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Interventional oncology provides image-guided therapies, including transarterial tumor embolization and percutaneous tumor ablation, for malignant tumors in a minimally invasive manner. As in other medical fields, the application of artificial intelligence (AI) in interventional oncology has garnered significant attention. This narrative review describes the current state of AI applications in interventional oncology based on recent literature. A literature search revealed a rapid increase in the number of studies relevant to this topic recently. Investigators have attempted to use AI for various tasks, including automatic segmentation of organs, tumors, and treatment areas; treatment simulation; improvement of intraprocedural image quality; prediction of treatment outcomes; and detection of post-treatment recurrence. Among these, the AI-based prediction of treatment outcomes has been the most studied. Various deep and conventional machine learning algorithms have been proposed for these tasks. Radiomics has often been incorporated into prediction and detection models. Current literature suggests that AI is potentially useful in various aspects of interventional oncology, from treatment planning to post-treatment follow-up. However, most AI-based methods discussed in this review are still at the research stage, and few have been implemented in clinical practice. To achieve widespread adoption of AI technologies in interventional oncology procedures, further research on their reliability and clinical utility is necessary. Nevertheless, considering the rapid research progress in this field, various AI technologies will be integrated into interventional oncology practices in the near future., (© 2024. The Author(s).)

This review explores the potential applications of Large Language Models (LLMs) in nuclear medicine, especially nuclear medicine examinations such as PET and SPECT, reviewing recent advancements in both fields. Despite the rapid adoption of LLMs in various medical specialties, their integration into nuclear medicine has not yet been sufficiently explored. We first discuss the latest developments in nuclear medicine, including new radiopharmaceuticals, imaging techniques, and clinical applications. We then analyze how LLMs are being utilized in radiology, particularly in report generation, image interpretation, and medical education. We highlight the potential of LLMs to enhance nuclear medicine practices, such as improving report structuring, assisting in diagnosis, and facilitating research. However, challenges remain, including the need for improved reliability, explainability, and bias reduction in LLMs. The review also addresses the ethical considerations and potential limitations of AI in healthcare. In conclusion, LLMs have significant potential to transform existing frameworks in nuclear medicine, making it a critical area for future research and development., (© 2024. The Author(s) under exclusive licence to The Japanese Society of Nuclear Medicine.)

The synthesis and structural analysis of (Me 3 SiC 5 H 4 )TiCl 2 (OAr) [OAr = O-2,6- i Pr 2 -4-RC 6 H 2 ; R = H, SiEt 3 ] revealed that it exhibits higher catalytic activities than ( t BuC 5 H 4 )TiCl 2 (OAr), Cp*TiCl 2 (OAr), with efficient comonomer incorporation in ethylene/styrene copolymerization in the presence of a methylaluminoxane (MAO) cocatalyst. The catalytic activity in the copolymerization increased upon increasing the charged styrene concentration along with the increase in the styrene content in the copolymers, whereas the activities of other catalysts showed the opposite trend. (Me 3 SiC 5 H 4 )TiCl 2 (O-2,6- i Pr 2 C 6 H 3 ) displayed the most suitable catalyst performance in terms of its activity and styrene incorporation, affording amorphous copolymers with styrene contents higher than 50 mol% (up to 63.6 mol%) and with random styrene incorporation confirmed by 13 C-NMR spectra.

Magnetic resonance imaging (MRI) is an essential tool for evaluating pelvic disorders affecting the prostate, bladder, uterus, ovaries, and/or rectum. Since the diagnostic pathway of pelvic MRI can involve various complex procedures depending on the affected organ, the Reporting and Data System (RADS) is used to standardize image acquisition and interpretation. Artificial intelligence (AI), which encompasses machine learning and deep learning algorithms, has been integrated into both pelvic MRI and the RADS, particularly for prostate MRI. This review outlines recent developments in the use of AI in various stages of the pelvic MRI diagnostic pathway, including image acquisition, image reconstruction, organ and lesion segmentation, lesion detection and classification, and risk stratification, with special emphasis on recent trends in multi-center studies, which can help to improve the generalizability of AI., (© 2024. Italian Society of Medical Radiology.)

