CT imaging correlates of genomic expression for oral cavity squamous cell carcinoma

Predictive value of quantitative 18F-FDG-PET radiomics analysis in patients with head and neck squamous cell carcinoma

BACKGROUND

Radiomics is aimed at image-based tumor phenotyping, enabling application within clinical-decision-support-systems to improve diagnostic accuracy and allow for personalized treatment. The purpose was to identify predictive 18-fluor-fluoro-2-deoxyglucose (¹⁸F-FDG) positron-emission tomography (PET) radiomic features to predict recurrence, distant metastasis, and overall survival in patients with head and neck squamous cell carcinoma treated with chemoradiotherapy.

METHODS

Between 2012 and 2018, 103 retrospectively (training cohort) and 71 consecutively included patients (validation cohort) underwent ¹⁸F-FDG-PET/CT imaging. The 434 extracted radiomic features were subjected, after redundancy filtering, to a projection resulting in outcome-independent meta-features (factors). Correlations between clinical, first-order ¹⁸F-FDG-PET parameters (e.g., SUVmean), and factors were assessed. Factors were combined with ¹⁸F-FDG-PET and clinical parameters in a multivariable survival regression and validated. A clinically applicable risk-stratification was constructed for patients' outcome.

RESULTS

Based on 124 retained radiomic features from 103 patients, 8 factors were constructed. Recurrence prediction was significantly most accurate by combining HPV-status, SUVmean, SUVpeak, factor 3 (histogram gradient and long-run-low-grey-level-emphasis), factor 4 (volume-difference, coarseness, and grey-level-non-uniformity), and factor 6 (histogram variation coefficient) (CI = 0.645). Distant metastasis prediction was most accurate assessing metabolic-active tumor volume (MATV)(CI = 0.627). Overall survival prediction was most accurate using HPV-status, SUVmean, SUVmax, factor 1 (least-axis-length, non-uniformity, high-dependence-of-high grey-levels), and factor 5 (aspherity, major-axis-length, inversed-compactness and, inversed-flatness) (CI = 0.764).

CONCLUSIONS

Combining HPV-status, first-order ¹⁸F-FDG-PET parameters, and complementary radiomic factors was most accurate for time-to-event prediction. Predictive phenotype-specific tumor characteristics and interactions might be captured and retained using radiomic factors, which allows for personalized risk stratification and optimizing personalized cancer care.

TRIAL REGISTRATION

Trial NL3946 (NTR4111), local ethics commission reference: Prediction 2013.191 and 2016.498. Registered 7 August 2013, https://www.trialregister.nl/trial/3946.

Radiomic analysis identifies tumor subtypes associated with distinct molecular and microenvironmental factors in head and neck squamous cell carcinoma

PURPOSE

To identify whether radiomic features from pre-treatment computed tomography (CT) scans can predict molecular differences between head and neck squamous cell carcinoma (HNSCC) using The Cancer Imaging Archive (TCIA) and The Cancer Genome Atlas (TCGA).

METHODS

77 patients from the TCIA with HNSCC had imaging suitable for analysis. Radiomic features were extracted and unsupervised consensus clustering was performed to identify subtypes. Genomic data was extracted from the matched patients in the TCGA database. We explored relationships between radiomic features and molecular profiles of tumors, including the tumor immune microenvironment. A machine learning method was used to build a model predictive of CD8 + T-cells. An independent cohort of 83 HNSCC patients was used to validate the radiomic clusters.

RESULTS

We initially extracted 104 two-dimensional radiomic features, and after feature stability tests and removal of volume dependent features, reduced this to 67 features for subsequent analysis. Consensus clustering based on these features resulted in two distinct clusters. The radiomic clusters differed by primary tumor subsite (p = 0.0096), HPV status (p = 0.0127), methylation-based clustering results (p = 0.0025), and tumor immune microenvironment. A random forest model using radiomic features predicted CD8 + T-cells independent of HPV status with R² = 0.30 (p < 0.0001) on cross validation. Consensus clustering on the validation cohort resulted in two distinct clusters that differ in tumor subsite (p = 1.3 × 10^-7) and HPV status (p = 4.0 × 10^-7).

CONCLUSION

Radiomic analysis can identify biologic features of tumors such as HPV status and T-cell infiltration and may be able to provide other information in the near future to help with patient stratification.

Imaging-Genomic Study of Head and Neck Squamous Cell Carcinoma: Associations Between Radiomic Phenotypes and Genomic Mechanisms via Integration of The Cancer Genome Atlas and The Cancer Imaging Archive

PURPOSE

Recent data suggest that imaging radiomic features of a tumor could be indicative of important genomic biomarkers. Understanding the relationship between radiomic and genomic features is important for basic cancer research and future patient care. We performed a comprehensive study to discover the imaginggenomic associations in head and neck squamous cell carcinoma (HNSCC) and explore the potential of predicting tumor genomic alternations using radiomic features.

METHODS

Our retrospective study integrated whole-genome multiomics data from The Cancer Genome Atlas with matched computed tomography imaging data from The Cancer Imaging Archive for the same set of 126 patients with HNSCC. Linear regression and gene set enrichment analysis were used to identify statistically significant associations between radiomic imaging and genomic features. Random forest classifier was used to predict the status of two key HNSCC molecular biomarkers, human papillomavirus and disruptive TP53 mutation, on the basis of radiomic features.

