Trial reportScientific reports2019
Revealing Tumor Habitats from Texture Heterogeneity Analysis for Classification of Lung Cancer Malignancy and Aggressiveness.
Trial report in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers.
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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.
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Who cites it
32 citing papers in PubMed.
- A Machine Learning Model Based on Unsupervised Clustering Multihabitat to Predict the Pathological Grading of Meningiomas.BioMed research international · 2022Trial
- Preoperative CT-Based Habitat Radiomics Classifiers Predict Recurrence in Non-Small Cell Lung Cancer.medRxiv : the preprint server for health sciences · 2026Article
- Assessment of intratumor heterogeneity in non-small cell lung cancer by unsupervised K-means clustering of radiomics features based on multiphase computed tomography images.Quantitative imaging in medicine and surgery · 2025Article
- Development and application of a novel tumor habitat analysis technique based on dynamical modeling.Medical physics · 2025Article
- Habitat-based radiomics from contrast-enhanced CT and clinical data to predict lymph node metastasis in clinical N0 peripheral lung adenocarcinoma ≤ 3 cm.Scientific reports · 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
- Metastatic Lung Adenocarcinomas: Development and Evaluation of Radiomic-Based Methods to Measure Baseline Intra-Patient Inter-Tumor Lesion Heterogeneity.Journal of imaging informatics in medicine · 2025Article
- Revolutionizing breast cancer Ki-67 diagnosis: ultrasound radiomics and fully connected neural networks (FCNN) combination method.Breast cancer research and treatment · 2024Article
- 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
- Evolving and Novel Applications of Artificial Intelligence in Thoracic Imaging.Diagnostics (Basel, Switzerland) · 2024Review
- Hybrid deep spatial and statistical feature fusion for accurate MRI brain tumor classification.Frontiers in computational neuroscience · 2024Article
- Application of radiomics in diagnosis and treatment of lung cancer.Frontiers in pharmacology · 2023Review
- Multi-view radiomics and deep learning modeling for prostate cancer detection based on multi-parametric MRI.Frontiers in oncology · 2023Article
- Deep learning-based morphological feature analysis and the prognostic association study in colon adenocarcinoma histopathological images.Frontiers in oncology · 2023Article
- Radiomic Signatures Associated with CD8Cancers · 2022Review
- Tumor Connectomics: Mapping the Intra-Tumoral Complex Interaction Network Using Machine Learning.Cancers · 2022Article
- Investigation of the added value of CT-based radiomics in predicting the development of brain metastases in patients with radically treated stage III NSCLC.Therapeutic advances in medical oncology · 2022Article
- Habitat radiomics analysis of pet/ct imaging in high-grade serous ovarian cancer: Application to Ki-67 status and progression-free survival.Frontiers in physiology · 2022Article
- Robust imaging habitat computation using voxel-wise radiomics features.Scientific reports · 2021Article
- Challenges and opportunities for artificial intelligence in oncological imaging.Clinical radiology · 2021Review
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
We propose an approach for characterizing structural heterogeneity of lung cancer nodules using Computed Tomography Texture Analysis (CTTA). Measures of heterogeneity were used to test the hypothesis that heterogeneity can be used as predictor of nodule malignancy and patient survival. To do this, we use the National Lung Screening Trial (NLST) dataset to determine if heterogeneity can represent differences between nodules in lung cancer and nodules in non-lung cancer patients. 253 participants are in the training set and 207 participants in the test set. To discriminate cancerous from non-cancerous nodules at the time of diagnosis, a combination of heterogeneity and radiomic features were evaluated to produce the best area under receiver operating characteristic curve (AUROC) of 0.85 and accuracy 81.64%. Second, we tested the hypothesis that heterogeneity can predict patient survival. We analyzed 40 patients diagnosed with lung adenocarcinoma (20 short-term and 20 long-term survival patients) using a leave-one-out cross validation approach for performance evaluation. A combination of heterogeneity features and radiomic features produce an AUROC of 0.9 and an accuracy of 85% to discriminate long- and short-term survivors.
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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.