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MyPainPal, a Novel mHealth App to Improve Pain in Patients With Advanced Cancer: Single-Arm Pilot Study. MyPainPal，一款新型移动健康应用程序，可改善晚期癌症患者的疼痛：单臂试点研究。
IF 2.7 Q2 ONCOLOGY
JMIR Cancer
Pub Date : 2025-12-30 DOI: 10.2196/79942
Desiree R Azizoddin, Michael Hassett, Kris-Ann S Anderson, Daniela Kessler, Alexi Wright, Madeline Gorra, Benjamin Kematick, Isaac Chua, Douglas Brandoff, Kate Lally, Lida Nabati, Susan MacIsaac, James A Tulsky, Andrea Enzinger
<p><strong>Background: </strong>Pain is common among patients with advanced cancer and is often inadequately controlled. Opioids are central to treatment; yet, self-management is challenging, and clinicians lack scalable tools to monitor and support patients between visits.</p><p><strong>Objective: </strong>This study aimed to evaluate the feasibility and acceptability of MyPainPal (Dana-Faber Cancer Institute), a novel mobile health app designed to optimize cancer pain management. MyPainPal combines daily surveys assessing symptoms and analgesic use, algorithmic self-management support, tailored psychoeducation, and clinician monitoring. Secondary objectives were to explore preliminary clinical impact and identify priorities for refinement.</p><p><strong>Methods: </strong>This single-arm pilot study enrolled adults with advanced malignancies using opioids for moderate-to-severe pain from an outpatient palliative care clinic at a comprehensive cancer center. Participants used MyPainPal for 28 days while nurses monitored symptom responses via a secure portal, and also completed structured surveys at end-of-study. Primary assessment of usability and acceptability included the System Usability Scale (SUS; range 0-100), the Acceptability E-Scale (range 6-30), and ratings of satisfaction using a 5-point Likert scale. Semistructured debriefing interviews explored user experience, perceived impact, and suggestions for optimization.</p><p><strong>Results: </strong>Twenty participants with advanced cancer enrolled, with a mean age of 57 (SD 12.3) years, 55% (11/20) female, 80% (16/20) non-Hispanic White, with mixed cancer types. Over the 28-day study, patients logged into MyPainPal a median of 14 (IQR 8-17) times, and completed a median of 8 (IQR 5-14) symptom surveys, reflecting mean of 36% (SD 20%) of eligible (out-of-hospital) days on study. Usability and acceptability ratings of MyPainPal were high (mean SUS 78.3, SD 16.2; mean Acceptability E-Scale 24.0, SD 4.4); 79% rated overall satisfaction of greater than or equal to 4/5. Twenty percent of surveys generated an alert, prompting nurse outreach. In response, 5 participants had symptom medications changed and 2 had medication errors corrected. In debriefing interviews, many participants described that the intervention reduced barriers to pain reporting and facilitated timely and constructive interactions with care teams for symptom management. Several noted that the intervention validated their pain experience, reduced stigma around opioid use, enabled constructive conversations with providers, and promoted self-management. Patients recommended several survey modifications, including reducing their frequency and enabling more nuanced pain assessments. Participants underused the educational resources and suggested that they be featured more prominently. Some patients suggested that the MyPainPal app should be introduced earlier in patients' cancer pain trajectory when pain needs are higher and opioid
背景：疼痛在晚期癌症患者中很常见，但往往无法得到充分控制。阿片类药物是治疗的核心；然而，自我管理是具有挑战性的，临床医生缺乏可扩展的工具来监测和支持患者的两次就诊。目的：本研究旨在评估MyPainPal （Dana-Faber Cancer Institute）的可行性和可接受性，这是一款旨在优化癌症疼痛管理的新型移动健康应用程序。MyPainPal结合了评估症状和止痛药使用的日常调查、算法自我管理支持、量身定制的心理教育和临床医生监测。次要目标是探索初步临床影响并确定改进的优先事项。方法：这项单臂试点研究招募了在综合癌症中心门诊姑息治疗诊所使用阿片类药物治疗中度至重度疼痛的晚期恶性肿瘤成年人。参与者使用MyPainPal 28天，同时护士通过安全门户监控症状反应，并在研究结束时完成结构化调查。可用性和可接受性的主要评估包括系统可用性量表（SUS；范围0-100），可接受性e量表（范围6-30），以及使用5点李克特量表的满意度评级。半结构化的汇报访谈探讨了用户体验、感知影响和优化建议。结果：20名晚期癌症患者入组，平均年龄为57岁（SD 12.3）， 55%（11/20）为女性，80%（16/20）为非西班牙裔白人，混合癌症类型。在28天的研究中，患者登录MyPainPal的中位数为14 （IQR 8-17）次，完成了中位数8 （IQR 5-14）次症状调查，反映了研究中平均36% （SD 20%）的合格（院外）天数。MyPainPal的可用性和可接受性评分较高（平均SUS 78.3, SD 16.2；平均可接受性e量表24.0,SD 4.4）；79%的人认为整体满意度大于或等于4/5。20%的调查产生了警报，促使护士伸出援手。结果显示，5名患者的症状药物发生了改变，2名患者的用药错误得到了纠正。在汇报采访中，许多参与者描述说，干预措施减少了疼痛报告的障碍，并促进了及时和建设性的互动与护理团队的症状管理。一些人指出，干预措施验证了他们的疼痛经历，减少了对阿片类药物使用的耻辱感，促进了与提供者的建设性对话，并促进了自我管理。患者建议对调查进行一些修改，包括减少检查频率和进行更细致的疼痛评估。与会者未充分利用教育资源，并建议将这些资源放在更突出的位置。一些患者建议，当疼痛需求较高且阿片类药物治疗新颖时，MyPainPal应用程序应更早地引入患者的癌症疼痛轨迹。结论：在这项试点研究中，MyPainPal在接受姑息治疗的晚期癌症患者中展示了可行性、可接受性和潜在临床影响的初步证据。该应用程序已经重建和优化，并关注患者的反馈，为未来的疗效研究做准备。试验注册：ClinicalTrials.gov NCT03717402；https://clinicaltrials.gov/study/NCT03717402。
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引用次数: 0
Designing an App to Facilitate Self-Management in Young Adult Survivors of Childhood Cancer: Development and Usability Study. 设计一个应用程序，以促进青少年癌症幸存者的自我管理：开发和可用性研究。
IF 2.7 Q2 ONCOLOGY
JMIR Cancer
Pub Date : 2025-12-24 DOI: 10.2196/77121
Meredith K Reffner Collins, Kristine Levonyan-Radloff, Jeffery McLaughlin, Margaret Masterson, Katie A Devine

Background: Young adult survivors of childhood cancer are at risk for late and long-term effects from their treatment, and less than 1 in 5 obtain risk-based care in adulthood. Transitioning young adult survivors from pediatric, parent-driven care to adult, self-driven care is a challenging process during which young adults face multiple barriers. Intervening during this period may facilitate better transition readiness. For this purpose, we previously developed the Managing Your Health (MYH) web-based intervention, which showed initial feasibility and acceptability; however, young adult participants wanted to access the intervention through a mobile app.

Objective: We used an iterative, cocreation design process to translate, build, and evaluate the usability of the MYH web-based intervention into a mobile app to be used in a future peer-mentoring educational intervention.

Methods: In phase 1, we conducted key informant workshops with 3 stakeholder groups to understand target users' needs and expectations related to the content and design of the mobile app. In phase 2, we conducted usability testing with young adult survivors of childhood cancer to evaluate the app's usability and subjective appeal.

