Evaluation of Risk Models and Biomarkers for the Optimization of Lung Cancer Screening

More deaths can be attributed to lung cancer, than to any other cancer type. Evidence collected over the last 10 years, from randomized trials in the USA and Europe, indicates that screening by means of low-dose computed tomography (LDCT) could reduce the number of lung cancer (LC) deaths by about 20%-24%. While these findings have led to the implementation of screening programs in the USA, South Korea and Poland, discussions on their optimal design and execution are still ongoing in various countries, including Germany. Optimizing screening means finding the right balance between mortality reduction and risks, harms, and monetary costs. LDCT-scans are expensive, expose participants to radiation and put them at risk for overdiagnosis, as well as at risk for unnecessary invasive and expensive confirmatory procedures triggered by false positive (FP) results. Minimizing the number of unnecessary screening and confirmatory examinations should be prioritized. While risk-based eligibility has been shown to best target candidates, questions regarding optimal screening frequency, accurate nodule evaluation, stop-screening criteria to reduce overdiagnosis, and the use of complementary non-invasive diagnostic methods, remain open. Statistical models and biomarkers have been developed to help answer these questions. However, there is limited evidence of their validity in data from screening contexts and populations other than those in which they were developed. The analyses presented in this thesis are based on data collected as part of the German Lung Cancer Screening Intervention (LUSI) trial in order to validate models that address the questions: 1) can candidates for biennial vs annual screening be identified on the basis of their LC risk? 2) can the number of FP test results be reduced by accurately estimating the malignancy of LDCT-detected nodules? 3) What was the extent of overdiagnosis in the LUSI trial and how does overdiagnosis risk relate to the age and remaining lifetime of participants? Additionally, blood samples from participants of the LUSI were measured to evaluate: 4) whether the well-validated diagnostic biomarker test EarlyCDT®-Lung is sensitive enough to detect tumors seen in LDCT images. The LCRAT+CT and Polynomial models predict LC risk based on subject characteristics and LDCT imaging findings. Results of this first external validation confirmed their ability to identify participants with LC detected within 1-2 years after first screening. Discrimination was higher compared to a criterion based on nodule size and, to a lesser degree, compared to a model based on smoking and subject characteristics (LCRAT). This suggested that while LDCT findings can enhance models, most of their performance can could be attributed to information on smoking. Skipping 50% of annual LDCT examinations (i.e., for participants with estimated risks <5th decile) would have caused <10% delayed diagnoses, indicating that candidates for biennial screening could be identified based on their predicted LC risks without compromising on early detection. Absolute risk estimates were, on average, below the observed LC rates, indicating poor calibration. Models developed using data from the Canadian screening study PanCan showed excellent ability to differentiate between tumors and non-malignant nodules seen on LDCT scans taken at first screening participation and to accurately predict absolute malignancy risk. However, they showed lower performance when applied on data of nodules detected in later rounds. In contrast, a model developed on data from the UKLS trial and models developed on data from clinical settings did not perform as well in any screening round. Excess incidence of screen-detected lung tumors, an estimator of overdiagnosis, was within the range of values reported by other trials after similar post-screening follow-up (ca. 5-6 years). Estimates of mean pre-clinical sojourn time (MPST) and LDCT detection sensitivity were obtained via mathematical modeling. The highest excess incidence and longest MPST estimates were found among adenocarcinomas. The proportion of tumors with long lead times predicted based on MPST estimates (e.g., 23% with lead times ≥8 years) suggested a substantial overdiagnosis risk for individuals with residual life expectancies shorter than these hypothetical lead times, for example for heavy smokers over the age of 75. The tumor autoantibody panel measured by EarlyCDT®-Lung, a test widely validated as a diagnostic tool in clinical settings and recently tested as a pre-screening tool in a large randomized Scottish trial (ECLS), was found to have insufficient sensitivity for the identification of lung tumors detected via LDCT and of participants with screen-detected pulmonary nodules for whom more invasive diagnostic procedures should be recommended. Overall, the findings presented in this thesis indicate that risk prediction models can help optimize LC screening by assigning participants to appropriate screening intervals, and by increasing the accuracy of nodule evaluation. However, there is a need for further external model validation and re-calibration. Additionally, while excess incidence can provide estimates of overdiagnosis risk at a population-level, a better approach would be to obtain model-based personalized estimates of tumor lead and residual lifetime. Better individualized decisions about whether to start or stop screening could be taken on the basis of the relationship between these estimates and the risk of overdiagnosis. Finally, although there is evidence for the potential of biomarkers to complement LC screening, the so far most promising candidate (EarlyCDT®-Lung) cannot be recommended as a pre-screening tool given its poor sensitivity for the identification of lung tumors detected via LDCT. In conclusion, while steps have been taken in the right direction, more research is required in order to answer all open questions regarding the optimal design of lung cancer screening programs

