Radiogenomics: radiobiology enters the era of big data and team science

Seventy Years of Dose-response Models: From the Target Theory to the Use of Big Databases Involving Cell Survival and DNA Repair

Radiobiological data, whether obtained at the clinical, biological or molecular level has significantly contributed to a better description and prediction of the individual dose-response to ionizing radiation and a better estimation of the radiation-induced risks. Particularly, over the last seventy years, the amount of radiobiological data has considerably increased, and permitted the mathematical formulas describing dose-response to become less empirical. A better understanding of the basic radiobiological mechanisms has also contributed to establish quantitative inter-correlations between clinical, biological and molecular biomarkers, refining again the mathematical models of description. Today, big data approaches and, more recently, artificial intelligence may finally complete and secure this long process of thinking from the multi-scale description of radiation-induced events to their prediction. Here, we reviewed the major dose-response models applied in radiobiology for quantifying molecular and cellular radiosensitivity and aimed to explain their evolution: Specifically, we highlighted the advances concerning the target theory with the cell survival models and the progressive introduction of the DNA repair process in the mathematical models. Furthermore, we described how the technological advances have changed the description of DNA double-strand break (DSB) repair kinetics by introducing the important notion of DSB recognition, independent of that of DSB repair. Initially developed separately, target theory on one hand and, DSB recognition and repair, on the other hand may be now fused into a unified model involving the cascade of phosphorylations mediated by the ATM kinase in response to any genotoxic stress.

MRI-Based Radiomics Methods for Predicting Ki-67 Expression in Breast Cancer: A Systematic Review and Meta-analysis

RATIONALE AND OBJECTIVES

The purpose of this systematic review and meta-analysis was to assess the quality and diagnostic accuracy of MRI-based radiomics for predicting Ki-67 expression in breast cancer.

MATERIALS AND METHODS

A systematic literature search was performed to find relevant studies published in different databases, including PubMed, Web of Science, and Embase up until March 10, 2023. All papers were independently evaluated for eligibility by two reviewers. Studies that matched research questions and provided sufficient data for quantitative synthesis were included in the systematic review and meta-analysis, respectively. The quality of the articles was assessed using Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) and Radiomics Quality Score (RQS) tools. The predictive value of MRI-based radiomics for Ki-67 antigen in patients with breast cancer was assessed using pooled sensitivity (SEN), specificity, and area under the curve (AUC). Meta-regression was performed to explore the cause of heterogeneity. Different covariates were used for subgroup analysis.

RESULTS

31 studies were included in the systematic review; among them, 21 reported sufficient data for meta-analysis. 20 training cohorts and five validation cohorts were pooled separately. The pooled sensitivity, specificity, and AUC of MRI-based radiomics for predicting Ki-67 expression in training cohorts were 0.80 [95% CI, 0.73-0.86], 0.82 [95% CI, 0.78-0.86], and 0.88 [95%CI, 0.85-0.91], respectively. The corresponding values for validation cohorts were 0.81 [95% CI, 0.72-0.87], 0.73 [95% CI, 0.62-0.82], and 0.84 [95%CI, 0.80-0.87], respectively. Based on QUADAS-2, some risks of bias were detected for reference standard and flow and timing domains. However, the quality of the included article was acceptable. The mean RQS score of the included articles was close to 6, corresponding to 16.6% of the maximum possible score. Significant heterogeneity was observed in pooled sensitivity and specificity of training cohorts (I2 > 75%). We found that using deep learning radiomic methods, magnetic field strength (3 T vs. 1.5 T), scanner manufacturer, region of interest structure (2D vs. 3D), route of tissue sampling, Ki-67 cut-off, logistic regression for model construction, and LASSO for feature reduction as well as PyRadiomics software for feature extraction had a great impact on heterogeneity according to our joint model analysis. Diagnostic performance in studies that used deep learning-based radiomics and multiple MRI sequences (e.g., DWI+DCE) was slightly higher. In addition, radiomic features derived from DWI sequences performed better than contrast-enhanced sequences in terms of specificity and sensitivity. No publication bias was found based on Deeks' funnel plot. Sensitivity analysis showed that eliminating every study one by one does not impact overall results.

CONCLUSION

This meta-analysis showed that MRI-based radiomics has a good diagnostic accuracy in differentiating breast cancer patients with high Ki-67 expression from low-expressing groups. However, the sensitivity and specificity of these methods still do not surpass 90%, restricting them from being used as a supplement to current pathological assessments (e.g., biopsy or surgery) to predict Ki-67 expression accurately.

Artificial intelligence in breast cancer: application and future perspectives

Breast cancer is one of the most common cancers and is one of the leading causes of cancer-related deaths in women worldwide. Early diagnosis and treatment are the key for a favorable prognosis. The application of artificial intelligence technology in the medical field is increasingly extensive, including image analysis, automated diagnosis, intelligent pharmaceutical system, personalized treatment and so on. AI-based breast cancer imaging, pathology and adjuvant therapy technology cannot only reduce the workload of clinicians, but also continuously improve the accuracy and sensitivity of breast cancer diagnosis and treatment. This paper reviews the application of AI in breast cancer, as well as looks ahead and poses challenges to the future development of AI for breast cancer detection and therapeutic, so as to provide ideas for future research.

Deep learning, radiomics and radiogenomics applications in the digital breast tomosynthesis: a systematic review

BACKGROUND

Recent advancements in computing power and state-of-the-art algorithms have helped in more accessible and accurate diagnosis of numerous diseases. In addition, the development of de novo areas in imaging science, such as radiomics and radiogenomics, have been adding more to personalize healthcare to stratify patients better. These techniques associate imaging phenotypes with the related disease genes. Various imaging modalities have been used for years to diagnose breast cancer. Nonetheless, digital breast tomosynthesis (DBT), a state-of-the-art technique, has produced promising results comparatively. DBT, a 3D mammography, is replacing conventional 2D mammography rapidly. This technological advancement is key to AI algorithms for accurately interpreting medical images.

OBJECTIVE AND METHODS

This paper presents a comprehensive review of deep learning (DL), radiomics and radiogenomics in breast image analysis. This review focuses on DBT, its extracted synthetic mammography (SM), and full-field digital mammography (FFDM). Furthermore, this survey provides systematic knowledge about DL, radiomics, and radiogenomics for beginners and advanced-level researchers.

RESULTS

A total of 500 articles were identified, with 30 studies included as the set criteria. Parallel benchmarking of radiomics, radiogenomics, and DL models applied to the DBT images could allow clinicians and researchers alike to have greater awareness as they consider clinical deployment or development of new models. This review provides a comprehensive guide to understanding the current state of early breast cancer detection using DBT images.

CONCLUSION

Using this survey, investigators with various backgrounds can easily seek interdisciplinary science and new DL, radiomics, and radiogenomics directions towards DBT.

A genome-wide association study of radiotherapy induced toxicity in head and neck cancer patients identifies a susceptibility locus associated with mucositis

PURPOSE

A two-stage genome-wide association study was carried out in head and neck cancer (HNC) patients aiming to identify genetic variants associated with either specific radiotherapy-induced (RT) toxicity endpoints or a general proneness to develop toxicity after RT.

