Cancer journal最新文献

{"title":"Errors in Germline Biomarker Testing: Significant Consequences and Missed Opportunities for Cancer Control in Patients and Their Families.","authors":"Suzanne M Mahon, Laura A McLaughlin, Usa Khemthong, Constance Owen","doi":"10.1097/PPO.0000000000000812","DOIUrl":"https://doi.org/10.1097/PPO.0000000000000812","url":null,"abstract":"<p><strong>Purpose: </strong>Germline biomarker testing to assess inherited risk for developing malignancy has evolved quickly from testing for 1 or 2 genes from a few laboratories to ordering panels of 80 or more genes available from multiple laboratories. Many health professionals did not receive foundational information in training yet are expected to identify and manage care for individuals and families with germline risk. Errors in testing do occur and can have significant adverse consequences including missed opportunities for prevention and detection for the patient and family, unnecessary risk-reducing surgery, and even death. By better understanding these errors and underlying causes, as well as the potential negative consequences due to these errors, strategies can be developed to help prevent future harm to patients.</p><p><strong>Methods: </strong>Using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) reporting system a literature review was completed to identify case reports of errors in germline testing for hereditary cancer. Ovid MEDLINE, Scopus, and EBSCOHOST CINAHL, were searched from January 2009 through February 2025. Reference lists were reviewed to identify additional case reports. Each case report was abstracted to identify error(s), consequence(s), and potential error(s) prevented.</p><p><strong>Results: </strong>A total of 106 cases were identified from the search of the databases and 1 case using archival methods. Sixty-six (61%) cases described more than one error and 61 (44%) described more than one negative consequence. In 48 (45%) cases one or more additional errors were prevented when a genetics professional was consulted. The most common errors were misinterpretation of data, failure to take a full family history and review previous test results, not recognizing a syndrome, and selecting the wrong test or wrong laboratory for testing. Negative consequences included missed opportunities for prevention and detection for the patient and potentially other family members, one or more unnecessary risk-reducing surgeries in 10 patients, late detection of malignancy in 8 patients, and 7 patient deaths.</p><p><strong>Discussion: </strong>Although germline testing seems to be a simple laboratory test, many errors occur that have avoidable adverse consequences for both the patient and family. Errors may occur because of a lack of foundational knowledge in comprehensive risk assessment and on how to order the best test in a laboratory capable of detecting a pathogenic variant as well as how to interpret germline testing results. Increasing the number of genetics professionals, implementing new delivery models, and increasing educational efforts in nongenetics professionals could prevent and decrease errors.</p>","PeriodicalId":9655,"journal":{"name":"Cancer journal","volume":"32 2","pages":""},"PeriodicalIF":2.2,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147509899","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}

{"title":"Economic Evaluation of Particle Therapy for Cancer Treatment: A Systematic Review.","authors":"Seyyed M M J Sarayi, Sara Nofal, Mehdi Hemmati, Samuel Garvin, Ethan N Maldonado, Alex Elliott, David P Farris, Maria A Lopez-Olivo, Iakovos Toumazis","doi":"10.1097/PPO.0000000000000815","DOIUrl":"https://doi.org/10.1097/PPO.0000000000000815","url":null,"abstract":"<p><p>Particle therapy represents a cutting-edge modality in radiation oncology that offers potential advantages over conventional radiation therapy; however, its cost-effectiveness has not been established. We conducted a systematic search of several electronic databases to synthesize contemporary evidence on the cost-effectiveness of particle therapy. The methodological quality of the included studies was assessed, and reported outcomes on costs, health benefits, and incremental cost-effectiveness ratios (ICERs) were extracted. We identified 57 studies, which collectively reported 134 comparisons for particle therapies. In general, studies were of good quality, although better reporting of data sources and modeling assumptions is warranted. Included studies primarily evaluated the cost-effectiveness of proton therapy, used photon therapy as the comparator, utilized a Markov Cohort model, and used the payer's perspective. Findings were inconsistent, with the reported ICERs ranging from $55 to $1,066,266 per unit of benefit gained, with discrepancies across cancer sites and treatment modalities. Particle therapy is cost-effective for specific groups but may not be universally cost-effective. More research is warranted to identify subgroups for which particle therapy is cost-effective.</p>","PeriodicalId":9655,"journal":{"name":"Cancer journal","volume":"32 2","pages":""},"PeriodicalIF":2.2,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147509908","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}

