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61.

Purpose

The purpose of this study was to assess the potential of a deep learning model to discriminate between benign and malignant breast lesions using magnetic resonance imaging (MRI) and characterize different histological subtypes of breast lesions.

Materials and methods

We developed a deep learning model that simultaneously learns to detect lesions and characterize them. We created a lesion-characterization model based on a single two-dimensional T1-weighted fat suppressed MR image obtained after intravenous administration of a gadolinium chelate selected by radiologists. The data included 335 MR images from 335 patients, representing 17 different histological subtypes of breast lesions grouped into four categories (mammary gland, benign lesions, invasive ductal carcinoma and other malignant lesions). Algorithm performance was evaluated on an independent test set of 168 MR images using weighted sums of the area under the curve (AUC) scores.

Results

We obtained a cross-validation score of 0.817 weighted average receiver operating characteristic (ROC)-AUC on the training set computed as the mean of three-shuffle three-fold cross-validation. Our model reached a weighted mean AUC of 0.816 on the independent challenge test set.

Conclusion

This study shows good performance of a supervised-attention model with deep learning for breast MRI. This method should be validated on a larger and independent cohort.  相似文献   
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In a prospective study, we sought to determine acceptability of linkage of administrative and clinical trial data among Canadian patients and Research Ethics Boards (REBs). The goal is to develop a more harmonized approach to data, with potential to improve clinical trial conduct through enhanced data quality collected at reduced cost and inconvenience for patients. On completion of the original LY.12 randomized clinical trial in lymphoma (NCT00078949), participants were invited to enrol in the Long-term Innovative Follow-up Extension (LIFE) component. Those consenting to do so provided comprehensive identifying information to facilitate linkage with their administrative data. We prospectively designed a global assessment of this innovative approach to clinical trial follow-up including rates of REB approval and patient consent. The pre-specified benchmark for patient acceptability was 80%. Of 16 REBs who reviewed the research protocol, 14 (89%) provided approval; two in Quebec declined due to small patient numbers. Of 140 patients invited to participate, 115 (82%, 95% CI 76 to 88%) from across 9 Canadian provinces provided consent and their full name, date of birth, health insurance number and postal code to facilitate linkage with their administrative data for long-term follow-up. Linkage of clinical trial and administrative data is feasible and acceptable. Further collaborative work including many stakeholders is required to develop an optimized secure approach to research. A more coordinated national approach to health data could facilitate more rapid testing and identification of new effective treatments across multiple jurisdictions and diseases from diabetes to COVID-19.  相似文献   
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