Preoperative assessment of lymph node metastasis in Colon Cancer patients using machine learning: a pilot study View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2020-04-25

AUTHORS

Aydin Eresen, Yu Li, Jia Yang, Junjie Shangguan, Yury Velichko, Vahid Yaghmai, Al B. Benson, Zhuoli Zhang

ABSTRACT

BackgroundPreoperative detection of lymph node (LN) metastasis is critical for planning treatments in colon cancer (CC). The clinical diagnostic criteria based on the size of the LNs are not sensitive to determine metastasis using CT images. In this retrospective study, we investigated the potential value of CT texture features to diagnose LN metastasis using preoperative CT data and patient characteristics by developing quantitative prediction models.MethodsA total of 390 CC patients, undergone surgical resection, were enrolled in this monocentric study. 390 histologically validated LNs were collected from patients and randomly separated into training (312 patients, 155 metastatic and 157 normal LNs) and test cohorts (78 patients, 39 metastatic and 39 normal LNs). Six patient characteristics and 146 quantitative CT imaging features were analyzed and key variables were determined using either exhaustive search or least absolute shrinkage algorithm. Two kernel-based support vector machine classifiers (patient-characteristic model and radiomic-derived model), generated with 10-fold cross-validation, were compared with the clinical model that utilizes long-axis diameter for diagnosis of metastatic LN. The performance of the models was evaluated on the test cohort by computing accuracy, sensitivity, specificity, and area under the receiver operating curve (AUC).ResultsThe clinical model had an overall diagnostic accuracy of 64.87%; specifically, accuracy of 65.38% and 62.82%, sensitivity of 83.87% and 84.62%, and specificity of 47.13% and 41.03% for training and test cohorts, respectively. The patient-demographic model obtained accuracy of 67.31% and 73.08%, the sensitivity of 62.58% and 69.23%, and specificity of 71.97% and 76.23% for training and test cohorts, respectively. Besides, the radiomic-derived model resulted in an accuracy of 81.09% and 79.49%, sensitivity of 83.87% and 74.36%, and specificity of 78.34% and 84.62% for training and test cohorts, respectively. Furthermore, the diagnostic performance of the radiomic-derived model was significantly higher than clinical and patient-demographic models (p < 0.02) according to the DeLong method.ConclusionsThe texture of the LNs provided characteristic information about the histological status of the LNs. The radiomic-derived model leveraging LN texture provides better preoperative diagnostic accuracy for the detection of metastatic LNs compared to the clinically accepted diagnostic criteria and patient-demographic model. More... »

PAGES

30

Identifiers

URI

http://scigraph.springernature.com/pub.10.1186/s40644-020-00308-z

DOI

http://dx.doi.org/10.1186/s40644-020-00308-z

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1127144707

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/32334635


Indexing Status Check whether this publication has been indexed by Scopus and Web Of Science using the SN Indexing Status Tool
Incoming Citations Browse incoming citations for this publication using opencitations.net