Results: Participants in the key informant workshops (n=13) agreed that the content of the proposed app matched the barriers faced by young adult survivors of childhood cancer. Participants provided suggestions about the design of the app, including content and features, although there were mixed views about the inclusion of gamification features. Usability testing participants (n=25) rated the app highly on measures of technology acceptance, usability, and aesthetic appeal. Participants' qualitative comments suggested that they found the app to be useful, easy to use, and likable or familiar relative to other existing apps. Participants suggested a variety of features to enhance the app, including adding features to enhance usability and reformatting certain aspects of the app to enhance interactivity and feedback to the user. Suggestions with uniformly positive reports were used to refine the app, while suggestions with mixed enthusiasm were not prioritized in refining the app.

Conclusions: We engaged target users of an educational app in an iterative app design process to create a product that would meet the needs and expectations of those users. Results suggested that the app was generally viewed as acceptable, useful, and visually appealing. Common suggestions for improvement, such as reformatting quizzes to enhance interactivity and provide feedback regarding correct answers, were used to refine the app. The refined app will be used in the future intervention efficacy trial.

Trial registration: ClinicalTrials NCT06763770; https://clinicaltrials.gov/study/NCT06763770.

背景：儿童癌症的年轻成年幸存者面临治疗的晚期和长期影响的风险，不到五分之一的人在成年后获得基于风险的护理。将年轻的成年幸存者从父母驱动的儿科护理过渡到成人自我驱动的护理是一个具有挑战性的过程，在这个过程中，年轻人面临着多重障碍。在此期间进行干预可能有助于更好地做好过渡准备。为此，我们先前开发了基于网络的健康管理（MYH）干预措施，初步显示出可行性和可接受性；然而，年轻的成年参与者希望通过移动应用程序访问干预。目的：我们使用了一个迭代的、共同创造的设计过程来翻译、构建和评估MYH基于网络的干预的可用性，并将其转化为一个移动应用程序，用于未来的同伴指导教育干预。方法：在第一阶段，我们与3个利益相关者小组进行了关键信息研讨会，以了解目标用户与移动应用程序的内容和设计相关的需求和期望。在第二阶段，我们对儿童癌症的年轻成年幸存者进行了可用性测试，以评估应用程序的可用性和主观吸引力。结果：关键信息研讨会的参与者（n=13）一致认为，拟议的应用程序的内容与儿童癌症的年轻成年幸存者面临的障碍相匹配。参与者对应用程序的设计提出了建议，包括内容和功能，尽管对包含游戏化功能的看法不一。可用性测试参与者（n=25）在技术接受度、可用性和审美吸引力方面对应用程序进行了高度评价。参与者的定性评论表明，他们发现该应用程序有用，易于使用，与其他现有应用程序相比，它更讨人喜欢或更熟悉。与会者建议了各种功能来增强应用程序，包括增加功能以增强可用性，并重新格式化应用程序的某些方面，以增强交互性和对用户的反馈。结论：我们让教育类应用的目标用户参与到迭代的应用设计过程中，从而创造出一款能够满足这些用户需求和期望的产品。结果表明，该应用程序通常被认为是可接受的，有用的，并且在视觉上很吸引人。常见的改进建议，如重新格式化测验以增强交互性，并提供正确答案的反馈，被用于改进应用程序。改进后的应用程序将用于未来的干预效果试验。试验注册：ClinicalTrials NCT06763770；https://clinicaltrials.gov/study/NCT06763770。
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引用次数: 0
FHIR Standard-Based Oncology Data Model for Cancer Screening: Design and Implementation Study. 基于FHIR标准的肿瘤数据模型用于癌症筛查：设计和实施研究。
IF 2.7 Q2 ONCOLOGY
JMIR Cancer
Pub Date : 2025-12-02 DOI: 10.2196/79011
Manisha Mantri, Sayali Satokar, Pritam Tambe, Cheenmaya Bhutad
<p><strong>Background: </strong>Cancer is a leading cause of death worldwide. Early detection through screening, diagnosis, and effective management can reduce cancer mortality. Risk assessment is crucial for improving outcomes by identifying high-risk individuals based on family history, genetics, lifestyle, and environment. Such targeted screening enhances accuracy and resource efficiency. However, the complex nature of oncology data-which includes clinical observations, lab results, radiology images, treatment regimens, and genetic information-presents significant challenges for data interoperability and exchange.</p><p><strong>Objective: </strong>This study proposes an oncology data model (ODM) based on the Fast Healthcare Interoperability Resources (FHIR) standard to facilitate the capturing, sharing, and processing of oncology data across various cancer care stages. We particularly focused on screening and risk assessment for 5 cancers: breast, cervical, esophageal, lung, and oral, within the Meghalaya Fourth Industrial Revolution for Sustainable Transformation Cancer Care pilot project in India.</p><p><strong>Methods: </strong>The ODM incorporates data elements from a cancer patient's journey across 5 phases: encounter, risk assessment, clinical investigation, treatment, and outcome. Essential oncology data elements were modeled using the Health Level 7 FHIR Revision 4 standard. Custom FHIR profiles were developed for cancer-specific use cases, with terminology mapped to Systematized Nomenclature of Medicine-Clinical Terms, Logical Observation Identifiers Names and Codes, and the International Classification of Diseases, 10th Revision. The implementation guide (IG) was created using FHIR Shorthand, SUSHI Unshortens Short Hand Inputs, and the Health Level 7 IG Publisher. Technical and clinical validation and a stakeholder usability assessment were conducted using a demonstration tool designed for implementer training and adoption.</p><p><strong>Results: </strong>The data model enhances interoperability across the cancer care continuum, from screening to treatment. The resulting IG includes 25 oncology-specific resource profiles and 50 standardized terminology value sets that support both semantic and syntactic interoperability. Central to the model are the FHIR Questionnaire and QuestionnaireResponse resources, customized for structured data collection in clinical and community settings, supporting cancer screening workflows. Technical validation yielded FHIR conformance and terminology binding, while clinical validation by oncologists and public health experts confirmed the usability and relevance of 5 screening questionnaires. The demonstration tool promoted stakeholder engagement and practical evaluation of the FHIR profiles.</p><p><strong>Conclusions: </strong>The FHIR-based ODM offers a unified framework for structured, interoperable cancer data exchange from screening to after treatment. This study marks the first comprehensive Indian
背景：癌症是世界范围内死亡的主要原因。通过筛查、诊断和有效管理的早期发现可以降低癌症死亡率。通过根据家族史、遗传、生活方式和环境来确定高危人群，风险评估对于改善预后至关重要。这种有针对性的筛选提高了准确性和资源效率。然而，肿瘤数据（包括临床观察、实验室结果、放射学图像、治疗方案和遗传信息）的复杂性为数据互操作性和交换带来了重大挑战。目的：本研究提出了一种基于快速医疗互操作性资源（FHIR）标准的肿瘤数据模型（ODM），以促进不同癌症护理阶段肿瘤数据的捕获、共享和处理。在印度梅加拉亚邦第四次工业革命促进可持续转型癌症护理试点项目中，我们特别关注五种癌症的筛查和风险评估：乳腺癌、宫颈癌、食道癌、肺癌和口腔癌。方法：ODM整合了癌症患者经历的5个阶段的数据元素：遭遇、风险评估、临床调查、治疗和结果。基本肿瘤学数据元素使用Health Level 7 FHIR Revision 4标准建模。针对癌症特定用例开发了自定义的FHIR概况，并将术语映射到《系统化医学-临床术语命名法》、《逻辑观察标识符名称和代码》以及《国际疾病分类，第十版》。实施指南（IG）是使用FHIR简写、SUSHI Unshortens Short Hand input和Health Level 7 IG Publisher创建的。使用为实施者培训和采用而设计的演示工具进行技术和临床验证以及利益相关者可用性评估。结果：该数据模型增强了从筛查到治疗整个癌症护理连续体的互操作性。最终的IG包括25个肿瘤学特定资源配置文件和50个支持语义和语法互操作性的标准化术语值集。该模型的核心是FHIR问卷和questionairerresponse资源，为临床和社区环境中的结构化数据收集定制，支持癌症筛查工作流程。技术验证产生了FHIR一致性和术语绑定，而肿瘤学家和公共卫生专家的临床验证证实了5个筛查问卷的可用性和相关性。演示工具促进了利益相关者的参与和对FHIR概况的实际评估。结论：基于fhr的ODM为从筛查到治疗后的结构化、可互操作的癌症数据交换提供了一个统一的框架。这项研究标志着印度首次将FHIR标准应用于肿瘤筛查和风险评估。与Ayushman Bharat digital Mission等国家数字卫生系统集成，可以确保筛查项目、医院和登记处之间一致的数据共享。未来的工作将侧重于实际模型的部署，在多个地区进行评估，扩展到治疗和生存数据，并促进国家采用，为癌症政策、研究和精确肿瘤学工作提供信息。
{"title":"FHIR Standard-Based Oncology Data Model for Cancer Screening: Design and Implementation Study.","authors":"Manisha Mantri, Sayali Satokar, Pritam Tambe, Cheenmaya Bhutad","doi":"10.2196/79011","DOIUrl":"10.2196/79011","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Cancer is a leading cause of death worldwide. Early detection through screening, diagnosis, and effective management can reduce cancer mortality. Risk assessment is crucial for improving outcomes by identifying high-risk individuals based on family history, genetics, lifestyle, and environment. Such targeted screening enhances accuracy and resource efficiency. However, the complex nature of oncology data-which includes clinical observations, lab results, radiology images, treatment regimens, and genetic information-presents significant challenges for data interoperability and exchange.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;This study proposes an oncology data model (ODM) based on the Fast Healthcare Interoperability Resources (FHIR) standard to facilitate the capturing, sharing, and processing of oncology data across various cancer care stages. We particularly focused on screening and risk assessment for 5 cancers: breast, cervical, esophageal, lung, and oral, within the Meghalaya Fourth Industrial Revolution for Sustainable Transformation Cancer Care pilot project in India.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;The ODM incorporates data elements from a cancer patient's journey across 5 phases: encounter, risk assessment, clinical investigation, treatment, and outcome. Essential oncology data elements were modeled using the Health Level 7 FHIR Revision 4 standard. Custom FHIR profiles were developed for cancer-specific use cases, with terminology mapped to Systematized Nomenclature of Medicine-Clinical Terms, Logical Observation Identifiers Names and Codes, and the International Classification of Diseases, 10th Revision. The implementation guide (IG) was created using FHIR Shorthand, SUSHI Unshortens Short Hand Inputs, and the Health Level 7 IG Publisher. Technical and clinical validation and a stakeholder usability assessment were conducted using a demonstration tool designed for implementer training and adoption.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;The data model enhances interoperability across the cancer care continuum, from screening to treatment. The resulting IG includes 25 oncology-specific resource profiles and 50 standardized terminology value sets that support both semantic and syntactic interoperability. Central to the model are the FHIR Questionnaire and QuestionnaireResponse resources, customized for structured data collection in clinical and community settings, supporting cancer screening workflows. Technical validation yielded FHIR conformance and terminology binding, while clinical validation by oncologists and public health experts confirmed the usability and relevance of 5 screening questionnaires. The demonstration tool promoted stakeholder engagement and practical evaluation of the FHIR profiles.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;The FHIR-based ODM offers a unified framework for structured, interoperable cancer data exchange from screening to after treatment. This study marks the first comprehensive Indian","PeriodicalId":45538,"journal":{"name":"JMIR Cancer","volume":"11 ","pages":"e79011"},"PeriodicalIF":2.7,"publicationDate":"2025-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12709152/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145662348","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Effectiveness and Adherence of Nutritional Management via Electronic Patient-Reported Outcomes Platform in Patients With Cancer: Multicenter Prospective Longitudinal Cohort Study. 通过电子患者报告结果平台对癌症患者进行营养管理的有效性和依从性：多中心前瞻性纵向队列研究。
IF 2.7 Q2 ONCOLOGY
JMIR Cancer
Pub Date : 2025-11-28 DOI: 10.2196/75633
Jiaxin Huang, Si-Wei Xie, Le Tian, Hui-Min Qu, Xi Zhang, Zhi-Gang Feng, Xin-Yi Wang, Zhen-Guang Du, Ming-Hui Zhang, Shu-Qing Wei, Jun Li, Li-Li Hong, Zhi-Cheng Zhou, Wen-Hui Yang, Wen-Hao Hu, Qian-Tong Dong, Ning Li, Min Yang, Meng Tang, Chen-Xin Song, Bao-Hua Zou, Sheng-Ling Qin, Rong Qin, Minghua Cong

