Magnetic resonance linear accelerator technology and adaptive radiation therapy: An overview for clinicians.

Radiation therapy (RT) continues to play an important role in the treatment of cancer. Adaptive RT (ART) is a novel method through which RT treatments are evolving. With the ART approach, computed tomography or magnetic resonance (MR) images are obtained as part of the treatment delivery process. This enables the adaptation of the irradiated volume to account for changes in organ and/or tumor position, movement, size, or shape that may occur over the course of treatment. The advantages and challenges of ART maybe somewhat abstract to oncologists and clinicians outside of the specialty of radiation oncology. ART is positioned to affect many different types of cancer. There is a wide spectrum of hypothesized benefits, from small toxicity improvements to meaningful gains in overall survival. The use and application of this novel technology should be understood by the oncologic community at large, such that it can be appropriately contextualized within the landscape of cancer therapies. Likewise, the need to test these advances is pressing. MR-guided ART (MRgART) is an emerging, extended modality of ART that expands upon and further advances the capabilities of ART. MRgART presents unique opportunities to iteratively improve adaptive image guidance. However, although the MRgART adaptive process advances ART to previously unattained levels, it can be more expensive, time-consuming, and complex. In this review, the authors present an overview for clinicians describing the process of ART and specifically MRgART.

Artificial intelligence applied to image-guided radiation therapy (IGRT): a systematic review by the Young Group of the Italian Association of Radiotherapy and Clinical Oncology (yAIRO).

INTRODUCTION: The advent of image-guided radiation therapy (IGRT) has recently changed the workflow of radiation treatments by ensuring highly collimated treatments. Artificial intelligence (AI) and radiomics are tools that have shown promising results for diagnosis, treatment optimization and outcome prediction. This review aims to assess the impact of AI and radiomics on modern IGRT modalities in RT. METHODS: A PubMed/MEDLINE and Embase systematic review was conducted to investigate the impact of radiomics and AI to modern IGRT modalities. The search strategy was "Radiomics" AND "Cone Beam Computed Tomography"; "Radiomics" AND "Magnetic Resonance guided Radiotherapy"; "Radiomics" AND "on board Magnetic Resonance Radiotherapy"; "Artificial Intelligence" AND "Cone Beam Computed Tomography"; "Artificial Intelligence" AND "Magnetic Resonance guided Radiotherapy"; "Artificial Intelligence" AND "on board Magnetic Resonance Radiotherapy" and only original articles up to 01.11.2022 were considered. RESULTS: A total of 402 studies were obtained using the previously mentioned search strategy on PubMed and Embase. The analysis was performed on a total of 84 papers obtained following the complete selection process. Radiomics application to IGRT was analyzed in 23 papers, while a total 61 papers were focused on the impact of AI on IGRT techniques. DISCUSSION: AI and radiomics seem to significantly impact IGRT in all the phases of RT workflow, even if the evidence in the literature is based on retrospective data. Further studies are needed to confirm these tools' potential and provide a stronger correlation with clinical outcomes and gold-standard treatment strategies.

Artificial intelligence in radiotherapy.

Radiotherapy is a discipline closely integrated with computer science. Artificial intelligence (AI) has developed rapidly over the past few years. With the explosive growth of medical big data, AI promises to revolutionize the field of radiotherapy through highly automated workflow, enhanced quality assurance, improved regional balances of expert experiences, and individualized treatment guided by multi-omics. In addition to independent researchers, the increasing number of large databases, biobanks, and open challenges significantly facilitated AI studies on radiation oncology. This article reviews the latest research, clinical applications, and challenges of AI in each part of radiotherapy including image processing, contouring, planning, quality assurance, motion management, and outcome prediction. By summarizing cutting-edge findings and challenges, we aim to inspire researchers to explore more future possibilities and accelerate the arrival of AI radiotherapy.

Risk management patterns in radiation oncology-results of a national survey within the framework of the Patient Safety in German Radiation Oncology (PaSaGeRO) project.

