Results 151 to 160 of about 42,977 (292)

Molecular and genomic advances in breast cancer: A comprehensive review of predictive and therapeutic innovations

Precision Medical Sciences, EarlyView.
Breast cancer subtypes, estrogen receptor‐positive (ER+), HER2‐enriched, and TNBC, are defined by genomic and epigenetic signatures. Multi‐omics profiling, immunotherapy, liquid biopsy, and AI‐driven radiogenomics enable precision medicine. Tools like Oncotype DX and PAM50 support personalized care.
Samina Malik, Arif Malik, Shahzad Ahmad, Hassan Mohsin, Shazia Iqbal   +4 more
wiley   +1 more source

Machine learning‐based predictive models versus traditional risk scores in hemodialysis patients with comorbid urolithiasis

Precision Medical Sciences, EarlyView.
Machine learning‐based predictive models outperform traditional risk scores in hemodialysis patients with comorbid urolithiasis by capturing nonlinear, dialysis‐specific interactions. These approaches enable more accurate prediction of stone recurrence, sepsis, hospitalization, and mortality, supporting personalized risk stratification and precision ...
Dipal Chaulagain, Muhammad Riaz, Mirza Muhammad Hadeed Khawar, Jawad Hameed, Malik Sohail Riaz   +4 more
wiley   +1 more source

The Evolving Status of Radiomics [PDF]

JNCI: Journal of the National Cancer Institute, 2020
Philip O Alderson, Ronald M Summers
openaire   +2 more sources

Advances in mucosal injury mechanisms in radiation‐induced esophagitis associated with thoracic radiotherapy

Precision Radiation Oncology, EarlyView.
There is no specific therapeutic drug regimen for radiation esophagitis. In previous studies, radiation esophagitis was considered a self‐limiting disease; however, current clinical approaches are limited to symptomatic treatment, which yields unsatisfactory therapeutic outcomes.
Hao Zhang, Liangxue Luo, Chaofen Zhao, Bibo Wu, Jing Zhang, Lingfeng Liu, Bing Lu, Shengfa Su   +7 more
wiley   +1 more source

A study on the value of ultrasound strain elastography-based radiomics nomogram in the differential diagnosis of breast masses

Scientific Reports
The aim of this study was to construct a radiomics nomogram for prediction of breast masses (BMs) by analyzing the clinical characteristics of the patients as well as radiomics features of two-dimensional (2D) ultrasound images and strain elastography ...
Yunpei Zhu, Yanping Dou, Qifan Hu, Hui Wang   +3 more
doaj   +1 more source

A new perspective on radiotherapy in the comprehensive treatment of cancer

Precision Radiation Oncology, EarlyView.
Radiotherapy remains a key component in the comprehensive management of cancer. Its effectiveness is significantly improved through strategic combinations with chemotherapy, targeted therapy, and immunotherapy, providing a range of therapeutic outcomes from cure to palliative care.
Yingze Ma, Hongxuan Yu, Shijie Shang, Shixin Shan, Dawei Chen   +4 more
wiley   +1 more source

A framework for artificial intelligence in cancer research and precision oncology

npj Precision Oncology, 2023
Raquel Perez-Lopez, Jorge S. Reis-Filho, Jakob Nikolas Kather   +2 more
doaj   +1 more source

The clinical implications and interpretability of computational medical imaging (radiomics) in brain tumors

Insights into Imaging
Radiomics has widespread applications in the field of brain tumor research. However, radiomic analyses often function as a ‘black box’ due to their use of complex algorithms, which hinders the translation of brain tumor radiomics into clinical ...
Yixin Wang, Zongtao Hu, Hongzhi Wang
doaj   +1 more source

Cancer Classification with Radiomics

Radiomics involves the extraction of high-dimensional quantitative features from medical images to aid clinical decision-making. While radiomics has shown promise in predicting disease characteristics, concerns regarding confounders, reproducibility, and

core   +1 more source

From prediction to intervention: Paradigm shifts in causal AI for precision medicine and large‐scale cohorts

VIEW, EarlyView.
Large‐scale cohorts and multimodal biomedical data have enabled powerful predictive models for clinical risk stratification, but prediction alone cannot guide effective interventions. This review introduces causal artificial intelligence as a design‐first framework that integrates target trial emulation, causal discovery, and robust effect estimation ...
Linlin Cao, Yudian Xu, Yifan Chen, Honghao Huang, Wei Xu, Jun Pu   +5 more
wiley   +1 more source