Translation of PET radiotracers for cancer imaging: recommendations from the National Cancer Imaging Translational Accelerator (NCITA) consensus meeting.

Background: The clinical translation of positron emission tomography (PET) radiotracers for cancer management presents complex challenges. We have developed consensus-based recommendations for preclinical and clinical assessment of novel and established radiotracers, applied to image different cancer types, to improve the standardisation of translational methodologies and accelerate clinical implementation.

Methods: A consensus process was developed using the RAND/UCLA Appropriateness Method (RAM) to gather insights from a multidisciplinary panel of 38 key stakeholders on the appropriateness of preclinical and clinical methodologies and stakeholder engagement for PET radiotracer translation.

MRI of the Rectum: A Decade into DISTANCE, Moving to DISTANCED.

Over the past decade, advancements in rectal cancer research have reshaped treatment paradigms. Historically, treatment for locally advanced rectal cancer has focused on neoadjuvant long-course chemoradiotherapy, followed by total mesorectal excision. Interest in organ preservation strategies has been strengthened by the introduction of total neoadjuvant therapy with improved rates of complete clinical response.

3D distortion-free, reduced FOV diffusion-prepared gradient echo at 3 T.

Purpose: To develop a 3D distortion-free reduced-FOV diffusion-prepared gradient-echo sequence and demonstrate its application in vivo for diffusion imaging of the spinal cord in healthy volunteers.

Methods: A 3D multi-shot reduced-FOV diffusion-prepared gradient-echo acquisition is achieved using a slice-selective tip-down pulse in the phase-encoding direction in the diffusion preparation, combined with magnitude stabilizers, centric k-space encoding, and 2D phase navigators to correct for intershot phase errors. The accuracy of the ADC values obtained using the proposed approach was evaluated in a diffusion phantom and compared to the tabulated reference ADC values and to the ADC values obtained using a standard spin echo diffusion-weighted single-shot EPI sequence (DW-SS-EPI).

Temporary treatment cessation compared with continuation of tyrosine kinase inhibitors for adults with renal cancer: the STAR non-inferiority RCT.

Background: There is interest in using treatment breaks in oncology, to reduce toxicity without compromising efficacy.

Trial Design: A Phase II/III multicentre, open-label, parallel-group, randomised controlled non-inferiority trial assessing treatment breaks in patients with renal cell carcinoma.

Methods: Patients with locally advanced or metastatic renal cell carcinoma, starting tyrosine kinase inhibitor as first-line treatment at United Kingdom National Health Service hospitals.

Advanced imaging for earlier diagnosis and morbidity prevention in multiple myeloma: A British Society of Haematology and UK Myeloma Society Good Practice Paper.

This Good Practice Paper provides recommendations for the use of advanced imaging for earlier diagnosis and morbidity prevention in multiple myeloma. It describes how advanced imaging contributes to optimal healthcare resource utilisation by in newly diagnosed and relapsed myeloma, and provides a perspective on future directions of myeloma imaging, including machine learning assisted reporting.

Self-Supervised Anomaly Detection from Anomalous Training Data via Iterative Latent Token Masking.

Anomaly detection and segmentation pose an important task across sectors ranging from medical imaging analysis to industry quality control. However, current unsupervised approaches require training data to not contain any anomalies, a requirement that can be especially challenging in many medical imaging scenarios. In this paper, we propose Iterative Latent Token Masking, a self-supervised framework derived from a robust statistics point of view, translating an iterative model fitting with M-estimators to the task of anomaly detection.

Magnetic resonance imaging evaluation of cochlear and vestibular nerve calibre: a case-control study in Ménière's disease and endolymphatic hydrops.

Purpose: To compare the calibre of the cochlear (CN), superior vestibular (SVN) and inferior vestibular (IVN) nerves on magnetic resonance imaging (MRI), both between Ménière's Disease (MD) ears and clinical controls, and between inner ears with and without endolymphatic hydrops (EH) on MRI.

Methods: A retrospective case-control study evaluated patients undergoing MRI for suspected hydropic ear disease from 9/2017 to 8/2022. The CN, SVN, IVN and facial nerve (FN) diameters and cross-sectional areas (CSA) were measured on T2-weighted sequences whilst EH was evaluated on delayed post-gadolinium MRI.

