MACHINE LEARNING TO PREDICTE CANCER

Disclosed herein are methods for determining a subject level risk of metastatic cancer comprising training and/or deploying a model to determine: 1) a lymph node level risk implied by an individual lymph node; and/or 2) a lymph node-dependent subject level risk. Thus, the method may identify high or...

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Bibliographic Details
Main Authors ESTEPA RODOLFO SANTIAGO JOSE, KINSEY CHRISTOPHER MICHAEL, STEVENSON CHRISTOPHER S, VOSKOW JR GEOFF
Format Patent
LanguageChinese
English
Published 03.10.2023
Subjects
CALCULATING
COMPUTING
COUNTING
IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
PHYSICS
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Summary:Disclosed herein are methods for determining a subject level risk of metastatic cancer comprising training and/or deploying a model to determine: 1) a lymph node level risk implied by an individual lymph node; and/or 2) a lymph node-dependent subject level risk. Thus, the method may identify high or low risk patients with sarcoidosis and optionally enable guidance of intervention in cancer patients, for example via treatment. 本文公开了用于确定转移癌的受试者水平风险的方法，其包括训练和/或部署模型以确定：1)个体淋巴结累及的淋巴结水平风险；以及/或者2)淋巴结累及的受试者水平风险。因此，该方法可识别具有结节病的高风险或低风险的患者，并且任选地使得能够例如经由治疗来指导癌症患者的干预。
Bibliography:Application Number: CN202180092579