancer were categorised as the training (n=201) and validation (n=26) group and the testing group (n=30) using processed CT images from either vendor. The trained deep-learning model was applied to test group patients with (30 females; mean age = 59.2 ± 15.8 years) and without (19 males, 21 females; mean age = 64 ± 15.9 years) breast cancer. Image-based diagnostic performance of the deep-learning model was evaluated with the area under the receiver operating characteristic curve (AUC). Two radiologists and three radiology residents were asked to detect malignant lesions by recording a four-point diagnostic confidence score before and after referring to the result from the deep-learning model, and their diagnostic performance was evaluated using jackknife alternative free-response receiver operating characteristic analysis by calculating the figure of merit (FOM).RESULTS:
The AUCs of the trained deep-learning model on the validation and test data were 0.976 and 0.967, respectively. After referencing with the result of the deep learning model, the FOMs of readers significantly improved (reader 1/2/3/4/5 from 0.933/0.962/0.883/0.944/0.867 to 0.958/0.968/0.917/0.947/0.900; p=0.038).CONCLUSION:
Deep learning can help radiologists and radiology residents detect breast cancer on CT.
Texto completo:
1
Banco de datos:
MEDLINE
Asunto principal:
Radiología
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Neoplasias de la Mama
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Aprendizaje Profundo
Límite:
Adult
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Aged
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Female
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Humans
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Male
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Middle aged
Idioma:
En
Año:
2024
Tipo del documento:
Article