Research Appraisals
Evidence-based critical appraisals of the latest medical research, systematically evaluated using Oxford CEBM methodology.
Showing 8 appraisals
Medical physics
Reference-Free large language model agents for physician-guided radiotherapy treatment planning
BACKGROUND: Large language models (LLMs) have recently demonstrated exceptional capabilities, offering the potential to streamline workflows and enhance efficiency across diverse tasks. However, their application in domain-specific areas, such as radiotherapy treatment planning, remains challenging due to the lack of publicly available, specialized knowledge required for these tasks. PURPOSE: To investigate the critical architectural and functional components necessary to adapt an off-the-shelf large language model into a clinically viable planning agent for inverse treatment planning in intensity-modulated radiation therapy (IMRT). MATERIALS/METHODS: Twenty head-and-neck (HN) cancer patients who received IMRT at our institution were retrospectively collected under IRB approval. The LLM agent was implemented to directly interact with the clinical treatment planning system (TPS) to iteratively extract intermediate plan states and propose new constraint values to guide inverse optimization. Its decision-making was informed by real-time plan evaluations and prior optimization outcomes, enabling adaptive refinement of planning strategies across iterations. The agent was equipped with three core capabilities: comprehension of clinical planning objectives, contextual understanding of the optimization environment, and arithmetic proficiency for quantitative reasoning. Treatment planning was conducted in a reference-free inference setting, wherein the LLM operated without prior exposure to manually generated treatment plans and without any fine-tuning or task-specific training. LLM-generated treatment plans were compared with clinically approved plans created by certified dosimetrists. Key dosimetric endpoints were evaluated and statistically analyzed. Paired comparisons between LLM-generated and clinical plans were performed using the Wilcoxon signed-rank test. RESULTS: LLM-generated plans achieved comparable organ-at-risk (OAR) sparing relative to clinical plans, while demonstrating improved hot spot control (Dmax: 106.5% vs. 108.8%, p < 0.05) and superior conformity (conformity index: 1.18 vs. 1.39, p < 0.05 for boost PTV; 1.82 vs. 1.88, p = 0.47 for primary PTV). CONCLUSIONS: This study demonstrates the feasibility of a reference-free, LLM-driven workflow for automated IMRT treatment planning in a commercial TPS. The proposed approach provides a generalizable solution that could reduce planning variability and support broader adoption of AI-based planning strategies.
2 Aug 2026
Read appraisal →Journal of radiation research
A model-based selection of oral cancer patient for passive scattering proton beam therapy
BACKGROUND AND PURPOSE: To identify patients with oral cancer (OC) who benefit from proton therapy versus photon therapy using a model-based approach (MBA) to reduce late adverse events. MATERIAL/METHODS: Passive scattering proton beam therapy (PSPT) and volumetric modulated arc therapy (VMAT) plans were established for 40 patients with OC, consisting of a dose of 70 Gy (relative biological effectiveness) in 35 fractions. At least 95% of the planning target volume received 95% of the prescribed dose, and the dose to the organs at risk was restricted to be as low as possible. Normal tissue complication probability (NTCP) was used to estimate the risk of late adverse events, including dysgeusia, xerostomia and osteoradionecrosis of the mandible. Patients who were considered as candidates for PSPT were selected if the NTCP difference between both plans (ΔNTCP) exceeded the threshold defined in the Dutch national indication protocol. RESULTS: PSPT reduced the doses to the spinal cord, pharynx, oral cavity and contralateral salivary glands compared with VMAT. PSPT was also superior for the risk evaluation of each late adverse event calculated by incorporating dose metrics and patient baseline risk factors. For the MBA using ΔNTCP and the thresholds, PSPT was indicated in 24 of the 40 cases, and the remaining 16 cases were considered to be equivalent to VMAT. CONCLUSION: MBA was used to select OC cases that were candidates for PSPT. Compared with VMAT, the risk of expected late adverse events was significantly reduced with PSPT, and 60% of patients were considered eligible for PSPT.
