EP3430384A1 - Procédé automatisé pour évaluer un risque de cancer au moyen d'échantillons de tissu, et système associé - Google Patents

Procédé automatisé pour évaluer un risque de cancer au moyen d'échantillons de tissu, et système associé

Info

Publication number
EP3430384A1
EP3430384A1 EP17765612.1A EP17765612A EP3430384A1 EP 3430384 A1 EP3430384 A1 EP 3430384A1 EP 17765612 A EP17765612 A EP 17765612A EP 3430384 A1 EP3430384 A1 EP 3430384A1
Authority
EP
European Patent Office
Prior art keywords
cancer
risk
processor
parameter
interest
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Pending
Application number
EP17765612.1A
Other languages
German (de)
English (en)
Other versions
EP3430384A4 (fr
Inventor
Ying Gu
Jason T.K. HWANG
Kenneth P.H. PRITZKER
Ranju Ralhan
Mi SHEN
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Proteocyte Diagnostics Inc
Original Assignee
Proteocyte Diagnostics Inc
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Proteocyte Diagnostics Inc filed Critical Proteocyte Diagnostics Inc
Publication of EP3430384A1 publication Critical patent/EP3430384A1/fr
Publication of EP3430384A4 publication Critical patent/EP3430384A4/fr
Pending legal-status Critical Current

Links

Classifications

    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
    • G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
    • G01N33/53—Immunoassay; Biospecific binding assay; Materials therefor
    • G01N33/574—Immunoassay; Biospecific binding assay; Materials therefor for cancer
    • G01N33/57484—Immunoassay; Biospecific binding assay; Materials therefor for cancer involving compounds serving as markers for tumor, cancer, neoplasia, e.g. cellular determinants, receptors, heat shock/stress proteins, A-protein, oligosaccharides, metabolites
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/0002—Inspection of images, e.g. flaw detection
    • G06T7/0012—Biomedical image inspection
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/60—Analysis of geometric attributes
    • G06T7/62—Analysis of geometric attributes of area, perimeter, diameter or volume
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N2333/00—Assays involving biological materials from specific organisms or of a specific nature
    • G01N2333/435—Assays involving biological materials from specific organisms or of a specific nature from animals; from humans
    • G01N2333/46—Assays involving biological materials from specific organisms or of a specific nature from animals; from humans from vertebrates
    • G01N2333/47—Assays involving proteins of known structure or function as defined in the subgroups
    • G01N2333/4701—Details
    • G01N2333/4727—Calcium binding proteins, e.g. calmodulin
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/10—Image acquisition modality
    • G06T2207/10024—Color image
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/30—Subject of image; Context of image processing
    • G06T2207/30004—Biomedical image processing
    • G06T2207/30024—Cell structures in vitro; Tissue sections in vitro

