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Purpose Checkpoint inhibition therapy using monoclonal antibodies against programmed cell death protein 1 (PD-1) or its ligand (PD-L1) is now standard management of non-small cell lung cancer (NSCLC). PD-L1 expression is a validated and approved prognostic and predictive biomarker for anti-PD-1/PD-L1 therapy. Technetium-99 m [99mTc]-labelled anti-PD-L1 single-domain antibody (NM-01) SPECT/CT quantification correlates with PD-L1 expression in NSCLC, presenting an opportunity for non-invasive assessment. The aim of this study was to determine the inter- and intraobserver agreement of the quantitative assessment of [99mTc]NM-01 SPECT/CT in NSCLC. Methods [99mTc]NM-01 SPECT/CT studies of 21 consecutive NSCLC participants imaged for the evaluation of PD-L1 expression were analysed. Three independent observers measured maximum counts in a tumour region of interest (ROImax) of primary lung, metastatic lesions and normal tissue references of both 1 and 2 h post-injection (n = 42) anonymised studies using a manual technique. Intraclass correlation coefficients (ICC) were calculated, and Bland–Altman plot analysis was performed to determine inter- and intraobserver agreement. Results Intraclass correlation of primary lung tumour-to-blood pool (T:BP; ICC 0.83, 95% CI 0.73–0.90) and lymph node metastasis-to-blood pool (LN:BP; ICC 0.87, 0.81–0.92) measures of [99mTc]NM-01 uptake was good to excellent between observers. Freehand ROImax of T (ICC 0.94), LN (ICC 0.97), liver (ICC 0.97) and BP (ICC 0.90) reference tissues also demonstrated excellent interobserver agreement. ROImax scoring of healthy lung demonstrated moderate to excellent interobserver agreement (ICC 0.84) and improved when measured consistently at the level of the aortic arch (ICC 0.89). Manual ROImax re-scoring of T, LN, T:BP and LN:BP using [99mTc]NM-01 SPECT/CT following a 42-day interval was consistent with excellent intraobserver agreement (ICC range 0.95–0.97). Conclusion Good to excellent inter- and intraobserver agreement of the quantitative assessment of [99mTc]NM-01 SPECT/CT in NSCLC was demonstrated in this study, including T:BP which has been shown to correlate with PD-L1 status. [99mTc]NM-01 SPECT/CT has the potential to reliably and non-invasively assess PD-L1 expression. Clinical trial registration ClinicalTrials.gov identifier no. NCT02978196. Registered 30th November 2016.

Understanding the internal physiological changes accompanying the aging process is an important aspect of medical image interpretation, with the expected changes acting as a baseline when reporting abnormal findings. Deep learning has recently been demonstrated to allow the accurate estimation of patient age from chest X-rays, and shows potential as a health indicator and mortality predictor. In this paper we present a novel comparative study of the relative performance of radiologists versus state-of-the-art deep learning models on two tasks: (a) patient age estimation from a single chest X-ray, and (b) ranking of two time-separated images of the same patient by age. We train our models with a heterogeneous database of 1.8M chest X-rays with ground truth patient ages and investigate the limitations on model accuracy imposed by limited training data and image resolution, and demonstrate generalisation performance on public data. To explore the large performance gap between the models and humans on these age-prediction tasks compared with other radiological reporting tasks seen in the literature, we incorporate our age prediction model into a conditional Generative Adversarial Network (cGAN) allowing visualisation of the semantic features identified by the prediction model as significant to age prediction, comparing the identified features with those relied on by clinicians., Comment: 13 pages, 8 figures, MICCAI 2022

