A Web-Based Peer Support Network to Help Care Partners of People With Serious Illness: Co-Design Study.

BACKGROUND: Care partners of people with serious illness experience significant challenges and unmet needs during the patient's treatment period and after their death. Learning from others with shared experiences can be valuable, but opportunities are not consistently available. OBJECTIVE: This study aims to design and prototype a regional, facilitated, and web-based peer support network to help active and bereaved care partners of persons with serious illness be better prepared to cope with the surprises that arise during serious illness and in bereavement. METHODS: An 18-member co-design team included active care partners and those in bereavement, people who had experienced serious illness, regional health care and support partners, and clinicians. It was guided by facilitators and peer network subject-matter experts. We conducted design exercises to identify the functions and specifications of a peer support network. Co-design members independently prioritized network specifications, which were incorporated into an early iteration of the web-based network. RESULTS: The team prioritized two functions: (1) connecting care partners to information and (2) facilitating emotional support. The design process generated 24 potential network specifications to support these functions. The highest priorities included providing a supportive and respectful community; connecting people to trusted resources; reducing barriers to asking for help; and providing frequently asked questions and responses. The network platform had to be simple and intuitive, provide technical support for users, protect member privacy, provide publicly available information and a private discussion forum, and be easily accessible. It was feasible to enroll members in the ConnectShareCare web-based network over a 3-month period. CONCLUSIONS: A co-design process supported the identification of critical features of a peer support network for care partners of people with serious illnesses in a rural setting, as well as initial testing and use. Further testing is underway to assess the long-term viability and impact of the network.

Organizational Intent, Organizational Structures, and Reviewer Mental Models Influence Mortality Review Processes.

Objective: To identify the factors that influence the mortality review process at health systems, including how mortality review is conducted, cases are adjudicated, and results are used. Methods: We conducted a qualitative analysis of the mortality review processes of 6 US health systems from February 1, 2021 to June 31, 2021. The data sources included individual and small-group semi-structured interviews with mortality review team members and a content analysis of site artifacts (eg, guiding principles, chart abstraction forms, review workflows, and clinical pathways developed from past mortality reviews). We analyzed each site's mortality review process, goals and incentives for mortality review, historical and evolving aspects of mortality review, personnel involved, and post-review use of findings. Results: Across the 6 systems, we interviewed a total of 24 mortality review experts and analyzed 26 site documents. We identified 3 thematic factors that influence mortality review processes: organizational intent, organizational structures for mortality review, and the mental models of individuals involved in the review process. Two subthemes emerged within organizational intent: (1) identifying preventable deaths to lower (clinical or financial) risk and (2) using death cases to guide system improvement. Sites varied in governance and decision rights concerning mortality review and adjudication, with 2 subthemes within organizational structures: (1) centralized-hierarchical and (2) decentralized or multidisciplinary. The analysis of mental models of participating reviewers revealed 2 themes: (1) confirmation of preventability and (2) identification of patterns or "signals." Conclusion: Understanding the factors that influence mortality review allows health systems to better leverage mortality review for institutional improvement and to develop training that builds shared mental models to enhance the review process.

Clinically informed machine learning elucidates the shape of hospice racial disparities within hospitals.

Racial disparities in hospice care are well documented for patients with cancer, but the existence, direction, and extent of disparity findings are contradictory across the literature. Current methods to identify racial disparities aggregate data to produce single-value quality measures that exclude important patient quality elements and, consequently, lack information to identify actionable equity improvement insights. Our goal was to develop an explainable machine learning approach that elucidates healthcare disparities and provides more actionable quality improvement information. We infused clinical information with engineering systems modeling and data science to develop a time-by-utilization profile per patient group at each hospital using US Medicare hospice utilization data for a cohort of patients with advanced (poor-prognosis) cancer that died April-December 2016. We calculated the difference between group profiles for people of color and white people to identify racial disparity signatures. Using machine learning, we clustered racial disparity signatures across hospitals and compared these clusters to classic quality measures and hospital characteristics. With 45,125 patients across 362 hospitals, we identified 7 clusters; 4 clusters (n = 190 hospitals) showed more hospice utilization by people of color than white people, 2 clusters (n = 106) showed more hospice utilization by white people than people of color, and 1 cluster (n = 66) showed no difference. Within-hospital racial disparity behaviors cannot be predicted from quality measures, showing how the true shape of disparities can be distorted through the lens of quality measures. This approach elucidates the shape of hospice racial disparities algorithmically from the same data used to calculate quality measures.

