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Abstract Purpose This study aimed to use propensity score matching (PSM) to explore the long-term outcomes and failure patterns in locally advanced rectal cancer (LARC) patients with positive versus negative lateral pelvic lymph node (LPLN). Materials and methods Patients with LARC were retrospectively divided into LPLN-positive and LPLN-negative groups. Clinical characteristics were compared between the groups using the chi-square test. PSM was applied to balance these differences. Progression-free survival (PFS) and overall survival (OS), and local–regional recurrence (LRR) and distant metastasis (DM) rates were compared between the groups using the Kaplan–Meier method and log-rank tests. Results A total of 651 LARC patients were included, 160 (24.6%) of whom had positive LPLN and 491 (75.4%) had negative LPLN. Before PSM, the LPLN-positive group had higher rates of lower location (53.1% vs. 43.0%, P = 0.025), T4 stage (37.5% vs. 23.2%, P = 0.002), mesorectal fascia (MRF)-positive (53.9% vs. 35.4%, P

Abstract Background and purpose Various deep learning auto-segmentation (DLAS) models have been proposed, some of which have been commercialized. However, the issue of performance degradation is notable when pretrained models are deployed in the clinic. This study aims to enhance precision of a popular commercial DLAS product in rectal cancer radiotherapy by localized fine-tuning, addressing challenges in practicality and generalizability in real-world clinical settings. Materials and methods A total of 120 Stage II/III mid-low rectal cancer patients were retrospectively enrolled and divided into three datasets: training (n = 60), external validation (ExVal, n = 30), and generalizability evaluation (GenEva, n = 30) datasets respectively. The patients in the training and ExVal dataset were acquired on the same CT simulator, while those in GenEva were on a different CT simulator. The commercial DLAS software was first localized fine-tuned (LFT) for clinical target volume (CTV) and organs-at-risk (OAR) using the training data, and then validated on ExVal and GenEva respectively. Performance evaluation involved comparing the LFT model with the vendor-provided pretrained model (VPM) against ground truth contours, using metrics like Dice similarity coefficient (DSC), 95th Hausdorff distance (95HD), sensitivity and specificity. Results LFT significantly improved CTV delineation accuracy (p

PurposeRepeated cone-beam CT (CBCT) scans for image-guided radiotherapy (IGRT) increase the health risk of radiation-induced malignancies. Patient-enrolled studies to optimize scan protocols are inadequate. We proposed a virtual clinical trial-based approach to evaluate projection-reduced low-dose CBCT for IGRT.Materials and methodsA total of 71 patients were virtually enrolled with 26 head, 23 thorax and 22 pelvis scans. Projection numbers of full-dose CBCT scans were reduced to 1/2, 1/4, and 1/8 of the original to simulate low-dose scans. Contrast-to-noise ratio (CNR) values in fat and muscle were measured in the full-dose and low-dose images. CBCT images were registered to planning CT to derive 6-degree-of-freedom couch shifts. Registration errors were statistically analyzed with the Wilcoxon paired signed-rank test.ResultsAs projection numbers were reduced, CNR values descended and the magnitude of registration errors increased. The mean CNR values of full-dose and half-dose CBCT were >3.0. For full-dose and low-dose CBCT (i.e. 1/2, 1/4 and 1/8 full-dose), the mean registration errors were< ± 0.4 mm in translational directions (LAT, LNG, VRT) and ±0.2 degree in rotational directions (Pitch, Roll, Yaw); the mean magnitude of registration errors were< 1 mm in translation and< 0.5 degree in rotation. The couch shift differences between full-dose and low-dose CBCT were not statistically significant (p>0.05) in all the directions.ConclusionThe results indicate that while the impact of dose-reduction on CBCT couch shifts is not significant, the impact on CNR values is significant. Further validation on optimizing CBCT imaging dose is required.

This study evaluated the feasibilities and outcomes following four-dimensional magnetic resonance imaging (4D-MRI) assisted stereotactic body radiation therapy (SBRT) for unresectable colorectal liver metastases (CRLMs). From March 2018 to January 2022, we identified 76 unresectable CRLMs patients with 123 lesions who received 4D-MRI guided SBRT in our institution. 4D-MRI simulation with or without abdominal compression was conducted for all patients. The prescription dose was 50–65 Gy in 5–12 fractions. The image quality of computed tomography (CT) and MRI were compared using the Clarity Score. Clinical outcomes and toxicity profiles were evaluated. 4D-MRI improved the image quality compared with CT images (mean Clarity Score: 1.67 vs 2.88, P

BackgroundThe aim of this article was to establish the clinical prognostic models and identify the predictive radiation dosimetric parameters for thrombocytopenia during concurrent chemoradiotherapy for rectal cancer.MethodsIn this retrospective cohort study, patients with rectal adenocarcinoma undergoing concurrent long-term chemoradiotherapy were included. The primary outcome of interest was grade 2 or higher (2+) thrombocytopenia (platelet(PLT) count