Designing clinical trials based on modern imaging and metastasis-directed treatments in patients with oligometastatic breast cancer: a consensus recommendation from the EORTC Imaging and Breast Cancer Groups.

Breast cancer remains the most common cause of cancer death among women. Despite its considerable histological and molecular heterogeneity, those characteristics are not distinguished in most definitions of oligometastatic disease and clinical trials of oligometastatic breast cancer. After an exhaustive review of the literature covering all aspects of oligometastatic breast cancer, 35 experts from the European Organisation for Research and Treatment of Cancer Imaging and Breast Cancer Groups elaborated a Delphi questionnaire aimed at offering consensus recommendations, including oligometastatic breast cancer definition, optimal diagnostic pathways, and clinical trials required to evaluate the effect of diagnostic imaging strategies and metastasis-directed therapies. The main recommendations are the introduction of modern imaging methods in metastatic screening for an earlier diagnosis of oligometastatic breast cancer and the development of prospective trials also considering the histological and molecular complexity of breast cancer. Strategies for the randomisation of imaging methods and therapeutic approaches in different subsets of patients are also addressed.

Practitioners' views on community implementation of point-of-care ultrasound (POCUS) in the UK: a qualitative interview study.

BACKGROUND: Implementing Point-of-care ultrasound (POCUS) in community practice could help to decide upon and prioritise initial treatment, procedures and appropriate specialist referral or conveyance to hospital. A recent literature review suggests that image quality, portability and cost of ultrasound devices are all improving with widening indications for community POCUS, but evidence about community POCUS use is needed in the UK. We aimed to explore views of clinical practitioners, actively using ultrasound, on their experiences of using POCUS and potential facilitators and barriers to its wider implementation in community settings in the UK. METHODS: We conducted a qualitative interview study with practitioners from community and secondary care settings actively using POCUS in practice. A convenience sample of eligible participants from different clinical specialties and settings was recruited using social media adverts, through websites of relevant research groups and snowball sampling. Individual semi-structured interviews were conducted online using Microsoft Teams. These were recorded, transcribed verbatim, and analysed using a Framework approach supported by NVivo 12. RESULTS: We interviewed 16 practitioners aged between 40 and 62 years from different professional backgrounds, including paramedics, emergency physicians, general practitioners, and allied health professionals. Participants identified key considerations and facilitators for wider implementation of POCUS in community settings in the UK: resource requirements for deployment and support of working devices; sufficient time and a skilled workforce; attention to training, education and support needs; ensuring proper governance, guidelines and quality assurance; workforce considerations; enabling ease of use in assisting decision making with consideration of unintended consequences; and more robust evidence to support perceptions of improved patient outcomes and experience. CONCLUSIONS: POCUS could be useful for improving patient journey and health outcomes in community care, but this requires further research to evaluate outcomes. The facilitators identified could help make community POCUS a reality.

Dual-Stream Spatiotemporal Networks with Feature Sharing for Monitoring Animals in the Home Cage.

This paper presents a spatiotemporal deep learning approach for mouse behavioral classification in the home-cage. Using a series of dual-stream architectures with assorted modifications for optimal performance, we introduce a novel feature sharing approach that jointly processes the streams at regular intervals throughout the network. The dataset in focus is an annotated, publicly available dataset of a singly-housed mouse. We achieved even better classification accuracy by ensembling the best performing models; an Inception-based network and an attention-based network, both of which utilize this feature sharing attribute. Furthermore, we demonstrate through ablation studies that for all models, the feature sharing architectures consistently outperform the conventional dual-stream having standalone streams. In particular, the inception-based architectures showed higher feature sharing gains with their increase in accuracy anywhere between 6.59% and 15.19%. The best-performing models were also further evaluated on other mouse behavioral datasets.

Potentials and caveats of AI in hybrid imaging.

