Computed tomography radiomics identification of T1–2 and T3–4 stages of esophageal squamous cell carcinoma: two-dimensional or three-dimensional?
Background To evaluate two-dimensional (2D) and three-dimensional (3D) computed tomography (CT) radiomics analysis for the T stage of esophageal squamous cell carcinoma (ESCC). Methods 398 patients with pathologically confirmed ESCC were divided into training and testing sets. All patients underwent...
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creator | Li, Yang Yang, Li Gu, Xiaolong Wang, Qi Shi, Gaofeng Zhang, Andu Yue, Meng Wang, Mingbo Ren, Jialiang |
description | Background
To evaluate two-dimensional (2D) and three-dimensional (3D) computed tomography (CT) radiomics analysis for the T stage of esophageal squamous cell carcinoma (ESCC).
Methods
398 patients with pathologically confirmed ESCC were divided into training and testing sets. All patients underwent chest CT scans preoperatively. For each tumor, based on CT images, a 2D region of interest (ROI) was outlined on the largest cross-sectional area, and a 3D ROI was outlined layer by layer on each section of the tumor. The radiomics platform was used for feature extraction. For feature selection, stepwise logistic regression was used. The receiver operating characteristic (ROC) curve was used to assess the diagnostic performance of the 2D radiomics model versus the 3D radiomics model. The differences were compared using the DeLong test. The value of the clinical utility of the two radiomics models was evaluated.
Results
1595 radiomics features were extracted. After screening, two radiomics models were constructed. In the training set, the difference between the area under the curve (AUC) of the 2D radiomics model (AUC = 0.831) and the 3D radiomics model (AUC = 0.830) was not statistically significant (p = 0.973). In the testing set, the difference between the AUC of the 2D radiomics model (AUC = 0.807) and the 3D radiomics model (AUC = 0.797) was also not statistically significant (p = 0.748). A 2D model was equally useful as a 3D model in clinical situations.
Conclusion
The performance of 2D radiomics model is comparable to that of 3D radiomics model in distinguishing between the T1-2 and T3-4 stages of ESCC. In addition, 2D radiomics model may be a more feasible option due to the shorter time required for segmenting the ROI. |
doi_str_mv | 10.1007/s00261-023-04070-1 |
format | Article |
fullrecord | <record><control><sourceid>proquest_pubme</sourceid><recordid>TN_cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_10789855</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2878016216</sourcerecordid><originalsourceid>FETCH-LOGICAL-c426t-d0b3731c339c683970076355717ad7560184f1765baea80889b84b31cee53b233</originalsourceid><addsrcrecordid>eNp9kc1u1DAUhS1ERau2L8ACWWLDJnBtxz9hU6ERf1Klboa15TjOjKs4Tu2EqjveAfGCPAkeprQDC1a-9v3usY8PQs8JvCYA8k0GoIJUQFkFNUioyBN0QpkQFQBXTw_qY3Se8zUAEMEJofwZOmZS1YxLcYJ-rGKYltl1eI4hbpKZtnc4mc7H4G3GvnPj7HtvzezjiGOP1-Tnt-8Um7HDa1bKGufZbFze9VyO07ZszIDzzWJCXDK2bhiwNcn6MQbzFs-3sep8cGMuggWMCc_b5Nzh4cUZOurNkN35_XqKvnx4v159qi6vPn5evbusbE3FXHXQMsmIZayxQrFGlo8RjHNJpOkkF0BU3RMpeGucUaBU06q6LQPOcdZSxk7RxV53WtrgOlvMJjPoKflg0p2Oxuu_O6Pf6k38qglI1SjOi8Kre4UUbxaXZx183nk2oyv2NVVSlY-nRBT05T_odVxSsVuohtRNTSTdUXRP2RRzTq5_eA0BvUte75PXJXn9O3lNytCLQx8PI39yLgDbA7m0xo1Lj3f_R_YXHt68Iw</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2914941726</pqid></control><display><type>article</type><title>Computed tomography radiomics identification of T1–2 and T3–4 stages of esophageal squamous cell carcinoma: two-dimensional or three-dimensional?</title><source>SpringerLink Journals</source><creator>Li, Yang ; Yang, Li ; Gu, Xiaolong ; Wang, Qi ; Shi, Gaofeng ; Zhang, Andu ; Yue, Meng ; Wang, Mingbo ; Ren, Jialiang</creator><creatorcontrib>Li, Yang ; Yang, Li ; Gu, Xiaolong ; Wang, Qi ; Shi, Gaofeng ; Zhang, Andu ; Yue, Meng ; Wang, Mingbo ; Ren, Jialiang</creatorcontrib><description>Background
To evaluate two-dimensional (2D) and three-dimensional (3D) computed tomography (CT) radiomics analysis for the T stage of esophageal squamous cell carcinoma (ESCC).
