Editorial: Modeling for Prediction of Radiation-Induced Toxicity to Improve Therapeutic Ratio in the Modern Radiation Therapy Era

1 Prostate Cancer Program, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy, 2 Unit of Medical Physics, San Raffaele Hospital, Milan, Italy, 3 Unit of Radiation Oncology, Regina Elena National Cancer Institute (IRCCS), Rome, Italy, 4 Department of Oncology and Hemato-Oncology, Università degli Studi di Milano, Milan, Italy, 5 Unit of Radiation Oncology 1, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy, 6 Radiation Oncology Clinical Department, National Center for Oncological Hadrontherapy (CNAO), Pavia, Italy


INTRODUCTION
Radiation therapy (RT) represents a mainstay of treatment for many cancer types, either as a single modality or within a multidisciplinary approach, including surgery and systemic therapy. From a general perspective, when planning a curative radiotherapy course, its potential benefits should be weighed against the risk of acute and late tissue/organ damage. In other words, the main goal of radiotherapy is to improve the clinical outcome by increasing the therapeutic ratio, i.e., the ratio between tumor control probability (TCP) and normal tissue complication probability (NTCP). Although modern radiotherapy techniques, such as Intensity Modulated RT (IMRT), often coupled with advanced in-room imaging (Image Guided RT, IGRT), Stereotactic Body RT (SBRT), particle RT, including proton ion and carbon ion RT, allow a better sparing of normal tissues due to their improved conformity and precision, radiation-induced toxicity is still a matter of concern. Indeed, dose tolerance of many healthy tissues, called organs at risk, is a little less than or equal to the dose needed to eradicate cancers.
It is acknowledged that the risk of some induced side effects during and after the course of curative radiotherapy may be related to radiation doses delivered to multiple organs at risk rather than to the dose received by a specific organ. Additionally, various patient-related factors, including comorbidities and genetic, genomic and biological/microenvironment features, may act as modifiers of the dose-response curve. Thus, predicting toxicity by analyzing the relationship among all determinants of radiation response of healthy tissues could improve the therapeutic ratio and the management of side effects.
The situations where consistent results were available. In the case of controversial results or still more of a lack of reliable information, the document critically discussed the controversial points, often suggesting urgent lines of research and giving clear warnings around the uncertainty of the proposed recommendations.
During the "post-QUANTEC" years, the field's progress has been relevant, confirming its vitality, with many research groups continuously contributing with ideas and new data. Besides, new challenges entered into the arena, substantially modifying the traditional aspects dealing with clinical dose-volume effects studies (2).
Among them, probably the most important is the shift from NTCP dose-based modelling to the broader field of more "comprehensive" predictive models. In the hypothetical case that two patients receive exactly the "same dose distribution", the risk of toxicity is always modulated by the single individual profile.
The fact that "dose is not enough" was clear from the early days of radiobiology. It is receiving constantly growing attention in the current "omics" era (3): the availability of individual information characterizing the patients and potentially influencing their reaction to radiation is more and more essential, especially in the era of image-guided IMRT in which organs are efficiently spared in most patients.
This implies the need to have access to data including individually assessed clinical, biological and genetic information and to face the issue of modeling the response of normal tissue to radiation in a more and more "phenomenological" approach (4), requiring robust methods for the selection of the most predictive variables (both dosimetric and non-dosimetric) and the adoption of advanced data mining/machine learning methods to manage large databases, including a large number of patients and lots of variables.
Treatment planning optimization is driven by the knowledge, often not exhaustive, of quantitative dose-volume effect relationships. NTCP models are also increasingly used in protocols of model-based selection of patients for proton therapy (5-7), impacting both the single patient treatment and National Health Systems (efficiency and costs). Therefore, every progress in this field has a vast and rapid impact on how patients are treated everywhere. This is an active field of research and practice, involving many radiation oncologists, medical physicists, biologists, and data scientists in a multiprofessional scenario.

TOPICS COVERED IN THIS RESEARCH TOPIC
This Research Topic includes Original Research Papers, Reviews, Mini Reviews and Perspective and Opinion articles focusing on: • The state-of-the-art of modeling approaches and their contribution towards personalized cancer treatment; • The improvements of knowledge on dose-volume relationships for different organs; • The integration of clinical/genetic/genomic/biological/ microenvironment/imaging features in prediction models; • Pre-clinical research on radiation induced damage to normal tissues using animal models; • Voxel-based approaches to analysis of radiation induced toxicity.

PAPERS INCLUDED IN THIS RESEARCH TOPIC
This Research Topic includes 30 original articles, 2 review, 1 mini-review and 1 perspective article. The papers are from 160 authors and 18 countries on four continents. In particular, there are 19 works involving several centers and countries from one continent (10 from Europe, 6 from Asia, 2 from United States, and 1 from Australia) 9 international papers including countries both from Europe and other continents, and 6 papers from Italian centers. Authors' affiliations are equally distributed among academies and hospitals. These summary statistics mirror the broad interest in modeling radiation-induced toxicity, the highly multidisciplinary background of people involved in the field, and the vital relationship between academic and clinical research teams.

CONCLUSIONS
The QUANTEC papers were published as a special issue of the Red Journal in March 2010 and became hugely successful with copies of QUANTEC dose constraints tables hanging in most dose-planning office spaces and hundreds to a thousand citations each of the published papers. However, as we passed the tenth anniversary of QUANTEC, there is a need for a renewed coordinated effort to take the use of mathematical bioeffect models for decision support and treatment plan comparison in radiation oncology to the next level for a range of reasons, including: (i) understanding that patient related risk factors may substantially impact organ tolerance, (ii) documented problems with external validation of dose-response models, (iii) more complicated associations of dose distribution to toxicity than a single dose-volume metric in a well-defined tissue structure, (iv) normal tissue effect models are being proposed for comparing competing high-cost treatment options (e.g. hadrons vs. photons).
The 34 papers published in this Research Topic constitute a vital contribution to the field. New interesting results are included, new topics and challenges are approached. The Research Topic witnesses the broad involvement of multidisciplinary teams towards a better understanding of the complex relationships between dose and biological response of healthy tissues, with the final aim of reaching improved optimization and personalization of radiotherapy treatments.