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1. A Pilot Study on Patient-specific Computational Forecasting of Prostate Cancer Growth during Active Surveillance Using an Imaging-informed Biomechanistic Model

   https://doi.org/10.1158/2767-9764.CRC-23-0449  

   Lorenzo, Guillermo ; Heiselman, Jon S. ; Liss, Michael A. ; Miga, Michael I. ; Gomez, Hector ; Yankeelov, Thomas E. ; Reali, Alessandro ; Hughes, Thomas J. R. ( March 2024 , Cancer Research Communications)

   Active surveillance (AS) is a suitable management option for newly diagnosed prostate cancer, which usually presents low to intermediate clinical risk. Patients enrolled in AS have their tumor monitored via longitudinal multiparametric MRI (mpMRI), PSA tests, and biopsies. Hence, treatment is prescribed when these tests identify progression to higher-risk prostate cancer. However, current AS protocols rely on detecting tumor progression through direct observation according to population-based monitoring strategies. This approach limits the design of patient-specific AS plans and may delay the detection of tumor progression. Here, we present a pilot study to address these issues by leveraging personalized computational predictions of prostate cancer growth. Our forecasts are obtained with a spatiotemporal biomechanistic model informed by patient-specific longitudinal mpMRI data (T2-weighted MRI and apparent diffusion coefficient maps from diffusion-weighted MRI). Our results show that our technology can represent and forecast the global tumor burden for individual patients, achieving concordance correlation coefficients from 0.93 to 0.99 across our cohort (n = 7). In addition, we identify a model-based biomarker of higher-risk prostate cancer: the mean proliferation activity of the tumor (P = 0.041). Using logistic regression, we construct a prostate cancer risk classifier based on this biomarker that achieves an area under the ROC curve of 0.83. We further show that coupling our tumor forecasts with this prostate cancer risk classifier enables the early identification of prostate cancer progression to higher-risk disease by more than 1 year. Thus, we posit that our predictive technology constitutes a promising clinical decision-making tool to design personalized AS plans for patients with prostate cancer.

   Personalization of a biomechanistic model of prostate cancer with mpMRI data enables the prediction of tumor progression, thereby showing promise to guide clinical decision-making during AS for each individual patient.

    

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2. Reenvisioning Patient Education with Smart Hospital Patient Rooms

   https://doi.org/10.1145/3631419  

   Dawson, Joshua ; Phanich, K. Jens ; Wiese, Jason ( December 2023 , Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies)

   Smart hospital patient rooms incorporate various smart devices to allow digital control of the entertainment --- such as TV and soundbar --- and the environment --- including lights, blinds, and thermostat. This technology can benefit patients by providing a more accessible, engaging, and personalized approach to their care. Many patients arrive at a rehabilitation hospital because they suffered a life-changing event such as a spinal cord injury or stroke. It can be challenging for patients to learn to cope with the changed abilities that are the new norm in their lives. This study explores ways smart patient rooms can support rehabilitation education to prepare patients for life outside the hospital's care. We conducted 20 contextual inquiries and four interviews with rehabilitation educators as they performed education sessions with patients and informal caregivers. Using thematic analysis, our findings offer insights into how smart patient rooms could revolutionize patient education by fostering better engagement with educational content, reducing interruptions during sessions, providing more agile education content management, and customizing therapy elements for each patient's unique needs. Lastly, we discuss design opportunities for future smart patient room implementations for a better educational experience in any healthcare context. 

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3. Developing behavior-based diabetic retinopathy screening guidelines

   Lee, T ; Dorali, P ; Shahmoradi, Z ; Weng, C. Y. ( June 2021 , Investigative ophthalmology visual science)

   null (Ed.)