Methods
398 patients with pathologically confirmed ESCC were divided into training and testing sets. All patients underwent chest CT scans preoperatively. For each tumor, based on CT images, a 2D region of interest (ROI) was outlined on the largest cross-sectional area, and a 3D ROI was outlined layer by layer on each section of the tumor. The radiomics platform was used for feature extraction. For feature selection, stepwise logistic regression was used. The receiver operating characteristic (ROC) curve was used to assess the diagnostic performance of the 2D radiomics model versus the 3D radiomics model. The differences were compared using the DeLong test. The value of the clinical utility of the two radiomics models was evaluated.
Results
1595 radiomics features were extracted. After screening, two radiomics models were constructed. In the training set, the difference between the area under the curve (AUC) of the 2D radiomics model (AUC = 0.831) and the 3D radiomics model (AUC = 0.830) was not statistically significant (p = 0.973). In the testing set, the difference between the AUC of the 2D radiomics model (AUC = 0.807) and the 3D radiomics model (AUC = 0.797) was also not statistically significant (p = 0.748). A 2D model was equally useful as a 3D model in clinical situations.
Conclusion
The performance of 2D radiomics model is comparable to that of 3D radiomics model in distinguishing between the T1-2 and T3-4 stages of ESCC. In addition, 2D radiomics model may be a more feasible option due to the shorter time required for segmenting the ROI.</description><identifier>ISSN: 2366-0058</identifier><identifier>ISSN: 2366-004X</identifier><identifier>EISSN: 2366-0058</identifier><identifier>DOI: 10.1007/s00261-023-04070-1</identifier><identifier>PMID: 37843576</identifier><language>eng</language><publisher>New York: Springer US</publisher><subject>Computed tomography ; Esophageal cancer ; Esophageal carcinoma ; Feature extraction ; Gastroenterology ; Hepatology ; Hollow Organ GI ; Imaging ; Medicine ; Medicine & Public Health ; Radiology ; Radiomics ; Squamous cell carcinoma ; Statistical analysis ; Three dimensional models ; Tomography ; Training ; Tumors ; Two dimensional models</subject><ispartof>Abdominal imaging, 2024-01, Vol.49 (1), p.288-300</ispartof><rights>The Author(s) 2023</rights><rights>2023. The Author(s).</rights><rights>The Author(s) 2023. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c426t-d0b3731c339c683970076355717ad7560184f1765baea80889b84b31cee53b233</cites><orcidid>0000-0001-8549-342X</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s00261-023-04070-1$$EPDF$$P50$$Gspringer$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s00261-023-04070-1$$EHTML$$P50$$Gspringer$$Hfree_for_read</linktohtml><link.rule.ids>230,314,776,780,881,27901,27902,41464,42533,51294</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/37843576$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Li, Yang</creatorcontrib><creatorcontrib>Yang, Li</creatorcontrib><creatorcontrib>Gu, Xiaolong</creatorcontrib><creatorcontrib>Wang, Qi</creatorcontrib><creatorcontrib>Shi, Gaofeng</creatorcontrib><creatorcontrib>Zhang, Andu</creatorcontrib><creatorcontrib>Yue, Meng</creatorcontrib><creatorcontrib>Wang, Mingbo</creatorcontrib><creatorcontrib>Ren, Jialiang</creatorcontrib><title>Computed tomography radiomics identification of T1–2 and T3–4 stages of esophageal squamous cell carcinoma: two-dimensional or three-dimensional?</title><title>Abdominal imaging</title><addtitle>Abdom Radiol</addtitle><addtitle>Abdom Radiol (NY)</addtitle><description>Background
To evaluate two-dimensional (2D) and three-dimensional (3D) computed tomography (CT) radiomics analysis for the T stage of esophageal squamous cell carcinoma (ESCC).
