IP Library › Granted Patent US 12,590,949
Granted Patent B2
US 12,590,949 · App. 16/630,031 · Granted Mar 31, 2026

Radiomics-based imaging tool to monitor tumor-lymphocyte infiltration and outcome in cancer patients treated by anti-PD-1/PD-L1

Inventors: Charles Ferte (Bethesda, MD); Elaine Johanna Limkin (Cachan, FR); Roger Sun (Paris, FR); Eric Deutsch (Paris, FR)
Assignees: Institut National de la Sante et de la Recherche Medicale; Universite Paris-Saclay
G01N33/5005A61B6/5217G01N33/505G01N33/574G06T7/0012G16H30/40G16H50/20G01N2800/52G01N2800/7028
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Quick Facts
Patent No.
US 12,590,949
App. No.
16/630,031
Granted
Mar 31, 2026
Kind
B2
Abstract

The present invention proposes a radiomics-based biomarker for detecting the presence and the density of tumor infiltrating CD8 T-cells in a solid tumor without having to use any biopsy of said tumor. The invention also proposes to use this information to assess the immune phenotype of said solid tumor. In a particular embodiment, the invention proposes to prognose the survival and/or the treatment efficiency of cancer patients treated with immunotherapy such as anti-PD-1/PD-L1 monotherapy.

Claims (48)

1 . A method for evaluating the spatial density of a CD8+ T cells infiltrate in a solid tumor, said method comprising:

a) specifying two regions of interest (ROI) in a radiological image of a region of a tumoral tissue of a solid tumor, said radiological image including a plurality of voxels, wherein the two ROI are:

a solid tumor volume and

a peripheral ring volume created around the solid tumor margin;

b) extracting a set of features from said two ROI, wherein the set of features comprises:

Min Value of the tumor as conventional variable,

at least three Gray-level Run Length Matrix (GLRLM) variables selected from:

GLRLM short-run high gray-level emphasis (GLRLM_SRHGE) of the solid tumor,

GLRLM short-run low gray-level emphasis (GLRLM_SRLGE) of the peripheral ring volume,

GLRLM low gray-level run emphasis (GLRLM_LGRE) of the peripheral ring volume, and

GLRLM long-run low gray-level emphasis (GLRLM_LRLGE) of the peripheral ring volume;

acquisition marker kilovoltage peak (kVp), and

location of adenopathy (VOI_Adenopathy) and location of head and neck cancer (VOI_head_and_neck), wherein the values of VOI Adenopathy and VOI head and neck are null when the target tumors are not from adenopathy or head-and-neck; and

c) providing the extracted features to a trained machine learning predictor which calculates a quantitative score of the spatial density of the CD8+ T cells infiltrate in the solid tumor.

2 . The method of claim 1 , wherein said radiological image is generated using non-invasive imaging technologies.

3 . The method of claim 1 , wherein said radiological image is generated using scanners, magnetic resonance imaging (MRI), or PET (Positron Emission Tomography).

4 . The method of claim 1 , wherein said radiological image is generated using a computed tomography (CT) scan.

5 . The method of claim 1 , wherein said set of features comprises:

GLRLM short-run high gray-level emphasis (GLRLM_SRHGE) of the tumor,

GLRLM short-run low gray-level emphasis (GLRLM_SRLGE) of the ring,

GLRLM low gray-level run emphasis (GLRLM_LGRE) of the ring, and

GLRLM long-run low gray-level emphasis (GLRLM_LRLGE) of the ring.

6 . The method of claim 1 , wherein said solid tumor is squamous cell carcinoma, small-cell lung cancer, non-small cell lung cancer, glioma, gastrointestinal cancer, renal cancer, ovarian cancer, liver cancer, colorectal cancer, endometrial cancer, kidney cancer, prostate cancer, thyroid cancer, neuroblastoma, brain cancer, central nervous system cancer, pancreatic cancer, glioblastoma multiforme, cervical cancer, stomach cancer, bladder cancer, malignant hepatoma, breast cancer, colon carcinoma, head and neck cancer, gastric cancer, germ cell tumor, pediatric sarcoma, rhabdomyosarcoma, Ewing's sarcoma, osteosarcoma, soft tissue sarcoma, sinonasal NK/T-cell lymphoma, myeloma, melanoma, multiple myeloma, or benign solid tumors such as uterine leiomyosarcoma.

