Normal Tissue Complication Probability Models Research Articles

Dosimetric and NTCP analyses for selecting parotid gland cancer patients for proton therapy.

To perform a dosimetric and a normal tissue complication probability (NTCP) comparison between intensity modulated proton therapy and photon volumetric modulated arc therapy in a cohort of patients with parotid gland cancers in a post-operative or radical setting. From May 2011 to September 2021, 37 parotid gland cancers patients treated at two institutions were eligible. Inclusion criteria were as follows: patients aged ⩾ 18 years, diagnosis of parotid gland cancers candidate for postoperative radiotherapy or definitive radiotherapy, presence of written informed consent for the use of anonymous data for research purposes. Organs at risk (OARs) were retrospectively contoured. Target coverage goal was defined as D95 > 98%. Six NTCP models were selected. NTCP profiles were calculated for each patient using an internally-developed Python script in RayStation TPS. Average differences in NTCP between photon and proton plans were tested for significance with a two-sided Wilcoxon signed-rank test. Seventy-four plans were generated. A lower Dmean to the majority of organs at risk (inner ear, cochlea, oral cavity, pharyngeal constrictor muscles, contralateral parotid and submandibular gland) was obtained with intensity modulated proton therapy vs volumetric modulated arc therapy with statistical significance (p < .05). Ten (27%) patients had a difference in NTCP (photon vs proton plans) greater than 10% for hearing loss and tinnitus: among them, seven qualified for both endpoints, two patients for hearing loss only, and one for tinnitus. In the current study, nearly one-third of patients resulted eligible for proton therapy and they were the most likely to benefit in terms of prevention of hearing loss and tinnitus.

Evaluation of an automated clinical decision system with deep learning dose prediction and NTCP model for prostate cancer proton therapy.

To evaluate the feasibility of using a deep learning dose prediction approach to identify patients who could benefit most from proton therapy based on the normal tissue complication probability (NTCP) model.&#xD;Approach: Two 3D UNets were established to predict photon and proton doses. A dataset of 95 patients with localized prostate cancer was randomly partitioned into 55, 10, and 30 for training, validation, and testing, respectively. We selected NTCP models for late rectum bleeding and acute urinary urgency of grade 2 or higher to quantify the benefit of proton therapy. Propagated uncertainties of predicted ΔNTCPs resulting from the dose prediction errors were calculated. Patient selection accuracies for a single endpoint and a composite evaluation were assessed under different ΔNTCP thresholds.&#xD;Main results: Our deep learning-based dose prediction technique can reduce the time spent on plan comparison from approximately 2 days to as little as 5 seconds. The expanded uncertainty of predicted ΔNTCPs for rectum and bladder endpoints propagated from the dose prediction error were 0.0042 and 0.0016, respectively, which is less than one-third of the acceptable tolerance. The averaged selection accuracies for rectum bleeding, urinary urgency, and composite evaluation were 90%, 93.5%, and 93.5%, respectively.&#xD;Significance: Our study demonstrates that deep learning dose prediction and NTCP evaluation scheme could distinguish the NTCP differences between photon and proton treatment modalities. In addition, the dose prediction uncertainty does not significantly influence the decision accuracy of NTCP-based patient selection for proton therapy. Therefore, automated deep learning dose prediction and NTCP evaluation schemes can potentially be used to screen large patient populations and to avoid unnecessary delays in the start of prostate cancer radiotherapy in the future.

The role of the soft palate dose regarding normal tissue toxicities in older adults with head and neck cancer undergoing definitive radiotherapy.

