Virological rebound to ≥5000 copies/ml was associated with arrested immune reconstitution, rising IL-6 and increased risk of clinical disease progression. As viral load testing becomes more available in sub-Saharan Africa, repeat testing algorithms will be required to identify those with virological rebound, who need switching to prevent disease progression, whilst preventing unnecessary second-line regimen initiation in the majority of children with detectable viraemia who remain at low risk of disease progression.As viral load testing becomes more available in sub-Saharan Africa, repeat testing algorithms will be required to identify those with virological rebound, who need switching to prevent disease progression, whilst preventing unnecessary second-line regimen initiation in the majority of children with detectable viraemia who remain at low risk of disease progression. The applications of machine learning in clinical radiology practice and in particular oncologic imaging practice are steadily evolving. However, there are several potential hurdles for widespread implementation of machine learning in oncologic imaging, including the lack of availability of a large number of annotated data sets and lack of use of consistent methodology and terminology for reporting the findings observed on the staging and follow-up imaging studies that apply to a wide spectrum of solid tumors. This short review discusses some potential hurdles to the implementation of machine learning in oncologic imaging, opportunities for improvement, and potential solutions that can facilitate robust machine learning from the vast number of radiology reports and annotations generated by the dictating radiologists.The applications of machine learning in clinical radiology practice and in particular oncologic imaging practice are steadily evolving. However, there are several potential hurdles for widespread implementation of machine learning in oncologic imaging, including the lack of availability of a large number of annotated data sets and lack of use of consistent methodology and terminology for reporting the findings observed on the staging and follow-up imaging studies that apply to a wide spectrum of solid tumors. This short review discusses some potential hurdles to the implementation of machine learning in oncologic imaging, opportunities for improvement, and potential solutions that can facilitate robust machine learning from the vast number of radiology reports and annotations generated by the dictating radiologists. To develop and validate an Osseous Tumor Reporting and Data System (OT-RADS) with the hypothesis that the proposed guideline is reliable and assists in separating benign from malignant osseous tumors with a good area under the curve, and that could assist further patient management. In this multireader cross-sectional validation study, an agreement was reached for OT-RADS categories based on previously described magnetic resonance imaging features and consensus of expert musculoskeletal radiologists. World Health Organization classification was used, and a wide spectrum of benign and malignant osseous tumors was evaluated. Magnetic resonance imaging categories were as follows OT-RADS 0-incomplete imaging; OT-RADS I-negative; OT-RADS II-definitely benign; OT-RADS III-probably benign; OT-RADS IV-suspicious for malignancy or indeterminate; OT-RADS V-highly suggestive of malignancy; and OT-RADS VI-known biopsy-proven malignancy or recurrent malignancy in the tumor bed. Four blinded readers categorized each tu Reporting and Data System lexicon is reliable and helps stratify tumors into benign and malignant categories. It can be practically used by radiologists to guide patient management, improve multidisciplinary communications, and potentially impact outcomes. Pericardiocentesis is a well-known procedure commonly performed by either image-guided intervention or surgical approaches. Computed tomography (CT)-guided pericardiocentesis is not widely used, but provides certain advantages. In the article we will discuss our technique of CT-guided therapeutic pericardiocentesis, including advantages and disadvantages, patient selection, possible approaches, and complications. The retrospective study included 121 therapeutic pericardial drainages performed under CT guidance. Demographic data of the patients, entry site, and needle orientation were recorded. Procedure complications and their significance were analyzed. One hundred and twenty-one pericardial drainages with catheter insertion were performed under CT guidance on 119 patients presenting with clinically significant pericardial effusion. The most common approach was at the left anterior chest wall. The rate of minor complications was 5.8%, no major complications occurred. Therapeutic pericardiocentesis can be obtained under CT guidance in a safe and effective manner. Recommendations for building a patient-centered protocol with an interdisciplinary team are discussed. Patient selection, procedural guidance, and lessons to avoid complications are reviewed.Therapeutic pericardiocentesis can be obtained under CT guidance in a safe and effective manner. Recommendations for building a patient-centered protocol with an interdisciplinary team are discussed. Patient selection, procedural guidance, and lessons to avoid complications are reviewed. To our knowledge, few studies have investigated anatomy of the Meckel cave with neuroimaging modalities. The present study aimed to characterize it using magnetic resonance imaging (MRI). Following conventional MRI examination, a total of 101 patients underwent T2-weighted imaging in thin-sliced coronal and sagittal sections, and 11 patients underwent constructive interference steady-state sequences in thin-sliced sagittal sections. Moreover, 3 injected cadaver heads were dissected. In the cadaver specimens, the size and extent of the cerebrospinal fluid-filled space between the Gasserian ganglion and surrounding arachnoids were difficult to define. On the T2-weighted imaging, the Meckel cave was delineated with variable morphologies and left-right asymmetry. DIRECT RED 80 cell line On the sagittal images, the shape of the Meckel cave could be classified into 3 different types, bulbous, oval, and flat, with the oval being the most frequent that comprised 60%. Furthermore, on the sagittal constructive interference steady-state images, parts of the trigeminal nerve distributed in the Meckel cave were delineated in all patients.