Definitive treatment of lung cancer with radiotherapy is challenging, as respiratory motion and anatomical changes can increase the risk of severe off-target effects during radiotherapy. Online adaptive radiotherapy (ART) is an evolving approach that enables timely modification of a treatment plan during the interfraction of radiotherapy, in response to physiologic or anatomic variations, aiming to improve the dose distribution for precise targeting and delivery in lung cancer patients. The effectiveness of online ART depends on the seamless integration of multiple components: sufficient quality of linear accelerator-integrated imaging guidance, deformable image registration, automatic recontouring, and efficient quality assurance and workflow. This review summarizes the present status of online ART for lung cancer, including key technologies, as well as the challenges and areas of active research in this field.
Humans ; Lung Neoplasms/radiotherapy* ; Radiotherapy Planning, Computer-Assisted/methods*

Objective:To explore the effects of multimodal imaging on the performance of automatic segmentation of glioblastoma targets for radiotherapy based on a deep learning approach.Methods:The computed tomography (CT) images and the contrast-enhanced T1 weighted (T1C) sequence and the T2 fluid attenuated inversion recovery (T2- FLAIR) sequence of magnetic resonance imaging (MRI) of 30 patients with glioblastoma were collected. The gross tumor volumes (GTV) and their corresponding clinical target volumes CTV1 and CTV2 of the 30 patients were manually delineated according to the criteria of the Radiation Therapy Oncology Group (RTOG). Moreover, four different datasets were designed, namely a unimodal CT dataset (only containing the CT sequences of 30 cases), a multimodal CT-T1C dataset (containing the CT and T1C sequences of 30 cases), a multimodal CT-T2-FLAIR dataset (containing the CT and T2- FLAIR sequences of the 30 cases), and a trimodal CT-MRI dataset (containing the CT, T1C, and T2- FLAIR sequences of 30 cases). For each dataset, the data of 25 cases were used for training the modified 3D U-Net model, while the data of the rest five cases were used for testing. Furthermore, this study evaluated the segmentation performance of the GTV, CTV1, and CTV2 of the testing cases obtained using the 3D U-Net model according to the indices including Dice similarity coefficient (DSC), 95% Hausdorff distance (HD95), and relative volume error (RVE).Results:The best automatic segmentation result of GTV were achieved using the CT-MRI dataset. Compared with the segmentation result using the CT dataset (DSC: 0.94 vs. 0.79, HD95: 2.09 mm vs. 12.33 mm, and RVE: 1.16% vs. 20.14%), there were statistically significant differences in DSC ( t=3.78, P<0.05) and HD95 ( t=4.07, P<0.05) obtained using the CT-MRI dataset. Highly consistent automatic segmentation result of CTV1 and CTV2 were also achieved using the CT-MRI dataset (DSC: 0.90 vs. 0.91, HD95: 3.78 mm vs. 2.41 mm, RVE: 3.61% vs. 5.35%). However, compared to the CT dataset, there were no statistically significant differences in DSC and HD95 of CTV1 and CTV2 ( P>0.05). Additionally, the 3D U-Net model yielded some errors in predicting the upper and lower bounds of GTV and the adjacent organs (e.g., the brainstem and eyeball) of CTV2. Conclusions:The modified 3D U-Net model based on the multimodal CT-MRI dataset can achieve better segmentation result of glioblastoma targets and its application potentially benefits clinical practice.