A narrative review on current imaging applications of artificial intelligence and radiomics in oncology: Focus on the three most common cancers
The use of artificial intelligence (AI) and radiomics in the healthcare setting to advance
disease diagnosis and management and facilitate the creation of new therapeutics is …
disease diagnosis and management and facilitate the creation of new therapeutics is …
Evaluation of the efficacy of neoadjuvant chemotherapy for breast cancer
H Wang, X Mao - Drug design, development and therapy, 2020 - Taylor & Francis
Neoadjuvant chemotherapy is increasingly used in breast cancer, especially for
downstaging the primary tumor in the breast and the metastatic axillary lymph node …
downstaging the primary tumor in the breast and the metastatic axillary lymph node …
Clinical and inflammatory features based machine learning model for fatal risk prediction of hospitalized COVID-19 patients: results from a retrospective cohort study
X Guan, B Zhang, M Fu, M Li, X Yuan, Y Zhu… - Annals of …, 2021 - Taylor & Francis
Objectives To appraise effective predictors for COVID-19 mortality in a retrospective cohort
study. Methods A total of 1270 COVID-19 patients, including 984 admitted in Sino French …
study. Methods A total of 1270 COVID-19 patients, including 984 admitted in Sino French …
Recent advancements in artificial intelligence for breast cancer: Image augmentation, segmentation, diagnosis, and prognosis approaches
Breast cancer is a significant global health burden, with increasing morbidity and mortality
worldwide. Early screening and accurate diagnosis are crucial for improving prognosis …
worldwide. Early screening and accurate diagnosis are crucial for improving prognosis …
Radiomics in breast MRI: Current progress toward clinical application in the era of artificial intelligence
H Satake, S Ishigaki, R Ito, S Naganawa - La radiologia medica, 2022 - Springer
Breast magnetic resonance imaging (MRI) is the most sensitive imaging modality for breast
cancer diagnosis and is widely used clinically. Dynamic contrast-enhanced MRI is the basis …
cancer diagnosis and is widely used clinically. Dynamic contrast-enhanced MRI is the basis …
Multimodal deep learning models for the prediction of pathologic response to neoadjuvant chemotherapy in breast cancer
The achievement of the pathologic complete response (pCR) has been considered a metric
for the success of neoadjuvant chemotherapy (NAC) and a powerful surrogate indicator of …
for the success of neoadjuvant chemotherapy (NAC) and a powerful surrogate indicator of …
Potential value and impact of data mining and machine learning in clinical diagnostics
M Saberi-Karimian, Z Khorasanchi… - Critical reviews in …, 2021 - Taylor & Francis
Data mining involves the use of mathematical sciences, statistics, artificial intelligence, and
machine learning to determine the relationships between variables from a large sample of …
machine learning to determine the relationships between variables from a large sample of …
[HTML][HTML] A stacking ensemble model of various machine learning models for daily runoff forecasting
M Lu, Q Hou, S Qin, L Zhou, D Hua, X Wang, L Cheng - Water, 2023 - mdpi.com
Background: Open Access Editor's Choice Article A Stacking Ensemble Model of Various
Machine Learning Models for Daily Runoff Forecasting by Mingshen Lu 1, 2, Qinyao Hou 1 …
Machine Learning Models for Daily Runoff Forecasting by Mingshen Lu 1, 2, Qinyao Hou 1 …
Machine learning in breast MRI
Machine‐learning techniques have led to remarkable advances in data extraction and
analysis of medical imaging. Applications of machine learning to breast MRI continue to …
analysis of medical imaging. Applications of machine learning to breast MRI continue to …
[HTML][HTML] Applied machine learning in cancer research: A systematic review for patient diagnosis, classification and prognosis
Artificial Intelligence (AI) has recently altered the landscape of cancer research and medical
oncology using traditional Machine Learning (ML) algorithms and cutting-edge Deep …
oncology using traditional Machine Learning (ML) algorithms and cutting-edge Deep …