Citations

Background Interventional radiotherapy (IRT), also known as brachytherapy, has become a cornerstone of radiation oncology due to its ability to maximize therapeutic efficacy while minimizing side effects to surrounding tissues. This bibliometric analysis aims to examine the evolution of IRT over the past decade. Material and Methods A string comprising IRT-related keywords was developed, and data were extracted from the Scopus database for English-language documents published between January 2014 and December 2024. The analysis was performed using Bibliometrix package Results A total of 15 755 documents from 2150 sources were analyzed, revealing an average annual growth rate of 1.03%. Publication trends were stable from 2014 to 2018, surged from 2019, and peaked in 2021, with subsequent levels remaining above pre-2019 figures. Original articles accounted for 72.7% of the literature, with key contributions from journals such as “Brachytherapy” and the “Journal of Contemporary Brachytherapy”. The analysis highlighted strong collaborative networks, with significant input from North American and European institutions, and identified focal research themes including uterine cervical cancer and prostate brachytherapy. Emerging topics, such as deep learning, artificial intelligence, and personalized dosimetry, underscore the field's shift towards precision medicine. Conclusions The findings reflect IRT's dynamic research landscape and its growing integration into modern oncologic practice, emphasizing the need for continued interdisciplinary efforts and clinical trials to advance this minimally invasive treatment modality further. Highlights • A decade-long bibliometric analysis of 15 755 publications on interventional radiotherapy (IRT) reveals stable growth, with a notable surge in research activity post-2019. • Uterine, cervical, and prostate cancers remain the most studied clinical indications for IRT globally. • North American and European institutions dominate collaborative networks, reflecting the strength of transatlantic research partnerships. • Emerging research themes include artificial intelligence, deep learning, and personalized dosimetry—marking a transition toward precision oncology. • The field's evolving landscape highlights the increasing clinical and scientific significance of IRT in contemporary cancer care.

Abstract Large Language Models (LLMs) demonstrate strong capabilities in healthcare applications. However, recruiting human evaluators to assess usability and effectiveness becomes challenging in the large-scale LLM-generated outcomes. Existing metrics are often outdated for evaluating LLM-based healthcare technologies. This paper proposes a Research through Evaluation (RtE) approach that can refine metrics and improve assessments of LLM-generated outputs. We deploy RtE in a retrospective comparative study of prostate cancer patient inquiries, comparing responses from human clinical care teams with those generated by a GPT-4-based in-basket bot. Through three rounds of co-evaluations with clinical professionals and an LLM-as-graders study with four LLMs, the RtE method shows that starting with small, iterative evaluations helps human graders adjust metrics to suit their daily practices better. Additionally, in the LLM-as-graders study, LLMs exhibit similar behavior, preferring the in-basket bot's responses over the clinical care team. Our methodology and findings demonstrate that the RtE method can enhance the transparency and robustness of evaluation metrics for LLM-generated content, with the potential to generalize to evaluations in other clinical practices.

LBA5012 Background: There is a critical evidence gap on the relative benefits and harms of proton therapy (PT) compared with photon-based intensity-modulated radiation therapy (IMRT) in prostate cancer. Herein we report early results from COMPPARE, a non-randomized comparative study of outcomes with PT and IMRT in prostate cancer. Methods: Between 2018-2022, 2524 patients from 51 facilities (28 PT and 23 IMRT) enrolled in COMPPARE, including 1501 treated with PT and 1023 treated with IMRT, and were followed prospectively. Treatment naïve patients with localized prostate cancer except those with very high-risk disease were eligible. Treatment was pragmatic with daily standard fractionation (1.8-2.1 Gy) and moderate hypofractionation (2.4-3.1 Gy) permitted. Use of rectal spacers and androgen deprivation were at physician discretion. Patient-reported quality of life data and treatment team-reported toxicity and disease control outcomes were collected at baseline and pre-specified time intervals via VisionTreeOptimalCare; treatment planning and delivery data were collected via the Advanced Treatment Consortium. We analyzed 1404 PT patients and 939 IMRT patients. We report the early primary endpoint of the study, a comparison of patient reported bowel urgency and bowel frequency at 2-years using the EPIC tool, and the secondary endpoints of 2-year CTCAE v5 ≥ grade 2 gastrointestinal toxicity outcomes and 3-year freedom from biochemical progression (FFBP) results reported by the study teams. Results: Study sample size was targeted at 1,500 PT patients and 1,000 IMRT patients, and median follow-up time was 4.0 years. The primary endpoint and secondary toxicity 2-year endpoints focused on the alternative hypothesis of PT superiority. All results include inverse probability of treatment weighting. Using generalized linear mixed effects models, we found there to be no significant difference in EPIC bowel urgency scores (p = 0.324) or bowel frequency scores (p = 0.514) between PT and IMRT cohorts over time up to the 2-year endpoint. For grade 2 gastrointestinal toxicity, the 2-year cumulative incidence was 5.2% for PT and 5.6% for IMRT. Gray’s Test for competing risks showed a hazard ratio (HR) of 0.91 [95% confidence interval (CI): 0.65-1.28, p = 0.6] for PT versus IMRT. The exploratory non-inferiority endpoint of 3-year FFBP was 98.0% for PT and 97.9% for IMRT, and Gray’s Test showed a HR of 0.95 (95% CI: 0.52-1.71, p = 0.90). Conclusions: Analysis of early results of COMPPARE show no significant differences between PT and IMRT in patient reported bowel frequency and urgency, grade ≥2 gastrointestinal toxicity, or FFBP. Assessment of long-term disease control, late toxicity and secondary malignancy will require longer follow-up. Clinical trial information: NCT03561220 .

