Background: Organs at risk (OARs) delineation is a crucial step of radiotherapy (RT) treatment planning workflow. Time-consuming and inter-observer variability are main issues in manual OAR delineation, mainly in the head and neck (H & N) district. Deep-learning based auto-segmentation is a promising strategy to improve OARs contouring in radiotherapy departments. A comparison of deep-learning-generated auto-contours (AC) with manual contours (MC) was performed by three expert radiation oncologists from a single center. Methods: Planning computed tomography (CT) scans of patients undergoing RT treatments for H&N cancers were considered. CT scans were processed by Limbus Contour auto-segmentation software, a commercial deep-learning auto-segmentation based software to generate AC. H&N protocol was used to perform AC, with the structure set consisting of bilateral brachial plexus, brain, brainstem, bilateral cochlea, pharyngeal constrictors, eye globes, bilateral lens, mandible, optic chiasm, bilateral optic nerves, oral cavity, bilateral parotids, spinal cord, bilateral submandibular glands, lips and thyroid. Manual revision of OARs was performed according to international consensus guidelines. The AC and MC were compared using the Dice similarity coefficient (DSC) and 95% Hausdorff distance transform (DT). Results: A total of 274 contours obtained by processing CT scans were included in the analysis. The highest values of DSC were obtained for the brain (DSC 1.00), left and right eye globes and the mandible (DSC 0.98). The structures with greater MC editing were optic chiasm, optic nerves and cochleae. Conclusions: In this preliminary analysis, deep-learning auto-segmentation seems to provide acceptable H&N OAR delineations. For less accurate organs, AC could be considered a starting point for review and manual adjustment. Our results suggest that AC could become a useful time-saving tool to optimize workload and resources in RT departments.

Artificial Intelligence (AI) is revolutionising many aspects of radiotherapy (RT), opening scenarios that were unimaginable just a few years ago. The aim of this study is to propose a Deep Leaning (DL) approach able to quickly generate synthetic Computed Tomography (CT) images from low-dose Cone Beam CT (CBCT) acquired on a modern linear accelerator integrating AI. Methods: A total of 53 patients treated in the pelvic region were enrolled and split into training (30), validation (9), and testing (14). A Generative Adversarial Network (GAN) was trained for 200 epochs. The image accuracy was evaluated by calculating the mean and mean absolute error (ME and ME) between sCT and CT. RT treatment plans were calculated on CT and sCT images, and dose accuracy was evaluated considering Dose Volume Histogram (DVH) and gamma analysis. Results: A total of 4507 images were selected for training. The MAE and ME values in the test set were 36 ± 6 HU and 7 ± 6 HU, respectively. Mean gamma passing rates for 1%/1 mm, 2%/2 mm, and 3%/3 mm tolerance criteria were respectively 93.5 ± 3.4%, 98.0 ± 1.3%, and 99.2 ± 0.7%, with no difference between curative and palliative cases. All the DVH parameters analysed were within 1 Gy of the difference between sCT and CT. Conclusion: This study demonstrated that sCT generation using the DL approach is feasible on low-dose CBCT images. The proposed approach can represent a valid tool to speed up the online adaptive procedure and remove CT simulation from the RT workflow.

Background/Objectives: High-dose-rate (HDR) interventional radiotherapy (IRT) represents a valuable treatment alternative for basal cell carcinoma (BCC) of the midface, especially in the presence of esthetic or functional issues related to surgery. This retrospective study examines the clinical outcomes of patients who underwent HDR-IRT at our institution. Methods: Patients (pts) with histologically confirmed midface BCC were treated with HDR-IRT using superficial, interstitial, or endocavitary implants depending on tumor characteristics. Fractionation regimens were tailored to tumor location: 45 Gy in 9 fractions twice a day (the cheeks/lips) and 44 Gy in 14 fractions (the nose, with the first and last fractions delivering 4 Gy each, administered once daily, and the remaining 12 fractions delivering 3 Gy each, administered twice daily (BID) with an inter-fraction interval of at least 6 h). Treatment efficacy was assessed based on local control rates, toxicity (CTCAE criteria), and cosmetic outcomes. Results: Eight patients were considered. The most common tumor site was the nose (seven patients, 87.5%), followed by the upper lip. Tumors were either primary (three patients, 37.5%) or residual/recurrent after previous surgery with involved margins (five patients, 62.5%). The median follow-up was 6 months (range: 1–19 months). Clinical local control was achieved in all cases, with persistent alteration at dermoscopy in one patient six months after the completion of treatment. Acute toxicities were minimal, with two patients developing grade 3 skin toxicity, which resolved within 1–3 months with topical management. Cosmetic outcomes were favorable across all patients. Conclusions: HDR-IRT is a well-tolerated, effective, and cosmetically favorable treatment for midface BCC. Our institutional experience supports its use as an alternative to surgery in cosmetically sensitive areas and in selected patients/lesions unfit for surgery. Endocavitary implants appear to be an effective option to cover the full thickness nasal wall without resorting to interstitial implants.