Fluorodeoxyglucose 18F (18F FDG) is commonly used and established protocols and quantitative methods are in place for PET scans. Recent advancements in [18F]FDG-PET technology are paving the way for individualized treatment decisions. This review explores how [18F]FDG-PET can be leveraged to establish individualized radiotherapy treatment regimens. Dose painting, gradient dose prescription, and response-adapted dose prescription guided by [18F]FDG-PET are part of the process. An assessment of the current situation, progress, and future prospects of these advancements is given for each tumor type.
Patient-derived cancer models have facilitated a deeper understanding of cancer and the evaluation of anti-cancer treatments for many years. New procedures for delivering radiation have amplified the value of these models for examining radiation sensitizers and the radiation response specific to each patient. Despite the advancements in patient-derived cancer models yielding more clinically relevant results, crucial questions persist regarding the optimal application of patient-derived xenografts and spheroid cultures. Mouse and zebrafish models, used as personalized predictive avatars in patient-derived cancer models, are discussed, along with a review of the advantages and disadvantages related to patient-derived spheroids. Furthermore, the employment of extensive collections of patient-originated models for the creation of predictive algorithms, intended to direct therapeutic choices, is examined. To finalize, we scrutinize methods for building patient-derived models, focusing on key determinants of their effectiveness as both representations and models of cancer biology.
Recent breakthroughs in circulating tumor DNA (ctDNA) methodologies offer a compelling chance to integrate this emerging liquid biopsy technique with the field of radiogenomics, the study of how tumor genomic profiles relate to radiotherapy efficacy and side effects. The traditional relationship between ctDNA levels and metastatic tumor burden exists, though recent, ultra-sensitive technologies enable ctDNA assessment following curative-intent radiotherapy of localized disease, either to detect minimal residual disease or to track post-treatment disease progression. Moreover, numerous investigations have highlighted the practical application of ctDNA analysis in a range of cancer types, including sarcoma, head and neck, lung, colon, rectal, bladder, and prostate cancers, when treated with radiotherapy or chemoradiotherapy. Peripheral blood mononuclear cells, collected alongside ctDNA to eliminate mutations from clonal hematopoiesis, are also available for single nucleotide polymorphism testing. This allows for the possible identification of patients at increased risk for radiotoxicity. Eventually, future ctDNA testing will be utilized to more thoroughly analyze local recurrence risk, facilitating a more precise approach to adjuvant radiation therapy post-surgery for patients with localized disease and guiding ablative radiation protocols for patients with oligometastatic disease.
Quantitative image analysis, formally recognized as radiomics, has the objective of assessing numerous quantitative characteristics extracted from acquired medical images, employing manually designed or automated feature extraction techniques. Bio-based chemicals In radiation oncology, which utilizes computed tomography (CT), magnetic resonance imaging (MRI), and positron emission tomography (PET) in treatment planning, dose calculation, and image guidance, radiomics offers considerable potential across various clinical applications. Radiomics stands to predict radiotherapy outcomes, encompassing aspects like local control and treatment-related toxicity, by analyzing features extracted from pretreatment and ongoing treatment imaging. Taking into account individual predictions for treatment results, the radiotherapy dose can be adjusted to specifically meet the requirements and preferences of each patient. Personalized treatment strategies can benefit from radiomics' capability to discern subtle variations within tumors, highlighting high-risk areas beyond mere size or intensity metrics. Radiomics-powered treatment response prediction allows for personalized dose adjustments and fractionation strategies. To ensure broader applicability of radiomics models across diverse institutions, varying scanner types, and patient demographics, there's a crucial need for harmonized and standardized image acquisition protocols, aiming to reduce inconsistencies in imaging data.
Personalized radiotherapy clinical decision-making depends on the development of tumor biomarkers responsive to radiation, a crucial goal in the field of precision cancer medicine. High-throughput molecular assay results, analyzed through modern computational techniques, can potentially identify individual tumor characteristics, and establish tools to comprehend disparate patient responses to radiotherapy. Clinicians can thus leverage the advancements in molecular profiling and computational biology, including machine learning. Despite this, the mounting complexity of data generated through high-throughput and omics-based assays necessitates a careful and considered selection of analytical methods. Subsequently, the proficiency of advanced machine learning procedures in detecting subtle data patterns entails a critical examination of the factors influencing the results' generalizability. We scrutinize the computational framework for tumor biomarker development, detailing common machine learning methods and their utilization in radiation biomarker discovery using molecular datasets, as well as current challenges and future directions.
