Total-Body PET in Theranostics: Reshaping Treatment Planning and Follow-Up.

Glioblastoma is the most aggressive type of primary adult brain tumour. The median survival of patients with glioblastoma remains approximately 15 months, and the 5-year survival rate is <10%. Current treatment options are limited, and the standard of care has remained relatively constant since 2011. Over the last decade, a range of different treatment regimens have been investigated with very limited success. Tumour recurrence is almost inevitable with the current treatment strategies, as glioblastoma tumours are highly heterogeneous and invasive. Additionally, another challenging issue facing patients with glioblastoma is how to distinguish between tumour progression and treatment effects, especially when relying on routine diagnostic imaging techniques in the clinic. The specificity of routine imaging for identifying tumour progression early or in a timely manner is poor due to the appearance similarity of post-treatment effects. Here, we concisely describe the current status and challenges in the assessment and early prediction of therapy response and the early detection of tumour progression or recurrence. We also summarize and discuss studies of advanced approaches such as quantitative imaging, liquid biomarker discovery and machine intelligence that hold exceptional potential to aid in the therapy monitoring of this malignancy and early prediction of therapy response, which may decisively transform the conventional detection methods in the era of precision medicine.

Nuclear medicine stands at the forefront of the precision medicine revolution, offering unique insights into molecular processes that complement and enhance other precision approaches. This chapter explores the integration of nuclear medicine with cutting-edge technologies and methodologies in the era of precision medicine, focusing on three key areas: integration with genomics and proteomics, radiomics and texture analysis, and personalised treatment planning and monitoring. The synergy between nuclear medicine and genomics/proteomics has opened new avenues for understanding disease biology and developing targeted therapies. By combining molecular imaging data with genomic and proteomic profiles, researchers and clinicians can gain a more comprehensive view of disease processes, enabling more accurate diagnosis, prognosis, and treatment selection. The chapter discusses examples of this integration in oncology, highlighting how it has improved patient stratification and treatment response prediction. Radiomics and texture analysis have expanded the information that can be extracted from nuclear medicine images, providing new biomarkers for various clinical applications. The chapter explores how these advanced image analysis techniques can reveal subtle patterns and features not apparent to the human eye, enhancing tumour characterisation, treatment response assessment, and prognostication. In the realm of personalised treatment planning and monitoring, nuclear medicine techniques enable truly individualised approaches. From initial target identification and characterisation to adaptive therapy strategies and longterm surveillance, molecular imaging plays a crucial role in guiding treatment decisions and assessing efficacy. The chapter discusses various applications, including theranostics, PET-guided radiotherapy, and novel approaches to monitoring immunotherapy response. Challenges and future directions are addressed, including the need for standardisation, the potential of artificial intelligence in image analysis, and the development of novel tracers and theranostic pairs. The integration of nuclear medicine data with other biomarkers and its incorporation into clinical decision support systems are highlighted as key areas for future development. By providing a comprehensive overview of these advances, this chapter illustrates how nuclear medicine is driving innovations in precision medicine, offering non-invasive, quantitative assessments of molecular processes that bridge the gap between scientific discoveries and clinical application. As the field continues to evolve, nuclear medicine is poised to play an increasingly central role in translating the promise of precision medicine into improved patient outcomes across a broad spectrum of diseases.