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/2008/11", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Medical and Health Sciences", 
        "type": "DefinedTerm"
      }, 
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/1112", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Oncology and Carcinogenesis", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Adult", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Aged", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Colonic Neoplasms", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Female", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Humans", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Lymphatic Metastasis", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Machine Learning", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Male", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Middle Aged", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Pilot Projects", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Preoperative Period", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Retrospective Studies", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Sensitivity and Specificity", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Tomography, X-Ray Computed", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "Department of Radiology, Feinberg School of Medicine, Northwestern University, 737 N. Michigan Ave, Suite 1600, 60611, Chicago, IL, USA", 
          "id": "http://www.grid.ac/institutes/grid.16753.36", 
          "name": [
            "Department of Radiology, Feinberg School of Medicine, Northwestern University, 737 N. Michigan Ave, Suite 1600, 60611, Chicago, IL, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Eresen", 
        "givenName": "Aydin", 
        "id": "sg:person.0673022574.02", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0673022574.02"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Department of Gastrointestinal Surgery, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, China", 
          "id": "http://www.grid.ac/institutes/grid.412521.1", 
          "name": [
            "Department of Radiology, Feinberg School of Medicine, Northwestern University, 737 N. Michigan Ave, Suite 1600, 60611, Chicago, IL, USA", 
            "Department of Gastrointestinal Surgery, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, China"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Li", 
        "givenName": "Yu", 
        "id": "sg:person.0757632010.58", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0757632010.58"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Department of Radiology, Feinberg School of Medicine, Northwestern University, 737 N. Michigan Ave, Suite 1600, 60611, Chicago, IL, USA", 
          "id": "http://www.grid.ac/institutes/grid.16753.36", 
          "name": [
            "Department of Radiology, Feinberg School of Medicine, Northwestern University, 737 N. Michigan Ave, Suite 1600, 60611, Chicago, IL, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Yang", 
        "givenName": "Jia", 
        "id": "sg:person.013172121233.61", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013172121233.61"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Department of Radiology, Feinberg School of Medicine, Northwestern University, 737 N. Michigan Ave, Suite 1600, 60611, Chicago, IL, USA", 
          "id": "http://www.grid.ac/institutes/grid.16753.36", 
          "name": [
            "Department of Radiology, Feinberg School of Medicine, Northwestern University, 737 N. Michigan Ave, Suite 1600, 60611, Chicago, IL, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Shangguan", 
        "givenName": "Junjie", 
        "id": "sg:person.01035705477.86", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01035705477.86"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Department of Radiology, Feinberg School of Medicine, Northwestern University, 737 N. Michigan Ave, Suite 1600, 60611, Chicago, IL, USA", 
          "id": "http://www.grid.ac/institutes/grid.16753.36", 
          "name": [
            "Department of Radiology, Feinberg School of Medicine, Northwestern University, 737 N. Michigan Ave, Suite 1600, 60611, Chicago, IL, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Velichko", 
        "givenName": "Yury", 
        "id": "sg:person.012737315246.18", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.012737315246.18"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Robert H. Lurie Comprehensive Cancer Center of Northwestern University, 675 N. St. Clair, 21st Floor, 60611, Chicago, IL, USA", 
          "id": "http://www.grid.ac/institutes/grid.16753.36", 
          "name": [
            "Department of Radiology, Feinberg School of Medicine, Northwestern University, 737 N. Michigan Ave, Suite 1600, 60611, Chicago, IL, USA", 
            "Department of Radiological Sciences, School of Medicine, University of California, Irvine, CA, USA", 
