ReviewNPJ precision oncology2023
Radiological artificial intelligence - predicting personalized immunotherapy outcomes in lung cancer.
Review in NPJ precision oncology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
22 citing papers in PubMed.
- CT-based deep learning radiomics predicts micropapillary/solid components in lung adenocarcinoma.Journal of thoracic disease · 2026Article
- Integrating Ultrasound and DCE-MRI Improves Accuracy in Differentiating Breast Adenosis from Carcinoma.Breast cancer (Dove Medical Press) · 2026Article
- Economic Impact of Targeted and Immunotherapies in Treating Operable Esophageal and Non-Small Cell Lung Cancers.Annals of thoracic surgery short reports · 2025Article
- Radiogenomics: transforming lung cancer care through non-invasive imaging and genomic integration.Medical oncology (Northwood, London, England) · 2025Review
- Constructing the cure: engineering the next wave of antibody and cellular immune therapies.Journal for immunotherapy of cancer · 2025Review
- Temporal and spatial omics technologies for 4D profiling.Nature methods · 2025Review
- Future prospects of deep learning in esophageal cancer diagnosis and clinical decision support (Review).Oncology letters · 2025Review
- Bridging surgical oncology and personalized medicine: the role of artificial intelligence and machine learning in thoracic surgery.Annals of medicine and surgery (2012) · 2025Review
- Machine learning-driven imaging data for early prediction of lung toxicity in breast cancer radiotherapy.Scientific reports · 2025Article
- Artificial Intelligence in Thoracic Surgery: A Review Bridging Innovation and Clinical Practice for the Next Generation of Surgical Care.Journal of clinical medicine · 2025Review
- A Thorough Review of the Clinical Applications of Artificial Intelligence in Lung Cancer.Cancers · 2025Review
- Single-cell spatial immune profiling for precision immunotherapy in Lynch syndrome.Journal of the National Cancer Center · 2025Article
- Convergence of evolving artificial intelligence and machine learning techniques in precision oncology.NPJ digital medicine · 2025Article
- A multi-modal deep learning model for prediction of Ki-67 for meningiomas using pretreatment MR images.NPJ precision oncology · 2025Article
- Harnessing multi-omics and artificial intelligence: revolutionizing prognosis and treatment in hepatocellular carcinoma.Frontiers in immunology · 2025Article
- Immunotherapy biomarkers in brain metastases: insights into tumor microenvironment dynamics.Frontiers in immunology · 2025Review
- Mime: A flexible machine-learning framework to construct and visualize models for clinical characteristics prediction and feature selection.Computational and structural biotechnology journal · 2024Article
- Personalized prediction of immunotherapy response in lung cancer patients using advanced radiomics and deep learning.Cancer imaging : the official publication of the International Cancer Imaging Society · 2024Article
- Article
- Immune checkpoint blockade resistance in lung cancer: emerging mechanisms and therapeutic opportunities.Trends in pharmacological sciences · 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
11 authors.
Funding
Abstract
Personalized medicine has revolutionized approaches to treatment in the field of lung cancer by enabling therapies to be specific to each patient. However, physicians encounter an immense number of challenges in providing the optimal treatment regimen for the individual given the sheer complexity of clinical aspects such as tumor molecular profile, tumor microenvironment, expected adverse events, acquired or inherent resistance mechanisms, the development of brain metastases, the limited availability of biomarkers and the choice of combination therapy. The integration of innovative next-generation technologies such as deep learning-a subset of machine learning-and radiomics has the potential to transform the field by supporting clinical decision making in cancer treatment and the delivery of precision therapies while integrating numerous clinical considerations. In this review, we present a brief explanation of the available technologies, the benefits of using these technologies in predicting immunotherapy response in lung cancer, and the expected future challenges in the context of precision medicine.
Identifiers
What Socratic holds
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 Socratic graph.