From measures to action: can integrating quality measures provide system-wide insights for quality improvement decision making?

BACKGROUND: Quality improvement decision makers are left to develop an understanding of quality within their healthcare system from a deluge of narrowly focused measures that reflect existing fragmentation in care and lack a clear method for triggering improvement. A one-to-one metric-to-improvement strategy is intractable and leads to unintended consequences. Although composite measures have been used and their limitations noted in the literature, what remains unknown is 'Can integrating multiple quality measures provide a systemic understanding of care quality across a healthcare system?' METHODS: We devised a four-part data-driven analytic strategy to determine if consistent insights exist about the differential utilisation of end-of-life care using up to eight publicly available end-of-life cancer care quality measures across National Cancer Institute and National Comprehensive Cancer Network-designated cancer hospitals/centres. We performed 92 experiments that included 28 correlation analyses, 4 principal component analyses, 6 parallel coordinate analyses with agglomerative hierarchical clustering across hospitals and 54 parallel coordinate analyses with agglomerative hierarchical clustering within each hospital. RESULTS: Across 54 centres, integrating quality measures provided no consistent insights across different integration analyses. In other words, we could not integrate quality measures to describe how the underlying quality constructs of interest-intensive care unit (ICU) visits, emergency department (ED) visits, palliative care use, lack of hospice, recent hospice, use of life-sustaining therapy, chemotherapy and advance care planning-are used relative to each other across patients. Quality measure calculations lack interconnection information to construct a story that provides insights about where, when or what care is provided to which patients. And yet, we posit and discuss why administrative claims data-used to calculate quality measures-do contain such interconnection information. CONCLUSION: While integrating quality measures does not provide systemic information, new systemic mathematical constructs designed to convey interconnection information can be developed from the same administrative claims data to support quality improvement decision making.

What is in the palliative care 'syringe'? A systems perspective.

The diffusion of palliative care has been rapid, yet uncertainty remains regarding palliative care's "active ingredients." The National Consensus Project Guidelines for Quality Palliative Care identified eight domains of palliative care. Despite these identified domains, when pressed to describe the specific maneuvers used in clinical encounters, palliative care providers acknowledge that "it's complex." The field of systems has been used to explain complexity across many different types of systems. Specifically, engineering systems develop a representation of a system that helps manage complexity to help humans better understand the system. Our goal was to develop a system model of what palliative care providers do such that the elements of the model can be described concretely and sequentially, aggregated to describe the high-level domains currently described by palliative care, and connected to the complexity described by providers and the literature. Our study design combined methodological elements from both qualitative research and systems engineering modeling. The model drew on participant observation and debriefing semi-structured interviews with interdisciplinary palliative care team members by a systems engineer. The setting was an interdisciplinary palliative care service in a US rural academic medical center. In the developed system model, we identified 59 functions provided to patients, families, non-palliative care provider(s), and palliative care provider(s). The high-level functions related to measurement, decision-making, and treatment address up to 8 states of an individual, including an overall holistic state, physical state, psychological state, spiritual state, cultural state, personal environment state, and clinical environment state. In contrast to previously described expert consensus domain-based descriptions of palliative care, this model more directly connects palliative care provider functions to emergent behaviors that may explain system-level mechanisms of action for palliative care. Thus, a systems modeling approach provides insights into the challenges surrounding the recurring question of what is in the palliative care "syringe."

