ArticleEuropean journal of radiology2025
Can MRI radiomics distinguish residual adenocarcinoma from acellular mucin in treated rectal cancer?
Article in European journal of radiology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 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
7 citing papers in PubMed.
- Lateral pelvic lymph nodes in rectal cancer: a narrative review.Abdominal radiology (New York) · 2026Review
- Can ADC differentiate cellular from acellular mucin in mucinous adenocarcinoma tumor beds after treatment of rectal cancer? A multicenter study.European radiology · 2026Article
- Challenges in rectal cancer MRI: from image acquisition to interpretation.Abdominal radiology (New York) · 2026Review
- Rectal cancer MRI: if you use the correct spin, patient care will be win-win.European radiology · 2026Article
- Mucin quantity on MRI and outcomes following total neoadjuvant therapy in patients with rectal cancer.European radiology · 2026Article
- Towards destigmatization of mucin in the era of TNT for locally advanced rectal cancer.European radiology · 2026Article
- Research Progress of MRI-based Radiomics in Rectal CancerCurrent medical imaging · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
21 authors.
Funding
Abstract
backgroundWe explored the use of MRI T2-weighted imaging (T2WI) radiomics to help distinguish acellular mucin from cellular mucin (residual tumor) to inform patient management.
methodsThis retrospective, multi-institutional study included consecutive patients with rectal adenocarcinoma containing mucin on restaging MRI from March 2012-January 2020. Radiologists segmented 3-mm-thick T2WI. Data were split into training (n = 122), validation (n = 40), and test (n = 42) sets. Sensitivity, specificity, PPV, NPV, and AUC of the developed radiomic models were investigated on the test set. Histopathology was the reference standard. Two radiologists independently reviewed 42 patients using a unique five-point Likert scale for the probability of cellular mucin. DeLong-Delong or McNemar's test was used to compare the AUC and diagnostic performance, respectively, of the radiomics model to that of the human readers.
resultsOf 204 patients (mean age, 56.6 ± 13.3 years; 129 men and 75 women), 39/204 (19 %) had acellular mucin whereas 165/204 (81 %) had cellular mucin. The radiomics model demonstrated a sensitivity of 90 % (27/30, 95 % CI: 74-97 %), specificity of 83 % (10/12, 95 % CI: 55-95 %), PPV of 93 % (27/29, 95 % CI: 78-98 %), NPV of 77 % (10/13, 95 % CI: 50-92 %), and AUC of 0.84 (95 % CI: 0.65-0.99), whereas the human readers showed inferior sensitivities of 47 % (14/30) (95 % CI: 28-66 %) and 50 % (15/30) (95 % CI: 31-69 %) and AUCs of 0.58 (95 % CI: 0.41-0.74) (DeLong's p = 0.06 [95 % CI: -0.55, 0.01]) and 0.73 (95 % CI: 0.57-0.88) (DeLong's p = 0.43 [95 % CI: -0.39, 0.16]).
conclusionThe developed MRI radiomics model predicts cellular mucin post neoadjuvant chemoradiation with higher sensitivity than human readers.
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.