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

Background & purpose –Late radiation-associated dysphagia after head and neck cancer (HNC) significantly impacts patient’s health and quality of life. Conventional normal tissue complication probability (NTCP) models use discrete dose parameters to predict toxicity risk but fail to fully capture the complexity of this side effect. Deep learning (DL) offers potential improvements by incorporating 3D dose data for all anatomical structures involved in swallowing. This study aims to enhance dysphagia prediction with 3D DL NTCP models compared to conventional NTCP models.Materials & methods –A multi-institutional cohort of 1484 HNC patients was used to train and validate a 3D DL model (Residual Network) incorporating 3D dose distributions, organ-at-risk segmentations, and CT scans, with or without patient- or treatment-related data. Predictions of grade ≥2 dysphagia (CTCAEv4) at six months post-treatment were evaluated using area under the curve (AUC) and calibration curves. Results were compared to a conventional NTCP model based on pre-treatment dysphagia, tumour location, and mean dose to swallowing organs. Attention maps highlighting regions of interest for individual patients were assessed.Results –DL models outperformed the conventional NTCP model in both the independent test set (AUC=0.80–0.84 versus 0.76) and external test set (AUC=0.73–0.74 versus 0.63) in AUC and calibration. Attention maps showed a focus on the oral cavity and superior pharyngeal constrictor muscle.Conclusion –DL NTCP models performed better than the conventional NTCP model, suggesting the benefit of using 3D-input over the conventional discrete dose parameters. Attention maps highlighted relevant regions linked to dysphagia, supporting the utility of DL for improved predictions.

Dosimetric Evaluation of Incorporating Patient Geometric Variations Into Adaptive Plan Optimization Through Probabilistic Treatment Planning in Head and Neck Cancers

Evaluation of a comprehensive set of normal tissue complication probability models for patients with head and neck cancer in an international cohort

The release of a homogeneous high dose to the tumour region has been one of the cornerstones of radiotherapy (RT) treatment since its early days. According to the organ type and to the cancer histology, different doses are required in order to inactivate malignant cells, thus stopping proliferation. However, radiation-induced cell killing is a stochastic process. Tumour control probability (TCP) models have been developed in order to assign a success rate to a given RT treatment. At the same time, there is the need to keep the risks of normal tissue toxicity at an acceptable level. Normal tissue complication probability (NTCP) models provide a means of doing this. Traditionally, TCP and NTCP models combine clinical outcomes with dosimetric information in terms of dose-volume histograms (DVH). Model parameters are derived by mathematical fits to clinical observations and are subsequently used to estimate the risk of tumour relapse or toxicity. In both types of models, all of the patient dosimetric information is condensed into the DVHs, which represents a potential limitation on their descriptive and predictive power. This choice, related to historical and practical reasons, does not allow the full complexity of the 3D dose distribution in the patient to be taken into account. Neglecting these aspects might be relevant in a modern RT setting, which often includes the presence of high dose gradient regions. This has motivated research on ‘advanced’ TCP and NTCP models, able to tackle the problem by looking at a different scale, e.g. in tumour sub-regions or at the single voxel level. This is relevant not only from the purely dosimetric point of view. Increasing evidence is reported on the heterogeneity of cancer tissues, suggesting that non-uniform dose distributions could result in improved survival, for instance if targeted to take into account sub volumes with high clonogen density or hypoxic radioresistant regions. Similarly, radiation-induced side effects are part of a complex biological response, which depends not only on cell killing, but also on the inflammatory response and in some cases on the interplay among different organs. Obviously, conventional NTCP models cannot describe this scenario, and the development of more advanced mathematical tools is needed. This review will be focused on the discussion of recent studies showing possible directions for moving the field of TCP and NTCP modelling forward. Without diminishing the role and usefulness of available models, the aim is to shed light on the benefits that might be achieved by ‘enhanced’ modelling. This could represent an important step in the gradual transition of radiation therapy towards a form of precision medicine.

