The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Date of Patent:
Jun. 04, 2019

Filed:

Oct. 17, 2014
Applicant:

Stichting Maastricht Radiation Oncology, Maastricht, NL;

Inventors:

Philippe Lambin, Maastricht, NL;

Sara Joao Botelho de Carvalho, Maastricht, NL;

Ralph Theodoor Hubertina Leijenaar, Maastricht, NL;

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06T 7/00 (2017.01); G06K 9/62 (2006.01); G06K 9/52 (2006.01); A61B 5/00 (2006.01); A61B 5/01 (2006.01); A61B 5/055 (2006.01); A61B 6/03 (2006.01); A61B 6/00 (2006.01); A61B 8/08 (2006.01);
U.S. Cl.
CPC ...
G06T 7/0016 (2013.01); A61B 5/0095 (2013.01); A61B 5/015 (2013.01); A61B 5/055 (2013.01); A61B 5/7275 (2013.01); A61B 6/032 (2013.01); A61B 6/037 (2013.01); A61B 6/5235 (2013.01); A61B 6/5247 (2013.01); A61B 8/085 (2013.01); G06K 9/527 (2013.01); G06K 9/629 (2013.01); G06K 2209/053 (2013.01); G06T 2207/10072 (2013.01); G06T 2207/10104 (2013.01); G06T 2207/20064 (2013.01); G06T 2207/20076 (2013.01); G06T 2207/30024 (2013.01); G06T 2207/30096 (2013.01);
Abstract

The present invention relates to an image analysis method for providing information for supporting illness development prediction regarding a neoplasm in a human or animal body. The method includes receiving for the neoplasm first and second image data at a first and second moment in time, and deriving for a plurality of image features a first and a second image feature parameter value from the first and second image data. These feature parameter values being a quantitative representation of a respective image feature. Further, calculating an image feature difference value by calculating a difference between the first and second image feature parameter value, and based on a prediction model deriving a predictive value associated with the neoplasm for supporting treatment thereof. The prediction model includes a plurality of multiplier values associated with image features. For calculating the predictive value the method includes multiplying each image feature difference value with its associated multiplier value and combining the multiplied image feature difference values.


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