Systems and methods for treating, diagnosing and predicting the occurrence of a medical condition


Ontology type: sgo:Patent     


Patent Info

DATE

N/A

AUTHORS

Mikhail Teverovskiy , David A. Verbel , Olivier Saidi

ABSTRACT

Methods and systems are provided that use clinical information, molecular information and computer-generated morphometric information in a predictive model for predicting the occurrence (e.g., recurrence) of a medical condition, for example, cancer. In an embodiment, a model that predicts prostate cancer recurrence is provided, where the model is based on features including seminal vesicle involvement, surgical margin involvement, lymph node status, androgen receptor (AR) staining index of tumor, a morphometric measurement of epithelial nuclei, and at least one morphometric measurement of stroma. In another embodiment, a model that predicts clinical failure post prostatectomy is provided, wherein the model is based on features including biopsy Gleason score, lymph node involvement, prostatectomy Gleason score, a morphometric measurement of epithelial cytoplasm, a morphometric measurement of epithelial nuclei, a morphometric measurement of stroma, and intensity of androgen receptor (AR) in racemase (AMACR)-positive epithelial cells. More... »

JSON-LD is the canonical representation for SciGraph data.

TIP: You can open this SciGraph record using an external JSON-LD service: JSON-LD Playground Google SDTT

[
  {
    "@context": "https://springernature.github.io/scigraph/jsonld/sgcontext.json", 
    "about": [
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/3142", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "name": "Mikhail Teverovskiy", 
        "type": "Person"
      }, 
      {
        "name": "David A. Verbel", 
        "type": "Person"
      }, 
      {
        "name": "Olivier Saidi", 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "https://doi.org/10.1002/(sici)1097-0142(19970215)79:4<857::aid-cncr24>3.0.co;2-y", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1000402969"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1006/cbmr.1998.1500", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1001337050"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/s0022-5347(05)64820-3", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1005218271"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1001/jama.294.4.433", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1005557650"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1056/nejm199912093412401", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1009618013"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/(sici)1097-0215(19991222)84:6<594::aid-ijc9>3.0.co;2-d", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1011537396"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1097/01.ju.0000107247.81471.06", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1013152534"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/s1359-6446(03)02600-x", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1014766430"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/jnci/90.10.766", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1016489321"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1158/1055-9965.epi-04-0801", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1016998700"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/s0031-3203(01)00178-9", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1018519932"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/nmeth1008", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1019003615", 
          "https://doi.org/10.1038/nmeth1008"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1001/jama.291.22.2713", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1019249000"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/s0022-5347(01)69079-7", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1019785366"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1146/annurev.genom.2.1.343", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1019815147"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1053/j.semdp.2005.11.001", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1019893005"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/s0090-4295(99)00471-9", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1020362336"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.urology.2005.02.028", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1021947213"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/bf01589116", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1022481421", 
          "https://doi.org/10.1007/bf01589116"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/cyto.10149", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1023261961"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1001/jama.295.14.1658", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1023420684"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/1097-0142(19930415)71:8<2574::aid-cncr2820710823>3.0.co;2-1", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1023915312"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1128/mcb.26.5.1908-1916.2006", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1026795868"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1006/cbmr.1998.1488", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1027582292"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/s0047-6374(02)00164-1", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1029067716"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/nm791", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1030132345", 
          "https://doi.org/10.1038/nm791"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1097/00000478-200407000-00013", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1030147549"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/cncr.21157", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1030157373"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1200/jco.2005.01.867", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1031124508"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1080/095400997116748", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1033160244"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.humpath.2004.05.010", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1034470189"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/ng1060", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1038400003", 
          "https://doi.org/10.1038/ng1060"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1001/jama.1982.03320430047030", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1038627387"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/1097-0142(19920701)70:1<161::aid-cncr2820700126>3.0.co;2-5", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1040813430"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/s0140-6736(03)12713-4", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1041988705"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/35090585", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1044713641", 
          "https://doi.org/10.1038/35090585"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/s0094-0143(03)00050-8", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1045496223"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/nm972", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1047154433", 
          "https://doi.org/10.1038/nm972"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1097/01.ju.0000113794.34810.d0", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1047671511"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1073/pnas.97.1.262", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1048892448"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/mc.10018", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1049617930"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1080/09553000400029460", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1049687782"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1001/jama.294.2.238", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1050131812"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/s1078-1439(02)00177-1", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1050271043"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/jnci/djj190", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1051830168"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/s1535-6108(02)00030-2", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1053589488"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1080/01621459.1958.10501452", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1058299418"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/34.244679", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061155889"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/5.241507", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061179093"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/72.623209", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061218943"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/78.650102", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061229999"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/83.136597", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061239012"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/tbme.2003.812194", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061525910"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1200/jco.20.4.951", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1064202830"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1200/jco.2002.11.021", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1064203165"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1200/jco.2002.12.019", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1064203198"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1200/jco.2003.06.100", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1064203679"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1200/jco.1999.17.5.1499", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1074460872"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1159/000473642", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1077836862"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/s0022-5347(17)32416-3", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1082397962"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/s0022-5347(17)32416-3", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1082397962"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/icip.2001.958629", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1094175106"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/hicss.2004.1265355", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1094797706"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/indico.2004.1497783", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1094845074"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "description": "