Bioanalytical assays are available for virtually all drugs used in humans, partly because of the regulatory requirements to characterize a drug's pharmacokinetic properties during its preclinical and clinical development. However, only a few drugs are subject to routine therapeutic drug monitoring (TDM) in patients, including several immunosuppressive drugs, antibiotics, antiepileptics, antidepressants, digoxin and methotrexate. Major reasons for this relatively small number of drugs include a lack of a straightforward relationship between serum/blood levels and effect, a wide therapeutic window and an unfavourable balance between intra- and interpatient and intra- and interoccasion variability in pharmacokinetics 1, 2. Moreover, there are surprisingly few prospective randomized data available to demonstrate a true beneficial effect of routine TDM, especially when looking at defined outcome parameters. Instead, most published data rather suggest that TDM might benefit patients 2-8. Another important reason for the relatively low number of drugs for which levels are monitored routinely might be that interpretation of drug levels may be perceived to be complicated. TDM data are often used to adjust dose regimens using fairly challenging pharmacokinetic calculations, or even using population pharmacokinetic models and Bayesian forecasting embedded in sophisticated software packages 9, 10. Although deemed useful by most clinical pharmacologists, and for some even a 'raison d'être', this relatively complicated use of data tends to scare off clinicians, minimizing the use of TDM. In our opinion, straightforward, easy-to-use TDM will result in its much broader use by the average clinician, which can be achieved by implementing user-friendly information technology tools, but can also be achieved by returning to user-friendly sampling strategies, such as the use of trough levels wherever possible and by considering alternative matrices such as saliva or dried blood spots 11, 12. Broader application is further supported by the rapidly developing field of pharmacogenetics, which makes it possible to identify patients who might benefit from higher or lower doses of some drugs without even having to determine a drug level 13. However, despite being able to explain some variability in the pharmacokinetics of some drugs, some aspects relevant for drug exposure are simply not covered by pharmacogenetics such as ontogeny in paediatric patients, poor adherence, and drug–drug and drug–food interactions, which can easily be monitored adequately by measuring trough levels for most drugs 14-17. A more practical but also important reason for the relatively small number of drugs for which levels are measured routinely is the limited availability of drug assays with turnaround times adequate for patient care. Most drug assays available in routine clinical chemistry and toxicology laboratories are automated immunoassays, which are fairly easy to perform and have a relatively short turnaround time. Other methodologies to determine drug levels are mostly chromatography based, such as high-performance liquid chromatography combined with ultraviolet detection (HPLC-UV) and liquid chromatography combined with mass spectrometry (LC-MS). While usually having a longer turnaround time and needing specialized technologists to perform them, these methods are much more versatile than automated assays and allow the development of assays for individual drugs by clinical laboratories themselves, so-called 'laboratory-developed tests' (LDTs) or 'in-house' assays. The recent growth in the number of LC-MS instruments in many clinical laboratories around the world could, therefore, produce enormous growth in quantitative 'in house' assays for TDM but this has not happened yet. The discrepancy between the increased availability of instruments and the relatively modest number of TDM assays may be because, for most drugs, levels are requested only rarely, which makes it difficult to cover the costs of developing, validating and maintaining a clinical assay for such a drug, even for large reference laboratories, and even though developing and validating chromatographic assays are much easier and cheaper than for most immunoassays. This situation might be a 'catch 22' as it is likely that some drug levels are not requested, for the simple reason that an assay for it does not exist, or is not easily accessible. In summary, the limited availability of drug assays, the lack of strong data demonstrating a positive effect on clinical outcome, and logistical sampling and interpretive challenges all contribute to underutilization of TDM. Expanding the number of drug assays, improving access to these assays, and simplifying blood sampling and data interpretation will not only improve the current status of TDM, but also better position TDM in the era of precision medicine. Precision medicine is an emerging approach for disease treatment and prevention that takes into account individual variability in genes, environment and lifestyle. A near consequence of precision medicine, especially with the inclusion of a systems biology approach, is the selection of drugs entirely tailored to a specific patient and her or his disease. This could mean prescribing according to the label, but it could also involve off-label use, such as using an antidiabetic drug to treat breast cancer or using an antibiotic to treat a specific form of childhood epilepsy. Although this practice is expanding into all disease areas, most headway has been made in oncology. Indeed, an incredibly exciting therapeutic approach that is currently entering the clinic is the treatment of cancer patients with (combinations of) drugs based on a systems biology analysis of tumour gene expression data in the absence and presence of pharmacological perturbation; this identifies relevant pathways, master regulator genes and actionable proteins, and optimal combinations of compounds that target these 18. This approach, which is currently being tested in various settings, will revolutionize pharmacotherapy, in oncology as well as other disease areas, and TDM can be of immense value to advance this novel way of treating patients. One example involves treating a patient with prostate cancer with a combination of the well-known mechanistic target of rapamycin (mTOR) inhibitor, rapamycin, and an experimental drug, DP0325901, which together optimally target the Forkhead box protein 1 / Centromere protein F (FOXM1/CENPF) pathway that was identified using a systems biology analysis of tumour expression data in the absence and presence of pharmacological perturbation 18. In this example, both drugs would be taken orally and it is not known if there is any interaction between the two drugs, or between the experimental drug and other drugs that are concomitantly prescribed to patients with prostate cancer. Measuring circulating levels of both drugs would help to characterize the pharmacokinetics of these drugs in this particular combination, in this particular patient population. Measuring levels of both drugs might also directly benefit this patient. Pharmacokinetic data are available for both rapamycin and DP0325901, so from the literature a preliminary estimate could be made regarding what levels to expect and, perhaps, even when to adjust the dose 19. Recently, a few perspectives and mini-reviews have described the opportunities for TDM in the era of precision medicine 20, 21. Although positive in nature, the scope of all of these papers was mostly restricted to reviewing drugs that are currently already monitored, although a recent editorial by Martin et al. in the British Journal of Clinical Pharmacology expanded this to more experimental drugs, by calling for more clinical pharmacology in the era of personalized medicine 22. The above-mentioned example of rapamycin and DP0325901 identifies additional tremendous opportunities for clinical pharmacologists, (bio-)chemists, pharmacists and pathologists who are active in the TDM field. Systems biology-driven selection of combinations of drugs will lead to unforeseen off-label use of registered drugs, as well as an increasing number of experimental drugs used to treat patients who are not part of a clinical study protocol. To some extent, this practice will take place within the grey area of combining patient care and clinical research. This research would benefit from generating pharmacokinetic data in patient groups for whom such data do not yet exist. Simultaneously, however, individual patients might benefit from dose adjustments based on rapidly determined drug levels that are compared with the scarce pharmacokinetic data available. In a sense, laboratories would, therefore, simultaneously generate both drug development and TDM data. This exciting and novel application of TDM requires extensive assay development and validation, easy access to the validated assays, and rapid turnaround times so that assays can be used for drug development and individual patient care. Such an endeavour would entail a new set of bioanalytical, regulatory, interpretive and financial challenges. The development and validation of these new assays require collaboration between individual laboratories, national and international clinical chemistry societies, and industry. In addition, each new assay needs assessment and approval by the respective national regulatory/accreditation services. Finally, optimal interpretation of the scarce data requires significant input from national and international medical, pharmacology and pharmaceutical societies, including the British Pharmacological Society and the International Association for Therapeutic Drug Monitoring and Clinical Toxicology. This new and exciting era of precision medicine has created never-before-seen opportunities for TDM in support of drug development and patient care. All that is required to seize these opportunities is tenacity, creativity, flexibility and collaboration.

