Taman Upadhaya
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View article: Author Correction: Multi-institutional atlas of brain metastases informs spatial modeling for precision imaging and personalized therapy
Author Correction: Multi-institutional atlas of brain metastases informs spatial modeling for precision imaging and personalized therapy Open
View article: Multi-institutional atlas of brain metastases informs spatial modeling for precision imaging and personalized therapy
Multi-institutional atlas of brain metastases informs spatial modeling for precision imaging and personalized therapy Open
Brain metastases are a frequent and debilitating manifestation of advanced cancer. Here, we collect and analyze neuroimaging of 3,065 cancer patients with 13,067 brain metastases, representing an extensive collection for research. We find …
View article: Unraveling Radiomics Complexity: Strategies for Optimal Simplicity in Predictive Modeling
Unraveling Radiomics Complexity: Strategies for Optimal Simplicity in Predictive Modeling Open
Background: The high dimensionality of radiomic feature sets, the variability in radiomic feature types and potentially high computational requirements all underscore the need for an effective method to identify the smallest set of predict…
View article: The Image Biomarker Standardization Initiative: Standardized Convolutional Filters for Reproducible Radiomics and Enhanced Clinical Insights
The Image Biomarker Standardization Initiative: Standardized Convolutional Filters for Reproducible Radiomics and Enhanced Clinical Insights Open
Filters are commonly used to enhance specific structures and patterns in images, such as vessels or peritumoral regions, to enable clinical insights beyond the visible image using radiomics. However, their lack of standardization restricts…
View article: Application of CT-based foundational artificial intelligence and radiomics models for prediction of survival for lung cancer patients treated on the NRG/RTOG 0617 clinical trial
Application of CT-based foundational artificial intelligence and radiomics models for prediction of survival for lung cancer patients treated on the NRG/RTOG 0617 clinical trial Open
Objectives To apply CT-based foundational artificial intelligence (AI) and radiomics models for predicting overall survival (OS) for patients with locally advanced non-small cell lung cancer (NSCLC). Methods Data for 449 patients retrospec…
View article: The Image Biomarker Standardization Initiative: Standardized convolutional filters for quantitative radiomics Authors and affiliations
The Image Biomarker Standardization Initiative: Standardized convolutional filters for quantitative radiomics Authors and affiliations Open
View article: Cancer Informatics for Cancer Centers: Sharing Ideas on How to Build an Artificial Intelligence–Ready Informatics Ecosystem for Radiation Oncology
Cancer Informatics for Cancer Centers: Sharing Ideas on How to Build an Artificial Intelligence–Ready Informatics Ecosystem for Radiation Oncology Open
In August 2022, the Cancer Informatics for Cancer Centers brought together cancer informatics leaders for its biannual symposium, Precision Medicine Applications in Radiation Oncology, co-chaired by Quynh-Thu Le, MD (Stanford University), …
View article: OncoBERT: Building an Interpretable Transfer Learning Bidirectional Encoder Representations from Transformers Framework for Longitudinal Survival Prediction of Cancer Patients
OncoBERT: Building an Interpretable Transfer Learning Bidirectional Encoder Representations from Transformers Framework for Longitudinal Survival Prediction of Cancer Patients Open
Deep learning transformer models have exhibited exceptional performance in various clinical tasks, including cancer outcome prediction, when applied to electronic health records (EHR). Inspired by the success of bidirectional encoder repre…
View article: Comparison and Fusion of Machine Learning Algorithms for Prospective Validation of PET/CT Radiomic Features Prognostic Value in Stage II-III Non-Small Cell Lung Cancer
Comparison and Fusion of Machine Learning Algorithms for Prospective Validation of PET/CT Radiomic Features Prognostic Value in Stage II-III Non-Small Cell Lung Cancer Open
Machine learning (ML) algorithms for selecting and combining radiomic features into multiparametric prediction models have become popular; however, it has been shown that large variations in performance can be obtained by relying on differ…
View article: Non-invasive imaging prediction of tumor hypoxia: A novel developed and externally validated CT and FDG-PET-based radiomic signatures
Non-invasive imaging prediction of tumor hypoxia: A novel developed and externally validated CT and FDG-PET-based radiomic signatures Open
View article: The Image Biomarker Standardization Initiative: Standardized Quantitative Radiomics for High-Throughput Image-based Phenotyping
The Image Biomarker Standardization Initiative: Standardized Quantitative Radiomics for High-Throughput Image-based Phenotyping Open
Background Radiomic features may quantify characteristics present in medical imaging. However, the lack of standardized definitions and validated reference values have hampered clinical use. Purpose To standardize a set of 174 radiomic fea…
View article: PO-0733 Non-invasive imaging for tumor hypoxia: a novel validated CT and FDG-PET-based Radiomic signature.
PO-0733 Non-invasive imaging for tumor hypoxia: a novel validated CT and FDG-PET-based Radiomic signature. Open
View article: Multimodal radiomics in neuro-oncology
Multimodal radiomics in neuro-oncology Open
Le glioblastome multiforme (GBM) est une tumeur de grade IV représentant 49% de toutes les tumeurs cérébrales. Malgré des modalités de traitement agressives (radiothérapie, chimiothérapie et résection chirurgicale), le pronostic est mauvai…
View article: A framework for multimodal imaging-based prognostic model building: Preliminary study on multimodal MRI in Glioblastoma Multiforme
A framework for multimodal imaging-based prognostic model building: Preliminary study on multimodal MRI in Glioblastoma Multiforme Open