Purpose Treatment of metastatic colorectal cancer frequently includes antiangiogenic agents such as bevacizumab. Size measurements are inadequate to assess treatment response to these agents, and newer response assessment criteria are needed. We aimed to evaluate F-18-FDG PET-derived texture parameters in a preclinical colorectal cancer model as alternative metrics of response to treatment with bevacizumab. Materials and methods Fourteen CD1 athymic mice injected in the flank with 5x106 LS174T cells (human colorectal carcinoma) were either untreated controls (n=7) or bevacizumab treated (n=7). After 2 weeks, mice underwent F-18-FDG PET/CT. Calliper-measured tumor growth (Delta(vol)) and final tumor volume (Vol(cal)), F-18-FDG PET metabolically active volume (Vol(met)), mean metabolism (Met(mean)), and maximum metabolism (Met(max)) were measured. Twenty-four texture features were compared between treated and untreated mice. Immunohistochemical mean tumor vascular density was estimated by anti-CD-34 staining after tumor resection. Results Treated mice had significantly lower tumor vascular density (P=0.032), confirming the antiangiogenic therapeutic effect of bevacizumab. None of the conventional measures were different between the two groups: Delta(vol) (P=0.9), Vol(cal) (P=0.7), Vol(met) (P=0.28), Met(max) (P=0.7), or Met(mean) (P=0.32). One texture parameter, GLSZM-SZV (visually indicating that the F-18-FDG PET images of treated mice comprise uniformly sized clusters of different activity) had significantly different means between the two groups of mice (P=0.001). Conclusion F-18-FDG PET derived texture parameters, particularly GLSZM-SZV, may be valid biomarkers of tumor response to treatment with bevacizumab, before change in volume.

PurposeWith the availability of ultra-sensitive PSA assays, early biochemical relapse (eBCR) of prostate cancer is increasingly being detected at values much lower than the conventional threshold of 0.2ng/ml. Accurate localisation of disease in this setting may allow treatment modification and improved outcomes, especially in patients with pelvis-confined or extra-pelvic oligometastasis (defined as up to three pelvic nodal or distant sites). We aimed to measure the detection rate of [68]Ga-PSMA-HBNED-CC (PSMA)-PET/CT and its influence on patient management in eBCR of prostate cancer following radical prostatectomy (RP).MethodsWe retrospectively identified 28 patients who underwent PSMA-PET/CT for post-RP eBCR (PSA <0.5ng/ml) at our tertiary care cancer centre. Two nuclear medicine physicians independently recorded the sites of PSMA-PET/CT positivity. Multidisciplinary meeting records were accessed to determinechanges in management decisions following PSMA-PET/CT scans.ResultsThe mean age of patients was 65.6years (range: 50-76.2years); median PSA was 0.22ng/ml (interquartile range: 0.15ng/ml to 0.34ng/ml). Thirteen patients (46.4%) had received radiotherapy in the past. PSMA-PET/CT was positive in 17 patients (60.7%). Only one patient had polymetastasis (>3 sites); the remainder either had prostatectomy bed recurrence (n=2), pelvic oligometastasis (n=10), or extra-pelvic oligometastasis (n=4). PSMA-PET/CT resulted in management change in 12 patients (42.8%), involving stereotactic body radiotherapy (n=6), salvage radiotherapy (n=4), and systemic treatment (n=2).ConclusionsOur findings show that PSMA-PET/CT has a high detection rate in the eBCR setting following RP, with a large proportion of patients found to have fewer than three lesions. PSMA-PET/CT may be of value in patients with early PSA failure, and impact on the choice of potentially curative salvage treatments.

