Search

Highlights • Radiomics studies in NSCLC suffer from a number of limitations. • No single radiomic signature has been translated into clinical use. • Identification of limitations can help future studies to expedite biomarker translation., Radiomics has become a popular image analysis method in the last few years. Its key hypothesis is that medical images harbor biological, prognostic and predictive information that is not revealed upon visual inspection. In contrast to previous work with a priori defined imaging biomarkers, radiomics instead calculates image features at scale and uses statistical methods to identify those most strongly associated to outcome. This builds on years of research into computer aided diagnosis and pattern recognition. While the potential of radiomics to aid personalized medicine is widely recognized, several technical limitations exist which hinder biomarker translation. Aspects of the radiomic workflow lack repeatability or reproducibility under particular circumstances, which is a key requirement for the translation of imaging biomarkers into clinical practice. One of the most commonly studied uses of radiomics is for personalized medicine applications in Non-Small Cell Lung Cancer (NSCLC). In this review, we summarize reported methodological limitations in CT based radiomic analyses together with suggested solutions. We then evaluate the current NSCLC radiomics literature to assess the risk associated with accepting the published conclusions with respect to these limitations. We review different complementary scoring systems and initiatives that can be used to critically appraise data from radiomics studies. Wider awareness should improve the quality of ongoing and future radiomics studies and advance their potential as clinically relevant biomarkers for personalized medicine in patients with NSCLC.

Objective To investigate the effects of Image Biomarker Standardisation Initiative (IBSI) compliance, harmonisation of calculation settings and platform version on the statistical reliability of radiomic features and their corresponding ability to predict clinical outcome. Methods The statistical reliability of radiomic features was assessed retrospectively in three clinical datasets (patient numbers: 108 head and neck cancer, 37 small-cell lung cancer, 47 non-small-cell lung cancer). Features were calculated using four platforms (PyRadiomics, LIFEx, CERR and IBEX). PyRadiomics, LIFEx and CERR are IBSI-compliant, whereas IBEX is not. The effects of IBSI compliance, user-defined calculation settings and platform version were assessed by calculating intraclass correlation coefficients and confidence intervals. The influence of platform choice on the relationship between radiomic biomarkers and survival was evaluated using univariable cox regression in the largest dataset. Results The reliability of radiomic features calculated by the different software platforms was only excellent (ICC > 0.9) for 4/17 radiomic features when comparing all four platforms. Reliability improved to ICC > 0.9 for 15/17 radiomic features when analysis was restricted to the three IBSI-compliant platforms. Failure to harmonise calculation settings resulted in poor reliability, even across the IBSI-compliant platforms. Software platform version also had a marked effect on feature reliability in CERR and LIFEx. Features identified as having significant relationship to survival varied between platforms, as did the direction of hazard ratios. Conclusion IBSI compliance, user-defined calculation settings and choice of platform version all influence the statistical reliability and corresponding performance of prognostic models in radiomics. Key Points • Reliability of radiomic features varies between feature calculation platforms and with choice of software version. • Image Biomarker Standardisation Initiative (IBSI) compliance improves reliability of radiomic features across platforms, but only when calculation settings are harmonised. • IBSI compliance, user-defined calculation settings and choice of platform version collectively affect the prognostic value of features.

Perianal Crohn’s Disease (pCD) is a common manifestation of Crohn’s Disease. Absence of reliable disease measures makes disease monitoring unreliable. Qualitative MRI has been increasingly used for diagnosing and monitoring pCD and has shown potential for assessing response to treatment. Quantitative MRI sequences, such as diffusion-weighted imaging (DWI), dynamic contrast enhancement (DCE) and magnetisation transfer (MT), along with T2 relaxometry, offer opportunities to improve diagnostic capability. Quantitative MRI sequences (DWI, DCE, MT and T2) were used in a cohort of 25 pCD patients before and 12 weeks after biological therapy at two different field strengths (1.5 and 3 T). Disease activity was measured with the Perianal Crohn’s Disease Activity index (PDAI) and serum C-reactive protein (CRP). Diseased tissue areas on MRI were defined by a radiologist. A baseline model to predict outcome at 12 weeks was developed. No differences were seen in the quantitative MR measured in the diseased tissue regions from baseline to 12 weeks; however, PDAI and CRP decreased. Baseline PDAI, CRP, T2 relaxometry and surgical history were found to have a moderate ability to predict response after 12 weeks of biological treatment. Validation in larger cohorts with MRI and clinical measures are needed in order to further develop the model.

Radiomics refers to the extraction of mineable data from medical imaging and has been applied within oncology to improve diagnosis, prognostication, and clinical decision support, with the goal of delivering precision medicine. The authors provide a practical approach for successfully implementing a radiomic workflow from planning and conceptualization through manuscript writing. Applications in oncology typically are either classification tasks that involve computing the probability of a sample belonging to a category, such as benign versus malignant, or prediction of clinical events with a time-to-event analysis, such as overall survival. The radiomic workflow is multidisciplinary, involving radiologists and data and imaging scientists, and follows a stepwise process involving tumor segmentation, image preprocessing, feature extraction, model development, and validation. Images are curated and processed before segmentation, which can be performed on tumors, tumor subregions, or peritumoral zones. Extracted features typically describe the distribution of signal intensities and spatial relationship of pixels within a region of interest. To improve model performance and reduce overfitting, redundant and nonreproducible features are removed. Validation is essential to estimate model performance in new data and can be performed iteratively on samples of the dataset (cross-validation) or on a separate hold-out dataset by using internal or external data. A variety of noncommercial and commercial radiomic software applications can be used. Guidelines and artificial intelligence checklists are useful when planning and writing up radiomic studies. Although interest in the field continues to grow, radiologists should be familiar with potential pitfalls to ensure that meaningful conclusions can be drawn.

