---
references:
- id: ludwig_detailed_2022
  abstract: >-
    Purpose/Objective

    Whereas the prevalence of lymph node level (LNL) involvement in head & neck
    squamous cell carcinomas (HNSCC) has been reported, the details of lymphatic
    progression patterns are insufficiently quantified. In this study, we
    investigate how the risk of metastases in each LNL depends on the
    involvement of upstream LNLs, T-category, HPV status and other risk factors.

    Materials/Methods

    We retrospectively analyzed patients with newly diagnosed oropharyngeal
    squamous cell carcinoma (OPSCC) treated at a single institution, resulting
    in a dataset of 287 patients. For all patients, involvement of LNLs I-VII
    was recorded individually based on available diagnostic modalities (PET,
    MRI, CT, FNA) together with clinicopathological factors. To analyze the
    dataset, a web-based graphical user interface (GUI) was developed, which
    allows querying the number of patients with a certain combination of
    co-involved LNLs and tumor characteristics.

    Results

    The full dataset and GUI is part of the publication. Selected findings are:
    Ipsilateral level IV was involved in 27% of patients with level II and III
    involvement, but only in 2% of patients with level II but not III
    involvement. Prevalence of involvement of ipsilateral levels II, III, IV, V
    was 79%, 34%, 7%, 3% for early T-category patients (T1/T2) and 85%, 50%,
    17%, 9% for late T-category (T3/T4), quantifying increasing involvement with
    T-category. Contralateral levels II, III, IV were involved in 41%, 19%, 4%
    and 12%, 3%, 2% for tumors with and without midline extension, respectively.
    T-stage dependence of LNL involvement was more pronounced in HPV negative
    than positive tumors, but overall involvement was similar. Ipsilateral level
    VII was involved in 14% and 6% of patients with primary tumors in the tonsil
    and the base of tongue, respectively.

    Conclusions

    Detailed quantification of LNL involvement in HNSCC depending on involvement
    of upstream LNLs and clinicopathological factors may allow for further
    personalization of CTV-N definition in the future.
  accessed:
    - year: 2022
      month: 10
      day: 25
  author:
    - family: Ludwig
      given: Roman
    - family: Hoffmann
      given: Jean-Marc
    - family: Pouymayou
      given: Bertrand
    - family: Däppen
      given: Martina Broglie
    - family: Morand
      given: Grégoire
    - family: Guckenberger
      given: Matthias
    - family: Grégoire
      given: Vincent
    - family: Balermpas
      given: Panagiotis
    - family: Unkelbach
      given: Jan
  citation-key: ludwig_detailed_2022
  container-title: Radiotherapy and Oncology
  container-title-short: Radiotherapy and Oncology
  DOI: 10.1016/j.radonc.2022.01.035
  ISSN: 0167-8140
  issued:
    - year: 2022
      month: 4
      day: 1
  language: en
  page: 1-7
  source: ScienceDirect
  title: >-
    Detailed patient-individual reporting of lymph node involvement in
    oropharyngeal squamous cell carcinoma with an online interface
  type: article-journal
  URL: https://www.sciencedirect.com/science/article/pii/S0167814022000615
  volume: '169'

- id: ludwig_dataset_2022
  abstract: >-
    Dataset

    We provide a dataset on lymph node level (LNL) involvement in 287 patients
    with newly diagnosed oropharyngeal squamous cell carcinoma (OPSCC). For each
    patient, ipsilateral and contralateral LNL involvement for levels I to VII
    is reported together with clinicopathological factors including TNM-stage,
    primary tumor subsite, tumor lateralization, HPV status, sex, age, smoking
    status, and primary treatment. LNL involvement was assessed individually
    based on available diagnostic modalities (PET, MRI, CT, fine needle
    aspiration) by reviewing pathology and radiology reports together with the
    radiological images. The data is shared as a CSV-table with rows of patients
    and columns of patient/tumor-specific information and the involvement of
    individual LNL based on the respective diagnostic modalities.

    Reuse potential

    Patterns of lymphatic progression have never been reported on a
    patient-individual basis in as much detail as provided in this dataset. The
    data can be used to build quantitative models for lymphatic tumor
    progression to estimate the probability of occult metastases in LNLs. This
    may in turn allow for further personalization of the elective clinical
    target volume definition in radiotherapy and the extent of neck dissection
    for surgically treated patients. The data can be pooled with other data to
    build large multi-institutional datasets on lymphatic metastatic progression
    in the future.

