JCO Clinical Cancer Informatics最新文献

{"title":"Perceptions of Implementing Real-Time Electronic Patient-Reported Outcomes and Digital Analytics in a Majority-Minority Cancer Center.","authors":"Daniela Arcos, Mary Dagsi, Reem Nasr, Carolyn Nguyen, Ding Quan Ng, Alexandre Chan","doi":"10.1200/CCI-24-00188","DOIUrl":"10.1200/CCI-24-00188","url":null,"abstract":"<p><strong>Purpose: </strong>Electronic patient-reported outcome (ePRO) tools are increasingly used to provide first-hand information on patient's symptoms and quality of life. This study explored how patients and health care providers (HCPs) perceive the use of a digital real-time ePRO tool, coupled with digital analytics at a cancer center located in a majority-minority county. Furthermore, we described the implementation barriers and facilitators identified from the participants' perspectives.</p><p><strong>Methods: </strong>We conducted a qualitative substudy as part of a larger implementation study conducted at University of California Irvine Chao Family Comprehensive Cancer Center. Patients and HCPs completed semistructured interviews and a focus group discussion. Thematic analysis was used to identify key themes regarding perceived impact of the intervention on patient's care and implementation factors.</p><p><strong>Results: </strong>A total of 31 participants, comprising 15 patients (67% English-speaking, 33% Spanish-speaking) and 16 HCPs (43.8% pharmacists, 37.5% physicians, 18.8% nurses), were interviewed. The utilization of real-time ePRO was perceived to beneficially affect patient care, improve patient-provider communication, and increase symptom awareness. Implementation facilitators included ease of comprehension and completion within the infusion center. Barriers included the need to incorporate results in electronic medical records and create real-time referral pathways to address patient's needs.</p><p><strong>Conclusion: </strong>The use of real-time ePRO in a majority-minority population was perceived to enhance patient-centered oncology care, yet implementation barriers must be addressed for successful integration in clinical settings. The findings from this study may inform implementation strategies to reduce health disparities.</p>","PeriodicalId":51626,"journal":{"name":"JCO Clinical Cancer Informatics","volume":"8 ","pages":"e2400188"},"PeriodicalIF":3.3,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11594559/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142689414","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":"Using Machine Learning Models to Predict Pathologic Complete Response to Neoadjuvant Chemotherapy in Breast Cancer.","authors":"Rayhan Erlangga Rahadian, Hong Qi Tan, Bryan Shihan Ho, Arjunan Kumaran, Andre Villanueva, Joy Sng, Ryan Shea Ying Cong Tan, Tira Jing Ying Tan, Veronique Kiak Mien Tan, Benita Kiat Tee Tan, Geok Hoon Lim, Yiyu Cai, Wen Long Nei, Fuh Yong Wong","doi":"10.1200/CCI.24.00071","DOIUrl":"https://doi.org/10.1200/CCI.24.00071","url":null,"abstract":"<p><strong>Purpose: </strong>Neoadjuvant chemotherapy (NAC) is increasingly used in breast cancer. Predictive modeling is useful in predicting pathologic complete response (pCR) to NAC. We test machine learning (ML) models to predict pCR in breast cancer and explore methods of handling missing data.</p><p><strong>Methods: </strong>Four hundred and ninety-nine patients with breast cancer treated with NAC in two centers in Singapore (National Cancer Centre Singapore [NCCS] and KK Hospital) between January 2014 and December 2017 were included. Eleven clinical features were used to train five different ML models. Listwise deletion and imputation were evaluated on handling missing data. Model performance was evaluated by AUC and calibration (Brier score). Feature importance from the best performing model in the external testing data set was calculated using Shapley additive explanations.</p><p><strong>Results: </strong>Seventy-two (24.6%), 18 (24.7%), and 31 (24.8%) patients attained pCR in NCCS training, NCCS testing, and KK Women's and Children's Hospital (KKH) testing data sets, respectively. The random forest (RF) base and imputed models have the highest AUCs in the KKH cohort of 0.794 (95% CI, 0.709 to 0.873) and 0.795 (95% CI, 0.706 to 0.871), respectively, and were the best calibrated with the lowest Brier score. No statistically significant difference was noted between AUCs of the base and imputed models in all data sets. The imputed model had a larger positive predictive value (PPV; 98.2% <i>v</i> 95.1%) and negative predictive value (NPV; 96.7% <i>v</i> 90.0%) than the base model in the KKH data set. Estrogen receptor intensity, human epidermal growth factor 2 intensity, and age at diagnosis were the three most important predictors.