Methods
398 patients with pathologically confirmed ESCC were divided into training and testing sets. All patients underwent chest CT scans preoperatively. For each tumor, based on CT images, a 2D region of interest (ROI) was outlined on the largest cross-sectional area, and a 3D ROI was outlined layer by layer on each section of the tumor. The radiomics platform was used for feature extraction. For feature selection, stepwise logistic regression was used. The receiver operating characteristic (ROC) curve was used to assess the diagnostic performance of the 2D radiomics model versus the 3D radiomics model. The differences were compared using the DeLong test. The value of the clinical utility of the two radiomics models was evaluated.
Results
1595 radiomics features were extracted. After screening, two radiomics models were constructed. In the training set, the difference between the area under the curve (AUC) of the 2D radiomics model (AUC = 0.831) and the 3D radiomics model (AUC = 0.830) was not statistically significant (p = 0.973). In the testing set, the difference between the AUC of the 2D radiomics model (AUC = 0.807) and the 3D radiomics model (AUC = 0.797) was also not statistically significant (p = 0.748). A 2D model was equally useful as a 3D model in clinical situations.
Conclusion
The performance of 2D radiomics model is comparable to that of 3D radiomics model in distinguishing between the T1-2 and T3-4 stages of ESCC. In addition, 2D radiomics model may be a more feasible option due to the shorter time required for segmenting the ROI.</description><subject>Computed tomography</subject><subject>Esophageal cancer</subject><subject>Esophageal carcinoma</subject><subject>Feature extraction</subject><subject>Gastroenterology</subject><subject>Hepatology</subject><subject>Hollow Organ GI</subject><subject>Imaging</subject><subject>Medicine</subject><subject>Medicine & Public Health</subject><subject>Radiology</subject><subject>Radiomics</subject><subject>Squamous cell carcinoma</subject><subject>Statistical analysis</subject><subject>Three dimensional models</subject><subject>Tomography</subject><subject>Training</subject><subject>Tumors</subject><subject>Two dimensional 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Qi</creator><creator>Shi, Gaofeng</creator><creator>Zhang, Andu</creator><creator>Yue, Meng</creator><creator>Wang, Mingbo</creator><creator>Ren, Jialiang</creator><general>Springer US</general><general>Springer Nature B.V</general><scope>C6C</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7RV</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>8AO</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>8FH</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>BHPHI</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FR3</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>JQ2</scope><scope>K7-</scope><scope>K9.</scope><scope>KB0</scope><scope>LK8</scope><scope>M0S</scope><scope>M1P</scope><scope>M7P</scope><scope>M7Z</scope><scope>NAPCQ</scope><scope>P5Z</scope><scope>P62</scope><scope>P64</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>7X8</scope><scope>5PM</scope><orcidid>https://orcid.org/0000-0001-8549-342X</orcidid></search><sort><creationdate>20240101</creationdate><title>Computed tomography radiomics identification of T1–2 and T3–4 stages of esophageal squamous cell carcinoma: two-dimensional or three-dimensional?</title><author>Li, Yang ; Yang, Li ; Gu, Xiaolong ; Wang, Qi ; Shi, Gaofeng ; Zhang, Andu ; Yue, Meng ; Wang, Mingbo ; Ren, Jialiang</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c426t-d0b3731c339c683970076355717ad7560184f1765baea80889b84b31cee53b233</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2024</creationdate><topic>Computed tomography</topic><topic>Esophageal cancer</topic><topic>Esophageal carcinoma</topic><topic>Feature extraction</topic><topic>Gastroenterology</topic><topic>Hepatology</topic><topic>Hollow Organ GI</topic><topic>Imaging</topic><topic>Medicine</topic><topic>Medicine & Public Health</topic><topic>Radiology</topic><topic>Radiomics</topic><topic>Squamous cell carcinoma</topic><topic>Statistical analysis</topic><topic>Three dimensional