DAART: a deep learning platform for deeply accelerated adaptive radiation therapy for lung cancer - Hooshangnejad, Hamed; Chen, Quan; Feng, Xue; Zhang, Rui; Farjam, Reza; Voong, Khinh Ranh; Hales, Russell K.; Du, Yong; Jia, Xun; Ding, Kai (Frontiers in Oncology, 2023)
Purpose: Implementation of the novel deeply accelerated adaptive radiation therapy (DAART) approach for lung cancer radiotherapy (RT). Lung cancer is the most common cause of cancer-related death and RT is the preferred medically inoperable treatment for early-stage non-small cell lung cancer (NSCLC). In the current lengthy workflow, it takes a median of four weeks from diagnosis to RT treatment which can result in the complete restaging and loss of local control with delay. We implemented DAART approach, featuring a novel deepPERFECT system, to address the unwanted delay between diagnosis and treatment initiation. Materials and Methods: We developed deepPERFECT to adapt the initial diagnostic imaging to treatment setup to allow the initial RT planning and verification. We used the data from 15 NSCLC patients treated with RT for training the model and testing its performance. We conducted a virtual clinical trial to evaluate the treatment quality of our proposed DAART for lung cancer radiotherapy. Results: We found that deepPERFECT predicts the planning CT with high-intensity fidelity of mean 83 HU and 14 HU for the body and lungs. The shape of the body and lungs on synthesized CT was highly conformal with a dice similarity coefficient (DSC) of 0.91 and 0.97 and Hausdorff distance (HD) of 7.9mm, and 4.9mm, respectively, compared with planning CT scan. The tumor showed less conformality which warrants the acquisition of treatment Day1 CT and online adaptive RT. An initial plan was designed on synthesized CT and then was adapted to treatment Day1 CT using the adapt to position (ATP) and adapt to shape (ATS) method. Noninferior plan quality was achieved by the ATP scenario, while all ATS-adapted plans showed good plan quality. Conclusion: DAART reduces the common online ART (ART) treatment course by at least two weeks, resulting in a 50% shorter time to treatment to lower the chance of restaging and loss of local control.