Contrast-enhanced computed tomography radiomics in predicting primary site response to neoadjuvant chemotherapy in high-risk neuroblastoma
Purpose To explore the clinical value of contrast-enhanced computed tomography (CECT) radiomics in predicting primary site response to neoadjuvant chemotherapy in high-risk neuroblastoma. Materials and methods Seventy patients were retrospectively included and separated into very good partial respon...
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| Veröffentlicht in: | Abdominal imaging 2023-03, Vol.48 (3), p.976-986 |
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| creator | Wang, Haoru Qin, Jinjie Chen, Xin Zhang, Ting Zhang, Li Ding, Hao Pan, Zhengxia He, Ling |
| description | Purpose
To explore the clinical value of contrast-enhanced computed tomography (CECT) radiomics in predicting primary site response to neoadjuvant chemotherapy in high-risk neuroblastoma.
Materials and methods
Seventy patients were retrospectively included and separated into very good partial response (VGPR) group and non-VGPR group according to the changes in primary tumor volume. The clinical features with statistical difference between the two groups were used to construct the clinical models using a logistic regression (LR) algorithm. The radiomics models based on different radiomics features selected by Kruskal–Wallis (KW) test and recursive feature elimination (RFE) were established using support vector machine (SVM) and LR algorithms. The radiomics score (Radscore) and clinical features were integrated into the combined models. Leave-one-out cross-validation (LOOCV) was used to validate the predictive performance of models in the entire dataset.
Results
The optimal clinical model achieved an area under the curve (AUC) of 0.767 [95% confidence interval (CI): 0.638, 0.896] and an accuracy of 0.771 after LOOCV. The AUCs of the best KW + SVM, KW + LR, RFE + SVM, and RFE + LR radiomics models were 0.816, 0.826, 0.853, and 0.850, respectively, and the corresponding AUCs after LOOCV were 0.780, 0.785, 0.755, and 0.772, respectively. The AUC and accuracy after LOOCV of the optimal combined model was 0.804 (95% CI: 0.694, 0.915) and 0.814, respectively. The Delong test showed a statistical difference in predictive performance between the optimal clinical and combined models after LOOCV (
Z
= 2.003,
P
= 0.045). The decision curve analysis showed that the combined model performs better than the clinical model.
Conclusion
The CECT radiomics models have a favorable predictive performance in predicting VGPR of high-risk neuroblastoma to neoadjuvant chemotherapy. When integrating radiomics features and clinical features, the predictive performance of the combined models can be further improved.
Graphical abstract |
| doi_str_mv | 10.1007/s00261-022-03774-0 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_miscellaneous_2758354979</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2778132604</sourcerecordid><originalsourceid>FETCH-LOGICAL-c375t-f484f7c5fb2788ab547e22924f9dda33f36d967191c12134222bc098ca4e2ebf3</originalsourceid><addsrcrecordid>eNp9kc1u1DAUhS0EotXQF2BRRWLTjYv_nSzRiP5IldjAOnKcm4mHiZ3aDtK8Qp8aDzOlqAs29pH8nXOvfBD6SMk1JUR_ToQwRTFhDBOutcDkDTpnXClMiKzf_qPP0EVKW0IIVZJSJt-jM66kpoo05-hpHXyOJmUMfjTeQl_ZMM1LLiKHKWyimcd9FU3vwuRsqpyv5gi9s9n5TZFuMnFfJZehipDm4BMUY-UhmH67_DI-V3aEKeQRStT-4B_dZsTRpZ-FWmLodmV8mMwH9G4wuwQXp3uFftx8_b6-ww_fbu_XXx6w5VpmPIhaDNrKoWO6rk0nhQbGGiaGpu8N5wNXfaM0bailjHLBGOssaWprBDDoBr5CV8fcOYbHBVJuJ5cs7HamLL2klmlZcyka3RT00yt0G5boy3aF0jXlTBFRKHakbAwpRRja07e0lLSHstpjWW0pq_1TVjlX6PIUvXQT9H8tz9UUgB-BVJ78BuLL7P_E_gYNAaIv</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2778132604</pqid></control><display><type>article</type><title>Contrast-enhanced computed tomography radiomics in predicting primary site response to neoadjuvant chemotherapy in high-risk neuroblastoma</title><source>MEDLINE</source><source>Springer Journals</source><creator>Wang, Haoru ; Qin, Jinjie ; Chen, Xin ; Zhang, Ting ; Zhang, Li ; Ding, Hao ; Pan, Zhengxia ; He, Ling</creator><creatorcontrib>Wang, Haoru ; Qin, Jinjie ; Chen, Xin ; Zhang, Ting ; Zhang, Li ; Ding, Hao ; Pan, Zhengxia ; He, Ling</creatorcontrib><description>Purpose
To explore the clinical value of contrast-enhanced computed tomography (CECT) radiomics in predicting primary site response to neoadjuvant chemotherapy in high-risk neuroblastoma.
