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:Background & aimsPathologists quantify liver steatosis as the fraction of lipid droplet-containing hepatocytes out of all hepatocytes, whereas the magnetic resonance-determined proton density fat fraction (PDFF) reflects the tissue triacylglycerol concentration. We investigated the linearity, agreement, and correspondence thresholds between histological steatosis and PDFF across the full clinical spectrum of liver fat content associated with non-alcoholic fatty liver disease.MethodsUsing individual patient-level measurements, we conducted a systematic review and meta-analysis of studies comparing histological steatosis with PDFF determined by magnetic resonance spectroscopy or imaging in adults with suspected non-alcoholic fatty liver disease. Linearity was assessed by meta-analysis of correlation coefficients and by linear mixed modelling of pooled data, agreement by Bland-Altman analysis, and thresholds by receiver operating characteristic analysis. To explain observed differences between the methods, we used RNA-seq to determine the fraction of hepatocytes in human liver biopsies.ResultsEligible studies numbered 9 (N = 597). The relationship between PDFF and histology was predominantly linear (r = 0.85 [95% CI, 0.80-0.89]), and their values approximately coincided at 5% steatosis. Above 5% and towards higher levels of steatosis, absolute values of the methods diverged markedly, with histology exceeding PDFF by up to 3.4-fold. On average, 100% histological steatosis corresponded to a PDFF of 33.0% (29.5-36.7%). Targeting at a specificity of 90%, optimal PDFF thresholds to predict histological steatosis grades were ≥5.75% for ≥S1, ≥15.50% for ≥S2, and ≥21.35% for S3. Hepatocytes comprised 58 ± 5% of liver cells, which may partly explain the lower values of PDFF vs. histology.ConclusionsHistological steatosis and PDFF have non-perfect linearity and fundamentally different scales of measurement. Liver fat values obtained using these methods may be rendered comparable by conversion equations or threshold values.Impact and implicationsMagnetic resonance-proton density fat fraction (PDFF) is increasingly being used to measure liver fat in place of the invasive liver biopsy. Understanding the relationship between PDFF and histological steatosis fraction is important for preventing misjudgement of clinical status or treatment effects in patient care. Our analysis revealed that histological steatosis fraction is often significantly higher than PDFF, and their association varies across the spectrum of fatty liver severity. These findings are particularly important for physicians and clinical researchers, who may use these data to interpret PDFF measurements in the context of histologically evaluated liver fat content.

Project description:PurposeOsteoradionecrosis (ORN) of the mandible represents a severe, debilitating complication of radiation therapy (RT) for head and neck cancer (HNC). At present, no normal tissue complication probability (NTCP) models for risk of ORN exist. The aim of this study was to develop a multivariable clinical/dose-based NTCP model for the prediction of ORN any grade (ORNI-IV) and grade IV (ORNIV) after RT (±chemotherapy) in patients with HNC.Methods and materialsIncluded patients with HNC were treated with (chemo-)RT between 2005 and 2015. Mandible bone radiation dose-volume parameters and clinical variables (ie, age, sex, tumor site, pre-RT dental extractions, chemotherapy history, postoperative RT, and smoking status) were considered as potential predictors. The patient cohort was randomly divided into a training (70%) and independent test (30%) cohort. Bootstrapped forward variable selection was performed in the training cohort to select the predictors for the NTCP models. Final NTCP model(s) were validated on the holdback test subset.ResultsOf 1259 included patients with HNC, 13.7% (n = 173 patients) developed any grade ORN (ORNI-IV primary endpoint) and 5% (n = 65) ORNIV (secondary endpoint). All dose and volume parameters of the mandible bone were significantly associated with the development of ORN in univariable models. Multivariable analyses identified D30% and pre-RT dental extraction as independent predictors for both ORNI-IV and ORNIV best-performing NTCP models with an area under the curve (AUC) of 0.78 (AUCvalidation = 0.75 [0.69-0.82]) and 0.81 (AUCvalidation = 0.82 [0.74-0.89]), respectively.ConclusionsThis study presented NTCP models based on mandible bone D30% and pre-RT dental extraction that predict ORNI-IV and ORNIV (ie, needing invasive surgical intervention) after HNC RT. Our results suggest that less than 30% of the mandible should receive a dose of 35 Gy or more for an ORNI-IV risk lower than 5%. These NTCP models can improve ORN prevention and management by identifying patients at risk of ORN.

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 (ΔNTCP) converts into clinical outcome on the patient level.Materials and methodsΔNTCP 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 ΔNTCP 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 ΔNTCP.

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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