Related Experiment Video
Updated: Sep 26, 2025
Identifying PD-1/PD-L1 Inhibitors with Surface Plasmon Resonance Technology
Published on: May 2, 2025
When artificial intelligence meets PD-1/PD-L1 inhibitors: Population screening, response prediction and efficacy
1Shanghai Lung Cancer Center, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai, 200030, China; School of Medicine, Shanghai Jiao Tong University, Shanghai, 200025, China.
Abstract:
Programmed cell death protein-1 (PD-1) and its ligand (programmed death ligand 1, PD-L1) inhibitors, as the rising stars of immunotherapy, have been widely used in clinical practice, and the corresponding population screening, response prediction and efficacy evaluation have become increasingly important in clinic. Artificial Intelligence (AI) can help us uncover effective information from clinical data such as medical history, images, laboratory results, sequencing data, etc., which can help us solve above problems and enrich the methodology of clinical research. In this way, AI researches related to PD-1/PD-L1 inhibitors have been emerging. Based on an introduction of AI fundamentals in medicine, this review systematically summarizes the existing AI studies related to PD-1/PD-L1 immunotherapy in three aspects: population screening, response prediction and efficacy evaluation, and briefly outlooks the development direction of AI studies related to PD-1/PD-L1 inhibitors.
Insights
Artificial Intelligence (AI) aids in selecting patients, predicting responses, and evaluating efficacy for Programmed Cell Death Protein-1 (PD-1) and its ligand (PD-L1) inhibitor immunotherapies. This review explores AI
Area of Science:
- Immunotherapy
- Artificial Intelligence in Medicine
- Oncology
Background:
- Programmed cell death protein-1 (PD-1) and its ligand (PD-L1) inhibitors are key immunotherapies.
- Effective patient selection, response prediction, and efficacy evaluation are crucial for PD-1/PD-L1 inhibitor therapy.
- Artificial Intelligence (AI) offers powerful tools for analyzing complex clinical data.
Purpose of the Study:
- To systematically review current AI applications in PD-1/PD-L1 immunotherapy.
- To summarize AI's role in population screening, response prediction, and efficacy evaluation.
- To provide an outlook on future AI research directions in this field.
Main Methods:
- Literature review of AI studies related to PD-1/PD-L1 immunotherapy.
- Categorization of AI applications into population screening, response prediction, and efficacy evaluation.
- Introduction to fundamental AI concepts in medicine.
Main Results:
- AI is increasingly utilized to analyze diverse clinical data (medical history, images, lab results, sequencing).
- AI facilitates improved patient stratification for PD-1/PD-L1 inhibitor treatment.
- AI methods enhance the prediction of treatment response and monitoring of efficacy.
Conclusions:
- AI significantly contributes to optimizing the clinical application of PD-1/PD-L1 inhibitors.
- AI-driven insights are essential for advancing personalized immunotherapy strategies.
- Future research should focus on further developing and validating AI methodologies for immunotherapy.
More Related Videos
06:07Monitoring PD-1-Blocking Antibodies Bound to T Cells Derived from a Drop of Peripheral Blood
Published on: February 5, 2020
10:29Semi-automatic PD-L1 Characterization and Enumeration of Circulating Tumor Cells from Non-small Cell Lung Cancer Patients by Immunofluorescence
Published on: August 14, 2019