Materials and methods
Seventy patients were retrospectively included and separated into very good partial response (VGPR) group and non-VGPR group according to the changes in primary tumor volume. The clinical features with statistical difference between the two groups were used to construct the clinical models using a logistic regression (LR) algorithm. The radiomics models based on different radiomics features selected by Kruskal–Wallis (KW) test and recursive feature elimination (RFE) were established using support vector machine (SVM) and LR algorithms. The radiomics score (Radscore) and clinical features were integrated into the combined models. Leave-one-out cross-validation (LOOCV) was used to validate the predictive performance of models in the entire dataset.
Results
The optimal clinical model achieved an area under the curve (AUC) of 0.767 [95% confidence interval (CI): 0.638, 0.896] and an accuracy of 0.771 after LOOCV. The AUCs of the best KW + SVM, KW + LR, RFE + SVM, and RFE + LR radiomics models were 0.816, 0.826, 0.853, and 0.850, respectively, and the corresponding AUCs after LOOCV were 0.780, 0.785, 0.755, and 0.772, respectively. The AUC and accuracy after LOOCV of the optimal combined model was 0.804 (95% CI: 0.694, 0.915) and 0.814, respectively. The Delong test showed a statistical difference in predictive performance between the optimal clinical and combined models after LOOCV (
Z
= 2.003,
P
= 0.045). The decision curve analysis showed that the combined model performs better than the clinical model.
Conclusion
The CECT radiomics models have a favorable predictive performance in predicting VGPR of high-risk neuroblastoma to neoadjuvant chemotherapy. When integrating radiomics features and clinical features, the predictive performance of the combined models can be further improved.
Graphical abstract</description><identifier>ISSN: 2366-0058</identifier><identifier>ISSN: 2366-004X</identifier><identifier>EISSN: 2366-0058</identifier><identifier>DOI: 10.1007/s00261-022-03774-0</identifier><identifier>PMID: 36571609</identifier><language>eng</language><publisher>New York: Springer US</publisher><subject>Accuracy ; Algorithms ; Chemotherapy ; Computed tomography ; Confidence intervals ; Decision analysis ; Gastroenterology ; Hepatology ; Humans ; Imaging ; Medicine ; Medicine & Public Health ; Neoadjuvant Therapy ; Neuroblastoma ; Performance prediction ; Peritoneum ; Radiology ; Radiomics ; Regression analysis ; Retrospective Studies ; Risk ; Statistical analysis ; Statistics ; Support vector machines ; Tomography ; Tomography, X-Ray Computed</subject><ispartof>Abdominal imaging, 2023-03, Vol.48 (3), p.976-986</ispartof><rights>The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.</rights><rights>2022. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c375t-f484f7c5fb2788ab547e22924f9dda33f36d967191c12134222bc098ca4e2ebf3</citedby><cites>FETCH-LOGICAL-c375t-f484f7c5fb2788ab547e22924f9dda33f36d967191c12134222bc098ca4e2ebf3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s00261-022-03774-0$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s00261-022-03774-0$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,776,780,27901,27902,41464,42533,51294</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/36571609$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Wang, Haoru</creatorcontrib><creatorcontrib>Qin, Jinjie</creatorcontrib><creatorcontrib>Chen, Xin</creatorcontrib><creatorcontrib>Zhang, Ting</creatorcontrib><creatorcontrib>Zhang, Li</creatorcontrib><creatorcontrib>Ding, Hao</creatorcontrib><creatorcontrib>Pan, Zhengxia</creatorcontrib><creatorcontrib>He, Ling</creatorcontrib><title>Contrast-enhanced computed tomography radiomics in predicting primary site response to neoadjuvant chemotherapy in high-risk neuroblastoma</title><title>Abdominal imaging</title><addtitle>Abdom Radiol</addtitle><addtitle>Abdom Radiol (NY)</addtitle><description>Purpose
To explore the clinical value of contrast-enhanced computed tomography (CECT) radiomics in predicting primary site response to neoadjuvant chemotherapy in high-risk neuroblastoma.
Materials and methods
Seventy patients were retrospectively included and separated into very good partial response (VGPR) group and non-VGPR group according to the changes in primary tumor volume. The clinical features with statistical difference between the two groups were used to construct the clinical models using a logistic regression (LR) algorithm. The radiomics models based on different radiomics features selected by Kruskal–Wallis (KW) test and recursive feature elimination (RFE) were established using support vector machine (SVM) and LR algorithms. The radiomics score (Radscore) and clinical features were integrated into the combined models. Leave-one-out cross-validation (LOOCV) was used to validate the predictive performance of models in the entire dataset.
