ArticleCancers2021
Quantification of Cancer-Developing Idiopathic Pulmonary Fibrosis Using Whole-Lung Texture Analysis of HRCT Images.
Article in Cancers, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- Lobar-level radiomic clustering reveals background lung changes associated with lung cancer risk: a new perspective for early screening.Insights into imaging · 2026Article
- A narrative review of the early diagnosis and treatment of idiopathic pulmonary fibrosis with lung cancer.Journal of thoracic disease · 2025Review
- Radiomics and Artificial Intelligence in Pulmonary Fibrosis.Journal of imaging informatics in medicine · 2025Review
- From Hypersensitivity Pneumonitis to Lung Adenocarcinoma: A Case Report Illustrating Diagnostic Complexity.Cureus · 2025Article
- Construction and validation of an EGFR-related risk signature identifiedTranslational cancer research · 2025Article
- Integrating Radiomics Signature into Clinical Pathway for Patients with Progressive Pulmonary Fibrosis.Diagnostics (Basel, Switzerland) · 2025Review
- Radiomics predict the WHO/ISUP nuclear grade and survival in clear cell renal cell carcinoma.Insights into imaging · 2024Article
- Combined expert-in-the-loop-random forest multiclass segmentation U-net based artificial intelligence model: evaluation of non-small cell lung cancer in fibrotic and non-fibrotic microenvironments.Journal of translational medicine · 2024Article
- Imaging at the nexus: how state of the art imaging techniques can enhance our understanding of cancer and fibrosis.Journal of translational medicine · 2024Review
- A Multichannel CT and Radiomics-Guided CNN-ViT (RadCT-CNNViT) Ensemble Network for Diagnosis of Pulmonary Sarcoidosis.Diagnostics (Basel, Switzerland) · 2024Article
- Identification of oxidative stress-related diagnostic markers and immune infiltration features for idiopathic pulmonary fibrosis by bibliometrics and bioinformatics.Frontiers in medicine · 2024Review
- Radiomics-Assisted Computed Tomography-Based Analysis to Evaluate Lung Morphology Characteristics after Congenital Diaphragmatic Hernia.Journal of clinical medicine · 2023Article
- Recent Advances of Artificial Intelligence Applications in Interstitial Lung Diseases.Diagnostics (Basel, Switzerland) · 2023Review
- Quantitative CT analysis of lung parenchyma to improve malignancy risk estimation in incidental pulmonary nodules.European radiology · 2023Article
- Artificial-intelligence-based computed tomography histogram analysis predicting tumor invasiveness of lung adenocarcinomas manifesting as radiological part-solid nodules.Frontiers in oncology · 2023Article
- The Potential and Emerging Role of Quantitative Imaging Biomarkers for Cancer Characterization.Cancers · 2022Review
- Radiomics for the Prediction of Response to Antifibrotic Treatment in Patients with Idiopathic Pulmonary Fibrosis: A Pilot Study.Diagnostics (Basel, Switzerland) · 2022Article
- The applications of CT with artificial intelligence in the prognostic model of idiopathic pulmonary fibrosis.Therapeutic advances in respiratory diseaseReview
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7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Idiopathic pulmonary fibrosis (IPF) patients have a significantly higher risk of developing lung cancer (LC). There is only limited evidence of the use of texture-based radiomics features from high-resolution computed tomography (HRCT) images for risk stratification of IPF patients for LC. We retrospectively enrolled subjects who suffered from IPF in this study. Clinical data including age, gender, smoking status, and pulmonary function were recorded. Non-contrast chest CT for fibrotic score calculation and determination of three dimensional measures of whole-lung texture and emphysema were performed using a promising deep learning imaging platform. The results revealed that among 116 subjects with IPF (90 non-cancer and 26 lung cancer cases), the radiomics features showed significant differences between non-cancer and cancer patients. In the training cohort, the diagnostic accuracy using selected radiomics features with AUC of 0.66-0.73 (sensitivity of 80.0-85.0% and specificity of 54.2-59.7%) was not inferior to that obtained using traditional risk factors, such as gender, smoking status, and emphysema (%). In the validation cohort, the combination of radiomics features and traditional risk factors produced a diagnostic accuracy of 0.87 AUC and an accuracy of 75.0%. In this study, we found that whole-lung CT texture analysis is a promising tool for LC risk stratification of IPF patients.
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