bims-rebome 2025-07-20 papers

Cancers (Basel). 2025 Jul 02. pii: 2218. [Epub ahead of print]17(13):

Integration of Next Generation Sequencing Data to Inform Survival Prediction of Patients with Spine Metastasis.

Alexandra Giantini-Larsen, Alexander D Ramos, Axel Martin, Katherine S Panageas, Caroline E Kostrzewa, Zaki Abou-Mrad, Adam Schmitt, Jacqueline F Bromberg, Anton Safonov, Charles M Rudin, William Christopher Newman, Mark H Bilsky, Ori Barzilai.

Background/Objectives: Spinal metastatic disease is a life-altering problem for individuals with cancer. Prognostication is key for tailored treatment of spinal metastases. This manuscript provides a comprehensive overview of the genomic profiles of metastatic spine tumors and investigates the potential of mutational data to stratify overall survival (OS) across various histologies. Methods: This is a cohort study of consecutive patients with spine metastatic disease whose tumors were sequenced on a next generation sequencing platform; a machine learning (ML) algorithm was used to stratify OS risk. Results: Targeted sequencing and stratification of OS risk of 282 spine metastases (breast (84), non-small cell lung (56), prostate (49), other (93)) was performed. TP53 (HR 1.80; 95% CI 1.26, 2.56) and KEAP1 (HR 3.95, 95% CI 2.24, 6.98) mutations were associated with poor survival across the entire cohort in univariate Cox proportional hazards models. The ML algorithm categorized breast cancer metastasis into low- and high-risk groups, revealing a median OS of 71 compared to 22 months (HR 3.3, p < 0.001). TP53 mutations and ESR1 mutations conferred poor prognosis. In lung cancer, low- and high-risk groups with median OS of 30 and 6 months (HR 8.3, p < 0.001), respectively, were identified with poor prognosis linked to MET amplification. No significant prognostic associations were identified for spinal prostate metastases. Conclusions: Metastatic spine tumor molecular data allows for the identification of prognostic groups. We present an open-source machine learning algorithm utilizing genomic mutational data that may aid in prognostication and tailored decision making.

Keywords: genomics; machine learning; spine metastasis

DOI: https://doi.org/10.3390/cancers17132218

Cancers (Basel). 2025 Jun 20. pii: 2073. [Epub ahead of print]17(13):

Evaluating the Accuracy of Privacy-Preserving Large Language Models in Calculating the Spinal Instability Neoplastic Score (SINS).

Li Yi Tammy Chan, Ding Zhou Matthew Chan, Yi Liang Tan, Qai Ven Yap, Wilson Ong, Aric Lee, Shuliang Ge, Wenxin Naomi Leow, Andrew Makmur, Yonghan Ting, Ee Chin Teo, Tan Jiong Hao, Naresh Kumar, James Thomas Patrick Decourcy Hallinan.

Background: Large language models (LLMs) have emerged as powerful tools in healthcare. In diagnostic radiology, LLMs can assist in the computation of the Spine Instability Neoplastic Score (SINS), which is a critical tool for assessing spinal metastases. However, the accuracy of LLMs in calculating the SINS based on radiological reports remains underexplored. Objective: This study evaluates the accuracy of two institutional privacy-preserving LLMs-Claude 3.5 and Llama 3.1-in computing the SINS from radiology reports and electronic medical records, comparing their performance against clinician readers. Methods: A retrospective analysis was conducted on 124 radiology reports from patients with spinal metastases. Three expert readers established a reference standard for the SINS calculation. Two orthopaedic surgery residents and two LLMs (Claude 3.5 and Llama 3.1) independently calculated the SINS. The intraclass correlation coefficient (ICC) was used to measure the inter-rater agreement for the total SINS, while Gwet's Kappa was used to measure the inter-rater agreement for the individual SINS components. Results: Both LLMs and clinicians demonstrated almost perfect agreement with the reference standard for the total SINS. Between the two LLMs, Claude 3.5 (ICC = 0.984) outperformed Llama 3.1 (ICC = 0.829). Claude 3.5 was also comparable to the clinician readers with ICCs of 0.926 and 0.986, exhibiting a near-perfect agreement across all individual SINS components [0.919-0.990]. Conclusions: Claude 3.5 demonstrated high accuracy in calculating the SINS and may serve as a valuable adjunct in clinical workflows, potentially reducing clinician workload while maintaining diagnostic reliability. However, variations in LLM performance highlight the need for further validation and optimisation before clinical integration.

Keywords: artificial intelligence; large language models; radiology; spine metastases

DOI: https://doi.org/10.3390/cancers17132073

Cureus. 2025 Jun;17(6): e86049

Current Advancements in the Diagnosis and Treatment of Metastatic Spinal Cord Compression and Its Postintervention Care: A Comprehensive Review.

Avikalp Kishore, Alessio Piccioni, Ivana L Fiallos Vinueza, Ahmed Rasool Younus Khan, Moutasm Kasab, Deepika Lakshminarasimhan, Duaa Behbehani, Jahed Bushnaq, Olamide Ogunfunwa, Naga V Bittu.