Background: Personalized nutritional management during cancer remains challenging in clinical practice. The development of an electronic patient-reported outcome platform (ePROM) provides novel opportunities.

Objective: This study aimed to evaluate the effectiveness and adherence of nutritional management using ePROM in patients with cancer.

Methods: This multicenter prospective longitudinal cohort study included 6124 patients diagnosed with cancer. Exposure was defined as adherence to the ePROM journal, measured by the longest consecutive month of weekly entries. Dietary intake was reported via food selection, voice input, or meal photos. The primary outcomes were adequate energy intake (EI, ≥25 kcal/kg/day) and protein intake (PI, ≥1 g/kg/day), defined according to European Society for Clinical Nutrition and Metabolism (ESPEN) guidelines. Logistic regression analysis was conducted to identify the factors associated with EI and PI, reporting odds ratios (ORs) and 95% CIs. A restricted cubic spline plot was used to illustrate the association between the adjusted ORs and adherence duration. The semMediation approach was applied to assess the impact of multiple mediators on the outcomes.

Results: The study cohort comprised 3741/6124 (61.1%) men and 2383 (38.9%) women, with a median age of 60.85 (IQR 53.3-68.3) years. Overall, 1024/6124 (16.7%) and 2591/6124 (42.3%) patients achieved adequate EI and PI scores, respectively. At one month, 499/1024 patients (48.7%) in the adequate EI group and 1287/2591 (49.7%) in the adequate PI group continued journaling, compared with 1879/5100 (36.8%) and 1091/3533 (30.9%) in the corresponding inadequate groups (P<.001). This trend remained significant in the second, third, and sixth months. Logistic regression analysis demonstrated that longer adherence to ePROM journaling was independently associated with adequate EI (OR 1.05, 95% CI 1.01-1.08; P=.01) and PI (OR 1.22, 95% CI 1.16-1.28; P<.001) after adjusting for confounders. Mediation analysis revealed that most symptoms did not significantly mediate these effects, except for constipation, reflux, and delirium, which showed statistical significance but minimal indirect effects.

Conclusions: Nutritional management via ePROM is a feasible approach, with improved effectiveness as adherence duration increases. The observed benefits resulted primarily from direct effects rather than from symptom improvement.