Artificial intelligence in cancer imaging: Clinical challenges and applications

Judgement, as one of the core tenets of medicine, relies upon the integration of multilayered data with nuanced decision making. Cancer offers a unique context for medical decisions given not only its variegated forms with evolution of disease but also the need to take into account the individual condition of patients, their ability to receive treatment, and their responses to treatment. Challenges remain in the accurate detection, characterization, and monitoring of cancers despite improved technologies. Radiographic assessment of disease most commonly relies upon visual evaluations, the interpretations of which may be augmented by advanced computational analyses. In particular, artificial intelligence (AI) promises to make great strides in the qualitative interpretation of cancer imaging by expert clinicians, including volumetric delineation of tumors over time, extrapolation of the tumor genotype and biological course from its radiographic phenotype, prediction of clinical outcome, and assessment of the impact of disease and treatment on adjacent organs. AI may automate processes in the initial interpretation of images and shift the clinical workflow of radiographic detection, management decisions on whether or not to administer an intervention, and subsequent observation to a yet to be envisioned paradigm. Here, the authors review the current state of AI as applied to medical imaging of cancer and describe advances in 4 tumor types (lung, brain, breast, and prostate) to illustrate how common clinical problems are being addressed. Although most studies evaluating AI applications in oncology to date have not been vigorously validated for reproducibility and generalizability, the results do highlight increasingly concerted efforts in pushing AI technology to clinical use and to impact future directions in cancer care

A Low-Dose CT-Based Radiomic Model to Improve Characterization and Screening Recall Intervals of Indeterminate Prevalent Pulmonary Nodules.

Lung cancer (LC) is currently one of the main causes of cancer-related deaths worldwide. Low-dose computed tomography (LDCT) of the chest has been proven effective in secondary prevention (i.e., early detection) of LC by several trials. In this work, we investigated the potential impact of radiomics on indeterminate prevalent pulmonary nodule (PN) characterization and risk stratification in subjects undergoing LDCT-based LC screening. As a proof-of-concept for radiomic analyses, the first aim of our study was to assess whether indeterminate PNs could be automatically classified by an LDCT radiomic classifier as solid or sub-solid (first-level classification), and in particular for sub-solid lesions, as non-solid versus part-solid (second-level classification). The second aim of the study was to assess whether an LCDT radiomic classifier could automatically predict PN risk of malignancy, and thus optimize LDCT recall timing in screening programs. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC), accuracy, positive predictive value, negative predictive value, sensitivity, and specificity. The experimental results showed that an LDCT radiomic machine learning classifier can achieve excellent performance for characterization of screen-detected PNs (mean AUC of 0.89 ± 0.02 and 0.80 ± 0.18 on the blinded test dataset for the first-level and second-level classifiers, respectively), providing quantitative information to support clinical management. Our study showed that a radiomic classifier could be used to optimize LDCT recall for indeterminate PNs. According to the performance of such a classifier on the blinded test dataset, within the first 6 months, 46% of the malignant PNs and 38% of the benign ones were identified, improving early detection of LC by doubling the current detection rate of malignant nodules from 23% to 46% at a low cost of false positives. In conclusion, we showed the high potential of LDCT-based radiomics for improving the characterization and optimizing screening recall intervals of indeterminate PNs

Lung cancer screening in the NELSON trial: balancing harms and benefits

__Abstract__ In this thesis, the harms and benefits of lung cancer screening using low-dose computed tomography were investigated. Data of the Dutch-Belgian NELSON trial were used to quantify its harms and benefits and develop strategies to improve the balance between them. If the NELSON trial demonstrates that low-dose CT screening is an effective method to reduce mortality from lun

Computer-aided Diagnosis of Pulmonary Nodules in Thoracic Computed Tomography.