Development of dynamic health care delivery heatmaps for end-of-life cancer care: a cohort study.

OBJECTIVE: Measures of variation in end-of-life (EOL) care intensity across hospitals are typically summarised using unidimensional measures. These measures do not capture the full dimensionality of complex clinical care trajectories over time that are needed to inform quality improvement efforts. The objective is to develop a novel visual map of EOL care trajectories that illustrates multidimensional utilisation over time. SETTING: United States' National Cancer Institute or National Comprehensive Cancer Network (NCI/NCCN)-designated hospitals. PARTICIPANTS: We identified Medicare claims for fee-for-service beneficiaries with poor prognosis cancers who died between April and December 2016 and received the preponderance of treatment in the last 6 months of life at an NCI/NCCN-designated hospital. DESIGN: For each beneficiary, we transformed each Medicare claim into two elements to generate a two-dimensional individual-level heatmap. On the y-axis, each claim was classified into a categorical description of the service delivered by a healthcare resource. On the x-axis, the date for each claim was converted into the day number prior to death it occurred on. We then summed up individual-level heatmaps of patients attributed to each hospital to generate two-dimensional hospital-level heatmaps. We used four case studies to illustrate the feasibility of interpreting these heatmaps and to shed light on how they might be used to guide value-based, quality improvement initiatives. RESULTS: We identified nine distinct EOL care delivery patterns from hospital-level heatmaps based on signal intensity and patterns for inpatient, outpatient and home-based hospice services. We illustrate that in most cases, heatmaps illustrating patterns of multidimensional healthcare utilisation over time provide more information about care trajectories and highlight more heterogeneity than current unidimensional measures. CONCLUSIONS: This study illustrates the feasibility of representing multidimensional EOL utilisation over time as a heatmap. These heatmaps may provide potentially actionable insights into hospital-level care delivery patterns, and the approach may generalise to other serious illness populations.

Healthcare Quality Improvement: The Need for a Macro-Systems Approach.

While the structure of healthcare systems evolved out of the need to address acute conditions, the function of healthcare systems evolved to primarily address chronic conditions. The healthcare delivery system organically developed to respond to "one-off" acute illness or injury. Subsequently, healthcare delivery systems grew into legacy systems that evolved into complex systems over time. Healthcare delivery for acute conditions tends to utilize a specific part or form of the healthcare delivery system. In contrast, healthcare delivery for chronic conditions forces patients to seek care over time between different places or healthcare entities. Because of the self-contained structural organization of these healthcare delivery systems, they were not designed to provide coordinated, integrated, and longitudinal care over time and place. Consequently, today's complex legacy healthcare delivery system requires significant improvement in the quality of care delivered to patients, especially those with chronic conditions. As a complex and legacy system, the most appropriate approach to improve the quality of delivered care is through a re-design quality improvement process, rather than a new system design process. In this paper, we describe the conceptual framework for quality improvement (QI) and the current micro and macro level approaches to quality improvement. We applied the current quality improvement approaches to the QI conceptual framework. We identified the limitations in current quality improvement processes in complex healthcare systems at the macro-level, pointing to the need for macro-systems approaches to healthcare quality improvement.

Development of a Visualization Tool for Healthcare Decision-Making using Electronic Medical Records: A Systems Approach to Viewing a Patient Record.

Healthcare delivery systems are widely accepted as socio-technical systems. Unlike other socio-technical systems, healthcare systems leave very little decision-making to technical automation and control. Instead, the healthcare delivery system relies on human healthcare resources for decision-making. Human decision-making is imperative to the clinical delivery of care to patients and to the operational processes that support care delivery, quality improvement, and other organizational management activities. For these clinical and operational activities, human resources make healthcare decisions using healthcare data typically housed in electronic medical records (EMRs). Unfortunately, EMR systems were first designed with the functional capability to store data, and, second to a lesser degree, to retrieve data. The literature recognizes the need to improve the retrieval of information from EMR systems. More specifically, there remains the need to directly view a patient's holistic health and healthcare trajectory. At this time, decision-makers are left to mentally build this holistic picture in their mind by sequentially clicking through many sections of the EMR. Therefore, in this paper, we develop a visualization tool to organize and present an individual's health and healthcare trajectory by describing a patient record holistically from a system architecture perspective. This approach is based on a previously developed system model for healthcare delivery and individual health outcomes.

