| Predicate |
Object |
| assignee |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentassignee/MD5_57f3e2af72b2933c004ed61dfccaaee1 |
| classificationCPCAdditional |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06T2207-30024
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G01N2800-54
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06T2207-10056
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G01N2800-52
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06T7-11 |
| classificationCPCInventive |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06K9-6265
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G01N33-57415
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06T7-0012
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G16H50-30
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06F18-2193
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06V20-698
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06V10-7796
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06T7-12 |
| classificationIPCInventive |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06V20-69
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G01N33-574
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06K9-62
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06T7-12
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06T7-00
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G16H50-30 |
| filingDate |
2021-12-09-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| inventor |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_05570762629e51b58e4479b61fbebe76
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_9e27ee82b49839546a3f60ffb633b931
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_c4f550c62b7a8a776ef37546273411b4
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_dbc9633fd8f2e9ef214babbbf7292c3f |
| publicationDate |
2022-05-19-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| publicationNumber |
US-2022156930-A1 |
| titleOfInvention |
Cancer risk stratification based on histopathological tissue slide analysis |
| abstract |
The subject disclosure presents systems and computer-implemented methods for providing reliable risk stratification for early-stage cancer patients by predicting a recurrence risk of the patient and to categorize the patient into a high or low risk group. A series of slides depicting serial sections of cancerous tissue are automatically analyzed by a digital pathology system, a score for the sections is calculated, and a Cox proportional hazards regression model is used to stratify the patient into a low or high risk group. The Cox proportional hazards regression model may be used to determine a whole-slide scoring algorithm based on training data comprising survival data for a plurality of patients and their respective tissue sections. The coefficients may differ based on different types of image analysis operations applied to either whole-tumor regions or specified regions within a slide. |
| isCitedBy |
http://rdf.ncbi.nlm.nih.gov/pubchem/patent/CN-114998348-A |
| priorityDate |
2014-12-03-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| type |
http://data.epo.org/linked-data/def/patent/Publication |