Development and Patient-Level Internal Validation of an MRI-Based Radiomics Machine Learning Model for Preoperative Prediction of Cervical Lymph Node Metastasis in Oral Squamous Cell Carcinoma: A Pilot Study.
- 1Department of Dentomaxillofacial Radiology, Faculty of Dentistry, Ankara University, 06560 Ankara, Türkiye.
- 2Department of Otorhinolaryngology and Head and Neck Surgery, Faculty of Medicine, Near East University, 99138 Kyrenia, Cyprus.
- 3Schulich School of Medicine and Dentistry, Western University, London, ON N6A 5C1, Canada.
Abstract
Background/Objectives: Cervical lymph node metastasis (LNM) is the main prognostic factor in oral squamous cell carcinoma (OSCC) and guides the extent of neck treatment. This retrospective pilot study aimed to develop MRI-based radiomics machine learning models for the preoperative prediction of LNM in OSCC and to validate them internally at the patient level. Methods: Preoperative MRI of 74 patients (32 LNM-positive, 42 LNM-negative on histopathology) was analysed. In each patient the largest morphologically suspicious cervical lymph node was segmented volumetrically on axial contrast-enhanced T1-weighted images, and 1409 IBSI-compliant radiomic features were extracted. Performance was estimated by 10 repeats of 5-fold nested cross-validation with the patient as the unit of partitioning; standardisation, correlation pruning, LASSO selection, hyperparameter tuning and threshold choice were confined to the training folds. Six classifiers were compared. Results: LASSO retained a median of nine features (range 6-13). SVM performed best, with an AUC of 0.781 (95% CI 0.667-0.884), a sensitivity of 0.72, a specificity of 0.76 and a balanced accuracy of 0.74; XGBoost (AUC 0.774) and logistic regression (AUC 0.769) performed comparably. A slice-level analysis of the same data, in which slices from the same patient could fall into both training and test sets, gave an apparent AUC of 0.878 but a specificity of only 0.33. Conclusions: MRI radiomic features of cervical lymph nodes carry moderate patient-level information on LNM in OSCC, and slice-level designs can substantially misrepresent performance. External validation in larger multicentre cohorts is required before clinical use.
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BibTeX
@article{ozturk2026development,
title = {{Development and Patient-Level Internal Validation of an MRI-Based Radiomics Machine Learning Model for Preoperative Prediction of Cervical Lymph Node Metastasis in Oral Squamous Cell Carcinoma: A Pilot Study.}},
author = {İrem Öztürk and Ferhat Erişir and Kaan Orhan and Gürkan Ünsal},
journal = {Diagnostics (Basel, Switzerland)},
year = {2026},
month = oct,
doi = {10.3390/diagnostics16193220},
url = {https://europepmc.org/article/MED/42882375},
note = {CC BY},
}
RIS
TY - JOUR
TI - Development and Patient-Level Internal Validation of an MRI-Based Radiomics Machine Learning Model for Preoperative Prediction of Cervical Lymph Node Metastasis in Oral Squamous Cell Carcinoma: A Pilot Study.
AU - İrem Öztürk
AU - Ferhat Erişir
AU - Kaan Orhan
AU - Gürkan Ünsal
PY - 2026
DA - 2026/10/05
JO - Diagnostics (Basel, Switzerland)
DO - 10.3390/diagnostics16193220
UR - https://europepmc.org/article/MED/42882375
AB - Background/Objectives: Cervical lymph node metastasis (LNM) is the main prognostic factor in oral squamous cell carcinoma (OSCC) and guides the extent of neck treatment. This retrospective pilot study aimed to develop MRI-based radiomics machine learning models for the preoperative prediction of LNM in OSCC and to validate them internally at the patient level. Methods: Preoperative MRI of 74 patients (32 LNM-positive, 42 LNM-negative on histopathology) was analysed. In each patient the largest morphologically suspicious cervical lymph node was segmented volumetrically on axial contrast-enhanced T1-weighted images, and 1409 IBSI-compliant radiomic features were extracted. Performance was estimated by 10 repeats of 5-fold nested cross-validation with the patient as the unit of partitioning; standardisation, correlation pruning, LASSO selection, hyperparameter tuning and threshold choice were confined to the training folds. Six classifiers were compared. Results: LASSO retained a median of nine features (range 6-13). SVM performed best, with an AUC of 0.781 (95% CI 0.667-0.884), a sensitivity of 0.72, a specificity of 0.76 and a balanced accuracy of 0.74; XGBoost (AUC 0.774) and logistic regression (AUC 0.769) performed comparably. A slice-level analysis of the same data, in which slices from the same patient could fall into both training and test sets, gave an apparent AUC of 0.878 but a specificity of only 0.33. Conclusions: MRI radiomic features of cervical lymph nodes carry moderate patient-level information on LNM in OSCC, and slice-level designs can substantially misrepresent performance. External validation in larger multicentre cohorts is required before clinical use.
SN - 2075-4418
ER -