Combined artificial intelligence and radiologist model for predicting rectal cancer treatment response from magnetic resonance imaging: an external validation study
Purpose To evaluate an MRI-based radiomic texture classifier alone and combined with radiologist qualitative assessment in predicting pathological complete response (pCR) using restaging MRI with internal training and external validation. Methods Consecutive patients with locally advanced rectal can...
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| Vydané v: | Abdominal imaging Ročník 47; číslo 8; s. 2770 - 2782 |
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| Hlavní autori: | , , , , , , , , , , , , , |
| Médium: | Journal Article |
| Jazyk: | English |
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New York
Springer US
01.08.2022
Springer Nature B.V |
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| ISSN: | 2366-0058, 2366-004X, 2366-0058 |
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| Abstract | Purpose
To evaluate an MRI-based radiomic texture classifier alone and combined with radiologist qualitative assessment in predicting pathological complete response (pCR) using restaging MRI with internal training and external validation.
Methods
Consecutive patients with locally advanced rectal cancer (LARC) who underwent neoadjuvant therapy followed by total mesorectal excision from March 2012 to February 2016 (Memorial Sloan Kettering Cancer Center/internal dataset,
n
= 114, 41% female, median age = 55) and July 2014 to October 2015 (Instituto do Câncer do Estado de São Paulo/external dataset,
n
= 50, 52% female, median age = 64.5) were retrospectively included. Two radiologists (R1, senior; R2, junior) independently evaluated restaging MRI, classifying patients (radiological complete response vs radiological partial response). Model A (
n
= 33 texture features), model B (
n
= 91 features including texture, shape, and edge features), and two combination models (model A + B + R1, model A + B + R2) were constructed. Pathology served as the reference standard for neoadjuvant treatment response. Comparison of the classifiers’ AUCs on the external set was done using DeLong’s test.
Results
Models A and B had similar discriminative ability (
P
= 0.3; Model B AUC = 83%, 95% CI 70%–97%). Combined models increased inter-reader agreement compared with radiologist-only interpretation (
κ
= 0.82, 95% CI 0.70–0.89 vs
k
= 0.25, 95% CI 0.11–0.61). The combined model slightly increased junior radiologist specificity, positive predictive value, and negative predictive values (93% vs 90%, 57% vs 50%, and 91% vs 90%, respectively).
Conclusion
We developed and externally validated a combined model using radiomics and radiologist qualitative assessment, which improved inter-reader agreement and slightly increased the diagnostic performance of the junior radiologist in predicting pCR after neoadjuvant treatment in patients with LARC.
Graphical abstract |
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| AbstractList | PurposeTo evaluate an MRI-based radiomic texture classifier alone and combined with radiologist qualitative assessment in predicting pathological complete response (pCR) using restaging MRI with internal training and external validation.MethodsConsecutive patients with locally advanced rectal cancer (LARC) who underwent neoadjuvant therapy followed by total mesorectal excision from March 2012 to February 2016 (Memorial Sloan Kettering Cancer Center/internal dataset, n = 114, 41% female, median age = 55) and July 2014 to October 2015 (Instituto do Câncer do Estado de São Paulo/external dataset, n = 50, 52% female, median age = 64.5) were retrospectively included. Two radiologists (R1, senior; R2, junior) independently evaluated restaging MRI, classifying patients (radiological complete response vs radiological partial response). Model A (n = 33 texture features), model B (n = 91 features including texture, shape, and edge features), and two combination models (model A + B + R1, model A + B + R2) were constructed. Pathology served as the reference standard for neoadjuvant treatment response. Comparison of the classifiers’ AUCs on the external set was done using DeLong’s test.ResultsModels A and B had similar discriminative ability (P = 0.3; Model B AUC = 83%, 95% CI 70%–97%). Combined models increased inter-reader agreement compared with radiologist-only interpretation (κ = 0.82, 95% CI 0.70–0.89 vs k = 0.25, 95% CI 0.11–0.61). The combined model slightly increased junior radiologist specificity, positive predictive value, and negative predictive values (93% vs 90%, 57% vs 50%, and 91% vs 90%, respectively).ConclusionWe developed and externally validated a combined model using radiomics and radiologist qualitative assessment, which improved inter-reader agreement and slightly increased the diagnostic performance of the junior radiologist in predicting pCR after neoadjuvant treatment in patients with LARC. To evaluate an MRI-based radiomic texture classifier