期刊
DIAGNOSTICS
卷 12, 期 7, 页码 -出版社
MDPI
DOI: 10.3390/diagnostics12071601
关键词
deep learning; machine learning; multicenter; locally advanced rectal cancer; response prediction to nCRT
资金
- Ministry of Science, Research and the Arts Baden-Wurttemberg (MWK)
- German Research Foundation (DFG) [INST 35/1314-1 FUGG, INST 35/1503-1 FUGG]
- DFG [428149221]
This retrospective study evaluated the generalizability of a deep learning model for predicting the response of rectal cancer to neoadjuvant chemoradiotherapy using a multicenter dataset. The study found that the predictive performance dropped significantly in a multicenter setting compared to previous single- or two-center settings.
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.
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