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1. Composite quantitative knee structure metrics predict the development of accelerated knee osteoarthritis: data from the osteoarthritis initiative

   https://doi.org/10.1186/s12891-020-03338-7  

   Harkey, Matthew S.; Davis, Julie E.; Price, Lori Lyn; Ward, Robert J.; MacKay, James W.; Eaton, Charles B.; Lo, Grace H.; Barbe, Mary F.; Zhang, Ming; Pang, Jincheng; et al (December 2020, BMC Musculoskeletal Disorders)

   Abstract Background We aimed to determine if composite structural measures of knee osteoarthritis (KOA) progression on magnetic resonance (MR) imaging can predict the radiographic onset of accelerated knee osteoarthritis. Methods We used data from a nested case-control study among participants from the Osteoarthritis Initiative without radiographic KOA at baseline. Participants were separated into three groups based on radiographic disease progression over 4 years: 1) accelerated (Kellgren-Lawrence grades [KL] 0/1 to 3/4), 2) typical (increase in KL, excluding accelerated osteoarthritis), or 3) no KOA (no change in KL). We assessed tibiofemoral cartilage damage (four regions: medial/lateral tibia/femur), bone marrow lesion (BML) volume (four regions: medial/lateral tibia/femur), and whole knee effusion-synovitis volume on 3 T MR images with semi-automated programs. We calculated two MR-based composite scores. Cumulative damage was the sum of standardized cartilage damage. Disease activity was the sum of standardized volumes of effusion-synovitis and BMLs. We focused on annual images from 2 years before to 2 years after radiographic onset (or a matched time for those without knee osteoarthritis). To determine between group differences in the composite metrics at all time points, we used generalized linear mixed models with group (3 levels) and time (up to 5 levels). For our prognostic analysis, we used multinomial logistic regression models to determine if one-year worsening in each composite metric change associated with future accelerated knee osteoarthritis (odds ratios [OR] based on units of 1 standard deviation of change). Results Prior to disease onset, the accelerated KOA group had greater average disease activity compared to the typical and no KOA groups and this persisted up to 2 years after disease onset. During a pre-radiographic disease period, the odds of developing accelerated KOA were greater in people with worsening disease activity [versus typical KOA OR (95% confidence interval [CI]): 1.58 (1.08 to 2.33); versus no KOA: 2.39 (1.55 to 3.71)] or cumulative damage [versus typical KOA: 1.69 (1.14 to 2.51); versus no KOA: 2.11 (1.41 to 3.16)]. Conclusions MR-based disease activity and cumulative damage metrics may be prognostic markers to help identify people at risk for accelerated onset and progression of knee osteoarthritis. 

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2. A data denoising approach to optimize functional clustering of single cell RNA-sequencing data

   https://doi.org/10.1109/BIBM49941.2020.9313483  

   Wan, Changlin; Jia, Dongya; Zhao, Yue; Chang, Wennan; Cao, Sha; Wang, Xiao; Zhang, Chi (December 2020, Proceedings - 2020 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020)

   null (Ed.)

   Single cell RNA-sequencing (scRNA-seq) technology enables comprehensive transcriptomic profiling of thousands of cells with distinct phenotypic and physiological states in a complex tissue. Substantial efforts have been made to characterize single cells of distinct identities from scRNA-seq data, including various cell clustering techniques. While existing approaches can handle single cells in terms of different cell (sub)types at a high resolution, identification of the functional variability within the same cell type remains unsolved. In addition, there is a lack of robust method to handle the inter-subject variation that often brings severe confounding effects for the functional clustering of single cells. In this study, we developed a novel data denoising and cell clustering approach, namely CIBS, to provide biologically explainable functional classification for scRNA-seq data. CIBS is based on a systems biology model of transcriptional regulation that assumes a multi-modality distribution of the cells’ activation status, and it utilizes a Boolean matrix factorization approach on the discretized expression status to robustly derive functional modules. CIBS is empowered by a novel fast Boolean Matrix Factorization method, namely PFAST, to increase the computational feasibility on large scale scRNA-seq data. Application of CIBS on two scRNA-seq datasets collected from cancer tumor micro-environment successfully identified subgroups of cancer cells with distinct expression patterns of epithelial-mesenchymal transition and extracellular matrix marker genes, which was not revealed by the existing cell clustering analysis tools. The identified cell groups were significantly associated with the clinically confirmed lymph-node invasion and metastasis events across different patients. Index Terms—Cell clustering analysis, Data denoising, Boolean matrix factorization, Cancer microenvirionment, Metastasis. 

