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Predicting Patient Reported Outcomes of Cognitive Function Using Connectome-Based Predictive Modeling in Breast Cancer.

ABSTRACT: Being able to predict who will likely experience cancer related cognitive impairment (CRCI) could enhance patient care and potentially reduce economic and human costs associated with this adverse event. We aimed to determine if post-treatment patient reported CRCI could also be predicted from baseline resting state fMRI in patients with breast cancer. 76 newly diagnosed patients (n = 42 planned for chemotherapy; n = 34 not planned for chemotherapy) and 50 healthy female controls were assessed at 3 times points [T1 (prior to treatment); T2 (1 month post chemotherapy); T3 (1 year after T2)], and at yoked intervals for controls. Data collection included self-reported executive dysfunction, memory function, and psychological distress and resting state fMRI data converted to connectome matrices for each participant. Statistical analyses included linear mixed modeling, independent t tests, and connectome-based predictive modeling (CPM). Executive dysfunction increased over time in the chemotherapy group and was stable in the other two groups (p < 0.001). Memory function decreased over time in both patient groups compared to controls (p < 0.001). CPM models successfully predicted executive dysfunction and memory function scores (r > 0.31, p < 0.002). Support vector regression with a radial basis function (SVR RBF) showed the highest performance for executive dysfunction and memory function (r = 0.68; r = 0.44, p's < 0.001). Baseline neuroimaging may be useful for predicting patient reported cognitive outcomes which could assist in identifying patients in need of surveillance and/or early intervention for treatment-related cognitive effects.

SUBMITTER: Henneghan AM

PROVIDER: S-EPMC8006573 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:IntroductionTrait mindfulness refers to one's disposition or tendency to pay attention to their experiences in the present moment, in a non-judgmental and accepting way. Trait mindfulness has been robustly associated with positive mental health outcomes, but its neural underpinnings are poorly understood. Prior resting-state fMRI studies have associated trait mindfulness with within- and between-network connectivity of the default-mode (DMN), fronto-parietal (FPN), and salience networks. However, it is unclear how generalizable the findings are, how they relate to different components of trait mindfulness, and how other networks and brain areas may be involved.MethodsTo address these gaps, we conducted the largest resting-state fMRI study of trait mindfulness to-date, consisting of a pre-registered connectome predictive modeling analysis in 367 adults across three samples collected at different sites.ResultsIn the model-training dataset, we did not find connections that predicted overall trait mindfulness, but we identified neural models of two mindfulness subscales, Acting with Awareness and Non-judging. Models included both positive networks (sets of pairwise connections that positively predicted mindfulness with increasing connectivity) and negative networks, which showed the inverse relationship. The Acting with Awareness and Non-judging positive network models showed distinct network representations involving FPN and DMN, respectively. The negative network models, which overlapped significantly across subscales, involved connections across the whole brain with prominent involvement of somatomotor, visual and DMN networks. Only the negative networks generalized to predict subscale scores out-of-sample, and not across both test datasets. Predictions from both models were also negatively correlated with predictions from a well-established mind-wandering connectome model.ConclusionsWe present preliminary neural evidence for a generalizable connectivity models of trait mindfulness based on specific affective and cognitive facets. However, the incomplete generalization of the models across all sites and scanners, limited stability of the models, as well as the substantial overlap between the models, underscores the difficulty of finding robust brain markers of mindfulness facets.

Project description:PurposePatient-reported outcomes (PROs) have been widely used to measure breast cancer (BC) treatment outcomes. However, evidence is still limited on using routinely PROs to personalize treatment decision-making, including or not chemotherapy, targeted therapy, and radiotherapy. Using patient baseline PRO scores, we aimed to use PROs before treatment initiation to predict improvement or decline in health-related quality of life (HRQoL) due to treatment that they receive.MethodsIn two French cancer sites, women with non-metastatic BC completed the EORTC QLQ-C30 and QLQ-BR23 and BREAST-Q questionnaires to assess their PROs at baseline and again at 6 months. The outcome measured was post-operative change in PROs with minimal important difference for QLQ-C30 domains. We performed multivariate ordinal logistic regression to estimate the incremental probability of post-operative PRO improvements and deteriorations depending upon treatment options and baseline HRQoL.ResultsOne hundred twenty-seven women completed questionnaires. Chemotherapy had significant negative impacts on Global health status (GHS) and on physical and social functioning. Chemotherapy and radiotherapy increased patient fatigue scores after adjusting for clinical factors (p< 0.01 and p< 0.05, respectively). The incremental probability of GHS deteriorations for chemotherapy was +0.3, +0.5, and +0.34 for patients with baseline GHS scores of 40, 70, and 100, respectively. This showed that different pre-treatment PROs might predict differential effects of chemotherapy on women change in HRQoL.ConclusionPatients with different baseline PRO scores may experience dissimilar impacts from BC treatments on post-operative PROs in terms of improvements and deteriorations. Oncologists might decide to adapt the treatment option based on a given level of the negative impact. Future studies should concentrate on incorporating this information into routine clinical decision-making strategies to optimize the treatment benefit for patients.

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Predicting Patient Reported Outcomes of Cognitive Function Using Connectome-Based Predictive Modeling in Breast Cancer.

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