State-of-the-art patient management frequently mandates the investigation of both anatomy and physiology of the patients. Hybrid imaging modalities such as the PET/MRI, PET/CT and SPECT/CT have the ability to provide both structural and functional information of the investigated tissues in a single examination. With the introduction of such advanced hardware fusion, new problems arise such as the exceedingly large amount of multi-modality data that requires novel approaches of how to extract a maximum of clinical information from large sets of multi-dimensional imaging data. Artificial intelligence (AI) has emerged as one of the leading technologies that has shown promise in facilitating highly integrative analysis of multi-parametric data. Specifically, the usefulness of AI algorithms in the medical imaging field has been heavily investigated in the realms of (1) image acquisition and reconstruction, (2) post-processing and (3) data mining and modelling. Here, we aim to provide an overview of the challenges encountered in hybrid imaging and discuss how AI algorithms can facilitate potential solutions. In addition, we highlight the pitfalls and challenges in using advanced AI algorithms in the context of hybrid imaging and provide suggestions for building robust AI solutions that enable reproducible and transparent research.

Incorporating radiomics into clinical trials: expert consensus endorsed by the European Society of Radiology on considerations for data-driven compared to biologically driven quantitative biomarkers.

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.

Use of modern imaging methods to facilitate trials of metastasis-directed therapy for oligometastatic disease in prostate cancer: a consensus recommendation from the EORTC Imaging Group.

Oligometastatic disease represents a clinical and anatomical manifestation between localised and polymetastatic disease. In prostate cancer, as with other cancers, recognition of oligometastatic disease enables focal, metastasis-directed therapies. These therapies potentially shorten or postpone the use of systemic treatment and can delay further metastatic progression, thus increasing overall survival. Metastasis-directed therapies require imaging methods that definitively recognise oligometastatic disease to validate their efficacy and reliably monitor response, particularly so that morbidity associated with inappropriately treating disease subsequently recognised as polymetastatic can be avoided. In this Review, we assess imaging methods used to identify metastatic prostate cancer at first diagnosis, at biochemical recurrence, or at the castration-resistant stage. Standard imaging methods recommended by guidelines have insufficient diagnostic accuracy for reliably diagnosing oligometastatic disease. Modern imaging methods that use PET-CT with tumour-specific radiotracers (choline or prostate-specific membrane antigen ligand), and increasingly whole-body MRI with diffusion-weighted imaging, allow earlier and more precise identification of metastases. The European Organisation for Research and Treatment of Cancer (EORTC) Imaging Group suggests clinical algorithms to integrate modern imaging methods into the care pathway at the various stages of prostate cancer to identify oligometastatic disease. The EORTC proposes clinical trials that use modern imaging methods to evaluate the benefits of metastasis-directed therapies.

Inflammatory breast cancer: the disease, the biology, the treatment.

Inflammatory breast cancer (IBC) is a rare and aggressive form of invasive breast cancer accounting for 2.5% of all breast cancer cases. It is characterized by rapid progression, local and distant metastases, younger age of onset, and lower overall survival compared with other breast cancers. Historically, IBC is a lethal disease with less than a 5% survival rate beyond 5 years when treated with surgery or radiation therapy. Because of its rarity, IBC is often misdiagnosed as mastitis or generalized dermatitis. This review examines IBC's unique clinical presentation, pathology, epidemiology, imaging, and biology and details current multidisciplinary management of the disease, which comprises systemic therapy, surgery, and radiation therapy.

Transrectal quantitative shear wave elastography in the detection and characterisation of prostate cancer.

