Rukhshana Dina Rabbani

Metastatic spinal cord compression (MSCC) is a common complication in cancer patients, occurring in 3–5% of diagnosed cases annually, and serves as the initial manifestation of malignancy in 20% of patients. Timely diagnosis and management are critical due to the risk of irreversible neurological damage and the significant impact on both quality and quantity of life. The National Institute for Health and Care Excellence (NICE) recommends that patients presenting with back pain accompanied by neurological signs and/or symptoms undergo whole-spine magnetic resonance imaging (MRI) within 24 h. This retrospective study at Medway Maritime Hospital in England aimed to assess adherence to these guidelines by reviewing the time from presentation to MRI for patients exhibiting symptoms and/or signs of MSCC. Data for 69 patients were collected over one year using electronic patient records and the acute oncology service database. Analysis revealed that MRI was conducted within 24 h in only 43 out of 69 cases (62%), and 16 out of 25 delayed cases (i.e., MRI done beyond the recommended 24 h window) experienced delays of more than 48 h. To improve guideline adherence, interventions such as informational flyers and regular MSCC training sessions, including trainee teaching and presentations during grand rounds, were implemented. A follow-up re-audit involving 113 patients over one year demonstrated improved adherence to the 24 h MRI guideline, with 81 out of 113 cases (71%) meeting the target. The second cycle also documented reasons for delays, identifying patient compliance and pain control as primary factors. Additionally, the timing of steroid administration following suspicion of MSCC was recorded. Future studies should re-assess adherence, focus on better documentation of delay causes, enhance pain management before MRI scans, and ensure prompt steroid administration.

Prostate cancer, a leading cause of cancer-related mortality among men, often presents challenges in accurate diagnosis and effective monitoring. This systematic review explores the potential of exosomal biomolecules as noninvasive biomarkers for the diagnosis, prognosis, and treatment response of prostate cancer. A thorough systematic literature search through online public databases (Medline via PubMed, Scopus, and Web of science) using structured search terms and screening using predefined eligibility criteria resulted in 137 studies that we analyzed in this systematic review. We evaluated the findings from these clinical studies, revealing that the load of exosomes in the blood and urine of prostate cancer patients, which includes microRNAs (miRNAs), proteins, and lipids, demonstrates disease-specific changes. It also shows that some exosomal markers can differentiate between malignant and benign hyperplasia of the prostate, predict disease aggressiveness, and monitor treatment efficacy. Notably, miRNA emerged as the most frequently studied biomolecule, demonstrating superior diagnostic potential compared to traditional methods like prostate-specific antigen (PSA) testing. The analysis also highlights the pressing need for a standardised analytic approach through multi-centre studies to validate the full potential of exosomal biomarkers for the diagnosis and monitoring of prostate cancer.

The technological capability of artificial intelligence (AI) continues to advance with great strength. Recently, the release of large language models has taken the world by storm with concurrent excitement and concern. As a consequence of their impressive ability and versatility, their provide a potential opportunity for implementation in oncology. Areas of possible application include supporting clinical decision making, education, and contributing to cancer research. Despite the promises that these novel systems can offer, several limitations and barriers challenge their implementation. It is imperative that concerns, such as accountability, data inaccuracy, and data protection, are addressed prior to their integration in oncology. As the progression of artificial intelligence systems continues, new ethical and practical dilemmas will also be approached; thus, the evaluation of these limitations and concerns will be dynamic in nature. This review offers a comprehensive overview of the potential application of large language models in oncology, as well as concerns surrounding their implementation in cancer care.