The number of older adults with head and neck squamous cell carcinoma (HNSCC) is continuously increasing. Older HNSCC patients may be more vulnerable to radiotherapy-related toxicities, so that extrapolation of available normal tissue complication probability (NTCP) models to this population may not be appropriate. Hence, we aimed to investigate the correlation between organ at risk (OAR) doses and chronic toxicities in older patients with HNSCC undergoing definitive radiotherapy. Patients treated with definitive radiotherapy, either alone or with concomitant systemic treatment, between 2009 and 2019 in a large tertiary cancer center were eligible for this analysis. OARs were contoured based on international consensus guidelines, and EQD2 doses using α/ß values of 3Gy for late effects were calculated based on the radiation treatment plans. Treatment-related toxicities were graded according to Common Terminology Criteria for Adverse Events version 5.0. Logistic regression analyses were carried out, and NTCP models were developed and internally validated using the bootstrapping method. A total of 180 patients with a median age of 73 years fulfilled the inclusion criteria and were analyzed. Seventy-three patients developed chronic moderate xerostomia (grade 2), 34 moderate dysgeusia (grade 2), and 59 moderate-to-severe (grade 2-3) dysphagia after definitive radiotherapy. The soft palate dose was significantly associated with all analyzed toxicities (xerostomia: OR = 1.028, dysgeusia: OR = 1.022, dysphagia: OR = 1.027) in the multivariable regression. The superior pharyngeal constrictor muscle was also significantly related to chronic dysphagia (OR = 1.030). Consecutively developed and internally validated NTCP models were predictive for the analyzed toxicities (optimism-corrected AUCs after bootstrapping: AUCxerostomia=0.64, AUCdysgeusia=0.60, AUCdysphagia=0.64). Our data suggest that the dose to the soft palate is associated with chronic moderate xerostomia, moderate dysgeusia and moderate-to-severe dysphagia in older HNSCC patients undergoing definitive radiotherapy. If validated in external studies, efforts should be undertaken to reduce the soft palate dose in these patients.

Predicting the risk of neurocognitive decline after brain irradiation in adult patients with a primary brain tumor.

Deterioration of neurocognitive function in adult patients with a primary brain tumor is the most concerning side effect of radiotherapy. This study was aimed to develop and evaluate Normal-Tissue Complication Probability (NTCP) models using clinical and dose-volume measures for 6-month, 1-year and 2-year Neurocognitive Decline (ND) post-radiotherapy. A total of 219 patients with a primary brain tumor treated with radical photon and/or proton radiotherapy (RT) between 2019 and 2022 were included. Controlled Oral Word Association (COWA) test, Hopkins Verbal Learning Test-Revised (HVLTR) and Trail Making Test (TMT) were used to objectively measure ND. A comprehensive set of potential clinical and dose-volume measures on several brain structures were considered for statistical modelling. Clinical, dose-volume and combined models were constructed and internally tested in terms of discrimination (Area Under the Curve, AUC), calibration (Mean Absolute Error, MAE) and net benefit. 50%, 44.5% and 42.7% of the patients developed ND at 6-month, 1-year and 2-year timepoints, respectively. Following predictors were included in the combined model for 6-month ND: age at radiotherapy>56 years (OR=5.71), overweight (OR=0.49), obesity (OR=0.35), chemotherapy (OR=2.23), brain V20Gy≥20% (OR=3.53), brainstem volume≥26cc (OR=0.39) and hypothalamus volume≥0.5cc (OR=0.4). Decision curve analysis showed that the combined models had the highest net benefits at 6-month (AUC=0.79, MAE=0.021), 1-year (AUC=0.72, MAE=0.027) and 2-year (AUC=0.69, MAE=0.038) timepoints. The proposed NTCP models use easy-to-obtain predictors to identify patients at high-risk of ND after brain RT. These models can potentially provide a base for RT-related decisions and post-therapy neurocognitive rehabilitation interventions.

A deep learning model for translating CT to ventilation imaging: analysis of accuracy and impact on functional avoidance radiotherapy planning.