Definitions

  • Warnakulasuriya et al. 201 1
  • Warnakulasuriya et al. 2015
  • biomarkers have been proposed for association with oral lesion risk, including but not limited to hypermethylation of endothelin receptor type B (EDNRB) and kinesin family member 1 A (KIF1 A) (Pattani et al., 2010), loss of heterozygosity (Zhang et al., 2012), p16 methylation/HPV (Liu et al., 2015), DNA content (Xiao et al., 2015), and S100A7 (Kaur, R. et al. , 2014).
  • EDNRB endothelin receptor type B
  • KIF1 A kinesin family member 1 A
  • US 2014/0235487 provides a quantitative method for determining the risk of developing oral cancers wherein certain morphological data of individual cells are used to calculate a risk score.
  • a method, and system that can provide a reliable means of determining the risk of a subject developing cancer, such as head and neck or oral cancer.
  • SUMMARY OF THE DESCRIPTION [0009] In general, described herein is a method of automatically quantifying a risk score representative of the risk that a subject, or individual, will develop a cancer. The method is based on a tissue sample obtained from the subject and prepared to visually identify at least one biological marker. The preparation may involve, for example, staining.
  • a method of prognosing the risk of developing a cancer in a subject comprising: [0011] - preparing a tissue sample obtained from the subject for visually identifying at least one biological marker associated with the cancer; [0012] - digitally scanning the prepared tissue sample with a digital scanner to generate a scanned image of the sample; [0013] - analyzing the scanned image with an image analyzer to identify at least one region of interest and to quantify at least one parameter that characterizes the presence of the at least one biological marker; [0014] - transmitting the at least one quantified parameter to a processor, the processor being programmed to execute an algorithm for determining a risk score representative of the risk of the individual developing the cancer based on the at least one quantified parameter; and, [0015] - executing the algorithm to generate the risk score.
  • a system for prognosing the risk of developing a cancer in a subject comprising: [0017] - a scanning module for digitally scanning a biological sample obtained from the subject and generating a scanned image of the sample, the sample being pre-treated to visually identify at least one biological marker associated with the cancer; [0018] - a visualization module for analyzing the scanned image to identify at least one region of interest and to quantify at least one parameter that characterizes the presence of the at least one biological marker; and, [0019] - a processor programmed to execute an algorithm for determining a risk score representative of the risk of the individual developing the cancer based on the at least quantified parameter.
  • FIG. 1 illustrates the automated region of interest (ROI) selection.
  • Figure 2 illustrates the automated labelling of the ROI.
  • Figure 3 illustrates an example of overlapping ROIs.
  • Figures 4a and 4b illustrate examples of input screens from the VisiopharmTM software used for the image analysis in the example.
  • Figures 5a and 5b shows the Kaplan-Meier survival curves for the two groups of cases studied in the example.
  • the present description relates to a method, in particular an automated method, of determining a subject's (i.e. patient's) risk of developing a cancer.
  • the description involves conducting an automated image analysis of a tissue sample that has been stained for one or more biological markers associated with the cancer.
  • markers may be cell morphologic properties (such as protein or nucleic acid markers for example.
  • the one or more biological markers are one or more protein biomarkers.
  • the cancer in question may be any cancer to which the biological marker has been correlated. The correlation may be a reduction or increase in the amount or concentration of the marker when the cancer is present.
  • a preferred biological marker is one or more protein markers.
  • the image analysis would involve treating a biological sample obtained from the subject with an agent for visually detecting the presence or absence of the protein marker.
  • the means of visually detecting the presence of a given protein involves the use of 3,3'-diaminobenzidine (or "DAB").
  • DAB 3,3'-diaminobenzidine
  • the sample obtained from the subject is contacted with an antibody specific to at least one of the protein markers under investigation, the antibody being conjugated with a peroxidase enzyme.
  • the sample is treated with hydrogen peroxide and DAB.
  • the DAB is thereby oxidized, forming a brown precipitate.
  • the brown precipitate can then be visually detected and the presence and intensity of such color enables the detection of the protein(s).
  • the visualization system first identifies a region of interest (ROI) within the sample.
  • ROI is preferably identified or delineated based on the detected concentration of the marker(s) in question in the tissue as well as the location of the marker(s) in the tissue.
  • Such location information would preferably also include topologic location information concerning the location of the marker(s) within the tissue.
  • the information may indicate the amount of the marker(s) in the epithelium
  • This detection may involve a "heat map" methodology, wherein the intensity of a stain color (e.g. brown, in the case of DAB) indicates concentration of the marker.
