Abstract:
RATIONALE AND OBJECTIVES:Histological subtypes of lung cancers are critical for clinical treatment decision. In this study, we attempt to use 3D deep learning and radiomics methods to automatically distinguish lung adenocarcinomas (ADC), squamous cell carcinomas (SCC), and small cell lung cancers (SCLC) respectively on Computed Tomography images, and then compare their performance. MATERIALS AND METHODS:920 patients (mean age 61.2, range, 17-87; 340 Female and 580 Male) with lung cancer, including 554 patients with ADC, 175 patients with lung SCC and 191 patients with SCLC, were included in this retrospective study from January 2013 to August 2018. Histopathologic analysis was available for every patient. The classification models based on 3D deep learning (named the ProNet) and radiomics (named com_radNet) were designed to classify lung cancers into the three types mentioned above according to histopathologic results. The training, validation and testing cohorts counted 0.70, 0.15, and 0.15 of the whole datasets respectively. RESULTS:The ProNet model used to classify the three types of lung cancers achieved the F1-scores of 90.0%, 72.4%, 83.7% in SCC, ADC, and SCLC respectively, and the weighted average F1-score of 73.2%. For com_radNet, the F1-scores achieved 83.1%, 75.4%, 85.1% in SCC, ADC, and SCLC, and the weighted average F1-score was 72.2%. The area under the receiver operating characteristic curve of the ProNet model and com_radNet were 0.840 and 0.789, and the accuracy were 71.6% and 74.7% respectively. CONCLUSION:The ProNet and com_radNet models we developed can achieve high performance in distinguishing ADC, SCC, and SCLC and may be promising approaches for non-invasive predicting histological subtypes of lung cancers.
journal_name
Acad Radioljournal_title
Academic radiologyauthors
Guo Y,Song Q,Jiang M,Guo Y,Xu P,Zhang Y,Fu CC,Fang Q,Zeng M,Yao Xdoi
10.1016/j.acra.2020.06.010subject
Has Abstractpub_date
2020-07-01 00:00:00eissn
1076-6332issn
1878-4046pii
S1076-6332(20)30360-3pub_type
杂志文章abstract:RATIONALE AND OBJECTIVES:The purpose of this study was to evaluate residents' use of a radiology curriculum CD-ROM designed to provide ready access to the department's curricula, study materials, and Internet resources. MATERIALS AND METHODS:A survey questionnaire eliciting feedback about the usability and value of th...
journal_title:Academic radiology
pub_type: 杂志文章
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pub_type: 杂志文章
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pub_type: 杂志文章
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pub_type: 杂志文章
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pub_type: 杂志文章,meta分析,评审
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journal_title:Academic radiology
pub_type: 杂志文章
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journal_title:Academic radiology
pub_type: 杂志文章
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journal_title:Academic radiology
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journal_title:Academic radiology
pub_type: 杂志文章
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更新日期:2007-01-01 00:00:00
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journal_title:Academic radiology
pub_type: 临床试验,杂志文章,多中心研究,随机对照试验
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pub_type: 临床试验,杂志文章
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pub_type: 杂志文章
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journal_title:Academic radiology
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journal_title:Academic radiology
pub_type: 杂志文章
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pub_type: 临床试验,杂志文章
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journal_title:Academic radiology
pub_type: 杂志文章
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pub_type: 杂志文章,评审
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更新日期:2016-12-01 00:00:00