Results 141 to 150 of about 254,610 (274)

Combating cancer immunotherapy resistance: a nano‐medicine perspective

Cancer Communications, EarlyView.
Abstract Cancer immunotherapy offers renewed hope for treating this disease. However, cancer cells possess inherent mechanisms that enable them to circumvent each stage of the immune cycle, thereby evading anti‐cancer immunity and leading to resistance.
Xiangyi Kong, Xintong Xie, Juan Wu, Xiangyu Wang, Wenxiang Zhang, Shuowen Wang, Daria Valerievna Abbasova, Yi Fang, Hongnan Jiang, Jidong Gao, Jing Wang   +10 more
wiley   +1 more source

AI in Oncology: Transforming Cancer Detection through Machine Learning and Deep Learning Applications [PDF]

arXiv
Artificial intelligence (AI) has potential to revolutionize the field of oncology by enhancing the precision of cancer diagnosis, optimizing treatment strategies, and personalizing therapies for a variety of cancers. This review examines the limitations of conventional diagnostic techniques and explores the transformative role of AI in diagnosing and ...
arxiv  

Thyroid Cancer Discovered Incidentally during Treatment for an Unrelated Head and Neck Cancer

, 1966
R. Lee Clark, Robert C. Hickey, James J. Butler, Michael L. Ibañez, Alando J. Ballantyne   +4 more
openalex   +2 more sources

The Prognosis and Insurability of Thyroid Cancer Patients [PDF]

, 1970
Edgar L. Frazell, David Schottenfeld, R. V. P. Hutter   +2 more
openalex   +1 more source

Multiomics identifies the link between intratumor steatosis and the exhausted tumor immune microenvironment in hepatocellular carcinoma

Hepatology, EarlyView., 2022
Steatotic HCC can be diagnosed by MRI and forms a pro‐tumor immune microenvironment with high expression of PD‐L1, M2 polarization of macrophages and activation of CAFs, which may be a good target for combined immunotherapy. Abstract Background and Aims Immunotherapy has become the standard‐of‐care treatment for hepatocellular carcinoma (HCC), but its ...
Hiroki Murai, Takahiro Kodama, Kazuki Maesaka, Shoichiro Tange, Daisuke Motooka, Yutaka Suzuki, Yasuyuki Shigematsu, Kentaro Inamura, Yoshihiro Mise, Akio Saiura, Yoshihiro Ono, Yu Takahashi, Yota Kawasaki, Satoshi Iino, Shogo Kobayashi, Masashi Idogawa, Takashi Tokino, Tomomi Hashidate‐Yoshida, Hideo Shindou, Masanori Miyazaki, Yasuharu Imai, Satoshi Tanaka, Eiji Mita, Kazuyoshi Ohkawa, Hayato Hikita, Ryotaro Sakamori, Tomohide Tatsumi, Hidetoshi Eguchi, Eiichi Morii, Tetsuo Takehara   +29 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  

The Histogenesis of Thyroid Cancer. [PDF]

, 1971
Nicoale Simionescu
openalex   +1 more source

Time Kinetics and prognosis roles of calcitonin after surgery for medullary thyroid carcinoma

World Journal of Surgical Oncology
Background Medullary thyroid carcinoma (MTC) is a malignant tumor with low incidence. Currently, most studies have focused on the prognostic risk factors of MTC, whatever, time kinetic and risk factors related to calcitonin normalization (CN) and ...
Fengli Guo, Guiming Fu, Fangxuan Li, Yitong Hua, Zhongyu Wang, Xiangqian Zheng, Jingzhu Zhao, Ming Gao   +7 more
doaj   +1 more source

The effect of sex and of sex and thyroid hormones on the induction of cancers in the salivary glands of rats.

, 1966
Aaron Glucksman, C. P. Cherry
openalex   +2 more sources

de novo variant calling identifies cancer mutation signatures in the 1000 Genomes Project

Human Mutation, Volume 43, Issue 12, Page 1979-1993, December 2022., 2022
Abstract Detection of de novo variants (DNVs) is critical for studies of disease‐related variation and mutation rates. To accelerate DNV calling, we developed a graphics processing units‐based workflow. We applied our workflow to whole‐genome sequencing data from three parent‐child sequenced cohorts including the Simons Simplex Collection (SSC), Simons
Jeffrey K. Ng, Pankaj Vats, Elyn Fritz‐Waters, Stephanie Sarkar, Eleanor I. Sams, Evin M. Padhi, Zachary L. Payne, Shawn Leonard, Marc A. West, Chandler Prince, Lee Trani, Marshall Jansen, George Vacek, Mehrzad Samadi, Timothy T. Harkins, Craig Pohl, Tychele N. Turner   +16 more
wiley   +1 more source