ArticleFrontiers in oncology2018
Radiomics Evaluation of Histological Heterogeneity Using Multiscale Textures Derived From 3D Wavelet Transformation of Multispectral Images.
Article in Frontiers in oncology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers.
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Who cites it
38 citing papers in PubMed.
- Multi-DECT image-based intra- and peritumoral interpretable radiomics and artificial intelligence models for preoperative prediction of pathological grading of non-muscle-invasive bladder cancer.Abdominal radiology (New York) · 2026Article
- Interpretable MRI Radiomics for Preoperative Meningioma Consistency Prediction.Journal of imaging informatics in medicine · 2026Article
- Decoding the Rotation Effect: A Retrospective Analysis of Lesion Orientation and Its Impact on Wavelet-Based Radiomics Feature Extraction and Lung Cancer Classification.Journal of imaging informatics in medicine · 2026Article
- A radiomic model leveraging conventional and Hessian matrix-based radiomic features from DCE-MRI for predicting efficacy of neoadjuvant chemotherapy in patients with HER2-low breast cancer.Frontiers in medicine · 2025Article
- Automated classification of pathological differentiation in head and neck squamous cell carcinoma using combined radiomics models from CET1WI and T2WI.Clinical oral investigations · 2024Article
- Multimodal Ultrasound Radiomic Technology for Diagnosing Benign and Malignant Thyroid Nodules of Ti-Rads 4-5: A Multicenter Study.Sensors (Basel, Switzerland) · 2024Article
- Prediction of prognosis in glioblastoma with radiomics features extracted by synthetic MRI images using cycle-consistent GAN.Physical and engineering sciences in medicine · 2024Article
- Machine learning model to preoperatively predict T2/T3 staging of laryngeal and hypopharyngeal cancer based on the CT radiomic signature.European radiology · 2024Article
- Prediction of lymphovascular invasion of gastric cancer based on contrast-enhanced computed tomography radiomics.Frontiers in oncology · 2024Article
- Heterogeneity of Glycolytic Phenotype Determined byDiagnostics (Basel, Switzerland) · 2023Article
- Applications of discriminative and deep learning feature extraction methods for whole slide image analysis: A survey.Journal of pathology informatics · 2023Review
- Article
- Intratumoral and peritumoral radiomics model based on abdominal ultrasound for predicting Ki-67 expression in patients with hepatocellular cancer.Frontiers in oncology · 2023Article
- Predicting necessity of daily online adaptive replanning based on wavelet image features for MRI guided adaptive radiation therapy.Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology · 2022Article
- Wavelet transformation can enhance computed tomography texture features: a multicenter radiomics study for grade assessment of COVID-19 pulmonary lesions.Quantitative imaging in medicine and surgery · 2022Article
- Synthetic MRI improves radiomics-based glioblastoma survival prediction.NMR in biomedicine · 2022Article
- Contrast-enhanced CT-based radiomics model for differentiating risk subgroups of thymic epithelial tumors.BMC medical imaging · 2022Article
- MRI-based radiomics analysis for preoperative evaluation of lymph node metastasis in hypopharyngeal squamous cell carcinoma.Frontiers in oncology · 2022Article
- A potential biomarker based on clinical-radiomics nomogram for predicting survival and adjuvant chemotherapy benefit in resected node-negative, early-stage lung adenocarcinoma.Journal of thoracic disease · 2022Article
- Can Autism Be Diagnosed with Artificial Intelligence? A Narrative Review.Diagnostics (Basel, Switzerland) · 2021Review
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Abstract
purposeColorectal cancer (CRC) is markedly heterogeneous and develops progressively toward malignancy through several stages which include stroma (ST), benign hyperplasia (BH), intraepithelial neoplasia (IN) or precursor cancerous lesion, and carcinoma (CA). Identification of the malignancy stage of CRC pathology tissues (PT) allows the most appropriate therapeutic intervention.
methodsThis study investigates multiscale texture features extracted from CRC pathology sections using 3D wavelet transform (3D-WT) filter. Multiscale features were extracted from digital whole slide images of 39 patients that were segmented in a pre-processing step using an active contour model. The capacity for multiscale texture to compare and classify between PTs was investigated using ANOVA significance test and random forest classifier models, respectively.
results12 significant features derived from the multiscale texture (i.e., variance, entropy, and energy) were found to discriminate between CRC grades at a significance value of
conclusionOur results suggest that multiscale texture features based on 3D-WT are sensitive enough to discriminate between CRC grades with the entropy feature, the best predictor of pathology grade.
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