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In the rapidly evolving field of medical image analysis utilizing artificial intelligence (AI), the selection of appropriate computational models is critical for accurate diagnosis and patient care. This literature review provides a comprehensive comparison of vision transformers (ViTs) and convolutional neural networks (CNNs), the two leading techniques in the field of deep learning in medical imaging. We conducted a survey systematically. Particular attention was given to the robustness, computational efficiency, scalability, and accuracy of these models in handling complex medical datasets. The review incorporates findings from 36 studies and indicates a collective trend that transformer-based models, particularly ViTs, exhibit significant potential in diverse medical imaging tasks, showcasing superior performance when contrasted with conventional CNN models. Additionally, it is evident that pre-training is important for transformer applications. We expect this work to help researchers and practitioners select the most appropriate model for specific medical image analysis tasks, accounting for the current state of the art and future trends in the field. [ABSTRACT FROM AUTHOR]

Background: The cancer genome contains several driver mutations. However, in some cases, no known drivers have been identified; these remaining areas of unmet needs, leading to limited progress in cancer therapy. Whole-genome sequencing (WGS) can identify non-coding alterations associated with the disease. Consequently, exploration of non-coding regions using WGS and other omics data such as ChIP-sequencing (ChIP-seq) to discern novel alterations and mechanisms related to tumorigenesis have been attractive these days. Methods: Integrated multi-omics analyses, including WGS, ChIP-seq, DNA methylation, and RNA-sequencing (RNA-seq), were conducted on samples from patients with non-clinically actionable genetic alterations (non-CAGAs) in lung adenocarcinoma (LUAD). Second-level cluster analysis was performed to reinforce the correlations associated with patient survival, as identified by RNA-seq. Subsequent differential gene expression analysis was performed to identify potential druggable targets. Results: Differences in H3K27ac marks in non-CAGAs LUAD were found and confirmed by analyzing RNA-seq data, in which mastermind-like transcriptional coactivator 2 (MAML2) was suppressed. The down-regulated genes whose expression was correlated to MAML2 expression were associated with patient prognosis. WGS analysis revealed somatic mutations associated with the H3K27ac marks in the MAML2 region and high levels of DNA methylation in MAML2 were observed in tumor samples. The second-level cluster analysis enabled patient stratification and subsequent analyses identified potential therapeutic target genes and treatment options. Conclusions: We overcome the persistent challenges of identifying alterations or driver mutations in coding regions related to tumorigenesis through a novel approach combining multi-omics data with clinical information to reveal the molecular mechanisms underlying non-CAGAs LUAD, stratify patients to improve patient prognosis, and identify potential therapeutic targets. This approach may be applicable to studies of other cancers with unmet needs. [ABSTRACT FROM AUTHOR]

