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Radiomics describes the extraction of multiple features from medical images, including molecular imaging modalities, that with bioinformatic approaches, provide additional clinically relevant information that may be invisible to the human eye. This information may complement standard radiological interpretation with data that may better characterize a disease or that may provide predictive or prognostic information. Progressing from predefined image features, often describing heterogeneity of voxel intensities within a volume of interest, there is increasing use of machine learning to classify disease characteristics and deep learning methods based on artificial neural networks that can learn features without a priori definition and without the need for preprocessing of images. There have been advances in standardization and harmonization of methods to a level that should support multicenter studies. However, in this relatively early phase of research in the field, there are limited aspects that have been adopted into routine practice. Most of the reports in the molecular imaging field describe radiomic approaches in cancer using 18 F-fluorodeoxyglucose positron emission tomography ( 18 F-FDG-PET). In this review, we will describe radiomics in molecular imaging and summarize the pertinent literature in lung cancer where reports are most prevalent and mature., (Copyright © 2020 Elsevier Inc. All rights reserved.)

Purpose: The purpose of this study was to determine if 18 F-fluorodeoxyglucose positron emission tomography/magnetic resonance imaging ( 18 F-FDG PET/MRI) features are associated with contemporaneous metastases in patients with oesophageal/gastroesophageal cancer., Methods: Following IRB approval and informed consent, patients underwent a staging PET/MRI following 18 F-FDG injection (326 ± 28 MBq) and 156 ± 23 min uptake time. First-order histogram and second-order grey level co-occurrence matrix features were computed for PET standardized uptake value (SUV) and MRI T1-W, T2-W, diffusion weighted (DWI) and apparent diffusion coefficient (ADC) images for the whole tumour volume. K-means clustering assessed the correlation of feature-pairs with metastases. Multivariate analysis of variance (MANOVA) was performed to assess the statistical separability of the groups identified by feature-pairs. Sensitivity (SN), specificity (SP), positive predictive value (PPV), negative predictive value (NPV), and accuracy (ACC) were calculated for these features and compared with SUV max , ADC mean and maximum diameter alone for predicting contemporaneous metastases., Results: Twenty patients (18 males, 2 female; median 67 years, range 52-86) comprised the final study cohort; ten patients had metastases. Lower second-order SUV entropy combined with higher second-order ADC entropy were the best feature-pair for discriminating metastatic patients, MANOVA p value <0.001 (SN = 80%, SP = 80%, PPV = 80%, NPV = 80%, ACC = 80%). SUV max (SN = 30%, SP = 80%, PPV = 60%, NPV = 53%, ACC = 55%), ADC mean (SN = 20%, SP = 70%, PPV = 40%, NPV = 47%, ACC = 45%) and tumour maximum diameter (SN = 10%, SP = 90%, PPV = 50%, NPV = 50%, ACC = 50%) had poorer sensitivity and accuracy., Conclusion: High ADC entropy combined with low SUV entropy is associated with a higher prevalence of metastases and a promising initial signature for future study.

Purpose: Despite the growing use of fluorine-18-fluorodeoxyglucose (F-FDG) PET texture analysis to measure intratumoural heterogeneity in cancer research, the biologic basis of F-FDG PET-derived texture variables is poorly understood. We aimed to assess correlations between F-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 F-FDG PET imaging before tumour resection. We tested nine F-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 cm (median: 17.9 cm). 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 F-FDG PET-derived indices including SUV-lacunarity, metabolically active tumour volume, SUVmean, entropy, skewness, and kurtosis, and thus may explain the biological basis of F-FDG PET-uptake heterogeneity in non-small-cell lung cancer.

