Comparison of quantitative and qualitative scoring approaches for radiation-induced pulmonary fibrosis as applied to a preliminary investigation into the efficacy of mesenchymal stem cell delivery methods in a rat model.

OBJECTIVES: Compare a quantitative, algorithm-driven, and qualitative, pathologist-driven, scoring of radiation-induced pulmonary fibrosis (RIPF). And using these scoring models to derive preliminary comparisons on the effects of different mesenchymal stem cell (MSC) administration modalities in reducing RIPF. METHODS: 25 rats were randomized into 5 groups: non-irradiated control (CG), irradiated control (CR), intraperitoneally administered granulocyte-macrophage colony stimulating factor or GM-CSF (Drug), intravascularly administered MSC (IV), and intratracheally administered MSC (IT). All groups, except CG, received an 18 Gy conformal dose to the right lung. Drug, IV and IT groups were treated immediately after irradiation. After 24 weeks of observation, rats were euthanized, their lungs excised, fixed and stained with Masson's Trichrome. Samples were anonymized and RIPF was scored qualitatively by a certified pathologist and quantitatively using ImageScope. An analysis of association was conducted, and two binary classifiers trained to validate the integrity of both qualitative and quantitative scoring. Differences between the treatment groups, as assessed by the pathologist score, were then tested by variance component analysis and mixed models for differences in RIPF outcomes. RESULTS: There is agreement between qualitative and quantitative scoring for RIPF grades from 4 to 7. Both classifiers performed similarly on the testing set (AUC = 0.923) indicating accordance between the qualitative and quantitative scoring. For comparisons between MSC infusion modalities, the Drug group had better outcomes (mean pathologist scoring of 3.96), correlating with significantly better RIPF outcomes than IV [lower by 0.97, p = 0.047, 95% CI = (0.013, 1.918)] and resulting in an improvement over CR [lower by 0.93, p = 0.037, 95% CI = (0.062, 1.800]. CONCLUSION: Quantitative image analysis may help in the assessment of therapeutic interventions for RIPF and can serve as a scoring surrogate in differentiating between severe and mild cases of RIPF. Preliminary data demonstrate that the use of GM-CSF was best correlated with lower RIPF severity. ADVANCES IN KNOWLEDGE: Quantitative image analysis can be a viable supplemental system of quality control and triaging in situations where pathologist work hours or resources are limited. The use of different MSC administration methods can result in different degrees of MSC efficacy and study outcomes.

A comparative analysis of longitudinal computed tomography and histopathology for evaluating the potential of mesenchymal stem cells in mitigating radiation-induced pulmonary fibrosis.

Radiation-induced pulmonary fibrosis (RIPF) is a debilitating side effect that occurs in up to 30% of thoracic irradiations in breast and lung cancer patients. RIPF remains a major limiting factor to dose escalation and an obstacle to applying more promising new treatments for cancer cure. Limited treatment options are available to mitigate RIPF once it occurs, but recently, mesenchymal stem cells (MSCs) and a drug treatment stimulating endogenous stem cells (GM-CSF) have been investigated for their potential in preventing this disease onset. In a pre-clinical rat model, we contrasted the application of longitudinal computed tomography (CT) imaging and classical histopathology to quantify RIPF and to evaluate the potential of MSCs in mitigating RIPF. Our results on histology demonstrate promises when MSCs are injected endotracheally (but not intravenously). While our CT analysis highlights the potential of GM-CSF treatment. Advantages and limitations of both analytical methods are contrasted in the context of RIPF.

Mesenchymal Stem Cells Adopt Lung Cell Phenotype in Normal and Radiation-induced Lung Injury Conditions.

Lung tissue exposure to ionizing irradiation can invariably occur during the treatment of a variety of cancers leading to increased risk of radiation-induced lung disease (RILD). Mesenchymal stem cells (MSCs) possess the potential to differentiate into epithelial cells. However, cell culture methods of primary type II pneumocytes are slow and cannot provide a sufficient number of cells to regenerate damaged lungs. Moreover, effects of ablative radiation doses on the ability of MSCs to differentiate in vitro into lung cells have not been investigated yet. Therefore, an in vitro coculture system was used, where MSCs were physically separated from dissociated lung tissue obtained from either healthy or high ablative doses of 16 or 20 Gy whole thorax irradiated rats. Around 10±5% and 20±3% of cocultured MSCs demonstrated a change into lung-specific Clara and type II pneumocyte cells when MSCs were cocultured with healthy lung tissue. Interestingly, in cocultures with irradiated lung biopsies, the percentage of MSCs changed into Clara and type II pneumocytes cells increased to 40±7% and 50±6% at 16 Gy irradiation dose and 30±5% and 40±8% at 20 Gy irradiation dose, respectively. These data suggest that MSCs to lung cell differentiation is possible without cell fusion. In addition, 16 and 20 Gy whole thorax irradiation doses that can cause varying levels of RILD, induced different percentages of MSCs to adopt lung cell phenotype compared with healthy lung tissue, providing encouraging outlook for RILD therapeutic intervention for ablative radiotherapy prescriptions.

Bayesian network ensemble as a multivariate strategy to predict radiation pneumonitis risk.

PURPOSE: Prediction of radiation pneumonitis (RP) has been shown to be challenging due to the involvement of a variety of factors including dose-volume metrics and radiosensitivity biomarkers. Some of these factors are highly correlated and might affect prediction results when combined. Bayesian network (BN) provides a probabilistic framework to represent variable dependencies in a directed acyclic graph. The aim of this study is to integrate the BN framework and a systems' biology approach to detect possible interactions among RP risk factors and exploit these relationships to enhance both the understanding and prediction of RP. METHODS: The authors studied 54 nonsmall-cell lung cancer patients who received curative 3D-conformal radiotherapy. Nineteen RP events were observed (common toxicity criteria for adverse events grade 2 or higher). Serum concentration of the following four candidate biomarkers were measured at baseline and midtreatment: alpha-2-macroglobulin, angiotensin converting enzyme (ACE), transforming growth factor, interleukin-6. Dose-volumetric and clinical parameters were also included as covariates. Feature selection was performed using a Markov blanket approach based on the Koller-Sahami filter. The Markov chain Monte Carlo technique estimated the posterior distribution of BN graphs built from the observed data of the selected variables and causality constraints. RP probability was estimated using a limited number of high posterior graphs (ensemble) and was averaged for the final RP estimate using Bayes' rule. A resampling method based on bootstrapping was applied to model training and validation in order to control under- and overfit pitfalls. RESULTS: RP prediction power of the BN ensemble approach reached its optimum at a size of 200. The optimized performance of the BN model recorded an area under the receiver operating characteristic curve (AUC) of 0.83, which was significantly higher than multivariate logistic regression (0.77), mean heart dose (0.69), and a pre-to-midtreatment change in ACE (0.66). When RP prediction was made only with pretreatment information, the AUC ranged from 0.76 to 0.81 depending on the ensemble size. Bootstrap validation of graph features in the ensemble quantified confidence of association between variables in the graphs where ten interactions were statistically significant. CONCLUSIONS: The presented BN methodology provides the flexibility to model hierarchical interactions between RP covariates, which is applied to probabilistic inference on RP. The authors' preliminary results demonstrate that such framework combined with an ensemble method can possibly improve prediction of RP under real-life clinical circumstances such as missing data or treatment plan adaptation.