PURPOSE: Risk management (RM) is a key component of patient safety in radiation oncology (RO). We investigated current approaches on RM in German RO within the framework of the Patient Safety in German Radiation Oncology (PaSaGeRO) project. Aim was not only to evaluate a status quo of RM purposes but furthermore to discover challenges for sustainable RM that should be addressed in future research and recommendations. METHODS: An online survey was conducted from June to August 2021, consisting of 18 items on prospective and reactive RM, protagonists of RM, and self-assessment concerning RM. The survey was designed using LimeSurvey and invitations were sent by e­mail. Answers were requested once per institution. RESULTS: In all, 48 completed questionnaires from university hospitals, general and non-academic hospitals, and private practices were received and considered for evaluation. Prospective and reactive RM was commonly conducted within interprofessional teams; 88% of all institutions performed prospective risk analyses. Most institutions (71%) reported incidents or near-events using multiple reporting systems. Results were presented to the team in 71% for prospective analyses and 85% for analyses of incidents. Risk conferences take place in 46% of institutions. 42% nominated a manager/committee for RM. Knowledge concerning RM was mostly rated "satisfying" (44%). However, 65% of all institutions require more information about RM by professional societies. CONCLUSION: Our results revealed heterogeneous patterns of RM in RO departments, although most departments adhered to common recommendations. Identified mismatches between recommendations and implementation of RM provide baseline data for future research and support definition of teaching content.

Reinventing radiation therapy with machine learning and imaging bio-markers (radiomics): State-of-the-art, challenges and perspectives.

Radiation therapy is a pivotal cancer treatment that has significantly progressed over the last decade due to numerous technological breakthroughs. Imaging is now playing a critical role on deployment of the clinical workflow, both for treatment planning and treatment delivery. Machine-learning analysis of predefined features extracted from medical images, i.e. radiomics, has emerged as a promising clinical tool for a wide range of clinical problems addressing drug development, clinical diagnosis, treatment selection and implementation as well as prognosis. Radiomics denotes a paradigm shift redefining medical images as a quantitative asset for data-driven precision medicine. The adoption of machine-learning in a clinical setting and in particular of radiomics features requires the selection of robust, representative and clinically interpretable biomarkers that are properly evaluated on a representative clinical data set. To be clinically relevant, radiomics must not only improve patients' management with great accuracy but also be reproducible and generalizable. Hence, this review explores the existing literature and exposes its potential technical caveats, such as the lack of quality control, standardization, sufficient sample size, type of data collection, and external validation. Based upon the analysis of 165 original research studies based on PET, CT-scan, and MRI, this review provides an overview of new concepts, and hypotheses generating findings that should be validated. In particular, it describes evolving research trends to enhance several clinical tasks such as prognostication, treatment planning, response assessment, prediction of recurrence/relapse, and prediction of toxicity. Perspectives regarding the implementation of an AI-based radiotherapy workflow are presented.

A Century of Fractionated Radiotherapy: How Mathematical Oncology Can Break the Rules.

Radiotherapy is involved in 50% of all cancer treatments and 40% of cancer cures. Most of these treatments are delivered in fractions of equal doses of radiation (Fractional Equivalent Dosing (FED)) in days to weeks. This treatment paradigm has remained unchanged in the past century and does not account for the development of radioresistance during treatment. Even if under-optimized, deviating from a century of successful therapy delivered in FED can be difficult. One way of exploring the infinite space of fraction size and scheduling to identify optimal fractionation schedules is through mathematical oncology simulations that allow for in silico evaluation. This review article explores the evidence that current fractionation promotes the development of radioresistance, summarizes mathematical solutions to account for radioresistance, both in the curative and non-curative setting, and reviews current clinical data investigating non-FED fractionated radiotherapy.

MRI-guided Radiation Therapy: An Emerging Paradigm in Adaptive Radiation Oncology.