Purpose: Radiomics describes the extraction of multiple, otherwise invisible, features from medical images that, with bioinformatic approaches, can be used to provide additional information that can predict underlying tumor biology and behavior., Methods and Materials: Radiomic signatures can be used alone or with other patient-specific data to improve tumor phenotyping, treatment response prediction, and prognosis, noninvasively. The data describing 18 F-fluorodeoxyglucose positron emission tomography radiomics, often using texture or heterogeneity parameters, are increasing rapidly., Results: In relation to radiation therapy practice, early data have reported the use of radiomic approaches to better define tumor volumes and predict radiation toxicity and treatment response., Conclusions: Although at an early stage of development, with many technical challenges remaining and a need for standardization, promise nevertheless exists that PET radiomics will contribute to personalized medicine, especially with the availability of increased computing power and the development of machine-learning approaches for imaging., (Copyright © 2017 The Author(s). Published by Elsevier Inc. All rights reserved.)

Since 2010 the portfolio of positron emission tomography (PET)-based imaging has been expanded by industry with the introduction of combined whole-body PET/MRI systems with the intent of merging PET-based molecular imaging with the strengths of MRI. PET/MRI has created a lot of hype in the scientific community but comparatively little traction in the clinic. The first years of whole-body PET/MRI were used to address inherent technical challenges; however, it is now time to make use of the full potential of this integrated imaging modality. This opinion piece highlights the continuing challenges for the clinical adoption of PET/MRI and cautions against putting too much emphasis on comparisons with clinical PET/CT. In order for PET/MRI to enter clinical practice, cross-specialty co-operation must be pursued with rigour and use-case scenarios must be propagated, following long-awaited expansion of reimbursement strategies and protocol standardization.

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., (Copyright © 2016 Elsevier Inc. All rights reserved.)

18F-fluorodeoxyglucose (18F-FDG) positron emission tomography-computed tomography (PET/CT) is established for characterising indeterminate pulmonary nodules and staging lung cancer where there is curative intent. Whilst a sensitive technique, specificity for characterising lung cancer is limited. There is recognition that evaluation of other aspects of abnormal cancer biology in addition to glucose metabolism may be more helpful in characterising tumours and predicting response to novel targeted cancer therapeutics. Therefore, efforts have been made to develop and evaluate new radiopharmaceuticals in order to improve the sensitivity and specificity of PET imaging in lung cancer with regards to characterisation, treatment stratification and therapeutic monitoring. 18F-fluorothymidine (18F-FLT) is a marker of cellular proliferation. It shows a lower accumulation in tumours than 18F-FDG as it only accumulates in the cells that are in the S phase of growth and demonstrates a low sensitivity for nodal staging. Its main role is in evaluating treatment response. Methionine is an essential amino acid. 11C-methionine is more specific and sensitive than 18F-FDG in differentiating benign and malignant thoracic nodules. 18Ffluoromisonidazole (18F-FMISO) is used for imaging tumour hypoxia. Tumour response to treatment is significantly related to the level of tumour oxygenation. Angiogenesis is the process by which new blood vessels are formed in tumours and is involved in tumour growth and metastatic tumour spread and is a therapeutic target. Most clinical studies have focused on targeted integrin PET imaging of which αvβ3 integrin is the most extensively investigated. It is upregulated on activated endothelial cells in association with tumour angiogenesis. Neuroendocrine tumour tracers, particularly 68Ga-DOTA-peptides, have an established role in imaging of carcinoid tumours. Whilst most of these tracers have predominantly been used in the research environment, they offer exciting opportunities for improving staging, characterisation, stratification and response assessment in an era of increased personalised therapy in lung cancer., (Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.)

