Nuclear medicine communications · 2025
Automatic Lugano staging for risk stratification in lymphoma: a multicenter PET radiomics and machine learning study with survival analysis.
СтатьяМногоцентровое исследованиеНазвание 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.
Hasanabadi Setareh, Aghamiri Seyed Mahmud Reza, Abin Ahmad Ali, Cheraghi Maryam, Bakhshayesh Karam Mehrdad, Vosoughi Habibeh, Emami Farshad, Arabi Hossein
Аннотация
BACKGROUND: Lymphoma staging plays a pivotal role in treatment planning and prognosis. Yet, it still relies on manual interpretation of PET/computed tomography (CT) images, which is time-consuming, subjective, and prone to variability. This study introduces a novel radiomics-based machine learning model for automated lymphoma staging to improve diagnostic accuracy and streamline clinical workflow.
METHODS: Imaging data from 241 patients with histologically confirmed lymphoma were retrospectively analyzed. Radiomics features were extracted from segmented lymph nodes and extranodal lesions using PET/CT. Three machine learning classifiers (Logistic Regression, Random Forest, and XGBoost) were trained to distinguish between early-stage (I-II) and advanced-stage (III-IV) lymphoma. Model performance was evaluated using area under the curve (AUC), sensitivity, specificity, and accuracy together with survival analysis.
RESULTS: Among the three models evaluated, the logistic regression model incorporating both nodal and extranodal radiomic features performed the best, achieving an AUC of 0.87 and a sensitivity of 0.88 in the external validation cohort. Including extranodal features significantly improved classification accuracy compared to nodal-only models (AUC: 0.87 vs. 0.75). Survival analysis revealed advanced-stage patients had a fourfold higher mortality risk (hazard ratio: 0.22-0.26, P = 0.0036) and a median survival of 84 months. Key radiomic features, such as tumor shape irregularity and heterogeneity, were strongly associated with staging, aligning with Lugano criteria for extranodal spread.
CONCLUSION: This study demonstrated the potential of PET radiomics features for automated Lugano staging. Adding extranodal features significantly improved staging accuracy and informed treatment.
METHODS: Imaging data from 241 patients with histologically confirmed lymphoma were retrospectively analyzed. Radiomics features were extracted from segmented lymph nodes and extranodal lesions using PET/CT. Three machine learning classifiers (Logistic Regression, Random Forest, and XGBoost) were trained to distinguish between early-stage (I-II) and advanced-stage (III-IV) lymphoma. Model performance was evaluated using area under the curve (AUC), sensitivity, specificity, and accuracy together with survival analysis.
RESULTS: Among the three models evaluated, the logistic regression model incorporating both nodal and extranodal radiomic features performed the best, achieving an AUC of 0.87 and a sensitivity of 0.88 in the external validation cohort. Including extranodal features significantly improved classification accuracy compared to nodal-only models (AUC: 0.87 vs. 0.75). Survival analysis revealed advanced-stage patients had a fourfold higher mortality risk (hazard ratio: 0.22-0.26, P = 0.0036) and a median survival of 84 months. Key radiomic features, such as tumor shape irregularity and heterogeneity, were strongly associated with staging, aligning with Lugano criteria for extranodal spread.
CONCLUSION: This study demonstrated the potential of PET radiomics features for automated Lugano staging. Adding extranodal features significantly improved staging accuracy and informed treatment.