Objective: Non-invasive distinction between squamous cell carcinoma and adenocarcinoma subtypes of non-small-cell lung cancer (NSCLC) may be beneficial to patients unfit for invasive diagnostic procedures or when tissue is insufficient for diagnosis. The purpose of our study was to compare the performance of random forest algorithms utilizing CT radiomics and/or semantic features in classifying NSCLC. Methods: Two thoracic radiologists scored 11 semantic features on CT scans of 106 patients with NSCLC. A set of 115 radiomics features was extracted from the CT scans. Random forest models were developed from semantic (RM-sem), radiomics (RM-rad), and all features combined (RM-all). External validation of models was performed using an independent test data set (n = 100) of CT scans. Model performance was measured with out-of-bag error and area under curve (AUC), and compared using receiver-operating characteristics curve analysis on the test data set. Results: The median (interquartile-range) error rates of the models were: RF-sem 24.5 % (22.6 - 37.5 %), RF-rad 35.8 % (34.9 - 38.7 %), and RM-all 37.7 % (37.7 - 37.7). On training data, both RF-rad and RF-all gave perfect discrimination (AUC = 1), which was significantly higher than that achieved by RF-sem (AUC = 0.78; p < 0.0001). On test data, however, RM-sem model (AUC = 0.82) out-performed RM-rad and RM-all (AUC = 0.5 and AUC = 0.56; p < 0.0001), neither of which was significantly different from random guess ( p = 0.9 and 0.6 respectively). Conclusion: Non-invasive classification of NSCLC can be done accurately using random forest classification models based on well-known CT-derived descriptive features. However, radiomics-based classification models performed poorly in this scenario when tested on independent data and should be used with caution, due to their possible lack of generalizability to new data. Advances in knowledge: Our study describes novel CT-derived random forest models based on radiologist-interpretation of CT scans (semantic features) that can assist NSCLC classification when histopathology is equivocal or when histopathological sampling is not possible. It also shows that random forest models based on semantic features may be more useful than those built from computational radiomic features.

Purpose Despite the growing use of fluorine-18-fluorodeoxyglucose (18F-FDG) PET texture analysis to measure intratumoural heterogeneity in cancer research, the biologic basis of 18F-FDG PET-derived texture variables is poorly understood. We aimed to assess correlations between 18F-FDG PET-derived texture variables and whole-slide image (WSI)-derived metrics of tumour cellularity and spatial heterogeneity. Patients and methods Twenty-two patients with non-small-cell lung cancer prospectively underwent 18F-FDG PET imaging before tumour resection. We tested nine 18F-FDG PET parameters: metabolically active tumour volume, total lesion glycolysis, mean standardized uptake value (SUVmean), first-order entropy, energy, skewness, kurtosis, grey-level co-occurrence matrix entropy and lacunarity (SUV-lacunarity). From the haematoxylin and eosin-stained WSIs, we derived mean tumour-cell density (MCD) and lacunarity (path-lacunarity). Spearman’s correlation analysis and agglomerative hierarchical clustering were performed to assess variable associations. Results Tumour volumes ranged from 2.2 to 74 cm3 (median: 17.9 cm3). MCD correlated positively with total lesion glycolysis (rs: 0.46, P: 0.007) and SUVmean (rs : 0.55; P: 0.008) and negatively with skewness and kurtosis (rs: −0.47 for both; P: 0.028 and 0.026, respectively). SUV-lacunarity and path-lacunarity were positively correlated (rs: 0.5; P: 0.018). On cluster analysis, larger tumours trended towards higher SUVmean and entropy with a predominance of tightly concentrated high SUV-voxels (negative skewness and low kurtosis on the histogram); on WSI analysis such larger tumours also displayed generally higher MCD and low SUV-lacunarity and path-lacunarity. Conclusion Our data suggest that histopathological MCD and lacunarity are associated with several commonly used 18F-FDG PET-derived indices including SUV-lacunarity, metabolically active tumour volume, SUVmean, entropy, skewness, and kurtosis, and thus may explain the biological basis of 18F-FDG PET-uptake heterogeneity in non-small-cell lung cancer.

