OBJECTIVE: Large language model (LLM) artificial intelligences may help physicians appeal insurer denials of prescribed medical services, a task that delays patient care and contributes to burnout. We evaluated LLM performance at this task for denials of radiotherapy services.
METHODS: We evaluated generative pretrained transformer 3.5 (GPT-3.5; OpenAI, San Francisco, CA), GPT-4, GPT-4 with internet search functionality (GPT-4web), and GPT-3.5ft. The latter was developed by fine-tuning GPT-3.5 via an OpenAI application programming interface with 53 examples of appeal letters written by radiation oncologists. Twenty test prompts with simulated patient histories were programmatically presented to the LLMs, and output appeal letters were scored by three blinded radiation oncologists for language representation, clinical detail inclusion, clinical reasoning validity, literature citations, and overall readiness for insurer submission.
RESULTS: Interobserver agreement between radiation oncologists\' scores was moderate or better for all domains (Cohen\'s kappa coefficients: 0.41-0.91). GPT-3.5, GPT-4, and GPT-4web wrote letters that were on average linguistically clear, summarized provided clinical histories without confabulation, reasoned appropriately, and were scored useful to expedite the insurance appeal process. GPT-4 and GPT-4web letters demonstrated superior clinical reasoning and were readier for submission than GPT-3.5 letters (P < .001). Fine-tuning increased GPT-3.5ft confabulation and compromised performance compared with other LLMs across all domains (P < .001). All LLMs, including GPT-4web, were poor at supporting clinical assertions with existing, relevant, and appropriately cited primary literature.
CONCLUSIONS: When prompted appropriately, three commercially available LLMs drafted letters that physicians deemed would expedite appealing insurer denials of radiotherapy services. LLMs may decrease this task\'s clerical workload on providers. However, LLM performance worsened when fine-tuned with a task-specific, small training data set.

OBJECTIVE: Most research on artificial intelligence-based auto-contouring as template (AI-assisted contouring) for organs-at-risk (OARs) stem from high-income countries. The effect and safety are, however, likely to depend on local factors. This study aimed to investigate the effects of AI-assisted contouring and teaching on contouring time and contour quality among radiation oncologists (ROs) working in low- and middle-income countries (LMICs).
METHODS: Ninety-seven ROs were randomly assigned to either manual or AI-assisted contouring of eight OARs for two head-and-neck cancer cases with an in-between teaching session on contouring guidelines. Thereby, the effect of teaching (yes/no) and AI-assisted contouring (yes/no) was quantified. Second, ROs completed short-term and long-term follow-up cases all using AI assistance. Contour quality was quantified with Dice Similarity Coefficient (DSC) between ROs\' contours and expert consensus contours. Groups were compared using absolute differences in medians with 95% CIs.
RESULTS: AI-assisted contouring without previous teaching increased absolute DSC for optic nerve (by 0.05 [0.01; 0.10]), oral cavity (0.10 [0.06; 0.13]), parotid (0.07 [0.05; 0.12]), spinal cord (0.04 [0.01; 0.06]), and mandible (0.02 [0.01; 0.03]). Contouring time decreased for brain stem (-1.41 [-2.44; -0.25]), mandible (-6.60 [-8.09; -3.35]), optic nerve (-0.19 [-0.47; -0.02]), parotid (-1.80 [-2.66; -0.32]), and thyroid (-1.03 [-2.18; -0.05]). Without AI-assisted contouring, teaching increased DSC for oral cavity (0.05 [0.01; 0.09]) and thyroid (0.04 [0.02; 0.07]), and contouring time increased for mandible (2.36 [-0.51; 5.14]), oral cavity (1.42 [-0.08; 4.14]), and thyroid (1.60 [-0.04; 2.22]).
CONCLUSIONS: The study suggested that AI-assisted contouring is safe and beneficial to ROs working in LMICs. Prospective clinical trials on AI-assisted contouring should, however, be conducted upon clinical implementation to confirm the effects.

