MRI-based delta-radiomics predicts pathologic complete response in high-grade soft-tissue sarcoma patients treated with neoadjuvant therapy
•Delta-radiomics predicts pathological complete response to neoadjuvant therapy.•Delta-radiomic models were superior to tumor volume and RECIST.•Delta-radiomic models predicted response to chemotherapy and radiotherapy.•Delta-radiomic models were superior to pre- or post-therapeutic radiomic models....
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| creator | Peeken, Jan C. Asadpour, Rebecca Specht, Katja Chen, Eleanor Y. Klymenko, Olena Akinkuoroye, Victor Hippe, Daniel S. Spraker, Matthew B Schaub, Stephanie K. Dapper, Hendrik Knebel, Carolin Mayr, Nina A. Gersing, Alexandra S. Woodruff, Henry C. Lambin, Philippe Nyflot, Matthew J. Combs, Stephanie E. |
| description | •Delta-radiomics predicts pathological complete response to neoadjuvant therapy.•Delta-radiomic models were superior to tumor volume and RECIST.•Delta-radiomic models predicted response to chemotherapy and radiotherapy.•Delta-radiomic models were superior to pre- or post-therapeutic radiomic models.•Delta-radiomic predictors may be associated with overall survival.
In high-grade soft-tissue sarcomas (STS) the standard of care encompasses multimodal therapy regimens. While there is a growing body of evidence for prognostic pretreatment radiomic models, we hypothesized that temporal changes in radiomic features following neoadjuvant treatment (“delta-radiomics”) may be able to predict the pathological complete response (pCR).
MRI scans (T1-weighted with fat-saturation and contrast-enhancement (T1FSGd) and T2-weighted with fat-saturation (T2FS)) of patients with STS of the extremities and trunk treated with neoadjuvant therapy were gathered from two independent institutions (training: 103, external testing: 53 patients). pCR was defined as |
| doi_str_mv | 10.1016/j.radonc.2021.08.023 |
| format | Article |
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In high-grade soft-tissue sarcomas (STS) the standard of care encompasses multimodal therapy regimens. While there is a growing body of evidence for prognostic pretreatment radiomic models, we hypothesized that temporal changes in radiomic features following neoadjuvant treatment (“delta-radiomics”) may be able to predict the pathological complete response (pCR).
MRI scans (T1-weighted with fat-saturation and contrast-enhancement (T1FSGd) and T2-weighted with fat-saturation (T2FS)) of patients with STS of the extremities and trunk treated with neoadjuvant therapy were gathered from two independent institutions (training: 103, external testing: 53 patients). pCR was defined as <5% viable cells. After segmentation and preprocessing, 105 radiomic features were extracted. Delta-radiomic features were calculated by subtraction of features derived from MRI scans obtained before and after neoadjuvant therapy. After feature reduction, machine learning modeling was performed in 100 iterations of 3-fold nested cross-validation. Delta-radiomic models were compared with single timepoint models in the testing cohort.
The combined delta-radiomic models achieved the best area under the receiver operating characteristic curve (AUC) of 0.75. Pre-therapeutic tumor volume was the best conventional predictor (AUC 0.70). The T2FS-based delta-radiomic model had the most balanced classification performance with a balanced accuracy of 0.69. Delta-radiomic models achieved better reproducibility than single timepoint radiomic models, RECIST or the peri-therapeutic volume change. Delta-radiomic models were significantly associated with survival in multivariate Cox regression.
