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1. (WO2019067557) MORPHOMETRIC GENOTYPING OF CELLS IN LIQUID BIOPSY USING OPTICAL TOMOGRAPHY
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Pub. No.: WO/2019/067557 International Application No.: PCT/US2018/052880
Publication Date: 04.04.2019 International Filing Date: 26.09.2018
IPC:
G01N 33/487 (2006.01) ,G01N 15/14 (2006.01) ,G01N 23/046 (2018.01) ,G06T 7/60 (2017.01)
G PHYSICS
01
MEASURING; TESTING
N
INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
33
Investigating or analysing materials by specific methods not covered by groups G01N1/-G01N31/131
48
Biological material, e.g. blood, urine; Haemocytometers
483
Physical analysis of biological material
487
of liquid biological material
G PHYSICS
01
MEASURING; TESTING
N
INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
15
Investigating characteristics of particles; Investigating permeability, pore-volume or surface-area of porous materials
10
Investigating individual particles
14
Electro-optical investigation
[IPC code unknown for G01N 23/046]
G PHYSICS
06
COMPUTING; CALCULATING; COUNTING
T
IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
7
Image analysis, e.g. from bit-mapped to non bit-mapped
60
Analysis of geometric attributes, e.g. area, centre of gravity, perimeter, from an image
Applicants:
VISIONGATE, INC. [US/US]; 10220 S 51ST ST STE 2 Phoenix, Arizona 85044-5231, US
Inventors:
MEYER, Michael G.; US
SUSSMAN, Daniel J.; US
KATDARE, Rahul; US
KELBAUSKAS, Laimonis; US
NELSON, Alan C.; US
MASTRANGELO, Randall; US
Agent:
LEONE, George; US
Priority Data:
62/563,54226.09.2017US
Title (EN) MORPHOMETRIC GENOTYPING OF CELLS IN LIQUID BIOPSY USING OPTICAL TOMOGRAPHY
(FR) GÉNOTYPAGE MORPHOMÉTRIQUE DE CELLULES DANS UNE BIOPSIE LIQUIDE AU MOYEN D’UNE TOMOGRAPHIE OPTIQUE
Abstract:
(EN) A classification training method for training classifiers adapted to identify specific mutations associated with different cancer including identifying driver mutations. First cells from mutation cell lines derived from conditions having the number of driver mutations are acquired and 3D image feature data from the number of first cells is identified. 3D cell imaging data from the number of first cells and from other malignant cells is generated, where cell imaging data includes a number of first individual cell images. A second set of 3D cell imaging data is generated from a set of normal cells where the number of driver mutations are expected to occur, where the second set of cell imaging data includes second individual cell images. Supervised learning is conducted based on cell line status as ground truth to generate a classifier.
(FR) La présente invention concerne un procédé d’apprentissage de classification pour l’apprentissage de classificateurs adaptés pour identifier des mutations spécifiques associées à différents cancers, comprenant l’identification de mutations inductrices. Des premières cellules de lignées cellulaires mutantes dérivées d’affections ayant la pluralité de mutations inductrices sont acquises et les données de caractéristiques d’image 3D à partir de la pluralité de premières cellules sont identifiées. Des données d’imagerie cellulaire 3D à partir de la pluralité de premières cellules et d’autres cellules malignes sont générées, les données d’imagerie cellulaire comprenant une pluralité de premières images de cellules individuelles. Un deuxième ensemble de données d’imagerie cellulaire 3D est généré à partir d’un ensemble de cellules normales dans lesquelles il est attendu que la pluralité de mutations inductrices survienne, le deuxième ensemble de données d’imagerie cellulaire comprenant des deuxièmes images de cellules individuelles. Un apprentissage supervisé est conduit sur la base de l’état de la lignée cellulaire en tant qu’observation directe pour générer un classificateur.
front page image
Designated States: AE, AG, AL, AM, AO, AT, AU, AZ, BA, BB, BG, BH, BN, BR, BW, BY, BZ, CA, CH, CL, CN, CO, CR, CU, CZ, DE, DJ, DK, DM, DO, DZ, EC, EE, EG, ES, FI, GB, GD, GE, GH, GM, GT, HN, HR, HU, ID, IL, IN, IR, IS, JO, JP, KE, KG, KH, KN, KP, KR, KW, KZ, LA, LC, LK, LR, LS, LU, LY, MA, MD, ME, MG, MK, MN, MW, MX, MY, MZ, NA, NG, NI, NO, NZ, OM, PA, PE, PG, PH, PL, PT, QA, RO, RS, RU, RW, SA, SC, SD, SE, SG, SK, SL, SM, ST, SV, SY, TH, TJ, TM, TN, TR, TT, TZ, UA, UG, US, UZ, VC, VN, ZA, ZM, ZW
African Regional Intellectual Property Organization (ARIPO) (BW, GH, GM, KE, LR, LS, MW, MZ, NA, RW, SD, SL, ST, SZ, TZ, UG, ZM, ZW)
Eurasian Patent Office (AM, AZ, BY, KG, KZ, RU, TJ, TM)
European Patent Office (EPO) (AL, AT, BE, BG, CH, CY, CZ, DE, DK, EE, ES, FI, FR, GB, GR, HR, HU, IE, IS, IT, LT, LU, LV, MC, MK, MT, NL, NO, PL, PT, RO, RS, SE, SI, SK, SM, TR)
African Intellectual Property Organization (BF, BJ, CF, CG, CI, CM, GA, GN, GQ, GW, KM, ML, MR, NE, SN, TD, TG)
Publication Language: English (EN)
Filing Language: English (EN)