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  1. Article ; Online: Image based prognosis in head and neck cancer using convolutional neural networks: a case study in reproducibility and optimization.

    Mateus, Pedro / Volmer, Leroy / Wee, Leonard / Aerts, Hugo J W L / Hoebers, Frank / Dekker, Andre / Bermejo, Inigo

    Scientific reports

    2023  Volume 13, Issue 1, Page(s) 18176

    Abstract: 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 lack of reproducibility of most such studies, which ... ...

    Abstract 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 lack of reproducibility of most such studies, which has impeded closer examination of the models and, in turn, their implementation in healthcare. On the other hand, the performance of these models is highly dependent on decisions on architecture and image pre-processing. In this work, we assess the reproducibility of three studies that use CNNs for head and neck cancer outcome prediction by attempting to reproduce the published results. In addition, we propose a new network structure and assess the impact of image pre-processing and model selection criteria on performance. We used two publicly available datasets: one with 298 patients for training and validation and another with 137 patients from a different institute for testing. All three studies failed to report elements required to reproduce their results thoroughly, mainly the image pre-processing steps and the random seed. Our model either outperforms or achieves similar performance to the existing models with considerably fewer parameters. We also observed that the pre-processing efforts significantly impact the model's performance and that some model selection criteria may lead to suboptimal models. Although there have been improvements in the reproducibility of deep learning models, our work suggests that wider implementation of reporting standards is required to avoid a reproducibility crisis.
    MeSH term(s) Humans ; Reproducibility of Results ; Neural Networks, Computer ; Head and Neck Neoplasms/diagnostic imaging ; Image Processing, Computer-Assisted/methods ; Prognosis
    Language English
    Publishing date 2023-10-24
    Publishing country England
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 2615211-3
    ISSN 2045-2322 ; 2045-2322
    ISSN (online) 2045-2322
    ISSN 2045-2322
    DOI 10.1038/s41598-023-45486-5
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Article ; Online: Response to: Comment on: The extent of unnecessary tooth loss due to extractions prior to radiotherapy based on radiation field and dose in patients with head and neck cancer.

    Buurman, Doke J M / Speksnijder, Caroline M / Granzier, Marlies E / Timmer, Veronique C M L / Hoebers, Frank J P / Kessler, Peter

    Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology

    2023  Volume 190, Page(s) 110026

    MeSH term(s) Humans ; Tooth Loss ; Head and Neck Neoplasms/radiotherapy ; Radiotherapy Dosage ; Radiation Oncology
    Language English
    Publishing date 2023-11-24
    Publishing country Ireland
    Document type Letter
    ZDB-ID 605646-5
    ISSN 1879-0887 ; 0167-8140
    ISSN (online) 1879-0887
    ISSN 0167-8140
    DOI 10.1016/j.radonc.2023.110026
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  3. Article ; Online: The extent of unnecessary tooth loss due to extractions prior to radiotherapy based on radiation field and dose in patients with head and neck cancer.

    Buurman, Doke J M / Speksnijder, Caroline M / Granzier, Marlies E / Timmer, Veronique C M L / Hoebers, Frank J P / Kessler, Peter

    Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology

    2023  Volume 187, Page(s) 109847

    Abstract: Background and purpose: Prior to radiotherapy (RT), teeth with poor prognosis that pose a risk for post-RT osteoradionecrosis (ORN) are removed. To allow enough time for adequate wound healing prior to RT, decisions are made based on the estimated ... ...

    Abstract Background and purpose: Prior to radiotherapy (RT), teeth with poor prognosis that pose a risk for post-RT osteoradionecrosis (ORN) are removed. To allow enough time for adequate wound healing prior to RT, decisions are made based on the estimated radiation dose. This study aimed to gain insight into (1) the overall number of teeth extracted and (2) the patient and tumor characteristics associated with the number of redundantly extracted teeth.
    Materials and methods: Patients with head and neck cancer (HNC), treated with RT between 2015 and 2019, were included in this cross-sectional study. For each extracted tooth the radiation dose was calculated retrospectively. The cut-off point for valid extraction was set at ≥ 40 Gy in accordance with the national protocol. Potential factors for doses ≥ 40 Gy were identified, including age, sex, tumor location, tumor (T) and nodal stage (N), overall tumor stage and number of teeth extracted.
    Results: A total of 1759 teeth were removed from 358 patients. Of these 1759 teeth, 1274 (74%) appeared to have been removed redundantly, based on the mean dose (D
    Conclusion: To our knowledge this is the first study to provide insight into the amount of teeth redundantly extracted prior to RT and represents a step forward in de-escalating the damage to the masticatory system prior to RT.
    MeSH term(s) Humans ; Retrospective Studies ; Tooth Loss ; Cross-Sectional Studies ; Head and Neck Neoplasms/radiotherapy ; Tooth Extraction
    Language English
    Publishing date 2023-08-04
    Publishing country Ireland
    Document type Journal Article
    ZDB-ID 605646-5
    ISSN 1879-0887 ; 0167-8140
    ISSN (online) 1879-0887
    ISSN 0167-8140
    DOI 10.1016/j.radonc.2023.109847
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  4. Article ; Online: Machine learning for normal tissue complication probability prediction: Predictive power with versatility and easy implementation.