Radiation therapy (RT) continues to be one of the mainstays of cancer treatment. Considerable efforts have been recently devoted to integrating MRI into clinical RT planning and monitoring. This integration, known as MRI-guided RT, has been motivated by the superior soft-tissue contrast, organ motion visualization, and ability to monitor tumor and tissue physiologic changes provided by MRI compared with CT. Offline MRI is already used for treatment planning at many institutions. Furthermore, MRI-guided linear accelerator systems, allowing use of MRI during treatment, enable improved adaptation to anatomic changes between RT fractions compared with CT guidance. Efforts are underway to develop real-time MRI-guided intrafraction adaptive RT of tumors affected by motion and MRI-derived biomarkers to monitor treatment response and potentially adapt treatment to physiologic changes. These developments in MRI guidance provide the basis for a paradigm change in treatment planning, monitoring, and adaptation. Key challenges to advancing MRI-guided RT include real-time volumetric anatomic imaging, addressing image distortion because of magnetic field inhomogeneities, reproducible quantitative imaging across different MRI systems, and biologic validation of quantitative imaging. This review describes emerging innovations in offline and online MRI-guided RT, exciting opportunities they offer for advancing research and clinical care, hurdles to be overcome, and the need for multidisciplinary collaboration.

Management of multiple brain metastases: a patterns of care survey within the German Society for Radiation Oncology.

PURPOSE: The treatment of brain metastases (BM) has changed considerably in recent years and in particular, the management of multiple BM is currently undergoing a paradigm shift and treatment may differ from current guidelines. This survey was designed to analyze the patterns of care in the management of multiple BM. METHODS: An online survey consisting of 36 questions was distributed to the members of the German Society for Radiation Oncology (DEGRO). RESULTS: In total, 193 physicians out of 111 institutions within the German Society for Radiation oncology responded to the survey. Prognostic scores for decision making were not used regularly. Whole brain radiotherapy approaches (WBRT) are the preferred treatment option for patients with multiple BM, although stereotactic radiotherapy treatments are chosen by one third depending on prognostic scores and overall number of BM. Routine hippocampal avoidance (HA) in WBRT is only used by a minority. In multiple BM of driver-mutated non-small cell lung cancer origin up to 30% favor sole TKI therapy as upfront treatment and would defer upfront radiotherapy. CONCLUSION: In multiple BM WBRT without hippocampal avoidance is still the preferred treatment modality of choice regardless of GPA and mutational status, while SRT is only used in patients with good prognosis. Evidence for both, SRS and hippocampal avoidance radiotherapy, is growing albeit the debate over the appropriate treatment in multiple BM is yet not fully clarified. Further prospective assessment of BM management-ideally as randomized trials-is required to align evolving concepts with the proper evidence and to update current guidelines.

Ovarian transposition in patients with cervical cancer prior to pelvic radiotherapy: a systematic review.

Ovarian transposition aims to minimize ovarian exposure and damage during pelvic radiotherapy. One or both ovaries are separated from the uterus and mobilized away from the area where the radiation will be administered. A review of the available literature was conducted to evaluate the efficacy and safety of ovarian transposition among pre-menopausal women diagnosed with cervical cancer and eligible for pelvic radiotherapy. Outcomes evaluated were ovarian function preservation and complication rates. We also searched for information on pregnancy/live birth rates after ovarian transposition. Our search yielded a total of 635 manuscripts, of which 33 were considered eligible. A total of 28 full texts were selected for the current review, including 1377 patients who underwent ovarian transposition. The median or mean follow-up ranged between 7 and 87 months. Ovarian function preservation after ovarian transposition and pelvic radiotherapy, with or without chemotherapy, was 61.7% (431/699 patients), ranging from 16.6% to 100%. A total of 12 studies reported on 117 complications, accounting for 8.5%. Ovarian metastases were described in 5 (0.4%). Data about fertility preservation after ovarian transposition are scarce and definitive conclusions cannot be drawn. Based on the available data, ovarian transposition could be performed on young patients with tumors smaller than 4 cm, and it should be avoided in those with bulky tumors. A risk/benefit assessment should be carefully evaluated by a multidisciplinary team, and the decision regarding ovarian transposition should be always guided by the values and informed preferences of the patient.

