Personalized Treatment Recommendations Research Articles

Feasibility of functional precision medicine for guiding treatment of relapsed or refractory pediatric cancers

Children with rare, relapsed or refractory cancers often face limited treatment options, and few predictive biomarkers are available that can enable personalized treatment recommendations. The implementation of functional precision medicine (FPM), which combines genomic profiling with drug sensitivity testing (DST) of patient-derived tumor cells, has potential to identify treatment options when standard-of-care is exhausted. The goal of this prospective observational study was to generate FPM data for pediatric patients with relapsed or refractory cancer. The primary objective was to determine the feasibility of returning FPM-based treatment recommendations in real time to the FPM tumor board (FPMTB) within a clinically actionable timeframe (<4 weeks). The secondary objective was to assess clinical outcomes from patients enrolled in the study. Twenty-five patients with relapsed or refractory solid and hematological cancers were enrolled; 21 patients underwent DST and 20 also completed genomic profiling. Median turnaround times for DST and genomics were within 10 days and 27 days, respectively. Treatment recommendations were made for 19 patients (76%), of whom 14 received therapeutic interventions. Six patients received subsequent FPM-guided treatments. Among these patients, five (83%) experienced a greater than 1.3-fold improvement in progression-free survival associated with their FPM-guided therapy relative to their previous therapy, and demonstrated a significant increase in progression-free survival and objective response rate compared to those of eight non-guided patients. The findings from our proof-of-principle study illustrate the potential for FPM to positively impact clinical care for pediatric and adolescent patients with relapsed or refractory cancers and warrant further validation in large prospective studies. ClinicalTrials.gov registration: NCT03860376.

A scoping review of web-based, interactive, personalized decision-making tools available to support breast cancer treatment and survivorship care.

We reviewed existing personalized, web-based, interactive decision-making tools available to guide breast cancer treatment and survivorship care decisions in clinical settings. The study was conducted using the Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews(PRISMA-ScR). We searched PubMed and related databases for interactive web-based decision-making tools developed to support breast cancer treatment and survivorship care from 2013 to 2023. Information on each tool's purpose, target population, data sources, individual and contextual characteristics, outcomes, validation, and usability testing were extracted. We completed a quality assessment for each tool using the International Patient Decision Aid Standard (IPDAS)instrument. We found 54 tools providing personalized breast cancer outcomes (e.g., recurrence) and treatment recommendations (e.g., chemotherapy) based on individual clinical (e.g., stage), genomic (e.g., 21-gene-recurrence score), behavioral (e.g., smoking), and contextual (e.g., insurance) characteristics. Forty-five tools were validated, and nine had undergone usability testing. However, validation and usability testing included mostly White, educated, and/or insured individuals. The average quality assessment score of the tools was 16 (range: 6-46; potential maximum: 63). There was wide variation in the characteristics, quality, validity, and usability of the tools. Future studies should consider diverse populations for tool development and testing. There are tools available to support personalized breast cancer treatment and survivorship care decisions in clinical settings. It is important for both cancer survivors and physicians to carefully consider the quality, validity, and usability of these tools before using them to guide care decisions.

Prognostic nomograms for predicting long-term overall survival in spindle cell melanoma: a population-based study.

There are few research findings on the survival prognosis of spindle cell melanoma (SCM), which is an unusual kind of melanoma. The purpose of this study was to develop a thorough nomogram for predicting the overall survival (OS) of patients with SCM and to assess its validity by comparing it with the conventional American Joint Committee on Cancer (AJCC) staging system. The Surveillance, Epidemiology, and End Results database was searched, and 2,015 patients with SCM were selected for the analysis. The patients were randomly divided into training (n = 1,410) and validation (n = 605) cohorts by using R software. Multivariate Cox regression was performed to identify predictive factors. A nomogram was established based on these characteristics to predict OS in SCM. The calibration curve, concordance index (C-index), area under the receiver operating characteristic curve, and decision-curve analysis were utilized to assess the accuracy and reliability of the model. The net reclassification improvement and integrated discrimination improvement were also applied in this model to evaluate its differences with the AJCC model. The developed nomogram suggests that race, AJCC stage, chemotherapy status, regional node examination status, marital status, and sex have the greatest effects on OS in SCM. The nomogram had a higher C-index than the AJCC staging system (0.751 versus 0.633 in the training cohort and 0.747 versus 0.650 in the validation cohort). Calibration plots illustrated that the model was capable of being calibrated. These criteria demonstrated that the nomogram outperforms the AJCC staging system alone. The nomogram developed in this study is sufficiently reliable for forecasting the risk and prognosis of SCM, which may facilitate personalized treatment recommendations in upcoming clinical trials.