Bone metastases are common in patients with advanced breast cancer. Given the significant associated morbidity, the introduction of new, effective systemic therapies, and the improvement in survival time, early detection and response assessment of skeletal metastases have become even more important. Although planar bone scanning has recognized limitations, in particular, poor specificity in staging and response assessment, it continues to be the main method in current clinical practice for staging of the skeleton in patients at risk of bone metastases. However, the accuracy of bone scanning can be improved with the addition of SPECT/CT. There have been reported improvements in sensitivity and specificity for staging of the skeleton with either bone-specific PET/CT tracers, such as (18)F-NaF, or tumor-specific tracers, such as (18)F-FDG, although these methods are less widely available and more costly. There is a paucity of data on the use of (18)F-NaF PET/CT for response assessment in breast cancer, but there is increasing evidence that (18)F-FDG PET/CT may improve on current methods in this regard. At the same time, interest and experience in using whole-body morphologic MRI augmented with diffusion-weighted imaging for both staging and response assessment in the skeleton have been increasing. However, data on comparisons of these methods with PET methods to determine the best technique for current clinical practice or for clinical trials are insufficient. There are early data supporting the use (18)F-FDG PET/MRI to assess malignant disease in the skeleton, with the possibility of taking advantage of the synergies offered by combining morphologic, physiologic, and metabolic imaging., (© 2016 by the Society of Nuclear Medicine and Molecular Imaging, Inc.)

Imaging of cancer with 18F-fluorodeoxyglucose positron emission tomography (18F-FDG PET) has become a standard component of diagnosis and staging in oncology, and is becoming more important as a quantitative monitor of individual response to therapy. In this article we investigate the challenging problem of predicting a patient's response to neoadjuvant chemotherapy from a single 18F-FDG PET scan taken prior to treatment. We take a "radiomics" approach whereby a large amount of quantitative features is automatically extracted from pretherapy PET images in order to build a comprehensive quantification of the tumor phenotype. While the dominant methodology relies on hand-crafted texture features, we explore the potential of automatically learning low- to high-level features directly from PET scans. We report on a study that compares the performance of two competing radiomics strategies: an approach based on state-of-the-art statistical classifiers using over 100 quantitative imaging descriptors, including texture features as well as standardized uptake values, and a convolutional neural network, 3S-CNN, trained directly from PET scans by taking sets of adjacent intra-tumor slices. Our experimental results, based on a sample of 107 patients with esophageal cancer, provide initial evidence that convolutional neural networks have the potential to extract PET imaging representations that are highly predictive of response to therapy. On this dataset, 3S-CNN achieves an average 80.7% sensitivity and 81.6% specificity in predicting non-responders, and outperforms other competing predictive models.

Purpose: To determine if first-order and high-order textural features on fluorine 18 ((18)F) fluorodeoxyglucose (FDG) positron emission tomography (PET) images of non-small cell lung cancer (NSCLC) (a) at baseline, (b) at 6 weeks, or (c) the percentage change between baseline and 6 weeks can predict response or survival in patients treated with erlotinib., Materials and Methods: Institutional review board approval was obtained for post hoc analysis of data from a prospective single-center study for which informed consent was obtained. The study included 47 patients with NSCLC who underwent (18)F-FDG PET/computed tomography (CT) at baseline (n = 47) and 6 weeks (n = 40) after commencing treatment with erlotinib. First-order and high-order primary tumor texture features reflecting image heterogeneity, standardized uptake values, metabolic tumor volume, and total lesion glycolysis were measured for all (18)F-FDG PET studies. Response to erlotinib was assessed by using the Response Evaluation Criteria in Solid Tumors (RECIST) on CT images obtained at 12 weeks (n = 32). Associations between PET parameters, overall survival (OS), and RECIST-based treatment response were tested by Cox and logistic regression analyses, respectively., Results: Median OS was 14.1 months. According to CT RECIST at 12 weeks, there were 21 nonresponders and 11 responders. Response to erlotinib was associated with reduced heterogeneity (first-order standard deviation, P = .01; entropy, P = .001; uniformity, P = .001). At multivariable analysis, high-order contrast at 6 weeks (P = .002) and percentage change in first-order entropy (P = .03) were independently associated with survival. Percentage change in first-order entropy was also independently associated with treatment response (P = .01)., Conclusion: Response to erlotinib is associated with reduced heterogeneity at (18)F-FDG PET. Changes in first-order entropy are independently associated with OS and treatment response.

