• Epidermal growth factor receptor
• Hypopharyngeal squamous cell carcinoma
• Immunohistochemistry
• Magnetic resonance imaging
• Positron emission tomography

• Humans
• Squamous Cell Carcinoma of Head and Neck/diagnostic imaging
• Pilot Projects
• Fluorodeoxyglucose F18
• Magnetic Resonance Imaging/methods
• Positron-Emission Tomography/methods
• Head and Neck Neoplasms
• ErbB Receptors
• Radiopharmaceuticals

• FDG PET
• GLUT
• KI 67
• epidermal growth factor receptor
• hypoxia-inducible factor-1α
• p53
• vascular endothelial growth factor

The purpose of this study is to test if functional multiparametric imaging with ¹⁸F-FDG-PET/MRI correlates spatially with immunohistochemical biomarker status within a lesion of head and neck squamous cell carcinoma (HNSCC), and also whether a biopsy with the highest FDG uptake was more likely to have the highest PD-L1 expression or the highest percentage of vital tumour cells (VTC) compared with a random biopsy.

Thirty-one patients with HNSCC were scanned on an integrated PET/MRI scanner with FDG prior to surgery in this prospective study. Imaging was quantified with SUV, ADC and K^trans. A 3D-morphometric MRI scan of the specimen was used to co-register the patient and the specimen scans. All specimens were sectioned in consecutive slices, and slices from six different locations were selected randomly from each tumour. Core biopsies were performed to construct TMA blocks for IHC staining with the ten predefined biomarkers. The spatial correlation was assessed with a partial correlation analysis.

Twenty-eight patients with a total of 33 lesions were eligible for further analysis. There were significant correlations between the three imaging biomarkers and some of the IHC biomarkers. Moreover, a biopsy taken from the most FDG-avid part of the tumour did not have a statistically significantly higher probability of higher PD-L1 expression or VTC, compared with a random biopsy.

We found statistically significant correlations between functional imaging parameters and key molecular cancer markers.

• Aged
• Cohort Studies
• Contrast Media/metabolism
• Female
• Fluorodeoxyglucose F18/metabolism
• Humans
• Lymph Nodes/pathology
• Magnetic Resonance Imaging/methods
• Male
• Middle Aged
• Oropharyngeal Neoplasms/diagnostic imaging
• Oropharyngeal Neoplasms/pathology
• Positron Emission Tomography Computed Tomography/methods
• Positron-Emission Tomography/methods
• Radiopharmaceuticals
• Retrospective Studies
• Squamous Cell Carcinoma of Head and Neck/diagnostic imaging
• Squamous Cell Carcinoma of Head and Neck/pathology
• Tomography, X-Ray Computed/methods
• Tumor Burden

• Dual time point
• Primary breast cancer
• ΔSUVmax%

• Positron Emission Tomography Computed Tomography
• histopathology
• immunohistochemistry
• microRNAs
• neoplasms

• fdg pet
• hnscc
• ki67
• suv

• Fluorodeoxyglucose F18
• Head and Neck Neoplasms/diagnostic imaging
• Head and Neck Neoplasms/metabolism
• Humans
• Ki-67 Antigen/metabolism
• Positron-Emission Tomography
• Squamous Cell Carcinoma of Head and Neck/diagnostic imaging
• Squamous Cell Carcinoma of Head and Neck/metabolism

• EGFR
• HIF-1α
• HNSCC
• KI 67
• PET
• SUV
• VEGF
• p53

Imaging techniques can provide information about the tumor non-invasively and have been shown to provide information about the underlying genetic makeup. Correlating image-based phenotypes (radiomics) with genomic analyses is an emerging area of research commonly referred to as “radiogenomics” or “imaging-genomics”. The purpose of this study was to assess the potential for using an automated, quantitative radiomics platform on magnetic resonance (MR) breast imaging for inferring underlying activity of clinically relevant gene pathways derived from RNA sequencing of invasive breast cancers prior to therapy.

We performed quantitative radiomic analysis on 47 invasive breast cancers based on dynamic contrast enhanced 3 Tesla MR images acquired before surgery and obtained gene expression data by performing total RNA sequencing on corresponding fresh frozen tissue samples. We used gene set enrichment analysis to identify significant associations between the 186 gene pathways and the 38 image-based features that have previously been validated.

All radiomic size features were positively associated with multiple replication and proliferation pathways and were negatively associated with the apoptosis pathway. Gene pathways related to immune system regulation and extracellular signaling had the highest number of significant radiomic feature associations, with an average of 18.9 and 16 features per pathway, respectively. Tumors with upregulation of immune signaling pathways such as T-cell receptor signaling and chemokine signaling as well as extracellular signaling pathways such as cell adhesion molecule and cytokine-cytokine interactions were smaller, more spherical, and had a more heterogeneous texture upon contrast enhancement. Tumors with higher expression levels of JAK/STAT and VEGF pathways had more intratumor heterogeneity in image enhancement texture. Other pathways with robust associations to image-based features include metabolic and catabolic pathways.