Background Whole-body magnetic resonance imaging is advocated as an alternative to standard pathways for staging cancer. Objectives The objectives were to compare diagnostic accuracy, efficiency, patient acceptability, observer variability and cost-effectiveness of whole-body magnetic resonance imaging and standard pathways in staging newly diagnosed non-small-cell lung cancer (Streamline L) and colorectal cancer (Streamline C). Design The design was a prospective multicentre cohort study. Setting The setting was 16 NHS hospitals. Participants Consecutive patients aged ≥ 18 years with histologically proven or suspected colorectal (Streamline C) or non-small-cell lung cancer (Streamline L). Interventions Whole-body magnetic resonance imaging. Standard staging investigations (e.g. computed tomography and positron emission tomography–computed tomography). Reference standard Consensus panel decision using 12-month follow-up data. Main outcome measures The primary outcome was per-patient sensitivity difference between whole-body magnetic resonance imaging and standard staging pathways for metastasis. Secondary outcomes included differences in specificity, the nature of the first major treatment decision, time and number of tests to complete staging, patient experience and cost-effectiveness. Results Streamline C – 299 participants were included. Per-patient sensitivity for metastatic disease was 67% (95% confidence interval 56% to 78%) and 63% (95% confidence interval 51% to 74%) for whole-body magnetic resonance imaging and standard pathways, respectively, a difference in sensitivity of 4% (95% confidence interval –5% to 13%; p = 0.51). Specificity was 95% (95% confidence interval 92% to 97%) and 93% (95% confidence interval 90% to 96%) respectively, a difference of 2% (95% confidence interval –2% to 6%). Pathway treatment decisions agreed with the multidisciplinary team treatment decision in 96% and 95% of cases, respectively, a difference of 1% (95% confidence interval –2% to 4%). Time for staging was 8 days (95% confidence interval 6 to 9 days) and 13 days (95% confidence interval 11 to 15 days) for whole-body magnetic resonance imaging and standard pathways, respectively, a difference of 5 days (95% confidence interval 3 to 7 days). The whole-body magnetic resonance imaging pathway was cheaper than the standard staging pathway: £216 (95% confidence interval £211 to £221) versus £285 (95% confidence interval £260 to £310). Streamline L – 187 participants were included. Per-patient sensitivity for metastatic disease was 50% (95% confidence interval 37% to 63%) and 54% (95% confidence interval 41% to 67%) for whole-body magnetic resonance imaging and standard pathways, respectively, a difference in sensitivity of 4% (95% confidence interval –7% to 15%; p = 0.73). Specificity was 93% (95% confidence interval 88% to 96%) and 95% (95% confidence interval 91% to 98%), respectively, a difference of 2% (95% confidence interval –2% to 7%). Pathway treatment decisions agreed with the multidisciplinary team treatment decision in 98% and 99% of cases, respectively, a difference of 1% (95% confidence interval –2% to 4%). Time for staging was 13 days (95% confidence interval 12 to 14 days) and 19 days (95% confidence interval 17 to 21 days) for whole-body magnetic resonance imaging and standard pathways, respectively, a difference of 6 days (95% confidence interval 4 to 8 days). The whole-body magnetic resonance imaging pathway was cheaper than the standard staging pathway: £317 (95% confidence interval £273 to £361) versus £620 (95% confidence interval £574 to £666). Participants generally found whole-body magnetic resonance imaging more burdensome than standard imaging but most participants preferred the whole-body magnetic resonance imaging staging pathway if it reduced time to staging and/or number of tests. Limitations Whole-body magnetic resonance imaging was interpreted by practitioners blinded to other clinical data, which may not fully reflect how it is used in clinical practice. Conclusions In colorectal and non-small-cell lung cancer, the whole-body magnetic resonance imaging staging pathway has similar accuracy to standard staging pathways, is generally preferred by patients, improves staging efficiency and has lower staging costs. Future work should address the utility of whole-body magnetic resonance imaging for treatment response assessment. Trial registration Current Controlled Trials ISRCTN43958015 and ISRCTN50436483. Funding This project was funded by the NIHR Health Technology Assessment programme and will be published in full in Health Technology Assessment; Vol. 23, No. 66. See the NIHR Journals Library website for further project information.

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 18F-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 5×106 LS174T cells (human colorectal carcinoma) were either untreated controls (n=7) or bevacizumab treated (n=7). After 2 weeks, mice underwent 18F-FDG PET/CT. Calliper-measured tumor growth (Δvol) and final tumor volume (Volcal), 18F-FDG PET metabolically active volume (Volmet), mean metabolism (Metmean), and maximum metabolism (Metmax) 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: Δvol (P=0.9), Volcal (P=0.7), Volmet (P=0.28), Metmax (P=0.7), or Metmean (P=0.32). One texture parameter, GLSZM-SZV (visually indicating that the 18F-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 18F-FDG PET derived texture parameters, particularly GLSZM-SZV, may be valid biomarkers of tumor response to treatment with bevacizumab, before change in volume.

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Additional file 1. Appendix 1. WBMRI Imaging Protocol ��� PET/MRI Oesophagus.

