Radiomics combined with clinical characteristics predicted the progression-free survival time in first-line targeted therapy for advanced non-small cell lung cancer ... View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2022-04-14

AUTHORS

Jian-man Zhu, Lei Sun, Linjing Wang, Tong-Chong Zhou, Yawei Yuan, Xin Zhen, Zhi-Wei Liao

ABSTRACT

ObjectiveThis study was to explore the most appropriate radiomics modeling method to predict the progression-free survival of EGFR-TKI treatment in advanced non-small cell lung cancer with EGFR mutations. Different machine learning methods may vary considerably and the selection of a proper model is essential for accurate treatment outcome prediction. Our study were established 176 discrimination models constructed with 22 feature selection methods and 8 classifiers. The predictive performance of each model were evaluated using the AUC, ACC, sensitivity and specificity, where the optimal model was identified.ResultsThere were totally 107 radiomics features and 7 clinical features obtained from each patient. After feature selection, the top-ten most relevant features were fed to train 176 models. Significant performance variations were observed in the established models, with the best performance achieved by the logistic regression model using gini-index feature selection (AUC = 0.797, ACC = 0.722, sensitivity = 0.758, specificity = 0.693). The median R-score was 0.518 (IQR, 0.023–0.987), and the patients were divided into high-risk and low-risk groups based on this cut-off value. The KM survival curves of the two groups demonstrated evident stratification results (p = 0.000). More... »

