Results 151 to 160 of about 150,574 (342)

Thyroid Cancer Malignancy Prediction From Whole Slide Cytopathology Images [PDF]

Proceedings of Machine Learning Research, 2019, Vol. 106, 2019
We consider preoperative prediction of thyroid cancer based on ultra-high-resolution whole-slide cytopathology images. Inspired by how human experts perform diagnosis, our approach first identifies and classifies diagnostic image regions containing informative thyroid cells, which only comprise a tiny fraction of the entire image. These local estimates
arxiv  

Analysis of thyroid carcinoma based on material registered in japan during 1977-1 986 with special reference to predominance of papillary type [PDF]

, 1992
Haruo Ezaki, Satoshi Ebihara, Yoshihide Fujimoto, Futoshi Iida, Kunihiko Ito, Kanji Kuma, Masaru Lzuo, M. Makiuchi, Hiyoshimaru Oyamada, Naoya Matoba, K Yagawa   +10 more
openalex   +1 more source

Real‐World Data Analysis of Genomic Alterations Detected by a Dual DNA–RNA Comprehensive Genomic Profiling Test

Cancer Science, EarlyView.
This study analyzed 1356 cases using GenMineTOP, Japan's first dual DNA–RNA CGP test, and identified oncogenic alterations in 91.5% of cases. The RNA panel detected 105 fusion events, including 11 NTRK fusions. Furthermore, the analysis clarified the independent effects of genomic abnormalities and cancer type on RNA expression, providing valuable ...
Kousuke Watanabe, Miho Ogawa, Aya Shinozaki‐Ushiku, Shuichi Tsutsumi, Kenji Tatsuno, Hiroyuki Aburatani, Hidenori Kage, Katsutoshi Oda   +7 more
wiley   +1 more source

Machine Learning and Transformers for Thyroid Carcinoma Diagnosis: A Review [PDF]

arXiv
The growing interest in developing smart diagnostic systems to help medical experts process extensive data for treating incurable diseases has been notable. In particular, the challenge of identifying thyroid cancer (TC) has seen progress with the use of machine learning (ML) and big data analysis, incorporating Transformers to evaluate TC prognosis ...
arxiv  

Dataset of differential gene expression between total normal human thyroid and histologically normal thyroid adjacent to papillary thyroid carcinoma

Data in Brief, 2019
This article contains further data and information from our published manuscript [1]. We aim to identify significant transcriptome alterations of total normal human thyroid vs. histologically normal thyroid adjacent to papillary thyroid carcinoma.
Lorenza Vitale, Allison Piovesan, Francesca Antonaros, Pierluigi Strippoli, Maria Chiara Pelleri, Maria Caracausi   +5 more
doaj  

Can Artificial Intelligence Software be Utilised for Thyroid Multi‐Disciplinary Team Outcomes?

Clinical Otolaryngology, EarlyView.
ABSTRACT Objectives ChatGPT is one of the most publicly available artificial intelligence (AI) softwares. Ear, nose and throat (ENT) services are often stretched due to the increasing incidence of thyroid malignancies. This study aims to investigate whether there is a role for AI software in providing accurate thyroid multidisciplinary team (MDT ...
Amir Habeeb, Kim Hui Lim, Xenofon Kochilas, Nazir Bhat, Furrat Amen, Samuel Chan   +5 more
wiley   +1 more source

Breast Tumor Classification Using EfficientNet Deep Learning Model [PDF]

arXiv
Precise breast cancer classification on histopathological images has the potential to greatly improve the diagnosis and patient outcome in oncology. The data imbalance problem largely stems from the inherent imbalance within medical image datasets, where certain tumor subtypes may appear much less frequently.
arxiv  

Growth factor production by human thyroid carcinoma cells: abundant expression of a platelet-derived growth factor-B-like protein by a human papillary carcinoma cell line. [PDF]

, 1993
KEIICHI MATSUO, Shanhong Tang, Behrooz G. Sharifi, Stanley A. Rubin, Rhona Schreck, James A. Fagin   +5 more
openalex   +1 more source

