Risk factors for the long-term persistent genitourinary toxicity after stereotactic body radiation therapy for localized prostate cancer: A single-center, retrospective study of 306 patients.

OBJECTIVES: To identify risk factors for the long-term persistent genitourinary toxicity (GUT) after stereotactic body radiation therapy (SBRT) for localized prostate cancer (PCa). METHODS: A total of 306 patients who underwent SBRT at our institution between March 2017 and April 2022 were retrospectively evaluated. SBRT was performed at 35 Gy in five fractions over 5 or 10 days. Factors related to the long-term persistence of acute GUT after SBRT were analyzed. RESULTS: During the median follow-up period of 39.1 months, 203 (66%) patients experienced any grade of acute GUT, which remained in 78 (26%) patients 6 months after SBRT. Multivariate analysis revealed that age ≥75 years was consistently a significant independent risk factor for any grade of acute GUT 6, 12, and 24 months after SBRT (hazard ratio [HR] 2.31, p = 0.010; HR 2.84, p = 0001; and HR 3.05, p = 0.009, respectively). Older age was not a significant risk factor for the development of grade ≥2 acute GUT. The duration of acute GUT was significantly longer in the older group than in the nonolder group (median duration = 234 vs. 61 days, p < 0.001), and the incidence of persistent GUT was significantly more frequent in the older group beyond 6 months after SBRT. CONCLUSIONS: Older age is a significant independent risk factor for the long-term persistent GUT after SBRT for localized PCa.

Clinical evaluation of deep learning and atlas-based auto-segmentation for organs at risk delineation.

Manual delineation of organs at risk and clinical target volumes is essential in radiotherapy planning. Atlas-based auto-segmentation (ABAS) algorithms have become available and been shown to provide accurate contouring for various anatomical sites. Recently, deep learning auto-segmentation (DL-AS) algorithms have emerged as the state-of-the-art in medical image segmentation. This study aimed to evaluate the effect of auto-segmentation on the clinical workflow for contouring different anatomical sites of cancer, such as head and neck (H&N), breast, abdominal region, and prostate. Patients with H&N, breast, abdominal, and prostate cancer (n = 30 each) were enrolled in the study. Twenty-seven different organs at four sites were evaluated. RayStation was used to apply the ABAS. Siemens AI-Rad Companion Organs RT was used to apply the DL-AS. Evaluations were performed with similarity indices using geometric methods, time-evaluation, and qualitative scoring visual evaluations by radiation oncologists. The DL-AS algorithm was more accurate than ABAS algorithm on geometric indices for half of the structures. The qualitative scoring results of the two algorithms were significantly different, and DL-AS was more accurate on many contours. DL-AS had 41%, 29%, 86%, and 15% shorter edit times in the HnN, breast, abdomen, and prostate groups, respectively, than ABAS. There were no correlations between the geometric indices and visual assessments. The time required to edit the contours was considerably shorter for DL-AS than for ABAS. Auto-segmentation with deep learning could be the first step for clinical workflow optimization in radiotherapy.

[The effect of weekly docetaxel and concurrent radiation for advanced breast cancer--case report].

Patients with locally advanced breast cancer were treated with intravenously administration of weekly docetaxel (20 mg/m2) and concurrent radiation therapy (66-70 Gy). A complete response was achieved in both cases and toxicities were tolerable. The protocol was effective as a radical or palliative treatment for locally advanced breast cancer.