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  1. Article ; Online: Evaluation of an artificial intelligence U-net algorithm for pulmonary nodule tracking on chest computed tomography images.

    Takeshita, Yuhei / Onozawa, Shiro / Katase, Shichiro / Shirakawa, Yuya / Yamashita, Kouji / Shudo, Jun / Nakanishi, Akihito / Akahori, Sadato / Yokoyama, Kenichi

    The Journal of international medical research

    2024  Volume 52, Issue 2, Page(s) 3000605241230033

    Abstract: Objectives: To apply image registration in the follow up of lung nodules and verify the feasibility of automatic tracking of lung nodules using an artificial intelligence (AI) method.: Methods: For this retrospective, observational study, patients ... ...

    Abstract Objectives: To apply image registration in the follow up of lung nodules and verify the feasibility of automatic tracking of lung nodules using an artificial intelligence (AI) method.
    Methods: For this retrospective, observational study, patients with pulmonary nodules 5-30 mm in diameter on computed tomography (CT) and who had at least six months follow-up were identified. Two radiologists defined a 'correct' cuboid circumscribing each nodule which was used to judge the success/failure of nodule tracking. An AI algorithm was applied in which a U-net type neural network model was trained to predict the deformation vector field between two examinations. When the estimated position was within a defined cuboid, the AI algorithm was judged a success.
    Results: In total, 49 lung nodules in 40 patients, with a total of 368 follow-up CT examinations were examined. The success rate for each time evaluation was 94% (345/368) and for 'nodule-by-nodule evaluation' was 78% (38/49). Reasons for a decrease in success rate were related to small nodules and those that decreased in size.
    Conclusion: Automatic tracking of lung nodules is highly feasible.
    MeSH term(s) Humans ; Artificial Intelligence ; Retrospective Studies ; Lung Neoplasms ; Solitary Pulmonary Nodule ; Algorithms ; Tomography, X-Ray Computed/methods
    Language English
    Publishing date 2024-02-06
    Publishing country England
    Document type Observational Study ; Journal Article
    ZDB-ID 184023-x
    ISSN 1473-2300 ; 0300-0605 ; 0142-2596
    ISSN (online) 1473-2300
    ISSN 0300-0605 ; 0142-2596
    DOI 10.1177/03000605241230033
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    In stock of ZB MED Cologne/Königswinter

    Zs.A 925: Show issues Location:
    Je nach Verfügbarkeit (siehe Angabe bei Bestand)
    bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular
    Jg. 1995 - 2021: Lesesall (1.OG)
    ab Jg. 2022: Lesesaal (EG)

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  2. Article ; Online: Radiological features of thyroid-like low-grade nasopharyngeal papillary adenocarcinoma: case series and systematic review.

    Baba, Akira / Matsushima, Satoshi / Kessoku, Hisashi / Omura, Kazuhiro / Kurokawa, Ryo / Fukasawa, Nei / Takeshita, Yuhei / Yamauchi, Hideomi / Ogino, Nobuhiro / Kayama, Reina / Uchihara, Kimiyuki / Yoshimatsu, Lynn / Ojiri, Hiroya

    Neuroradiology

    2023  Volume 66, Issue 2, Page(s) 249–259

    Abstract: Purpose: To comprehensively summarize the clinical data and CT/MRI characteristics of thyroid-like low-grade nasopharyngeal papillary adenocarcinoma (TL-LGNPPA).: Methods: Twenty-seven lesions from 25 study articles identified through a systematic ... ...

    Abstract Purpose: To comprehensively summarize the clinical data and CT/MRI characteristics of thyroid-like low-grade nasopharyngeal papillary adenocarcinoma (TL-LGNPPA).
    Methods: Twenty-seven lesions from 25 study articles identified through a systematic review and three lesions from our institution associated with TL-LGNPPA were evaluated.
    Results: The mean age of the patients at diagnosis was 35.7 years, and the male-to-female ratio was nearly half. The chief complaint was nasal obstruction, followed by epistaxis. All patients underwent excision. None of the patients had neck nodes or distant metastases. All patients survived with no locoregional/distant recurrence during 3-93 months of follow-up. All lesions were located at the posterior edge of the nasal septum, attached to the nasopharyngeal parietal wall, and showed no laterality. The mean lesion diameter was 1.7 cm. The margins of lesions were well-defined and lobulated, followed by well-defined smooth margins. None of lesions were associated with parapharyngeal space or skull base destruction. All lesions were iso- and low-density on non-contrast CT. Adjacent skull base sclerosis was detected in 63.6% of lesions. High signal intensity on T2-weighted imaging and mostly iso-signal intensity on T1-weighted imaging compared to muscle tissue. Most lesions were heterogeneous and exhibited moderate contrast enhancement. Relatively large lesions (≥1.4 cm) tended to be more lobulated than smooth margins compared to relatively small lesions (<1.4 cm) (p = 0.016).
    Conclusion: We summarized the clinical and radiological features of TL-LGNPPA to facilitate accurate diagnosis and appropriate management.
    MeSH term(s) Adult ; Female ; Humans ; Male ; Adenocarcinoma, Papillary/diagnostic imaging ; Adenocarcinoma, Papillary/pathology ; Magnetic Resonance Imaging ; Thyroid Gland/pathology
    Language English
    Publishing date 2023-12-16
    Publishing country Germany
    Document type Systematic Review ; Journal Article
    ZDB-ID 123305-1
    ISSN 1432-1920 ; 0028-3940
    ISSN (online) 1432-1920
    ISSN 0028-3940
    DOI 10.1007/s00234-023-03254-2
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    Zs.A 658: Show issues Location:
    Je nach Verfügbarkeit (siehe Angabe bei Bestand)
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    Jg. 1995 - 2021: Lesesall (1.OG)
    ab Jg. 2022: Lesesaal (EG)