Enhancing gestational diabetes mellitus risk assessment and treatment through GDMPredictor: a machine learning approach.

Gestational diabetes mellitus (GDM) is a serious health concern that affects pregnant women worldwide and can lead to adverse pregnancy outcomes. Early detection of high-risk individuals and the implementation of appropriate treatment can enhance these outcomes. We conducted a study on a cohort of 3467 pregnant women during their pregnancy, with a total of 5649 clinical and biochemical records collected. We utilized this dataset as our training dataset to develop a web server called GDMPredictor. The GDMPredictor utilizes advanced machine learning techniques to predict the risk of GDM in pregnant women. We also personalize treatment recommendations based on essential biochemical indicators, such as A1MG, BMG, CysC, CO2, TBA, FPG, and CREA. Our assessment of GDMPredictor's effectiveness involved training it on the dataset of 3467 pregnant women and measuring its ability to predict GDM risk using an AUC and auPRC. GDMPredictor demonstrated an impressive level of precision by achieving an AUC score of 0.967. To tailor our treatment recommendations, we use the GDM risk level to identify higher risk candidates who require more intensive care. The GDMPredictor can accept biochemical indicators for predicting the risk of GDM at any period from 1 to 24weeks, providing healthcare professionals with an intuitive interface to identify high-risk patients and give optimal treatment recommendations. The GDMPredictor presents a valuable asset for clinical practice, with the potential to change the management of GDM in pregnant women. Its high accuracy and efficiency make it a reliable tool for doctors to improve patient outcomes. Early identification of high-risk individuals and tailored treatment can improve maternal and fetal health outcomes http://www.bioinfogenetics.info/GDM/ .

ASPiRATION: Australian observational cohort study of comprehensive genomic profiling in metastatic lung cancer tissue.

ASPiRATION is a national prospective observational cohort study assessing the feasibility, clinical and economic value of up-front tissue-based comprehensive genomic profiling (CGP) to identify actionable genomic alterations in participants with newly diagnosed metastatic non-squamous non-small-cell lung cancerin Australia. This study will enrol 1000 participants with tumor available for CGP and standard of caremolecular testing (EGFR/ALK/ROS1). Participants with actionable variants may receive novel targeted treatments through ASPiRATION-specific substudies, other trials/programs. Clinical outcome data will be collected for a minimum of 2 years. Study outcomes are descriptive, including the ability of CGP to identify additional actionable variants, leading to personalized treatment recommendations, and will describe the feasibility, efficiency, cost and utility of implementation of CGP nationally.

Optimizing adjuvant treatment options for patients with glioblastoma.

This study focused on minimizing the costs and toxic effects associated with unnecessary chemotherapy. We sought to optimize the adjuvant therapy strategy, choosing between radiotherapy (RT) and chemoradiotherapy (CRT), for patients based on their specific characteristics. This selection process utilized an innovative deep learning method. We trained six machine learning (ML) models to advise on the most suitable treatment for glioblastoma (GBM) patients. To assess the protective efficacy of these ML models, we employed various metrics: hazards ratio (HR), inverse probability treatment weighting (IPTW)-adjusted HR (HRa), the difference in restricted mean survival time (dRMST), and the number needed to treat (NNT). The Balanced Individual Treatment Effect for Survival data (BITES) model emerged as the most effective, demonstrating significant protective benefits (HR: 0.53, 95% CI, 0.48-0.60; IPTW-adjusted HR: 0.65, 95% CI, 0.55-0.78; dRMST: 7.92, 95% CI, 7.81-8.15; NNT: 1.67, 95% CI, 1.24-2.41). Patients whose treatment aligned with BITES recommendations exhibited notably better survival rates compared to those who received different treatments, both before and after IPTW adjustment. In the CRT-recommended group, a significant survival advantage was observed when choosing CRT over RT (p < 0.001). However, this was not the case in the RT-recommended group (p = 0.06). Males, older patients, and those whose tumor invasion is confined to the ventricular system were more frequently advised to undergo RT. Our study suggests that BITES can effectively identify GBM patients likely to benefit from CRT. These ML models show promise in transforming the complex heterogeneity of real-world clinical practice into precise, personalized treatment recommendations.

