Comparative analysis of traditional machine learning and automated machine learning: advancing inverted papilloma versus associated squamous cell carcinoma diagnosis

Hosseinzadeh, Farideh; Mohammadi, S. Saeed; Palmer, James N.; Kohanski, Michael A.; Adappa, Nithin D.; Chang, Michael T.; Hwang, Peter H.; Nayak, Jayakar V.; Patel, Zara M.

doi:10.1002/alr.23438

Int Forum Allergy Rhinol

2024

DOI: 10.1002/alr.23438

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Comparative analysis of traditional machine learning and automated machine learning: advancing inverted papilloma versus associated squamous cell carcinoma diagnosis

Farideh Hosseinzadeh,

S. Saeed Mohammadi,

James N. Palmer

et al.

Abstract: Key Points Inverted papilloma conversion to squamous cell carcinoma is not always easy to predict. AutoML requires much less technical knowledge and skill to use than traditional ML. AutoML surpassed the traditional ML algorithm in differentiating IP from IP‐SCC.

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Cited by 2 publications

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Artificial intelligence and MRI in sinonasal tumors discrimination: where do we stand?

Gravante,

Arosio,

Curti

et al. 2024

Eur Arch Otorhinolaryngol

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Artificial intelligence and MRI in sinonasal tumors discrimination: where do we stand?

Gravante,

Arosio,

Curti

et al. 2024

Eur Arch Otorhinolaryngol

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Prediction of recurrence-free survival and risk factors of sinonasal inverted papilloma after surgery by machine learning models

Miao,

Cheng,

et al. 2024

Eur J Med Res

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Objectives Our research aims to construct machine learning prediction models to identify patients proned to recurrence after inverted papilloma (IP) surgery and guide their follow-up treatment. Methods This study collected 210 patients underwent IP resection surgery at a university hospital from January 2010 to December 2023. Six machine learning algorithms including ExtraSurvivalTrees (EST), GradientBoostingSurvivalAnalysis (GBSA), RandomSurvivalForest (RSF), SurvivalSVM, Coxnet and Coxph, were used to construct the prediction models. Shapley Additive Explanations (SHAP) values were used to explain the importance of various features in predicting IP recurrence. Results We found that the recurrence rate of IP patients is 20.00%, with a median recurrence time of 35.5 months. Multivariate Cox regression analysis identified mild or moderate dysplasia as an independent risk factor for recurrence. The EST model performs the best in predicting postoperative recurrence of IP, with C-index of 0.968 and 0.878 in the training and testing sets. SHAP emphasizes five important predictive factors for recurrence, including bone defects, orbital involvement, smoking, no processing of tumor attachment sites and drinking. Conclusions To our knowledge, this is the first study to use multiple ML models to predict postoperative recurrence of IP. The EST model has the best predictive performance, with SHAP emphasizing several key predictive factors for IP recurrence. This study emphasizes the practicality of machine learning algorithms in predicting IP clinical outcomes, providing valuable insights into the potential for improving clinical decision-making. Supplementary Information The online version contains supplementary material available at 10.1186/s40001-024-02099-6.

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Comparative analysis of traditional machine learning and automated machine learning: advancing inverted papilloma versus associated squamous cell carcinoma diagnosis

Abstract: Key Points Inverted papilloma conversion to squamous cell carcinoma is not always easy to predict. AutoML requires much less technical knowledge and skill to use than traditional ML. AutoML surpassed the traditional ML algorithm in differentiating IP from IP‐SCC.

Cited by 2 publications

References 7 publications

Artificial intelligence and MRI in sinonasal tumors discrimination: where do we stand?

Artificial intelligence and MRI in sinonasal tumors discrimination: where do we stand?

Prediction of recurrence-free survival and risk factors of sinonasal inverted papilloma after surgery by machine learning models

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