            "Robert H. Lurie Comprehensive Cancer Center of Northwestern University, 675 N. St. Clair, 21st Floor, 60611, Chicago, IL, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Yaghmai", 
        "givenName": "Vahid", 
        "id": "sg:person.01134174205.19", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01134174205.19"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Division of Hematology and Oncology, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA", 
          "id": "http://www.grid.ac/institutes/grid.16753.36", 
          "name": [
            "Robert H. Lurie Comprehensive Cancer Center of Northwestern University, 675 N. St. Clair, 21st Floor, 60611, Chicago, IL, USA", 
            "Division of Hematology and Oncology, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Benson", 
        "givenName": "Al B.", 
        "id": "sg:person.013621452577.37", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013621452577.37"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Robert H. Lurie Comprehensive Cancer Center of Northwestern University, 675 N. St. Clair, 21st Floor, 60611, Chicago, IL, USA", 
          "id": "http://www.grid.ac/institutes/grid.16753.36", 
          "name": [
            "Department of Radiology, Feinberg School of Medicine, Northwestern University, 737 N. Michigan Ave, Suite 1600, 60611, Chicago, IL, USA", 
            "Robert H. Lurie Comprehensive Cancer Center of Northwestern University, 675 N. St. Clair, 21st Floor, 60611, Chicago, IL, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Zhang", 
        "givenName": "Zhuoli", 
        "id": "sg:person.013232656557.16", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013232656557.16"
        ], 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "sg:pub.10.1038/nrclinonc.2017.141", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1092061102", 
          "https://doi.org/10.1038/nrclinonc.2017.141"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s00423-016-1377-4", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1015106019", 
          "https://doi.org/10.1007/s00423-016-1377-4"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s00330-014-3420-6", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1010879560", 
          "https://doi.org/10.1007/s00330-014-3420-6"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1186/s41747-018-0068-z", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1109834161", 
          "https://doi.org/10.1186/s41747-018-0068-z"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s00261-019-01900-z", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1112140025", 
          "https://doi.org/10.1007/s00261-019-01900-z"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/bf02660767", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1040325183", 
          "https://doi.org/10.1007/bf02660767"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/bf00994018", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1025150743", 
          "https://doi.org/10.1007/bf00994018"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1186/s40644-016-0104-2", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1053746619", 
          "https://doi.org/10.1186/s40644-016-0104-2"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2020-04-25", 
    "datePublishedReg": "2020-04-25", 
    "description": "BackgroundPreoperative detection of lymph node (LN) metastasis is critical for planning treatments in colon cancer (CC). The clinical diagnostic criteria based on the size of the LNs are not sensitive to determine metastasis using CT images. In this retrospective study, we investigated the potential value of CT texture features to diagnose LN metastasis using preoperative CT data and patient characteristics by developing quantitative prediction models.MethodsA total of 390 CC patients, undergone surgical resection, were enrolled in this monocentric study. 390 histologically validated LNs were collected from patients and randomly separated into training (312 patients, 155 metastatic and 157 normal LNs) and test cohorts (78 patients, 39 metastatic and 39 normal LNs). Six patient characteristics and 146 quantitative CT imaging features were analyzed and key variables were determined using either exhaustive search or least absolute shrinkage algorithm. Two kernel-based support vector machine classifiers (patient-characteristic model and radiomic-derived model), generated with 10-fold cross-validation, were compared with the clinical model that utilizes long-axis diameter for diagnosis of metastatic LN. The performance of the models was evaluated on the test cohort by computing accuracy, sensitivity, specificity, and area under the receiver operating curve (AUC).ResultsThe clinical model had an overall diagnostic accuracy of 64.87%; specifically, accuracy of 65.38% and 62.82%, sensitivity of 83.87% and 84.62%, and specificity of 47.13% and 