Research Appraisals
Evidence-based critical appraisals of the latest medical research, systematically evaluated using Oxford CEBM methodology.
Showing 4 appraisals
BMJ health & care informatics
Detection of cancer recurrence from Thai-English electronic medical records using sentence embeddings.
OBJECTIVE: This study developed and validated monolingual and bilingual sentence-bidirectional encoder representations from transformers (SBERT) models for detecting cancer recurrence within Thai-English electronic medical records (EMRs) from Thai cancer hospitals. METHOD: A multicentre dataset of 32 436 documents from 1250 patients was used for model development. External validation involved an independent dataset of 9244 documents from 384 patients across two Thai cancer hospitals. Performance was benchmarked against a fine-tuned PubMedBERT (MetBERT). RESULTS: The development dataset included breast (43.9%), colorectal (12.1%), cervical (28.0%) and head and neck (16.0%) cancers. MetBERT achieved the highest area under the precision-recall curve (AUPRC) for locoregional versus no recurrence (11.1%) and locoregional versus distant recurrence (91.7%), while monolingual-SBERT excelled at distant versus no recurrence (32.0%). External validation demonstrated MetBERT superiority for locoregional versus no recurrence (9.30%-21.50%). For distant versus no recurrence, bilingual-SBERT performed best with AUPRC 17.55%-24.39%. While MetBERT led in distinguishing locoregional versus distant recurrence (88.30%-94.70%), bilingual-SBERT demonstrated robust external validation performance (AUPRC 85.25%-91.80%). DISCUSSION: Low AUPRC values (9%-32%) reflect the extreme class imbalance in real-world data (~1% recurrence prevalence). Despite this, fine-tuned MetBERT achieved highest performance, while bilingual-SBERT demonstrated superior robustness during external validation. This validates sentence embedding models for handling mixed Thai-English medical records in multilingual clinical environments. CONCLUSION: Sentence embedding frameworks provide a practical, generalisable solution for detecting cancer recurrence within multilingual EMRs. Despite text-length constraints, these models are suitable for clinical integration as a screening tool for cancer registry workflows.
4 July 2026
Read appraisal →Physics in medicine and biology
Deep learning-based dose prediction to enhance planning efficiency in cervical brachytherapy with hybrid applicators
Objective.This study aimed to develop a dose prediction model for hybrid applicators in cervical cancer brachytherapy.Approach.A total of 216 previous treatment plans were collected and divided into three datasets: training (161 plans), testing (40 plans), and clinical evaluation (15 plans). The treatment plans included three hybrid applicator types: tandem and ovoids combined with interstitial needles, tandem and ring combined with interstitial needles, and tandem and cylinder combined with interstitial needles. The model was validated on the testing dataset using mean absolute error (MAE) for voxel-wise dose evaluation and on the clinical evaluation dataset using dice similarity coefficient (DSC) and dose statistic differences.Main results.The model achieved a voxel-wise MAE of 0.45 ± 0.27 Gy and showed no statistically significant differences (p> 0.05) in all key dose-volume histogram parameters when compared to clinical plans. The DSC values ranged from 0.74 to 0.77. Small dosimetric differences between the optimized and manually generated plans were observed for the high-risk clinical target volume (HR-CTV) D98%, HR-CTV D90%, and bladder D2cc.Significance.The developed dose prediction model demonstrates significant potential for application in hybrid applicator brachytherapy. This model can provide guidance for dose delivery during dwell time optimization. As a reference for optimization, it has the potential to streamline the planning process, reduce planning time, and improve consistency in adaptive workflows.
3 July 2026
Read appraisal →Biomedical physics & engineering express
RADIANT: A fully configurable radiotherapy dose prediction framework
Objective.Treatment planning is a multi-disciplinary effort that requires medical decision-making, specialized training, and access to specialized software. Recently, deep learning has arisen as a powerful method for predicting patient-specific dose distributions. This work presents the radiotherapy dose inference and analysis toolkit (RADIANT), an open-source, fully configurable framework for 3D radiotherapy dose prediction. Built upon the Medical Imaging Segmentation Toolkit, RADIANT supports diverse network architectures, loss functions, and training strategies.Approach.We demonstrate its capabilities on cervical and prostate treatment plans generated with the radiation planning assistant, and on head and neck cancer plans from the American Association of Physicists in Medicine OpenKBP challenge data. For cervical cancer, we trained and compared nnU-Net, FMG-Net, W-Net, ddU-Net, and Swin UNETR using clinical metrics such as dose score, homogeneity index, and percent errors inD95,D98, andD99. Benchmarking was also performed on the OpenKBP dataset using dose score and dose-volume histogram (DVH) score for comparison with top challenge submissions.Results.Our results indicate that RADIANT provides a scalable platform for the rapid development and benchmarking of dose prediction models for selected cancer sites (cervix, prostate, head and neck). For cervical cancer, the best configuration (nnU-Net architecture, mean absolute error loss, cosine learning rate scheduler) achieved a dose score of 1.31 and sub-1%errors inD95andD98on the training set, and a dose score of 1.20 on the test set. The same configuration performed best on prostate data with a dose score of 1.96 on a test set. On the head and neck OpenKBP data, RADIANT achieved a dose score of 2.702 and a DVH score of 1.495, competitive with top challenge results.Significance.This work provides an open-source, fully configurable framework for deep learning-based dose prediction. RADIANT facilitates reproducible experimentation across data preprocessing, model configuration, training, and evaluation for multiple cancer sites and anatomical structures.
30 May 2026
Read appraisal →Blood
Brentuximab vedotin and nivolumab in combination with chemotherapy for nonbulky, early-stage classical Hodgkin lymphoma
Most patients with early-stage classical Hodgkin lymphoma (cHL) are treated with doxorubicin, bleomycin, vinblastine, and dacarbazine with or without radiation therapy, although studies are now evaluating the incorporation of novel agents paired with abbreviated chemotherapy. We present the efficacy and safety of AN+AD (brentuximab vedotin [BV] and nivolumab in combination with doxorubicin and dacarbazine) in patients with early-stage cHL. In this phase 2 study, patients with nonbulky (<10 cm) Ann Arbor stage I or II cHL received 4 cycles of AN+AD. The primary end point was complete response (CR) rate at end of treatment (EOT) by investigator. At the time of this analysis, 154 patients received ≥1 dose of AN+AD. The objective response rate at EOT was 96% (95% confidence interval [CI], 91.7-98.6), and the CR rate was 92% (95% CI, 86.0-95.4). In the favorable (n = 56) and unfavorable (n = 97) subgroups, CR rates were 95% (95% CI, 85.1-98.9) and 91% (95% CI, 83.1-95.7), respectively. The proportion of patients with duration of CR of at least 2 years was 96% (95% CI, 90.9-98.4). At a median follow-up of 27.9 months, the estimated 2-year progression-free survival rate was 97% (95% CI, 92.0-98.8). Any-grade and grade ≥3 treatment-emergent adverse events occurred in 99% and 44% of patients, respectively; no events of febrile neutropenia were reported. Any-grade treatment-emergent immune-mediated adverse events occurred in 22% of patients. One disease-related death was reported after the safety reporting period. Results from this study support the use of BV and nivolumab in combination with limited chemotherapy for patients with nonbulky, early-stage cHL. This trial was registered at www.clinicaltrials.gov as NCT03646123.
10 Apr 2026
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