The GAINED phase 3 trial (ClinicalTrials.gov identifier: NCT01659099) evaluated a PET-driven consolidative strategy in patients with diffuse large B-cell lymphoma. In this post hoc analysis, we aimed to compare the prognostic value of the per-protocol PET interpretation criteria (Menton 2011 consensus) with the change in the SUVmax (ΔSUVmax) alone. Methods: Real-time central review of 18F-FDG PET/CT

Androgen deprivation therapy (ADT) is known to influence the prostate-specific membrane antigen (PSMA) expression of prostate cancer, potentially complicating the interpretation of PSMA ligand PET findings and affecting PSMA radioligand therapy. However, the impact of ADT on PSMA ligand biodistribution in nontumorous organs is not well understood. Methods: Men (n = 112) with histologically proven prostate

Nonspecific lymph node uptake on 18F-FDG PET/CT imaging is a significant pitfall for tumor staging. Fibroblast activation protein α expression on cancer-associated fibroblasts and some tumor cells is less sensitive to acute inflammatory stimuli, and fibroblast activation protein–directed PET may overcome this limitation. Methods: Eighteen patients from our prospective observational study underwent

Visual Abstract

Glaucoma is a chronic neuro-degenerative condition that is one of the world’s leading causes of irreversible but preventable blindness. The blindness is generally caused by the lack of timely detection and treatment. Early screening is thus essential for early treatment to preserve vision and maintain life quality. Color fundus photography and Optical Coherence Tomography (OCT) are the two most cost-effective

Annotated images for rare disease diagnosis are extremely hard to collect. Therefore, identifying rare diseases under a few-shot learning (FSL) setting is significant. Existing FSL methods transfer useful and global knowledge from base classes with abundant training samples to enrich features of novel classes with few training samples, but still face difficulties when being applied to medical images

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Any clinically-deployed image-processing pipeline must be robust to the full range of inputs it may be presented with. One popular approach to this challenge is to develop predictive models that can provide a measure of their uncertainty. Another approach is to use generative modelling to quantify the likelihood of inputs. Inputs with a low enough likelihood are deemed to be out-of-distribution and

The use of convolutional neural networks (CNNs) has allowed accurate white matter (WM) tract segmentation on diffusion magnetic resonance imaging (dMRI). To train the CNN-based segmentation models, a large number of scans on which WM tracts are annotated need to be collected, and these annotated scans can be accumulated over a long period of time. However, when novel WM tracts that are different from

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The prostate-specific membrane antigen (PSMA) inhibitor [177Lu]Lu-PSMA-617 has been previously demonstrated to be noninferior to docetaxel in achieving a biochemical response in chemotherapy-naïve metastatic castration-resistant prostate cancer patients. Here, we report the final analysis of overall survival (OS) for a phase 2 randomized, controlled trial. Methods: Forty chemotherapy-naïve, PSMA-positive

Visual Abstract

We present KeyMorph, a deep learning-based image registration framework that relies on automatically detecting corresponding keypoints. State-of-the-art deep learning methods for registration often are not robust to large misalignments, are not interpretable, and do not incorporate the symmetries of the problem. In addition, most models produce only a single prediction at test-time. Our core insight

Pathological brain lesions exhibit diverse appearance in brain images, in terms of intensity, texture, shape, size, and location. Comprehensive sets of data and annotations are difficult to acquire. Therefore, unsupervised anomaly detection approaches have been proposed using only normal data for training, with the aim of detecting outlier anomalous voxels at test time. Denoising methods, for instance

Magnetic resonance imaging (MRI) is increasingly being used to delineate morphological changes underlying neurological disorders. Successfully detecting these changes depends on the MRI data quality. Unfortunately, image artifacts frequently compromise the MRI utility, making it critical to screen the data. Currently, quality assessment requires visual inspection, a time-consuming process that suffers

Open international challenges are becoming the de facto standard for assessing computer vision and image analysis algorithms. In recent years, new methods have extended the reach of pulmonary airway segmentation that is closer to the limit of image resolution. Since EXACT’09 pulmonary airway segmentation, limited effort has been directed to the quantitative comparison of newly emerged algorithms driven

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The performance of learning-based algorithms improves with the amount of labelled data used for training. Yet, manually annotating data is particularly difficult for medical image segmentation tasks because of the limited expert availability and intensive manual effort required. To reduce manual labelling, active learning (AL) targets the most informative samples from the unlabelled set to annotate

Our objective was to evaluate the prognostic value of end-of-treatment prostate-specific membrane antigen (PSMA) PET/CT (PSMA-PET) in patients with metastatic castration-resistant prostate cancer (mCRPC) treated with 177Lu-PSMA radioligand therapy (PSMA-RLT). Methods: This was a single-center retrospective study. mCRPC patients who underwent PSMA-RLT with available baseline PSMA-PET (bPET) and end-of-treatment

The aim of this retrospective analysis was to evaluate health-related quality of life (HRQoL) for patients with metastatic castration-resistant prostate cancer (mCRPC) receiving consecutive cycles of 177Lu-prostate-specific membrane antigen (PSMA) radioligand therapy (RLT) using the reliable and validated European Organization for Research and Treatment of Cancer core quality-of-life (QoL) questionnaire

Visual Abstract

Congenital heart disease (CHD) is the most common type of birth defect. Without timely detection and treatment, approximately one-third of children with CHD would die in the infant period. However, due to the complicated heart structures, early diagnosis of CHD and its types is quite challenging, even for experienced radiologists. Here, we present an artificial intelligence (AI) system that achieves

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