Clinical knowledge-guided deep reinforcement learning for sepsis antibiotic dosing recommendations

Sepsis is the third leading cause of death worldwide. Antibiotics are an important component in the treatment of sepsis. The use of antibiotics is currently facing the challenge of increasing antibiotic resistance (Evans et al., 2021). Sepsis medication prediction can be modeled as a Markov decision process, but existing methods fail to integrate with medical knowledge, making the decision process potentially deviate from medical common sense and leading to underperformance. (Wang et al., 2021). In this paper, we use Deep Q-Network (DQN) to construct a Sepsis Anti-infection DQN (SAI-DQN) model to address the challenge of determining the optimal combination and duration of antibiotics in sepsis treatment. By setting sepsis clinical knowledge as reward functions to guide DQN complying with medical guidelines, we formed personalized treatment recommendations for antibiotic combinations. The results showed that our model had a higher average value for decision-making than clinical decisions. For the test set of patients, our model predicts that 79.07% of patients will achieve a favorable prognosis with the recommended combination of antibiotics. By statistically analyzing decision trajectories and drug action selection, our model was able to provide reasonable medication recommendations that comply with clinical practices. Our model was able to improve patient outcomes by recommending appropriate antibiotic combinations in line with certain clinical knowledge.

Steatotic Liver Disease: Pathophysiology and Emerging Pharmacotherapies.

Steatotic liver disease (SLD) displays a dynamic and complex disease phenotype. Consequently, the metabolic dysfunction-associated steatotic liver disease (MASLD)/metabolic dysfunction-associated steatohepatitis (MASH) therapeutic pipeline is expanding rapidly and in multiple directions. In parallel, noninvasive tools for diagnosing and monitoring responses to therapeutic interventions are being studied, and clinically feasible findings are being explored as primary outcomes in interventional trials. The realization that distinct subgroups exist under the umbrella of SLD should guide more precise and personalized treatment recommendations and facilitate advancements in pharmacotherapeutics. This review summarizes recent updates of pathophysiology-based nomenclature and outlines both effective pharmacotherapeutics and those in the pipeline for MASLD/MASH, detailing their mode of action and the current status of phase 2 and 3 clinical trials. Of the extensive arsenal of pharmacotherapeutics in the MASLD/MASH pipeline, several have been rejected, whereas other, mainly monotherapy options, have shown only marginal benefits and are now being tested as part of combination therapies, yet others are still in development as monotherapies. Although the Food and Drug Administration (FDA) has recently approved resmetirom, additional therapeutic approaches in development will ideally target MASH and fibrosis while improving cardiometabolic risk factors. Due to the urgent need for the development of novel therapeutic strategies and the potential availability of safety and tolerability data, repurposing existing and approved drugs is an appealing option. Finally, it is essential to highlight that SLD and, by extension, MASLD should be recognized and approached as a systemic disease affecting multiple organs, with the vigorous implementation of interdisciplinary and coordinated action plans. SIGNIFICANCE STATEMENT: Steatotic liver disease (SLD), including metabolic dysfunction-associated steatotic liver disease and metabolic dysfunction-associated steatohepatitis, is the most prevalent chronic liver condition, affecting more than one-fourth of the global population. This review aims to provide the most recent information regarding SLD pathophysiology, diagnosis, and management according to the latest advancements in the guidelines and clinical trials. Collectively, it is hoped that the information provided furthers the understanding of the current state of SLD with direct clinical implications and stimulates research initiatives.

The quality traits of artificial intelligence operations in predicting mental healthcare professionals’ perceptions: A case study in the psychotherapy division