Malignant spinal cord compression (MSCC) is a serious oncological emergency that can result in neurological impairment and significant pain. It is associated with high morbidity, mortality, and a decrease in quality of life. This review reviews the current literature concerning diagnosis, guidelines, timely intervention, and management options in oncology patients with MSCC. A literature search of PubMed using relevant keywords and MeSH terms yielded 604 articles. Of these, 31 studies met the predefined inclusion criteria and were selected for detailed data extraction and synthesis into a narrative review. MRI remains the gold standard for diagnosis, with newer systems like short tau inversion recovery (STIR) sequences being used, and includes the Bilsky grading system to improve classification and diagnosis for treatment. Radiotherapy and surgery continue to be the main treatment options. A multidisciplinary, personalized approach improves the amount of function recovered, pain control, and quality of life. New advances include robotic-assisted surgery and immunotherapy as future management options. Early recognition and clear, evidence-based guidelines are essential to reduce the morbidity and mortality of MSCC. This includes immediate administration of steroids, MRI imaging, and timely referral to appropriate departments, including acute oncology and neurosurgery. Additional research is needed to determine the main factors that impact patient recovery and quality of life.

Keywords: diagnosis; malignant spinal cord compression; metastatic spinal cord compression; multidisciplinary approach; radiotherapy; steroids

DOI: https://doi.org/10.7759/cureus.86049

Cancers (Basel). 2025 Jun 27. pii: 2166. [Epub ahead of print]17(13):

Advances in Supportive Care for Multiple Myeloma-Related Bone Disease-A Review.

Saadia Abbas, Haleema Sadia Adil, Shahzad Raza, Janita Basit, Karan Arul, Faiz Anwer, Muhammad Hamza Habib.

Multiple myeloma (MM) is an incurable plasma cell malignancy characterized by the increased production of monoclonal immunoglobulin. Patients with MM are at high risk of suffering from disease-related complications. Osteolytic bone disease is one of the most common disease-related complications, resulting in chronic pain and skeletal pathologies that contribute significantly to high morbidity and mortality rates among MM patients. In addition to standard anti-MM therapy, management of disease-related sequelae is integral to improving quality of life in MM patients. Bisphosphates have long been the mainstay treatment for patients with myeloma bone disease (MBD) due to their ability to reduce the incidence of skeletal-related adverse events. However, in recent years, a deeper understanding of the complex biology and pathophysiology associated with myeloma bone disease has led to the development of novel therapies that have the potential to improve MM patient management and outcomes. This narrative review uses the most recent extant publications to review all such advancements. It aims to summarize evidence-based strategies for the management of myeloma bone disease and therapy-associated adverse events whilst highlighting current guidelines on optimal bisphosphonate use and providing an overview of promising new agents currently in clinical development.

Keywords: bone pain; myeloma bone disease; myeloma bone pain; palliative care; supportive care

DOI: https://doi.org/10.3390/cancers17132166

Support Care Cancer. 2025 Jul 11. 33(8): 685

Safety, feasibility, and effectiveness of exercise interventions in patients with hematologic malignancy during chemotherapy: a systematic review and meta-analysis.

Yuqin Luo, Yufang Ren, Yuan Li, Runyu Yang, Yuyan Cheng, Yulu Chen, Yan Deng, Jingyu Zheng, Yingli Wang.

PURPOSE: To evaluate the safety, feasibility, and effectiveness of exercise interventions aimed at improving the physical and/or mental health of patients with hematologic malignancies (HM) during chemotherapy.
METHODS: We systematically searched nine electronic databases for studies from their inception up to February 2024. The review process strictly adhered to the Cochrane guidelines and followed the PRISMA checklist for reporting systematic reviews. Sensitivity and subgroup analyses were conducted. When appropriate, we ran meta-regressions to locate the source of heterogeneity. Where statistical pooling was not appropriate, trials were instead summarized in narrative form.
RESULTS: Thirty-three studies were included in this systematic review, of which 19 were included in the meta-analysis. Five studies reported 19 exercise-related no serious adverse events. Twenty-eight studies reported that exercise interventions were feasible or had high participation rates. The results revealed significant improvements in physical fitness parameters among the exercise intervention group compared to the control group. Specifically, we observed substantial enhancements in the timed walking distance (TMD) (SMD = 0.696; 95%CI, [0.455,0.988]; p < 0.001)), timed up and go test (TUG) (SMD = -0.570; 95%CI, [-0.867, -0.273]; p < 0.001), peak oxygen consumption (VO2 peak) (SMD = 0.532; 95%CI, [0.261, 0.804]; p < 0.001), lower muscle strength (SMD = 0.346; 95%CI, [0.018,0.674]; p = 0.039) and upper muscle strength. Moreover, our findings demonstrated that exercise intervention significantly reduced depression scores (SMD = -0.692; 95%CI, [-0.631,-0.157]; p = 0.001) compared to usual care. However, non significant effects were found for anxiety and quality of life (QOL). The narrative summary of evidence for fatigue showed uncertain intervention effects, and our meta-regression analysis did not identify any covariates significantly associated with fatigue outcomes.
CONCLUSION: Generally, the exercise intervention appear safe and feasible, and can improve physical fitness and depression in adult patients with HM during chemotherapy. Currently, there is inconclusive evidence regarding QOL, fatigue and anxiety. Further trials with larger sample size and longer follow-up periods are warranted to evaluate the effects of exercise interventions for patients with HM.

Keywords: Exercise; Fatigue; Feasibility; Hematologic malignancy; Meta-analysis; Physical fitness; Quality of life; Safety

DOI: https://doi.org/10.1007/s00520-025-09748-4