Lung cancer is the leading cause of cancer death in the United States. The five-year survival rate is 15% because most patients present with advanced disease. If lung cancer is detected and treated at its earliest stage, the five-year survival rate has been reported as high as 92%. Computed tomography (CT) has been shown to be more sensitive than chest radiography in detecting abnormal lung lesions (nodules), especially when they are small. However, each thin-slice thoracic CT scan can contain hundreds of images. Large amounts of image data, radiologist fatigue, and diagnostic uncertainty may lead to missed cancers or unnecessary biopsies. We address these issues by developing a computer-aided diagnosis (CAD) system that would serve as a second reader for radiologists by analyzing nodules and providing a malignancy estimate using computer vision and machine learning techniques. To segment the nodules, we extended the active contour (AC) model to 3D by adding new energy terms. The classification accuracy, quantified by the area (Az) under the receiver operating characteristic curve, was used as the figure-of-merit to guide segmentation parameter optimization. The effect of CT acquisition parameters on 3DAC segmentation was systematically studied by imaging simulated nodules in chest phantoms. We conducted simulation studies to compare the relative performance of feature selection and classification methods and to examine the bias and variance introduced due to limited training sample sizes. We also designed new feature descriptors to describe the nodule surface, which were combined with texture and morphological features extracted from the nodule volume and the surrounding tissue to characterize the nodule. Stepwise feature selection was used to search for the subset of most effective features to be used in the linear discriminant analysis classifier. The CAD system achieved a test Az of 0.86±0.02 in a leave-one-case-out resampling scheme for 256 nodules from 152 patients. We conducted an observer study with six thoracic radiologists and found that their average Az in assessing nodule malignancy increased significantly (p<0.05) from 0.83±0.03 without CAD to 0.85±0.04 with CAD. These results indicate the potential usefulness of CAD as a second reader for radiologists in characterizing lung nodules.Ph.D.Biomedical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/60814/1/tway_1.pd

Low-dose computed tomography for lung cancer screening in high risk populations: a systematic review and economic evaluation

This is the final version. Available from NIHR Journals Library via the DOI in this record.The dataset associated with this article is located in ORE at: https://doi.org/10.24378/exe.564Background Diagnosis of lung cancer frequently occurs in its later stages. Low-dose computed tomography (LDCT) could detect lung cancer early. Objectives To estimate the effectiveness and cost-effectiveness of LDCT lung cancer screening in high risk populations. Methods Clinical effectiveness A systematic review of randomised controlled trials (RCTs) comparing LDCT screening programmes with usual care (no screening) or other imaging screening programme (such as chest X-ray (CXR)) was conducted. Bibliographic sources included MEDLINE, Embase, Web of Science and the Cochrane Library. Meta-analyses, including network meta-analyses, were performed. Cost-effectiveness An independent economic model employing discrete event simulation and using a natural history model calibrated to results from a large RCT was developed. There were twelve different population eligibility criteria and four intervention frequencies (single screen, triple screen, annual screening and biennial screening) and a no screening control arm. Results Clinical effectiveness Twelve RCTs were included, four of which currently contribute evidence on mortality. Meta-analysis of these demonstrated that LDCT with up to 9.80 years of follow-up was associated with a non-statistically significant decrease in lung cancer mortality (pooled RR 0.94, 95% CI 0.74 to 1.19). The findings also showed that LDCT screening demonstrated a non-statistically significant increasein all-cause mortality. Given the considerable heterogeneity detected between studies for both outcomes, the results should be treated with caution. Network meta-analysis including six RCTs was performed to assess the relative effectiveness of LDCT, CXR and usual care. The results showed that LDCT was ranked as the best screening strategy in terms of lung cancer mortality reduction. CXR had a 99.7% probability of being the worst intervention with usual care intermediate. Cost-effectiveness Screening programmes are predicted to be more effective than no screening, reduce lung cancer mortality and result in more lung cancer diagnoses. Screening programmes also increase costs. Screening for lung cancer is unlikely to be cost-effective at a threshold of £20,000/QALY, but may be cost-effective at a threshold of £30,000/QALY. The incremental cost-effectiveness ratio for a single screen in smokers aged 60–75 years with at least a 3% risk of lung cancer is £28,169 per QALY. Sensitivity and scenario analyses were conducted. Screening was only cost-effective at a threshold of £20,000/QALY in a minority of analyses. Limitations Clinical effectiveness The largest of the included RCTs compared LDCT with CXR screening rather than no screening. Cost-effectiveness A representative cost to the NHS of lung cancer has not been recently estimated according to key variables such as stage at diagnosis. Certain costs associated with running a screening programme have not been included. Conclusions LDCT screening may be clinically effective in reducing lung cancer mortality but there is considerable uncertainty. There is evidence that a single round of screening could be considered cost-effective at conventional thresholds, but there is significant uncertainty about the effect on costs and the magnitude of benefits. Future work Effectiveness and cost-effectiveness estimates should be updated with the anticipated results from several ongoing RCTs (particularly NELSON).This report was commissioned by the NIHR Health Technology Assessment Programme as project number 14/151/0

