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Predicting cancer outcomes with radiomics and artificial intelligence in radiology
The successful use of artificial intelligence (AI) for diagnostic purposes has prompted the
application of AI-based cancer imaging analysis to address other, more complex, clinical …
application of AI-based cancer imaging analysis to address other, more complex, clinical …
Artificial intelligence-driven radiomics study in cancer: the role of feature engineering and modeling
Modern medicine is reliant on various medical imaging technologies for non-invasively
observing patients' anatomy. However, the interpretation of medical images can be highly …
observing patients' anatomy. However, the interpretation of medical images can be highly …
Predicting treatment response from longitudinal images using multi-task deep learning
Radiographic imaging is routinely used to evaluate treatment response in solid tumors.
Current imaging response metrics do not reliably predict the underlying biological response …
Current imaging response metrics do not reliably predict the underlying biological response …
A CT-based deep learning radiomics nomogram for predicting the response to neoadjuvant chemotherapy in patients with locally advanced gastric cancer: a …
Background Accurate prediction of treatment response to neoadjuvant chemotherapy
(NACT) in individual patients with locally advanced gastric cancer (LAGC) is essential for …
(NACT) in individual patients with locally advanced gastric cancer (LAGC) is essential for …
Radiomics for survival risk stratification of clinical and pathologic stage IA pure-solid non–small cell lung cancer
Background Radiomics-based biomarkers enable the prognostication of resected non–small
cell lung cancer (NSCLC), but their effectiveness in clinical stage and pathologic stage IA …
cell lung cancer (NSCLC), but their effectiveness in clinical stage and pathologic stage IA …
Technological advances in cancer immunity: from immunogenomics to single-cell analysis and artificial intelligence
Immunotherapies play critical roles in cancer treatment. However, given that only a few
patients respond to immune checkpoint blockades and other immunotherapeutic strategies …
patients respond to immune checkpoint blockades and other immunotherapeutic strategies …
Noninvasive imaging of the tumor immune microenvironment correlates with response to immunotherapy in gastric cancer
The tumor immune microenvironment (TIME) is associated with tumor prognosis and
immunotherapy response. Here we develop and validate a CT-based radiomics score (RS) …
immunotherapy response. Here we develop and validate a CT-based radiomics score (RS) …
Biology-guided deep learning predicts prognosis and cancer immunotherapy response
Substantial progress has been made in using deep learning for cancer detection and
diagnosis in medical images. Yet, there is limited success on prediction of treatment …
diagnosis in medical images. Yet, there is limited success on prediction of treatment …
Radiomics in precision medicine for gastric cancer: opportunities and challenges
Objectives Radiomic features derived from routine medical images show great potential for
personalized medicine in gastric cancer (GC). We aimed to evaluate the current status and …
personalized medicine in gastric cancer (GC). We aimed to evaluate the current status and …
Radiological tumour classification across imaging modality and histology
Radiomics refers to the high-throughput extraction of quantitative features from radiological
scans and is widely used to search for imaging biomarkers for the prediction of clinical …
scans and is widely used to search for imaging biomarkers for the prediction of clinical …