ArticleNature communications2021
Deep learning for the prediction of early on-treatment response in metastatic colorectal cancer from serial medical imaging.
Article in Nature communications, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT00561470 (A Multinational, Randomized, Double-blind Study, Comparing the Efficacy of Aflibercept Once Every 2 Weeks Versus Placebo in Patients With Metastatic Colorectal Cancer), which is not on this map. Cited by 58 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
A Multinational, Randomized, Double-blind Study, Comparing the Efficacy of Aflibercept Once Every 2 Weeks Versus Placebo in Patients With Metastatic Colorectal Cancer (MCRC) Treated With Irinotecan / 5-FU Combination (FOLFIRI) After Failure of an Oxaliplatin Based Regimen
Who cites it
58 citing papers in PubMed, 97 citations in OpenAlex.
- Article
- VEGFC as a prognostic cytokine biomarker linking lymph node metastasis to immune suppression in breast cancer.European cytokine network · 2026Article
- Deep learning-based spatiotemporal estimation of lesion changes for patient-level assessment of breast cancer lung metastases on longitudinal CT.NPJ precision oncology · 2026Article
- Interpretable multimodal PET/CT-EHR fusion via mixture-of-experts for prognostic stratification in mantle cell lymphoma: a multicenter study.BMC medicine · 2026Article
- Integrating artificial intelligence (AI) into colorectal cancer reporting.The Journal of pathology · 2026Review
- Review
- Foundation Model-Enabled Multimodal Deep Learning for Prognostic Prediction in Colorectal Cancer with Incomplete Modalities: A Multi-Institutional Retrospective Study.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- A Sensor-Oriented Multimodal Medical Data Acquisition and Modeling Framework for Tumor Grading and Treatment Response Analysis.Sensors (Basel, Switzerland) · 2026Article
- Multispectral PCCT and CBCT imaging for high precision radiotherapy through translation of imaging parameters with machine learning validation.Scientific reports · 2026Article
- Deep-Learning Serial CT Prediction of Survival in Immunotherapy-Treated Non-Small Cell Lung Cancer.JAMA network open · 2026Article
- A deep learning framework to iDentify prOgnostically releVant cancEr Regions (DOVER) within whole slide histopathology images.Cancer letters · 2026Article
- Preoperative CT deep learning of tumor and visceral adipose tissue combined with postoperative pathology predicts colorectal cancer recurrence: a multicenter study.Frontiers in molecular biosciences · 2026Article
- Colorectal Lesion diagnosis using transformer and deep learning with multiscale feature interface.PloS one · 2026Article
- Deep learning-assisted diagnosis of pubertal breast development on multicenter ultrasound images.iScience · 2025Article
- PAM: a propagation-based model for segmenting any 3D objects across multi-modal medical images.NPJ digital medicine · 2025Article
- Radiology-based artificial intelligence for predicting targeted therapy response in pan-cancer: a comprehensive review.Journal of translational medicine · 2025Review
- Evaluating the application of dynamic prediction models in oncological prognostic studies with repeated measurement predictors.NPJ precision oncology · 2025Article
- Development and validation of an AI-augmented deep learning model for survival prediction in de novo metastatic colorectal cancer.Discover oncology · 2025Article
- Prediction of neoadjuvant chemotherapy efficacy in patients with HER2-low breast cancer based on ultrasound radiomics.Cancer imaging : the official publication of the International Cancer Imaging Society · 2025Article
- The Rise of AI-Assisted Diagnosis: Will Pathologists Be Partners or Bystanders?Diagnostics (Basel, Switzerland) · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 1 institution in 1 country.
Funding
Abstract
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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.