MATERIALS AND METHODS

The analysis included 1780 HNC patients treated with primary RT for laryngeal or oro/hypopharyngeal cancers. In a non-hypothesis-driven explorative discovery study, associations were tested in 1183 patients treated within The Danish Head and Neck Cancer Group. Significant associations were later tested in an independent Dutch cohort of 597 HNC patients and if replicated, summary data obtained from discovery and replication studies were meta-analysed. Further validation of significantly replicated findings was pursued in an Asian cohort of 235 HNC patients with nasopharynx as the primary tumour site.

RESULTS

We found and replicated a significant association between a locus on chromosome 5 and mucositis with a pooled OR for rs1131769*C in meta-analysis = 1.95 (95% CI 1.48-2.41; ppooled = 4.34 × 10-16).

CONCLUSION

This first exploratory GWAS in European cohorts of HNC patients identified and replicated a risk locus for mucositis. A larger Meta-GWAS to identify further risk variants for RT-induced toxicity in HNC patients is warranted.

Molecular Biology in Treatment Decision Processes-Neuro-Oncology Edition

Computational approaches including machine learning, deep learning, and artificial intelligence are growing in importance in all medical specialties as large data repositories are increasingly being optimised. Radiation oncology as a discipline is at the forefront of large-scale data acquisition and well positioned towards both the production and analysis of large-scale oncologic data with the potential for clinically driven endpoints and advancement of patient outcomes. Neuro-oncology is comprised of malignancies that often carry poor prognosis and significant neurological sequelae. The analysis of radiation therapy mediated treatment and the potential for computationally mediated analyses may lead to more precise therapy by employing large scale data. We analysed the state of the literature pertaining to large scale data, computational analysis, and the advancement of molecular biomarkers in neuro-oncology with emphasis on radiation oncology. We aimed to connect existing and evolving approaches to realistic avenues for clinical implementation focusing on low grade gliomas (LGG), high grade gliomas (HGG), management of the elderly patient with HGG, rare central nervous system tumors, craniospinal irradiation, and re-irradiation to examine how computational analysis and molecular science may synergistically drive advances in personalised radiation therapy (RT) and optimise patient outcomes.

Does Gender Difference Effect Radiation-Induced Lung Toxicity? An Experimental Study by Genetic and Histopathological Predictors

Several studies have reported differences in radiation toxicity between the sexes, but these differences have not been tested with respect to histopathology and genes. This animal study aimed to show an association between histopathological findings of radiation-induced lung toxicity and the genes ATM, SOD2, TGF-β1, XRCC1, XRCC3 and HHR2. In all, 120 animals were randomly divided into 2 control groups (male and female) and experimental groups comprising fifteen rats stratified by sex, radiotherapy (0 Gy vs. 10 Gy), and time to sacrifice (6, 12, and 24 weeks postirradiation). Histopathological evaluations for lung injury, namely, intra-alveolar edema, alveolar neutrophils, intra-alveolar erythrocytes, activated macrophages, intra-alveolar fibrosis, hyaline arteriosclerosis, and collapse were performed under a light microscope using a grid system; the evaluations were semi quantitatively scored. Then, the alveolar wall thickness was measured. Real-time quantitative reverse transcription PCR (RT-qPCR) was used to determine gene expression differences in ATM, TGF-β1, XRCC1, XRCC3, SOD2 and HHR2L among the groups. Histopathological data showed that radiation-induced acute, subacute, and chronic lung toxicity were worse in male rats. The expression levels of the evaluated genes were significantly higher in females than males in the control group, but this difference was lost over time after radiotherapy. Less toxicity in females may be attributable to the fact that the expression of the evaluated genes was higher in normal lung tissue in females than in males and the changes in gene expression patterns in the postradiotherapy period played a protective role in females. Additional data related to pulmonary function, lung weights, imaging, or outcomes are needed to support this data that is based on histopathology alone.

Current Advances and Challenges in Radiomics of Brain Tumors

Imaging diagnosis is crucial for early detection and monitoring of brain tumors. Radiomics enable the extraction of a large mass of quantitative features from complex clinical imaging arrays, and then transform them into high-dimensional data which can subsequently be mined to find their relevance with the tumor's histological features, which reflect underlying genetic mutations and malignancy, along with grade, progression, therapeutic effect, or even overall survival (OS). Compared to traditional brain imaging, radiomics provides quantitative information linked to meaningful biologic characteristics and application of deep learning which sheds light on the full automation of imaging diagnosis. Recent studies have shown that radiomics' application is broad in identifying primary tumor, differential diagnosis, grading, evaluation of mutation status and aggression, prediction of treatment response and recurrence in pituitary tumors, gliomas, and brain metastases. In this descriptive review, besides establishing a general understanding among protocols, results, and clinical significance of these studies, we further discuss the current limitations along with future development of radiomics.

Proof of Concept of a Binary Blood Assay for Predicting Radiosensitivity

Radiation therapy (RT), either alone or in combination with surgery and/or chemotherapy is a keystone of cancers treatment. Early toxicity is common, sometimes leading to discontinuation of treatment. Recent studies stressed the role of the phosphorylated ATM (pATM) protein in RT-toxicity genesis and its ability in predicting individual radiosensitivity (IRS) in fibroblasts. Here we assessed the reliability of the pATM quantification in lymphocytes to predict IRS. A first retrospective study was performed on 150 blood lymphocytes of patients with several cancer types. Patients were divided into 2 groups, according to the grade of experienced toxicity. The global quantity of pATM molecules was assessed by ELISA on lymphocytes to determine the best threshold value. Then, the binary assay was assessed on a validation cohort of 36 patients with head and neck cancers. The quantity of pATM molecules in each sample of the training cohort was found in agreement with the observed Common Terminology Criteria for Adverse Events (CTCAE) grades with an AUC = 0.71 alone and of 0.77 combined to chemotherapy information. In the validation cohort, the same test was conducted with the following performances: sensitivity = 0.84, specificity = 0.54, AUC = 0.70 and 0.72 combined to chemotherapy. This study provides the basis of an easy to perform assay for clinical use.

Radiomic and radiogenomic modeling for radiotherapy: strategies, pitfalls, and challenges

The power of predictive modeling for radiotherapy outcomes has historically been limited by an inability to adequately capture patient-specific variabilities; however, next-generation platforms together with imaging technologies and powerful bioinformatic tools have facilitated strategies and provided optimism. Integrating clinical, biological, imaging, and treatment-specific data for more accurate prediction of tumor control probabilities or risk of radiation-induced side effects are high-dimensional problems whose solutions could have widespread benefits to a diverse patient population-we discuss technical approaches toward this objective. Increasing interest in the above is specifically reflected by the emergence of two nascent fields, which are distinct but complementary: radiogenomics, which broadly seeks to integrate biological risk factors together with treatment and diagnostic information to generate individualized patient risk profiles, and radiomics, which further leverages large-scale imaging correlates and extracted features for the same purpose. We review classical analytical and data-driven approaches for outcomes prediction that serve as antecedents to both radiomic and radiogenomic strategies. Discussion then focuses on uses of conventional and deep machine learning in radiomics. We further consider promising strategies for the harmonization of high-dimensional, heterogeneous multiomics datasets (panomics) and techniques for nonparametric validation of best-fit models. Strategies to overcome common pitfalls that are unique to data-intensive radiomics are also discussed.