Radiotherapy planning incorporating functional lung images has the potential to reduce pulmonary toxicity. Free-breathing 4DCT-derived ventilation image (CTVI) may help quantify lung function. This study introduces a novel deep-learning model directly translating planning CT images into CTVI. We investigated the accuracy of generated images and the impact on functional avoidance planning. Paired planning CT and 4DCT scans from 48 patients with NSCLC were randomized to training (n = 41) and testing (n = 7) data sets. The ventilation maps were generated from 4DCT using a Jacobian-based algorithm to provide ground truth labels (CTVI4DCT). A 3D U-Net-based model was trained to map CT to synthetic CTVI (CTVISyn) and validated using fivefold cross-validation. The highest-performing model was applied to the testing set. Spearman's correlation (rs) and Dice similarity coefficient (DSC) determined voxel-wise and functional-wise concordance between CTVI4DCT and CTVISyn. Three plans were designed per patient in the testing set: one clinical plan without CTVI and two functional avoidance plans combined with CTVI4DCT or CTVISyn, aimed at sparing high-functional lungs defined as the top 50% of the percentile ventilation ranges. Dose-volume (DVH) parameters regarding the planning target volume (PTV) and organs at risk (OARs) were recorded. Radiation pneumonitis (RP) risk was estimated using a dose-function (DFH)-based normal tissue complication probability (NTCP) model. CTVISyn showed a mean rs value of 0.65 ± 0.04 compared to CTVI4DCT. Mean DSC values over the top 50% and 60% of ventilation ranges were 0.41 ± 0.07 and 0.52 ± 0.10, respectively. In the test set (n = 7), all patients' RP-risk benefited from CTVI4DCT-guided plans (Riskmean_4DCT_vs_Clinical: 29.24% vs. 49.12%, P = 0.016), and six patients benefited from CTVISyn-guided plans (Riskmean_Syn_vs_Clinical: 31.13% vs. 49.12%, P = 0.022). There were no significant differences in DVH and DFH metrics between CTVISyn and CTVI4DCT-guided plan (P > 0.05). Using deep-learning techniques, CTVISyn generated from planning CT exhibited a moderate-to-high correlation with CTVI4DCT. The CTVISyn-guided plans were comparable to the CTVI4DCT-guided plans, effectively reducing pulmonary toxicity in patients while maintaining acceptable plan quality. Further prospective trials are needed to validate these findings.

49 Development of a Normal Tissue Complication Probability Model for Dysphagia in PATHOS trial patients

49 Development of a Normal Tissue Complication Probability Model for Dysphagia in PATHOS trial patients

Skin dose-volume predictors of moderate-severe late side effects after whole breast radiotherapy

BackgroundToxicity after whole breast Radiotherapy is a relevant issue, impacting the quality-of-life of a not negligible number of patients. We aimed to develop a Normal Tissue Complication Probability (NTCP) model predicting late toxicities by combining dosimetric parameters of the breast dermis and clinical factors. MethodsThe skin structure was defined as the outer CT body contour's 5 mm inner isotropic expansion. It was retrospectively segmented on a large mono-institutional cohort of early-stage breast cancer patients enrolled between 2009 and 2017 (n = 1066). Patients were treated with tangential-field RT, delivering 40 Gy in 15 fractions to the whole breast. Toxicity was reported during Follow-Up (FU) using SOMA/LENT scoring. The study endpoint was moderate-severe late side effects consisting of Fibrosis-Atrophy-Telangiectasia-Pain (FATP G ≥ 2) developed within 42 months after RT completion. A machine learning pipeline was designed with a logistic model combining clinical factors and absolute skin DVH (cc) parameters as output. ResultsThe FATP G2 + rate was 3.8 %, with 40/1066 patients experiencing side effects. After the preprocessing of variables, a cross-validation was applied to define the best-performing model. We selected a 4-variable model with Post-Surgery Cosmetic alterations (Odds Ratio, OR = 7.3), Aromatase Inhibitors (as a protective factor with OR = 0.45), V20 Gy (50 % of the prescribed dose, OR = 1.02), and V42 Gy (105 %, OR = 1.09). Factors were also converted into an adjusted V20Gy. ConclusionsThe association between late reactions and skin DVH when delivering 40 Gy/15 fr was quantified, suggesting an independent role of V20 and V42. Few clinical factors heavily modulate the risk.

Interobserver delineation variability of computed tomography-based radiomic features of the parotid gland.

To assess the interobserver delineation variability of radiomic features of the parotid gland from computed tomography (CT) images and evaluate the correlation of these features for head and neck cancer (HNC) radiotherapy patients. Contrast-enhanced CT images of 20 HNC patients were utilized. The parotid glands were delineated by treating radiation oncologists (ROs), a selected RO and AccuContour auto-segmentation software. Dice similarity coefficients (DSCs) between each pair of observers were calculated. A total of 107 radiomic features were extracted, whose robustness to interobserver delineation was assessed using the intraclass correlation coefficient (ICC). Pearson correlation coefficients (r) were calculated to determine the relationship between the features. The influence of excluding unrobust features from normal tissue complication probability (NTCP) modeling was investigated for severe oral mucositis (grade ≥3). The average DSC was 0.84 (95% confidence interval, 0.83-0.86). Most of the shape features demonstrated robustness (ICC ≥0.75), while the first-order and texture features were influenced by delineation variability. Among the three observers investigated, 42 features were sufficiently robust, out of which 36 features exhibited weak correlation (|r|<0.8). No significant difference in the robustness level was found when comparing manual segmentation by a single RO or automated segmentation with the actual clinical contour data made by treating ROs. Excluding unrobust features from the NTCP model for severe oral mucositis did not deteriorate the model performance. Interobserver delineation variability had substantial impact on radiomic features of the parotid gland. Both manual and automated segmentation methods contributed similarly to this variation.