alone and combined with radiologist qualitative assessment in predicting pathological complete response (pCR) using restaging MRI with internal training and external validation.PURPOSETo evaluate an MRI-based radiomic texture classifier alone and combined with radiologist qualitative assessment in predicting pathological complete response (pCR) using restaging MRI with internal training and external validation.Consecutive patients with locally advanced rectal cancer (LARC) who underwent neoadjuvant therapy followed by total mesorectal excision from March 2012 to February 2016 (Memorial Sloan Kettering Cancer Center/internal dataset, n = 114, 41% female, median age = 55) and July 2014 to October 2015 (Instituto do Câncer do Estado de São Paulo/external dataset, n = 50, 52% female, median age = 64.5) were retrospectively included. Two radiologists (R1, senior; R2, junior) independently evaluated restaging MRI, classifying patients (radiological complete response vs radiological partial response). Model A (n = 33 texture features), model B (n = 91 features including texture, shape, and edge features), and two combination models (model A + B + R1, model A + B + R2) were constructed. Pathology served as the reference standard for neoadjuvant treatment response. Comparison of the classifiers' AUCs on the external set was done using DeLong's test.METHODSConsecutive patients with locally advanced rectal cancer (LARC) who underwent neoadjuvant therapy followed by total mesorectal excision from March 2012 to February 2016 (Memorial Sloan Kettering Cancer Center/internal dataset, n = 114, 41% female, median age = 55) and July 2014 to October 2015 (Instituto do Câncer do Estado de São Paulo/external dataset, n = 50, 52% female, median age = 64.5) were retrospectively included. Two radiologists (R1, senior; R2, junior) independently evaluated restaging MRI, classifying patients (radiological complete response vs radiological partial response). Model A (n = 33 texture features), model B (n = 91 features including texture, shape, and edge features), and two combination models (model A + B + R1, model A + B + R2) were constructed. Pathology served as the reference standard for neoadjuvant treatment response. Comparison of the classifiers' AUCs on the external set was done using DeLong's test.Models A and B had similar discriminative ability (P = 0.3; Model B AUC = 83%, 95% CI 70%-97%). Combined models increased inter-reader agreement compared with radiologist-only interpretation (κ = 0.82, 95% CI 0.70-0.89 vs k = 0.25, 95% CI 0.11-0.61). The combined model slightly increased junior radiologist specificity, positive predictive value, and negative predictive values (93% vs 90%, 57% vs 50%, and 91% vs 90%, respectively).RESULTSModels A and B had similar discriminative ability (P = 0.3; Model B AUC = 83%, 95% CI 70%-97%). Combined models increased inter-reader agreement compared with radiologist-only interpretation (κ = 0.82, 95% CI 0.70-0.89 vs k = 0.25, 95% CI 0.11-0.61). The combined model slightly increased junior radiologist specificity, positive predictive value, and negative predictive values (93% vs 90%, 57% vs 50%, and 91% vs 90%, respectively).We developed and externally validated a combined model using radiomics and radiologist qualitative assessment, which improved inter-reader agreement and slightly increased the diagnostic performance of the junior radiologist in predicting pCR after neoadjuvant treatment in patients with LARC.CONCLUSIONWe developed and externally validated a combined model using radiomics and radiologist qualitative assessment, which improved inter-reader agreement and slightly increased the diagnostic performance of the junior radiologist in predicting pCR after neoadjuvant treatment in patients with LARC. To evaluate an MRI-based radiomic texture classifier alone and combined with radiologist qualitative assessment in predicting pathological complete response (pCR) using restaging MRI with internal training and external validation. Consecutive patients with locally advanced rectal cancer (LARC) who underwent neoadjuvant therapy followed by total mesorectal excision from March 2012 to February 2016 (Memorial Sloan Kettering Cancer Center/internal dataset, n = 114, 41% female, median age = 55) and July 2014 to October 2015 (Instituto do Câncer do Estado de São Paulo/external dataset, n = 50, 52% female, median age = 64.5) were retrospectively included. Two radiologists (R1, senior; R2, junior) independently evaluated restaging MRI, classifying patients (radiological complete response vs radiological partial response). Model A (n = 33 texture features), model B (n = 91 features including texture, shape, and edge features), and two combination models (model A + B + R1, model A + B + R2) were constructed. Pathology served as the reference standard for neoadjuvant treatment response. Comparison of the classifiers' AUCs on the external set was done using DeLong's test. Models A and B had similar discriminative ability (P = 0.3; Model