Background: Few studies have examined the use of two-dimensional computed tomography (2D CT) and three-dimensional (3D) reconstruction images to determine the intersegmental plane (ISP) for pulmonary segmentectomy, but a systematic approach and nomenclature are currently lacking. This current study used 3D reconstruction of CT imaging to analyze variations in the right lower lobe's pulmonary ISP and created a simplified model to determine the optimum cutting plane (CP) for clinical application for operative planning and use during thoracoscopic anatomical partial lobectomy (APL).
Methods: Between January 2018 and October 2019, 325 patients with pulmonary lesions were identified who underwent thin-slice CT scans of the chest. The ISPs were identified by analyzing the 2D CT scans and 3D reconstruction images and the anatomical characteristics segmental boundary. The CP for the thoracoscopic procedure was then determined within the safe surgical margins, and a simplified CP model was created.
Results: The boundary between adjacent lung segment A and segment B was be expressed as "ISP: Sa-Sb". The ISP was divided into venous ISP (VISP), arterial ISP (AISP), and bronchial ISP (BISP). The proposed model of the CP can be expressed as follows: CP (f) = (V/A/B) ISP (x) + (V/A/B) sub ISP (y) + (V/A/B) sub-sub ISP (z).
Conclusions: This report is a first attempt to provide a nomenclature for identifying the ISP, and create a simplified model for determining the CP for thoracoscopic partial lobectomy.
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http://dx.doi.org/10.21037/tlcr-21-525 | DOI Listing |
Natl Sci Rev
January 2025
Key Laboratory for Thermal Science and Power Engineering of Ministry of Education, Department of Engineering Mechanics, Tsinghua University, Beijing 100084, China.
The high thermopower of ionic thermoelectric (-TE) materials holds promise for miniaturized waste-heat recovery devices and thermal sensors. However, progress is hampered by laborious trial-and-error experimentations, which lack theoretical underpinning. Herein, by introducing the simplified molecular-input line-entry system, we have addressed the challenge posed by the inconsistency of -TE material types, and present a machine learning model that evaluates the Seebeck coefficient with an of 0.
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January 2025
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BMC Med Imaging
January 2025
Electronics and Communications, Arab Academy for Science, Heliopolis, Cairo, 2033, Egypt.
Invasive breast cancer diagnosis and treatment planning require an accurate assessment of human epidermal growth factor receptor 2 (HER2) expression levels. While immunohistochemical techniques (IHC) are the gold standard for HER2 evaluation, their implementation can be resource-intensive and costly. To reduce these obstacles and expedite the procedure, we present an efficient deep-learning model that generates high-quality IHC-stained images directly from Hematoxylin and Eosin (H&E) stained images.
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