BACKGROUND: Shear wave imaging (SWI) is a new ultrasound technique whose application facilitates quantitative tissue elasticity assessment during transrectal ultrasound biopsies of the prostate gland. The aim of this study was to determine whether SWI quantitative data can differentiate between benign and malignant areas within prostate glands in men suspected of prostate cancer (PCa). METHODS: We conducted a protocol-based, prospective, prebiopsy quantitative SWI of prostate glands in 50 unscreened men suspected of prostate cancer between July 2011 and May 2012. The ultrasound image of whole prostate gland was arbitrarily divided into 12 zones for sampling biopsies, as is carried out in routine clinical practice. Each region was imaged by grey scale and SWI imaging techniques. Each region was further biopsied irrespective of findings of grey scale or SWI on ultrasound. Additional biopsies were taken if SWI abnormal area was felt to be outside of these 12 zones. Quantitative assessment of SWI abnormal areas was obtained in kilopascals (kPa) from abnormal regions shown by SWI and compared with histopathology. Sensitivity, specificity, positive and negative predictive values, and likelihood ratios were calculated for SWI (histopathology was a reference standard). RESULTS: Fifty patients, with a mean age of 69 ± 6.2 years, were recruited into the study. Thirty-three (66%) patients were diagnosed with PCa, while an additional 4 (8%) had atypia in at least one of the 12 prostate biopsies. Thirteen (26%) patients had a benign biopsy. Data analysed per core for SWI findings showed that for patients with PSA <20 µg/L, the sensitivity and specificity of SWI for PCa detection were 0.9 and 0.88, respectively, while in patients with PSA >20 µg/L, the sensitivity and specificity were 0.93 and 0.93, respectively. In addition, PCa had significantly higher stiffness values compared to benign tissues (p <0.05), with a trend toward stiffness differences in different Gleason grades. CONCLUSION: SWI provides quantitative assessment of the prostatic tissues and, in our preliminary observation, provides better diagnostic accuracy than grey-scale ultrasound imaging.

Human CD34+ cells in experimental myocardial infarction: long-term survival, sustained functional improvement, and mechanism of action.

RATIONALE: Human CD34(+) cells have been used in clinical trials for treatment of myocardial infarction (MI). However, it is unknown how long the CD34(+) cells persist in hearts, whether the improvement in cardiac function is sustained, or what are the underlying mechanisms. OBJECTIVE: We sought to track the fate of injected human CD34(+) cells in the hearts of severe combined immune deficiency (SCID) mice after experimental MI and to determine the mechanisms of action. METHODS AND RESULTS: We used multimodality molecular imaging to track the fate of injected human CD34(+) cells in the hearts of SCID mice after experimental MI, and used selective antibody blocking to determine the mechanisms of action. Bioluminescence imaging showed that injected CD34(+) cells survived in the hearts for longer than 12 months. The PET signal from the injected cells was detected in the wall of the left ventricle. Cardiac MRI showed that left ventricular ejection fraction was significantly improved in the treated mice compared to the control mice for up to 52 weeks (P<0.05). Furthermore, treatment with anti-alpha4beta1 showed that generation of human-derived cardiomyocytes was inhibited, whereas anti-vascular endothelial growth factor (VEGF) treatment blocked the production of human-derived endothelial cells. However, the improvement in cardiac function was abolished only in the anti-VEGF, but not anti-alpha4beta1, treated group. CONCLUSIONS: Angiogenesis and/or paracrine effect, but not myogenesis, is responsible for functional improvement following CD34(+) cells therapy.

Standardised lesion segmentation for imaging biomarker quantitation: a consensus recommendation from ESR and EORTC.

