ArticleAnnals of translational medicine2019
Automated quantitative assessment of oncological disease progression using deep learning.
Article in Annals of translational medicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 3 of them syntheses that pooled it.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
11 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- The role of deep learning in diagnostic imaging of spondyloarthropathies: a systematic review.European radiology · 2025Pooled it
- Artificial intelligence for X-ray scaphoid fracture detection: a systematic review and diagnostic test accuracy meta-analysis.European radiology · 2024Pooled it
- Twenty Years On: RECIST as a Biomarker of Response in Solid Tumours an EORTC Imaging Group - ESOI Joint Paper.Frontiers in oncology · 2021Pooled it
- From manual clinical criteria to machine learning algorithms: Comparing outcome endpoints derived from diverse electronic health record data modalities.PLOS digital health · 2025Article
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- Artificial intelligence in neuro-oncology.Frontiers in neuroscience · 2023Review
- Capsule Endoscopy in Crohn's Disease-From a Relative Contraindication to Habitual Monitoring Tool.Diagnostics (Basel, Switzerland) · 2021Review
- Differentiation Between Malignant and Benign Endoscopic Images of Gastric Ulcers Using Deep Learning.Clinical and experimental gastroenterology · 2021Article
- A Convolutional Neural Network Deep Learning Model Trained on CD Ulcers Images Accurately Identifies NSAID Ulcers.Frontiers in medicine · 2021Article
- Comparison of deep learning models for natural language processing-based classification of non-English head CT reports.Neuroradiology · 2020Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
PubMed holds no abstract for this paper.
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.