Aim To determine if a positive circumferential resection margin (CRM) can be predicted in patients with esophageal adenocarcinoma undergoing neo-adjuvant chemotherapy prior to surgery Methods 223 patients were included in this study. Multivariable analyses of clinico-pathological and computed tomography (CT) imaging predictors were performed to evaluate the likelihood of a positive CRM at initial staging and following neoadjuvant chemotherapy, with prediction models constructed. Area under curve (AUC) with 95% confidence interval (CI) from 1000 bootstrapping was assessed. Results Advanced clinical T-stage (T3-4) (odds ratio, OR 2.93 95%CI 1.03-9.48) and poor tumor differentiation (OR 2.84, 95%CI 1.39-6.01) were independently associated with an increased risk of a positive CRM. Non response to chemotherapy as estimated by CT independently corresponded with an increased risk of CRM positivity (OR 3.38 95% CI 1.43-8.50). Additional CT evidence of local invasion/pleural thickening and higher CT tumor volume (14cm3) improved the performance of a prediction model, including all above parameters; with AUC (c-index) of 0.76 (0.68-0.83). Conclusion Combining advanced clinical T-stage, poor tumor differentiation, CT non-response to chemotherapy, higher CT tumor volume and local invasion may be helpful in selecting patients for intensification of neo-adjuvant treatment prior to resection.

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 18F-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.

We evaluated magnetic resonance imaging (MRI) voxel heterogeneity following trastuzumab and/or cisplatin in a HER2+ esophageal xenograft (OE19) as a potential response biomarker. OE19 xenografts treated with saline (controls), monotherapy, or combined cisplatin and trastuzumab underwent 9.4-T MRI. Tumor MRI parametric maps of T1 relaxation time (pre/post contrast), T2 relaxation time, T2* relaxation rate (R2*), and apparent diffusion coefficient obtained before (TIME0), after 24 hours (TIME1), and after 2 weeks of treatment (TIME2) were analyzed. Voxel histogram and fractal parameters (from the whole tumor, rim and center, and as a ratio of rim‐to‐center) were derived. Tumors were stained for immunohistochemical markers of hypoxia (CA-IX), angiogenesis (CD34), and proliferation (Ki-67). Combination therapy reduced xenograft growth rate (relative change, ∆ +0.58 ± 0.43 versus controls, ∆ +4.1 ± 1.0; P = 0.008). More spatially homogeneous voxel distribution between the rim to center was noted after treatment for combination therapy versus controls, respectively, for contrast-enhanced T1 relaxation time (90th percentile: ratio 1.00 versus 0.88, P = 0.009), T2 relaxation time (mean: 1.00 versus 0.92, P = 0.006; median: 0.98 versus 0.91, P = 0.006; 75th percentile: 1.02 versus 0.94, P = 0.007), and R2* (10th percentile: 0.99 versus 1.26, P = 0.003). We found that combination and trastuzumab monotherapy reduced MRI spatial heterogeneity and growth rate compared to the control or cisplatin groups, the former providing adjunctive tumor response information.

Background: Texture features are being increasingly evaluated in 18F-fluorodeoxyglucose positron emission tomography (18F-FDG PET) as adjunctive imaging biomarkers in a number of different cancers. Whilst studies have reported repeatability between scans, there have been no studies that have specifically investigated the effect that the time of acquisition post-injection of 18F-FDG has on texture features. The aim of this study was to investigate if texture features change between scans performed at different time points post-injection. Results: Fifty-four patients (30 male, 24 female, mean age 35.1 years) with neurofibromatosis-1 and suspected malignant transformation of a neurofibroma underwent 18F-FDG PET/computed tomography (CT) scans at 101.5 ± 15.0 and 251.7 ± 18.4 min post-injection of 350 MBq 18F-FDG to a standard clinical protocol. Following tumour segmentation on both early and late scans, first- (n = 37), second- (n = 25) and high-order (n = 31) statistical features, as well as fractal texture features (n = 6), were calculated and a comparison was made between the early and late scans for each feature. Of the 54 tumours, 30 were benign and 24 malignant on histological analysis or on clinical follow-up for at least 5 years. Overall, 25/37 first-order, 9/25 second-order, 13/31 high-order and 3/6 fractal features changed significantly (p < 0.05) between early and late scans. The corresponding proportions for the 30 benign tumours alone were 22/37, 7/25, 8/31 and 2/6 and for the 24 malignant tumours, 11/37, 6/25, 8/31 and 0/6, respectively. Conclusions: Several texture features change with time post-injection of 18F-FDG. Thus, when comparing texture features in intra- and inter-patient studies, it is essential that scans are obtained at a consistent time post-injection of 18F-FDG.

