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Articles by Brinton LA
TRIPOD+AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods
Research Methods & Reporting BMJ 2024; 385 doi: https://doi.org/10.1136/bmj-2023-078378 Cite this as: BMJ 2024;385:e078378 Linked Opinion Making the black box more transparent: improving the reporting of artificial intelligence studies in healthcare Linked Editorial TRIPOD+AI: an updated reporting guideline for clinical prediction models Gary S Collins, professor1, Karel G M Moons, professor2, Paula Dhiman, senior researcher in medical statistics1, Richard D Riley, professor3 4, Andrew L...
Advances in breast cancer risk modeling: integrating clinics, imaging, pathology and artificial intelligence for personalized risk assessment
https://orcid.org/0000-0002-9269-0146 Division of Breast Surgery, IEO European Institute of Oncology, IRCCS, Milan, 20141, Italy Department of Oncology and Hemato-Oncology, University of Milan, Milan, 20141, Italy European Cancer Prevention Organization (ECP), Milan, 20141, Italy
Development and validation of clinical prediction models for breast cancer incidence and mortality: a protocol for a dual cohort study
Methods and analysis Study population and data sources for model development An open cohort of women aged between 20 and 90 years at entry into the QResearch database will be identified (study years 2000–2020). Cohort entry will be the earliest of 20th birthday, or date of registration with the general practice plus 1 year. Study participants must have a recorded NHS number in the QResearch database to facilitate data linkages and be alive at the start of the study.
Breast Density Awareness and Knowledge in a Mammography Screening Cohort of Predominantly Hispanic Women: Does Breast Density Notification Matter?
Research Articles Jessica D. Austin, Mariangela Agovino, Carmen B. Rodriguez, Mary Beth Terry, Rachel C. Shelton, Ying Wei, Elise Desperito, Karen M.
Predicting the risk of cancer in adults using supervised machine learning: a scoping review
Predicting the risk of cancer in adults using supervised machine learning: a scoping review Abstract Objectives The purpose of this scoping review is to: (1) identify existing supervised machine learning (ML) approaches on the prediction of cancer in asymptomatic adults; (2) to compare the performance of ML models with each other and (3) to identify potential gaps in research. Design Scoping review using the population, concept and context approach.
Predicting melanoma risk: theory, practice and future challenges
Melanoma ManagementVol. 1, No. 2 Management Perspective David Whiteman *Cancer Control Group, QIMR Berghofer Medical Research Institute, PO Royal Brisbane & Women's Hospital, Brisbane, Queensland, Australia; E-mail Address: david.whiteman@qimrberghofer.edu.au Abstract SUMMARY The incidence of melanoma continues to rise in most fair-skinned populations.
Predicting Breast Cancer by Applying Deep Learning to Linked Health Records and Mammograms
* A.A.B. and M.C. contributed equally to this work. Abstract A deep learning algorithm trained on a linked data set of mammograms and electronic health records achieved breast cancer detection accuracy comparable to radiologists as defined by the Breast Cancer Surveillance Consortium benchmark for screening digital mammography and revealed additional clinical risk features. Background Computational models on the basis of deep neural networks are increasingly used to analyze health care data.
A Deep Learning Mammography-based Model for Improved Breast Cancer Risk Prediction
Summary We developed a deep learning model that uses full-field mammograms and traditional risk factors, and found that our model was more accurate than the Tyrer-Cusick model (version 8), a current clinical standard. Key Points ■ A deep learning (DL) mammography-based model identified women at high risk for breast cancer and placed 31% of all patients with future breast cancer in the top risk decile compared with only 18% by the Tyrer-Cuzick model (version 8).
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