41.03% for training and test cohorts, respectively. The patient-demographic model obtained accuracy of 67.31% and 73.08%, the sensitivity of 62.58% and 69.23%, and specificity of 71.97% and 76.23% for training and test cohorts, respectively. Besides, the radiomic-derived model resulted in an accuracy of 81.09% and 79.49%, sensitivity of 83.87% and 74.36%, and specificity of 78.34% and 84.62% for training and test cohorts, respectively. Furthermore, the diagnostic performance of the radiomic-derived model was significantly higher than clinical and patient-demographic models (p\u2009<\u20090.02) according to the DeLong method.ConclusionsThe texture of the LNs provided characteristic information about the histological status of the LNs. The radiomic-derived model leveraging LN texture provides better preoperative diagnostic accuracy for the detection of metastatic LNs compared to the clinically accepted diagnostic criteria and patient-demographic model.", 
    "genre": "article", 
    "id": "sg:pub.10.1186/s40644-020-00308-z", 
    "isAccessibleForFree": true, 
    "isFundedItemOf": [
      {
        "id": "sg:grant.4104005", 
        "type": "MonetaryGrant"
      }, 
      {
        "id": "sg:grant.9018717", 
        "type": "MonetaryGrant"
      }, 
      {
        "id": "sg:grant.5504653", 
        "type": "MonetaryGrant"
      }
    ], 
    "isPartOf": [
      {
        "id": "sg:journal.1032121", 
        "issn": [
          "1740-5025", 
          "1470-7330"
        ], 
        "name": "Cancer Imaging", 
        "publisher": "Springer Nature", 
        "type": "Periodical"
      }, 
      {
        "issueNumber": "1", 
        "type": "PublicationIssue"
      }, 
      {
        "type": "PublicationVolume", 
        "volumeNumber": "20"
      }
    ], 
    "keywords": [
      "test cohort", 
      "patient characteristics", 
      "metastatic LNs", 
      "diagnostic criteria", 
      "colon cancer", 
      "clinical model", 
      "quantitative CT imaging features", 
      "diagnostic accuracy", 
      "lymph node metastasis", 
      "clinical diagnostic criteria", 
      "CT imaging features", 
      "colon cancer patients", 
      "preoperative diagnostic accuracy", 
      "long-axis diameter", 
      "CT texture features", 
      "overall diagnostic accuracy", 
      "monocentric study", 
      "LN metastasis", 
      "surgical resection", 
      "node metastasis", 
      "retrospective study", 
      "CC patients", 
      "preoperative assessment", 
      "cancer patients", 
      "imaging features", 
      "histological status", 
      "planning treatment", 
      "DeLong method", 
      "metastasis", 
      "cohort", 
      "patients", 
      "preoperative CT data", 
      "pilot study", 
      "diagnostic performance", 
      "CT images", 
      "specificity", 
      "CT data", 
      "resection", 
      "lymph", 
      "cancer", 
      "diagnosis", 
      "study", 
      "sensitivity", 
      "training", 
      "potential value", 
      "treatment", 
      "total", 
      "criteria", 
      "status", 
      "Ln", 
      "assessment", 
      "detection", 
      "kernel-based Support Vector Machine (SVM) classifier", 
      "features", 
      "search", 
      "characteristics", 
      "prediction model", 
      "data", 
      "model", 
      "variables", 
      "receiver", 
      "curves", 
      "diameter", 
      "area", 
      "key variables", 
      "information", 
      "values", 
      "support vector machine classifier", 
      "accuracy", 
      "texture features", 
      "size", 
      "method", 
      "vector machine classifier", 
      "machine classifier", 
      "images", 
      "performance", 
      "learning", 
      "machine learning", 
      "characteristic information", 
      "classifier", 
      "quantitative prediction model", 
      "texture", 
      "exhaustive search", 
      "algorithm", 
      "shrinkage algorithm"
    ], 
    "name": "Preoperative assessment of lymph node metastasis in Colon Cancer patients using machine learning: a pilot study", 
    "pagination": "30", 
    "productId": [
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1127144707"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1186/s40644-020-00308-z"
        ]
      }, 
      {
        "name": "pubmed_id", 
        "type": "PropertyValue", 
        "value": [
          "32334635"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1186/s40644-020-00308-z", 
      "https://app.dimensions.ai/details/publication/pub.1127144707"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2022-09-02T16:05", 
    "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
    "sdPublisher": {
      "name": "Springer Nature - SN SciGraph project", 
      "type": "Organization"
    }, 
    "sdSource": "s3://com-springernature-scigraph/baseset/20220902/entities/gbq_results/article/article_864.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "https://doi.org/10.1186/s40644-020-00308-z"
  }
]
 