Low-dose computed tomography for lung cancer screening in high-risk populations: a systematic review and economic evaluation

BACKGROUND: Diagnosis of lung cancer frequently occurs in its later stages. Low-dose computed tomography (LDCT) could detect lung cancer early. OBJECTIVES: To estimate the clinical effectiveness and cost-effectiveness of LDCT lung cancer screening in high-risk populations. DATA SOURCES: Bibliographic sources included MEDLINE, EMBASE, Web of Science and The Cochrane Library. METHODS: Clinical effectiveness – a systematic review of randomised controlled trials (RCTs) comparing LDCT screening programmes with usual care (no screening) or other imaging screening programmes [such as chest X-ray (CXR)] was conducted. Bibliographic sources included MEDLINE, EMBASE, Web of Science and The Cochrane Library. Meta-analyses, including network meta-analyses, were performed. Cost-effectiveness – an independent economic model employing discrete event simulation and using a natural history model calibrated to results from a large RCT was developed. There were 12 different population eligibility criteria and four intervention frequencies [(1) single screen, (2) triple screen, (3) annual screening and (4) biennial screening] and a no-screening control arm. RESULTS: Clinical effectiveness – 12 RCTs were included, four of which currently contribute evidence on mortality. Meta-analysis of these demonstrated that LDCT, with ≤ 9.80 years of follow-up, was associated with a non-statistically significant decrease in lung cancer mortality (pooled relative risk 0.94, 95% confidence interval 0.74 to 1.19). The findings also showed that LDCT screening demonstrated a non-statistically significant increase in all-cause mortality. Given the considerable heterogeneity detected between studies for both outcomes, the results should be treated with caution. Network meta-analysis, including six RCTs, was performed to assess the relative clinical effectiveness of LDCT, CXR and usual care. The results showed that LDCT was ranked as the best screening strategy in terms of lung cancer mortality reduction. CXR had a 99.7% probability of being the worst intervention and usual care was ranked second. Cost-effectiveness – screening programmes are predicted to be more effective than no screening, reduce lung cancer mortality and result in more lung cancer diagnoses. Screening programmes also increase costs. Screening for lung cancer is unlikely to be cost-effective at a threshold of £20,000/quality-adjusted life-year (QALY), but may be cost-effective at a threshold of £30,000/QALY. The incremental cost-effectiveness ratio for a single screen in smokers aged 60–75 years with at least a 3% risk of lung cancer is £28,169 per QALY. Sensitivity and scenario analyses were conducted. Screening was only cost-effective at a threshold of £20,000/QALY in only a minority of analyses. LIMITATIONS: Clinical effectiveness – the largest of the included RCTs compared LDCT with CXR screening rather than no screening. Cost-effectiveness – a representative cost to the NHS of lung cancer has not been recently estimated according to key variables such as stage at diagnosis. Certain costs associated with running a screening programme have not been included. CONCLUSIONS: LDCT screening may be clinically effective in reducing lung cancer mortality, but there is considerable uncertainty. There is evidence that a single round of screening could be considered cost-effective at conventional thresholds, but there is significant uncertainty about the effect on costs and the magnitude of benefits. FUTURE WORK: Clinical effectiveness and cost-effectiveness estimates should be updated with the anticipated results from several ongoing RCTs [particularly the NEderlands Leuvens Longkanker Screenings ONderzoek (NELSON) screening trial]. STUDY REGISTRATION: This study is registered as PROSPERO CRD42016048530. FUNDING: The National Institute for Health Research Health Technology Assessment programme