Background: Circumportal pancreas is a rare morphological variant with clinical significance due to the high risk of postoperative pancreatic fistula in patients undergoing pancreaticoduodenectomy. Type IIIA (suprasplenic anteportal) is the most common type of circumportal pancreas. We present two cases of type IIIA treated with minimally invasive pancreaticoduodenectomy, and review the literature on patients with circumportal pancreas who underwent pancreatic surgery. Case presentation: Case 1: Laparoscopic Pancreaticoduodenectomy for Non-functioning Pancreatic Neuroendocrine Neoplasm with Circumportal Pancreas. A 69-year-old female with no prior medical history presented with a pancreatic head mass detected during routine ultrasound. CT revealed a 20 mm hypervascular tumor in pancreas head and a suprasplenic circumportal pancreas with an anteportal duct. The main pancreatic duct (MPD) was not in the parenchyma on the dorsal side of the portal vein (PV). Laparoscopic pancreaticoduodenectomy was performed. The anteportal side was resected with an ultrasonic device, and the retroportal side with a mesh-reinforced stapler. Pancreaticojejunostomy was performed without complications. Case 2: Robot-assisted Pancreaticoduodenectomy for Pancreatic Head Cancer and Non-functioning Pancreatic Neuroendocrine Neoplasm in the pancreatic tail with Circumportal Pancreas. A 72-year-old male with no prior medical history presented with a dilated main pancreatic duct on ultrasound. Diagnosed with pancreatic head cancer (Stage IIA), he underwent neoadjuvant chemotherapy. Contrast-enhanced CT revealed pancreatic cancer in the head and a tumor in the tail with unknown pathology. Robot-assisted pancreaticoduodenectomy was performed, and pancreatectomy on the left side of the tail tumor was planned. Intraoperative findings revealed a circumportal pancreas with the MPD not running through the dorsal parenchyma. After resected the parenchyma on the left side of the tail tumor, parenchyma on the dorsal side of the PV was dissected using SynchroSeal®. Pancreaticojejunostomy was performed without complications. The postoperative course was uneventful. Conclusions: The optimal location and method of pancreatic resection should be selected according to the type of circumportal pancreas and the location of the lesion to be resected to minimize the risk of pancreatic fistula. Minimally invasive surgery for circumportal pancreas remains challenging even for surgical teams with sufficient experience and skills, and careful consideration are necessary for its application. [ABSTRACT FROM AUTHOR]

Sensor degradation poses a critical yet 'often overlooked' challenge in accurately predicting the remaining useful life (RUL) of nuclear reactor pressure vessels (RPVs), hindering safe and efficient plant operation. This paper introduces an approach to RUL estimation that explicitly addresses sensor degradation, a significant departure from conventional methods. We model neutron embrittlement, a dominant degradation process in RPV steel, as a Wiener process and leverage real-world surveillance capsule data for insightful parameterization. Maximum likelihood estimation is utilized to characterize the degradation dynamics in the model. A Kalman filter then seamlessly integrates the degradation model with sensor measurements, effectively compensating for degradation-induced errors and providing refined state estimates. These estimates power a robust RUL prediction framework. Our results expose the profound impact of sensor degradation on conventional RUL predictions. By directly confronting sensor degradation, our method yields substantially more accurate and reliable RUL estimates. This work marks a significant advancement in the field of materials degradation, offering a powerful tool to optimize nuclear power plant safety and longevity. [ABSTRACT FROM AUTHOR]

Osteochondroma rarely occurs in the ribs; therefore, the treatment is not standardized. There are few studies of resection via complete thoracoscopic surgery (CTS), although video‐assisted thoracoscopic surgery with mini‐thoracotomy has been reported. Herein, we report a case of costal osteochondroma managed with CTS. A 23‐year‐old woman presented to our hospital due to left chest pain. Chest computed tomography revealed a bone‐like structure protruding into the thoracic cavity from the left fourth rib. Thus, surgery was performed to obtain a definitive diagnosis and provide appropriate treatment. The tumor was resected from the base at the border of the normal bone via CTS using three 5.5‐mm ports. A pathological diagnosis of costal osteochondroma was made. The patient had an uneventful clinical course and did not present with a recurrence 1 year after surgery. Therefore, CTS can be a good approach for cases with slim and stalked costal osteochondroma. [ABSTRACT FROM AUTHOR]