Multivariable prognostic modelling to improve prediction of colorectal cancer recurrence: the PROSPeCT trial.

Objective: Improving prognostication to direct personalised therapy remains an unmet need. This study prospectively investigated promising CT, genetic, and immunohistochemical markers to improve the prediction of colorectal cancer recurrence.

Material And Methods: This multicentre trial (ISRCTN 95037515) recruited patients with primary colorectal cancer undergoing CT staging from 13 hospitals.

National myeloma patient survey shows continuing inappropriate imaging and geographical inequalities.

Objective: To evaluate the provision of imaging at diagnosis of myeloma from the service user perspective with a specific focus on how the experiences of patients align with the National Institute for Health and Care Excellence (NICE) guidelines (NG35, 2016) on first-line imaging practice for myeloma in the United Kingdom.

Methods: A national survey was performed to evaluate access to imaging from the patient's perspective. Patients with myeloma who received their diagnosis between 2017 and March 2022 were invited to participate.

Multicentre validation of CT grey-level co-occurrence matrix features for overall survival in primary oesophageal adenocarcinoma.

Background: Personalising management of primary oesophageal adenocarcinoma requires better risk stratification. Lack of independent validation of proposed imaging biomarkers has hampered clinical translation. We aimed to prospectively validate previously identified prognostic grey-level co-occurrence matrix (GLCM) CT features for 3-year overall survival.

The Image Biomarker Standardization Initiative: Standardized Convolutional Filters for Reproducible Radiomics and Enhanced Clinical Insights.

Filters are commonly used to enhance specific structures and patterns in images, such as vessels or peritumoral regions, to enable clinical insights beyond the visible image using radiomics. However, their lack of standardization restricts reproducibility and clinical translation of radiomics decision support tools. In this special report, teams of researchers who developed radiomics software participated in a three-phase study (September 2020 to December 2022) to establish a standardized set of filters.

Perceptions of radiologists on structured reporting for cancer imaging-a survey by the European Society of Oncologic Imaging (ESOI).

Objectives: To assess radiologists' current use of, and opinions on, structured reporting (SR) in oncologic imaging, and to provide recommendations for a structured report template.

Materials And Methods: An online survey with 28 questions was sent to European Society of Oncologic Imaging (ESOI) members. The questionnaire had four main parts: (1) participant information, e.

Patient Reidentification from Chest Radiographs: An Interpretable Deep Metric Learning Approach and Its Applications.

Purpose: To train an explainable deep learning model for patient reidentification in chest radiograph datasets and assess changes in model-perceived patient identity as a marker for emerging radiologic abnormalities in longitudinal image sets.

Materials And Methods: This retrospective study used a set of 1 094 537 frontal chest radiographs and free-text reports from 259 152 patients obtained from six hospitals between 2006 and 2019, with validation on the public ChestX-ray14, CheXpert, and MIMIC-CXR datasets. A deep learning model was trained for patient reidentification and assessed on patient identity confirmation, retrieval of patient images from a database based on a query image, and radiologic abnormality prediction in longitudinal image sets.

Development and validation of open-source deep neural networks for comprehensive chest x-ray reading: a retrospective, multicentre study.

Background: Artificial intelligence (AI) systems for automated chest x-ray interpretation hold promise for standardising reporting and reducing delays in health systems with shortages of trained radiologists. Yet, there are few freely accessible AI systems trained on large datasets for practitioners to use with their own data with a view to accelerating clinical deployment of AI systems in radiology. We aimed to contribute an AI system for comprehensive chest x-ray abnormality detection.

Weakly supervised segmentation models as explainable radiological classifiers for lung tumour detection on CT images.

Purpose: Interpretability is essential for reliable convolutional neural network (CNN) image classifiers in radiological applications. We describe a weakly supervised segmentation model that learns to delineate the target object, trained with only image-level labels ("image contains object" or "image does not contain object"), presenting a different approach towards explainable object detectors for radiological imaging tasks.

Methods: A weakly supervised Unet architecture (WSUnet) was trained to learn lung tumour segmentation from image-level labelled data.