  • the ROI determination would preferably require a threshold intensity to be determined initially. Once the threshold intensity is set, the visualization system would automatically identify the ROI. Such ROI may be visually represented by one or more boundary lines to identify areas of marker concentration that exceed the concentration threshold. These regions, or "hot spots", superimposed on a digital image of the sample. [0032] After this, the visualization system then conducts a further data acquisition step on the ROIs that are identified.
  • the system calculates the value of at least one parameter representative of the presence of the biological marker.
  • the visualization system calculates the values of at least two parameters, namely: (1) a first parameter, P1 , comprising for example a value representing the percentage of the ROI area that is positive for the marker(s) in question; and (2) a second parameter, P2, comprising for example a value representing the average cell size of the cells within the ROI.
  • a first parameter, P1 comprising for example a value representing the percentage of the ROI area that is positive for the marker(s) in question
  • P2 comprising for example a value representing the average cell size of the cells within the ROI.
  • this step can be conducted automatically using software associated with the visualization system. It will be understood that in other embodiments, further parameters may be determined either quantitatively or qualitatively.
  • the presence of the marker(s) in question may be determined at the subcellular level, thus providing data on the sub-cellular localization of the marker(s) within the cells in the ROI. It will be understood that the description is not limited to the number of other parameters that may be incorporated into the analysis.
  • the visualization system transmits such data to a processor for further processing.
  • the processor may be part of or otherwise associated with the same hardware system used to conduct the visualization procedure or it may be associated with a separate hardware component such as a local or remote computer or server.
  • the processor is programmed to receive the values of P1 and P2 to perform one or more further mathematical operations on same.
  • the processor of the present description is encoded to execute a first algorithm to calculate a risk score, RS.
  • the risk score is calculated by multiplying each of the parameters, P1 and P2, with a suitable weighting factor and then subtracting the weighted average cell size value from the weighted marker-positive area value.
  • the weighting factors can vary based on the marker or markers in question and on the desired sensitivity of the analysis.
  • the values for the weighting factors can be calculated using any statistical modelling techniques or methods as would be known in the art.
  • weighting factors may involve linear regression or Cox regression methods based on a given data set.
  • weighting factors i.e. w 1 and w 2
  • w 1 and w 2 weighting factors
  • the processor is also programmed to calculate a probability value, PV, that the cancer will develop within a time period t.
  • PV a probability value
  • - PV is, as indicated above, the probability of developing cancer within a time period t.
  • - S(t) is the probability of not developing cancer within the time period t.
  • the system for performing the visualization of the sample need not necessarily be physically located together with the processor that conducts the aforementioned risk calculations.
  • the visualization procedure can be performed at a lab located in one location.
  • the results, i.e. quantified parameters P1 and P2 can then be transferred or transmitted to another location where the processor may be situated, which processor can the execute the subsequent calculations.
  • the processor calculating the risk value(s) may be located a separate office, such as an office of a data analysis service provider.
  • the service provider can then provide a clinician or physician or patient with the calculated risk value.
  • the above description has involved two entities, the lab and the service provider. However, it will be understood that any number (i.e. one or more entities) may be involved in the data analysis/manipulation process. [0038] As described in the example below, the above automated method was used to determine the risk of developing oral cancer in patients, wherein the protein marker S100A7 was utilized. This marker has been described as a known biomarker for head and neck cancer (Ralhan et al., 2008; Tripathi et al., 2010) and later for oral mucosal dysplasia (Kaur et al. 2014).
  • each of the steps of preparing the tissue sample, scanning the tissue sample, analyzing the scanned image and processing the quantified parameter(s) can be performed at discrete locations or in the same location as needed. Further any group or subset of the steps can be performed at the same location or at different locations.
  • the data generated by each step can be transmitted in any manner as would be commonly known.
  • the data from one step can be passed to the other step over any data communication network, or may be physically transported from one location to another by means of a memory device, such as a USB device, disk etc.
  • a memory device such as a USB device, disk etc.
  • the present description is not limited by the means by which data is transmitted.
  • the description also encompasses a system for performing the aforementioned method steps.