Background The DREAMtherapy (Dual REctal Angiogenesis MEK inhibition radiotherapy) trial is a novel intertwined design whereby two tyrosine kinase inhibitors (cediranib and selumetinib) were independently evaluated with rectal chemoradiotherapy (CRT) in an efficient manner to limit the extended follow-up period often required for radiotherapy studies. Patients and methods Cediranib or selumetinib was commenced 10 days before and then continued with RT (45 Gy/25#/5 wks) and capecitabine (825 mg/m2 twice a day (BID)). When three patients in the cediranib 15-mg once daily (OD) cohort were in the surveillance period, recruitment to the selumetinib cohort commenced. This alternating schedule was followed throughout. Three cediranib (15, 20 and 30 mg OD) and two selumetinib cohorts (50 and 75 mg BID) were planned. Circulating and imaging biomarkers of inflammation/angiogenesis were evaluated. Results In case of cediranib, dose-limiting diarrhoea, fatigue and skin reactions were seen in the 30-mg OD cohort, and therefore, 20 mg OD was defined as the maximum tolerated dose. Forty-one percent patients achieved a clinical or pathological complete response (7/17), and 53% (9/17) had an excellent clinical or pathological response (ECPR). Significantly lower level of pre-treatment plasma tumour necrosis factor alpha (TNFα) was found in patients who had an ECPR. In case of selumetinib, the 50-mg BID cohort was poorly tolerated (fatigue and diarrhoea); a reduced dose cohort of 75-mg OD was opened which was also poorly tolerated, and further recruitment was abandoned. Of the 12 patients treated, two attained an ECPR (17%). Conclusions This novel intertwined trial design is an effective way to independently investigate multiple agents with radiotherapy. The combination of cediranib with CRT was well tolerated with encouraging efficacy. TNFα emerged as a potential predictive biomarker of response and warrants further evaluation.

The annual global incidence of cervical cancer is approximately 604 000 cases/342 000 deaths, making it the fourth most common cancer in women. Cervical cancer is a major healthcare problem in low and middle income countries where 85% of new cases and deaths occur. Secondary prevention measures have reduced incidence and mortality in developed countries over the past 30 years, but cervical cancer remains a major cause of cancer deaths in women. For women who present with Fédération Internationale de Gynécologie et d’Obstétrique (FIGO 2018) stages IB3 or upwards, chemoradiation is the established treatment. Despite high rates of local control, overall survival is less than 50%, largely due to distant relapse. Reducing the health burden of cervical cancer requires greater individualization of treatment, identifying those at risk of relapse and progression for modified or intensified treatment. Hypoxia is a well known feature of solid tumors and an established therapeutic target. Low tumorous oxygenation increases the risk of local invasion, metastasis and treatment failure. While meta-analyses show benefit, many individual trials targeting hypoxia failed in part due to not selecting patients most likely to benefit. This review summarizes the available hypoxia-targeted strategies and identifies further research and new treatment paradigms needed to improve patient outcomes. The applications and limitations of hypoxia biomarkers for treatment selection and response monitoring are discussed. Finally, areas of greatest unmet clinical need are identified to measure and target hypoxia and therefore improve cervical cancer outcomes.

Abstract Existing quantitative imaging biomarkers (QIBs) are associated with known biological tissue characteristics and follow a well-understood path of technical, biological and clinical validation before incorporation into clinical trials. In radiomics, novel data-driven processes extract numerous visually imperceptible statistical features from the imaging data with no a priori assumptions on their correlation with biological processes. The selection of relevant features (radiomic signature) and incorporation into clinical trials therefore requires additional considerations to ensure meaningful imaging endpoints. Also, the number of radiomic features tested means that power calculations would result in sample sizes impossible to achieve within clinical trials. This article examines how the process of standardising and validating data-driven imaging biomarkers differs from those based on biological associations. Radiomic signatures are best developed initially on datasets that represent diversity of acquisition protocols as well as diversity of disease and of normal findings, rather than within clinical trials with standardised and optimised protocols as this would risk the selection of radiomic features being linked to the imaging process rather than the pathology. Normalisation through discretisation and feature harmonisation are essential pre-processing steps. Biological correlation may be performed after the technical and clinical validity of a radiomic signature is established, but is not mandatory. Feature selection may be part of discovery within a radiomics-specific trial or represent exploratory endpoints within an established trial; a previously validated radiomic signature may even be used as a primary/secondary endpoint, particularly if associations are demonstrated with specific biological processes and pathways being targeted within clinical trials. Key Points • Data-driven processes like radiomics risk false discoveries due to high-dimensionality of the dataset compared to sample size, making adequate diversity of the data, cross-validation and external validation essential to mitigate the risks of spurious associations and overfitting. • Use of radiomic signatures within clinical trials requires multistep standardisation of image acquisition, image analysis and data mining processes. • Biological correlation may be established after clinical validation but is not mandatory.

