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      Predictive Modeling of Thoracic Radiotherapy Toxicity and the Potential Role of Serum Alpha-2-Macroglobulin

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          Abstract

          Background: To investigate the impact of alpha-2-macroglobulin (A2M), a suspected intrinsic radioprotectant, on radiation pneumonitis and esophagitis using multifactorial predictive models.

          Materials and Methods: Baseline A2M levels were obtained for 258 patients prior to thoracic radiotherapy (RT). Dose-volume characteristics were extracted from treatment plans. Spearman's correlation (Rs) test was used to correlate clinical and dosimetric variables with toxicities. Toxicity prediction models were built using least absolute shrinkage and selection operator (LASSO) logistic regression on 1,000 bootstrapped datasets.

          Results: Grade ≥2 esophagitis and pneumonitis developed in 61 (23.6%) and 36 (14.0%) patients, respectively. The median A2M level was 191 mg/dL (range: 94–511). Never/former/current smoker status was 47 (18.2%)/179 (69.4%)/32 (12.4%). We found a significant negative univariate correlation between baseline A2M levels and esophagitis (Rs = −0.18/ p = 0.003) and between A2M and smoking status (Rs = 0.13/ p = 0.04). Further significant parameters for grade ≥2 esophagitis included age (Rs = −0.32/ p < 0.0001), chemotherapy use (Rs = 0.56/ p < 0.0001), dose per fraction (Rs = −0.57/ p < 0.0001), total dose (Rs = 0.35/ p < 0.0001), and several other dosimetric variables with Rs > 0.5 ( p < 0.0001). The only significant non-dosimetric parameter for grade ≥2 pneumonitis was sex (Rs = −0.32/ p = 0.037) with higher risk for women. For pneumonitis D15 (lung) (Rs = 0.19/ p = 0.006) and D45 (heart) (Rs = 0.16/ p = 0.016) had the highest correlation. LASSO models applied on the validation data were statistically significant and resulted in areas under the receiver operating characteristic curve of 0.84 (esophagitis) and 0.78 (pneumonitis). Multivariate predictive models did not require A2M to reach maximum predictive power.

          Conclusion: This is the first study showing a likely association of higher baseline A2M values with lower risk of radiation esophagitis and with smoking status. However, the baseline A2M level was not a significant risk factor for radiation pneumonitis.

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          Most cited references52

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          Quantitative Analyses of Normal Tissue Effects in the Clinic (QUANTEC): an introduction to the scientific issues.

          Advances in dose-volume/outcome (or normal tissue complication probability, NTCP) modeling since the seminal Emami paper from 1991 are reviewed. There has been some progress with an increasing number of studies on large patient samples with three-dimensional dosimetry. Nevertheless, NTCP models are not ideal. Issues related to the grading of side effects, selection of appropriate statistical methods, testing of internal and external model validity, and quantification of predictive power and statistical uncertainty, all limit the usefulness of much of the published literature. Synthesis (meta-analysis) of data from multiple studies is often impossible because of suboptimal primary analysis, insufficient reporting and variations in the models and predictors analyzed. Clinical limitations to the current knowledge base include the need for more data on the effect of patient-related cofactors, interactions between dose distribution and cytotoxic or molecular targeted agents, and the effect of dose fractions and overall treatment time in relation to nonuniform dose distributions. Research priorities for the next 5-10 years are proposed. Copyright 2010 Elsevier Inc. All rights reserved.
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            Predicting radiation pneumonitis after chemoradiation therapy for lung cancer: an international individual patient data meta-analysis.

