Initial Experience of CT-Based Online Adaptive Radiotherapy for Nasopharyngeal Carcinoma With a Novel Integrated Platform: A Case Report.

Intensity-modulated radiation therapy (IMRT) improves tumor control and reduces long-term radiation-induced complications of patients with nasopharyngeal carcinoma (NPC), contingent upon accurate contouring and precise delivery of treatment plans. Online adaptive radiotherapy (ART) involves real-time treatment plan modification based on the variations in targets and organs at risk (OARs) to uphold treatment planning accuracy. This study describes the first reported case of fan beam computed tomography (FBCT)-guided online ART for NPC using a novel integrated platform. Online ART was performed at the 25th fraction in this case, as tumors and the patient's anatomy were observed to regress inter-fractionally, necessitating adjustments to the contours based on the anatomy of the day. Online ART plan optimized target volume coverage while reducing doses to OARs. Notably, online ART significantly improved radiotherapy efficiency. This patient achieved a clinical complete response 12 weeks post-treatment, with Epstein-Barr virus DNA levels reduced to 0 copies/ml. Currently, the patient is alive without evidence of high-grade toxicity or local recurrence at approximately 10 months post-treatment. This case confirms the feasibility and dosimetric benefit of online ART for NPC using a novel integrated platform. Further research is needed to confirm its clinical benefits.

Analysis of Individualized Silicone Rubber Bolus Using Fan Beam Computed Tomography in Postmastectomy Radiotherapy: A Dosimetric Evaluation and Skin Acute Radiation Dermatitis Survey.

Objective: To investigate the dosimetric effects of using individualized silicone rubber (SR) bolus on the target area and organs at risk (OARs) during postmastectomy radiotherapy (PMRT), as well as evaluate skin acute radiation dermatitis (ARD). Methods: A retrospective study was performed on 30 patients with breast cancer. Each patient was prepared with an individualized SR bolus of 3 mm thickness. Fan-beam computed tomography (FBCT) was performed at the first and second fractions, and then once a week for a total of 5 times. Dosimetric metrics such as homogeneity index (HI), conformity index (CI), skin dose (SD), and OARs including the heart, lungs, and spinal cord were compared between the original plan and the FBCTs. The acute side effects were recorded. Results: In targets' dosimetric metrics, there were no significant differences in Dmean and V105% between planning computed tomography (CT) and actual treatments (P > .05), while the differences in D95%, V95%, HI, and CI were statistically significant (P < .05). In OARs, there were no significant differences between the Dmean, V5, and V20 of the affected lung, V5 of the heart and Dmax of the spinal cord (P > .05) except the V30 of affected lung, which was slightly lower than the planning CT (P < .05). In SD, both Dmax and Dmean in actual treatments were increased than plan A, and the difference was statistically significant (P < .05), while the skin-V20 and skin-V30 has no difference. Among the 30 patients, only one patient had no skin ARD, and 5 patients developed ARD of grade 2, while the remaining 24 patients were grade 1. Conclusion: The OR bolus showed good anastomoses and high interfraction reproducibility with the chest wall, and did not cause deformation during irradiation. It ensured accurate dose delivery of the target and OARs during the treatment, which may increase SD by over 101%. In this study, no cases of grade 3 skin ARD were observed. However, the potential of using OR bolus to reduce grade 1 and 2 skin ARD warrants further investigation with a larger sample size.

Clinical evaluation of deep learning-based clinical target volume three-channel auto-segmentation algorithm for adaptive radiotherapy in cervical cancer.

OBJECTIVES: Accurate contouring of the clinical target volume (CTV) is a key element of radiotherapy in cervical cancer. We validated a novel deep learning (DL)-based auto-segmentation algorithm for CTVs in cervical cancer called the three-channel adaptive auto-segmentation network (TCAS). METHODS: A total of 107 cases were collected and contoured by senior radiation oncologists (ROs). Each case consisted of the following: (1) contrast-enhanced CT scan for positioning, (2) the related CTV, (3) multiple plain CT scans during treatment and (4) the related CTV. After registration between (1) and (3) for the same patient, the aligned image and CTV were generated. Method 1 is rigid registration, method 2 is deformable registration, and the aligned CTV is seen as the result. Method 3 is rigid registration and TCAS, method 4 is deformable registration and TCAS, and the result is generated by a DL-based method. RESULTS: From the 107 cases, 15 pairs were selected as the test set. The dice similarity coefficient (DSC) of method 1 was 0.8155 ± 0.0368; the DSC of method 2 was 0.8277 ± 0.0315; the DSCs of method 3 and 4 were 0.8914 ± 0.0294 and 0.8921 ± 0.0231, respectively. The mean surface distance and Hausdorff distance of methods 3 and 4 were markedly better than those of method 1 and 2. CONCLUSIONS: The TCAS achieved comparable accuracy to the manual delineation performed by senior ROs and was significantly better than direct registration.