This exploratory analysis demonstrated that MRI-based delta-radiomics improves prediction of pCR over tumor volume and RECIST. Delta-radiomics may one day function as a biomarker for personalized treatment adaptations.</description><identifier>ISSN: 0167-8140</identifier><identifier>EISSN: 1879-0887</identifier><identifier>DOI: 10.1016/j.radonc.2021.08.023</identifier><identifier>PMID: 34506832</identifier><language>eng</language><publisher>Ireland: Elsevier B.V</publisher><subject>Delta radiomics ; Humans ; Machine Learning ; Magnetic Resonance Imaging ; MRI ; Neoadjuvant radiotherapy ; Neoadjuvant Therapy ; Reproducibility of Results ; Response prediction ; Retrospective Studies ; Sarcoma - diagnostic imaging ; Sarcoma - therapy ; Soft-tissue sarcoma</subject><ispartof>Radiotherapy and oncology, 2021-11, Vol.164, p.73-82</ispartof><rights>2021 Elsevier B.V.</rights><rights>Copyright © 2021 Elsevier B.V. All rights reserved.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c408t-492add125c43510064be8b731f9c71298eada390ea4d0a823555b7288f822e503</citedby><cites>FETCH-LOGICAL-c408t-492add125c43510064be8b731f9c71298eada390ea4d0a823555b7288f822e503</cites><orcidid>0000-0003-2427-4404 ; 0000-0001-7911-5123 ; 0000-0003-2679-9853 ; 0000-0002-5148-379X ; 0000-0001-7961-0191</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://www.sciencedirect.com/science/article/pii/S0167814021067177$$EHTML$$P50$$Gelsevier$$H</linktohtml><link.rule.ids>314,776,780,3537,27901,27902,65306</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/34506832$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Peeken, Jan C.</creatorcontrib><creatorcontrib>Asadpour, Rebecca</creatorcontrib><creatorcontrib>Specht, Katja</creatorcontrib><creatorcontrib>Chen, Eleanor Y.</creatorcontrib><creatorcontrib>Klymenko, Olena</creatorcontrib><creatorcontrib>Akinkuoroye, Victor</creatorcontrib><creatorcontrib>Hippe, Daniel S.</creatorcontrib><creatorcontrib>Spraker, Matthew B</creatorcontrib><creatorcontrib>Schaub, Stephanie K.</creatorcontrib><creatorcontrib>Dapper, Hendrik</creatorcontrib><creatorcontrib>Knebel, Carolin</creatorcontrib><creatorcontrib>Mayr, Nina A.</creatorcontrib><creatorcontrib>Gersing, Alexandra S.</creatorcontrib><creatorcontrib>Woodruff, Henry C.</creatorcontrib><creatorcontrib>Lambin, Philippe</creatorcontrib><creatorcontrib>Nyflot, Matthew J.</creatorcontrib><creatorcontrib>Combs, Stephanie E.</creatorcontrib><title>MRI-based delta-radiomics predicts pathologic complete response in high-grade soft-tissue sarcoma patients treated with neoadjuvant therapy</title><title>Radiotherapy and oncology</title><addtitle>Radiother Oncol</addtitle><description>•Delta-radiomics predicts pathological complete response to neoadjuvant therapy.•Delta-radiomic models were superior to tumor volume and RECIST.•Delta-radiomic models predicted response to chemotherapy and radiotherapy.•Delta-radiomic models were superior to pre- or post-therapeutic radiomic models.•Delta-radiomic predictors may be associated with overall survival.
In high-grade soft-tissue sarcomas (STS) the standard of care encompasses multimodal therapy regimens. While there is a growing body of evidence for prognostic pretreatment radiomic models, we hypothesized that temporal changes in radiomic features following neoadjuvant treatment (“delta-radiomics”) may be able to predict the pathological complete response (pCR).
MRI scans (T1-weighted with fat-saturation and contrast-enhancement (T1FSGd) and T2-weighted with fat-saturation (T2FS)) of patients with STS of the extremities and trunk treated with neoadjuvant therapy were gathered from two independent institutions (training: 103, external testing: 53 patients). pCR was defined as <5% viable cells. After segmentation and preprocessing, 105 radiomic features were extracted. Delta-radiomic features were calculated by subtraction of features derived from MRI scans obtained before and after neoadjuvant therapy. After feature reduction, machine learning modeling was performed in 100 iterations of 3-fold nested cross-validation. Delta-radiomic models were compared with single timepoint models in the testing cohort.
The combined delta-radiomic models achieved the best area under the receiver operating characteristic curve (AUC) of 0.75. Pre-therapeutic tumor volume was the best conventional predictor (AUC 0.70). The T2FS-based delta-radiomic model had the most balanced classification performance with a balanced accuracy of 0.69. Delta-radiomic models achieved better reproducibility than single timepoint radiomic models, RECIST or the peri-therapeutic volume change. Delta-radiomic models were significantly associated with survival in multivariate Cox regression.
