Analysis of super-enhancer using machine learning and its application to medical biology.

The analysis of super-enhancers (SEs) has recently attracted attention in elucidating the molecular mechanisms of cancer and other diseases. SEs are genomic structures that strongly induce gene expression and have been reported to contribute to the overexpression of oncogenes. Because the analysis of SEs and integrated analysis with other data are performed using large amounts of genome-wide data, artificial intelligence technology, with machine learning at its core, has recently begun to be utilized. In promoting precision medicine, it is important to consider information from SEs in addition to genomic data; therefore, machine learning technology is expected to be introduced appropriately in terms of building a robust analysis platform with a high generalization performance. In this review, we explain the history and principles of SE, and the results of SE analysis using state-of-the-art machine learning and integrated analysis with other data are presented to provide a comprehensive understanding of the current status of SE analysis in the field of medical biology. Additionally, we compared the accuracy between existing machine learning methods on the benchmark dataset and attempted to explore the kind of data preprocessing and integration work needed to make the existing algorithms work on the benchmark dataset. Furthermore, we discuss the issues and future directions of current SE analysis.

Mechanism of ERBB2 gene overexpression by the formation of super-enhancer with genomic structural abnormalities in lung adenocarcinoma without clinically actionable genetic alterations.

BACKGROUND: In an extensive genomic analysis of lung adenocarcinomas (LUADs), driver mutations have been recognized as potential targets for molecular therapy. However, there remain cases where target genes are not identified. Super-enhancers and structural variants are frequently identified in several hundred loci per case. Despite this, most cancer research has approached the analysis of these data sets separately, without merging and comparing the data, and there are no examples of integrated analysis in LUAD. METHODS: We performed an integrated analysis of super-enhancers and structural variants in a cohort of 174 LUAD cases that lacked clinically actionable genetic alterations. To achieve this, we conducted both WGS and H3K27Ac ChIP-seq analyses using samples with driver gene mutations and those without, allowing for a comprehensive investigation of the potential roles of super-enhancer in LUAD cases. RESULTS: We demonstrate that most genes situated in these overlapped regions were associated with known and previously unknown driver genes and aberrant expression resulting from the formation of super-enhancers accompanied by genomic structural abnormalities. Hi-C and long-read sequencing data further corroborated this insight. When we employed CRISPR-Cas9 to induce structural abnormalities that mimicked cases with outlier ERBB2 gene expression, we observed an elevation in ERBB2 expression. These abnormalities are associated with a higher risk of recurrence after surgery, irrespective of the presence or absence of driver mutations. CONCLUSIONS: Our findings suggest that aberrant gene expression linked to structural polymorphisms can significantly impact personalized cancer treatment by facilitating the identification of driver mutations and prognostic factors, contributing to a more comprehensive understanding of LUAD pathogenesis.

Comparison of Vision Transformers and Convolutional Neural Networks in Medical Image Analysis: A Systematic Review.

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.

Sketch-based semantic retrieval of medical images.

The volume of medical images stored in hospitals is rapidly increasing; however, the utilization of these accumulated medical images remains limited. Existing content-based medical image retrieval (CBMIR) systems typically require example images, leading to practical limitations, such as the lack of customizable, fine-grained image retrieval, the inability to search without example images, and difficulty in retrieving rare cases. In this paper, we introduce a sketch-based medical image retrieval (SBMIR) system that enables users to find images of interest without the need for example images. The key concept is feature decomposition of medical images, which allows the entire feature of a medical image to be decomposed into and reconstructed from normal and abnormal features. Building on this concept, our SBMIR system provides an easy-to-use two-step graphical user interface: users first select a template image to specify a normal feature and then draw a semantic sketch of the disease on the template image to represent an abnormal feature. The system integrates both types of input to construct a query vector and retrieves reference images. For evaluation, ten healthcare professionals participated in a user test using two datasets. Consequently, our SBMIR system enabled users to overcome previous challenges, including image retrieval based on fine-grained image characteristics, image retrieval without example images, and image retrieval for rare cases. Our SBMIR system provides on-demand, customizable medical image retrieval, thereby expanding the utility of medical image databases.

Advances in cancer DNA methylation analysis with methPLIER: use of non-negative matrix factorization and knowledge-based constraints to enhance biological interpretability.