&lt;p&gt;As advancements in healthcare technologies continue to emerge, the integration of AI-Technology has brought about significant transformations in various healthcare sectors. While substantial advancements have been made in applying AI to enhance physical health, its implementation in the field of mental health is still in its early stages. This descriptive study aims to address this gap by exploring the perspectives of mental health professionals (MHPs) on the acceptance and utilization of AI technology. Unified Theory of Acceptance and Use of Technology (UTAUT) was utilized to assess MHPs’ attitudes and beliefs towards AI implementation in psychotherapeutic practices. The sample was compromised of 349 MHPs. The findings reveal the task characteristic (TC) domain as the most influential domain, followed by Performance expectancy (PE), Behavioural intentions (BI), Personal innovativeness in IT (PT), Social influence (SI), Effort expectancy (EE), Perceived substitution crisis (PSC), Technology characteristic (TECH), and Initial trust (IT). The study also identifies statistically significant differences in AI usage based on gender variable, with females demonstrating a higher level of AI usage in comparison to males. Furthermore, the study highlights diverse applications of AI in the field of mental health, including AI-assisted assessments (AAA), chatbots for psychotherapy support (CPS), and data analytics for personalized treatment recommendations (DAPTR). By incorporating mental healthcare professionals’ (MHPs) perspectives, this research significantly contributes to a comprehensive understanding of the acceptance and utilization of AI technology in psychotherapy. The findings offer valuable insights into MHPs’ perceptions, concerns, and perceived advantages associated with integrating AI technology within clinical settings in the field of mental health.&lt;/p&gt;

Antiplatelet therapy guided by CYP2C19 point-of-care pharmacogenetics plus multidimensional treatment decisions.

Aim: Implementation of CYP2C19 point-of-care (POC) pharmacogenetic (PGx) testing with personalized treatment recommendations. Methods: POC CYP2C19 genotyping plus expert evaluation of risk factors for ischemic and bleeding events. Results: 167 patients underwent PGx testing, 54 (32.3%) were CYP2C19 loss of function carriers, and POC versus standard PGx analysis results for *2 and *3 variants matched in 100%. Antiplatelet therapy was adjusted in 44 patients (26.3%), but always required consideration of patient-specific factors. Conclusion: CYP2C19 POC-PGx is reliable and offers clinically relevant advantages for immediate evidence-based adaptations of antiplatelet therapy, whereas in less acute cases conventional PGx testing can also have advantages. Antiplatelet therapy has become more complex, and implementation of PGx-based personalized antiplatelet therapy requires complementary expert knowledge.

Ayurveda Medical System in Ancient India

Ayurveda, an ancient medical system originating in India over 5,000 years ago, represents a holistic approach to health and well-being, emphasizing the balance between mind, body, and spirit. Rooted in the sacred texts known as the Vedas, particularly the Atharvaveda, Ayurveda is built upon the principles of maintaining equilibrium among the three doshas: Vata (air and ether), Pitta (fire and water), and Kapha (water and earth). Each individual’s unique constitution, or prakriti, is determined by the predominant dosha and guides personalized treatment and lifestyle recommendations. Central to Ayurveda is the concept of Agni, or digestive fire, which is considered crucial for overall health. Proper digestion and elimination are seen as the foundation of physical and mental well-being. Ayurvedic practices encompass a wide range of therapeutic measures, including dietary guidelines, herbal remedies, yoga, meditation, and detoxification processes such as Panchakarma. Panchakarma involves five primary procedures—Vamana (therapeutic vomiting), Virechana (purgation), Basti (enema), Nasya (nasal administration), and Raktamokshana (bloodletting)—designed to cleanse the body of toxins and restore balance. Herbal medicine is a cornerstone of Ayurveda, utilizing a diverse array of plants, minerals, and metals. Prominent herbs include Ashwagandha, Turmeric, and Triphala, each known for their multifaceted health benefits. Ayurveda also places significant importance on lifestyle practices tailored to the individual’s constitution and the natural rhythms of the environment, advocating for daily routines (dinacharya) and seasonal routines (ritucharya) to harmonize with nature. Ayurveda’s influence extends beyond medicine, permeating Indian culture and daily life. Its resurgence in modern times reflects a growing interest in natural and preventive healthcare. While contemporary scientific validation is ongoing, Ayurveda’s comprehensive and integrative approach continues to be valued for its potential to enhance quality of life and foster sustainable health practices.

Deep Attention Q-Network for Personalized Treatment Recommendation.

Tailoring treatment for severely ill patients is crucial yet challenging to achieve optimal healthcare outcomes. Recent advances in reinforcement learning offer promising personalized treatment recommendations. However, they often rely solely on a patient's current physiological state, which may not accurately represent the true health status of the patient. This limitation hampers policy learning and evaluation, undermining the effectiveness of the treatment. In this study, we propose Deep Attention Q-Network for personalized treatment recommendation, leveraging the Transformer architecture within a deep reinforcement learning framework to efficiently integrate historical observations of patients. We evaluated our proposed method on two real-world datasets: sepsis and acute hypotension patients, demonstrating its superiority over state-of-the-art methods. The source code for our model is available at https://github.com/stevenmsm/RL-ICU-DAQN.

