Causal analysis of radiotherapy safety incidents based on a hybrid model of HFACS and Bayesian network.

Objective: This research investigates the role of human factors of all hierarchical levels in radiotherapy safety incidents and examines their interconnections. Methods: Utilizing the human factor analysis and classification system (HFACS) and Bayesian network (BN) methodologies, we created a BN-HFACS model to comprehensively analyze human factors, integrating the hierarchical structure. We examined 81 radiotherapy incidents from the radiation oncology incident learning system (RO-ILS), conducting a qualitative analysis using HFACS. Subsequently, parametric learning was applied to the derived data, and the prior probabilities of human factors were calculated at each BN-HFACS model level. Finally, a sensitivity analysis was conducted to identify the human factors with the greatest influence on unsafe acts. Results: The majority of safety incidents reported on RO-ILS were traced back to the treatment planning phase, with skill errors and habitual violations being the primary unsafe acts causing these incidents. The sensitivity analysis highlighted that the condition of the operators, personnel factors, and environmental factors significantly influenced the occurrence of incidents. Additionally, it underscored the importance of organizational climate and organizational process in triggering unsafe acts. Conclusion: Our findings suggest a strong association between upper-level human factors and unsafe acts among radiotherapy incidents in RO-ILS. To enhance radiation therapy safety and reduce incidents, interventions targeting these key factors are recommended.

Which Modality of SFRT Should be Considered First for Bulky Tumor Radiation Therapy, GRID or LATTICE?

Spatially fractionated radiation therapy (SFRT), also known as the GRID and LATTICE radiotherapy (GRT, LRT), the concept of treating tumors by delivering a spatially modulated dose with highly non-uniform dose distributions, is a treatment modality of growing interest in radiation oncology, physics, and radiation biology. Clinical experience in SFRT has suggested that GRID and LATTICE therapy can achieve a high response and low toxicity in the treatment of refractory and bulky tumors. Limited initially to GRID therapy using block collimators, advanced, and versatile multi-leaf collimators, volumetric modulated arc technologies and particle therapy have since increased the capabilities and individualization of SFRT and expanded the clinical investigation of SFRT to various dosing regimens, multiple malignancies, tumor types and sites. As a 3D modulation approach outgrown from traditional 2D GRID, LATTICE therapy aims to reconfigure the traditional SFRT as spatial modulation of the radiation is confined solely to the tumor volume. The distinctively different beam geometries used in LATTICE therapy have led to appreciable variations in dose-volume distributions, compared to GRID therapy. The clinical relevance of the variations in dose-volume distribution between LATTICE and traditional GRID therapies is a crucial factor in determining their adoption in clinical practice. In this Point-Counterpoint contribution, the authors debate the pros and cons of GRID and LATTICE therapy. Both modalities have been used in clinics and their applicability and optimal use have been discussed in this article.

Navigating the Critical Translational Questions for Implementing FLASH in the Clinic.

The "FLASH effect" is an increased therapeutic index, that is, reduced normal tissue toxicity for a given degree of anti-cancer efficacy, produced by ultra-rapid irradiation delivered on time scales orders of magnitude shorter than currently conventional in the clinic for the same doses. This phenomenon has been observed in numerous preclinical in vivo tumor and normal tissue models. While the underlying biological mechanism(s) remain to be elucidated, a path to clinical implementation of FLASH can be paved by addressing several critical translational questions. Technological questions pertinent to each beam type (eg, electron, proton, photon) also dictate the logical progression of experimentation required to move forward in safe and decisive clinical trials. Here we review the available preclinical data pertaining to these questions and how they may inform strategies for FLASH cancer therapy clinical trials.

[Informed consent in radiotherapy care]. / Consentement aux soins en radiothérapie.

Obtaining consent to care requires the radiation oncologist to provide loyal information and to ensure that the patient understands it. Proof of such an approach rests with the practitioner. The French Society for Radiation Oncology (SFRO) does not recommend the signature of a consent form by the patient but recommends that the radiation oncologist be able to provide all the elements demonstrating the reality of a complete information circuit.

Artificial intelligence solution to accelerate the acquisition of MRI images: Impact on the therapeutic care in oncology in radiology and radiotherapy departments.