Purpose: We introduce a novel, generalized tracer kinetic model selection framework to quantify microvascular characteristics of liver and tumor tissue in gadoxetate-enhanced dynamic contrast-enhanced MRI (DCE-MRI). Methods: Our framework includes a hierarchy of nested models, from which physiological parameters are derived in 2 regimes, corresponding to the active transport and free diffusion of gadoxetate. We use simulations to show the sensitivity of model selection and parameter estimation to temporal resolution, time-series duration, and noise. We apply the framework in 8 healthy volunteers (time-series duration up to 24 minutes) and 10 patients with hepatocellular carcinoma (6 minutes). Results: The active transport regime is preferred in 98.6% of voxels in volunteers, 82.1% of patients’ non-tumorous liver, and 32.2% of tumor voxels. Interpatient variations correspond to known co-morbidities. Simulations suggest both datasets have sufficient temporal resolution and signal-to-noise ratio, while patient data would be improved by using a time-series duration of at least 12 minutes. Conclusions: In patient data, gadoxetate exhibits different kinetics: (a) between liver and tumor regions and (b) within regions due to liver disease and/or tumor heterogeneity. Our generalized framework selects a physiological interpretation at each voxel, without preselecting a model for each region or duplicating time-consuming optimizations for models with identical functional forms.

Imaging biomarkers require technical, biological, and clinical validation to be translated into robust tools in research or clinical settings. This study contributes to the technical validation of radiomic features from magnetic resonance imaging (MRI) by evaluating the repeatability of features from four MR sequences: pre-contrast T1- and T2-weighted images, pre-contrast quantitative T1 maps (qT1), and contrast-enhanced T1-weighted images. Fifty-one patients with colorectal cancer liver metastases were scanned twice, up to 7 days apart. Repeatability was quantified using the intraclass correlation coefficient (ICC) and repeatability coefficient (RC), and the impact of non-Gaussian feature distributions and image normalisation was evaluated. Most radiomic features had non-Gaussian distributions, but Box&ndash, Cox transformations enabled ICCs and RCs to be calculated appropriately for an average of 97% of features across sequences. ICCs ranged from 0.30 to 0.99, with volume and other shape features tending to be most repeatable, volume ICC &gt, 0.98 for all sequences. 19% of features from non-normalised images exhibited significantly different ICCs in pair-wise sequence comparisons. Normalisation tended to increase ICCs for pre-contrast T1- and T2-weighted images, and decrease ICCs for qT1 maps. RCs tended to vary more between sequences than ICCs, showing that evaluations of feature performance depend on the chosen metric. This work suggests that feature-specific repeatability, from specific combinations of MR sequence and pre-processing steps, should be evaluated to select robust radiomic features as biomarkers in specific studies. In addition, as different repeatability metrics can provide different insights into a specific feature, consideration of the appropriate metric should be taken in a study-specific context.

The current standard of care for the management of inoperable stage 3 non-small cell lung cancer (NSCLC) is concurrent chemoradiotherapy (cCRT) using radiotherapy dose-fractionation and chemotherapy regimens that were established 3 decades ago. In an attempt to improve the chances of long-term control from cCRT, dose-escalation of the radiotherapy dose was assessed in the RTOG 0617 randomised control study comparing the standard 60 Gy in 30 fractions with a high-dose arm receiving 74 Gy in 37 fractions. Following the publication of this trial the thoracic oncology community were surprised to learn that there was worse survival in the dose-escalated arm and that for now the standard of care must remain with the lower dose. In this article we review the RTOG 0617 paper with subsequent analyses and studies to explore why the use of dose-escalated cCRT in stage 3 NSCLC has not shown the benefits that were expected. The overarching theme of this opinion piece is how heterogeneity between stage 3 NSCLC cases in terms of patient, tumour, and clinical factors may obscure the potential benefits of dose-escalation by causing imbalances in the arms of studies such as RTOG 0617. We also examine recent advances in the staging, management, and technological delivery of radiotherapy in NSCLC and how these may be employed to optimise cCRT trials in the future and ensure that any potential benefits of dose-escalation can be detected.

Existing quantitative imaging biomarkers (QIBs) are associated with known biological tissue characteristics and follow a well-understood path of technical, biological and clinical validation before incorporation into clinical trials. In radiomics, novel data-driven processes extract numerous visually imperceptible statistical features from the imaging data with no a priori assumptions on their correlation with biological processes. The selection of relevant features (radiomic signature) and incorporation into clinical trials therefore requires additional considerations to ensure meaningful imaging endpoints. Also, the number of radiomic features tested means that power calculations would result in sample sizes impossible to achieve within clinical trials. This article examines how the process of standardising and validating data-driven imaging biomarkers differs from those based on biological associations. Radiomic signatures are best developed initially on datasets that represent diversity of acquisition protocols as well as diversity of disease and of normal findings, rather than within clinical trials with standardised and optimised protocols as this would risk the selection of radiomic features being linked to the imaging process rather than the pathology. Normalisation through discretisation and feature harmonisation are essential pre-processing steps. Biological correlation may be performed after the technical and clinical validity of a radiomic signature is established, but is not mandatory. Feature selection may be part of discovery within a radiomics-specific trial or represent exploratory endpoints within an established trial; a previously validated radiomic signature may even be used as a primary/secondary endpoint, particularly if associations are demonstrated with specific biological processes and pathways being targeted within clinical trials. • Data-driven processes like radiomics risk false discoveries due to high-dimensionality of the dataset compared to sample size, making adequate diversity of the data, cross-validation and external validation essential to mitigate the risks of spurious associations and overfitting. • Use of radiomic signatures within clinical trials requires multistep standardisation of image acquisition, image analysis and data mining processes. • Biological correlation may be established after clinical validation but is not mandatory.

