Radiotherapy and Oncology最新文献

Background: Re-irradiation in radiotherapy presents complexities that require dedicated tools to generate optimal re-treatment plans. This study presents a robust workflow that considers fractionation size, anatomical variations between treatments, and cumulative bias doses to improve the re-irradiation planning process.

Methods: The workflow was automated in MIM® Software and the Elekta© Monaco® treatment planning system. Prior treatment doses are deformably mapped, converted to equivalent dose in 2 Gy fractions (EQD2), and accumulated onto the re-treatment planning CT. Two MIM extensions were developed to estimate voxel-wise dose mapping uncertainties and to convert the cumulative EQD2 into a physical dose distribution equivalent to the re-treatment fractionation size. This dose distribution is used in Monaco as bias to optimize the re-irradiation plan. The workflow was retrospectively tested with data from 14 patients, and the outcomes were compared to the manually optimized plans (MOPs) clinically utilized.

Results: Bias-dose guided plans (BDGPs) demonstrated a median reduction of the critical organ at risk (OAR) cumulative EQD2 metrics of 240 cGy (range: 1909 cGy, -187 cGy, p = 0.002). BDGPs allowed higher target coverage in cases where the MOP approach implied dose de-escalation of the target. The dose mapping uncertainties resulted in OAR cumulative EQD2 metrics increments ranging from 10 cGy to 730 cGy.

Conclusions: We introduced a re-irradiation planning workflow using commercially available software that accounts for anatomic and fraction size variations and improves planning efficiency. Employing voxel-level bias dose guidance demonstrated OAR-sparing benefits while maximizing prescription dose coverage to targets. The workflow's robustness tools aid informed clinical decision-making.

Background and purpose: Radiation therapy (RT) is highly effective, but its success depends on accurate, manual target delineation, which is time-consuming, labor-intensive, and prone to variability. Despite AI advancements in auto-contouring normal tissues, accurate RT target volume delineation remains challenging. This study presents Radformer, a novel visual language model that integrates text-rich clinical data with medical imaging for accurate automated RT target volume delineation.

Materials and methods: We developed Radformer, an innovative network that utilizes a hierarchical vision transformer as its backbone and integrates large language models (LLMs) to extract and embed clinical data in text-rich form. The model features a novel visual language attention module (VLAM) to combine visual and linguistic features, enabling language-aware visual encoding (LAVE). The Radformer was evaluated on a dataset of 2985 patients with head-and-neck cancer who underwent RT. Quantitative evaluations were performed utilizing metrics such as the Dice similarity coefficient (DSC), intersection over union (IOU), and 95th percentile Hausdorff distance (HD95).

Results: The Radformer demonstrated superior performance in segmenting RT target volumes compared to state-of-the-art models. On the head-and-neck cancer dataset, Radformer achieved a mean DSC of 0.76 ± 0.09 versus 0.66 ± 0.09, a mean IOU of 0.69 ± 0.08 versus 0.59 ± 0.07, and a mean HD95 of 7.82 ± 6.87 mm versus 14.28 ± 6.85 mm for gross tumor volume delineation, compared to the baseline 3D-UNETR.

Conclusions: The Radformer model offers a clinically optimal means of RT target auto-delineation by integrating both imaging and clinical data through a visual language model. This approach improves the accuracy of RT target volume delineation, facilitating broader AI-assisted automation in RT treatment planning.

Purpose: We provide for the first time a comprehensive situational diagnosis and propose an artificial intelligence (AI)-assisted nationwide plan of implementation, attending the most urgent needs.

Methods: Baseline information was collected from open-source databases of the Peruvian Government. Data on cancer incidence from the Health Authorities and GLOBOCAN were collected and compared. The existing external-beam radiotherapy (EBRT) devices and brachytherapy (BT) units were identified and information on their obsolescence was additionally collected. The ten most common cancer entities with RT indication were considered for the analysis. Utilizing open-source softwares, population clusters based on density, cancer incidence, geographic distribution, existing facilities able to be implemented with radiotherapy and travel times for patients were defined. A coding for identifying the best possible locations with AI was developed, keeping the allocation of resources to the minimum possible. A projection until 2030 on required resources was additionally elaborated.

Results: As of 2023 eight additional EBRT and seven BT devices were needed to cover the existing demand. The artificial-intelligence algorithm yielded the regions where these resources should be primarily allocated. An increase in demand of approximately 22% is expected until 2030, which translates into additional 23 EBRT and 16 BT devices, considering the replacement of obsolete units until then.

Conclusion: Increased investment pace is required to cover the existing RT demand in Peru. This AI-assisted analysis might help prioritize allocation of resources. The code employed in this work will be made publicly available, so this method could be replicated in other developing economies.