7 . The method of claim 1 , wherein said set of features consists essentially of said MinValue, at least three GLRLM variables, kVp, and said VOI_Adenopathy and VOI_head_and_neck variables, wherein the values of VOI Adenopathy and VOI head and neck are null when the target tumors are not from adenopathy or head-and-neck.

8 . A method for predicting the outcome or the efficiency of an anti-cancer treatment for a cancer patient with a tumor, said method comprising:

a) specifying two regions of interest (ROI) in a radiological image of a region of a tumoral tissue of a solid tumor, said radiological image including a plurality of voxels, wherein the two ROI are:

a solid tumor volume and

a peripheral ring volume created around the solid tumor margin;

b) extracting a set of features from said two ROI, wherein the set of features comprises:

Min Value of the tumor as conventional variable,

at least three Gray-level Run Length Matrix (GLRLM) variables selected from

GLRLM short-run high gray-level emphasis (GLRLM_SRHGE) of the solid tumor,

GLRLM short-run low gray-level emphasis (GLRLM_SRLGE) of the peripheral ring volume,

GLRLM low gray-level run emphasis (GLRLM_LGRE) of the peripheral ring volume, and

GLRLM long-run low gray-level emphasis (GLRLM_LRLGE) of the peripheral ring volume;

acquisition marker kilovoltage peak (kVp), and

location of adenopathy (VOI_Adenopathy) and location of head and neck cancer (VOI_head_and_neck), wherein the values of VOI Adenopathy and VOI head and neck are null when the target tumors are not from adenopathy or head-and-neck; and

c) providing the extracted features to a trained machine learning predictor which calculates a quantitative score of the spatial density of the CD8+ T cells infiltrate in the solid tumor.

9 . The method of claim 8 , wherein said anti-cancer treatment is a chemotherapeutic treatment, an immunotherapeutic treatment, a radiotherapeutic treatment, and/or surgery.

10 . The method of claim 8 , wherein said anti-cancer treatment is an immunotherapeutic treatment with anti-PD-1 and/or anti-PD-L1 drugs.

11 . The method of claim 8 , wherein said radiological image is generated using a CT scan.

12 . The method of claim 8 , wherein said set of features comprises:

GLRLM short-run high gray-level emphasis (GLRLM_SRHGE) of the solid tumor,

GLRLM short-run low gray-level emphasis (GLRLM_SRLGE) of the peripheral ring volume,

GLRLM low gray-level run emphasis (GLRLM_LGRE) of the peripheral ring volume, and

GLRLM long-run low gray-level emphasis (GLRLM_LRLGE) of the peripheral ring volume.

13 . The method of claim 8 , wherein said set of features consists essentially of said MinValue, at least three GLRLM variables, kVp, and said VOI_Adenopathy and VOI_head_and_neck variables, wherein the values of VOI Adenopathy and VOI head and neck are null when the target tumors are not from adenopathy or head-and-neck.

14 . The method of claim 8 , wherein said method is for assessing progression-free survival (PFS) or overall survival (OS) of the cancer patient.

Assignments (2)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Oct 20, 2025
From: INSTITUT GUSTAVE ROUSSY
To: INSTITUT GUSTAVE ROUSSY; INSTITUT NATIONAL DE LA SANTE ET DE LA RECHERCHE MEDICALE (INSERM); UNIVERSITE PARIS-SACLAY
Reel/Frame 072600/0367 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded May 29, 2020
From: FERTE, CHARLES; LIMKIN, JOHANNA; SUN, ROGER; DEUTSCH, ERIC
To: INSTITUT GUSTAVE ROUSSY
Reel/Frame 052785/0807 →
Priority Claims (1)
EP 18305680 · Jun 1, 2018 · regional
Continuity (2)
Provisional Application 62532139 · Jul 13, 2017
Related Publication 20210003555A1 · Jan 7, 2021
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