We provide further evidence that MR imaging of breast tumors can infer underlying gene expression by using RNA sequencing. Size and shape features were appropriately correlated with proliferative and apoptotic pathways. Given the high number of radiomic feature associations with immune pathways, our results raise the possibility of using MR imaging to distinguish tumors that are more immunologically active, although further studies are necessary to confirm this observation.

The online version of this article (10.1186/s40644-019-0233-5) contains supplementary material, which is available to authorized users.

• Breast cancer
• Imaging genomics
• Radiogenomics

• Humans
• Molecular Imaging/methods
• Neoplasms/diagnosis
• Neoplasms/diagnostic imaging
• Neoplasms/pathology
• Neoplasms/therapy
• Precision Medicine
• Prognosis
• Technetium/therapeutic use
• Tomography, Emission-Computed, Single-Photon/methods

The purpose of this study was to determine the predictive power for treatment outcome of a machine-learning algorithm combining magnetic resonance imaging (MRI)-derived data in patients with sinonasal squamous cell carcinomas (SCCs). Thirty-six primary lesions in 36 patients were evaluated. Quantitative morphological parameters and intratumoral characteristics from T2-weighted images, tumor perfusion parameters from arterial spin labeling (ASL) and tumor diffusion parameters of five diffusion models from multi-b-value diffusion-weighted imaging (DWI) were obtained. Machine learning by a non-linear support vector machine (SVM) was used to construct the best diagnostic algorithm for the prediction of local control and failure. The diagnostic accuracy was evaluated using a 9-fold cross-validation scheme, dividing patients into training and validation sets. Classification criteria for the division of local control and failure in nine training sets could be constructed with a mean sensitivity of 0.98, specificity of 0.91, positive predictive value (PPV) of 0.94, negative predictive value (NPV) of 0.97, and accuracy of 0.96. The nine validation data sets showed a mean sensitivity of 1.0, specificity of 0.82, PPV of 0.86, NPV of 1.0, and accuracy of 0.92. In conclusion, a machine-learning algorithm using various MR imaging-derived data can be helpful for the prediction of treatment outcomes in patients with sinonasal SCCs.

• diffusion
• machine learning
• magnetic resonance imaging
• perfusion
• squamous cell carcinoma of the head and neck
• texture analysis

Introduction: Texture analysis is an emergent imaging technique to quantify heterogeneity in radiological images. It is still unclear whether this technique is capable to reflect tumor microstructure. The present study sought to correlate histopathology parameters with texture features derived from contrast-enhanced CT images in head and neck squamous cell carcinomas (HNSCC).

Materials and Methods: Twenty-eight patients with histopathological proven HNSCC were retrospectively analyzed. In every case EGFR, VEGF, Hif1-alpha, Ki67, p53 expression derived from immunhistochemical specimen were semiautomatically calculated. Furthermore, mean cell count was estimated. Texture analysis was performed on contrast-enhanced CT images as a whole lesion measurement. Spearman's correlation analysis was performed, adjusted with Benjamini-Hochberg correction for multiple tests.

Results: Several texture features correlated with histopathological parameters. After correction only CT texture joint entropy and CT entropy correlation with Hif1-alpha expression remained statistically significant (ρ = −0.60 and ρ = −0.50, respectively).

Conclusions: CT texture joint entropy and CT entropy were associated with Hif1-alpha expression in HNSCC and might be able to reflect hypoxic areas in this entity.

• CT
• HNSCC
• Hif1-alpha
• Ki67
• texture analysis

• head and neck neoplasms
• neovascularization
• pathologic
• positron emission tomography

• DWI
• Head and neck
• MRI
• PET
• Prognosis
• Squamous cell carcinoma

Positron Emission Tomography (PET) is a functional imaging modality widely used in clinical oncology. Over the years the sensitivity and specificity of PET has improved with the advent of specific radiotracers, increased technical accuracy of PET scanners and incremental experience of Radiologists. However, significant limitations exist—most notably false positives and false negatives. Additionally, the accuracy of PET varies between cancer types and in some cancers, is no longer considered a standard imaging modality. This review considers the relative influence of macroscopic tumour features such as size and morphology on 2-Deoxy-2-[¹⁸F]fluoroglucose ([¹⁸F]FDG) uptake by tumours which, though well described in the literature, lacks a comprehensive assessment of biomolecular features which may influence [¹⁸F]FDG uptake. The review aims to discuss the potential influence of individual molecular markers of glucose transport, glycolysis, hypoxia and angiogenesis in addition to the relationships between these key cellular processes and their influence on [¹⁸F]FDG uptake. Finally, the potential role for biomolecular profiling of individual tumours to predict positivity on PET imaging is discussed to enhance accuracy and clinical utility.