  • Such a system would include a digital scanning system or device (also referred to herein as a scanning module, which would be understood to encompass hardware and associated software), that is capable of scanning a biological sample that has been treated to visually identify one or more biological markers.
  • the system would also include an image analysis system, or visualization system (also referred to herein as a visualization module, which would be understood to encompass hardware and associated software), for analyzing the digital image generated by the scanning system.
  • the visualization system may, for example, identify the regions of interest in the scanned image and also generate one or more quantified parameters representative of the presence and concentration etc. of the marker.
  • the system described herein would preferably also include a processor for receiving the quantified parameter(s) and for executing an algorithm that calculates a risk score based on the value(s) of the
  • OPL Oral pre-malignant lesion
  • the Aalen-Link-Tsiatis estimate used to estimate the variance of expected cancer-free survival probability, provided the 95% confidence interval (CI) of the cancer-free survival curve.
  • CI 95% confidence interval
  • histopathological dysplasia grading was found to outperform histopathological dysplasia grading in two clinical indices.
  • the sensitivity between the low-risk vs. non-low-risk using the present method was 96% compared to the mild vs. non-mild dysplasia grading which was 75%, with a negative predictive value of 80% and 59%, respectively.
  • the present method is believed to better categorize a patient's 5-year risk of OPLs progressing to cancer. The method can be easily incorporated into clinical practice as no additional tissue samples are needed for the assessment.
  • Materials and methods [0049] Tissue biopsy slides [0050] 150 samples were used in this application.
  • tissue biopsy slides, tissue microarrays and immunohistochemistry staining have been described previously (Kaur, Sawhney et al. 2013). The staining was performed at a commercial clinical lab accredited in the province of Ontario, Canada. The slides were then digitally scanned on a Hamamatsu Nanozoomer-XRTM slide scanner. The images of the slides were visualized using
  • VisiopharmTM VIS software version 5.0.1 .1 122, Hoersholm, Denmark. Clinical information for each sample such as dysplasia grading, gender, age, etc. was provided by Mount Sinai Hospital. This project including the informed consent form was approved by Mount Sinai Hospital Research Ethics Board (project 13-0197-E).
  • VisiopharmTM APPs for automated regions of interest identification, cell classification, and counting [0052] The ROI selection, cell classification, and counting were performed using Visiopharm VIS. Five independent VisiopharmTM APPs were used in tandem to carry out the process.
  • APP1 Whole Tissue selection. The tissue on a slide was outlined for further analysis. Glass with no tissue or with staining debris was excluded.
  • APP2 Whole Tissue to DAB Area conversion. DAB (3,3'-diaminobenzidine) positive regions (intensity below a user-defined threshold) on the tissue were selected.
  • APP3 DAB Area to cell classification. Cell nuclei were labeled in the DAB positive regions.
  • APP4 DAB Heat mapping. A heat map was generated throughout the DAB positive region based on the density of nuclei (density was defined as number of nuclei per 10 ⁇ diameter circle). Five hottest spots (with highest density of nuclei) were selected and five 500 ⁇ diameter circles were created the centers at each of the hot spot. These circles might overlap, giving irregular shapes ( Figures 1 and 3).
  • APP5 Nuclei classification and Positive DAB% calculation. Only tissue found inside the regions of interest (ROIs) as determined in APP4 were included at this stage. Areas void of tissue (background) was designed to be absent from any analysis. The nuclei were re-labeled which not necessarily overlapped with the labels in APP3. Cytoplasm were classified and labeled as either positive or negative for DAB staining. Data on two parameters were recorded, the average size of cells and the percentage of DAB positive areas versus the total area of ROIs ( Figure 2). [0058] Algorithm [0059] The present algorithm has two major steps. The first step, "image analysis", is used to obtain measurements of S100A7 from slide images.
  • image analysis is used to obtain measurements of S100A7 from slide images.
  • the second step "risk calculation” is used to feed the measurements from the first step to a formula to produce cancer progression probability. These two steps are discussed further below.
  • Table 1 summarises the results of the 150 cases that were reviewed in this study. [0075] Table 1
  • the algorithm used in the present example was found to classify dysplasia more accurately than histopathological grading in relation to cancer progression.
  • a patient can be classified into one of three risk groups.
  • the Nelson-Aalen-Breslow estimate was used to calculate the baseline cancer free survival curve. Based on risk score and the baseline cancer free survival curve, the expected cancer free survival probability for a patient can be calculated.
  • the Aalen-Link-Tsiatis estimate was used to estimate the variance of expected cancer free survival probability. Based on the variance and expected cancer free survival probability, the 95% confidence interval (CI) of the cancer free survival curve is generated.
  • Dysplasia grading (mild, moderate, 0.67 0.67 severe)

