Auris, nasus, larynx · 2024
Development of machine learning models to predict papillary carcinoma in thyroid nodules: The role of immunological, radiologic, cytologic and radiomic features.
СтатьяНазвание и аннотация: Перевод пока не подготовлен
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Canali Luca, Gaino Francesca, Costantino Andrea, Guizzardi Mathilda, Carnicelli Giorgia, Gullà Federica, Russo Elena, Spriano Giuseppe, Giannitto Caterina, Mercante Giuseppe
Аннотация
OBJECTIVE: Approximately 30 % of thyroid nodules yield an indeterminate diagnosis through conventional diagnostic strategies. The aim of this study was to develop machine learning (ML) models capable of identifying papillary thyroid carcinomas using preoperative variables.
METHODS: Patients with thyroid nodules undergoing thyroid surgery were enrolled in a retrospective monocentric study. Six 2-class supervised ML models were developed to predict papillary thyroid carcinoma, by sequentially incorporating clinical-immunological, ultrasonographic, cytological, and radiomic variables.
RESULTS: Out of 186 patients, 92 nodules (49.5 %) were papillary thyroid carcinomas in the histological report. The Area Under the Curve (AUC) ranged from 0.41 to 0.61 using only clinical-immunological variables. All ML models exhibited an increased performance when ultrasound variables were included (AUC: 0.95-0.97). The addition of cytological (AUC: 0.86-0.97) and radiomic (AUC: 0.88-0.97) variables did not further improve ML models' performance.
CONCLUSION: ML algorithms demonstrated low accuracy when trained with clinical-immunological data. However, the inclusion of radiological data significantly improved the models' performance, while cytopathological and radiomics data did not further improve the accuracy.
LEVEL OF EVIDENCE: Level 4.
METHODS: Patients with thyroid nodules undergoing thyroid surgery were enrolled in a retrospective monocentric study. Six 2-class supervised ML models were developed to predict papillary thyroid carcinoma, by sequentially incorporating clinical-immunological, ultrasonographic, cytological, and radiomic variables.
RESULTS: Out of 186 patients, 92 nodules (49.5 %) were papillary thyroid carcinomas in the histological report. The Area Under the Curve (AUC) ranged from 0.41 to 0.61 using only clinical-immunological variables. All ML models exhibited an increased performance when ultrasound variables were included (AUC: 0.95-0.97). The addition of cytological (AUC: 0.86-0.97) and radiomic (AUC: 0.88-0.97) variables did not further improve ML models' performance.
CONCLUSION: ML algorithms demonstrated low accuracy when trained with clinical-immunological data. However, the inclusion of radiological data significantly improved the models' performance, while cytopathological and radiomics data did not further improve the accuracy.
LEVEL OF EVIDENCE: Level 4.