Predicting cancer outcomes with radiomics and artificial intelligence in radiology

K Bera, N Braman, A Gupta, V Velcheti… - Nature reviews Clinical …, 2022‏ - nature.com
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 …

Artificial intelligence-driven radiomics study in cancer: the role of feature engineering and modeling

YP Zhang, XY Zhang, YT Cheng, B Li, XZ Teng… - Military Medical …, 2023‏ - Springer
Modern medicine is reliant on various medical imaging technologies for non-invasively
observing patients' anatomy. However, the interpretation of medical images can be highly …

Predicting treatment response from longitudinal images using multi-task deep learning

C **, H Yu, J Ke, P Ding, Y Yi, X Jiang, X Duan… - Nature …, 2021‏ - nature.com
Radiographic imaging is routinely used to evaluate treatment response in solid tumors.
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 …

Y Cui, J Zhang, Z Li, K Wei, Y Lei, J Ren, L Wu… - …, 2022‏ - thelancet.com
Background Accurate prediction of treatment response to neoadjuvant chemotherapy
(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

T Wang, Y She, Y Yang, X Liu, S Chen, Y Zhong… - Radiology, 2022‏ - pubs.rsna.org
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 …

Technological advances in cancer immunity: from immunogenomics to single-cell analysis and artificial intelligence

Y Xu, GH Su, D Ma, Y **ao, ZM Shao… - Signal Transduction and …, 2021‏ - nature.com
Immunotherapies play critical roles in cancer treatment. However, given that only a few
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

W Huang, Y Jiang, W **ong, Z Sun, C Chen… - Nature …, 2022‏ - nature.com
The tumor immune microenvironment (TIME) is associated with tumor prognosis and
immunotherapy response. Here we develop and validate a CT-based radiomics score (RS) …

Biology-guided deep learning predicts prognosis and cancer immunotherapy response

Y Jiang, Z Zhang, W Wang, W Huang, C Chen… - Nature …, 2023‏ - nature.com
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 …

Radiomics in precision medicine for gastric cancer: opportunities and challenges

Q Chen, L Zhang, S Liu, J You, L Chen, Z **… - European …, 2022‏ - Springer
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 …

Radiological tumour classification across imaging modality and histology

J Wu, C Li, M Gensheimer, S Padda, F Kato… - Nature machine …, 2021‏ - nature.com
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 …