ArticleNature machine intelligence2020
A Shallow Convolutional Neural Network Predicts Prognosis of Lung Cancer Patients in Multi-Institutional CT-Image Data.
Article in Nature machine intelligence, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 41 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
41 citing papers in PubMed.
- A hybrid deep learning approach integrating CNN and transformer for lung cancer classification using CT scans.Scientific reports · 2026Article
- Deep-Learning Serial CT Prediction of Survival in Immunotherapy-Treated Non-Small Cell Lung Cancer.JAMA network open · 2026Article
- Application of deep learning in prognostic prediction of non-small cell lung cancer.Frontiers in cell and developmental biology · 2026Review
- Thorax-encompassing multi-modality PET/CT deep learning model for resected lung cancer prognostication: A retrospective, multicenter study.Medical physics · 2025Article
- AI-assisted SERS imaging method for label-free and rapid discrimination of clinical lymphoma.Journal of nanobiotechnology · 2025Article
- Integrating Machine Learning and Deep Learning for Predicting Non-Surgical Root Canal Treatment Outcomes Using Two-Dimensional Periapical Radiographs.Diagnostics (Basel, Switzerland) · 2025Article
- Multimodal deep learning improves recurrence risk prediction in pediatric low-grade gliomas.Neuro-oncology · 2025Article
- A CT-based deep learning model to predict local recurrence-free survival in primary retroperitoneal sarcoma.Frontiers in medicine · 2025Article
- Optimized deep learning approach for lung cancer detection using flying fox optimization and bidirectional generative adversarial networks.PeerJ. Computer science · 2025Article
- Research trends of artificial intelligence and radiomics in lung cancer: a bibliometric analysis.Quantitative imaging in medicine and surgery · 2024Article
- Seven oral traditional Chinese medicine combined with chemotherapy for the treatment of non-small cell lung cancer: a network meta-analysis.Pharmaceutical biology · 2024Article
- An international study presenting a federated learning AI platform for pediatric brain tumors.Nature communications · 2024Article
- An Advanced Lung Carcinoma Prediction and Risk Screening Model Using Transfer Learning.Diagnostics (Basel, Switzerland) · 2024Article
- Enhancing Lung Cancer Survival Prediction: 3D CNN Analysis of CT Images Using Novel GTV1-SliceNum Feature and PEN-BCE Loss Function.Diagnostics (Basel, Switzerland) · 2024Article
- Automated detection of otosclerosis with interpretable deep learning using temporal bone computed tomography images.Heliyon · 2024Article
- Application of artificial intelligence in the diagnosis, treatment, and recurrence prediction of peritoneal carcinomatosis.Heliyon · 2024Review
- Added prognostic value of 3D deep learning-derived features from preoperative MRI for adult-type diffuse gliomas.Neuro-oncology · 2024Article
- Article
- A 3D lung lesion variational autoencoder.Cell reports methods · 2024Article
- Deep learning-assisted survival prognosis in renal cancer: A CT scan-based personalized approach.Heliyon · 2024Article
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
Lung cancer is the most common fatal malignancy in adults worldwide, and non-small cell lung cancer (NSCLC) accounts for 85% of lung cancer diagnoses. Computed tomography (CT) is routinely used in clinical practice to determine lung cancer treatment and assess prognosis. Here, we developed LungNet, a shallow convolutional neural network for predicting outcomes of NSCLC patients. We trained and evaluated LungNet on four independent cohorts of NSCLC patients from four medical centers: Stanford Hospital (n = 129), H. Lee Moffitt Cancer Center and Research Institute (n = 185), MAASTRO Clinic (n = 311) and Charité - Universitätsmedizin (n=84). We show that outcomes from LungNet are predictive of overall survival in all four independent survival cohorts as measured by concordance indices of 0.62, 0.62, 0.62 and 0.58 on cohorts 1, 2, 3, and 4, respectively. Further, the survival model can be used, via transfer learning, for classifying benign vs malignant nodules on the Lung Image Database Consortium (n = 1010), with improved performance (AUC=0.85) versus training from scratch (AUC=0.82). LungNet can be used as a noninvasive predictor for prognosis in NSCLC patients and can facilitate interpretation of CT images for lung cancer stratification and prognostication.
Identifiers
What OpenQuestion 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 OpenQuestion graph.