A holistic profile for cancer-related fatigue for women with breast cancer – a qualitative study

Objective: Cancer- related fatigue (CRF) is one of the most reported long-term effects after breast cancer and severely impacts quality of life. To come towards optimal treatment of multidimensional CRF, the first step is to use a holistic approach to develop a holistic patient profile including the patient’s experience and impact of CRF on their life. Methods and measures: Four semi- structured focus groups with twenty- seven breast cancer patients and fourteen interviews with healthcare professionals (HCPs) were held. Reflexive thematic analysis was used to define (sub)themes for the holistic patient profile. The themes of the interviews and focus groups were compared for validity. Results: Breast cancer patients and HCPs described the same five major themes, consisting of experience of CRF, impact and consequences, coping, personality, and CRF treatment. Experience of CRF consists of cognitive, emotional, and physical aspects. Impact and consequences include work, family, partner relation, social contact and hobbies, body, and misunderstanding. Coping consists of twelve (mal)adaptive strategies. Personality and CRF treatment were summarised as themes. Conclusions: A first holistic patient profile was introduced for CRF for breast cancer. This profile can be conceptualized into a questionnaire to collect information for personalized treatment recommendations and monitoring of CRF over time.

Radiomic signatures reveal multiscale intratumor heterogeneity associated with tissue tolerance and survival in re-irradiated nasopharyngeal carcinoma: a multicenter study

BackgroundPost-radiation nasopharyngeal necrosis (PRNN) is a severe adverse event following re-radiotherapy for patients with locally recurrent nasopharyngeal carcinoma (LRNPC) and associated with decreased survival. Biological heterogeneity in recurrent tumors contributes to the different risks of PRNN. Radiomics can be used to mine high-throughput non-invasive image features to predict clinical outcomes and capture underlying biological functions. We aimed to develop a radiogenomic signature for the pre-treatment prediction of PRNN to guide re-radiotherapy in patients with LRNPC.MethodsThis multicenter study included 761 re-irradiated patients with LRNPC at four centers in NPC endemic area and divided them into training, internal validation, and external validation cohorts. We built a machine learning (random forest) radiomic signature based on the pre-treatment multiparametric magnetic resonance images for predicting PRNN following re-radiotherapy. We comprehensively assessed the performance of the radiomic signature. Transcriptomic sequencing and gene set enrichment analyses were conducted to identify the associated biological processes.ResultsThe radiomic signature showed discrimination of 1-year PRNN in the training, internal validation, and external validation cohorts (area under the curve (AUC) 0.713–0.756). Stratified by a cutoff score of 0.735, patients with high-risk signature had higher incidences of PRNN than patients with low-risk signature (1-year PRNN rates 42.2–62.5% vs. 16.3–18.8%, P < 0.001). The signature significantly outperformed the clinical model (P < 0.05) and was generalizable across different centers, imaging parameters, and patient subgroups. The radiomic signature had prognostic value concerning its correlation with PRNN-related deaths (hazard ratio (HR) 3.07–6.75, P < 0.001) and all causes of deaths (HR 1.53–2.30, P < 0.01). Radiogenomics analyses revealed associations between the radiomic signature and signaling pathways involved in tissue fibrosis and vascularity.ConclusionsWe present a radiomic signature for the individualized risk assessment of PRNN following re-radiotherapy, which may serve as a noninvasive radio-biomarker of radiation injury-associated processes and a useful clinical tool to personalize treatment recommendations for patients with LANPC.

Evaluation of the effectiveness of a nationwide precision medicine program for patients with advanced non-small cell lung cancer in Germany: a historical cohort analysis

