BMC medical imaging · 2026
Predicting placenta accreta spectrum antenatally: a deep learning-radiomics dual-model based on intraoperative clinical and histopathologic criteria.
СтатьяНазвание и аннотация: Перевод в очереди
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.
Hu Yumin, Shen Chenqi, Shen Di, Chen Xiaojun, Zhao Hongyan, Li Xia, Zhou Qing, Shi Feng, Huang Yechao, Xu Wensong, Lu Chenying
Аннотация
PURPOSE: Definitive diagnosis of placenta accreta spectrum (PAS) disorders occurs at delivery by using combined intraoperative clinical and histopathologic diagnostic criteria per the recently updated International Federation of Gynecology and Obstetrics (FIGO) classification system. The purpose of this study is to construct a dual diagnostic model based on intraoperative clinical and histopathologic criteria for PAS in accordance with FIGO's diagnostic criteria.
MATERIALS AND METHODS: This study pooled data from two centers. Diagnostic models based on histopathologic and intraoperative clinical criteria were separately developed using radiomics, deep learning, and clinical features. Subsequently, these individual models were integrated. Moreover, the correlations among clinical indicators, radiomics, and deep-learning features were explored. The Mann-Whitney U test was used for continuous variables with non-normal distribution. Categorical data were analyzed by Fisher's exact test or 2 × 2 or R×C χ2 test. A P value < 0.05 was considered significant.
RESULTS: For the FIGO intraoperative clinical criteria model, the fusion model's AUC was 0.964 (0.946-0.982) in the training set and 0.949 (0.902-0.996) in the test set. For the FIGO histopathologic criteria model, the training-set AUC was 0.961 (0.940-0.982), and the validation cohort AUC was 0.936 (0.883-0.990). In addition, several clinical factors and magnetic resonance imaging (MRI) features of PAS were found to correlate with radiomics scores and deep learning scores in both the intraoperative clinical and histopathologic criteria models.
CONCLUSION: In this study, we constructed a dual model that includes FIGO intraoperative clinical and histopathologic criteria for predicting PAS. The two models that integrate deep learning, radiomics, and clinical features have good performance.
MATERIALS AND METHODS: This study pooled data from two centers. Diagnostic models based on histopathologic and intraoperative clinical criteria were separately developed using radiomics, deep learning, and clinical features. Subsequently, these individual models were integrated. Moreover, the correlations among clinical indicators, radiomics, and deep-learning features were explored. The Mann-Whitney U test was used for continuous variables with non-normal distribution. Categorical data were analyzed by Fisher's exact test or 2 × 2 or R×C χ2 test. A P value < 0.05 was considered significant.
RESULTS: For the FIGO intraoperative clinical criteria model, the fusion model's AUC was 0.964 (0.946-0.982) in the training set and 0.949 (0.902-0.996) in the test set. For the FIGO histopathologic criteria model, the training-set AUC was 0.961 (0.940-0.982), and the validation cohort AUC was 0.936 (0.883-0.990). In addition, several clinical factors and magnetic resonance imaging (MRI) features of PAS were found to correlate with radiomics scores and deep learning scores in both the intraoperative clinical and histopathologic criteria models.
CONCLUSION: In this study, we constructed a dual model that includes FIGO intraoperative clinical and histopathologic criteria for predicting PAS. The two models that integrate deep learning, radiomics, and clinical features have good performance.