Objectives: Measuring tumour heterogeneity by textural analysis in (18)F-fluorodeoxyglucose positron emission tomography ((18)F-FDG PET) provides predictive and prognostic information but technical aspects of image processing can influence parameter measurements. We therefore tested effects of image smoothing, segmentation and quantisation on the precision of heterogeneity measurements., Methods: Sixty-four (18)F-FDG PET/CT images of oesophageal cancer were processed using different Gaussian smoothing levels (2.0, 2.5, 3.0, 3.5, 4.0 mm), maximum standardised uptake value (SUVmax) segmentation thresholds (45%, 50%, 55%, 60%) and quantisation (8, 16, 32, 64, 128 bin widths). Heterogeneity parameters included grey-level co-occurrence matrix (GLCM), grey-level run length matrix (GLRL), neighbourhood grey-tone difference matrix (NGTDM), grey-level size zone matrix (GLSZM) and fractal analysis methods. The concordance correlation coefficient (CCC) for the three processing variables was calculated for each heterogeneity parameter., Results: Most parameters showed poor agreement between different bin widths (CCC median 0.08, range 0.004-0.99). Segmentation and smoothing showed smaller effects on precision (segmentation: CCC median 0.82, range 0.33-0.97; smoothing: CCC median 0.99, range 0.58-0.99)., Conclusions: Smoothing and segmentation have only a small effect on the precision of heterogeneity measurements in (18)F-FDG PET data. However, quantisation often has larger effects, highlighting a need for further evaluation and standardisation of parameters for multicentre studies., Key Points: • Heterogeneity measurement precision in (18) F-FDG PET is influenced by image processing methods. • Quantisation shows large effects on precision of heterogeneity parameters in (18) F-FDG PET/CT. • Smoothing and segmentation show comparatively smaller effects on precision of heterogeneity parameters.

Non-small-cell lung cancer (NSCLC) is the commonest cancer worldwide but survival remains poor with a high risk of relapse, particularly after nonsurgical treatment. Hypoxia is present in a variety of solid tumours, including NSCLC. It is associated with treatment resistance and a poor prognosis, although when recognised may be amenable to different treatment strategies. Thus, noninvasive assessment of intratumoral hypoxia could be used to stratify patients for modification of subsequent treatment to improve tumour control. Molecular imaging approaches targeting hypoxic cells have shown some early success in the clinical setting. This review evaluates the evidence for hypoxia imaging using PET in NSCLC and explores its potential clinical utility.

Unlabelled: This study explores the potential for multifunctional imaging to provide a signature for V-KI-RAS2 Kirsten rat sarcoma viral oncogene homolog (KRAS) gene mutations in colorectal cancer., Methods: This prospective study approved by the institutional review board comprised 33 patients undergoing PET/CT before surgery for proven primary colorectal cancer. Tumor tissue was examined histologically for presence of the KRAS mutations and for expression of hypoxia-inducible factor-1 (HIF-1) and minichromosome maintenance protein 2 (mcm2). The following imaging parameters were derived for each tumor: (18)F-FDG uptake ((18)F-FDG maximum standardized uptake value [SUVmax]), CT texture (expressed as mean of positive pixels [MPP]), and blood flow measured by dynamic contrast-enhanced CT. A recursive decision tree was developed in which the imaging investigations were applied sequentially to identify tumors with KRAS mutations. Monte Carlo analysis provided mean values and 95% confidence intervals for sensitivity, specificity, and accuracy., Results: The final decision tree comprised 4 decision nodes and 5 terminal nodes, 2 of which identified KRAS mutants. The true-positive rate, false-positive rate, and accuracy (95% confidence intervals) of the decision tree were 82.4% (63.9%-93.9%), 0% (0%-10.4%), and 90.1% (79.2%-96.0%), respectively. KRAS mutants with high (18)F-FDG SUVmax and low MPP showed greater frequency of HIF-1 expression (P = 0.032). KRAS mutants with low (18)F-FDG SUV(max), high MPP, and high blood flow expressed mcm2 (P = 0.036)., Conclusion: Multifunctional imaging with PET/CT and recursive decision-tree analysis to combine measurements of tumor (18)F-FDG uptake, CT texture, and perfusion has the potential to identify imaging signatures for colorectal cancers with KRAS mutations exhibiting hypoxic or proliferative phenotypes.

