JCO Clinical Cancer Informatics最新文献

{"title":"Leveraging Radiotherapy Data for Precision Oncology: Veterans Affairs Granular Radiotherapy Information Database.","authors":"Evangelia Katsoulakis, Cecelia J Madison, Rishabh Kapoor, Ryan A Melson, Anthony Gao, Jiantao Bian, Ryan M Hausler, Peter N Danilov, Nicholas G Nickols, Abhishek A Solanki, William C Sleeman, Jatinder R Palta, Scott L DuVall, Julie A Lynch, Reid F Thompson, Maria Kelly","doi":"10.1200/CCI-24-00219","DOIUrl":"10.1200/CCI-24-00219","url":null,"abstract":"<p><strong>Purpose: </strong>Despite the frequency with which patients with cancer receive radiotherapy, integrating radiation oncology data with other aspects of the clinical record remains challenging because of siloed and variable software systems, high data complexity, and inconsistent data encoding. Recognizing these challenges, the Veterans Affairs (VA) National Radiation Oncology Program (NROP) is developing Granular Radiotherapy Information Database (GRID), a platform and pipeline to combine radiotherapy data across the VA with the goal of both better understanding treatment patterns and outcomes and enhancing research and data analysis capabilities.</p><p><strong>Methods: </strong>This study represents a proof-of-principle retrospective cohort analysis and review of select radiation treatment data from the VA Radiation Oncology Quality Surveillance Program (VAROQS) initiative. Key radiation oncology data elements were extracted from Digital Imaging and Communications in Medicine Radiotherapy extension (DICOM-RT) files and combined into a single database using custom scripts. These data were transferred to the VA's Corporate Data Warehouse (CDW) for integration and comparison with the VA Cancer Registry System and tumor sequencing data.</p><p><strong>Results: </strong>The final cohort includes 1,568 patients, 766 of whom have corresponding DICOM-RT data. All cases were successfully linked to the CDW; 18.8% of VAROQS cases were not reported in the existing VA cancer registry. The VAROQS data contributed accurate radiation treatment details that were often erroneous or missing from the cancer registry record. Tumor sequencing data were available for approximately 5% of VAROQS cases. Finally, we describe a clinical dosimetric analysis leveraging GRID.</p><p><strong>Conclusion: </strong>NROP's GRID initiative aims to integrate VA radiotherapy data with other clinical data sets. It is anticipated to generate the single largest collection of radiation oncology-centric data merged with detailed clinical and genomic data, primed for large-scale quality assurance, research reuse, and discovery science.</p>","PeriodicalId":51626,"journal":{"name":"JCO Clinical Cancer Informatics","volume":"9 ","pages":"e2400219"},"PeriodicalIF":3.3,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11841735/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143411480","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":"Radiomics and Deep Learning Prediction of Immunotherapy-Induced Pneumonitis From Computed Tomography.","authors":"David S Smith, Levente Lippenszky, Michele L LeNoue-Newton, Neha M Jain, Kathleen F Mittendorf, Christine M Micheel, Patrick A Cella, Jan Wolber, Travis J Osterman","doi":"10.1200/CCI-24-00198","DOIUrl":"10.1200/CCI-24-00198","url":null,"abstract":"<p><strong>Purpose: </strong>Primary barriers to application of immune checkpoint inhibitor (ICI) therapy for cancer include severe side effects (such as potentially life threatening pneumonitis [PN]), which can cause the discontinuation of treatment. Predicting which patients may develop PN while on ICI would improve both safety and potential efficacy because treatments could be safely administered for longer or discontinued before severe toxicity.</p><p><strong>Methods: </strong>Starting from a cohort of 3,351 patients with cancer who received previous ICI therapy at the Vanderbilt University Medical Center, we curated 2,700 contrast chest computed tomography (CT) volumes for 671 patients. Three different pure imaging models predicted the potential for PN using only a single time point before the first ICI dose.</p><p><strong>Results: </strong>The first model used 109 radiomics features only and achieved an AUC of 0.747 (CI, 0.705 to 0.789) with a positive predictive value (PPV) of 0.244 (CI, 0.211 to 0.276) at a sensitivity of 0.553 (CI, 0.485 to 0.621) using mainly features describing the global lung properties. The second model used a convolutional neural network (CNN) on the raw CTs to improve to an AUC of 0.819 (CI, 0.781 to 0.857) with a PPV of 0.244 (CI, 0.203 to 0.284) at a sensitivity of 0.743 (CI, 0.681 to 0.806). The third model combined both radiomics and deep learning but, with an AUC of 0.829 (CI, 0.797 to 0.862) and a PPV of 0.254 (CI, 0.228 to 0.281) at a sensitivity of 0.780 (CI, 0.721 to 0.840), did not show a significant improvement on the CNN-only model.