PAGES

140

Identifiers

URI

http://scigraph.springernature.com/pub.10.1186/s13104-022-06019-x

DOI

http://dx.doi.org/10.1186/s13104-022-06019-x

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1147105411

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/35422007


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": "Carcinoma, Non-Small-Cell Lung", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "ErbB Receptors", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Humans", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Lung Neoplasms", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Mutation", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Progression-Free Survival", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "Department of Radiation Oncology, Affiliated Cancer Hospital & Institute of Guangzhou Medical University, 510095, Guangzhou, Guangdong, China", 
          "id": "http://www.grid.ac/institutes/grid.410737.6", 
          "name": [
            "Department of Radiation Oncology, Affiliated Cancer Hospital & Institute of Guangzhou Medical University, 510095, Guangzhou, Guangdong, China"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Zhu", 
        "givenName": "Jian-man", 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Department of Radiation Oncology, Affiliated Zhujiang Hospital of Southern Medical University, 510280, Guangzhou, Guangdong, China", 
          "id": "http://www.grid.ac/institutes/grid.284723.8", 
          "name": [
            "Department of Radiation Oncology, Affiliated Zhujiang Hospital of Southern Medical University, 510280, Guangzhou, Guangdong, China"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Sun", 
        "givenName": "Lei", 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Radiotherapy Center, Affiliated Cancer Hospital & Institute of Guangzhou Medical University, 510095, Guangzhou, Guangdong, China", 
          "id": "http://www.grid.ac/institutes/grid.410737.6", 
          "name": [
            "Radiotherapy Center, Affiliated Cancer Hospital & Institute of Guangzhou Medical University, 510095, Guangzhou, Guangdong, China"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Wang", 
        "givenName": "Linjing", 
        "id": "sg:person.010044035377.87", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010044035377.87"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Department of Radiation Oncology, Affiliated Cancer Hospital & Institute of Guangzhou Medical University, 510095, Guangzhou, Guangdong, China", 
          "id": "http://www.grid.ac/institutes/grid.410737.6", 
          "name": [
            "Department of Radiation Oncology, Affiliated Cancer Hospital & Institute of Guangzhou Medical University, 510095, Guangzhou, Guangdong, China"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Zhou", 
        "givenName": "Tong-Chong", 
        "id": "sg:person.01204321631.85", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01204321631.85"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Department of Radiation Oncology, Affiliated Cancer Hospital & Institute of Guangzhou Medical University, 510095, Guangzhou, Guangdong, China", 
          "id": "http://www.grid.ac/institutes/grid.410737.6", 
          "name": [
            "Department of Radiation Oncology, Affiliated Cancer Hospital & Institute of Guangzhou Medical University, 510095, Guangzhou, Guangdong, China"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Yuan", 
        "givenName": "Yawei", 
        "id": "sg:person.01071447336.55", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01071447336.55"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "School of Biomedical Engineering, Southern Medical University, 510515, Guangzhou, Guangdong, China", 
          "id": "http://www.grid.ac/institutes/grid.284723.8", 
          "name": [
            "School of Biomedical Engineering, Southern Medical University, 510515, Guangzhou, Guangdong, China"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Zhen", 
        "givenName": "Xin", 
        "id": "sg:person.01323520527.67", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01323520527.67"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Department of Radiation Oncology, Affiliated Cancer Hospital & Institute of Guangzhou Medical University, 510095, Guangzhou, Guangdong, China", 
          "id": "http://www.grid.ac/institutes/grid.410737.6", 
          "name": [
            "Department of Radiation Oncology, Affiliated Cancer Hospital & Institute of Guangzhou Medical University, 510095, Guangzhou, Guangdong, China"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Liao", 
        "givenName": "Zhi-Wei", 
        "id": "sg:person.01023042040.18", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01023042040.18"
