Invention Application
- Patent Title: MACHINE LEARNING-BASED SYSTEMS AND METHODS FOR PREDICTING LIVER CANCER RECURRENCE IN LIVER TRANSPLANT PATIENTS
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Application No.: PCT/US2022/075886Application Date: 2022-09-02
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Publication No.: WO2023034955A1Publication Date: 2023-03-09
- Inventor: LUO, Jianhua , SINGHI, Aatur Dilip , REN, Baoguo , TSENG, Chien-Cheng , MICHALOPOULOS, George , NALESNIK, Michael A. , WOOD-TRAGESER, Michelle A. , LIU, Shuchang , YU, Yanping
- Applicant: UNIVERSITY OF PITTSBURGH-OF THE COMMONWEALTH SYSTEM OF HIGHER EDUCATION , LUO, Jianhua , SINGHI, Aatur Dilip , REN, Baoguo , TSENG, Chien-Cheng , MICHALOPOULOS, George , NALESNIK, Michael A. , WOOD-TRAGESER, Michelle A. , LIU, Shuchang , YU, Yanping
- Applicant Address: 1st Floor Gardner Steel Conference Center; 431 Mckean Drive; 143 Sweetwater Drive; 3978 N Monet Ct; 8181 Streamside Dr; 172 Lancaster Avenue; 778 Venango Avenue; 80 York Drive; 3760 Allendale Circle; 431 Mckean Drive
- Assignee: UNIVERSITY OF PITTSBURGH-OF THE COMMONWEALTH SYSTEM OF HIGHER EDUCATION,LUO, Jianhua,SINGHI, Aatur Dilip,REN, Baoguo,TSENG, Chien-Cheng,MICHALOPOULOS, George,NALESNIK, Michael A.,WOOD-TRAGESER, Michelle A.,LIU, Shuchang,YU, Yanping
- Current Assignee: UNIVERSITY OF PITTSBURGH-OF THE COMMONWEALTH SYSTEM OF HIGHER EDUCATION,LUO, Jianhua,SINGHI, Aatur Dilip,REN, Baoguo,TSENG, Chien-Cheng,MICHALOPOULOS, George,NALESNIK, Michael A.,WOOD-TRAGESER, Michelle A.,LIU, Shuchang,YU, Yanping
- Current Assignee Address: 1st Floor Gardner Steel Conference Center; 431 Mckean Drive; 143 Sweetwater Drive; 3978 N Monet Ct; 8181 Streamside Dr; 172 Lancaster Avenue; 778 Venango Avenue; 80 York Drive; 3760 Allendale Circle; 431 Mckean Drive
- Agency: ANDERSON, Bjorn G et al.
- Priority: US63/239,999 2021-09-02
- Main IPC: G06N20/10
- IPC: G06N20/10 ; G06N20/20 ; G16B25/10 ; C12Q1/6886 ; G16H50/20
Abstract:
Described herein are prediction models based on the transcriptomic, exomic, and/or radiological analyses on tissue samples to predict the likelihood of the original cancer (such as Hepatocellular carcinoma (HCC)) recurrence into the liver transplant. An example computerimplemented method for predicting the likelihood of liver cancer recurrence 5 into a liver transplant includes receiving gene expression data related to a liver tissue sample for a subject having a liver cancer, inputting the gene expression data into a trained machine learning model, and predicting, using the trained machine learning model, a risk of recurrence of the liver cancer in the subject after liver transplantation.
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