    Co-submission

    This paper supports the original scientific article by Roman Ludwig,
    Jean-Marc Hoffmann, Bertrand Pouymayou, Grégoire Morand, Martina Broglie
    Däppen, Matthias Guckenberger, Vincent Grégoire, Panagiotis Balermpas, Jan
    Unkelbach, “Detailed patient-individual reporting of lymph node involvement
    in oropharyngeal squamous cell carcinoma with an online interface”,
    Radiotherapy & Oncology [1]
  accessed:
    - year: 2022
      month: 10
      day: 25
  author:
    - family: Ludwig
      given: Roman
    - family: Hoffmann
      given: Jean-Marc
    - family: Pouymayou
      given: Bertrand
    - family: Morand
      given: Grégoire
    - family: Däppen
      given: Martina Broglie
    - family: Guckenberger
      given: Matthias
    - family: Grégoire
      given: Vincent
    - family: Balermpas
      given: Panagiotis
    - family: Unkelbach
      given: Jan
  citation-key: ludwig_dataset_2022
  container-title: Data in Brief
  container-title-short: Data in Brief
  DOI: 10.1016/j.dib.2022.108345
  ISSN: 2352-3409
  issued:
    - year: 2022
      month: 8
      day: 1
  language: en
  page: '108345'
  source: ScienceDirect
  title: >-
    A dataset on patient-individual lymph node involvement in oropharyngeal
    squamous cell carcinoma
  type: article-journal
  URL: https://www.sciencedirect.com/science/article/pii/S2352340922005479
  volume: '43'

- id: bauwens_prevalence_2021
  abstract: "OBJECTIVE: In oropharyngeal squamous cell carcinoma (OP-SCC), the prevalence and distribution of clinical and pathological lymph node metastasis in the neck have been extensively reported. It served as the basis for consensus recommendations on the selection of the lymph node levels in the neck requiring a treatment. The objective of the study is to compare the prevalence and distribution of neck node metastases in HPV+ and HPV- OP-SCC from a large series of patients with OP-SCC who underwent a cervical lymph-node dissection (LND) as part of their treatment.\nMETHODS: The study concentrated on OP-SCC patients treated by various neck node dissection (LND) procedures from January 2014 to December 2018 in 3 French institutions. Patients with prior head and neck cancer, prior neck surgery, the use of induction chemotherapy, or patients with carcinoma of unknown primary were excluded. HPV-status was assessed by p16 immunohistochemistry. For each patient, the clinical and the pathological nodal status, as well as the distribution of the positive nodes in each neck level (from Ia to V) were reported.\nRESULTS: Two-hundred and sixty-three patients were included (126 p16-negative (p16-), and 137 p16-positive (p16+). The rate of clinical positive node (cN+) reached 54% and 88.3% in the p16- and p16+ groups, respectively (p\_<\_0.001); the corresponding rate of pathological positive node (pN+) reached 61.9% and 91.2%, respectively (p\_<\_0.001). Regarding the clinical lymph node distribution, in p16+ patients, more positive nodes were observed in the ipsilateral level IV (p\_=\_0.003), and less positive nodes were observed in the contralateral levels III and IV (p\_=\_0.003 and p\_=\_0.045, respectively). Regarding the pathologic lymph node distribution in the ipsilateral neck, in the cN0 patients, no significant difference was observed between p16- and p16+ patients (p\_=\_0.33 to 1); in the cN+ patients, the nodes were distributed in levels Ib, II, III, IV and V without differences between the p16- and the p16+ patients. In the contralateral neck of p16- patients, nodes metastases were mainly observed in levels II, III and IV, whereas for the p16+ patients, positive nodes were only observed in level II (p\_=\_0.03).\nCONCLUSION: This study demonstrated the higher prevalence of cN+ and pN+ in p16+ OP-SCC patients, but without meaningful difference in the distribution of the lymph node drainage between p16- and p16+ OP-SCC. It indicates that no difference should be made between p16- and p16+ patients regarding the extend of neck treatment."
  author:
    - family: Bauwens
      given: Laurence
    - family: Baltres
      given: Aline
    - family: Fiani
      given: Danny-Joe
    - family: Zrounba
      given: Philippe
    - family: Buiret
      given: Guillaume
    - family: Fleury
      given: Bertrand
    - family: Benzerdjeb
      given: Nazim
    - family: Grégoire
      given: Vincent
  citation-key: bauwens_prevalence_2021
  container-title: Radiotherapy and Oncology
  container-title-short: Radiother Oncol
  DOI: 10.1016/j.radonc.2021.01.028
  ISSN: 1879-0887
  issued:
    - year: 2021
      month: 4
  language: eng
  page: 122-129
  PMID: '33545255'
  source: PubMed
  title: >-
    Prevalence and distribution of cervical lymph node metastases in
    HPV-positive and HPV-negative oropharyngeal squamous cell carcinoma
  type: article-journal
  volume: '157'

