AI Article Synopsis
- The study aimed to assess the effectiveness of ultrasound-based deep learning (DL) models in differentiating between breast phyllodes tumors (PTs) and fibroadenomas (FAs) while supporting radiologists of varying experience levels.
- Researchers collected 1180 ultrasound images from 539 patients and trained five different DL models, finding that the Xception model achieved the highest diagnostic performance, surpassing all radiologists in accuracy.
- The DL model not only showed better predictive capabilities than experienced radiologists but also improved the accuracy of diagnosis for radiologists with different levels of expertise by up to 4%.
Article Abstract
Objectives: To evaluate the performance of ultrasound-based deep learning (DL) models in distinguishing breast phyllodes tumours (PTs) from fibroadenomas (FAs) and their clinical utility in assisting radiologists with varying diagnostic experiences.
Methods: We retrospectively collected 1180 ultrasound images from 539 patients (247 PTs and 292 FAs). Five DL network models with different structures were trained and validated using nodule regions annotated by radiologists on breast ultrasound images. DL models were trained using the methods of transfer learning and 3-fold cross-validation. The model demonstrated the best evaluation index in the 3-fold cross-validation was selected for comparison with radiologists' diagnostic decisions. Two-round reader studies were conducted to investigate the value of DL model in assisting 6 radiologists with different levels of experience.
Results: Upon testing, Xception model demonstrated the best diagnostic performance (area under the receiver-operating characteristic curve: 0.87; 95% CI, 0.81-0.92), outperforming all radiologists (all P < .05). Additionally, the DL model enhanced the diagnostic performance of radiologists. Accuracy demonstrated improvements of 4%, 4%, and 3% for senior, intermediate, and junior radiologists, respectively.
Conclusions: The DL models showed superior predictive abilities compared to experienced radiologists in distinguishing breast PTs from FAs. Utilizing the model led to improved efficiency and diagnostic performance for radiologists with different levels of experience (6-25 years of work).
Advances In Knowledge: We developed and validated a DL model based on the largest available dataset to assist in diagnosing PTs. This model has the potential to allow radiologists to discriminate 2 types of breast tumours which are challenging to identify with precision and accuracy, and subsequently to make more informed decisions about surgical plans.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1093/bjr/tqae147 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Want AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!