Comparative effect of Tai Chi and aerobic exercise on cognitive function in advanced lung cancer survivors with perceived cognitive impairment: a three-arm randomized controlled trial with mediation analysis.

PURPOSE: Cancer-related cognitive impairment is prevalent in metastatic lung cancer survivors. This study aimed to compare the effectiveness of aerobic exercise and Tai Chi on perceived cognitive function and the mediating role of psychoneurological symptoms with perceived cognitive impairment. METHODS: In a subgroup of a parent randomized clinical trial, participants who reported cognitive impairment underwent a 16-week aerobic exercise (n = 49), Tai Chi (n = 48), and control (n = 54) groups. Measures included perceived cognitive function and psychoneurological symptoms (sleep disturbance, fatigue, anxiety, and depression) assessed at baseline (T0), 16-week (T1), and 1 year (T2). RESULTS: Participants in Tai Chi showed significant improvements compared to aerobic exercise and control groups in perceived cognitive function at T1 (AE: between-group difference, 6.52; P < 0.001; CG: 8.34; P < 0.001) and T2 (AE: between-group difference, 3.55; P = 0.05; CG: 5.94; P < 0.001). Sleep disturbance, fatigue, anxiety, and depression at month 12 explained 24%, 31%, 32%, and 24% of the effect of the intervention on cognitive function at month 12, respectively. Only anxiety at month 4 explained 23% of the intervention effect at month 12. CONCLUSIONS: Tai Chi demonstrated beneficial effects on cognitive function in advanced lung cancer survivors with perceived cognitive impairment. Improvement in cognitive function was mediated by reducing sleep disturbance, fatigue, anxiety, and depression, highlighting the importance of addressing these symptoms in future interventions to improve cognitive function, with anxiety playing a significant role at an earlier stage. IMPLICATIONS FOR CANCER SURVIVORS: Tai Chi is a potentially safe complementary therapeutic option for managing cognitive impairment in this vulnerable population. TRIAL REGISTRATION: ClinicalTrials.gov identifier: NCT04119778; retrospectively registered on 8 October 2019.

Tai Chi and Aerobic Exercise on Cancer-Related Dyspnea in Advanced Lung Cancer Patients: A Randomized Clinical Trial.

CONTEXT: Dyspnea, a prevalent and debilitating symptom in patients with advanced lung cancer, negatively affects symptom burden and prognosis. Physical activity has emerged as a promising non-pharmacological intervention for managing dyspnea. OBJECTIVES: This study compared the effectiveness of two widely-recognized physical activity modalities, namely Tai Chi (TC) and aerobic exercise (AE) for treating dyspnea in patients with advanced lung cancer. METHODS: Patients with advanced lung cancer (n=226) were randomized into TC, AE, or control groups. There was no baseline dyspnea requirement for patients. The AE group received two 60-minute supervised sessions and home-based exercises per month, the TC group received 60-minute sessions twice weekly, and the control group received exercise guidelines for 16 weeks. The primary outcome (sleep quality) of the study has been previously reported. In this secondary analysis, we focused on dyspnea outcomes, including overall and lung cancer-specific dyspnea. Assessments were conducted at baseline (T0), 16 weeks (T1), and one year (T2). RESULTS: Compared to the control group, TC significantly improved overall dyspnea at T1 (between-group difference, -8.69; P=0.03) and T2 (between-group difference, -11.45; P=0.01), but not AE. Both AE (between-group difference, -11.04; P=0.01) and TC (between-group difference, -14.19; P<0.001) significantly alleviated lung cancer-specific dyspnea at T2 compared with the control group. CONCLUSION: Both TC and AE alleviate dyspnea severity in patients with advanced lung cancer, and continuous exercise can yield substantial improvements. Due to its multi-component nature, Tai Chi has a greater effect on dyspnea.

Glioblastoma and radiotherapy: A multicenter AI study for Survival Predictions from MRI (GRASP study).

BACKGROUND: The aim was to predict survival of glioblastoma at 8 months after radiotherapy (a period allowing for completing a typical course of adjuvant temozolomide), by applying deep learning to the first brain MRI after radiotherapy completion. METHODS: Retrospective and prospective data were collected from 206 consecutive glioblastoma, isocitrate dehydrogenase -wildtype patients diagnosed between March 2014 and February 2022 across 11 UK centers. Models were trained on 158 retrospective patients from 3 centers. Holdout test sets were retrospective (n = 19; internal validation), and prospective (n = 29; external validation from 8 distinct centers). Neural network branches for T2-weighted and contrast-enhanced T1-weighted inputs were concatenated to predict survival. A nonimaging branch (demographics/MGMT/treatment data) was also combined with the imaging model. We investigated the influence of individual MR sequences; nonimaging features; and weighted dense blocks pretrained for abnormality detection. RESULTS: The imaging model outperformed the nonimaging model in all test sets (area under the receiver-operating characteristic curve, AUC P = .038) and performed similarly to a combined imaging/nonimaging model (P > .05). Imaging, nonimaging, and combined models applied to amalgamated test sets gave AUCs of 0.93, 0.79, and 0.91. Initializing the imaging model with pretrained weights from 10 000s of brain MRIs improved performance considerably (amalgamated test sets without pretraining 0.64; P = .003). CONCLUSIONS: A deep learning model using MRI images after radiotherapy reliably and accurately determined survival of glioblastoma. The model serves as a prognostic biomarker identifying patients who will not survive beyond a typical course of adjuvant temozolomide, thereby stratifying patients into those who might require early second-line or clinical trial treatment.

Tumour mutational burden is overestimated by target cancer gene panels.

Background: Tumour mutational burden (TMB) has emerged as a predictive marker for responsiveness to immune checkpoint inhibitors (ICI) in multiple tumour types. It can be calculated from somatic mutations detected from whole exome or targeted panel sequencing data. As mutations are unevenly distributed across the cancer genome, the clinical implications from TMB calculated using different genomic regions are not clear. Methods: Pan-cancer data of 10,179 samples were collected from The Cancer Genome Atlas cohort and 6,831 cancer patients with either ICI or non-ICI treatment outcomes were derived from published papers. TMB was calculated as the count of non-synonymous mutations and normalised by the size of genomic regions. Dirichlet method, linear regression and Poisson calibration models are used to unify TMB from different gene panels. Results: We found that panels based on cancer genes usually overestimate TMB compared to whole exome, potentially leading to misclassification of patients to receive ICI. The overestimation is caused by positive selection for mutations in cancer genes and cannot be completely addressed by the removal of mutational hotspots. We compared different approaches to address this discrepancy and developed a generalised statistical model capable of interconverting TMB derived from whole exome and different panel sequencing data, enabling TMB correction for patient stratification for ICI treatment. We show that in a cohort of lung cancer patients treated with ICI, when using a TMB cutoff of 10 mut/Mb, our corrected TMB outperforms the original panel-based TMB. Conclusion: Cancer gene-based panels usually overestimate TMB, and these findings will be valuable for unifying TMB calculations across cancer gene panels in clinical practice.