Proceedings of the International Cancer Imaging Society (ICIS) 16th Annual Teaching Course

Table of contents O1 Tumour heterogeneity: what does it mean? Dow-Mu Koh O2 Skeletal sequelae in adult survivors of childhood cancer Sue Creviston Kaste O3 Locoregional effects of breast cancer treatment Sarah J Vinnicombe O4 Imaging of cancer therapy-induced CNS toxicity Giovanni Morana, Andrea Rossi O5 Screening for lung cancer Christian J. Herold O6Risk stratification of lung nodules Theresa C. McLoud O7 PET imaging of pulmonary nodules Kirk A Frey O8 Transarterial tumour therapy Bernhard Gebauer O9 Interventional radiology in paediatric oncology Derek Roebuck O10 Image guided prostate interventions Jurgen J. Fütterer O11 Imaging cancer predisposition syndromes Alexander J. Towbin O12Chest and chest wall masses Thierry AG Huisman O13 Abdominal masses: good or bad? Anne MJB Smets O14 Hepatobiliary MR contrast: enhanced liver MRI for HCC diagnosis and management Giovanni Morana O15 Role of US elastography and multimodality fusion for managing patients with chronic liver disease and HCC Jeong Min Lee O16 Opportunities and challenges in imaging metastatic disease Hersh Chandarana O17 Diagnosis, treatment monitoring, and follow-up of lymphoma Marius E. Mayerhoefer, Markus Raderer, Alexander Haug O18 Managing high-risk and advanced prostate cancer Matthias Eiber O19 Immunotherapy: imaging challenges Bernhard Gebauer O20 RECIST and RECIST 1.1 Andrea Rockall O21 Challenges of RECIST in oncology imaging basics for the trainee and novice Aslam Sohaib O22 Lymphoma: PET for interim and end of treatment response assessment: a users’ guide to the Deauville Score Victoria S Warbey O23 Available resources Hebert Alberto Vargas O24 ICIS e-portal and the online learning community Dow-Mu Koh O25 Benign lesions that mimic pancreatic cancer Jay P Heiken O26 Staging and reporting pancreatic malignancies Isaac R Francis, Mahmoud, M Al-Hawary, Ravi K Kaza O27 Intraductal papillary mucinous neoplasm Giovanni Morana O28 Cystic pancreatic tumours Mirko D’Onofrio O29 Diffusion-weighted imaging of head and neck tumours Harriet C. Thoeny O30 Radiation injury in the head and neck Ann D King O31 PET/MR of paediatric brain tumours Giovanni Morana, Arnoldo Piccardo, Maria Luisa Garrè, Andrea Rossi O32 Structured reporting and beyond Hebert Alberto Vargas O33 Massachusetts General Hospital experience with structured reporting Theresa C. McLoud O34 The oncologist’s perspective: what the oncologist needs to know Nick Reed O35 Towards the cure of all children with cancer: global initiatives in pediatric oncology Carlos Rodriguez-Galindo O36 Multiparametric imaging of renal cancers Hersh Chandarana O37 Linking imaging features of renal disease and their impact on management strategies Hebert Alberto Vargas O38 Adrenals, retroperitoneum and peritoneum Isaac R Francis, Ashish P Wasnik O39 Lung and pleura Stefan Diederich O40 Advances in MRI Jurgen J. Fütterer O41 Advances in molecular imaging Wim J.G. Oyen O42 Incorporating advanced imaging, impact on treatment selection and patient outcome Cheng Lee Chaw, Nicholas van As S1 Combining ADC-histogram features improves performance of MR diffusion-weighted imaging for Lymph node characterisation in cervical cancer Igor Vieira, Frederik De Keyzer, Elleke Dresen, Sileny Han, Ignace