Methods and systems are provided that use clinical information, molecular information and computer-generated morphometric information in a predictive model for predicting the occurrence (e.g., recurrence) of a medical condition, for example, cancer. In an embodiment, a model that predicts prostate cancer recurrence is provided, where the model is based on features including seminal vesicle involvement, surgical margin involvement, lymph node status, androgen receptor (AR) staining index of tumor, a morphometric measurement of epithelial nuclei, and at least one morphometric measurement of stroma. In another embodiment, a model that predicts clinical failure post prostatectomy is provided, wherein the model is based on features including biopsy Gleason score, lymph node involvement, prostatectomy Gleason score, a morphometric measurement of epithelial cytoplasm, a morphometric measurement of epithelial nuclei, a morphometric measurement of stroma, and intensity of androgen receptor (AR) in racemase (AMACR)-positive epithelial cells.

", "id": "sg:patent.US-7461048-B2", "keywords": [ "method", "diagnosing", "occurrence", "medical condition", "clinical information", "molecular information", "morphometrics", "predictive model", "Recurrence", "Neoplasm", "embodiment", "prostate cancer recurrence", "feature", "seminal vesicle", "surgical margin", "lymph node status", "androgen receptor", "index", "morphometric measurement", "nucleus", "stroma", "clinical failure", "prostatectomy", "wherein", "biopsy", "lymph node involvement", "cytoplasm", "intensity", "epithelial cell" ], "name": "Systems and methods for treating, diagnosing and predicting the occurrence of a medical condition", "recipient": [ { "id": "https://www.grid.ac/institutes/grid.421010.6", "type": "Organization" } ], "sameAs": [ "https://app.dimensions.ai/details/patent/US-7461048-B2" ], "sdDataset": "patents", "sdDatePublished": "2019-03-07T15:32", "sdLicense": "https://scigraph.springernature.com/explorer/license/", "sdPublisher": { "name": "Springer Nature - SN SciGraph project", "type": "Organization" }, "sdSource": "s3://com.uberresearch.data.dev.patents-pipeline/full_run_10/sn-export/5eb3e5a348d7f117b22cc85fb0b02730/0000100128-0000348334/json_export_1a463c9e.jsonl", "type": "Patent" } ]
 

Download the RDF metadata as:  json-ld nt turtle xml License info

HOW TO GET THIS DATA PROGRAMMATICALLY:

JSON-LD is a popular format for linked data which is fully compatible with JSON.

curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/patent.US-7461048-B2'

N-Triples is a line-based linked data format ideal for batch operations.

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/patent.US-7461048-B2'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/patent.US-7461048-B2'

RDF/XML is a standard XML format for linked data.