OBJECTIVES: To determine the importance placed by patients on attributes associated with whole-body MRI (WB-MRI) and standard cancer staging pathways and ascertain drivers of preference. METHODS: Patients recruited to two multi-centre diagnostic accuracy trials comparing WB-MRI with standard staging pathways in lung and colorectal cancer were invited to complete a discrete choice experiment (DCE), choosing between a series of alternate pathways in which 6 attributes (accuracy, time to diagnosis, scan duration, whole-body enclosure, radiation exposure, total scan number) were varied systematically. Data were analysed using a conditional logit regression model and marginal rates of substitution computed. The relative importance of each attribute and probabilities of choosing WB-MRI-based pathways were estimated. RESULTS: A total of 138 patients (mean age 65, 61% male, lung n = 72, colorectal n = 66) participated (May 2015 to September 2016). Lung cancer patients valued time to diagnosis most highly, followed by accuracy, radiation exposure, number of scans, and time in the scanner. Colorectal cancer patients valued accuracy most highly, followed by time to diagnosis, radiation exposure, and number of scans. Patients were willing to wait 0.29 (lung) and 0.45 (colorectal) weeks for a 1% increase in pathway accuracy. Patients preferred WB-MRI-based pathways (probability 0.64 [lung], 0.66 [colorectal]) if they were equivalent in accuracy, total scan number, and time to diagnosis compared with a standard staging pathway. CONCLUSIONS: Staging pathways based on first-line WB-MRI are preferred by the majority of patients if they at least match standard pathways for diagnostic accuracy, time to diagnosis, and total scan number. KEY POINTS: • WB-MRI staging pathways are preferred to standard pathways by the majority of patients provided they at least match standard staging pathways for accuracy, total scan number, and time to diagnosis. • For patients with lung cancer, time to diagnosis was the attribute valued most highly, followed by accuracy, radiation dose, number of additional scans, and time in a scanner. Preference for patients with colorectal cancer was similar. • Most (63%) patients were willing to trade attributes, such as faster diagnosis, for improvements in pathway accuracy and reduced radiation exposure.

Objectives To determine the importance placed by patients on attributes associated with whole-body MRI (WB-MRI) and standard cancer staging pathways and ascertain drivers of preference. Methods Patients recruited to two multi-centre diagnostic accuracy trials comparing WB-MRI with standard staging pathways in lung and colorectal cancer were invited to complete a discrete choice experiment (DCE), choosing between a series of alternate pathways in which 6 attributes (accuracy, time to diagnosis, scan duration, whole-body enclosure, radiation exposure, total scan number) were varied systematically. Data were analysed using a conditional logit regression model and marginal rates of substitution computed. The relative importance of each attribute and probabilities of choosing WB-MRI-based pathways were estimated. Results A total of 138 patients (mean age 65, 61% male, lung n = 72, colorectal n = 66) participated (May 2015 to September 2016). Lung cancer patients valued time to diagnosis most highly, followed by accuracy, radiation exposure, number of scans, and time in the scanner. Colorectal cancer patients valued accuracy most highly, followed by time to diagnosis, radiation exposure, and number of scans. Patients were willing to wait 0.29 (lung) and 0.45 (colorectal) weeks for a 1% increase in pathway accuracy. Patients preferred WB-MRI-based pathways (probability 0.64 [lung], 0.66 [colorectal]) if they were equivalent in accuracy, total scan number, and time to diagnosis compared with a standard staging pathway. Conclusions Staging pathways based on first-line WB-MRI are preferred by the majority of patients if they at least match standard pathways for diagnostic accuracy, time to diagnosis, and total scan number. Key Points • WB-MRI staging pathways are preferred to standard pathways by the majority of patients provided they at least match standard staging pathways for accuracy, total scan number, and time to diagnosis. • For patients with lung cancer, time to diagnosis was the attribute valued most highly, followed by accuracy, radiation dose, number of additional scans, and time in a scanner. Preference for patients with colorectal cancer was similar. • Most (63%) patients were willing to trade attributes, such as faster diagnosis, for improvements in pathway accuracy and reduced radiation exposure. Electronic supplementary material The online version of this article (10.1007/s00330-019-06153-4) contains supplementary material, which is available to authorized users.