PURPOSE: MRI is essential in the management of brain tumours. However, long waiting times reduce patient accessibility. Reducing acquisition time could improve access but at the cost of spatial resolution and diagnostic quality. A commercially available artificial intelligence (AI) solution, SubtleMR™, can increase the resolution of acquired images. The objective of this prospective study was to evaluate the impact of this algorithm that halves the acquisition time on the detectability of brain lesions in radiology and radiotherapy. MATERIAL AND METHODS: The T1/T2 MRI of 33 patients with brain metastases or meningiomas were analysed. Images acquired quickly have a matrix divided by two which halves the acquisition time. The visual quality and lesion detectability of the AI images were evaluated by radiologists and radiation oncologist as well as pixel intensity and lesions size. RESULTS: The subjective quality of the image is lower for the AI images compared to the reference images. However, the analysis of lesion detectability shows a specificity of 1 and a sensitivity of 0.92 and 0.77 for radiology and radiotherapy respectively. Undetected lesions on the IA image are lesions with a diameter less than 4mm and statistically low average gadolinium-enhancement contrast. CONCLUSION: It is possible to reduce MRI acquisition times by half using the commercial algorithm to restore the characteristics of the image and obtain good specificity and sensitivity for lesions with a diameter greater than 4mm.

Evaluating ChatGPT to test its robustness as an interactive information database of radiation oncology and to assess its responses to common queries from radiotherapy patients: A single institution investigation.

PURPOSE: Commercial vendors have created artificial intelligence (AI) tools for use in all aspects of life and medicine, including radiation oncology. AI innovations will likely disrupt workflows in the field of radiation oncology. However, limited data exist on using AI-based chatbots about the quality of radiation oncology information. This study aims to assess the accuracy of ChatGPT, an AI-based chatbot, in answering patients' questions during their first visit to the radiation oncology outpatient department and test knowledge of ChatGPT in radiation oncology. MATERIAL AND METHODS: Expert opinion was formulated using a set of ten standard questions of patients encountered in outpatient department practice. A blinded expert opinion was taken for the ten questions on common queries of patients in outpatient department visits, and the same questions were evaluated on ChatGPT version 3.5 (ChatGPT 3.5). The answers by expert and ChatGPT were independently evaluated for accuracy by three scientific reviewers. Additionally, a comparison was made for the extent of similarity of answers between ChatGPT and experts by a response scoring for each answer. Word count and Flesch-Kincaid readability score and grade were done for the responses obtained from expert and ChatGPT. A comparison of the answers of ChatGPT and expert was done with a Likert scale. As a second component of the study, we tested the technical knowledge of ChatGPT. Ten multiple choice questions were framed with increasing order of difficulty - basic, intermediate and advanced, and the responses were evaluated on ChatGPT. Statistical testing was done using SPSS version 27. RESULTS: After expert review, the accuracy of expert opinion was 100%, and ChatGPT's was 80% (8/10) for regular questions encountered in outpatient department visits. A noticeable difference was observed in word count and readability of answers from expert opinion or ChatGPT. Of the ten multiple-choice questions for assessment of radiation oncology database, ChatGPT had an accuracy rate of 90% (9 out of 10). One answer to a basic-level question was incorrect, whereas all answers to intermediate and difficult-level questions were correct. CONCLUSION: ChatGPT provides reasonably accurate information about routine questions encountered in the first outpatient department visit of the patient and also demonstrated a sound knowledge of the subject. The result of our study can inform the future development of educational tools in radiation oncology and may have implications in other medical fields. This is the first study that provides essential insight into the potentially positive capabilities of two components of ChatGPT: firstly, ChatGPT's response to common queries of patients at OPD visits, and secondly, the assessment of the radiation oncology knowledge base of ChatGPT.

Associations Between Radiation Oncologist Demographic Factors and Segmentation Similarity Benchmarks: Insights From a Crowd-Sourced Challenge Using Bayesian Estimation.

PURPOSE: The quality of radiotherapy auto-segmentation training data, primarily derived from clinician observers, is of utmost importance. However, the factors influencing the quality of clinician-derived segmentations are poorly understood; our study aims to quantify these factors. METHODS: Organ at risk (OAR) and tumor-related segmentations provided by radiation oncologists from the Contouring Collaborative for Consensus in Radiation Oncology data set were used. Segmentations were derived from five disease sites: breast, sarcoma, head and neck (H&N), gynecologic (GYN), and GI. Segmentation quality was determined on a structure-by-structure basis by comparing the observer segmentations with an expert-derived consensus, which served as a reference standard benchmark. The Dice similarity coefficient (DSC) was primarily used as a metric for the comparisons. DSC was stratified into binary groups on the basis of structure-specific expert-derived interobserver variability (IOV) cutoffs. Generalized linear mixed-effects models using Bayesian estimation were used to investigate the association between demographic variables and the binarized DSC for each disease site. Variables with a highest density interval excluding zero were considered to substantially affect the outcome measure. RESULTS: Five hundred seventy-four, 110, 452, 112, and 48 segmentations were used for the breast, sarcoma, H&N, GYN, and GI cases, respectively. The median percentage of segmentations that crossed the expert DSC IOV cutoff when stratified by structure type was 55% and 31% for OARs and tumors, respectively. Regression analysis revealed that the structure being tumor-related had a substantial negative impact on binarized DSC for the breast, sarcoma, H&N, and GI cases. There were no recurring relationships between segmentation quality and demographic variables across the cases, with most variables demonstrating large standard deviations. CONCLUSION: Our study highlights substantial uncertainty surrounding conventionally presumed factors influencing segmentation quality relative to benchmarks.

