ArticleScientific reports2024
An AI-based approach for modeling the synergy between radiotherapy and immunotherapy.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to 2 registered trials, which are not on this map. Cited by 14 papers.
What it found
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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.
A Phase I/II Clinical Trial of NALIRIFOX Combined With Adebrelimab and PULSAR as First-Line Treatment for Locally Advanced Unresectable or Metastatic Pancreatic Ductal Adenocarcinoma
A Phase II Clinical Trial of Anlotinib Combined With Cadonilimab and PULSAR in Previously Treated Advanced Unresectable or Metastatic Biliary Tract Cancer
Who cites it
14 citing papers in PubMed, 15 citations in OpenAlex.
- Personalized Ultrafractionated Stereotactic Adaptive Radiotherapy in Lung Cancer and Lung Metastases: A Systematic Review.Cancers · 2026Review
- Closing the Translational Gap: Closed-Loop AI Discovery Frameworks for Experimental Validation and Clinical Implementation in Cancer Therapeutics.Cancer medicine · 2026Review
- Leveraging Artificial Intelligence and Large Language Models for Cancer Immunotherapy.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- Artificial intelligence and transforming cancer care.Discover oncology · 2026Review
- Application of artificial intelligence in medical imaging for tumor diagnosis and treatment: a comprehensive approach.Discover oncology · 2025Review
- Roles of the phagocytosis checkpoint in radiotherapy.Cell death & disease · 2025Review
- Gradient-Based Radiomics for Outcome Prediction and Decision-Making in PULSAR: A Preliminary Study.International journal of particle therapy · 2025Article
- Harnessing artificial intelligence for predicting breast cancer recurrence: a systematic review of clinical and imaging data.Discover oncology · 2025Review
- Optimizing Cancer Treatment: Exploring the Role of AI in Radioimmunotherapy.Diagnostics (Basel, Switzerland) · 2025Review
- Advances in radiotherapy enhancing the efficacy of immune checkpoint inhibitors in malignant.Frontiers in oncology · 2025Review
- Multiomics-Based Outcome Prediction in Personalized Ultra-Fractionated Stereotactic Adaptive Radiotherapy (PULSAR).Cancers · 2024Article
- Article
- Understanding the PULSAR effect in combined radiotherapy and immunotherapy using transformer-based attention mechanisms.Frontiers in oncology · 2024Article
- Digital pathology and multimodal learning on oncology data.BJR artificial intelligence · 2024Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Personalized, ultra-fractionated stereotactic adaptive radiotherapy (PULSAR) is designed to administer tumoricidal doses in a pulsed mode with extended intervals, spanning weeks or months. This approach leverages longer intervals to adapt the treatment plan based on tumor changes and enhance immune-modulated effects. In this investigation, we seek to elucidate the potential synergy between combined PULSAR and PD-L1 blockade immunotherapy using experimental data from a Lewis Lung Carcinoma (LLC) syngeneic murine cancer model. Employing a long short-term memory (LSTM) recurrent neural network (RNN) model, we simulated the treatment response by treating irradiation and anti-PD-L1 as external stimuli occurring in a temporal sequence. Our findings demonstrate that: (1) The model can simulate tumor growth by integrating various parameters such as timing and dose, and (2) The model provides mechanistic interpretations of a "causal relationship" in combined treatment, offering a completely novel perspective. The model can be utilized for in-silico modeling, facilitating exploration of innovative treatment combinations to optimize therapeutic outcomes. Advanced modeling techniques, coupled with additional efforts in biomarker identification, may deepen our understanding of the biological mechanisms underlying the combined treatment.
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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.