Purpose Despite the growing use of 18-fluoro-2-deoxyglucose positron emission tomography ( 18 F-FDG PET) texture analysis to measure intratumoural heterogeneity in cancer research, the biologic basis of 18 F-FDG PET-derived texture variables (TV) is poorly understood. We aimed to assess correlations between 18 F-FDG PET-derived TVs and whole-slide image (WSI)-derived metrics of tumour cellularity and spatial heterogeneity. Methods Twenty-two patients with non-small cell lung cancer (NSCLC) prospectively underwent 18 F-FDG PET imaging before tumour resection. We tested 9 18 F-FDG PET parameters: metabolically active tumour volume (MATV), total lesion glycolysis (TLG), mean standardised uptake value (SUVmean), first-order entropy, energy, skewness, kurtosis, grey-level co-occurrence matrix entropy, and lacunarity (SUV-lacunarity). From the haematoxylin and eosin-stained WSIs, we derived mean tumour-cell density (MCD) and lacunarity (Path-lacunarity). Spearman’s correlation analysis and agglomerative hierarchical clustering were performed to assess variable associations. Our data suggest that histopathological MCD and lacunarity are associated with several commonly used 18 F-FDG PET-derived indices including SUV-lacunarity, MATV, SUV mean , entropy, skewness, and kurtosis, and thus may explain biological basis of 18 F-FDG PET-uptake heterogeneity in NSCLC.

Measures of tumour heterogeneity derived from 18-fluoro-2-deoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) scans are increasingly reported as potential biomarkers of non-small cell lung cancer (NSCLC) for classification and prognostication. Several segmentation algorithms have been used to delineate tumours, but their effects on the reproducibility and predictive and prognostic capability of derived parameters have not been evaluated. The purpose of our study was to retrospectively compare various segmentation algorithms in terms of inter-observer reproducibility and prognostic capability of texture parameters derived from non-small cell lung cancer (NSCLC) 18F-FDG PET/CT images.

Background: Measures of tumour heterogeneity derived from 18-fluoro-2-deoxyglucose positron emission tomography/computed tomography (F-FDG PET/CT) scans are increasingly reported as potential biomarkers of non-small cell lung cancer (NSCLC) for classification and prognostication. Several segmentation algorithms have been used to delineate tumours, but their effects on the reproducibility and predictive and prognostic capability of derived parameters have not been evaluated. The purpose of our study was to retrospectively compare various segmentation algorithms in terms of inter-observer reproducibility and prognostic capability of texture parameters derived from non-small cell lung cancer (NSCLC) F-FDG PET/CT images. Fifty three NSCLC patients (mean age 65.8 years; 31 males) underwent pre-chemoradiotherapy F-FDG PET/CT scans. Three readers segmented tumours using freehand (FH), 40% of maximum intensity threshold (40P), and fuzzy locally adaptive Bayesian (FLAB) algorithms. Intraclass correlation coefficient (ICC) was used to measure the inter-observer variability of the texture features derived by the three segmentation algorithms. Univariate cox regression was used on 12 commonly reported texture features to predict overall survival (OS) for each segmentation algorithm. Model quality was compared across segmentation algorithms using Akaike information criterion (AIC). Results: 40P was the most reproducible algorithm (median ICC 0.9; interquartile range [IQR] 0.85–0.92) compared with FLAB (median ICC 0.83; IQR 0.77–0.86) and FH (median ICC 0.77; IQR 0.7–0.85). On univariate cox regression analysis, 40P found 2 out of 12 variables, i.e. first-order entropy and grey-level co-occurence matrix (GLCM) entropy, to be significantly associated with OS; FH and FLAB found 1, i.e., first-order entropy. For each tested variable, survival models for all three segmentation algorithms were of similar quality, exhibiting comparable AIC values with overlapping 95% CIs. Conclusions: Compared with both FLAB and FH, segmentation with 40P yields superior inter-observer reproducibility of texture features. Survival models generated by all three segmentation algorithms are of at least equivalent utility. Our findings suggest that a segmentation algorithm using a 40% of maximum threshold is acceptable for texture analysis of F-FDG PET in NSCLC.