B AUC = 83%, 95% CI 70%-97%). Combined models increased inter-reader agreement compared with radiologist-only interpretation (κ = 0.82, 95% CI 0.70-0.89 vs k = 0.25, 95% CI 0.11-0.61). The combined model slightly increased junior radiologist specificity, positive predictive value, and negative predictive values (93% vs 90%, 57% vs 50%, and 91% vs 90%, respectively). We developed and externally validated a combined model using radiomics and radiologist qualitative assessment, which improved inter-reader agreement and slightly increased the diagnostic performance of the junior radiologist in predicting pCR after neoadjuvant treatment in patients with LARC. Purpose To evaluate an MRI-based radiomic texture classifier alone and combined with radiologist qualitative assessment in predicting pathological complete response (pCR) using restaging MRI with internal training and external validation. Methods Consecutive patients with locally advanced rectal cancer (LARC) who underwent neoadjuvant therapy followed by total mesorectal excision from March 2012 to February 2016 (Memorial Sloan Kettering Cancer Center/internal dataset, n = 114, 41% female, median age = 55) and July 2014 to October 2015 (Instituto do Câncer do Estado de São Paulo/external dataset, n = 50, 52% female, median age = 64.5) were retrospectively included. Two radiologists (R1, senior; R2, junior) independently evaluated restaging MRI, classifying patients (radiological complete response vs radiological partial response). Model A ( n = 33 texture features), model B ( n = 91 features including texture, shape, and edge features), and two combination models (model A + B + R1, model A + B + R2) were constructed. Pathology served as the reference standard for neoadjuvant treatment response. Comparison of the classifiers’ AUCs on the external set was done using DeLong’s test. Results Models A and B had similar discriminative ability ( P = 0.3; Model B AUC = 83%, 95% CI 70%–97%). Combined models increased inter-reader agreement compared with radiologist-only interpretation ( κ = 0.82, 95% CI 0.70–0.89 vs k = 0.25, 95% CI 0.11–0.61). The combined model slightly increased junior radiologist specificity, positive predictive value, and negative predictive values (93% vs 90%, 57% vs 50%, and 91% vs 90%, respectively). Conclusion We developed and externally validated a combined model using radiomics and radiologist qualitative assessment, which improved inter-reader agreement and slightly increased the diagnostic performance of the junior radiologist in predicting pCR after neoadjuvant treatment in patients with LARC. Graphical abstract |
| Author | Capanu, Marinela Nahas, Caio S. R. Ferreira, Felipe R. Veeraraghavan, Harini Horvat, Natally Nahas, Sergio C. Sosa, Ramon E. Fuqua, James L. Zheng, Junting Bates, David D. B. Jayaprakasam, Vetri Sudar Fernandes, Maria Clara Petkovska, Iva Cerri, Giovanni G. |
| AuthorAffiliation | d Department of Surgery, University of Sao Paulo, Sao Paulo, SP, Brazil b Department of Radiology, University of Sao Paulo, Sao Paulo, SP, Brazil a Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA e Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA c Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, USA |
| AuthorAffiliation_xml | – name: b Department of Radiology, University of Sao Paulo, Sao Paulo, SP, Brazil – name: c Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, USA – name: e Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA – name: d Department of Surgery, University of Sao Paulo, Sao Paulo, SP, Brazil – name: a Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA |
| Author_xml | – sequence: 1 givenname: Natally surname: Horvat fullname: Horvat, Natally organization: Department of Radiology, Memorial Sloan Kettering Cancer Center, Department of Radiology, University of Sao Paulo – sequence: 2 givenname: Harini surname: Veeraraghavan fullname: Veeraraghavan, Harini organization: Department of Medical Physics, Memorial Sloan Kettering Cancer Center – sequence: 3 givenname: Caio S. R. surname: Nahas fullname: Nahas, Caio S. R. organization: Department of Surgery, University of Sao Paulo – sequence: 4 givenname: David D. B. surname: Bates fullname: Bates, David D. B. organization: Department of Radiology, Memorial Sloan Kettering Cancer Center – sequence: 5 givenname: Felipe R. surname: Ferreira fullname: Ferreira, Felipe R. organization: Department of Radiology, University of Sao Paulo – sequence: 6 givenname: Junting surname: Zheng fullname: Zheng, Junting organization: Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center – sequence: 7 givenname: Marinela surname: Capanu fullname: Capanu, Marinela organization: Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center – sequence: 8 givenname: James L. surname: Fuqua fullname: Fuqua, James L. organization: Department of Radiology, Memorial Sloan Kettering Cancer Center – sequence: 9 givenname: Maria Clara surname: Fernandes fullname: Fernandes, Maria Clara organization: Department of