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3. Novel Preoperative Risk Stratification Using Digital Phenotyping Applying a Scalable Machine-Learning Approach

   https://doi.org/10.1213/ANE.0000000000006753  

   Laferrière-Langlois, Pascal; Imrie, Fergus; Geraldo, Marc-Andre; Wingert, Theodora; Lahrichi, Nadia; van der Schaar, Mihaela; Cannesson, Maxime (December 2023, Anesthesia & Analgesia)

   BACKGROUND:Classification of perioperative risk is important for patient care, resource allocation, and guiding shared decision-making. Using discriminative features from the electronic health record (EHR), machine-learning algorithms can create digital phenotypes among heterogenous populations, representing distinct patient subpopulations grouped by shared characteristics, from which we can personalize care, anticipate clinical care trajectories, and explore therapies. We hypothesized that digital phenotypes in preoperative settings are associated with postoperative adverse events including in-hospital and 30-day mortality, 30-day surgical redo, intensive care unit (ICU) admission, and hospital length of stay (LOS). METHODS:We identified all laminectomies, colectomies, and thoracic surgeries performed over a 9-year period from a large hospital system. Seventy-seven readily extractable preoperative features were first selected from clinical consensus, including demographics, medical history, and lab results. Three surgery-specific datasets were built and split into derivation and validation cohorts using chronological occurrence. Consensusk-means clustering was performed independently on each derivation cohort, from which phenotypes’ characteristics were explored. Cluster assignments were used to train a random forest model to assign patient phenotypes in validation cohorts. We reconducted descriptive analyses on validation cohorts to confirm the similarity of patient characteristics with derivation cohorts, and quantified the association of each phenotype with postoperative adverse events by using the area under receiver operating characteristic curve (AUROC). We compared our approach to American Society of Anesthesiologists (ASA) alone and investigated a combination of our phenotypes with the ASA score. RESULTS:A total of 7251 patients met inclusion criteria, of which 2770 were held out in a validation dataset based on chronological occurrence. Using segmentation metrics and clinical consensus, 3 distinct phenotypes were created for each surgery. The main features used for segmentation included urgency of the procedure, preoperative LOS, age, and comorbidities. The most relevant characteristics varied for each of the 3 surgeries. Low-risk phenotype alpha was the most common (2039 of 2770, 74%), while high-risk phenotype gamma was the rarest (302 of 2770, 11%). Adverse outcomes progressively increased from phenotypes alpha to gamma, including 30-day mortality (0.3%, 2.1%, and 6.0%, respectively), in-hospital mortality (0.2%, 2.3%, and 7.3%), and prolonged hospital LOS (3.4%, 22.1%, and 25.8%). When combined with the ASA score, digital phenotypes achieved higher AUROC than the ASA score alone (hospital mortality: 0.91 vs 0.84; prolonged hospitalization: 0.80 vs 0.71). CONCLUSIONS:For 3 frequently performed surgeries, we identified 3 digital phenotypes. The typical profiles of each phenotype were described and could be used to anticipate adverse postoperative events. 

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4. Development and application of a novel tumor habitat analysis technique based on dynamical modeling

   https://doi.org/10.1002/mp.18032  

   Stevens, Jack B; Je, Jihyeon; Riley, Breylon A; Mowery, Yvonne M; Brizel, David M; Liu, Jian‐Guo; Wang, Chunhao; Lafata, Kyle J (September 2025, Medical Physics)

