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Machine Learning on a Genome-wide Association Study to Predict Late Genitourinary Toxicity After Prostate Radiation Therapy.

ABSTRACT: PURPOSE:Late genitourinary (GU) toxicity after radiation therapy limits the quality of life of prostate cancer survivors; however, efforts to explain GU toxicity using patient and dose information have remained unsuccessful. We identified patients with a greater congenital GU toxicity risk by identifying and integrating patterns in genome-wide single nucleotide polymorphisms (SNPs). METHODS AND MATERIALS:We applied a preconditioned random forest regression method for predicting risk from the genome-wide data to combine the effects of multiple SNPs and overcome the statistical power limitations of single-SNP analysis. We studied a cohort of 324 prostate cancer patients who were self-assessed for 4 urinary symptoms at 2 years after radiation therapy using the International Prostate Symptom Score. RESULTS:The predictive accuracy of the method varied across the symptoms. Only for the weak stream endpoint did it achieve a significant area under the curve of 0.70 (95% confidence interval 0.54-0.86; P = .01) on hold-out validation data that outperformed competing methods. Gene ontology analysis highlighted key biological processes, such as neurogenesis and ion transport, from the genes known to be important for urinary tract functions. CONCLUSIONS:We applied machine learning methods and bioinformatics tools to genome-wide data to predict and explain GU toxicity. Our approach enabled the design of a more powerful predictive model and the determination of plausible biomarkers and biological processes associated with GU toxicity.

SUBMITTER: Lee S

PROVIDER: S-EPMC5886789 | biostudies-literature | 2018 May

REPOSITORIES: biostudies-literature

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Project description:PURPOSE:The aims of this study were to systematically review tolerance doses for late distinct gastrointestinal (GI), genitourinary (GU), and sexual dysfunction (SD) symptoms after external beam radiation therapy (EBRT) alone and treatments involving brachytherapy (BT) for prostate cancer after Quantitative Analysis of Normal Tissue Effects in the Clinic (QUANTEC) and ultimately to perform quantitative syntheses of identified dose/volume tolerances represented by dose-volume histogram (DVH) thresholds, that is, statistically significant (P ? .05) cutoff points between symptomatic and asymptomatic patients in a certain study. METHODS AND MATERIALS:PubMed was scrutinized for full-text articles in English after QUANTEC (January 1, 2010). The inclusion criteria were randomized controlled trials, case-control studies, or cohort studies with tolerance doses for late distinct symptoms ?3 months after primary radiation therapy for prostate cancer (N > 30). All DVH thresholds were converted into equivalent doses in 2-Gy fractions (EQD2?/?) and were fitted with a linear or linear-quadratic function (goodness of fit, R2). The review was registered on PROSPERO (CRD42016042464). RESULTS:From 33 identified studies, which included 36 to 746 patients per symptom domain, the majority of dose/volume tolerances were derived for GI toxicity after EBRT alone (GI, 97 thresholds; GU, 8 thresholds; SD, 1 threshold). For 5 symptoms (defecation urgency, diarrhea, fecal incontinence, proctitis, and rectal bleeding), relationships between dose/volume tolerances across studies (R2 = 0.93 [0.82-1.00]), and across symptoms, leading to a curve for overall GI toxicity (R2 = 0.98), could be determined. For these symptoms, mainly rectal thresholds were found throughout low and high doses (10 Gy ? equivalent dose in 2-Gy fractions using ?/? = 3Gy (EQD23) ? 50 Gy and 55 Gy ? EQD23 ? 78 Gy, respectively). For BT with or without EBRT, dose/volume tolerances were also mainly identified for GI toxicity (GI, 14 thresholds; GU, 4 thresholds; SD, 2 thresholds) with the largest number of DVH thresholds concerning rectal bleeding (5 thresholds). CONCLUSIONS:Updated dose/volume tolerances after QUANTEC were found for 17 GI, GU, or SD symptoms. A DVH curve described the relationship between dose/volume tolerances across 5 GI symptoms after EBRT alone. Restricting treatments for EBRT alone using the lower boundaries of this curve is likely to limit overall GI toxicity, but this should be explored prospectively. Dose/volume tolerances for GU and SD toxicity after EBRT alone and after BT with or without EBRT were scarce and support further research including data-sharing initiatives to untangle the dose/volume relationships for these symptoms.

