Research Appraisals
Evidence-based critical appraisals of the latest medical research, systematically evaluated using Oxford CEBM methodology.
Showing 3 appraisals
Studies in health technology and informatics
Digital GO: A Clinical Decision-Support Dashboard for Gynecologic Oncology Tumor Board
Multidisciplinary Tumor Board (MTB) meetings are essential for coordinated oncologic decision-making but are often hindered by the complexity and fragmentation of patient data. This study presents the development of an interactive digital dashboard designed to streamline data access and visualization during ovarian cancer MTB sessions. The system leverages structured knowledge extracted from unstructured clinical reports using in-house large language model (LLM) pipelines. These outputs are harmonized within a cloud environment through dedicated processing workflows and mapped into a domain ontology, to ensure interoperability. The resulting dashboard, implemented using a dedicated functional module, enables both cohort-level exploration and patient-specific visualization, including longitudinal event timelines. Preliminary results demonstrate the feasibility of integrating LLM-based data extraction with a scalable visualization framework, offering a step toward the digital transformation of multidisciplinary oncology workflows.
24 May 2026
Read appraisal →Journal of clinical ultrasound : JCU
Deep Learning Radiomics Model Based on Ultrasound Images Predicts Myometrial Infiltration of Endometrial Cancer
OBJECTIVE: This study aims to develop and validate a deep learning radiomics (DLR) model based on ultrasound images for non-invasively distinguishing the myometrial infiltration (MI) of patients with endometrial cancer (EC). METHODS: We retrospectively collected 310 patients with EC who underwent surgical resection from center 1 from September 2017 to January 2025, forming a training group and a validation group. An external testing group was comprised of 80 patients from center 2. We extracted deep learning (DL) features and radiomics features from ultrasound images, establishing a DLR model after dimensionality reduction. The receiver operating characteristic analysis was used to evaluate the practicality of the proposed model. The utility of the proposed model was evaluated using receiver operating characteristic, calibration, and decision curve analysis. RESULTS: A total of 390 EC patients were included in the study. In the validation set, the AUC of the radiomics model was 0.874 (0.774-0.949), and in the testing group, the AUC of the DL model was 0.844 (0.699-0.957). The DLR model was superior to radiomics models and DL models in interpreting images, and also outperformed the diagnostic performance of advanced ultrasound physicians. CONCLUSION: The DLR model based on ultrasound images can accurately and non-invasively distinguish the MI depth of EC patients, assisting doctors in formulating more favorable treatment plans for patients.
5 May 2026
Read appraisal →Clinical cancer research : an official journal of the American Association for Cancer Research
Camonsertib, an ATRi, in Combination with Low-Dose Gemcitabine in Solid Tumors with DNA Damage Response Aberrations: Preclinical and Phase Ib Results.
PURPOSE: The utility of combination treatment with gemcitabine and camonsertib, an ataxia telangiectasia and Rad3-related kinase inhibitor, in mediating tumor cell death was assessed in preclinical models, prompting clinical investigation. The phase Ib TRESR study (NCT04497116) aimed to evaluate the safety, tolerability, and preliminary efficacy of the combination in patients with advanced solid tumors harboring DNA damage repair (DDR) gene alterations. PATIENTS AND METHODS: Cell lines and tumor xenografts were tested across a range of dose levels and schedules. Patients (N = 76) harboring tumors with DDR gene alterations received camonsertib (80-120 mg) and de-escalating gemcitabine (1,000-100 mg/m2) in 21- or 28-day cycles on an intermittent dosing regimen. Safety, tolerability, and preliminary efficacy were assessed to identify an optimal dosing regimen. RESULTS: In preclinical models, low-dose camonsertib (1/3 maximum tolerated dose) and gemcitabine led to tumor regression and was well tolerated with minimal body weight loss observed. In patients, synergistic toxicities were observed, primarily myelosuppression, resulting in gemcitabine de-escalation. The introduction of a 1 week on/1 week off schedule in combination with low-dose gemcitabine allowed for spontaneous neutrophil recovery, fewer dose modifications, and improved tolerability. Tumor responses were primarily observed in patients with gynecologic cancers, with tumor control maintained for greater than 1 year in some patients. CONCLUSIONS: Camonsertib and low-dose gemcitabine demonstrated preliminary clinical activity, but due to challenging tolerability, further evaluation is warranted to identify the optimal dosing regimen and subset of patients who may benefit most from this combination.
17 Apr 2026
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