背景：癌症期间的个性化营养管理在临床实践中仍然具有挑战性。电子患者报告结果平台（ePROM）的发展提供了新的机会。目的：本研究旨在评估ePROM对癌症患者营养管理的有效性和依从性。方法：这项多中心前瞻性纵向队列研究纳入了6124例诊断为癌症的患者。暴露被定义为坚持ePROM日志，以每周记录的最长连续月份来衡量。通过食物选择、语音输入或用餐照片来报告饮食摄入量。主要结局是足够的能量摄入（EI,≥25 kcal/kg/day）和蛋白质摄入（PI,≥1 g/kg/day），根据欧洲临床营养与代谢学会（ESPEN）指南定义。进行Logistic回归分析以确定与EI和PI相关的因素，报告优势比（ORs）和95% ci。使用限制性三次样条图来说明调整后的ORs与坚持时间之间的关系。采用semMediation方法评估多个中介对结果的影响。结果：研究队列包括3741/6124（61.1%）名男性和2383（38.9%）名女性，中位年龄为60.85 （IQR 53.3-68.3）岁。总体而言，1024/6124（16.7%）和2591/6124（42.3%）患者分别获得了足够的EI和PI评分。1个月时，EI充足组499/1024例患者（48.7%）和PI充足组1287/2591例患者（49.7%）继续记录日记，而相应的PI不足组分别为1879/5100例（36.8%）和1091/3533例（30.9%）。结论：通过ePROM进行营养管理是一种可行的方法，随着坚持时间的延长，其有效性得到提高。观察到的益处主要来自直接效果，而不是来自症状的改善。
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引用次数: 0
Comparison of Machine Learning Models for Colon Cancer Survival: Predictive Modeling Approach. 结肠癌生存的机器学习模型的比较：预测建模方法。
IF 2.7 Q2 ONCOLOGY
JMIR Cancer
Pub Date : 2025-11-26 DOI: 10.2196/72665
Reuben Adatorwovor, Motolani E Ogunsanya, Bin Huang, Richard Charnigo, Olufunmilola Abraham
<p><strong>Background: </strong>Colon cancer is a leading cause of cancer-related deaths worldwide, with survival influenced by risk factors, treatment type, and patient characteristics. Traditional statistical models, such as Kaplan-Meier curves, have been widely used to estimate survival probabilities. However, these models often have difficulty handling complex interactions, covariates, and nonlinear relationships between risk factors. Recently, machine learning (ML) techniques have emerged as promising tools for improving survival prediction by handling large covariates and capturing complex patterns.</p><p><strong>Objective: </strong>This study compares several ML models to accurately estimate colon cancer survival by leveraging data from the Kentucky Cancer Registry. By identifying key risk factors, these analyses aim to improve risk stratification, treatment planning, and prognosis for overall colon cancer survival within subgroups.</p><p><strong>Methods: </strong>We conducted a retrospective analysis of colon cancer cases diagnosed between 2010 and 2022 (n=33,825), using Kentucky Cancer Registry data linked to mortality records, with approval from the University of Kentucky Institutional Review Board (#63067). We compared multiple predictive modeling techniques, including Cox proportional hazards, accelerated failure time models, Extreme Gradient Boosting, random survival forests, least absolute shrinkage and selection operator (LASSO), and elastic net regression, to estimate survival probabilities. The Kaplan-Meier method provided baseline survival estimates, and multivariate models, including ML approaches, evaluated contributions of key risk factors. Model performance was compared across evaluation metrics such as the Brier score, concordance index, out-of-bag error, and Continuous Ranked Probability Score. Missing data were handled via multiple imputation, and leave-one-out cross-validation was applied to reduce overfitting.</p><p><strong>Results: </strong>The ML models identified key covariates influencing survival outcomes, such as age, treatment type, positive nodes, tumor stage, smoking, and comorbidities. In the overall model, patients who refused or received no treatment had a 3.24-fold higher risk of mortality compared to those who underwent surgery at primary and regional sites. Elevated mortality risk was also observed among smokers (24% higher than non-smokers) and Appalachian residents (7% higher than non-Appalachian residents). Our overall model achieved a concordance index of 0.8146, with strong discriminatory performance across subgroups, including early-age diagnosis (0.8175), late-age diagnosis (0.7841), Appalachia (0.8135), non-Appalachia (0.8126), White patients (0.8164), and Black patients (0.7881). The results highlight the strengths and limitations of each ML approach, with the random survival forest and LASSO models outperforming traditional methods such as the Cox model in prediction accuracy and model discrimin
背景：结肠癌是世界范围内癌症相关死亡的主要原因，其生存受危险因素、治疗类型和患者特征的影响。传统的统计模型，如Kaplan-Meier曲线，已被广泛用于估计生存概率。然而，这些模型通常难以处理复杂的相互作用、协变量和风险因素之间的非线性关系。最近，机器学习（ML）技术已经成为通过处理大协变量和捕获复杂模式来改善生存预测的有前途的工具。目的：本研究比较了几种机器学习模型，利用肯塔基州癌症登记处的数据准确估计结肠癌的生存。通过识别关键危险因素，这些分析旨在改善亚组内结肠癌总体生存的风险分层、治疗计划和预后。方法：我们对2010年至2022年间诊断的结肠癌病例（n=33,825）进行了回顾性分析，使用肯塔基大学机构审查委员会（#63067）批准的肯塔基州癌症登记处与死亡率记录相关的数据。我们比较了多种预测建模技术，包括Cox比例风险、加速失效时间模型、极端梯度增强、随机生存森林、最小绝对收缩和选择算子（LASSO）以及弹性净回归，以估计生存概率。Kaplan-Meier方法提供了基线生存估计，多变量模型（包括ML方法）评估了关键风险因素的贡献。模型的性能通过评估指标进行比较，如Brier评分、一致性指数、包外误差和连续排序概率评分。缺失数据通过多次输入处理，并采用留一交叉验证来减少过拟合。结果：ML模型确定了影响生存结果的关键协变量，如年龄、治疗类型、阳性淋巴结、肿瘤分期、吸烟和合并症。在整个模型中，拒绝或未接受治疗的患者死亡风险比在原发性和局部部位接受手术的患者高3.24倍。在吸烟者（比不吸烟者高24%）和阿巴拉契亚居民（比非阿巴拉契亚居民高7%）中也观察到较高的死亡风险。我们的整体模型的一致性指数为0.8146，在亚组间表现出很强的歧视性，包括早期诊断（0.8175）、晚期诊断（0.7841）、阿巴拉契亚（0.8135）、非阿巴拉契亚（0.8126）、白人（0.8164）和黑人（0.7881）。结果突出了每种机器学习方法的优势和局限性，随机生存森林和LASSO模型在预测精度和模型判别方面优于传统方法，如Cox模型。结论：我们的研究证明了ML在识别与结肠癌生存相关的危险因素方面的效用，在所有统计模型中，淋巴结阳性、诊断年龄、接受的治疗、临床肿瘤大小、肿瘤分级、吸烟状况、地理区域和婚姻状况成为主要预测因素。这一对比分析为临床决策和预后提供了有价值的见解，突出了ML识别不同亚组特定风险因素的潜力，最终推进了结肠癌患者的个性化护理。
{"title":"Comparison of Machine Learning Models for Colon Cancer Survival: Predictive Modeling Approach.","authors":"Reuben Adatorwovor, Motolani E Ogunsanya, Bin Huang, Richard Charnigo, Olufunmilola Abraham","doi":"10.2196/72665","DOIUrl":"https://doi.org/10.2196/72665","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Colon cancer is a leading cause of cancer-related deaths worldwide, with survival influenced by risk factors, treatment type, and patient characteristics. Traditional statistical models, such as Kaplan-Meier curves, have been widely used to estimate survival probabilities. However, these models often have difficulty handling complex interactions, covariates, and nonlinear relationships between risk factors. Recently, machine learning (ML) techniques have emerged as promising tools for improving survival prediction by handling large covariates and capturing complex patterns.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;This study compares several ML models to accurately estimate colon cancer survival by leveraging data from the Kentucky Cancer Registry. By identifying key risk factors, these analyses aim to improve risk stratification, treatment planning, and prognosis for overall colon cancer survival within subgroups.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;We conducted a retrospective analysis of colon cancer cases diagnosed between 2010 and 2022 (n=33,825), using Kentucky Cancer Registry data linked to mortality records, with approval from the University of Kentucky Institutional Review Board (#63067). We compared multiple predictive modeling techniques, including Cox proportional hazards, accelerated failure time models, Extreme Gradient Boosting, random survival forests, least absolute shrinkage and selection operator (LASSO), and elastic net regression, to estimate survival probabilities. The Kaplan-Meier method provided baseline survival estimates, and multivariate models, including ML approaches, evaluated contributions of key risk factors. Model performance was compared across evaluation metrics such as the Brier score, concordance index, out-of-bag error, and Continuous Ranked Probability Score. Missing data were handled via multiple imputation, and leave-one-out cross-validation was applied to reduce overfitting.