ArticleTranslational oncology2017
MRI Texture Analysis Reflects Histopathology Parameters in Thyroid Cancer - A First Preliminary Study.
Article in Translational oncology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 papers.
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Who cites it
39 citing papers in PubMed, 66 citations in OpenAlex.
- CT-Based Texture Analysis in Indeterminate Pediatric Renal and Pararenal Masses.The Indian journal of radiology & imaging · 2026Article
- Integrative molecular-radiopathomic characterization of AIRE-driven immune subtypes in bladder cancer.NPJ digital medicine · 2025Article
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- Associations between MRI radiomics analysis and tumor-micro milieu in uterine cervical cancer.Journal of cancer research and clinical oncology · 2025Article
- CT Texture Analysis in Breast Cancer Patients Undergoing CT-Guided Bone Biopsy: Correlations With Histopathology.Breast cancer : basic and clinical research · 2025Article
- The diagnostic value of convolutional neural networks in thyroid cancer detection using ultrasound images.Frontiers in oncology · 2025Article
- CT Texture Analysis of Perihilar Cholangiocarcinoma-Associations With Tumor Grading, Tumor Markers and Clinical Outcome.Cancer reports (Hoboken, N.J.) · 2024Article
- Impact of radiomics features, pulmonary emphysema score and muscle mass on the rate of pneumothorax and chest tube insertion in CT-guided lung biopsies.Respiratory research · 2024Article
- Impact of quantitative CT texture analysis on the outcome of CT-guided bone biopsy.Journal of bone oncology · 2024Article
- Prediction of non-perfusion volume ratio for uterine fibroids treated with ultrasound-guided high-intensity focused ultrasound based on MRI radiomics combined with clinical parameters.Biomedical engineering online · 2023Article
- Prediction of Ki-67 expression in gastrointestinal stromal tumors using radiomics of plain and multiphase contrast-enhanced CT.European radiology · 2023Article
- Computed tomography texture analysis to discriminate fungal and non-fungal infected fluid collections.Journal of medical imaging (Bellingham, Wash.) · 2023Article
- Deep Learning to Predict the Cell Proliferation and Prognosis of Non-Small Cell Lung Cancer Based on FDG-PET/CT Images.Diagnostics (Basel, Switzerland) · 2023Article
- Abnormal neonatal brain microstructure in gestational diabetes mellitus revealed by MRI texture analysis.Scientific reports · 2023Article
- Computed Tomography Embolus Texture Analysis as a Prognostic Marker of Acute Pulmonary Embolism.Angiology · 2023Article
- Radiomics-Based Machine Learning Model for Predicting Overall and Progression-Free Survival in Rare Cancer: A Case Study for Primary CNS Lymphoma Patients.Bioengineering (Basel, Switzerland) · 2023Article
- CT texture analysis and node-RADS CT score of mediastinal lymph nodes - diagnostic performance in lung cancer patients.Cancer imaging : the official publication of the International Cancer Imaging Society · 2022Article
- Magnetic resonance imaging-based radiomics signature for preoperative prediction of Ki67 expression in bladder cancer.Cancer imaging : the official publication of the International Cancer Imaging Society · 2021Article
- CT Texture Analysis of Pulmonary Neuroendocrine Tumors-Associations with Tumor Grading and Proliferation.Journal of clinical medicine · 2021Article
- Radiomics feature stability of open-source software evaluated on apparent diffusion coefficient maps in head and neck cancer.Scientific reports · 2021Article
Corrections and comments
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectThyroid cancer represents the most frequent malignancy of the endocrine system with an increasing incidence worldwide. Novel imaging techniques are able to further characterize tumors and even predict histopathology features. Texture analysis is an emergent imaging technique to extract extensive data from an radiology images. The present study was therefore conducted to identify possible associations between texture analysis and histopathology parameters in thyroid cancer.
methodsThe radiological database was retrospectively reviewed for thyroid carcinoma. Overall, 13 patients (3 females, 23.1%) with a mean age of 61.6 years were identified. The MaZda program was used for texture analysis. The T1-precontrast and T2-weighted images were analyzed and overall 279 texture feature for each sequence was investigated. For every patient cell count, Ki67-index and p53 count were investigated.
resultsSeveral significant correlations between texture features and histopathology were identified. Regarding T1-weighted images, S(0;1)Sum Averg correlated the most with cell count (r=0.82). An inverse correlations with S(5;0)AngScMom, S(5;0)DifVarnc S(5;0), DiffEntrp and GrNonZeros (r=-0.69, -0.66, -0.69 and -0.63, respectively) was also identified. For T2-weighted images, Variance with r=0.63 was the highest coefficient, WavEnLL_S3 correlated inversely with cell count (r=-0.57). WavEnLL_S2 derived from T1-weighted images was the highest coefficient r=-0.80, S(0;5)SumVarnc was positively with r=0.74. Regarding T2-weighted images WavEnHL_s-1 was inverse correlated with Ki67 index (r=-0.77). S(1;0)Correlat was with r=0.75 the best correlation with Ki67 index. For T1-weighed images S(5;0)SumofSqs was the best with r=0.65 with p53 count. For T2-weighted images S(1;-1)SumEntrp was the inverse correlation with r=-0.72, whereas S(0;4)AngScMom correlated positively with r=0.63.
conclusionsMRI texture analysis derived from conventional sequences reflects histopathology features in thyroid cancer. This technique might be a novel noninvasive modality to further characterize thyroid cancer in clinical oncology.
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