    Samant, Pratik / Ruysscher, Dirk de / Hoebers, Frank / Canters, Richard / Hall, Emma / Nutting, Chris / Maughan, Tim / Van den Heuvel, Frank

    Clinical and translational radiation oncology

    2023  Volume 39, Page(s) 100595

    Abstract: Background and purpose: A popular Normal tissue Complication (NTCP) model deployed to predict radiotherapy (RT) toxicity is the Lyman-Burman Kutcher (LKB) model of tissue complication. Despite the LKB model's popularity, it can suffer from numerical ... ...

    Abstract Background and purpose: A popular Normal tissue Complication (NTCP) model deployed to predict radiotherapy (RT) toxicity is the Lyman-Burman Kutcher (LKB) model of tissue complication. Despite the LKB model's popularity, it can suffer from numerical instability and considers only the generalized mean dose (GMD) to an organ. Machine learning (ML) algorithms can potentially offer superior predictive power of the LKB model, and with fewer drawbacks. Here we examine the numerical characteristics and predictive power of the LKB model and compare these with those of ML.
    Materials and methods: Both an LKB model and ML models were used to predict G2 Xerostomia on patients following RT for head and neck cancer, using the dose volume histogram of parotid glands as the input feature. Model speed, convergence characteristics and predictive power was evaluated on an independent training set.
    Results: We found that only global optimization algorithms could guarantee a convergent and predictive LKB model. At the same time our results showed that ML models remained unconditionally convergent and predictive, while staying robust to gradient descent optimization. ML models outperform LKB in Brier score and accuracy but compare to LKB in ROC-AUC.
    Conclusion: We have demonstrated that ML models can quantify NTCP better than or as well as LKB models, even for a toxicity that the LKB model is particularly well suited to predict. ML models can offer this performance while offering fundamental advantages in model convergence, speed, and flexibility, and so could offer an alternative to the LKB model that could potentially be used in clinical RT planning decisions.
    Language English
    Publishing date 2023-02-10
    Publishing country Ireland
    Document type Journal Article
    ISSN 2405-6308
    ISSN (online) 2405-6308
    DOI 10.1016/j.ctro.2023.100595
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  5. Article ; Online: Clinical implementation and validation of an automated adaptive workflow for proton therapy.

    Taasti, Vicki Trier / Hazelaar, Colien / Vaassen, Femke / Vaniqui, Ana / Verhoeven, Karolien / Hoebers, Frank / van Elmpt, Wouter / Canters, Richard / Unipan, Mirko

    Physics and imaging in radiation oncology

    2022  Volume 24, Page(s) 59–64

    Abstract: Background and purpose: Treatment quality of proton therapy can be monitored by repeat-computed tomography scans (reCTs). However, manual re-delineation of target contours can be time-consuming. To improve the workflow, we implemented an automated reCT ... ...

    Abstract Background and purpose: Treatment quality of proton therapy can be monitored by repeat-computed tomography scans (reCTs). However, manual re-delineation of target contours can be time-consuming. To improve the workflow, we implemented an automated reCT evaluation, and assessed if automatic target contour propagation would lead to the same clinical decision for plan adaptation as the manual workflow.
    Materials and methods: This study included 79 consecutive patients with a total of 250 reCTs which had been manually evaluated. To assess the feasibility of automated reCT evaluation, we propagated the clinical target volumes (CTVs) deformably from the planning-CT to the reCTs in a commercial treatment planning system. The dose-volume-histogram parameters were extracted for manually re-delineated (CTV
    Results: In 92% (N = 229) of the reCTs correct flagging was obtained. Only 4% (N = 9) of the reCTs presented with false negatives (i.e., at least one clinical constraint failed for CTV
    Conclusion: Deformable target contour propagation was clinically acceptable. A script-based automatic reCT evaluation workflow has been introduced in routine clinical practice.
    Language English
    Publishing date 2022-09-27
    Publishing country Netherlands
    Document type Journal Article
    ISSN 2405-6316
    ISSN (online) 2405-6316
    DOI 10.1016/j.phro.2022.09.009
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  6. Article ; Online: Additional parameters to improve the prognostic value of the 8th edition of the UICC classification for human papillomavirus-related oropharyngeal tumors.

