A CT-based radiomics classification model for the prediction of histological type and tumour grade in retroperitoneal sarcoma (RADSARC-R): a retrospective multicohort analysis
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Author
Arthur, AmaniOrton, Matthew R
Emsley, Robby
Vit, Sharon
Kelly-Morland, Christian
Strauss, Dirk
Lunn, Jason
Doran, Simon
Lmalem, Hafida
Nzokirantevye, Axelle
Litiere, Saskia
Bonvalot, Sylvie
Haas, Rick
Gronchi, Alessandro
Van Gestel, Dirk
Ducassou, Anne
Raut, Chandrajit P
Meeus, Pierre
Spalek, Mateusz
Hatton, Matthew
Le Pechoux, Cecile
Thway, Khin
Fisher, Cyril
Jones, Robin
Huang, Paul H
Messiou, Christina
Publication date
2023-11-24Subject
Oncology. Pathology.
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Background: Retroperitoneal sarcomas are tumours with a poor prognosis. Upfront characterisation of the tumour is difficult, and under-grading is common. Radiomics has the potential to non-invasively characterise the so-called radiological phenotype of tumours. We aimed to develop and independently validate a CT-based radiomics classification model for the prediction of histological type and grade in retroperitoneal leiomyosarcoma and liposarcoma. Methods: A retrospective discovery cohort was collated at our centre (Royal Marsden Hospital, London, UK) and an independent validation cohort comprising patients recruited in the phase 3 STRASS study of neoadjuvant radiotherapy in retroperitoneal sarcoma. Patients aged older than 18 years with confirmed primary leiomyosarcoma or liposarcoma proceeding to surgical resection with available contrast-enhanced CT scans were included. Using the discovery dataset, a CT-based radiomics workflow was developed, including manual delineation, sub-segmentation, feature extraction, and predictive model building. Separate probabilistic classifiers for the prediction of histological type and low versus intermediate or high grade tumour types were built and tested. Independent validation was then performed. The primary objective of the study was to develop radiomic classification models for the prediction of retroperitoneal leiomyosarcoma and liposarcoma type and histological grade. Findings: 170 patients recruited between Oct 30, 2016, and Dec 23, 2020, were eligible in the discovery cohort and 89 patients recruited between Jan 18, 2012, and April 10, 2017, were eligible in the validation cohort. In the discovery cohort, the median age was 63 years (range 27-89), with 83 (49%) female and 87 (51%) male patients. In the validation cohort, median age was 59 years (range 33-77), with 46 (52%) female and 43 (48%) male patients. The highest performing model for the prediction of histological type had an area under the receiver operator curve (AUROC) of 0·928 on validation, based on a feature set of radiomics and approximate radiomic volume fraction. The highest performing model for the prediction of histological grade had an AUROC of 0·882 on validation, based on a radiomics feature set. Interpretation: Our validated radiomics model can predict the histological type and grade of retroperitoneal sarcomas with excellent performance. This could have important implications for improving diagnosis and risk stratification in retroperitoneal sarcomas. Funding: Wellcome Trust, European Organisation for Research and Treatment of Cancer-Soft Tissue and Bone Sarcoma Group, the National Institutes for Health, and the National Institute for Health and Care Research Biomedical Research Centre at The Royal Marsden NHS Foundation Trust and The Institute of Cancer Research.Citation
Arthur, A., Orton, M. R., Emsley, R., Vit, S., Kelly-Morland, C., Strauss, D., Lunn, J., Doran, S., Lmalem, H., Nzokirantevye, A., Litiere, S., Bonvalot, S., Haas, R., Gronchi, A., Van Gestel, D., Ducassou, A., Raut, C. P., Meeus, P., Spalek, M., Hatton, M., … Messiou, C. (2023). A CT-based radiomics classification model for the prediction of histological type and tumour grade in retroperitoneal sarcoma (RADSARC-R): a retrospective multicohort analysis. The Lancet. Oncology, 24(11), 1277–1286. https://doi.org/10.1016/S1470-2045(23)00462-XType
ArticleAdditional Links
http://www.sciencedirect.com/science/journal/14702045PMID
37922931Journal
The Lancet OncologyPublisher
Elsevierae974a485f413a2113503eed53cd6c53
10.1016/S1470-2045(23)00462-X