Deep learning for the prediction of early on-treatment response in metastatic colorectal cancer from serial medical imaging
Open accessAbstract
In current clinical practice, tumor response assessment is usually based on tumor size change on serial computerized tomography (CT) scan images. However, evaluation of tumor response to anti-vascular endothelial growth factor therapies in metastatic colorectal cancer (mCRC) is limited because morphological change in tumor may occur earlier than tumor size change. Here we present an analysis utilizing a deep learning (DL) network to characterize tumor morphological change for response assessment in mCRC patients. We retrospectively analyzed 1,028 mCRC patients who were prospectively included in the VELOUR trial (NCT00561470). We found that DL network was able to predict early on-treatment response in mCRC and showed better performance than its size-based counterpart with C-Index: 0.649 (95% CI: 0.619,0.679) vs. 0.627 (95% CI: 0.567,0.638), p = 0.009, z-test. The integration of DL network with size-based methodology could further improve the prediction performance to C-Index: 0.694 (95% CI: 0.661,0.720), which was superior to size/DL-based-only models (all p < 0.001, z-test). Our study suggests that DL network could provide a noninvasive mean for quantitative and comprehensive characterization of tumor morphological change, which may potentially benefit personalized early on-treatment decision making.
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Lu et al. (2021) developed a deep learning network to predict early on-treatment response in metastatic colorectal cancer, which outperformed traditional methods.
“For instance, Lu et al. (2021) developed a deep learning network to predict early on-treatment response in metastatic colorectal cancer, which outperformed traditional methods [13].”
From Integrating machine learning and artificial intelligence in life-course epidemiology: pathways to innovative public health solutions (2024) · cites this paper as [13]
ML and AI techniques have been applied to predict cancer prognosis and estimate treatment response based on genomic and clinical data.
“These techniques have been applied to predict cancer prognosis and estimate treatment response based on genomic and clinical data [8–14].”
From Integrating machine learning and artificial intelligence in life-course epidemiology: pathways to innovative public health solutions (2024) · cites this paper as [13]
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“For instance, Lu et al. (2021) developed a deep learning network to predict early on-treatment response in metastatic colorectal cancer, which outper-formed traditional methods [13].”
BackgroundExploring and bypassing resistance to targeted therapies in colorectal cancer
Favier, S., Xiong, W., Chalabi-Dchar, M., Chettab, K., Piercey, O., Wu, T., Fenouil, T., Albaret, M.-A., Jordheim, L. P., Diaz, J.-J., et al. (2025) · Biomedicine & Pharmacotherapy · doi:10.1016/j.biopha.2025.118741 · Cited by 12 · see citing works
- Authors
- Lu, L., Dercle, L., Zhao, B., & Schwartz, L. H.
- Affiliations
- Columbia University Irving Medical Center (US)
- Corresponding author
- Schwartz, Lawrence H.
- Venue
- Nature Communications
- Published
- 2021-11-17
- Volume
- 12
- Issue
- 1
- Pages
- 6654-6654
- ISSN
- 2041-1723 (ISSN-L)
- PMC
- PMC8599694
- OpenAlex
- W3214055905
- Cited by
- 104
- References
- 51
- Citations by year
- 2026: 15 · 2025: 30 · 2024: 33 · 2023: 14 · 2022: 12
- FWCI
- 7.79
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Nature Communications (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- U.S. Department of Health & Human Services | NIH | National Cancer Institute (U01 CA225431)
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
- Health Sciences › Medicine › Oncology › Colorectal Cancer Treatments and Studies
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Medical Imaging Techniques and Applications
- Keywords
- metastatic colorectal cancer, tumor response assessment, anti-VEGF therapy, deep learning, serial CT imaging, tumor size change
- MeSH terms
- Deep Learning, Antineoplastic Combined Chemotherapy Protocols, Humans, Predictive Value of Tests, Prognosis, Retrospective Studies, Tomography, X-Ray Computed, Colorectal Neoplasms, Treatment Outcome, Vascular Endothelial Growth Factor A
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