ArticleFrontiers in oncology2021
Multidisciplinary Tumor Board Smart Virtual Assistant in Locally Advanced Cervical Cancer: A Proof of Concept.
Article in Frontiers in oncology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT07318701 (Multi-agent Large Language Models for Multidisciplinary Decision Support in Cervical Cancer During Pregnancy), which is not on this map. Cited by 12 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Multi-agent Large Language Models for Multidisciplinary Decision Support in Cervical Cancer During Pregnancy
Who cites it
12 citing papers in PubMed, 18 citations in OpenAlex.
- AI-driven smart patient retrieval for precision oncology.Nature reviews. Cancer · 2026Article
- Magnetic resonance-guided stereotactic body radiotherapy (MRIgSBRT) for nodal disease in oligometastatic setting: a single-institution study.Clinical & experimental metastasis · 2026Article
- Personalised medicine through AI-enhanced integration of diagnostic imaging and radiation therapy.European radiology experimental · 2025Review
- Chat GPT Performance in Multi-Disciplinary Boards-Should AI Be a Member of Cancer Boards?Healthcare (Basel, Switzerland) · 2025Article
- Recent advances in applications of machine learning in cervical cancer research: a focus on prediction models.Obstetrics & gynecology science · 2025Article
- Clinical applications of large language models in medicine and surgery: A scoping review.The Journal of international medical research · 2025Article
- Large Language Models in Cancer Imaging: Applications and Future Perspectives.Journal of clinical medicine · 2025Review
- The value of virtual molecular tumor boards for informed clinical decision-making.The oncologist · 2024Article
- Evaluating ChatGPT as an adjunct for the multidisciplinary tumor board decision-making in primary breast cancer cases.Archives of gynecology and obstetrics · 2023Article
- Factors Affecting the Adoption of Artificial Intelligence-Enabled Virtual Assistants for Leukemia Self-Management.Cureus · 2023Article
- Data-driven support to decision-making in molecular tumour boards for lymphoma: A design science approach.Frontiers in oncology · 2022Article
- Establishing Artificial Intelligence-Powered Virtual Tumor Board Meetings in Pakistan.Cancer control : journal of the Moffitt Cancer CenterArticle
Corrections and comments
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Authors and funding
15 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
aimThe first prototype of the "Multidisciplinary Tumor Board Smart Virtual Assistant" is presented, aimed to (i) Automated classification of clinical stage starting from different free-text diagnostic reports; (ii) Resolution of inconsistencies by identifying controversial cases drawing the clinician's attention to particular cases worthy for multi-disciplinary discussion; (iii) Support environment for education and knowledge transfer to junior staff; (iv) Integrated data-driven decision making and standardized language and interpretation. PATIENTS AND
methodData from patients affected by Locally Advanced Cervical Cancer (LACC), FIGO stage IB2-IVa, treated between 2015 and 2018 were extracted. Magnetic Resonance (MR), Gynecologic examination under general anesthesia (EAU), and Positron Emission Tomography-Computed Tomography (PET-CT) performed at the time of diagnosis were the items from the Electronic Health Records (eHRs) considered for analysis. An automated extraction of eHR that capture the patient's data before the diagnosis and then, through Natural Language Processing (NLP), analysis and categorization of all data to transform source information into structured data has been performed.
resultsIn the first round, the system has been used to retrieve all the eHR for the 96 patients with LACC. The system has been able to classify all patients belonging to the training set and - through the NLP procedures - the clinical features were analyzed and classified for each patient. A second important result was the setup of a predictive model to evaluate the patient's staging (accuracy of 94%). Lastly, we created a user-oriented operational tool targeting the MTB who are confronted with the challenge of large volumes of patients to be diagnosed in the most accurate way.
conclusionThis is the first proof of concept concerning the possibility of creating a smart virtual assistant for the MTB. A significant benefit could come from the integration of these automated methods in the collaborative, crucial decision stages.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.