PURPOSE: MRI biomarkers of tumor response to treatment are typically obtained from parameters derived from a model applied to pre-treatment and post-treatment data. However, as tumors are spatially and temporally heterogeneous, different models may be necessary in different tumor regions, and model suitability may change over time. This work evaluates how the suitability of two diffusion-weighted (DW) MRI models varies spatially within tumors at the voxel level and in response to radiotherapy, potentially allowing inference of qualitatively different tumor microenvironments.METHODS: DW-MRI data were acquired in CT26 subcutaneous allografts before and after radiotherapy. Restricted and time-independent diffusion models were compared, with regions well-described by the former hypothesized to reflect cellular tissue, and those well-described by the latter expected to reflect necrosis or oedema. Technical and biological validation of the percentage of tissue described by the restricted diffusion microstructural model (termed %MM) was performed through simulations and histological comparison.RESULTS: Spatial and radiotherapy-related variation in model suitability was observed. %MM decreased from a mean of 64% at baseline to 44% 6 days post-radiotherapy in the treated group. %MM correlated negatively with the percentage of necrosis from histology, but overestimated it due to noise. Within MM regions, microstructural parameters were sensitive to radiotherapy-induced changes.CONCLUSIONS: There is spatial and radiotherapy-related variation in different models' suitability for describing diffusion in tumor tissue, suggesting the presence of different and changing tumor sub-regions. The biological and technical validation of the proposed %MM cancer imaging biomarker suggests it correlates with, but overestimates, the percentage of necrosis.

Motivation Probabilistic latent semantic analysis (pLSA) is commonly applied to describe mass spectra (MS) images. However, the method does not provide certain outputs necessary for the quantitative scientific interpretation of data. In particular, it lacks assessment of statistical uncertainty and the ability to perform hypothesis testing. We show how linear Poisson modelling advances pLSA, giving covariances on model parameters and supporting χ2 testing for the presence/absence of MS signal components. As an example, this is useful for the identification of pathology in MALDI biological samples. We also show potential wider applicability, beyond MS, using magnetic resonance imaging (MRI) data from colorectal xenograft models. Results Simulations and MALDI spectra of a stroke-damaged rat brain show MS signals from pathological tissue can be quantified. MRI diffusion data of control and radiotherapy-treated tumours further show high sensitivity hypothesis testing for treatment effects. Successful χ2 and degrees-of-freedom are computed, allowing null-hypothesis thresholding at high levels of confidence. Availability and implementation Open-source image analysis software available from TINA Vision, www.tina-vision.net. Supplementary information Supplementary data are available at Bioinformatics online.

Hypoxia is known to be a poor prognostic indicator for nearly all solid tumours and also is predictive of treatment failure for radiotherapy, chemotherapy, surgery and targeted therapies. Imaging has potential to identify, spatially map and quantify tumour hypoxia prior to therapy, as well as track changes in hypoxia on treatment. At present no hypoxia imaging methods are available for routine clinical use. Research has largely focused on positron emission tomography (PET)-based techniques, but there is gathering evidence that MRI techniques may provide a practical and more readily translational alternative. In this review we focus on the potential for imaging hypoxia by measuring changes in longitudinal relaxation [R (1); termed oxygen-enhanced MRI or tumour oxygenation level dependent (TOLD) MRI] and effective transverse relaxation [R (2)*; termed blood oxygenation level dependent (BOLD) MRI], induced by inhalation of either 100% oxygen or the radiosensitising hyperoxic gas carbogen. We explain the scientific principles behind oxygen-enhanced MRI and BOLD and discuss significant studies and their limitations. All imaging biomarkers require rigorous validation in order to translate into clinical use and the steps required to further develop oxygen-enhanced MRI and BOLD MRI into decision-making tools are discussed.

Purpose: Hypoxia is associated with poor prognosis and is predictive of poor response to cancer treatments, including radiotherapy. Developing noninvasive biomarkers that both detect hypoxia prior to treatment and track change in tumor hypoxia following treatment is required urgently. Experimental Design: We evaluated the ability of oxygen-enhanced MRI (OE-MRI) to map and quantify therapy-induced changes in tumor hypoxia by measuring oxygen-refractory signals in perfused tissue (perfused Oxy-R). Clinical first-in-human study in patients with non–small cell lung cancer (NSCLC) was performed alongside preclinical experiments in two xenograft tumors (Calu6 NSCLC model and U87 glioma model). Results: MRI perfused Oxy-R tumor fraction measurement of hypoxia was validated with ex vivo tissue pathology in both xenograft models. Calu6 and U87 experiments showed that MRI perfused Oxy-R tumor volume was reduced relative to control following single fraction 10-Gy radiation and fractionated chemoradiotherapy (P < 0.001) due to both improved perfusion and reduced oxygen consumption rate. Next, evaluation of 23 patients with NSCLC showed that OE-MRI was clinically feasible and that tumor perfused Oxy-R volume is repeatable [interclass correlation coefficient: 0.961 (95% CI, 0.858–0.990); coefficient of variation: 25.880%]. Group-wise perfused Oxy-R volume was reduced at 14 days following start of radiotherapy (P = 0.015). OE-MRI detected between-subject variation in hypoxia modification in both xenograft and patient tumors. Conclusions: These findings support applying OE-MRI biomarkers to monitor hypoxia modification, to stratify patients in clinical trials of hypoxia-modifying therapies, to identify patients with hypoxic tumors that may fail treatment with immunotherapy, and to guide adaptive radiotherapy by mapping regional hypoxia.

Dynamic contrast-enhanced (DCE) MR imaging is used increasingly often to evaluate tumor angiogenesis and the efficacy of antiangiogenic drugs. In clinical practice DCE-MR imaging applications are largely centered on lesion detection, characterization, and localization. In research, DCE-MR imaging helps inform decision making in early-phase clinical trials by showing efficacy and by selecting dose and schedule. However, the role of these techniques in patient selection is uncertain. Future research is required to optimize existing DCE-MR imaging methods and to fully validate these biomarkers for wider use in patient care and in drug development.

