Background: Many patients with cancer experience depression and anxiety, and an associated decrease in quality of life (QOL) during radiation therapy (RT). The main objective of the study was to determine the benefits of psychosocial interventions for cancer patients who received RT. Methods: Patients with cancer (n = 178) who agreed to participate in the study were randomized to the intervention arm (n = 89) or the control arm (n = 89). Patients in the intervention group received psychosocial care during RT, whereas the control group received RT only. The benefits of the intervention were evaluated using the Zung Self-rating Depression Scale (SDS) to measure depression, the Self-rating Anxiety Scale (SAS) to assess anxiety, and the European Organization for Research and Treatment of Cancer Quality of Life Questionnaire-Core 30 (EORTC QLQ-C30) to survey health-related QOL. The association between intervention and survival was also assessed. Results: Patients randomly assigned to the intervention arm showed significant improvements on symptoms of depression (p < 0.05) and anxiety (p < 0.05), health-related QOL (p < 0.05) (i.e. better global health status, and physical and emotional functioning, and less insomnia) when compared with controls. In the subset analysis, female patients, those that received high dose irradiation, and those that underwent adjuvant chemotherapy could benefit more from psychosocial intervention. There was no difference between the two groups in disease-free survival (DFS) (2-year DFS 79.8% in the intervention arm and 76.4% in the control arm; p = 0.527) and overall survival (OS) (2-year OS 83.1% in the intervention arm and 84.3% in the control arm; p = 0.925) Conclusions: Psychosocial intervention is a cost-effective approach that can improve a patient’s mood and QOL both during and after RT. However, the intervention was not found to reduce the risk of cancer recurrence and death. Trial registration: ChiCTR-TRC-12002438

Fault detection and root cause identification are both important tasks in Multivariate Statistical Process Control (MSPC) for improving process and product quality. Most traditional control charts, including Hotelling's T² chart and the Multivariate Exponential Weighted Moving Average (MEWMA) chart, separate the two tasks into independent and successive procedures by signaling the existence of process faults followed by auxiliary methods to locate root causes. This paper proposes an integrated procedure, a Variable-Selection-based MEWMA (VS-MEWMA) chart, for multivariate process monitoring and fault diagnosis by utilizing dimensionality reduction techniques. The VS-MEWMA chart first locates potentially out-of-control variables via variable selection and then deploys such information in the monitoring statistics with the reduction in dimensionality providing increased sensitivity to out-of-control conditions. When a signal is given, the algorithm also identifies the suspected variables for further root cause diagnosis. Both numerical simulations and real examples are presented to illustrate the performance of the proposed chart, as well as design guidelines.

Increasing evidence has revealed the roles of long noncoding RNAs (lncRNAs) as tumor biomarkers. Here, we introduce an immune-associated nine-lncRNA signature for predicting distant metastasis in locoregionally advanced nasopharyngeal carcinoma (LA-NPC). The nine lncRNAs are identified through microarray profiling, followed by RT–qPCR validation and selection using a machine learning method in the training cohort (n = 177). This nine-lncRNA signature classifies patients into high and low risk groups, which have significantly different distant metastasis-free survival. Validations in the Guangzhou internal (n = 177) and Guilin external (n = 150) cohorts yield similar results, confirming that the signature is an independent risk factor for distant metastasis and outperforms anatomy-based metrics in identifying patients with high metastatic risk. Integrative analyses show that this nine-lncRNA signature correlates with immune activity and lymphocyte infiltration, which is validated by digital pathology. Our results suggest that the immune-associated nine-lncRNA signature can serve as a promising biomarker for metastasis prediction in LA-NPC.

To explore the prognostic value of radiomics-based and digital pathology-based imaging biomarkers from macroscopic magnetic resonance imaging (MRI) and microscopic whole-slide images for patients with nasopharyngeal carcinoma (NPC). We recruited 220 NPC patients and divided them into training (n = 132), internal test (n = 44), and external test (n = 44) cohorts. The primary endpoint was failure-free survival (FFS). Radiomic features were extracted from pretreatment MRI and selected and integrated into a radiomic signature. The histopathological signature was extracted from whole-slide images of biopsy specimens using an end-to-end deep-learning method. Incorporating two signatures and independent clinical factors, a multi-scale nomogram was constructed. We also tested the correlation between the key imaging features and genetic alternations in an independent cohort of 16 patients (biological test cohort). Both radiomic and histopathologic signatures presented significant associations with treatment failure in the three cohorts (C-index: 0.689–0.779, all p < 0.050). The multi-scale nomogram showed a consistent significant improvement for predicting treatment failure compared with the clinical model in the training (C-index: 0.817 versus 0.730, p < 0.050), internal test (C-index: 0.828 versus 0.602, p < 0.050) and external test (C-index: 0.834 versus 0.679, p < 0.050) cohorts. Furthermore, patients were stratified successfully into two groups with distinguishable prognosis (log-rank p < 0.0010) using our nomogram. We also found that two texture features were related to the genetic alternations of chromatin remodeling pathways in another independent cohort. The multi-scale imaging features showed a complementary value in prognostic prediction and may improve individualized treatment in NPC.