Do We Need Complex Image Features to Personalize Treatment of Patients with Locally Advanced Rectal Cancer?
Shahzadi I, Lattermann A, Linge A, Zwanenburg A, Baldus C, Peeken JC, Combs SE, Baumann M, Krause M, Troost EGC, Loeck S (2021)
Publication Type: Conference contribution
Publication year: 2021
Journal
Publisher: Springer Science and Business Media Deutschland GmbH
Book Volume: 12907 LNCS
Pages Range: 775-785
Conference Proceedings Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Event location: Virtual, Online
ISBN: 9783030872335
DOI: 10.1007/978-3-030-87234-2_73
Abstract
Radiomics has shown great potential for outcome prognosis and presents a promising approach for improving personalized cancer treatment. In radiomic analyses, features of different complexity are extracted from clinical imaging datasets, which are correlated to the endpoints of interest using machine-learning approaches. However, it is generally unclear if more complex features have a higher prognostic value and show a robust performance in external validation. Therefore, in this study, we developed and validated radiomic signatures for outcome prognosis after neoadjuvant radiochemotherapy in locally advanced rectal cancer (LARC) using computed tomography (CT) and T2-weighted magnetic resonance imaging (MRI) of two independent institutions (training/validation: 94/28 patients). For the prognosis of tumor response and freedom from distant metastases (FFDM), we used different imaging features extracted from the gross tumor volume: less complex morphological and first-order (MFO) features, more complex second-order texture (SOT) features, and both feature classes combined. Analyses were performed for both imaging modalities separately and combined. Performance was assessed by the area under the curve (AUC) and the concordance index (CI) for tumor response and FFDM, respectively. Overall, radiomic features showed prognostic value for both endpoints. Combining MFO and SOT features led to equal or higher performance in external validation compared to MFO and SOT features alone. The best results were observed after combining MRI and CT features (AUC = 0.76, CI = 0.65). In conclusion, promising biomarker signatures combining MRI and CT were developed for outcome prognosis in LARC. Further external validation is pending before potential clinical application.
Involved external institutions
Germany (DE)How to cite
APA:
Shahzadi, I., Lattermann, A., Linge, A., Zwanenburg, A., Baldus, C., Peeken, J.C.,... Loeck, S. (2021). Do We Need Complex Image Features to Personalize Treatment of Patients with Locally Advanced Rectal Cancer? In Marleen de Bruijne, Philippe C. Cattin, Stéphane Cotin, Nicolas Padoy, Stefanie Speidel, Yefeng Zheng, Caroline Essert (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 775-785). Virtual, Online: Springer Science and Business Media Deutschland GmbH.
MLA:
Shahzadi, Iram, et al. "Do We Need Complex Image Features to Personalize Treatment of Patients with Locally Advanced Rectal Cancer?" Proceedings of the 24th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2021, Virtual, Online Ed. Marleen de Bruijne, Philippe C. Cattin, Stéphane Cotin, Nicolas Padoy, Stefanie Speidel, Yefeng Zheng, Caroline Essert, Springer Science and Business Media Deutschland GmbH, 2021. 775-785.
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