Purpose: Malignant pleural mesothelioma (MPM) is a disease with poor prognosis despite multimodal therapy but there is variation in survival between patients. Prognostic information is therefore potentially valuable in managing patients, particularly in the context of clinical trials where patients could be stratified according to risk. Therefore we have evaluated the prognostic ability of parameters derived from baseline 2-[(18)F]fluoro-2-deoxy-D-glucose positron emission tomography/computed tomography ((18)F-FDG PET/CT)., Methods: In order to determine the relationships between metabolic activity and prognosis we reviewed all (18)F-FDG PET/CT scans used for pretreatment staging of MPM patients in our institution between January 2005 and December 2011 (n = 60) and measured standardised uptake values (SUV) including mean, maximum and peak values, metabolic tumour volume (MTV) and total lesion glycolysis (TLG). Overall survival (OS) or time to last censor was recorded, as well as histological subtypes., Results: Median follow-up was 12.7 months (1.9-60.9) and median OS was 14.1 months (1.9-54.9). By univariable analysis histological subtype (p = 0.013), TLG (p = 0.024) and MTV (p = 0.038) were significantly associated with OS and SUVmax was borderline (p = 0.051). On multivariable analysis histological subtype and TLG were associated with OS but at borderline statistical significance (p = 0.060 and 0.058, respectively). No statistically significant differences in any PET parameters were found between the epithelioid and non-epithelioid histological subtypes., Conclusion: (18)F-FDG PET/CT parameters that take into account functional volume (MTV, TLG) show significant associations with survival in patients with MPM before adjusting for histological subtype and are worthy of further evaluation to determine their ability to stratify patients in clinical trials.

Unlabelled: 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 = 0.004) and higher contrast (mean, 0.11 vs. 0.044; P = 0.002) and busyness (mean, 0.76 vs. 0.37; P = 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 = 0.003; 12.6 vs. 25.8 mo, P = 0.002; and 12.9 vs. 20.5 mo, P = 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 = 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 = 0.015, and median not reached vs. 24 mo, P = 0.02; and for busyness: median of 20.5 vs. 12.6 mo, P = 0.01, and median not reached vs. 24 mo, P = 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.

(18)F-Fluorodeoxyglucose positron emission tomography/computed tomography ((18)F-FDG PET/CT) is now routinely used in oncological imaging for diagnosis and staging and increasingly to determine early response to treatment, often employing semiquantitative measures of lesion activity such as the standardized uptake value (SUV). However, the ability to predict the behaviour of a tumour in terms of future therapy response or prognosis using SUVs from a baseline scan prior to treatment is limited. It is recognized that medical images contain more useful information than may be perceived with the naked eye, leading to the field of "radiomics" whereby additional features can be extracted by computational postprocessing techniques. In recent years, evidence has slowly accumulated showing that parameters obtained by texture analysis of radiological images, reflecting the underlying spatial variation and heterogeneity of voxel intensities within a tumour, may yield additional predictive and prognostic information. It is hoped that measurement of these textural features may allow better tissue characterization as well as better stratification of treatment in clinical trials, or individualization of future cancer treatment in the clinic, than is possible with current imaging biomarkers. In this review we focus on the literature describing the emerging methods of texture analysis in (18)FDG PET/CT, as well as other imaging modalities, and how the measurement of spatial variation of voxel grey-scale intensity within an image may provide additional predictive and prognostic information, and postulate the underlying biological mechanisms.