</p><p><strong>Conclusion: </strong>ML, particularly RF, demonstrates reasonable accuracy in pCR prediction after NAC. Imputing missing fields in the data can improve the PPV and NPV of the pCR prediction model.</p>","PeriodicalId":51626,"journal":{"name":"JCO Clinical Cancer Informatics","volume":"8 ","pages":"e2400071"},"PeriodicalIF":3.3,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142693885","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":"Identifying Oncology Patients at High Risk for Potentially Preventable Emergency Department Visits Using a Novel Definition.","authors":"Lauren Fleshner, Sonal Gandhi, Andrew Lagree, Louise Boulard, Robert C Grant, Alex Kiss, Monika K Krzyzanowska, Ivy Cheng, William T Tran","doi":"10.1200/CCI-24-00147","DOIUrl":"https://doi.org/10.1200/CCI-24-00147","url":null,"abstract":"<p><strong>Purpose: </strong>Patients with cancer visit the emergency department (ED) frequently. While some ED visits are necessary, others may be potentially preventable ED visits (PPEDs). Reducing PPEDs is important to improve quality of care and reduce costs. However, a robust definition and the characteristics of patients at risk remain unclear. This study aimed to describe oncology-related PPEDs and identify characteristics of patients at the highest risk for PPEDs to help target interventions and minimize avoidable ED visits.</p><p><strong>Methods: </strong>A retrospective study was conducted using four clinical and administrative databases. All ED visits by oncology patients between April 1, 2019, and April 1, 2021, were identified. A novel definition of PPEDs was explored, specifically visits that resulted in immediate discharge from the ED or admissions <48 hours. Trends in ED use, including PPEDs, were evaluated using descriptive statistics, logistic regression, and machine learning (ML) modeling.</p><p><strong>Results: </strong>During the 2-year period, 6,689 oncology patients visited the ED (N = 13,415 visits). A total of 62.1% of visits were classified as PPEDs. PPEDs were most common among patients with stage I to III breast cancer and those on systemic therapy. Characteristics of patients at high risk for non-PPEDs included stage IV disease with either lung or GI carcinomas and shorter distances to the ED. The highest-performing ML model yielded an AUC of 0.819.</p><p><strong>Conclusion: </strong>Our novel definition of PPEDs appears promising in identifying oncology patients who could avoid the ED with targeted interventions. This work demonstrated that patients with early-stage disease, those with breast cancer, and those on systemic therapy are at the highest risk for PPEDs and may benefit from proactive interventions to avoid the ED. Although our definition requires validation, using ML models for more robust predictive modeling appears promising.</p>","PeriodicalId":51626,"journal":{"name":"JCO Clinical Cancer Informatics","volume":"8 ","pages":"e2400147"},"PeriodicalIF":3.3,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142548860","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":"Optimizing End Points for Phase III Cancer Trials.","authors":"Steven E Schild","doi":"10.1200/CCI-24-00210","DOIUrl":"https://doi.org/10.1200/CCI-24-00210","url":null,"abstract":"","PeriodicalId":51626,"journal":{"name":"JCO Clinical Cancer Informatics","volume":"8 ","pages":"e2400210"},"PeriodicalIF":3.3,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142591689","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":"Patients Facing Large Language Models in Oncology: A Narrative Review.","authors":"Charles Raynaud, David Wu, Jarod Levy, Matteo Marengo, Jean-Emmanuel Bibault","doi":"10.1200/CCI-24-00149","DOIUrl":"https://doi.org/10.1200/CCI-24-00149","url":null,"abstract":"<p><p>The integration of large language models (LLMs) into oncology is transforming patients' journeys through education, diagnosis, treatment monitoring, and follow-up. This review examines the current landscape, potential benefits, and associated ethical and regulatory considerations of the application of LLMs for patients in the oncologic domain.</p>","PeriodicalId":51626,"journal":{"name":"JCO Clinical Cancer Informatics","volume":"8 ","pages":"e2400149"},"PeriodicalIF":3.3,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142607285","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":"Bias in Prediction Models to Identify Patients With Colorectal Cancer at High Risk for Readmission After Resection.","authors":"Mary M Lucas, Mario Schootman, Jonathan A Laryea, Sonia T Orcutt, Chenghui Li, Jun Ying, Jennifer A Rumpel, Christopher C Yang","doi":"10.1200/CCI.23.00194","DOIUrl":"10.1200/CCI.23.00194","url":null,"abstract":"<p><strong>Purpose: </strong>Machine learning algorithms are used for predictive modeling in medicine, but studies often do not evaluate or report on the potential biases of the models. Our purpose was to develop clinical prediction models for readmission after surgery in colorectal cancer (CRC) patients and to examine their potential for racial bias.