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/pub.10.1186/s40644-020-00308-z'

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

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1186/s40644-020-00308-z'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1186/s40644-020-00308-z'

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

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1186/s40644-020-00308-z'


 

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

298 TRIPLES      21 PREDICATES      132 URIs      116 LITERALS      21 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1186/s40644-020-00308-z schema:about N118c106fa85549cb8dcca3672eaa660a
2 N1c4a4458704e44a18ed2f1362660aa84
3 N20e9a3e1c9c94d75ae2f9e31df74500d
4 N374193d517e94cc2909bd9768a1ac311
5 N394e07779c3f4636abd041da94fe45cc
6 N493e0b54f55d448595e7c036de8d109a
7 N636a5403488141ce95dac30221516c23
8 N73887360b99240249d2adda82b3d5548
9 N8c28a598f8924ae9b90d0353f9ec9098
10 N8faf2fd324844d4d97c989d04af5f4b6
11 Na4e15f20fb574f5bb54537b2a8afa01f
12 Nd2e700e442c64c6695ef7fb2c1952f95
13 Ne1234fd8b05b4d6fad97061ce50b64ad
14 Nf4e6304dd3514c72b9b495390134ebe2
15 anzsrc-for:11
16 anzsrc-for:1112
17 schema:author Nfdecf0b132ea44c3abc1b02875229f10
18 schema:citation sg:pub.10.1007/bf00994018
19 sg:pub.10.1007/bf02660767
20 sg:pub.10.1007/s00261-019-01900-z
21 sg:pub.10.1007/s00330-014-3420-6
22 sg:pub.10.1007/s00423-016-1377-4
23 sg:pub.10.1038/nrclinonc.2017.141
24 sg:pub.10.1186/s40644-016-0104-2
25 sg:pub.10.1186/s41747-018-0068-z
26 schema:datePublished 2020-04-25
27 schema:datePublishedReg 2020-04-25
28 schema:description BackgroundPreoperative detection of lymph node (LN) metastasis is critical for planning treatments in colon cancer (CC). The clinical diagnostic criteria based on the size of the LNs are not sensitive to determine metastasis using CT images. In this retrospective study, we investigated the potential value of CT texture features to diagnose LN metastasis using preoperative CT data and patient characteristics by developing quantitative prediction models.MethodsA total of 390 CC patients, undergone surgical resection, were enrolled in this monocentric study. 390 histologically validated LNs were collected from patients and randomly separated into training (312 patients, 155 metastatic and 157 normal LNs) and test cohorts (78 patients, 39 metastatic and 39 normal LNs). Six patient characteristics and 146 quantitative CT imaging features were analyzed and key variables were determined using either exhaustive search or least absolute shrinkage algorithm. Two kernel-based support vector machine classifiers (patient-characteristic model and radiomic-derived model), generated with 10-fold cross-validation, were compared with the clinical model that utilizes long-axis diameter for diagnosis of metastatic LN. The performance of the models was evaluated on the test cohort by computing accuracy, sensitivity, specificity, and area under the receiver operating curve (AUC).ResultsThe clinical model had an overall diagnostic accuracy of 64.87%; specifically, accuracy of 65.38% and 62.82%, sensitivity of 83.87% and 84.62%, and specificity of 47.13% and 41.03% for training and test cohorts, respectively. The patient-demographic model obtained accuracy of 67.31% and 73.08%, the sensitivity of 62.58% and 69.23%, and specificity of 71.97% and 76.23% for training and test cohorts, respectively. Besides, the radiomic-derived model resulted in an accuracy of 81.09% and 79.49%, sensitivity of 83.87% and 74.36%, and specificity of 78.34% and 84.62% for training and test cohorts, respectively. Furthermore, the diagnostic performance of the radiomic-derived model was significantly higher than clinical and patient-demographic models (p < 0.02) according to the DeLong method.ConclusionsThe texture of the LNs provided characteristic information about the histological status of the LNs. The radiomic-derived model leveraging LN texture provides better preoperative diagnostic accuracy for the detection of metastatic LNs compared to the clinically accepted diagnostic criteria and patient-demographic model.