Objective: The purpose of this article is to assess the effect of observers on combined metabolic-vascular parameters in colorectal cancer., Subjects and Methods: Twenty-five prospective patients (12 men and 13 women; mean age, 66.9 years) with proven primary colorectal adenocarcinoma underwent integrated (18)F-FDG PET/perfusion CT to assess tumor metabolism (mean and maximum standardized uptake value [SUV(mean) and SUV(max), respectively]) and vascularization (blood flow [BF], blood volume [BV], permeability surface-area product, and standardized perfusion value). Intra- and interobserver agreement for PET, perfusion CT, and combined metabolic-flow parameters were determined by Bland-Altman statistics and intraclass correlation coefficients (ICCs)., Results: The mean tumor size was 3.8 ± 1.6 cm; there were five stage IA/B, six stage IIA/B, eight stage IIIA/B, and six stage IV tumors. Intra- and interobserver agreement for individual parameters was fair to good, with mean differences between observers of -0.74 for SUV(max), -0.16 for SUV(mean), 9.72 for BF, 0.15 for BV, -0.76 for permeability surface-area product, and 0.09 for standardized perfusion value. ICCs were 0.44-0.99 and 0.38-0.89 for intra- and interobserver agreement, respectively. Interobserver agreement was variable for combined metabolic-flow parameters but better for metabolic-flow difference than for metabolic-flow ratio: ICCs were 0.69-0.88 for the metabolic-flow difference and 0.44-0.94 for the metabolic-flow ratio., Conclusion: Combined parameters to assess the metabolic-flow relationship are influenced by observer variation. Intra- and interobserver agreement are better for the metabolic-flow differences than for the ratios, suggesting that metabolic-flow differences may be a more robust parameter for clinical practice.

Unlabelled: The aim of this study was to assess the in vivo flow-metabolic phenotype in primary colorectal cancer with integrated (18)F-FDG PET/perfusion CT and its relationship to gold standard histopathologic assessment of angiogenesis and hypoxia., Methods: 45 patients (26 male and 19 female; mean age, 67.6 y) with primary colorectal cancer underwent integrated (18)F-FDG PET/perfusion CT, deriving tumor glucose metabolism (maximum standardized uptake value) and regional blood flow. From this cohort, 35 underwent surgery subsequently, without intervening neoadjuvant treatment, allowing histopathologic correlation with tumor stage, CD105 microvessel density, vascular endothelial growth factor (VEGF), glucose transporter protein 1 (Glut-1), and hypoxia-inducible factor 1 expression., Results: The flow-metabolic ratio was significantly lower for tumors with higher VEGF (3.65 vs. 5.98; P = 0.01) or hypoxia-inducible factor 1 expression (3.63 vs. 5.48; P = 0.04) versus tumors with lower expression. There were significant negative correlations between the tumor flow-metabolic ratio and VEGF expression (r = -0.55, P = 0.0008), indicating that tumors with low blood flow but higher metabolism were associated with higher VEGF expression. Flow and metabolism were coupled in higher-stage (stage III/IV) tumors but not lower-stage tumors (stage I/II) (r = 0.47, P = 0.03, vs. r = 0.09, P = 0.65, respectively., Conclusion: Tumors with a low-flow-high-metabolism phenotype demonstrated higher VEGF expression and may reflect a more angiogenic phenotype.

Unlabelled: Inflammation and angiogenesis are hypothesized to be important factors contributing to plaque vulnerability, whereas calcification is suggested to confer stability. To investigate this in vivo, we combined CT angiography and PET and compared the findings with immunohistochemistry for patients undergoing carotid endarterectomy., Methods: Twenty-one consecutive patients (18 men, 3 women; mean age ± SD, 68.3 ± 7.3) undergoing carotid endarterectomy were recruited for combined carotid (18)F-FDG PET/CT angiography. Plaque (18)F-FDG uptake was quantified with maximum standardized uptake value, and CT angiography quantified percentage plaque composition (calcium and lipid). Surgical specimens underwent ex vivo CT aiding image registration, followed by immunohistochemical staining for CD68 (macrophage density) and vascular endothelial growth factor (angiogenesis). Relationships between imaging and immunohistochemistry were assessed with Spearman rank correlation and multivariable regression., Results: The mean (±SD) surgically excised carotid plaque (18)F-FDG metabolism was 2.4 (±0.5) versus 2.2 (±0.3) contralaterally (P = 0.027). There were positive correlations between plaque (18)F-FDG metabolism and immunohistochemistry with CD68 (ρ = 0.55; P = 0.011) and vascular endothelial growth factor (ρ = 0.47; P = 0.031). There was an inverse relationship between plaque (18)F-FDG metabolism and plaque percentage calcium composition on CT (ρ = -0.51; P = 0.018) and between calcium composition and immunohistochemistry with CD68 (ρ = -0.57; P = 0.007). Regression showed that maximum standardized uptake value and calcium composition were independently significant predictors of angiogenesis, and calcium composition was a predictor of macrophage density., Conclusion: We provide in vivo evidence that increased plaque metabolism is associated with increased biomarkers of angiogenesis and inflammation, whereas plaque calcification is inversely related to PET and histologic biomarkers of inflammation.