With improvements in molecular treatment, it is increasingly important to differentiate non-small cell lung cancer (NSCLC) subtypes, i.e., adenocarcinoma(ADCA) from squamous cell cancer(SCCA). Many patients cannot undergo invasive biopsy procedures and so non-invasive classification methods would be helpful in their management. Most studies using CT scans for this purpose have used either semantic (visual assessment of CT images by a radiologist) or computational texture features, yielding modest accuracy. We hypothesized that combined semantic and computational assessment of CT scans would improve the accuracy of CT in NSCLC classification. 67 patients (38 ADC, 29 SCCA) underwent contrast-enhanced chest CT for lung cancer staging. Tumor volumes of interests (VOI) were drawn semi-automatically. 10 qualitative semantic and 361 computational texture features were derived from the VOIs. Univariate and multivariate logistic regression models(MLRM) were developed for combinations of semantic and texture features. Sensitivity, specificity, and area under the receiver operating characteristic (AUROC) curve were computed. 10-fold cross-validation was used to prevent overfitting. Univariate models found two semantic (air-bronchogram, shape) and five texture parameters (wavelet-transform based: GLCM_Correlation, GLRL_LGRE, GLRL_LGRE, GLRL_LGRE, and original VOI-based GLSZM_ZSN[1]) to be most predictive of tumor class (p-value <0.01). Sensitivity, specificity, and AUROC for MLRM utilizing semantic features alone was 64.2%, 73.3%, and 0.76, and that of MLRM for texture features alone was 74.6%, 72.3%, and 0.79, respectively. For combined model involving semantic and texture features (i.e., air-bronchogram and GLCM_Correlation), respective values were 81.2%, 90%, and 0.9. [1] GLCM: gray-level cooccurence matrix, GLRL_LGRE: gray-level run-length matrix-derived low gray run emphasis, GLSZM_ZSN: Gray-level size-zone matrix-derived zone-size nonuniformity. Combined semantic and computational texture assessment of lung cancer CT images is highly accurate in differentiation of SCCA and ADCA.

OBJECTIVE. Texture analysis involves the mathematic processing of medical images to derive sets of numeric quantities that measure heterogeneity. Studies on lung cancer have shown that texture analysis may have a role in characterizing tumors and predicting patient outcome. This article outlines the mathematic basis of and the most recent literature on texture analysis in lung cancer imaging. We also describe the challenges facing the clinical implementation of texture analysis.CONCLUSION. Texture analysis of lung cancer images has been applied successfully to FDG PET and CT scans. Different texture parameters have been shown to be predictive of the nature of disease and of patient outcome. In general, it appears that more heterogeneous tumors on imaging tend to be more aggressive and to be associated with poorer outcomes and that tumor heterogeneity on imaging decreases with treatment. Despite these promising results, there is a large variation in the reported data and strengths of association.

As an integrated system, hybrid positron emission tomography/magnetic resonance imaging (PET/MRI) is able to provide simultaneously complementary high-resolution anatomic, molecular, and functional information, allowing comprehensive cancer phenotyping in a single imaging examination. In addition to an improved patient experience by combining 2 separate imaging examinations and streamlining the patient pathway, the superior soft tissue contrast resolution of MRI and the ability to acquire multiparametric MRI data is advantageous over computed tomography. For gastrointestinal cancers, this would improve tumor staging, assessment of neoadjuvant response, and of the likelihood of a complete (R0) resection in comparison with positron emission tomography or computed tomography.

PET/MRI is a new hybrid imaging modality and has the potential to become a powerful imaging tool. It is currently one of the most active areas of research in diagnostic imaging. The characterisation of an incidental renal lesion can be difficult. In particular, the differentiation of an oncocytoma from other solid renal lesions such as renal cell carcinoma (RCC) represents a diagnostic challenge. We describe the detection of an incidental renal oncocytoma in a 79-year gentleman who underwent a re-staging 18F-Choline PET/MRI following a rise in PSA values (4.07, nadir 1.3).