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;The ML models identified key covariates influencing survival outcomes, such as age, treatment type, positive nodes, tumor stage, smoking, and comorbidities. In the overall model, patients who refused or received no treatment had a 3.24-fold higher risk of mortality compared to those who underwent surgery at primary and regional sites. Elevated mortality risk was also observed among smokers (24% higher than non-smokers) and Appalachian residents (7% higher than non-Appalachian residents). Our overall model achieved a concordance index of 0.8146, with strong discriminatory performance across subgroups, including early-age diagnosis (0.8175), late-age diagnosis (0.7841), Appalachia (0.8135), non-Appalachia (0.8126), White patients (0.8164), and Black patients (0.7881). The results highlight the strengths and limitations of each ML approach, with the random survival forest and LASSO models outperforming traditional methods such as the Cox model in prediction accuracy and model discrimin","PeriodicalId":45538,"journal":{"name":"JMIR Cancer","volume":"11 ","pages":"e72665"},"PeriodicalIF":2.7,"publicationDate":"2025-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12655889/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145641024","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Exploring Perspectives of Patients With Cancer on Implementing Electronic Patient-Reported Outcome Measures to Enhance Patient-Centered Care: Qualitative Study. 探讨癌症患者实施电子患者报告结果措施以加强以患者为中心的护理的观点：定性研究。
IF 2.7 Q2 ONCOLOGY
JMIR Cancer
Pub Date : 2025-11-25 DOI: 10.2196/79144
Terese Solvoll Skåre, Tonje Lundeby, Jo-Åsmund Lund, Elias David Lundereng, Stein Kaasa, Nienke de Glas, Karianne Røssummoen Øyen, Kristin Vassbotn Guldhav, May Helen Midtbust
<p><strong>Background: </strong>Systematic symptom management is a crucial component in patient-centered cancer care. Despite the development of numerous electronic patient-reported outcome measure (ePROM) tools, integrating these tools into clinical practice remains challenging. Engaging key stakeholders, including patients, in the development of ePROM tools is pivotal to fostering the adoption of such tools. As part of an innovation and implementation study aimed at enhancing efficiency and patient-centered care (PCC) through the development of digital PCC pathways, we explored the perspectives of patients with cancer on current clinical practice regarding symptom management and PCC, as well as their needs and preferences related to ePROMs.</p><p><strong>Objective: </strong>This study aims to explore the perspectives of patients with cancer on PCC and symptom management, including their experience with current clinical practice and their views on how ePROMs might enhance patient-centered follow-up.</p><p><strong>Methods: </strong>A 2-stage qualitative design was used. In stage 1, semistructured individual interviews were conducted to gain an in-depth understanding of patients' experiences with current clinical practice, including perceived challenges and unmet needs. Stage 2 involved structured interviews to further explore patients' perspectives on the potential role of ePROMs in enhancing patient-centered follow-up.</p><p><strong>Results: </strong>A total of 10 patients were included in the study, participating in either or both stages. Two main themes were developed through a reflexive thematic analysis process: (1) symptom management in the shadow of disease-centered care, and (2) ePROMs: bridging holistic care and disease management. Theme 1 highlighted how patients made sense of symptom management within a health care context primarily focused on disease treatment and progression. Their narratives revealed that biomedical concerns often dominated clinical encounters, while patients' broader lived experiences and symptom-related needs were marginalized. Patients shared an understanding that it was their own responsibility to redirect the focus of clinical consultations toward symptoms. While they generally expressed satisfaction with the care received, they also described a sense of unmet needs that remained unaddressed. The second theme explored how patients made sense of the potential role of an ePROM tool in supporting more patient-centered cancer care. Their accounts revealed both perceived barriers and facilitators to its use, shaped by the expectations and needs that contrasted with current clinical practices. Central to this was a belief, emerging through engagement with the conceptual tool's functionalities, that it could enable a more holistic approach to care, extending beyond physical symptom to encompass the lived experience of cancer.</p><p><strong>Conclusions: </strong>Patients often felt personally responsible for ensuring t
背景：系统的症状管理是以患者为中心的癌症治疗的重要组成部分。尽管开发了许多电子患者报告结果测量（ePROM）工具，但将这些工具整合到临床实践中仍然具有挑战性。让包括患者在内的关键利益相关者参与ePROM工具的开发对于促进此类工具的采用至关重要。作为一项创新和实施研究的一部分，旨在通过发展数字PCC途径提高效率和以患者为中心的护理（PCC），我们探讨了癌症患者对当前临床实践中症状管理和PCC的看法，以及他们对eprom的需求和偏好。目的：本研究旨在探讨癌症患者对PCC和症状管理的看法，包括他们目前临床实践的经验以及他们对eprom如何加强以患者为中心的随访的看法。方法：采用两阶段定性设计。在第一阶段，进行了半结构化的个人访谈，以深入了解患者在当前临床实践中的经历，包括感知到的挑战和未满足的需求。第二阶段包括结构化访谈，以进一步探讨患者对eprom在加强以患者为中心的随访中的潜在作用的看法。结果：共有10例患者被纳入研究，参与其中一个或两个阶段。通过反身性主题分析过程，开发了两个主要主题：(1)以疾病为中心的护理阴影下的症状管理；(2)eprom：连接整体护理和疾病管理。主题1强调了在主要关注疾病治疗和进展的卫生保健背景下，患者如何理解症状管理。他们的叙述表明，生物医学问题经常主导临床接触，而患者更广泛的生活经历和症状相关的需求被边缘化。患者都认为，将临床咨询的重点转向症状是他们自己的责任。虽然他们普遍对所得到的照顾表示满意，但他们也描述了一种未满足的需求仍未得到解决的感觉。第二个主题探讨了患者如何理解ePROM工具在支持更多以患者为中心的癌症治疗中的潜在作用。他们的描述揭示了其使用的感知障碍和促进因素，由与当前临床实践形成对比的期望和需求形成。其核心是一种信念，通过与概念工具的功能的接触，它可以实现更全面的护理方法，超越身体症状，涵盖癌症的生活经历。结论：患者常常觉得自己有责任确保症状得到解决，这表明随访和沟通方面存在不足。eprom被认为是一种很有前途的工具，可以通过放大患者的声音和实现更全面和响应性的随访来加强PCC。将eprom集成到常规护理中可以提高症状的可见性，促进患者和卫生保健专业人员之间的共同理解，并支持更公平的护理提供。
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引用次数: 0
Use of an Integrated Knowledge Translation Approach to Develop an Electronic Patient-Reported Outcome System for Cancer Rehabilitation: Tutorial. 使用综合知识翻译方法开发癌症康复电子患者报告结果系统：教程。
IF 2.7 Q2 ONCOLOGY
JMIR Cancer
Pub Date : 2025-11-24 DOI: 10.2196/74123
Christian Lopez, Sarah E Neil-Sztramko, Kristin L Campbell, David M Langelier, Tran Truong, Yuliya Gavrylyuk, Pia Nyakairu, Laura Parente, Audrey Durand, Jackie L Bender, Gillian Strudwick, Rupali Bhati, Jonathan Greenland, Tony Reiman, Jennifer M Jones
<p><strong>Unlabelled: </strong>Electronic prospective surveillance models (ePSMs) have the potential to improve the management of cancer-related impairments by systematically screening patients using electronic patient-reported outcomes during and after treatment, and linking them to tailored self-management resources and rehabilitation programs. However, their successful implementation into routine care requires careful consideration of patient and provider needs and must align with clinical workflows, which may vary across settings and require adaptation to the local context. The aim of this paper is to describe the development of REACH, a web-based ePSM designed to remotely screen for physical cancer-related impairments and direct patients to rehabilitation resources based on need. The development of REACH followed an integrated knowledge translation (iKT) approach, engaging key knowledge users including patients, clinicians, administrators, and information technology specialists. The development process involved collaboration across 5 working groups. The system content and logic group selected the impairments to be screened, measures used, frequency of screening, and resources recommended based on results of a survey with oncology providers and researchers, patient feedback, a literature review, and