Evaluation of deep learning-based multiparametric MRI oropharyngeal primary tumor auto-segmentation and investigation of input channel effects: Results from a prospective imaging registry

Autor: Kareem A. Wahid, Sara Ahmed, Renjie He, Lisanne V. van Dijk, Jonas Teuwen, Brigid A. McDonald, Vivian Salama, Abdallah S.R. Mohamed, Travis Salzillo, Cem Dede, Nicolette Taku, Stephen Y. Lai, Clifton D. Fuller, Mohamed A. Naser

Publikováno v: Clinical and Translational Radiation Oncology, Vol 32, Iss , Pp 6-14 (2022)

Background/Purpose: Oropharyngeal cancer (OPC) primary gross tumor volume (GTVp) segmentation is crucial for radiotherapy. Multiparametric MRI (mpMRI) is increasingly used for OPC adaptive radiotherapy but relies on manual segmentation. Therefore, we

Externí odkaz: https://doaj.org/article/ddd64c41230343b4b61f9c1202cfe2cf

Cluster-Based Toxicity Estimation of Osteoradionecrosis via Unsupervised Machine Learning: Moving Beyond Single Dose-Parameter Normal Tissue Complication Probability by Using Whole Dose-Volume Histograms for Cohort Risk Stratification

Autor: Seyedmohammadhossein Hosseinian, Mehdi Hemmati, Cem Dede, Travis C. Salzillo, Lisanne V. van Dijk, Abdallah S. R. Mohamed, Stephen Y. Lai, Andrew J. Schaefer, Clifton D. Fuller

Publikováno v: medRxiv

PurposeGiven the limitations of extant models for normal tissue complication probability estimation for osteoradionecrosis (ORN) of the mandible, the purpose of this study was to enrich statistical inference by exploiting structural properties of dat

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::8f8a490aaf6206fd4922ed7ef5d5efe4
https://doi.org/10.1101/2023.03.24.23287710

Quality assurance assessment of intra-acquisition diffusion-weighted and T2-weighted magnetic resonance imaging registration and contour propagation for head and neck cancer radiotherapy

Autor: Mohamed A. Naser, Kareem A. Wahid, Sara Ahmed, Vivian Salama, Cem Dede, Benjamin W. Edwards, Ruitao Lin, Brigid McDonald, Travis C. Salzillo, Renjie He, Yao Ding, Moamen Abobakr Abdelaal, Daniel Thill, Nicolette O'Connell, Virgil Willcut, John P. Christodouleas, Stephen Y. Lai, Clifton D. Fuller, Abdallah S. R. Mohamed

Publikováno v: Medical physicsREFERENCES.

Adequate image registration of anatomical and functional magnetic resonance imaging (MRI) scans is necessary for MR-guided head and neck cancer (HNC) adaptive radiotherapy planning. Despite the quantitative capabilities of diffusion-weighted imaging

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c7a6a916cdfb5f073be1068530415804
https://pubmed.ncbi.nlm.nih.gov/36519973

Head and Neck Cancer Primary Tumor Auto Segmentation Using Model Ensembling of Deep Learning in PET/CT Images

Autor: Mohamed Naser, Moamen Abobakr Abdelaal, Renjie He, Kareem Wahid, Lisanne Van Dijk, Clifton Fuller, Cem Dede, Abdallah Mohamed

Publikováno v: Head Neck Tumor Segm Chall (2021)
Lecture Notes in Computer Science ISBN: 9783030982522

Auto-segmentation of primary tumors in oropharyngeal cancer using PET/CT images is an unmet need that has the potential to improve radiation oncology workflows. In this study, we develop a series of deep learning models based on a 3D Residual Unet (R

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b2e54552eec30f1b9dbc09488bf785c7
https://pubmed.ncbi.nlm.nih.gov/35399869

Combining Tumor Segmentation Masks with PET/CT Images and Clinical Data in a Deep Learning Framework for Improved Prognostic Prediction in Head and Neck Squamous Cell Carcinoma

Autor: Clifton D. Fuller, Cem Dede, Kareem Wahid, Abdallah S.R. Mohamed, Renjie He, Lisanne V. van Dijk, Moamen Abobakr Abdelaal, Mohamed A. Naser

Publikováno v: Head and neck tumor segmentation and outcome prediction : second challenge, HECKTOR 2021, held in conjunction with MICCAI 2021, Strasbourg, France, September 27, 2021, Proceedings. Head and Neck Tumor Segmentation Challenge (2nd : 2021 ..., 13209, 300-307

PET/CT images provide a rich data source for clinical prediction models in head and neck squamous cell carcinoma (HNSCC). Deep learning models often use images in an end-to-end fashion with clinical data or no additional input for predictions. Howeve

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ec57257cb954018164db78c65246fd63
https://research.rug.nl/en/publications/152a457d-1f91-41ae-9f30-64f620e7ecd5