ArticleInternational journal of computer assisted radiology and surgery2020
CT-based radiomics for prediction of histologic subtype and metastatic disease in primary malignant lung neoplasms.
Article in International journal of computer assisted radiology and surgery, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
32 citing papers in PubMed.
- A predictive nomogram for two-year growth of CT-indeterminate small pulmonary nodules.European radiology · 2022Trial
- End-to-end prognostication in pancreatic cancer by multimodal deep learning: a retrospective, multicenter study.European radiology · 2025Article
- Review of the application of dual-energy CT combined with radiomics in the diagnosis and analysis of lung cancer.Journal of applied clinical medical physics · 2025Review
- The prognostic value of radiogenomics using CT in patients with lung cancer: a systematic review.Insights into imaging · 2024Review
- Prognostic nomogram combiningScientific reports · 2024Article
- Preoperative CT-based radiomic prognostic index to predict the benefit of postoperative radiotherapy in patients with non-small cell lung cancer: a multicenter study.Cancer imaging : the official publication of the International Cancer Imaging Society · 2024Article
- Developments and future prospects of personalized medicine in head and neck squamous cell carcinoma diagnoses and treatments.Cancer reports (Hoboken, N.J.) · 2024Review
- Article
- Research in the application of artificial intelligence to lung cancer diagnosis.Frontiers in medicine · 2024Review
- Diagnostic value of a CT-based radiomics nomogram for discrimination of benign and early stage malignant ovarian tumors.European journal of medical research · 2023Article
- Additional Value of PET and CT Image-Based Features in the Detection of Occult Lymph Node Metastases in Lung Cancer: A Systematic Review of the Literature.Diagnostics (Basel, Switzerland) · 2023Review
- Article
- Application of radiomics in diagnosis and treatment of lung cancer.Frontiers in pharmacology · 2023Review
- Applying a CT texture analysis model trained with deep-learning reconstruction images to iterative reconstruction images in pulmonary nodule diagnosis.Journal of applied clinical medical physics · 2022Article
- Artificial intelligence for multimodal data integration in oncology.Cancer cell · 2022Review
- Form Factors as Potential Imaging Biomarkers to Differentiate Benign vs. Malignant Lung Lesions on CT Scans.Sensors (Basel, Switzerland) · 2022Article
- Radiomic Quantification for MRI Assessment of Sacroiliac Joints of Patients with Spondyloarthritis.Journal of digital imaging · 2022Article
- Article
- The Effects of Perinodular Features on Solid Lung Nodule Classification.Journal of digital imaging · 2021Article
- A radiomic nomogram based on arterial phase of CT for differential diagnosis of ovarian cancer.Abdominal radiology (New York) · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
Abstract
purposeAs some of the most important factors for treatment decision of lung cancer (which is the deadliest neoplasm) are staging and histology, this work aimed to associate quantitative contrast-enhanced computed tomography (CT) features from malignant lung tumors with distant and nodal metastases (according to clinical TNM staging) and histopathology (according to biopsy and surgical resection) using radiomics assessment.
methodsA local cohort of 85 patients were retrospectively (2010-2017) analyzed after approval by the institutional research review board. CT images acquired with the same protocol were semiautomatically segmented by a volumetric segmentation method. Tumors were characterized by quantitative CT features of shape, first-order, second-order, and higher-order textures. Statistical and machine learning analyses assessed the features individually and combined with clinical data.
resultsUnivariate and multivariate analyses identified 40, 2003, and 45 quantitative features associated with distant metastasis, nodal metastasis, and histopathology (adenocarcinoma and squamous cell carcinoma), respectively. A machine learning model yielded the highest areas under the receiver operating characteristic curves of 0.92, 0.84, and 0.88 to predict the same previous patterns.
conclusionSeveral radiomic features (including wavelet energies, information measures of correlation and maximum probability from co-occurrence matrix, busyness from neighborhood intensity-difference matrix, directionalities from Tamura's texture, and fractal dimension estimation) significantly associated with distant metastasis, nodal metastasis, and histology were discovered in this work, presenting great potential as imaging biomarkers for pathological diagnosis and target therapy decision.
Indexed as
Identifiers
31722085What OpenQuestion holds
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.