Risk Stratification for Oropharyngeal Squamous Cell Carcinoma Using Texture Analysis on CT - A Step Beyond HPV Status.

Background and Purpose: Human papillomavirus-associated oropharyngeal squamous cell carcinoma (OPSCC) is increasingly prevalent. Despite the overall more favorable outcome, the observed heterogeneous treatment response within this patient group highlights the need for additional means to prognosticate and guide clinical decision-making. Promising prediction models using radiomics from primary OPSCC have been derived. However, no model/s using metastatic lymphadenopathy exist to allow prognostication in those instances when the primary tumor is not seen. The aim of our study was to evaluate whether radiomics using metastatic lymphadenopathy allows for the development of a useful risk assessment model comparable to the primary tumor and whether additional knowledge of the HPV status further improves its prognostic efficacy. Materials and Methods: 80 consecutive patients diagnosed with stage III-IV OPSCC between February 2009 and October 2015, known human papillomavirus status, and pre-treatment CT images were retrospectively identified. Manual segmentation of primary tumor and metastatic lymphadenopathy was performed and the extracted texture features were used to develop multivariate assessment models to prognosticate treatment response. Results: Texture analysis of either the primary or metastatic lymphadenopathy from pre-treatment enhanced CT images can be used to develop models for the stratification of treatment outcomes in OPSCC patients. AUCs range from .78 to .85 for the various OPSCC groups tested, indicating high predictive capability of the models. Conclusions: This preliminary study can form the basis multi-centre trial that may help optimize treatment and improve quality of life in patients with OPSCC in the era of personalized medicine.

Radiogenomic Models Using Machine Learning Techniques to Predict EGFR Mutations in Non-Small Cell Lung Cancer.

BACKGROUND: The purpose of this study was to build radiogenomics models from texture signatures derived from computed tomography (CT) and 18F-FDG PET-CT (FDG PET-CT) images of non-small cell lung cancer (NSCLC) with and without epidermal growth factor receptor (EGFR) mutations. METHODS: Fifty patients diagnosed with NSCLC between 2011 and 2015 and with known EGFR mutation status were retrospectively identified. Texture features extracted from pretreatment CT and FDG PET-CT images by manual contouring of the primary tumor were used to develop multivariate logistic regression (LR) models to predict EGFR mutations in exon 19 and exon 20. RESULTS: An LR model evaluating FDG PET-texture features was able to differentiate EGFR mutant from wild type with an area under the curve (AUC), sensitivity, specificity, and accuracy of 0.87, 0.76, 0.66, and 0.71, respectively. The model derived from CT texture features had an AUC, sensitivity, specificity, and accuracy of 0.83, 0.84, 0.73, and 0.78, respectively. FDG PET-texture features that could discriminate between mutations in EGFR exon 19 and 21 demonstrated AUC, sensitivity, specificity, and accuracy of 0.86, 0.84, 0.73, and 0.78, respectively. Based on CT texture features, the AUC, sensitivity, specificity, and accuracy were 0.75, 0.81, 0.69, and 0.75, respectively. CONCLUSION: Non-small cell lung cancer texture analysis using FGD-PET and CT images can identify tumors with mutations in EGFR. Imaging signatures could be valuable for pretreatment assessment and prognosis in precision therapy.

Arterial spin labelling reveals multi-regional cerebral hypoperfusion in patients with transient ischemic attack that are unrelated to ischemia location: A proof-of-concept study.

Background and Aims: Patients with transient ischemic attack (TIA) have a substantially increased risk of early dementia. In this exploratory study, we aim to determine whether patients with TIA have 1) measurable regional cerebral hypoperfusion unrelated to the location of ischemia, and 2) determine the relationship of regional cerebral blood flow (rCBF) with their cognitive profiles. Methods: Patients with TIA (N = 49) and seventy-nine (N = 79) age and sex matched controls underwent formal neuropsychological testing and MRI. Quantitative arterial spin labelling rCBF maps (mL/min/100 g) were registered to the corresponding high resolution T1-weighted image. Linear regression was used to determine the association between demographic, clinical and cognitive variables and rCBF. Results: Patients with TIA had significantly (p < 0.05) lower cognitive scores in the MMSE, MOCA, ACE-R, WAIS-IV DS Coding and Trail Making Tests A and B compared to controls. TIA patients had significantly lower rCBF in the left entorhinal cortex (p = 0.03), right posterior cingulate (p = 0.04), and right precuneus (p = 0.05), after adjusting for age and sex, that were unrelated to the regional anatomical volume and DWI positivity. Regional hypoperfusion in the right posterior cingulate and right precuneus was associated with impaired visual memory (BVMT total, p = 0.05 for both regions) and slower processing speed (TMT A, p = 0.04 and p = 0.01), respectively after adjusting for age and sex. Conclusions: TIA patients have patterns of regional hypoperfusion in multiple cortical regions unrelated to the parcellated regional anatomical volume or the presence of a DWI lesion. Regional hypoperfusion in patients with TIA may be an early marker conferring risk of future cognitive decline that needs to be confirmed by future studies.