BACKGROUND: Lesion/tissue segmentation on digital medical images enables biomarker extraction, image-guided therapy delivery, treatment response measurement, and training/validation for developing artificial intelligence algorithms and workflows. To ensure data reproducibility, criteria for standardised segmentation are critical but currently unavailable. METHODS: A modified Delphi process initiated by the European Imaging Biomarker Alliance (EIBALL) of the European Society of Radiology (ESR) and the European Organisation for Research and Treatment of Cancer (EORTC) Imaging Group was undertaken. Three multidisciplinary task forces addressed modality and image acquisition, segmentation methodology itself, and standards and logistics. Devised survey questions were fed via a facilitator to expert participants. The 58 respondents to Round 1 were invited to participate in Rounds 2-4. Subsequent rounds were informed by responses of previous rounds. RESULTS/CONCLUSIONS: Items with ≥ 75% consensus are considered a recommendation. These include system performance certification, thresholds for image signal-to-noise, contrast-to-noise and tumour-to-background ratios, spatial resolution, and artefact levels. Direct, iterative, and machine or deep learning reconstruction methods, use of a mixture of CE marked and verified research tools were agreed and use of specified reference standards and validation processes considered essential. Operator training and refreshment were considered mandatory for clinical trials and clinical research. Items with a 60-74% agreement require reporting (site-specific accreditation for clinical research, minimal pixel number within lesion segmented, use of post-reconstruction algorithms, operator training refreshment for clinical practice). Items with ≤ 60% agreement are outside current recommendations for segmentation (frequency of system performance tests, use of only CE-marked tools, board certification of operators, frequency of operator refresher training). Recommendations by anatomical area are also specified.

Development of a targeted gene vector platform based on simian adenovirus serotype 24.

Efforts to develop adenovirus vectors suitable for genetic interventions in humans have identified three major limitations of the most frequently used vector prototype, human adenovirus serotype 5 (Ad5). These limitations--widespread preexisting anti-Ad5 immunity in humans, the high rate of transduction of normal nontarget tissues, and the lack of target-specific gene delivery--justify the exploration of other Ad serotypes as vector prototypes. In this paper, we describe the development of an alternative vector platform using simian Ad serotype 24 (sAd24). We found that sAd24 virions formed unstable complexes with blood coagulation factor X and, because of that, transduced the liver and other organs at low levels when administered intravenously. The overall pattern of biodistribution of sAd24 particles was similar, however, to that of Ad5, and the intravenously injected sAd24 was cleared by Kupffer cells, leading to their depletion. We modified the virus's fiber protein to design a Her2-specific derivative of sAd24 capable of infecting target human tumor cells in vitro. In the presence of neutralizing anti-Ad5 antibodies, Her2-mediated infection with targeted sAd24 compared favorably to that with the Ad5-derived vector. When used to target Her2-expressing tumors in animals, this fiber-modified vector achieved a higher level of gene transfer to metastasis-containing murine lungs than to tumor-free lungs. In aggregate, these studies provide important insights into sAd24 biology, identify its advantages and limitations as a vector prototype, and are thus essential for further development of an sAd24-based gene delivery platform.

The influence of field strength and different clinical breast MRI protocols on the outcome of texture analysis using foam phantoms.

PURPOSE: Texture analysis (TA) has proved to be useful to distinguish different tissues and disease states using magnetic resonance imaging (MRI). TA has been successfully applied clinically to improve identification of abnormalities in the brain, liver, and bone and, more recently, has been used to enhance the specificity of breast MRI. This preclinical study used a custom-made phantom containing different grades of reticulated foam embedded in agarose gel to assess the capability of TA to distinguish between different texture objects, under different imaging conditions. The aim was to assess whether TA could be used reliably with clinical protocols that were not optimized for texture analysis and also to investigate the effect that changing imaging sequence parameters would have on the outcome of TA. METHODS: Clinical fast gradient echo sequences and two different breast RF coils were used in order to reflect standard clinical practice. Three protocols were used: (1) a high spatial resolution protocol run on a 1.5 Tesla (T) MRI scanner, (2) a parameter matched sequence run on a 3.0 T magnet, and (3) a high temporal resolution protocol also run on a 3.0 T magnet.For each protocol, three sequence parameters (repetition time, bandwidth/echo time, and flip angle) were altered from the baseline values to assess the impact of changes in acquisition parameters on the outcome of TA. RESULTS: TA was performed using MAZDA software and clearly differentiated four foam phantoms when using the wavelet transform method (WAV), also moderately so with the co-occurrence matrix method (COM). The outcome was generally improved for imaging protocols acquired on the 3.0 T scanner, particularly for the high spatial resolution protocol where changes to the acquisition parameters influenced the TA, especially changes to the bandwidth/echo time. For the other protocols, TA outcome was less affected by changes to the imaging parameters. CONCLUSIONS: This phantom study shows that acquisition parameters and protocols that are typically used for clinical breast imaging can result in good TA. Our findings suggest that changes to sequence parameters may not greatly influence the outcome of texture analysis, but rather that spatial resolution may be the most important factor to consider.

