The University of Osaka · Medicine
Professor Masahiro Yanagawa's research lab specializes in advanced medical imaging, particularly in the application of high-resolution computed tomography (CT) for early lung cancer detection and characterization. The lab focuses on improving diagnostic accuracy in lung adenocarcinoma by evaluating thin-section and ultra-high-resolution CT for predicting tumor invasiveness and pathological features. A key direction involves comparing radiological assessment with deep learning algorithms to enhance the precision of non-invasive imaging biomarkers. The lab also investigates metabolic imaging using PERCIST to better predict treatment response in esophageal cancer, aiming to refine clinical decision-making.
Figures are computed from collected data and may differ slightly.
• Ultra-high-resolution CT (U-HRCT) can improve spatial resolution. • U-HRCT can reduce streak and dark band artifacts. • U-HRCT can provide higher image quality than conventional area detector CT. • In U-HRCT, the volume mode is less noisy than the super-high-resolution mode. • U-HRCT may provide more detailed information about the lung anatomy and pathology.
RECIST based on the anatomic size reduction rate did not demonstrate the correlation between therapeutic responses and prognosis in patients with esophageal cancer receiving neoadjuvant chemotherapy. However, PERCIST was found to be the strongest independent predictor of outcomes. Given the significance of noninvasive radiologic imaging in formulating clinical treatment strategies, PERCIST might be considered more suitable for evaluation of chemotherapeutic response to esophageal cancer than REC
Background High-spatial-resolution (HSR) CT provides detailed information and clear delineation of lung anatomy and disease states. HSR CT may have high diagnostic performance for predicting invasiveness of lung adenocarcinoma. Purpose To examine the diagnostic performance of HSR CT in predicting the invasiveness of lung adenocarcinoma. Materials and Methods In this retrospective study, 89 consecutive patients with adenocarcinoma in situ (AIS), minimally invasive adenocarcinoma (MIA), or invasiv
Two volumetric measurements (solid volume, ≥1.5 cm(3); percentage of solid volume, ≥63%) were found to be independent indicators associated with increased likelihood of recurrence and/or death in patients with stage I adenocarcinoma.
To evaluate thin-section computed tomography (CT) (TSCT) features that differentiate adenocarcinoma in situ (AIS), minimally invasive adenocarcinoma (MIA), and invasive adenocarcinoma (IVA), and to determine the size of solid portion on CT that correlates to pathological invasive components. Forty-eight patients were included. Nodules were classified into ground-glass nodule (GGN), part-solid, solid, and heterogeneous. Visual density of GGNs was subjectively evaluated using reference standard im
Abstract To compare results for radiological prediction of pathological invasiveness in lung adenocarcinoma between radiologists and a deep learning (DL) system. Ninety patients (50 men, 40 women; mean age, 66 years; range, 40–88 years) who underwent pre-operative chest computed tomography (CT) with 0.625-mm slice thickness were included in this retrospective study. Twenty-four cases of adenocarcinoma in situ (AIS), 20 cases of minimally invasive adenocarcinoma (MIA), and 46 cases of invasive ad
The image quality of standard-dose garnet-based-detector CT (high-resolution) was significantly improved. Although highly reduced-dose garnet-based-detector CT (high-resolution mode) provided more image noise, overall image quality was not different between conventional-detector CT and garnet-based-detector CT.
Although there is no solid agreement for artificial intelligence (AI), it refers to a computer system with intelligence similar to that of humans. Deep learning appeared in 2006, and more than 10 years have passed since the third AI boom was triggered by improvements in computing power, algorithm development, and the use of big data. In recent years, the application and development of AI technology in the medical field have intensified internationally. There is no doubt that AI will be used in c
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