JCO Clinical Cancer Informatics最新文献

{"title":"Machine Learning to Predict the Individual Risk of Treatment-Relevant Toxicity for Patients With Breast Cancer Undergoing Neoadjuvant Systemic Treatment.","authors":"Lie Cai, Thomas M Deutsch, Chris Sidey-Gibbons, Michelle Kobel, Fabian Riedel, Katharina Smetanay, Carlo Fremd, Laura Michel, Michael Golatta, Joerg Heil, Andreas Schneeweiss, André Pfob","doi":"10.1200/CCI.24.00010","DOIUrl":"10.1200/CCI.24.00010","url":null,"abstract":"<p><strong>Purpose: </strong>Toxicity to systemic cancer treatment represents a major anxiety for patients and a challenge to treatment plans. We aimed to develop machine learning algorithms for the upfront prediction of an individual's risk of experiencing treatment-relevant toxicity during the course of treatment.</p><p><strong>Methods: </strong>Clinical records were retrieved from a single-center, consecutive cohort of patients who underwent neoadjuvant treatment for early breast cancer. We developed and validated machine learning algorithms to predict grade 3 or 4 toxicity (anemia, neutropenia, deviation of liver enzymes, nephrotoxicity, thrombopenia, electrolyte disturbance, or neuropathy). We used 10-fold cross-validation to develop two algorithms (logistic regression with elastic net penalty [GLM] and support vector machines [SVMs]). Algorithm predictions were compared with documented toxicity events and diagnostic performance was evaluated via area under the curve (AUROC).</p><p><strong>Results: </strong>A total of 590 patients were identified, 432 in the development set and 158 in the validation set. The median age was 51 years, and 55.8% (329 of 590) experienced grade 3 or 4 toxicity. The performance improved significantly when adding referenced treatment information (referenced regimen, referenced summation dose intensity product) in addition to patient and tumor variables: GLM AUROC 0.59 versus 0.75, <i>P</i> = .02; SVM AUROC 0.64 versus 0.75, <i>P</i> = .01.</p><p><strong>Conclusion: </strong>The individual risk of treatment-relevant toxicity can be predicted using machine learning algorithms. We demonstrate a promising way to improve efficacy and facilitate proactive toxicity management of systemic cancer treatment.</p>","PeriodicalId":51626,"journal":{"name":"JCO Clinical Cancer Informatics","volume":"8 ","pages":"e2400010"},"PeriodicalIF":3.3,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11670908/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142883088","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}

{"title":"Actionability of Synthetic Data in a Heterogeneous and Rare Health Care Demographic: Adolescents and Young Adults With Cancer.","authors":"Joshi Hogenboom, Aiara Lobo Gomes, Andre Dekker, Winette Van Der Graaf, Olga Husson, Leonard Wee","doi":"10.1200/CCI.24.00056","DOIUrl":"10.1200/CCI.24.00056","url":null,"abstract":"<p><strong>Purpose: </strong>Research on rare diseases and atypical health care demographics is often slowed by high interparticipant heterogeneity and overall scarcity of data. Synthetic data (SD) have been proposed as means for data sharing, enlargement, and diversification, by artificially generating real phenomena while obscuring the real patient data. The utility of SD is actively scrutinized in health care research, but the role of sample size for actionability of SD is insufficiently explored. We aim to understand the interplay of actionability and sample size by generating SD sets of varying sizes from gradually diminishing amounts of real individuals' data. We evaluate the actionability of SD in a highly heterogeneous and rare demographic: adolescents and young adults (AYAs) with cancer.</p><p><strong>Methods: </strong>A population-based cross-sectional cohort study of 3,735 AYAs was subsampled at random to produce 13 training data sets of varying sample sizes. We studied four distinct generator architectures built on the open-source Synthetic Data Vault library. Each architecture was used to generate SD of varying sizes on the basis of each aforementioned training subsets. SD actionability was assessed by comparing the resulting SD with their respective real data against three metrics-veracity, utility, and privacy concealment.