29 schema:genre article
30 schema:isAccessibleForFree true
31 schema:isPartOf N79fa8650cc4b484eb2c6858f3a1b3fe6
32 Nbf6ac190f93746f495973a2268f7cde6
33 sg:journal.1032121
34 schema:keywords CC patients
35 CT data
36 CT images
37 CT imaging features
38 CT texture features
39 DeLong method
40 LN metastasis
41 Ln
42 accuracy
43 algorithm
44 area
45 assessment
46 cancer
47 cancer patients
48 characteristic information
49 characteristics
50 classifier
51 clinical diagnostic criteria
52 clinical model
53 cohort
54 colon cancer
55 colon cancer patients
56 criteria
57 curves
58 data
59 detection
60 diagnosis
61 diagnostic accuracy
62 diagnostic criteria
63 diagnostic performance
64 diameter
65 exhaustive search
66 features
67 histological status
68 images
69 imaging features
70 information
71 kernel-based Support Vector Machine (SVM) classifier
72 key variables
73 learning
74 long-axis diameter
75 lymph
76 lymph node metastasis
77 machine classifier
78 machine learning
79 metastasis
80 metastatic LNs
81 method
82 model
83 monocentric study
84 node metastasis
85 overall diagnostic accuracy
86 patient characteristics
87 patients
88 performance
89 pilot study
90 planning treatment
91 potential value
92 prediction model
93 preoperative CT data
94 preoperative assessment
95 preoperative diagnostic accuracy
96 quantitative CT imaging features
97 quantitative prediction model
98 receiver
99 resection
100 retrospective study
101 search
102 sensitivity
103 shrinkage algorithm
104 size
105 specificity
106 status
107 study
108 support vector machine classifier
109 surgical resection
110 test cohort
111 texture
112 texture features
113 total
114 training
115 treatment
116 values
117 variables
118 vector machine classifier
119 schema:name Preoperative assessment of lymph node metastasis in Colon Cancer patients using machine learning: a pilot study
120 schema:pagination 30
121 schema:productId N116ca03a54504026af075f7f766d0849
122 N90fc3dc4394b4b888dd4837b3301ace4
123 Nc3076c34bbad41368d1e4715db0302d7
124 schema:sameAs https://app.dimensions.ai/details/publication/pub.1127144707
125 https://doi.org/10.1186/s40644-020-00308-z
126 schema:sdDatePublished 2022-09-02T16:05
127 schema:sdLicense https://scigraph.springernature.com/explorer/license/
128 schema:sdPublisher N58de9303f49449798abb8ee684126023
129 schema:url https://doi.org/10.1186/s40644-020-00308-z
130 sgo:license sg:explorer/license/
131 sgo:sdDataset articles
132 rdf:type schema:ScholarlyArticle
133 N116ca03a54504026af075f7f766d0849 schema:name pubmed_id
134 schema:value 32334635
135 rdf:type schema:PropertyValue
136 N118c106fa85549cb8dcca3672eaa660a schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
137 schema:name Tomography, X-Ray Computed
138 rdf:type schema:DefinedTerm
139 N1c4a4458704e44a18ed2f1362660aa84 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
140 schema:name Aged
141 rdf:type schema:DefinedTerm
142 N20e9a3e1c9c94d75ae2f9e31df74500d schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
143 schema:name Humans
144 rdf:type schema:DefinedTerm
145 N25930f63b3e24cc58456c348b83a2723 rdf:first sg:person.012737315246.18
146 rdf:rest N85fe13c4766444fdafab5b0640b0b89f
147 N374193d517e94cc2909bd9768a1ac311 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
148 schema:name Adult
149 rdf:type schema:DefinedTerm
150 N394e07779c3f4636abd041da94fe45cc schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
151 schema:name Male
152 rdf:type schema:DefinedTerm
153 N3fadf52c78e64872b204153b9f8f5f8f rdf:first sg:person.013232656557.16
154 rdf:rest rdf:nil
155 N493e0b54f55d448595e7c036de8d109a schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
156 schema:name Female
157 rdf:type schema:DefinedTerm
158 N4cb8e9b1b65041d6afc7ae5220fc15a6 rdf:first sg:person.01035705477.86
159 rdf:rest N25930f63b3e24cc58456c348b83a2723
