OBJECTIVE. The objective of our study was to estimate and compare the performance of diffusion-weighted imaging (DWI) with other MRI techniques including T2-weighted MRI for the detection of prosta...

Advances in our understanding of the role of magnetic resonance imaging (MRI) for the detection of prostate cancer have enabled its integration into clinical routines in the past two decades. The Prostate Imaging Reporting and Data System (PI-RADS) is an established imaging-based scoring system that scores the probability of clinically significant prostate cancer on MRI to guide management. Image fusion technology allows one to combine the superior soft tissue contrast resolution of MRI, with real-time anatomical depiction using ultrasound or computed tomography. This allows the accurate mapping of prostate cancer for targeted biopsy and treatment. Machine learning provides vast opportunities for automated organ and lesion depiction that could increase the reproducibility of PI-RADS categorisation, and improve co-registration across imaging modalities to enhance diagnostic and treatment methods that can then be individualised based on clinical risk of malignancy. In this article, we provide a comprehensive and contemporary review of advancements, and share insights into new opportunities in this field.

Background: Chest radiography (CXR) is performed more widely and readily than CT for the management of coronavirus disease (COVID-19), but there remains little data on its clinical utility. This study aims to assess the diagnostic performance of CXR, with emphasis on its predictive value, for severe COVID-19 disease. Methods: A retrospective cohort study was conducted, 358 chest radiographs were performed on 109 COVID-19 patients (median age 44.4 years, 58 males and 30 with comorbidities) admitted between 22 January 2020 and 15 March 2020. Each CXR was reviewed and scored by three radiologists in consensus using a 72-point COVID-19 Radiographic Score (CRS). Disease severity was determined by the need for supplemental oxygen and mechanical ventilation. Results: Patients who needed supplemental oxygen (n=19, 17.4%) were significantly older (P<0.001) and significantly more of them had co-morbidities (P=0.011). They also had higher C-reactive protein (CRP) (P<0.001), higher lactate dehydrogenase (LDH) (P<0.001), lower lymphocyte count (P<0.001) and lower hemoglobin (Hb) (P=0.001). Their initial (CRS_initial) and maximal CRS (CRS_max) were higher (P<0.001). Adjusting for age and baseline hemoglobin, the AUROC of CRS_max (0.983) was as high as CRP_max (0.987) and higher than the AUROC for lymphocyte count_min (0.897), and LDH_max (0.900). The AUROC for CRS_initial was slightly lower (0.930). CRS_initial ≥5 had a sensitivity of 63% and specificity of 92% in predicting the need for oxygen, and 73% sensitivity and 88% specificity in predicting the need for mechanical ventilation. CRS between the 6^th and 10^th day from the onset of symptoms (CRS_D6-10) ≥5 had a sensitivity of 89% and specificity of 95% in predicting the need for oxygen, and 100% sensitivity and 86% specificity in predicting the need for mechanical ventilation. Conclusions: Adjusting for key confounders of age and baseline Hb, CRS_max performed comparable to or better than laboratory markers in the diagnosis of severe disease. CXR performed between the 6^th and 10^th days from symptom onset was a better predictor of severe disease than CXR performed earlier at presentation. A benign clinical course was seen in CXR that were normal or had very mild abnormalities.

Prostate cancer is one of the most common malignant tumors in men. Magnetic resonance imaging (MRI) has evolved to an important tool for the diagnosis of prostate cancer. Targeted biopsy is required for accurate diagnosis. This often requires MRI-ultrasound (MRI-US) fusion, as the biopsy is usually performed using transrectal ultrasound. Accurate prostate segmentation on MRI is essential for MRI-US fusion biopsy. However, the variation in prostate shape, appearance, and size makes the automatic segmentation challenging, given the limit of the annotated data. In this paper, we propose a method using multi-scale and Channel-wise Self-Attention (CSA) to re-calibrate the feature maps from multiple layers. By embedding the multi-scale CSA on the skip-connection in a UNet structure, called as UCAnet, we show the consistent improvement of the prostate segmentation in Dice, IoU and ASSD. For comparison, we also investigate the single-scale CSA in the networks, and incorporate the vision transformer to test if a transformer would boost the performance. Experiments on a public dataset with 204 prostate MRI scans show that UCAnet achieves the best performance and outperforms the state-of-the-art methods for prostate segmentation such as ENet, UNet, USE-Net and TransUNet.