DR-CSI for In Vivo Tumor Heterogeneity and Lymph Node Metastasis Prediction in Oral Tongue Squamous Cell Carcinoma

Motivation: Preoperative assessment of cervical lymph node metastasis (CLNM) is critical for prognosis and treatment in oral tongue squamous cell carcinoma (OTSCC), but current imaging methods lack the precision to capture tumor microstructure accurately. Goal(s): We aimed to assess the feasibility of DR-CSI in characterizing tumor heterogeneity and predicting CLNM in OTSCC. Approach: We enrolled 57 OTSCC patients who underwent DR-CSI, segmenting D-T2 spectra into five subregions. We compared DR-CSI volume fractions to differentiate normal tissue, CLNM-negative, and CLNM-positive groups. Results: DR-CSI metrics, especially VB and VD, distinguished CLNM-positive cases and outperformed ADC in predicting CLNM. Impact: DR-CSI enables detailed in vivo mapping of tumor microstructure in OTSCC, facilitating precise lymph node metastasis prediction and offering a powerful tool for non-invasive risk stratification. Its use could improve staging accuracy and optimize treatment planning for OTSCC.

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Publication Details

Journal
Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition
Published
2025-09-16
DOI
https://doi.org/10.58530/2025/0126
Primary Topic
Radiomics and Machine Learning in Medical Imaging
Type
article
Field-Weighted Citation Impact
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article

DR-CSI for In Vivo Tumor Heterogeneity and Lymph Node Metastasis Prediction in Oral Tongue Squamous Cell Carcinoma

Wentao Hu, Yongming Dai, Peng Hu, Siyu Li et al.
Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition
Radiomics and Machine Learning in Medical Imaging
article

DR-CSI for In Vivo Tumor Heterogeneity and Lymph Node Metastasis Prediction in Oral Tongue Squamous Cell Carcinoma

Wentao Hu, Yongming Dai, Peng Hu, Siyu Li, Dongmei Wu, Yingwei Wu
article en

Abstract

Motivation: Preoperative assessment of cervical lymph node metastasis (CLNM) is critical for prognosis and treatment in oral tongue squamous cell carcinoma (OTSCC), but current imaging methods lack the precision to capture tumor microstructure accurately. Goal(s): We aimed to assess the feasibility of DR-CSI in characterizing tumor heterogeneity and predicting CLNM in OTSCC. Approach: We enrolled 57 OTSCC patients who underwent DR-CSI, segmenting D-T2 spectra into five subregions. We compared DR-CSI volume fractions to differentiate normal tissue, CLNM-negative, and CLNM-positive groups. Results: DR-CSI metrics, especially VB and VD, distinguished CLNM-positive cases and outperformed ADC in predicting CLNM. Impact: DR-CSI enables detailed in vivo mapping of tumor microstructure in OTSCC, facilitating precise lymph node metastasis prediction and offering a powerful tool for non-invasive risk stratification. Its use could improve staging accuracy and optimize treatment planning for OTSCC.

Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition
Openalex Percentile: Top 64%
Radiomics and Machine Learning in Medical Imaging
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