- id: ludwig_hidden_2021
  abstract: >-
    Currently, elective clinical target volume (CTV-N) definition for head and
    neck squamous cell carcinoma (HNSCC) is mostly based on the prevalence of
    nodal involvement for a given tumor location. In this work, we propose a
    probabilistic model for lymphatic metastatic spread that can quantify the
    risk of microscopic involvement in lymph node levels (LNL) given the
    location of macroscopic metastases and T-category. This may allow for
    further personalized CTV-N definition based on an individual patient’s state
    of disease. We model the patient's state of metastatic lymphatic progression
    as a collection of hidden binary random variables that indicate the
    involvement of LNLs. In addition, each LNL is associated with observed
    binary random variables that indicate whether macroscopic metastases are
    detected. A hidden Markov model (HMM) is used to compute the probabilities
    of transitions between states over time. The underlying graph of the HMM
    represents the anatomy of the lymphatic drainage system. Learning of the
    transition probabilities is done via Markov chain Monte Carlo sampling and
    is based on a dataset of HNSCC patients in whom involvement of individual
    LNLs was reported. The model is demonstrated for ipsilateral metastatic
    spread in oropharyngeal HNSCC patients. We demonstrate the model's
    capability to quantify the risk of microscopic involvement in levels III and
    IV, depending on whether macroscopic metastases are observed in the upstream
    levels II and III, and depending on T-category. In conclusion, the
    statistical model of lymphatic progression may inform future, more
    personalized, guidelines on which LNL to include in the elective CTV.
    However, larger multi-institutional datasets for model parameter learning
    are required for that.
  accessed:
    - year: 2021
      month: 9
      day: 27
  author:
    - family: Ludwig
      given: Roman
    - family: Pouymayou
      given: Bertrand
    - family: Balermpas
      given: Panagiotis
    - family: Unkelbach
      given: Jan
  citation-key: ludwig_hidden_2021
  container-title: Scientific Reports
  container-title-short: Sci Rep
  DOI: 10.1038/s41598-021-91544-1
  ISSN: 2045-2322
  issue: '1'
  issued:
    - year: 2021
      month: 12
  language: en
  page: '12261'
  source: DOI.org (Crossref)
  title: A hidden Markov model for lymphatic tumor progression in the head and neck
  type: article-journal
  URL: http://www.nature.com/articles/s41598-021-91544-1
  volume: '11'

- id: pouymayou_bayesian_2019
  abstract: >-
    Many tumors including head and neck squamous cell carcinoma (HNSCC) spread
    along the lymphatic network. Current imaging modalities can only detect
    sufficiently large metastases. Therefore, adjacent lymph node levels (LNL)
    are irradiated electively since they may harbor microscopic tumors. We apply
    Bayesian Networks (BN) to model lymphatic tumor progression. The model can
    subsequently be used to personalize the risk estimation of microscopic lymph
    node metastases in newly diagnosed patients based on their distribution of
    macroscopic metastases. A BN is a graphical representation of a joint
    probability distribution. We represent LNLs by binary random variables
    corresponding to the BN nodes. Each LNL is associated with a hidden
    microscopic state and an observed macroscopic state (e.g. 18F-FDG-PET/CT
    imaging). The primary tumor is represented by network input nodes. We
    demonstrate the concept for early T-stage oropharyngeal carcinomas and their
    spread to ipsilateral lymph node levels (LNL) Ib to IV. We show that the BN
    parameters can be efficiently learnt by merging pathology findings on
    microscopic tumor progression (which is limited to a few published studies)
    and imaging data on macroscopic tumor progression such as CT and 18F-FDG-PET
    (which are widely available in clinical practice). The trained network can
    be used to quantify how the distribution of macroscopic metastases impacts
    the probability of microscopic involvement of the remaining LNLs. The
    analysis suggests that the risk of microscopic involvement of level IV
    exceeds 5% only if level III harbors metastases. Excluding level IV from the
    elective CTV for other patients would reduce the integral dose delivered to
    the patient and potentially reduce acute and late side effects.
  author:
    - family: Pouymayou
      given: Bertrand
    - family: Balermpas
      given: Panagiotis
    - family: Riesterer
      given: Oliver
    - family: Guckenberger
      given: Matthias
    - family: Unkelbach
      given: Jan
  citation-key: pouymayou_bayesian_2019
  container-title: Physics in Medicine & Biology
  DOI: 10.1088/1361-6560/ab2a18
  ISSN: 1361-6560
  issue: '16'
  issued:
    - year: 2019
      month: 8
      day: 14
  page: '165003'
  publisher: IOP Publishing
  title: >-
    A Bayesian network model of lymphatic tumor progression for personalized
    elective CTV definition in head and neck cancers
  type: article-journal
  URL: http://dx.doi.org/10.1088/1361-6560/ab2a18
  volume: '64'