Patient Safety and Satisfaction With Fully Remote Management of Radiation Oncology Care.

Importance: Patients of Memorial Sloan Kettering Cancer Center in New York, New York, are now offered a choice of either in-person or remote telehealth visits for radiation oncology care. However, safety and satisfaction among patients receiving treatment with fully remote physician management is unclear. Objective: To analyze patient safety and satisfaction, financial implications, and environmental consequences associated with fully remote management among a cohort of patients treated with radiotherapy. Design, Setting, and Participants: This single-institution retrospective cohort study was performed at Memorial Sloan Kettering Cancer Center, with patients treated with radiation who opted for fully remote management between October 1, 2020, and October 31, 2022. Data on patient safety events were prospectively collected with an in-house quality improvement reporting system. Patient satisfaction surveys were distributed electronically before, during, and after treatment. Patient transportation costs and environmental consequences were estimated based on differences in travel distance. Data analysis was performed from March 14 through September 19, 2023. Exposure: Radiotherapy with fully remote physician management. Main Outcomes and Measures: Satisfaction rates among patients opting for fully remote management were analyzed via surveys administered electronically after visits with clinicians. Patient safety events, defined as staff-reported actual events and near misses that had the potential to affect patient care, were reviewed. Rates and types of safety events were analyzed and compared with patients treated by onsite clinicians. Distances between patient home zip codes and treatment site locations were compared with estimated cost savings and decreased emissions. Results: This study included 2817 patients who received radiation oncology care with fully remote physician management. The median age of patients was 65 (range, 9-99) years, and more than half were men (1467 [52.1%]). Of the 764 safety events reported, 763 (99.9%) did not reach patients or caused no harm to patients. Nearly all survey respondents (451 [97.6%]) rated patient satisfaction as good to very good across all domains. For treatment with fully remote physician management, out-of-pocket cost savings totaled $612 912.71 ($466.45 per patient) and decreased carbon dioxide emissions by 174 metric tons. Conclusions and Relevance: In this study, radiation oncology care provided by fully remote clinicians was safe and feasible, with no serious patient events. High patient satisfaction, substantial cost savings, and decreased environmental consequences were observed. These findings support the continuation of a fully remote management option for select patients in the post-COVID-19 era.

Insurance Denials and Patient Treatment in a Large Academic Radiation Oncology Center.

Importance: Insurance barriers to cancer care can cause significant patient and clinician burden. Objective: To investigate the association of insurance denial with changes in technique, dose, and time to delivery of radiation oncology treatment. Design, Setting, and Participants: In this single-institution cohort analysis, data were collected from patients with payer-denied authorization for radiation therapy (RT) from November 1, 2021, to December 8, 2022. Data were analyzed from December 15, 2022, to December 31, 2023. Exposure: Insurance denial for RT. Main Outcomes and Measures: Association of these denials with changes in RT technique, dose, and time to treatment delivery was assessed using χ2 tests. Results: A total of 206 cases (118 women [57.3%]; median age, 58 [range, 26-91] years) were identified. Most insurers (199 [96.6%]) were commercial payers, while 7 (3.4%) were Medicare or Medicare Advantage. One hundred sixty-one patients (78.2%) were younger than 65 years. Of 206 cases, 127 (61.7%) were ultimately authorized without any change to the requested RT technique or prescription dose; 56 (27.2%) were authorized after modification to RT technique and/or prescription dose required by the payer. Of 21 cases with required prescription dose change, the median decrease in dose was 24.0 (range, 2.3-51.0) Gy. Of 202 cases (98.1%) with RT delivered, 72 (34.9%) were delayed for a mean (SD) of 7.8 (9.1) days and median of 5 (range, 1-49) days. Four cases (1.9%) ultimately did not receive any authorization, with 3 (1.5%) not undergoing RT, and 1 (0.5%) seeking treatment at another institution. Conclusions and Relevance: In this cohort study of patients with payer-denied cases, most insurance denials in radiation oncology were ultimately approved on appeal; however, RT technique and/or effectiveness may be compromised by payer-mandated changes. Further investigation and action to recognize the time and financial burdens on clinicians and clinical effects on patients caused by insurance denials of RT is needed.

