Opportunity cost of annual screening mammography

Keen, John D.

doi:10.1002/cncr.31197

Cited by 2 publications

(1 citation statement)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Breast cancer is the most common cancer among women in 157 out of 185 countries, leading to 670,000 deaths globally in 2022 1 . Despite technological advancements such as tomosynthesis, introduced to enhance breast cancer screening and early-stage diagnosis for more effective treatment, challenges persist in the frequent occurrence of false positive mammograms, variability among expert readers, patient anxiety, as well as financial and opportunity costs 2,3,4,5,6 . Given these obstacles, alongside efforts promoting screening access, significant endeavors have been made in developing software for computer-aided diagnosis (CAD) to interpret abnormal mammograms 7,8,9,10,11,12,13,14 .…”

Section: Introductionmentioning

confidence: 99%

Transparency in Computer-Aided Breast Cancer Diagnosis Tool Development: A CBIS-DDSM Case Study

Liao,

Aagaard

2024

Preprint

View full text Add to dashboard Cite

Accessible mammography datasets and innovative machine learning techniques are at the front line for computer-aided breast cancer diagnosis. However, the opacity surrounding private datasets and the unclear methodology behind the selection of subset images from publicly available databases for model training and testing, coupled with the arbitrary incompleteness or inaccessibility of code, markedly intensifies the obstacles in replicating and validating the model's efficacy. These challenges, in turn, erect barriers for subsequent researchers striving to learn and advance in this field. To address these limitations, we provide a pilot codebase covering the entire image preprocessing to model construction and evaluation pipeline, utilizing publicly available Curated Breast Imaging Subset of Digital Database for Screening Mammography (CBIS-DDSM) mass subset, including both full images and regions of interests (ROIs). The pipeline achieves an area under the receiver operating characteristic curve (AUROC) of 86.3% [95% CI: 0.842, 0.885], representing the state of the art performance for benign and malignant classification with selected mammography images. Collectively, our efforts hold promise in accelerating global software development for breast cancer diagnosis by leveraging our codebase and structure, while also integrating other advancements in the field.

show abstract