Multi-institutional Normal Tissue Complication Probability (NTCP) Prediction Model for Mandibular Osteoradionecrosis: Results from the PREDMORN Study

Laia Humbert-Vidan; Christian R. Hansen; Steven Petit; Carles Muñoz-Montplet; Katrina Hueniken; Abdallah S.R. Mohamed; Deborah P. Saunders; Vinod Patel; Gerda M. Verduijn; Wilma D. Heemsbergen; Arjen van der Schaaf; Max Witjes; Suzanne P.M. de Vette; Mohammad Moharrami; Abdul A. Khan; Jordi Marruecos Querol; Irene Oliveras Cancio; Mike Oliver; Peter Reich; Stacey A. Santi; Andrew G. Pearce; Stephen Y. Lai; Andrew P. King; Ali Hosni; Andrew J. Hope; Erin E. Watson; Johannes A. Langendijk; Jørgen Johansen; Amy C. Moreno; Clifton D. Fuller; Lisanne V. van Dijk; Teresa Guerrero Urbano

doi:10.1016/j.ijrobp.2025.12.044

Multi-institutional Normal Tissue Complication Probability (NTCP) Prediction Model for Mandibular Osteoradionecrosis: Results from the PREDMORN Study

Laia Humbert-Vidan^*
, Christian R. Hansen
, Steven Petit
, Carles Muñoz-Montplet
, Katrina Hueniken
, Abdallah S.R. Mohamed
, Deborah P. Saunders
, Vinod Patel
, Gerda M. Verduijn
, Wilma D. Heemsbergen
, Arjen van der Schaaf
, Max Witjes
, Suzanne P.M. de Vette
, Mohammad Moharrami
, Abdul A. Khan
, Jordi Marruecos Querol
, Irene Oliveras Cancio
, Mike Oliver
, Peter Reich
, Stacey A. Santi

Andrew G. Pearce, Stephen Y. Lai, Andrew P. King, Ali Hosni, Andrew J. Hope, Erin E. Watson, Johannes A. Langendijk, Jørgen Johansen, Amy C. Moreno, Clifton D. Fuller, Lisanne V. van Dijk, Teresa Guerrero Urbano

^*Corresponding author for this work

Radiotherapy

Research output: Contribution to journal › Article › Academic › peer-review

Abstract

Purpose:

Mandibular osteoradionecrosis (ORN) is a severe late complication affecting patients with head and neck cancer (HNC) treated with radiation therapy (RT) that significantly impacts patients’ quality of life and can require costly interventions. Although radiation dose is a key factor, other clinical and demographic risk factors also influence ORN development. Previous predictive models have primarily been single-institutional, limiting their generalizability. In this first analysis from the PREDMORN Consortium, we have aimed to reproduce existing statistical association and modeling analyses on the largest and most diverse mandibular ORN cohort worldwide to allow comparison with previous studies.

Methods and Materials:

This retrospective multi-institutional study included 3928 patients with HNC (622 ORN cases) from 8 institutions. Clinical, demographic, and dosimetric variables were analyzed to develop a prediction model (any grade ORN vs no ORN) using forward stepwise logistic regression with correlation-based variable preselection. The ORN normal tissue complication probability (NTCP) model was developed on 80% of data from 6 institutions, tested on the remaining unseen 20%, and externally validated on a matched cohort (58 patients, 19 ORN cases) and a large population-based cohort (2687 patients, 215 ORN cases).

Results:

Key predictors of ORN were D_30%, V_70Gy, pre-RT dental extractions, and smoking status. The ORN NTCP model demonstrated very good calibration on the population-based external cohort (Brier score, 0.077; Log Loss, 0.281). Model discrimination improved on a subcohort including oropharyngeal and locally advanced larynx/hypopharynx cancer cases only (AUC from 0.69 to 0.75 and from 0.65 to 0.67 on the matched and the population-based external cohorts, respectively).

