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dc.contributor.authorMbanu, Peter
dc.contributor.authorVasquez Osorio, Eliana
dc.contributor.authorMercer, Joe
dc.contributor.authorMalcomson, Lee
dc.contributor.authorKochhar, Rohit
dc.contributor.authorRenehan, Andrew G
dc.contributor.authorvan Herk, Marcel
dc.contributor.authorSaunders, Mark P
dc.contributor.authorMistry, Hitesh
dc.date.accessioned2022-01-11T11:59:38Z
dc.date.available2022-01-11T11:59:38Z
dc.date.issued2021en
dc.identifier.citationMbanu P, Osorio EV, Mistry H, Mercer J, Malcomson L, Kochhar R, et al. Prediction of clinical complete response in rectal cancer using clinical and radiomics features. Radiotherapy and Oncology. 2021;161:S73-S4.en
dc.identifier.urihttp://hdl.handle.net/10541/624834
dc.description.abstractPurpose or Objective In patients with rectal cancer, watch and wait for a clinical complete response (cCR) after neoadjuvant chemo-radiotherapy has the potential to avoid major surgery and stoma formation. But, other than tumour size, there are currently no predictors for cCR. To better optimise neoadjuvant strategies, we evaluated the predictive characteristics of clinical and radiomics variables. Materials and Methods From the OnCoRe (The Rectal Cancer Oncological Complete Response Database) database, we performed a matched case-control study in 304 patients (152 cCR; 152 non-cCR) deriving training (N=200), and validation (N=104) sets based on the patient’s date of diagnosis to mimic a prospective study. We collected pre-treatment demographic and routine laboratory parameters. We segmented the gross tumour volume on T2W pre-treatment MR Images, which were normalised using histogram-based normalisation. We extracted 1781 radiomics features per patient (1430 features accepted as stable features for analysis based on ICC >0.9). We used principal component analysis to cluster radiomics features. The ROC AUC was used to evaluate predictive power. Results A multivariable clinical model that included full blood count parameters, alkaline phosphatase, albumin, and tumour diameter had modest predictive characteristics. In the radiomics analysis, four clusters were identified using principal components analysis – predictive characteristics of these alone were modest. The addition of radiomics variables to clinical variables marginally improved prediction, and this improvement remained after validation. A significant drop in ROC AUC on the validation cohort with the models containing clinical variables is due to calibration drift, a known phenomenon with clinical variable over time. Patients with cCR were treated from 2008-2013 in the training cohort and 2013-2019 in the validation cohort.en
dc.language.isoenen
dc.titlePrediction of clinical complete response in rectal cancer using clinical and radiomics featuresen
dc.typeMeetings and Proceedingsen
dc.contributor.departmentChristie NHS Foundation Trust, Department of Clinical Oncology, Manchesteren
dc.identifier.journalRadiotherapy and Oncologyen
dc.description.noteen]


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