The Era of Radiogenomics in Precision Medicine: An Emerging Approach to Support Diagnosis, Treatment Decisions, and Prognostication in Oncology

With the rapid development of new technologies, including artificial intelligence and genome sequencing, radiogenomics has emerged as a state-of-the-art science in the field of individualized medicine. Radiogenomics combines a large volume of quantitative data extracted from medical images with individual genomic phenotypes and constructs a prediction model through deep learning to stratify patients, guide therapeutic strategies, and evaluate clinical outcomes. Recent studies of various types of tumors demonstrate the predictive value of radiogenomics. And some of the issues in the radiogenomic analysis and the solutions from prior works are presented. Although the workflow criteria and international agreed guidelines for statistical methods need to be confirmed, radiogenomics represents a repeatable and cost-effective approach for the detection of continuous changes and is a promising surrogate for invasive interventions. Therefore, radiogenomics could facilitate computer-aided diagnosis, treatment, and prediction of the prognosis in patients with tumors in the routine clinical setting. Here, we summarize the integrated process of radiogenomics and introduce the crucial strategies and statistical algorithms involved in current studies.

Radiomics for liver tumours

Current research, especially in oncology, increasingly focuses on the integration of quantitative, multiparametric and functional imaging data. In this fast-growing field of research, radiomics may allow for a more sophisticated analysis of imaging data, far beyond the qualitative evaluation of visible tissue changes. Through use of quantitative imaging data, more tailored and tumour-specific diagnostic work-up and individualized treatment concepts may be applied for oncologic patients in the future. This is of special importance in cross-sectional disciplines such as radiology and radiation oncology, with already high and still further increasing use of imaging data in daily clinical practice. Liver targets are generally treated with stereotactic body radiotherapy (SBRT), allowing for local dose escalation while preserving surrounding normal tissue. With the introduction of online target surveillance with implanted markers, 3D-ultrasound on conventional linacs and hybrid magnetic resonance imaging (MRI)-linear accelerators, individualized adaptive radiotherapy is heading towards realization. The use of big data such as radiomics and the integration of artificial intelligence techniques have the potential to further improve image-based treatment planning and structured follow-up, with outcome/toxicity prediction and immediate detection of (oligo)progression. The scope of current research in this innovative field is to identify and critically discuss possible application forms of radiomics, which is why this review tries to summarize current knowledge about interdisciplinary integration of radiomics in oncologic patients, with a focus on investigations of radiotherapy in patients with liver cancer or oligometastases including multiparametric, quantitative data into (radio)-oncologic workflow from disease diagnosis, treatment planning, delivery and patient follow-up.

Radiomics at a Glance: A Few Lessons Learned from Learning Approaches

Simple Summary

Radiomics has become a prominent component of medical imaging research and many studies show its specific value as a support tool for clinical decision-making processes. Radiomic data are typically analyzed with statistical and machine learning methods, which change depending on the disease context and the imaging modality. We found a certain bias in the literature towards the use of such methods and believe that this limitation may influence the capacity of producing accurate and reliable decisions. Therefore, in view of the relevance of various types of learning methods, we report their significance and discuss their unrevealed potential.

Abstract

Processing and modeling medical images have traditionally represented complex tasks requiring multidisciplinary collaboration. The advent of radiomics has assigned a central role to quantitative data analytics targeting medical image features algorithmically extracted from large volumes of images. Apart from the ultimate goal of supporting diagnostic, prognostic, and therapeutic decisions, radiomics is computationally attractive due to specific strengths: scalability, efficiency, and precision. Optimization is achieved by highly sophisticated statistical and machine learning algorithms, but it is especially deep learning that stands out as the leading inference approach. Various types of hybrid learning can be considered when building complex integrative approaches aimed to deliver gains in accuracy for both classification and prediction tasks. This perspective reviews some selected learning methods by focusing on both their significance for radiomics and their unveiled potential.

Overall survival prediction in glioblastoma patients using structural magnetic resonance imaging (MRI): advanced radiomic features may compensate for lack of advanced MRI modalities

Purpose: Glioblastoma, the most common and aggressive adult brain tumor, is considered noncurative at diagnosis, with 14 to 16 months median survival following treatment. There is increasing evidence that noninvasive integrative analysis of radiomic features can predict overall and progression-free survival, using advanced multiparametric magnetic resonance imaging (Adv-mpMRI). If successfully applicable, such noninvasive markers can considerably influence patient management. However, most patients prior to initiation of therapy typically undergo only basic structural mpMRI (Bas-mpMRI, i.e., T1, T1-Gd, T2, and T2-fluid-attenuated inversion recovery) preoperatively, rather than Adv-mpMRI that provides additional vascularization (dynamic susceptibility contrast-MRI) and cell-density (diffusion tensor imaging) related information. Approach: We assess a retrospective cohort of 101 glioblastoma patients with available Adv-mpMRI from a previous study, which has shown that an initial feature panel (IFP, i.e., intensity, volume, location, and growth model parameters) extracted from Adv-mpMRI can yield accurate overall survival stratification. We focus on demonstrating that equally accurate prediction models can be constructed using augmented radiomic feature panels (ARFPs, i.e., integrating morphology and textural descriptors) extracted solely from widely available Bas-mpMRI, obviating the need for using Adv-mpMRI. We extracted 1612 radiomic features from distinct tumor subregions to build multivariate models that stratified patients as long-, intermediate-, or short-survivors. Results: The classification accuracy of the model utilizing Adv-mpMRI protocols and the IFP was 72.77% and degraded to 60.89% when using only Bas-mpMRI. However, utilizing the ARFP on Bas-mpMRI improved the accuracy to 74.26%. Furthermore, Kaplan-Meier analysis demonstrated superior classification of subjects into short-, intermediate-, and long-survivor classes when using ARFP extracted from Bas-mpMRI. Conclusions: This quantitative evaluation indicates that accurate survival prediction in glioblastoma patients is feasible using solely Bas-mpMRI and integrative advanced radiomic features, which can compensate for the lack of Adv-mpMRI. Our finding holds promise for generalization across multiple institutions that may not have access to Adv-mpMRI and to better inform clinical decision-making about aggressive interventions and clinical trials.