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/patent.US-7461048-B2'


 

This table displays all metadata directly associated to this object as RDF triples.

253 TRIPLES      14 PREDICATES      105 URIs      36 LITERALS      2 BLANK NODES

Subject Predicate Object
1 sg:patent.US-7461048-B2 schema:about anzsrc-for:3142
2 schema:author N3dacc7bedd594b4a8a2e48d76510da18
3 schema:citation sg:pub.10.1007/bf01589116
4 sg:pub.10.1038/35090585
5 sg:pub.10.1038/ng1060
6 sg:pub.10.1038/nm791
7 sg:pub.10.1038/nm972
8 sg:pub.10.1038/nmeth1008
9 https://doi.org/10.1001/jama.1982.03320430047030
10 https://doi.org/10.1001/jama.291.22.2713
11 https://doi.org/10.1001/jama.294.2.238
12 https://doi.org/10.1001/jama.294.4.433
13 https://doi.org/10.1001/jama.295.14.1658
14 https://doi.org/10.1002/(sici)1097-0142(19970215)79:4<857::aid-cncr24>3.0.co;2-y
15 https://doi.org/10.1002/(sici)1097-0215(19991222)84:6<594::aid-ijc9>3.0.co;2-d
16 https://doi.org/10.1002/1097-0142(19920701)70:1<161::aid-cncr2820700126>3.0.co;2-5
17 https://doi.org/10.1002/1097-0142(19930415)71:8<2574::aid-cncr2820710823>3.0.co;2-1
18 https://doi.org/10.1002/cncr.21157
19 https://doi.org/10.1002/cyto.10149
20 https://doi.org/10.1002/mc.10018
21 https://doi.org/10.1006/cbmr.1998.1488
22 https://doi.org/10.1006/cbmr.1998.1500
23 https://doi.org/10.1016/j.humpath.2004.05.010
24 https://doi.org/10.1016/j.urology.2005.02.028
25 https://doi.org/10.1016/s0022-5347(01)69079-7
26 https://doi.org/10.1016/s0022-5347(05)64820-3
27 https://doi.org/10.1016/s0022-5347(17)32416-3
28 https://doi.org/10.1016/s0031-3203(01)00178-9
29 https://doi.org/10.1016/s0047-6374(02)00164-1
30 https://doi.org/10.1016/s0090-4295(99)00471-9
31 https://doi.org/10.1016/s0094-0143(03)00050-8
32 https://doi.org/10.1016/s0140-6736(03)12713-4
33 https://doi.org/10.1016/s1078-1439(02)00177-1
34 https://doi.org/10.1016/s1359-6446(03)02600-x
35 https://doi.org/10.1016/s1535-6108(02)00030-2
36 https://doi.org/10.1053/j.semdp.2005.11.001
37 https://doi.org/10.1056/nejm199912093412401
38 https://doi.org/10.1073/pnas.97.1.262
39 https://doi.org/10.1080/01621459.1958.10501452
40 https://doi.org/10.1080/095400997116748
41 https://doi.org/10.1080/09553000400029460
42 https://doi.org/10.1093/jnci/90.10.766
43 https://doi.org/10.1093/jnci/djj190
44 https://doi.org/10.1097/00000478-200407000-00013
45 https://doi.org/10.1097/01.ju.0000107247.81471.06
46 https://doi.org/10.1097/01.ju.0000113794.34810.d0
47 https://doi.org/10.1109/34.244679
48 https://doi.org/10.1109/5.241507
49 https://doi.org/10.1109/72.623209
50 https://doi.org/10.1109/78.650102
51 https://doi.org/10.1109/83.136597
52 https://doi.org/10.1109/hicss.2004.1265355
53 https://doi.org/10.1109/icip.2001.958629
54 https://doi.org/10.1109/indico.2004.1497783
55 https://doi.org/10.1109/tbme.2003.812194
56 https://doi.org/10.1128/mcb.26.5.1908-1916.2006
57 https://doi.org/10.1146/annurev.genom.2.1.343
58 https://doi.org/10.1158/1055-9965.epi-04-0801
59 https://doi.org/10.1159/000473642
60 https://doi.org/10.1200/jco.1999.17.5.1499
61 https://doi.org/10.1200/jco.20.4.951
62 https://doi.org/10.1200/jco.2002.11.021
63 https://doi.org/10.1200/jco.2002.12.019
64 https://doi.org/10.1200/jco.2003.06.100
65 https://doi.org/10.1200/jco.2005.01.867