In the era of precision medicine, imaging plays a critical role in evaluating treatment response to various oncologic therapies. For decades, conventional morphologic assessments using cross-sectional imaging have been the standard for monitoring the effectiveness of systemic and locoregional therapies in patients with cancer. However, the development of new functional imaging tools has widened the scope of imaging from mere response assessment to patient selection and outcome prediction. Dual-energy CT (DECT), known for its superior material differentiation capabilities, shows promise in enhancing treatment response evaluation. DECT-based iodine quantification methods are increasingly being investigated as surrogates for assessing tumor vascularity and physiology, which is particularly important in patients undergoing emerging targeted therapies. The purpose of this review article is to discuss the current and emerging role of DECT in assessing treatment response in patients with malignant abdominal tumors. Keywords: CT-Dual Energy, Transcatheter Tumor Therapy, Tumor Response, Iodine Uptake, Therapeutic Response © RSNA, 2025.

Trying to predict what genetic counseling will look like in the era of precision medicine is a continuous challenge. According to the National Institutes of Health, precision medicine is an emerging approach for disease treatment and prevention that takes into account individual variability in genes, environment, and lifestyle. In order to explore the future of genetic counseling practice in this era, this article examines the current genetic counseling practice, internal and external forces that most likely will continue to shape the genetic counseling profession, and discusses the most important aspects of what genetic counselors have to offer in the era of precision medicine.

