Materials Map

Discover the materials research landscape. Find experts, partners, networks.

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The Materials Map is an open tool for improving networking and interdisciplinary exchange within materials research. It enables cross-database search for cooperation and network partners and discovering of the research landscape.

The dashboard provides detailed information about the selected scientist, e.g. publications. The dashboard can be filtered and shows the relationship to co-authors in different diagrams. In addition, a link is provided to find contact information.

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The Materials Map is still under development. In its current state, it is only based on one single data source and, thus, incomplete and contains duplicates. We are working on incorporating new open data sources like ORCID to improve the quality and the timeliness of our data. We will update Materials Map as soon as possible and kindly ask for your patience.

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in Cooperation with on an Cooperation-Score of 37%

Topics

Publications (3/3 displayed)

  • 2024Epitaxial growth of copper phthalocyanine on a large single-grain of thin film alkyl perylene diimidecitations
  • 2022Dielectric property measurements for the rapid differentiation of thoracic lymph nodes using XGBoost in patients with non-small cell lung cancer: a self-control clinical trial.4citations
  • 2001Pulsed Laser Epitaxy and Magnetic Properties of Single Phase Y-Type Magnetoplumbite Thin Films8citations

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Fujikake, Hideo
1 / 1 shared
Koganezawa, Tomoyuki
1 / 9 shared
Shibata, Yosei
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Maruyama, Shingo
1 / 2 shared
Takagawa, Yusuke
1 / 1 shared
Tanaka, Shuhei
1 / 1 shared
Hasegawa, Tetsuya
1 / 1 shared
Ueno, Koichiro
1 / 1 shared
Itaka, Kenji
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Ahmet, Parhat
1 / 1 shared
Koinuma, Hideomi
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Ohkubo, Isao
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Chikyow, Toyohiro
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Co-Authors (by relevance)

  • Fujikake, Hideo
  • Koganezawa, Tomoyuki
  • Shibata, Yosei
  • Maruyama, Shingo
  • Takagawa, Yusuke
  • Tanaka, Shuhei
  • Hasegawa, Tetsuya
  • Ueno, Koichiro
  • Itaka, Kenji
  • Ahmet, Parhat
  • Koinuma, Hideomi
  • Ohkubo, Isao
  • Chikyow, Toyohiro
OrganizationsLocationPeople

article

Dielectric property measurements for the rapid differentiation of thoracic lymph nodes using XGBoost in patients with non-small cell lung cancer: a self-control clinical trial.

  • Matsumoto, Yuji
Abstract

<h4>Background</h4>One of the important criteria for thoracic surgeons in making surgical strategies is whether the thoracic lymph nodes (LNs) are metastatic. Frozen section (FS) is widely used as an intraoperative diagnostic method, which is time-consuming and expensive. The dielectric property, including permittivity and conductivity, varies with different tissues. The extreme gradient boosting (XGBoost) is a powerful classifier and widely used. Thus, this study aims to develop the rapid differentiation method combining dielectric property and XGBoost, and assess its efficacy on the thoracic LNs in patients with non-small cell lung cancer (NSCLC).<h4>Methods</h4>This was a single center self-control clinical trial with paraffin pathology section (PPS) results as gold diagnosis. The LNs from the pathologically diagnosed patients with NSCLC were recruited, which were measured by open-ended coaxial probe for the dielectric property within 1-4,000 MHz after removal from the patients and then were sent to perform FS and PPS diagnosis. The XGBoost combining with dielectric property was developed to differentiate malignant LNs from benign LNs. The classified efficacy was determined using the receiver operator characteristic (ROC) curve and area under the curve (AUC).<h4>Results</h4>A total of 204 LNs from 67 NSCLC patients were analyzed. The mean values of the two parameters differed significantly (P<0.001) between benign and malignant LNs. The AUC for permittivity and conductivity were 0.850 [95% confidence interval (CI): 0.786 to 0.915; P<0.001] and 0.887 (95% CI: 0.828 to 0.946; P<0.001), respectively. The AUC was 0.893 (95% CI: 0.834 to 0.951; P<0.001) when the two parameters were combined. After the application of the XGBoost, the AUC was 0.968 (95% CI: 0.918 to 1.000; P<0.001), and the accuracy was 87.80%. Its sensitivity was 58.33% and the specificity was 100%. When the Synthetic Minority Oversampling Technique (SMOTE) algorithm was used, the AUC was 0.954 (95% CI: 0.883 to 1.000; P<0.001) and the accuracy was 92.68%. Its sensitivity was 83.33% and the specificity was 96.55%.<h4>Conclusions</h4>This method might be useful for thoracic surgeons during surgery, for its relatively high efficacy in rapid differentiation of LNs for patients with NSCLC.

Topics
  • impedance spectroscopy
  • gold
  • chemical ionisation