66 schema:description <p num="p-0001">Methods and systems are provided that use clinical information, molecular information and computer-generated morphometric information in a predictive model for predicting the occurrence (e.g., recurrence) of a medical condition, for example, cancer. In an embodiment, a model that predicts prostate cancer recurrence is provided, where the model is based on features including seminal vesicle involvement, surgical margin involvement, lymph node status, androgen receptor (AR) staining index of tumor, a morphometric measurement of epithelial nuclei, and at least one morphometric measurement of stroma. In another embodiment, a model that predicts clinical failure post prostatectomy is provided, wherein the model is based on features including biopsy Gleason score, lymph node involvement, prostatectomy Gleason score, a morphometric measurement of epithelial cytoplasm, a morphometric measurement of epithelial nuclei, a morphometric measurement of stroma, and intensity of androgen receptor (AR) in racemase (AMACR)-positive epithelial cells.</p>
67 schema:keywords Neoplasm
68 Recurrence
69 androgen receptor
70 biopsy
71 clinical failure
72 clinical information
73 cytoplasm
74 diagnosing
75 embodiment
76 epithelial cell
77 feature
78 index
79 intensity
80 lymph node involvement
81 lymph node status
82 medical condition
83 method
84 molecular information
85 morphometric measurement
86 morphometrics
87 nucleus
88 occurrence
89 predictive model
90 prostate cancer recurrence
91 prostatectomy
92 seminal vesicle
93 stroma
94 surgical margin
95 wherein
96 schema:name Systems and methods for treating, diagnosing and predicting the occurrence of a medical condition
97 schema:recipient https://www.grid.ac/institutes/grid.421010.6
98 schema:sameAs https://app.dimensions.ai/details/patent/US-7461048-B2
99 schema:sdDatePublished 2019-03-07T15:32
100 schema:sdLicense https://scigraph.springernature.com/explorer/license/
101 schema:sdPublisher N5b7c191ccf9e4bedbb6304d5b243de3d
102 sgo:license sg:explorer/license/
103 sgo:sdDataset patents
104 rdf:type sgo:Patent
105 N078682f61e3d4a49990557dba2b442dd rdf:first N10822a67b51347319d3845d890b1d419
106 rdf:rest Nb9a189d4cd81498d9af9102bcbb70fb3
107 N10822a67b51347319d3845d890b1d419 schema:name David A. Verbel
108 rdf:type schema:Person
109 N3dacc7bedd594b4a8a2e48d76510da18 rdf:first Nfa54dcbcbc5b40229cd719373441563d
110 rdf:rest N078682f61e3d4a49990557dba2b442dd
111 N5b7c191ccf9e4bedbb6304d5b243de3d schema:name Springer Nature - SN SciGraph project
112 rdf:type schema:Organization
113 Nb9a189d4cd81498d9af9102bcbb70fb3 rdf:first Nfe156643f3244ad6a32059729f4e5e84
114 rdf:rest rdf:nil
115 Nfa54dcbcbc5b40229cd719373441563d schema:name Mikhail Teverovskiy
116 rdf:type schema:Person
117 Nfe156643f3244ad6a32059729f4e5e84 schema:name Olivier Saidi
118 rdf:type schema:Person
119 anzsrc-for:3142 schema:inDefinedTermSet anzsrc-for:
120 rdf:type schema:DefinedTerm
121 sg:pub.10.1007/bf01589116 schema:sameAs https://app.dimensions.ai/details/publication/pub.1022481421
122 https://doi.org/10.1007/bf01589116
123 rdf:type schema:CreativeWork
124 sg:pub.10.1038/35090585 schema:sameAs https://app.dimensions.ai/details/publication/pub.1044713641
