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Article
Radiology, Nuclear Medicine & Medical Imaging
Jing Li et al.
Summary: This study compared and combined CT and mp-MRI radiomics for the identification of pathological response to neoadjuvant chemotherapy in gastric cancer (GC). The results showed that mp-MRI radiomics provided similar results to CT radiomics for early identification of pathological response, and the multimodal radiomics nomogram further improved the capability.
JOURNAL OF MAGNETIC RESONANCE IMAGING
(2023)
Review
Oncology
Siyu Zhang et al.
Summary: This review summarizes the applications, limitations and prospects of MRI-based radiomics in the treatment of locally advanced rectal cancer (LARC). Radiomics uses high-dimensional quantitative features extracted from medical imaging data to predict treatment response and prognosis, and has the potential to become an imaging biomarker for treatment prediction. However, there are still limitations and challenges in its current application.
Review
Health Care Sciences & Services
Adrienne Kline et al.
Summary: This review summarizes current studies on multi-modal data fusion in the health sector, highlighting the common use of multi-modal methods in neurology and oncology and the improved predictive performance achieved through data fusion. However, the lack of clear clinical deployment strategies, FDA approval, and analysis of biases and healthcare disparities in diverse sub-populations was noted.
NPJ DIGITAL MEDICINE
(2022)
Article
Radiology, Nuclear Medicine & Medical Imaging
Qiuying Chen et al.
Summary: Radiomic features derived from routine medical images have the potential to predict therapy response and prognosis in gastric cancer. However, the current research in this field is heterogeneous and of relatively low quality. Efforts towards standardization and collaboration are needed to fully utilize radiomics in clinical applications.
EUROPEAN RADIOLOGY
(2022)
Article
Radiology, Nuclear Medicine & Medical Imaging
Joshua D. Shur et al.
Summary: Radiomics is the extraction of mineable data from medical imaging to improve oncology diagnosis and prognostication, with a multidisciplinary workflow involving planning, data extraction, and validation. Applications in oncology typically involve classification tasks and prediction of clinical events, requiring collaboration between radiologists and data scientists.
Review
Medicine, General & Internal
Alfonso Reginelli et al.
Summary: This study aims to analyze the impact of texture analysis in predicting treatment response and stratifying prognosis in oncology, considering different pathologies, through the quantification and identification of parameters related to tumors by radiologists using texture analysis.
Review
Radiology, Nuclear Medicine & Medical Imaging
Janita E. van Timmeren et al.
INSIGHTS INTO IMAGING
(2020)
Article
Oncology
Wujie Chen et al.
FRONTIERS IN ONCOLOGY
(2019)
Article
Radiology, Nuclear Medicine & Medical Imaging
Robert J. Gillies et al.
Article
Radiology, Nuclear Medicine & Medical Imaging
Chiao-Yun Chen et al.
