Radiomics-based analysis of CT imaging for the preoperative prediction of invasiveness in pure ground-glass nodule lung adenocarcinomas

Key points Radiomics have potentials to differentiate the invasiveness of pGGNs lung adenocarcinoma. Clinical-radiographic feature adds discriminative value to radiomics in pGGNs pathological subtype. Combined clinical-radiographic-radiomic model can successfully stratify patients into noninvasive p...
Ausführliche Beschreibung

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Autor*in:	Hui Feng [verfasserIn] Gaofeng Shi [verfasserIn] Qian Xu [verfasserIn] Jialiang Ren [verfasserIn] Lijia Wang [verfasserIn] Xiaojia Cai [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	Radiomics Lung adenocarcinoma Ground glass opacity Tomography (X-Ray Computed) Pulmonary nodules

Übergeordnetes Werk:	In: Insights into Imaging - SpringerOpen, 2013, 14(2023), 1, Seite 12
Übergeordnetes Werk:	volume:14 ; year:2023 ; number:1 ; pages:12

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1186/s13244-022-01363-9

Katalog-ID:	DOAJ080971652

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spelling	10.1186/s13244-022-01363-9 doi (DE-627)DOAJ080971652 (DE-599)DOAJd3da51145e914cb097caecddaa602fe2 DE-627 ger DE-627 rakwb eng R895-920 Hui Feng verfasserin aut Radiomics-based analysis of CT imaging for the preoperative prediction of invasiveness in pure ground-glass nodule lung adenocarcinomas 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Key points Radiomics have potentials to differentiate the invasiveness of pGGNs lung adenocarcinoma. Clinical-radiographic feature adds discriminative value to radiomics in pGGNs pathological subtype. Combined clinical-radiographic-radiomic model can successfully stratify patients into noninvasive pGGNs and invasive pGGNs in patients with resectable lung adenocarcinoma. Radiomics Lung adenocarcinoma Ground glass opacity Tomography (X-Ray Computed) Pulmonary nodules Medical physics. Medical radiology. Nuclear medicine Gaofeng Shi verfasserin aut Qian Xu verfasserin aut Jialiang Ren verfasserin aut Lijia Wang verfasserin aut Xiaojia Cai verfasserin aut In Insights into Imaging SpringerOpen, 2013 14(2023), 1, Seite 12 (DE-627)621547425 (DE-600)2543323-4 18694101 nnns volume:14 year:2023 number:1 pages:12 https://doi.org/10.1186/s13244-022-01363-9 kostenfrei https://doaj.org/article/d3da51145e914cb097caecddaa602fe2 kostenfrei https://doi.org/10.1186/s13244-022-01363-9 kostenfrei https://doaj.org/toc/1869-4101 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4277 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 14 2023 1 12
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allfieldsGer	10.1186/s13244-022-01363-9 doi (DE-627)DOAJ080971652 (DE-599)DOAJd3da51145e914cb097caecddaa602fe2 DE-627 ger DE-627 rakwb eng R895-920 Hui Feng verfasserin aut Radiomics-based analysis of CT imaging for the preoperative prediction of invasiveness in pure ground-glass nodule lung adenocarcinomas 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Key points Radiomics have potentials to differentiate the invasiveness of pGGNs lung adenocarcinoma. Clinical-radiographic feature adds discriminative value to radiomics in pGGNs pathological subtype. Combined clinical-radiographic-radiomic model can successfully stratify patients into noninvasive pGGNs and invasive pGGNs in patients with resectable lung adenocarcinoma. Radiomics Lung adenocarcinoma Ground glass opacity Tomography (X-Ray Computed) Pulmonary nodules Medical physics. Medical radiology. Nuclear medicine Gaofeng Shi verfasserin aut Qian Xu verfasserin aut Jialiang Ren verfasserin aut Lijia Wang verfasserin aut Xiaojia Cai verfasserin aut In Insights into Imaging SpringerOpen, 2013 14(2023), 1, Seite 12 (DE-627)621547425 (DE-600)2543323-4 18694101 nnns volume:14 year:2023 number:1 pages:12 https://doi.org/10.1186/s13244-022-01363-9 kostenfrei https://doaj.org/article/d3da51145e914cb097caecddaa602fe2 kostenfrei https://doi.org/10.1186/s13244-022-01363-9 kostenfrei https://doaj.org/toc/1869-4101 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4277 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 14 2023 1 12
