Cancer journal最新文献_第6页

Modern Radiosurgery Approaches for Brain Metastases. 脑转移瘤的现代放射外科治疗方法。

IF 2.1 4区医学

Cancer journal Pub Date : 2026-01-01 Epub Date: 2026-01-27 DOI: 10.1097/PPO.0000000000000804

Brittney Chau, Eric Lin Chang, Adam Garsa

引用次数: 0

Innovative Diagnostic and Therapeutic Approaches for Radiation Necrosis. 放射性坏死的创新诊断和治疗方法。

IF 2.1 4区医学

Cancer journal Pub Date : 2026-01-01 Epub Date: 2026-01-27 DOI: 10.1097/PPO.0000000000000808

Adannia Ufondu, Jana Haroun, Samuel Chao, Praveen Pendyala

{"title":"Innovative Diagnostic and Therapeutic Approaches for Radiation Necrosis.","authors":"Adannia Ufondu, Jana Haroun, Samuel Chao, Praveen Pendyala","doi":"10.1097/PPO.0000000000000808","DOIUrl":"10.1097/PPO.0000000000000808","url":null,"abstract":"Radiation necrosis (RN) is a serious complication of stereotactic radiosurgery (SRS), affecting ∼5% to 25% of patients treated for brain metastases. Because RN is a late effect of radiation therapy with a typical onset of 6 to 12 months after treatment, the incidence of RN is expected to rise as advancements in systemic therapy prolong survival among patients with brain metastases. The exact radiobiological mechanisms behind RN are still unclear but likely driven by a self-sustaining feedback loop involving vascular injury, ischemia, neuroinflammation and perivascular fibrosis. Although multiple diagnostic tools are currently used or being investigated to help distinguish RN from tumor progression (TP), including MR perfusion, MR spectroscopy, and Amino Acid PET/CT, the clinical utility of each of these tools is limited by suboptimal specificity, lesion size criteria, limited access and susceptibility to treatment-related factors such as prior bevacizumab. First line management of symptomatic RN is steroids, with escalation to bevacizumab or aggressive, local therapies such as LITT and resection for steroid refractory RN. Refining our understanding of normal brain tissue dose tolerances, characterizing the safety profile of concurrent administration of SRS with different precision systemic therapies and integrating novel risk stratification tools such as the Recursive Partitioning Analysis (RPA) for RN will be key to reducing the risk of RN after SRS. These prevention strategies along with the development of novel therapies informed by enhanced insight into the pathophysiology of RN promise to improve long-term quality of life for patients with brain metastases.","PeriodicalId":9655,"journal":{"name":"Cancer journal","volume":"32 1","pages":""},"PeriodicalIF":2.1,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146028414","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Advanced Imaging Techniques for the Detection and Follow-up of Brain Metastases. 脑转移瘤检测与随访的先进影像技术。

IF 2.1 4区医学

Cancer journal Pub Date : 2026-01-01 Epub Date: 2026-01-27 DOI: 10.1097/PPO.0000000000000805

Hang Yu, Hany Soliman, Kang Liang Zeng, Chia-Lin Tseng, Jay Detsky, Hanbo Chen, Greg J Stanisz, Rachel W Chan, Charles H Cunningham, Nicole I C Cappelletto, Angus Lau, Paula Alcaide Leon, Sunit Das, Chinthaka Heyn, Arjun Sahgal, Pejman Jabehdar Maralani

{"title":"Advanced Imaging Techniques for the Detection and Follow-up of Brain Metastases.","authors":"Hang Yu, Hany Soliman, Kang Liang Zeng, Chia-Lin Tseng, Jay Detsky, Hanbo Chen, Greg J Stanisz, Rachel W Chan, Charles H Cunningham, Nicole I C Cappelletto, Angus Lau, Paula Alcaide Leon, Sunit Das, Chinthaka Heyn, Arjun Sahgal, Pejman Jabehdar Maralani","doi":"10.1097/PPO.0000000000000805","DOIUrl":"10.1097/PPO.0000000000000805","url":null,"abstract":"Optimal management of patients with brain metastases relies on both accurate detection on initial imaging and accurate differentiation between treatment-related effects from progression on follow-up imaging. Contrast-enhanced MRI remains the standard technique for initial and follow-up imaging, though advanced MRI techniques, such as perfusion MRI, diffusion-weighted MRI, MR spectroscopy, and chemical exchange saturation transfer (CEST), have also emerged as complementary examinations, particularly in distinguishing TRE from true progression. Molecular imaging with PET, especially using amino acid tracers, also demonstrates high diagnostic accuracy in discriminating between treatment-related effects and tumour progression. Artificial intelligence models, including those using radiomics and deep learning approaches, have been developed to enhance diagnostic performance and to streamline workflow. However, their clinical utility is limited by significant inter-study heterogeneity, lack of standardization, and lack of large-scale external validation. This review summarizes the current literature on advanced imaging techniques in BM diagnosis and response assessment.","PeriodicalId":9655,"journal":{"name":"Cancer journal","volume":"32 1","pages":""},"PeriodicalIF":2.1,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146028477","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Decoding Destiny: Can We Deliver Artificial Intelligence-Powered Patient-centered Care? 解码命运：我们能提供人工智能驱动的以病人为中心的护理吗？

IF 2.2 4区医学

Cancer journal Pub Date : 2025-11-01 Epub Date: 2025-11-18 DOI: 10.1097/PPO.0000000000000801