Mechanistic model of radiotherapy-induced lung fibrosis using coupled 3D agent-based and Monte Carlo simulations

BackgroundMechanistic modelling of normal tissue toxicities is unfolding as an alternative to the phenomenological normal tissue complication probability models. The latter, currently used in the clinics, rely exclusively on limited patient data and neglect spatial dose distribution information. Among the various approaches, agent-based models are appealing as they provide the means to include patient-specific parameters and simulate long-term effects in complex systems. However, Monte Carlo tools remain the state-of-the-art for modelling radiation transport and provide measurements of the delivered dose with unmatched precision.MethodsIn this work, we develop and characterize a coupled 3D agent-based – Monte Carlo model that mechanistically simulates the onset of the radiation-induced lung fibrosis in an alveolar segment. To the best of our knowledge, this is the first such model.ResultsOur model replicates extracellular matrix patterns, radiation-induced lung fibrosis severity indexes and functional subunits survivals that show qualitative agreement with experimental studies and are consistent with our past results. Moreover, in accordance with experimental results, higher functional subunits survival and lower radiation-induced lung fibrosis severity indexes are achieved when a 5-fractions treatment is simulated. Finally, the model shows increased sensitivity to more uniform protons dose distributions with respect to more heterogeneous ones from photon irradiation.ConclusionsThis study lays thus the groundwork for further investigating the effects of different radiotherapeutic treatments on the onset of radiation-induced lung fibrosis via mechanistic modelling.

Predicting radiation pneumonitis in lung cancer: a EUD-based machine learning approach for volumetric modulated arc therapy patients.

This study aims to develop an optimal machine learning model that uses lung equivalent uniform dose (lung EUD to predict radiation pneumonitis (RP) occurrence in lung cancer patients treated with volumetric modulated arc therapy (VMAT). We analyzed a cohort of 77 patients diagnosed with locally advanced squamous cell lung cancer (LASCLC) receiving concurrent chemoradiotherapy with VMAT. Patients were categorized based on the onset of grade II or higher radiation pneumonitis (RP 2+). Dose volume histogram data, extracted from the treatment planning system, were used to compute the lung EUD values for both groups using a specialized numerical analysis code. We identified the parameter α, representing the most significant relative difference in lung EUD between the two groups. The predictive potential of variables for RP2+, including physical dose metrics, lung EUD, normal tissue complication probability (NTCP) from the Lyman-Kutcher-Burman (LKB) model, and lung EUD-calibrated NTCP for affected and whole lung, underwent both univariate and multivariate analyses. Relevant variables were then employed as inputs for machine learning models: multiple logistic regression (MLR), support vector machine (SVM), decision tree (DT), and K-nearest neighbor (KNN). Each model's performance was gauged using the area under the curve (AUC), determining the best-performing model. The optimal α-value for lung EUD was 0.3, maximizing the relative lung EUD difference between the RP 2+ and non-RP 2+ groups. A strong correlation coefficient of 0.929 (P< 0.01) was observed between lung EUD (α = 0.3) and physical dose metrics. When examining predictive capabilities, lung EUD-based NTCP for the affected lung (AUC: 0.862) and whole lung (AUC: 0.815) surpassed LKB-based NTCP for the respective lungs. The decision tree (DT) model using lung EUD-based predictors emerged as the superior model, achieving an AUC of 0.98 in both training and validation datasets. The likelihood of developing RP 2+ has shown a significant correlation with the advancements in RT technology. From traditional 3-D conformal RT, lung cancer treatment methodologies have transitioned to sophisticated techniques like static IMRT. Accurately deriving such a dose-effect relationship through NTCP modeling of RP incidence is statistically challenging due to the increased number of degrees-of-freedom. To the best of our knowledge, many studies have not clarified the rationale behind setting the α-value to 0.99 or 1, despite the closely aligned calculated lung EUD and lung mean dose MLD. Perfect independence among variables is rarely achievable in real-world scenarios. Four prominent machine learning algorithms were used to devise our prediction models. The inclusion of lung EUD-based factors substantially enhanced their predictive performance for RP 2+. Our results advocate for the decision tree model with lung EUD-based predictors as the optimal prediction tool for VMAT-treated lung cancer patients. Which could replace conventional dosimetric parameters, potentially simplifying complex neural network structures in prediction models.