</p><p><strong>Results: </strong>All examined generator architectures yielded actionable data when generating SD with sizes similar to the real data. Large SD sample size increased veracity but generally increased privacy risks. Using fewer training participants led to faster convergence in veracity, but partially exacerbated privacy concealment issues.</p><p><strong>Conclusion: </strong>SD is a potentially promising option for data sharing and data augmentation, yet sample size plays a significant role in its actionability. SD generation should go hand-in-hand with consistent scrutiny, and sample size should be carefully considered in this process.</p>","PeriodicalId":51626,"journal":{"name":"JCO Clinical Cancer Informatics","volume":"8 ","pages":"e2400056"},"PeriodicalIF":3.3,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11627331/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142774439","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}

{"title":"Toward the Clinically Effective Evaluation of Artificial Intelligence-Generated Responses.","authors":"Silambarasan Anbumani, Ergun Ahunbay","doi":"10.1200/CCI-24-00258","DOIUrl":"https://doi.org/10.1200/CCI-24-00258","url":null,"abstract":"","PeriodicalId":51626,"journal":{"name":"JCO Clinical Cancer Informatics","volume":"8 ","pages":"e2400258"},"PeriodicalIF":3.3,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142814874","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Measurement of Completeness and Timeliness of Linked Electronic Health Record Pharmacy Data for Early Detection of Nonadherence to Breast Cancer Adjuvant Endocrine Therapy.","authors":"Chelsea McPeek, Shirlene Paul, Jordan Lieberenz, Mia Levy","doi":"10.1200/CCI.24.00115","DOIUrl":"10.1200/CCI.24.00115","url":null,"abstract":"<p><strong>Purpose: </strong>This retrospective cohort study evaluated whether linked electronic health record (EHR) pharmacy data were adequately complete and timely to detect primary nonadherence to breast cancer adjuvant endocrine therapy (AET).</p><p><strong>Materials and methods: </strong>Linked EHR pharmacy data were extracted from the EHR for patients with stage 0 to III breast cancer who had their first prescription order for AET between 2016 and 2021. Patients with the first dispense event within 90 days of the prescription were classified as having sufficient or insufficient data available for early detection of primary adherence.</p><p><strong>Results: </strong>A total of 1,446 eligible patients had a first AET prescription order between 2016 and 2021; these orders were routed to 871 unique pharmacies, of which 856 (98.2%) were contracted with the linked EHR pharmacy database and 15 (1.8%) were not contracted. Among the 1,428 patients with a first prescription sent to a contract pharmacy, 164 (13%) had incomplete linked EHR pharmacy data refresh events to assess primary adherence. Among the 1,244 patients with at least 1 refresh event after their first prescription, 82% occurred within 90 days and were sufficiently timely for early detection of primary adherence. Overall, 32% of patients would benefit from an intervention to verify or improve primary adherence to AET.</p><p><strong>Conclusion: </strong>Although linked EHR pharmacy data have adequate completeness of contract pharmacy data, local configurations of data refresh events tailored to medication reconciliation workflows are incomplete (13%) and insufficiently timely (32%) to fully support clinical decision support (CDS) for early detection of primary medication nonadherence. Prospective CDS interventions using linked EHR pharmacy data are possible with enhancements to the frequency and timeliness of refresh events.</p>","PeriodicalId":51626,"journal":{"name":"JCO Clinical Cancer Informatics","volume":"8 ","pages":"e2400115"},"PeriodicalIF":3.3,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11658023/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142819612","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}

