JCO Clinical Cancer Informatics最新文献

{"title":"CFO: Calibration-Free Odds Bayesian Designs for Dose Finding in Clinical Trials.","authors":"Jialu Fang, Ninghao Zhang, Wenliang Wang, Guosheng Yin","doi":"10.1200/CCI-24-00184","DOIUrl":"10.1200/CCI-24-00184","url":null,"abstract":"<p><strong>Purpose: </strong>Calibration-free odds type (CFO-type) designs have been demonstrated to be robust, model-free, and practically useful, which have become the state-of-the-art approach for dose finding. However, a key challenge for implementing such designs is a lack of accessible tools. We develop a user-friendly <i>R</i> package and <i>Shiny</i> web-based software to facilitate easy implementation of CFO-type designs. Moreover, we incorporate randomization into the CFO framework.</p><p><strong>Methods: </strong>We created the <i>R</i> package CFO and leveraged <i>R Shiny</i> to build an interactive web application, CFO suite, for implementing CFO-type designs. We introduce the randomized CFO (rCFO) design by integrating the exploration-exploitation mechanism into the CFO framework.</p><p><strong>Results: </strong>The CFO package and CFO suite encompass various variants tailored to different clinical settings. Beyond the fundamental CFO design, these include the two-dimensional CFO (2dCFO) for drug-combination trials, accumulative CFO (aCFO) for accommodating all dose information, rCFO for integrating exploration-exploitation via randomization, time-to-event CFO (TITE-CFO), and fractional CFO (fCFO) for addressing late-onset toxicity. Using all information and addressing delayed toxicity outcomes, TITE-aCFO and fractional-aCFO are also included. The package provides functions for determining the subsequent cohort dose, selecting the maximum tolerated dose, and conducting simulations to evaluate performance, with results presented through textual and graphical outputs.</p><p><strong>Conclusion: </strong>The CFO package and CFO suite provide comprehensive and flexible tools for implementing CFO-type designs in phase I clinical trials. This work is highly significant as it integrates all existing CFO-type designs to facilitate novel trial designs with enhanced performance. In addition, this promotes the spread of statistical methods using a user-friendly <i>R</i> package and <i>Shiny</i> software. It strengthens collaborations between biostatisticians and clinicians, further enhancing trial performance in terms of efficiency and accuracy.</p>","PeriodicalId":51626,"journal":{"name":"JCO Clinical Cancer Informatics","volume":"9 ","pages":"e2400184"},"PeriodicalIF":3.3,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11797228/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143071238","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 of Technology on Quality of Thoracic Multidisciplinary Cancer Conferences.","authors":"Opuruiche Ibekwe, Carmelo Gaudioso, Kristopher M Attwood, Saraswati Pokharel, Charles L Roche, Chukwumere E Nwogu","doi":"10.1200/CCI-24-00156","DOIUrl":"https://doi.org/10.1200/CCI-24-00156","url":null,"abstract":"<p><strong>Purpose: </strong>Complex cancers require discussion at multidisciplinary cancer conferences (MCCs) to determine the best management. This study assessed the impact of a tumor board (TB)-specific information technology platform on the quality of information presented, case discussions, and care plans at thoracic MCCs.</p><p><strong>Methods: </strong>Between September 2020 and February 2022, using a before-after study design, we prospectively collected data through direct observation of thoracic MCCs at an academic cancer center. In addition, we reviewed medical records to assess the rate of change in care plans, compliance of all care plans with national guidelines, concordance of treatment received with MCC recommendations, and time from MCC presentation to treatment. Observational data were collected using a validated tool, Metric for the Observation of Decision-Making. We used SAS version 9.4 (SAS Institute Inc, Cary, NC) for statistical analyses.</p><p><strong>Results: </strong>We identified 151 and 166 thoracic cancer cases before and after implementation of the information technology platform, respectively. The overall quality of case presentation and discussion, represented by a mean composite score (summation of individual variables scored on a 1-5 scale, poor to excellent), increased from 56.8 to 82.0 (<i>P</i> < .001). This improvement was also observed across multiple subcomponents of the composite score all with <i>P</i> < .001. There was no statistically significant difference between the two cohorts in rate of change in care plans by the MCC, care plan compliance with national guidelines, and concordance of treatment received with MCC recommendations.