https://www.paradigmpress.org/crms <p><a href="https://www.paradigmpress.org/crms/about"> <img src="https://www.paradigmpress.org/public/journals/16/journalThumbnail_en_US.jpg" /> </a></p> Paradigm Academic Press Limited en-US Current Research in Medical Sciences 2958-0390 https://www.paradigmpress.org/crms/article/view/2092 <p><strong><em><u>Objective:</u></em></strong> To investigate the effect of symptom management strategies in palliative care for patients undergoing chemotherapy, analyze their impacts on symptom relief, quality of life and treatment adherence, and establish a nurse-led integrated intervention program. <strong><em><u>Methods:</u></em></strong> A total of 154 chemotherapy patients from a tertiary hospital were enrolled. A quantitative multi-method design was used, including questionnaire survey, Spearman correlation analysis and linear regression analysis to evaluate the current status and predictive effect of symptom management. <strong><em><u>Results:</u></em></strong> Symptom management in palliative care was generally effective: pain management (3.65), nausea and vomiting control (3.60), emotional and psychological support (3.62), spiritual care (3.60), fatigue management (3.55). Palliative care-enhanced components significantly improved symptom relief (3.63), quality of life (3.61) and treatment adherence (3.58). Symptom management strategies had a very strong positive correlation with palliative care outcomes (r=0.972, P&lt;0.001) and significantly predicted patient outcomes (R²=0.953, P&lt;0.001). <strong><em><u>Conclusion:</u></em></strong> Symptom management for chemotherapy patients in palliative care is generally effective but remains to be optimized. The quality of symptom management directly determines the effect of palliative care. A nurse-led integrated symptom management program is recommended to strengthen assessment, education, psychological support and spiritual care, so as to further improve patient prognosis and quality of life.</p> Yanqiu Xie Zheng Xie Yexu Qiao Minerva B. De Ala Copyright (c) 2026 2026-05-21 2026-05-21 5 3 1 8 10.63593/CRMS.2026.05.01 https://www.paradigmpress.org/crms/article/view/2093 <p>T1-weighted magnetic resonance imaging (T1WI) sequences are emerging as a powerful tool for the assessment of high-grade serous ovarian cancer (HGSOC), providing unique insights into tumour biology, the composition of the microenvironment, and treatment response. The intrinsic T1 signal changes in HGSOC arise from intrinsic pathophysiological features such as intratumoural haemorrhage, necrosis and increased protein content; these factors shorten T1 relaxation times and are independent of contrast agent administration. Following contrast administration, dynamic T1 changes reflect vascular remodelling and extracellular matrix expansion—hallmark features of aggressive disease—whilst genomic instability (particularly BRCA-associated phenotypes) further modulates T1-visible tissue characteristics. Technological advances have driven the evolution of T1 assessment from qualitative imaging towards quantitative pixel-level T1 mapping and dynamic contrast-enhanced (DCE) modelling, thereby enabling the extraction of pharmacokinetic parameters associated with tumour biology. The integration of synthetic MRI with machine learning algorithms has significantly improved the reproducibility and accuracy of T1 feature quantification. Clinically, pre-treatment T1 metrics show promise in predicting platinum resistance, whilst early post-contrast T1 kinetics during neoadjuvant chemotherapy correlate with treatment response and progression-free survival, often demonstrating superior predictive value compared to CA-125 trends. Compared with diffusion-weighted imaging, FDG-PET/CT and conventional CT, T1-based detection methods exhibit higher specificity in identifying microenvironmental features, can reduce inflammatory false positives, and improve the detection rate of peritoneal lesions by enhancing soft tissue contrast. Practical application requires standardisation of protocols regarding timing, fat suppression and magnetic field strength, whilst consensus must be reached on the definition of regions of interest and clinically relevant thresholds. Despite these advances, challenges remain, including inter-platform variability, interference from iron deposition and ascites, and a lack of prospective, multicentre validation, which hinder its inclusion in current guidelines. Looking ahead, AI-driven radiomics, multi-parametric MRI features, and the role of T1 sequences as dynamic biomarkers for anti-angiogenic therapies will position T1 sequences at the forefront of precision oncology and therapeutic diagnostic strategies for HGSOC.</p> Yuqiao Zhang Copyright (c) 2026 2026-05-21 2026-05-21 5 3 9 20 10.63593/CRMS.2026.05.02