Immunotherapy has potential to improve outcome for cancer patients. The MRI biomarker apparent diffusion co-efficient (ADC) can map response to radiotherapy (RT) through its sensitivity to changes in tumor fluid and cellularity. The role of ADC in evaluating immunotherapy agents is unknown, despite investigators using ADC change as an exploratory endpoint in early phase clinical trials. Our study sought to evaluate and validate ADC changes induced by both RT and immunotherapy agents in syngeneic mouse tumor models. Three experiments were performed in BALB/c mice bearing CT26 colorectal cancers: (1) single 10Gy fraction RT versus control (sham); (2) TLR 7/8 agonist R848 versus control (saline); (3) anti-PD-L1 antibody versus control (saline). We acquired MRI data at 7T Bruker system at days 0, 3, 7, +/- 10 after therapy start, with tumors measuring between 250-300 mm3 at day 0. Median ADC and the inter-quartile range (IQR; a measure of tumor heterogeneity) were derived. Three further equivalent experiments were performed in BALB/c mice bearing triple negative 4T1 breast tumors. All CT26 and 4T1 tumors were bisected at cull for immunohistochemistry and FACS analysis. For CT26 model, RT-induced tumor growth inhibition (p In conclusion, our data are the first to evaluate ADC changes induced by RT and immunotherapy agents in syngeneic mouse models of cancer. RT, R848 and anti-PD-L1 induced different ADC responses, each with varied relationships to immune cells. This highlights the need for extensive validation before diffusion weighted MRI biomarkers can be used in clinical trials to monitor response to immunotherapies alone or in combination with RT. Citation Format: Grazyna Lipowska-Bhalla, Damien J. McHugh, Muhammad Babur, Isabel Peset Martin, Michael Berks, Ross A. Little, Susan Cheung, Yvonne Watson, Denis G. Alferez, Kaye J. Williams, Jamie Honeychurch, James P. O'Connor. Diffusion weighted MRI evaluation of response to immunotherapy and radiotherapy in CT26 and 4T1 syngeneic mouse models of cancer [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 2781.

Magnetic resonance imaging (MRI) is a highly versatile imaging modality that can be used to measure features of the tumour microenvironment including cell death, proliferation, metabolism, angiogenesis, and hypoxia. Mapping and quantifying these pathophysiological features has the potential to alter the use of adaptive radiotherapy planning. Although these methods are available for use on diagnostic machines, several challenges exist for implementing these functional MRI methods on the MRI-linear accelerators (linacs). This review considers these challenges and potential solutions.

Clinical workflows for the non-invasive detection and characterization of disease states could benefit from optical-imaging biomarkers. In this Perspective, we discuss opportunities and challenges towards the clinical implementation of optical-imaging biomarkers for the early detection of cancer by analysing two case studies: the assessment of skin lesions in primary care, and the surveillance of patients with Barrett’s oesophagus in specialist care. We stress the importance of technical and biological validations and clinical-utility assessments, and the need to address implementation bottlenecks. In addition, we define a translational roadmap for the widespread clinical implementation of optical imaging-technologies.

Purpose: To evaluate between-site agreement of apparent diffusion coefficient (ADC) measurements in preclinical magnetic resonance imaging (MRI) systems. Materials and Methods: A miniaturized thermally stable ice-water phantom was devised. ADC (mean and interquartile range) was measured over several days, on 4.7T, 7T, and 9.4T Bruker, Agilent, and Magnex small-animal MRI systems using a common protocol across seven sites. Day-to-day repeatability was expressed as percent variation of mean ADC between acquisitions. Cross-site reproducibility was expressed as 1.96 x standard deviation of percent deviation of ADC values. Results: ADC measurements were equivalent across all seven sites with a cross-site ADC reproducibility of 6.3%. Mean day-to-day repeatability of ADC measurements was 2.3%, and no site was identified as presenting different measurements than others (analysis of variance [ANOVA] P = 0.02, post-hoc test n.s.). Between-slice ADC variability was negligible and similar between sites (P = 0.15). Mean within-region-of-interest ADC variability was 5.5%, with one site presenting a significantly greater variation than the others (P = 0.0013). Conclusion: Absolute ADC values in preclinical studies are comparable between sites and equipment, provided standardized protocols are employed.

OBJECTIVE:Application of whole body diffusion-weighted MRI (WB-DWI) for oncology are rapidly increasing within both research and routine clinical domains. However, WB-DWI as a quantitative imaging biomarker (QIB) has significantly slower adoption. To date, challenges relating to accuracy and reproducibility, essential criteria for a good QIB, have limited widespread clinical translation. In recognition, a UK workgroup was established in 2016 to provide technical consensus guidelines (to maximise accuracy and reproducibility of WB-MRI QIBs) and accelerate the clinical translation of quantitative WB-DWI applications for oncology.METHODS:A panel of experts convened from cancer centres around the UK with subspecialty expertise in quantitative imaging and/or the use of WB-MRI with DWI. A formal consensus method was used to obtain consensus agreement regarding best practice. Questions were asked about the appropriateness or otherwise on scanner hardware and software, sequence optimisation, acquisition protocols, reporting, and ongoing quality control programs to monitor precision and accuracy and agreement on quality control.RESULTS:The consensus panel was able to reach consensus on 73% (255/351) items and based on consensus areas made recommendations to maximise accuracy and reproducibly of quantitative WB-DWI studies performed at 1.5T. The panel were unable to reach consensus on the majority of items related to quantitative WB-DWI performed at 3T.CONCLUSION:This UK Quantitative WB-DWI Technical Workgroup consensus provides guidance on maximising accuracy and reproducibly of quantitative WB-DWI for oncology. The consensus guidance can be used by researchers and clinicians to harmonise WB-DWI protocols which will accelerate clinical translation of WB-DWI-derived QIBs.