Individual response of humans to ionising radiation: governing factors and importance for radiological protection

Tissue reactions and stochastic effects after exposure to ionising radiation are variable between individuals but the factors and mechanisms governing individual responses are not well understood. Individual responses can be measured at different levels of biological organization and using different endpoints following varying doses of radiation, including: cancers, non-cancer diseases and mortality in the whole organism; normal tissue reactions after exposures; and, cellular endpoints such as chromosomal damage and molecular alterations. There is no doubt that many factors influence the responses of people to radiation to different degrees. In addition to the obvious general factors of radiation quality, dose, dose rate and the tissue (sub)volume irradiated, recognized and potential determining factors include age, sex, life style (e.g., smoking, diet, possibly body mass index), environmental factors, genetics and epigenetics, stochastic distribution of cellular events, and systemic comorbidities such as diabetes or viral infections. Genetic factors are commonly thought to be a substantial contributor to individual response to radiation. Apart from a small number of rare monogenic diseases such as ataxia telangiectasia, the inheritance of an abnormally responsive phenotype among a population of healthy individuals does not follow a classical Mendelian inheritance pattern. Rather it is considered to be a multi-factorial, complex trait.

Clinical and epidemiological observations on individual radiation sensitivity and susceptibility

Purpose: To summarize existing knowledge and to understand individual response to radiation exposure, the MELODI Association together with CONCERT European Joint Programme has organized a workshop in March 2018 on radiation sensitivity and susceptibility.Methods: The workshop reviewed the current evidence on this matter, to inform the MELODI Strategic Research Agenda (SRA), to determine social and scientific needs and to come up with recommendations for suitable and feasible future research initiatives to be taken for the benefit of an improved medical diagnosis and treatment as well as for radiation protection.Results: The present paper gives an overview of the current evidence in this field, including potential effect modifiers such as age, gender, genetic profile, and health status of the exposed population, based on clinical and epidemiological observations.Conclusion: The authors conclude with the following recommendations for the way forward in radiation research: (a) there is need for large (prospective) cohort studies; (b) build upon existing radiation research cohorts; (c) use data from well-defined cohorts with good exposure assessment and biological material already collected; (d) focus on study quality with standardized data collection and reporting; (e) improve statistical analysis; (f) cooperation between radiobiology and epidemiology; and (g) take consequences of radiosensitivity and radiosusceptibility into account.

REQUITE: A prospective multicentre cohort study of patients undergoing radiotherapy for breast, lung or prostate cancer

PURPOSE

REQUITE aimed to establish a resource for multi-national validation of models and biomarkers that predict risk of late toxicity following radiotherapy. The purpose of this article is to provide summary descriptive data.

METHODS

An international, prospective cohort study recruited cancer patients in 26 hospitals in eight countries between April 2014 and March 2017. Target recruitment was 5300 patients. Eligible patients had breast, prostate or lung cancer and planned potentially curable radiotherapy. Radiotherapy was prescribed according to local regimens, but centres used standardised data collection forms. Pre-treatment blood samples were collected. Patients were followed for a minimum of 12 (lung) or 24 (breast/prostate) months and summary descriptive statistics were generated.

RESULTS

The study recruited 2069 breast (99% of target), 1808 prostate (86%) and 561 lung (51%) cancer patients. The centralised, accessible database includes: physician- (47,025 forms) and patient- (54,901) reported outcomes; 11,563 breast photos; 17,107 DICOMs and 12,684 DVHs. Imputed genotype data are available for 4223 patients with European ancestry (1948 breast, 1728 prostate, 547 lung). Radiation-induced lymphocyte apoptosis (RILA) assay data are available for 1319 patients. DNA (n = 4409) and PAXgene tubes (n = 3039) are stored in the centralised biobank. Example prevalences of 2-year (1-year for lung) grade ≥2 CTCAE toxicities are 13% atrophy (breast), 3% rectal bleeding (prostate) and 27% dyspnoea (lung).

CONCLUSION

The comprehensive centralised database and linked biobank is a valuable resource for the radiotherapy community for validating predictive models and biomarkers.

PATIENT SUMMARY

Up to half of cancer patients undergo radiation therapy and irradiation of surrounding healthy tissue is unavoidable. Damage to healthy tissue can affect short- and long-term quality-of-life. Not all patients are equally sensitive to radiation "damage" but it is not possible at the moment to identify those who are. REQUITE was established with the aim of trying to understand more about how we could predict radiation sensitivity. The purpose of this paper is to provide an overview and summary of the data and material available. In the REQUITE study 4400 breast, prostate and lung cancer patients filled out questionnaires and donated blood. A large amount of data was collected in the same way. With all these data and samples a database and biobank were created that showed it is possible to collect this kind of information in a standardised way across countries. In the future, our database and linked biobank will be a resource for research and validation of clinical predictors and models of radiation sensitivity. REQUITE will also enable a better understanding of how many people suffer with radiotherapy toxicity.

The Nucleoshuttling of the ATM Protein: A Unified Model to Describe the Individual Response to High- and Low-Dose of Radiation?

The evaluation of radiation-induced (RI) risks is of medical, scientific, and societal interest. However, despite considerable efforts, there is neither consensual mechanistic models nor predictive assays for describing the three major RI effects, namely radiosensitivity, radiosusceptibility, and radiodegeneration. Interestingly, the ataxia telangiectasia mutated (ATM) protein is a major stress response factor involved in the DNA repair and signaling that appears upstream most of pathways involved in the three precited RI effects. The rate of the RI ATM nucleoshuttling (RIANS) was shown to be a good predictor of radiosensitivity. In the frame of the RIANS model, irradiation triggers the monomerization of cytoplasmic ATM dimers, which allows ATM monomers to diffuse in nucleus. The nuclear ATM monomers phosphorylate the H2AX histones, which triggers the recognition of DNA double-strand breaks and their repair. The RIANS model has made it possible to define three subgroups of radiosensitivity and provided a relevant explanation for the radiosensitivity observed in syndromes caused by mutated cytoplasmic proteins. Interestingly, hyper-radiosensitivity to a low dose and adaptive response phenomena may be also explained by the RIANS model. In this review, the relevance of the RIANS model to describe several features of the individual response to radiation was discussed.

Precision Radiotherapy and Radiation Risk Assessment: How Do We Overcome Radiogenomic Diversity?

Precision medicine is a rapidly developing area that aims to deliver targeted therapies based on individual patient characteristics. However, current radiation treatment is not yet personalized; consequently, there is a critical need for specific patient characteristics of both tumor and normal tissues to be fully incorporated into dose prescription. Furthermore, current risk assessment following environmental, occupational, or accidental exposures to radiation is based on population effects, and does not account for individual diversity underpinning radiosensitivity. The lack of personalized approaches in both radiotherapy and radiation risk assessment resulted in the current situation where a population-based model, effective dose, is being used. In this review article, to stimulate scientific discussion for precision medicine in both radiotherapy and radiation risk assessment, we propose a novel radiological concept and metric - the personalized dose and the personalized risk index - that incorporate individual physiological, lifestyle-related and genomic variations and radiosensitivity, outlining the potential clinical application for precision medicine. We also review on recent progress in both genomics and biobanking research, which is promising for providing novel insights into individual radiosensitivity, and for creating a novel conceptual framework of precision radiotherapy and radiation risk assessment.

Validation of a normal tissue complication probability model for late unfavourable aesthetic outcome after breast-conserving therapy

PURPOSE

To validate a normal tissue complication probability (NTCP) model for late unfavourable aesthetic outcome (AO) after breast-conserving therapy.

