ArticleDiagnostics (Basel, Switzerland)2021
A Cascaded Neural Network for Staging in Non-Small Cell Lung Cancer Using Pre-Treatment CT.
Article in Diagnostics (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed, 11 citations in OpenAlex.
- Deep Learning-Driven Multimodal Integration of miRNA and Radiomic for Lung Cancer Diagnosis.Biosensors · 2025Review
- Unified deep learning models for enhanced lung cancer prediction with ResNet-50-101 and EfficientNet-B3 using DICOM images.BMC medical imaging · 2024Article
- A CT-based transfer learning approach to predict NSCLC recurrence: The added-value of peritumoral region.PloS one · 2023Article
- AI/ML advances in non-small cell lung cancer biomarker discovery.Frontiers in oncology · 2023Review
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Authors and funding
5 authors at 3 institutions in 1 country.
Funding
Abstract
BACKGROUND AND
aimTumor staging in non-small cell lung cancer (NSCLC) is important for treatment and prognosis. Staging involves expert interpretation of imaging, which we aim to automate with deep learning (DL). We proposed a cascaded DL method comprised of two steps to classification between early- and advanced-stage NSCLC using pretreatment computed tomography.
methodsWe developed and tested a DL model to classify between early- and advanced-stage using training (
resultsOur model was tested in two test cohorts of CPTAC and TCGA. In CPTAC, our model achieved accuracy of 0.8649, sensitivity of 0.8000, specificity of 0.9412, and area under the curve (AUC) of 0.8206 compared to other approaches (AUC 0.6824-0.7206) for classifying between early- and advanced-stages. In TCGA, our model achieved accuracy of 0.8077, sensitivity of 0.7692, specificity of 0.8462, and AUC of 0.8343.
conclusionOur cascaded DL model for classification NSCLC patients into early-stage and advanced-stage showed promising results and could help future NSCLC research.
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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.