The power of specialty palliative care: moving towards a systems perspective.

Three palliative care clinical trials were presented at the 2020 American Society for Clinical Oncology Annual Meeting. The heterogeneity in populations, models of care, study design, and assessment of clinical outcomes across these three studies show the broad opportunities for research into interventions for palliative care. In this Viewpoint, we summarise the characteristics of these studies, discuss their novel features and lingering questions, and offer a suggestion for further expanding the focus of clinical trials for delivery of palliative care in the future. We particularly argue that the propensity to characterise palliative care as if it was a clinical or biomedical intervention hampers the design and evaluation of complex clinical interventions that influence clinicians, systems for health-care delivery, individual patients, and their families.

End-of-life quality metrics among medicare decedents at minority-serving cancer centers: A retrospective study.

BACKGROUND: We calculated the performance of National Cancer Institute (NCI)/National Comprehensive Cancer Network (NCCN) cancer centers' end-of-life (EOL) quality metrics among minority and white decedents to explore center-attributable sources of EOL disparities. METHODS: We conducted a retrospective cohort study of Medicare beneficiaries with poor-prognosis cancers who died between April 1, 2016 and December 31, 2016 and had any inpatient services in the last 6 months of life. We attributed patients' EOL treatment to the center at which they received the preponderance of EOL inpatient services and calculated eight risk-adjusted metrics of EOL quality (hospice admission ≤3 days before death; chemotherapy last 14 days of life; ≥2 emergency department (ED) visits; intensive care unit (ICU) admission; or life-sustaining treatment last 30 days; hospice referral; palliative care; advance care planning last 6 months). We compared performance between patients across and within centers. RESULTS: Among 126,434 patients, 10,119 received treatment at one of 54 NCI/NCCN centers. In aggregate, performance was worse among minorities for ED visits (10.3% vs 7.4%, P < .01), ICU admissions (32.9% vs 30.4%, P = .03), no hospice referral (39.5% vs 37.0%, P = .03), and life-sustaining treatment (19.4% vs 16.2%, P < .01). Despite high within-center correlation for minority and white metrics (0.61-0.79; P < .01), five metrics demonstrated worse performance as the concentration of minorities increased: ED visits (P = .03), ICU admission (P < .01), no hospice referral (P < .01), and life-sustaining treatments (P < .01). CONCLUSION: EOL quality metrics vary across NCI/NCCN centers. Within center, care was similar for minority and white patients. Minority-serving centers had worse performance on many metrics.

Structuring and Visualizing Healthcare Claims Data Using Systems Architecture Methodology.

Healthcare delivery systems around the world are in crisis. The need to improve health outcomes while decreasing healthcare costs have led to an imminent call to action to transform the healthcare delivery system. While Bioinformatics and Biomedical Engineering have primarily focused on biological level data and biomedical technology, there is clear evidence of the importance of the delivery of care on patient outcomes. Classic singular decomposition approaches from reductionist science are not capable of explaining complex systems. Approaches and methods from systems science and systems engineering are utilized to structure healthcare delivery system data. Specifically, systems architecture is used to develop a multi-scale and multi-dimensional characterization of the healthcare delivery system, defined here as the Healthcare Delivery System Knowledge Base. This paper is the first to contribute a new method of structuring and visualizing a multi-dimensional and multi-scale healthcare delivery system using systems architecture in order to better understand healthcare delivery.