{"title":"Decoding Destiny: Can We Deliver Artificial Intelligence-Powered Patient-centered Care?","authors":"Edward S Kim","doi":"10.1097/PPO.0000000000000801","DOIUrl":"https://doi.org/10.1097/PPO.0000000000000801","url":null,"abstract":"","PeriodicalId":9655,"journal":{"name":"Cancer journal","volume":"31 6","pages":""},"PeriodicalIF":2.2,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145539257","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}

{"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":"<p><p>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.</p>","PeriodicalId":9655,"journal":{"name":"Cancer journal","volume":"31 6","pages":""},"PeriodicalIF":2.2,"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}

{"title":"Artificial Intelligence in Supportive Oncology and Symptom Management Opportunities.","authors":"Elyssa N Kim, Krisstina Gowin, Anne Reb, Diya Sandhu, Erica Veguilla, Finly Zachariah, Richard T Lee","doi":"10.1097/PPO.0000000000000800","DOIUrl":"https://doi.org/10.1097/PPO.0000000000000800","url":null,"abstract":"<p><p>Artificial intelligence (AI) is rapidly transforming medical care, including in oncology, offering promising avenues for enhancing supportive care and symptom management. This review synthesizes current research on AI applications in this critical domain, exploring its potential to personalize interventions and improve patient-reported outcomes in oncology supportive care. We examine AI-driven tools for symptom monitoring, predictive analytics for adverse events, and personalized supportive care recommendations. Emphasis is placed on the integration of machine learning algorithms for real-time data analysis, enabling proactive interventions and timely symptom relief. We highlight challenges in translating AI-based solutions into clinical practice, including data privacy, algorithm bias, applicability for all patients, and the need for rigorous validation studies. Ultimately, the integration of AI in supportive oncology holds the potential to revolutionize patient-centered care, optimizing symptom control and improving the quality of life for individuals facing cancer.</p>","PeriodicalId":9655,"journal":{"name":"Cancer journal","volume":"31 6","pages":""},"PeriodicalIF":2.2,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145539303","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}

{"title":"Integrating AI into the Clinical Workflows Across the Cancer Care Continuum: Opportunities and Challenges.","authors":"Urmila Kulkarni Kale, Gorkey Vemulapalli","doi":"10.1097/PPO.0000000000000799","DOIUrl":"10.1097/PPO.0000000000000799","url":null,"abstract":"<p><p>Cancer cases are projected to hit 35 million worldwide by 2050, posing a significant burden on health care systems. The cancer care continuum has evolved to precision medicine practices, provisioning personalized treatments based on multimodal and multiomics data. Contextual analysis of such diverse, voluminous, spatiotemporal patient data is beyond human cognitive capacity. Artificial Intelligence (AI) technologies are reshaping the data mining paradigm in healthcare by delivering novel data-led insights in real time. AI-based methods for cancer risk predictions, diagnosis, prognosis, and therapeutics are developed, validated, and approved, indicating readiness for integration in clinical workflows. Additional validation of AI models using real-world data representing diverse populations is recommended to address clinical, technical, regulatory, ethical, and legal challenges, along with trust issues. Integrating AI tools into cancer care workflows to augment clinical decision-making, without compromising clinical autonomy and patient safety, is essential to address the increasing demand for cancer care by 2050.</p>","PeriodicalId":9655,"journal":{"name":"Cancer journal","volume":"31 6","pages":""},"PeriodicalIF":2.2,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145539210","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}

