From patterns to patients: Advances in clinical machine learning for cancer diagnosis, prognosis, and treatment

K Swanson, E Wu, A Zhang, AA Alizadeh, J Zou - Cell, 2023 - cell.com
Machine learning (ML) is increasingly used in clinical oncology to diagnose cancers, predict
patient outcomes, and inform treatment planning. Here, we review recent applications of ML …

Artificial intelligence and machine learning in cancer imaging

DM Koh, N Papanikolaou, U Bick, R Illing… - Communications …, 2022 - nature.com
An increasing array of tools is being developed using artificial intelligence (AI) and machine
learning (ML) for cancer imaging. The development of an optimal tool requires …

Breast cancer screening for women at higher-than-average risk: updated recommendations from the ACR

DL Monticciolo, MS Newell, L Moy, CS Lee… - Journal of the American …, 2023 - Elsevier
Early detection decreases breast cancer death. The ACR recommends annual screening
beginning at age 40 for women of average risk and earlier and/or more intensive screening …

[HTML][HTML] Application of deep learning in breast cancer imaging

L Balkenende, J Teuwen, RM Mann - Seminars in Nuclear Medicine, 2022 - Elsevier
This review gives an overview of the current state of deep learning research in breast cancer
imaging. Breast imaging plays a major role in detecting breast cancer at an earlier stage, as …

Toward robust mammography-based models for breast cancer risk

A Yala, PG Mikhael, F Strand, G Lin, K Smith… - Science Translational …, 2021 - science.org
Improved breast cancer risk models enable targeted screening strategies that achieve
earlier detection and less screening harm than existing guidelines. To bring deep learning …

[HTML][HTML] Artificial intelligence for breast cancer detection in mammography and digital breast tomosynthesis: State of the art

I Sechopoulos, J Teuwen, R Mann - Seminars in cancer biology, 2021 - Elsevier
Screening for breast cancer with mammography has been introduced in various countries
over the last 30 years, initially using analog screen-film-based systems and, over the last 20 …

Toward generalizability in the deployment of artificial intelligence in radiology: role of computation stress testing to overcome underspecification

T Eche, LH Schwartz, FZ Mokrane… - Radiology: Artificial …, 2021 - pubs.rsna.org
The clinical deployment of artificial intelligence (AI) applications in medical imaging is
perhaps the greatest challenge facing radiology in the next decade. One of the main …

Predicting breast cancer 5-year survival using machine learning: A systematic review

J Li, Z Zhou, J Dong, Y Fu, Y Li, Z Luan, X Peng - PloS one, 2021 - journals.plos.org
Background Accurately predicting the survival rate of breast cancer patients is a major issue
for cancer researchers. Machine learning (ML) has attracted much attention with the hope …

Novel approaches to screening for breast cancer

RM Mann, R Hooley, RG Barr, L Moy - Radiology, 2020 - pubs.rsna.org
Screening for breast cancer reduces breast cancer–related mortality and earlier detection
facilitates less aggressive treatment. Unfortunately, current screening modalities are …

[HTML][HTML] Artificial intelligence and early detection of pancreatic cancer: 2020 summative review

B Kenner, ST Chari, D Kelsen, DS Klimstra, SJ Pandol… - Pancreas, 2021 - journals.lww.com
Despite considerable research efforts, pancreatic cancer is associated with a dire prognosis
and a 5-year survival rate of only 10%. Early symptoms of the disease are mostly …