</p><p><strong>Conclusion: </strong>This new model suggests the utility of deep learning in PN prediction over traditional pure radiomics and promises better management for patients receiving ICI and the ability to better stratify patients in immunotherapy drug trials.</p>","PeriodicalId":51626,"journal":{"name":"JCO Clinical Cancer Informatics","volume":"9 ","pages":"e2400198"},"PeriodicalIF":3.3,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11867800/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143469943","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":"Patient-Reported Outcomes: Comparing Functional Avoidance and Standard Thoracic Radiation Therapy in Lung Cancer.","authors":"Spencer J Poiset, Joseph Lombardo, Edward Castillo, Richard Castillo, Bernard Jones, Moyed Miften, Brian Kavanagh, Adam P Dicker, Cullen Boyle, Nicole L Simone, Benjamin Movsas, Inga Grills, Chad G Rusthoven, Yevgeniy Vinogradskiy, Lydia Wilson","doi":"10.1200/CCI-24-00202","DOIUrl":"10.1200/CCI-24-00202","url":null,"abstract":"<p><strong>Purpose: </strong>Novel methods generate functional images using image processing techniques combined with four-dimensional computed tomography (4DCT) data (4DCT-ventilation). 4DCT-ventilation was implemented in a phase II, multicenter functional avoidance clinical trial. The work compares functional avoidance patient-reported outcomes (PROs) against historical standards.</p><p><strong>Methods: </strong>Patients with locally advanced lung cancer undergoing curative-intent chemoradiation were accrued. 4DCT-ventilation imaging was generated and functional avoidance treatment plans created reduced dose to functional lung. PRO instruments included Functional Assessment of Cancer Therapy Lung questionnaire and accompanying subscales (including the Trial Outcome Index [TOI]), EuroQol-5 Dimension (EQ-5D), and EQ-Visual Analog Scale (EQ-VAS). The average change from baseline and percentage of clinically meaningful declines were calculated. We compared results against PROs from RTOG 0617 and PACIFIC trial data using Student t-tests and chi-square tests.</p><p><strong>Results: </strong>Fifty-nine patients completed baseline PRO surveys. The median age was 65 (44-86) years, non-small cell lung cancer comprised 83%, and median dose was 60 Gy in 30 fractions. The percent of patients with clinically meaningful decline in FACT-TOI at 12 months was 47.8% for RTOG 0617% and 26.8% for functional avoidance (<i>P</i> = .03). The functional avoidance cohort demonstrated a significantly (<i>P</i> = .012) higher change in EQ-VAS score at 12 months (9.9 ± 3.3; average ± SE) compared with the PACIFIC cohort (1.6 ± 0.6).</p><p><strong>Conclusion: </strong>The current work demonstrates improved PROs from a phase II functional avoidance trial in certain subscales (FACT-TOI and EQ-VAS) compared with PROs from seminal studies (RTOG 0617 and PACIFIC). The presented data support investigation of 4DCT functional avoidance in a phase III setting.</p>","PeriodicalId":51626,"journal":{"name":"JCO Clinical Cancer Informatics","volume":"9 ","pages":"e2400202"},"PeriodicalIF":3.3,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11801246/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143191190","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":"Developing and Validating an Automatic Support System for Tumor Coding in Pathology Reports in Spanish.","authors":"Fabián Villena, Pablo Báez, Sergio Peñafiel, Matías Rojas, Inti Paredes, Jocelyn Dunstan","doi":"10.1200/CCI.24.00124","DOIUrl":"10.1200/CCI.24.00124","url":null,"abstract":"<p><strong>Purpose: </strong>Pathology reports provide valuable information for cancer registries to understand, plan, and implement strategies to mitigate the impact of cancer. However, coding essential information from unstructured reports is performed by experts in a time-consuming manual process. We developed and validated a novel two-step automatic coding system that first recognizes tumor morphology and topography mentions from free text and then suggests codes from the International Classification of Diseases for Oncology (ICD-O) in Spanish.