125 https://doi.org/10.1038/35090585
126 rdf:type schema:CreativeWork
127 sg:pub.10.1038/ng1060 schema:sameAs https://app.dimensions.ai/details/publication/pub.1038400003
128 https://doi.org/10.1038/ng1060
129 rdf:type schema:CreativeWork
130 sg:pub.10.1038/nm791 schema:sameAs https://app.dimensions.ai/details/publication/pub.1030132345
131 https://doi.org/10.1038/nm791
132 rdf:type schema:CreativeWork
133 sg:pub.10.1038/nm972 schema:sameAs https://app.dimensions.ai/details/publication/pub.1047154433
134 https://doi.org/10.1038/nm972
135 rdf:type schema:CreativeWork
136 sg:pub.10.1038/nmeth1008 schema:sameAs https://app.dimensions.ai/details/publication/pub.1019003615
137 https://doi.org/10.1038/nmeth1008
138 rdf:type schema:CreativeWork
139 https://doi.org/10.1001/jama.1982.03320430047030 schema:sameAs https://app.dimensions.ai/details/publication/pub.1038627387
140 rdf:type schema:CreativeWork
141 https://doi.org/10.1001/jama.291.22.2713 schema:sameAs https://app.dimensions.ai/details/publication/pub.1019249000
142 rdf:type schema:CreativeWork
143 https://doi.org/10.1001/jama.294.2.238 schema:sameAs https://app.dimensions.ai/details/publication/pub.1050131812
144 rdf:type schema:CreativeWork
145 https://doi.org/10.1001/jama.294.4.433 schema:sameAs https://app.dimensions.ai/details/publication/pub.1005557650
146 rdf:type schema:CreativeWork
147 https://doi.org/10.1001/jama.295.14.1658 schema:sameAs https://app.dimensions.ai/details/publication/pub.1023420684
148 rdf:type schema:CreativeWork
149 https://doi.org/10.1002/(sici)1097-0142(19970215)79:4<857::aid-cncr24>3.0.co;2-y schema:sameAs https://app.dimensions.ai/details/publication/pub.1000402969
150 rdf:type schema:CreativeWork
151 https://doi.org/10.1002/(sici)1097-0215(19991222)84:6<594::aid-ijc9>3.0.co;2-d schema:sameAs https://app.dimensions.ai/details/publication/pub.1011537396
152 rdf:type schema:CreativeWork
153 https://doi.org/10.1002/1097-0142(19920701)70:1<161::aid-cncr2820700126>3.0.co;2-5 schema:sameAs https://app.dimensions.ai/details/publication/pub.1040813430
154 rdf:type schema:CreativeWork
155 https://doi.org/10.1002/1097-0142(19930415)71:8<2574::aid-cncr2820710823>3.0.co;2-1 schema:sameAs https://app.dimensions.ai/details/publication/pub.1023915312
156 rdf:type schema:CreativeWork
157 https://doi.org/10.1002/cncr.21157 schema:sameAs https://app.dimensions.ai/details/publication/pub.1030157373
158 rdf:type schema:CreativeWork
159 https://doi.org/10.1002/cyto.10149 schema:sameAs https://app.dimensions.ai/details/publication/pub.1023261961
160 rdf:type schema:CreativeWork
161 https://doi.org/10.1002/mc.10018 schema:sameAs https://app.dimensions.ai/details/publication/pub.1049617930
162 rdf:type schema:CreativeWork
163 https://doi.org/10.1006/cbmr.1998.1488 schema:sameAs https://app.dimensions.ai/details/publication/pub.1027582292
164 rdf:type schema:CreativeWork
165 https://doi.org/10.1006/cbmr.1998.1500 schema:sameAs https://app.dimensions.ai/details/publication/pub.1001337050
166 rdf:type schema:CreativeWork
167 https://doi.org/10.1016/j.humpath.2004.05.010 schema:sameAs https://app.dimensions.ai/details/publication/pub.1034470189
168 rdf:type schema:CreativeWork