an environmental scan. The machine learning group explored predictive modeling approaches to optimize the assessment frequency using retrospective patient data. The implementation group identified features from existing systems that could be built to promote assessment completion and integration into clinical workflows through a scoping review, interviews with clinic staff, and focus groups with patients. The design group conducted co-design workshops and usability testing with patients to iteratively refine the interface and develop a prototype. Finally, the software development group converted the prototype to a web-based application and conducted privacy and security assessments and quality assurance. The integration of key knowledge users through an iKT approach played a critical role in determining the design and functionality of REACH. REACH allows patients to remotely complete assessments tailored to their cancer type and treatment status on any electronic device. The system generates automated advice based on the assessment responses, including links to educational resources for self-management, suggestions for community programs to register for, and recommendations to contact their oncology team for further assessment and possible referral to rehabilitation services. These recommended resources are stored in the patient's personalized library, organized by type and severity of cancer-related impairments reported, and are updated following each new electronic patient-reported outcomes assessment completed. Additional key system features include a patient-driven and structured process for managing high impairment scores, usability enhancem
未标记：电子前瞻性监测模型（ePSMs）有潜力通过在治疗期间和治疗后使用电子患者报告结果系统筛查患者，并将其与量身定制的自我管理资源和康复计划联系起来，从而改善癌症相关损伤的管理。然而，它们在常规护理中的成功实施需要仔细考虑患者和提供者的需求，并且必须与临床工作流程保持一致，这可能因环境而异，需要适应当地情况。本文的目的是描述REACH的发展，这是一个基于网络的ePSM，旨在远程筛查身体癌症相关的损伤，并根据需要指导患者康复资源。REACH的开发遵循了集成知识翻译（iKT）方法，吸引了包括患者、临床医生、管理人员和信息技术专家在内的关键知识用户。开发过程涉及5个工作组之间的协作。系统内容和逻辑组根据对肿瘤学提供者和研究人员的调查结果、患者反馈、文献综述和环境扫描选择要筛查的损伤、使用的措施、筛查频率和推荐的资源。机器学习小组探索了预测建模方法，利用回顾性患者数据优化评估频率。实施小组从现有系统中确定了可构建的特征，通过范围审查、与临床工作人员的访谈和与患者的焦点小组，促进评估的完成和融入临床工作流程。设计小组与患者进行了共同设计研讨会和可用性测试，反复改进界面并开发原型。最后，软件开发小组将原型转换为基于web的应用程序，并进行隐私和安全评估以及质量保证。通过iKT方法集成关键知识用户在确定REACH的设计和功能方面发挥了关键作用。REACH允许患者在任何电子设备上远程完成针对其癌症类型和治疗状态的评估。系统根据评估结果自动生成建议，包括链接到自我管理的教育资源，建议注册社区项目，建议联系肿瘤团队进行进一步评估，并可能转介到康复服务。这些推荐的资源存储在患者的个性化库中，根据报告的癌症相关损伤的类型和严重程度进行组织，并在每个新的电子患者报告结果评估完成后进行更新。系统的其他关键功能包括：患者驱动的结构化流程，用于管理高损伤评分，增强可用性以改善导航，以及确保数据安全的保障措施。REACH的开发证明了如何使用iKT方法来设计用户友好的、与临床相关的、与实施考虑相一致的ePSM。该系统已在加拿大4个癌症中心实施，目前正在对其实施情况进行评估，以便为未来的改进提供信息。
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引用次数: 0
Integrated, Cross-Entity Information on Preventive Measures for Bowel, Breast, and Prostate Cancer: Evaluation Study of the Web Application "Prevent-Take-Up". 肠癌、乳腺癌和前列腺癌预防措施的综合跨实体信息：Web应用程序“预防-摄取”的评估研究。
IF 2.7 Q2 ONCOLOGY
JMIR Cancer
Pub Date : 2025-11-19 DOI: 10.2196/76393
Angelika M R Kestler, Julian D Schwab, Tanja Jähnig, Michael Karl Melzer, Friedemann Zengerling, Stefan Lukac, Wolfgang Janni, Christian Bolenz, Anne Barzel, Hans A Kestler, Thomas Seufferlein
<p><strong>Background: </strong>About 40% of cancers are preventable through evidence-based interventions; however, uptake remains suboptimal. Knowledge and acceptance of primary and secondary preventive measures in the general population is not sufficient. We hypothesized that a web-based tool providing comprehensive, easily accessible, and individualized information on preventive strategies for multiple tumor entities could support informed decisions.</p><p><strong>Objective: </strong>This study aimed to evaluate an interactive web application offering guideline-based, risk-adapted information on preventive measures for colorectal, breast, and prostate cancer.</p><p><strong>Methods: </strong>The content of web application was developed based on German S3 guidelines. Evaluation questions consisted of the system usability scale questionnaire and queries developed by the Prevent-Take-up consortium. An initial version was tested by two focus groups comprising general practitioners (GPs), specialists and patients, revised and then made publicly available in GPs' offices, hospitals, and pharmacies. We report on the evaluation of the revised web application. Data were collected from 2022-2023. The web application also gathers information regarding family and individual risk factors and offers personalized recommendations (eg, to seek further information). Personal data related to specific recommendations were not stored. Participants receiving a recommendation from the web application were asked to anonymously answer questions about the information provided and the website application's functionality using a 5-point Likert scale. As breast cancer mainly occurs in women and prostate cancer only in men, the questions regarding prevention had intrinsic sex specific items and hence all data were evaluated by sex using descriptive statistics. The main evaluation questions were (1) usability or user-friendliness of the web application for cross-entity cancer prevention and (2) motivation of users to seek further preventive advice by the information received.</p><p><strong>Results: </strong>Data from the first 101 users (62 female, 38 male, one unspecified; predominantly aged 50-70 y) showed high score regarding user-friendliness (female 47/62, 76%; male 25/38, 65%), question comprehensibility (female 54/62, 87%; male 32/38, 83%), and the relevance of recommendations (female 47/62, 76%; male 24/38, 63%). A total of 37/62 (59%) of female and 16/38 (44%) of male participants appreciated the web application's functionality; 29/62 (47%) of female and 14/38 (37%) of male participants reported increased knowledge about prevention and early detection of colorectal, breast, and prostate cancers. Additionally, 44/62 (71%) of female and 18/38 (47%) of male participants expressed willingness to follow-up on the web application's recommendations and seek more information from their GPs.</p><p><strong>Conclusions: </strong>Our web application for risk-adapted prevention
背景：约40%的癌症可通过循证干预措施加以预防；然而，摄取仍然不够理想。一般人群对一级和二级预防措施的了解和接受程度是不够的。我们假设，一个基于网络的工具可以为多种肿瘤实体的预防策略提供全面、易于获取和个性化的信息，从而支持明智的决策。目的：本研究旨在评估一个交互式web应用程序，该应用程序提供基于指南的、风险适应的结肠直肠癌、乳腺癌和前列腺癌预防措施信息。方法：基于德国S3指南开发web应用程序内容。评估问题包括系统可用性量表问卷和由预防-摄取联盟开发的查询。最初的版本由由全科医生（gp）、专家和患者组成的两个焦点小组进行了测试，然后进行了修订，然后在全科医生办公室、医院和药房公开提供。我们报告了对修订后的web应用程序的评估。数据收集时间为2022-2023年。该网络应用程序还收集有关家庭和个人风险因素的信息，并提供个性化的建议（例如，寻求进一步的信息）。与具体推荐相关的个人数据未被存储。接受网络应用程序推荐的参与者被要求匿名回答有关所提供信息和网站应用程序功能的问题，使用5分李克特量表。由于乳腺癌主要发生在女性中，而前列腺癌仅发生在男性中，因此有关预防的问题具有内在的性别特异性项目，因此所有数据都使用描述性统计按性别进行评估。主要的评估问题是(1)跨实体预防癌症的web应用程序的可用性或用户友好性，以及(2)用户根据收到的信息寻求进一步预防建议的动机。结果：来自前101名用户（62名女性，38名男性，1名未指定，主要年龄在50-70岁之间）的数据显示，在用户友好性（女性47/62,76%；男性25/38,65%）、问题可理解性（女性54/62,87%；男性32/38,83%）和推荐相关性（女性47/62,76%；男性24/38,63%）方面得分很高。总共有37/62（59%）的女性参与者和16/38（44%）的男性参与者欣赏web应用程序的功能；29/62（47%）的女性和14/38（37%）的男性参与者表示，他们对预防和早期发现结直肠癌、乳腺癌和前列腺癌的了解有所增加。此外，44% /62（71%）的女性和18% /38（47%）的男性参与者表示愿意跟进网络应用程序的建议，并从他们的全科医生那里寻求更多信息。结论：我们针对多种癌症的风险适应性预防的网络应用程序被参与者评为用户友好型。在使用网络应用程序后，更多的女性参与者比男性更愿意寻求有关预防和早期检测措施的进一步信息。总的来说，我们的研究表明，网络应用程序可以成为提供综合和个性化预防建议的有用工具。
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引用次数: 0
Knowledge of Clinical Trials Among US Cancer Survivors: Cross-Sectional Study of HINTS-SEER Data. 美国癌症幸存者的临床试验知识：HINTS-SEER数据的横断面研究。
IF 2.7 Q2 ONCOLOGY
JMIR Cancer
Pub Date : 2025-11-19 DOI: 10.2196/76187
Aisha Tene Langford, Katrina Renee Ellis, Nancy Buderer, Navreet Singh

