Nuclear medicine communications · 2026
Predicting persistent/recurrent cervical lymph node metastasis in papillary thyroid carcinoma with PET/CT-based multimodal radiomics: an image-pathology matching study.
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.
Chen Pan, Duan Yifei, Liang Chuang, Xiao Shuitian, Chen Erhao, Wang Jing, Lu Jianxing, Feng Huijuan, Ouyang Wei
Аннотация
OBJECTIVE: Current evidence regarding radiomics-based assessment of persistent/recurrent cervical lymph node metastasis (CLNM) remains limited, particularly in the field of PET/CT in papillary thyroid cancer (PTC). Therefore, we aimed to construct a prediction model for persistent/recurrent CLNM of PTC by fluoro-18-deoxyglucose ( 18 F-FDG) PET/computed tomography (CT) multimodal radiomics.
METHODS: We retrospectively analyzed postoperative patients with PTC who underwent 18 F-FDG PET/CT at our hospital between June 2021 and June 2024. A total of 425 CLNs (219 metastatic and 206 nonmetastatic) from 158 patients were included, and the patients were randomly assigned to a training set and a test set at a ratio of 7 : 3. Pearson correlation analysis and least absolute shrinkage and selection operator regression were utilized to select PET/CT radiomic features for model development. Five radiomics models were developed based on distinct feature sets: clinical features alone, CT radiomics, PET radiomics, PET/CT radiomics, and integrated PET/CT radiomics combined with clinical features. Subsequently, a nomogram model was built by combining radscore and clinical features.
RESULTS: The integrated model demonstrated superior diagnostic accuracy, with areas under the curve (AUCs) of 0.944 in the training set and 0.922 in the test set. PET/CT models demonstrated the following AUCs: PET/CT (0.887), PET (0.834), CT (0.775), and clinical features alone (0.783). A nomogram incorporating clinical features and radscores was then developed, achieving C-indices of 0.937 in the training set and 0.895 in the test set.
CONCLUSION: The integration of PET/CT radiomics and clinical features may provide a noninvasive tool for predicting persistent/recurrent CLNM in postoperative PTC patients, with potential to support clinical decision‑making.
METHODS: We retrospectively analyzed postoperative patients with PTC who underwent 18 F-FDG PET/CT at our hospital between June 2021 and June 2024. A total of 425 CLNs (219 metastatic and 206 nonmetastatic) from 158 patients were included, and the patients were randomly assigned to a training set and a test set at a ratio of 7 : 3. Pearson correlation analysis and least absolute shrinkage and selection operator regression were utilized to select PET/CT radiomic features for model development. Five radiomics models were developed based on distinct feature sets: clinical features alone, CT radiomics, PET radiomics, PET/CT radiomics, and integrated PET/CT radiomics combined with clinical features. Subsequently, a nomogram model was built by combining radscore and clinical features.
RESULTS: The integrated model demonstrated superior diagnostic accuracy, with areas under the curve (AUCs) of 0.944 in the training set and 0.922 in the test set. PET/CT models demonstrated the following AUCs: PET/CT (0.887), PET (0.834), CT (0.775), and clinical features alone (0.783). A nomogram incorporating clinical features and radscores was then developed, achieving C-indices of 0.937 in the training set and 0.895 in the test set.
CONCLUSION: The integration of PET/CT radiomics and clinical features may provide a noninvasive tool for predicting persistent/recurrent CLNM in postoperative PTC patients, with potential to support clinical decision‑making.