Abstract Background Vorasidenib is Food and Drug Administration‐approved for treatment of grade 2 isocitrate dehydrogenase (IDH)–mutant gliomas and has demonstrated a benefit in progression‐free survival compared to a placebo. This study assessed the cost‐effectiveness of this treatment strategy compared to standard treatment: observation or chemoradiation (CRT). Methods The authors used an integrated Markov model to compare quality‐adjusted life‐years (QALY) gained and cost‐effectiveness of patients with IDH‐mutated low‐grade glioma (LGG) treated with vorasidenib, observation, or CRT. Outcomes were expressed as incremental cost‐effectiveness ratios (ICERs) per QALY gained. Treatment is considered cost‐effective if it meets the commonly accepted willingness‐to‐pay threshold of $100,000 per QALY gained. Results Based on model parameters, vorasidenib demonstrated an ICER of $356,756 per QALY when compared to CRT. When compared to observation, vorasidenib demonstrated an improved ICER of $215,191 per QALY. On one‐way sensitivity analysis compared to observation, median cost of vorasidenib and median overall survival were most likely to affect ICER with only marginal changes on analysis of other model factors. For ICER to become ≤$100,000 compared to observation, the cost of vorasidenib would need to drop to below $14,072 monthly or have a median survival gain of at least 11.9 years. The probabilistic sensitivity analysis demonstrated that vorasidenib was favored in none of 5000 Monte Carlo iterations. Conclusion Using the best available data, the current price of vorasidenib does not meet commonly accepted cost‐effectiveness thresholds in treatment of IDH‐mutated LGGs.

Retrieval-augmented generation (RAG) is rapidly emerging as a transformative paradigm for large language models (LLMs), especially in high-stakes domains like oncology that demand precision, factual grounding, and up-to-date knowledge. By pairing LLMs with external knowledge repositories, RAG systems explicitly ground model outputs in relevant retrieved documents, helping to reduce hallucinations and ensure responses reflect current evidence. In oncology, where clinical knowledge evolves continually with new research and drug approvals, RAG offers a way to integrate the latest data (e.g., trial results, guidelines, genomic databases) into decision-making. This review synthesizes the technical foundations of RAG, including its architecture and key components, and examines current applications in oncology such as clinical decision support, patient education, radiology reporting, pathology analysis, and genomics-driven precision medicine. We highlight recent studies that demonstrate RAG’s potential—for instance, improving treatment recommendations by incorporating genetic profiles and literature, and enhancing diagnostic accuracy by integrating guidelines. We also discuss emerging developments like multimodal RAG (combining text with imaging or other data), ensemble model approaches, and new explainability tools that trace model outputs to sources. Finally, we critically analyze the limitations and challenges of deploying RAG in healthcare, including computational costs, retrieval errors, noise or conflicts in retrieved information, and ethical and regulatory considerations. While RAG-based systems show promise in augmenting oncologists’ expertise with timely knowledge, careful implementation, high-quality curation of knowledge bases, and human oversight will be crucial for safe and effective adoption in clinical practice.

