Very Early Diagnosis and Management of Congenital Erythropoietic Porphyria

Congenital erythropoietic porphyria (CEP), a rare form of porphyria, is caused by a defect in the heme biosynthesis pathway of the enzyme uroporphyrinogen III synthase (UROS). Uroporphyrinogen III synthase deficiency leads to an accumulation of nonphysiological porphyrins in bone marrow, red blood cells, skin, bones, teeth, and spleen. Consequently, the exposure to sunlight causes severe photosensitivity, long-term intravascular hemolysis, and eventually, irreversible mutilating deformities. Several supportive therapies such as strict sun avoidance, physical sunblocks, red blood cells transfusions, hydroxyurea, and splenectomy are commonly used in the management of CEP. Currently, the only available curative treatment of CEP is hematopoietic stem cell transplantation (HSCT). In this article, we present a young girl in which precocious genetic testing enabled early diagnosis and allowed curative treatment with HSCT for CEP at the age of 3 months of age, that is, the youngest reported case thus far.

Role of Biomarkers in Hepatocellular Carcinoma and Their Disease Progression

Hepatocellular carcinoma (HCC) is one of the third leading and common lethal cancers worldwide. Early detection of tumorigenesis of hepatocellular carcinoma is through ultrasonography, computerized tomography (CT) scans, and magnetic resonance imaging (MRI) scans; however, these methods are not up to the mark, so a search for an efficient biomarker for early diagnosis and treatment of hepatocarcinogenesis is important. Proteomic and genomic approaches aid to develop new promising biomarkers for the diagnosis of HCC at the early stages. These biomarkers not only help in prognosis but also provide better therapeutic intervention against HCC. Among the different biomarker candidates, liquid biopsy [including circulating tumor cells (CTCs) and circulating tumor DNA (ctDNA)] has recently emerged as a noninvasive detection technique for the characterization of circulating cells, providing a strong basis and early diagnosis for the individualized treatment of patients. This review provides the current understanding of HCC biomarkers that predict the risk of HCC recurrence.

Assessment of Radiology Artificial Intelligence Software: A Validation and Evaluation Framework

Artificial intelligence (AI) software in radiology is becoming increasingly prevalent and performance is improving rapidly with new applications for given use cases being developed continuously, oftentimes with development and validation occurring in parallel. Several guidelines have provided reporting standards for publications of AI-based research in medicine and radiology. Yet, there is an unmet need for recommendations on the assessment of AI software before adoption and after commercialization. As the radiology AI ecosystem continues to grow and mature, a formalization of system assessment and evaluation is paramount to ensure patient safety, relevance and support to clinical workflows, and optimal allocation of limited AI development and validation resources before broader implementation into clinical practice. To fulfil these needs, we provide a glossary for AI software types, use cases and roles within the clinical workflow; list healthcare needs, key performance indicators and required information about software prior to assessment; and lay out examples of software performance metrics per software category. This conceptual framework is intended to streamline communication with the AI software industry and provide healthcare decision makers and radiologists with tools to assess the potential use of these software. The proposed software evaluation framework lays the foundation for a radiologist-led prospective validation network of radiology AI software. Learning Points: The rapid expansion of AI applications in radiology requires standardization of AI software specification, classification, and evaluation. The Canadian Association of Radiologists' AI Tech & Apps Working Group Proposes an AI Specification document format and supports the implementation of a clinical expert evaluation process for Radiology AI software.