ArticleCancer medicine2026
MRI-Based DeltaHabitat Radiomic Model Predicts Pathological Complete Response in Oral Cavity Cancer Treated With Neoadjuvant Chemoimmunotherapy.
Article in Cancer medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis 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
2 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Computational and AI-Enabled Imaging Biomarkers for Predicting and Assessing Immunotherapy Response in Oral Squamous Cell Carcinoma: A Systematic Review with Functional Meta-Synthesis.Medical sciences (Basel, Switzerland) · 2026Pooled it
- Integrating MR dynamic radiomics and clinical parameters for machine learning-based prediction of short-term response to induction chemotherapy in nasopharyngeal carcinoma.Frontiers in oncology · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
Abstract
objectiveThis study aimed to develop and validate an MRI-based DeltaHabitat radiomics model to predict pathological complete response (pCR) in oral cavity squamous cell carcinoma (OCSCC) patients treated with neoadjuvant chemoimmunotherapy.
methodsPatients treated with neoadjuvant chemoimmunotherapy and surgery were retrospectively included from one institution and randomly divided into training and test cohorts using a 7:3 ratio. The region of interest (ROI) for the primary tumor was manually delineated on contrast-enhanced T1-weighted MRI, and radiomic features were extracted. The volume of interest was segmented into three subregions using the K-means clustering algorithm. Following feature selection, five models were constructed to predict pCR in both the training and test cohorts. The efficacy of the models was evaluated using receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA).
resultsOne hundred and ninety-five patients were enrolled. The median age was 57 years, and 127 (65.1%) patients were male. Features were extracted from three separate regions, and a total of 5502 features were yielded. After the feature selection process, 12 features were retained. Among radiomic models, the DeltaHabitat model demonstrated a satisfactory area under the receiver operating characteristic curve (AUC) in both the training and test cohorts (0.923, 95% CI: 0.880-0.967; 0.878, 95% CI: 0.791-0.964, respectively).
conclusionMRI-based DeltaHabitat radiomics model demonstrated good performance in predicting pCR in OCSCC patients treated with neoadjuvant chemoimmunotherapy. This non-invasive approach may facilitate early identification of responders and support individualized treatment decision-making.
Indexed as
Identifiers
What Socratic 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 Socratic graph.