Role of vaginal brachytherapy boost following adjuvant external beam radiotherapy in cervical cancer: Turkish Society for Radiation Oncology Gynecologic Group Study (TROD 04-002).

OBJECTIVE: There are a limited number of studies supporting vaginal brachytherapy boost to external beam radiotherapy in the adjuvant treatment of cervical cancer. The aim of this study was to assess the impact of the addition of vaginal brachytherapy boost to adjuvant external beam radiotherapy on oncological outcomes and toxicity in patients with cervical cancer. METHODS: Patients treated with post-operative external beam radiotherapy ± chemotherapy ± vaginal brachytherapy between January 2001 and January 2019 were retrospectively evaluated. The treatment outcomes and prognostic factors were analyzed in patients treated with external beam radiotherapy with or without vaginal brachytherapy. RESULTS: A total of 480 patients were included in the analysis. The median age was 51 years (range 42-60). At least two intermediate risk factors were observed in 51% of patients, while 49% had at least one high-risk factor. The patients in the external beam radiotherapy + vaginal brachytherapy group had worse prognostic factors than the external beam radiotherapy alone group. With a median follow-up time of 56 months (range 33-90), the 5-year overall survival rate was 82%. There was no difference in 5-year overall survival (87% vs 79%, p=0.11), recurrence-free survival (74% vs 71%, p=0.49), local recurrence-free survival (78% vs 76%, p=0.16), and distant metastasis-free survival (85% vs 76%, p=0.09) rates between treatment groups. There was no benefit of addition of vaginal brachytherapy to external beam radiotherapy in patients with positive surgical margins. In multivariate analysis, stage (overall survival and local recurrence-free survival), tumor histology (recurrence-free survival, local recurrence-free survival and distant metastasis-free survival), parametrial invasion (recurrence-free survival and distant metastasis-free survival), lymphovascular space invasion (recurrence-free survival), and lymph node metastasis (distant metastasis-free survival) were found as negative prognostic factors. CONCLUSION: Adding vaginal brachytherapy boost to external beam radiotherapy did not provide any benefit in local control or survival in patients with cervical cancer.

Insights into the theranostic value of precision medicine on advanced radiotherapy to breast cancer.

Breast cancer is the most common cancer in women worldwide. "Breast cancer" encompasses a broad spectrum of diseases (i.e., subtypes) with significant epidemiological, clinical, and biological heterogeneity. Each of these subtypes has a different natural history and prognostic profile. Although tumour staging (TNM classification) still provides valuable information in the overall management of breast cancer, the current reality is that clinicians must consider other biological and molecular factors that directly influence treatment decision-making, including extent of surgery, indication for chemotherapy, hormonal therapy, and even radiotherapy (and treatment volumes). The management of breast cancer has changed radically in the last 15 years due to significant advances in our understanding of these tumours. While these changes have been extremely positive in terms of surgical and systemic management, they have also created significant uncertainties concerning integration of local and locoregional radiotherapy into the therapeutic scheme. In parallel, radiotherapy itself has also experienced major advances. Beyond the evident technological advances, new radiobiological concepts have emerged, and genomic data and other patient-specific factors must now be integrated into individualized treatment approaches. In this context, "precision medicine" seeks to provide an answer to these open questions and uncertainties. Although precision medicine has been much discussed in the last five years or so, the concept remains somewhat ambiguous, and it often appear to be used as a "catch-all" term. The present review aims to clarify the meaning of this term and, more importantly, to critically evaluate the role and impact of precision medicine on breast cancer radiotherapy. Finally, we will discuss the current and future of precision medicine in radiotherapy.

Palliative radiotherapy indications during the COVID-19 pandemic and in future complex logistic settings: the NORMALITY model.