• [18F]FDG PET/CT
• biomarker profiling
• cancer

Our purpose was to elucidate possible correlations between histogram parameters derived from dynamic contrast-enhanced MRI (DCE-MRI) with several histopathological features in head and neck squamous cell carcinomas (HNSCC).

Thirty patients with primary HNSCC were prospectively acquired. Histogram analysis was derived from the DCE-MRI parameters: K_trans, K_ep, and V_e. Additionally, in all cases, expression of human papilloma virus (p16) hypoxia-inducible factor-1-alpha (Hif1-alpha), vascular endothelial growth factor (VEGF), epidermal growth factor receptor (EGFR), and tumor suppressor protein p53 were estimated.

K_ep kurtosis was significantly higher in p16 tumors, and V_e min was significantly lower in p16 tumors compared to the p16 negative tumors. In the overall sample, K_ep entropy correlated well with EGFR expression (p=0.38, P=0.04). In p16 positive carcinomas, K_trans max correlated with VEGF expression (p=0.46, P=0.04), K_trans kurtosis correlated with Hif1-alpha expression (p=0.46, P=0.04), and K_trans entropy correlated with EGFR expression (p=0.50, P=0.03). Regarding K_ep parameters, mode correlated with VEGF expression (p=0.51, P=0.02), and entropy correlated with Hif1-alpha expression (p=0.47, P=0.04). In p16 negative carcinomas, K_ep mode correlated with Her2 expression (p=−0.72, P=0.03), V_e max correlated with p53 expression (p=−0.80, P=0.009), and V_e p10 correlated with EGFR expression (p=0.68, P=0.04).

DCE-MRI can reflect several histopathological features in HNSCC. Associations between DCE-MRI and histopathology in HNSCC depend on p16 status. K_ep kurtosis and V_e min can differentiate p16 positive and p16 negative carcinomas.

BACKGROUND: Our purpose was to evaluate associations of combined ¹⁸F-FDG-PET and MRI parameters with histopathological features in head and neck squamous cell carcinoma (HNSCC). METHODS: Overall, 22 patients with HNSCC were acquired (10 with G1/2 tumors and 12 with G3 tumors).¹⁸F-FDG-PET/CT and MRI was performed and maximum standardized uptake value (SUV_max), total lesion glycolysis (TLG) and metabolic tumor volume (MTV) were estimated. Neck MRI was obtained on a 3 T scanner. Diffusion weighted imaging was performed with estimation of apparent diffusion coefficient (ADC). Perfusion parameters K_trans,V_e, and K_ep were derived from dynamic contrast-enhanced (DCE) imaging. Different combined PET/MRI parameters were calculated as ratios: PET parameters divided by ADC or DCE MRI parameters. The following histopathological features were estimated: Ki 67, EGFR, VEGF, p53, hypoxia-inducible factor (HIF)-1α, and cell count. Spearman's correlation coefficient (p) was used for correlation analysis. P < .05 was taken to indicate statistical significance. RESULTS: In overall sample, cellularity correlated with SUV_max/ADC_min (P = .558, P = .007), TLG/ADC_min (P = .546, P = .009), and MTV/ADC_min (P = .468, P = .028). MTV/K_ep correlated with expression of HIF-1α (P = .450, P = 0,047). In G1/2 tumors, SUV_max/ADC_min correlated with HIF-1α (P = −.648, P = .043); MTV/K_ep (P = −.669, P = .034) and TLG/K_ep (P = −.644, P = .044) with Ki67. In G3 tumors, cellularity correlated with SUV_max/ADC_min (P = .832, P = .001), SUV_max/ADC_mean (P = .741, P = .006), and TLG/ADC_min (P = .678, P = .015). MTV/ADC_min and TLG/ADC_min tended to correlate with HIF-1α. CONCLUSION: Combined parameters of ¹⁸F-FDG-PET and MRI can reflect Ki 67, tumor cellularity and expression of HIF-1α in HNSCC. Associations between parameters of ¹⁸F-FDG-PET and MRI and histopathology depend on tumor grading.

• FDG uptake
• Head and neck squamous cell carcinoma
• P16-negative and p16-positive
• Prognostication

• Decision support systems, clinical
• Head and neck neoplasms
• Nomograms
• Patient selection
• Risk

• Head and neck cancer
• Molecular imaging
• Targeted therapy
• Tumor genetic profiling