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Related Concept Videos

Radiological Investigation II: MRI and Ventilation Perfusion Scan01:30

Radiological Investigation II: MRI and Ventilation Perfusion Scan

Description
Magnetic Resonance Imaging (MRI) and Ventilation Perfusion Scans are two radiological investigations that offer detailed diagnostic images of the body, particularly lung structures.
MRI
MRI uses magnetic fields and radiofrequency signals to distinguish between normal and abnormal tissues. This technology provides a more detailed diagnostic image than CT scans, enabling it to characterize pulmonary nodules, stage bronchogenic carcinoma, and evaluate inflammatory activity in...

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Related Experiment Video

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A Dorsal Skinfold Window Chamber Tumor Mouse Model for Combined Intravital Microscopy and Magnetic Resonance Imaging in Translational Cancer Research
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MRI-Based DeltaHabitat Radiomic Model Predicts Pathological Complete Response in Oral Cavity Cancer Treated With

Lin Ding1, Jialing Wu1, Yangxin Liang1

  • 1Department of Radiation Oncology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, China.

Cancer Medicine
|June 15, 2026
PubMed
Summary

An MRI-based DeltaHabitat radiomics model effectively predicts pathological complete response (pCR) in oral cavity squamous cell carcinoma (OCSCC) patients receiving neoadjuvant chemoimmunotherapy. This non-invasive tool aids in early identification of treatment responders.

Keywords:
magnetic resonance imagingneoadjuvant chemoimmunotherapyoral cavity squamous cell carcinomapathological complete responseradiomics

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Area of Science:

  • Oncology
  • Radiology
  • Medical Imaging

Background:

  • Oral cavity squamous cell carcinoma (OCSCC) presents a significant challenge in treatment response prediction.
  • Neoadjuvant chemoimmunotherapy is a key treatment modality for OCSCC, but predicting patient response remains difficult.
  • Accurate prediction of pathological complete response (pCR) is crucial for optimizing treatment strategies.

Purpose of the Study:

  • To develop and validate an MRI-based DeltaHabitat radiomics model.
  • To predict pathological complete response (pCR) in OCSCC patients.
  • To evaluate the model's performance in patients treated with neoadjuvant chemoimmunotherapy.

Main Methods:

  • Retrospective analysis of 195 OCSCC patients treated with neoadjuvant chemoimmunotherapy and surgery.
  • Manual delineation of the primary tumor region of interest (ROI) on contrast-enhanced T1-weighted MRI.
  • Extraction and selection of radiomic features, including segmentation into subregions using K-means clustering, to build predictive models.

Main Results:

  • The DeltaHabitat radiomics model demonstrated high predictive performance.
  • Area under the receiver operating characteristic curve (AUC) was 0.923 in the training cohort and 0.878 in the test cohort.
  • 12 radiomic features were selected after extensive feature extraction from 5502 initial features.

Conclusions:

  • The MRI-based DeltaHabitat radiomics model shows strong performance in predicting pCR for OCSCC.
  • This non-invasive approach can help identify treatment responders early.
  • The model supports personalized treatment decision-making for OCSCC patients.