Objective: This article reviews the current and future contributions of functional imaging techniques to improve diagnosis, prognosis, and treatment of colorectal cancer. In addition, evolving roles and challenges for their implementation will be covered., Conclusion: Functional imaging now has a growing role in colorectal cancer. Recent developments in imaging technologies and validation of these newer imaging techniques may lead to significant improvements in the management of patients with colorectal cancer.

In the past 10 years, overall survival and disease-free survival of patients with colorectal cancer (CRC) has improved substantially because of a combination of factors: (1) more accurate staging as a result of advances in imaging technology; (2) refinements in surgical technique; (3) 'curative' metastasectomy for patients with limited metastatic disease; (4) improvements in radiation therapy planning and greater precision of radiation therapy delivery; and (5) increasing chemotherapeutic options, including antiangiogenic and vascular targeting drugs. In this era of 'personalized medicine', the increasingly individualized treatment of patients with CRC has highlighted the need for functional imaging techniques in addition to conventional anatomic-based imaging. This review discusses the contribution of positron emission tomography to the clinical management of CRC. In addition, evolving techniques such as dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI), DCE computed tomography (perfusion CT), diffusion-weighted MRI, and blood oxygenation level-dependent MRI that might have a future role will be covered.

Background Radiomic features may quantify characteristics present in medical imaging. However, the lack of standardized definitions and validated reference values have hampered clinical use. Purpose To standardize a set of 174 radiomic features. Materials and Methods Radiomic features were assessed in three phases. In phase I, 487 features were derived from the basic set of 174 features. Twenty-five research teams with unique radiomics software implementations computed feature values directly from a digital phantom, without any additional image processing. In phase II, 15 teams computed values for 1347 derived features using a CT image of a patient with lung cancer and predefined image processing configurations. In both phases, consensus among the teams on the validity of tentative reference values was measured through the frequency of the modal value and classified as follows: less than three matches, weak; three to five matches, moderate; six to nine matches, strong; 10 or more matches, very strong. In the final phase (phase III), a public data set of multimodality images (CT, fluorine 18 fluorodeoxyglucose PET, and T1-weighted MRI) from 51 patients with soft-tissue sarcoma was used to prospectively assess reproducibility of standardized features. Results Consensus on reference values was initially weak for 232 of 302 features (76.8%) at phase I and 703 of 1075 features (65.4%) at phase II. At the final iteration, weak consensus remained for only two of 487 features (0.4%) at phase I and 19 of 1347 features (1.4%) at phase II. Strong or better consensus was achieved for 463 of 487 features (95.1%) at phase I and 1220 of 1347 features (90.6%) at phase II. Overall, 169 of 174 features were standardized in the first two phases. In the final validation phase (phase III), most of the 169 standardized features could be excellently reproduced (166 with CT; 164 with PET; and 164 with MRI). Conclusion A set of 169 radiomics features was standardized, which enabled verification and calibration of different radiomics software. © RSNA, 2020 Online supplemental material is available for this article. See also the editorial by Kuhl and Truhn in this issue.