Conclusions:

The PREDMORN NTCP model is the largest multi-institutional effort to date aimed at predicting ORN risk in patients with HNC using real-world data. The model demonstrated good generalizability when externally validated to a large population-based cohort. Our observations align with current guidelines and corroborate findings from smaller single-institution studies.

Original language	English
Journal	International Journal of Radiation Oncology Biology Physics
DOIs	https://doi.org/10.1016/j.ijrobp.2025.12.044
Publication status	E-pub ahead of print - 8 Jan 2026

Bibliographical note

Publisher Copyright:
© 2026 The Author(s)

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

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Humbert-Vidan, L., Hansen, C. R., Petit, S., Muñoz-Montplet, C., Hueniken, K., Mohamed, A. S. R., Saunders, D. P., Patel, V., Verduijn, G. M., Heemsbergen, W. D., van der Schaaf, A., Witjes, M., de Vette, S. P. M., Moharrami, M., Khan, A. A., Marruecos Querol, J., Cancio, I. O., Oliver, M., Reich, P., ... Guerrero Urbano, T. (2026). Multi-institutional Normal Tissue Complication Probability (NTCP) Prediction Model for Mandibular Osteoradionecrosis: Results from the PREDMORN Study. International Journal of Radiation Oncology Biology Physics. Advance online publication. https://doi.org/10.1016/j.ijrobp.2025.12.044

@article{5437708c28904fe49a33fb5a56e691e4,

title = "Multi-institutional Normal Tissue Complication Probability (NTCP) Prediction Model for Mandibular Osteoradionecrosis: Results from the PREDMORN Study",

abstract = "Purpose: Mandibular osteoradionecrosis (ORN) is a severe late complication affecting patients with head and neck cancer (HNC) treated with radiation therapy (RT) that significantly impacts patients{\textquoteright} quality of life and can require costly interventions. Although radiation dose is a key factor, other clinical and demographic risk factors also influence ORN development. Previous predictive models have primarily been single-institutional, limiting their generalizability. In this first analysis from the PREDMORN Consortium, we have aimed to reproduce existing statistical association and modeling analyses on the largest and most diverse mandibular ORN cohort worldwide to allow comparison with previous studies. Methods and Materials: This retrospective multi-institutional study included 3928 patients with HNC (622 ORN cases) from 8 institutions. Clinical, demographic, and dosimetric variables were analyzed to develop a prediction model (any grade ORN vs no ORN) using forward stepwise logistic regression with correlation-based variable preselection. The ORN normal tissue complication probability (NTCP) model was developed on 80\% of data from 6 institutions, tested on the remaining unseen 20\%, and externally validated on a matched cohort (58 patients, 19 ORN cases) and a large population-based cohort (2687 patients, 215 ORN cases). Results: Key predictors of ORN were D30\%, V70Gy, pre-RT dental extractions, and smoking status. The ORN NTCP model demonstrated very good calibration on the population-based external cohort (Brier score, 0.077; Log Loss, 0.281). Model discrimination improved on a subcohort including oropharyngeal and locally advanced larynx/hypopharynx cancer cases only (AUC from 0.69 to 0.75 and from 0.65 to 0.67 on the matched and the population-based external cohorts, respectively).Conclusions: The PREDMORN NTCP model is the largest multi-institutional effort to date aimed at predicting ORN risk in patients with HNC using real-world data. The model demonstrated good generalizability when externally validated to a large population-based cohort. Our observations align with current guidelines and corroborate findings from smaller single-institution studies.",

author = "Laia Humbert-Vidan and Hansen, \{Christian R.\} and Steven Petit and Carles Mu{\~n}oz-Montplet and Katrina Hueniken and Mohamed, \{Abdallah S.R.\} and Saunders, \{Deborah P.\} and Vinod Patel and Verduijn, \{Gerda M.\} and Heemsbergen, \{Wilma D.\} and \{van der Schaaf\}, Arjen and Max Witjes and \{de Vette\}, \{Suzanne P.M.\} and Mohammad Moharrami and Khan, \{Abdul A.\} and \{Marruecos Querol\}, Jordi and Cancio, \{Irene Oliveras\} and Mike Oliver and Peter Reich and Santi, \{Stacey A.\} and Pearce, \{Andrew G.\} and Lai, \{Stephen Y.\} and King, \{Andrew P.\} and Ali Hosni and Hope, \{Andrew J.\} and Watson, \{Erin E.\} and Langendijk, \{Johannes A.\} and J{\o}rgen Johansen and Moreno, \{Amy C.\} and Fuller, \{Clifton D.\} and \{van Dijk\}, \{Lisanne V.\} and \{Guerrero Urbano\}, Teresa",