MATERIALS/METHODS

The BCCT.core software evaluated the AO using standardized photographs of patients treated at the University Hospitals Leuven between April 2015 and April 2016. Dose maps in 2 Gy equivalents were calculated assuming α/β = 3.6 Gy. The discriminating ability of the model was described by the AUC of the receiver operating characteristic curve. A 95% confidence interval (CI) of AUC was calculated using 10,000 bootstrap replications. Calibration was evaluated with the calibration plot and Nagelkerke R². Patients with unfavourable AO at baseline were excluded. Patient, tumour and treatment characteristics were compared between the development and the validation cohort. The prognostic value of the characteristics in the validation cohort was further evaluated in univariable and multivariable analysis.

RESULTS

Out of 175 included patients, 166 were evaluated two years after RT and 44 (26.51%) had unfavourable AO. AUC was 0.66 (95% CI 0.56; 0.76). Calibration was moderate with small overestimations at higher risk. When applying all of the univariable significant clinicopathological and dosimetrical variables from the validation cohort in a multivariable model, the presence of a seroma and V45 were selected as significant risk factors for unfavourable AO (Odds Ratio 4.40 (95% CI 1.96; 9.86) and 1.14 (95% CI 1.03; 1.27), p-value <.001 and .01, respectively).

CONCLUSIONS

The NTCP model for unfavourable AO shows a moderate discrimination and calibration in the present prospective validation cohort with a small overestimation in the high risk patients.

A review of radiation genomics: integrating patient radiation response with genomics for personalised and targeted radiation therapy

Background

The success of radiation therapy for cancer patients is dependent on the ability to deliver a total tumouricidal radiation dose capable of eradicating all cancer cells within the clinical target volume, however, the radiation dose tolerance of the surrounding healthy tissues becomes the main dose-limiting factor. The normal tissue adverse effects following radiotherapy are common and significantly impact the quality of life of patients. The likelihood of developing these adverse effects following radiotherapy cannot be predicted based only on the radiation treatment parameters. However, there is evidence to suggest that some common genetic variants are associated with radiotherapy response and the risk of developing adverse effects. Radiation genomics is a field that has evolved in recent years investigating the association between patient genomic data and the response to radiation therapy. This field aims to identify genetic markers that are linked to individual radiosensitivity with the potential to predict the risk of developing adverse effects due to radiotherapy using patient genomic information. It also aims to determine the relative radioresponse of patients using their genetic information for the potential prediction of patient radiation treatment response.

Methods and materials

This paper reports on a review of recent studies in the field of radiation genomics investigating the association between genomic data and patients response to radiation therapy, including the investigation of the role of genetic variants on an individual’s predisposition to enhanced radiotherapy radiosensitivity or radioresponse.

Conclusion

The potential for early prediction of treatment response and patient outcome is critical in cancer patients to make decisions regarding continuation, escalation, discontinuation, and/or change in treatment options to maximise patient survival while minimising adverse effects and maintaining patients’ quality of life.

A New Challenge for Radiologists: Radiomics in Breast Cancer

INTRODUCTION

Over the last decade, the field of medical imaging experienced an exponential growth, leading to the development of radiomics, with which innumerable quantitative features are obtained from digital medical images, providing a comprehensive characterization of the tumor. This review aims to assess the role of this emerging diagnostic tool in breast cancer, focusing on the ability of radiomics to predict malignancy, response to neoadjuvant chemotherapy, prognostic factors, molecular subtypes, and risk of recurrence.

EVIDENCE ACQUISITION

A literature search on PubMed and on Cochrane database websites to retrieve English-written systematic reviews, review articles, meta-analyses, and randomized clinical trials published from August 2013 up to July 2018 was carried out.

RESULTS

Twenty papers (19 retrospective and 1 prospective studies) conducted with different conventional imaging modalities were included.

DISCUSSION

The integration of quantitative information with clinical, histological, and genomic data could enable clinicians to provide personalized treatments for breast cancer patients. Current limitations of a routinely application of radiomics are represented by the limited knowledge of its basics concepts among radiologists and by the lack of efficient and standardized systems of feature extraction and data sharing.

Radiogenomic Predictors of Adverse Effects following Charged Particle Therapy

Radiogenomics is the study of genomic factors that are associated with response to radiation therapy. In recent years, progress has been made toward identifying genetic risk factors linked with late radiation-induced adverse effects. These advances have been underpinned by the establishment of an international Radiogenomics Consortium with collaborative studies that expand cohort sizes to increase statistical power and efforts to improve methodologic approaches for radiogenomic research. Published studies have predominantly reported the results of research involving patients treated with photons using external beam radiation therapy. These studies demonstrate our ability to pool international cohorts to identify common single nucleotide polymorphisms associated with risk for developing normal tissue toxicities. Progress has also been achieved toward the discovery of genetic variants associated with radiation therapy-related subsequent malignancies. With the increasing use of charged particle therapy (CPT), there is a need to establish cohorts for patients treated with these advanced technology forms of radiation therapy and to create biorepositories with linked clinical data. While some genetic variants are likely to impact toxicity and second malignancy risks for both photons and charged particles, it is plausible that others may be specific to the radiation modality due to differences in their biological effects, including the complexity of DNA damage produced. In recognition that the formation of patient cohorts treated with CPT for radiogenomic studies is a high priority, efforts are underway to establish collaborations involving institutions treating cancer patients with protons and/or carbon ions as well as consortia, including the Proton Collaborative Group, the Particle Therapy Cooperative Group, and the Pediatric Proton Consortium Registry. These important radiogenomic CPT initiatives need to be expanded internationally to build on experience gained from the Radiogenomics Consortium and epidemiologists investigating normal tissue toxicities and second cancer risk.

Automated data mining of a plan-check database and example application

Purpose

The aim of this work was to present the development and example application of an automated data mining software platform that preforms bulk analysis of results and patient data passing through the 3D plan and delivery QA system, Mobius3D.

Methods

Python, matlab, and Java were used to create an interface that reads JavaScript Object Notation (JSON) created for every approved Mobius3D pre‐treatment plan‐check. The aforementioned JSON files contain all the information for every pre‐treatment QA check performed by Mobius3D, including all 3D dose, CT, structure set information, as well as all plan information and patient demographics. Two Graphical User Interfaces (GUIs) were created, the first is called Mobius3D‐Database (M3D‐DB) and presents the check results in both filterable tabular and graphical form. These data are presented for all patients and includes mean dose differences, 90% coverage, 3D gamma pass rate percentages, treatment sites, machine, beam energy, Multi‐Leaf Collimator (MLC) mode, treatment planning system (TPS), plan names, approvers, dates and times. Group statistics and statistical process control levels are then calculated based on filter settings. The second GUI, called Mobius3D organ at risk (M3DOAR), analyzes dose‐volume histogram data for all patients and all Organs‐at‐Risk (OAR). The design of the software is such that all treatment parameters and treatment site information are able to be filtered and sorted with the results, plots, and statistics updated.

Results

The M3D‐DB software can summarize and filter large numbers of plan‐checks from Mobius3D. The M3DOAR software is also able to analyze large amounts of dose‐volume data for patient groups which may prove useful in clinical trials, where OAR doses for large numbers of patients can be compared and correlated. Target DVHs can also be analyzed en mass.

