International journal of gynecological cancer : official journal of the International Gynecological Cancer Society · 2026
Predicting high-grade lesions and cervical cancer with supervised machine learning algorithms.
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.
López-Alza Laura C, Mejía-Pérez Dauris L, Amaya-Guio Jairo, Orjuela-Cañon Alvaro D
Аннотация
OBJECTIVE: To evaluate machine learning models for predicting high-grade squamous intra-epithelial lesions and cervical intra-epithelial neoplasia grade 2 or higher using sociodemographic and clinical parameters.
METHODS: A retrospective cohort study was conducted using data from the Subred Norte (Bogotá, Colombia), a public health network, between 2018 and 2024. Records lacking sociodemographic data were excluded. To address the 10% class imbalance, random under-sampling was applied to train balanced models, which were then compared with the original imbalanced models.
RESULTS: The final sample included 4367 patients (aged 21 to 65 years) with cytology, colposcopy, or biopsy reports. The real-world prevalence of histopathologically confirmed cervical intra-epithelial neoplasia grade 2 or higher in this cohort was 10%. Five machine learning classification methods were tested across 2 datasets: 2018-2022 (n = 3139, excluding human papillomavirus history due to under-reporting) and 2023-2024 (n = 1228, including human papillomavirus history). On the original imbalanced dataset (without human papillomavirus history), the random forest model achieved an area under the receiver operating characteristic curve of 0.91 and a specificity of 99%, but had a sensitivity of only 41%. After applying random under-sampling, the random forest model's sensitivity improved to 89%, with a precision of 84%. For the dataset including human papillomavirus history, the support vector machine model achieved the highest balanced performance, yielding similar metrics. However, because these models were developed and evaluated strictly within a single institution, these balanced metrics represent optimistic internal validation performance.
CONCLUSIONS: Machine learning models show promising performance for predicting cervical intra-epithelial neoplasia grade 2 or higher, with improved sensitivity after addressing class imbalance. These findings support further external, multi-center validation before clinical implementation.
METHODS: A retrospective cohort study was conducted using data from the Subred Norte (Bogotá, Colombia), a public health network, between 2018 and 2024. Records lacking sociodemographic data were excluded. To address the 10% class imbalance, random under-sampling was applied to train balanced models, which were then compared with the original imbalanced models.
RESULTS: The final sample included 4367 patients (aged 21 to 65 years) with cytology, colposcopy, or biopsy reports. The real-world prevalence of histopathologically confirmed cervical intra-epithelial neoplasia grade 2 or higher in this cohort was 10%. Five machine learning classification methods were tested across 2 datasets: 2018-2022 (n = 3139, excluding human papillomavirus history due to under-reporting) and 2023-2024 (n = 1228, including human papillomavirus history). On the original imbalanced dataset (without human papillomavirus history), the random forest model achieved an area under the receiver operating characteristic curve of 0.91 and a specificity of 99%, but had a sensitivity of only 41%. After applying random under-sampling, the random forest model's sensitivity improved to 89%, with a precision of 84%. For the dataset including human papillomavirus history, the support vector machine model achieved the highest balanced performance, yielding similar metrics. However, because these models were developed and evaluated strictly within a single institution, these balanced metrics represent optimistic internal validation performance.
CONCLUSIONS: Machine learning models show promising performance for predicting cervical intra-epithelial neoplasia grade 2 or higher, with improved sensitivity after addressing class imbalance. These findings support further external, multi-center validation before clinical implementation.