The Lung Image Database Consortium (LIDC) and Image Database Resource Initiative (IDRI): a completed reference database of lung nodules on CT scans.

PURPOSE: The development of computer-aided diagnostic (CAD) methods for lung nodule detection, classification, and quantitative assessment can be facilitated through a well-characterized repository of computed tomography (CT) scans. The Lung Image Database Consortium (LIDC) and Image Database Resource Initiative (IDRI) completed such a database, establishing a publicly available reference for the medical imaging research community. Initiated by the National Cancer Institute (NCI), further advanced by the Foundation for the National Institutes of Health (FNIH), and accompanied by the Food and Drug Administration (FDA) through active participation, this public-private partnership demonstrates the success of a consortium founded on a consensus-based process. METHODS: Seven academic centers and eight medical imaging companies collaborated to identify, address, and resolve challenging organizational, technical, and clinical issues to provide a solid foundation for a robust database. The LIDC/IDRI Database contains 1018 cases, each of which includes images from a clinical thoracic CT scan and an associated XML file that records the results of a two-phase image annotation process performed by four experienced thoracic radiologists. In the initial blinded-read phase, each radiologist independently reviewed each CT scan and marked lesions belonging to one of three categories ("nodule > or =3 mm," "nodule <3 mm," and "non-nodule > or =3 mm"). In the subsequent unblinded-read phase, each radiologist independently reviewed their own marks along with the anonymized marks of the three other radiologists to render a final opinion. The goal of this process was to identify as completely as possible all lung nodules in each CT scan without requiring forced consensus. RESULTS: The Database contains 7371 lesions marked "nodule" by at least one radiologist. 2669 of these lesions were marked "nodule > or =3 mm" by at least one radiologist, of which 928 (34.7%) received such marks from all four radiologists. These 2669 lesions include nodule outlines and subjective nodule characteristic ratings. CONCLUSIONS: The LIDC/IDRI Database is expected to provide an essential medical imaging research resource to spur CAD development, validation, and dissemination in clinical practice.

Neural architecture search of echocardiography view classifiers.

Purpose: Echocardiography is the most commonly used modality for assessing the heart in clinical practice. In an echocardiographic exam, an ultrasound probe samples the heart from different orientations and positions, thereby creating different viewpoints for assessing the cardiac function. The determination of the probe viewpoint forms an essential step in automatic echocardiographic image analysis. Approach: In this study, convolutional neural networks are used for the automated identification of 14 different anatomical echocardiographic views (larger than any previous study) in a dataset of 8732 videos acquired from 374 patients. Differentiable architecture search approach was utilized to design small neural network architectures for rapid inference while maintaining high accuracy. The impact of the image quality and resolution, size of the training dataset, and number of echocardiographic view classes on the efficacy of the models were also investigated. Results: In contrast to the deeper classification architectures, the proposed models had significantly lower number of trainable parameters (up to 99.9% reduction), achieved comparable classification performance (accuracy 88.4% to 96%, precision 87.8% to 95.2%, recall 87.1% to 95.1%) and real-time performance with inference time per image of 3.6 to 12.6 ms. Conclusion: Compared with the standard classification neural network architectures, the proposed models are faster and achieve comparable classification performance. They also require less training data. Such models can be used for real-time detection of the standard views.

Bone Metastases Are Measurable: The Role of Whole-Body MRI and Positron Emission Tomography.