        ], 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "sg:pub.10.1007/s00330-019-06024-y", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1112217380", 
          "https://doi.org/10.1007/s00330-019-06024-y"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2022-04-14", 
    "datePublishedReg": "2022-04-14", 
    "description": "ObjectiveThis study was to explore the most appropriate radiomics modeling method to predict the progression-free survival of EGFR-TKI treatment in advanced non-small cell lung cancer with EGFR mutations. Different machine learning methods may vary considerably and the selection of a proper model is essential for accurate treatment outcome prediction. Our study were established 176 discrimination models constructed with 22 feature selection methods and 8 classifiers. The predictive performance of each model were evaluated using the AUC, ACC, sensitivity and specificity, where the optimal model was identified.ResultsThere were totally 107 radiomics features and 7 clinical features obtained from each patient. After feature selection, the top-ten most relevant features were fed to train 176 models. Significant performance variations were observed in the established models, with the best performance achieved by the logistic regression model using gini-index feature selection (AUC\u2009=\u20090.797, ACC\u2009=\u20090.722, sensitivity\u2009=\u20090.758, specificity\u2009=\u20090.693). The median R-score was 0.518 (IQR, 0.023\u20130.987), and the patients were divided into high-risk and low-risk groups based on this cut-off value. The KM survival curves of the two groups demonstrated evident stratification results (p\u2009=\u20090.000).", 
    "genre": "article", 
    "id": "sg:pub.10.1186/s13104-022-06019-x", 
    "inLanguage": "en", 
    "isAccessibleForFree": true, 
    "isPartOf": [
      {
        "id": "sg:journal.1039457", 
        "issn": [
          "1756-0500"
        ], 
        "name": "BMC Research Notes", 
        "publisher": "Springer Nature", 
        "type": "Periodical"
      }, 
      {
        "issueNumber": "1", 
        "type": "PublicationIssue"
      }, 
      {
        "type": "PublicationVolume", 
        "volumeNumber": "15"
      }
    ], 
    "keywords": [
      "advanced non-small cell lung cancer", 
      "non-small cell lung cancer", 
      "cell lung cancer", 
      "lung cancer", 
      "EGFR mutations", 
      "progression-free survival time", 
      "progression-free survival", 
      "EGFR-TKI treatment", 
      "low-risk group", 
      "KM survival curves", 
      "logistic regression models", 
      "clinical characteristics", 
      "clinical features", 
      "treatment outcome prediction", 
      "survival time", 
      "survival curves", 
      "outcome prediction", 
      "radiomic features", 
      "patients", 
      "cancer", 
      "regression models", 
      "ResultsThere", 
      "group", 
      "therapy", 
      "mutations", 
      "AUC", 
      "survival", 
      "treatment", 
      "radiomics", 
      "predictive performance", 
      "R score", 
      "study", 
      "specificity", 
      "ACC", 
      "features", 
      "sensitivity", 
      "selection", 
      "method", 
      "model", 
      "stratification results", 
      "time", 
      "curves", 
      "results", 
      "characteristics", 
      "optimal model", 
      "values", 
      "discrimination model", 
      "relevant features", 
      "variation", 
      "feature selection", 
      "different machine", 
      "prediction", 
      "performance", 
      "feature selection method", 
      "proper model", 
      "selection method", 
      "classifier", 
      "significant performance variations", 
      "better performance", 
      "machine", 
      "performance variation", 
      "modeling method"
    ], 
    "name": "Radiomics combined with clinical characteristics predicted the progression-free survival time in first-line targeted therapy for advanced non-small cell lung cancer with EGFR mutation", 
    "pagination": "140", 
    "productId": [
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1147105411"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1186/s13104-022-06019-x"
        ]
      }, 
      {
        "name": "pubmed_id", 
        "type": "PropertyValue", 
        "value": [
          "35422007"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1186/s13104-022-06019-x", 
      "https://app.dimensions.ai/details/publication/pub.1147105411"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2022-06-01T22:25", 
    "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
    "sdPublisher": {
      "name": "Springer Nature - SN SciGraph project", 
      "type": "Organization"
    }, 
    "sdSource": "s3://com-springernature-scigraph/baseset/20220601/entities/gbq_results/article/article_935.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "https://doi.org/10.1186/s13104-022-06019-x"
  }
]
 