Myelin oligodendrocyte glycoprotein antibody-associated disease (MOGAD) has highly heterogeneous clinical presentations, in which encephalitis is an important phenotype. Moreover, MOGAD has been reported to exhibit diverse imaging findings. However, there have been no previous reports of cases with perivascular radial gadolinium enhancement in periventricular regions, commonly reported in autoimmune glial fibrillary acidic protein (GFAP) astrocytopathy. In this paper, we present two cases of MOGAD with this MRI feature, both of which underwent brain biopsy for the lesions. Brain biopsies revealed perivenous demyelination and inflammation consistent with acute disseminated encephalomyelitis (ADEM), with pronounced axonal damage in Case 1 and minimal axonal involvement in Case 2. Case 1 exhibited more severe cerebral atrophy than Case 2, correlating with the extent of axonal damage. Through these cases, we highlight the heterogeneity of radiological manifestations of MOGAD, expanding the spectrum beyond previously defined MRI patterns. Furthermore, histopathological analysis revealed distinct axonal involvement as a potential prognostic marker of brain atrophy. These observations emphasize the importance of considering MOGAD in the differential diagnosis, even in cases with atypical imaging findings, and highlight the significance of brain biopsy in guiding both diagnosis and prognosis., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Shimizu, Beck, Murayama, Hoshi, Sumikura, Higashida, Fukasaka, Shimada, Nagashima, Fujioka, Hatayama, Okuno, Mochizuki and Sakaguchi.)

Although several studies have been conducted on artificial intelligence (AI) use in mammography (MG), there is still a paucity of research on the diagnosis of metachronous bilateral breast cancer (BC), which is typically more challenging to diagnose. This study aimed to determine whether AI could enhance BC detection, achieving earlier or more accurate diagnoses than radiologists in cases of metachronous contralateral BC. We included patients who underwent unilateral BC surgery and subsequently developed contralateral BC. This retrospective study evaluated the AI-supported MG diagnostic system called FxMammo™. We evaluated the capability of FxMammo™ (FathomX Pte Ltd., Singapore) to diagnose BC more accurately or earlier than radiologists' assessments. This evaluation was supplemented by reviewing MG readings made by radiologists. Out of 1101 patients who underwent surgery, 10 who had initially undergone a partial mastectomy and later developed contralateral BC were analyzed. The AI system identified malignancies in six cases (60%), while radiologists identified five cases (50%). Notably, two cases (20%) were diagnosed solely by the AI system. Additionally, for these cases, the AI system had identified malignancies a year before the conventional diagnosis. This study highlights the AI system's effectiveness in diagnosing metachronous contralateral BC via MG. In some cases, the AI system consistently diagnosed cancer earlier than radiological assessments.

Autoimmune neutropenia (AIN) is an extremely rare condition, and there is no effective treatment option for this disorder. AIN can cause major complications in patients with perioperative infection. Herein, we present a 56-year-old female patient who was scheduled for breast cancer surgery. However, she was unexpectedly diagnosed with AIN. Thus, the surgery was postponed, and endocrine therapy was started. After 7 months of treatment, the surgery was performed. Granulocyte colony-stimulating factor was administered before the surgery, but the patient's neutrophil count did not increase. Thus, levofloxacin was administered during the surgery. The patient had fever (38.6°C) 1 day after the surgery. Her surgical wound did not present with redness, and there were no other signs of infection. The fever subsided on the second day after the surgery. Nevertheless, antibiotics were administered for 5 days. The patient was discharged on the sixth day after the surgery., Competing Interests: The authors declare that they have no conflicts of interest., (Copyright © 2024 Mio Adachi et al.)