Metastatic tumor deposits in bone marrow elicit differential bone responses that vary with the type of malignancy. This results in either sclerotic, lytic, or mixed bone lesions, which can change in morphology due to treatment effects and/or secondary bone remodeling. Hence, morphological imaging is regarded unsuitable for response assessment of bone metastases and in the current Response Evaluation Criteria In Solid Tumors 1.1 (RECIST1.1) guideline bone metastases are deemed unmeasurable. Nevertheless, the advent of functional and molecular imaging modalities such as whole-body magnetic resonance imaging (WB-MRI) and positron emission tomography (PET) has improved the ability for follow-up of bone metastases, regardless of their morphology. Both these modalities not only have improved sensitivity for visual detection of bone lesions, but also allow for objective measurements of bone lesion characteristics. WB-MRI provides a global assessment of skeletal metastases and for a one-step "all-organ" approach of metastatic disease. Novel MRI techniques include diffusion-weighted imaging (DWI) targeting highly cellular lesions, dynamic contrast-enhanced MRI (DCE-MRI) for quantitative assessment of bone lesion vascularization, and multiparametric MRI (mpMRI) combining anatomical and functional sequences. Recommendations for a homogenization of MRI image acquisitions and generalizable response criteria have been developed. For PET, many metabolic and molecular radiotracers are available, some targeting tumor characteristics not confined to cancer type (e.g. 18F-FDG) while other targeted radiotracers target specific molecular characteristics, such as prostate specific membrane antigen (PSMA) ligands for prostate cancer. Supporting data on quantitative PET analysis regarding repeatability, reproducibility, and harmonization of PET/CT system performance is available. Bone metastases detected on PET and MRI can be quantitatively assessed using validated methodologies, both on a whole-body and individual lesion basis. Both have the advantage of covering not only bone lesions but visceral and nodal lesions as well. Hybrid imaging, combining PET with MRI, may provide complementary parameters on the morphologic, functional, metabolic and molecular level of bone metastases in one examination. For clinical implementation of measuring bone metastases in response assessment using WB-MRI and PET, current RECIST1.1 guidelines need to be adapted. This review summarizes available data and insights into imaging of bone metastases using MRI and PET.

Twenty Years On: RECIST as a Biomarker of Response in Solid Tumours an EORTC Imaging Group - ESOI Joint Paper.

Response evaluation criteria in solid tumours (RECIST) v1.1 are currently the reference standard for evaluating efficacy of therapies in patients with solid tumours who are included in clinical trials, and they are widely used and accepted by regulatory agencies. This expert statement discusses the principles underlying RECIST, as well as their reproducibility and limitations. While the RECIST framework may not be perfect, the scientific bases for the anticancer drugs that have been approved using a RECIST-based surrogate endpoint remain valid. Importantly, changes in measurement have to meet thresholds defined by RECIST for response classification within thus partly circumventing the problems of measurement variability. The RECIST framework also applies to clinical patients in individual settings even though the relationship between tumour size changes and outcome from cohort studies is not necessarily translatable to individual cases. As reproducibility of RECIST measurements is impacted by reader experience, choice of target lesions and detection/interpretation of new lesions, it can result in patients changing response categories when measurements are near threshold values or if new lesions are missed or incorrectly interpreted. There are several situations where RECIST will fail to evaluate treatment-induced changes correctly; knowledge and understanding of these is crucial for correct interpretation. Also, some patterns of response/progression cannot be correctly documented by RECIST, particularly in relation to organ-site (e.g. bone without associated soft-tissue lesion) and treatment type (e.g. focal therapies). These require specialist reader experience and communication with oncologists to determine the actual impact of the therapy and best evaluation strategy. In such situations, alternative imaging markers for tumour response may be used but the sources of variability of individual imaging techniques need to be known and accounted for. Communication between imaging experts and oncologists regarding the level of confidence in a biomarker is essential for the correct interpretation of a biomarker and its application to clinical decision-making. Though measurement automation is desirable and potentially reduces the variability of results, associated technical difficulties must be overcome, and human adjudications may be required.