Conclusions

This work demonstrates a method to extract the large amount of treatment data for every patient that is stored by Mobius3D but not easily accessible. With scripting, it is possible to mine this data for research and clinical trials as well as patient and TPS QA.

Development and validation of a radiomic signature to predict HPV (p16) status from standard CT imaging: a multicenter study

OBJECTIVES

Human papillomavirus (HPV) positive oropharyngeal cancer (oropharyngeal squamous cell carcinoma, OPSCC) is biologically and clinically different from HPV negative OPSCC. Here, we evaluate the use of a radiomic approach to identify the HPV status of OPSCC.

METHODS

Four independent cohorts, totaling 778 OPSCC patients with HPV determined by p16 were collected. We randomly assigned 80% of all data for model training (N = 628) and 20% for validation (N = 150). On the pre-treatment CT images, 902 radiomic features were calculated from the gross tumor volume. Multivariable modeling was performed using least absolute shrinkage and selection operator. To assess the impact of CT artifacts in predicting HPV (p16), a model was developed on all training data (Mall) and on the artifact-free subset of training data (Mno art). Models were validated on all validation data (Vall), and the subgroups with (Vart) and without (Vno art) artifacts. Kaplan-Meier survival analysis was performed to compare HPV status based on p16 and radiomic model predictions.

RESULTS

The area under the receiver operator curve for Mall and Mno art ranged between 0.70 and 0.80 and was not significantly different for all validation data sets. There was a consistent and significant split between survival curves with HPV status determined by p16 [p = 0.007; hazard ratio (HR): 0.46], Mall (p = 0.036; HR: 0.55) and Mno art (p = 0.027; HR: 0.49).

CONCLUSION

This study provides proof of concept that molecular information can be derived from standard medical images and shows potential for radiomics as imaging biomarker of HPV status. Advances in knowledge: Radiomics has the potential to identify clinically relevant molecular phenotypes.

MR-spectroscopic imaging of glial tumors in the spotlight of the 2016 WHO classification

BACKGROUND

The purpose of this study is to map spatial metabolite differences across three molecular subgroups of glial tumors, defined by the IDH1/2 mutation and 1p19q-co-deletion, using magnetic resonance spectroscopy. This work reports a new MR spectroscopy based classification algorithm by applying a radiomics analytics pipeline.

MATERIALS

65 patients received anatomical and chemical shift imaging (5 × 5 × 20 mm voxel size). Tumor regions were segmented and registered to corresponding spectroscopic voxels. Spectroscopic features were computed (n = 860) in a radiomic approach and selected by a classification algorithm. Finally, a random forest machine-learning model was trained to predict the molecular subtypes.

RESULTS

A cluster analysis identified three robust spectroscopic clusters based on the mean silhouette widths. Molecular subgroups were significantly associated with the computed spectroscopic clusters (Fisher's Exact test p < 0.01). A machine-learning model was trained and validated by public available MRS data (n = 19). The analysis showed an accuracy rate in the Random Forest model by 93.8%.

CONCLUSIONS

MR spectroscopy is a robust tool for predicting the molecular subtype in gliomas and adds important diagnostic information to the preoperative diagnostic work-up of glial tumor patients. MR-spectroscopy could improve radiological diagnostics in the future and potentially influence clinical and surgical decisions to improve individual tumor treatment.

Acute Epithelial Toxicity Is Prognostic for Improved Prostate Cancer Response to Radiation Therapy: A Retrospective, Multicenter, Cohort Study

PURPOSE

To test the hypothesis that increased acute toxicity, measured using subdomains reflective of epithelial cell damage, will be associated with reduced late biochemical failure, as a surrogate for tumor radiosensitivity.

METHODS AND MATERIALS

The study design was retrospective, with discovery and validation cohorts involving routinely collected data. Eligible patients had prostate cancer, underwent radiation therapy with curative intent, and had acute toxicity assessed prospectively. The discovery cohort was from a single institution. Genitourinary and gastrointestinal acute toxicity related to epithelial cell damage (hematuria, dysuria, proctitis, or mucus) were related to freedom from late biochemical failure (FFBF; nadir + 2). The validation cohort was from two separate institutions.

RESULTS

In all, 503 patients were included in the discovery cohort and 658 patients in the validation cohort. In the validation cohort, patients with acute radiation toxicity reflecting epithelial damage had a longer FFBF on both univariate (hazard ratio [HR] 0.37; P = .004) and multivariate (HR 0.45; P = .035) analysis. The impact of acute toxicity on late FFBF seemed to be greater in patients treated with androgen deprivation (HR 0.19) than in those without (HR 0.48).

CONCLUSION

Patients reporting acute radiation toxicity reflective of epithelial cell damage during definitive radiation therapy for prostate cancer have significantly longer FFBF, consistent with an underlying genetic link between normal tissue and tumor radiosensitivity.

Personalising Prostate Radiotherapy in the Era of Precision Medicine: A Review

Prostate cancer continues to be the most commonly diagnosed cancer among Canadian men. The introduction of routine screening and advanced treatment options have allowed for a decrease in prostate cancer-related mortality, but outcomes following treatment continue to vary widely. In addition, the overtreatment of indolent prostate cancers causes unnecessary treatment toxicities and burdens health care systems. Accurate identification of patients who should undergo aggressive treatment, and those which should be managed more conservatively, needs to be implemented. More tumour and patient information is needed to stratify patients into low-, intermediate-, and high-risk groups to guide treatment options. This paper reviews the current literature on personalised prostate cancer management, including targeting tumour hypoxia, genomic and radiomic prognosticators, and radiobiological tumour targeting. A review of the current applications and future directions for the use of big data in radiation therapy is also presented. Prostate cancer management has a lot to gain from the implementation of personalised medicine into practice. Using specific tumour and patient characteristics to personalise prostate radiotherapy in the era of precision medicine will improve survival, decrease unnecessary toxicities, and minimise the heterogeneity of outcomes following treatment.

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

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.

Background, current role, and potential applications of radiogenomics

With the genomic revolution in the early 1990s, medical research has been driven to study the basis of human disease on a genomic level and to devise precise cancer therapies tailored to the specific genetic makeup of a tumor. To match novel therapeutic concepts conceived in the era of precision medicine, diagnostic tests must be equally sufficient, multilayered, and complex to identify the relevant genetic alterations that render cancers susceptible to treatment. With significant advances in training and medical imaging techniques, image analysis and the development of high-throughput methods to extract and correlate multiple imaging parameters with genomic data, a new direction in medical research has emerged. This novel approach has been termed radiogenomics. Radiogenomics aims to correlate imaging characteristics (ie, the imaging phenotype) with gene expression patterns, gene mutations, and other genome-related characteristics and is designed to facilitate a deeper understanding of tumor biology and capture the intrinsic tumor heterogeneity. Ultimately, the goal of radiogenomics is to develop imaging biomarkers for outcome that incorporate both phenotypic and genotypic metrics. Due to the noninvasive nature of medical imaging and its ubiquitous use in clinical practice, the field of radiogenomics is rapidly evolving and initial results are encouraging. In this article, we briefly discuss the background and then summarize the current role and the potential of radiogenomics in brain, liver, prostate, gynecological, and breast tumors.