INTRODUCTION: The COVID-19 pandemic has challenged healthcare systems worldwide over the last few months, and it continues to do so. Although some restrictions are being removed, it is not certain when the pandemic is going to be definitively over. Pandemics can be seen as a highly complex logistic scenario. From this perspective, some of the indications provided for palliative radiotherapy (PRT) during the COVID-19 pandemic could be maintained in the future in settings that limit the possibility of patients achieving symptom relief by radiotherapy. This paper has two aims: (1) to provide a summary of the indications for PRT during the COVID-19 pandemic; since some indications can differ slightly, and to avoid any possible contradictions, an expert panel composed of the Italian Association of Radiotherapy and Clinical Oncology (AIRO) and the Palliative Care and Supportive Therapies Working Group (AIRO-palliative) voted by consensus on the summary; (2) to introduce a clinical care model for PRT [endorsed by AIRO and by a spontaneous Italian collaborative network for PRT named "La Rete del Sollievo" ("The Net of Relief")]. The proposed model, denoted "No cOmpRoMise on quality of life by pALliative radiotherapy" (NORMALITY), is based on an AIRO-palliative consensus-based list of clinical indications for PRT and on practical suggestions regarding the management of patients potentially suitable for PRT but dealing with highly complex logistics scenarios (similar to the ongoing logistics limits due to COVID-19). MATERIAL AND METHODS: First, a summary of the available literature guidelines for PRT published during the COVID-19 pandemic was prepared. A systematic literature search based on the PRISMA approach was performed to retrieve the available literature reporting guideline indications fully or partially focused on PRT. Tables reporting each addressed clinical presentation and respective literature indications were prepared and distributed into two main groups: palliative emergencies and palliative non-emergencies. These summaries were voted in by consensus by selected members of the AIRO and AIRO-palliative panels. Second, based on the summary for palliative indications during the COVID-19 pandemic, a clinical care model to facilitate recruitment and delivery of PRT to patients in complex logistic scenarios was proposed. The summary tables were critically integrated and shuffled according to clinical presentations and then voted on in a second consensus round. Along with the adapted guideline indications, some methods of performing the first triage of patients and facilitating a teleconsultation preliminary to the first in-person visit were developed. RESULTS: After the revision of 161 documents, 13 papers were selected for analysis. From the papers, 19 clinical presentation items were collected; in total, 61 question items were extracted and voted on (i.e., for each presentation, more than one indication was provided from the literature). Two tables summarizing the PRT indications during the COVID-19 pandemic available from the literature (PRT COVID-19 summary tables) were developed: palliative emergencies and palliative non-emergencies. The consensus of the vote by the AIRO panel for the PRT COVID-19 summary was reached. The PRT COVID-19 summary tables for palliative emergencies and palliative non-emergencies were adapted for clinical presentations possibly associated with patients in complex clinical scenarios other than the COVID-19 pandemic. The two new indication tables (i.e., "Normality model of PRT indications") for both palliative emergencies and palliative non-emergencies were voted on in a second consensus round. The consensus rate was reached and strong. Written forms facilitating two levels of teleconsultation (triage and remote visits) were also developed, both in English and in Italian, to evaluate the patients for possible indications for PRT before scheduling clinical visits. CONCLUSION: We provide a comprehensive summary of the literature guideline indications for PRT during COVID-19 pandemic. We also propose a clinical care model including clinical indications and written forms facilitating two levels of teleconsultation (triage and remote visits) to evaluate the patients for indications of PRT before scheduling clinical visits. The normality model could facilitate the provision of PRT to patients in future complex logistic scenarios.

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.

Contemporary considerations in adjuvant radioiodine treatment of adults with differentiated thyroid cancer.