Gender Differences in Real-Home Sleep of Young and Older Couples.

STUDY OBJECTIVES: To understand gender differences in sleep quality, architecture and duration of young healthy couples in comparison to older couples in their natural sleep environment. DESIGN: Sleep was monitored in a naturalistic setting using a headband sleep monitoring device over a period of two weeks for young couples and home polysomnography for the older couples. PARTICIPANTS: Ten heterosexual young couples (male mean age: 28.2±1.0[SD] years/female mean age: 26.8±0.9 years) and 14 older couples (male mean age: 59.3±9.6 years/female mean age: 58.8±9.1 years). MEASUREMENTS AND RESULTS: In the young couples, total sleep time (395±66 vs. 367±54 min., p<0.05), sleep efficiency (97.0±3.0 vs. 91.1±7.9, p<0.001), and % REM (31.1±4.8 vs. 23.6±5.5, p<0.001) in males was higher than in females. In contrast, % light sleep (51.7±7.1 vs. 59.7±6.7, p<0.001) and number of arousals (2.9±1.9 vs. 5.3±1.9, p<0.001) were lower. These differences persisted after controlling for evening mood and various evening pre-sleep activities. In the older couples, there were no differences between genders. In addition, children in the household adversely impacted sleep. CONCLUSIONS: In couples recorded in the home, young males slept longer and had better sleep quality than young females. This difference appears to dissipate with age. In-home assessment of couples can aid in understanding of gender differences in sleep and how they are affected by age and social environment.

Technologically sensed social exposure related to slow-wave sleep in healthy adults.

OBJECTIVE: The aim of this study is to understand the relationship between automatically captured social exposure and detailed sleep parameters of healthy young adults. METHODS: This study was conducted in a real-world setting in a graduate-student housing community at a US university. Social exposure was measured using Bluetooth proximity sensing technology in mobile devices. Sleep was monitored in a naturalistic setting using a headband sleep monitoring device over a period of 2 weeks. The analysis included a total of 11 subjects (6 males and 5 females) aged 24-35 (149 subject nights). RESULTS: Slow-wave sleep showed a significant positive correlation (Spearman's rho = 0.51, p < 0.0001) with social exposure, whereas light non-REM (N1 + N2) sleep and wake time were found to be negatively correlated (rho = -0.25, p < 0.01; rho = -0.21, p < 0.01, respectively). The correlation of median slow-wave sleep with median social exposure per subject showed a strong positive significance (rho = 0.88, p < 0.001). On average, within subjects, following day's social exposure was higher when (slow-wave NREM + REM) percentage was high (Wilcoxon sign-ranked test, p < 0.05). CONCLUSIONS: Subjects with higher social exposure spent more time in slow-wave sleep. Following day's social exposure was found to be positively affected by previous night's (slow-wave NREM + REM) percentage. This suggests that sleep affects following day's social exposure and not vice versa. Capturing an individual's dynamic social behavior and sleep from their natural environment can provide novel insights into these relationships.

Change in BMI accurately predicted by social exposure to acquaintances.

Research has mostly focused on obesity and not on processes of BMI change more generally, although these may be key factors that lead to obesity. Studies have suggested that obesity is affected by social ties. However these studies used survey based data collection techniques that may be biased toward select only close friends and relatives. In this study, mobile phone sensing techniques were used to routinely capture social interaction data in an undergraduate dorm. By automating the capture of social interaction data, the limitations of self-reported social exposure data are avoided. This study attempts to understand and develop a model that best describes the change in BMI using social interaction data. We evaluated a cohort of 42 college students in a co-located university dorm, automatically captured via mobile phones and survey based health-related information. We determined the most predictive variables for change in BMI using the least absolute shrinkage and selection operator (LASSO) method. The selected variables, with gender, healthy diet category, and ability to manage stress, were used to build multiple linear regression models that estimate the effect of exposure and individual factors on change in BMI. We identified the best model using Akaike Information Criterion (AIC) and R(2). This study found a model that explains 68% (p<0.0001) of the variation in change in BMI. The model combined social interaction data, especially from acquaintances, and personal health-related information to explain change in BMI. This is the first study taking into account both interactions with different levels of social interaction and personal health-related information. Social interactions with acquaintances accounted for more than half the variation in change in BMI. This suggests the importance of not only individual health information but also the significance of social interactions with people we are exposed to, even people we may not consider as close friends.