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.

PurposeTreatment of metastatic colorectal cancer frequently includes anti-angiogenic agents such as bevacizumab. Size measurements are inadequate to assessment treatment response to these agents, and newer response assessment criteria are needed. We aimed to evaluate 18-fluoro-2-deoxyglucose positron emission tomography (18F-FDG PET)-derived texture parameters in a preclinical colorectal cancer model as alternative metrics of response to treatment with bevacizumab. Methods Fourteen CD1 athymic mice injected in the flank with 5 x 106 LS174T cells (human colorectal carcinoma) were either untreated controls (n=7) or bevacizumab treated (n=7). After 2 weeks, mice underwent 18FDG-PET/CT. Calliper-measured tumour growth (Δvol) and final tumour volume (Volcal), 18FDG-PET metabolically active volume (Volmet), mean metabolism (Metmean), and maximum metabolism (Metmax) were measured. Twenty-four texture features were compared between treated and untreated mice. Immunohistochemical mean tumour vascular density (MVD) was estimated by anti-CD-34 staining after tumour resection.ResultsTreated mice had significantly lower MVD (p=0.032), confirming anti-angiogenic therapeutic effect of bevacizumab. None of the conventional measures were different between the two groups: Δvol (p=0.9), Volcal (p=0.7), Volmet (p=0.28), Metmax (p=0.7), or Metmean (p=0.32). One texture parameter, GLSZM-SZV (visually indicating that the 18F-FDG PET images of treated mice comprise uniformly sized clusters of different activity)) had significantly different means between the two groups of mice. Conclusions18F-FDG PET derived texture parameters, particularly GLSZM-SZV, may be valid biomarkers of tumour response to treatment with bevacizumab, before change in volume.

Background: The aim of this study was to evaluate the role of image heterogeneity analysis of standard care magnetic resonance imaging (MRI) in patients with anal squamous cell carcinoma (ASCC) to predict chemoradiotherapy (CRT) outcome. The ability to predict disease recurrence following CRT has the potential to inform personalized radiotherapy approaches currently being explored in novel clinical trials. Methods: An IRB waiver was obtained for retrospective analysis of standard care MRIs from ASCC patients presenting between 2010 and 2014. Whole tumor 3D volume-of-interest (VOI) was outlined on T2-weighted (T2w) and diffusion weighted imaging (DWI) of the pre- and post-treatment scans. Independent imaging features most predictive of disease recurrence were added to the baseline clinico-pathological model and the predictive value of respective extended models was calculated using net reclassification improvement (NRI) algorithm. Cross-validation analysis was carried out to determine percentage error reduction with inclusion of imaging features to the baseline model for both endpoints. Results:Forty patients who underwent 1.5 T pelvic MRI at baseline and following completion of CRT were included. A combination of two baseline MR heterogeneity features (baseline T2w energy and DWI coefficient of variation) was most predictive of disease recurrence resulting in significant NRI (p< 0.001). This was confirmed in cross-validation analysis with 34.8% percentage error reduction for the primary endpoint and 18.1% reduction for the secondary endpoint with addition of imaging variables to baseline model. Conclusion: MRI heterogeneity analysis offers complementary information, in addition to clinical staging, in predicting outcome of CRT in anal SCC, warranting validation in larger datasets.

Our purpose was to establish whether noninvasive measurement of changes in 18 F-fluoride metabolic flux to bone mineral (K i ) by PET/ CT can provide incremental value in response assessment of bone metastases in breast cancer compared with SUV max and SUV mean . Methods: Twelve breast cancer patients starting endocrine treatment for de novo or progressive bone metastases were included. Static 18 F-fluoride PET/CT scans were acquired 60 min after injection, before and 8 wk after commencing treatment. Venous blood samples were taken at 55 and 85 min after injection to measure plasma 18 F-fluoride activity concentrations, and K i in individual bone metastases was calculated using a previously validated method. Percentage changes in K i , SUV max , and SUV mean were calculated from the same index lesions (#5 lesions) from each patient. Clinical response up to 24 wk, assessed in consensus by 2 experienced oncologists masked to PET imaging findings, was used as a reference standard. Results: Of the 4 patients with clinically progressive disease (PD), mean K i significantly increased (.25%) in all, SUV max in 3, and SUV mean in 2. Of the 8 non-PD patients, K i decreased or remained stable in 7, SUV max in 5, and SUV mean in 6. A significant mean percentage increase from baseline for K i , compared with SUV max and SUV mean , occurred in the 4 patients with PD (89.7% vs. 41.8% and 43.5%, respectively; P, 0.001). Conclusion: After 8 wk of endocrine treatment for bone-predominant metastatic breast cancer, K i more reliably differentiated PD from non-PD than did SUV max and SUV mean , probably because measurement of SUV underestimates fluoride clearance by not considering changes in input function.