Vergote, Philippe Moerman, Frederic Amant, Michel Koole, Vincent Vandecaveye S2 Whole-body diffusion-weighted MRI for surgical planning in patients with colorectal cancer and peritoneal metastases R Dresen, S De Vuysere, F De Keyzer, E Van Cutsem, A D’Hoore, A Wolthuis, V Vandecaveye S3 Role of apparent diffusion coefficient (ADC) diffusion-weighted MRI for predicting extra capsular extension of prostate cancer. P. Pricolo ([email protected]), S. Alessi, P. Summers, E. Tagliabue, G. Petralia S4 Generating evidence for clinical benefit of PET/CT – are management studies sufficient as surrogate for patient outcome? C. Pfannenberg, B. Gückel, SC Schüle, AC Müller, S. Kaufmann, N. Schwenzer, M. Reimold,C. la Fougere, K. Nikolaou, P. Martus S5 Heterogeneity of treatment response in skeletal metastases from breast cancer with 18F-fluoride and 18F-FDG PET GJ Cook, GK Azad, BP Taylor, M Siddique, J John, J Mansi, M Harries, V Goh S6 Accuracy of suspicious breast imaging—can we tell the patient? S Seth, R Burgul, A Seth S7 Measurement method of tumour volume changes during neoadjuvant chemotherapy affects ability to predict pathological response S Waugh, N Muhammad Gowdh, C Purdie, A Evans, E Crowe, A Thompson, S Vinnicombe S8 Diagnostic yield of CT IVU in haematuria screening F. Arfeen, T. Campion, E. Goldstraw S9 Percutaneous radiofrequency ablation of unresectable locally advanced pancreatic cancer: preliminary results D’Onofrio M, Ciaravino V, Crosara S, De Robertis R, Pozzi Mucelli R S10 Iodine maps from dual energy CT improve detection of metastases in staging examinations of melanoma patients M. Uhrig, D. Simons, H. Schlemmer S11Can contrast enhanced CT predict pelvic nodal status in malignant melanoma of the lower limb? Kate Downey S12 Current practice in the investigation for suspected Paraneoplastic Neurological Syndromes (PNS) and positive malignancy yield. S Murdoch, AS Al-adhami, S Viswanathan P1 Technical success and efficacy of Pulmonary Radiofrequency ablation: an analysis of 207 ablations S Smith, P Jennings, D Bowers, R Soomal P2 Lesion control and patient outcome: prospective analysis of radiofrequency abaltion in pulmonary colorectal cancer metastatic disease S Smith, P Jennings, D Bowers, R Soomal P3 Hepatocellular carcinoma in a post-TB patient: case of tropical infections and oncologic imaging challenges TM Mutala, AO Odhiambo, N Harish P4 Role of apparent diffusion coefficient (ADC) diffusion-weighted MRI for predicting extracapsular extension of prostate cancer P. Pricolo, S. Alessi, P. Summers, E. Tagliabue, G. Petralia P5 What a difference a decade makes; comparison of lung biopsies in Glasgow 2005 and 2015 M. Hall, M. Sproule, S. Sheridan P6 Solid pseudopapillary tumour of pancreas: imaging features of a rare neoplasm KY Thein, CH Tan, YL Thian, CM Ho P7 MDCT - pathological correlation in colon adenocarcinoma staging: preliminary experience S De Luca, C Carrera, V Blanchet, L Alarcón, E Eyheremnedy P8 Image guided biopsy of thoracic masses and reduction of pneumothorax risk: 25 years experience B K Choudhury, K Bujarbarua, G Barman P9 Tumour heterogeneity analysis of 18F-FDG-PET for characterisation of malignant peripheral nerve sheath tumours in neurofibromatosis-1 GJ Cook, E Lovat, M Siddique, V Goh, R Ferner, VS Warbey P10 Impact of introduction of vacuum