Machine Learning Enabled Prediction of Biologically Relevant Gene Expression Using CT‐Based Radiomic Features in Non‐Small Cell Lung Cancer
Shrey S Sukhadia, Christopher Sadee, Olivier Gevaert, Shivashankar H Nagaraj · 2024
Read Machine Learning Enabled Prediction of Biologically Relevant Gene Expression Using CT‐Based Radiomic Features in Non‐Small Cell Lung Cancer by Shrey S Sukhadia, Christopher Sadee, Olivier Gevaert, Shivashankar H Nagaraj (2024) in the Tower of Babel, a free online library of public-domain works preserved in clean, searchable text. No sign-up. The full text is served as a plain-text file at a predictable URL, where text is present: https://antarctic-labs.com/tower-of-babel/pmc-11667219.txt.
Description
Sukhadia, Sadee, Gevaert, and Nagaraj test whether features pulled from CT scans can predict gene activity in lung tumors, in this 2024 study. Joining imaging with genetics, a field called radiogenomics, could one day spare patients some biopsies. The team worked with two patient groups scanned five years apart.
Scans and gene data came from 116 patients in an older dataset and 44 in a newer one. Merging the two groups after careful alignment worked better than training on one and testing on the other. Certain texture features predicted high or low activity of three genes tied to lung cancer growth and drug resistance. The authors say the approach shows imaging can carry real biological signal. Larger studies should confirm the markers.
Catalog record
| CREATOR | Shrey S Sukhadia, Christopher Sadee, Olivier Gevaert, Shivashankar H Nagaraj |
|---|---|
| YEAR | 2024 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 33 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |