Use of radiomics to extract splenic features to predict prognosis of patients with gastric cancer
Radiomics allows for mining of imaging data to examine tissue characteristics non-invasively, which can be used to predict the prognosis of a patient. This study explored the use of imaging techniques to evaluate splenic tissue characteristics to predict the prognosis of patients with gastric cancer...
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| Veröffentlicht in: | European journal of surgical oncology 2020-10, Vol.46 (10), p.1932-1940 |
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| container_issue | 10 |
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| container_title | European journal of surgical oncology |
| container_volume | 46 |
| creator | Wang, Xiang Sun, Jing Zhang, Weiteng Yang, Xinxin Zhu, Ce Pan, Bujian Zeng, Yunpeng Xu, Jingxuan Chen, Xiaodong Shen, Xian |
| description | Radiomics allows for mining of imaging data to examine tissue characteristics non-invasively, which can be used to predict the prognosis of a patient. This study explored the use of imaging techniques to evaluate splenic tissue characteristics to predict the prognosis of patients with gastric cancer.
Computed tomography images from patients with gastric cancer were collected retrospectively. Splenic image characteristics, extracted with pyradiomics, of patients in the training group were randomly divided. Characteristics with a P value |
| doi_str_mv | 10.1016/j.ejso.2020.06.021 |
| format | Article |
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Computed tomography images from patients with gastric cancer were collected retrospectively. Splenic image characteristics, extracted with pyradiomics, of patients in the training group were randomly divided. Characteristics with a P value < 0.1 were selected for lasso regression to construct a survival risk model. Models for high-and low-risk groups were established. Patients were divided into the high- and low-risk groups for univariate and multivariate regression analysis of survival-related factors, and a visual prognostic prediction model was established.
The splenic characteristic prognostic model was consistent in the training and verification groups (p < 0.001 and p = 0.016, respectively). The two groups that displayed different splenic characteristics showed no statistical difference in other basic data except the tumour-node-metastasis (pTNM) stage (p = 0.007). Univariate and multivariate analysis of survival risk factors showed that splenic characteristics (p = 0.042), age (p < 0.001), tumor location (p = 0.002), and pTNM stage (p < 0.001) were independent risk factors for survival. The prognostic prediction model combined with splenic characteristics significantly improved the accuracy of prognosis, predicting one-and three-year survival rates.
Splenic features extracted from imaging technology can accurately predict the long-term survival of patients with gastric cancer. Splenic characteristic grouping can effectively improve the accuracy of survival prediction and gastric cancer prognosis.</description><identifier>ISSN: 0748-7983</identifier><identifier>EISSN: 1532-2157</identifier><identifier>DOI: 10.1016/j.ejso.2020.06.021</identifier><identifier>PMID: 32694053</identifier><language>eng</language><publisher>England: Elsevier Ltd</publisher><subject>Age Factors ; Aged ; Carcinoma - diagnostic imaging ; Carcinoma - pathology ; Carcinoma - surgery ; Computational Biology ; Female ; Gastrectomy ; Gastric cancer ; Humans ; Male ; Middle Aged ; Multivariate Analysis ; Neoplasm Staging ; Prognosis ; Pyloric Antrum - pathology ; Radiomics ; Spleen ; Spleen - diagnostic imaging ; Stomach Neoplasms - diagnostic imaging ; Stomach Neoplasms - pathology ; Stomach Neoplasms - surgery ; Survival ; Survival Rate ; Tomography, X-Ray Computed</subject><ispartof>European journal of surgical oncology, 2020-10, Vol.46 (10), p.1932-1940</ispartof><rights>2020 Elsevier Ltd, BASO ~ The Association for Cancer Surgery, and the European Society of Surgical Oncology</rights><rights>Copyright © 2020 Elsevier Ltd, BASO ~ The Association for Cancer Surgery, and the European Society of Surgical Oncology. All rights reserved.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c356t-759c5fd59b4a49034711b0324527172045ce61fefaf1d820235ad439196e54da3</citedby><cites>FETCH-LOGICAL-c356t-759c5fd59b4a49034711b0324527172045ce61fefaf1d820235ad439196e54da3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://dx.doi.org/10.1016/j.ejso.2020.06.021$$EHTML$$P50$$Gelsevier$$H</linktohtml><link.rule.ids>314,776,780,3536,27903,27904,45974</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/32694053$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Wang, Xiang</creatorcontrib><creatorcontrib>Sun, Jing</creatorcontrib><creatorcontrib>Zhang, Weiteng</creatorcontrib><creatorcontrib>Yang, Xinxin</creatorcontrib><creatorcontrib>Zhu, Ce</creatorcontrib><creatorcontrib>Pan, Bujian</creatorcontrib><creatorcontrib>Zeng, Yunpeng</creatorcontrib><creatorcontrib>Xu, Jingxuan</creatorcontrib><creatorcontrib>Chen, Xiaodong</creatorcontrib><creatorcontrib>Shen, Xian</creatorcontrib><title>Use of radiomics to extract splenic features to predict prognosis of patients with gastric cancer</title><title>European journal of surgical oncology</title><addtitle>Eur J Surg Oncol</addtitle><description>Radiomics allows for mining of imaging data to examine tissue characteristics non-invasively, which can be used to predict the prognosis of a patient. This study explored the use of imaging techniques to evaluate splenic tissue characteristics to predict the prognosis of patients with gastric cancer.
Computed tomography images from patients with gastric cancer were collected retrospectively. Splenic image characteristics, extracted with pyradiomics, of patients in the training group were randomly divided. Characteristics with a P value < 0.1 were selected for lasso regression to construct a survival risk model. Models for high-and low-risk groups were established. Patients were divided into the high- and low-risk groups for univariate and multivariate regression analysis of survival-related factors, and a visual prognostic prediction model was established.
The splenic characteristic prognostic model was consistent in the training and verification groups (p < 0.001 and p = 0.016, respectively). The two groups that displayed different splenic characteristics showed no statistical difference in other basic data except the tumour-node-metastasis (pTNM) stage (p = 0.007). Univariate and multivariate analysis of survival risk factors showed that splenic characteristics (p = 0.042), age (p < 0.001), tumor location (p = 0.002), and pTNM stage (p < 0.001) were independent risk factors for survival. The prognostic prediction model combined with splenic characteristics significantly improved the accuracy of prognosis, predicting one-and three-year survival rates.
Splenic features extracted from imaging technology can accurately predict the long-term survival of patients with gastric cancer. Splenic characteristic grouping can effectively improve the accuracy of survival prediction and gastric cancer prognosis.</description><subject>Age Factors</subject><subject>Aged</subject><subject>Carcinoma - diagnostic imaging</subject><subject>Carcinoma - pathology</subject><subject>Carcinoma - surgery</subject><subject>Computational Biology</subject><subject>Female</subject><subject>Gastrectomy</subject><subject>Gastric cancer</subject><subject>Humans</subject><subject>Male</subject><subject>Middle Aged</subject><subject>Multivariate Analysis</subject><subject>Neoplasm Staging</subject><subject>Prognosis</subject><subject>Pyloric Antrum - pathology</subject><subject>Radiomics</subject><subject>Spleen</subject><subject>Spleen - diagnostic imaging</subject><subject>Stomach Neoplasms - diagnostic imaging</subject><subject>Stomach Neoplasms - pathology</subject><subject>Stomach Neoplasms - surgery</subject><subject>Survival</subject><subject>Survival Rate</subject><subject>Tomography, X-Ray Computed</subject><issn>0748-7983</issn><issn>1532-2157</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNp9kEtPIzEQhC0EgmzgD3BYzZHLzPo5jiUuCPFYCYkLOVuOpyc4SsazboeFf49Dwh5XfehDV5W6PkIuGW0YZe2vVQMrjA2nnDa0bShnR2TClOA1Z0ofkwnVclZrMxNn5AfiilJqhDan5Ezw1kiqxIS4OUIV-yq5LsRN8FjlWMF7Ts7nCsc1DMFXPbi8TfB1GxN0odzGFJdDxIA79-hygCFj9Tfk12rpMKdi827wkM7JSe_WCBeHPSXz-7uX28f66fnh9-3NU-2FanOtlfGq75RZSCcNFVIztqCCS8U105xK5aFlPfSuZ92sdBbKdVIYZlpQsnNiSq72ueWzP1vAbDcBPazXboC4Rcslb9lM6DJTwvdSnyJigt6OKWxc-rCM2h1au7I7tHaH1tLWFrTF9POQv11soPtn-WZZBNd7AZSWbwGSRV-o-MIrgc-2i-F_-Z_M64qL</recordid><startdate>202010</startdate><enddate>202010</enddate><creator>Wang, Xiang</creator><creator>Sun, Jing</creator><creator>Zhang, Weiteng</creator><creator>Yang, Xinxin</creator><creator>Zhu, Ce</creator><creator>Pan, Bujian</creator><creator>Zeng, Yunpeng</creator><creator>Xu, Jingxuan</creator><creator>Chen, Xiaodong</creator><creator>Shen, Xian</creator><general>Elsevier Ltd</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7X8</scope></search><sort><creationdate>202010</creationdate><title>Use of radiomics to extract splenic features to predict prognosis of patients with gastric cancer</title><author>Wang, Xiang ; Sun, Jing ; Zhang, Weiteng ; Yang, Xinxin ; Zhu, Ce ; Pan, Bujian ; Zeng, Yunpeng ; Xu, Jingxuan ; Chen, Xiaodong ; Shen, Xian</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c356t-759c5fd59b4a49034711b0324527172045ce61fefaf1d820235ad439196e54da3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Age Factors</topic><topic>Aged</topic><topic>Carcinoma - diagnostic imaging</topic><topic>Carcinoma - pathology</topic><topic>Carcinoma - surgery</topic><topic>Computational Biology</topic><topic>Female</topic><topic>Gastrectomy</topic><topic>Gastric cancer</topic><topic>Humans</topic><topic>Male</topic><topic>Middle Aged</topic><topic>Multivariate Analysis</topic><topic>Neoplasm