Seminars in Radiation Oncology最新文献

The genomic era has significantly changed the practice of clinical oncology. The use of genomic-based molecular diagnostics including prognostic genomic signatures and new-generation sequencing has become routine for clinical decisions regarding cytotoxic chemotherapy, targeted agents and immunotherapy. In contrast, clinical decisions regarding radiation therapy (RT) remain uninformed about the genomic heterogeneity of tumors. In this review, we discuss the clinical opportunity to utilize genomics to optimize RT dose. Although from the technical perspective, RT has been moving towards a data-driven approach, RT prescription dose is still based on a one-size-fits all approach, with most RT dose based on cancer diagnosis and stage. This approach is in direct conflict with the realization that tumors are biologically heterogeneous, and that cancer is not a single disease. Here, we discuss how genomics can be integrated into RT prescription dose, the clinical potential for this approach and how genomic-optimization of RT dose could lead to new understanding of the clinical benefit of RT.

Patient-derived cancer models have been used for decades to improve our understanding of cancer and test anticancer treatments. Advances in radiation delivery have made these models more attractive for studying radiation sensitizers and understanding an individual patient's radiation sensitivity. Advances in the use of patient-derived cancer models lead to a more clinically relevant outcome, although many questions remain regarding the optimal use of patient-derived xenografts and patient-derived spheroid cultures. The use of patient-derived cancer models as personalized predictive avatars through mouse and zebrafish models is discussed, and the advantages and disadvantages of patient-derived spheroids are reviewed. In addition, the use of large repositories of patient-derived models to develop predictive algorithms to guide treatment selection is discussed. Finally, we review methods for establishing patient-derived models and identify key factors that influence their use as both avatars and models of cancer biology.

PET imaging with 2’-deoxy-2’-[18F]fluoro-D-glucose ([18F]FDG) has become one of the pillars in the management of malignant diseases. It has proven value in diagnostic workup, treatment policy, follow-up, and as prognosticator for outcome. [18F]FDG is widely available and standards have been developed for PET acquisition protocols and quantitative analyses. More recently, [18F]FDG-PET is also starting to be appreciated as a decision aid for treatment personalization. This review focuses on the potential of [18F]FDG-PET for individualized radiotherapy dose prescription. This includes dose painting, gradient dose prescription, and [18F]FDG-PET guided response-adapted dose prescription. The current status, progress, and future expectations of these developments for various tumor types are discussed.

Hypoxia (oxygen deprivation) occurs in most solid malignancies, albeit with considerable heterogeneity. Hypoxia is associated with an aggressive cancer phenotype by promotion of genomic instability, evasion of anti-cancer therapies including radiotherapy and enhancement of metastatic risk. Therefore, hypoxia results in poor cancer outcomes. Targeting hypoxia to improve cancer outcomes is an attractive therapeutic strategy. Hypoxia-targeted dose painting escalates radiotherapy dose to hypoxic sub-volumes, as quantified and spatially mapped using hypoxia imaging. This therapeutic approach could overcome hypoxia-induced radioresistance and improve patient outcomes without the need for hypoxia-targeted drugs. This article will review the premise and underpinning evidence for personalized hypoxia-targeted dose painting. It will present data on relevant hypoxia imaging biomarkers, highlight the challenges and potential benefit of this approach and provide recommendations for future research priorities in this field. Personalized hypoxia-based radiotherapy de-escalation strategies will also be addressed.