DNA methylation is an epigenetic modification that results in dynamic changes during ontogenesis and cell differentiation. DNA methylation patterns regulate gene expression and have been widely researched. While tools for DNA methylation analysis have been developed, most of them have focused on intergroup comparative analysis within a dataset; therefore, it is difficult to conduct cross-dataset studies, such as rare disease studies or cross-institutional studies. This study describes a novel method for DNA methylation analysis, namely, methPLIER, which enables interdataset comparative analyses. methPLIER combines Pathway Level Information Extractor (PLIER), which is a non-negative matrix factorization (NMF) method, with regularization by a knowledge matrix and transfer learning. methPLIER can be used to perform intersample and interdataset comparative analysis based on latent feature matrices, which are obtained via matrix factorization of large-scale data, and factor-loading matrices, which are obtained through matrix factorization of the data to be analyzed. We used methPLIER to analyze a lung cancer dataset and confirmed that the data decomposition reflected sample characteristics for recurrence-free survival. Moreover, methPLIER can analyze data obtained via different preprocessing methods, thereby reducing distributional bias among datasets due to preprocessing. Furthermore, methPLIER can be employed for comparative analyses of methylation data obtained from different platforms, thereby reducing bias in data distribution due to platform differences. methPLIER is expected to facilitate cross-sectional DNA methylation data analysis and enhance DNA methylation data resources.

A discrimination model by machine learning to avoid gastrectomy for early gastric cancer.

Aim: Gastrectomy is recommended for patients with early gastric cancer (EGC) because the possibility of lymph node metastasis (LNM) cannot be completely denied. The aim of this study was to develop a discrimination model to select patients who do not require surgery using machine learning. Methods: Data from 382 patients who received gastrectomy for gastric cancer and who were diagnosed with pT1b were extracted for developing a discrimination model. For the validation of this discrimination model, data from 140 consecutive patients who underwent endoscopic resection followed by gastrectomy, with a diagnosis of pT1b EGC, were extracted. We applied XGBoost to develop a discrimination model for clinical and pathological variables. The performance of the discrimination model was evaluated based on the number of cases classified as true negatives for LNM, with no false negatives for LNM allowed. Results: Lymph node metastasis was observed in 95 patients (25%) in the development cohort and 11 patients (8%) in the validation cohort. The discrimination model was developed to identify 27 (7%) patients with no indications for additional surgery due to the prediction of an LNM-negative status with no false negatives. In the validation cohort, 13 (9%) patients were identified as having no indications for additional surgery and no patients with LNM were classified into this group. Conclusion: The discrimination model using XGBoost algorithms could select patients with no risk of LNM from patients with pT1b EGC. This discrimination model was considered promising for clinical decision-making in relation to patients with EGC.

Perioperative Management and Surgical Outcomes of Colorectal Cancer Patients Undergoing Peritoneal Dialysis for End-Stage Kidney Disease.

INTRODUCTION: Despite the fact that the number of peritoneal dialysis (PD) patients is increasing, there is little evidence on the surgical outcomes of PD patients who have colorectal cancer surgery, and there is no consensus on the safety and practicality of continuing PD. METHODS: We retrospectively evaluated the short- and long-term results, as well as the feasibility of continuing PD, in eight patients with PD who had colorectal cancer surgery at our institution between January 2010 and January 2021. RESULTS: The scheduled open-fashioned resection was performed in one patient, whereas the other seven surgeries were all conducted laparoscopically, with no intraoperative conversion to laparotomy necessary. Except for one patient with a history of recurring PD-related peritonitis, the PD catheter was kept in seven of the eight cases. Five of the seven patients continuing PD underwent temporary postoperative hemodialysis. At a median of 24.5 months of postoperative monitoring, no infectious complications were observed, six cases continued PD, and no recurrence of colorectal cancer was observed in all cases. CONCLUSIONS: Routine curative-intent colorectal cancer surgery with the preservation of the PD catheter is possible and safe in individuals receiving PD. This patient population's short- and long-term oncological results are comparable to general surgical outcomes of those without chronic kidney disease. PD can be maintained for a long period of time following major colorectal cancer surgery.

Intraoperative Indocyanine Green Imaging for the Evaluation of Blood Perfusion Area in Cancer of the Splenic Flexure With an Occluded Inferior Mesenteric Artery: A Report of Two Cases.