allfieldsSound	10.1186/s13244-022-01363-9 doi (DE-627)DOAJ080971652 (DE-599)DOAJd3da51145e914cb097caecddaa602fe2 DE-627 ger DE-627 rakwb eng R895-920 Hui Feng verfasserin aut Radiomics-based analysis of CT imaging for the preoperative prediction of invasiveness in pure ground-glass nodule lung adenocarcinomas 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Key points Radiomics have potentials to differentiate the invasiveness of pGGNs lung adenocarcinoma. Clinical-radiographic feature adds discriminative value to radiomics in pGGNs pathological subtype. Combined clinical-radiographic-radiomic model can successfully stratify patients into noninvasive pGGNs and invasive pGGNs in patients with resectable lung adenocarcinoma. Radiomics Lung adenocarcinoma Ground glass opacity Tomography (X-Ray Computed) Pulmonary nodules Medical physics. Medical radiology. Nuclear medicine Gaofeng Shi verfasserin aut Qian Xu verfasserin aut Jialiang Ren verfasserin aut Lijia Wang verfasserin aut Xiaojia Cai verfasserin aut In Insights into Imaging SpringerOpen, 2013 14(2023), 1, Seite 12 (DE-627)621547425 (DE-600)2543323-4 18694101 nnns volume:14 year:2023 number:1 pages:12 https://doi.org/10.1186/s13244-022-01363-9 kostenfrei https://doaj.org/article/d3da51145e914cb097caecddaa602fe2 kostenfrei https://doi.org/10.1186/s13244-022-01363-9 kostenfrei https://doaj.org/toc/1869-4101 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4277 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 14 2023 1 12
language	English
source	In Insights into Imaging 14(2023), 1, Seite 12 volume:14 year:2023 number:1 pages:12
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topic_facet	Radiomics Lung adenocarcinoma Ground glass opacity Tomography (X-Ray Computed) Pulmonary nodules Medical physics. Medical radiology. Nuclear medicine
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container_title	Insights into Imaging
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callnumber-first	R - Medicine
author	Hui Feng
spellingShingle	Hui Feng misc R895-920 misc Radiomics misc Lung adenocarcinoma misc Ground glass opacity misc Tomography (X-Ray Computed) misc Pulmonary nodules misc Medical physics. Medical radiology. Nuclear medicine Radiomics-based analysis of CT imaging for the preoperative prediction of invasiveness in pure ground-glass nodule lung adenocarcinomas
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topic_title	R895-920 Radiomics-based analysis of CT imaging for the preoperative prediction of invasiveness in pure ground-glass nodule lung adenocarcinomas Radiomics Lung adenocarcinoma Ground glass opacity Tomography (X-Ray Computed) Pulmonary nodules
topic	misc R895-920 misc Radiomics misc Lung adenocarcinoma misc Ground glass opacity misc Tomography (X-Ray Computed) misc Pulmonary nodules misc Medical physics. Medical radiology. Nuclear medicine
topic_unstemmed	misc R895-920 misc Radiomics misc Lung adenocarcinoma misc Ground glass opacity misc Tomography (X-Ray Computed) misc Pulmonary nodules misc Medical physics. Medical radiology. Nuclear medicine
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title	Radiomics-based analysis of CT imaging for the preoperative prediction of invasiveness in pure ground-glass nodule lung adenocarcinomas
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title_full	Radiomics-based analysis of CT imaging for the preoperative prediction of invasiveness in pure ground-glass nodule lung adenocarcinomas
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title_sort	radiomics-based analysis of ct imaging for the preoperative prediction of invasiveness in pure ground-glass nodule lung adenocarcinomas
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title_auth	Radiomics-based analysis of CT imaging for the preoperative prediction of invasiveness in pure ground-glass nodule lung adenocarcinomas
abstract	Key points Radiomics have potentials to differentiate the invasiveness of pGGNs lung adenocarcinoma. Clinical-radiographic feature adds discriminative value to radiomics in pGGNs pathological subtype. Combined clinical-radiographic-radiomic model can successfully stratify patients into noninvasive pGGNs and invasive pGGNs in patients with resectable lung adenocarcinoma.
abstractGer	Key points Radiomics have potentials to differentiate the invasiveness of pGGNs lung adenocarcinoma. Clinical-radiographic feature adds discriminative value to radiomics in pGGNs pathological subtype. Combined clinical-radiographic-radiomic model can successfully stratify patients into noninvasive pGGNs and invasive pGGNs in patients with resectable lung adenocarcinoma.
abstract_unstemmed	Key points Radiomics have potentials to differentiate the invasiveness of pGGNs lung adenocarcinoma. Clinical-radiographic feature adds discriminative value to radiomics in pGGNs pathological subtype. Combined clinical-radiographic-radiomic model can successfully stratify patients into noninvasive pGGNs and invasive pGGNs in patients with resectable lung adenocarcinoma.
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title_short	Radiomics-based analysis of CT imaging for the preoperative prediction of invasiveness in pure ground-glass nodule lung adenocarcinomas
url	https://doi.org/10.1186/s13244-022-01363-9 https://doaj.org/article/d3da51145e914cb097caecddaa602fe2 https://doaj.org/toc/1869-4101
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Radiomics-based analysis of CT imaging for the preoperative prediction of invasiveness in pure ground-glass nodule lung adenocarcinomas

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