{"title":"Symptom Monitoring App Use Associated With Medication Adherence Among Woman Survivors of Breast Cancer on Adjuvant Endocrine Therapy.","authors":"Rebecca A Krukowski, Xin Hu, Sara Arshad, Janeane N Anderson, Edward Stepanski, Gregory A Vidal, Lee S Schwartzberg, Ilana Graetz","doi":"10.1200/CCI-24-00179","DOIUrl":"10.1200/CCI-24-00179","url":null,"abstract":"<p><strong>Purpose: </strong>Oral adjuvant endocrine therapy (AET) reduces the risk of cancer recurrence and death for women with hormone receptor-positive (HR+) breast cancer. Because of adverse symptoms and socioecologic barriers, AET adherence rates are low. We conducted post hoc analyses of a randomized trial of a remote symptom and adherence monitoring app to evaluate characteristics associated with higher app use, satisfaction, and how app use was associated with AET adherence.</p><p><strong>Methods: </strong>Patients prescribed AET were randomly assigned to receive one of three intervention conditions: app, app + feedback, or enhanced usual care. Baseline and 6-month follow-up surveys, app use, and pillbox-monitored AET adherence data for app and app + feedback participants were used. Logistic regression evaluated the association between sociodemographic/clinical characteristics and app utilization and satisfaction, and how app use was associated with AET adherence (>80%).</p><p><strong>Results: </strong>Overall, 163 women with early-stage HR+ breast cancer were included; 35.0% had high app use (≥75% of weeks enrolled). No sociodemographic characteristics were associated with app use. Satisfaction with the app was higher among those who were younger (88.9% for age 31-49 years <i>v</i> 54.9% for age 65+ years, <i>P</i> < .001), identified as White (76.8% <i>v</i> 60.1% for Black, <i>P</i> = .045), had lower health literacy (85.4% <i>v</i> 68.2% with higher health literacy, <i>P</i> = .017), or were nonurban residents (85.7% <i>v</i> 68.6% for urban, <i>P</i> = .021). Most participants (90.3%) with high app use were AET-adherent compared with 66.8% for those with lower app use (<i>P</i> < .001).</p><p><strong>Conclusion: </strong>Use of a remote monitoring app was similar across sociodemographic characteristics, and more frequent app use was associated with a higher likelihood of 6-month AET adherence. Encouraging women to monitor medication adherence and communicate adverse symptoms could improve AET adherence.</p>","PeriodicalId":51626,"journal":{"name":"JCO Clinical Cancer Informatics","volume":"8 ","pages":"e2400179"},"PeriodicalIF":3.3,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11631045/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142789699","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}

{"title":"Real-World and Clinical Trial Validation of a Deep Learning Radiomic Biomarker for PD-(L)1 Immune Checkpoint Inhibitor Response in Advanced Non-Small Cell Lung Cancer.","authors":"Chiharu Sako, Chong Duan, Kevin Maresca, Sean Kent, Taly Gilat Schmidt, Hugo J W L Aerts, Ravi B Parikh, George R Simon, Petr Jordan","doi":"10.1200/CCI.24.00133","DOIUrl":"10.1200/CCI.24.00133","url":null,"abstract":"<p><strong>Purpose: </strong>This study developed and validated a novel deep learning radiomic biomarker to estimate response to immune checkpoint inhibitor (ICI) therapy in advanced non-small cell lung cancer (NSCLC) using real-world data (RWD) and clinical trial data.</p><p><strong>Materials and methods: </strong>Retrospective RWD of 1,829 patients with advanced NSCLC treated with PD-(L)1 ICIs were collected from 10 academic and community institutions in the United States and Europe. The RWD included data sets for discovery (Data Set A-Discovery, n = 1,173) and independent test (Data Set B, n = 458). A radiomic pipeline, containing a deep learning feature extractor and a survival model, generated the computed tomography (CT) response score (CTRS) applied to the pretreatment routine CT/positron emission tomography (PET)-CT scan. An enhanced CTRS (eCTRS) also incorporated age, sex, treatment line, and lesion annotations. Performance was evaluated against progression-free survival (PFS) and overall survival (OS). Biomarker generalizability was further evaluated using a secondary analysis of a prospective clinical trial (ClinicalTrials.gov identifier: NCT02573259) evaluating the PD-1 inhibitor sasanlimab in second or later line of treatment (Data Set C, n = 54).