暂无摘要。

BACKGROUND: Interest in the oligometastatic prostate cancer (OMPC) is increasing, and various clinical studies have reported the benefits of metastasis-directed radiation therapy (MDRT) in OMPC. However, the recognition regarding the adopted definitions, methodologies of assessment, and therapeutic approaches is diverse among radiation oncologists. This study aims to evaluate the level of agreement for issues in OMPC among radiation oncologists.
METHODS: We generated 15 key questions (KQs) for OMPC relevant to definition, diagnosis, local therapies, and endpoints. Additionally, three clinical scenarios representing synchronous metastatic prostate cancer (mPC) (case 1), metachronous mPC with visceral metastasis (case 2), and metachronous mPC with castration-resistance and history of polymetastasis (case 3) were developed. The 15 KQs were adapted according to each scenario and transformed into 23 questions with 6-9 per scenario. The survey was distributed to 80 radiation oncologists throughout the Republic of Korea. Answer options with 0.0-29.9%, 30-49.9%, 50-69.9%, 70-79.9%, 80-89.9%, and 90-100% agreements were considered as no, minimal, weak, moderate, strong, and near perfect agreement, respectively.
RESULTS: Forty-five candidates voluntarily participated in this study. Among 23 questions, near perfect (n = 4), strong (n = 3), or moderate (n = 2) agreements were shown in nine. For the case recognized as OMPC with agreements of 93% (case 1), near perfect agreements on the application of definitive radiation therapy (RT) for whole metastatic lesions were achieved. While ≥70% agreements regarding optimal dose-fractionation for metastasis-directed RT (MDRT) has not been achieved, stereotactic body RT (SBRT) is favored by clinicians with higher clinical volume.
CONCLUSIONS: For the case recognized as OMPC, near perfect agreement for the application of definitive RT for whole metastatic lesions was reached. SBRT was more favored as a MDRT by clinicians with a higher clinical volume.

OBJECTIVE: For many malignancies, hypofractionated radiotherapy (HFRT) is an accepted standard associated with decreased treatment time and costs. United States provider beliefs regarding HFRT likely impact its adoption but are poorly studied. We surveyed US-based radiation oncologists (ROs) to gauge HFRT utilization rates for prostate (PC), breast (BC), and rectal cancer (RC) and to characterize the beliefs governing these decisions.
METHODS: From July to October 2021, an anonymized, online survey was electronically distributed to ROs actively practicing in the United States. Demographic and practice characteristic information was collected. Questions assessing rates of offering HFRT for PC, BC, and RC and perceived limitations towards using HFRT were administered.
RESULTS: A total of 203 eligible respondents (72% male, 72% White, 53% nonacademic practice, 69% with 11+ years in practice) were identified. Approximately 50% offered stereotactic body radiation therapy (SBRT) for early/favorable intermediate risk PC. Although >90% of ROs offered whole-breast HFRT for early-stage BC, only 33% offered accelerated partial-breast irradiation (APBI). Overall, 41% of ROs offered short-course neoadjuvant RT for RC. The primary reported barriers to HFRT utilization were lack of data, inexperience, and referring provider concerns.
CONCLUSIONS: HFRT is safe, effective, and beneficial, yet underutilized-particularly prostate SBRT, APBI, and short-course RT for RC. Skills retraining and education of ROs and referring providers may increase utilization rates.

OBJECTIVE: 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.
CONCLUSIONS: Our study highlights substantial uncertainty surrounding conventionally presumed factors influencing segmentation quality relative to benchmarks.

UNASSIGNED: To understand perspective on breast cancer using a survey.
UNASSIGNED: Questionnaire was distributed to 304 Japanese radiation oncologists (RadOncs) (response rate: 64.1%). Result was compared with a similar US survey.
UNASSIGNED: In a scenario with an 81-year-old patient with comorbidities, while most US RadOncs chose to tell that radiation might not be necessary, 2% of Japanese chose it. In a scenario with a healthy 65-year-old breast cancer patient with lumpectomy, while most US RadOncs chose to discuss omission of radiation, 24.5% of Japanese chose it.
UNASSIGNED: Differences were observed on radiotherapy for older early-stage breast cancer.

