ArticleEuropean radiology2023
Deep-learning-based 3D super-resolution MRI radiomics model: superior predictive performance in preoperative T-staging of rectal cancer.
Article in European radiology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 61 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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
61 citing papers in PubMed, 90 citations in OpenAlex.
- MST-Net: a multi-scale spatial transformation network for automated T2/T3 staging of rectal cancer on preoperative T2-weighted MRI.Insights into imaging · 2026Article
- Intratumor habitat aware deep learning framework for preoperative lymph node metastasis prediction in laryngeal carcinoma.Cancer imaging : the official publication of the International Cancer Imaging Society · 2026Article
- Redox-senescence function of PON1 in hepatocellular carcinoma and its non-invasive assessment using super-resolution radiomics: a multi-center study.Journal of translational medicine · 2026Article
- Deep learning-based super-resolution ultrashort echo time MRI for pulmonary nodule detection and longitudinal assessment in emphysema.BMC pulmonary medicine · 2026Article
- Fusion of MRI-radiomics and clinical features to improve pathological T-stage prediction of rectal cancer: a multicenter study.BMC cancer · 2026Article
- The Diagnostic Value of Deep Learning for Multi-Classification of Rectal Cancer T Staging Based on Regional Attention.Diagnostics (Basel, Switzerland) · 2026Article
- A CT-based 2.5D deep learning model for preoperative T-staging in gastric cancer: a retrospective multicenter study.Abdominal radiology (New York) · 2026Article
- Diagnostic Performance of a Deep Learning-Based Tool for the Detection and Staging of Rectal Cancers on Endoscopic Ultrasound: Prospective Study.Diagnostics (Basel, Switzerland) · 2026Article
- Deep learning-assisted prediction of hydrocephalus in preoperative-subarachnoid hemorrhage: a multi-center study.Quantitative imaging in medicine and surgery · 2026Article
- Deep learning 3D super-resolution radiomics model based on Gd-enhanced MRI for improving preoperative prediction of HCC pathological grading.Abdominal radiology (New York) · 2026Article
- Generative adversarial networks: multiparametric, multiregion super-resolution MRI in predicting lymph node metastasis in rectal cancer.Insights into imaging · 2026Article
- The Efficacy of a Super-Resolution Reconstruction Radiomics Model Based on T2WI for Predicting Placenta Accreta Spectrum Disorders: A Multicenter Study.Current medical imaging · 2026Article
- Research Progress of MRI-based Radiomics in Rectal CancerCurrent medical imaging · 2026Review
- Super-resolution ultrasound radiomics for pre-FNA prediction of nondiagnostic (Bethesda I) thyroid nodules.Frontiers in endocrinology · 2026Article
- Application of a MRI model based on tumor microenvironment habitat and peritumoral features in preoperative differentiation of rectal cancer T1-2/T3: a multicenter study.Frontiers in medicine · 2026Article
- Multi-Radiomics Features of Super-Resolution MRI for Predicting Minor Amputation in Wagner 3 Diabetic Foot Ulcers.Diabetes, metabolic syndrome and obesity : targets and therapy · 2026Article
- Ultrasound radiomics and deep learning for predicting antral follicle count and anti-Müllerian hormone.Scientific reports · 2025Article
- Noninvasive Prediction of Programmed Cell Death Protein-Ligand 1 Expression in Locally Advanced Non-small Cell Lung Cancer byAnnals of surgical oncology · 2025Article
- An interpretable machine learning model for predicting visceral pleural invasion in cT1 lung adenocarcinoma based on habitat analysis.Quantitative imaging in medicine and surgery · 2025Article
- Multicenter study of CT-based deep learning for predicting preoperative T staging and TNM staging in clear cell renal cell carcinoma.BMC cancer · 2025Article
1 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 1 institution in 1 country.
Funding
Abstract
objectivesTo investigate the feasibility and efficacy of a deep-learning (DL)-based three-dimensional (3D) super-resolution (SR) MRI radiomics model for preoperative T-staging prediction in rectal cancer (RC).
methodsSeven hundred six eligible RC patients (T1/2 = 287, T3/4 = 419) were retrospectively enrolled in this study and chronologically allocated into a training cohort (n = 565) and a validation cohort (n = 141). We conducted a deep-transfer-learning network on high-resolution (HR) T2-weighted imaging (T2WI) to enhance the z-resolution of the images and acquired the preoperative SRT2WI. The radiomics models named model
resultsModel
conclusionsModel KEY POINTS: • For the first time, DL-based 3D SR images were applied in radiomics analysis for clinical utility. • Compared with the visual assessment of expert radiologists and the conventional radiomics model based on HRT2WI, the SR radiomics model showed a more favorable capability in helping clinicians assess the invasion depth of RC preoperatively. • This is the largest radiomics study for T-staging prediction in RC.
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.