    Straetmans, Jos M J A A / Stuut, Marijn / Lacko, Martin / Hoebers, Frank / Speel, Ernst-Jan M / Kremer, Bernd

    Head & neck

    2022  Volume 44, Issue 8, Page(s) 1799–1815

    Abstract: Background: The prognostic reliability of the UICC's TNM classification (8th edition) for human papillomavirus (HPV)-positive tonsillar squamous cell carcinomas (TSCCs) compared to the 7th edition was explored, and its improvement by using additional ... ...

    Abstract Background: The prognostic reliability of the UICC's TNM classification (8th edition) for human papillomavirus (HPV)-positive tonsillar squamous cell carcinomas (TSCCs) compared to the 7th edition was explored, and its improvement by using additional anatomical and nonanatomical parameters.
    Methods: One hundred and ten HPV-positive and 225 HPV-negative TSCCs were retrospectively analyzed. Survival was correlated with patient and tumor characteristics (7th and 8th edition UICC TNM classification).
    Results: In HPV-positive TSCCs, the 8th edition UICC's TNM classification correlated better with prognosis than the 7th edition. Also, smoking status was a stronger prognosticator of survival than UICC staging. Non- or former smokers had a 5-year overall survival of 95.1% regardless of tumor stage. Furthermore, age (>65 years), cN3, and M1 classification were significant prognostic factors.
    Conclusion: The prognostic value of the 8th edition UICC's TNM classification improved significantly when compared to the 7th edition. Nonetheless, further improvement is possible by adding nonanatomical factors (smoking, age >65 year) and separating N0-N2 from N3.
    MeSH term(s) Aged ; Alphapapillomavirus ; Carcinoma, Squamous Cell/pathology ; Humans ; Neoplasm Staging ; Oropharyngeal Neoplasms/pathology ; Papillomaviridae ; Papillomavirus Infections/pathology ; Prognosis ; Reproducibility of Results ; Retrospective Studies
    Language English
    Publishing date 2022-05-17
    Publishing country United States
    Document type Journal Article
    ZDB-ID 645165-2
    ISSN 1097-0347 ; 0148-6403 ; 1043-3074
    ISSN (online) 1097-0347
    ISSN 0148-6403 ; 1043-3074
    DOI 10.1002/hed.27084
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  7. Article ; Online: Oropharyngeal dysphagia and cachexia: Intertwined in head and neck cancer.

    Willemsen, Anna C H / Pilz, Walmari / Hoeben, Ann / Hoebers, Frank J P / Schols, Annemie M W J / Baijens, Laura W J

    Head & neck

    2022  Volume 45, Issue 4, Page(s) 783–797

    Abstract: Background: This study aims to investigate the relationship between cancer cachexia and oropharyngeal dysphagia (OD) in patients with head and neck cancer (HNC) prior to chemoradiotherapy or bioradiotherapy (CRT/BRT).: Methods: A prospective cohort ... ...

    Abstract Background: This study aims to investigate the relationship between cancer cachexia and oropharyngeal dysphagia (OD) in patients with head and neck cancer (HNC) prior to chemoradiotherapy or bioradiotherapy (CRT/BRT).
    Methods: A prospective cohort study with patients with HNC undergoing CRT/BRT (2018-2021) was conducted. Body composition and skeletal muscle function were evaluated using bioelectrical impedance analysis, handgrip strength, and the short physical performance battery (SPPB). The M. D. Anderson Dysphagia Inventory (MDADI), Eating Assessment Tool (EAT)-10 questionnaire, and patient characteristics were collected. A standardized videofluoroscopic swallowing study was offered to patients.
    Results: Sixty-six patients were included. Twenty-six patients scored EAT-10 ≥ 3 and seventeen were cachectic. ACE-27 score >1, cachexia, abnormal SPPB-derived repeated chair-stand test, lower MDADI scores, and higher overall stage grouping showed potential predictive value (p ≤ 0.10) for EAT-10 ≥ 3. Using multivariable regression analysis, only cachexia remained a significant predictor of EAT-10 ≥ 3 (HR 9.000 [95%CI 2.483-32.619], p = 0.001).
    Conclusion: Cachexia independently predicted the presence of patient-reported OD.
    MeSH term(s) Humans ; Deglutition Disorders/etiology ; Prospective Studies ; Cachexia/etiology ; Hand Strength ; Head and Neck Neoplasms/complications ; Head and Neck Neoplasms/therapy ; Deglutition
    Language English
    Publishing date 2022-12-30
    Publishing country United States
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 645165-2
    ISSN 1097-0347 ; 0148-6403 ; 1043-3074
    ISSN (online) 1097-0347
    ISSN 0148-6403 ; 1043-3074
    DOI 10.1002/hed.27288
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  8. Article ; Online: Decisional Conflict in Patients with Advanced Laryngeal Carcinoma: A Multicenter Study.

