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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Veröffentlicht in:Abdominal imaging 2024-01, Vol.49 (1), p.288-300
Hauptverfasser: Li, Yang, Yang, Li, Gu, Xiaolong, Wang, Qi, Shi, Gaofeng, Zhang, Andu, Yue, Meng, Wang, Mingbo, Ren, Jialiang
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container_end_page 300
container_issue 1
container_start_page 288
container_title Abdominal imaging
container_volume 49
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.
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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 &amp; 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. 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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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