LEVEL OF EVIDENCE

5 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2017;47:604-620.

Fast and Binary Assay for Predicting Radiosensitivity Based on the Theory of ATM Nucleo-Shuttling: Development, Validation, and Performance

PURPOSE

To examine the possibility of predicting clinical radiosensitivity by quantifying the nuclear forms of autophosphorylated ATM protein (pATM) via a specific enzyme-linked immunosorbent assay (ELISA).

METHODS AND MATERIALS

This study was performed on 30 skin fibroblasts from 9 radioresistant patients and 21 patients with adverse tissue reaction events. Patients were divided into 2 groups: radioresistant (toxicity grade <2) and radiosensitive (toxicity grade ≥2). The quantity of nuclear pATM molecules was assessed by the ELISA method at 10 minutes and 1 hour after 2 Gy and compared with pATM immunofluorescence data.

RESULTS

The pATM ELISA data were in quantitative agreement with the immunofluorescence data. A receiver operating characteristic analysis was applied first to 2 data sets (a training set [n=14] and a validating [n=16] set) and thereafter to all the data with a 2-fold cross-validation method. The assay showed an area under the curve value higher than 0.8, a sensitivity of 0.8, and a specificity ranging from 0.75 to 1, which strongly documents the predictive power of the pATM ELISA.

CONCLUSION

This study showed that the assessment of nuclear pATM quantity after 2 Gy via an ELISA technique can be the basis of a predictive assay with the highest statistical performance among the available predictive approaches.

Radiomics: the bridge between medical imaging and personalized medicine

Radiomics, the high-throughput mining of quantitative image features from standard-of-care medical imaging that enables data to be extracted and applied within clinical-decision support systems to improve diagnostic, prognostic, and predictive accuracy, is gaining importance in cancer research. Radiomic analysis exploits sophisticated image analysis tools and the rapid development and validation of medical imaging data that uses image-based signatures for precision diagnosis and treatment, providing a powerful tool in modern medicine. Herein, we describe the process of radiomics, its pitfalls, challenges, opportunities, and its capacity to improve clinical decision making, emphasizing the utility for patients with cancer. Currently, the field of radiomics lacks standardized evaluation of both the scientific integrity and the clinical relevance of the numerous published radiomics investigations resulting from the rapid growth of this area. Rigorous evaluation criteria and reporting guidelines need to be established in order for radiomics to mature as a discipline. Herein, we provide guidance for investigations to meet this urgent need in the field of radiomics.

Advancing The Cancer Genome Atlas glioma MRI collections with expert segmentation labels and radiomic features

Gliomas belong to a group of central nervous system tumors, and consist of various sub-regions. Gold standard labeling of these sub-regions in radiographic imaging is essential for both clinical and computational studies, including radiomic and radiogenomic analyses. Towards this end, we release segmentation labels and radiomic features for all pre-operative multimodal magnetic resonance imaging (MRI) (n=243) of the multi-institutional glioma collections of The Cancer Genome Atlas (TCGA), publicly available in The Cancer Imaging Archive (TCIA). Pre-operative scans were identified in both glioblastoma (TCGA-GBM, n=135) and low-grade-glioma (TCGA-LGG, n=108) collections via radiological assessment. The glioma sub-region labels were produced by an automated state-of-the-art method and manually revised by an expert board-certified neuroradiologist. An extensive panel of radiomic features was extracted based on the manually-revised labels. This set of labels and features should enable i) direct utilization of the TCGA/TCIA glioma collections towards repeatable, reproducible and comparative quantitative studies leading to new predictive, prognostic, and diagnostic assessments, as well as ii) performance evaluation of computer-aided segmentation methods, and comparison to our state-of-the-art method.

Radiogenomics and radiotherapy response modeling

Advances in patient-specific information and biotechnology have contributed to a new era of computational medicine. Radiogenomics has emerged as a new field that investigates the role of genetics in treatment response to radiation therapy. Radiation oncology is currently attempting to embrace these recent advances and add to its rich history by maintaining its prominent role as a quantitative leader in oncologic response modeling. Here, we provide an overview of radiogenomics starting with genotyping, data aggregation, and application of different modeling approaches based on modifying traditional radiobiological methods or application of advanced machine learning techniques. We highlight the current status and potential for this new field to reshape the landscape of outcome modeling in radiotherapy and drive future advances in computational oncology.

"Radio-oncomics" : The potential of radiomics in radiation oncology

INTRODUCTION

Radiomics, a recently introduced concept, describes quantitative computerized algorithm-based feature extraction from imaging data including computer tomography (CT), magnetic resonance imaging (MRT), or positron-emission tomography (PET) images. For radiation oncology it offers the potential to significantly influence clinical decision-making and thus therapy planning and follow-up workflow.

METHODS

After image acquisition, image preprocessing, and defining regions of interest by structure segmentation, algorithms are applied to calculate shape, intensity, texture, and multiscale filter features. By combining multiple features and correlating them with clinical outcome, prognostic models can be created.

RESULTS

Retrospective studies have proposed radiomics classifiers predicting, e. g., overall survival, radiation treatment response, distant metastases, or radiation-related toxicity. Besides, radiomics features can be correlated with genomic information ("radiogenomics") and could be used for tumor characterization.

DISCUSSION

Distinct patterns based on data-based as well as genomics-based features will influence radiation oncology in the future. Individualized treatments in terms of dose level adaption and target volume definition, as well as other outcome-related parameters will depend on radiomics and radiogenomics. By integration of various datasets, the prognostic power can be increased making radiomics a valuable part of future precision medicine approaches.

CONCLUSION

This perspective demonstrates the evidence for the radiomics concept in radiation oncology. The necessity of further studies to integrate radiomics classifiers into clinical decision-making and the radiation therapy workflow is emphasized.

Multiple-response regression analysis links magnetic resonance imaging features to de-regulated protein expression and pathway activity in lower grade glioma

BACKGROUND AND PURPOSE

Lower grade gliomas (LGGs), lesions of WHO grades II and III, comprise 10-15% of primary brain tumors. In this first-of-a-kind study, we aim to carry out a radioproteomic characterization of LGGs using proteomics data from the TCGA and imaging data from the TCIA cohorts, to obtain an association between tumor MRI characteristics and protein measurements. The availability of linked imaging and molecular data permits the assessment of relationships between tumor genomic/proteomic measurements with phenotypic features.

MATERIALS AND METHODS

Multiple-response regression of the image-derived, radiologist scored features with reverse-phase protein array (RPPA) expression levels generated correlation coefficients for each combination of image-feature and protein or phospho-protein in the RPPA dataset. Significantly-associated proteins for VASARI features were analyzed with Ingenuity Pathway Analysis software. Hierarchical clustering of the results of the pathway analysis was used to determine which feature groups were most strongly correlated with pathway activity and cellular functions.

