BackgroundTumor spatial heterogeneity is an important prognostic factor, which may be reflected in medical imagesMethodsImage texture analysis is an approach of quantifying heterogeneity that may not be appreciated by the naked eye. Different methods can be applied including statistical-, model-, and transform-based methods.ResultsEarly evidence suggests that texture analysis has the potential to augment diagnosis and characterization as well as improve tumor staging and therapy response assessment in oncological practice.ConclusionThis review provides an overview of the application of texture analysis with different imaging modalities, CT, MRI, and PET, to date and describes the technical challenges that have limited its widespread clinical implementation so far. With further efforts to refine its application, image texture analysis has the potential to develop into a valuable clinical tool for oncologic imaging.Teaching Points• Tumor spatial heterogeneity is an important prognostic factor.• Image texture analysis is an approach of quantifying heterogeneity.• Different methods can be applied, including statistical-, model-, and transform-based methods.• Texture analysis could improve the diagnosis, tumor staging, and therapy response assessment.

There is evidence in some solid tumors that textural features of tumoral uptake in 18 F-FDG PET images are associated with response to chemoradiotherapy and survival. We have investigated whether a similar relationship exists in non-small cell lung cancer (NSCLC). Methods: Fifty-three patients (mean age, 65.8 y; 31 men, 22 women) with NSCLC treated with chemoradiotherapy underwent pretreatment 18 F-FDG PET/CT scans. Response was assessed by CT Response Evaluation Criteria in Solid Tumors (RECIST) at 12 wk. Overall survival (OS), progression-free survival (PFS), and local PFS (LPFS) were recorded. Primary tumor texture was measured by the parameters coarseness, contrast, busyness, and complexity. The following parameters were also derived from the PET data: primary tumor standardized uptake values (SUVs) (mean SUV, maximum SUV, and peak SUV), metabolic tumor volume, and total lesion glycolysis. Results: Compared with nonresponders, RECIST responders showed lower coarseness (mean, 0.012 vs. 0.027; P 5 0.004) and higher contrast (mean, 0.11 vs. 0.044; P 5 0.002) and busyness (mean, 0.76 vs. 0.37; P 5 0.027). Neither complexity nor any of the SUV parameters predicted RECIST response. By Kaplan-Meier analysis, OS, PFS, and LPFS were lower in patients with high primary tumor coarseness (median, 21.1 mo vs. not reached, P 5 0.003; 12.6 vs. 25.8 mo, P 5 0.002; and 12.9 vs. 20.5 mo, P 5 0.016, respectively). Tumor coarseness was an independent predictor of OS on multivariable analysis. Contrast and busyness did not show significant associations with OS (P 5 0.075 and 0.059, respectively), but PFS and LPFS were longer in patients with high levels of each (for contrast: median of 20.5 vs. 12.6 mo, P 5 0.015, and median not reached vs. 24 mo, P 5 0.02; and for busyness: median of 20.5 vs. 12.6 mo, P 5 0.01, and median not reached vs. 24 mo, P 5 0.006). Neither complexity nor any of the SUV parameters showed significant associations with the survival parameters. Conclusion: In NSCLC, baseline 18 F-FDG PET scan uptake showing abnormal texture as measured by coarseness, contrast, and busyness is associated with nonresponse to chemoradiotherapy by RECIST and with poorer prognosis. Measurement of tumor metabolic heterogeneity with these parameters may provide indices that can be used to stratify patients in clinical trials for lung cancer chemoradiotherapy.

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• Changes in CT body composition occur after neoadjuvant chemotherapy in oesophageal cancer. • Sarcopenia was more prevalent after neoadjuvant chemotherapy. • Fat mass, fat-free mass and weight decreased after neoadjuvant chemotherapy. • Changes in body composition were associated with CRM positivity. • Changes in body composition did not affect perioperative complications and survival.