169 https://doi.org/10.1016/j.urology.2005.02.028 schema:sameAs https://app.dimensions.ai/details/publication/pub.1021947213
170 rdf:type schema:CreativeWork
171 https://doi.org/10.1016/s0022-5347(01)69079-7 schema:sameAs https://app.dimensions.ai/details/publication/pub.1019785366
172 rdf:type schema:CreativeWork
173 https://doi.org/10.1016/s0022-5347(05)64820-3 schema:sameAs https://app.dimensions.ai/details/publication/pub.1005218271
174 rdf:type schema:CreativeWork
175 https://doi.org/10.1016/s0022-5347(17)32416-3 schema:sameAs https://app.dimensions.ai/details/publication/pub.1082397962
176 rdf:type schema:CreativeWork
177 https://doi.org/10.1016/s0031-3203(01)00178-9 schema:sameAs https://app.dimensions.ai/details/publication/pub.1018519932
178 rdf:type schema:CreativeWork
179 https://doi.org/10.1016/s0047-6374(02)00164-1 schema:sameAs https://app.dimensions.ai/details/publication/pub.1029067716
180 rdf:type schema:CreativeWork
181 https://doi.org/10.1016/s0090-4295(99)00471-9 schema:sameAs https://app.dimensions.ai/details/publication/pub.1020362336
182 rdf:type schema:CreativeWork
183 https://doi.org/10.1016/s0094-0143(03)00050-8 schema:sameAs https://app.dimensions.ai/details/publication/pub.1045496223
184 rdf:type schema:CreativeWork
185 https://doi.org/10.1016/s0140-6736(03)12713-4 schema:sameAs https://app.dimensions.ai/details/publication/pub.1041988705
186 rdf:type schema:CreativeWork
187 https://doi.org/10.1016/s1078-1439(02)00177-1 schema:sameAs https://app.dimensions.ai/details/publication/pub.1050271043
188 rdf:type schema:CreativeWork
189 https://doi.org/10.1016/s1359-6446(03)02600-x schema:sameAs https://app.dimensions.ai/details/publication/pub.1014766430
190 rdf:type schema:CreativeWork
191 https://doi.org/10.1016/s1535-6108(02)00030-2 schema:sameAs https://app.dimensions.ai/details/publication/pub.1053589488
192 rdf:type schema:CreativeWork
193 https://doi.org/10.1053/j.semdp.2005.11.001 schema:sameAs https://app.dimensions.ai/details/publication/pub.1019893005
194 rdf:type schema:CreativeWork
195 https://doi.org/10.1056/nejm199912093412401 schema:sameAs https://app.dimensions.ai/details/publication/pub.1009618013
196 rdf:type schema:CreativeWork
197 https://doi.org/10.1073/pnas.97.1.262 schema:sameAs https://app.dimensions.ai/details/publication/pub.1048892448
198 rdf:type schema:CreativeWork
199 https://doi.org/10.1080/01621459.1958.10501452 schema:sameAs https://app.dimensions.ai/details/publication/pub.1058299418
200 rdf:type schema:CreativeWork
201 https://doi.org/10.1080/095400997116748 schema:sameAs https://app.dimensions.ai/details/publication/pub.1033160244
202 rdf:type schema:CreativeWork
203 https://doi.org/10.1080/09553000400029460 schema:sameAs https://app.dimensions.ai/details/publication/pub.1049687782
204 rdf:type schema:CreativeWork
205 https://doi.org/10.1093/jnci/90.10.766 schema:sameAs https://app.dimensions.ai/details/publication/pub.1016489321
206 rdf:type schema:CreativeWork
207 https://doi.org/10.1093/jnci/djj190 schema:sameAs https://app.dimensions.ai/details/publication/pub.1051830168
208 rdf:type schema:CreativeWork
209 https://doi.org/10.1097/00000478-200407000-00013 schema:sameAs https://app.dimensions.ai/details/publication/pub.1030147549
210 rdf:type schema:CreativeWork
