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. 2023 Aug;45(8):2068-2078.
doi: 10.1002/hed.27434. Epub 2023 Jun 22.

Machine learning in laryngeal cancer: A pilot study to predict oncological outcomes and the role of adverse features

Gerardo Petruzzi  1 , Elisa Coden  2 , Oreste Iocca  3 , Pasquale di Maio  4 , Barbara Pichi  1 , Flaminia Campo  1 , Armando De Virgilio  5   6 , Mazzola Francesco  1 , Antonello Vidiri  7 , Raul Pellini  1
Affiliations

Machine learning in laryngeal cancer: A pilot study to predict oncological outcomes and the role of adverse features

Gerardo Petruzzi et al. Head Neck. 2023 Aug.

Abstract

Background: Laryngeal carcinoma (LC) remains a significant economic and emotional problem to the healthcare system and severe social morbidity. New tools as Machine Learning could allow clinicians to develop accurate and reproducible treatments.

Methods: This study aims to evaluate the performance of a ML-algorithm in predicting 1- and 3-year overall survival (OS) in a cohort of patients surgical treated for LC. Moreover, the impact of different adverse features on prognosis will be investigated. Data was collected on oncological FU of 132 patients. A retrospective review was performed to create a dataset of 23 variables for each patient.

Results: The decision-tree algorithm is highly effective in predicting the prognosis, with a 95% accuracy in predicting the 1-year survival and 82.5% in 3-year survival; The measured AUC area is 0.886 at 1-year Test and 0.871 at 3-years Test. The measured AUC area is 0.917 at 1-year Training set and 0.964 at 3-years Training set. Factors that affected 1yOS are: LNR, type of surgery, and subsite. The most significant variables at 3yOS are: number of metastasis, perineural invasion and Grading.

Conclusions: The integration of ML in medical practices could revolutionize our approach on cancer pathology.

Keywords: algorithm; artificial intelligence; laryngeal cancer; machine learning; oncological outcome; open surgery.

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References

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