Landscapes

  • Engineering & Computer Science (AREA)
  • Health & Medical Sciences (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Physics & Mathematics (AREA)
  • Immunology (AREA)
  • General Physics & Mathematics (AREA)
  • Molecular Biology (AREA)
  • Biomedical Technology (AREA)
  • Chemical & Material Sciences (AREA)
  • Urology & Nephrology (AREA)
  • Hematology (AREA)
  • General Health & Medical Sciences (AREA)
  • Theoretical Computer Science (AREA)
  • Cell Biology (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Geometry (AREA)
  • Medical Informatics (AREA)
  • Quality & Reliability (AREA)
  • Biotechnology (AREA)
  • Radiology & Medical Imaging (AREA)
  • Microbiology (AREA)
  • Hospice & Palliative Care (AREA)
  • Nuclear Medicine, Radiotherapy & Molecular Imaging (AREA)
  • Oncology (AREA)
  • Food Science & Technology (AREA)
  • Medicinal Chemistry (AREA)
  • Analytical Chemistry (AREA)
  • Biochemistry (AREA)
  • Pathology (AREA)
  • Investigating Or Analysing Biological Materials (AREA)

Abstract

La présente invention concerne un procédé et un système automatisés pour déterminer le risque de développer un cancer chez un sujet, le procédé comprenant la préparation d'un échantillon de tissu obtenu à partir du sujet pour identifier visuellement au moins un marqueur biologique associé au cancer, le balayage numérique de l'échantillon de tissu préparé, l'analyse de l'image scannée de l'échantillon de tissu pour identifier des régions d'intérêt, la quantification d'au moins un paramètre associé au marqueur, et l'exécution d'un algorithme au moyen du paramètre quantifié pour calculer un score de risque, le score de risque étant représentatif du risque que l'individu développe le cancer.
EP17765612.1A 2016-03-14 2017-03-14 Procédé automatisé pour évaluer un risque de cancer au moyen d'échantillons de tissu, et système associé Pending EP3430384A4 (fr)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
US201662308182P 2016-03-14 2016-03-14
PCT/CA2017/050333 WO2017156627A1 (fr) 2016-03-14 2017-03-14 Procédé automatisé pour évaluer un risque de cancer au moyen d'échantillons de tissu, et système associé

Publications (2)

Publication Number Publication Date
EP3430384A1 true EP3430384A1 (fr) 2019-01-23
EP3430384A4 EP3430384A4 (fr) 2019-07-31

Family

ID=59851352

Family Applications (1)

Application Number Title Priority Date Filing Date
EP17765612.1A Pending EP3430384A4 (fr) 2016-03-14 2017-03-14 Procédé automatisé pour évaluer un risque de cancer au moyen d'échantillons de tissu, et système associé

Country Status (2)

Country Link
EP (1) EP3430384A4 (fr)
WO (1) WO2017156627A1 (fr)

Family Cites Families (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CA2580795A1 (fr) * 2004-09-22 2006-04-06 Tripath Imaging, Inc. Methodes et compositions permettant d'evaluer un pronostic de cancer du sein
CA2630820A1 (fr) * 2005-11-25 2007-05-31 British Columbia Cancer Agency Branch Appareil et procedes d'evaluation automatisee d'une pathologie tissulaire
CA2797585C (fr) * 2010-04-27 2023-03-14 Prelude Corporation Biomarqueurs de cancer et leurs procedes d'utilisation
ES2739623T3 (es) * 2011-03-17 2020-02-03 Cernostics Inc Sistemas y composiciones para diagnosticar el esófago de Barrett y métodos para usarlos
CA2904441A1 (fr) * 2013-03-15 2014-09-18 Metamark Genetics, Inc. Compositions et procedes pour le pronostic de cancer
US10983126B2 (en) * 2013-05-31 2021-04-20 The Brigham And Women's Hospital System and method for analyzing tissue for the presence of cancer using bio-marker profiles

Also Published As

Publication number Publication date
WO2017156627A1 (fr) 2017-09-21
EP3430384A4 (fr) 2019-07-31