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  3. Article ; Online: Development and performance evaluation of a deep learning lung nodule detection system.

    Katase, Shichiro / Ichinose, Akimichi / Hayashi, Mahiro / Watanabe, Masanaka / Chin, Kinka / Takeshita, Yuhei / Shiga, Hisae / Tateishi, Hidekatsu / Onozawa, Shiro / Shirakawa, Yuya / Yamashita, Koji / Shudo, Jun / Nakamura, Keigo / Nakanishi, Akihito / Kuroki, Kazunori / Yokoyama, Kenichi

    BMC medical imaging

    2022  Volume 22, Issue 1, Page(s) 203

    Abstract: Background: Lung cancer is the leading cause of cancer-related deaths throughout the world. Chest computed tomography (CT) is now widely used in the screening and diagnosis of lung cancer due to its effectiveness. Radiologists must identify each small ... ...

    Abstract Background: Lung cancer is the leading cause of cancer-related deaths throughout the world. Chest computed tomography (CT) is now widely used in the screening and diagnosis of lung cancer due to its effectiveness. Radiologists must identify each small nodule shadow from 3D volume images, which is very burdensome and often results in missed nodules. To address these challenges, we developed a computer-aided detection (CAD) system that automatically detects lung nodules in CT images.
    Methods: A total of 1997 chest CT scans were collected for algorithm development. The algorithm was designed using deep learning technology. In addition to evaluating detection performance on various public datasets, its robustness to changes in radiation dose was assessed by a phantom study. To investigate the clinical usefulness of the CAD system, a reader study was conducted with 10 doctors, including inexperienced and expert readers. This study investigated whether the use of the CAD as a second reader could prevent nodular lesions in lungs that require follow-up examinations from being overlooked. Analysis was performed using the Jackknife Free-Response Receiver-Operating Characteristic (JAFROC).
    Results: The CAD system achieved sensitivity of 0.98/0.96 at 3.1/7.25 false positives per case on two public datasets. Sensitivity did not change within the range of practical doses for a study using a phantom. A second reader study showed that the use of this system significantly improved the detection ability of nodules that could be picked up clinically (p = 0.026).
    Conclusions: We developed a deep learning-based CAD system that is robust to imaging conditions. Using this system as a second reader increased detection performance.
    MeSH term(s) Humans ; Deep Learning ; Tomography, X-Ray Computed ; Lung Neoplasms/diagnostic imaging ; Phantoms, Imaging ; Lung/diagnostic imaging
    Language English
    Publishing date 2022-11-22
    Publishing country England
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 2061975-3
    ISSN 1471-2342 ; 1471-2342
    ISSN (online) 1471-2342
    ISSN 1471-2342
    DOI 10.1186/s12880-022-00938-8
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  4. Article ; Online: Clinical validity of non-contrast-enhanced VI-RADS: prospective study using 3-T MRI with high-gradient magnetic field.

    Watanabe, Masanaka / Taguchi, Satoru / Machida, Haruhiko / Tambo, Mitsuhiro / Takeshita, Yuhei / Kariyasu, Toshiya / Fukushima, Keita / Shimizu, Yuta / Okegawa, Takatsugu / Fukuhara, Hiroshi / Yokoyama, Kenichi

    European radiology

    2022  Volume 32, Issue 11, Page(s) 7513–7521

    Abstract: Objectives: To develop a modified Vesical Imaging Reporting and Data System (VI-RADS) without dynamic contrast-enhanced imaging (DCEI), termed "non-contrast-enhanced VI-RADS (NCE-VI-RADS)", and to assess the additive impact of denoising deep learning ... ...