Radiology, Memorial Sloan Kettering Cancer Center – sequence: 10 givenname: Ramon E. surname: Sosa fullname: Sosa, Ramon E. organization: Department of Radiology, Memorial Sloan Kettering Cancer Center – sequence: 11 givenname: Vetri Sudar surname: Jayaprakasam fullname: Jayaprakasam, Vetri Sudar organization: Department of Radiology, Memorial Sloan Kettering Cancer Center – sequence: 12 givenname: Giovanni G. surname: Cerri fullname: Cerri, Giovanni G. organization: Department of Radiology, University of Sao Paulo – sequence: 13 givenname: Sergio C. surname: Nahas fullname: Nahas, Sergio C. organization: Department of Surgery, University of Sao Paulo – sequence: 14 givenname: Iva orcidid: 0000-0002-4373-6494 surname: Petkovska fullname: Petkovska, Iva email: petkovsi@mskcc.org organization: Department of Radiology, Memorial Sloan Kettering Cancer Center |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/35710951$$D View this record in MEDLINE/PubMed |
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| ContentType | Journal Article |
| Copyright | The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 2022. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022. |
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| Keywords | Neoadjuvant therapy Watchful waiting Rectal cancer Magnetic resonance imaging Artificial intelligence |
| Language | English |
| License | 2022. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature. |
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| MergedId | FETCHMERGED-LOGICAL-c475t-64e91841bdc0d3312b0281329eb6adaf79a4ff92b72b4bf092cfe280244982b23 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 Author contributions: Natally Horvat: Conceptualization; Data curation; Formal analysis; Investigation; Methodology; Project administration; Resources; Software; Supervision; Validation; Visualization; Roles/Writing - original draft; Writing - review & editing. Harini Veeraraghavan: Conceptualization; Data curation; Formal analysis; Investigation; Methodology; Project administration; Resources; Software; Supervision; Validation; Visualization; Roles/Writing - original draft; Writing - review & editing. Caio S.R. Nahas: Data curation; Writing – review & editing. David D.B. Bates: Formal analysis; Writing – review & editing. Felipe R. Ferreira: Data curation; Formal analysis; Writing – review & editing. James L. Fuqua III: Formal analysis; Writing – review & editing. Maria Clara Fernandes: Formal analysis; Writing – review & editing. Ramon E. Sosa: Data curation; Writing – review & editing. Vetri Sudar Jayaprakasam: Formal analysis; Writing – review & editing. Giovanni G. Cerri: Project administration; Supervision; Writing – review & editing. Sergio C. Nahas: Data curation; Project administration; Supervision; Writing – review & editing. Iva Petkovska: Conceptualization; Data curation; Formal analysis; Investigation; Methodology; Project administration; Resources; Software; Supervision; Validation; Visualization; Roles/Writing - original draft; Writing - review & editing. Natally Horvat and Harini Veeraraghavan contributed equally to this study. |
| ORCID | 0000-0002-4373-6494 |
| OpenAccessLink | https://pmc.ncbi.nlm.nih.gov/articles/PMC10150388/pdf/nihms-1889612.pdf |
| PMID | 35710951 |
| PQID | 2691898695 |
| PQPubID | 31175 |
| PageCount | 13 |
| ParticipantIDs | pubmedcentral_primary_oai_pubmedcentral_nih_gov_10150388 proquest_miscellaneous_2678425745 proquest_journals_2691898695 pubmed_primary_35710951 crossref_primary_10_1007_s00261_022_03572_8 crossref_citationtrail_10_1007_s00261_022_03572_8 springer_journals_10_1007_s00261_022_03572_8 |
| PublicationCentury | 2000 |
| PublicationDate | 2022-08-01 |
| PublicationDateYYYYMMDD | 2022-08-01 |
| PublicationDate_xml | – month: 08 year: 2022 text: 2022-08-01 day: 01 |
| PublicationDecade | 2020 |
| PublicationPlace | New York |
| PublicationPlace_xml | – name: New York – name: United States |
| PublicationTitle | Abdominal imaging |
| PublicationTitleAbbrev | Abdom Radiol |
| PublicationTitleAlternate | Abdom Radiol (NY) |
| PublicationYear | 2022 |
| Publisher | Springer US Springer Nature B.V |
| Publisher_xml | – name: Springer US – name: Springer Nature B.V |
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To evaluate an MRI-based radiomic texture classifier alone and combined with radiologist qualitative assessment in predicting pathological complete... To evaluate an MRI-based radiomic texture classifier alone and combined with radiologist qualitative assessment in predicting pathological complete response... PurposeTo evaluate an MRI-based radiomic texture classifier alone and combined with radiologist qualitative assessment in predicting pathological complete... |
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| Title | Combined artificial intelligence and radiologist model for predicting rectal cancer treatment response from magnetic resonance imaging: an external validation study |
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