Purpose: The randomized phase II GORTEC 2014-04 and French Head and Neck Intergroup study showed deeper deterioration of the quality of life (HRQoL) and dramatically higher severe toxicity rates with similar overall survival rates using chemo-SABR compared to SABR alone in oligometastatic head and neck cancer (HNSCC) patients. We evaluated the costs associated with SABR-alone versus chemo-SABR and their associated costs (transportation, hospitalizations, etc).

Materials and methods: 69 HNSCC patients with 1-3 oligometastases and a controlled primary were randomized from September 2015 to October 2022. HRQoL by the QLQ-C30, QLQ-HN35, descriptive EQ5D-3L and visual EQ-VAS self-rated questionnaires were completed for clinical benefit and economic utility appraisal. Direct medical treatment-related costs (radiotherapy, anticancer drugs, hospital stays, serious adverse event management, medical imaging, biological surveillance and medical transports) were analyzed from randomization until 12 months (M12, including per protocol and salvage treatments) or death. Utility index scores and deterioration rates were used. Based on equivalent outcomes, a cost-minimization analysis was performed..

Results: Median EQ-5D-3L utility index scores were 0.84 at baseline and 0.87 at M12 for SABR-alone; corresponding to 0.85 and 0.57 for chemo-SABR. Rates of patients free of definitive EQ-VAS deterioration at M12 were 76.9 % and 63.8 % for SABR-alone and chemo-SABR. Mean quality-adjusted PFS was 12.1 and 11.0 months with SABR-alone and chemo-SABR. The mean total costs from the French Public health system perspective were €8,498 ± 3,599 for SABR-alone, and €48,034 ± 58,228 for chemo-SABR (p < 10^-4). Sensitivity analyses confirmed cost savings around €35,000-€40,000 per patient using SABR-alone. Anticancer drugs and hospital stays were cost drivers. The economic burden increased by 269 ± 66 % with chemo-SABR compared to SABR-alone (p < 10^-4).

Conclusions: in addition to clinical benefits, SABR-alone appears as the least costly option (by a factor of 5) for the management of oligometastases from HNSCC.

Existing evidence supports the benefits of radiation therapy (RT) for cancer patients however, it is underutilised. This scoping review aims to synthesise the current literature investigating patient and department level barriers and facilitators influencing the utilisation trends of RT. A systematic search strategy was developed to identify articles dated from 1993 to 2023. Four online databases (Medline, Embase, Scopus and CINAHL) were searched using key words. Eligible studies needed to report outcomes related to barriers and facilitators influencing utilisation of RT. Data was extracted and categorised into health professional, patient, and department level influences. The review resulted in 340 included studies with 298 (88 %) studies reporting on patient influences. More than half of these studies (n = 164; 55 %) reported accessibility concerns including distance and travel burden. Patient acceptability was reported in 88 (30 %) studies, patient affordability in 138 (46 %) studies, patient knowledge, and education in 92 (31 %) studies and patient health and demographics in 235 (79 %) studies. Of the department level influence papers (n = 242, 71 %), department availability such as infrastructure, staffing and waitlists were reported in 167 (69 %) papers. Department adequacy, including the quality, reputation and technology suitability of departments was reported in 60 (25 %) papers. Clinical pathway use was reported in 107 (44 %) papers. This scoping review identifies the broad range of patient and department level influences and facilitators affecting the global utilisation of RT. Recognition of such influences reducing access to RT will inform proposed interventions or educational strategies to overcome and address such barriers.

Background and purpose: Radiation induced image changes (IC) on MRI have been observed after proton therapy for brain tumours. This study aims to create predictive models, with and without taking into account patient variation, based on dose, linear energy transfer (LET) and periventricular zone (PVZ) in a national cohort of patients with glioma treated with pencil beam scanning (PBS).

Materials and methods: A cohort of 87 consecutive patients with oligodendroglioma or astrocytoma (WHO grade 2-4) treated with PBS from January 2019 to December 2021 was included. All patients were treated with three to four beams. Monte Carlo calculations of dose and LET were performed for all treatment plans. Lesion weighted as well as mixed effect logistic regression models were developed to predict IC in a voxel.

Results: 12 patients (14 %) developed ICs on the follow-up MR-scans. Mixed effect modelling accounting for interpatient variation was justified by the non-negligible inter class correlation coefficient (ICC = 0.33). The two approaches identified similar model features and marginal improvement in model performance was found, when increasing model parameters from two (AUC = 0.92/0.94) to three (AUC = 0.93/0.95) parameters. Univariate analysis showed that patients treated with narrow beam configurations had an increased incidence of IC (p = 0.01).

Conclusion: 14% of patients developed IC following PT. Lesion-weighted and mixed effect models resulted in similar model performance confirming increased risk of IC with increasing LET. The beam arrangement seems to influence the risk of IC and needs further investigation.