Oral cancer, which occurs in the mouth, lips, and tongue, is a multifactorial disease whose etiology involves environment, genetic, and epigenetic factors. Tobacco use and alcohol consumption are regarded as the primary risk factors for oral squamous cell carcinoma (OSCC), and betel use, other chemicals, radiation, environmental, and genetics are reported as relevant risk factors for oral carcinogenesis. The human papillomavirus infection is an independent risk factor. Traditional epidemiology studies have revealed that environmental carcinogens are risk factors for OSCC. Molecular epidemiology studies have revealed that the susceptibility to OSCC is influenced by both environmental and genetic risk factors. However, the details and mechanisms of risk factors involved in OSCC are unclear. Advanced methods and techniques used in human genome studies provide great opportunities for researchers to explore and identify (a) the details of such risk factors and (b) genetic susceptibility involved in OSCC. Human genome epidemiology is a new branch of epidemiology, which leads the epidemiology study from the molecular epidemiology era into the era of genome-wide association study. In the era of precision medicine, molecular epidemiology studies should focus on biomarkers for cancer genomics and their potential utility in clinical practice. Here, we briefly reviewed several molecular epidemiology studies of OSCC, focusing on biomarkers as valuable utility in risk assessment, clinical screening, diagnosis, and prognosis prediction of OSCC in the era of precision medicine.

In the era of precision medicine, translating genomics into clinical care will involve answering key questions in social and behavioral research. The scope of this research addresses assessing how clients perceive and use genomic information, and how effectively genetic counseling is meeting clients' needs. Outcomes are central to enhancing practice effectiveness, improving patient outcomes, and informing cost effective services to address workforce challenges. While genetic counseling is generally thought of as a clinical practice, genetic counselors contribute to research in several ways. Counselors are actively involved in interpretation of sequence data, collaborate in clinical research teams, and serve as lead investigators. This commentary highlights genetic counselors as social and behavioral scientists and reviews evidence generated by genetic counselors, describes advanced training in research, and posits key social and behavioral research questions for genetic counseling in translating genomic science in the era of precision medicine.

Background Whole-body diffusion-weighted (DW) MRI can help detect cancer with high sensitivity. However, the assessment of therapy response often requires information about tumor metabolism, which is measured with fluorine 18 fluorodeoxyglucose (FDG) PET. Purpose To compare tumor therapy response with whole-body DW MRI and FDG PET/MRI in children and young adults. Materials and Methods In this prospective, nonrandomized multicenter study, 56 children and young adults (31 male and 25 female participants; mean age, 15 years ± 4 [standard deviation]; age range, 6-22 years) with lymphoma or sarcoma underwent 112 simultaneous whole-body DW MRI and FDG PET/MRI between June 2015 and December 2018 before and after induction chemotherapy (ClinicalTrials.gov identifier: NCT01542879). The authors measured minimum tumor apparent diffusion coefficients (ADCs) and maximum standardized uptake value (SUV) of up to six target lesions and assessed therapy response after induction chemotherapy according to the Lugano classification or PET Response Criteria in Solid Tumors. The authors evaluated agreements between whole-body DW MRI- and FDG PET/MRI-based response classifications with Krippendorff α statistics. Differences in minimum ADC and maximum SUV between responders and nonresponders and comparison of timing for discordant and concordant response assessments after induction chemotherapy were evaluated with the Wilcoxon test. Results Good agreement existed between treatment response assessments after induction chemotherapy with whole-body DW MRI and FDG PET/MRI (α = 0.88). Clinical response prediction according to maximum SUV (area under the receiver operating characteristic curve = 100%; 95% confidence interval [CI]: 99%, 100%) and minimum ADC (area under the receiver operating characteristic curve = 98%; 95% CI: 94%, 100%) were similar (P = .37). Sensitivity and specificity were 96% (54 of 56 participants; 95% CI: 86%, 99%) and 100% (56 of 56 participants; 95% CI: 54%, 100%), respectively, for DW MRI and 100% (56 of 56 participants; 95% CI: 93%, 100%) and 100% (56 of 56 participants; 95% CI: 54%, 100%) for FDG PET/MRI. In eight of 56 patients who underwent imaging after induction chemotherapy in the early posttreatment phase, chemotherapy-induced changes in tumor metabolism preceded changes in proton diffusion (P = .002). Conclusion Whole-body diffusion-weighted MRI showed significant agreement with fluorine 18 fluorodeoxyglucose PET/MRI for treatment response assessment in children and young adults. © RSNA, 2020 Online supplemental material is available for this article.