</p><p><strong>Results: </strong>In RWD Test Data Set B, the CTRS identified patients with a high probability of response to ICI with a PFS hazard ratio (HR) of 0.46 (95% CI, 0.26 to 0.82) and an OS HR of 0.50 (95% CI, 0.28 to 0.92) in the first-line ICI monotherapy cohort, after adjustment for baseline covariates including the PD-L1 tumor proportion score. In Clinical Trial Data Set C, the CTRS demonstrated an adjusted PFS HR of 1.03 (95% CI, 0.43 to 2.47) and an OS HR of 0.33 (95% CI, 0.14 to 0.91). The CTRS and eCTRS outperformed traditional imaging biomarkers of lesion size in PFS and OS for RWD Test Data Set B and in OS for the Clinical Trial Data Set.</p><p><strong>Conclusion: </strong>The study developed and validated a deep learning radiomic biomarker using pretreatment routine CT/PET-CT scans to identify ICI benefit in advanced NSCLC.</p>","PeriodicalId":51626,"journal":{"name":"JCO Clinical Cancer Informatics","volume":"8 ","pages":"e2400133"},"PeriodicalIF":3.3,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11658027/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142822824","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}

{"title":"Development, Validation, and Clinical Utility of Electronic Patient-Reported Outcome Measure-Enhanced Prediction Models for Overall Survival in Patients With Advanced Non-Small Cell Lung Cancer Receiving Immunotherapy.","authors":"Kuan Liao, Sabine N van der Veer, Fabio Gomes, Corinne Faivre-Finn, Janelle Yorke, Matthew Sperrin","doi":"10.1200/CCI.24.00035","DOIUrl":"https://doi.org/10.1200/CCI.24.00035","url":null,"abstract":"<p><strong>Purpose: </strong>Electronic patient-reported outcome measures (ePROMs) are increasingly collected routinely in clinical practice and may be prognostic for survival in adults with advanced non-small cell lung cancer (NSCLC) in addition to clinical data. This study developed ePROM-enhanced models for predicting 1-year overall survival in patients with advanced NSCLC at the start of immunotherapy.</p><p><strong>Methods: </strong>This is a single-center study using consecutive patients from a tertiary cancer hospital in England. Using Cox proportional hazards models, we developed one clinical factor-only model and three ePROM-enhanced models, each including one of the following factors: quality of life (as measured by EuroQoL five-dimension five-level utility score) and overall symptom burden and number of moderate-to-severe symptoms (as measured by patient-reported version of Common Terminology Criteria for Adverse Events). Predictive performance was evaluated and compared through bootstrapping internal validation, and clinical utility was determined via decision curve analysis.</p><p><strong>Results: </strong>The clinical factor-only model contained age, histology, performance status, and neutrophile-to-lymphocyte ratio. While calibration was similar between the clinical factor-only and ePROM-enhanced models, the latter showed improved discrimination by 0.020 (95% CI, 0.011 to 0.024), 0.024 (95% CI, 0.016 to 0.031), and 0.024 (95% CI, 0.014 to 0.029) when enhanced with ePROMs on quality of life, overall symptom burden, and number of moderate-to-severe symptoms, respectively. If care decisions are to be made at risk thresholds between 25% and 75%, the ePROM-enhanced models led to higher net benefit than the clinical factor-only model and the default strategies of intervention for all and intervention for none.</p><p><strong>Conclusion: </strong>The ePROM-enhanced models outperformed the clinical factor-only model in predicting 1-year overall survival for patients with advanced NSCLC receiving immunotherapy and showed potential clinical utility for informing decisions in this population. Future studies should focus on validating the models in external data sets.