Differentiated thyroid cancer (DTC) is the most common endocrine malignancy with a growing incidence worldwide. The initial conventional management is surgery, followed by consideration of 131 I treatment that includes three options. These are termed remnant ablation (targeting benign thyroid remnant), adjuvant (targeting presumed microscopic DTC) and known disease (targeting macroscopic DTC) treatments. Some experts mostly rely on clinicopathologic assessment for recurrence risk to select patients for the 131 I treatment. Others, in addition, apply radioiodine imaging to guide their treatment planning, termed theranostics (aka theragnostics or radiotheragnostics). In patients with low-risk DTC, remnant ablation rather than adjuvant treatment is generally recommended and, in this setting, the ATA recommends a low 131 I activity. 131 I adjuvant treatment is universally recommended in patients with high-risk DTC (a primary tumor of any size with gross extrathyroidal extension) and is generally recommended in intermediate-risk DTC (primary tumor >4 cm in diameter, locoregional metastases, microscopic extrathyroidal extension, aggressive histology or vascular invasion). The optimal amount of 131 I activity for adjuvant treatment is controversial, but experts reached a consensus that the 131 I activity should be greater than that for remnant ablation. The main obstacles to establishing timely evidence through randomized clinical trials for 131 I therapy include years-to-decades delay in recurrence and low disease-specific mortality. This mini-review is intended to update oncologists on the most recent clinical, pathologic, laboratory and imaging variables, as well as on the current 131 I therapy-related definitions and management paradigms, which should optimally equip them for individualized patient guidance and treatment.

Update of the Standard Operating Procedure on the Use of Multiparametric Magnetic Resonance Imaging for the Diagnosis, Staging and Management of Prostate Cancer.

PURPOSE: We update the prior standard operating procedure for magnetic resonance imaging of the prostate, and summarize the available data about the technique and clinical use for the diagnosis and management of prostate cancer. This update includes practical recommendations on the use of magnetic resonance imaging for screening, diagnosis, staging, treatment and surveillance of prostate cancer. MATERIALS AND METHODS: A panel of clinicians from the American Urological Association and Society of Abdominal Radiology with expertise in the diagnosis and management of prostate cancer evaluated the current published literature on the use and technique of magnetic resonance imaging for this disease. When adequate studies were available for analysis, recommendations were made on the basis of data and when adequate studies were not available, recommendations were made on the basis of expert consensus. RESULTS: Prostate magnetic resonance imaging should be performed according to technical specifications and standards, and interpreted according to standard reporting. Data support its use in men with a previous negative biopsy and ongoing concerns about increased risk of prostate cancer. Sufficient data now exist to support the recommendation of magnetic resonance imaging before prostate biopsy in all men who have no history of biopsy. Currently, the evidence is insufficient to recommend magnetic resonance imaging for screening, staging or surveillance of prostate cancer. CONCLUSIONS: Use of prostate magnetic resonance imaging in the risk stratification, diagnosis and treatment pathway of men with prostate cancer is expanding. When quality prostate imaging is obtained, current evidence now supports its use in men at risk of harboring prostate cancer and who have not undergone a previous biopsy, as well as in men with an increasing prostate specific antigen following an initial negative standard prostate biopsy procedure.

Implementing a new scale for failure mode and effects analysis (FMEA) for risk analysis in a radiation oncology department.

PURPOSE: Patients and staffs are endangered by different failure modes during clinical routine in radiation oncology and risks are difficult to stratify. We implemented the method of failure mode and effects analysis (FMEA) via questionnaires in our institution and introduced an adapted scale applicable for radiation oncology. METHODS: Failure modes in physical treatment planning and daily routine were detected and stratified by ranking occurrence, severity, and detectability in a questionnaire. Multiplication of these values offers the risk priority number (RPN). We implemented an ordinal rating scale (ORS) as a combination of earlier published scales from the literature. This scale was optimized for German radiation oncology. We compared RPN using this ORS versus use of a rather subjective visual analogue rating scale (VRS). RESULTS: Mean RPN using ORS was 62.3 vs. 67.5 using VRS (p = 0.7). Use of ORS led to improved completeness of questionnaires (91 vs. 79%) and stronger agreement among the experts, especially concerning failure modes during radiation routine. The majority of interviewed experts found the analysis by using the ORS easier and expected a saving of time as well as higher intra- and interobserver reliability. CONCLUSION: The introduced rating scale together with a questionnaire survey provides merit for conducting FMEA in radiation oncology as results are comparable to the use of VRS and the process is facilitated.

Radiomics in radiation oncology-basics, methods, and limitations.