211 https://doi.org/10.1097/01.ju.0000107247.81471.06 schema:sameAs https://app.dimensions.ai/details/publication/pub.1013152534
212 rdf:type schema:CreativeWork
213 https://doi.org/10.1097/01.ju.0000113794.34810.d0 schema:sameAs https://app.dimensions.ai/details/publication/pub.1047671511
214 rdf:type schema:CreativeWork
215 https://doi.org/10.1109/34.244679 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061155889
216 rdf:type schema:CreativeWork
217 https://doi.org/10.1109/5.241507 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061179093
218 rdf:type schema:CreativeWork
219 https://doi.org/10.1109/72.623209 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061218943
220 rdf:type schema:CreativeWork
221 https://doi.org/10.1109/78.650102 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061229999
222 rdf:type schema:CreativeWork
223 https://doi.org/10.1109/83.136597 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061239012
224 rdf:type schema:CreativeWork
225 https://doi.org/10.1109/hicss.2004.1265355 schema:sameAs https://app.dimensions.ai/details/publication/pub.1094797706
226 rdf:type schema:CreativeWork
227 https://doi.org/10.1109/icip.2001.958629 schema:sameAs https://app.dimensions.ai/details/publication/pub.1094175106
228 rdf:type schema:CreativeWork
229 https://doi.org/10.1109/indico.2004.1497783 schema:sameAs https://app.dimensions.ai/details/publication/pub.1094845074
230 rdf:type schema:CreativeWork
231 https://doi.org/10.1109/tbme.2003.812194 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061525910
232 rdf:type schema:CreativeWork
233 https://doi.org/10.1128/mcb.26.5.1908-1916.2006 schema:sameAs https://app.dimensions.ai/details/publication/pub.1026795868
234 rdf:type schema:CreativeWork
235 https://doi.org/10.1146/annurev.genom.2.1.343 schema:sameAs https://app.dimensions.ai/details/publication/pub.1019815147
236 rdf:type schema:CreativeWork
237 https://doi.org/10.1158/1055-9965.epi-04-0801 schema:sameAs https://app.dimensions.ai/details/publication/pub.1016998700
238 rdf:type schema:CreativeWork
239 https://doi.org/10.1159/000473642 schema:sameAs https://app.dimensions.ai/details/publication/pub.1077836862
240 rdf:type schema:CreativeWork
241 https://doi.org/10.1200/jco.1999.17.5.1499 schema:sameAs https://app.dimensions.ai/details/publication/pub.1074460872
242 rdf:type schema:CreativeWork
243 https://doi.org/10.1200/jco.20.4.951 schema:sameAs https://app.dimensions.ai/details/publication/pub.1064202830
244 rdf:type schema:CreativeWork
245 https://doi.org/10.1200/jco.2002.11.021 schema:sameAs https://app.dimensions.ai/details/publication/pub.1064203165
246 rdf:type schema:CreativeWork
247 https://doi.org/10.1200/jco.2002.12.019 schema:sameAs https://app.dimensions.ai/details/publication/pub.1064203198
248 rdf:type schema:CreativeWork
249 https://doi.org/10.1200/jco.2003.06.100 schema:sameAs https://app.dimensions.ai/details/publication/pub.1064203679
250 rdf:type schema:CreativeWork
251 https://doi.org/10.1200/jco.2005.01.867 schema:sameAs https://app.dimensions.ai/details/publication/pub.1031124508
252 rdf:type schema:CreativeWork
253 https://www.grid.ac/institutes/grid.421010.6 schema:Organization
 




Preview window. Press ESC to close (or click here)


...