Magnetic resonance imaging-based prediction models for tumor stage and cervical lymph node metastasis of tongue squamous cell carcinoma

Antonello Vidiri; Simona Marzi; Francesca Piludu; Sonia Lucchese; Vincenzo Dolcetti; Eleonora Polito; Francesco Mazzola; Paolo Marchesi; Elisabetta Merenda; Isabella Sperduti; Raul Pellini; Renato Covello

doi:10.1016/j.csbj.2023.08.020

Magnetic resonance imaging-based prediction models for tumor stage and cervical lymph node metastasis of tongue squamous cell carcinoma

Comput Struct Biotechnol J. 2023 Aug 24:21:4277-4287. doi: 10.1016/j.csbj.2023.08.020. eCollection 2023.

Authors

Affiliations

¹ Radiology and Diagnostic Imaging Department, IRCCS Regina Elena National Cancer Institute, Via Elio Chianesi 53, 00144 Rome,Italy.
² Medical Physics Laboratory, IRCCS Regina Elena National Cancer Institute, Via Elio Chianesi 53, 0 0144 Rome, Italy.
³ Scuola di Specializzazione in Radiodiagnostica, Sapienza University of Rome, Policlinico Umberto I, Viale Regina Elena 324, 00161 Rome, Italy.
⁴ Department of Otolaryngology and Head and Neck Surgery, IRCCS Regina Elena National Cancer Institute, Via Elio Chianesi 53, 00144 Rome, Italy.
⁵ Department of Radiological, Oncological and Pathological Sciences, Sapienza University of Rome, Policlinico Umberto I, 00161 Rome, Italy.
⁶ Biostatistics Unit, IRCCS Regina Elena National Cancer Institute, Via Elio Chianesi 53, 00144 Rome, Italy.
⁷ Department of Pathology, IRCCS Regina Elena National Cancer Institute, Via Elio Chianesi 53, 00144 Rome, Italy.

Abstract

Purpose: To evaluate the ability of preoperative MRI-based measurements to predict the pathological T (pT) stage and cervical lymph node metastasis (CLNM) via machine learning (ML)-driven models trained in oral tongue squamous cell carcinoma (OTSCC).

Materials and methods: 108 patients with a new diagnosis of OTSCC were enrolled. The preoperative MRI study included post-contrast high-resolution T1-weighted images acquired in all patients. MRI-based depth of invasion (DOI) and tumor dimension-together with shape-based and intensity-based features-were extracted from the lesion volume segmentation. The entire dataset was randomly divided into a training set and a validation set, and the performances of different types of ML algorithms were evaluated and compared.

Results: MRI-based DOI and tumor dimension together with several shape-based and intensity-based signatures significantly discriminated the pT stage and LN status. The overall accuracy of the model for predicting the pT stage was 0.86 (95%CI, 0.78-0.92) and 0.81 (0.64-0.91) in the training and validation sets, respectively. There was no improvement in the model performance upon including shape-based and intensity-based features. The model for predicting CLNM based on DOI and tumor dimensions had a fair accuracy of 0.68 (0.57-0.78) and 0.69 (0.51-0.84) in the training and validation sets, respectively. The shape-based and intensity-based signatures have shown potential for improving the model sensitivity, with a comparable accuracy.

Conclusion: MRI-based models driven by ML algorithms could stratify patients with OTSCC according to the pT stages. They had a moderate ability to predict cervical lymph node metastasis.

Keywords: Depth of invasion; Lymph node metastasis; Machine learning; Magnetic resonance imaging; Tongue cancer.