ArticlePloS one2023
Deep pathomics: A new image-based tool for predicting response to treatment in stage III non-small cell lung cancer.
Article in PloS one, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 2 of them syntheses that pooled it.
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Who cites it
12 citing papers in PubMed, 2 syntheses or guidelines pooled it, 15 citations in OpenAlex.
- Deep learning or radiomics based on CT for predicting the response of gastric cancer to neoadjuvant chemotherapy: a meta-analysis and systematic review.Frontiers in oncology · 2024Pooled it
- Unveiling the landscape of pathomics in personalized immunotherapy for lung cancer: a bibliometric analysis.Frontiers in oncology · 2024Pooled it
- Virtual Evaluation of Hematoxylin & Eosin via Digital Pathology Survey (VEED) Project: Results from a Non-Inferiority Study of a Tabs-Based Staining Method.Diagnostics (Basel, Switzerland) · 2026Article
- Feasibility of No-Code Deep Learning for Diagnosing Bone Metastasis in Bone Scans: A Comparative Study of Teachable Machine and ResNet.Journal of imaging informatics in medicine · 2026Article
- Artificial Intelligence in Lung Cancer: A Narrative Review of Recent Advances in Diagnosis, Biomarker Discovery, and Drug Development.Pharmaceutics · 2026Review
- Inferring tumor immune microenvironment -related risk states from pretreatment H&E pathomics and clinical biomarkers to predict checkpoint inhibitor pneumonitis in advanced NSCLC: a multicenter multimodal study.Frontiers in immunology · 2026Article
- Exploratory Analysis of HMGB1 Genetic Variants and Their Potential Association with Lung Cancer Susceptibility and Chemotherapy Response in a Chinese Population.Human heredity · 2026Article
- A narrative review of pathologic response in non-small cell lung cancer: challenges, implications, and future directions.Translational lung cancer research · 2025Review
- Pathomics-based machine learning models for optimizing LungPro navigational bronchoscopy in peripheral lung lesion diagnosis: a retrospective study.Biomedical engineering online · 2025Article
- IDH-mutant glioma risk stratification via whole slide images: Identifying pathological feature associations.iScience · 2025Article
- Review
- Brain tumour detection from magnetic resonance imaging using convolutional neural networks.Contemporary oncology (Poznan, Poland) · 2023Article
Corrections and comments
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Authors and funding
15 authors at 3 institutions in 4 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Despite the advantages offered by personalized treatments, there is presently no way to predict response to chemoradiotherapy in patients with non-small cell lung cancer (NSCLC). In this exploratory study, we investigated the application of deep learning techniques to histological tissue slides (deep pathomics), with the aim of predicting the response to therapy in stage III NSCLC. We evaluated 35 digitalized tissue slides (biopsies or surgical specimens) obtained from patients with stage IIIA or IIIB NSCLC. Patients were classified as responders (12/35, 34.7%) or non-responders (23/35, 65.7%) based on the target volume reduction shown on weekly CT scans performed during chemoradiation treatment. Digital tissue slides were tested by five pre-trained convolutional neural networks (CNNs)-AlexNet, VGG, MobileNet, GoogLeNet, and ResNet-using a leave-two patient-out cross validation approach, and we evaluated the networks' performances. GoogLeNet was globally found to be the best CNN, correctly classifying 8/12 responders and 10/11 non-responders. Moreover, Deep-Pathomics was found to be highly specific (TNr: 90.1) and quite sensitive (TPr: 0.75). Our data showed that AI could surpass the capabilities of all presently available diagnostic systems, supplying additional information beyond that currently obtainable in clinical practice. The ability to predict a patient's response to treatment could guide the development of new and more effective therapeutic AI-based approaches and could therefore be considered an effective and innovative step forward in personalised medicine.
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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.