Radiotherapy and Oncology最新文献

Background and purpose: To ensure the proper implementation of stereotactic body radiotherapy (SBRT) for spinal metastases in a multicenter clinical trial, this study evaluated the compliance and variability in contouring for spine SBRT.

Materials and methods: A dummy run was conducted at 34 institutions. Computed tomography (CT) and T1/T2-weighted magnetic resonance imaging (MRI) of a patient with fifth thoracic spinal metastasis involving the vertebral body and right pedicle were provided. The observers performed CT-MRI registration and delineated the gross tumor volume (GTV), clinical target volume (CTV), and spinal cord. A radiation oncologist from the study office performed a central review of 17 checklist items. Agreement was assessed using the Dice similarity coefficient.

Results: An average of 3.0 protocol deviations per institution was observed. Deviations related to CTV setting in sectors adjacent to the GTV, the addition of a CTV margin to the paraspinal disease, and accurate delineation of the spinal cord were noted in 11, 13, and 12 institutions, respectively. The mean ± standard deviation Dice similarity coefficients for the GTV, CTV, and spinal cord were 0.84 ± 0.06, 0.85 ± 0.04, and 0.76 ± 0.09, respectively. Although nine institutions had clinically unacceptable errors, all met the criteria on the first resubmission. The mean Dice similarity coefficients for the spinal cord improved from 0.66 to 0.82 after resubmission.

Conclusion: This study revealed frequent deviations in CTV setting and spinal cord delineation. This dummy run clarified critical points in contouring for spine SBRT, contributing to the standardization of treatment planning.

Background and purpose: Radiation-induced aspiration is a serious complication following (chemo)radiation for head and neck cancer. The standard set of swallowing organs at risk (SWOARs) does not include all aspiration-related organs (OARs). An alternative proposed in earlier work includes a definition and delineation atlas for additional OARs, called Functional Swallowing Units (FSU). The purpose of this study was to compare two NTCP models for late aspiration, based on either SWOARs only or the FSU concept.

Methods and materials: Data from 189 patients were analysed. Aspiration at baseline (Asp_T0) and 6 months after treatment (Asp_T6) were scored according to Penetration Aspiration Scale (PAS). All SWOARs and FSUs were delineated and the DVH was recorded. Clinical factors and average dose (Dmean) to all aspiration-related OARs were included in multivariable analysis. Two models were built, model 1: including clinical factors and SWOARs only and model 2: including clinical factors, SWOARs and FSUs.

Results: Both final models included Asp_T0 and Dmean to the supraglottic larynx as predictors. Model 2 included the dose to three additional OARs as a predictor: 1) Anterior Segment (floor of mouth/ thyrohyoid muscles) 2) hyoglossus/styloglossus muscles complex (HSG) 3) upper oesophageal sphincter (UES). Adding FSUs to model 1 resulted in significant model updates and model 2 performed better than model 1 (AUC 0.79 vs. 0.75).

Conclusion: NTCP models for late aspiration may be improved by including the dose to aspiration-related OARs, defined by the FSU concept. In addition to the supraglottic larynx, sparing of the Anterior Segment, HSG and UES could further decrease the risk of radiation-induced aspiration, but this remains to be confirmed in clinical studies.

Purpose: This study aims to develop an artificial intelligence model to predict severe radiation-induced oral mucositis (RIOM) in patients with locally advanced nasopharyngeal carcinoma (LA-NPC) and verify the risk factors associated with severe RIOM.

Methods and materials: A total of 578 patients diagnosed with LA-NPC and undergoing radiotherapy were enrolled in this study. This cohort comprised 430 retrospective patients used for model development/validation, and 148 patients for the prospective verification study. Multifaceted data related to RIOM were collected to build an explainable multi-classifier fusion (MCF) model to identify severe RIOM associated risk factors. A prospective study was designed to validate the key risk factors.

Results: The MCF model demonstrated satisfactory performance in severe RIOM prediction when integrating all dosimetric, clinical, and oral features, with an AUC of 0.904, ACC of 0.849, SEN of 0.853 and SPE of 0.846 on the independent testing set. The dental calculus index of 2 was identified as a significant key risk factor for developing RIOM. The severe RIOM rate in the prospective intervention cohort was 8.1 % (95 % CI:4.3 %∼13.7 %), lower than that in the model development cohort, with a decrease of 31 % (95 % CI23.9 %∼36.8 %, p < 0.0001).

Conclusions: The developed model can serve as a valuable tool for providing timely alerts for high-risk patients with the severe RIOM and assisting physicians in optimizing treatment management. The dental calculus index is a key independent risk factor for severe RIOM. The effective control of the dental calculus can significantly mitigate the onset of severe RIOM.

Clinicaltrials: gov: NCT05858385.

Background and purpose: Artificial intelligence (AI) may extract prognostic information from MRI for localized prostate cancer. We evaluate whether AI-derived prostate and gross tumor volume (GTV) are associated with toxicity and oncologic outcomes after radiotherapy.

Materials and methods: We conducted a retrospective study of patients, who underwent radiotherapy between 2010-2017. We trained an AI segmentation algorithm to contour the prostate and GTV from patients treated with external-beam RT, and applied the algorithm to those treated with brachytherapy. AI prostate and GTV volumes were calculated from segmentation results. We evaluated whether AI GTV volume was associated with biochemical failure (BF) and metastasis. We evaluated whether AI prostate volume was associated with acute and late grade 2+ genitourinary toxicity, and International Prostate Symptom Score (IPSS) resolution for monotherapy and combination sets, separately.

Results: We identified 187 patients who received brachytherapy (monotherapy (N = 154) or combination therapy (N = 33)). AI GTV volume was associated with BF (hazard ratio (HR):1.28[1.14,1.44];p < 0.001) and metastasis (HR:1.34[1.18,1.53;p < 0.001). For the monotherapy subset, AI prostate volume was associated with both acute (adjusted odds ratio:1.16[1.07,1.25];p < 0.001) and late grade 2 + genitourinary toxicity (adjusted HR:1.04[1.01,1.07];p = 0.01), but not IPSS resolution (0.99[0.97,1.00];p = 0.13). For the combination therapy subset, AI prostate volume was not associated with either acute (p = 0.72) or late (p = 0.75) grade 2 + urinary toxicity. However, AI prostate volume was associated with IPSS resolution (0.96[0.93, 0.99];p = 0.01).

Conclusion: AI-derived prostate and GTV volumes may be prognostic for toxicity and oncologic outcomes after RT. Such information may aid in treatment decision-making, given differences in outcomes among RT treatment modalities.