Brain Connectomics Improve the Prediction of High-Risk Depression Profiles in the First Year following Breast Cancer Diagnosis

The interplay between sleep and cancer-related fatigue in breast cancer: A casual and computer-simulated network analysis

Objective

Sleep problems and cancer-related fatigue are common symptoms in women for breast cancer, during and after treatment. Identifying key intervention targets for this symptom cluster may improve patient reported outcomes. This study aimed to explore the relationship between sleep and cancer-related fatigue to identify optimal intervention targets.

Methods

In the "Be Resilient to Breast Cancer" program, self report data were collected on sleep and cancer-related fatigue the Multidimensional Fatigue Symptom Inventory-Short Form and the Pittsburgh Sleep Quality Index. Gaussian network analysis was employed to identify central symptoms and nodes, while a Bayesian network explored their causal relationships. Computer-simulated interventions were used to identify core symptoms as targets for intervention.

Results

General fatigue (Str ​= ​0.95, Bet ​= ​7, Clo ​= ​0.007) was considered the node with the strongest centrality. The daytime dysfunction item on the Pittsburgh sleep quality index had the strongest bridge strength. Core symptoms were identified as targets for intervention by the computer-simulated analysis.

Conclusions

Sleep quality is the strongest predictor of cancer-related fatigue from a casual networking perspective. Sleep latency and daytime dysfunction should be targeted to break the chained symptom interaction between sleep and cancer-related fatigue.

A multi-center study of symptoms in patients with esophageal cancer postoperatively: A networking analysis

PURPOSE

This study aimed to explore symptom clusters and the inter-relationship of symptoms in esophageal cancer (EC) patients during the first week after surgery.

METHODS

A cross-sectional survey across multiple centers was carried out using the EORTCQLQ-OES18. Patients with esophageal cancer within a week post-surgery were recruited from the "Be Resilient to Cancer" project in Guangdong, Hunan, and Sichuan provinces between January and September 2024. Exploratory factor analysis with a priori algorithm was used to identify symptom clusters and network analysis was employed to recognize the relationship among core and bridge symptoms.

RESULTS

The sample consisted of 501 patients with esophageal cancer, who were predominantly male (83%), married (93%) and 57% were ≥60 years. Three symptom clusters were identified: "reflux-pain", "eating", and "dysphagia-dry mouth". Acid or bile coming up (support = 40.1%, confidence = 1, lift = 2.53), eating difficulties (support = 40.1%, confidence = 0.990, lift = 2.408) and dry mouth (support = 42.9%, confidence = 0.808, lift = 1.298) were marked as sentinel symptoms for these clusters, respectively. Acid indigestion or heartburn was identified as the core symptom (EI = 1.142 without covariates and EI = 1.153 with covariates), and dry mouth served as the bridge symptoms (EI = 0.63 and EI = 0.656).

CONCLUSIONS

Addressing acid or bile coming up, eating difficulties, dry mouth are imperative to help relief symptom burden at the cluster level. Furthermore, targeting acid indigestion and heartburn are crucial to break the chains among different symptom clusters.

Navigating specific targets of breast cancer symptoms: An innovative computer-simulated intervention analysis

PURPOSE

To pinpoint optimal interventions by dissecting the complex symptom interactions, encompassing both their static and temporal dimensions.

METHODS

The study incorporated a cross-sectional survey utilizing the MD Anderson Symptom Inventory. Participants with breast cancer undergoing chemotherapy were recruited from the "Be Resilient to Breast Cancer" from April 2023 to June 2024. Static symptom interrelationships were elucidated using undirected and Bayesian network models, complemented by an exploration of their dynamic counterparts through computer-simulated interventions.

RESULTS

The study included 602 patients with breast cancer. Both undirected networks and computer-simulated interventions concurred on the symptoms of distress and fatigue as optimal alleviation targets. The Bayesian network and computer-simulated interventions both emphasized "shortness of breath" as preventive care. Notably, Distress appeared to be the most effective target for interventions, and compared to fatigue (decreasing score = 1.84-2.20, decreasing prevalence = 14.2-16.7%). Conversely, disturbed sleep, despite its high position in Bayesian network, had no propelling effects on increasing the network's overall symptom activity levels (increasing score<1).

CONCLUSIONS

Computer-simulated intervention integrating with traditional network analysis can improve intervention precision and efficacy by prioritizing individual symptom impacts, both statically and dynamically.

A Longitudinal Correlational Study of Psychological Resilience, Depression Disorder, and Brain Functional-Structural Hybrid Connectome in Breast Cancer

Purposes: To evaluate the association between psychological resilience, depression disorder (DD), and brain functional-structural hybrid connectome in patients with breast cancer before treatment (T0) and at 1 year. Methods: Between February 2017 and October 2019, 172 patients were longitudinally enrolled from a multicenter trial named as Be Resilient to Breast Cancer (BRBC) and completed resting-state functional magnetic resonance imaging (rs-fMRI) and diffusion tensor imaging (DTI) before the T0. Data-driven multivoxel pattern analysis (MVPA) and correlational tractography (CT) were performed to identify distinct functional-structural hybrid connectome. DD was diagnosed by psychiatry physicians according to Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5). Psychological resilience was collected by Resilience Scale Specific to Cancer (RS-SC) and tested as the mediation variable between hybrid connectome and DD. Results: Of the total sample of 172, 14.5% (N = 25) were diagnosed with DD. High psychological resilience was associated with a lower risk of DD (hazard ratio (HR) = 0.37, 95% confidence interval (CI), 0.17-0.82, p=0.0368). Frontal pole right (88.0%) in rs-fMRI and arcuate fasciculus_L (75.2%) in DTI were identified as main significant brain areas. Psychological resilience accounted for 10.01%-12.14% of direct effect between brain functional-structural hybrid connectome and 1-year DD. Conclusion: Psychological resilience predicts DD at 1 year and mediates the association between brain functional-structural hybrid connectome and DD at 1 year in patients diagnosed with breast cancer. Trial Registration: ClinicalTrials.gov identifier: NCT03026374.

Associations Between Brain Structural Connectivity and 1-Year Demoralization in Breast Cancer: A Longitudinal Diffusion Tensor Imaging Study

Purposes: This study aims to explore the association between brain structural connectivity and 1-year demoralization in patients with newly diagnosed breast cancer. Methods: Patients were enrolled from a multicenter longitudinal program named as Be Resilient to Breast Cancer (BRBC) between 2017 and 2019. Brain structural connectivity was assessed with diffusion tensor imaging (DTI) at baseline and the demoralization scale II collected self-report data at baseline and 1 year later. A data-driven correlational tractography was performed to recognize significant neural pathways associated with the group membership (increased vs. nonincreased demoralization). The incremental prediction values of Quantitative Anisotropy (QA) extracted from the significant white matter tracts against the group membership were evaluated. Results: 21.2% (N = 31) reported increased 1-year demoralization. Inferior fronto-occipital fasciculus (IFOF) was associated with 1-year demoralization in breast cancer. The incremental prediction values of QAs in net reclassification improvement (NRI) and integrated discrimination improvement (IDI) ranged from 8.11% to 46.89% and 9.12% to 23.95%, respectively, over the conventional tumor-nodal metatasis (TNM) staging model. Conclusion: Anisotropy in IFOF is a potential prediction neuromarker to 1-year demoralization in patients with newly diagnosed breast cancer. Trial Registration: ClinicalTrials.gov identifier: NCT03026374.