Automatically captured sociability and sleep quality in healthy adults.

Sleep and social interactions have been shown to have a considerable public health impact. However, little is known about how these affect each other in healthy individuals. This research is first to propose the exploration of the bidirectional relationship between technologically sensed sleep quality and quantified face-to-face social interactions. We detail a pilot study designed to study the relationship of sociability and sleep quality, both measured and perceived, of healthy adults. We capture real-world social interactions and measure sleep in a naturalistic setting using wireless sensing technologies. We find that it may not be the device-defined sleep quality (ZQ score) but our perceived sleep quality which affects our following day's sociability. Further, we also find perceived sleep quality is more strongly correlated to normalized ZQ scores than the actual scores. These intriguing insights raise several questions on how an individual's social life could be affected by sleep and indicate the usefulness of mobile sensing technologies in understanding public health phenomena.

MRI apparent diffusion coefficient reflects histopathologic subtype, axonal disruption, and tumor fraction in diffuse-type grade II gliomas.

The apparent diffusion coefficient (ADC) determined from MR diffusion tensor imaging (DTI) has shown promise for distinguishing World Health Organization grade II astrocytoma (AS) from the more prognostically favorable grade II oligodendroglioma (OD). Since mixed oligoastrocytomas (OAs) with codeletions in chromosomes 1p and 19q confer prognoses similar to those of OD, we questioned whether a previously determined ADC-based criterion for distinguishing OD and AS would hold on an independent set of gliomas that included OA with codeleted or intact 1p/19q chromosomes. We also questioned whether the ADC is associated with the tumor microstructure. ADC colormaps generated from presurgical DTI scans were used to guide the collection of biopsies from each tumor. The median normalized ADC distinguished OD from AS with 91% sensitivity and 92% specificity. 1p/19q codeleted OAs were always classified as ODs, while 1p/19q intact OAs were always classified as ASs. There were positive associations between the ADC and both the SMI-31 score of axonal disruption and the fraction of tumor cells in the biopsies. The ADC of OD and 1p/19q codeleted OA was more associated with tumor fraction, while the ADC of AS and 1p/19q intact OA was more associated with SMI-31 score. We conclude that our previously determined threshold median ADC can distinguish grade II OD and AS on a new patient cohort and that the distinctions extend to OA with codeleted and intact 1p/19q chromosomes. Further, the ADC in grade II gliomas is associated with the fraction of tumor cells and degree of axonal disruption in tumor subregions.

Sleep, mood and sociability in a healthy population.

Sleep and mood problems have a considerable public health impact with serious societal and significant financial effects. In this work, we study the relationship between these factors in the everyday life of healthy young adults. More importantly, we look at these factors from a social perspective, studying the impact that couples have on each other and the role that face-to-face interactions play. We find that there is a significant bi-directional relationship between mood and sleep. More interestingly, we find that the spouse's sleep and mood may have an effect on the subject's mood and sleep. Further, we find that subjects whose sleep is significantly correlated with mood tend to be more sociable. Finally, we observe that less sociable subjects show poor mood more often than their more sociable contemporaries. These novel insights, especially those involving sociability, measured from quantified face-to-face interaction data gathered through smartphones, open up several avenues to enhance public health research through the use of latest wireless sensing technologies.

Evaluation of diffusion parameters as early biomarkers of disease progression in glioblastoma multiforme.

