Advancing Precision Medicine: Algebraic Topology and Differential Geometry in Radiology and Computational Pathology.

Precision medicine aims to provide personalized care based on individual patient characteristics, rather than guideline-directed therapies for groups of diseases or patient demographics. Images-both radiology- and pathology-derived-are a major source of information on presence, type, and status of disease. Exploring the mathematical relationship of pixels in medical imaging ("radiomics") and cellular-scale structures in digital pathology slides ("pathomics") offers powerful tools for extracting both qualitative, and increasingly, quantitative data. These analytical approaches, however, may be significantly enhanced by applying additional methods arising from fields of mathematics such as differential geometry and algebraic topology that remain underexplored in this context. Geometry's strength lies in its ability to provide precise local measurements, such as curvature, that can be crucial for identifying abnormalities at multiple spatial levels. These measurements can augment the quantitative features extracted in conventional radiomics, leading to more nuanced diagnostics. By contrast, topology serves as a robust shape descriptor, capturing essential features such as connected components and holes. The field of topological data analysis was initially founded to explore the shape of data, with functional network connectivity in the brain being a prominent example. Increasingly, its tools are now being used to explore organizational patterns of physical structures in medical images and digitized pathology slides. By leveraging tools from both differential geometry and algebraic topology, researchers and clinicians may be able obtain a more comprehensive, multi-layered understanding of medical images and contribute to precision medicine's armamentarium.

Topological data analysis in medical imaging: current state of the art.

Machine learning, and especially deep learning, is rapidly gaining acceptance and clinical usage in a wide range of image analysis applications and is regarded as providing high performance in detecting anatomical structures and identification and classification of patterns of disease in medical images. However, there are many roadblocks to the widespread implementation of machine learning in clinical image analysis, including differences in data capture leading to different measurements, high dimensionality of imaging and other medical data, and the black-box nature of machine learning, with a lack of insight into relevant features. Techniques such as radiomics have been used in traditional machine learning approaches to model the mathematical relationships between adjacent pixels in an image and provide an explainable framework for clinicians and researchers. Newer paradigms, such as topological data analysis (TDA), have recently been adopted to design and develop innovative image analysis schemes that go beyond the abilities of pixel-to-pixel comparisons. TDA can automatically construct filtrations of topological shapes of image texture through a technique known as persistent homology (PH); these features can then be fed into machine learning models that provide explainable outputs and can distinguish different image classes in a computationally more efficient way, when compared to other currently used methods. The aim of this review is to introduce PH and its variants and to review TDA's recent successes in medical imaging studies.

Using a Machine Learning Algorithm to Predict Online Patient Portal Utilization: A Patient Engagement Study.

Objective: There is a low rate of online patient portal utilization in the U.S. This study aimed to utilize a machine learning approach to predict access to online medical records through a patient portal. Methods: This is a cross-sectional predictive machine learning algorithm-based study of Health Information National Trends datasets (Cycles 1 and 2; 2017-2018 samples). Survey respondents were U.S. adults (≥18 years old). The primary outcome was a binary variable indicating that the patient had or had not accessed online medical records in the previous 12 months. We analyzed a subset of independent variables using k-means clustering with replicate samples. A cross-validated random forest-based algorithm was utilized to select features for a Cycle 1 split training sample. A logistic regression and an evolved decision tree were trained on the rest of the Cycle 1 training sample. The Cycle 1 test sample and Cycle 2 data were used to benchmark algorithm performance. Results: Lack of access to online systems was less of a barrier to online medical records in 2018 (14%) compared to 2017 (26%). Patients accessed medical records to refill medicines and message primary care providers more frequently in 2018 (45%) than in 2017 (25%). Discussion: Privacy concerns, portal knowledge, and conversations between primary care providers and patients predict portal access. Conclusion: Methods described here may be employed to personalize methods of patient engagement during new patient registration.

Personal and cultural identity development in recently immigrated Hispanic adolescents: Links with psychosocial functioning.

OBJECTIVES: This study examined directionality between personal (i.e., coherence and confusion) and cultural identity (i.e., ethnic and U.S.) as well as their additive effects on psychosocial functioning in a sample of recently immigrated Hispanic adolescents. METHOD: The sample consisted of 302 recent (<5 years) immigrant Hispanic adolescents (53% boys; Mage = 14.51 years at baseline; SD = .88 years) from Miami and Los Angeles who participated in a longitudinal study. RESULTS: Results indicated a bidirectional relationship between personal identity coherence and both ethnic and U.S. identity. Ethnic and U.S. affirmation/commitment (A/C) positively and indirectly predicted optimism and negatively predicted rule breaking and aggression through coherence. However, confusion predicted lower self-esteem and optimism and higher depressive symptoms, rule breaking, unprotected sex, and cigarette use. Results further indicated significant site differences. In Los Angeles (but not Miami), ethnic A/C also negatively predicted confusion. CONCLUSION: Given the direct effects of coherence and confusion on nearly every outcome, it may be beneficial for interventions to target personal identity. However, in contexts such as Los Angeles, which has at least some ambivalence toward recently immigrated Hispanic adolescents, it may be more beneficial for interventions to also target cultural identity to reduce confusion and thus promote positive development. (PsycINFO Database Record

The role of acculturation and family functioning in predicting HIV risk behaviors among Hispanic delinquent youth.

The present study examined the relationship between Berry's acculturation typology and HIV risk behaviors and whether family functioning mediated any such effects. A total of 235 high risk Hispanic adolescents were categorized into one of Berry's four acculturation typologies through the use of cut-off scores on measures of Hispanicism and Americanism. Structural equation modeling was used to examine the effects of acculturation typology on HIV risk behaviors and the indirect effects of acculturation typology on HIV risk behaviors through family functioning. Acculturation typology was related to HIV risk behaviors. Family functioning partially mediated the effects of acculturation typology on the HIV risk behavior outcomes. These findings suggest that both Americanism and Hispanicism play an important role in the etiology of HIV risk behaviors among Hispanic youth and that both, along with family functioning, are important to consider when designing preventive interventions for this population.