RESULTS

Widespread and statistically significant associations were discovered between genomic features (including microRNA expression, somatic mutations, and transcriptional activity, copy number variations, and promoter region DNA methylation changes of pathways) and radiomic features characterizing the size, shape, and texture of tumor. Prediction of human papillomavirus and TP53 mutation status using radiomic features achieved areas under the receiver operating characteristic curve of 0.71 and 0.641, respectively.

CONCLUSION

Our exploratory study suggests that radiomic features are associated with genomic characteristics at multiple molecular layers in HNSCC and provides justification for continued development of radiomics as biomarkers for relevant genomic alterations in HNSCC.

Non-invasive tumor genotyping using radiogenomic biomarkers, a systematic review and oncology-wide pathway analysis

With targeted treatments playing an increasing role in oncology, the need arises for fast non-invasive genotyping in clinical practice. Radiogenomics is a rapidly evolving field of research aimed at identifying imaging biomarkers useful for non-invasive genotyping. Radiogenomic genotyping has the advantage that it can capture tumor heterogeneity, can be performed repeatedly for treatment monitoring, and can be performed in malignancies for which biopsy is not available. In this systematic review of 187 included articles, we compiled a database of radiogenomic associations and unraveled networks of imaging groups and gene pathways oncology-wide. Results indicated that ill-defined tumor margins and tumor heterogeneity can potentially be used as imaging biomarkers for 1p/19q codeletion in glioma, relevant for prognosis and disease profiling. In non-small cell lung cancer, FDG-PET uptake and CT-ground-glass-opacity features were associated with treatment-informing traits including EGFR-mutations and ALK-rearrangements. Oncology-wide gene pathway analysis revealed an association between contrast enhancement (imaging) and the targetable VEGF-signalling pathway. Although the need of independent validation remains a concern, radiogenomic biomarkers showed potential for prognosis prediction and targeted treatment selection. Quantitative imaging enhanced the potential of multiparametric radiogenomic models. A wealth of data has been compiled for guiding future research towards robust non-invasive genomic profiling.

Added value of a single-energy projection-based metal-artifact reduction algorithm for the computed tomography evaluation of oral cavity cancers

PURPOSE

The purpose of our study was to investigate the added value of the single-energy projection-based metallic artifact reduction (SEMAR) technique on the visualization and diagnostic confidence of oral cavity cancers.

MATERIALS AND METHODS

Contrast-enhanced CT was performed on 40 patients with dental metalwork. Of these, 18 patients had pathologically confirmed oral cavity cancer, and the other 22 patients had no lesions. CT attenuation and noise in the oral cavity were compared between the conventional and SEMAR images. Two radiologists visually graded the diagnostic confidence on a 5-point scale from 1 (definitely absent) to 5 (definitely present). The value of the SEMAR was assessed in a receiver-operating characteristic curve analysis.

RESULTS

The artifact of the SEMAR images was significantly lower than that of the conventional images (38.4 ± 18.0 HU vs. 187.7 ± 162.7 HU; P < 0.01). Reviewers 1 and 2 detected 10 and 7 more lesions with the SEMAR, respectively. Areas under the curve for the conventional and SEMAR reconstruction were 0.761 and 0.942, respectively, for reviewer 1 and 0.701 and 0.864, respectively, for reviewer 2.

CONCLUSION

The SEMAR significantly improved the oral cavity cancer visualization and diagnostic confidence.

Mutation and Transcriptional Profiling of Formalin-Fixed Paraffin Embedded Specimens as Companion Methods to Immunohistochemistry for Determining Therapeutic Targets in Oropharyngeal Squamous Cell Carcinoma (OPSCC): A Pilot of Proof of Principle

The role of molecular methods in the diagnosis of head and neck cancer is rapidly evolving and holds great potential for improving outcomes for all patients who suffer from this diverse group of malignancies . However, there is considerable debate as to the best clinical approaches, particularly for Next Generation Sequencing (NGS). The choices of NGS methods such as whole exome, whole genome, whole transcriptomes (RNA-Seq) or multiple gene resequencing panels, each have strengths and weakness based on data quality, the size of the data, the turnaround time for data analysis, and clinical actionability. There have also been a variety of gene expression signatures established from microarray studies that correlate with relapse and response to treatment, but none of these methods have been implemented as standard of care for oropharyngeal squamous cell carcinoma (OPSCC). Because many genomic methodologies are still far from the capabilities of most clinical laboratories, we chose to explore the use of a combination of off the shelf targeted mutation analysis and gene expression analysis methods to complement standard anatomical pathology methods. Specifically, we have used the Ion Torrent AmpliSeq cancer panel in combination with the NanoString nCounter Human Cancer Reference Kit on 8 formalin-fixed paraffin embedded (FFPE) OPSCC tumor specimens, (4) HPV-positive and (4) HPV-negative. Differential expression analysis between HPV-positive and negative groups showed that expression of several genes was highly likely to correlate with HPV status. For example, WNT1, PDGFA and OGG1 were all over-expressed in the positive group. Our results show the utility of these methods with routine FFPE clinical specimens to identify potential therapeutic targets which could be readily applied in a clinical trial setting for clinical laboratories lacking the instrumentation or bioinformatics infrastructure to support comprehensive genomics workflows. To the best of our knowledge, these preliminary experiments are among the earliest to combine both mutational and gene expression profiles using Ion Torrent and NanoString technologies. This reports serves as a proof of principle methodology in OPSCC.