Similar Documents

Publication Publication Date Title
US20220156930A1 (en) Cancer risk stratification based on histopathological tissue slide analysis
JP5184087B2 (ja) ガンの予後のためのマーカー候補を分析および最適化するための方法およびコンピュータープログラム製品
Horn et al. Immunostaining for p16INK4a used as a conjunctive tool improves interobserver agreement of the histologic diagnosis of cervical intraepithelial neoplasia
Davison et al. Independent blinded validation of a tissue systems pathology test to predict progression in patients with Barrett's esophagus
Etzkorn et al. Frequency of and risk factors for tumor upstaging after wide local excision of primary cutaneous melanoma
Bernet et al. AACE/ACE disease state commentary: molecular diagnostic testing of thyroid nodules with indeterminate cytopathology
Hwang et al. Individualized five-year risk assessment for oral premalignant lesion progression to cancer
Visser et al. Liquid biopsy-based decision support algorithms for diagnosis and subtyping of lung cancer
US9785752B1 (en) Method for stratifying and selecting candidates for receiving a specific therapeutic approach
Steuber et al. PROPOSe: a real-life prospective study of proclarix, a novel blood-based test to support challenging biopsy decision-making in prostate cancer
CN113841201A (zh) 用于与抗原无关地从头预测与癌症相关联的tcr组库的计算机化系统和方法
Atwater et al. The pursuit of noninvasive diagnosis of lung cancer
US11585816B2 (en) Automated method for assessing cancer risk using tissue samples, and system therefor
Zhu et al. BRAFV600E mutation combined with American College of Radiology thyroid imaging report and data system significantly changes surgical resection rate and risk of malignancy in thyroid cytopathology practice
Kocher et al. Tumor burden of lung metastases at initial staging in breast cancer patients detected by artificial intelligence as a prognostic tool for precision medicine
US20230282362A1 (en) Systems and methods for determining breast cancer prognosis and associated features
Narayanan et al. Unmasking the tissue microecology of ductal carcinoma in situ with deep learning
Ebrahim et al. Segmented linear correlations between bone scan index and prostate cancer biomarkers, alkaline phosphatase, and prostate specific antigen in patients with a Gleason score≥ 7
US20020012938A1 (en) Detection of epithelial dysplasia
EP3430384A1 (fr) Procédé automatisé pour évaluer un risque de cancer au moyen d'échantillons de tissu, et système associé
Zerka et al. A CT-based radiomics approach for CD8+ lymphocytes infiltrationstratification in patients with non-small cell lung cancer
CA2890898A1 (fr) Profilage metabolique dans un tissu et un serum indicateur de la differentiation de tumeur dans le cancer de la prostate
Pascual et al. Prevalence and associated malignancy of Bethesda category III cytologies of thyroid nodules assigned to the “cytological atypia” or “architectural atypia” groups
Yang et al. Development and validation of peritoneal metastasis in gastric cancer based on simplified clinicopathological features and serum tumour markers
Syryło et al. The clinical use of the BTA test for diagnosis of bladder cancer.

Legal Events

Date Code Title Description
STAA Information on the status of an ep patent application or granted ep patent

Free format text: STATUS: THE INTERNATIONAL PUBLICATION HAS BEEN MADE

PUAI Public reference made under article 153(3) epc to a published international application that has entered the european phase

Free format text: ORIGINAL CODE: 0009012

STAA Information on the status of an ep patent application or granted ep patent

Free format text: STATUS: REQUEST FOR EXAMINATION WAS MADE

17P Request for examination filed

Effective date: 20181012

AK Designated contracting states

Kind code of ref document: A1

Designated state(s): AL AT BE BG CH CY CZ DE DK EE ES FI FR GB GR HR HU IE IS IT LI LT LU LV MC MK MT NL NO PL PT RO RS SE SI SK SM TR

AX Request for extension of the european patent

Extension state: BA ME

STAA Information on the status of an ep patent application or granted ep patent

Free format text: STATUS: REQUEST FOR EXAMINATION WAS MADE

DAV Request for validation of the european patent (deleted)
DAX Request for extension of the european patent (deleted)
A4 Supplementary search report drawn up and despatched

Effective date: 20190702

RIC1 Information provided on ipc code assigned before grant

Ipc: G06T 7/00 20170101AFI20190626BHEP

Ipc: G01N 21/84 20060101ALI20190626BHEP

Ipc: G01N 33/574 20060101ALI20190626BHEP

Ipc: G01N 15/02 20060101ALI20190626BHEP

Ipc: G06T 7/62 20170101ALI20190626BHEP

STAA Information on the status of an ep patent application or granted ep patent

Free format text: STATUS: EXAMINATION IS IN PROGRESS

17Q First examination report despatched

Effective date: 20210507

STAA Information on the status of an ep patent application or granted ep patent

Free format text: STATUS: EXAMINATION IS IN PROGRESS