The genomics-based concept of precision medicine began to emerge following the completion of the Human Genome Project. In contrast to evidence-based medicine, precision medicine will allow doctors and scientists to tailor the treatment of different subpopulations of patients who differ in their susceptibility to specific diseases or responsiveness to specific therapies. The current precision medicine model was proposed to precisely classify patients into subgroups sharing a common biological basis of diseases for more effective tailored treatment to achieve improved outcomes. Precision medicine has become a term that symbolizes the new age of medicine. In this review, we examine the history, development, and future perspective of precision medicine. We also discuss the concepts, principles, tools, and applications of precision medicine and related fields. In our view, for precision medicine to work, two essential objectives need to be achieved. First, diseases need to be classified into various subtypes. Second, targeted therapies must be available for each specific disease subtype. Therefore, we focused this review on the progress in meeting these two objectives.

Ischemic stroke poses a significant global health challenge, necessitating ongoing exploration of its pathophysiology and treatment strategies. This comprehensive review integrates various aspects of ischemic stroke research, emphasizing crucial mechanisms, therapeutic approaches, and the role of clinical imaging in disease management. It discusses the multifaceted role of Netrin-1, highlighting its potential in promoting neurovascular repair and mitigating post-stroke neurological decline. It also examines the impact of blood–brain barrier permeability on stroke outcomes and explores alternative therapeutic targets such as statins and sphingosine-1-phosphate signaling. Neurocardiology investigations underscore the contribution of cardiac factors to post-stroke mortality, emphasizing the importance of understanding the brain–heart axis for targeted interventions. Additionally, the review advocates for early reperfusion and neuroprotective agents to counter-time-dependent excitotoxicity and inflammation, aiming to preserve tissue viability. Advanced imaging techniques, including DWI, PI, and MR angiography, are discussed for their role in evaluating ischemic penumbra evolution and guiding therapeutic decisions. By integrating molecular insights with imaging modalities, this interdisciplinary approach enhances our understanding of ischemic stroke and offers promising avenues for future research and clinical interventions to improve patient outcomes.

Pretreatment CT Perfusion (CTP) parameter rCBV < 42% lesion volume has recently been shown to predict 90-day mRS. In this study, we aim to assess the relationship between rCBV < 42% and a radiographic follow-up infarct volume delineated on FLAIR images. In this retrospective evaluation of our prospectively collected database, we included acute stroke patients triaged by multimodal CT imaging, including CT angiography and perfusion imaging, with confirmed anterior circulation large vessel occlusion between 9 January 2017 and 10 January 2023. Follow-up FLAIR imaging was used to determine the final infarct volume. Student t, Mann-Whitney-U, and Chi-Square tests were used to assess differences. Spearman’s rank correlation and linear regression analysis were used to assess associations between rCBV < 42% and follow-up infarct volume on FLAIR. In total, 158 patients (median age: 68 years, 52.5% female) met our inclusion criteria. rCBV < 42% (ρ = 0.56, p < 0.001) significantly correlated with follow-up-FLAIR infarct volume. On multivariable linear regression analysis, rCBV < 42% lesion volume (beta = 0.60, p < 0.001), ASPECTS (beta = −0.214, p < 0.01), mTICI (beta = −0.277, p < 0.001), and diabetes (beta = 0.16, p < 0.05) were independently associated with follow-up infarct volume. The rCBV < 42% lesion volume is independently associated with FLAIR follow-up infarct volume.

Up to 30% of ischemic stroke cases are due to large vessel occlusion (LVO), causing significant morbidity. Studies have shown that the collateral circulation of patients with acute ischemic stroke (AIS) secondary to LVO can predict their clinical and radiological outcomes. The aim of this study is to identify baseline patient characteristics that can help predict the collateral status of these patients for improved triage. In this IRB approved retrospective study, consecutive patients presenting with AIS secondary to anterior circulation LVO were identified between September 2019 and August 2021. The baseline patient characteristics, laboratory values, imaging features and outcomes were collected using a manual chart review. From the 181 consecutive patients initially reviewed, 54 were confirmed with a clinical diagnosis of AIS and anterior circulation LVO. In patients with poor collateral status, the body mass index (BMI) was found to be significantly lower compared to those with good collateral status (26.4 ± 5.6 vs. 31.7 ± 12.3; p = 0.045). BMI of >35 kg/m² was found to predict the presence of good collateral status. Age was found to be significantly higher (70.5 ± 9.6 vs. 58.9 ± 15.6; p = 0.034) in patients with poor collateral status and M1 strokes associated with older age and BMI.

At present, clinicians are expected to manage a large volume of complex clinical, laboratory, and imaging data, necessitating sophisticated analytic approaches. Machine learning-based models can use this vast amount of data to create forecasting models. We aimed to predict short- and medium-term functional outcomes in acute ischemic stroke (AIS) patients with proximal middle cerebral artery (MCA) occlusions using machine learning models with clinical, laboratory, and quantitative imaging data as inputs. Included were consecutive AIS patients with MCA M1 and proximal M2 occlusions. The XGBoost, LightGBM, CatBoost, and Random Forest were used to predict the outcome. Minimum redundancy maximum relevancy was used for selecting features. The primary outcomes were the National Institutes of Health Stroke Scale (NIHSS) shift and the modified Rankin Score (mRS) at 90 days. The algorithm with the highest area under the receiver operating characteristic curve (AUROC) for predicting the favorable and unfavorable outcome groups at 90 days was LightGBM. Random Forest had the highest AUROC when predicting the favorable and unfavorable groups based on the NIHSS shift. Using clinical, laboratory, and imaging parameters in conjunction with machine learning, we accurately predicted the functional outcome of AIS patients with proximal MCA occlusions.