MR-guided stereotactic radiotherapy of infra-diaphragmatic oligometastases: Evaluation of toxicity and dosimetric parameters

Background and purposeThe SOFT trial is a prospective, multicenter, phase 2 trial investigating magnetic resonance (MR)-guided stereotactic ablative radiotherapy (SABR) for abdominal, soft tissue metastases in patients with oligometastatic disease (OMD) (clinicaltrials.gov ID NCT04407897). We present the primary endpoint analysis of 1-year treatment-related toxicity (TRAE). Materials and methodsPatients with up to five oligometastases from non-hematological cancers were eligible for inclusion. A risk-adapted strategy prioritized fixed organs at risk (OAR) constraints over target coverage. Fractionation schemes were 45–67.5 Gy in 3–8 fractions. The primary endpoint was grade ≥ 4 TRAE within 12 months post-SABR. The association between the risk of gastrointestinal (GI) toxicity and clinical and dosimetric parameters was tested using a normal tissue complication probability model. ResultsWe included 121 patients with 147 oligometastatic targets, mainly located in the liver (41 %), lymph nodes (35 %), or adrenal glands (14 %). Nearly half of all targets (48 %, n = 71) were within 10 mm of a radiosensitive OAR. No grade 4 or 5 TRAEs, 3.5 % grade 3 TRAEs, and 43.7 % grade 2 TRAEs were reported within the first year of follow-up. We found a significant association between grade ≥ 2 GI toxicity and the parameters GI OAR D0.1cc, D1cc, and D20cc. ConclusionIn this phase II study of MR-guided SABR of oligometastases in the infra-diaphragmatic region, we found a low incidence of toxicity despite half of the lesions being within 10 mm of a radiosensitive OAR. GI OAR D0.1cc, D1cc, and D20cc were associated with grade ≥ 2 GI toxicity.

Using meta-analysis and CNN-NLP to review and classify the medical literature for normal tissue complication probability in head and neck cancer

PurposeThe study aims to enhance the efficiency and accuracy of literature reviews on normal tissue complication probability (NTCP) in head and neck cancer patients using radiation therapy. It employs meta-analysis (MA) and natural language processing (NLP).Material and methodsThe study consists of two parts. First, it employs MA to assess NTCP models for xerostomia, dysphagia, and mucositis after radiation therapy, using Python 3.10.5 for statistical analysis. Second, it integrates NLP with convolutional neural networks (CNN) to optimize literature search, reducing 3256 articles to 12. CNN settings include a batch size of 50, 50–200 epoch range and a 0.001 learning rate.ResultsThe study's CNN-NLP model achieved a notable accuracy of 0.94 after 200 epochs with Adamax optimization. MA showed an AUC of 0.67 for early-effect xerostomia and 0.74 for late-effect, indicating moderate to high predictive accuracy but with high variability across studies. Initial CNN accuracy of 66.70% improved to 94.87% post-tuning by optimizer and hyperparameters.ConclusionThe study successfully merges MA and NLP, confirming high predictive accuracy for specific model-feature combinations. It introduces a time-based metric, words per minute (WPM), for efficiency and highlights the utility of MA and NLP in clinical research.

