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Frank Hoebers

Explore the profile of Frank Hoebers including associated specialties, affiliations and a list of published articles. Areas
Snapshot
Articles 57
Citations 3276
Followers 0
Related Specialties
Oncology
Radiology
General Medicine
Top 10 Co-Authors
Philippe Lambin
Andre Dekker
Wouter van Elmpt
Ruud H Brakenhoff
C Rene Leemans
Hugo J W L Aerts
Bernd Kremer
Kathrin Scheckenbach
Catharina M L Zegers
Sebastian Sanduleanu
Published In
Radiother Oncol
Clin Transl Radiat Oncol
medRxiv
Head Neck
Phys Imaging Radiat Oncol
Affiliations
Soon will be listed here.
Recent Articles
1.
The effect of radiation dose to the brain on early self-reported cognitive function in brain and head-and-neck cancer patients
Vaassen F, Hofstede D, Zegers C, Dijkstra J, Hoeben A, Anten M, et al.
Clin Transl Radiat Oncol . 2025 Mar; 52:100929. PMID: 40028425
Purpose: Assess cognitive changes after radiotherapy (RT) in brain and head-and-neck (HN) cancer patients using patient-reported outcome measures (PROMs) and evaluate a dose-effect relationship for brain structures. Materials And Methods:...
2.
Ferroptosis-Related Gene Signatures: Prognostic Role in HPV-Positive Oropharyngeal Squamous Cell Carcinoma
Lenoci D, Serafini M, Lucchetta M, Cavalieri S, Brakenhoff R, Hoebers F, et al.
Cancers (Basel) . 2025 Feb; 17(3). PMID: 39941896
Background: Despite advances in the management of head and neck squamous cell carcinoma (HNSCC), prognostic models and treatment strategies remain inadequate, particularly for HPV-positive oropharyngeal squamous cell carcinoma (OPSCC). The...
3.
Deep learning with uncertainty estimation for automatic tumor segmentation in PET/CT of head and neck cancers: impact of model complexity, image processing and augmentation
Huynh B, Groendahl A, Tomic O, Liland K, Knudtsen I, Hoebers F, et al.
Biomed Phys Eng Express . 2024 Aug; 10(5). PMID: 39127060
Target volumes for radiotherapy are usually contoured manually, which can be time-consuming and prone to inter- and intra-observer variability. Automatic contouring by convolutional neural networks (CNN) can be fast and...
4.
Corrigendum: Head and neck cancer treatment outcome prediction: a comparison between machine learning with conventional radiomics features and deep learning radiomics
Huynh B, Groendahl A, Tomic O, Liland K, Knudtsen I, Hoebers F, et al.
Front Med (Lausanne) . 2024 May; 11:1421603. PMID: 38813378
[This corrects the article DOI: 10.3389/fmed.2023.1217037.].
5.
The effect of using a large language model to respond to patient messages
Chen S, Guevara M, Moningi S, Hoebers F, Elhalawani H, Kann B, et al.
Lancet Digit Health . 2024 Apr; 6(6):e379-e381. PMID: 38664108
No abstract available.
6.
Decisional Conflict in Patients with Advanced Laryngeal Carcinoma: A Multicenter Study
Heirman A, de Kort D, Petersen J, Al-Mamgani A, Eerenstein S, de Kleijn B, et al.
Laryngoscope . 2024 Feb; 134(8):3604-3610. PMID: 38366759
Objectives: Decision-making for patients with a locally advanced laryngeal carcinoma (T3 and T4) is challenging due to the treatment choice between organ preservation and laryngectomy, both with different and high...
7.
The development of a decision aid for patients with operable oropharyngeal carcinoma in the Netherlands - A mixed methods study
Heirman A, Dirven R, van der Molen L, Schreuder W, Hoebers F, Honings J, et al.
Oral Oncol . 2023 Dec; 149:106677. PMID: 38142550
Objective: The aim of this project is to create an interactive online patient decision aid (PDA) for oropharyngeal cancer (OPSCC) patients, eligible for transoral (robotic) surgery with an ultimate goal...
8.
Image based prognosis in head and neck cancer using convolutional neural networks: a case study in reproducibility and optimization
Mateus P, Volmer L, Wee L, Aerts H, Hoebers F, Dekker A, et al.
Sci Rep . 2023 Oct; 13(1):18176. PMID: 37875663
In the past decade, there has been a sharp increase in publications describing applications of convolutional neural networks (CNNs) in medical image analysis. However, recent reviews have warned of the...
9.
A clinicogenomic model including GARD predicts outcome for radiation treated patients with HPV+ oropharyngeal squamous cell carcinoma
Ho E, De Cecco L, Cavalieri S, Sedor G, Hoebers F, Brakenhoff R, et al.
medRxiv . 2023 Sep; PMID: 37745365
Background: Treatment decision-making in oropharyngeal squamous cell carcinoma (OPSCC) includes clinical stage, HPV status, and smoking history. Despite improvements in staging with separation of HPV-positive and -negative OPSCC in AJCC...
10.
Head and neck cancer treatment outcome prediction: a comparison between machine learning with conventional radiomics features and deep learning radiomics
Huynh B, Groendahl A, Tomic O, Liland K, Knudtsen I, Hoebers F, et al.
Front Med (Lausanne) . 2023 Sep; 10:1217037. PMID: 37711738
Background: Radiomics can provide in-depth characterization of cancers for treatment outcome prediction. Conventional radiomics rely on extraction of image features within a pre-defined image region of interest (ROI) which are...