            Radiation pneumonitis is a dose-limiting toxicity for patients undergoing concurrent chemoradiation therapy (CCRT) for non-small cell lung cancer (NSCLC). We performed an individual patient data meta-analysis to determine factors predictive of clinically significant pneumonitis. After a systematic review of the literature, data were obtained on 836 patients who underwent CCRT in Europe, North America, and Asia. Patients were randomly divided into training and validation sets (two-thirds vs one-third of patients). Factors predictive of symptomatic pneumonitis (grade ≥2 by 1 of several scoring systems) or fatal pneumonitis were evaluated using logistic regression. Recursive partitioning analysis (RPA) was used to define risk groups. The median radiation therapy dose was 60 Gy, and the median follow-up time was 2.3 years. Most patients received concurrent cisplatin/etoposide (38%) or carboplatin/paclitaxel (26%). The overall rate of symptomatic pneumonitis was 29.8% (n=249), with fatal pneumonitis in 1.9% (n=16). In the training set, factors predictive of symptomatic pneumonitis were lung volume receiving ≥20 Gy (V(20)) (odds ratio [OR] 1.03 per 1% increase, P=.008), and carboplatin/paclitaxel chemotherapy (OR 3.33, P 0.65). On RPA, the highest risk of pneumonitis (>50%) was in patients >65 years of age receiving carboplatin/paclitaxel. Predictors of fatal pneumonitis were daily dose >2 Gy, V(20), and lower-lobe tumor location. Several treatment-related risk factors predict the development of symptomatic pneumonitis, and elderly patients who undergo CCRT with carboplatin-paclitaxel chemotherapy are at highest risk. Fatal pneumonitis, although uncommon, is related to dosimetric factors and tumor location. Copyright © 2013 Elsevier Inc. All rights reserved.
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              CERR: a computational environment for radiotherapy research.

              A software environment is described, called the computational environment for radiotherapy research (CERR, pronounced "sir"). CERR partially addresses four broad needs in treatment planning research: (a) it provides a convenient and powerful software environment to develop and prototype treatment planning concepts, (b) it serves as a software integration environment to combine treatment planning software written in multiple languages (MATLAB, FORTRAN, C/C++, JAVA, etc.), together with treatment plan information (computed tomography scans, outlined structures, dose distributions, digital films, etc.), (c) it provides the ability to extract treatment plans from disparate planning systems using the widely available AAPM/RTOG archiving mechanism, and (d) it provides a convenient and powerful tool for sharing and reproducing treatment planning research results. The functional components currently being distributed, including source code, include: (1) an import program which converts the widely available AAPM/RTOG treatment planning format into a MATLAB cell-array data object, facilitating manipulation; (2) viewers which display axial, coronal, and sagittal computed tomography images, structure contours, digital films, and isodose lines or dose colorwash, (3) a suite of contouring tools to edit and/or create anatomical structures, (4) dose-volume and dose-surface histogram calculation and display tools, and (5) various predefined commands. CERR allows the user to retrieve any AAPM/RTOG key word information about the treatment plan archive. The code is relatively self-describing, because it relies on MATLAB structure field name definitions based on the AAPM/RTOG standard. New structure field names can be added dynamically or permanently. New components of arbitrary data type can be stored and accessed without disturbing system operation. CERR has been applied to aid research in dose-volume-outcome modeling, Monte Carlo dose calculation, and treatment planning optimization. In summary, CERR provides a powerful, convenient, and common framework which allows researchers to use common patient data sets, and compare and share research results.
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                Author and article information

                Contributors
                Journal
                Front Oncol
                Front Oncol
                Front. Oncol.
                Frontiers in Oncology
                Frontiers Media S.A.
                2234-943X
                06 August 2020
                2020
                : 10
                : 1395
                Affiliations
                [1] 1Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center , New York, NY, United States
                [2] 2Department of Medical Physics, Memorial Sloan Kettering Cancer Center , New York, NY, United States
                [3] 3Department of Laboratory Medicine, Memorial Sloan Kettering Cancer Center , New York, NY, United States
                Author notes

                Edited by: Claudio Fiorino, San Raffaele Hospital (IRCCS), Italy

                Reviewed by: Francesco Tommasino, University of Trento, Italy; Joseph Stancanello, Guerbet, France

                *Correspondence: Jung Hun Oh OhJ@ 123456mskcc.org

                This article was submitted to Radiation Oncology, a section of the journal Frontiers in Oncology

                Article
                10.3389/fonc.2020.01395
                7423838
                32850450
                e1afd41c-0ea8-48f2-ab29-f8bc2915ad86
                Copyright © 2020 von Reibnitz, Yorke, Oh, Apte, Yang, Pham, Thor, Wu, Fleisher, Gelb, Deasy and Rimner.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 15 January 2020
                : 02 July 2020
                Page count
                Figures: 4, Tables: 3, Equations: 0, References: 60, Pages: 9, Words: 6372
                Funding
                Funded by: Foundation for the National Institutes of Health 10.13039/100000009
                Categories
                Oncology
                Original Research

                Oncology & Radiotherapy
                alpha-2-macroglobulin (a2m),thoracic radiation,toxicity,radioprotection,predictive modeling

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