    Heirman, Anne N / de Kort, Daan P / Petersen, Japke F / Al-Mamgani, Abrahim / Eerenstein, Simone E J / de Kleijn, Bertram J / Hoebers, Frank / Tijink, Bernard M / Stuiver, Martijn M / van der Molen, Lisette / Dirven, Richard / Halmos, Gyorgy B / van den Brekel, Michiel W M

    The Laryngoscope

    2024  

    Abstract: 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 impact on function and quality of life ( ...

    Abstract 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 impact on function and quality of life (QoL). The complexity of these treatment decisions and their possible consequences might lead to decisional conflict (DC). This study aimed to explore the level of DC in locally advanced laryngeal carcinoma patients facing curative decision-making, and to identify possible associated factors.
    Methods: In this multicenter prospective cohort study, participants completed questionnaires on DC, level of shared decision-making (SDM), and a knowledge test directly after counseling and 6 months after treatment. Descriptive statistics and Spearman correlation tests were used to analyze the data.
    Results: Directly after counseling, almost all participants (44/45; 98%) experienced Clinically Significant DC score (CSDC >25, scale 0-100). On average, patients scored 47% (SD 20%) correct on the knowledge test. Questions related to radiotherapy were answered best (69%, SD 29%), whilst only 35% (SD 29%) of the questions related to laryngectomy were answered correctly. Patients' perceived level of SDM (scale 0-100) was 70 (mean, SD 16.2), and for physicians this was 70 (SD 1.7).
    Conclusion: Most patients with advanced larynx cancer experience high levels of DC. Low knowledge levels regarding treatment aspects indicate a need for better patient counseling.
    Level of evidence: Level IV Laryngoscope, 2024.
    Language English
    Publishing date 2024-02-17
    Publishing country United States
    Document type Journal Article
    ZDB-ID 80180-x
    ISSN 1531-4995 ; 0023-852X
    ISSN (online) 1531-4995
    ISSN 0023-852X
    DOI 10.1002/lary.31336
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  9. Article ; Online: The effect of using a large language model to respond to patient messages.

    Chen, Shan / Guevara, Marco / Moningi, Shalini / Hoebers, Frank / Elhalawani, Hesham / Kann, Benjamin H / Chipidza, Fallon E / Leeman, Jonathan / Aerts, Hugo J W L / Miller, Timothy / Savova, Guergana K / Gallifant, Jack / Celi, Leo A / Mak, Raymond H / Lustberg, Maryam / Afshar, Majid / Bitterman, Danielle S

    The Lancet. Digital health

    2024  

    Language English
    Publishing date 2024-04-24
    Publishing country England
    Document type Journal Article
    ISSN 2589-7500
    ISSN (online) 2589-7500
    DOI 10.1016/S2589-7500(24)00060-8
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  10. Article: Head and neck cancer treatment outcome prediction: a comparison between machine learning with conventional radiomics features and deep learning radiomics.

    Huynh, Bao Ngoc / Groendahl, Aurora Rosvoll / Tomic, Oliver / Liland, Kristian Hovde / Knudtsen, Ingerid Skjei / Hoebers, Frank / van Elmpt, Wouter / Malinen, Eirik / Dale, Einar / Futsaether, Cecilia Marie

    Frontiers in medicine

    2023  Volume 10, Page(s) 1217037

    Abstract: 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 typically fed to a ... ...

    Abstract 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 typically fed to a classification algorithm for prediction of a clinical endpoint. Deep learning radiomics allows for a simpler workflow where images can be used directly as input to a convolutional neural network (CNN) with or without a pre-defined ROI.
    Purpose: The purpose of this study was to evaluate (i) conventional radiomics and (ii) deep learning radiomics for predicting overall survival (OS) and disease-free survival (DFS) for patients with head and neck squamous cell carcinoma (HNSCC) using pre-treatment
    Materials and methods: FDG PET/CT images and clinical data of patients with HNSCC treated with radio(chemo)therapy at Oslo University Hospital (OUS;
    Results: CNNs trained directly on images achieved the highest performance on external data for both endpoints. Adding both clinical and radiomics features to these image-based models increased performance further. Conventional radiomics including clinical data could achieve competitive performance. However, feature selection on clinical and radiomics data lead to overfitting and poor cross-institutional generalizability. CNNs without tumor and node contours achieved close to on-par performance with CNNs including contours.
    Conclusion: High performance and cross-institutional generalizability can be achieved by combining clinical data, radiomics features and medical images together with deep learning models. However, deep learning models trained on images without contours can achieve competitive performance and could see potential use as an initial screening tool for high-risk patients.
    Language English
    Publishing date 2023-08-30
    Publishing country Switzerland
    Document type Journal Article
    ZDB-ID 2775999-4
    ISSN 2296-858X
    ISSN 2296-858X
    DOI 10.3389/fmed.2023.1217037
    Database MEDical Literature Analysis and Retrieval System OnLINE

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