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» Authors » Takafumi Nemoto

Takafumi Nemoto

Explore the profile of Takafumi Nemoto including associated specialties, affiliations and a list of published articles. Areas
Snapshot
Articles 8
Citations 57
Followers 0
Related Specialties
Oncology
Radiology
Biophysics
Top 10 Co-Authors
Atsuya Takeda
Etsuo Kunieda
Masamichi Yagi
Natsumi Futakami
Takeshi Akiba
Naoyuki Shigematsu
Takahisa Eriguchi
Eride Mutu
Akitomo Sugawara
Yuichiro Tsurugai
Published In
J Radiat Res
Radiol Phys Technol
Tokai J Exp Clin Med
Pract Radiat Oncol
JCO Clin Cancer Inform
Affiliations
Soon will be listed here.
Recent Articles
1.
[Effects of sample size and data augmentation on U-Net-based automatic segmentation of various organs]
Nemoto T, Futakami N, Kunieda E, Yagi M, Takeda A, Akiba T, et al.
Igaku Butsuri . 2023 Apr; 43(1):19. PMID: 37045760
No abstract available.
2.
Effect on Heart and Lung Doses Reduction of Abdominal and Thoracic Deep Inspiratory Breath-hold Assuming Involved-field Radiation Therapy in Patients with Simulated Esophageal Cancer
Mutu E, Akiba T, Matsumoto Y, Kunieda E, Nagao R, Fukuzawa T, et al.
Tokai J Exp Clin Med . 2023 Mar; 48(1):32-37. PMID: 36999391
Purpose: The purpose of this study was to evaluate the lung and heart doses in volumetric-modulated arc therapy (VMAT) using involved-field irradiation in patients with middle-to-lower thoracic esophageal cancer during...
3.
Relationship between Dose Prescription Methods and Local Control Rate in Stereotactic Body Radiotherapy for Early Stage Non-Small-Cell Lung Cancer: Systematic Review and Meta-Analysis
Eriguchi T, Takeda A, Nemoto T, Tsurugai Y, Sanuki N, Tateishi Y, et al.
Cancers (Basel) . 2022 Aug; 14(15). PMID: 35954478
Variations in dose prescription methods in stereotactic body radiotherapy (SBRT) for early stage non-small-cell lung cancer (ES-NSCLC) make it difficult to properly compare the outcomes of published studies. We conducted...
4.
Applying Artificial Neural Networks to Develop a Decision Support Tool for Tis-4N0M0 Non-Small-Cell Lung Cancer Treated With Stereotactic Body Radiotherapy
Nemoto T, Takeda A, Matsuo Y, Kishi N, Eriguchi T, Kunieda E, et al.
JCO Clin Cancer Inform . 2022 Jun; 6:e2100176. PMID: 35749675
Purpose: Clear evidence indicating whether surgery or stereotactic body radiation therapy (SBRT) is best for non-small-cell lung cancer (NSCLC) is lacking. SBRT has many advantages. We used artificial neural networks...
5.
Effects of sample size and data augmentation on U-Net-based automatic segmentation of various organs
Nemoto T, Futakami N, Kunieda E, Yagi M, Takeda A, Akiba T, et al.
Radiol Phys Technol . 2021 Jul; 14(3):318-327. PMID: 34254251
Deep learning has demonstrated high efficacy for automatic segmentation in contour delineation, which is crucial in radiation therapy planning. However, the collection, labeling, and management of medical imaging data can...
6.
Simple low-cost approaches to semantic segmentation in radiation therapy planning for prostate cancer using deep learning with non-contrast planning CT images
Nemoto T, Futakami N, Yagi M, Kunieda E, Akiba T, Takeda A, et al.
Phys Med . 2020 Sep; 78:93-100. PMID: 32950833
Purpose: Deep learning has shown great efficacy for semantic segmentation. However, there are difficulties in the collection, labeling and management of medical imaging data, because of ethical complications and the...
7.
Repeated Stereotactic Body Radiation Therapy for Hepatocellular Carcinoma
Eriguchi T, Tsukamoto N, Kuroiwa N, Nemoto T, Ogata T, Okubo Y, et al.
Pract Radiat Oncol . 2020 Aug; 11(1):44-52. PMID: 32791232
Purpose: In clinical practice, whether cirrhotic livers in patients with hepatocellular carcinoma (HCC) can withstand repeated stereotactic body radiation therapy (SBRT) remains unclear. This study aimed to evaluate the outcomes...
8.
Efficacy evaluation of 2D, 3D U-Net semantic segmentation and atlas-based segmentation of normal lungs excluding the trachea and main bronchi
Nemoto T, Futakami N, Yagi M, Kumabe A, Takeda A, Kunieda E, et al.
J Radiat Res . 2020 Feb; 61(2):257-264. PMID: 32043528
This study aimed to examine the efficacy of semantic segmentation implemented by deep learning and to confirm whether this method is more effective than a commercially dominant auto-segmentation tool with...