Dataset Information

Assessing the uncertainty in a normal tissue complication probability difference (?NTCP): radiation-induced liver disease (RILD) in liver tumour patients treated with proton vs X-ray therapy.

ABSTRACT: Modern radiotherapy technologies such as proton beam therapy (PBT) permit dose escalation to the tumour and minimize unnecessary doses to normal tissues. To achieve appropriate patient selection for PBT, a normal tissue complication probability (NTCP) model can be applied to estimate the risk of treatment-related toxicity relative to X-ray therapy (XRT). A methodology for estimating the difference in NTCP (?NTCP), including its uncertainty as a function of dose to normal tissue, is described in this study using the Delta method, a statistical method for evaluating the variance of functions, considering the variance-covariance matrix. We used a virtual individual patient dataset of radiation-induced liver disease (RILD) in liver tumour patients who were treated with XRT as a study model. As an alternative option for individual patient data, dose-bin data, which consists of the number of patients who developed toxicity in each dose level/bin and the total number of patients in that dose level/bin, are useful for multi-institutional data sharing. It provides comparable accuracy with individual patient data when using the Delta method. With reliable NTCP models, the ?NTCP with uncertainty might potentially guide the use of PBT; however, clinical validation and a cost-effectiveness study are needed to determine the appropriate ?NTCP threshold.

SUBMITTER: Kobashi K

PROVIDER: S-EPMC5868200 | biostudies-literature | 2018 Mar

REPOSITORIES: biostudies-literature

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Assessing the uncertainty in a normal tissue complication probability difference (∆NTCP): radiation-induced liver disease (RILD) in liver tumour patients treated with proton vs X-ray therapy.

Kobashi Keiji K Prayongrat Anussara A Kimoto Takuya T Toramatsu Chie C Dekura Yasuhiro Y Katoh Norio N Shimizu Shinichi S Ito Yoichi M YM Shirato Hiroki H

Journal of radiation research 20180301 suppl_1

Modern radiotherapy technologies such as proton beam therapy (PBT) permit dose escalation to the tumour and minimize unnecessary doses to normal tissues. To achieve appropriate patient selection for PBT, a normal tissue complication probability (NTCP) model can be applied to estimate the risk of treatment-related toxicity relative to X-ray therapy (XRT). A methodology for estimating the difference in NTCP (∆NTCP), including its uncertainty as a function of dose to normal tissue, is described in ...[more]

PMID: 29538699

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Project description:PurposeWe propose and simulate a model-based methodology to incorporate heterogeneous treatment benefit of proton therapy (PrT) versus photon therapy into randomized trial designs. We use radiation-induced pneumonitis (RP) as an exemplar. The aim is to obtain an unbiased estimate of how predicted difference in normal tissue complications probability (?NTCP) converts into clinical outcome on the patient level.Materials and methods?NTCP data from in silico treatment plans for photon therapy and PrT for patients with locally advanced lung cancer as well as randomly sampled clinical risk factors were included in simulations of trial outcomes. The model used at point of analysis of the trials was an iQUANTEC model. Trial outcomes were examined with Cox proportional hazards models, both in case of a correctly specified model and in a scenario where there is discrepancy between the dose metric used for ?NTCP and the dose metric associated with the "true" clinical outcome, that is, when the model is misspecified. We investigated how outcomes from such a randomized trial may feed into a model-based estimate of the patient-level benefit from PrT, by creating patient-specific predicted benefit probability distributions.ResultsSimulated trials showed benefit in accordance with that expected when the NTCP model was equal to the model for simulating outcome. When the model was misspecified, the benefit changed and we observed a reversal when the driver of outcome was high-dose dependent while the NTCP model was mean-dose dependent. By converting trial results into probability distributions, we demonstrated large heterogeneity in predicted benefit, and provided a randomized measure of the precision of individual benefit estimates.ConclusionsThe design allows for quantifying the benefit of PrT referral, based on the combination of NTCP models, clinical risk factors, and traditional randomization. A misspecified model can be detected through a lower-than-expected hazard ratio per predicted ?NTCP.

Project description:PurposeTo quantify the long-term evaluation of optic chiasma (OC) and/or optic nerve(s) (ONs) and to develop predictive models for radiation-induced optic neuropathy (RION) in nasopharyngeal carcinoma after intensity-modulated radiotherapy (IMRT).Methods and materialsA total of 3,662 patients' OC/ONs with full visual acuity and dosimetry data between 2010 and 2015 were identified. Critical dosimetry predictors of RION were chosen by machine learning and penalized regression for survival. A nomogram containing dosimetry and clinical variables was generated for predicting RION-free survival.ResultsThe median follow-up was 71.79 (2.63-120.9) months. Sixty-six eyes in 51 patients (1.39%) developed RION. Two patients were visual field deficient, and 49 patients had visual acuity of less than 0.1 (20/200). The median latency time was 36 (3-90) months. The 3-, 5-, and 8-year cumulative incidence of RION was 0.78%, 1.19%, and 1.97%, respectively. Dmax was the most critical dosimetry variable for RION (AUC: 0.9434, the optimal cutoff: 64.48 Gy). Patients with a Dmax ≥64.48 Gy had a significantly higher risk of RION (HR = 102.25; 95%CI, 24.86-420.59; P < 0.001). Age (>44 years) (HR = 2.234, 95% CI = 1.233-4.051, p = 0.008), advanced T stage (T3 vs. T1-2: HR = 7.516, 95% CI = 1.725-32.767, p=0.007; T4 vs. T1-2: HR = 37.189, 95% CI = 8.796-157.266, P < 0.001), and tumor infiltration/compression of the OC/ONs (HR = 4.572, 95% CI = 1.316-15.874, p=0.017) were significant clinical risk factors of RION. A nomogram comprising age, T stage, tumor infiltration/compression of the OC/ON, and Dmax significantly outperformed the model, with only Dmax predicting RION (C-index: 0.916 vs. 0.880, P < 0.001 in the training set; 0.899 vs. 0.874, P=0.038 in the test set). The nomogram-defined high-risk group had a worse 8-year RION-free survival.ConclusionsIn the IMRT era, Dmax <60 Gy is safe and represents an acceptable dose constraint for most NPC patients receiving IMRT. A reasonable trade-off for selected patients with unsatisfactory tumor coverage due to proximity to the optic apparatus would be Dmax <65 Gy. Caution should be exercised when treating elderly and advanced T-stage patients or those with tumor infiltration/compression of the OC/ON. Our nomogram shows strong efficacy in predicting RION.

Dataset Information

Assessing the uncertainty in a normal tissue complication probability difference (?NTCP): radiation-induced liver disease (RILD) in liver tumour patients treated with proton vs X-ray therapy.

Publications

Assessing the uncertainty in a normal tissue complication probability difference (∆NTCP): radiation-induced liver disease (RILD) in liver tumour patients treated with proton vs X-ray therapy.

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