Estimating how contouring differences affect normal tissue complication probability modelling

Background and purposeNormal tissue complication probability (NTCP) models are developed from large retrospective datasets where automatic contouring is often used to contour the organs at risk. This study proposes a methodology to estimate how discrepancies between two sets of contours are reflected on NTCP model performance. We apply this methodology to heart contours within a dataset of non-small cell lung cancer (NSCLC) patients. Materials and methodsOne of the contour sets is designated the ground truth and a dosimetric parameter derived from it is used to simulate outcomes via a predefined NTCP relationship. For each simulated outcome, the selected dosimetric parameters associated with each contour set are individually used to fit a toxicity model and their performance is compared. Our dataset comprised 605 stage IIA-IIIB NSCLC patients. Manual, deep learning, and atlas-based heart contours were available. ResultsHow contour differences were reflected in NTCP model performance depended on the slope of the predefined model, the dosimetric parameter utilized, and the size of the cohort. The impact of contour differences on NTCP model performance increased with steeper NTCP curves. In our dataset, parameters on the low range of the dose-volume histogram were more robust to contour differences. ConclusionsOur methodology can be used to estimate whether a given contouring model is fit for NTCP model development. For the heart in comparable datasets, average Dice should be at least as high as between our manual and deep learning contours for shallow NTCP relationships (88.5 ± 4.5 %) and higher for steep relationships.

Proton Therapy in Breast Cancer: A Review of Potential Approaches for Patient Selection.

Proton radiotherapy may be a compelling technical option for the treatment of breast cancer due to its unique physical property known as the "Bragg peak." This feature offers distinct advantages, promising superior dose conformity within the tumor area and reduced radiation exposure to surrounding healthy tissues, enhancing the potential for better treatment outcomes. However, proton therapy is accompanied by inherent challenges, primarily higher costs and limited accessibility when compared to well-developed photon irradiation. Thus, in clinical practice, it is important for radiation oncologists to carefully select patients before recommendation of proton therapy to ensure the transformation of dosimetric benefits into tangible clinical benefits. Yet, the optimal indications for proton therapy in breast cancer patients remain uncertain. While there is no widely recognized methodology for patient selection, numerous attempts have been made in this direction. In this review, we intended to present an inspiring summarization and discussion about the current practices and exploration on the approaches of this treatment decision-making process in terms of treatment-related side-effects, tumor control, and cost-efficiency, including the normal tissue complication probability (NTCP) model, the tumor control probability (TCP) model, genomic biomarkers, cost-effectiveness analyses (CEAs), and so on. Additionally, we conducted an evaluation of the eligibility criteria in ongoing randomized controlled trials and analyzed their reference value in patient selection. We evaluated the pros and cons of various potential patient selection approaches and proposed possible directions for further optimization and exploration. In summary, while proton therapy holds significant promise in breast cancer treatment, its integration into clinical practice calls for a thoughtful, evidence-driven strategy. By continuously refining the patient selection criteria, we can harness the full potential of proton radiotherapy while ensuring maximum benefit for breast cancer patients.

Proton beam therapy in paediatric cancer: Anticipating the opening of the Australian Bragg Centre for Proton Therapy and Research.

Proton Beam Therapy (PBT) has the potential to improve paediatric cancer care by reducing radiation exposure and thus long-term toxicities. Ethical concerns and debates surrounding the treatment, such as eligibility and accessibility, are ongoing in Australia. The Australian Bragg Centre for Proton Therapy and Research (ABCPTR) (named after Sir William Henry Bragg who described the Bragg peak in his laboratory at the University of Adelaide in 1903) aims to increase access to PBT in Australasia and offer a patient-centred care approach. Research is underway to assess PBT's safety and cost-effectiveness, using tools including Normal Tissue Complication Probability (NTCP) models. Collaborative efforts are focused on developing tailored survivorship clinics to enhance patient follow-up and quality of life. With the anticipated opening of the ABCPTR, Australia is preparing to take a significant step in radiation oncology, offering new research opportunities and creating a publicly funded treatment centre. The initiative aims to balance treatment efficacy with patient care, setting the stage for a future in which radiation therapy will reduce long-term side effects compared to the current standard of care. The implementation of PBT in Australia represents a complex and promising approach to paediatric oncology. This article provides an overview of the current landscape, highlighting the potential benefits and challenges of a treatment that could redefine the quality of survivorship and contribute to global research and best clinical practice.