The national Network Genomic Medicine (nNGM) Lung Cancer provides comprehensive and high-quality multiplex molecular diagnostics and standardized personalized treatment recommendation for patients with advanced non-small cell lung cancer (aNSCLC) in Germany. The primary aim of this study was to investigate the effectiveness of the nNGM precision medicine program in terms of overall survival (OS) using real-world data (RWD). A historical nationwide cohort analysis of patients with aNSCLC and initial diagnosis between 04/2019 and 06/2020 was conducted to compare treatment and OS of patients with and without nNGM-participation. Patients participating within the nNGM (nNGM group) were selected based on a prospective nNGM database. The electronic health records (EHR) of the prospective nNGM database were case-specifically linked to claims data (AOK, German health insurance). The control group was selected from claims data of patients receiving usual care without nNGM-participation (non-nNGM group). The minimum follow-up period was six months. Overall, n=509 patients in the nNGM group and n=7213 patients in the non-nNGM group met the inclusion criteria. Patients participating in the nNGM had a significantly improved OS compared to the non-nNGM group (median OS: 10.5 months vs. 8.7 months, p=0.008, HR=0.84, 95% CI: 0.74-0.95). The 1-year survival rates were 46.8% (nNGM) and 41.3% (non-nNGM). The use of approved tyrosine kinase inhibitors (TKI) in the first-line setting was significantly higher in the nNGM group than in the non-nNGM group (nNGM: 8.4% (43/509) vs. non-nNGM: 5.1% (366/7213), p=0.001). Overall, patients receiving first-line TKI treatment had significantly higher 1-year OS rates than patients treated with PD-1/PD-L1 inhibitors and/or chemotherapy (67.2% vs. 40.2%, p<0.001). This is the first study to demonstrate a significant survival benefit and higher utilization of targeted therapies for aNSCLC patients participating within nNGM. Our data indicate that precision medicine programs can enhance collaborative personalized lung cancer care and promote the implementation of treatment innovations and the latest scientific knowledge into clinical routine care. The study was funded by the AOK Federal Association Germany.

Deep Learning for the Prediction of the Survival of Midline Diffuse Glioma with an H3K27M Alteration.

The prognosis of diffuse midline glioma (DMG) patients with H3K27M (H3K27M-DMG) alterations is poor; however, a model that encourages accurate prediction of prognosis for such lesions on an individual basis remains elusive. We aimed to construct an H3K27M-DMG survival model based on DeepSurv to predict patient prognosis. Patients recruited from a single center were used for model training, and patients recruited from another center were used for external validation. Univariate and multivariate Cox regression analyses were used to select features. Four machine learning models were constructed, and the consistency index (C-index) and integrated Brier score (IBS) were calculated. We used the receiver operating characteristic curve (ROC) and area under the receiver operating characteristic (AUC) curve to assess the accuracy of predicting 6-month, 12-month, 18-month and 24-month survival rates. A heatmap of feature importance was used to explain the results of the four models. We recruited 113 patients in the training set and 23 patients in the test set. We included tumor size, tumor location, Karnofsky Performance Scale (KPS) score, enhancement, radiotherapy, and chemotherapy for model training. The accuracy of DeepSurv prediction is highest among the four models, with C-indexes of 0.862 and 0.811 in the training and external test sets, respectively. The DeepSurv model had the highest AUC values at 6 months, 12 months, 18 months and 24 months, which were 0.970 (0.919-1), 0.950 (0.877-1), 0.939 (0.845-1), and 0.875 (0.690-1), respectively. We designed an interactive interface to more intuitively display the survival probability prediction results provided by the DeepSurv model. The DeepSurv model outperforms traditional machine learning models in terms of prediction accuracy and robustness, and it can also provide personalized treatment recommendations for patients. The DeepSurv model may provide decision-making assistance for patients in formulating treatment plans in the future.

Mathematical modeling of radiotherapy: impact of model selection on estimating minimum radiation dose for tumor control.

Radiation therapy (RT) is one of the most common anticancer therapies. Yet, current radiation oncology practice does not adapt RT dose for individual patients, despite wide interpatient variability in radiosensitivity and accompanying treatment response. We have previously shown that mechanistic mathematical modeling of tumor volume dynamics can simulate volumetric response to RT for individual patients and estimation personalized RT dose for optimal tumor volume reduction. However, understanding the implications of the choice of the underlying RT response model is critical when calculating personalized RT dose. In this study, we evaluate the mathematical implications and biological effects of 2 models of RT response on dose personalization: (1) cytotoxicity to cancer cells that lead to direct tumor volume reduction (DVR) and (2) radiation responses to the tumor microenvironment that lead to tumor carrying capacity reduction (CCR) and subsequent tumor shrinkage. Tumor growth was simulated as logistic growth with pre-treatment dynamics being described in the proliferation saturation index (PSI). The effect of RT was simulated according to each respective model for a standard schedule of fractionated RT with 2 Gy weekday fractions. Parameter sweeps were evaluated for the intrinsic tumor growth rate and the radiosensitivity parameter for both models to observe the qualitative impact of each model parameter. We then calculated the minimum RT dose required for locoregional tumor control (LRC) across all combinations of the full range of radiosensitvity and proliferation saturation values. Both models estimate that patients with higher radiosensitivity will require a lower RT dose to achieve LRC. However, the two models make opposite estimates on the impact of PSI on the minimum RT dose for LRC: the DVR model estimates that tumors with higher PSI values will require a higher RT dose to achieve LRC, while the CCR model estimates that higher PSI values will require a lower RT dose to achieve LRC. Ultimately, these results show the importance of understanding which model best describes tumor growth and treatment response in a particular setting, before using any such model to make estimates for personalized treatment recommendations.