PURPOSE Cancer recurrence is a critical outcome for patients and physicians. Retrospective cancer recurrence data can evaluate recurrence-directed treatment and generate novel interventions targeting recurrent cancers. However, large cancer databases do not provide recurrence-related information, stymying study at scale, and consequently require significant manual record review. Automated evaluation of records may allow for the rapid generation of easily analyzed data sets, accelerating the evaluation of recurrence altogether. METHODS Patients treated with radiation therapy at one tertiary referral center from 2010 to 2018 with a verified cancer status (cancer recurrence v no cancer recurrence) were identified. Patients with recurrent disease were initially identified through manual record review, and the associated pathology report was collected. Google Automated Machine Learning with Natural Language Processing (AutoNLP) and Google Gemini 1.5 Pro were used to generate a model for binary classification, with comparison to the gold-standard manually developed data set. RESULTS A total of 7,054 patients were identified. 3,431 (48.6%) were female, with a median age of 64 years. Head and neck (1,482, 21%), breast (1,480, 21%), upper GI (1,307, 18.5%), and lung/thorax (1,107, 15.7%) were the most common disease sites. Recurrence was verified for 1,546 patients (21.9%) using pathology reports, of which 1,249 positive cases were paired with 651 negative pathology reports for model development. Google Gemini 1.5 Pro consistently outperformed AutoNLP across all measurements of accuracy, generating a greater absolute difference in precision, recall, negative predictive value, and specificity, and a higher likelihood of correct classification at the individual level, rendering Gemini superior in recurrence status extraction. CONCLUSION AutoNLP and Google Gemini 1.5 Pro are promising tools for identifying recurrence from pathology reports, with the latter demonstrating superior overall performance, making it particularly suitable for clinical translation.

357 Background: Prostate cancer (PCa) is the most incident cancer in adult males in the United States, and the second highest in the world. CTCAE is the standardized method to grade the severity of treatment-related adverse events (AEs), but are tedious to collect and subject to inter-observer variability. This study aimed to evaluate the performance of LLMs in automatically extracting and grading CTCAE toxicities from clinical notes and patient-reported outcomes of PCa patients from a clinical trial. Methods: A total of 55 patients from NCT02874014 trial, undergoing proton therapy (PT) for localized PCa, were included. The ground truth of CTCAE graded toxicities was obtained from the trial’s results. Clinical notes within 3 days, and the most recent patient questionnaires within 90 days of toxicity assessment were retrieved from the electronic health record. A comprehensive prompt was developed to extract and CTCAE grade toxicities from the retrieved records. LLMs used for this analysis were OpenAI GPT-4o and Gemini 2.0 Flash. Phase 1 evaluated both LLMs' performance against the ground truth. To ensure the metrics reflected the models’ performance based only on the information available in the text provided, cases where both LLMs contradicted the ground truth were manually reviewed. Phase 2 recalculated LLMs' performance metrics using this adapted ground truth. A conservative stepwise ensemble model was developed to leverage the complementary strengths of both LLMs. Results: In the conservative stepwise ensemble model, Gemini 2.0 Flash served as the initial screening platform to identify AEs in step 1. All cases with identified toxicity events were then reviewed and confirmed using OpenAI GPT-4o in step 2. This effectively reduced false positive rates while preserving high sensitivity. The ensemble model achieved strong performance metrics: binary accuracy (0.954), sensitivity (0.961), specificity (0.953), F1 score (0.836), and grade accuracy (0.932). Conclusions: This study demonstrates the feasibility of the use of LLMs for extraction and CTCAE toxicity grading in PCa patients treated with RT. The ensemble model achieved strong performance metrics. Further validation is needed for other cancer diagnosis and treatment modalities. Phase Model Binary Accuracy Precision Sensitivity Specificity F1 Score Grade Accuracy 1 Gemini 0.946 0.672 1.000 0.939 0.804 0.919 1 OpenAI 0.940 0.785 0.637 0.978 0.703 0.895 2 Gemini 0.956 0.735 1.000 0.950 0.848 0.927 2 OpenAI 0.954 0.917 0.679 0.991 0.780 0.903