The purpose of this study was to evaluate diffusion parameters at pre-, mid-, and post-radiation therapy (RT) in contrast-enhancing and nonenhancing lesions of postsurgical glioblastoma multiforme patients treated with the standard of care RT concurrently with temozolomide (TMZ) followed by adjuvant TMZ and an antiangiogenic drug. The diffusion parameters explored include baseline and short-term changes in apparent diffusion coefficient, fractional anisotropy, and eigenvalues. These diffusion parameters were examined as early markers for disease progression by relating them to clinical outcome of 6-month progression-free survival. The results indicated that changes from mid- to post-RT were significantly different between patients who progressed within 6 months vs those who were free of progression for 6 months after initiation of therapy. The study also showed that the changes in diffusion parameters from the mid- to post-RT scan may be more significant than those from pre- to mid-RT and pre- to post-RT. This is important because the mid-RT scan is currently not performed as part of the standard clinical care.

Multiparametric characterization of grade 2 glioma subtypes using magnetic resonance spectroscopic, perfusion, and diffusion imaging.

BACKGROUND AND PURPOSE: The purpose of this study was to derive quantitative parameters from magnetic resonance (MR) spectroscopic, perfusion, and diffusion imaging of grade 2 gliomas according to the World Health Organization and to investigate how these multiple imaging modalities can contribute to evaluating their histologic subtypes and spatial characteristics. MATERIALS AND METHODS: MR spectroscopic, perfusion, and diffusion images from 56 patients with newly diagnosed grade 2 glioma (24 oligodendrogliomas, 18 astrocytomas, and 14 oligoastrocytomas) were retrospectively studied. Metabolite intensities, relative cerebral blood volume (rCBV), and apparent diffusion coefficient (ADC) were statistically evaluated. RESULTS: The 75th percentile rCBV and median ADC were significantly different between oligodendrogliomas and astrocytomas (P < .0001) and between oligodendrogliomas and oligoastrocytomas (P < .001). Logistic regression analysis identified both 75th percentile rCBV and median ADC as significant variables in the differentiation of oligodendrogliomas from astrocytomas and oligoastrocytomas. Group differences in metabolite intensities were not significant, but there was a much larger variation in the volumes and maximum values of metabolic abnormalities for patients with oligodendroglioma compared with the other tumor subtypes. CONCLUSIONS: Perfusion and diffusion imaging provide quantitative MR parameters that can help to differentiate grade 2 oligodendrogliomas from grade 2 astrocytomas and oligoastrocytomas. The large variations in the magnitude and spatial extent of the metabolic lesions between patients and the fact that their values are not correlated with the other imaging parameters indicate that MR spectroscopic imaging may provide complementary information that is helpful in targeting therapy, evaluating residual disease, and assessing response to therapy.

Characterization of low-grade gliomas using RGB color maps derived from ADC histograms.

PURPOSE: To use normalized apparent diffusion coefficient (nADC) histograms from patients with grade II oligodendroglioma (OD) and astrocytoma (AC) to generate RGB color maps that emphasize the differences between normal-appearing white matter (NAWM), oligo-like, and astro-like regions. MATERIALS AND METHODS: NAWM and nonenhancing lesion (NEL) ADC values from 19 ODs and 11 ACs were summed to generate oligo-like (red), NAWM (green), and astro-like (blue) nADC histograms. These nADC histograms were then used to map nADC values to an RGB matrix. RESULTS: Color maps of oligodendroglial tumor regions were generally visualized in pink, while color maps of astrocytic tumor regions showed various shades of blue. This technique was also applied to 23 patients with the more mixed subtype, oligoastrocytoma (OA), which showed a mixture of both blue and pink, which in many cases appeared to bleed into each other and were blotchy. CONCLUSION: This technique allows for the visualization of biologically different regions within the whole tumor mass, which may aid in directing image-guided biopsies. This can be used to ensure that the biopsy is directed to regions that can more accurately define the dominant tumor characteristics.