Simple SummaryThe role of proton therapy as a radiation treatment option for locally advanced sinonasal cancer patients has increased in the last years, showing promising results in terms of clinical outcomes. Definition of strategies to identify patients who would benefit the most from proton therapy in terms of reduced toxicity is highly desirable, due to limited availability and higher costs of this treatment option. The novelty of our in silico study relies on assessing the suitability of a mixed dose volume histograms and normal tissue complication probability model-based approach, aiming at providing, to the scientific community, a patient selection criteria possibly leading the therapeutic choice between proton therapy and advanced photon techniques.(1) Background: In this work, we aim to provide selection criteria based on normal tissue complication probability (NTCP) models and additional explanatory dose-volume histogram parameters suitable for identifying locally advanced sinonasal cancer patients with orbital invasion benefitting from proton therapy. (2) Methods: Twenty-two patients were enrolled, and two advanced radiation techniques were compared: intensity modulated proton therapy (IMPT) and photon volumetric modulated arc therapy (VMAT). Plans were optimized with a simultaneous integrated boost modality: 70 and 56 Gy(RBE) in 35 fractions were prescribed to the high risk/low risk CTV. Several endpoints were investigated, classified for their severity and used as discriminating paradigms. In particular, when NTCP models were already available, a first selection criterion based on the delta-NTCP was adopted. Additionally, an overall analysis in terms of DVH parameters was performed. Furthermore, a second selection criterion based on a weighted sum of the NTCP and DVH was adopted. (3) Results: Four patients out of 22 (18.2%) were suitable for IMPT due to NTCP > 3% for at least one severe toxicity, 4 (18.2%) due to NTCP > 20% for at least three concurrent intermediate toxicities and 16 (72.7%) due to the mixed sum of NTCP and DVH criterion. Since, for some cases, both criteria were contemporary fulfilled, globally 17/22 patients (77.3%) would benefit from IMPT. (4) Conclusions: For this rare clinical scenario, the use of a strategy including DVH parameters and NTCPs when comparing VMAT and IMPT is feasible. We showed that patients affected by sinonasal cancer could profit from IMPT compared to VMAT in terms of optical and central nervous system organs at risk sparing.

Development of a multivariable normal tissue complication probability (NTCP) model for tube feeding dependence after curative radiotherapy/chemo-radiotherapy in head and neck cancer

Background: Mandibular osteoradionecrosis (ORN) is a severe late toxicity of head and neck radiotherapy (RT) characterized by non-healing necrotic bone, with an estimated incidence of 4-8% in the modern IMRT era [1]. Multiple patient, tumor, and treatment factors (e.g., dental extractions, tumor proximity, smoking, and high radiation dose) all contribute to ORN [1,2]. Predictive models that quantify individualized ORN risk could help guide preventive strategies, but there were no validated normal tissue complication probability (NTCP) models for ORN until recently [3]. This study creates a multi-institutional deep learning (DL) model that uses 3D dose distributions ("dosiomic" data) and clinical variables to predict mandibular ORN, with the hypothesis that a large, diverse dataset will improve predictive performance and generalizability. Methods: A retrospective multi-institutional cohort of 1,184 head and neck cancer patients (389 ORN cases) from seven centers was created [4]. Clinical, demographic, and dosimetric information (3D mandibular dose-volume maps and dose-volume histogram metrics) was gathered. The mandibular dose distribution was processed using late fusion convolutional neural network (DenseNet architecture), with clinical risk factors integrated via late fusion. Model training applied 5-fold cross-validation to 80% of the pooled data and was tested on an internal test set (20%) and an external hold-out institution. Performance was evaluated using the area under the ROC curve (AUC), calibration, and F1-score. Results: ORN patients received significantly higher mandibular radiation doses (median D30% and V70Gy), as well as a higher rate of pre-RT dental extractions and smoking [4]. The multi-modal DL model had an internal test AUC of ~0.72 and good calibration, similar to a logistic regression model (AUC ~0.69) [3]. In an independent external cohort, performance was lower (AUC ~0.60), indicating institutional heterogeneity [3]. In terms of discrimination and calibration, a late-fusion DL model performed slightly better than early-fusion and single-modality models [3]. Conclusions: This large-scale study demonstrates the feasibility of predicting ORN risk with multi-institutional data and deep learning. While the DL model performed similarly to traditional models, it offers spatial dose-response insights. Key predictors included mandibular dose-volume metrics (D30%, V70Gy) and dental risk factors [4]. Further research will concentrate on incorporating advanced architectures (e.g., transformers) and addressing domain shifts in order to improve generalizability. This approach shows promise for personalized ORN risk stratification, allowing for tailored prophylactic dental care or adaptive RT planning to reduce ORN in high-risk patients.