- id: unkelbach_role_2020
  abstract: >-
    Treatment planning in radiotherapy distinguishes three target volume
    concepts: the gross tumor volume (GTV), the clinical target volume (CTV),
    and the planning target volume (PTV). Over time, GTV definition and PTV
    margins have improved through the development of novel imaging techniques
    and better image guidance, respectively. CTV definition is sometimes
    considered the weakest element in the planning process. CTV definition is
    particularly complex since the extension of microscopic disease cannot be
    seen using currently available in-vivo imaging techniques. Instead, CTV
    definition has to incorporate knowledge of the patterns of tumor
    progression. While CTV delineation has largely been considered the domain of
    radiation oncologists, this paper, arising from a 2019 ESTRO Physics
    research workshop, discusses the contributions that medical physics and
    computer science can make by developing computational methods to support CTV
    definition. First, we overview the role of image segmentation algorithms,
    which may in part automate CTV delineation through segmentation of lymph
    node stations or normal tissues representing anatomical boundaries of
    microscopic tumor progression. The recent success of deep convolutional
    neural networks has also enabled learning entire CTV delineations from
    examples. Second, we discuss the use of mathematical models of tumor
    progression for CTV definition, using as example the application of glioma
    growth models to facilitate GTV-to-CTV expansion for glioblastoma that is
    consistent with neuroanatomy. We further consider statistical machine
    learning models to quantify lymphatic metastatic progression of tumors,
    which may eventually improve elective CTV definition. Lastly, we discuss
    approaches to incorporate uncertainty in CTV definition into treatment plan
    optimization as well as general limitations of the CTV concept in the case
    of infiltrating tumors without natural boundaries.
  accessed:
    - year: 2023
      month: 3
      day: 13
  author:
    - family: Unkelbach
      given: Jan
    - family: Bortfeld
      given: Thomas
    - family: Cardenas
      given: Carlos E.
    - family: Gregoire
      given: Vincent
    - family: Hager
      given: Wille
    - family: Heijmen
      given: Ben
    - family: Jeraj
      given: Robert
    - family: Korreman
      given: Stine S.
    - family: Ludwig
      given: Roman
    - family: Pouymayou
      given: Bertrand
    - family: Shusharina
      given: Nadya
    - family: Söderberg
      given: Jonas
    - family: Toma-Dasu
      given: Iuliana
    - family: Troost
      given: Esther G. C.
    - family: Vasquez Osorio
      given: Eliana
  citation-key: unkelbach_role_2020
  collection-title: >-
    Physics Special Issue: ESTRO Physics Research Workshops on Science in
    Development
  container-title: Radiotherapy and Oncology
  container-title-short: Radiotherapy and Oncology
  DOI: 10.1016/j.radonc.2020.10.002
  ISSN: 0167-8140
  issued:
    - year: 2020
      month: 12
      day: 1
  language: en
  page: 15-25
  source: ScienceDirect
  title: >-
    The role of computational methods for automating and improving clinical
    target volume definition
  type: article-journal
  URL: https://www.sciencedirect.com/science/article/pii/S0167814020308380
  volume: '153'
...