Supplemental material, Texture_appendix_1a_xyz1487273ad138b for Assessment of the Spatial Heterogeneity of Breast Cancers: Associations Between Computed Tomography and Immunohistochemistry by David K Woolf, Sonia P Li, Simone Detre, Alison Liu, Andrew Gogbashian, Ian C Simcock, James Stirling, Michael Kosmin, Gary J Cook, Muhammad Siddique, Mitch Dowsett, Andreas Makris and Vicky Goh in Biomarkers in Cancer

AimsAblation therapies are an innovative nephron-sparing alternative to radical nephrectomy for early stage renal cancers, although determination of treatment success is challenging. We aimed to undertake a systematic review of the literature to determine whether assessment of tumour perfusion may improve response assessment or alter clinical management when compared to standard imaging.Material and MethodsTwo radiologists performed independent primary literature searches for perfusion imaging in response assessment following ablative therapies (radiofrequency ablation and cryotherapy) focused on renal tumours.Results5 of 795 articles were eligible, totaling 110 patients. The study designs were heterogeneous with different imaging techniques, perfusion calculations, reference standard and follow-up periods. All studies found lower perfusion following treatment, with a return of ‘high grade’ perfusion in the 7/110 patients with residual or recurrent tumour. One study found perfusion curves were different between successfully ablated regions and residual tumour.ConclusionsStudies were limited by small sample size and heterogeneous methodology. No studies have investigated the impact of perfusion imaging on management. This review highlights the current lack of evidence for perfusion imaging in response assessment following renal ablation, however it suggests that there may be a future role. Further prospective research is required to address this.

Cancer remains a global killer alongside cardiovascular disease. A better understanding of cancer biology has transformed its management with an increasing emphasis on a personalized approach, so-called ‘precision cancer medicine’. Imaging has a key role to play in the management of cancer patients. Imaging biomarkers that objectively inform on tumor biology, the tumor environment, and tumor changes in response to an intervention complement genomic and molecular diagnostics. In this review we describe the key principles for imaging biomarker development and discuss the current status with respect to magnetic resonance imaging (MRI).

Bone metastases are common, cause significant morbidity and impact on healthcare resources. While radiographs, computed tomography (CT), magnetic resonance imaging (MRI) and bone scintigraphy have frequently been used for staging the skeleton, it is recognized that these methods are insensitive and non-specific for monitoring treatment response in a clinically relevant time frame. We summarize several recent reports on new functional and hybrid imaging methods including single photon emission CT / CT, positron emission tomography / CT and whole-body MRI with diffusion-weighted imaging.These modalities generally show improvements in diagnostic accuracy for staging and response assessment over standard imaging methods, with the ability to quantify biological processes related to the bone microenvironment as well as tumor cells.As some of these methods are now being adopted into routine clinical practice and clinical trials, further evaluation with comparative studies are still required to guide optimal and cost effective clinical management of patients with skeletal metastases.

Objectives: To explore the feasibility of magnetic resonance elastography (MRE) for characterizing indeterminate small renal tumors (SRT) as part of a multiparametric MR imaging protocol.Materials and Methods: Following institutional review board approval and informed consent, 21 prospective adults (15 men, median age 55 [range 25-72] years) with SRT were enrolled. Tumors (2 - 5 cm Ø) were imaged using three-directional, gradient echo MRE. Viscoelastic parametric maps (shear wave velocity [c] and attenuation [α]) were analyzed by two independent radiologists. Interobserver agreement (Bland-Altman statistics and intraclass correlation coefficients [ICC]) was assessed. Anatomical T2-weighted, dynamic contrast enhanced (DCE) and diffusion sequences completed the acquisition protocol. Imaging parameters were compared between groups (Mann-Whitney U test).Results: MRE quality was good in 18 cases. A cohort of 5 oncocytomas and 11 clearcell renal cell carcinomas (ccRCC) was analyzed. MRE viscoelastic parameters were the strongest imaging discriminators: oncocytomas displayed significantly lower shear velocity c (median, 0.77 m/s; interquartile range [IQR], 0.76-0.79) (P = 0.007) and higher shear attenuation α (median, 0.087 mm-1; IQR, 0.082-0.087) (P = 0.008) than ccRCC (medians, 0.92 m/s and 0.066 mm-1; IQR, 0.84-0.97 and 0.054-0.074 respectively). T2 signal intensity ratio (tumor/renal cortex) was lower in oncocytomas (P = 0.02). DCE and diffusion MR parameters overlapped substantially (P ≥ 0.1). Oncocytomas displayed a consistent MRE viscoelastic profile, corresponding to data point clustering in a bidimensional scatter plot. MRE ICC were 0.982 for c and 0.984 for α, indicating excellent interobserver agreement.Conclusions: MRE is feasible for SRT characterization; MRE viscoelastic parameters stronger discriminators between oncocytoma and ccRCC than anatomical, DCE and diffusion MR imaging parameters.