assisted excision (VAE) on screen detected high risk breast lesions L Potti, B Kaye, A Beattie, K Dutton P11 Can we reduce prevalent recall rate in breast screening? AA Seth, F Constantinidis, H Dobson P12 How to reduce prevalent recall rate? Identifying mammographic lesions with low Positive Predictive Value (PPV) AA Seth ([email protected]), F Constantinidis, H Dobson P13 Behaviour of untreated pulmonary thrombus in oncology patients diagnosed with incidental pulmonary embolism on CT R. Bradley, G. Bozas, G. Avery, A. Stephens, A. Maraveyas P14 A one-stop lymphoma biopsy service – is it possible? S Bhuva, CA Johnson, M Subesinghe, N Taylor P15 Changes in the new TNM classification for lung cancer (8th edition, effective January 2017) LE Quint, RM Reddy, GP Kalemkerian P16 Cancer immunotherapy: a review of adequate imaging assessment G González Zapico, E Gainza Jauregui, R Álvarez Francisco, S Ibáñez Alonso, I Tavera Bahillo, L Múgica Álvarez P17 Succinate dehydrogenase mutations and their associated tumours O Francies, R Wheeler, L Childs, A Adams, A Sahdev P18 Initial experience in the usefulness of dual energy technique in the abdomen SE De Luca, ME Casalini Vañek, MD Pascuzzi, T Gillanders, PM Ramos, EP Eyheremendy P19 Recognising the serious complication of Richter’s transformation in CLL patients C Stove, M Digby P20 Body diffusion-weighted MRI in oncologic practice: truths, tricks and tips M. Nazar, M. Wirtz, MD. Pascuzzi, F. Troncoso, F. Saguier, EP. Eyheremendy P21 Methotrexate-induced leukoencephalopathy in paediatric ALL Patients D.J. Quint, L. Dang, M. Carlson, S. Leber, F. Silverstein P22 Pitfalls in oncology CT reporting. A pictorial review R Rueben, S Viswanathan P23 Imaging of perineural extension in head and neck tumours B Nazir, TH Teo, JB Khoo P24 MRI findings of molecular subtypes of breast cancer: a pictorial primer K Sharma, N Gupta, B Mathew, T Jeyakumar, K Harkins P25 When cancer can’t wait! A pictorial review of oncological emergencies K Sharma, B Mathew, N Gupta, T Jeyakumar, S Joshua P26 MRI of pancreatic neuroendocrine tumours: an approach to interpretation D Christodoulou, S Gourtsoyianni, A Jacques, N Griffin, V Goh P27 Gynaecological cancers in pregnancy: a review of imaging CA Johnson, J Lee P28 Suspected paraneoplastic neurological syndromes - review of published recommendations to date, with proposed guideline/flowchart JA Goodfellow, AS Al-adhami, S Viswanathan P29 Multi-parametric MRI of the pelvis for suspected local recurrence of prostate cancer after radical prostatectomy R Bradley P30 Utilisation of PI-RADS version 2 in multi-parametric MRI of the prostate; 12-months experience R Bradley P31 Radiological assessment of the post-chemotherapy liver A Yong, S Jenkins, G Joseph P32 Skeletal staging with MRI in breast cancer – what the radiologist needs to know S Bhuva, K Partington P33 Perineural spread of lympoma: an educational review of an unusual distribution of disease CA Johnson, S Bhuva, M Subesinghe, N Taylor P34 Visually isoattenuating pancreatic adenocarcinoma. Diagnostic imaging tools. C Carrera, A Zanfardini, S De Luca, L Alarcón, V Blanchet, EP Eyheremendy P35 Imaging of larynx cancer: when is CT, MRI or FDG PET/CT the best test? K Cavanagh, E Lauhttp://deepblue.lib.umich.edu/bitstream/2027.42/134651/1/40644_2016_Article_79.pd