   Abstract BackgroundOropharyngeal cancer (OPC) exhibits varying responses to chemoradiation therapy, making treatment outcome prediction challenging. Traditional imaging‐based methods often fail to capture the spatial heterogeneity within tumors, which influences treatment resistance and disease progression. Advances in modeling techniques allow for more nuanced analysis of this heterogeneity, identifying distinct tumor regions, or habitats, that drive patient outcomes. PurposeTo interrogate the association between treatment‐induced changes in spatial heterogeneity and chemoradiation resistance of oropharyngeal cancer (OPC) based on a novel tumor habitat analysis. MethodsA mathematical model was used to estimate tumor time dynamics of patients with OPC based on the applied analysis of partial differential equations. The position and momentum of each voxel was propagated according to Fokker‐Planck dynamics, that is, a common model in statistical mechanics. The boundary conditions of the Fokker‐Planck equation were solved based on pre‐ and intra‐treatment (i.e., after 2 weeks of therapy)¹⁸F‐FDG‐PET SUV images of patients (n = 56) undergoing definitive (chemo)radiation for OPC as part of a previously conducted prospective clinical trial. Tumor‐specific time dynamics, measured based on the solution of the Fokker‐Planck equation, were generated for each patient. Tumor habitats (i.e., non‐overlapping subregions of the primary tumor) were identified by measuring vector similarity in voxel‐level time dynamics through a fuzzy c‐means clustering algorithm. The robustness of our habitat construction method was quantified using a mean silhouette metric to measure intra‐habitat variability. Fifty‐four habitat‐specific radiomic texture features were extracted from pre‐treatment SUV images and normalized by habitat volume. Univariate Kaplan‐Meier analyses were implemented as a feature selection method, where statistically significant features (p < 0.05, log‐rank) were used to construct a multivariate Cox proportional‐hazards model. Parameters from the resulting Cox model were then used to construct a risk score for each patient, based on habitat‐specific radiomic expression. The patient cohort was stratified by median risk score value and association with recurrence‐free survival (RFS) was evaluated via log‐rank tests. ResultsDynamic tumor habitat analysis partitioned the gross disease of each patient into three spatial subregions. Voxels within each habitat suggested differential response rates in different compartments of the tumor. The minimum mean silhouette value was 0.57 and maximum mean silhouette value was 0.8, where values above 0.7 indicated strong intra‐habitat consistency and values between 0.5 and 0.7 indicated reasonable intra‐habitat consistency. Nine radiomic texture features (three GLRLM, two GLCOM, and three GLSZM) and SUVmax were found to be prognostically significant and were used to build the multivariate Cox model. The resulting risk score was associated with RFS (p = 0.032). By contrast, potential confounding factors (primary tumor volume and mean SUV) were not significantly associated with RFS (p = 0.286 andp = 0.231, respectively). ConclusionWe interrogated spatial heterogeneity of oropharyngeal tumors through the application of a novel algorithm to identify spatial habitats on SUV images. Our habitat construction technique was shown to be robust and habitat‐specific feature spaces revealed distinct underlying radiomic expression patterns. Radiomic features were extracted from dynamic habitats and used to build a risk score which demonstrated prognostic value. 

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5. Biclustering reveals potential knee OA phenotypes in exploratory analyses: Data from the Osteoarthritis Initiative

   https://doi.org/10.1371/journal.pone.0266964  

   Nelson, Amanda E.; Keefe, Thomas H.; Schwartz, Todd A.; Callahan, Leigh F.; Loeser, Richard F.; Golightly, Yvonne M.; Arbeeva, Liubov; Marron, J. S. (May 2022, PLOS ONE)

   Shaharudin, Shazlin (Ed.)

   Objective To apply biclustering, a methodology originally developed for analysis of gene expression data, to simultaneously cluster observations and clinical features to explore candidate phenotypes of knee osteoarthritis (KOA) for the first time. Methods Data from the baseline Osteoarthritis Initiative (OAI) visit were cleaned, transformed, and standardized as indicated (leaving 6461 knees with 86 features). Biclustering produced submatrices of the overall data matrix, representing similar observations across a subset of variables. Statistical validation was determined using the novel SigClust procedure. After identifying biclusters, relationships with key outcome measures were assessed, including progression of radiographic KOA, total knee arthroplasty, loss of joint space width, and worsening Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) scores, over 96 months of follow-up. Results The final analytic set included 6461 knees from 3330 individuals (mean age 61 years, mean body mass index 28 kg/m 2 , 57% women and 86% White). We identified 6 mutually exclusive biclusters characterized by different feature profiles at baseline, particularly related to symptoms and function. Biclusters represented overall better (#1), similar (#2, 3, 6), and poorer (#4, 5) prognosis compared to the overall cohort of knees, respectively. In general, knees in biclusters #4 and 5 had more structural progression (based on Kellgren-Lawrence grade, total knee arthroplasty, and loss of joint space width) but tended to have an improvement in WOMAC pain scores over time. In contrast, knees in bicluster #1 had less incident and progressive KOA, fewer total knee arthroplasties, less loss of joint space width, and stable pain scores compared with the overall cohort. Significance We identified six biclusters within the baseline OAI dataset which have varying relationships with key outcomes in KOA. Such biclusters represent potential phenotypes within the larger cohort and may suggest subgroups at greater or lesser risk of progression over time. 

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