</p><p><strong>Materials and methods: </strong>We created an annotated corpus of tumor morphology and topography mentions consisting of 1,101 documents. We combined it with the CANTEMIST corpus (Cancer Text Mining Shared Task). Specifically, we implemented a named entity recognition (NER) model using the bidirectional long short-term memory network-conditional random field architecture enhanced with a stacked embedding layer. We applied transfer learning from state-of-the-art pretrained language models to obtain high-quality contextual representations, thus improving the detection of entities. The mentions found using this model were subsequently coded using a search engine tailored to the ICD-O codes.</p><p><strong>Results: </strong>Our NER models achieved an F1 score of 0.86 and 0.90 for tumor morphology and topography, respectively. The overall performance of our automatic coding system achieved an accuracy at five suggestions of 0.72 and 0.65 for tumor morphology and topography, respectively.</p><p><strong>Conclusion: </strong>These results demonstrate the feasibility of implementing natural language processing tools in the routine of a cancer center to extract and code valuable information from pathology reports. Our recommender system allows reliable and transparent coding at the moment of consultation. This publication shares the annotated corpus in Spanish, annotation guidelines, and source code to reproduce our experiments.</p>","PeriodicalId":51626,"journal":{"name":"JCO Clinical Cancer Informatics","volume":"9 ","pages":"e2400124"},"PeriodicalIF":3.3,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11872266/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143494326","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":"Validation of Clinical Dynamic Contrast-Enhanced Magnetic Resonance Imaging Perfusion Modeling and Neoadjuvant Chemotherapy Response Prediction in Breast Cancer Using ¹⁸FDG and ⁶⁴Cu-DOTA-Trastuzumab Positron Emission Tomography Studies.","authors":"John Whitman, Vikram Adhikarla, Lusine Tumyan, Joanne Mortimer, Wei Huang, Russell Rockne, Joesph R Peterson, John Cole","doi":"10.1200/CCI.23.00248","DOIUrl":"10.1200/CCI.23.00248","url":null,"abstract":"<p><strong>Purpose: </strong>Perfusion modeling presents significant opportunities for imaging biomarker development in breast cancer but has historically been held back by the need for data beyond the clinical standard of care (SoC) and uncertainty in the interpretability of results. We aimed to design a perfusion model applicable to breast cancer SoC dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) series with results stable to low temporal resolution imaging, comparable with published results using full-resolution DCE-MRI, and correlative with orthogonal imaging modalities indicative of biophysical markers.</p><p><strong>Methods: </strong>Subsampled high-temporal-resolution DCE-MRI series were run through our perfusion model and resulting fits were compared for consistency. The fits were also compared against previously published results from institutions using the full resolution series. The model was then evaluated on a separate cohort for validity of biomarker indications. Finally, the model was used as a fundamental part of predicting response to neoadjuvant chemotherapy (NACT).</p><p><strong>Results: </strong>Temporally subsampled DCE-MRI series yield perfusion fit variations on the scale of 1% of the tumor median value when input frames are varied. Fits generated from pseudoclinical series are within the variation range seen between imaging sites (ρ = 0.55), voxel-wise. The model also demonstrates significant correlations with orthogonal positron emission tomography imaging, indicating potential for use as a biomarker proxy. Specifically, using the perfusion fits as the grounding for a biophysical simulation of response, we correctly predict the pathologic complete response status after NACT in 15 of 18 patients, for an accuracy of 0.83, with a specificity and sensitivity of 0.83 as well.</p><p><strong>Conclusion: </strong>Clinical DCE-MRI data may be leveraged to provide stable perfusion fit results and indirectly interrogate the tumor microenvironment. These fits can then be used downstream for prediction of response to NACT with high accuracy.