Recently,deep learning (DL)‐based methods for the generation of synthetic computed tomography (sCT) have received significant research attention as an alternative to classical ones. We present here a systematic review of these methods by grouping them into three categories, according to their clinical applications: (i) to replace computed tomography in magnetic resonance (MR) based treatment planning, (ii) facilitate cone‐beam computed tomography based image‐guided adaptive radiotherapy, and (iii) derive attenuation maps for the correction of positron emission tomography. Appropriate database searching was performed on journal articles published between January 2014 and December 2020. The DL methods' key characteristics were extracted from each eligible study, and a comprehensive comparison among network architectures and metrics was reported. A detailed review of each category was given, highlighting essential contributions, identifying specific challenges, and summarizing the achievements. Lastly, the statistics of all the cited works from various aspects were analyzed, revealing the popularity and future trends and the potential of DL‐based sCT generation. The current status of DL‐based sCT generation was evaluated, assessing the clinical readiness of the presented methods.

The intricate relationships between innate immunity and brain diseases raise increased interest across the wide spectrum of neurodegenerative and neuropsychiatric disorders. Barriers, such as the blood–brain barrier, and innate immunity cells such as microglia, astrocytes, macrophages, and mast cells are involved in triggering disease events in these groups, through the action of many different cytokines. Chronic inflammation can lead to dysfunctions in large-scale brain networks. Neurodegenerative diseases, such as Alzheimer’s disease, Parkinson’s disease, Huntington’s disease, amyotrophic lateral sclerosis, and frontotemporal dementia, are associated with a substrate of dysregulated immune responses that impair the central nervous system balance. Recent evidence suggests that similar phenomena are involved in psychiatric diseases, such as depression, schizophrenia, autism spectrum disorders, and post-traumatic stress disorder. The present review summarizes and discusses the main evidence linking the innate immunological response in neurodegenerative and psychiatric diseases, thus providing insights into how the responses of innate immunity represent a common denominator between diseases belonging to the neurological and psychiatric sphere. Improved knowledge of such immunological aspects could provide the framework for the future development of new diagnostic and therapeutic approaches.

External-beam radiotherapy followed by high dose rate (HDR) brachytherapy is the standard-of-care for treating gynecologic cancers. The enhanced soft-tissue contrast provided by magnetic resonance imaging (MRI) makes it a valuable imaging modality for diagnosing and treating these cancers. However, in contrast to computed tomography (CT) imaging, the appearance of the brachytherapy catheters, through which radiation sources are inserted to reach the cancerous tissue later on, is often variable across images. This paper reports, for the first time, a new deep-learning-based method for fully automatic segmentation of multiple closely spaced brachytherapy catheters in intraoperative MRI. Represented in the data are 50 gynecologic cancer patients treated by MRI-guided HDR brachytherapy. For each patient, a single intraoperative MRI was used. 826 catheters in the images were manually segmented by an expert radiation physicist who is also a trained radiation oncologist. The number of catheters in a patient ranged between 10 and 35. A deep 3D convolutional neural network (CNN) model was developed and trained. In order to make the learning process more robust, the network was trained 5 times, each time using a different combination of shown patients. Finally, each test case was processed by the five networks and the final segmentation was generated by voting on the obtained five candidate segmentations. 4-fold validation was executed and all the patients were segmented. An average distance error of 2.0 ± 3.4 mm was achieved. False positive and false negative catheters were 6.7% and 1.5% respectively. Average Dice score was equal to 0.60 ± 0.17. The algorithm is available for use in the open source software platform 3D Slicer allowing for wide scale testing and research discussion. In conclusion, to the best of our knowledge, fully automatic segmentation of multiple closely spaced catheters from intraoperative MR images was achieved for the first time in gynecological brachytherapy.

Recent studies have clarified many still unknown aspects related to innate immunity and the blood-brain barrier relationship. They have also confirmed the close links between effector immune system cells, such as granulocytes, macrophages, microglia, natural killer cells and mast cells, and barrier functionality. The latter, in turn, is able to influence not only the entry of the cells of the immune system into the nervous tissue, but also their own activation. Interestingly, these two components and their interactions play a role of great importance not only in infectious diseases, but in almost all the pathologies of the central nervous system. In this paper, we review the main aspects in the field of vascular diseases (cerebral ischemia), of primitive and secondary neoplasms of Central Nervous System CNS, of CNS infectious diseases, of most common neurodegenerative diseases, in epilepsy and in demyelinating diseases (multiple sclerosis). Neuroinflammation phenomena are constantly present in all diseases; in every different pathological state, a variety of innate immunity cells responds to specific stimuli, differentiating their action, which can influence the blood-brain barrier permeability. This, in turn, undergoes anatomical and functional modifications, allowing the stabilization or the progression of the pathological processes.

Breast cancer is the most frequent cancer in women worldwide and late diagnosis often adversely affects the prognosis of the disease. Radiotherapy is commonly used to treat breast cancer, reducing the risk of recurrence after surgery. However, the eradication of radioresistant cancer cells, including cancer stem cells, remains the main challenge of radiotherapy. Recently, lipid droplets (LDs) have been proposed as functional markers of cancer stem cells, also being involved in increased cell tumorigenicity. LD biogenesis is a multistep process requiring various enzymes, including Diacylglycerol acyltransferase 2 (DGAT2). In this context, we evaluated the effect of PF-06424439, a selective DGAT2 inhibitor, on MCF7 breast cancer cells exposed to X-rays. Our results demonstrated that 72 h of PF-06424439 treatment reduced LD content and inhibited cell migration, without affecting cell proliferation. Interestingly, PF-06424439 pre-treatment followed by radiation was able to enhance radiosensitivity of MCF7 cells. In addition, the combined treatment negatively interfered with lipid metabolism-related genes, as well as with EMT gene expression, and modulated the expression of typical markers associated with the CSC-like phenotype. These findings suggest that PF-06424439 pre-treatment coupled to X-ray exposure might potentiate breast cancer cell radiosensitivity and potentially improve the radiotherapy effectiveness.

The coronavirus disease 19 (COVID-19) pandemic is having a dramatic impact on society and healthcare systems. In this complex scenario, lung computerized tomography (CT) may play an important prognostic role. However, datasets released so far present limitations that hamper the development of tools for quantitative analysis. In this paper, we present an open-source lung CT dataset comprising information on 50 COVID-19-positive patients. The CT volumes are provided along with (i) an automatic threshold-based annotation obtained with a Gaussian mixture model (GMM) and (ii) a scoring provided by an expert radiologist. This score was found to significantly correlate with the presence of ground glass opacities and the consolidation found with GMM. The dataset is freely available in an ITK-based file format under the CC BY-NC 4.0 license. The code for GMM fitting is publicly available, as well. We believe that our dataset will provide a unique opportunity for researchers working in the field of medical image analysis, and hope that its release will lay the foundations for the successfully implementation of algorithms to support clinicians in facing the COVID-19 pandemic.