| Autor: |
Antonello Vidiri, Simona Marzi, Francesca Piludu, Sonia Lucchese, Vincenzo Dolcetti, Eleonora Polito, Francesco Mazzola, Paolo Marchesi, Elisabetta Merenda, Isabella Sperduti, Raul Pellini, Renato Covello |
| Jazyk: |
angličtina |
| Rok vydání: |
2023 |
| Předmět: |
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| Zdroj: |
Computational and Structural Biotechnology Journal, Vol 21, Iss , Pp 4277-4287 (2023) |
| Druh dokumentu: |
article |
| ISSN: |
2001-0370 |
| DOI: |
10.1016/j.csbj.2023.08.020 |
| Popis: |
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. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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