Normal Tissue Complication Probability Modeling of Severe Radiation-Induced Lymphopenia Using Blood Dose for Patients With Hepatocellular Carcinoma

PurposeThis study aims to develop a normal tissue complication probability (NTCP) model to estimate the risk of severe radiation-induced lymphopenia (SRIL, absolute lymphocyte count (ALC) < 500/μL) by using the blood dose of hepatocellular carcinoma (HCC) patients. Methods and MaterialsWe retrospectively collected data from 75 HCC patients who received radiotherapy (RT) between 2015 and 2018. The hematological dose framework calculated blood dose-volume histograms (DVHs) using a predefined blood flow model, organ DVHs, the number of treatment fractions, and beam delivery time. A Lyman-Kutcher-Burman (LKB) model with a generalized equivalent dose was used to establish the NTCP model, reflecting the whole blood DVHs. The optimization of the LKB parameters was conducted by minimizing a negative log-likelihood function. ResultsThere were 6, 4, 18, 33, and 14 patients in the groups with radiation-induced lymphopenia grades 0, 1, 2, 3, and 4, respectively. The median pre/post-RT ALC were 1410/μL (range, 520–3710/μL) and 470/μL (range, 60–1760/μL), respectively. There was a correlation between blood mean dose and ALC depletion (Pearson r = -0.664; p < 0.001). The average blood mean doses in each radiation-induced lymphopenia group were 2.90 Gy (95% CI, 1.96–3.85 Gy) for grade 0–1, 5.29 Gy (95% CI, 4.12–6.45 Gy) for grade 2, 8.81 Gy (95% CI, 7.55–10.07 Gy) for grade 3, and 11.69 Gy (95% CI, 9.82–17.57 Gy) for grade 4. When applying developed an NTCP model to predict SRIL, the area under the receiver operating characteristic curve and Brier score were 0.89 and 0.12, respectively. ConclusionWe developed the first NTCP model based on whole blood DVHs for estimating SRIL after abdominal RT in HCC patients. Our results showed a strong correlation between blood dose and ALC depletion, suggesting the potential to predict the risk of SRIL occurrence for using blood dose.

The Role of Thoracic Vertebral Body Dosimetry in Minimizing Acute Hematologic Toxicities of Patients With Non-Small Cell Lung Cancer Receiving Lung Radiation Therapy and Immunotherapy

IntroductionHematologic toxicities (HTs) are among the most common toxicities of combined immunotherapy and radiotherapy (RT). It remains essential to prevent RT-induced HTs because they can cause treatment discontinuation (influencing antitumoral effects), and because lymphopenia might dampen the effects of immunotherapy. To date, there are no studies examining the impact of thoracic vertebral body (TVB) RT dose on HTs in non-small cell lung cancer (NSCLC) patients receiving combined lung RT and PD(L)-1 immunotherapy. MethodsFor standardization, all doses were reported as 2-Gy equivalents (EQD2). Mirroring publications prior to the immunotherapy era, TVB volumes referred to T1-T10 and specific dosimetric parameters (DmeanEQD2, V5EQD2‒V60EQD2) were analyzed. Logistic regression estimated associations between grade ≥3 HTs (HT3+) and dosimetric/clinical parameters. Normal tissue complication probability (NTCP) models were constructed by logistic regression analysis modeling for HT3+. Receiver operating characteristic (ROC) analysis delineated TVB dosimetric thresholds, stratification of which was able to evaluate post-RT absolute lymphocyte count (ALC) and immunotherapy responses. Areas under the curve (AUCs) for NTCP models were corroborated by bootstrapping (optimism-corrected) methodology. ResultsIn 132 patients, there were 26 (19.7%) instances of HT3+. On multivariate analysis, DmeanEQD2 and V5EQD2‒V20EQD2 associated with HT3+ (P<0.05 for all). The NTCP models illustrated a 50% probability of HT3+ at a DmeanEQD2=39.8 Gy, V5EQD2=87.4%, V10EQD2=77.0%, and V20EQD2=68.4%. ROC analysis delineated optimal thresholds of HT3+ with DmeanEQD2 ±30.2 Gy, V5EQD2 ±69.1%, V10EQD2 ±64.6%, and V20EQD2 ±53.5%. Patients treated with values above those cutoffs had over double the risk of HT3+, with significant differences in post-RT ALC and immunotherapy responses (P<0.05 for all). AUCs for each individual parameter ranged from 0.743 to 0.798, and combining all four aforementioned cutoffs into a ROC curve resulted in a qualitatively higher AUC (0.836). ConclusionsThis hypothesis-generating work suggests that TVB dosimetry may equate with HT3+ in NSCLC patients undergoing combined lung RT/immunotherapy. Applying TVB dose constraints in this population could reduce HT3+ and avoid dampening of immunotherapy responses, but prospective validation is required.