Poster: ECR 2016 / C-0773 / Artifacts and Diagnostic Pitfalls in PET/MRI by: A. Mallia , U. Bashir, J. Stirling, J. Joemon, J. MacKewn, G. Charles-Edwards, V. Goh, G. Cook; London/UK

Positron emission tomography (PET) combined with magnetic resonance imaging (MRI) is a hybrid technology which has recently gained interest as a potential cancer imaging tool. Compared with CT, MRI is advantageous due to its lack of ionizing radiation, superior soft-tissue contrast resolution, and wider range of acquisition sequences. Several studies have shown PET/MRI to be equivalent to PET/CT in most oncological applications, possibly superior in certain body parts, e.g., head and neck, pelvis, and in certain situations, e.g., cancer recurrence. This review will update the readers on recent advances in PET/MRI technology and review key literature, while highlighting the strengths and weaknesses of PET/MRI in cancer imaging.

OBJECTIVE:To determine the diagnostic accuracy of high-resolution MR imaging done at 1.5T in distinguishing bladder-restricted tumor from non-bladder-restricted tumor and compare the mean short axis dimension of metastatic pelvic lymph nodes with benign pelvic lymph nodes.STUDY DESIGN:Analytical study.PLACE AND DURATION OF STUDY:Shaukat Khanum Memorial Cancer Hospital, Lahore, Pakistan, from March 2008 to July 2011.METHODOLOGY:Patients with bladder cancer were enrolled. Based on pathologic T-staging following radical cystectomy, patients were assigned to one of two groups. Patients with stage T1 and T2 disease were assigned to the bladder-restricted tumor (BRT) group and those with stage T3 and T4 disease to the non-bladder-restricted tumor (NBRT). High-resolution unenhanced MR imaging done prior to cystectomy was reviewed retrospectively (1.5 T MRI unit; GE Healthcare). Results from MR imaging-based categorization were compared with pathology reports to fulfill the objective. Mean short-axis diameter of largest visible lymph nodes in patients with nodal metastasis was compared with mean short-axis diameter of largest visible lymph nodes in patients with benign lymph nodes.RESULTS:The accuracy of MRI in differentiating distinguishing bladder-restricted tumor from non-bladder-restricted tumor was 67.72%. The mean short axis diameter of metastatic lymph nodes was greater than that of non-metastatic lymph nodes, i.e., 7.4 mm and 5.4 mm respectively.CONCLUSION:Conventional high resolution 1.5T MRI does not appear to offer advantage over imaging done at low field strength scanners.

Masses involving the abdominal wall arise from a large number of aetiologies. This article will describe a diagnostic approach, imaging features of the most common causes of abdominal wall masses, and highly specific characteristics of less common diseases. A diagnostic algorithm for abdominal wall masses combines clinical history and imaging appearances to classify lesions. (C) 2014 The Royal College of Radiologists. Published by Elsevier Ltd. All rights reserved.

Objectives: To evaluate the efficacy of ultrasound (US) and US-guided fine-needle aspiration (US-FNA) for axillary nodal staging in breast cancer patients. Methods: A retrospective study of breast cancer patients at our institution from January 2007 to December 2009 revealed 449 cases in which axillary US was performed. The US appearance of the axillary lymph nodes was divided into 4 categories: suspicious, borderline, benign, and not visible. The results were compared with the final pathologic outcome and correlated with the tumor size on US and the pathologic nodal stage (pN). Results: Among the 449 patients, the lymph nodes were: not visualized in 124, suspicious in 180, borderline in 39, and benign in 106 cases. The overall sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy of US alone and combined US/US-FNA was 69.1%, 83.3%, 86.8%, 63%, and 74.6% and 69.1%, 100%, 100%, 67.2%, and 81.1%, respectively. The sensitivity of US increased with increasing tumor size and pN. A negative or benign US (combined n = 230) was associated with the pN0, pN1, pN2, and pN3 stage in 146, 66, 13, and 5 cases, respectively. Conclusions: Axillary staging using US criteria is about 70% sensitive and its sensitivity increases with increasing tumor size and pN stage.