models</topic><topic>Tomography</topic><topic>Training</topic><topic>Tumors</topic><topic>Two dimensional models</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Li, Yang</creatorcontrib><creatorcontrib>Yang, Li</creatorcontrib><creatorcontrib>Gu, Xiaolong</creatorcontrib><creatorcontrib>Wang, Qi</creatorcontrib><creatorcontrib>Shi, Gaofeng</creatorcontrib><creatorcontrib>Zhang, Andu</creatorcontrib><creatorcontrib>Yue, Meng</creatorcontrib><creatorcontrib>Wang, Mingbo</creatorcontrib><creatorcontrib>Ren, Jialiang</creatorcontrib><collection>Springer Nature OA Free Journals</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Nursing & Allied Health Database</collection><collection>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Medical Database (Alumni 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Aerospace Collection</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>Abdominal imaging</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Li, Yang</au><au>Yang, Li</au><au>Gu, Xiaolong</au><au>Wang, Qi</au><au>Shi, Gaofeng</au><au>Zhang, Andu</au><au>Yue, Meng</au><au>Wang, Mingbo</au><au>Ren, Jialiang</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Computed tomography radiomics identification of T1–2 and T3–4 stages of esophageal squamous cell carcinoma: two-dimensional or three-dimensional?</atitle><jtitle>Abdominal imaging</jtitle><stitle>Abdom Radiol</stitle><addtitle>Abdom Radiol (NY)</addtitle><date>2024-01-01</date><risdate>2024</risdate><volume>49</volume><issue>1</issue><spage>288</spage><epage>300</epage><pages>288-300</pages><issn>2366-0058</issn><issn>2366-004X</issn><eissn>2366-0058</eissn><abstract>Background
To evaluate two-dimensional (2D) and three-dimensional (3D) computed tomography (CT) radiomics analysis for the T stage of esophageal squamous cell carcinoma (ESCC).
Methods
398 patients with pathologically confirmed ESCC were divided into training and testing sets. All patients underwent chest CT scans preoperatively. For each tumor, based on CT images, a 2D region of interest (ROI) was outlined on the largest cross-sectional area, and a 3D ROI was outlined layer by layer on each section of the tumor. The radiomics platform was used for feature extraction. For feature selection, stepwise logistic regression was used. The receiver operating characteristic (ROC) curve was used to assess the diagnostic performance of the 2D radiomics model versus the 3D radiomics model. The differences were compared using the DeLong test. The value of the clinical utility of the two radiomics models was evaluated.
Results
1595 radiomics features were extracted. After screening, two radiomics models were constructed. In the training set, the difference between the area under the curve (AUC) of the 2D radiomics model (AUC = 0.831) and the 3D radiomics model (AUC = 0.830) was not statistically significant (p = 0.973). In the testing set, the difference between the AUC of the 2D radiomics model (AUC = 0.807) and the 3D radiomics model (AUC = 0.797) was also not statistically significant (p = 0.748). A 2D model was equally useful as a 3D model in clinical situations.
Conclusion
The performance of 2D radiomics model is comparable to that of 3D radiomics model in distinguishing between the T1-2 and T3-4 stages of ESCC. In addition, 2D radiomics model may be a more feasible option due to the shorter time required for segmenting the ROI.</abstract><cop>New York</cop><pub>Springer US</pub><pmid>37843576</pmid><doi>10.1007/s00261-023-04070-1</doi><tpages>13</tpages><orcidid>https://orcid.org/0000-0001-8549-342X</orcidid><oa>free_for_read</oa></addata></record> |
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subjects | Computed tomography Esophageal cancer Esophageal carcinoma Feature extraction Gastroenterology Hepatology Hollow Organ GI Imaging Medicine Medicine & Public Health Radiology Radiomics Squamous cell carcinoma Statistical analysis Three dimensional models Tomography Training Tumors Two dimensional models |
title | Computed tomography radiomics identification of T1–2 and T3–4 stages of esophageal squamous cell carcinoma: two-dimensional or three-dimensional? |
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