   Purpose : Personalized screening guidelines can be an effective strategy to prevent diabetic retinopathy (DR)-related vision loss. However, these strategies typically do not capture behavior-based factors such as a patient’s compliance or cost preferences. This study develops a mathematical model to identify screening policies that capture both DR progression and behavioral factors to provide personalized recommendations. Methods : A partially observable Markov decision process model (POMDP) is developed to provide personalized screening recommendations. For each patient, the model estimates the patient’s probability of having a sight-threatening diabetic eye disorder (STDED) yearly via Bayesian inference based on natural history, screening results, and compliance behavior. The model then determines a personalized, threshold-based recommendation for each patient annually--either no action (NA), teleretinal imaging (TRI), or clinical screening (CS)--based on the patient’s current probability of having STDED as well as patient-specific preference between cost saving ($) and QALY gain. The framework is applied to a hypothetical cohort of 40-year-old African American male patients. Results : For the base population with TRI and CS compliance rates of 65% and 55% and equal preference for cost and QALY, NA is identified as an optimal recommendation when the patient’s probability of having STDED is less than 0.72%, TRI when the probability is [0.72%, 2.09%], and CS when the probability is above 2.09%. Simulated against annual clinical screening, the model-based policy finds an average decrease of 7.07% in cost/QALY (95% CI; 6.93-7.23%) and 15.05% in blindness prevalence over a patient’s lifetime (95% CI; 14.88-15.23%). For patients with equal preference for cost and QALY, the model identifies 6 different types of threshold-based policies (See Fig 1). For patients with strong preference for QALY gain, CS-only policies had an increase in prevalence by a factor of 19.2 (see Fig 2). Conclusions : The POMDP model is highly flexible and responsive in incorporating behavioral factors when providing personalized screening recommendations. As a decision support tool, providers can use this modeling framework to provide unique, catered recommendations. 

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4. Development and initial clinical testing of a multiplexed circulating tumor cell assay in patients with clear cell renal cell carcinoma

   https://doi.org/10.1002/1878-0261.12931  

   Bade, Rory M. ; Schehr, Jennifer L. ; Emamekhoo, Hamid ; Gibbs, Benjamin K. ; Rodems, Tamara S. ; Mannino, Matthew C. ; Desotelle, Joshua A. ; Heninger, Erika ; Stahlfeld, Charlotte N. ; Sperger, Jamie M. ; et al ( March 2021 , Molecular Oncology)

   Although therapeutic options for patients with advanced renal cell carcinoma (RCC) have increased in the past decade, no biomarkers are yet available for patient stratification or evaluation of therapy resistance. Given the dynamic and heterogeneous nature of clear cell RCC (ccRCC), tumor biopsies provide limited clinical utility, but liquid biopsies could overcome these limitations. Prior liquid biopsy approaches have lacked clinically relevant detection rates for patients with ccRCC. This study employed ccRCC‐specific markers, CAIX and CAXII, to identify circulating tumor cells (CTC) from patients with metastatic ccRCC. Distinct subtypes of ccRCC CTCs were evaluated for PD‐L1 and HLA‐I expression and correlated with patient response to therapy. CTC enumeration and expression of PD‐L1 and HLA‐I correlated with disease progression and treatment response, respectively. Longitudinal evaluation of a subset of patients demonstrated potential for CTC enumeration to serve as a pharmacodynamic biomarker. Further evaluation of phenotypic heterogeneity among CTCs is needed to better understand the clinical utility of this new biomarker.

    

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5. From Medical Imaging to Radiomics: Role of Data Science for Advancing Precision Health

   https://doi.org/10.3390/jpm10010015  

   Capobianco, Enrico ; Dominietto, Marco ( March 2020 , Journal of Personalized Medicine)

   Treating disease according to precision health requires the individualization of therapeutic solutions as a cardinal step that is part of a process that typically depends on multiple factors. The starting point is the collection and assembly of data over time to assess the patient’s health status and monitor response to therapy. Radiomics is a very important component of this process. Its main goal is implementing a protocol to quantify the image informative contents by first mining and then extracting the most representative features. Further analysis aims to detect potential disease phenotypes through signs and marks of heterogeneity. As multimodal images hinge on various data sources, and these can be integrated with treatment plans and follow-up information, radiomics is naturally centered on dynamically monitoring disease progression and/or the health trajectory of patients. However, radiomics creates critical needs too. A concise list includes: (a) successful harmonization of intra/inter-modality radiomic measurements to facilitate the association with other data domains (genetic, clinical, lifestyle aspects, etc.); (b) ability of data science to revise model strategies and analytics tools to tackle multiple data types and structures (electronic medical records, personal histories, hospitalization data, genomic from various specimens, imaging, etc.) and to offer data-agnostic solutions for patient outcomes prediction; (c) and model validation with independent datasets to ensure generalization of results, clinical value of new risk stratifications, and support to clinical decisions for highly individualized patient management. 

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