160 N58de9303f49449798abb8ee684126023 schema:name Springer Nature - SN SciGraph project
161 rdf:type schema:Organization
162 N636a5403488141ce95dac30221516c23 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
163 schema:name Machine Learning
164 rdf:type schema:DefinedTerm
165 N71e851500eb5479db52cee4690d51f84 rdf:first sg:person.0757632010.58
166 rdf:rest N722a148b33df40f484135fd8643c4875
167 N722a148b33df40f484135fd8643c4875 rdf:first sg:person.013172121233.61
168 rdf:rest N4cb8e9b1b65041d6afc7ae5220fc15a6
169 N73887360b99240249d2adda82b3d5548 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
170 schema:name Colonic Neoplasms
171 rdf:type schema:DefinedTerm
172 N79fa8650cc4b484eb2c6858f3a1b3fe6 schema:issueNumber 1
173 rdf:type schema:PublicationIssue
174 N85fe13c4766444fdafab5b0640b0b89f rdf:first sg:person.01134174205.19
175 rdf:rest Nb0413c3dd80846c9b61f4735e42ac29c
176 N8c28a598f8924ae9b90d0353f9ec9098 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
177 schema:name Middle Aged
178 rdf:type schema:DefinedTerm
179 N8faf2fd324844d4d97c989d04af5f4b6 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
180 schema:name Preoperative Period
181 rdf:type schema:DefinedTerm
182 N90fc3dc4394b4b888dd4837b3301ace4 schema:name dimensions_id
183 schema:value pub.1127144707
184 rdf:type schema:PropertyValue
185 Na4e15f20fb574f5bb54537b2a8afa01f schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
186 schema:name Retrospective Studies
187 rdf:type schema:DefinedTerm
188 Nb0413c3dd80846c9b61f4735e42ac29c rdf:first sg:person.013621452577.37
189 rdf:rest N3fadf52c78e64872b204153b9f8f5f8f
190 Nbf6ac190f93746f495973a2268f7cde6 schema:volumeNumber 20
191 rdf:type schema:PublicationVolume
192 Nc3076c34bbad41368d1e4715db0302d7 schema:name doi
193 schema:value 10.1186/s40644-020-00308-z
194 rdf:type schema:PropertyValue
195 Nd2e700e442c64c6695ef7fb2c1952f95 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
196 schema:name Lymphatic Metastasis
197 rdf:type schema:DefinedTerm
198 Ne1234fd8b05b4d6fad97061ce50b64ad schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
199 schema:name Sensitivity and Specificity
200 rdf:type schema:DefinedTerm
201 Nf4e6304dd3514c72b9b495390134ebe2 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
202 schema:name Pilot Projects
203 rdf:type schema:DefinedTerm
204 Nfdecf0b132ea44c3abc1b02875229f10 rdf:first sg:person.0673022574.02
205 rdf:rest N71e851500eb5479db52cee4690d51f84
206 anzsrc-for:11 schema:inDefinedTermSet anzsrc-for:
207 schema:name Medical and Health Sciences
208 rdf:type schema:DefinedTerm
209 anzsrc-for:1112 schema:inDefinedTermSet anzsrc-for:
210 schema:name Oncology and Carcinogenesis
211 rdf:type schema:DefinedTerm
212 sg:grant.4104005 http://pending.schema.org/fundedItem sg:pub.10.1186/s40644-020-00308-z
213 rdf:type schema:MonetaryGrant
214 sg:grant.5504653 http://pending.schema.org/fundedItem sg:pub.10.1186/s40644-020-00308-z
215 rdf:type schema:MonetaryGrant
216 sg:grant.9018717 http://pending.schema.org/fundedItem sg:pub.10.1186/s40644-020-00308-z
217 rdf:type schema:MonetaryGrant
218 sg:journal.1032121 schema:issn 1470-7330
219 1740-5025
220 schema:name Cancer Imaging
221 schema:publisher Springer Nature
222 rdf:type schema:Periodical
223 sg:person.01035705477.86 schema:affiliation grid-institutes:grid.16753.36
224 schema:familyName Shangguan
225 schema:givenName Junjie
226 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01035705477.86
227 rdf:type schema:Person
228 sg:person.01134174205.19 schema:affiliation grid-institutes:grid.16753.36
229 schema:familyName Yaghmai
230 schema:givenName Vahid
231 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01134174205.19
232 rdf:type schema:Person
233 sg:person.012737315246.18 schema:affiliation grid-institutes:grid.16753.36
234 schema:familyName Velichko
235 schema:givenName Yury
236 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.012737315246.18
237 rdf:type schema:Person
238 sg:person.013172121233.61 schema:affiliation grid-institutes:grid.16753.36