In the present research, we have developed a model-based crisp logic function statistical classifier decision support system supplemented with treatment planning systems for radiation oncologists in the treatment of glioblastoma multiforme (GBM). This system is based on Monte Carlo radiation transport simulation and it recreates visualization of treatment environments on mathematical anthropomorphic brain (MAB) phantoms. Energy deposition within tumour tissue and normal tissues are graded by quality audit factors which ensure planned dose delivery to tumour site thereby minimising damages to healthy tissues. The proposed novel methodology predicts tumour growth response to radiation therapy from a patient-specific medicine quality audit perspective. Validation of the study was achieved by recreating thirty-eight patient-specific mathematical anthropomorphic brain phantoms of treatment environments by taking into consideration density variation and composition of brain tissues. Dose computations accomplished through water phantom, tissue-equivalent head phantoms are neither cost-effective, nor patient-specific customized and is often less accurate. The above-highlighted drawbacks can be overcome by using open-source Electron Gamma Shower (EGSnrc) software and clinical case reports for MAB phantom synthesis which would result in accurate dosimetry with due consideration to the time factors. Considerable dose deviations occur at the tumour site for environments with intraventricular glioblastoma, haematoma, abscess, trapped air and cranial flaps leading to quality factors with a lower logic value of 0. Logic value of 1 depicts higher dose deposition within healthy tissues and also leptomeninges for majority of the environments which results in radiation-induced laceration.

BACKGROUND: Patients with head and neck cancer (HNC) may experience substantial anatomical changes during the course of radiotherapy treatment. The implementation of adaptive radiotherapy (ART) proves effective in managing the consequent impact on the planned dose distribution.
METHODS: This narrative literature review comprehensively discusses the diverse strategies of ART in HNC and the documented dosimetric and clinical advantages associated with these approaches, while also addressing the current challenges for integration of ART into clinical practice.
CONCLUSIONS: Although based on mainly non-randomized and retrospective trials, there is accumulating evidence that ART has the potential to reduce toxicity and improve quality of life and tumor control in HNC patients treated with RT. However, several questions remain regarding accurate patient selection, the ideal frequency and timing of replanning, and the appropriate way for image registration and dose calculation. Well-designed randomized prospective trials, with a predetermined protocol for both image registration and dose summation, are urgently needed to further investigate the dosimetric and clinical benefits of ART.

BACKGROUND: Cervical cancer is a global public health concern. Despite ESGO recommendations and FIGO classification changes, management of locally advanced cervical cancer (LACC) remains debated in France. Our study aimed to review LACC treatment practices and assess adherence to ESGO recommendations among different practitioners.
METHODS: From February 2021 to August 2022, we conducted a survey among gynecologic oncology surgeons, radiation oncologists, and medical oncologists practicing in France and managing LACC (FIGO stages IB3-IVA) according to the 2018 FIGO classification. We analyzed responses against the 2018 ESGO recommendations as a \"gold standard.\"
RESULTS: Among 115 respondents (56% radiation oncologists, 30% surgeons, 13% medical oncologists), 48.6% of gynecologic surgeons didn\'t perform para-aortic lymphadenectomy (PAL) with significant radiologic pelvic involvement. PAL, when indicated by PET-CT, was more common in university hospitals (66.7% of surgeons). Surgeons in university hospitals also followed ESGO recommendations more closely. Overall, compliance with all ESGO recommendations was low: 5.7% of surgeons, 21.5% of radiation oncologists, and 60% of medical oncologists. Prophylactic para-aortic irradiation, per ESGO, was more frequent in comprehensive cancer centers (52% of radiation oncologists).
CONCLUSIONS: Adherence to ESGO recommendations for LACC treatment appears low in France, particularly in surgery, with limited PAL in cases of lymph node negativity on PET-CT. However, these recommendations are more often followed by surgeons in university hospitals and radiation oncologists in cancer centers. Adherence to these recommendations may impact patient survival and warrants evaluation of care quality, justifying the organization of LACC management in expert centers.