    Abstract Objectives: To develop a modified Vesical Imaging Reporting and Data System (VI-RADS) without dynamic contrast-enhanced imaging (DCEI), termed "non-contrast-enhanced VI-RADS (NCE-VI-RADS)", and to assess the additive impact of denoising deep learning reconstruction (dDLR) on NCE-VI-RADS.
    Methods: From January 2019 through December 2020, 163 participants who underwent high-gradient 3-T MRI of the bladder were prospectively enrolled. In total, 108 participants with pathologically confirmed bladder cancer by transurethral resection were analyzed. Tumors were evaluated based on VI-RADS (scores 1-5) by two readers independently: an experienced radiologist (reader 1) and a senior radiology resident (reader 2). Conventional VI-RADS assessment included all three imaging types (T2-weighted imaging [T2WI], diffusion-weighted imaging [DWI], and dynamic contrast-enhanced imaging [DCEI]). Also evaluated were NCE-VI-RADS comprising only non-contrast-enhanced imaging types (T2WI and DWI), and "NCE-VI-RADS with dDLR" comprising T2WI processed with dDLR and DWI. All systems were assessed using receiver-operating characteristic curve analysis and simple and/or weighted κ statistics.
    Results: Muscle invasion was identified in 23/108 participants (21%). Area under the curve (AUC) values for diagnosing muscle invasion were as follows: conventional VI-RADS, 0.94 and 0.91; NCE-VI-RADS, 0.93 and 0.91; and "NCE-VI-RADS with dDLR", 0.96 and 0.93, for readers 1 and 2, respectively. Simple κ statistics indicated substantial agreement for NCE-VI-RADS and almost perfect agreement for conventional VI-RADS and "NCE-VI-RADS with dDLR" between the two readers.
    Conclusion: NCE-VI-RADS achieved predictive accuracy for muscle invasion comparable to that of conventional VI-RADS. Additional use of dDLR improved the diagnostic accuracy of NCE-VI-RADS.
    Key points: • Non-contrast-enhanced Vesical Imaging Reporting and Data System (NCE-VI-RADS) was developed to avoid risk related to gadolinium-based contrast agent administration. • NCE-VI-RADS had predictive accuracy for muscle invasion comparable to that of conventional VI-RADS. • The additional use of denoising deep learning reconstruction (dDLR) might further improve the diagnostic accuracy of NCE-VI-RADS.
    MeSH term(s) Humans ; Data Systems ; Urinary Bladder/diagnostic imaging ; Urinary Bladder/pathology ; Prospective Studies ; Retrospective Studies ; Magnetic Resonance Imaging/methods ; Urinary Bladder Neoplasms/diagnostic imaging ; Urinary Bladder Neoplasms/pathology ; Magnetic Fields
    Language English
    Publishing date 2022-05-12
    Publishing country Germany
    Document type Journal Article
    ZDB-ID 1085366-2
    ISSN 1432-1084 ; 0938-7994 ; 1613-3749
    ISSN (online) 1432-1084
    ISSN 0938-7994 ; 1613-3749
    DOI 10.1007/s00330-022-08813-4
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    Zs.A 3300: Show issues Location:
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    Jg. 1995 - 2021: Lesesall (2.OG)
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  5. Article: Habitat niche specialization in an understory species in a warm temperate forest

    Takeshita, Yuhei / Muller, Onno / Yamada, Toshihiro

    Ecological research. 2009 Mar., v. 24, no. 2

    2009  

    Abstract: Relationships between microhabitat variables; understory light conditions in summer and winter, altitude, slope inclination and topographic categories (valley, ridge, and slope) and the distribution of Aucuba japonica Thunb. (Cornaceae), a common ... ...

    Abstract Relationships between microhabitat variables; understory light conditions in summer and winter, altitude, slope inclination and topographic categories (valley, ridge, and slope) and the distribution of Aucuba japonica Thunb. (Cornaceae), a common understory shrub species in Japan were examined using non-contagious 66, 20 x 20 m² quadrats. The Morishita's I δ suggested that A. japonica distributions were strongly heterogeneous among the quadrats. Therefore positive spatial autocorrelation of A. japonica at a within-quadrat level (<=20 m) was obvious. Moran's I statistics showed a significant positive spatial autocorrelation in A. japonica abundance within the distance shorter than 60 m. But the partial Mantel tests suggested that the mass effect from neighboring quadrats would little explain A. japonica abundance in a quadrat. The partial Mantel tests also clearly showed that A. japonica distributions were strongly structured by topography and understory light conditions. Using Monte Carlo randomization tests, we found that A. japonica was aggregately distributed in quadrats in valley which were covered by deciduous canopies. Better understory light conditions in winter together with valley edaphic conditions may increase the abundance of A. japonica there. It is concluded that habitat niche specialization is important in structuring distribution of A. japonica in this forest community.
    Keywords Aucuba japonica ; light ; spatial distribution ; topography
    Language English
    Dates of publication 2009-03
    Size p. 467-475.
    Publisher Springer Japan
    Publishing place Japan
    Document type Article
    ZDB-ID 233459-8
    ISSN 0912-3814
    ISSN 0912-3814
    DOI 10.1007/s11284-008-0523-z
    Database NAL-Catalogue (AGRICOLA)

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