note = "Publisher Copyright: {\textcopyright} 2026 The Author(s)",

year = "2026",

month = jan,

day = "8",

doi = "10.1016/j.ijrobp.2025.12.044",

language = "English",

journal = "International Journal of Radiation Oncology Biology Physics",

issn = "0360-3016",

publisher = "Elsevier Inc.",

}

Humbert-Vidan, L, Hansen, CR, Petit, S, Muñoz-Montplet, C, Hueniken, K, Mohamed, ASR, Saunders, DP, Patel, V, Verduijn, GM , Heemsbergen, WD, van der Schaaf, A, Witjes, M, de Vette, SPM, Moharrami, M, Khan, AA, Marruecos Querol, J, Cancio, IO, Oliver, M, Reich, P, Santi, SA, Pearce, AG, Lai, SY, King, AP, Hosni, A, Hope, AJ, Watson, EE, Langendijk, JA, Johansen, J, Moreno, AC, Fuller, CD, van Dijk, LV & Guerrero Urbano, T 2026, 'Multi-institutional Normal Tissue Complication Probability (NTCP) Prediction Model for Mandibular Osteoradionecrosis: Results from the PREDMORN Study', International Journal of Radiation Oncology Biology Physics. https://doi.org/10.1016/j.ijrobp.2025.12.044

Multi-institutional Normal Tissue Complication Probability (NTCP) Prediction Model for Mandibular Osteoradionecrosis: Results from the PREDMORN Study. / Humbert-Vidan, Laia; Hansen, Christian R.; Petit, Steven et al.
In: International Journal of Radiation Oncology Biology Physics, 08.01.2026.

Research output: Contribution to journal › Article › Academic › peer-review

TY - JOUR

T1 - Multi-institutional Normal Tissue Complication Probability (NTCP) Prediction Model for Mandibular Osteoradionecrosis

T2 - Results from the PREDMORN Study

AU - Humbert-Vidan, Laia

AU - Hansen, Christian R.

AU - Petit, Steven

AU - Muñoz-Montplet, Carles

AU - Hueniken, Katrina

AU - Mohamed, Abdallah S.R.

AU - Saunders, Deborah P.

AU - Patel, Vinod

AU - Verduijn, Gerda M.

AU - Heemsbergen, Wilma D.

AU - van der Schaaf, Arjen

AU - Witjes, Max

AU - de Vette, Suzanne P.M.

AU - Moharrami, Mohammad

AU - Khan, Abdul A.

AU - Marruecos Querol, Jordi

AU - Cancio, Irene Oliveras

AU - Oliver, Mike

AU - Reich, Peter

AU - Santi, Stacey A.

AU - Pearce, Andrew G.

AU - Lai, Stephen Y.

AU - King, Andrew P.

AU - Hosni, Ali

AU - Hope, Andrew J.

AU - Watson, Erin E.

AU - Langendijk, Johannes A.

AU - Johansen, Jørgen

AU - Moreno, Amy C.

AU - Fuller, Clifton D.

AU - van Dijk, Lisanne V.