Aim: Immune checkpoint inhibitors (ICIs) are less effective in mismatch repair (MMR)‐proficient (pMMR) colorectal cancers (CRCs) than in MMR‐deficient CRCs. Here, we investigated changes in the tumor microenvironment after neoadjuvant chemotherapy (NAC) without radiotherapy in locally advanced rectal cancer (LARC) and the potential of ICIs as therapeutic agents for pMMR CRCs. Methods: This was an ad hoc analysis of a KSCC1301 randomized phase II trial in which patients with untreated resectable LARC were randomly assigned to receive S‐1 and oxaliplatin or folinic acid, 5‐fluorouracil, and oxaliplatin as NAC. Forty‐nine patients were studied in this ad hoc analysis. As a reference cohort, we assessed 25 rectal cancer patients who underwent surgery without NAC outside the randomized trial. Immune checkpoint molecules (ICMs; PD‐1, PD‐L1, CTLA‐4, LAG3), tumor‐infiltrating lymphocytes (TILs; CD8, FOXP3), and other related proteins were evaluated by immunohistochemistry. Next‐generation sequencing (NGS) using Oncomine™ Comprehensive Assay version 3 was conducted in 23 patients. Results: The expression levels of PD‐1, CTLA‐4, and LAG3 in the NAC group were significantly higher than in reference patients (p < 0.001). Additionally, the infiltration of CD8+ and FOXP3+ T cells, and the CD8/FOXP3 ratio were significantly higher in the NAC group than in reference patients (p < 0.0001). NGS analysis revealed no specific gene alteration related to TILs or ICMs. Conclusion: We demonstrated changes in the tumor immune microenvironment after NAC in pMMR rectal cancer. NAC was associated with increased expression of ICMs and TILs. Rectal cancer could be susceptible to combined immunotherapy with chemotherapy. [ABSTRACT FROM AUTHOR]

In response to the imperative need for robotic involvement in the decommissioning of the Fukushima Daiichi Nuclear Power Plant, a proactive initiative was undertaken. This initiative led to the organization of the "7th Decommissioning Creative Robot Contest," in which pivotal challenges related to nuclear plant decommissioning were addressed. Recognizing inaccessible areas within a decommissioned plant, the primary objective of this study was to develop a remotely operated robot capable of executing decontamination tasks in such environments. Two distinct locomotion systems were designed: mecanum wheels and crawlers. The mecanum wheels, characterized by their ability to execute approximately 100 mm vertical movements, facilitated the robot to surmount obstacles, particularly by negotiating 100 mm steps by engaging the tracks. Simultaneously, to address decontamination tasks at elevated locations, a threefold expandable ladder truck system was integrated, augmenting the operational range of the robot. Participation in the contest provided a platform for showcasing the developed technologies and validating their practicality. Despite encountering challenges during competition, such as damage to one locomotion system, the other locomotion system can be used to reach the destination. Experience serves as a crucial means of verifying technological advancements. Issues such as malfunctions in the range of motion control due to faulty limit switch contacts and unexpected loads on the tire elevation motors were noted, which hindered the achievement of the anticipated results. Despite these drawbacks, the adopted structural design demonstrates notable advantages, particularly in terms of maneuverability. The combination of mecanum wheels and crawlers along with a ladder truck system demonstrated adaptability to various scenarios. This adaptability holds significant promise for future nuclear power plant decontamination applications. While the competition may not have yielded the desired outcome, the lessons learned and the verified practicality of the developed technologies reinforce their potential utility in addressing the future challenges of nuclear plant decommissioning. [ABSTRACT FROM AUTHOR]

Chest radiography, commonly known as CXR, is frequently utilized in clinical settings to detect cardiopulmonary conditions. However, even seasoned radiologists might offer different evaluations regarding the seriousness and uncertainty associated with observed abnormalities. Previous research has attempted to utilize clinical notes to extract abnormal labels for training deep-learning models in CXR image diagnosis. However, these methods often neglected the varying degrees of severity and uncertainty linked to different labels. In our study, we initially assembled a comprehensive new dataset of CXR images based on clinical textual data, which incorporated radiologists' assessments of uncertainty and severity. Using this dataset, we introduced a multi-relationship graph learning framework that leverages spatial and semantic relationships while addressing expert uncertainty through a dedicated loss function. Our research showcases a notable enhancement in CXR image diagnosis and the interpretability of the diagnostic model, surpassing existing state-of-the-art methodologies. The dataset address of disease severity and uncertainty we extracted is: https://physionet.org/content/cad-chest/1.0/.