Artificial intelligence for radiation oncology applications using public datasets

KA Wahid, E Glerean, J Sahlsten, J Jaskari… - Seminars in radiation …, 2022 - Elsevier
Artificial intelligence (AI) has exceptional potential to positively impact the field of radiation
oncology. However, large curated datasets-often involving imaging data and corresponding …

Large scale crowdsourced radiotherapy segmentations across a variety of cancer anatomic sites

KA Wahid, D Lin, O Sahin, M Cislo, BE Nelms, R He… - Scientific data, 2023 - nature.com
Clinician generated segmentation of tumor and healthy tissue regions of interest (ROIs) on
medical images is crucial for radiotherapy. However, interobserver segmentation variability …

[HTML][HTML] Intensity standardization methods in magnetic resonance imaging of head and neck cancer

KA Wahid, R He, BA McDonald, BM Anderson… - Physics and imaging in …, 2021 - Elsevier
Abstract Background and Purpose Conventional magnetic resonance imaging (MRI) poses
challenges in quantitative analysis because voxel intensity values lack physical meaning …

E pluribus unum: prospective acceptability benchmarking from the Contouring Collaborative for Consensus in Radiation Oncology crowdsourced initiative for …

D Lin, KA Wahid, BE Nelms, R He… - Journal of Medical …, 2023 - spiedigitallibrary.org
Purpose Contouring Collaborative for Consensus in Radiation Oncology (C3RO) is a
crowdsourced challenge engaging radiation oncologists across various expertise levels in …

Stability of dosiomic features against variations in dose calculation: An analysis based on a cohort of prostate external beam radiotherapy patients

L Sun, W Smith, C Kirkby - Journal of Applied Clinical Medical …, 2023 - Wiley Online Library
Introduction Interest in using higher order features of the planned 3D dose distributions (ie,
dosiomics) to predict radiotherapy outcomes is growing. This is driving many retrospective …

Deep learning auto-segmentation of cervical skeletal muscle for sarcopenia analysis in patients with head and neck cancer

MA Naser, KA Wahid, AJ Grossberg, B Olson… - Frontiers in …, 2022 - frontiersin.org
Background/Purpose Sarcopenia is a prognostic factor in patients with head and neck
cancer (HNC). Sarcopenia can be determined using the skeletal muscle index (SMI) …

Leveraging the Academic Artificial Intelligence Silecosystem to Advance the Community Oncology Enterprise

KJ McDonnell - Journal of Clinical Medicine, 2023 - mdpi.com
Over the last 75 years, artificial intelligence has evolved from a theoretical concept and
novel paradigm describing the role that computers might play in our society to a tool with …

[HTML][HTML] PyRaDiSe: A Python package for DICOM-RT-based auto-segmentation pipeline construction and DICOM-RT data conversion

E Rüfenacht, A Kamath, Y Suter, R Poel, E Ermiş… - Computer methods and …, 2023 - Elsevier
Background and objective: Despite fast evolution cycles in deep learning methodologies for
medical imaging in radiotherapy, auto-segmentation solutions rarely run in clinics due to the …

Muscle and adipose tissue segmentations at the third cervical vertebral level in patients with head and neck cancer

KA Wahid, B Olson, R Jain, AJ Grossberg… - Scientific Data, 2022 - nature.com
The accurate determination of sarcopenia is critical for disease management in patients with
head and neck cancer (HNC). Quantitative determination of sarcopenia is currently …

Deep learning auto-segmentation of cervical neck skeletal muscle for sarcopenia analysis using pre-therapy CT in patients with head and neck cancer

MA Naser, KA Wahid, AJ Grossberg, B Olson, R Jain… - medRxiv, 2021 - medrxiv.org
ABSTRACT Background/Purpose Sarcopenia is a prognostic factor in patients with head
and neck cancer (HNC). Sarcopenia can be determined using the skeletal muscle index …