http://rdf.ncbi.nlm.nih.gov/pubchem/patent/CN-114822824-A
Outgoing Links
| Predicate | Object |
|---|---|
| assignee | http://rdf.ncbi.nlm.nih.gov/pubchem/patentassignee/MD5_ada68032e6c5599de33d868231aaa52d |
| classificationCPCAdditional | http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06T2207-10081 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06T2207-30096 |
| classificationCPCInventive | http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G16H50-20 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06T7-0012 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06T7-11 |
| classificationIPCInventive | http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G16H50-20 http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06T7-11 http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06T7-00 |
| filingDate | 2022-05-11-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| inventor | http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_5721bc731701230f31a261b713663ed8 http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_34c517b96c13565d012e938c0ae623bd http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_6d6d436c35094179a1df12a4bca706e5 |
| publicationDate | 2022-07-29-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| publicationNumber | CN-114822824-A |
| titleOfInvention | Construction of a prediction model for the efficacy of RAS and BRAF gene wild-type colorectal cancer patients with liver metastases based on radiomic features |
| abstract | The invention belongs to the technical field of intelligent medical treatment, and in particular relates to a prediction model of curative effect of RAS and BRAF gene wild-type colorectal cancer liver metastasis patients based on imaging omics features and its application. The present invention has successfully constructed a model for predicting the curative effect of liver metastases from RAS and BRAF gene wild-type colorectal cancer patients receiving advanced first-line bevacizumab combined with chemotherapy based on the CT imaging omics features before treatment. 1000 times Lasso-Logistic analysis was used to obtain 7 image group characteristics, and the multivariate logistic regression method was used to build a radiomics prediction model. The model constructed by the present invention starts from clinical practical problems and has important clinical application and promotion value. |
| priorityDate | 2022-05-11-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| type | http://data.epo.org/linked-data/def/patent/Publication |
Incoming Links
Total number of triples: 48.