Results
The optimal clinical model achieved an area under the curve (AUC) of 0.767 [95% confidence interval (CI): 0.638, 0.896] and an accuracy of 0.771 after LOOCV. The AUCs of the best KW + SVM, KW + LR, RFE + SVM, and RFE + LR radiomics models were 0.816, 0.826, 0.853, and 0.850, respectively, and the corresponding AUCs after LOOCV were 0.780, 0.785, 0.755, and 0.772, respectively. The AUC and accuracy after LOOCV of the optimal combined model was 0.804 (95% CI: 0.694, 0.915) and 0.814, respectively. The Delong test showed a statistical difference in predictive performance between the optimal clinical and combined models after LOOCV (
Z
= 2.003,
P
= 0.045). The decision curve analysis showed that the combined model performs better than the clinical model.
Conclusion
The CECT radiomics models have a favorable predictive performance in predicting VGPR of high-risk neuroblastoma to neoadjuvant chemotherapy. When integrating radiomics features and clinical features, the predictive performance of the combined models can be further improved.
Graphical abstract</description><subject>Accuracy</subject><subject>Algorithms</subject><subject>Chemotherapy</subject><subject>Computed tomography</subject><subject>Confidence intervals</subject><subject>Decision analysis</subject><subject>Gastroenterology</subject><subject>Hepatology</subject><subject>Humans</subject><subject>Imaging</subject><subject>Medicine</subject><subject>Medicine & Public Health</subject><subject>Neoadjuvant Therapy</subject><subject>Neuroblastoma</subject><subject>Performance prediction</subject><subject>Peritoneum</subject><subject>Radiology</subject><subject>Radiomics</subject><subject>Regression analysis</subject><subject>Retrospective Studies</subject><subject>Risk</subject><subject>Statistical analysis</subject><subject>Statistics</subject><subject>Support vector machines</subject><subject>Tomography</subject><subject>Tomography, X-Ray Computed</subject><issn>2366-0058</issn><issn>2366-004X</issn><issn>2366-0058</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><sourceid>BENPR</sourceid><recordid>eNp9kc1u1DAUhS0EotXQF2BRRWLTjYv_nSzRiP5IldjAOnKcm4mHiZ3aDtK8Qp8aDzOlqAs29pH8nXOvfBD6SMk1JUR_ToQwRTFhDBOutcDkDTpnXClMiKzf_qPP0EVKW0IIVZJSJt-jM66kpoo05-hpHXyOJmUMfjTeQl_ZMM1LLiKHKWyimcd9FU3vwuRsqpyv5gi9s9n5TZFuMnFfJZehipDm4BMUY-UhmH67_DI-V3aEKeQRStT-4B_dZsTRpZ-FWmLodmV8mMwH9G4wuwQXp3uFftx8_b6-ww_fbu_XXx6w5VpmPIhaDNrKoWO6rk0nhQbGGiaGpu8N5wNXfaM0bailjHLBGOssaWprBDDoBr5CV8fcOYbHBVJuJ5cs7HamLL2klmlZcyka3RT00yt0G5boy3aF0jXlTBFRKHakbAwpRRja07e0lLSHstpjWW0pq_1TVjlX6PIUvXQT9H8tz9UUgB-BVJ78BuLL7P_E_gYNAaIv</recordid><startdate>20230301</startdate><enddate>20230301</enddate><creator>Wang, Haoru</creator><creator>Qin, Jinjie</creator><creator>Chen, Xin</creator><creator>Zhang, Ting</creator><creator>Zhang, Li</creator><creator>Ding, Hao</creator><creator>Pan, Zhengxia</creator><creator>He, Ling</creator><general>Springer US</general><general>Springer Nature B.V</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>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>PRINS</scope><scope>7X8</scope></search><sort><creationdate>20230301</creationdate><title>Contrast-enhanced computed tomography radiomics in predicting primary site response to neoadjuvant chemotherapy in high-risk neuroblastoma</title><author>Wang, Haoru ; Qin, Jinjie ; Chen, Xin ; Zhang, Ting ; Zhang, Li ; Ding, Hao ; Pan, Zhengxia ; He, Ling</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c375t-f484f7c5fb2788ab547e22924f9dda33f36d967191c12134222bc098ca4e2ebf3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Accuracy</topic><topic>Algorithms</topic><topic>Chemotherapy</topic><topic>Computed tomography</topic><topic>Confidence intervals</topic><topic>Decision analysis</topic><topic>Gastroenterology</topic><topic>Hepatology</topic><topic>Humans</topic><topic>Imaging</topic><topic>Medicine</topic><topic>Medicine & Public Health</topic><topic>Neoadjuvant Therapy</topic><topic>Neuroblastoma</topic><topic>Performance prediction</topic><topic>Peritoneum</topic><topic>Radiology</topic><topic>Radiomics</topic><topic>Regression analysis</topic><topic>Retrospective Studies</topic><topic>Risk</topic><topic>Statistical analysis</topic><topic>Statistics</topic><topic>Support vector machines</topic><topic>Tomography</topic><topic>Tomography, X-Ray