Project description:Purpose: Dosimetric predictors of toxicity after Stereotactic Body Radiation Therapy (SBRT) are not well-established. We sought to develop a multivariate model that predicts Common Terminology Criteria for Adverse Events (CTCAE) late grade 2 or greater genitourinary (GU) toxicity by interrogating the entire dose-volume histogram (DVH) from a large cohort of prostate cancer patients treated with SBRT on prospective trials. Methods: Three hundred and thirty-nine patients with late CTCAE toxicity data treated with prostate SBRT were identified and analyzed. All patients received 40 Gy in five fractions, every other day, using volumetric modulated arc therapy. For each patient, we examined 910 candidate dosimetric features including maximum dose, volumes of each organ [CTV, organs at risk (OARs)], V100%, and other granular volumetric/dosimetric indices at varying volumetric/dosimetric values from the entire DVH as well as ADT use to model and predict toxicity from SBRT. Training and validation subsets were generated with 90 and 10% of the patients in our cohort, respectively. Predictive accuracy was assessed by calculating the area under the receiver operating curve (AROC). Univariate analysis with student t-test was first performed on each candidate DVH feature. We subsequently performed advanced machine-learning multivariate analyses including classification and regression tree (CART), random forest, boosted tree, and multilayer neural network. Results: Median follow-up time was 32.3 months (range 3-98.9 months). Late grade ?2 GU toxicity occurred in 20.1% of patients in our series. No single dosimetric parameter had an AROC for predicting late grade ?2 GU toxicity on univariate analysis that exceeded 0.599. Optimized CART modestly improved prediction accuracy, with an AROC of 0.601, whereas other machine learning approaches did not improve upon univariate analyses. Conclusions: CART-based machine learning multivariate analyses drawing from 910 dosimetric features and ADT use modestly improves upon clinical prediction of late GU toxicity alone, yielding an AROC of 0.601. Biologic predictors may enhance predictive models for identifying patients at risk for late toxicity after SBRT.

Project description:This research uses machine-learned computational analyses to predict the cognitive performance impairment of rats induced by irradiation. The experimental data in the analyses is from a rodent model exposed to ≤15 cGy of individual galactic cosmic radiation (GCR) ions: 4He, 16O, 28Si, 48Ti, or 56Fe, expected for a Lunar or Mars mission. This work investigates rats at a subject-based level and uses performance scores taken before irradiation to predict impairment in attentional set-shifting (ATSET) data post-irradiation. Here, the worst performing rats of the control group define the impairment thresholds based on population analyses via cumulative distribution functions, leading to the labeling of impairment for each subject. A significant finding is the exhibition of a dose-dependent increasing probability of impairment for 1 to 10 cGy of 28Si or 56Fe in the simple discrimination (SD) stage of the ATSET, and for 1 to 10 cGy of 56Fe in the compound discrimination (CD) stage. On a subject-based level, implementing machine learning (ML) classifiers such as the Gaussian naïve Bayes, support vector machine, and artificial neural networks identifies rats that have a higher tendency for impairment after GCR exposure. The algorithms employ the experimental prescreen performance scores as multidimensional input features to predict each rodent's susceptibility to cognitive impairment due to space radiation exposure. The receiver operating characteristic and the precision-recall curves of the ML models show a better prediction of impairment when 56Fe is the ion in question in both SD and CD stages. They, however, do not depict impairment due to 4He in SD and 28Si in CD, suggesting no dose-dependent impairment response in these cases. One key finding of our study is that prescreen performance scores can be used to predict the ATSET performance impairments. This result is significant to crewed space missions as it supports the potential of predicting an astronaut's impairment in a specific task before spaceflight through the implementation of appropriately trained ML tools. Future research can focus on constructing ML ensemble methods to integrate the findings from the methodologies implemented in this study for more robust predictions of cognitive decrements due to space radiation exposure.

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Machine Learning on a Genome-wide Association Study to Predict Late Genitourinary Toxicity After Prostate Radiation Therapy.

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