Staging</topic><topic>Prognosis</topic><topic>Pyloric Antrum - pathology</topic><topic>Radiomics</topic><topic>Spleen</topic><topic>Spleen - diagnostic imaging</topic><topic>Stomach Neoplasms - diagnostic imaging</topic><topic>Stomach Neoplasms - pathology</topic><topic>Stomach Neoplasms - surgery</topic><topic>Survival</topic><topic>Survival Rate</topic><topic>Tomography, X-Ray Computed</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Wang, Xiang</creatorcontrib><creatorcontrib>Sun, Jing</creatorcontrib><creatorcontrib>Zhang, Weiteng</creatorcontrib><creatorcontrib>Yang, Xinxin</creatorcontrib><creatorcontrib>Zhu, Ce</creatorcontrib><creatorcontrib>Pan, Bujian</creatorcontrib><creatorcontrib>Zeng, Yunpeng</creatorcontrib><creatorcontrib>Xu, Jingxuan</creatorcontrib><creatorcontrib>Chen, Xiaodong</creatorcontrib><creatorcontrib>Shen, Xian</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>MEDLINE - Academic</collection><jtitle>European journal of surgical oncology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Wang, Xiang</au><au>Sun, Jing</au><au>Zhang, Weiteng</au><au>Yang, Xinxin</au><au>Zhu, Ce</au><au>Pan, Bujian</au><au>Zeng, Yunpeng</au><au>Xu, Jingxuan</au><au>Chen, Xiaodong</au><au>Shen, Xian</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Use of radiomics to extract splenic features to predict prognosis of patients with gastric cancer</atitle><jtitle>European journal of surgical oncology</jtitle><addtitle>Eur J Surg Oncol</addtitle><date>2020-10</date><risdate>2020</risdate><volume>46</volume><issue>10</issue><spage>1932</spage><epage>1940</epage><pages>1932-1940</pages><issn>0748-7983</issn><eissn>1532-2157</eissn><abstract>Radiomics allows for mining of imaging data to examine tissue characteristics non-invasively, which can be used to predict the prognosis of a patient. This study explored the use of imaging techniques to evaluate splenic tissue characteristics to predict the prognosis of patients with gastric cancer.
Computed tomography images from patients with gastric cancer were collected retrospectively. Splenic image characteristics, extracted with pyradiomics, of patients in the training group were randomly divided. Characteristics with a P value < 0.1 were selected for lasso regression to construct a survival risk model. Models for high-and low-risk groups were established. Patients were divided into the high- and low-risk groups for univariate and multivariate regression analysis of survival-related factors, and a visual prognostic prediction model was established.
The splenic characteristic prognostic model was consistent in the training and verification groups (p < 0.001 and p = 0.016, respectively). The two groups that displayed different splenic characteristics showed no statistical difference in other basic data except the tumour-node-metastasis (pTNM) stage (p = 0.007). Univariate and multivariate analysis of survival risk factors showed that splenic characteristics (p = 0.042), age (p < 0.001), tumor location (p = 0.002), and pTNM stage (p < 0.001) were independent risk factors for survival. The prognostic prediction model combined with splenic characteristics significantly improved the accuracy of prognosis, predicting one-and three-year survival rates.
Splenic features extracted from imaging technology can accurately predict the long-term survival of patients with gastric cancer. Splenic characteristic grouping can effectively improve the accuracy of survival prediction and gastric cancer prognosis.</abstract><cop>England</cop><pub>Elsevier Ltd</pub><pmid>32694053</pmid><doi>10.1016/j.ejso.2020.06.021</doi><tpages>9</tpages></addata></record> |
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| subjects | Age Factors Aged Carcinoma - diagnostic imaging Carcinoma - pathology Carcinoma - surgery Computational Biology Female Gastrectomy Gastric cancer Humans Male Middle Aged Multivariate Analysis Neoplasm Staging Prognosis Pyloric Antrum - pathology Radiomics Spleen Spleen - diagnostic imaging Stomach Neoplasms - diagnostic imaging Stomach Neoplasms - pathology Stomach Neoplasms - surgery Survival Survival Rate Tomography, X-Ray Computed |
| title | Use of radiomics to extract splenic features to predict prognosis of patients with gastric cancer |
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