Cancer stem cells (CSCs), circulating tumor cells (CTCs) and their interplay with cancer associated fibroblasts (CAFs): a new world of targets and treatments

B Aramini, V Masciale, C Arienti, M Dominici, F Stella… - Cancers, 2022 - mdpi.com
Simple Summary The world of small molecules in solid tumors as cancer stem cells (CSCs),
circulating tumor cells (CTCs) and cancer-associated fibroblasts (CAFs) continues to be …

Application of artificial intelligence techniques to predict risk of recurrence of breast cancer: a systematic review

C Mazo, C Aura, A Rahman, WM Gallagher… - Journal of Personalized …, 2022 - mdpi.com
Breast cancer is the most common disease among women, with over 2.1 million new
diagnoses each year worldwide. About 30% of patients initially presenting with early stage …

Machine learning classifiers on breast cancer recurrences

VPC Magboo, MSA Magboo - Procedia Computer Science, 2021 - Elsevier
Breast cancer remains to be a leading cause of cancer-related deaths among women.
Mortality is mainly attributed to metastasis and recurrence. Hence, early detection of breast …

Machine learning-based models for the prediction of breast cancer recurrence risk

D Zuo, L Yang, Y Jin, H Qi, Y Liu, L Ren - BMC Medical Informatics and …, 2023 - Springer
Breast cancer is the most common malignancy diagnosed in women worldwide. The
prevalence and incidence of breast cancer is increasing every year; therefore, early …

Machine learning algorithms to predict breast cancer recurrence using structured and unstructured sources from electronic health records

L González-Castro, M Chávez, P Duflot, V Bleret… - Cancers, 2023 - mdpi.com
Simple Summary Breast cancer is a heterogeneous disease characterized by different risks
of relapse, which makes it challenging to predict progression and select the most …

3D Breast Cancer Segmentation in DCE‐MRI Using Deep Learning With Weak Annotation

GE Park, SH Kim, Y Nam, J Kang… - Journal of Magnetic …, 2024 - Wiley Online Library
Background Deep learning models require large‐scale training to perform confidently, but
obtaining annotated datasets in medical imaging is challenging. Weak annotation has …

Application of machine learning algorithms in predicting HIV infection among men who have sex with men: Model development and validation

J He, J Li, S Jiang, W Cheng, J Jiang, Y Xu… - Frontiers in Public …, 2022 - frontiersin.org
Background Continuously growing of HIV incidence among men who have sex with men
(MSM), as well as the low rate of HIV testing of MSM in China, demonstrates a need for …

Diabetes mellitus risk prediction in the presence of class imbalance using flexible machine learning methods

S Sadeghi, D Khalili, A Ramezankhani… - BMC Medical Informatics …, 2022 - Springer
Background Early detection and prediction of type two diabetes mellitus incidence by
baseline measurements could reduce associated complications in the future. The low …

Predicting icd-9 codes using self-report of patients

A Singaravelan, CH Hsieh, YK Liao, JL Hsu - Applied Sciences, 2021 - mdpi.com
The International Classification of Diseases (ICD) is a globally recognized medical
classification system that aids in the identification of diseases and the regulation of health …

[HTML][HTML] Artificial intelligence empowered digital health technologies in cancer survivorship care: A scoping review

L Pan, X Wu, Y Lu, H Zhang, Y Zhou, X Liu, S Liu… - Asia-Pacific Journal of …, 2022 - Elsevier
Objective The objectives of this systematic review are to describe features and specific
application scenarios for current cancer survivorship care services of Artificial intelligence …