26 July 2026
Read appraisal →Head and neck pathology
A Deep Learning AI Model for Histopathological Diagnosis and Grading of Mucoepidermoid Carcinoma of Salivary Glands
PURPOSE: This study aims to evaluate the effectiveness of deep learning AI model in the diagnosis and grading of mucoepidermoid carcinoma. METHODS: We developed a multiple instance learning based-deep learning model which consisted of two modules. One to perform slide-level classification of whole slide images into mucoepidermoid carcinoma and non-mucoepidermoid carcinoma classes and the second one to perform grading into low, intermediate, and high grades. A total of 194 whole slide images were included in the study. Of these, 92 corresponded to mucoepidermoid carcinoma, while the remaining 102 represented non-mucoepidermoid carcinoma cases. RESULTS: Our results showed that the classification model achieved 90.62% accuracy, 90.62% macro F1 score, 90.60% weighted F1 score, 91.67% macro precision, 91.18% macro recall. The grading model achieved an accuracy of 73.3%, weighted F1-score of 73.9%, weighted precision of 76.1% and weighted recall of 73.3%. CONCLUSION: The proposed deep learning classification model achieved promising performance in differentiating mucoepidermoid carcinoma cases. However, the grading model achieved an overall accuracy of 73.3%. Thus, depending on AI solely is still questionable, however it can be used to augment the work of the pathologist.
22 July 2026
Read appraisal →Radiation protection dosimetry
Impact of neutron sieve therapy for deep site in boron neutron capture therapy using Monte Carlo calculation
Recent Boron neutron capture therapy (BNCT) has shifted to accelerator-based systems. One of them is a compact neutron generator developed using silicon carbide (SiC) semiconductors by Fukushima SiC Applied Technology Co. However, neutron penetration remains a critical challenge for treating deep-seated tumors. This study investigated the potential of neutron sieve therapy, originally developed to enhance the depth dose distribution in X-ray therapy, to improve BNCT dose distribution via Monte Carlo simulations based on the Fukushima SiC BNCT system. Dose distribution changes were investigated using polyethylene and 6LiF-plastic in block and sieve shapes. The results show that the maximum thermal neutron fluence depth was shallower (deeper) with polyethylene (6LiF-plastic) filters. The sieve filter moderately altered the dose distribution compared to the block filter due to density differences. The maximum dose point shifted 8 mm deeper using a sieve filter composed of 6LiF-plastic and polyethylene, with a 5.5% increase at 20 mm depth in neutron fluence at the same skin dose.
8 July 2026
Read appraisal →Biomaterials
Artificial intelligence in biomaterials for oral oncology
Oral cancer and oral potentially malignant disorders (OPMDs) remain a significant challenge in diagnosis and therapy, primarily due to inherent limitations in early detection, targeted treatment, and postoperative rehabilitation. Conventional diagnostic and therapeutic modalities often lack sufficient sensitivity, specificity, and effectiveness in restoring oral function. Biomaterials including nanoparticles, hydrogels, and scaffolds, offer versatile solutions by virtue of their tuneable properties, biocompatibility, and versatility in drug delivery and tissue engineering. However, their clinical translation is limited by the need for personalisation and lingering efficacy concerns. Artificial intelligence (AI) has emerged as a transformative approach to advance the design, optimisation, and application of biomaterials in oral oncology. By integrating machine learning (ML) and data-driven modelling, AI enhances diagnostic accuracy through biosensing and radiomic analysis, guides the rational design of drug carriers and dosing regimens, and facilitates computer-aided scaffold fabrication for maxillofacial reconstruction. This review summarises recent advances at the intersection of AI and biomaterials in the context of oral cancer and OPMDs, highlighting innovations in early detection, targeted therapy, and postoperative repair. It also discusses current barriers, including data quality, model generalizability, and regulatory oversight, and outlines future directions for interdisciplinary research. When properly integrated, AI-enabled biomaterials hold considerable potential to deliver more precise, efficient, and patient-tailored solutions for oral cancer management.
