Predicting recurrence and overall survival using radiomic features correlated with PD-L1 expression in early stage non-small cell lung cancer (ES-NSCLC)
US11574404B2 · kind B2 · utility
Assignees
Inventors
Key dates
| Filing date | Feb 18, 2019 |
| Grant date | Feb 7, 2023 |
| Priority date | — |
| Expiry date | Sep 7, 2041 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06V2201/03
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Embodiments include controlling a processor to perform operations, the operations comprising accessing a digitized image of a region of tissue (ROT) demonstrating cancerous pathology; extracting a set of radiomic features from the digitized image, where the set of radiomic features are positively correlated with programmed death-ligand 1 (PD-L1) expression; providing the set of radiomic features to a machine learning classifier; receiving, from the machine learning classifier, a probability that the region of tissue will experience cancer recurrence, where the machine learning classifier computes the probability based, at least in part, on the set of radiomic features; generating a classification of the region of tissue as likely to experience recurrence or non-recurrence based, at least in part, on the probability; and displaying the classification and at least one of the probability, the set of radiomic features, or the digitized image.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.