What scans we will read: imaging instrumentation trends in clinical oncology.

Oncological diseases account for a significant portion of the burden on public healthcare systems with associated costs driven primarily by complex and long-lasting therapies. Through the visualization of patient-specific morphology and functional-molecular pathways, cancerous tissue can be detected and characterized non-invasively, so as to provide referring oncologists with essential information to support therapy management decisions. Following the onset of stand-alone anatomical and functional imaging, we witness a push towards integrating molecular image information through various methods, including anato-metabolic imaging (e.g., PET/CT), advanced MRI, optical or ultrasound imaging.This perspective paper highlights a number of key technological and methodological advances in imaging instrumentation related to anatomical, functional, molecular medicine and hybrid imaging, that is understood as the hardware-based combination of complementary anatomical and molecular imaging. These include novel detector technologies for ionizing radiation used in CT and nuclear medicine imaging, and novel system developments in MRI and optical as well as opto-acoustic imaging. We will also highlight new data processing methods for improved non-invasive tissue characterization. Following a general introduction to the role of imaging in oncology patient management we introduce imaging methods with well-defined clinical applications and potential for clinical translation. For each modality, we report first on the status quo and, then point to perceived technological and methodological advances in a subsequent status go section. Considering the breadth and dynamics of these developments, this perspective ends with a critical reflection on where the authors, with the majority of them being imaging experts with a background in physics and engineering, believe imaging methods will be in a few years from now.Overall, methodological and technological medical imaging advances are geared towards increased image contrast, the derivation of reproducible quantitative parameters, an increase in volume sensitivity and a reduction in overall examination time. To ensure full translation to the clinic, this progress in technologies and instrumentation is complemented by advances in relevant acquisition and image-processing protocols and improved data analysis. To this end, we should accept diagnostic images as "data", and - through the wider adoption of advanced analysis, including machine learning approaches and a "big data" concept - move to the next stage of non-invasive tumour phenotyping. The scans we will be reading in 10 years from now will likely be composed of highly diverse multi-dimensional data from multiple sources, which mandate the use of advanced and interactive visualization and analysis platforms powered by Artificial Intelligence (AI) for real-time data handling by cross-specialty clinical experts with a domain knowledge that will need to go beyond that of plain imaging.

In-vivo Barrett's esophagus digital pathology stage classification through feature enhancement of confocal laser endomicroscopy.

Barrett's esophagus (BE) is a premalignant condition that has an increased risk to turn into esophageal adenocarcinoma. Classification and staging of the different changes (BE in particular) in the esophageal mucosa are challenging since they have a very similar appearance. Confocal laser endomicroscopy (CLE) is one of the newest endoscopy tools that is commonly used to identify the pathology type of the suspected area of the esophageal mucosa. However, it requires a well-trained physician to classify the image obtained from CLE. An automatic stage classification of esophageal mucosa is presented. The proposed model enhances the internal features of CLE images using an image filter that combines fractional integration with differentiation. Various features are then extracted on a multiscale level, to classify the mucosal tissue into one of its four types: normal squamous (NS), gastric metaplasia (GM), intestinal metaplasia (IM or BE), and neoplasia. These sets of features are used to train two conventional classifiers: support vector machine (SVM) and random forest. The proposed method was evaluated on a dataset of 96 patients with 557 images of different histopathology types. The SVM classifier achieved the best performance with 96.05% accuracy based on a leave-one-patient-out cross-validation. Additionally, the dataset was divided into 60% training and 40% testing; the model achieved an accuracy of 93.72% for the testing data using the SVM. The presented model showed superior performance when compared with four state-of-the-art methods. Accurate classification is essential for the intestinal metaplasia grade, which most likely develops into esophageal cancer. Not only does our method come to the aid of physicians for more accurate diagnosis by acting as a second opinion, but it also acts as a training method for junior physicians who need practice in using CLE. Consequently, this work contributes to an automatic classification that facilitates early intervention and decreases samples of required biopsy.