Todd B Sheridan
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Explore the profile of Todd B Sheridan including associated specialties, affiliations and a list of published articles.
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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.
Significance of Lymphovascular Space Invasion by the Sarcomatous Component in Uterine Carcinosarcoma
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...