</p><p><strong>Methods: </strong>We used the 2012-2020 American College of Surgeons' National Surgical Quality Improvement Program (ACS-NSQIP) Participant Use File and Targeted Colectomy File. Patients were categorized into four race groups - White, Black or African American, Other, and Unknown/Not Reported. Potential predictive features were identified from studies of risk factors of 30-day readmission in CRC patients. We compared four machine learning-based methods - logistic regression (LR), multilayer perceptron (MLP), random forest (RF), and XGBoost (XGB). Model bias was assessed using false negative rate (FNR) difference, false positive rate (FPR) difference, and disparate impact.</p><p><strong>Results: </strong>In all, 112,077 patients were included, 67.2% of whom were White, 9.2% Black, 5.6% Other race, and 18% with race not recorded. There were significant differences in the AUROC, FPR and FNR between race groups across all models. Notably, patients in the 'Other' race category had higher FNR compared to Black patients in all but the XGB model, while Black patients had higher FPR than White patients in some models. Patients in the 'Other' category consistently had the lowest FPR. Applying the 80% rule for disparate impact, the models consistently met the threshold for unfairness for the 'Other' race category.</p><p><strong>Conclusion: </strong>Predictive models for 30-day readmission after colorectal surgery may perform unequally for different race groups, potentially propagating to inequalities in delivery of care and patient outcomes if the predictions from these models are used to direct care.</p>","PeriodicalId":51626,"journal":{"name":"JCO Clinical Cancer Informatics","volume":"8 ","pages":""},"PeriodicalIF":3.3,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11741203/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143016054","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":"Use of Patient-Reported Outcomes in Risk Prediction Model Development to Support Cancer Care Delivery: A Scoping Review.","authors":"Roshan Paudel, Samira Dias, Carrie G Wade, Christine Cronin, Michael J Hassett","doi":"10.1200/CCI-24-00145","DOIUrl":"10.1200/CCI-24-00145","url":null,"abstract":"<p><strong>Purpose: </strong>The integration of patient-reported outcomes (PROs) into electronic health records (EHRs) has enabled systematic collection of symptom data to manage post-treatment symptoms. The use and integration of PRO data into routine care are associated with overall treatment success, adherence, and satisfaction. Clinical trials have demonstrated the prognostic value of PROs including physical function and global health status in predicting survival. It is unknown to what extent routinely collected PRO data are used in the development of risk prediction models (RPMs) in oncology care. The objective of the scoping review is to assess how PROs are used to train risk RPMs to predict patient outcomes in oncology care.</p><p><strong>Methods: </strong>Using the scoping review methodology outlined in the Joanna Briggs Institute Manual for Evidence Synthesis, we searched four databases (MEDLINE, CINAHL, Embase, and Web of Science) to locate peer-reviewed oncology articles that used PROs as predictors to train models. Study characteristics including settings, clinical outcomes, and model training, testing, validation, and performance data were extracted for analyses.</p><p><strong>Results: </strong>Of the 1,254 studies identified, 18 met inclusion criteria. Most studies performed retrospective analyses of prospectively collected PRO data to build prediction models. Post-treatment survival was the most common outcome predicted. Discriminative performance of models trained using PROs was better than models trained without PROs. Most studies did not report model calibration.</p><p><strong>Conclusion: </strong>Systematic collection of PROs in routine practice provides an opportunity to use patient-reported data to develop RPMs. Model performance improves when PROs are used in combination with other comprehensive data sources.</p>","PeriodicalId":51626,"journal":{"name":"JCO Clinical Cancer Informatics","volume":"8 ","pages":"e2400145"},"PeriodicalIF":3.3,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11534280/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142562529","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":"Classifying Tumor Reportability Status From Unstructured Electronic Pathology Reports Using Language Models in a Population-Based Cancer Registry Setting.","authors":"Lovedeep Gondara, Jonathan Simkin, Gregory Arbour, Shebnum Devji, Raymond Ng","doi":"10.1200/CCI.24.00110","DOIUrl":"10.1200/CCI.24.00110","url":null,"abstract":"<p><strong>Purpose: </strong>Population-based cancer registries (PBCRs) collect data on all new cancer diagnoses in a defined population. Data are sourced from pathology reports, and the PBCRs rely on manual and rule-based solutions. This study presents a state-of-the-art natural language processing (NLP) pipeline, built by fine-tuning pretrained language models (LMs). The pipeline is deployed at the British Columbia Cancer Registry (BCCR) to detect reportable tumors from a population-based feed of electronic pathology.