{"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":"<p><p>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.</p>","PeriodicalId":9655,"journal":{"name":"Cancer journal","volume":"31 6","pages":""},"PeriodicalIF":2.2,"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}

{"title":"Human-machine Interaction in the Age of Generative AI.","authors":"Dipesh Niraula, Monique O Shotande, Issam El Naqa","doi":"10.1097/PPO.0000000000000797","DOIUrl":"https://doi.org/10.1097/PPO.0000000000000797","url":null,"abstract":"<p><p>Generative artificial intelligence (Gen-AI) powered technologies are increasingly integrated across virtually all fields, including oncology, poised to fundamentally transform human-machine interaction (HMI). In biomedicine and oncology, Gen-AI tools are forming the foundation for intuitive patient-facing and clinician-facing interfaces that increase accessibility and efficiency of health care applications, enhance patient experience, and improve clinical workflows, ultimately optimizing patient outcomes. Despite Gen-AI's great potential in health care, limitations related to data quality and learning algorithms can create persistent challenges to patient safety, warranting a thorough HMI evaluation by end-users and experts that goes beyond traditional statistical validation. In parallel, a legal framework for assigning liability among developers, deployers, maintainers, and end-users is essential to ensure fairness and promote safe and beneficial application of clinical AI.</p>","PeriodicalId":9655,"journal":{"name":"Cancer journal","volume":"31 6","pages":""},"PeriodicalIF":2.2,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145539223","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}

{"title":"Advancing Federal Coordination to Address Drug Shortages.","authors":"Emeka E Duru, Kwame Kissi-Twum, Kenechukwu C Ben-Umeh, T Joseph Mattingly","doi":"10.1097/PPO.0000000000000787","DOIUrl":"10.1097/PPO.0000000000000787","url":null,"abstract":"<p><p>Persistent shortages of essential medicines in the United States, especially generic oncology drugs, continue to compromise timely cancer care and patient safety. The presence of multiple high-level reports from federal agencies and industry experts has outlined similar recommendations, including the creation of a unified essential medicines list, transparent supply chain monitoring, domestic manufacturing incentives, and centralized federal coordination, among others, giving an optimistic direction. This manuscript synthesizes key findings from these reports and highlights misalignment across agency roles and priorities as a barrier to sustained progress. Case studies of cisplatin, vincristine, and methotrexate shortages underscore the high stakes of inaction. Drawing on recent coordination successes during the COVID-19 response, we propose a practical path forward: establishing a central federal coordinating body, legislating an essential medicines list developed using an established criticality-reach-vulnerability framework, reforming procurement incentives, and expanding the Strategic National Stockpile.</p>","PeriodicalId":9655,"journal":{"name":"Cancer journal","volume":"31 5","pages":""},"PeriodicalIF":2.2,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145147886","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}

{"title":"The Oncology Drug Shortages and Its Impact on Community Hospitals.","authors":"Lyndsey Reich, Kevin B Knopf","doi":"10.1097/PPO.0000000000000794","DOIUrl":"10.1097/PPO.0000000000000794","url":null,"abstract":"<p><p>The ongoing shortage of oncology drugs, particularly generic chemotherapies like platinum agents, has had a disproportionate impact on community and safety net hospitals in the United States and globally. These institutions, often serving rural and underserved populations, face significant challenges due to limited financial resources. This article examines the practical implications of these shortages through the lens of a community hospital, where creative solutions were employed to maximize limited resources where drug shortages were concerned. This article also highlights the emergence of gray and black markets, raising concerns about drug quality, especially in low-income and middle-income countries. Broader market dynamics-including rising platinum prices and recent health care policy changes-threaten to deepen disparities in cancer care. Systemic reforms are required to improve supply chain resilience, ensure equitable drug access, and protect vulnerable institutions and populations from the consequences of ongoing and future drug shortages.</p>","PeriodicalId":9655,"journal":{"name":"Cancer journal","volume":"31 5","pages":""},"PeriodicalIF":2.2,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145147929","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}