Over the past years, the quantity and complexity of imaging data available for the clinical management of patients with solid tumors has increased substantially. Without the support of methods from the field of artificial intelligence (AI) and machine learning, a complete evaluation of the available image information is hardly feasible in clinical routine. Especially in radiotherapy planning, manual detection and segmentation of lesions is laborious, time consuming, and shows significant variability among observers. Here, AI already offers techniques to support radiation oncologists, whereby ultimately, the productivity and the quality are increased, potentially leading to an improved patient outcome. Besides detection and segmentation of lesions, AI allows the extraction of a vast number of quantitative imaging features from structural or functional imaging data that are typically not accessible by means of human perception. These features can be used alone or in combination with other clinical parameters to generate mathematical models that allow, for example, prediction of the response to radiotherapy. Within the large field of AI, radiomics is the subdiscipline that deals with the extraction of quantitative image features as well as the generation of predictive or prognostic mathematical models. This review gives an overview of the basics, methods, and limitations of radiomics, with a focus on patients with brain tumors treated by radiation therapy.

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.

Applications of radiomics and machine learning for radiotherapy of malignant brain tumors.

BACKGROUND: Magnetic resonance imaging (MRI) and amino acid positron-emission tomography (PET) of the brain contain a vast amount of structural and functional information that can be analyzed by machine learning algorithms and radiomics for the use of radiotherapy in patients with malignant brain tumors. METHODS: This study is based on comprehensive literature research on machine learning and radiomics analyses in neuroimaging and their potential application for radiotherapy in patients with malignant glioma or brain metastases. RESULTS: Feature-based radiomics and deep learning-based machine learning methods can be used to improve brain tumor diagnostics and automate various steps of radiotherapy planning. In glioma patients, important applications are the determination of WHO grade and molecular markers for integrated diagnosis in patients not eligible for biopsy or resection, automatic image segmentation for target volume planning, prediction of the location of tumor recurrence, and differentiation of pseudoprogression from actual tumor progression. In patients with brain metastases, radiomics is applied for additional detection of smaller brain metastases, accurate segmentation of multiple larger metastases, prediction of local response after radiosurgery, and differentiation of radiation injury from local brain metastasis relapse. Importantly, high diagnostic accuracies of 80-90% can be achieved by most approaches, despite a large variety in terms of applied imaging techniques and computational methods. CONCLUSION: Clinical application of automated image analyses based on radiomics and artificial intelligence has a great potential for improving radiotherapy in patients with malignant brain tumors. However, a common problem associated with these techniques is the large variability and the lack of standardization of the methods applied.

Radiation treatment of hemato-oncological patients in times of the COVID-19 pandemic : Expert recommendations from the radiation oncology panels of the German Hodgkin Study Group and the German Lymphoma Alliance.

PURPOSE: The coronavirus pandemic is affecting global health systems, endangering daily patient care. Hemato-oncological patients are particularly vulnerable to infection, requiring decisive recommendations on treatment and triage. The aim of this survey amongst experts on radiation therapy (RT) for lymphoma and leukemia is to delineate typical clinical scenarios and to provide counsel for high-quality care. METHODS: A multi-item questionnaire containing multiple-choice and free-text questions was developed in a peer-reviewed process and sent to members of the radiation oncology panels of the German Hodgkin Study Group and the German Lymphoma Alliance. Answers were assessed online and analyzed centrally. RESULTS: Omission of RT was only considered in a minority of cases if alternative treatment options were available. Hypofractionated regimens and reduced dosages may be used for indolent lymphoma and fractures due to multiple myeloma. Overall, there was a tendency to shorten RT rather than to postpone or omit it. Even in case of critical resource shortage, panelists agreed to start emergency RT for typical indications (intracranial pressure, spinal compression, superior vena cava syndrome) within 24 h. Possible criteria to consider for patient triage are the availability of (systemic) options, the underlying disease dynamic, and the treatment rationale (curative/palliative). CONCLUSION: RT for hemato-oncological patients receives high-priority and should be maintained even in later stages of the pandemic. Hypofractionation and shortened treatment schedules are feasible options for well-defined constellations, but have to be discussed in the clinical context.