Radiomics describes the extraction of multiple, otherwise invisible, features from medical images that, with bioinformatic approaches, can be used to provide additional information that may predict underlying tumor biology and behavior. Radiomic signatures can be used alone or with other patient-specific data to improve tumor phenotyping, treatment response prediction and prognosis, non-invasively. The literature describing 18F-fluorodeoxyglucose positron emission tomography (18F-FDG PET) radiomics, often using texture or heterogeneity parameters, is increasing rapidly. In relation to radiotherapy practice there are early data reporting the use of radiomic approaches to better define tumor volumes and to predict radiation toxicity and treatment response. Whilst at an early stage of development, with many technical challenges remaining and a need for standardization, there is nevertheless promise that PET radiomics will contribute to personalized medicine, particularly with the availability of increased computing power and the development of machine learning approaches for imaging.

International audience

Background: 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.Fifty three NSCLC patients (mean age 65.8 years; 31 males) underwent pre-chemoradiotherapy 18F-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 modelsfor 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 18F-FDG PET in NSCLC.

Purpose To assess the day-to-day repeatability of global and local-regional magnetic resonance (MR) imaging texture features derived from primary rectal cancer. Materials and Methods After ethical approval and patient informed consent were obtained, two pretreatment T2-weighted axial MR imaging studies performed prospectively with the same imaging unit on 2 consecutive days in 14 patients with rectal cancer (11 men [mean age, 61.7 years], three women [mean age, 70.0 years]) were analyzed to extract (a) global first-order statistical histogram and model-based fractal features reflecting the whole-tumor voxel intensity histogram distribution and repeating patterns, respectively, without spatial information and (b) local-regional second-order and high-order statistical texture features reflecting the intensity and spatial interrelationships between adjacent in-plane or multiplanar voxels or regions, respectively. Repeatability was assessed for 46 texture features, and mean difference, 95% limits of agreement, within-subject coefficient of variation (wCV), and repeatability coefficient (r) were recorded. Results Repeatability was better for global parameters than for most local-regional parameters. In particular, histogram mean, median, and entropy, fractal dimension mean and standard deviation, and second-order entropy, homogeneity, difference entropy, and inverse difference moment demonstrated good repeatability, with narrow limits of agreement and wCVs of 10% or lower. Repeatability was poorest for the following high-order gray-level run-length (GLRL) gray-level zone size matrix (GLZSM) and neighborhood gray-tone difference matrix (NGTDM) parameters: GLRL intensity variability, GLZSM short-zone emphasis, GLZSM intensity nonuniformity, GLZSM intensity variability, GLZSM size zone variability, and NGTDM complexity, demonstrating wider agreement limits and wCVs of 50% or greater. Conclusion MR imaging repeatability is better for global texture parameters than for local-regional texture parameters, indicating that global texture parameters should be sufficiently robust for clinical practice. Online supplemental material is available for this article.