Purpose Most dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) data are evaluated for individual patients with cohorts analyzed to detect significant changes from baseline values, repeating the process at each posttreatment timepoint. Our study aimed to develop a statistically valid model for the complete time course of DCE-MRI data in a patient cohort. Materials and Methods Data from 10 patients with colorectal cancer liver metastases were analyzed, including two baseline scans and four post-bevacizumab scans. Apparent changes in tumor median Ktrans were adjusted for changes in observed enhancing tumor fraction (EnF) by multiplying Ktrans by EnF (KEnF). A mixed-effects model (MEM) was defined to describe the KEnF time course for all patients simultaneously by assuming a three-parameter indirect response model with model parameters lognormally distributed across patients. Results The typical cohort time course showed a KEnF reduction to 59% of baseline at 24 hours, returning to 65% of baseline values by day 12. Interpatient variability of model parameters ranged from 11% to 307%. Conclusion The MEM approach has potential for comparing responses at a group level in clinical trials with different doses, schedules, or combination regimens. Furthermore, the KEnF biomarker successfully resolved confounds in interpreting Ktrans arising from therapy induced changes in the volume of enhancing tumor. J. Magn. Reson. Imaging 2015;41:132–141. © 2014 Wiley Periodicals, Inc.

Hypoxia was identified as a microenvironmental component of solid tumours over 60 years ago and was immediately recognised as a potential barrier to therapy through the reliance of radiotherapy on oxygen to elicit maximal cytotoxicity. Over the last two decades both clinical and experimental studies have markedly enhanced our understanding of how hypoxia influences cellular behaviour and therapy response. Furthermore, they have confirmed early assumptions that low oxygenation status in tumours is an exploitable target in cancer therapy. Generally such approaches will be more beneficial to patients with hypoxic tumours, necessitating the use of biomarkers that reflect oxygenation status. Tissue biomarkers have shown utility in many studies. Further significant advances have been made in the non-invasive measurement of tumour hypoxia with positron emission tomography, magnetic resonance imaging and other imaging modalities. Here, we describe the complexities of defining and measuring tumour hypoxia and highlight the therapeutic approaches to combat it.

The management of solid tumors has been transformed by the advent of VEGF pathway inhibitors. Early clinical evaluation of these drugs has used pharmacodynamic biomarkers derived from advanced imaging such as dynamic MRI, computed tomography (CT), and ultrasound to establish proof of principle. We have reviewed published studies that used these imaging techniques to determine whether the same biomarkers relate to survival in renal, hepatocellular, and brain tumors in patients treated with VEGF inhibitors. Data show that in renal cancer, pretreatment measurements of Ktrans and early pharmacodynamic reduction in tumor enhancement and density have prognostic significance in patients treated with VEGF inhibitors. A weaker, but significant, relationship is seen with subtle early size change (10% in one dimension) and survival. Data from high-grade glioma suggest that pretreatment fractional blood volume and Ktrans were prognostic of overall survival. However, lack of control data with other therapies prevents assessment of the predictive nature of these biomarkers, and such studies are urgently required. Clin Cancer Res; 18(24); 6588–98. ©2012 AACR.

Imaging biomarkers (IBs) are integral to the routine management of patients with cancer. IBs used daily in oncology include clinical TNM stage, objective response and left ventricular ejection fraction. Other CT, MRI, PET and ultrasonography biomarkers are used extensively in cancer research and drug development. New IBs need to be established either as useful tools for testing research hypotheses in clinical trials and research studies, or as clinical decision-making tools for use in healthcare, by crossing 'translational gaps' through validation and qualification. Important differences exist between IBs and biospecimen-derived biomarkers and, therefore, the development of IBs requires a tailored 'roadmap'. Recognizing this need, Cancer Research UK (CRUK) and the European Organisation for Research and Treatment of Cancer (EORTC) assembled experts to review, debate and summarize the challenges of IB validation and qualification. This consensus group has produced 14 key recommendations for accelerating the clinical translation of IBs, which highlight the role of parallel (rather than sequential) tracks of technical (assay) validation, biological/clinical validation and assessment of cost-effectiveness; the need for IB standardization and accreditation systems; the need to continually revisit IB precision; an alternative framework for biological/clinical validation of IBs; and the essential requirements for multicentre studies to qualify IBs for clinical use.

About 100 early-phase clinical trials and investigator-led studies of targeted antivascular therapies--both anti-angiogenic and vascular-targeting agents--have reported data derived from T1-weighted dynamic contrast-enhanced (DCE)-MRI. However, the role of DCE-MRI for decision making during the drug-development process remains controversial. Despite well-documented guidelines on image acquisition and analysis, several key questions concerning the role of this technique in early-phase trial design remain unanswered. This Review describes studies of single-agent antivascular therapies, in which DCE-MRI parameters are incorporated as pharmacodynamic biomarkers. We discuss whether these parameters, such as volume transfer constant (K(trans)), are reproducible and reliable biomarkers of both drug efficacy and proof of concept, and whether they assist in dose selection and drug scheduling for subsequent phase II trials. Emerging evidence indicates that multiparametric analysis of DCE-MRI data offers greater insight into the mechanism of drug action than studies measuring a single parameter, such as K(trans). We also provide an overview of current data and appraise the future directions of this technique in oncology trials. Finally, major hurdles in imaging biomarker development, validation and qualification that hinder a wide application of DCE-MRI techniques in clinical trials are addressed.