Computed</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Wang, Haoru</creatorcontrib><creatorcontrib>Qin, Jinjie</creatorcontrib><creatorcontrib>Chen, Xin</creatorcontrib><creatorcontrib>Zhang, Ting</creatorcontrib><creatorcontrib>Zhang, Li</creatorcontrib><creatorcontrib>Ding, Hao</creatorcontrib><creatorcontrib>Pan, Zhengxia</creatorcontrib><creatorcontrib>He, Ling</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE 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Edition)</collection><collection>Biological Sciences</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>PML(ProQuest Medical Library)</collection><collection>Biological Science Database</collection><collection>Biochemistry Abstracts 1</collection><collection>Nursing & Allied Health Premium</collection><collection>ProQuest advanced technologies & aerospace journals</collection><collection>ProQuest Advanced Technologies & 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>ProQuest Central China</collection><collection>MEDLINE - Academic</collection><jtitle>Abdominal imaging</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Wang, Haoru</au><au>Qin, Jinjie</au><au>Chen, Xin</au><au>Zhang, Ting</au><au>Zhang, Li</au><au>Ding, Hao</au><au>Pan, Zhengxia</au><au>He, Ling</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Contrast-enhanced computed tomography radiomics in predicting primary site response to neoadjuvant chemotherapy in high-risk neuroblastoma</atitle><jtitle>Abdominal imaging</jtitle><stitle>Abdom Radiol</stitle><addtitle>Abdom Radiol (NY)</addtitle><date>2023-03-01</date><risdate>2023</risdate><volume>48</volume><issue>3</issue><spage>976</spage><epage>986</epage><pages>976-986</pages><issn>2366-0058</issn><issn>2366-004X</issn><eissn>2366-0058</eissn><abstract>Purpose
To explore the clinical value of contrast-enhanced computed tomography (CECT) radiomics in predicting primary site response to neoadjuvant chemotherapy in high-risk neuroblastoma.
Materials and methods
Seventy patients were retrospectively included and separated into very good partial response (VGPR) group and non-VGPR group according to the changes in primary tumor volume. The clinical features with statistical difference between the two groups were used to construct the clinical models using a logistic regression (LR) algorithm. The radiomics models based on different radiomics features selected by Kruskal–Wallis (KW) test and recursive feature elimination (RFE) were established using support vector machine (SVM) and LR algorithms. The radiomics score (Radscore) and clinical features were integrated into the combined models. Leave-one-out cross-validation (LOOCV) was used to validate the predictive performance of models in the entire dataset.
Results
The optimal clinical model achieved an area under the curve (AUC) of 0.767 [95% confidence interval (CI): 0.638, 0.896] and an accuracy of 0.771 after LOOCV. The AUCs of the best KW + SVM, KW + LR, RFE + SVM, and RFE + LR radiomics models were 0.816, 0.826, 0.853, and 0.850, respectively, and the corresponding AUCs after LOOCV were 0.780, 0.785, 0.755, and 0.772, respectively. The AUC and accuracy after LOOCV of the optimal combined model was 0.804 (95% CI: 0.694, 0.915) and 0.814, respectively. The Delong test showed a statistical difference in predictive performance between the optimal clinical and combined models after LOOCV (
Z
= 2.003,
P
= 0.045). The decision curve analysis showed that the combined model performs better than the clinical model.
Conclusion
The CECT radiomics models have a favorable predictive performance in predicting VGPR of high-risk neuroblastoma to neoadjuvant chemotherapy. When integrating radiomics features and clinical features, the predictive performance of the combined models can be further improved.
Graphical abstract</abstract><cop>New York</cop><pub>Springer US</pub><pmid>36571609</pmid><doi>10.1007/s00261-022-03774-0</doi><tpages>11</tpages></addata></record> |
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| subjects | Accuracy Algorithms Chemotherapy Computed tomography Confidence intervals Decision analysis Gastroenterology Hepatology Humans Imaging Medicine Medicine & Public Health Neoadjuvant Therapy Neuroblastoma Performance prediction Peritoneum Radiology Radiomics Regression analysis Retrospective Studies Risk Statistical analysis Statistics Support vector machines Tomography Tomography, X-Ray Computed |
| title | Contrast-enhanced computed tomography radiomics in predicting primary site response to neoadjuvant chemotherapy in high-risk neuroblastoma |
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