Machine learning approaches hold great potential for the automated detection of lung nodules in chest radiographs, but training the algorithms requires vary large amounts of manually annotated images, which are difficult to obtain. Weak labels indicating whether a radiograph is likely to contain pulmonary nodules are typically easier to obtain at scale by parsing historical free-text radiological reports associated to the radiographs. Using a repositotory of over 700,000 chest radiographs, in this study we demonstrate that promising nodule detection performance can be achieved using weak labels through convolutional neural networks for radiograph classification. We propose two network architectures for the classification of images likely to contain pulmonary nodules using both weak labels and manually-delineated bounding boxes, when these are available. Annotated nodules are used at training time to deliver a visual attention mechanism informing the model about its localisation performance. The first architecture extracts saliency maps from high-level convolutional layers and compares the estimated position of a nodule against the ground truth, when this is available. A corresponding localisation error is then back-propagated along with the softmax classification error. The second approach consists of a recurrent attention model that learns to observe a short sequence of smaller image portions through reinforcement learning. When a nodule annotation is available at training time, the reward function is modified accordingly so that exploring portions of the radiographs away from a nodule incurs a larger penalty. Our empirical results demonstrate the potential advantages of these architectures in comparison to competing methodologies.

PURPOSE The purpose of this study was to determine if 18F-fluorodeoxyglucose positron emission tomography/magnetic resonance imaging (18F-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 18F-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 SUVmax, ADCmean 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

OBJECTIVES: To determine the importance placed by patients on attributes associated with whole-body MRI (WB-MRI) and standard cancer staging pathways and ascertain drivers of preference. METHODS: Patients recruited to two multi-centre diagnostic accuracy trials comparing WB-MRI with standard staging pathways in lung and colorectal cancer were invited to complete a discrete choice experiment (DCE), choosing between a series of alternate pathways in which 6 attributes (accuracy, time to diagnosis, scan duration, whole-body enclosure, radiation exposure, total scan number) were varied systematically. Data were analysed using a conditional logit regression model and marginal rates of substitution computed. The relative importance of each attribute and probabilities of choosing WB-MRI-based pathways were estimated. RESULTS: A total of 138 patients (mean age 65, 61% male, lung n = 72, colorectal n = 66) participated (May 2015 to September 2016). Lung cancer patients valued time to diagnosis most highly, followed by accuracy, radiation exposure, number of scans, and time in the scanner. Colorectal cancer patients valued accuracy most highly, followed by time to diagnosis, radiation exposure, and number of scans. Patients were willing to wait 0.29 (lung) and 0.45 (colorectal) weeks for a 1% increase in pathway accuracy. Patients preferred WB-MRI-based pathways (probability 0.64 [lung], 0.66 [colorectal]) if they were equivalent in accuracy, total scan number, and time to diagnosis compared with a standard staging pathway. CONCLUSIONS: Staging pathways based on first-line WB-MRI are preferred by the majority of patients if they at least match standard pathways for diagnostic accuracy, time to diagnosis, and total scan number. KEY POINTS: • WB-MRI staging pathways are preferred to standard pathways by the majority of patients provided they at least match standard staging pathways for accuracy, total scan number, and time to diagnosis. • For patients with lung cancer, time to diagnosis was the attribute valued most highly, followed by accuracy, radiation dose, number of additional scans, and time in a scanner. Preference for patients with colorectal cancer was similar. • Most (63%) patients were willing to trade attributes, such as faster diagnosis, for improvements in pathway accuracy and reduced radiation exposure.

The identification of cancer-promoting genetic alterations is challenging particularly in highly unstable and heterogeneous cancers, such as esophageal adenocarcinoma (EAC). Here we describe a machine learning algorithm to identify cancer genes in individual patients considering all types of damaging alterations simultaneously. Analysing 261 EACs from the OCCAMS Consortium, we discover helper genes that, alongside well-known drivers, promote cancer. We confirm the robustness of our approach in 107 additional EACs. Unlike recurrent alterations of known drivers, these cancer helper genes are rare or patient-specific. However, they converge towards perturbations of well-known cancer processes. Recurrence of the same process perturbations, rather than individual genes, divides EACs into six clusters differing in their molecular and clinical features. Experimentally mimicking the alterations of predicted helper genes in cancer and pre-cancer cells validates their contribution to disease progression, while reverting their alterations reveals EAC acquired dependencies that can be exploited in therapy.

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.

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.

Esophageal, esophago-gastric, and gastric cancers are major causes of cancer morbidity and cancer death. For patients with potentially resectable disease, multi-modality treatment is recommended as it provides the best chance of survival. However, quality of life may be adversely affected by therapy, and with a wide variation in outcome despite multi-modality therapy, there is a clear need to improve patient stratification. Radiomic approaches provide an opportunity to improve tumor phenotyping. In this review we assess the evidence to date and discuss how these approaches could improve outcome in esophageal, esophago-gastric, and gastric cancer.