Direct seeding is employed to circumvent the labor-intensive process of rice (Oryza sativa) transplantation, but this approach requires varieties with vigorous low-temperature germination (LTG) when sown in cold climates. To investigate the genetic basis of LTG, we identified the quantitative trait locus (QTL) qLTG11 from rice variety Arroz da Terra, which shows rapid seed germination at lower temperatures, using QTL-seq. We delineated the candidate region to a 52-kb interval containing GENERAL REGULATORY FACTOR14h (GF14h) gene, which is expressed during seed germination. The Arroz da Terra GF14h allele encodes functional GF14h, whereas Japanese rice variety Hitomebore harbors a 4-bp deletion in the coding region. Knocking out functional GF14h in a near-isogenic line (NIL) carrying the Arroz da Terra allele decreased LTG, whereas overexpressing functional GF14h in Hitomebore increased LTG, indicating that GF14h is the causal gene behind qLTG11. Analysis of numerous Japanese rice accessions revealed that the functional GF14h allele was lost from popular varieties during modern breeding. We generated a NIL in the Hitomebore background carrying a 172-kb genomic fragment from Arroz da Terra including GF14h. The NIL showed superior LTG compared to Hitomebore, with otherwise comparable agronomic traits. The functional GF14h allele from Arroz da Terra represents a valuable resource for direct seeding in cold regions., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Sugimura et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Purpose: To compare different biopsy systems with different-sized needles by determining the weight of the tissue cores, which is one of the important factors for precise pathological diagnoses, and to provide a rationale for choosing the appropriate breast biopsy system with the appropriate needle for breast cancer biopsy., Methods: Six different vacuum-assisted biopsy (VAB) systems and one core needle biopsy (CNB) system with different-sized needles in different modes were compared, representing 15 total combinations. Tissue cores were obtained from a chicken breast phantom, which is a common substitute for human breast tissue. Five cores were taken for each combination and weighed., Results: The CNB combination provided significantly lighter tissue cores compared with the VAB combinations with the same-size (14-G) needle (P < 0.01). The combinations using the thickest needle obtained the heaviest among all systems (P < 0.02). The untethered battery-free VAB system yielded the lightest specimen among the VAB systems with the same-sized (12-G) needle (P < 0.04). The percent coefficient of variation (%CV) of the core weights obtained using VAB without a basket was significantly smaller compared with the core weights obtained using VAB with a basket (P < 0.01)., Conclusion: VAB systems can yield larger tissue cores compared with CNB systems. The size of the tissue cores varies even with the same-sized needle among different VAB systems. When performing a breast tissue biopsy, it is important to consider not only CNB versus VAB but also what specific device to use with which needle size., (© 2024. The Author(s), under exclusive licence to The Japan Society of Ultrasonics in Medicine.)

Determining the reliability of nanofiltration (NF) membranes for the removal of contaminants of emerging concern, including polyfluoroalkyl substances (PFASs), pharmaceuticals, and personal care products (PPCPs), is important for ensuring drinking water safety. This study aimed to clarify the factors that influence the removal of nine major PFASs during submerged NF treatment via extrapolation based on the factors that influence PPCP removal. The rejection of nine PFASs in ultra-filtered dam water by a polypiperazine-amide (NF270) membrane increased from 71 % to 94 % at a low permeate flux of 5 L/m 2 h as the PFAS molecular dimensions increased. PFASs with a carboxylic acid (-CO 2 H) were rejected to a greater extent than PFASs with a sulfo group (-SO 3 H). Further, negatively charged PFASs or PPCPs were rejected to a greater extent than uncharged and positively charged PPCPs. Our findings suggest that the rejection of PFASs can vary because of the (i) clearance distance between the PFASs' molecular dimensions and NF membrane pore diameter and (ii) intensity of electrostatic repulsion between the PFASs' functional groups and NF membrane surface. Our study indicates that submerged NF can achieve high PFAS rejection; however, variations in rejection among PFASs can become more prominent owing to a low permeate flux., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 The Author(s).)

Background: Although low high-density lipoprotein cholesterol (HDL-C) levels are a common metabolic abnormality associated with insulin resistance, their role in cardiovascular risk stratification remains controversial. Recently, we developed a simple, high-throughput, cell-free assay system to evaluate the "cholesterol uptake capacity (CUC)" as a novel concept for HDL functionality. In this study, we assessed the CUC in patients with hypertriglyceridemia and diabetes mellitus., Methods: The CUC was measured using cryopreserved serum samples from 285 patients who underwent coronary angiography or percutaneous coronary intervention between December 2014 and May 2019 at Kobe University Hospital., Results: The CUC was significantly lower in diabetic patients (n = 125) than in nondiabetic patients (93.0 vs 100.7 arbitrary units (A.U.), P = 0.002). Patients with serum triglyceride (TG) levels >150 mg/dL (n = 94) also had a significantly lower CUC (91.8 vs 100.0 A.U., P = 0.004). Furthermore, the CUC showed a significant inverse correlation with TG, hemoglobin A1c (Hb A1c), homeostasis model assessment of insulin resistance (HOMA-IR), and body mass index (BMI). Finally, the HDL-C/Apolipoprotein A1 (ApoA1) ratio, calculated as a surrogate index of HDL particle size, was significantly positively correlated with the CUC (r2 = 0.49, P < 0.001), but inversely correlated with TG levels (r2 = -0.30, P < 0.001)., Conclusions: The CUC decreased in patients with hypertriglyceridemia and diabetes mellitus, and HDL particle size was a factor defining the CUC and inversely correlated with TG levels, suggesting that impaired CUC in insulin-resistant states was partially due to the shift in HDL towards smaller particles. These findings provide a better understanding of the mechanisms underlying impaired HDL functionality., (Published by Oxford University Press on behalf of Association for Diagnostics & Laboratory Medicine 2024.)