1 July 2026
Read appraisal →Scientific reports
Oral cancer in the era of precision medicine: molecular targets and technological innovations
This topical Collection presents a series of studies that examine molecular mechanisms, diagnostic approaches, and therapeutic strategies relevant to oral cancer. The included investigations explore topics ranging from metastasis-associated pathways and immune evasion to emerging detection technologies such as deep learning-based impedance spectroscopy and volatile organic compound analysis. Together, these studies reflect the growing convergence of molecular oncology, digital diagnostics, and translational clinical research in shaping the future of oral cancer management. This Collection represents an ongoing effort to consolidate current knowledge and guide future research in oral squamous cell carcinoma.
28 June 2026
Read appraisal →International journal of hyperthermia : the official journal of European Society for Hyperthermic Oncology, North American Hyperthermia Group
The role of capacitive hyperthermia as an adjunct treatment in oncology: a systematic review of randomized phase III trials.
INTRODUCTION: Capacitive hyperthermia (cHT) has been established for superficial tumors but the efficacy of cHT for intermediate or deep-seated tumors remains a subject of debate. This review is an evaluation of the current clinical evidence on the effectiveness of cHT in the treatment of tumors that are located deeper within the body. METHODS: A systematic review was conducted in accordance with the PRISMA guidelines of Phase III randomized controlled trials that included cancer patients of all ages and sexes with confirmed malignant tumors undergoing radiotherapy (RT) and/or chemotherapy (CT), with or without additional cHT. The literature search was conducted in PubMed and the Cochrane Register of Controlled Trials (CENTRAL). The primary endpoints included overall survival (OS), tumor response (TR; encompassing complete response (CR)), local control (LC), and cHT treatment-related toxicity (TRT). RESULTS: A total of nine randomized phase III trials were included in the study. The combination of cHT with RT and/or CT was found to improve both LC and OS in three out of five trials conducted in patients with cervical cancer. The analysis of both head and neck cancer trials demonstrated a marked increase in TR rates in cases where cHT was employed. Conversely, no such benefit was observed in non-small cell lung cancers and in a large multicenter cervical cancer trial. The treatment was well tolerated, with mild to moderate adverse events being the most common. CONCLUSION: It has been demonstrated that the combination of cHT with either RT or CT may improve TR and LC and, in certain cases, OS when compared with RT with or without CT alone. The occurrence of significant side effects is rare.
24 June 2026
Read appraisal →Biomedical physics & engineering express
The impact of data consistency on deep learning models for nasopharyngeal cancer organ auto-segmentation
Purpose. To investigate how annotation consistency influences deep learning-based auto-contouring performance for organs-at-risk (OARs) in nasopharyngeal cancer radiotherapy.Methods. We evaluated CT scans from 1,301 nasopharyngeal carcinoma patients: 65 contoured by Physician A, 76 by Physician B, and 1,160 by heterogeneous multi-physician teams. Three cohorts (50 samples each for Physicians A/B; 1,000 for multi-physician) with standardized U-Net training protocols generated Models A, B, and C. Model C underwent physician-specific fine-tuning. Performance was quantified via Dice similarity coefficients (DSC) against ground-truth contours across 14 critical OARs.Results. Each model achieved peak accuracy on physician-matched test data. Critically, small-consistency models (A/B) outperformed large-heterogeneous Model C on target cohorts (Model A: 0.777 versus Model C's 0.743 on Test A; Model B: 0.806 versus 0.765 on Test B). Physician-specific fine-tuning closed institutional data gaps, boosting Model C's DSC to 0.795 (+7.08% versus original) on Test A and 0.814 (+6.32%) on Test B-surpassing both original Model C and dedicated small-data models (Model A: +2.39%; Model B: +0.97%).Conclusion. Annotation consistency supersedes dataset scale as the primary performance driver for OAR auto-contouring. Small high-consistency datasets enable optimal native model accuracy, whereas fine-tuning large pre-trained models with targeted physician data closes domain adaptation gaps and delivers state-of-the-art segmentation, advancing precision radiotherapy for head and neck oncology..
11 Apr 2026
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