FRCR Purpose:To determine the association between tumor heterogeneity, morphologic tumor response, and overall survival in primary esophageal cancer treated with chemotherapy and radiation therapy (CRT). Materials and Methods:After an institutional review board waiver was obtained, contrast material-enhanced computed tomographic (CT) studies in 36 patients with stage T2 or greater esophageal tumors who underwent contrast-enhanced CT before and after CRT between 2005 and 2008 were analyzed in terms of whole-tumor texture, with quantification of entropy, uniformity, mean graylevel intensity, kurtosis, standard deviation of the histogram, and skewness for fine to coarse textures (filters 1.0-2.5, respectively). The association between texture parameters and survival time was assessed by using Kaplan-Meier analysis and a Cox proportional hazards model. Survival models involving texture parameters and combinations of texture and morphologic response assessment were compared with morphologic assessment alone by means of receiver operating characteristic (ROC) analysis. Results:Posttreatment medium entropy of less than 7.356 (median overall survival, 33.2 vs 11.7 months; P = .0002), coarse entropy of less than 7.116 (median overall survival, 33.2 vs 11.7 months; P = .0002), and medium uniformity of 0.007 or greater (median overall survival, 33.2 vs 11.7 months; P = .0002) were associated with improved survival time. These remained significant prognostic factors after adjustment for stage and age: entropy (filter 2.0: hazard ratio [HR] = 5.038, P = .0004; filter 2.5: HR = 5.038, P = .0004) and uniformity (HR = 0.199, P = .0004). Survival models that included a combination of pretreatment entropy and uniformity with maximal wall thickness assessment, respectively, performed better than morphologic assessment alone (area under the ROC curve, 0.767 vs 0.487 [P = .00005] and 0.802 vs 0.487 [P = .0003]). Conclusion:Posttreatment texture parameters are associated with survival time, and the combination of pretreatment texture parameters and maximal wall thickness performed better in survival models than morphologic tumor response alone.q RSNA, 2013

In recent years, there has been increasing interest in the influence of body composition on oncological patient outcomes. Visceral obesity, sarcopenia and sarcopenic obesity have been identified as adverse factors in cancer patients. Imaging quantification of body composition such as lean muscle mass and fat distribution is a potentially valuable tool. This review describes the following imaging techniques that may be used to assess body composition: dual-energy X-ray absorptiometry (DXA), computed tomography (CT) and magnetic resonance imaging (MRI). CT and MRI are acquired as part of oncological patient care, thus providing an opportunity to integrate body composition assessment into the standard clinical pathway and allowing supportive care to be commenced as appropriate to improve outcome.Main Messages• Sarcopenia, sarcopenic obesity and visceral obesity are adverse prognostic factors in cancer patients.• CT and MRI are the current gold standard in body composition evaluation.• Body composition may affect chemotherapy tolerance and toxicities.

ObjectivesThe primary aim was to determine the sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of the bedside head-impulse test (bHIT) using the video HIT (vHIT) as the gold standard for quantifying the function of the vestibulo-ocular reflex (VOR). Secondary aims were to determine the bHIT inter-rater reliability and sensitivity in detecting unilateral and bilateral vestibulopathy.MethodsIn this prospective study, 500 consecutive outpatients presenting to a tertiary neuro-otology clinic with vertigo or dizziness of various vestibular etiologies who did not have any of the pre-defined exclusion criteria were recruited. Bedside HITs were done by three experienced neuro-otology clinicians masked to the diagnosis, and the results were compared with the vHIT. The patients were likewise blinded to the bHIT and vHIT findings. Patients with VOR deficits were identified on the vHIT by referencing to the pre-selected “pathological” gain of <0.7. The data were then analyzed using standard statistical methods.ResultsFor the primary outcome (vHIT “pathological” VOR gain <0.7), the three-rater mean bHIT sensitivity = 66.0%, PPV = 44.3%, specificity = 86.2%, and NPV = 93.9%. Shifting the “pathological” threshold from 0.6 to 0.9 caused the bHIT sensitivity to decrease while the PPV increased. Specificity and NPV tended to remain stable. Inter-rater agreement was moderate (Krippendorff’s alpha = 0.54). For unilateral vestibulopathy, overall bHIT sensitivity = 69.6%, reaching 86.67% for severely reduced unilateral gain. For VOR asymmetry <40% and >40%, the bHIT sensitivity = 51.7 and 83%, respectively. For bilateral vestibulopathy, overall bHIT sensitivity = 66.3%, reaching 86.84% for severely reduced bidirectional gains.ConclusionFor the primary outcome, the bHIT had moderate sensitivity and low PPV. While the study did not elucidate the best choice for vHIT reference, it demonstrated how the bHIT test properties varied with vHIT thresholds: selecting a lower threshold improved the sensitivity but diminished the PPV, while a higher threshold had the opposite effect. The VOR was most likely normal if the bHIT was negative due to its high NPV. The bHIT was moderately sensitive for detecting unilateral and bilateral vestibulopathy overall, but better for certain subgroups.

Radiomics is an evolving field in which the extraction of large amounts of features from diagnostic medical images may be used to predict underlying molecular and genetic characteristics, thereby improving treatment response prediction and prognostication and potentially allowing personalisation of cancer treatment. There is increasing interest in extracting additional data from PET images, particularly novel features that describe the heterogeneity of voxel intensities, but a number of potential limitations need to be recognised and overcome. Nevertheless, some early data suggest that extraction of additional quantitative data may offer further predictive and prognostic information in individual patients.