iRadiology最新文献

Colorectal cancer (CRC) is a global health challenge with high morbidity and mortality. Radiomics, an emerging field, utilizes quantitative imaging features extracted from medical images for CRC diagnosis, staging, treatment response assessment, and prognostication. This review highlights the potential of radiomics for personalized CRC management. Radiomics enables noninvasive tumor characterization, aiding in early detection and accurate diagnosis, and it can be used to predict tumor stage, lymph node involvement, and prognosis. Furthermore, radiomics guides personalized therapies by assessing the treatment response and identifying patients who could benefit. Challenges include standardizing imaging protocols and analysis techniques. Robust validation frameworks and user-friendly software are needed for the integration of radiomics into clinical practice. Despite challenges, radiomics offers valuable insights into tumor biology, treatment response, and prognosis in CRC. Overcoming technical and clinical hurdles will unlock its full potential in CRC management.

Varicose veins of the round ligaments during pregnancy are rare with only 16 reported cases in literature in the last 65 years [1]. It can easily be mistaken for an inguinal hernia. Anatomically, the round ligament extends from the lateral uterus to the labium majorus containing veins, arteries, lymphatic channels, and nerves. Round ligament varices are prominent veins within the round ligament and are more common in pregnancy because pregnancy promotes increased venous flow and decreased venous tone [2]. During physical examination, it is difficult to differentiate between round ligament varicosity and inguinal hernia.

A 29-year-old woman, in the 30th week of pregnancy, presented to the emergency department with a complaint of a small, painless palpable left groin mass. The surgeon's clinical diagnosis was inguinal hernia and she was referred to the radiology department for diagnostic ultrasound. Doppler sonography showed an asymmetric left inguinal mass composed of multiple anechoic tubular ducts (Figure 1) with a venous flow pattern (Figure 2), consistent with round ligament varices. The patient was treated conservatively without any complication in pregnancy.

The diagnosis of varicosities of the round ligaments can be made on grayscale and color Doppler sonography [2]. Rapid identification and diagnosis of round ligament varices are important to avoid unnecessary surgical treatment. Ultrasound is the gold standard for the diagnosis with a classic gray-scale ultrasound image of a “bag of worms” appearance associated with varicose veins and a venous flow pattern on Doppler imaging [3].

Dragan Vasin: Conceptualization (lead); investigation (equal); visualization (equal). Danijela Sekulić: Conceptualization (equal); investigation (equal); software (equal).

There are no conflicts of interest to declare.

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Authors obtained inform patient consent.

Alpha emitters are radionuclides with good pharmacological characteristics for the treatment of cancer because they decay by emitting high linear energy transfer particles. Recent advancements in isotope production and purification and the generation of novel techniques for optimum targeting have led to the development of targeted alpha therapy (TAT). The great cytotoxic potential of α-particle emissions combined with monoclonal antibodies, peptides, small compounds, or nanoparticles has led to investigations of TAT in the pre-clinical context and more recently, in oncology clinical trials. Numerous studies have shown that TAT is effective both in vitro and in vivo. The first α-emitter to obtain FDA approval for the treatment of prostate cancer with metastatic bone lesions was radium-223 dichloride. Many clinical trials are being conducted to evaluate the efficiency and safety of several radionuclides in cancer treatment, including radium-223, astatine-211, actinium-225, bismuth-213, lead-212, and thorium-227. This review provides an overview of the therapeutic use of these radionuclides and a summary of the studies that lay the groundwork for future clinical advancement.

Adoptive T-cell therapy (ACT), which is an important type of live cell therapy, has achieved unprecedented success in treating hematological malignancies. Recent studies have shown that ACT is also a promising treatment for solid tumors. Visualizing the in vivo fates (distribution, homing, infiltration, proliferation, and exhaustion) of the immune cells used for ACT (ACT immune cells) is of great importance to promote basic research and clinical translation of ACT. Optical imaging techniques, including bioluminescence, fluorescence, and photoacoustic imaging, have the advantages of high sensitivity, high spatiotemporal resolution, minimal exposure to harmful radiation, and simple instrumentation. Recently, various types of optical imaging probes, including bioluminescence, fluorescence, and photoacoustic imaging probes, have been used to visualize ACT immune cells in vivo and evaluate the molecular mechanism, efficacy, and side effects of ACT. In this review, the optical imaging probes and labeling methods that have been used for in vivo visualization of ACT immune cells are summarized, and the opportunities and challenges of using optical imaging to visualize ACT immune cells in vivo are discussed.