RESULTS

The multiple-response regression approach identified multiple proteins associated with each VASARI imaging feature. VASARI features were found to be correlated with expression of IL8, PTEN, PI3K/Akt, Neuregulin, ERK/MAPK, p70S6K and EGF signaling pathways.

CONCLUSION

Radioproteomics analysis might enable an insight into the phenotypic consequences of molecular aberrations in LGGs.

Data-Based Radiation Oncology: Design of Clinical Trials in the Toxicity Biomarkers Era

The ability to stratify patients using a set of biomarkers, which predict that toxicity risk would allow for radiotherapy (RT) modulation and serve as a valuable tool for precision medicine and personalized RT. For patients presenting with tumors with a low risk of recurrence, modifying RT schedules to avoid toxicity would be clinically advantageous. Indeed, for the patient at low risk of developing radiation-associated toxicity, use of a hypofractionated protocol could be proposed leading to treatment time reduction and a cost-utility advantage. Conversely, for patients predicted to be at high risk for toxicity, either a more conformal form or a new technique of RT, or a multidisciplinary approach employing surgery could be included in the trial design to avoid or mitigate RT when the potential toxicity risk may be higher than the risk of disease recurrence. In addition, for patients at high risk of recurrence and low risk of toxicity, dose escalation, such as a greater boost dose, or irradiation field extensions could be considered to improve local control without severe toxicities, providing enhanced clinical benefit. In cases of high risk of toxicity, tumor control should be prioritized. In this review, toxicity biomarkers with sufficient evidence for clinical testing are presented. In addition, clinical trial designs and predictive models are described for different clinical situations.

Computational methods using genome-wide association studies to predict radiotherapy complications and to identify correlative molecular processes

The biological cause of clinically observed variability of normal tissue damage following radiotherapy is poorly understood. We hypothesized that machine/statistical learning methods using single nucleotide polymorphism (SNP)-based genome-wide association studies (GWAS) would identify groups of patients of differing complication risk, and furthermore could be used to identify key biological sources of variability. We developed a novel learning algorithm, called pre-conditioned random forest regression (PRFR), to construct polygenic risk models using hundreds of SNPs, thereby capturing genomic features that confer small differential risk. Predictive models were trained and validated on a cohort of 368 prostate cancer patients for two post-radiotherapy clinical endpoints: late rectal bleeding and erectile dysfunction. The proposed method results in better predictive performance compared with existing computational methods. Gene ontology enrichment analysis and protein-protein interaction network analysis are used to identify key biological processes and proteins that were plausible based on other published studies. In conclusion, we confirm that novel machine learning methods can produce large predictive models (hundreds of SNPs), yielding clinically useful risk stratification models, as well as identifying important underlying biological processes in the radiation damage and tissue repair process. The methods are generally applicable to GWAS data and are not specific to radiotherapy endpoints.

Decision support systems for personalized and participative radiation oncology

A paradigm shift from current population based medicine to personalized and participative medicine is underway. This transition is being supported by the development of clinical decision support systems based on prediction models of treatment outcome. In radiation oncology, these models 'learn' using advanced and innovative information technologies (ideally in a distributed fashion - please watch the animation: http://youtu.be/ZDJFOxpwqEA) from all available/appropriate medical data (clinical, treatment, imaging, biological/genetic, etc.) to achieve the highest possible accuracy with respect to prediction of tumor response and normal tissue toxicity. In this position paper, we deliver an overview of the factors that are associated with outcome in radiation oncology and discuss the methodology behind the development of accurate prediction models, which is a multi-faceted process. Subsequent to initial development/validation and clinical introduction, decision support systems should be constantly re-evaluated (through quality assurance procedures) in different patient datasets in order to refine and re-optimize the models, ensuring the continuous utility of the models. In the reasonably near future, decision support systems will be fully integrated within the clinic, with data and knowledge being shared in a standardized, dynamic, and potentially global manner enabling truly personalized and participative medicine.

Precision medicine and molecular imaging: new targeted approaches toward cancer therapeutic and diagnosis

This paper presents a review of the importance and role of precision medicine and molecular imaging technologies in cancer diagnosis with therapeutics and diagnostics purposes. Precision medicine is progressively becoming a hot topic in all disciplines related to biomedical investigation and has the capacity to become the paradigm for clinical practice. The future of medicine lies in early diagnosis and individually appropriate treatments, a concept that has been named precision medicine, i.e. delivering the right treatment to the right patient at the right time. Molecular imaging is quickly being recognized as a tool with the potential to ameliorate every aspect of cancer treatment. On the other hand, emerging high-throughput technologies such as omics techniques and systems approaches have generated a paradigm shift for biological systems in advanced life science research. In this review, we describe the precision medicine, difference between precision medicine and personalized medicine, precision medicine initiative, systems biology/medicine approaches (such as genomics, radiogenomics, transcriptomics, proteomics, and metabolomics), P4 medicine, relationship between systems biology/medicine approaches and precision medicine, and molecular imaging modalities and their utility in cancer treatment and diagnosis. Accordingly, the precision medicine and molecular imaging will enable us to accelerate and improve cancer management in future medicine.

Radiogenomics: A systems biology approach to understanding genetic risk factors for radiotherapy toxicity?

Adverse reactions in normal tissue after radiotherapy (RT) limit the dose that can be given to tumour cells. Since 80% of individual variation in clinical response is estimated to be caused by patient-related factors, identifying these factors might allow prediction of patients with increased risk of developing severe reactions. While inactivation of cell renewal is considered a major cause of toxicity in early-reacting normal tissues, complex interactions involving multiple cell types, cytokines, and hypoxia seem important for late reactions. Here, we review 'omics' approaches such as screening of genetic polymorphisms or gene expression analysis, and assess the potential of epigenetic factors, posttranslational modification, signal transduction, and metabolism. Furthermore, functional assays have suggested possible associations with clinical risk of adverse reaction. Pathway analysis incorporating different 'omics' approaches may be more efficient in identifying critical pathways than pathway analysis based on single 'omics' data sets. Integrating these pathways with functional assays may be powerful in identifying multiple subgroups of RT patients characterised by different mechanisms. Thus 'omics' and functional approaches may synergise if they are integrated into radiogenomics 'systems biology' to facilitate the goal of individualised radiotherapy.

Individual response to ionizing radiation

The human response to ionizing radiation (IR) varies among individuals. The first evidence of the individual response to IR was reported in the beginning of the 20th century. Considering nearly one century of observations, we here propose three aspects of individual IR response: radiosensitivity for early or late adverse tissue events after radiotherapy on normal tissues (non-cancer effects attributable to cell death); radiosusceptibility for IR-induced cancers; and radiodegeneration for non-cancer effects that are often attributable to mechanisms other than cell death (e.g., cataracts and circulatory disease). All the molecular and cellular mechanisms behind IR-induced individual effects are not fully elucidated. However, some specific assays may help their quantification according to the dose and to the genetic status. Accumulated data on individual factors have suggested that the individual IR response cannot be ignored and raises some clinical and societal issues. The individual IR response therefore needs to be taken into account to better evaluate the risks related to IR exposure.