Background: Clinical trials are important for all stages of the cancer control continuum, including cancer survivorship.

Objective: The purpose of this study was to evaluate correlates of general clinical trial knowledge among US adult cancer survivors.

Methods: We conducted a cross-sectional analysis of the National Cancer Institute's 2021 Health Information National Trends Survey. Cancer survivors were recruited from 3 Surveillance, Epidemiology, and End Results registries: Iowa Cancer Registry, Greater Bay Area Cancer Registry, and New Mexico Tumor Registry. Data collection occurred from January 11 to August 20, 2021. Eligible participants had a cancer diagnosis prior to 2018. The primary outcome was self-reported knowledge of clinical trials, assessed by the question: "How would you describe your level of knowledge about clinical trials?" Responses were dichotomized as knowing "a lot" or "a little bit" versus "don't know anything." Independent variables included sociodemographic characteristics, patient-centered communication, health information seeking (including watching health-related videos on YouTube), and confidence in obtaining cancer-related information. We used survey-weighted logistic regression to examine univariable and multivariable associations with clinical trial knowledge. A total of 2 a priori hypotheses were specified: (1) cancer survivors with a higher perceived quality of patient-centered communication would have greater knowledge of clinical trials than those with a lower perceived quality of patient-centered communication and (2) cancer survivors who were "completely confident" in their ability to obtain cancer-related information would have greater knowledge of clinical trials than those less confident. Odds ratios (ORs), 95% CIs, and P values were estimated using SAS (version 9.4; SAS Institute Inc, Cary, NC, USA).

Results: Among cancer survivors (N=1207) included in the analysis, 269 (22.3%) reported that they did not know anything about clinical trials, while 938 (77.7%) reported knowing "a lot" or "a little." Neither of the 2 a priori hypotheses was supported. In the multivariable weighted logistic regression model, greater knowledge of clinical trials was significantly associated with non-Hispanic White race compared with all other races (OR 2.55, 95% CI 1.59, 4.08; P<.001), having a college degree compared with less than a college degree (OR 3.50, 95% CI 2.25, 5.46; P<.001), seeking cancer information from any source (OR 3.04, 95% CI 2.10-4.40; P<.001) compared with not, and ever watched health-related videos on YouTube (OR 2.71, 95% CI 1.49-4.94; P=.002) compared with never watched. In contrast, female sex assigned at birth was associated with lower odds of clinical trial knowledge compared with male sex assigned at birth (OR 0.57, 95% CI 0.41-0.80; P<.001).