Radiopharmaceutical therapy (RPT) is an invigorated form of cancer therapy that systemically delivers targeted radioactive drugs to cancer cells. Theranostics is a type of RPT that utilizes imaging, either of the RPT drug directly or a companion diagnostic, to inform whether a patient will benefit from the treatment. Given the ability to image the drug onboard theranostic treatments also lends itself readily to patient-specific dosimetry, which is a physics-based process that determines the overall absorbed dose burden to healthy organs and tissues and tumors in patients. While companion diagnostics identify who will benefit from RPT treatments, dosimetry determines how much activity these beneficiaries can receive to maximize therapeutic efficacy. Clinical data is starting to accrue suggesting tremendous benefits when dosimetry is performed for RPT patients. RPT dosimetry, which was once performed by florid and often inaccurate workflows, can now be performed more efficiently and accurately with FDA-cleared dosimetry software. Therefore, there is no better time for the field of oncology to adopt this form of personalize medicine to improve outcomes for cancer patients.

Improvements in radiotherapy delivery have enabled higher therapeutic doses and improved efficacy, contributing to the growing number of long-term cancer survivors. These survivors are at risk of developing late toxicity from radiotherapy, and the inability to predict who is most susceptible results in substantial impact on quality of life and limits further curative dose escalation. A predictive assay or algorithm for normal tissue radiosensitivity would allow more personalized treatment planning, reducing the burden of late toxicity, and improving the therapeutic index. Progress over the last 10 years has shown that the etiology of late clinical radiotoxicity is multifactorial and informs development of predictive models that combine information on treatment (eg, dose, adjuvant treatment), demographic and health behaviors (eg, smoking, age), co-morbidities (eg, diabetes, collagen vascular disease), and biology (eg, genetics, ex vivo functional assays). AI has emerged as a useful tool and is facilitating extraction of signal from large datasets and development of high-level multivariable models. Some models are progressing to evaluation in clinical trials, and we anticipate adoption of these into the clinical workflow in the coming years. Information on predicted risk of toxicity could prompt modification of radiotherapy delivery (eg, use of protons, altered dose and/or fractionation, reduced volume) or, in rare instances of very high predicted risk, avoidance of radiotherapy. Risk information can also be used to assist treatment decision-making for cancers where efficacy of radiotherapy is equivalent to other treatments (eg, low-risk prostate cancer) and can be used to guide follow-up screening in instances where radiotherapy is still the best choice to maximize tumor control probability. Here, we review promising predictive assays for clinical radiotoxicity and highlight studies that are progressing to develop an evidence base for clinical utility.

Estimation of patient prognosis plays a central role in guiding decision making for the palliative management of metastatic disease, and a number of statistical models have been developed to provide survival estimates for patients in this context. In this review, we discuss several well-validated survival prediction models for patients receiving palliative radiotherapy to sites outside of the brain. Key considerations include the type of statistical model, model performance measures and validation procedures, studies’ source populations, time points used for prognostication, and details of model output. We then briefly discuss underutilization of these models, the role of decision support aids, and the need to incorporate patient preference in shared decision making for patients with metastatic disease who are candidates for palliative radiotherapy

In patients with advanced cancer, radiation therapy is considered at various time points in the patient's clinical course from diagnosis to death. As some patients are living longer with metastatic cancer on novel therapeutics, radiation oncologists are increasingly using radiation therapy as an ablative therapy in appropriately selected patients. However, most patients with metastatic cancer still eventually die of their disease. For those without effective targeted therapy options or those who are not candidates for immunotherapy, the time frame from diagnosis to death is still relatively short. Given this evolving landscape, prognostication has become increasingly challenging. Thus, radiation oncologists must be diligent about defining the goals of therapy and considering all treatment options from ablative radiation to medical management and hospice care. The risks and benefits of radiation therapy vary based on an individual patient's prognosis, goals of care, and the ability of radiation to help with their cancer symptoms without undue toxicity over the course of their expected lifetime. When considering recommending a course of radiation, physicians must broaden their understanding of risks and benefits to include not only physical symptoms, but also various psychosocial burdens. These include financial burdens to the patient, to their caregiver and to the healthcare system. The burden of time spent at the end-of-life receiving radiation therapy must also be considered. Thus, the consideration of radiation therapy at the end-of-life can be complex and requires careful attention to the whole patient and their goals of care.