Over the last few decades there has been considerable research into quantifying the cerebral microvasculature with imaging, for use in studies of the human brain and various pathologies including cerebral tumours. This review highlights key issues in dynamic contrast-enhanced CT, dynamic contrast-enhanced MRI and arterial spin labelling, the various applications of which are considered elsewhere in this special issue of the British Journal of Radiology.

Purpose: Treatment efficacy and toxicity are difficult to predict in lymphoma patients. In this study, the utility of circulating biomarkers in predicting and/or monitoring treatment efficacy/toxicity were investigated. Patients and methods Circulating biomarkers of cell death (nucleosomal DNA (nDNA) and cytokeratin 18 (CK18)), and circulating FLT3 ligand, a potential biomarker of myelosuppression, were assessed before and serially after standard chemotherapy in 49 patients with Hodgkin and non-Hodgkin lymphoma. Cytokeratin 18 is not expressed in lymphoma cells so is a potential biomarker of epithelial toxicity in this setting. Tumour response was assessed before and after completion of chemotherapy by 2D and 3D computed tomography radiological response. Results: Baseline nDNA level was significantly higher in all lymphoma subtypes compared with 61 healthy controls and was prognostic for progression-free survival in diffuse large B-cell lymphoma (DLBCL). Decreases in nDNA levels were observed in the first week after chemotherapy; in FL, early falls in nDNA predicted for long remission following therapy. In DLBCL, elevations in nDNA occurred in cases with progressive disease. Circulating CK18 increased within 48 h of chemotherapy and was significantly higher in patients experiencing epithelial toxicity graded >3 by Common Terminology for Classification of Adverse Events criteria. FLT3 ligand was elevated within 3–8 days of chemotherapy initiation and predicted those patients who subsequently developed neutropenic sepsis. Conclusion: These data suggest circulating biomarkers contribute useful information regarding tumour response and toxicity in patients receiving standard chemotherapy and have potential utility in the development of individualised treatment approaches in lymphoma. These biomarkers are now being tested within multicentre phase III trials to progress their qualification.

Dynamic contrast-enhanced MRI is becoming a standard tool for imaging-based trials of anti-vascular/angiogenic agents in cancer. So far, however, biomarkers derived from DCE-MRI parameter maps have largely neglected the fact that the maps have spatial structure and instead focussed on distributional summary statistics. Such statistics - e.g., biomarkers based on median values - neglect the spatial arrangement of parameters, which may carry important diagnostic and prognostic information. This article describes two types of heterogeneity biomarker that are sensitive to both parameter values and their spatial arrangement. Methods based on Renyi fractal dimensions and geometrical properties are developed, both of which attempt to describe the complexity of DCE-MRI parameter maps. Experiments using simulated data show that the proposed biomarkers are sensitive to changes that distribution-based summary statistics cannot detect and demonstrate that heterogeneity biomarkers could be applied in the drug trial setting. An experiment using 23 DCE-MRI parameter maps of gliomas - a class of tumour that is graded on the basis of heterogeneity - shows that the proposed heterogeneity biomarkers are able to differentiate between low- and high-grade tumours. Magn Reson Med, 2009. © 2009 Wiley-Liss, Inc.

Recent advances in magnetic resonance imaging (MRI) have seen the development of techniques that allow quantitative imaging of a number of anatomical and physiological descriptors. These techniques have been increasingly applied to cancer imaging where they can provide some insight into tumour microvascular structure and physiology. This review details technical approaches and application of quantitative MRI, focusing particularly on perfusion imaging and its role in neuro-oncology.

Interest in new blood vessel formation in tumours (angiogenesis) has led to the development of imaging strategies for investigating the microvascular structure and function of tumours. One such technique is dynamic contrast-enhanced MRI (DCE-MRI), where gadolinium based contrast agents are injected intravenously and serial image acquisitions are performed as the contrast agent passes through the tumour vascular bed. Sophisticated analyses can then be applied in order to produce indirect measures of parameters that represent blood flow, vascular volume, capillary permeability and surface area. The technique has been used to characterise malignant disease and to evaluate the effect of therapies that target tumour blood vessels. This article reviews the important image acquisition and data analysis principles behind DCE-MRI and highlights its use in clinical and research medicine to date.

Purpose: To define a simple radiologic biomarker of prognosis in patients with advanced epithelial ovarian carcinoma on first-line chemotherapy. Experimental Design: Twenty-seven patients receiving platinum-based chemotherapy with >2 cm residual disease [International Federation of Gynecology and Obstetrics (FIGO) stages IIIC or IV] after surgery were identified. The proportion of enhancing tumor tissue—the enhancing fraction—was calculated on pre-chemotherapy computed tomography scans at four Hounsfield unit (HU) thresholds and assessed for correlation with CA125 response, Response Evaluation Criteria in Solid Tumors (RECIST) radiologic response, and time to progression. Discriminative power was assessed by leave-one-out discriminant analysis. Results: Pre-chemotherapy residual tumor volume did not correlate with clinical outcome. Pre-chemotherapy enhancing fraction at all thresholds significantly correlated with CA125 response (P < 0.001, ρ = 0.553 for 50 HU; P < 0.001, ρ = 0.565 for 60 HU; P < 0.001, ρ = 0.553 for 70 HU; P = 0.001, ρ = 0.516 for 80 HU). Significant correlations were also shown for radiologic response at all thresholds. Enhancing fraction predicted CA125 response with 81.9% to 86.4% specificity and Response Evaluation Criteria in Solid Tumors response with 74.9% to 76.8% specificity at 95% sensitivity (dependent on threshold). Enhancing fraction correlated with time to progression at the 60 HU (P = 0.045, ρ = 0.336) and 70 HU (P = 0.042; ρ = 0.340) thresholds. Conclusion: Pre-chemotherapy enhancing fraction is a simple quantitative radiologic measure. Further evaluation in larger trials is required to confirm the potential of enhancing fraction as a predictive factor, particularly for patients who may benefit from the addition of antiangiogenic therapy.