A joint ESTRO and AAPM guideline for development, clinical validation and reporting of artificial intelligence models in radiation therapy.

BACKGROUND AND PURPOSE: Artificial Intelligence (AI) models in radiation therapy are being developed with increasing pace. Despite this, the radiation therapy community has not widely adopted these models in clinical practice. A cohesive guideline on how to develop, report and clinically validate AI algorithms might help bridge this gap. METHODS AND MATERIALS: A Delphi process with all co-authors was followed to determine which topics should be addressed in this comprehensive guideline. Separate sections of the guideline, including Statements, were written by subgroups of the authors and discussed with the whole group at several meetings. Statements were formulated and scored as highly recommended or recommended. RESULTS: The following topics were found most relevant: Decision making, image analysis, volume segmentation, treatment planning, patient specific quality assurance of treatment delivery, adaptive treatment, outcome prediction, training, validation and testing of AI model parameters, model availability for others to verify, model quality assurance/updates and upgrades, ethics. Key references were given together with an outlook on current hurdles and possibilities to overcome these. 19 Statements were formulated. CONCLUSION: A cohesive guideline has been written which addresses main topics regarding AI in radiation therapy. It will help to guide development, as well as transparent and consistent reporting and validation of new AI tools and facilitate adoption.

Autologous Peripheral Blood Stem Cell Transplantation and Timing of Local Radiation Therapy in Patients With Malignant Lymphoma: Results of the Japanese Radiation Oncology Study Group Questionnaire Survey.

BACKGROUND/AIM: Malignant lymphoma (ML) including Hodgkin's lymphoma and non-Hodgkin's lymphoma is often treated with local radiation therapy (RT) in combination with autologous hematopoietic stem cell transplantation (ASCT) to prevent relapse; however, the efficacy and optimal timing of this approach is unclear. In this study, a national survey conducted by the Japanese Radiation Oncology Study Group reviewed ML cases from 2011 to 2019 to determine whether RT should be added to ASCT, focusing on the use of autologous peripheral blood stem cell transplantation (auto-PBSCT), a predominant form of ASCT. PATIENTS AND METHODS: The survey encompassed 92 patients from 11 institutes, and assessed histological ML types, treatment regimens, timing of RT relative to auto-PBSCT, and associated adverse events. RESULTS: The results indicated no significant differences in adverse events, including myelosuppression, based on the timing of RT in relation to auto-PBSCT. However, anemia was more prevalent when RT was administered before auto-PBSCT, and there was a higher incidence of neutropenia recovery delay in patients receiving RT after auto-PBSCT. CONCLUSION: This study provides valuable insights into the variable practices of auto-PBSCT and local RT in ML treatment, emphasizing the need for optimized timing of these therapies to improve patient outcomes and reduce complications.

A survey on brachytherapy training of gynecological cancer focusing on the competence of residents in China.

BACKGROUND: The brachytherapy is an indispensable treatment for gynecological tumors, but the quality and efficiency of brachytherapy training for residents is still unclear. METHODS: An anonymous questionnaire was designed to collect information on gynecological brachytherapy (GBT) training for radiation oncology residents from 28 training bases in China. The questionnaire content was designed based on the principle of competency based medical education (CBME). The Likert scale was employed to evaluate self-reported competence and comprehension regarding GBT. A total of 132 senior residents were included in the final analysis. RESULTS: 53.79% (71/132) of senior residents had experience in performing image-guided GBT, whereas 76.52% (101/132) had observed the procedure during their standardized residency training. The proportion of senior residents who reported having the self-reported competence to independently complete the GBT was 78.03% for intracavity GBT, 75.00% for vaginal stump GBT, and 50.03% for interstitial GBT, respectively. The number of successful completion of Interstitial, intracavity and vaginal GBT was correlated with the self- confidence of trainees after standardized training. In particular, the independent completion of interstitial GBT for more than 20 cases was an independent factor for the self-reported competence of senior residents. During the training period, 50.76% and 56.82% of the residents had not participated in the specialized examinations and professional GBT courses. CONCLUSIONS: The study revealed that the self-confidence of residents to independently complete brachytherapy was relatively high, and the specialized curriculum setting and training process assessment for brachytherapy training still need to be strengthened in the future.