World journal of radiology · 2026
Immediate post-microwave ablation computed tomography model may better predict short-term response of stage I non-small cell lung cancer.
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.
Qin Qing-Jin, Hou Ming-Yuan, Tian Juan-Juan, Sun Yan-Zhuo, Jing Xiao-Han, Zuo Tai-Yang
Аннотация
BACKGROUND: Microwave ablation (MWA) is indicated for treating inoperable early-stage non-small cell lung cancer.
AIM: To evaluate its therapeutic effect by developing multimodal models that integrate computed tomography (CT) radiomics features and simple clinical characteristics.
METHODS: This study enrolled 50 patients with stage I non-small cell lung cancer who were treated with MWA. Corresponding CT images were collected before and after the MWA, and radiomics features were extracted using the 3D-slicer software platform. In the study, statistical analyses were performed using R studio software. Firstly, radiomics features related to local tumor progression (LTP) at 6 months were screened from the pre-MWA and immediate post-MWA CT radiomics features by the max-relevance and min-redundancy features. This study aimed to evaluate its therapeutic effect by developing multimodal models that integrate CT radiomics features and simple clinical characteristics. Based on the selected radiomics features, prediction models for 6-month LTP were constructed. These models' performance was evaluated by the area under the receiver operating characteristic (ROC) curve (AUC).
RESULTS: Three pre-MWA CT radiomics features and three post-MWA CT radiomics features were confirmed to be correlated with the LTP. For the ROC curves that only covered the pre-MWA CT radiomics, AUC was 0.689. For the ROC curves integrating the pre-MWA CT radiomics plus simple clinical characteristics, AUC was 0.849. For the ROC curves that only covered the immediate post-MWA CT radiomics features, AUC was 0.849. For the ROC curves integrating the immediate post-MWA CT features plus simple clinical characteristics, AUC was 0.907.
CONCLUSION: This exploratory preliminary analysis revealed that the predictive model integrating immediate post-microwave-ablation CT radiomic features and clinical indicators yields superior prognostic performance relative to the model constructed solely from preoperative CT radiomic features.
AIM: To evaluate its therapeutic effect by developing multimodal models that integrate computed tomography (CT) radiomics features and simple clinical characteristics.
METHODS: This study enrolled 50 patients with stage I non-small cell lung cancer who were treated with MWA. Corresponding CT images were collected before and after the MWA, and radiomics features were extracted using the 3D-slicer software platform. In the study, statistical analyses were performed using R studio software. Firstly, radiomics features related to local tumor progression (LTP) at 6 months were screened from the pre-MWA and immediate post-MWA CT radiomics features by the max-relevance and min-redundancy features. This study aimed to evaluate its therapeutic effect by developing multimodal models that integrate CT radiomics features and simple clinical characteristics. Based on the selected radiomics features, prediction models for 6-month LTP were constructed. These models' performance was evaluated by the area under the receiver operating characteristic (ROC) curve (AUC).
RESULTS: Three pre-MWA CT radiomics features and three post-MWA CT radiomics features were confirmed to be correlated with the LTP. For the ROC curves that only covered the pre-MWA CT radiomics, AUC was 0.689. For the ROC curves integrating the pre-MWA CT radiomics plus simple clinical characteristics, AUC was 0.849. For the ROC curves that only covered the immediate post-MWA CT radiomics features, AUC was 0.849. For the ROC curves integrating the immediate post-MWA CT features plus simple clinical characteristics, AUC was 0.907.
CONCLUSION: This exploratory preliminary analysis revealed that the predictive model integrating immediate post-microwave-ablation CT radiomic features and clinical indicators yields superior prognostic performance relative to the model constructed solely from preoperative CT radiomic features.