Nanna Maria (Marianna) Sijtsema

About Nanna Maria (Marianna) Sijtsema

Nanna Maria (Marianna) Sijtsema, With an exceptional h-index of 31 and a recent h-index of 21 (since 2020), a distinguished researcher at Rijksuniversiteit Groningen, specializes in the field of radiomics, image biomarkers, deep learning, proton therapy, adaptive radiotherapy.

His recent articles reflect a diverse array of research interests and contributions to the field:

Late-xerostomia prediction model based on 18F-FDG PET image biomarkers of the main salivary glands

Facts and Needs to Improve Radiomics Reproducibility

A comparative study of federated learning methods for COVID-19 detection

Deep learning-based outcome prediction using PET/CT and automatically predicted probability maps of primary tumor in patients with oropharyngeal cancer

METhodological RadiomICs Score (METRICS): a quality scoring tool for radiomics research endorsed by EuSoMII

Proton and photon radiotherapy in stage III NSCLC: Effects on hematological toxicity and adjuvant immune therapy

PD-0248 Can surface imaging predict the impact of anatomical deformations on proton breast treatment?

MO-0062 External validation of radiomics and deep learning models for recurrence-free survival prediction

Nanna Maria (Marianna) Sijtsema Information

University

Position

University Medical Center Groningen Department of Radiation Oncology The

Citations(all)

5351

Citations(since 2020)

4084

Cited By

2245

hIndex(all)

31

hIndex(since 2020)

21

i10Index(all)

52

i10Index(since 2020)

38

Email

University Profile Page

Google Scholar

Nanna Maria (Marianna) Sijtsema Skills & Research Interests

radiomics

image biomarkers

deep learning

proton therapy

adaptive radiotherapy

Top articles of Nanna Maria (Marianna) Sijtsema

Late-xerostomia prediction model based on 18F-FDG PET image biomarkers of the main salivary glands

Radiotherapy and Oncology

2024/5/1

Facts and Needs to Improve Radiomics Reproducibility

2024/2/25

A comparative study of federated learning methods for COVID-19 detection

Scientific Reports

2024/2/16

Deep learning-based outcome prediction using PET/CT and automatically predicted probability maps of primary tumor in patients with oropharyngeal cancer

Computer Methods and Programs in Biomedicine

2024/2/1

Proton and photon radiotherapy in stage III NSCLC: Effects on hematological toxicity and adjuvant immune therapy

Radiotherapy and Oncology

2024/1/1

PD-0248 Can surface imaging predict the impact of anatomical deformations on proton breast treatment?

Radiotherapy and Oncology

2023/5/1

MO-0062 External validation of radiomics and deep learning models for recurrence-free survival prediction

Radiotherapy and Oncology

2023/5/1

A comparative study of federated learning models for covid-19 detection

arXiv preprint arXiv:2303.16141

2023/3/28

Deep Learning and Radiomics Based PET/CT Image Feature Extraction from Auto Segmented Tumor Volumes for Recurrence-Free Survival Prediction in Oropharyngeal Cancer Patients

Head and Neck Tumor Segmentation and Outcome Prediction: Third Challenge, HECKTOR 2022, Held in Conjunction with MICCAI 2022, Singapore, September 22, 2022, Proceedings

2023/3/17

Survival prediction for stage I-IIIA non-small cell lung cancer using deep learning

Radiotherapy and oncology

2023/3/1

Validation of the 18F-FDG PET image biomarker model predicting late xerostomia after head and neck cancer radiotherapy

Radiotherapy and Oncology

2023/3/1

Deep learning aided oropharyngeal cancer segmentation with adaptive thresholding for predicted tumor probability in FDG PET and CT images

Physics in Medicine & Biology

2023/2/23

Application of radiomics in understanding tumor biological behaviors and treatment response

Frontiers in Oncology

2023

TransRP: Transformer-based PET/CT feature extraction incorporating clinical data for recurrence-free survival prediction in oropharyngeal cancer

2023

The hidden adversarial vulnerabilities of medical federated learning

arXiv preprint arXiv:2310.13893

2023/10/21

Head and neck cancer predictive risk estimator to determine control and therapeutic outcomes of radiotherapy (HNC-PREDICTOR): development, international multi-institutional …

European Journal of Cancer

2023/1/1

Tackling Heterogeneity in Medical Federated learning via Vision Transformers

arXiv preprint arXiv:2310.09444

2023/10/13

Fed-Safe: Securing Federated Learning in Healthcare Against Adversarial Attacks

arXiv preprint arXiv:2310.08681

2023/10/12

Exploring adversarial attacks in federated learning for medical imaging

arXiv preprint arXiv:2310.06227

2023/10/10

See List of Professors in Nanna Maria (Marianna) Sijtsema University(Rijksuniversiteit Groningen)

Co-Authors

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