AU - Guerrero Urbano, Teresa

PY - 2026/1/8

Y1 - 2026/1/8

N2 - Purpose: Mandibular osteoradionecrosis (ORN) is a severe late complication affecting patients with head and neck cancer (HNC) treated with radiation therapy (RT) that significantly impacts patients’ quality of life and can require costly interventions. Although radiation dose is a key factor, other clinical and demographic risk factors also influence ORN development. Previous predictive models have primarily been single-institutional, limiting their generalizability. In this first analysis from the PREDMORN Consortium, we have aimed to reproduce existing statistical association and modeling analyses on the largest and most diverse mandibular ORN cohort worldwide to allow comparison with previous studies. Methods and Materials: This retrospective multi-institutional study included 3928 patients with HNC (622 ORN cases) from 8 institutions. Clinical, demographic, and dosimetric variables were analyzed to develop a prediction model (any grade ORN vs no ORN) using forward stepwise logistic regression with correlation-based variable preselection. The ORN normal tissue complication probability (NTCP) model was developed on 80% of data from 6 institutions, tested on the remaining unseen 20%, and externally validated on a matched cohort (58 patients, 19 ORN cases) and a large population-based cohort (2687 patients, 215 ORN cases). Results: Key predictors of ORN were D30%, V70Gy, pre-RT dental extractions, and smoking status. The ORN NTCP model demonstrated very good calibration on the population-based external cohort (Brier score, 0.077; Log Loss, 0.281). Model discrimination improved on a subcohort including oropharyngeal and locally advanced larynx/hypopharynx cancer cases only (AUC from 0.69 to 0.75 and from 0.65 to 0.67 on the matched and the population-based external cohorts, respectively).Conclusions: The PREDMORN NTCP model is the largest multi-institutional effort to date aimed at predicting ORN risk in patients with HNC using real-world data. The model demonstrated good generalizability when externally validated to a large population-based cohort. Our observations align with current guidelines and corroborate findings from smaller single-institution studies.

AB - Purpose: Mandibular osteoradionecrosis (ORN) is a severe late complication affecting patients with head and neck cancer (HNC) treated with radiation therapy (RT) that significantly impacts patients’ quality of life and can require costly interventions. Although radiation dose is a key factor, other clinical and demographic risk factors also influence ORN development. Previous predictive models have primarily been single-institutional, limiting their generalizability. In this first analysis from the PREDMORN Consortium, we have aimed to reproduce existing statistical association and modeling analyses on the largest and most diverse mandibular ORN cohort worldwide to allow comparison with previous studies. Methods and Materials: This retrospective multi-institutional study included 3928 patients with HNC (622 ORN cases) from 8 institutions. Clinical, demographic, and dosimetric variables were analyzed to develop a prediction model (any grade ORN vs no ORN) using forward stepwise logistic regression with correlation-based variable preselection. The ORN normal tissue complication probability (NTCP) model was developed on 80% of data from 6 institutions, tested on the remaining unseen 20%, and externally validated on a matched cohort (58 patients, 19 ORN cases) and a large population-based cohort (2687 patients, 215 ORN cases). Results: Key predictors of ORN were D30%, V70Gy, pre-RT dental extractions, and smoking status. The ORN NTCP model demonstrated very good calibration on the population-based external cohort (Brier score, 0.077; Log Loss, 0.281). Model discrimination improved on a subcohort including oropharyngeal and locally advanced larynx/hypopharynx cancer cases only (AUC from 0.69 to 0.75 and from 0.65 to 0.67 on the matched and the population-based external cohorts, respectively).Conclusions: The PREDMORN NTCP model is the largest multi-institutional effort to date aimed at predicting ORN risk in patients with HNC using real-world data. The model demonstrated good generalizability when externally validated to a large population-based cohort. Our observations align with current guidelines and corroborate findings from smaller single-institution studies.

UR - https://www.scopus.com/pages/publications/105029438548

U2 - 10.1016/j.ijrobp.2025.12.044

DO - 10.1016/j.ijrobp.2025.12.044

M3 - Article

C2 - 41642169

AN - SCOPUS:105029438548

SN - 0360-3016

JO - International Journal of Radiation Oncology Biology Physics

JF - International Journal of Radiation Oncology Biology Physics

ER -

Multi-institutional Normal Tissue Complication Probability (NTCP) Prediction Model for Mandibular Osteoradionecrosis: Results from the PREDMORN Study

Abstract

Bibliographical note

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