Histogram Analysis Parameters Derived from Conventional T1- and T2-Weighted Images Can Predict Different Histopathological Features Including Expression of Ki67, EGFR, VEGF, HIF-1α, and p53 and Cell Count in Head and Neck Squamous Cell Carcinoma.
Hans Jonas MeyerLeonard LeifelsGordian HamerlaAnne Kathrin HöhnAlexey SurovPublished in: Molecular imaging and biology (2020)
Multiple associations between histogram parameters derived from T1w and T2w images and clinically relevant histopathological features were found in HNSCC. Therefore, imaging parameters can be also used as surrogate markers for tumor cellularity, proliferation, and vascularization in HNSCC. The identified correlations differed significantly between p16-positive and p16-negative cancers.
Keyphrases
- contrast enhanced
- deep learning
- small cell lung cancer
- convolutional neural network
- poor prognosis
- endothelial cells
- optical coherence tomography
- magnetic resonance
- single cell
- diffusion weighted
- diffusion weighted imaging
- tyrosine kinase
- cell therapy
- radiation therapy
- neoadjuvant chemotherapy
- squamous cell carcinoma
- machine learning
- long non coding rna
- lymph node
- network analysis