ArticleScientific reports2021
Robust imaging habitat computation using voxel-wise radiomics features.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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Who cites it
24 citing papers in PubMed, 30 citations in OpenAlex.
- Clinico-biological-radiomics (CBR) based machine learning for improving the diagnostic accuracy of FDG-PET false-positive lymph nodes in lung cancer.European journal of medical research · 2023Trial
- Intratumoral habitat imaging for progression-free survival prediction of patients with high-risk, non-metastatic clear cell renal cell carcinoma.Abdominal radiology (New York) · 2026Article
- Predicting early recurrence of craniopharyngioma using multi-omics radiomic modeling: a retrospective cohort study.Neurosurgical review · 2026Article
- Multimodal combined model integrating 2.5D deep learning and habitat radiomics for malignancy discrimination in ≤2 cm BI-RADS 4 breast lesions.Quantitative imaging in medicine and surgery · 2026Article
- ASO Author Reflections: Beyond Whole-Tumor Analysis-Unveiling the Prognostic Power of PET Radiomic Habitats in Invasive Lung Adenocarcinoma.Annals of surgical oncology · 2026Article
- Development and validation of a CT-based habitat radiomics model for predicting pathological grading in non-small cell lung cancer.Frontiers in medicine · 2026Article
- Contrast-enhanced CT-based habitat radiomics for distinguishing low-risk thymomas from high-risk thymomas: a multicenter study.Japanese journal of radiology · 2026Article
- Review
- Predicting the progressive-free survival of ALK-positive lung cancer: machine learning algorithm based on habitat imaging.Journal of thoracic disease · 2025Article
- Integrative radiomics analysis of peri-tumoral and habitat zones for predicting major pathological response to neoadjuvant immunotherapy and chemotherapy in non-small cell lung cancer.Translational lung cancer research · 2025Article
- Habitat Analysis in Tumor Imaging: Advancing Precision Medicine Through Radiomic Subregion Segmentation.Cancer management and research · 2025Review
- Prognostic and Predictive Biomarkers of Oligometastatic NSCLC: New Insights and Clinical Applications.JTO clinical and research reports · 2024Review
- Radiogenomics Pilot Study: Association Between Radiomics and Single Nucleotide Polymorphism-Based Microarray Copy Number Variation in Diagnosing Renal Oncocytoma and Chromophobe Renal Cell Carcinoma.International journal of molecular sciences · 2024Article
- MRI-based habitat imaging in cancer treatment: current technology, applications, and challenges.Cancer imaging : the official publication of the International Cancer Imaging Society · 2024Review
- Habitat radiomics and deep learning fusion nomogram to predict EGFR mutation status in stage I non-small cell lung cancer: a multicenter study.Scientific reports · 2024Article
- Finding the Pieces to Treat the Whole: Using Radiomics to Identify Tumor Habitats.Radiology. Artificial intelligence · 2024Article
- Identification of Precise 3D CT Radiomics for Habitat Computation by Machine Learning in Cancer.Radiology. Artificial intelligence · 2024Article
- Prediction of early postoperative recurrence of hepatocellular carcinoma by habitat analysis based on different sequence of contrast-enhanced CT.Frontiers in oncology · 2024Article
- Assessment of brain cancer atlas maps with multimodal imaging features.Journal of translational medicine · 2023Review
- Clinical features combined with ultrasound-based radiomics nomogram for discrimination between benign and malignant lesions in ultrasound suspected supraclavicular lymphadenectasis.Frontiers in oncology · 2023Article
Corrections and comments
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Authors and funding
6 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Tumor heterogeneity has been postulated as a hallmark of treatment resistance and a cure constraint in cancer patients. Conventional quantitative medical imaging (radiomics) can be extended to computing voxel-wise features and aggregating tumor subregions with similar radiological phenotypes (imaging habitats) to elucidate the distribution of tumor heterogeneity within and among tumors. Despite the promising applications of imaging habitats, they may be affected by variability of radiomics features, preventing comparison and generalization of imaging habitats techniques. We performed a comprehensive repeatability and reproducibility analysis of voxel-wise radiomics features in more than 500 lung cancer patients with computed tomography (CT) images and demonstrated the effect of voxel-wise radiomics variability on imaging habitats computation in 30 lung cancer patients with test-retest images. Repeatable voxel-wise features characterized texture heterogeneity and were reproducible regardless of the applied feature extraction parameters. Imaging habitats computed using robust radiomics features were more stable than those computed using all features in test-retest CTs from the same patient. Nine voxel-wise radiomics features (joint energy, joint entropy, sum entropy, maximum probability, difference entropy, Imc1, Imc2, Idn and Idmn) were repeatable and reproducible. This supports their application for computing imaging habitats in lung tumors towards the discovery of previously unseen tumor heterogeneity and the development of novel non-invasive imaging biomarkers for precision medicine.
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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.