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

The longitudinal association between resilience and sleep quality in breast cancer

PURPOSE

To estimate the longitudinal association between resilience and sleep quality in patients with newly diagnosed breast cancer within the first 6 months.

METHOD

Between July 2023 and September 2023, 155 newly diagnosed BC patients were recruited to participate in the Be Resilience to Breast Cancer program (Abbreviated as BRBC). They completed the 10-item Connor-Davidson Resilience scale and Pittsburgh Sleep Quality Index Scale. The following three timepoints were set to collect the data, including 1 month after initial diagnosis (T0), 3 months (T1), and 6 months (T2). Data were analyzed using Cross-lagged Panel Model (CLPM), and Parallel Latent Growth Model (PLGM).

RESULTS

Excluded questionnaires with a large number of missing items and finally 125 patients were included, with the response rate of 83.3%. CLPM indicated that resilience at T1 predicted PSQI at T2 (r = -0.168, P < 0.001), and PSQI at T1 predicted resilience at T2 (r = -0.112, P< 0.001). PLGM demonstrated that changes in resilience was significantly associated with changes in PSQI (r = -0.874, P< 0.001).

CONCLUSION

A longitudinal association between resilience and sleep quality was confirmed in patients with newly diagnosed breast cancer. Resilience was a protective factor in the development of sleep quality.

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.