Radical resection for cancer of the splenic flexure requires careful consideration of the dissection line so that blood flow in the remnant bowel is maintained, particularly when the root of the inferior mesenteric artery (IMA) is already occluded. Intraoperative indocyanine green (ICG) imaging is a promising method for evaluating blood perfusion of organs and vessels. However, there are few reports on the use of ICG to determine the dissection line in patients with altered blood flow. In this article, we describe two cases of successful resection of splenic flexure cancer (SFC) in patients with an occluded IMA under ICG guidance. Case one was a 76-year-old man with a diagnosis of stage III SFC who had previously undergone endovascular aortic repair without reimplantation of the IMA. Intraoperative ICG imaging revealed that the left side of the colon was perfused mainly by the left branch of the middle colic artery (MCA). We performed a hemicolectomy with preservation of the MCA-left colic artery (LCA) arcade and resected an enlarged lymph node en bloc. Case two was a 77-year-old man with a diagnosis of stage II SFC in whom the root of the IMA appeared to be occluded by arteriosclerosis. Computed tomography showed that the LCA was anastomosed to the accessory middle colic artery (AMCA) while the left branch of the MCA was joined to the marginal artery. Intraoperative ICG imaging revealed that the left side of the colon was perfused by the AMCA and not the MCA. By preserving the AMCA-LCA arcade, we were able to safely divide the left branch of the MCA. Both patients were discharged with no symptoms of bowel ischemia or recurrence of cancer during follow-up. Interindividual variation in vessel branching patterns and dominant vessels in the descending and distal transverse colon may result from congenital factors or acquired disease. Detailed information on blood perfusion is required to avoid postoperative bowel ischemia. This report is the first to focus on patients with SFC and altered blood flow. We show that ICG imaging might be a reasonable option for determining an adequate surgical dissection area.

Is laparoscopic liver resection safe for super-elderly patients (aged ≥80)? A propensity score-matched analysis.

BACKGROUND/PURPOSE: The safety of laparoscopic liver resection in super-elderly patients with comorbidities is unknown. We used propensity score matching to evaluate the utility and safety of laparoscopic liver resection in super-elderly patients. METHODS: Two-hundred and five patients who underwent laparoscopic liver resection were retrospectively reviewed. They were classified into two groups based on age: ≥80 years (elderly group, n = 49) and <80 years (control group, n = 156). Propensity score matching (PSM) was performed based on preoperative clinical parameters. The intraoperative and postoperative outcomes were compared. RESULTS: After matching, 45 patients were included in each group. The intraoperative blood loss was identical between the control and elderly groups (60 vs 60 mL, respectively, P = .588); the frequency of serious postoperative complications (Clavien-Dindo class ≥3, 1/45 vs 1/45, P = 1.00) was also similar. There was no significant difference in terms of the exacerbation of malignancy (22.2% vs 11.1%, P = .258) or other diseases (8.9% vs 22.2%, P = .144). There was no difference in overall survival before and after PSM. However, 5-year overall survival excluding primary cancer-related death showed a difference after PSM (90.7% vs 70.4%; P = .048). CONCLUSIONS: Laparoscopic liver resection is feasible and safe in super-elderly patients. The long-term prognosis was poor in patients affected by other illnesses compared to the younger population with similar risk profiles, but there was no difference in overall survival.

Introducing AI to the molecular tumor board: one direction toward the establishment of precision medicine using large-scale cancer clinical and biological information.

Since U.S. President Barack Obama announced the Precision Medicine Initiative in his New Year's State of the Union address in 2015, the establishment of a precision medicine system has been emphasized worldwide, particularly in the field of oncology. With the advent of next-generation sequencers specifically, genome analysis technology has made remarkable progress, and there are active efforts to apply genome information to diagnosis and treatment. Generally, in the process of feeding back the results of next-generation sequencing analysis to patients, a molecular tumor board (MTB), consisting of experts in clinical oncology, genetic medicine, etc., is established to discuss the results. On the other hand, an MTB currently involves a large amount of work, with humans searching through vast databases and literature, selecting the best drug candidates, and manually confirming the status of available clinical trials. In addition, as personalized medicine advances, the burden on MTB members is expected to increase in the future. Under these circumstances, introducing cutting-edge artificial intelligence (AI) technology and information and communication technology to MTBs while reducing the burden on MTB members and building a platform that enables more accurate and personalized medical care would be of great benefit to patients. In this review, we introduced the latest status of elemental technologies that have potential for AI utilization in MTB, and discussed issues that may arise in the future as we progress with AI implementation.