A multivariable normal tissue complication probability model for predicting radiation-induced hypothyroidism in nasopharyngeal carcinoma patients in the modern radiotherapy era.

Radiation-induced hypothyroidism (RHT) is a common long-term complication for nasopharyngeal carcinoma (NPC) survivors. A model using clinical and dosimetric factors for predicting risk of RHT could suggest a proper dose-volume parameters for the treatment planning in an individual level. We aim to develop a multivariable normal tissue complication probability (NTCP) model for RHT in NPC patients after intensity-modulated radiotherapy or volumetric modulated arc therapy. The model was developed using retrospective clinical data and dose-volume data of the thyroid and pituitary gland based on a standard backward stepwise multivariable logistic regression analysis and was then internally validated using 10-fold cross-validation. The final NTCP model consisted of age, pretreatment thyroid-stimulating hormone and mean thyroid dose. The model performance was good with an area under the receiver operating characteristic curve of 0.749 on an internal (200 patients) and 0.812 on an external (25 patients) validation. The mean thyroid dose at ≤45Gy was suggested for treatment plan, owing to an RHT incidence of 2% versus 61% in the >45Gy group.

NTCP modelling of xerostomia after radiotherapy for oropharyngeal cancer using the PRO-CTCAE and CTCAE scoring systems at different time-points post-RT

PurposeThis study aims at determining the parameter values of three normal tissue complication probability (NTCP) models for the contralateral parotid gland, contralateral submandibular gland (SMG) and contralateral salivary glands regarding the endpoint of xerostomia 6–24 months after radiotherapy for oropharynx cancer. MethodsThe treatment and outcome data of 231 patients with favorable risk, HPV-associated oropharyngeal squamous cell carcinoma are analyzed. 60 Gy intensity modulated radiotherapy was delivered to all the patients. The presence and severity of xerostomia was recorded (pre- and post- radiotherapy) by the PRO-CTCAE and the CTCAE scoring systems. In both scoring systems, patients with a change in symptom severity (from baseline) of ≥ 2 were considered responders. ResultsXerostomia was observed in 61.3 %, 39.2 %, 28.6 % and 27.0 % of the patients based on the PRO-CTCAE scoring system at 6-, 12-, 18- and 24-months post-RT, respectively. The AUCs of the contralateral salivary glands ranged between 0.58–0.64 in the LKB model with the gEUD ranging between 20.3 Gy and 24.7 Gy. ConclusionsBased on the PRO-CTCAE scores, mean dose < 22 Gy, V50 < 10 % for the contralateral salivary glands and mean dose < 18 Gy, V45 < 10 % for the contralateral parotid were found to significantly reduce by a factor of 2–3 the risk for radiation induced xerostomia that is observed at 6–24 months post-RT, respectively. Also, gEUD < 22 Gy to the contralateral salivary glands and < 18 Gy to the contralateral parotid was found to significantly reduce the risk for radiation induced xerostomia that is observed at 6–24 months post-RT by 2.0–2.3 times.

Radiomics and dosiomics for predicting radiation-induced hypothyroidism and guiding intensity-modulated radiotherapy

To guide individualized intensity-modulated radiotherapy (IMRT), we developed and prospectively validated a multiview radiomics risk model for predicting radiation-induced hypothyroidism in patients with nasopharyngeal carcinoma. And simulated radiotherapy plans with same dose-volume-histogram (DVH) but different dose distributions were redesigned to explore the clinical application of the multiview radiomics risk model. The radiomics and dosiomics were built based on selected radiomics and dosiomics features from planning computed tomography and dose distribution, respectively. The multiview radiomics risk model that integrated radiomics, dosiomics, DVH parameters, and clinical factors had better performance than traditional normal tissue complication probability models. And multiview radiomics risk model could identify differences of patient hypothyroidism-free survival that cannot be stratified by traditional models. Besides, two redesigned simulated plans further verified the clinical application and advantage of the multiview radiomics risk model. The multiview radiomics risk model was a promising method to predict radiation-induced hypothyroidism and guide individualized IMRT.