Advancements in Heart Failure Management: A Comprehensive Narrative Review of Emerging Therapies.

Heart failure is a substantial and escalating global health challenge, affecting millions worldwide. This complex syndrome arises from diverse etiologies, encompassing ischemic heart disease, hypertension, valvular abnormalities, and cardiomyopathies. Heart failure is characterized by the heart's inability to pump blood efficiently to meet the body's metabolic demands, leading to debilitating symptoms, frequent hospitalizations, and high mortality rates. Traditionally, the management of Heart failure has focused on alleviating symptoms, reducing fluid retention, and enhancing cardiac contractility. These goals have been achieved through a combination of pharmacological therapies such as angiotensin-converting enzyme inhibitors, beta-blockers, and diuretics, often complemented by device-based interventions like implantable cardioverter-defibrillators and cardiac resynchronization therapy. However, despite these advances, the relentless progression of heart failure remains a significant clinical challenge. Neurohormonal activation, cardiac fibrosis, and cellular remodeling are just a few of the intricate processes contributing to the disease's progression. In recent years, researchers and clinicians have embarked on a quest to identify novel therapeutic approaches that address these underlying mechanisms. One such avenue of exploration involves the revolutionary field of gene therapy, with promising gene-editing techniques, such as CRISPR-Cas9, offering potential routes for correcting genetic mutations that contribute to heart failure. Additionally, regenerative medicine approaches, including stem cell therapy and tissue engineering, hold significant promise for repairing damaged cardiac tissue and restoring function. Furthermore, precision medicine initiatives have gained traction, aiming to tailor heart failure therapies to individual patient profiles, taking into account genetics, biomarkers, and comorbidities. Integrating artificial intelligence and machine learning in heart failure management has also enabled the development of predictive models for early intervention, risk stratification, and personalized treatment recommendations. This narrative review navigates the intricate landscape of emerging therapies for heart failure, emphasizing their potential to revolutionize the field by targeting the disease's fundamental mechanisms. By exploring these innovative approaches, we aspire to provide a comprehensive perspective on the evolving paradigm of heart failuremanagement, fostering a hopeful outlook for patients and clinicians alike.

Balancing evidence-based care with patient-centered individualized care

Weak evidence, when manifested in clinical guidelines, can translate into biased vascular care. In vascular surgery, we have few randomized controlled trials with appropriate representation of females and persons of color, so generalizability of trial results can be problematic. Physicians are required to balance evidenced-based care (which is only as good as the underlying evidence) with personalized treatment recommendations that are often based on demographics, social circumstances, and/or existing therapeutic relationships. Biases, whether implicit or explicit, have an oversized effect on treatment decisions, and patient outcomes. In this commentary, we propose three principles to strengthen the vascular surgery evidence foundation and patient-centered decision-making going forward: (1) generating evidence designed for individualized care, (2) constructing clinical guidelines that are context specific and complexity aware, and (3) strengthening the training and support for surgeons to deliver patient-centered individualized care.

The Power of AI-Assisted Diagnosis

The rapid advancements in artificial intelligence (AI) have unleashed a wave of transformative technologies, and one area that has witnessed significant progress is AI-assisted diagnosis in healthcare. With the ability to analyze vast amounts of medical data, learn from patterns, and make accurate predictions, AI systems hold immense potential to revolutionize the diagnostic process, enabling earlier detection, improved accuracy, and personalized treatment recommendations. This review aims to explore the impact of AI in healthcare, specifically focusing on its role in assisting physicians with diagnosis, highlighting the benefits, challenges, and ethical considerations associated with the integration of AI systems into clinical practice. Through the utilization of AI's capabilities, the enhancement of patient outcomes, optimization of resource allocation, and the reshaping of medical professionals' approaches to diagnosis and treatment can be achieved.