</p>","PeriodicalId":51626,"journal":{"name":"JCO Clinical Cancer Informatics","volume":"9 ","pages":"e2300248"},"PeriodicalIF":3.3,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11902905/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142985624","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":"ImpACT Project: Improving Access to Clinical Trials in Victoria, an Artificial Intelligence-Based Approach.","authors":"Maria L Bechelli, Kris Ivanova, Suan Siang Tan, Beena Kumar, Dayna Swiatek, Surein Arulananda, Sue M Evans","doi":"10.1200/CCI.24.00137","DOIUrl":"10.1200/CCI.24.00137","url":null,"abstract":"<p><strong>Purpose: </strong>Enhancing the speed and efficiency of clinical trial recruitment is a key objective across international health systems. This study aimed to use artificial intelligence (AI) applied in the Victorian Cancer Registry (VCR), a population-based cancer registry, to assess (1) if VCR received all relevant pathology reports for three clinical trials, (2) AI accuracy in auto-extracting information from pathology reports for recruitment, and (3) the number of participants approached for trial enrollment using the AI approach compared with standard hospital-based recruitment.</p><p><strong>Methods: </strong>To verify pathology report accessibility for VCR trial enrollment, reports from the laboratory were cross-referenced. To determine the accuracy of a Rapid Case Ascertainment (RCA) module of the AI software in extracting key clinical variables from the pathology report, data were compared with manually reviewed reports. To examine the effectiveness of the AI recruitment approach, the number of patients approached for recruitment was compared with standard practice.</p><p><strong>Results: </strong>Of the 195 reports provided by the pathology laboratory, 185 (94.9%) were received by VCR, 73 of 195 (37.4%) were eligible for the studies, and 5 of 73 (6.8%) eligible cases had not been received by the VCR. The RCA module demonstrated an accuracy of 93% and an F1 score of 0.94 in extracting key clinical variables. However, the RCA false-positive rate was 10% and the false-negative rate was 5%. The standard hospital approach selected fewer cases for approach to clinical trials compared with the RCA module approach, 8 of 336 (2.4%) versus 12 of 336 (3.6%), respectively.</p><p><strong>Conclusion: </strong>Using AI to screen potentially eligible cases for recruitment to three clinical trials resulted in a 50% increase in eligible cases being approached for enrollment.</p>","PeriodicalId":51626,"journal":{"name":"JCO Clinical Cancer Informatics","volume":"9 ","pages":"e2400137"},"PeriodicalIF":3.3,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11732263/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142958620","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":"Erratum: Volumetric Breast Density Estimation From Three-Dimensional Reconstructed Digital Breast Tomosynthesis Images Using Deep Learning.","authors":"","doi":"10.1200/CCI-24-00325","DOIUrl":"https://doi.org/10.1200/CCI-24-00325","url":null,"abstract":"","PeriodicalId":51626,"journal":{"name":"JCO Clinical Cancer Informatics","volume":"9 ","pages":"e2400325"},"PeriodicalIF":3.3,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142980693","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":"Delta-Radiomics Using Machine Learning Classifiers With Auxiliary Data Sets to Predict Disease Progression During Magnetic Resonance-Guided Radiotherapy in Adrenal Metastases.","authors":"Jesutofunmi A Fajemisin, John M Bryant, Payman G Saghand, Matthew N Mills, Kujtim Latifi, Eduardo G Moros, Vladimir Feygelman, Jessica M Frakes, Sarah E Hoffe, Kathryn E Mittauer, Tugce Kutuk, Rupesh Kotecha, Issam El Naqa, Stephen A Rosenberg","doi":"10.1200/CCI.24.00002","DOIUrl":"https://doi.org/10.1200/CCI.24.00002","url":null,"abstract":"<p><strong>Purpose: </strong>Adaptive radiotherapy accounts for interfractional anatomic changes. We hypothesize that changes in the gross tumor volumes identified during daily scans could be analyzed using delta-radiomics to predict disease progression events. We evaluated whether an auxiliary data set could improve prediction performance.</p><p><strong>Materials and methods: </strong>We analyzed 108 patients (n = 90 internal; n = 18 external) who received ablative radiotherapy. The internal data set included 42 patients with adrenal cancer, 23 patients with lung cancer, and 25 patients with pancreatic cancer, with the clinical end point of progression-free survival events. The median dose was 50 Gy, which was delivered over five fractions. The delta features are the ratio of the features of the last to first treatment fraction, F5/F1, and the concatenation of the first and last fraction features, F1||F5. Decision tree classifier with and without auxiliary data sets, and the external data set was used exclusively for independent testing of the final models.