</p><p><strong>Methods: </strong>We fine-tune two publicly available LMs, GatorTron and BlueBERT, which are pretrained on clinical text. Fine-tuning is done using BCCR's pathology reports. For the final decision making, we combine both models' output using an OR approach. The fine-tuning data set consisted of 40,000 reports from the diagnosis year of 2021, and the test data sets consisted of 10,000 reports from the diagnosis year 2021, 20,000 reports from diagnosis year 2022, and 400 reports from diagnosis year 2023.</p><p><strong>Results: </strong>The retrospective evaluation of our proposed approach showed boosted reportable accuracy, maintaining the true reportable threshold of 98%.</p><p><strong>Conclusion: </strong>Disadvantages of rule-based NLP in cancer surveillance include manual effort in rule design and sensitivity to language change. Deep learning approaches demonstrate superior performance in classification. PBCRs distinguish reportability status of incoming electronic cancer pathology reports. Deep learning methods provide significant advantages over rule-based NLP.</p>","PeriodicalId":51626,"journal":{"name":"JCO Clinical Cancer Informatics","volume":"8 ","pages":"e2400110"},"PeriodicalIF":3.3,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11593994/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142677700","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":"Rethinking Human Abstraction as the Gold Standard.","authors":"Kirk D Wyatt, Brian T Furner, Samuel L Volchenboum","doi":"10.1200/CCI-24-00218","DOIUrl":"10.1200/CCI-24-00218","url":null,"abstract":"<p><p>@PedsDataCommons discusses automated approaches for data extraction from electronic health records.</p>","PeriodicalId":51626,"journal":{"name":"JCO Clinical Cancer Informatics","volume":"8 ","pages":"e2400218"},"PeriodicalIF":3.3,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142717766","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":"Automated Electronic Health Record Data Extraction and Curation Using ExtractEHR.","authors":"Tamara P Miller, Kelly D Getz, Edward Krause, Yun Gun Jo, Sandhya Charapala, M Monica Gramatages, Karen Rabin, Michael E Scheurer, Jennifer J Wilkes, Brian T Fisher, Richard Aplenc","doi":"10.1200/CCI.24.00100","DOIUrl":"10.1200/CCI.24.00100","url":null,"abstract":"<p><strong>Purpose: </strong>Although the potential transformative effect of electronic health record (EHR) data on clinical research in adult patient populations has been very extensively discussed, the effect on pediatric oncology research has been limited. Multiple factors contribute to this more limited effect, including the paucity of pediatric cancer cases in commercial EHR-derived cancer data sets and phenotypic case identification challenges in pediatric federated EHR data.</p><p><strong>Methods: </strong>The ExtractEHR software package was initially developed as a tool to improve clinical trial adverse event reporting but has expanded its use cases to include the development of multisite EHR data sets and the support of cancer cohorts. ExtractEHR enables customized, automated data extraction from the EHR that, when implemented across multiple hospitals, can create pediatric cancer EHR data sets to address a very wide range of research questions in pediatric oncology. After ExtractEHR data acquisition, EHR data can be cleaned and graded using CleanEHR and GradeEHR, companion software packages.</p><p><strong>Results: </strong>ExtractEHR has been installed at four leading pediatric institutions: Children's Healthcare of Atlanta, Children's Hospital of Philadelphia, Texas Children's Hospital, and Seattle Children's Hospital.</p><p><strong>Conclusion: </strong>ExtractEHR has supported multiple use cases, including five clinical epidemiology studies, multicenter clinical trials, and cancer cohort assembly. Work is ongoing to develop Fast Health care Interoperability Resources ExtractEHR and implement other sustainability and scalability enhancements.</p>","PeriodicalId":51626,"journal":{"name":"JCO Clinical Cancer Informatics","volume":"8 ","pages":"e2400100"},"PeriodicalIF":3.3,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11608624/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142717764","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}