The advent of Deep Learning (DL) has significantly propelled the field of diagnostic radiology forward by enhancing image analysis and interpretation. The introduction of the Transformer architecture, followed by the development of Large Language Models (LLMs), has further revolutionized this domain. LLMs now possess the potential to automate and refine the radiology workflow, extending from report generation to assistance in diagnostics and patient care. The integration of multimodal technology with LLMs could potentially leapfrog these applications to unprecedented levels.However, LLMs come with unresolved challenges such as information hallucinations and biases, which can affect clinical reliability. Despite these issues, the legislative and guideline frameworks have yet to catch up with technological advancements. Radiologists must acquire a thorough understanding of these technologies to leverage LLMs' potential to the fullest while maintaining medical safety and ethics. This review aims to aid in that endeavor., (© 2024. The Author(s).)

Purpose: This study aimed to enhance the diagnostic accuracy of contrast-enhanced breast magnetic resonance imaging (MRI) using gadobutrol for differentiating benign breast lesions from malignant ones. Moreover, this study sought to address the limitations of current imaging techniques and criteria based on the Breast Imaging Reporting and Data System (BI-RADS)., Materials and Methods: In a multicenter retrospective study conducted in Japan, 200 women were included, comprising 100 with benign lesions and 100 with malignant lesions, all classified under BI-RADS categories 3 and 4. The MRI protocol included 3D fast gradient echo T1- weighted images with fat suppression, with gadobutrol as the contrast agent. The analysis involved evaluating patient and lesion characteristics, including age, size, location, fibroglandular tissue, background parenchymal enhancement (BPE), signal intensity, and the findings of mass and non-mass enhancement. In this study, univariate and multivariate logistic regression analyses were performed, along with decision tree analysis, to identify significant predictors for the classification of lesions., Results: Differences in lesion characteristics were identified, which may influence malignancy risk. The multivariate logistic regression model revealed age, lesion location, shape, and signal intensity as significant predictors of malignancy. Decision tree analysis identified additional diagnostic factors, including lesion margin and BPE level. The decision tree models demonstrated high diagnostic accuracy, with the logistic regression model showing an area under the curve of 0.925 for masses and 0.829 for non-mass enhancements., Conclusion: This study underscores the importance of integrating patient age, lesion location, and BPE level into the BI-RADS criteria to improve the differentiation between benign and malignant breast lesions. This approach could minimize unnecessary biopsies and enhance clinical decision-making in breast cancer diagnostics, highlighting the effectiveness of gadobutrol in breast MRI evaluations., (© 2024. The Author(s).)

The rapid advancement of artificial intelligence (AI) in healthcare has revolutionized the industry, offering significant improvements in diagnostic accuracy, efficiency, and patient outcomes. However, the increasing adoption of AI systems also raises concerns about their environmental impact, particularly in the context of climate change. This review explores the intersection of climate change and AI in healthcare, examining the challenges posed by the energy consumption and carbon footprint of AI systems, as well as the potential solutions to mitigate their environmental impact. The review highlights the energy-intensive nature of AI model training and deployment, the contribution of data centers to greenhouse gas emissions, and the generation of electronic waste. To address these challenges, the development of energy-efficient AI models, the adoption of green computing practices, and the integration of renewable energy sources are discussed as potential solutions. The review also emphasizes the role of AI in optimizing healthcare workflows, reducing resource waste, and facilitating sustainable practices such as telemedicine. Furthermore, the importance of policy and governance frameworks, global initiatives, and collaborative efforts in promoting sustainable AI practices in healthcare is explored. The review concludes by outlining best practices for sustainable AI deployment, including eco-design, lifecycle assessment, responsible data management, and continuous monitoring and improvement. As the healthcare industry continues to embrace AI technologies, prioritizing sustainability and environmental responsibility is crucial to ensure that the benefits of AI are realized while actively contributing to the preservation of our planet., Competing Interests: Declaration of competing interest The authors have no competing interests to disclose in relation with this article., (Copyright © 2024 The Author(s). Published by Elsevier Masson SAS.. All rights reserved.)

