ArticleEuropean radiology2021
Prediction of micropapillary and solid pattern in lung adenocarcinoma using radiomic values extracted from near-pure histopathological subtypes.
Article in European radiology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers.
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31 citing papers in PubMed, 40 citations in OpenAlex.
- Assessment of high-grade pattern in lung invasive nonmucinous adenocarcinoma based on multiparametric features from dual-layer detector spectral computed tomography combined with extracellular volume fraction.Quantitative imaging in medicine and surgery · 2026Article
- Radiomics to understand pre-treatment tumor biology for resectable non-small cell lung cancer.Frontiers in oncology · 2026Review
- Development and validation of a CT-based habitat radiomics model for predicting pathological grading in non-small cell lung cancer.Frontiers in medicine · 2026Article
- Bio-interpretable ensemble learning model for invasive pulmonary adenocarcinoma grade using CT and histopathology images.NPJ precision oncology · 2025Article
- Novel insights into predicting the presence of micropapillary and solid components in stage IA lung adenocarcinoma using machine learning models of modifiable risk factors.Annals of medicine · 2025Article
- Development and validation of a computed tomography-based radiomics-clinical model to preoperatively predict high-grade patterns within lung invasive adenocarcinoma.Journal of thoracic disease · 2025Article
- [Predicting Invasive Non-mucinous Lung Adenocarcinoma IASLC Grading: A Nomogram Based on Dual-energy CT Imaging and Conventional Features].Zhongguo fei ai za zhi = Chinese journal of lung cancer · 2025Article
- Consensus clustering based on CT radiomics has potential for risk stratification of patients with clinical T1 stage lung adenocarcinoma.BMC medical imaging · 2025Article
- Enhancing the intraoperative identification of high-grade patterns in invasive lung adenocarcinoma via radiomics.Translational lung cancer research · 2025Article
- Intratumoral and peritumoral radiomics model for the preoperative prediction of cribriform component in invasive lung adenocarcinoma: a multicenter study.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2025Article
- CT radiomics from intratumor and peritumor regions for predicting poorly differentiated invasive nonmucinous pulmonary adenocarcinoma.Scientific reports · 2025Article
- Differentiating high-grade patterns and predominant subtypes for IASLC grading in invasive pulmonary adenocarcinoma using radiomics and clinical-semantic features.Cancer imaging : the official publication of the International Cancer Imaging Society · 2025Article
- Quantifying intratumoral heterogeneity within sub-regions to predict high-grade patterns in clinical stage I solid lung adenocarcinoma.BMC cancer · 2025Article
- Enhancing Identification of High-Risk cN0 Lung Adenocarcinoma Patients Using MRI-Based Radiomic Features.Cancer research and treatment · 2025Article
- AI-based prediction of pathological risk factors in lung adenocarcinoma from CT imaging: bridging innovation and clinical practice.Frontiers in oncology · 2025Review
- Article
- Prediction of solid and micropapillary components in lung invasive adenocarcinoma: radiomics analysis from high-spatial-resolution CT data with 1024 matrix.Japanese journal of radiology · 2024Article
- Feasibility of UTE-MRI-based radiomics model for prediction of histopathologic subtype of lung adenocarcinoma: in comparison with CT-based radiomics model.European radiology · 2024Article
- Preoperative CT-based radiomics nomogram to predict the micropapillary pattern in lung adenocarcinoma of size 2 cm or less.Frontiers in oncology · 2024Article
- A pretreatment prediction model of grade 3 tumors classed by the IASLC grading system in lung adenocarcinoma.BMC pulmonary medicine · 2023Article
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Authors and funding
10 authors at 2 institutions in 1 country.
Funding
Abstract
objectivesNear-pure lung adenocarcinoma (ADC) subtypes demonstrate strong stratification of radiomic values, providing basic information for pathological subtyping. We sought to predict the presence of high-grade (micropapillary and solid) components in lung ADCs using quantitative image analysis with near-pure radiomic values.
methodsOverall, 103 patients with lung ADCs of various histological subtypes were enrolled for 10-repetition, 3-fold cross-validation (cohort 1); 55 were enrolled for testing (cohort 2). Histogram and textural features on computed tomography (CT) images were assessed based on the "near-pure" pathological subtype data. Patch-wise high-grade likelihood prediction was performed for each voxel within the tumour region. The presence of high-grade components was then determined based on a volume percentage threshold of the high-grade likelihood area. To compare with quantitative approaches, consolidation/tumour (C/T) ratio was evaluated on CT images; we applied radiological invasiveness (C/T ratio > 0.5) for the prediction.
resultsIn cohort 1, patch-wise prediction, combined model (C/T ratio and patch-wise prediction), whole-lesion-based prediction (using only the "near-pure"-based prediction model), and radiological invasiveness achieved a sensitivity and specificity of 88.00 ± 2.33% and 75.75 ± 2.82%, 90.00 ± 0.00%, and 77.12 ± 2.67%, 66.67% and 90.41%, and 90.00% and 45.21%, respectively. The sensitivity and specificity, respectively, for cohort 2 were 100.0% and 95.35% using patch-wise prediction, 100.0% and 95.35% using combined model, 75.00% and 95.35% using whole-lesion-based prediction, and 100.0% and 69.77% using radiological invasiveness.
conclusionUsing near-pure radiomic features and patch-wise image analysis demonstrated high levels of sensitivity and moderate levels of specificity for high-grade ADC subtype-detecting. KEY POINTS: • The radiomic values extracted from lung adenocarcinoma with "near-pure" histological subtypes provide useful information for high-grade (micropapillary and solid) components detection. • Using near-pure radiomic features and patch-wise image analysis, high-grade components of lung adenocarcinoma can be predicted with high sensitivity and moderate specificity. • Using near-pure radiomic features and patch-wise image analysis has potential role in facilitating the prediction of the presence of high-grade components in lung adenocarcinoma prior to surgical resection.
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