</p>","PeriodicalId":51626,"journal":{"name":"JCO Clinical Cancer Informatics","volume":"8 ","pages":"e2400035"},"PeriodicalIF":3.3,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142734551","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Application of Artificial Intelligence in Symptom Monitoring in Adult Cancer Survivorship: A Systematic Review.","authors":"Sanam Tabataba Vakili, Darren Haywood, Deborah Kirk, Aalaa M Abdou, Ragisha Gopalakrishnan, Sarina Sadeghi, Helena Guedes, Chia Jie Tan, Carla Thamm, Rhys Bernard, Henry C Y Wong, Elaine P Kuhn, Jennifer Y Y Kwan, Shing Fung Lee, Nicolas H Hart, Catherine Paterson, Deepti A Chopra, Amanda Drury, Elwyn Zhang, Shayan Raeisi Dehkordi, Fredrick D Ashbury, Grigorios Kotronoulas, Edward Chow, Michael Jefford, Raymond J Chan, Rouhi Fazelzad, Srinivas Raman, Muna Alkhaifi","doi":"10.1200/CCI.24.00119","DOIUrl":"https://doi.org/10.1200/CCI.24.00119","url":null,"abstract":"<p><strong>Purpose: </strong>The adoption of artificial intelligence (AI) in health care may afford new avenues for personalized and patient-centered care. This systematic review explored the role of AI in symptom monitoring for adult cancer survivors.</p><p><strong>Methods: </strong>A comprehensive search was performed from inception to November 2023 in seven bibliographic databases and three clinical trial registries. This PROSPERO registered review (ID: CRD42023476027) assessed reports of empirical research studies of AI use in symptom monitoring (physical and psychological symptoms) across all cancer types in adults.</p><p><strong>Results: </strong>A total of 18,530 reports were identified, of which 41 met review criteria and were analyzed. Included studies were predominantly published between 2021 and 2023, originated in the United States (39.0%) and Japan (14.6%), and primarily used cohort designs (80.5%), followed by cross-sectional designs (12.2%). The mean sample size was 617.14 (standard deviation = 1,401.37), with most studies primarily including multiple tumor types (31.7%) or breast cancer survivors (26.8%). Machine learning algorithms (43.9%) was the most used AI method, followed by natural language processing (29.3%), AI-driven chatbots (17.1%), and decision support tools (9.8%). The most common inputs to the AI algorithms were textual data, patient-reported symptoms, and physiologic measurements. The most examined symptom was pain (34.2% of studies), followed by fatigue and nausea (17.1% of studies each). Overall, the review showed increasing AI technology use in the prediction and monitoring of cancer symptoms.</p><p><strong>Conclusion: </strong>AI is being used to enhance symptom monitoring in various cancer settings. When considering integration into clinical practice, standardization of data capture, the use of analytics, investing in infrastructure, and the end-user experience should be considered for successful implementation and monitoring the improvement of patient outcomes.</p>","PeriodicalId":51626,"journal":{"name":"JCO Clinical Cancer Informatics","volume":"8 ","pages":"e2400119"},"PeriodicalIF":3.3,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142774444","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Volumetric Breast Density Estimation From Three-Dimensional Reconstructed Digital Breast Tomosynthesis Images Using Deep Learning.","authors":"Vinayak S Ahluwalia, Nehal Doiphode, Walter C Mankowski, Eric A Cohen, Sarthak Pati, Lauren Pantalone, Spyridon Bakas, Ari Brooks, Celine M Vachon, Emily F Conant, Aimilia Gastounioti, Despina Kontos","doi":"10.1200/CCI.24.00103","DOIUrl":"10.1200/CCI.24.00103","url":null,"abstract":"<p><strong>Purpose: </strong>Breast density is a widely established independent breast cancer risk factor. With the increasing utilization of digital breast tomosynthesis (DBT) in breast cancer screening, there is an opportunity to estimate volumetric breast density (VBD) routinely. However, current available methods extrapolate VBD from two-dimensional (2D) images acquired using DBT and/or depend on the existence of raw DBT data, which is rarely archived by clinical centers because of storage constraints.</p><p><strong>Methods: </strong>We retrospectively analyzed 1,080 nonactionable three-dimensional (3D) reconstructed DBT screening examinations acquired between 2011 and 2016. Reference tissue segmentations were generated using previously validated software that uses 3D reconstructed slices and raw 2D DBT data. We developed a deep learning (DL) model that segments dense and fatty breast tissue from background. We then applied this model to estimate %VBD and absolute dense volume (ADV) in cm³ in a separate case-control sample (180 cases and 654 controls). We created two conditional logistic regression models, relating each model-derived density measurement to likelihood of contralateral breast cancer diagnosis, adjusted for age, BMI, family history, and menopausal status.