Hereditary angioedema (HAE) is a rare hereditary disorder characterized by episodic swelling and life-threatening airway obstruction caused by laryngeal angioedema. In most HAE patients, reduced level of serum C1-Inhibitor (type-I-HAE) or presence of aberrant C1-Inhibitor (type-II-HAE) result in the lost of regulation of the complementary system and contact activation system with downstream over-activation of bradykinin - the chief mediator leading to angioedema. Type-III HAE (HAE-nl-C1INH) is rare without deficient or dysfunction of C1-Inhibitor, often with genetic aberrant related to the contact activation system. The prevalence of HAE in the population is estimated at 1 in 50,000 individuals, often with early onset, but due to the heterogeneity of the disease, there is frequently a significant delay in diagnosis. Recently, better awareness by physicians, more access to diagnostic tools, better management and prophylaxis has decreased morbidity and mortality. A focus in HAE patient care shift from management of attacks with on-demand medication, to use of prophylaxis to reduce attacks has improved the overall quality of life of patients with HAE. One area in HAE research that has not been emphasized is the long-term consequence of C1-INH deficiency in HAE patients, other than the typical manifestations of HAE, as evidence have emerged linking this disorder with increased risk of cardiovascular diseases, auto-immune disorders, and malignancy. This review aims to gather the current knowledge and evidence of potential consequence of C1-Inhibitor deficiency in HAE aside from angioedema with emphasis in the improvement of long-term care and overall quality of life for HAE patients.

Background: Percutaneous coronary intervention (PCI) for lesions with eruptive calcified nodules (CNs) is associated with worse outcomes compared with that for other calcified lesions. We aimed to clarify the relationship between eruptive CNs at index PCI, optical coherence tomography (OCT) findings at the 8-month follow-up, and clinical outcomes using serial OCT., Methods: This retrospective observational study used data from a prospective, single-centre registry. We conducted consecutive PCI for calcified lesions requiring rotational atherectomy (RA) with OCT guidance. We categorized 51 patients (54 lesions) into those with (16 patients [16 lesions]) and without eruptive CNs (35 patients [38 lesions])., Results: Post-PCI, stent expansion was comparable between the 2 groups, and CN-like protrusion was found in 75% of lesions with eruptive CNs. Follow-up OCT at 8 months revealed in-stent CNs in 54% of treated eruptive CN lesions, whereas lesions without eruptive CNs lacked in-stent CNs. Multivariate linear regression analysis demonstrated that eruptive CN was associated with maximum neointimal tissue (NIT) thickness (regression coefficient 0.303; 95% confidence interval, 0.057-0.549; P = 0.02). Consequently, patients with eruptive CNs exhibited a higher clinically driven target lesion revascularization (TLR) rate than did those without at 1 year (31.3% vs 2.9%, P = 0.009) and 5 years (43.8% vs 11.4%, P = 0.02). TLR primarily occurred in lesions with maximum eruptive CN arc angles > 180°., Conclusions: Following RA treatment with acceptable stent expansion, eruptive CNs before PCI correlated with greater NIT formation with in-stent CNs, resulting in a higher TLR rate, particularly in lesions with maximum eruptive CN arc angles exceeding 180°., (Copyright © 2024 Canadian Cardiovascular Society. Published by Elsevier Inc. All rights reserved.)

We herein report a 79-year-old woman with subacute progressive ataxic sensory neuropathy. The patient's symptoms began with numbness in the lower extremities, which rapidly deteriorated, resulting in gait disturbance and abnormal sensations in the extremities, reaching a peak over a period of approximately two months. Nerve conduction studies revealed pure axonal-type sensory polyneuropathy. The expeditious progression of the disease initially prompted suspicion of Guillain-Barré syndrome or paraneoplastic syndrome. Nevertheless, after comprehensive evaluations, the conclusive diagnosis was confirmed as ataxic sensory neuropathy with Sjögren's syndrome. Intensive immunotherapy was administered; however, it was ineffective in halting disease progression. Consequently, this case underscores the significance of an early comprehensive diagnosis and prompt immunotherapy for ataxic sensory neuropathy associated with Sjögren's syndrome.