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/s13104-022-06019-x'

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/s13104-022-06019-x'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1186/s13104-022-06019-x'

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

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1186/s13104-022-06019-x'


 

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

198 TRIPLES      22 PREDICATES      95 URIs      86 LITERALS      13 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1186/s13104-022-06019-x schema:about N2efb349e6f9949b2916e6eef20c305f4
2 N2f238f5022f1446799f47aedd6642120
3 N57723a2239cf4777b5f9ed50f37ee9d9
4 N69fc64316e374df58043b3613f6bd229
5 N81a9faac0f7f49308f095efb2bfca611
6 Nd97d7e5c95fa45eab3fa6369088dba66
7 anzsrc-for:11
8 anzsrc-for:1112
9 schema:author Nd19e07aabe66466e83cb7f14ced10317
10 schema:citation sg:pub.10.1007/s00330-019-06024-y
11 schema:datePublished 2022-04-14
12 schema:datePublishedReg 2022-04-14
13 schema:description ObjectiveThis study was to explore the most appropriate radiomics modeling method to predict the progression-free survival of EGFR-TKI treatment in advanced non-small cell lung cancer with EGFR mutations. Different machine learning methods may vary considerably and the selection of a proper model is essential for accurate treatment outcome prediction. Our study were established 176 discrimination models constructed with 22 feature selection methods and 8 classifiers. The predictive performance of each model were evaluated using the AUC, ACC, sensitivity and specificity, where the optimal model was identified.ResultsThere were totally 107 radiomics features and 7 clinical features obtained from each patient. After feature selection, the top-ten most relevant features were fed to train 176 models. Significant performance variations were observed in the established models, with the best performance achieved by the logistic regression model using gini-index feature selection (AUC = 0.797, ACC = 0.722, sensitivity = 0.758, specificity = 0.693). The median R-score was 0.518 (IQR, 0.023–0.987), and the patients were divided into high-risk and low-risk groups based on this cut-off value. The KM survival curves of the two groups demonstrated evident stratification results (p = 0.000).
14 schema:genre article
15 schema:inLanguage en
16 schema:isAccessibleForFree true
17 schema:isPartOf N5bd6b9397b744cc69bdbc67f7fbecd5b
18 Nd49f9ebf0e7e4af9bdb3ea20770abbde
19 sg:journal.1039457
20 schema:keywords ACC
21 AUC
22 EGFR mutations
23 EGFR-TKI treatment
24 KM survival curves
25 R score
26 ResultsThere
27 advanced non-small cell lung cancer
28 better performance
29 cancer
30 cell lung cancer
31 characteristics
32 classifier
33 clinical characteristics
34 clinical features
35 curves
36 different machine
37 discrimination model
38 feature selection
39 feature selection method
40 features
41 group
42 logistic regression models
43 low-risk group
44 lung cancer
45 machine
46 method
47 model
48 modeling method
49 mutations
50 non-small cell lung cancer
51 optimal model
52 outcome prediction
53 patients
54 performance
55 performance variation
56 prediction
57 predictive performance
58 progression-free survival
59 progression-free survival time
60 proper model
61 radiomic features
62 radiomics
63 regression models
64 relevant features
65 results
66 selection
67 selection method
68 sensitivity
69 significant performance variations
70 specificity
71 stratification results
72 study
73 survival
74 survival curves
75 survival time
76 therapy
77 time
78 treatment
79 treatment outcome prediction
80 values
81 variation
82 schema:name Radiomics combined with clinical characteristics predicted the progression-free survival time in first-line targeted therapy for advanced non-small cell lung cancer with EGFR mutation
83 schema:pagination 140
84 schema:productId N0c2b4083b1244896974c153f7186ba3c
85 Nb063a3cfc01d4d36b63c19f5e2007d39
86 Nc1ae906c44fe4107b605568948d8b29e
87 schema:sameAs https://app.dimensions.ai/details/publication/pub.1147105411
88 https://doi.org/10.1186/s13104-022-06019-x
89 schema:sdDatePublished 2022-06-01T22:25
90 schema:sdLicense https://scigraph.springernature.com/explorer/license/
91 schema:sdPublisher N929847a88cca48aa9061477878694abe
92 schema:url https://doi.org/10.1186/s13104-022-06019-x
93 sgo:license sg:explorer/license/
94 sgo:sdDataset articles
95 rdf:type schema:ScholarlyArticle
96 N0c2b4083b1244896974c153f7186ba3c schema:name pubmed_id
97 schema:value 35422007
98 rdf:type schema:PropertyValue
99 N2efb349e6f9949b2916e6eef20c305f4 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
100 schema:name Carcinoma, Non-Small-Cell Lung
101 rdf:type schema:DefinedTerm
102 N2f238f5022f1446799f47aedd6642120 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
103 schema:name Lung Neoplasms
104 rdf:type schema:DefinedTerm
105 N45be6f5a6ff44e06a1016bd4a338e019 schema:affiliation grid-institutes:grid.410737.6
106 schema:familyName Zhu
107 schema:givenName Jian-man
108 rdf:type schema:Person
109 N57723a2239cf4777b5f9ed50f37ee9d9 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