Conclusions: Sociodemographic characteristics

背景：临床试验对癌症控制的所有阶段都很重要，包括癌症生存期。目的：本研究的目的是评估美国成年癌症幸存者的一般临床试验知识的相关因素。方法：我们对国家癌症研究所的2021年健康信息国家趋势调查进行了横断面分析。癌症幸存者从3个监测、流行病学和最终结果登记处招募：爱荷华州癌症登记处、大湾区癌症登记处和新墨西哥州肿瘤登记处。数据收集时间为2021年1月11日至8月20日。符合条件的参与者在2018年之前被诊断患有癌症。主要结果是自我报告的临床试验知识，通过以下问题进行评估：“你如何描述你对临床试验的知识水平？”回答被分为“知道很多”、“知道一点点”和“什么都不知道”。自变量包括社会人口学特征、以患者为中心的沟通、健康信息搜索（包括在YouTube上观看与健康相关的视频）和获取癌症相关信息的信心。我们使用调查加权逻辑回归来检验与临床试验知识的单变量和多变量关联。我们提出了2个先验假设：(1)以患者为中心的沟通感知质量较高的癌症幸存者比以患者为中心的沟通感知质量较低的癌症幸存者更了解临床试验；(2)对自己获得癌症相关信息的能力“完全自信”的癌症幸存者比不太自信的癌症幸存者更了解临床试验。使用SAS（版本9.4;SAS Institute Inc, Cary， NC， USA）估计优势比（ORs）、95% ci和P值。结果：在纳入分析的癌症幸存者（N=1207）中，269人（22.3%）报告他们对临床试验一无所知，而938人（77.7%）报告知道“很多”或“一点”。这两个先验假设都没有得到支持。在多变量加权logistic回归模型中，与所有其他种族相比，更多的临床试验知识与非西班牙裔白人种族显著相关(OR 2.55, 95% CI 1.59, 4.08； p结论：社会人口统计学特征和寻求健康的行为，包括在YouTube上观看与健康相关的视频，与癌症幸存者的临床试验知识相关。这些发现突出了利用YouTube作为促进临床试验意识和加强幸存者癌症特异性信息寻求技能以改善临床试验信息获取的机会。
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引用次数: 0
Evaluation of Cancer Survivors' Experience of Using AI-Based Conversational Tools: Qualitative Study. 评估癌症幸存者使用基于人工智能的会话工具的经验：定性研究。
IF 2.7 Q2 ONCOLOGY
JMIR Cancer
Pub Date : 2025-11-14 DOI: 10.2196/77390
Saif Khairat, Hanna Mehraby, Safoora Masoumi, Melissa Coffel, Callie Rockey-Bartlett, Andrea Huang, William Wood, Ethan Basch
<p><strong>Background: </strong>Cancer survivorship is a complicated, chronic, and long-lasting experience, causing uncertainty and a wide range of physical and emotional health concerns. Due to the complexity of cancer, patients often seek out multiple sources of health information to better understand the aspects of their cancer diagnosis. The high variability among patients with cancer presents significant challenges in treatment, prognosis, and overall disease management. Artificial intelligence (AI) chatbots can further personalize cancer care delivery. However, there is a knowledge gap regarding cancer survivors' perceived facilitators and barriers to adopting and using AI chatbots.</p><p><strong>Objective: </strong>In this study, we examined cancer survivors' experiences of using existing AI chatbots and identified their facilitators and barriers to the adoption of AI chatbots.</p><p><strong>Methods: </strong>We conducted a qualitative study to investigate the perceptions of cancer survivors, conducting semistructured interviews to understand their prior use of existing AI chatbots in general. We asked the participants about their perceptions regarding AI chatbot acceptability and comfort level; trust and adherence; and concerns, barriers, and suggestions. We used the Consolidated Criteria for Reporting Qualitative Research (COREQ) checklist for this qualitative report.</p><p><strong>Results: </strong>Of 21 participants, 17 (81%) were female patients with breast cancer, 15 (71%) were aged 50 to 64 years, 19 (90%) were White, and 9 (43%) had a graduate degree. Participants' responses were grouped into three overarching themes: (1) patients' perceptions of interacting with chatbots compared to health care professionals, (2) patient-chatbot interaction, and (3) chatbot information processing. All participants who were interviewed reported that they would prefer interacting with health care professionals over a chatbot. The lack of empathy shown by chatbots was a major concern among cancer survivors. Many patients criticized chatbots for tending to provide a general overarching response to their questions rather than being specific to their cancer diagnosis. The main concerns of cancer survivors with using chatbots were the overabundance of general information that was often not relevant to their diagnosis and privacy of patient information.</p><p><strong>Conclusions: </strong>The findings of this study underscore the critical importance of empathetic responses during AI chatbot interactions for cancer survivors, as the lack of personalized and emotional responses can lead to distrust and frustration. Clinically, these tools should be integrated as supplementary resources to enhance patient engagement while preserving essential human support. Policymakers need to develop guidelines that promote responsible use of AI in cancer care, prioritizing patient confidentiality and trustworthiness. AI chatbots have the potential to significantly improve
背景：癌症幸存者是一个复杂的、慢性的、长期的经历，引起不确定性和广泛的生理和心理健康问题。由于癌症的复杂性，患者经常寻求多种来源的健康信息，以更好地了解他们的癌症诊断的各个方面。癌症患者之间的高变异性在治疗、预后和整体疾病管理方面提出了重大挑战。人工智能（AI）聊天机器人可以进一步个性化癌症治疗。然而，关于癌症幸存者认为的促进因素和采用和使用人工智能聊天机器人的障碍，存在知识差距。目的：在本研究中，我们研究了癌症幸存者使用现有人工智能聊天机器人的经历，并确定了他们采用人工智能聊天机器人的促进因素和障碍。方法：我们进行了一项定性研究，调查癌症幸存者的看法，通过半结构化访谈了解他们之前使用现有人工智能聊天机器人的情况。我们询问了参与者对人工智能聊天机器人的可接受性和舒适度的看法；信任和坚持；以及顾虑、障碍和建议。我们在这个定性报告中使用了报告定性研究的综合标准（COREQ）清单。结果：在21名参与者中，17名（81%）为女性乳腺癌患者，15名（71%）年龄在50至64岁之间，19名（90%）为白人，9名（43%）具有研究生学位。参与者的回答分为三个主要主题：(1)与医疗保健专业人员相比，患者对与聊天机器人互动的看法；(2)患者与聊天机器人的互动；(3)聊天机器人信息处理。所有接受采访的参与者都报告说，他们更喜欢与医疗保健专业人员互动，而不是聊天机器人。聊天机器人缺乏同理心是癌症幸存者的一个主要担忧。许多患者批评聊天机器人倾向于对他们的问题提供一个笼统的回答，而不是针对他们的癌症诊断。癌症幸存者使用聊天机器人的主要担忧是，通常与他们的诊断无关的一般信息过多，以及患者信息的隐私。结论：本研究的发现强调了人工智能聊天机器人互动过程中移情反应对癌症幸存者的重要性，因为缺乏个性化和情感反应会导致不信任和沮丧。在临床上，这些工具应作为补充资源加以整合，以提高患者参与度，同时保留必要的人力支持。政策制定者需要制定指导方针，促进在癌症治疗中负责任地使用人工智能，优先考虑患者的机密性和可信度。人工智能聊天机器人有可能显著改善为癌症幸存者提供的支持，但解决已确定的障碍并提高用户接受度至关重要。
{"title":"Evaluation of Cancer Survivors' Experience of Using AI-Based Conversational Tools: Qualitative Study.","authors":"Saif Khairat, Hanna Mehraby, Safoora Masoumi, Melissa Coffel, Callie Rockey-Bartlett, Andrea Huang, William Wood, Ethan Basch","doi":"10.2196/77390","DOIUrl":"10.2196/77390","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Cancer survivorship is a complicated, chronic, and long-lasting experience, causing uncertainty and a wide range of physical and emotional health concerns. Due to the complexity of cancer, patients often seek out multiple sources of health information to better understand the aspects of their cancer diagnosis. The high variability among patients with cancer presents significant challenges in treatment, prognosis, and overall disease management. Artificial intelligence (AI) chatbots can further personalize cancer care delivery. However, there is a knowledge gap regarding cancer survivors' perceived facilitators and barriers to adopting and using AI chatbots.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;In this study, we examined cancer survivors' experiences of using existing AI chatbots and identified their facilitators and barriers to the adoption of AI chatbots.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;We conducted a qualitative study to investigate the perceptions of cancer survivors, conducting semistructured interviews to understand their prior use of existing AI chatbots in general. We asked the participants about their perceptions regarding AI chatbot acceptability and comfort level; trust and adherence; and concerns, barriers, and suggestions. We used the Consolidated Criteria for Reporting Qualitative Research (COREQ) checklist for this qualitative report.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;Of 21 participants, 17 (81%) were female patients with breast cancer, 15 (71%) were aged 50 to 64 years, 19 (90%) were White, and 9 (43%) had a graduate degree. Participants' responses were grouped into three overarching themes: (1) patients' perceptions of interacting with chatbots compared to health care professionals, (2) patient-chatbot interaction, and (3) chatbot information processing. All participants who were interviewed reported that they would prefer interacting with health care professionals over a chatbot. The lack of empathy shown by chatbots was a major concern among cancer survivors. Many patients criticized chatbots for tending to provide a general overarching response to their questions rather than being specific to their cancer diagnosis. The main concerns of cancer survivors with using chatbots were the overabundance of general information that was often not relevant to their diagnosis and privacy of patient information.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;The findings of this study underscore the critical importance of empathetic responses during AI chatbot interactions for cancer survivors, as the lack of personalized and emotional responses can lead to distrust and frustration. Clinically, these tools should be integrated as supplementary resources to enhance patient engagement while preserving essential human support. Policymakers need to develop guidelines that promote responsible use of AI in cancer care, prioritizing patient confidentiality and trustworthiness. AI chatbots have the potential to significantly improve","PeriodicalId":45538,"journal":{"name":"JMIR Cancer","volume":"11 ","pages":"e77390"},"PeriodicalIF":2.7,"publicationDate":"2025-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12617959/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145524619","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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