Amita Shukla-Dave
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Explore the profile of Amita Shukla-Dave including associated specialties, affiliations and a list of published articles.
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117
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2384
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Recent Articles
1.
Apte A, LoCastro E, Iyer A, Elguindi S, Jiang J, Oh J, et al.
bioRxiv
. 2025 Feb;
PMID: 39896472
This work introduces a user-friendly, cloud-based software framework for conducting Artificial Intelligence (AI) analyses of medical images. The framework allows users to deploy AI-based workflows by customizing software and hardware...
2.
Arita Y, Kwee T, Akin O, Shigeta K, Paudyal R, Roest C, et al.
Insights Imaging
. 2025 Jan;
16(1):7.
PMID: 39747744
Bladder cancer is the 10th most common and 13th most deadly cancer worldwide, with urothelial carcinomas being the most common type. Distinguishing between non-muscle-invasive bladder cancer (NMIBC) and muscle-invasive bladder...
3.
Aliotta E, Paudyal R, Dresner A, Shukla-Dave A, Lee N, Cervino L, et al.
Phys Imaging Radiat Oncol
. 2024 Oct;
32:100653.
PMID: 39399877
Background And Purpose: Quantitative Diffusion Weighted Imaging (DWI) has potential value in guiding head and neck (HN) cancer radiotherapy. However, clinical translation has been hindered by severe distortions in standard...
4.
Boss M, Malyarenko D, Partridge S, Obuchowski N, Shukla-Dave A, Winfield J, et al.
Radiology
. 2024 Oct;
313(1):e233055.
PMID: 39377680
The apparent diffusion coefficient (ADC) provides a quantitative measure of water mobility that can be used to probe alterations in tissue microstructure due to disease or treatment. Establishment of the...
5.
Margolis D, Chatterjee A, deSouza N, Fedorov A, Fennessy F, Maier S, et al.
AJR Am J Roentgenol
. 2024 Oct;
PMID: 39356481
Prostate MRI has traditionally relied on qualitative interpretation. However, quantitative components hold the potential to markedly improve performance. The ADC from DWI is probably the most widely recognized quantitative MRI...
6.
Aliotta E, Paudyal R, Diplas B, Han J, Hu Y, Oh J, et al.
Phys Imaging Radiat Oncol
. 2024 Jul;
31:100603.
PMID: 39040433
Background And Purpose: Volume regression during radiotherapy can indicate patient-specific treatment response. We aimed to identify pre-treatment multimodality imaging (MMI) metrics from positron emission tomography (PET), magnetic resonance imaging (MRI),...
7.
Samala R, Drukker K, Shukla-Dave A, Chan H, Sahiner B, Petrick N, et al.
BJR Artif Intell
. 2024 Jun;
1(1):ubae006.
PMID: 38828430
Innovation in medical imaging artificial intelligence (AI)/machine learning (ML) demands extensive data collection, algorithmic advancements, and rigorous performance assessments encompassing aspects such as generalizability, uncertainty, bias, fairness, trustworthiness, and interpretability....
8.
Mahmood U, Shukla-Dave A, Chan H, Drukker K, Samala R, Chen Q, et al.
BJR Artif Intell
. 2024 Mar;
1(1):ubae003.
PMID: 38476957
The adoption of artificial intelligence (AI) tools in medicine poses challenges to existing clinical workflows. This commentary discusses the necessity of context-specific quality assurance (QA), emphasizing the need for robust...
9.
Paudyal R, Jiang J, Han J, Diplas B, Riaz N, Hatzoglou V, et al.
BJR Artif Intell
. 2024 Mar;
1(1):ubae004.
PMID: 38476956
Objectives: Auto-segmentation promises greater speed and lower inter-reader variability than manual segmentations in radiation oncology clinical practice. This study aims to implement and evaluate the accuracy of the auto-segmentation algorithm,...
10.
Lee N, Sherman E, Schoder H, Wray R, Boyle J, Singh B, et al.
J Clin Oncol
. 2024 Jan;
42(8):940-950.
PMID: 38241600
Purpose: Standard curative-intent chemoradiotherapy for human papillomavirus (HPV)-related oropharyngeal carcinoma results in significant toxicity. Since hypoxic tumors are radioresistant, we posited that the aerobic state of a tumor could identify...