Zobrazeno 1 - 10
of 989
pro vyhledávání: '"Khan Seema A"'
Autor:
Fabian, Carol J., Mudaranthakam, Dinesh Pal, Gajewski, Byron, Young, Kate, Winblad, Onalisa, Khan, Seema A., Garber, Judy E., Esserman, Laura J., Yee, Lisa D., Nye, Lauren, Powers, Kandy R., Ranallo, Lori, Kreutzjans, Amy L., Pittman, Krystal, Altman, Christy, Metheny, Trina, Zelenchuk, Adrian, Komm, Barry S., Kimler, Bruce F.
Publikováno v:
In Contemporary Clinical Trials November 2024 146
Ductal carcinoma in situ of the breast: finding the balance between overtreatment and undertreatment
Publikováno v:
In The Lancet 22-28 June 2024 403(10445):2734-2746
Autor:
Lee, Oukseub, Bazzi, Latifa A., Xu, Yanfei, Pearson, Erik, Wang, Minhua, Hosseini, Omid, Akasha, Azza M., Choi, Jennifer Nam, Karlan, Scott, Pilewskie, Melissa, Kocherginsky, Masha, Benante, Kelly, Helland, Thomas, Mellgren, Gunnar, Dimond, Eileen, Perloff, Marjorie, Heckman-Stoddard, Brandy M., Khan, Seema A.
Publikováno v:
In Biomedicine & Pharmacotherapy February 2024 171
Autor:
Khan, Seema Ahsan
Publikováno v:
In Surgical Oncology Clinics of North America October 2023 32(4):631-646
Autor:
Majid, Nabeela, Siddiqi, Mohammad Khursheed, Hassan, Md. Nadir, Malik, Sadia, Khan, Seema, Khan, Rizwan Hasan
Publikováno v:
In Biomaterials Advances August 2023 151
Publikováno v:
BMC Cancer, Vol 8, Iss 1, p 36 (2008)
Abstract Background In women with duct carcinoma in-situ (DCIS) receiving breast conservation therapy (BCT), in-breast recurrences are seen in approximately 10%, but cannot be accurately predicted using clinical and histological criteria. We performe
Externí odkaz:
https://doaj.org/article/10cdbc96c6364a8ea901c382de9d41b7
Autor:
Lee, Oukseub, Wang, Minhua, Hosseini, Omid, Bosland, Maarten C., Muzzio, Miguel, Helenowski, Irene, Khan, Seema A.
Publikováno v:
In Biomedicine & Pharmacotherapy June 2023 162
Autor:
Tsukioki, Takahiro1 (AUTHOR) ph4y4htb@okayama-u.ac.jp, Khan, Seema A.2 (AUTHOR), Shien, Tadahiko1 (AUTHOR)
Publikováno v:
Genes & Environment. 12/12/2023, Vol. 45 Issue 1, p1-11. 11p.
Akademický článek
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Extracting genetic information from a full range of sequencing data is important for understanding diseases. We propose a novel method to effectively explore the landscape of genetic mutations and aggregate them to predict cancer type. We used multin
Externí odkaz:
http://arxiv.org/abs/1809.10681