ReviewCancers2023
Deep Learning for Lung Cancer Diagnosis, Prognosis and Prediction Using Histological and Cytological Images: A Systematic Review.
Review in Cancers, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers, 1 of them a synthesis that pooled it.
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.
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Who cites it
35 citing papers in PubMed, 1 synthesis or guideline pooled it, 61 citations in OpenAlex.
- Transforming histologic assessment: artificial intelligence in cancer diagnosis and personalized treatment.British journal of cancer · 2025Pooled it
- Transfer Learning in Convolutional Neural Network to Differentiate Follicular Adenoma Versus Follicular Carcinoma of Thyroid on Aspiration Cytology Material.Cytopathology : official journal of the British Society for Clinical Cytology · 2026Article
- Artificial intelligence for lung cancer classification in cytology specimens: A systematic review and diagnostic test accuracy meta-analysis of benign-malignant diagnosis and ADC/SCC/SCLC subtyping.Journal of pathology informatics · 2026Review
- Review
- Systematic review and meta-analysis of AI in lung cancer metastasis imaging for diagnosis and prognosis.NPJ digital medicine · 2026Article
- Pathology Foundation Models: Evolution, Current Landscape, Challenges and Opportunities from a Technical and Clinical Perspective.Bioengineering (Basel, Switzerland) · 2026Review
- Diagnostic Accuracy of Sputum and Bronchoscopy-Guided Cytology Compared With Bronchial Biopsy in Pulmonary Lesions.Cureus · 2026Article
- Deep learning framework for predicting EGFR mutation status from H&E whole slide images in lung adenocarcinoma.BMC cancer · 2026Article
- Clinical validation of lightweight CNN architectures for reliable multi-class classification of lung cancer using histopathological imaging techniques.Scientific reports · 2026Article
- Automatic and accurate auxiliary detection of lung cancer pathological classification based on novel lightweight deep learning model.Discover oncology · 2026Article
- MLHNet-Lung: an attention-guided multi-level CNN-transformer fusion framework with CBAM and GeM pooling for explainable multiclass lung CT image classification.Frontiers in medicine · 2026Article
- Automatic identification of clinically importantEmerging microbes & infections · 2025Article
- Automated classification of lung cancer subtypes cells using microscopic images and ensembled deep learning architectures.Scientific reports · 2025Article
- Translating Features to Findings: Deep Learning for Melanoma Subtype Prediction.Dermatopathology (Basel, Switzerland) · 2025Review
- Gastroenterology in the age of artificial intelligence: Bridging technology and clinical practice.World journal of gastroenterology · 2025Review
- Development and evaluation of deep learning models for detecting and classifying various bone tumours in full-field limb radiographs using automated object detection models.Bone & joint research · 2025Article
- Emerging Techniques of Translational Research in Immuno-Oncology: A Focus on Non-Small Cell Lung Cancer.Cancers · 2025Review
- Systematic scoping review of external validation studies of AI pathology models for lung cancer diagnosis.NPJ precision oncology · 2025Article
- Deep learning in histopathology images for prediction of oncogenic driver molecular alterations in lung cancer: a systematic review and meta-analysis.Translational lung cancer research · 2025Article
- Segmentation of Non-Small Cell Lung Carcinomas: Introducing DRU-Net and Multi-Lens Distortion.Journal of imaging · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Lung cancer is one of the deadliest cancers worldwide, with a high incidence rate, especially in tobacco smokers. Lung cancer accurate diagnosis is based on distinct histological patterns combined with molecular data for personalized treatment. Precise lung cancer classification from a single H&E slide can be challenging for a pathologist, requiring most of the time additional histochemical and special immunohistochemical stains for the final pathology report. According to WHO, small biopsy and cytology specimens are the available materials for about 70% of lung cancer patients with advanced-stage unresectable disease. Thus, the limited available diagnostic material necessitates its optimal management and processing for the completion of diagnosis and predictive testing according to the published guidelines. During the new era of Digital Pathology, Deep Learning offers the potential for lung cancer interpretation to assist pathologists' routine practice. Herein, we systematically review the current Artificial Intelligence-based approaches using histological and cytological images of lung cancer. Most of the published literature centered on the distinction between lung adenocarcinoma, lung squamous cell carcinoma, and small cell lung carcinoma, reflecting the realistic pathologist's routine. Furthermore, several studies developed algorithms for lung adenocarcinoma predominant architectural pattern determination, prognosis prediction, mutational status characterization, and PD-L1 expression status estimation.
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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.