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Verfasst von:Wichtmann, Barbara [VerfasserIn]   i
 Albert, Steffen [VerfasserIn]   i
 Zhao, Wenzhao [VerfasserIn]   i
 Maurer, Angelika [VerfasserIn]   i
 Rödel, Claus [VerfasserIn]   i
 Hofheinz, Ralf-Dieter [VerfasserIn]   i
 Hesser, Jürgen [VerfasserIn]   i
 Zöllner, Frank G. [VerfasserIn]   i
 Attenberger, Ulrike [VerfasserIn]   i
Titel:Are we there yet? The value of deep learning in a multicenter setting for response prediction of locally advanced rectal cancer to neoadjuvant chemoradiotherapy
Verf.angabe:Barbara D. Wichtmann, Steffen Albert, Wenzhao Zhao, Angelika Maurer, Claus Rödel, Ralf-Dieter Hofheinz, Jürgen Hesser, Frank G. Zöllner and Ulrike I. Attenberger
E-Jahr:2022
Jahr:30 June 2022
Umfang:15 S.
Fussnoten:Dieser Artikel gehört zum Special issue: Artificial intelligence in clinical medical imaging analysis ; Gesehen am 02.01.2024
Titel Quelle:Enthalten in: Diagnostics
Ort Quelle:Basel : MDPI, 2011
Jahr Quelle:2022
Band/Heft Quelle:12(2022), 7, Artikel-ID 1601, Seite 1-15
ISSN Quelle:2075-4418
Abstract:This retrospective study aims to evaluate the generalizability of a promising state-of-the-art multitask deep learning (DL) model for predicting the response of locally advanced rectal cancer (LARC) to neoadjuvant chemoradiotherapy (nCRT) using a multicenter dataset. To this end, we retrained and validated a Siamese network with two U-Nets joined at multiple layers using pre- and post-therapeutic T2-weighted (T2w), diffusion-weighted (DW) images and apparent diffusion coefficient (ADC) maps of 83 LARC patients acquired under study conditions at four different medical centers. To assess the predictive performance of the model, the trained network was then applied to an external clinical routine dataset of 46 LARC patients imaged without study conditions. The training and test datasets differed significantly in terms of their composition, e.g., T-/N-staging, the time interval between initial staging/nCRT/re-staging and surgery, as well as with respect to acquisition parameters, such as resolution, echo/repetition time, flip angle and field strength. We found that even after dedicated data pre-processing, the predictive performance dropped significantly in this multicenter setting compared to a previously published single- or two-center setting. Testing the network on the external clinical routine dataset yielded an area under the receiver operating characteristic curve of 0.54 (95% confidence interval [CI]: 0.41, 0.65), when using only pre- and post-therapeutic T2w images as input, and 0.60 (95% CI: 0.48, 0.71), when using the combination of pre- and post-therapeutic T2w, DW images, and ADC maps as input. Our study highlights the importance of data quality and harmonization in clinical trials using machine learning. Only in a joint, cross-center effort, involving a multidisciplinary team can we generate large enough curated and annotated datasets and develop the necessary pre-processing pipelines for data harmonization to successfully apply DL models clinically.
DOI:doi:10.3390/diagnostics12071601
URL:Bitte beachten Sie: Dies ist ein Bibliographieeintrag. Ein Volltextzugriff für Mitglieder der Universität besteht hier nur, falls für die entsprechende Zeitschrift/den entsprechenden Sammelband ein Abonnement besteht oder es sich um einen OpenAccess-Titel handelt.

kostenfrei: Volltext: https://doi.org/10.3390/diagnostics12071601
 kostenfrei: Volltext: https://www.mdpi.com/2075-4418/12/7/1601
 DOI: https://doi.org/10.3390/diagnostics12071601
Datenträger:Online-Ressource
Sprache:eng
Sach-SW:deep learning
 locally advanced rectal cancer
 machine learning
 multicenter
 response prediction to nCRT
K10plus-PPN:1877043559
Verknüpfungen:→ Zeitschrift

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