</p><p><strong>Results: </strong>The DL model achieved unweighted and weighted Dice scores of 0.88 (standard deviation [SD] = 0.08) and 0.76 (SD = 0.15), respectively, on the held-out test set, demonstrating good agreement between the model and 3D reference segmentations. There was a significant association between the odds of breast cancer diagnosis and model-derived VBD (odds ratio [OR], 1.41 [95 % CI, 1.13 to 1.77]; <i>P</i> = .002), with an AUC of 0.65 (95% CI, 0.60 to 0.69). ADV was also significantly associated with breast cancer diagnosis (OR, 1.45 [95% CI, 1.22 to 1.73]; <i>P</i> < .001) with an AUC of 0.67 (95% CI, 0.62 to 0.71).</p><p><strong>Conclusion: </strong>DL-derived density measures derived from 3D reconstructed DBT images are associated with breast cancer diagnosis.</p>","PeriodicalId":51626,"journal":{"name":"JCO Clinical Cancer Informatics","volume":"8 ","pages":"e2400103"},"PeriodicalIF":3.3,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11643139/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142803132","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}

{"title":"Serious Games for Serious Pain: Development and Initial Testing of a Cognitive Behavioral Therapy Game for Patients With Advanced Cancer Pain.","authors":"Desiree R Azizoddin, Sara M DeForge, Robert R Edwards, Ashton R Baltazar, Kristin L Schreiber, Matthew Allsop, Justice Banson, Gabe Oseuguera, Michael Businelle, James A Tulsky, Andrea C Enzinger","doi":"10.1200/CCI.24.00111","DOIUrl":"10.1200/CCI.24.00111","url":null,"abstract":"<p><strong>Purpose: </strong>Cancer-related pain is prevalent among people with advanced cancer. To improve accessibility and engagement with pain-cognitive behavioral therapy (pain-CBT), we developed and tested a serious game hosted within a mobile health intervention that delivers pain-CBT and pharmacologic support. The game focuses on teaching and practicing cognitive restructuring (CR), a central pain-CBT intervention component.</p><p><strong>Methods: </strong>The pain-CBT game was developed through partnerships with commercial and academic game developers, graphic designers, clinical experts, and patients. Patients with metastatic cancer and pain participated in iterative, semistructured interviews. They described their experience playing each level and reflected on relevance, clarity, usability, and potential changes. Content codes captured patients' suggestions and informed game refinements.</p><p><strong>Results: </strong>The final game includes five levels that prompt players to distinguish between adaptive and maladaptive thoughts that are pain- and cancer-specific. The levels vary in objective (eg, hiking and sledding), interaction type (eg, dragging and tapping), and mode of feedback (eg, audio and animation). Fourteen participants reviewed the game. Patients appreciated the pain- and cancer-specific thought examples, with a few noting that the thoughts made them feel less alone. Many stated that the game was fun, relatable, and an engaging distraction. Others noted that the game provided helpful CR practice and prompted reflection. For example, one 40-year-old woman said the game \"brings [a thought] to the forefront so you can acknowledge it, and then maybe you could let it go or… do something about it.\"</p><p><strong>Conclusion: </strong>Patients coping with cancer pain found the CR game helpful, enjoyable, and satisfactory. Serious games have the potential to increase engagement while facilitating learning and rehearsal of psychological skills for pain. Future testing will evaluate the efficacy of this serious game.</p>","PeriodicalId":51626,"journal":{"name":"JCO Clinical Cancer Informatics","volume":"8 ","pages":"e2400111"},"PeriodicalIF":3.3,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11668515/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142640351","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}