</p><p><strong>Results: </strong>During internal training, for the F1||F5 model, the inclusion of the lung data set increased our AUC receiver operator characteristic curve (ROC) from 0.53 ± 0.12 to 0.61 ± 0.11, whereas the pancreatic data set increased our AUC-ROC to 0.60 ± 0.14. For the F5/F1 model, the inclusion of the lung auxiliary data increased our AUC-ROC from 0.52 ± 0.13 to 0.65 ± 0.11, whereas it modestly changed by 0.62 ± 0.13 with the pancreas. During external testing, for the F5/F1 model, we reported an AUC-ROC of 0.60 with the lung auxiliary data and 0.43 with the pancreatic data. Also, for the F5||F1 model, we reported an AUC-ROC of 0.70 with the lung auxiliary and 0.60 with the pancreatic data.</p><p><strong>Conclusion: </strong>Decision trees provided an explainable model on the external data set. The validation of our model on an external data set may be the first step to biologically adapted radiotherapy recognizing radiomics signals for potential recurrence.</p>","PeriodicalId":51626,"journal":{"name":"JCO Clinical Cancer Informatics","volume":"9 ","pages":"e2400002"},"PeriodicalIF":3.3,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143034901","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":"Evaluating Cancer Screening in the Era of Advanced Causal Inference Methods: Innovation, Adherence, and Health Equity Considerations.","authors":"Rebecca A Miksad, Somnath Sarkar","doi":"10.1200/CCI-24-00214","DOIUrl":"https://doi.org/10.1200/CCI-24-00214","url":null,"abstract":"","PeriodicalId":51626,"journal":{"name":"JCO Clinical Cancer Informatics","volume":"9 ","pages":"e2400214"},"PeriodicalIF":3.3,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142962531","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":"Development and Validation of Emoji Response Scales for Assessing Patient-Reported Outcomes.","authors":"Carrie A Thompson, Paul J Novotny, Kathleen Yost, Alicia C Bartz, Lauren Rogak, Amylou C Dueck","doi":"10.1200/CCI-24-00148","DOIUrl":"https://doi.org/10.1200/CCI-24-00148","url":null,"abstract":"<p><strong>Purpose: </strong>Emoji are digital images or icons used to express an idea or emotion in electronic communication. The purpose of this study was to develop and evaluate the psychometric properties of two patient-reported scales that incorporate emoji.</p><p><strong>Methods: </strong>The Emoji Response Scale developed for this study has two parts: the Emoji-Ordinal and Emoji-Mood scales. A pilot study was designed to validate the ordinal nature of the Emoji-Ordinal Scale. Twenty patients were shown all possible pairs of five emoji and asked to select the most positive from each pair. The psychometric ordering was assessed using Coombs unfolding and Thurstone scaling. A separate pilot study was designed to determine which emoji to include in the Emoji-Mood Scale. Ten common feelings experienced by patients with cancer were chosen by the study team. Patients and providers were asked to select the one emoji that best represented each feeling from the selection. The most commonly selected emotions and representative emoji were chosen for the Emoji-Mood Scale. In a randomized study of 294 patients, Spearman correlations, Wilcoxon tests, and Bland-Altman analyses determined the construct validity of the scales compared with Linear Analog Scale Assessments (LASA) and Patient-Reported Outcomes Measurement Information System (PROMIS) scores.</p><p><strong>Results: </strong>Ninety-five percent of patients selected the same ordering among the ordinal emoji, and Thurstone scaling confirmed the ordinal nature of the response scale. The construct validity of the scales was high with correlations between the Emoji-Ordinal Scale and the LASA scale of 0.70 for emotional well-being, 0.72 for physical well-being, 0.74 for overall quality of life, and -0.81 for fatigue. Emoji-Mood Scale ratings were strongly related to PROMIS global mental, global physical, fatigue, anxiety, sleep disturbance, and social activity scales (<i>P</i> < .0001).</p><p><strong>Conclusion: </strong>This study provides evidence that scales incorporating emoji are valid for collecting patient-reported outcomes.</p>","PeriodicalId":51626,"journal":{"name":"JCO Clinical Cancer Informatics","volume":"9 ","pages":"e2400148"},"PeriodicalIF":3.3,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142958610","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}