110 schema:name Humans
111 rdf:type schema:DefinedTerm
112 N5bd6b9397b744cc69bdbc67f7fbecd5b schema:volumeNumber 15
113 rdf:type schema:PublicationVolume
114 N69fc64316e374df58043b3613f6bd229 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
115 schema:name ErbB Receptors
116 rdf:type schema:DefinedTerm
117 N81a9faac0f7f49308f095efb2bfca611 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
118 schema:name Mutation
119 rdf:type schema:DefinedTerm
120 N8a2a5c5ccba84d3c991f6e892f32fcb1 schema:affiliation grid-institutes:grid.284723.8
121 schema:familyName Sun
122 schema:givenName Lei
123 rdf:type schema:Person
124 N8d7df4f2e08d47a09e78552733dfaf9e rdf:first sg:person.010044035377.87
125 rdf:rest Nd3d8bb63b1c34e56ac1fa847348a2905
126 N929847a88cca48aa9061477878694abe schema:name Springer Nature - SN SciGraph project
127 rdf:type schema:Organization
128 N973a5ea861b445b1ba0ae14e6eb0f318 rdf:first sg:person.01323520527.67
129 rdf:rest Nbe5276bbf67b47cfb99903ad04977fae
130 Nb063a3cfc01d4d36b63c19f5e2007d39 schema:name doi
131 schema:value 10.1186/s13104-022-06019-x
132 rdf:type schema:PropertyValue
133 Nbe5276bbf67b47cfb99903ad04977fae rdf:first sg:person.01023042040.18
134 rdf:rest rdf:nil
135 Nbf30dae1cee74dd495c6224321e073ce rdf:first sg:person.01071447336.55
136 rdf:rest N973a5ea861b445b1ba0ae14e6eb0f318
137 Nc1ae906c44fe4107b605568948d8b29e schema:name dimensions_id
138 schema:value pub.1147105411
139 rdf:type schema:PropertyValue
140 Nd19e07aabe66466e83cb7f14ced10317 rdf:first N45be6f5a6ff44e06a1016bd4a338e019
141 rdf:rest Nd1ae8cfdb73842ed9b960c5cec81a355
142 Nd1ae8cfdb73842ed9b960c5cec81a355 rdf:first N8a2a5c5ccba84d3c991f6e892f32fcb1
143 rdf:rest N8d7df4f2e08d47a09e78552733dfaf9e
144 Nd3d8bb63b1c34e56ac1fa847348a2905 rdf:first sg:person.01204321631.85
145 rdf:rest Nbf30dae1cee74dd495c6224321e073ce
146 Nd49f9ebf0e7e4af9bdb3ea20770abbde schema:issueNumber 1
147 rdf:type schema:PublicationIssue
148 Nd97d7e5c95fa45eab3fa6369088dba66 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
149 schema:name Progression-Free Survival
150 rdf:type schema:DefinedTerm
151 anzsrc-for:11 schema:inDefinedTermSet anzsrc-for:
152 schema:name Medical and Health Sciences
153 rdf:type schema:DefinedTerm
154 anzsrc-for:1112 schema:inDefinedTermSet anzsrc-for:
155 schema:name Oncology and Carcinogenesis
156 rdf:type schema:DefinedTerm
157 sg:journal.1039457 schema:issn 1756-0500
158 schema:name BMC Research Notes
159 schema:publisher Springer Nature
160 rdf:type schema:Periodical
161 sg:person.010044035377.87 schema:affiliation grid-institutes:grid.410737.6
162 schema:familyName Wang
163 schema:givenName Linjing
164 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010044035377.87
165 rdf:type schema:Person
166 sg:person.01023042040.18 schema:affiliation grid-institutes:grid.410737.6
167 schema:familyName Liao
168 schema:givenName Zhi-Wei
169 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01023042040.18
170 rdf:type schema:Person
171 sg:person.01071447336.55 schema:affiliation grid-institutes:grid.410737.6
172 schema:familyName Yuan
173 schema:givenName Yawei
174 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01071447336.55
175 rdf:type schema:Person
176 sg:person.01204321631.85 schema:affiliation grid-institutes:grid.410737.6
177 schema:familyName Zhou
178 schema:givenName Tong-Chong
179 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01204321631.85
180 rdf:type schema:Person
181 sg:person.01323520527.67 schema:affiliation grid-institutes:grid.284723.8
182 schema:familyName Zhen
183 schema:givenName Xin
184 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01323520527.67
185 rdf:type schema:Person
186 sg:pub.10.1007/s00330-019-06024-y schema:sameAs https://app.dimensions.ai/details/publication/pub.1112217380
187 https://doi.org/10.1007/s00330-019-06024-y
188 rdf:type schema:CreativeWork
189 grid-institutes:grid.284723.8 schema:alternateName Department of Radiation Oncology, Affiliated Zhujiang Hospital of Southern Medical University, 510280, Guangzhou, Guangdong, China
190 School of Biomedical Engineering, Southern Medical University, 510515, Guangzhou, Guangdong, China
191 schema:name Department of Radiation Oncology, Affiliated Zhujiang Hospital of Southern Medical University, 510280, Guangzhou, Guangdong, China
192 School of Biomedical Engineering, Southern Medical University, 510515, Guangzhou, Guangdong, China
193 rdf:type schema:Organization
194 grid-institutes:grid.410737.6 schema:alternateName Department of Radiation Oncology, Affiliated Cancer Hospital & Institute of Guangzhou Medical University, 510095, Guangzhou, Guangdong, China
195 Radiotherapy Center, Affiliated Cancer Hospital & Institute of Guangzhou Medical University, 510095, Guangzhou, Guangdong, China
196 schema:name Department of Radiation Oncology, Affiliated Cancer Hospital & Institute of Guangzhou Medical University, 510095, Guangzhou, Guangdong, China
197 Radiotherapy Center, Affiliated Cancer Hospital & Institute of Guangzhou Medical University, 510095, Guangzhou, Guangdong, China
198 rdf:type schema:Organization
 




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


...