Role of Imaging in the Era of Precision Medicine

Simple SummaryWorldwide gastrointestinal (GI) malignancies account for about 25% of the global cancer incidence. For some malignancies, screening programs, such as routine colon cancer screenings, have largely aided in the early diagnosis of those at risk. However, even after diagnosis, many GI malignancies lack robust biomarkers to serve as definitive staging and prognostic tools to aid in clinical decision-making. Radiomics uses high-throughput data to extract various features from medical images with the potential to aid personalized precision medicine. Machine learning is a technique for analyzing and predicting by learning from sample data, finding patterns in it, and applying it to new data. We reviewed the fundamental concepts of radiomics such as imaging data acquisition, lesion segmentation, feature design, and interpretation specific to GI cancer studies and assessed the clinical applications of radiomics and machine learning in diagnosis, staging, evaluation of tumor prognosis, and treatment response.Gastrointestinal (GI) cancers, consisting of a wide spectrum of pathologies, have become a prominent health issue globally. Despite medical imaging playing a crucial role in the clinical workflow of cancers, standard evaluation of different imaging modalities may provide limited information. Accurate tumor detection, characterization, and monitoring remain a challenge. Progress in quantitative imaging analysis techniques resulted in ”radiomics”, a promising methodical tool that helps to personalize diagnosis and treatment optimization. Radiomics, a sub-field of computer vision analysis, is a bourgeoning area of interest, especially in this era of precision medicine. In the field of oncology, radiomics has been described as a tool to aid in the diagnosis, classification, and categorization of malignancies and to predict outcomes using various endpoints. In addition, machine learning is a technique for analyzing and predicting by learning from sample data, finding patterns in it, and applying it to new data. Machine learning has been increasingly applied in this field, where it is being studied in image diagnosis. This review assesses the current landscape of radiomics and methodological processes in GI cancers (including gastric, colorectal, liver, pancreatic, neuroendocrine, GI stromal, and rectal cancers). We explain in a stepwise fashion the process from data acquisition and curation to segmentation and feature extraction. Furthermore, the applications of radiomics for diagnosis, staging, assessment of tumor prognosis and treatment response according to different GI cancer types are explored. Finally, we discussed the existing challenges and limitations of radiomics in abdominal cancers and investigate future opportunities.

Tracer-kinetic analysis of dynamic contrast-enhanced magnetic resonance imaging data is commonly performed with the well-known Tofts model and nonlinear least squares (NLLS) regression. This approach yields point estimates of model parameters, uncertainty of these estimates can be assessed e.g. by an additional bootstrapping analysis. Here, we present a Bayesian probabilistic modeling approach for tracer-kinetic analysis with a Tofts model, which yields posterior probability distributions of perfusion parameters and therefore promises a robust and information-enriched alternative based on a framework of probability distributions. In this manuscript, we use the quantitative imaging biomarkers alliance (QIBA) Tofts phantom to evaluate the Bayesian tofts model (BTM) against a bootstrapped NLLS approach. Furthermore, we demonstrate how Bayesian posterior probability distributions can be employed to assess treatment response in a breast cancer DCE-MRI dataset using Cohen’s d. Accuracy and precision of the BTM posterior distributions were validated and found to be in good agreement with the NLLS approaches, and assessment of therapy response with respect to uncertainty in parameter estimates was found to be excellent. In conclusion, the Bayesian modeling approach provides an elegant means to determine uncertainty via posterior distributions within a single step and provides honest information about changes in parameter estimates.

