Method of adjudicating an imaged lesion

Patent metadata

Field	Value
Publication number	US-2024370999-A1
Application number	US-202418652286-A
Country	US
Kind code	A1
Filing date	May 1, 2024
Priority date	May 4, 2023
Publication date	Nov 7, 2024
Grant date	—

Abstract

A computer-implemented method of adjudicating an imaged lesion, comprising: receiving a diagnostic image showing a lesion; processing the diagnostic image in a machine learning algorithm previously trained to classify the lesion and to propose, based on a lesion class for the lesion, a blood test panel suited to adjudicate the lesion; and outputting the proposed blood test panel to a user.

First claim

What is claimed is:

1 . A computer-implemented method for use in adjudicating an imaged lesion, the computer-implemented method comprising:
obtaining a diagnostic image showing a lesion; inputting the diagnostic image to a machine learning algorithm previously trained to classify the lesion and to propose, based on a lesion class for the lesion, a blood test panel including markers suited to adjudicate the lesion; and outputting the blood test panel to a user.

2 . The computer-implemented method according to claim 1 , wherein the blood test panel comprises markers including at least one of: circulating tumor DNA, circulating tumor cells, proteins, circulating RNA, metabolites, sugars, or exosomes.

3 . The computer-implemented method according to claim 1 , wherein the machine learning algorithm is trained to estimate a malignancy risk of the lesion, and to include at least one marker with a large effect size in the blood test panel when the malignancy risk is at a threshold between two risk groups.

4 . The computer-implemented method according to claim 1 , wherein the blood test panel includes one or more of: an operating point along a receiver operating characteristic curve of the blood test panel, a decision threshold of a blood marker, or a weight of a blood marker.

5 . The computer-implemented method according to claim 1 , wherein the machine learning algorithm is trained to identify at least one laboratory analysis technique for analysis of the blood test panel.

6 . The computer-implemented method according to claim 5 , wherein the at least one laboratory analysis technique is: a next generation sequencing technique, a polymerase chain reaction technique, a mass spectrometry technique, an immunoassay technique, a fluorescence in-situ hybridization technique, or an electrochemical sensing technique.

7 . The computer-implemented method according to claim 1 , wherein the machine learning algorithm is trained to identify a number of further biological markers to assist in adjudicating the lesion.

8 . The computer-implemented method according to claim 7 , wherein a further biological marker is obtainable from a nasal swab, urine, a bronchial swab, or saliva.

9 . The computer-implemented method according to claim 1 , wherein the diagnostic image is a computed tomography image.

10 . The computer-implemented method according to claim 1 , wherein the lesion is a pulmonary lesion, an adrenal lesion, a renal lesion, a hepatic lesion, a pancreatic lesion, or a mammary lesion.

11 . The computer-implemented method according to claim 1 , wherein the machine learning algorithm is a two-step classifier configured to
classify the lesion into one of a plurality of defined classes, identify a number of blood markers suited to adjudicate the lesion, and output the blood test panel including the blood markers.

12 . A method of adjudicating an imaged lesion, the method comprising:
obtaining a diagnostic image showing a lesion; processing the diagnostic image using the computer-implemented method according to claim 1 and receiving the blood test panel; and obtaining a blood sample and performing laboratory processing of the blood sample according to the blood test panel.

13 . A data processing apparatus configured to perform the computer-implemented method according to claim 1 , wherein the data processing apparatus comprises:
an input interface configured to obtain the diagnostic image; the machine learning algorithm previously trained to classify the lesion and to specify, based on the lesion class, the blood test panel suited to adjudicate the lesion; and an output device configured to output the blood test panel to the user.

14 . A non-transitory computer program product comprising instructions which, when executed by a computer, cause the computer to carry out the computer-implemented method according to claim 1 .

15 . A non-transitory computer-readable storage medium storing computer-executable instructions that, when executed at a computer, cause the computer to carry out the computer-implemented method of claim 1 .

16 . The computer-implemented method according to claim 2 , wherein the machine learning algorithm is trained to estimate a malignancy risk of the lesion, and to include at least one marker with a large effect size in the blood test panel when the malignancy risk is at a threshold between two risk groups.

17 . The computer-implemented method according to claim 16 , wherein the machine learning algorithm is trained to identify at least one laboratory analysis technique for analysis of the blood test panel.

18 . The computer-implemented method according to claim 16 , wherein the machine learning algorithm is trained to identify a number of further biological markers to assist in adjudicating the lesion.

19 . The computer-implemented method according to claim 2 , wherein the machine learning algorithm is trained to identify at least one laboratory analysis technique for analysis of the blood test panel.

20 . The computer-implemented method according to claim 19 , wherein the machine learning algorithm is trained to identify a number of further biological markers to assist in adjudicating the lesion.

Assignees

• Siemens Healthineers Ag

Inventors

• HOELZER Philipp
• Heckel Tobias
• Assmann Stefan
• Karabayir Ayse
• Klatt Torbjoern
• Gutsche Robin
• Schmidt Sebastian
• Kamen Ali
• Singh Vivek
• Brost Alexander
• Siebert Matthias
• Sperl Jonathan

Classifications

• G06T2207/30204

  Marker · CPC title

• G06T2207/30068

  Mammography; Breast · CPC title

• G06T2207/30056

  Liver; Hepatic · CPC title

• G06T2207/30084

  Kidney; Renal · CPC title

• G06T2207/30061

  Lung · CPC title