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Todd B Sheridan

Explore the profile of Todd B Sheridan including associated specialties, affiliations and a list of published articles. Areas
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Articles 18
Citations 213
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Recent Articles
1.
Rubinstein J, Domanskyi S, Sheridan T, Sanderson B, Park S, Kaster J, et al.
Cancer Res . 2024 Dec; 85(5):987-1002. PMID: 39700408
Resistance of BRAF-mutant melanomas to targeted therapy arises from the ability of cells to enter a persister state, evade treatment with relative dormancy, and repopulate the tumor when reactivated. A...
2.
Rubinstein J, Domanskyi S, Sheridan T, Sanderson B, Park S, Kaster J, et al.
bioRxiv . 2024 Feb; PMID: 38370717
Statement Of Significance: Tumor evolution is accelerated by application of anti-cancer therapy, resulting in clonal expansions leading to dormancy and subsequently resistance, but the dynamics of this process are incompletely...
3.
Mukashyaka P, Sheridan T, Foroughi Pour A, Chuang J
EBioMedicine . 2023 Dec; 99:104908. PMID: 38101298
Background: Deep learning has revolutionized digital pathology, allowing automatic analysis of hematoxylin and eosin (H&E) stained whole slide images (WSIs) for diverse tasks. WSIs are broken into smaller images called...
4.
Mukashyaka P, Sheridan T, Foroughi Pour A, Chuang J
bioRxiv . 2023 Aug; PMID: 37577691
Deep learning has revolutionized digital pathology, allowing for automatic analysis of hematoxylin and eosin (H&E) stained whole slide images (WSIs) for diverse tasks. In such analyses, WSIs are typically broken...
5.
Rubinstein J, Foroughi Pour A, Zhou J, Sheridan T, White B, Chuang J
J Surg Oncol . 2022 Oct; 127(3):426-433. PMID: 36251352
Background And Objectives: Deep learning utilizing convolutional neural networks (CNNs) applied to hematoxylin & eosin (H&E)-stained slides numerically encodes histomorphological tumor features. Tumor heterogeneity is an emerging biomarker in colon...
6.
Foroughi Pour A, White B, Park J, Sheridan T, Chuang J
Sci Rep . 2022 Jun; 12(1):9428. PMID: 35676395
Convolutional neural networks (CNNs) are revolutionizing digital pathology by enabling machine learning-based classification of a variety of phenotypes from hematoxylin and eosin (H&E) whole slide images (WSIs), but the interpretation...
7.
Sheridan T, Walavalkar V, Yates J, Owens C, Fischer A
J Am Soc Cytopathol . 2019 Oct; 9(1):26-32. PMID: 31564532
Introduction: Because of the high rates of false-negative or nondiagnostic ureteral Piranha microbiopsies associated with low cellularity, we assessed the effect of processing these using cytology. Materials And Methods: We...
8.
Matsuo K, Takazawa Y, Ross M, Elishaev E, Yunokawa M, Sheridan T, et al.
Surg Oncol . 2018 Sep; 27(3):433-440. PMID: 30217299
Objective: To examine significance of sarcoma dominance (SD) patterns in uterine carcinosarcoma (UCS). Methods: This is a secondary analysis of multicenter retrospective study examining women with stages I-IV UCS who...
9.
Matsuo K, Takazawa Y, Ross M, Elishaev E, Yunokawa M, Sheridan T, et al.
Ann Surg Oncol . 2018 Aug; 25(12):3676-3684. PMID: 30105438
Purpose: To propose a categorization model of uterine carcinosarcoma (UCS) based on tumor cell types (carcinoma and sarcoma) and sarcoma dominance. Methods: This secondary analysis of a prior multicenter retrospective...
10.
Matsuo K, Takazawa Y, Ross M, Elishaev E, Yunokawa M, Sheridan T, et al.
Ann Surg Oncol . 2018 Jul; 25(9):2756-2766. PMID: 29971677
Objective: The aim of this study was to examine the significance of lymphovascular space invasion (LVSI) with a sarcomatous component on the tumor characteristics and clinical outcomes of women with...