Precision Medicine with Imprecise Therapy: Computational Modeling for Chemotherapy in Breast Cancer

To compare tumor therapy response assessments with whole-body diffusion-weighted imaging (WB-DWI) and 18F-fluorodeoxyglucose ([18F]FDG) PET/MRI in pediatric patients with Hodgkin lymphoma and non-Hodgkin lymphoma. In a retrospective, non-randomized single-center study, we reviewed serial simultaneous WB-DWI and [18F]FDG PET/MRI scans of 45 children and young adults (27 males; mean age, 13years ± 5 [standard deviation]; age range, 1-21years) with Hodgkin lymphoma (n = 20) and non-Hodgkin lymphoma (n = 25) between February 2018 and October 2022. We measured minimum tumor apparent diffusion coefficient (ADCmin) and maximum standardized uptake value (SUVmax) of up to six target lesions and assessed therapy response according to Lugano criteria and modified criteria for WB-DWI. We evaluated the agreement between WB-DWI- and [18F]FDG PET/MRI-based response classifications with Gwet's agreement coefficient (AC). After induction chemotherapy, 95% (19 of 20) of patients with Hodgkin lymphoma and 72% (18 of 25) of patients with non-Hodgkin lymphoma showed concordant response in tumor metabolism and proton diffusion. We found a high agreement between treatment response assessments on WB-DWI and [18F]FDG PET/MRI (Gwet's AC = 0.94; 95% confidence interval [CI]: 0.82, 1.00) in patients with Hodgkin lymphoma, and a lower agreement for patients with non-Hodgkin lymphoma (Gwet's AC = 0.66; 95% CI: 0.43, 0.90). After completion of therapy, there was an excellent agreement between WB-DWI and [18F]FDG PET/MRI response assessments (Gwet's AC = 0.97; 95% CI: 0.91, 1). Therapy response of Hodgkin lymphoma can be evaluated with either [18F]FDG PET or WB-DWI, whereas patients with non-Hodgkin lymphoma may benefit from a combined approach. Hodgkin lymphoma and non-Hodgkin lymphoma exhibit different patterns of tumor response to induction chemotherapy on diffusion-weighted MRI and PET/MRI. • Diffusion-weighted imaging has been proposed as an alternative imaging to assess tumor response without ionizing radiation. • After induction therapy, whole-body diffusion-weighted imaging and PET/MRI revealed a higher agreement in patients with Hodgkin lymphoma than in those with non-Hodgkin lymphoma. • At the end of therapy, whole-body diffusion-weighted imaging and PET/MRI revealed an excellent agreement for overall tumor therapy responses for all lymphoma types.

In recent years, medicine has undergone profound changes, strongly entering a new phase defined as the "era of precision medicine". In this context, patient clinical management involves various scientific approaches that allow for a comprehensive pathology evaluation: from preventive processes (where applicable) to genetic and diagnostic studies. In this scenario, biobanks play an important role and, over the years, have gained increasing prestige, moving from small deposits to large collections of samples of various natures. Disease-oriented biobanks are rapidly developing as they provide useful information for the management of complex diseases, such as melanoma. Indeed, melanoma, given its highly heterogeneous characteristics, is one of the oncologic diseases with the greatest clinical and therapeutic management complexity. So, the possibility of extrapolating tissue, genetic and imaging data from dedicated biobanks could result in more selective study approaches. In this review, we specifically analyze the several biobank types to evaluate their role in technology development, patient monitoring and research of new biomarkers, especially in the melanoma context.

The numbers of diagnostic, prognostic, and predictive immunohistochemistry (IHC) tests are increasing; the implementation and validation of new IHC tests, revalidation of existing tests, as well as the on-going need for daily quality assurance monitoring present significant challenges to clinical laboratories. There is a need for proper quality tools, specifically tissue tools that will enable laboratories to successfully carry out these processes. This paper clarifies, through the lens of laboratory tissue tools, how validation, verification, and revalidation of IHC tests can be performed in order to develop and maintain high quality "fit-for-purpose" IHC testing in the era of precision medicine. This is the final part of the 4-part series "Evolution of Quality Assurance for Clinical Immunohistochemistry in the Era of Precision Medicine."