239 schema:familyName Yang
240 schema:givenName Jia
241 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013172121233.61
242 rdf:type schema:Person
243 sg:person.013232656557.16 schema:affiliation grid-institutes:grid.16753.36
244 schema:familyName Zhang
245 schema:givenName Zhuoli
246 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013232656557.16
247 rdf:type schema:Person
248 sg:person.013621452577.37 schema:affiliation grid-institutes:grid.16753.36
249 schema:familyName Benson
250 schema:givenName Al B.
251 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013621452577.37
252 rdf:type schema:Person
253 sg:person.0673022574.02 schema:affiliation grid-institutes:grid.16753.36
254 schema:familyName Eresen
255 schema:givenName Aydin
256 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0673022574.02
257 rdf:type schema:Person
258 sg:person.0757632010.58 schema:affiliation grid-institutes:grid.412521.1
259 schema:familyName Li
260 schema:givenName Yu
261 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0757632010.58
262 rdf:type schema:Person
263 sg:pub.10.1007/bf00994018 schema:sameAs https://app.dimensions.ai/details/publication/pub.1025150743
264 https://doi.org/10.1007/bf00994018
265 rdf:type schema:CreativeWork
266 sg:pub.10.1007/bf02660767 schema:sameAs https://app.dimensions.ai/details/publication/pub.1040325183
267 https://doi.org/10.1007/bf02660767
268 rdf:type schema:CreativeWork
269 sg:pub.10.1007/s00261-019-01900-z schema:sameAs https://app.dimensions.ai/details/publication/pub.1112140025
270 https://doi.org/10.1007/s00261-019-01900-z
271 rdf:type schema:CreativeWork
272 sg:pub.10.1007/s00330-014-3420-6 schema:sameAs https://app.dimensions.ai/details/publication/pub.1010879560
273 https://doi.org/10.1007/s00330-014-3420-6
274 rdf:type schema:CreativeWork
275 sg:pub.10.1007/s00423-016-1377-4 schema:sameAs https://app.dimensions.ai/details/publication/pub.1015106019
276 https://doi.org/10.1007/s00423-016-1377-4
277 rdf:type schema:CreativeWork
278 sg:pub.10.1038/nrclinonc.2017.141 schema:sameAs https://app.dimensions.ai/details/publication/pub.1092061102
279 https://doi.org/10.1038/nrclinonc.2017.141
280 rdf:type schema:CreativeWork
281 sg:pub.10.1186/s40644-016-0104-2 schema:sameAs https://app.dimensions.ai/details/publication/pub.1053746619
282 https://doi.org/10.1186/s40644-016-0104-2
283 rdf:type schema:CreativeWork
284 sg:pub.10.1186/s41747-018-0068-z schema:sameAs https://app.dimensions.ai/details/publication/pub.1109834161
285 https://doi.org/10.1186/s41747-018-0068-z
286 rdf:type schema:CreativeWork
287 grid-institutes:grid.16753.36 schema:alternateName Department of Radiology, Feinberg School of Medicine, Northwestern University, 737 N. Michigan Ave, Suite 1600, 60611, Chicago, IL, USA
288 Division of Hematology and Oncology, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
289 Robert H. Lurie Comprehensive Cancer Center of Northwestern University, 675 N. St. Clair, 21st Floor, 60611, Chicago, IL, USA
290 schema:name Department of Radiological Sciences, School of Medicine, University of California, Irvine, CA, USA
291 Department of Radiology, Feinberg School of Medicine, Northwestern University, 737 N. Michigan Ave, Suite 1600, 60611, Chicago, IL, USA
292 Division of Hematology and Oncology, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
293 Robert H. Lurie Comprehensive Cancer Center of Northwestern University, 675 N. St. Clair, 21st Floor, 60611, Chicago, IL, USA
294 rdf:type schema:Organization
295 grid-institutes:grid.412521.1 schema:alternateName Department of Gastrointestinal Surgery, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, China
296 schema:name Department of Gastrointestinal Surgery, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, China
297 Department of Radiology, Feinberg School of Medicine, Northwestern University, 737 N. Michigan Ave, Suite 1600, 60611, Chicago, IL, USA
298 rdf:type schema:Organization
 




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


...