ArticleScientific reports2023
Wavelet radiomics features from multiphase CT images for screening hepatocellular carcinoma: analysis and comparison.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 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
17 citing papers in PubMed.
- Predicting chemotherapy response in pediatric lymphoma using pre-treatment contrast-enhanced CT radiomics: A hypothesis-generating study.Abdominal radiology (New York) · 2026Article
- MRI-based clinical-radiologic model to predict the efficacy and prognosis of interventional with systemic therapy in advanced hepatocellular carcinoma: a multicenter study.Journal of translational medicine · 2026Article
- Multi-Phasic CECT Peritumoral Radiomics Predict Treatment Response to Bevacizumab-Based Chemotherapy in RAS-Mutated Colorectal Liver Metastases.Bioengineering (Basel, Switzerland) · 2026Article
- Explainable machine learning-based multiphase contrast-enhanced CT radiomics for noninvasively predicting GPC3 expression in hepatocellular carcinoma: a bicentric study.American journal of translational research · 2026Article
- A radiomics-deep learning nomogram integrating intratumoral and peritumoral DCE-MRI features for pCR prediction in breast cancer.Frontiers in oncology · 2026Article
- Radiomics application using non-contrast computed tomography for predicting uric acid kidney stones.BMC medical imaging · 2025Article
- CT-based machine learning radiomics predicts Ki-67 expression level and its relationship with overall survival in resectable pancreatic ductal adenocarcinoma.Abdominal radiology (New York) · 2025Article
- Graph feature selection for enhancing radiomic stability and reproducibility across multiple institutions in head and neck cancer.Scientific reports · 2025Article
- Toward New Assessment in Sarcoma Identification and Grading Using Artificial Intelligence Techniques.Diagnostics (Basel, Switzerland) · 2025Article
- Comprehensive review of Bayesian network applications in gastrointestinal cancers.World journal of clinical oncology · 2025Review
- Preoperative multiparameter MRI-based prediction of Ki-67 expression in primary central nervous system lymphoma.Precision radiation oncology · 2025Article
- Article
- 3D synergistic tumor-liver analysis further improves the efficacy prediction in hepatocellular carcinoma: a multi-center study.BMC cancer · 2025Article
- Preoperative prediction of lymph node metastasis in intrahepatic cholangiocarcinoma: an integrative approach combining ultrasound-based radiomics and inflammation-related markers.BMC medical imaging · 2025Article
- Development and validation of machine learning-based MRI radiomics models for preoperative lymph node staging in T3 rectal cancer.Frontiers in oncology · 2025Article
- A study on the prediction of targeted therapy efficacy in advanced lung adenocarcinoma patients with EGFR mutations using CT-based delta-radiomics model.Frontiers in medicine · 2025Article
- Prediction models for cognitive impairment in middle-aged patients with cerebral small vessel disease.Frontiers in neurology · 2025Article
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
No grant is acknowledged in the PubMed record.
Abstract
Early detection of liver malignancy based on medical image analysis plays a crucial role in patient prognosis and personalized treatment. This task, however, is challenging due to several factors, including medical data scarcity and limited training samples. This paper presents a study of three important aspects of radiomics feature from multiphase computed tomography (CT) for classifying hepatocellular carcinoma (HCC) and other focal liver lesions: wavelet-transformed feature extraction, relevant feature selection, and radiomics features-based classification under the inadequate training samples. Our analysis shows that combining radiomics features extracted from the wavelet and original CT domains enhance the classification performance significantly, compared with using those extracted from the wavelet or original domain only. To facilitate the multi-domain and multiphase radiomics feature combination, we introduce a logistic sparsity-based model for feature selection with Bayesian optimization and find that the proposed model yields more discriminative and relevant features than several existing methods, including filter-based, wrapper-based, or other model-based techniques. In addition, we present analysis and performance comparison with several recent deep convolutional neural network (CNN)-based feature models proposed for hepatic lesion diagnosis. The results show that under the inadequate data scenario, the proposed wavelet radiomics feature model produces comparable, if not higher, performance metrics than the CNN-based feature models in terms of area under the curve.
Indexed as
Identifiers
What 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.