Citrin deficiency is a congenital secondary urea cycle disorder lacking useful disease models for effective treatment development. In this study, human induced pluripotent stem cells (iPSCs) were generated from two patients with citrin deficiency and differentiated into hepatocyte-like cells (HLCs). Citrin-deficient HLCs produced albumin and liver-specific markers but completely lacked citrin protein and expressed argininosuccinate synthase only weakly. In addition, ammonia concentrations in a medium cultured with citrin-deficient HLCs were higher than with control HLCs. Sodium pyruvate administration significantly reduced ammonia concentrations in the medium of citrin-deficient HLCs and slightly reduced ammonia in HLCs differentiated from control iPSCs, though this change was not significant. Our results suggest that sodium pyruvate may be an efficient treatment for patients with citrin deficiency. Citrin-deficient iPSCs are a pathological liver model for congenital urea cycle disorders to clarify pathogenesis and develop novel therapies., Competing Interests: None., (© 2024 The Authors. Published by Elsevier Inc.)

Myogenesis is regulated mainly by transcription factors known as Myogenic Regulatory Factors (MRFs), and the transcription is affected by epigenetic modifications. However, the epigenetic regulation of myogenesis is poorly understood. Here, we focused on the epigenomic modification enzyme, PHF2, which demethylates histone 3 lysine 9 dimethyl (H3K9me2) during myogenesis. Phf2 mRNA was expressed during myogenesis, and PHF2 was localized in the nuclei of myoblasts and myotubes. We generated Phf2 knockout C2C12 myoblasts using the CRISPR/Cas9 system and analyzed global transcriptional changes via RNA-sequencing. Phf2 knockout (KO) cells 2 d post differentiation were subjected to RNA sequencing. Gene ontology (GO) analysis revealed that Phf2 KO impaired the expression of the genes related to skeletal muscle fiber formation and muscle cell development. The expression levels of sarcomeric genes such as Myhs and Mybpc2 were severely reduced in Phf2 KO cells at 7 d post differentiation, and H3K9me2 modification of Mybpc2, Mef2c and Myh7 was increased in Phf2 KO cells at 4 d post differentiation. These findings suggest that PHF2 regulates sarcomeric gene expression via epigenetic modification., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Fukushima et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

A 44-year-old woman with a subacute onset of an altered mental status, urinary retention, and fluctuating blood pressure was initially diagnosed with anti-N-methyl-d-aspartate receptor (NMDAR) encephalitis, meeting the criteria of Graus et al. Cardiac arrest occurred, which required pacemaker placement. She subsequently showed profound flaccid limb paralysis, with magnetic resonance imaging demonstrating focal necrotic lesions localized in the anterior horn of the longitudinal segments of the spinal cord and in the pontine tegmentum. Enteroviruses or autoimmune encephalitis-associated autoantibodies were not detected. We herein report a case of acute flaccid myelitis with profound psychiatric symptoms and dysautonomia, resembling NMDAR encephalitis.

A woman in her 70s, initially suspected of having fibroadenoma due to a well-defined mass in her breast, underwent regular mammography and ultrasound screenings. Over several years, no appreciable alterations in the mass were observed, maintaining the fibroadenoma diagnosis. However, in the fourth year, an ultrasound indicated slight enlargement and peripheral irregularities in the mass, even though the mammography images at that time showed no alterations. Interestingly, mammography images over time showed the gradual disappearance of previously observed arterial calcification around the mass. Pathological examination eventually identified the mass as invasive ductal carcinoma. Although the patient had breast tissue arterial calcification typical of atherosclerosis, none was present around the tumor-associated arteries. This case highlights the importance of monitoring arterial calcification changes in mammography, suggesting that they are crucial indicators in breast cancer diagnosis, beyond observing size and shape alterations.