This exploratory analysis demonstrated that MRI-based delta-radiomics improves prediction of pCR over tumor volume and RECIST. Delta-radiomics may one day function as a biomarker for personalized treatment adaptations.</description><subject>Delta radiomics</subject><subject>Humans</subject><subject>Machine Learning</subject><subject>Magnetic Resonance Imaging</subject><subject>MRI</subject><subject>Neoadjuvant radiotherapy</subject><subject>Neoadjuvant Therapy</subject><subject>Reproducibility of Results</subject><subject>Response prediction</subject><subject>Retrospective Studies</subject><subject>Sarcoma - diagnostic imaging</subject><subject>Sarcoma - therapy</subject><subject>Soft-tissue sarcoma</subject><issn>0167-8140</issn><issn>1879-0887</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNp9kUFv1DAQhS0EotvCP0AoRy4OYztZOxckVBWoVISE4Gw59mTjVRIH2ynqb-BP49W2HDl5Dt9743mPkDcMagZs__5YR-PCYmsOnNWgauDiGdkxJTsKSsnnZFcwSRVr4IJcpnQEAA5CviQXomlhrwTfkT9fv9_S3iR0lcMpG1pMfZi9TdUa0Xmby2DyGKZw8LayYV4nzFhFTGtYElZ-qUZ_GOmhCLFKYcg0-5S2MptYcHOSe1yKT45ocln02-exWjAYd9zuzZKrPGI068Mr8mIwU8LXj-8V-fnp5sf1F3r37fPt9cc7ahtQmTYdN84x3tpGtAxg3_SoeinY0FnJeKfQOCM6QNM4MIqLtm17yZUaFOfYgrgi786-awy_NkxZzz5ZnCZTPrUlzVvJOib3ihW0OaM2hpQiDnqNfjbxQTPQpxr0UZ9r0KcaNChdaiiyt48btn5G90_0lHsBPpwBLHfee4w62RKSLYlHtFm74P-_4S-7z51k</recordid><startdate>202111</startdate><enddate>202111</enddate><creator>Peeken, Jan C.</creator><creator>Asadpour, Rebecca</creator><creator>Specht, Katja</creator><creator>Chen, Eleanor Y.</creator><creator>Klymenko, Olena</creator><creator>Akinkuoroye, Victor</creator><creator>Hippe, Daniel S.</creator><creator>Spraker, Matthew B</creator><creator>Schaub, Stephanie K.</creator><creator>Dapper, Hendrik</creator><creator>Knebel, Carolin</creator><creator>Mayr, Nina A.</creator><creator>Gersing, Alexandra S.</creator><creator>Woodruff, Henry C.</creator><creator>Lambin, Philippe</creator><creator>Nyflot, Matthew J.</creator><creator>Combs, Stephanie E.</creator><general>Elsevier 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>7X8</scope><orcidid>https://orcid.org/0000-0003-2427-4404</orcidid><orcidid>https://orcid.org/0000-0001-7911-5123</orcidid><orcidid>https://orcid.org/0000-0003-2679-9853</orcidid><orcidid>https://orcid.org/0000-0002-5148-379X</orcidid><orcidid>https://orcid.org/0000-0001-7961-0191</orcidid></search><sort><creationdate>202111</creationdate><title>MRI-based delta-radiomics predicts pathologic complete response in high-grade soft-tissue sarcoma patients treated with neoadjuvant therapy</title><author>Peeken, Jan C. ; 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In high-grade soft-tissue sarcomas (STS) the standard of care encompasses multimodal therapy regimens. While there is a growing body of evidence for prognostic pretreatment radiomic models, we hypothesized that temporal changes in radiomic features following neoadjuvant treatment (“delta-radiomics”) may be able to predict the pathological complete response (pCR).
MRI scans (T1-weighted with fat-saturation and contrast-enhancement (T1FSGd) and T2-weighted with fat-saturation (T2FS)) of patients with STS of the extremities and trunk treated with neoadjuvant therapy were gathered from two independent institutions (training: 103, external testing: 53 patients). pCR was defined as <5% viable cells. After segmentation and preprocessing, 105 radiomic features were extracted. Delta-radiomic features were calculated by subtraction of features derived from MRI scans obtained before and after neoadjuvant therapy. After feature reduction, machine learning modeling was performed in 100 iterations of 3-fold nested cross-validation. Delta-radiomic models were compared with single timepoint models in the testing cohort.
The combined delta-radiomic models achieved the best area under the receiver operating characteristic curve (AUC) of 0.75. Pre-therapeutic tumor volume was the best conventional predictor (AUC 0.70). The T2FS-based delta-radiomic model had the most balanced classification performance with a balanced accuracy of 0.69. Delta-radiomic models achieved better reproducibility than single timepoint radiomic models, RECIST or the peri-therapeutic volume change. Delta-radiomic models were significantly associated with survival in multivariate Cox regression.
This exploratory analysis demonstrated that MRI-based delta-radiomics improves prediction of pCR over tumor volume and RECIST. Delta-radiomics may one day function as a biomarker for personalized treatment adaptations.</abstract><cop>Ireland</cop><pub>Elsevier B.V</pub><pmid>34506832</pmid><doi>10.1016/j.radonc.2021.08.023</doi><tpages>10</tpages><orcidid>https://orcid.org/0000-0003-2427-4404</orcidid><orcidid>https://orcid.org/0000-0001-7911-5123</orcidid><orcidid>https://orcid.org/0000-0003-2679-9853</orcidid><orcidid>https://orcid.org/0000-0002-5148-379X</orcidid><orcidid>https://orcid.org/0000-0001-7961-0191</orcidid><oa>free_for_read</oa></addata></record> |
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| subjects | Delta radiomics Humans Machine Learning Magnetic Resonance Imaging MRI Neoadjuvant radiotherapy Neoadjuvant Therapy Reproducibility of Results Response prediction Retrospective Studies Sarcoma - diagnostic imaging Sarcoma - therapy Soft-tissue sarcoma |
| title | MRI-based delta-radiomics predicts pathologic complete response in high-grade soft-tissue sarcoma patients treated with neoadjuvant therapy |
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