Khan, Muhammad Ali

ABSTRACT Background Symptom management in myeloproliferative neoplasms (MPN) remains challenging despite advancements in disease‐directed therapies. This study assessed the impact of demographic, clinical, laboratory and treatment‐related variables on total symptom scores (TSS) and individual symptom scores in patients with polycythemia vera (PV), essential thrombocythemia (ET), and myelofibrosis (MF). Methods A cohort of 553 patients (PV: 200; ET: 190; MF: 163) was identified through a retrospective chart review. Symptom scores, demographic information and clinical variables were captured at the time of the first TSS assessment. Laboratory results within 1 month and treatment information within 90 days prior to the symptom assessment were captured. Univariable linear regression, followed by multivariable regression analyses using a robust variance estimator were performed, with a p value < 0.05 indicating a significant association. Results MF patients experienced the highest symptom burden and fatigue was the most reported symptom across all MPN subtypes. In PV, depression ( β : 10.53; p = 0.001) was associated with a higher TSS whereas older age (−0.17; 0.01) and higher hemoglobin (−1.24; 0.003) were associated with a lower TSS. In ET, depression ( β : 14.19; p < 0.001) was associated with a higher TSS, whereas in MF, depression (12.28; < 0.001) and higher WBC count (0.22; 0.02) were associated with a higher TSS. Depression and non‐White race in PV, depression in ET, and depression, low hemoglobin, and higher WBC count in MF were associated with multiple symptoms. Conclusions Integrating depression screening and management and optimizing hematologic parameters alongside disease‐directed therapy is crucial to improving patient outcomes.

Abstract Artificial intelligence (AI) holds significant potential to enhance various aspects of oncology, spanning the cancer care continuum. This review provides an overview of current and emerging AI applications, from risk assessment and early detection to treatment and supportive care. AI‐driven tools are being developed to integrate diverse data sources, including multi‐omics and electronic health records, to improve cancer risk stratification and personalize prevention strategies. In screening and diagnosis, AI algorithms show promise in augmenting the accuracy and efficiency of medical image analysis and histopathology interpretation. AI also offers opportunities to refine treatment planning, optimize radiation therapy, and personalize systemic therapy selection. Furthermore, AI is explored for its potential to improve survivorship care by tailoring interventions and to enhance end‐of‐life care through improved symptom management and prognostic modeling. Beyond care delivery, AI augments clinical workflows, streamlines the dissemination of up‐to‐date evidence, and captures critical patient‐reported outcomes for clinical decision support and outcomes assessment. However, the successful integration of AI into clinical practice requires addressing key challenges, including rigorous validation of algorithms, ensuring data privacy and security, and mitigating potential biases. Effective implementation necessitates interdisciplinary collaboration and comprehensive education for health care professionals. The synergistic interaction between AI and clinical expertise is crucial for realizing the potential of AI to contribute to personalized and effective cancer care. This review highlights the current state of AI in oncology and underscores the importance of responsible development and implementation.