[HTML][HTML] Predictive value of 18F-FDG PET/CT radiomics for EGFR mutation status in non-small cell lung cancer: a systematic review and meta-analysis
N Ma, W Yang, Q Wang, C Cui, Y Hu, Z Wu - Frontiers in Oncology, 2024 - frontiersin.org
Objective This study aimed to evaluate the value of 18 F-FDG PET/CT radiomics in
predicting EGFR gene mutations in non-small cell lung cancer by meta-analysis. Methods …
predicting EGFR gene mutations in non-small cell lung cancer by meta-analysis. Methods …
Artificial intelligence in fracture detection on radiographs: a literature review
A Lo Mastro, E Grassi, D Berritto, A Russo… - Japanese Journal of …, 2024 - Springer
Fractures are one of the most common reasons of admission to emergency department
affecting individuals of all ages and regions worldwide that can be misdiagnosed during …
affecting individuals of all ages and regions worldwide that can be misdiagnosed during …
MRI radiomics-based interpretable model and nomogram for preoperative prediction of Ki-67 expression status in primary central nervous system lymphoma
E Zhao, YF Yang, M Bai, H Zhang, YY Yang… - Frontiers in …, 2024 - frontiersin.org
Objectives To investigate the value of interpretable machine learning model and nomogram
based on clinical factors, MRI imaging features, and radiomic features to predict Ki-67 …
based on clinical factors, MRI imaging features, and radiomic features to predict Ki-67 …
[HTML][HTML] CT-based radiomics for predicting Ki-67 expression in lung cancer: a systematic review and meta-analysis
X Luo, R Zheng, J Zhang, J He, W Luo, Z Jiang… - Frontiers in …, 2024 - frontiersin.org
Background Radiomics, an emerging field, presents a promising avenue for the accurate
prediction of biomarkers in different solid cancers. Lung cancer remains a significant global …
prediction of biomarkers in different solid cancers. Lung cancer remains a significant global …
Deep Learning in Breast Cancer Imaging: State of the Art and Recent Advancements in Early 2024
A Carriero, L Groenhoff, E Vologina, P Basile, M Albera - Diagnostics, 2024 - mdpi.com
The rapid advancement of artificial intelligence (AI) has significantly impacted various
aspects of healthcare, particularly in the medical imaging field. This review focuses on …
aspects of healthcare, particularly in the medical imaging field. This review focuses on …
Accuracy of deep learning in the differential diagnosis of coronary artery stenosis: a systematic review and meta-analysis
L Tu, Y Deng, Y Chen, Y Luo - BMC Medical Imaging, 2024 - Springer
Background In recent years, as deep learning has received widespread attention in the field
of heart disease, some studies have explored the potential of deep learning based on …
of heart disease, some studies have explored the potential of deep learning based on …
Radiomics diagnostic performance for predicting lymph node metastasis in esophageal cancer: a systematic review and meta-analysis
D Ma, T Zhou, J Chen, J Chen - BMC Medical Imaging, 2024 - Springer
Background Esophageal cancer, a global health concern, impacts predominantly men,
particularly in Eastern Asia. Lymph node metastasis (LNM) significantly influences …
particularly in Eastern Asia. Lymph node metastasis (LNM) significantly influences …
Enhancing Ki-67 Prediction in Breast Cancer: Integrating Intratumoral and Peritumoral Radiomics From Automated Breast Ultrasound via Machine Learning
F Li, T Zhu, M Lin, X Zhang, Y Zhang, A Zhou… - Academic …, 2024 - Elsevier
Rationale and Objectives Traditional Ki-67 evaluation in breast cancer (BC) via core needle
biopsy is limited by repeatability and heterogeneity. The automated breast ultrasound …
biopsy is limited by repeatability and heterogeneity. The automated breast ultrasound …
An XGBoost Machine Learning Based Model for Predicting Ki-67 Value ≥ 15% in T2NXM0 Stage Primary Breast Cancer Receiving Neoadjuvant Chemotherapy …
Y Lu, F Yang, Y Tao, P An - Technology in Cancer Research …, 2024 - journals.sagepub.com
Objective: To establish a model based on clinical and delta-radiomic features within
ultrasound images using XGBoost machine learning to predict proliferation-associated …
ultrasound images using XGBoost machine learning to predict proliferation-associated …
KAISO Promotes Poor Prognosis in Hepatocellular Carcinoma Patients by Enhancing Neutrophil Infiltration via IGFBP1
J Zhou, Y Pang, H Wang, Y Wang, Q Li, T Yang - 2024 - researchsquare.com
Background KAISO is a transcriptional regulator involved in gene expression, cell
proliferation, and apoptosis, linked to cancer prognosis and tumor aggressiveness, making it …
proliferation, and apoptosis, linked to cancer prognosis and tumor aggressiveness, making it …