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Particle therapies are becoming increasingly available clinically due to their beneficial energy deposition profile, sparing healthy tissues. This may be further promoted with ultra-high dose rates, termed FLASH. This review comprehensively summarises current knowledge based on studies relevant to proton- and carbon-FLASH therapy. As electron-FLASH literature presents important radiobiological findings that form the basis of proton and carbon-based FLASH studies, a summary of key electron-FLASH papers is also included. Preclinical data suggest three key mechanisms by which proton and carbon-FLASH are able to reduce normal tissue toxicities compared to conventional dose rates, with equipotent, or enhanced, tumour kill efficacy. However, a degree of caution is needed in clinically translating these findings as: most studies use transmission and do not conform the Bragg peak to tumour volume; mechanistic understanding is still in its infancy; stringent verification of dosimetry is rarely provided; biological assays are prone to limitations which need greater acknowledgement.

The combination of visual assessment of whole body [18F]FDG PET images and evaluation of bone marrow samples by Multiparameter Flow Cytometry (MFC) or Next-Generation Sequencing (NGS) is currently the most common clinical practice for the detection of Measurable Residual Disease (MRD) in Multiple Myeloma (MM) patients. In this study, radiomic features extracted from the bone marrow biopsy locations are analyzed and compared to those extracted from the whole bone marrow in order to study the representativeness of these biopsy locations in the image-based MRD assessment. Whole body [18F]FDG PET of 39 patients with newly diagnosed MM were included in the database, and visually evaluated by experts in nuclear medicine. A methodology for the segmentation of biopsy sites from PET images, including sternum and posterior iliac crest, and their subsequent quantification is proposed. First, starting from the bone marrow segmentation, a segmentation of the biopsy sites is performed. Then, segmentations are quantified extracting SUV metrics and radiomic features from the [18F]FDG PET images and are evaluated by Mann–Whitney U-tests as valuable features differentiating PET+/PET− and MFC+ /MFC− groups. Moreover, correlation between whole bone marrow and biopsy sites is studied by Spearman ρ rank. Classification performance of the radiomics features is evaluated applying seven machine learning algorithms. Statistical analyses reveal that some images features are significant in PET+/PET− differentiation, such as SUVmax, Gray Level Non-Uniformity or Entropy, especially with a balanced database where 16 of the features show a p value 18F]FDG PET images in MRD assessment in MM patients.

Distal radius fractures (DRFs) are one of the most common types of wrist fracture and can be subdivided into intra- and extra-articular fractures. Compared with extra-articular DRFs which spare the joint surface, intra-articular DRFs extend to the articular surface and can be more difficult to treat. Identification of articular involvement can provide valuable information about the characteristics of fracture patterns. In this study, a two-stage ensemble deep learning framework was proposed to differentiate intra- and extra-articular DRFs automatically on posteroanterior (PA) view wrist X-rays. The framework firstly detects the distal radius region of interest (ROI) using an ensemble model of YOLOv5 networks, which imitates the clinicians’ search pattern of zooming in on relevant regions to assess abnormalities. Secondly, an ensemble model of EfficientNet-B3 networks classifies the fractures in the detected ROIs into intra- and extra-articular. The framework achieved an area under the receiver operating characteristic curve of 0.82, an accuracy of 0.81, a true positive rate of 0.83 and a false positive rate of 0.27 (specificity of 0.73) for differentiating intra- from extra-articular DRFs. This study has demonstrated the potential in automatic DRF characterization using deep learning on clinically acquired wrist radiographs and can serve as a baseline for further research in incorporating multi-view information for fracture classification.

Quality control in mammography is a very important element. One of the parameters indicating the appropriate image quality is the threshold image contrast. The CDMAM phantom is used to measure this parameter. It is currently available in two versions 3.4 and 4.0. The aim of this work is to compare the threshold image contrast readings obtained with the CDMAM 3.4 and CDMAM 4.0 phantoms. In the measurements, 9 CDMAM 4.0 phantoms were used to check the difference in indications of individual copies. The phantom whose readings were closest to the average of all readings was used for comparative measurements with the CDMAM 3.4 phantom. Measurements were made on 40 mammography devices. The obtained images were read with the software provided by the phantom manufacturer and the CDMAM Analysis v2.3.0 (NCCPM) software. The average percentage difference between the minimum and maximum values indicated by the CDMAM 4.0 phantoms was 10.09%. Using the CDMAM Analysis v2.3.0 (NCCPM) software, the average difference in readings between the CDMAM 3.4 and CDMAM 4.0 phantoms is 7.93%, and when using the software provided by the phantom manufacturer, it is as much as 60.15%. The obtained results of the threshold image contrast are affected by the type of software used for reading and the accuracy of the execution of individual elements of the phantom. It is recommended to use CDMAM Analysis v2.3.0 (NCCPM) software or the latest software provided by the phantom manufacturer to read the phantom images.