Study Design: Retrospective diagnostic study., Objective: To automatically detect osteolytic bone metastasis lesions in the thoracolumbar region using conventional computed tomography (CT) scans, we developed a new deep learning (DL)-based computer-aided detection model., Summary of Background Data: Radiographic detection of bone metastasis is often difficult, even for orthopedic surgeons and diagnostic radiologists, with a consequent risk for pathologic fracture or spinal cord injury. If we can improve detection rates, we will be able to prevent the deterioration of patients' quality of life at the end stage of cancer., Materials and Methods: This study included CT scans acquired at Tokyo Medical and Dental University (TMDU) Hospital between 2016 and 2022. A total of 263 positive CT scans that included at least one osteolytic bone metastasis lesion in the thoracolumbar spine and 172 negative CT scans without bone metastasis were collected for the datasets to train and validate the DL algorithm. As a test data set, 20 positive and 20 negative CT scans were separately collected from the training and validation datasets. To evaluate the performance of the established artificial intelligence (AI) model, sensitivity, precision, F1-score, and specificity were calculated. The clinical utility of our AI model was also evaluated through observer studies involving six orthopaedic surgeons and six radiologists., Results: Our AI model showed a sensitivity, precision, and F1-score of 0.78, 0.68, and 0.72 (per slice) and 0.75, 0.36, and 0.48 (per lesion), respectively. The observer studies revealed that our AI model had comparable sensitivity to orthopaedic or radiology experts and improved the sensitivity and F1-score of residents., Conclusion: We developed a novel DL-based AI model for detecting osteolytic bone metastases in the thoracolumbar spine. Although further improvement in accuracy is needed, the current AI model may be applied to current clinical practice., Level of Evidence: Level III., Competing Interests: The authors report no conflicts of interest., (Copyright © 2023 The Author(s). Published by Wolters Kluwer Health, Inc.)

Background/aim: Antiviral hepatitis and systemic therapies for hepatocellular carcinoma (HCC) remarkably progressed in the recent 10 years. This study aimed to reveal the actual transition and changes in the prognosis and background liver disease in non-advanced HCC in the past 20 years., Methods: This retrospectively recruited 566 patients who were diagnosed with non-advanced HCC from February 2002 to February 2022. The prognosis was analyzed by subdividing according to the diagnosis date (period I: February 2002-April 2009 and period Ⅱ: May 2009-February 2022)., Results: Patients in period II (n = 351) were significantly older, with lower albumin-bilirubin (ALBI) scores and alpha-fetoprotein (AFP) and more anti-viral therapy, systemic therapy, and hepatic arterial infusion chemotherapy as compared with those in period I (n = 215). The etiology ratio of the background liver disease revealed decreased hepatitis C virus from 70.6% to 49.0% and increased non-B, non-C from 17.7% to 39.9% from periods I to Ⅱ. The multivariate analysis revealed older age and higher ALBI score in Barcelona Clinic Liver Cancer (BCLC) 0/A stage, AFP of >20 ng/mL, and higher ALBI score in BCLC B stage as independent prognosis factors. Fine-Gray competing risk model analysis revealed that liver-related deaths significantly decreased in period II as compared to period I, especially for BCLC stage 0/A (HR: 0.656; 95%CI: 0.442-0.972, P = 0.036)., Conclusion: The characteristics of patients with non-advanced HCC have changed over time. Appropriate background liver management led to better liver-related prognoses in BCLC 0/A., Competing Interests: NO authors have competing interests., (Copyright: © 2024 Kaneko et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Malignant pericardial mesothelioma (MPM) is extremely rare, and peritoneal dissemination has not yet been reported. There is no consensus regarding appropriate pharmacological treatment for MPM, including immune checkpoint inhibitors (ICIs). We herein report a 36-year-old man with MPM diagnosed by peritoneal dissemination and treated with an ICI. Cytology of the ascites revealed malignant peritonitis, and a re-evaluation of a pericardial biopsy performed at the previous hospital led to a diagnosis of MPM. The patient was treated with nivolumab and showed a clinical response despite several complications, such as renal dysfunction and performance status deterioration. This case report provides suggestive information for the diagnosis and ICI therapy of a rare type of mesothelioma.

Propylene polymerizations with different ketimide-modified half-titanocene catalysts, Cp'TiCl 2 (N=C t Bu 2 ) [Cp' = C 5 H 5 ( 1 ), C 5 Me 5 ( 2 ), Me 3 SiC 5 H 4 ( 3 )], with MAO as a cocatalyst, were investigated. The obtained polymers were studied in detail by determining their microstructure, molar masses, thermal, and mechanical properties. The Cp*-ketimide, (C 5 Me 5 )TiCl 2 (N=C t Bu 2 ) ( 2 ), exhibited higher catalytic activities than Cp'TiCl 2 (N=C t Bu 2 ) ( 1 , 3 ), yielding higher molar mass polymers, M w up to 1400 Kg/mol. All the synthesized polypropylenes (PP) are atactic and highly regioregular, with predominant rrrr pentads, especially PP prepared with catalyst 1 . Differential scanning calorimetry (DSC) established that the polymers are fully amorphous aPP, and no melting endotherm events are detected. Glass transition temperatures were detected between -2 and 2 °C. These polypropylenes have been established to be high-performance thermoplastic elastomers endowed with remarkably high ductility, and a tensile strain at break higher than 2000%.