Clinical staging and histopathology have been the standard for treatment allocation in cancer care throughout history. This approach, though extremely practical and fruitful over the years, has clearly revealed a deficiency in these data's ability to capture the full spectrum and diversity of disease trajectories amongst patients. The availability of efficient and affordable DNA and RNA sequencing has made precision therapy a tangible possibility. This achievement, a result of systemic oncologic therapy, is due to the significant promise demonstrated by targeted therapies in patients harboring oncogene-driver mutations. selleck chemicals llc Correspondingly, a considerable amount of studies have investigated predictive indicators for how patients will react to systemic therapies in a variety of cancers. In radiation oncology, the application of genomics and transcriptomics to optimize radiation therapy regimens, including dose and fractionation, is experiencing rapid development, yet remains a nascent field. The genomic adjusted radiation dose/radiation sensitivity index is a notable early achievement in the field, aiming for a pan-cancer approach to genomically-guided radiation therapy. This encompassing method is further augmented by a histology-focused approach to precisely targeting radiation therapy. A survey of the literature regarding histology-specific, molecular biomarkers for precision radiotherapy emphasizes the importance of commercially available and prospectively validated options.
The application of genomics has revolutionized the landscape of clinical oncology. Genomic-based molecular diagnostics, including prognostic genomic signatures and next-generation sequencing, are now a standard part of clinical decisions regarding cytotoxic chemotherapy, targeted agents, and immunotherapy. Clinical judgments about radiation therapy (RT) are, unfortunately, detached from the genomic complexities of the tumor. This review examines the clinical potential of genomics in optimizing radiation therapy (RT) dosage. Although radiation therapy is undergoing a transformation towards data-driven techniques, the current prescription of radiation therapy dosage continues to be predominantly a generalized approach reliant upon cancer type and stage. This methodology directly contradicts the acknowledgement that tumors are biologically diverse, and that cancer isn't a single disease process. Epimedium koreanum We investigate the integration of genomics into radiation therapy treatment protocols focusing on dose prescription, assess its clinical relevance, and examine how genomic-driven radiation therapy dose optimization may contribute to a more profound understanding of radiation therapy's clinical effects.
The consequence of low birth weight (LBW) extends to elevated risks of both short- and long-term morbidity and mortality, beginning in early life and continuing into adulthood. While researchers have diligently worked to improve birth outcomes, the pace of progress has unfortunately lagged behind expectations.
This comprehensive review of English-language clinical trials investigated the effectiveness of antenatal interventions aimed at mitigating environmental exposures, particularly toxin reduction, and promoting improved sanitation, hygiene, and health-seeking behaviors in pregnant women, with the goal of enhancing birth outcomes.
Between March 17, 2020, and May 26, 2020, we conducted eight systematic searches across various databases: MEDLINE (OvidSP), Embase (OvidSP), Cochrane Database of Systematic Reviews (Wiley Cochrane Library), Cochrane Central Register of Controlled Trials (Wiley Cochrane Library), and CINAHL Complete (EbscoHOST).
Concerning strategies to curb indoor air pollution, four documents stand out. Two randomized controlled trials (RCTs), a systematic review and meta-analysis (SRMA), and a single RCT investigate these issues. Preventative antihelminth treatment and antenatal counselling to reduce unnecessary cesarean sections feature in the interventions. Analysis of the published literature reveals that interventions designed to alleviate indoor air pollution (LBW RR 090 [056, 144], PTB OR 237 [111, 507]) or preventative antihelminth treatment (LBW RR 100 [079, 127], PTB RR 088 [043, 178]) are not likely to have a discernible effect on the rates of low birth weight or premature birth. Data supporting antenatal counseling strategies against cesarean sections is limited. Randomized controlled trials (RCTs) have not produced sufficient published research on the effectiveness of other interventions.