Edward S Kim

引用次数: 0

Harnessing Artificial Intelligence to Transform Clinical Trials and Cancer Care: Opportunities and Challenges. 利用人工智能改变临床试验和癌症治疗：机遇与挑战。

IF 2.1 4区医学

Cancer journal Pub Date : 2025-11-01 Epub Date: 2025-11-18 DOI: 10.1097/PPO.0000000000000796

Jennifer H Benbow, Edward S Kim

{"title":"Harnessing Artificial Intelligence to Transform Clinical Trials and Cancer Care: Opportunities and Challenges.","authors":"Jennifer H Benbow, Edward S Kim","doi":"10.1097/PPO.0000000000000796","DOIUrl":"10.1097/PPO.0000000000000796","url":null,"abstract":"Artificial intelligence (AI) is working toward the reality of speeding up oncology drug development, offering the ability to cut years off the pipeline while maintaining patient safety and personalized care. Machine learning (ML) models analyze historical and real-world data to optimize eligibility criteria, simulate in silico cohorts, flag protocol risks, and recommend real-time adaptations. Natural language processing enhances patient screening by extracting patient data from electronic health records to match diverse patient populations to trials faster than traditional methods. AI-driven analysis of data from electronic wearables and imaging enables early toxicity and efficacy signals, allowing providers real-time monitoring. However, the same code that accelerates technology can also amplify bias, increase data security issues, hallucinate unsafe recommendations, and raise legal and ethical alarms. Safeguards, including transparent model reporting, bias mitigation, robust cybersecurity, clinician oversight, and education for providers and patients, are essential. Harnessed responsibly, AI can transform clinical trials and oncology care without sacrificing empathy, accountability, and patient-centered values.","PeriodicalId":9655,"journal":{"name":"Cancer journal","volume":"31 6","pages":""},"PeriodicalIF":2.1,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145539226","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Artificial Intelligence in Supportive Oncology and Symptom Management Opportunities. 人工智能在支持肿瘤学和症状管理的机会。

IF 2.1 4区医学

Cancer journal Pub Date : 2025-11-01 Epub Date: 2025-11-18 DOI: 10.1097/PPO.0000000000000800

Elyssa N Kim, Krisstina Gowin, Anne Reb, Diya Sandhu, Erica Veguilla, Finly Zachariah, Richard T Lee

引用次数: 0

Integrating AI into the Clinical Workflows Across the Cancer Care Continuum: Opportunities and Challenges. 将人工智能整合到癌症护理连续体的临床工作流程中：机遇与挑战。

IF 2.1 4区医学

Cancer journal Pub Date : 2025-11-01 Epub Date: 2025-11-18 DOI: 10.1097/PPO.0000000000000799

Urmila Kulkarni Kale, Gorkey Vemulapalli

引用次数: 0

Active Inference AI and the Spatial Web for Medicine: A New Paradigm for Medical Research, Treatment, and Education. 主动推理人工智能和医学空间网络：医学研究、治疗和教育的新范式。

IF 2.1 4区医学

Cancer journal Pub Date : 2025-11-01 Epub Date: 2025-11-18 DOI: 10.1097/PPO.0000000000000798

Dan Mapes

{"title":"Active Inference AI and the Spatial Web for Medicine: A New Paradigm for Medical Research, Treatment, and Education.","authors":"Dan Mapes","doi":"10.1097/PPO.0000000000000798","DOIUrl":"10.1097/PPO.0000000000000798","url":null,"abstract":"A new branch of artificial intelligence called Active Inference AI is changing the very foundations of how medical knowledge is created, applied, and taught. And this new AI is combining with an entirely new evolution of the Internet, called the Spatial Web, which is changing how medical knowledge will be shared globally. Active Inference AI and the Spatial Web have been developed together to create a powerful new environment for medicine and science in general to evolve to an entirely new level. Until now, large-scale AI models called LLMs (Large Language Models) have been dominating the AI marketplace. But these are general-purpose AIs. They are expensive to create, they are massively data-hungry, and they are imprecise and not designed for specialized domains like medicine. In contrast, this new Active Inference AI-inspired by neuroscience-is designed specifically for medicine and other applications requiring high accuracy and explainable results. This new AI does not use LLM technology but relies on small, domain-specific models built from expert-curated knowledge graphs and factor graphs. This novel approach enables reasoning, learning, and decision-making within well-defined medical contexts, allowing for the precision, adaptability, and interpretability missing in LLMs. This report outlines how Active Inference AI can: (1) accelerate medical research by simulating hypotheses and causal pathways. (2) Enhance medical treatment through adaptive, real-time digital twins and precision diagnostics. (3) Revolutionize medical education by creating dynamic, interactive, and semantically accurate learning environments.","PeriodicalId":9655,"journal":{"name":"Cancer journal","volume":"31 6","pages":""},"PeriodicalIF":2.1,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145539118","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Human-machine Interaction in the Age of Generative AI. 生成式人工智能时代的人机交互。

IF 2.1 4区医学

Cancer journal Pub Date : 2025-11-01 Epub Date: 2025-11-18 DOI: 10.1097/PPO.0000000000000797

Dipesh Niraula, Monique O Shotande, Issam El Naqa

引用次数: 0

Advancing Federal Coordination to Address Drug Shortages. 推进联邦协调解决药品短缺问题。

IF 2.1 4区医学

Cancer journal Pub Date : 2025-09-01 Epub Date: 2025-09-25 DOI: 10.1097/PPO.0000000000000787

Emeka E Duru, Kwame Kissi-Twum, Kenechukwu C Ben-Umeh, T Joseph Mattingly

引用次数: 0