Distinguishing minimally invasive carcinoma and adenocarcinoma in situ from invasive adenocarcinoma with intratumoral and peri-tumoral textural features
US11464473B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Mar 22, 2019 |
| Grant date | Oct 11, 2022 |
| Priority date | — |
| Expiry date | Jul 20, 2041 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06V2201/03
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Embodiments include controlling a processor to access a radiological image of a region of lung tissue, where the radiological image includes a ground glass (GGO) nodule; define a tumoral region by segmenting the GGO nodule, where defining the tumoral region includes defining a tumoral boundary; define a peri-tumoral region based on the tumoral boundary; extract a set of radiomic features from the peri-tumoral region and the tumoral region; provide the set of radiomic features to a machine learning classifier trained to distinguish minimally invasive adenocarcinoma (MIA) and adenocarcinoma in situ (AIS) from invasive adenocarcinoma; receive, from the machine learning classifier, a probability that the GGO nodule is invasive adenocarcinoma, where the machine learning classifier computes the probability based on the set of radiomic features; generate a classification of the GGO nodule as MIA or AIS, or invasive adenocarcinoma, based, at least in part, on the probability; and display the classification.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.