Multiple competing normal tissue complication probability (NTCP) models have been proposed for predicting symptomatic radiation-induced lung injury in human. In this paper we tested the efficacy of four common NTCP models applied quantitatively to sub-clinical X-ray computed tomography (CT)-density changes in the lung following radiotherapy. Radiotherapy planning datasets and follow-up chest CTs were obtained in eight patients treated for targets within the lung or hilar region. Image pixel-wise radiation dose exposure versus change in observable CT Hounsfield units was recorded for early (2–5 months) and late (6–9 months) time-points. Four NTCP models, Lyman, Logistic, Weibull and Poisson, were fit to the population data. The quality of fits was assessed by five statistical criteria. All four models fit the data significantly (p < 0.05) well at early, late and cumulative time points. The Lyman model fitted best for early effects while the Weibull Model fitted best for late effects. No significant difference was found between the fits of the models and with respect to parameters D50 and γ50. The D50 estimates were more robust than γ50 to image registration error. For analyzing population-based sub-clinical CT pixel intensity-based dose response, all four models performed well.

Dose–volume toxicity modeling for de-intensified chemo-radiation therapy for HPV-positive oropharynx cancer

This work aims to evaluate the predictive strength of the relative seriality, parallel and Lyman–Kutcher–Burman (LKB) normal tissue complication probability (NTCP) models regarding the incidence of radiation pneumonitis (RP), in a group of patients following lung cancer radiotherapy and also to examine their correlation with pulmonary function tests (PFTs). The study was based on 47 patients who received radiation therapy for stage III non-small-cell lung cancer. For each patient, lung dose volume histograms (DVHs) and the clinical treatment outcome were available. Clinical symptoms, radiological findings and pulmonary function tests incorporated in a post-treatment follow-up period of 18 months were used to assess the manifestation of radiation induced complications. Thirteen of the 47 patients were scored as having radiation induced pneumonitis, with RTOG criteria grade ⩾ 3 and 28 of the 47 with RTOG criteria grade ⩾ 2. Using this material, different methods of estimating the likelihood of radiation effects were evaluated, by analysing patient data based on their full dose distributions and associating the calculated complication rates with the clinical follow-up records. Lungs were evaluated as a paired organ as well as individual lungs. Of the NTCP models examined in the overall group considering the dose distribution in the ipsilateral lung, all models were able to predict radiation induced pneumonitis only in the case of grade 2 radiation pneumonitis score, with the LKB model giving the best results (χ2-test: probability of agreement between the observed and predicted results Pχ(χ2) = 0.524 using the 0.05 significance level). The NTCP modelling considering lungs as a paired organ did not give statistically acceptable results. In the case of lung cancer radiotherapy, the application of different published radiobiological parameters alters the NTCP results, but not excessively as in the case of breast cancer radiotherapy. In this relatively small group of lung cancer patients, no positive statistical correlation could be established between the incidence of radiation pneumonitis as estimated by NTCP models and the pulmonary function test evaluation. However, the use of PFTs as markers or predictors for the incidence or severity of radiation induced pneumonitis must be investigated further.

Toward a model-based patient selection strategy for proton therapy: External validation of photon-derived normal tissue complication probability models in a head and neck proton therapy cohort

External validation of a multifactorial normal tissue complication probability model for tube feeding dependence at 6 months after definitive radiotherapy for head and neck cancer

NTCP Modeling of Patient Reported Xerostomia after De-Intensified Chemoradiotherapy for HPV Associated Oropharynx Cancer

A prospective cohort study of hepatic toxicity after stereotactic body radiation therapy for hepatocellular carcinoma

Cross-modality applicability of rectal normal tissue complication probability models from photon- to proton-based radiotherapy