Prediction recurrence in stage I epidermal growth factor receptor-mutated non-small cell lung cancer using multi-modal data.

Introduction: Integrated recurrence prediction models that combine clinical, imaging, and genetic data are lacking for epidermal growth factor receptor (EGFR)-mutated stage I non-small cell lung cancer (NSCLC). We developed a recurrence prediction model for Stage I EGFR-mutated NSCLC by integrating clinical, radiological, and whole-exome sequencing (WES) data.

Methods: A total of 306 patients with Stage I EGFR-mutated NSCLC were stratified into training (n = 206) and validation (n = 100) cohorts using stratified random sampling.

SEgene identifies links between super enhancers and gene expression across cell types.

Enhancers are non-coding DNA regions that facilitate gene transcription, with a specialized subset, super-enhancers, known to exert exceptionally strong transcriptional activation effects. Super-enhancers have been implicated in oncogenesis, and their identification is achievable through histone mark chromatin immunoprecipitation followed by sequencing data using existing analytical tools. However, conventional super-enhancer detection methodologies often do not accurately reflect actual gene expression levels, and the large volume of identified super-enhancers complicates comprehensive analysis.

Establishment of a machine learning model for predicting splenic hilar lymph node metastasis.

Upper gastrointestinal cancer (UGC) sometimes metastasizes to the splenic hilum lymph node (SHLN). However, surgical removal of SHLN is technically difficult, and the risk of postoperative complications is high. Although there are models that predict SHLN metastasis, they usually only provide point estimates of risk, and there is a lack of sufficient information.

Analysis of Inertial Measurement Unit Data for an AI-Based Physical Function Assessment System Using In-Clinic-like Movements.

Assessing objective physical function in patients with cancer is crucial for evaluating their ability to tolerate invasive treatments. Current assessment methods, such as the timed up and go (TUG) test and the short physical performance battery, tend to require additional resources and time, limiting their practicality in routine clinical practice. To address these challenges, we developed a system to assess physical function based on movements observed during clinical consultations and aimed to explore relevant features from inertial measurement unit data collected during those movements.

Introduction of AI Technology for Objective Physical Function Assessment.

Objective physical function assessment is crucial for determining patient eligibility for treatment and adjusting the treatment intensity. Existing assessments, such as performance status, are not well standardized, despite their frequent use in daily clinical practice. This paper explored how artificial intelligence (AI) could predict physical function scores from various patient data sources and reviewed methods to measure objective physical function using this technology.

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.

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.

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.

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.

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.

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.

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.

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.