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is now frequently used in early clinical trial assessment of antiangiogenic and vascular disrupting compounds. Evidence of drug efficacy and dose-dependent response has been demonstrated with some angiogenesis inhibitors. This review highlights the critical issues that influence T(1)-weighted DCE-MRI data acquisition and analysis, identifies important areas for future development and reviews the clinical trial findings to date.

Rationale and Objectives The quantitative analysis of dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) data is subject to model fitting errors caused by motion during the time-series data acquisition. However, the time-varying features that occur as a result of contrast enhancement can confound motion correction techniques based on conventional registration similarity measures. We have therefore developed a heuristic, locally controlled tracer kinetic model-driven registration procedure, in which the model accounts for contrast enhancement, and applied it to the registration of abdominal DCE-MRI data at high temporal resolution. Materials and Methods Using severely motion-corrupted data sets that had been excluded from analysis in a clinical trial of an antiangiogenic agent, we compared the results obtained when using different models to drive the tracer kinetic model-driven registration with those obtained when using a conventional registration against the time series mean image volume. Results Using tracer kinetic model-driven registration, it was possible to improve model fitting by reducing the sum of squared errors but the improvement was only realized when using a model that adequately described the features of the time series data. The registration against the time series mean significantly distorted the time series data, as did tracer kinetic model-driven registration using a simpler model of contrast enhancement. Conclusion When an appropriate model is used, tracer kinetic model-driven registration influences motion-corrupted model fit parameter estimates and provides significant improvements in localization in three-dimensional parameter maps. This has positive implications for the use of quantitative DCE-MRI for example in clinical trials of antiangiogenic or antivascular agents.

Renal-cell carcinoma (RCC) during pregnancy is rare. Laparoscopic nephrectomy has been used effectively and safely in nonpregnant patients with RCC. We report a case of a 34-year-old pregnant woman with RCC, which we believe to be the first such case to be managed by laparoscopic nephrectomy.

Objectives: To assess the potential impact of new health guidelines on prostate cancer services within one NHS Trust and consider alternative follow-up of stable patients with advanced disease. Patients and methods: Patients diagnosed within a one-year period were identified. Incidence and number treated by each modality were found. Retrospective cost analysis for those receiving hormonal therapy was assessed. Current performance was assessed against the 2005 prostate Cancer Plan targets with a projected estimate of resources necessary to achieve the targets. Results: 152 patients were newly diagnosed with prostate cancer where 54.5% received hormonal treatment, utilising 8 full consultant clinics per year and costing £400,000 and 1.5 nurse specialist sessions were needed per week. Only 35% of patients received their first treatment within two months. Estimates suggest than an additional 30 annual consultant clinics are required to meet national targets and deliver a quality service. Conclusion: Substanti...

Nature Reviews Clinical Oncology 14, 452 (2017) In the HTML and PDF versions of this Correspondence, the authors declared no competing interests, but an involvement of John C. Waterton with Alderley Imaging Ltd and Bioxydyn Ltd was indicated in the author biographies section. This information has now also been incorporated into the competing interests statement in the HTML and PDF versions of the Correspondence.

11521 Background: VEGF inhibitor (VEGFi) use is compromised by lack of predictive/ response biomarkers. Previously, we identified plasma Tie2 (pTie2) as a vascular response biomarker (VRB) for bevacizumab (bev) in ovarian cancer (OC). Here, we applied dynamic contrast-enhanced MRI (DCE-MRI) and circulating biomarkers in colorectal cancer (CRC), to validate pTie2 as the first tumor VRB. Methods: Seventy patients were recruited, with untreated, mCRC and ≥1 lesion of 3-10cm diameter for DCE-MRI. Patients received bev 10mg/kg for 2 weeks to elicit a biomarker response and then FOLFOX6/bev until progressive disease (PD) Thirteen circulating and 6 imaging biomarkers were measured before and during treatment and at PD. Unsupervised correlation analysis identified bev-induced biomarker correlations. Biomarkers were evaluated by clustered parameter-time course studies to determine their epithelial or vascular origin. Clinical significance was determined by relating the biomarker data to tumor 3D volumetric change assessed by MRI and PFS. The emergent vascular biomarker signal was modelled with epithelial biomarkers to assess the independent contribution of the vascular compartment to PD. Results: Bev induced significant correlations between pTie2, Ang2 and Ktrans. Cluster analysis of Tie2 concentration-time course curves showed that pTie2 reflected tumor Ktransbut not CK18, an epithelial antigen, i.e. changes in pTie2 reflected tumor vascular biology Patients who had the greatest area under the pTie2-time curve had tumors with high Ktransand/or low pVEGFR2, pre-treatment. They also had the greatest reduction in tumor volume and longest PFS. Fusion of pTie2 and CK18 data significantly improved modelling of PD. Conclusions: Bev impacts tumor vasculature causing proportional changes in pTie2. Information from pTie2 adds clinical value to that derived from the epithelial compartment. Thus (i) pTie2 is the first vascular response biomarker for bev and probably all VEGFi and (ii) demonstration of separate vascular and epithelial compartments in ovarian and CRC validates the vascular compartment as a target. This work identifies the first assay that could optimise use of VEGFi. Clinical trial information: 2009-011377-33.