Objectives: The aim of the study was to investigate the serological factors in predicting symptom improvement in myasthenia gravis (MG) patients who underwent plasmapheresis (PP)., Methods: The relationship between symptom improvement and change in immunoglobin G (IgG) and acetylcholine receptor antibody (AchR-Ab) levels in 21 MG patients after PP was analyzed., Results: Patients were divided into two groups: the favorable (n = 11) or unfavorable (n = 10) response groups around the median MG composite improvement rate (50 [29, 56]%) 2 weeks after a PP course. In all patients, the IgG reduction rate before and after PP was higher in the favorable than the unfavorable response group. In AChR-Ab-positive patients, IgG and AChR-Ab reduction rates were higher in the favorable than the unfavorable response group., Conclusion: IgG reduction rate is a predictor of symptom improvement in MG patients who underwent PP., (© 2023 International Society for Apheresis and Japanese Society for Apheresis.)

This review provides an overview of the application of artificial intelligence (AI) in radiation therapy (RT) from a radiation oncologist's perspective. Over the years, advances in diagnostic imaging have significantly improved the efficiency and effectiveness of radiotherapy. The introduction of AI has further optimized the segmentation of tumors and organs at risk, thereby saving considerable time for radiation oncologists. AI has also been utilized in treatment planning and optimization, reducing the planning time from several days to minutes or even seconds. Knowledge-based treatment planning and deep learning techniques have been employed to produce treatment plans comparable to those generated by humans. Additionally, AI has potential applications in quality control and assurance of treatment plans, optimization of image-guided RT and monitoring of mobile tumors during treatment. Prognostic evaluation and prediction using AI have been increasingly explored, with radiomics being a prominent area of research. The future of AI in radiation oncology offers the potential to establish treatment standardization by minimizing inter-observer differences in segmentation and improving dose adequacy evaluation. RT standardization through AI may have global implications, providing world-standard treatment even in resource-limited settings. However, there are challenges in accumulating big data, including patient background information and correlating treatment plans with disease outcomes. Although challenges remain, ongoing research and the integration of AI technology hold promise for further advancements in radiation oncology., (© The Author(s) 2023. Published by Oxford University Press on behalf of The Japanese Radiation Research Society and Japanese Society for Radiation Oncology.)

Encephalitis caused by antibodies targeting the leucine-rich glioma-inactivated 1 protein receptor, which belongs to the anti-voltage-gated potassium channel receptor complex, is characterized by hyponatremia, progressive cognitive impairment, seizures, and psychiatric disorders. The patient initially presented with faciobrachial dystonic seizures and subsequently developed encephalopathy. Brain magnetic resonance imaging revealed atypical unilateral hyperintense signals in the cerebral cortex and white matter. Intravenous corticosteroid pulse therapy effectively improved faciobrachial dystonic seizures and brain lesions.

In this review, we address the issue of fairness in the clinical integration of artificial intelligence (AI) in the medical field. As the clinical adoption of deep learning algorithms, a subfield of AI, progresses, concerns have arisen regarding the impact of AI biases and discrimination on patient health. This review aims to provide a comprehensive overview of concerns associated with AI fairness; discuss strategies to mitigate AI biases; and emphasize the need for cooperation among physicians, AI researchers, AI developers, policymakers, and patients to ensure equitable AI integration. First, we define and introduce the concept of fairness in AI applications in healthcare and radiology, emphasizing the benefits and challenges of incorporating AI into clinical practice. Next, we delve into concerns regarding fairness in healthcare, addressing the various causes of biases in AI and potential concerns such as misdiagnosis, unequal access to treatment, and ethical considerations. We then outline strategies for addressing fairness, such as the importance of diverse and representative data and algorithm audits. Additionally, we discuss ethical and legal considerations such as data privacy, responsibility, accountability, transparency, and explainability in AI. Finally, we present the Fairness of Artificial Intelligence Recommendations in healthcare (FAIR) statement to offer best practices. Through these efforts, we aim to provide a foundation for discussing the responsible and equitable implementation and deployment of AI in healthcare., (© 2023. The Author(s).)