Integrated Genomic Characterization of Papillary Thyroid Carcinoma

Cell, 2014
N. Agrawal, Rehan Akbani, B. A. Aksoy, Adrian Ally, H. Arachchi, S. Asa, J. Auman, M. Balasundaram, S. Balu, S. Baylin, M. Behera, Brady Bernard, R. Beroukhim, J. Bishop, Aaron D. Black, T. Bodenheimer, L. Boice, Moiz Bootwalla, Jay Bowen, R. Bowlby, C. Bristow, Robin Brookens, Denise Brooks, Robert Bryant, Elizabeth Buda, Y. Butterfield, T. Carling, R. Carlsen, S. Carter, S. Carty, T. Chan, Amy Y. Chen, A. Cherniack, Dorothy Cheung, L. Chin, Juok Cho, Andy Chu, E. Chuah, K. Cibulskis, G. Ciriello, A. Clarke, G. Clayman, L. Cope, J. Copland, K. Covington, Ludmila V. Danilova, Tanja Davidsen, John A. Demchok, D. Dicara, Noreen Dhalla, R. Dhir, Sheliann S. Dookran, Gideon Dresdner, Jonathan V. Eldridge, G. Eley, A. El-Naggar, S. Eng, J. Fagin, T. Fennell, R. Ferris, Sheila A Fisher, S. Frazer, J. Frick, S. Gabriel, I. Ganly, Jianjiong Gao, L. Garraway, J. Gastier-Foster, G. Getz, Nils Gehlenborg, R. Ghossein, R. Gibbs, T. Giordano, Karen Gómez-Hernández, J. Grimsby, Benjamin E. Gross, R. Guin, Angela Hadjipanayis, H. Harper, D. Hayes, David I Heiman, J. Herman, K. Hoadley, Matan Hofree, R. Holt, A. Hoyle, F. Huang, Meijuan Huang, C. Hutter, T. Ideker, L. Iype, A. Jacobsen, S. Jefferys, Corbin D. Jones, Steven J. M. Jones, K. Kasaian, E. Kebebew, F. Khuri, Jaegil Kim, R. Kramer, R. Kreisberg, R. Kucherlapati, D. Kwiatkowski, M. Ladanyi, Phillip H. Lai, P. Laird, E. Lander, M. Lawrence, Darlene Lee, E. Lee, Semin Lee, William Lee, K. Leraas, T. Lichtenberg, Lee T Lichtenstein, Pei Lin, Shiyun Ling, Jinze Liu, Wenbin Liu, Yingchun Liu, V. Livolsi, Yiling Lu, Yussanne Ma, Harshad S. Mahadeshwar, M. Marra, Michael Mayo, D. McFadden, S. Meng, M. Meyerson, P. Mieczkowski, Michael B. Miller, G. Mills, Richard A. Moore, Lisle E. Mose, A. Mungall, Bradley A. Murray, Y. Nikiforov, M. Noble, A. Ojesina, T. Owonikoko, B. Ozenberger, A. Pantazi, Michael G Parfenov, P. Park, J. Parker, Evan O. Paull, C. Pedamallu, C. Perou, J. Prins, A. Protopopov, S. Ramalingam, N. Ramirez, Ricardo Ramirez, Benjamin J. Raphael, W. Rathmell, X. Ren, Sheila M. Reynolds, Esther Rheinbay, M. Ringel, M. Rivera, J. Roach, A. G. Robertson, Mara W Rosenberg, M. Rosenthal, S. Sadeghi, G. Saksena, C. Sander, Netty G Santoso, J. Schein, N. Schultz, S. Schumacher, R. Seethala, J. Seidman, Y. Şenbabaoğlu, S. Seth, S. Sharpe, K. Shaw, J. Shen, R. Shen, S. Sherman, Margi Sheth, Yan Shi, I. Shmulevich, G. Sica, J. Simons, Rileen Sinha, Payal Sipahimalani, R. Smallridge, H. Sofia, Matthew G. Soloway, Xingzhi Song, C. Sougnez, C. Stewart, P. Stojanov, Joshua M. Stuart, S. O. Sumer, Yichao Sun, B. Tabak, Angela Tam, Donghui Tan, Jiabin Tang, R. Tarnuzzer, B. Taylor, N. Thiessen, L. Thorne, V. Thorsson, R. Tuttle, C. Umbricht, D. V. Berg, Fabio Vandin, Umadevi Veluvolu, R. Verhaak, Michelle Vinco, Douglas Voet, Vonn Walter, Zhining Wang, S. Waring, P. Weinberger, Nils Weinhold, J. Weinstein, D. Weisenberger, D. Wheeler, M. Wilkerson, Jocelyn Wilson, M. Williams, D. Winer, L. Wise, Junyuan Wu, Liu Xi, A. W. Xu, Liming Yang, Lixing Yang, Travis Zack, M. Zeiger, Dong Zeng, J. Zenklusen, N. Zhao, Hailei Zhang, Jianhua Zhang, J. Zhang, Wei Zhang, E. Zmuda, L. Zou   +242 more
semanticscholar   +1 more source

Pathology-genomic fusion via biologically informed cross-modality graph learning for survival analysis [PDF]

arXiv
The diagnosis and prognosis of cancer are typically based on multi-modal clinical data, including histology images and genomic data, due to the complex pathogenesis and high heterogeneity. Despite the advancements in digital pathology and high-throughput genome sequencing, establishing effective multi-modal fusion models for survival prediction and ...
arxiv