A 3-dimensional Resnet model for assessment of drug efficacy in 3D cancer models using optical coherence tomography.

Authors

Gavrielle R Untracht, Jan Kaminski, Eike Guldenring, Boye Schnack Nielsen, Kim Holmstrøm, Katrine Hommelhoff Jensen, Peter E Andersen

Published in

PloS one. Volume 21. Issue 7. Pages e0353170. Epub Jul 24, 2026.

Abstract

Ninety percent of drugs fail during clinical trials, mainly due to lack of clinical efficacy. Recent developments in in vitro models such as 3D tumor heterospheroids have led to improvements in failure rates, but the relative lack of standardized evaluation methods for 3D cultures limits their utility in high-throughput screening. Optical coherence tomography (OCT) shows significant promise for high-throughput screening of 3D models; however, the optimal classification model and key image features for assessing drug efficacy in OCT images of spheroids has yet to be explored in detail. In this study, we investigate whether OCT combined with machine learning methods can be used to identify biomarkers of drug efficacy in 3D tumor spheroid models. We further compare the performance of two different models to determine the optimal configuration for accurate classification. Volumetric OCT images were acquired of co-cultured HT29 spheroids treated with 3 different concentrations of cisplatin. A two-dimensional multi-view ResNet model and a three-dimensional ResNet model were used to classify the images and to identify key image features associated with each group. Differences between spheroids treated with different concentrations of cisplatin are clearly visible in the OCT images. Our model was able to classify the images based on cisplatin concentration with 71.9% accuracy using the 2D multi-view model and 91.2% accuracy using the 3D model. Key features in the 3D model significantly improved the model accuracy. These results underscore the possibility that OCT could be used for high-throughput screening of drugs using 3D in vitro models and highlight key identifying features for further investigation.

PMID:
42497140
Bibliographic data and abstract were imported from PubMed on 25 Jul 2026.

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