Abstract Background Partial breast irradiation (PBI) is an effective treatment for early-stage breast cancer. However, evidence comparing different PBI modalities is limited. Methods This trial emulation uses electronic health records from a multi-state large tertiary health system. Three PBI modalities were compared: proton radiation therapy, photon radiation therapy, and applicator-based brachytherapy. Inverse probability weighting (IPW) was used to balance confounders. The primary outcome was ipsilateral breast recurrence (IBR). Results Between 2013 and 2023, 1041 women with early-stage breast cancer were treated with PBI. Of these, 1026 patients were included in the analyses. The median age was 66 years; 22.51% had ductal carcinoma in situ (DCIS), and 97.56% were estrogen receptor positive. With median follow-up of 38.2 months, the rate of IBR in the overall group was 3.05% (95% confidence interval [CI] = 1.99% to 4.65%) at 3 years. After IPW, compared with photon PBI, the hazard ratio for IBR for proton PBI was 2.11 (95% CI = 0.70 to 6.33) and for brachytherapy, 3.86 (95% CI = 1.39 to 10.69). Compared with proton PBI, the hazard ratio for IBR for brachytherapy was 1.83 (95% CI = 0.88 to 3.81). IBR risk was similar across PBI modalities for patients with tumor size ≤ 10 mm and in patients treated for DCIS. Among patients with tumor size >10 mm, the hazard ratio for IBR for brachytherapy was 7.64 (95% CI = 1.64 to 35.58) and 3.59 (95% CI = 1.22 to 10.56) relative to photons and protons, respectively. Conclusion PBI with applicator-based brachytherapy was associated with higher IBR than photon or proton PBI in patients with tumor size > 10 mm, suggesting the need for more careful PBI patient selection for this modality.

Objectives: For locally advanced rectal adenocarcinoma (R-ACA), a nonoperative management (NOM) approach has emerged as a guideline-supported treatment option. However, the variables associated with NOM receipt are unknown. Methods: Utilizing the National Cancer Database, we performed a retrospective cohort study of adults with stage 1 to 3 R-ACA managed with curative intent from 2004 to 2018. The primary outcome was the proportion of patients receiving NOM versus surgery. The secondary outcome was survival among NOM patients. Results: A total of 128,297 patients were included. In all, 115,888 (90.3%) received surgery and 12,409 (9.7%) received NOM. Receipt of NOM was associated with age above 70, Charlson-Deyo score of 0, race (Black, Asian or Pacific Islander, or other vs. White), insurance status, geographical region, treatment in a community facility, year of diagnosis (2012-2018 vs. 2004-2011), tumor grade 1 versus ≥ 2, clinical T-stage ≥ 2, and clinical N1 or N2 versus N0. In the NOM cohort, poorer overall survival was associated with age 70 and above, male sex, Charlson-Deyo score ≥ 1, insurance status, geographical region, rural urban density versus metro/urban, treatment in a community facility, year of diagnosis (2004-2011 vs. 2012-2018), clinical T4 versus T1, clinical N1 or N2 versus N0, grade 3 versus 1, treatment with a radiotherapy dose <45 Gy versus 45 to 54 Gy, and omission of chemotherapy. Conclusions: Several demographic factors and social determinants of health were associated with receipt of NOM and overall survival. With the increasing utilization of NOM, it will be important to understand the drivers of treatment decisions and influences on access to the desired treatment approach.

Abstract Introduction While randomized clinical trials (RCT) confirmed superiority of Mepitel film (MF) in reducing acute radiation dermatitis (ARD) compared to standard-of-care (SoC), the incremental cost difference has limited its use. A cost-effectiveness analysis (CEA) was conducted from a Canadian healthcare payer’s perspective to guide policy decisions. Methods: A decision model was constructed to perform a CEA for MF compared to SoC (moisturisers) for prevention of grade 2 or higher ARD following adjuvant hypo-fractionated whole-breast radiotherapy (RT) based on a Canadian multicentre RCT. Direct and indirect cost data were collected from two oncology centres in Canada. Quality-of-life (QoL) utility values were derived from mapping Dermatology Life Quality Index (DLQI) scores for patients with grade 2 or higher ARD at week 6 of RT to EQ-5D. A willingness-to-pay (WTF) threshold of CAD 50,000 per quality-adjusted life years (QALY) gained was used. One-way sensitivity analysis was performed to account for uncertainty in decision model assumptions. Results: Base case analysis demonstrated that MF is cost-effective in preventing grade 2 or higher ARD as compared with SoC with an incremental cost-effectiveness ratio (ICER) of CAD 3,366 per QALY gained. When nursing costs were included, MF resulted in an ICER of CAD 2,823 per QALY gained. One-way sensitivity analysis showed that the results were most sensitive to the QoL utility value for ARD. All sensitivity variations remained well below the WTP threshold. Conclusions: MF is a cost-effective intervention for preventing high-grade ARD and should be recommended for patients with breast cancer undergoing adjuvant RT.