A 48-year-old woman was admitted to our hospital because of neck tightness, cough, hoarseness, and nocturnal dyspnea. Physical examination revealed left neck swelling, but her thyroid gland was small on ultrasound examination. A clear demarcation of the thyroid gland depicting fibrotic invasion was difficult to find and positron emission tomography/computed tomography suggested chronic thyroiditis (Figure 1a–e). The parathyroid hormone and calcium levels were within normal limits. Biopsy revealed some fibrocytes and lymphocytes but no signs of malignancy. The patient was finally diagnosed with Riedel's thyroiditis. She was treated with prednisone and methotrexate. Cervical ultrasonography performed throughout follow-up showed no significant change in the thyroid volume; however, the compressive symptoms disappeared and hoarseness was relieved. The hypoechoic areas surrounding the carotid arteries gradually decreased after treatment (Figure 1f–h). No further changes were obvious beyond 1 year.

Xinlong Shi analyzed the data and prepared the first draft of the manuscript. Xinlong Shi and Yu Xia participated in the conception and design of the study, Xinlong Shi constructively revised the manuscript; Yu Xia participated in and supervised the study throughout, and he shared corresponding authorship. All authors commented on previous versions of the manuscript and approved the final version.

The authors declare no conflicts of interest.

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Due to their inherent tunable spectrum, high brightness, excellent biostability and biocompatibility, and functionalization of surfaces, semiconducting polymer dots (Pdots) are now playing an essential role in fluorescent (FL) imaging and disease treatment through bioconjugation with peptides or biomimetic materials. In particular, biomimetic Pdots exhibit their capability in targeted imaging of lesion and increased efficacy for targeting disease treatment. This review will inspect the recent advances in the design and functionalization strategies of biomodified and biomimetic Pdots for enhanced disease detection and therapy. More importantly, the application of these two modifications in targeted FL imaging and cancer treatment is to be addressed in detail. Meanwhile, the main challenges and prospects of biomimetic and biomodified Pdots are to be discussed, which will pave a new avenue for improved disease detection and imaging-guided treatment.

Medical imaging is playing an increasingly crucial role in disease diagnosis. Numerous deep learning-based methods have been developed for image-guided automatic disease diagnosis. Most of the methods have harnessed conventional convolutional neural networks, which are directly applied in the regular image domain. However, some irregular spatial patterns revealed in medical images are also critical to disease diagnosis, since they can describe latent relations in different image regions of a subject (e.g., different focal lesions in an image) or between different groups (e.g., Alzheimer's disease and healthy control). Therefore, how to exploit and analyze irregular spatial patterns and their relations has become a research challenge in the field of image-guided disease diagnosis. To address this challenge, graph neural networks (GNNs) are proposed to perform the convolution operation on graphs. Graphs can naturally represent irregular spatial structures. Because of their ability to aggregate node features, edge features, and graph structure information to capture and learn hidden spatial patterns in irregular structures, GNN-based algorithms have achieved promising results in the detection of various diseases. In this paper, we introduce commonly used GNN-based algorithms and systematically review their applications to disease diagnosis. We summarize the workflow of GNN-based applications in disease diagnosis, ranging from localizing the regions of interest and edge construction to modeling. Furthermore, we discuss the limitations and outline potential research directions for GNNs in disease diagnosis.

The liver is a multifaceted organ that is responsible for many critical functions encompassing amino acid, carbohydrate, and lipid metabolism, all of which make a healthy liver essential for the human body. Contemporary imaging methodologies have remarkable diagnostic accuracy in discerning focal liver lesions; however, a comprehensive understanding of diffuse liver diseases is a requisite for radiologists to accurately diagnose or predict the progression of such lesions within clinical contexts. Nonetheless, the conventional attributes of radiological features, including morphology, size, margin, density, signal intensity, and echoes, limit their clinical utility. Radiomics is a widely used approach that is characterized by the extraction of copious image features from radiographic depictions, which gives it considerable potential in addressing this limitation. It is worth noting that functional or molecular alterations occur significantly prior to the morphological shifts discernible by imaging modalities. Consequently, the explication of potential mechanisms by multiomics analyses (encompassing genomics, epigenomics, transcriptomics, proteomics, and metabolomics) is essential for investigating putative signal pathway regulations from a radiological viewpoint. In this review, we elaborate on the principal pathological categorizations of diffuse liver diseases, the evaluation of multiomics approaches pertaining to diffuse liver diseases, and the prospective value of predictive models. Accordingly, the overarching objective of this review is to scrutinize the interrelations between radiological features and bioinformatics as well as to consider the development of prediction models predicated on radiobioinformatics as integral components of clinical decision support systems for diffuse liver diseases.