</p><p><strong>Conclusion: </strong>Technology improves the quality of information and discussion at TBs. However, this study did not demonstrate an impact on compliance with practice guidelines. Practitioners should explore the available TB technology platforms to optimize the conduct of MCCs in their respective institutions.</p>","PeriodicalId":51626,"journal":{"name":"JCO Clinical Cancer Informatics","volume":"9 ","pages":"e2400156"},"PeriodicalIF":3.3,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143517266","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":"Erratum: Identification of Novel DNA Methylation Prognostic Biomarkers for AML With Normal Cytogenetics.","authors":"Cândida Cardoso, Daniel Pestana, Sreemol Gokuladhas, Ana D Marreiros, Justin M O'Sullivan, Alexandra Binnie, Mónica Teotónio Fernandes, Pedro Castelo-Branco","doi":"10.1200/CCI-25-00012","DOIUrl":"https://doi.org/10.1200/CCI-25-00012","url":null,"abstract":"","PeriodicalId":51626,"journal":{"name":"JCO Clinical Cancer Informatics","volume":"9 ","pages":"e2500012"},"PeriodicalIF":3.3,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143442955","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":"Prognosis of p16 and Human Papillomavirus Discordant Oropharyngeal Cancers and the Exploration of Using Natural Language Processing to Analyze Free-Text Pathology Reports.","authors":"Ethan Shin, Justin Choi, Tony K W Hung, Chester Poon, Nadeem Riaz, Yao Yu, Jung Julie Kang","doi":"10.1200/CCI-24-00177","DOIUrl":"https://doi.org/10.1200/CCI-24-00177","url":null,"abstract":"<p><strong>Purpose: </strong>Treatment deintensification for human papillomavirus-positive (HPV+)-associated oropharyngeal cancer (OPC) has been the catalyst of experts worldwide. In situ hybridization is optimal to identify HPV+ OPC, but immunohistochemistry for its surrogate p16INK4a (p16) is standard-of-care given its availability and sensitivity. HPV testing is not required for clinical management, so treatments are often administered on the basis of p16 status alone. However, the prognosis of p16/HPV discordant tumors is uncertain.</p><p><strong>Materials and methods: </strong>This cohort study included 727 consecutive patients with OPC with digitized unstructured pathology reports receiving curative radiation therapy at an academic cancer center. Natural language processing (NLP) methods were used to classify biomarker status and compared against manually derived classification. Patients were excluded if either p16 or HPV testing was not performed or equivocal. Primary end points were progression-free survival (PFS), cancer-specific survival (CSS), and overall survival.</p><p><strong>Results: </strong>NLP classified p16 and HPV status from a majority (91%) of reports. Accuracy, positive predictive value, sensitivity, and <i>F</i>-score for NLP-derived p16/HPV were 84%/82%, 91%/87%, 90%/89%, and 90%/88%, respectively. Four groups were identified: p16-positive (p16+)/HPV+ (75%), p16+/HPV-negative (HPV-; 13%), p16-negative (p16-)/HPV- (10%), and p16-/HPV+ (2%). There was no statistically significant difference in outcomes between p16+/HPV- and p16-/HPV- patients (5-year PFS 76.1% <i>v</i> 68.9%; <i>P</i> = .12; 5-year CSS 81.5% <i>v</i> 84.9%; <i>P</i> = .22). Number needed to harm calculations estimated one excess cancer-related death for every 10 p16+/HPV- patients, compared with that expected with p16+/HPV+ patients.</p><p><strong>Conclusion: </strong>NLP classified head and neck cancer pathology reports with high concordance with gold-standard categorization, but a conspicuous portion of reports could not be interpreted. p16/HPV discordant OPC constitutes a noteworthy minority of patients. The inferior prognosis of p16+/HPV- suggests that p16 alone for prognostication is insufficient-especially when considering treatment de-escalation.</p>","PeriodicalId":51626,"journal":{"name":"JCO Clinical Cancer Informatics","volume":"9 ","pages":"e2400177"},"PeriodicalIF":3.3,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143450861","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":"Incorporating Structured and Unstructured Data Sources to Identify and Characterize Hereditary Cancer Testing Among Veterans With Metastatic Castration-Resistant Prostate Cancer.","authors":"Danielle Candelieri-Surette, Anna Hung, Fatai Y Agiri, Mengke Hu, Elizabeth E Hanchrow, Kyung Min Lee, Nai-Chung N Chang, Ming Yin, Jeffrey W Shevach, Weiyan Li, Tyler J Nelson, Anthony Gao, Kathryn M Pridgen, Martin W Schoen, Scott L DuVall, Yu-Ning Wong, Julie A Lynch, Patrick R Alba","doi":"10.1200/CCI-24-00189","DOIUrl":"10.1200/CCI-24-00189","url":null,"abstract":"<p><strong>Purpose: </strong>This study introduces an integrated approach using structured and unstructured data from an electronic health record to identify and characterize patient utilization of hereditary cancer genetic testing among patients with metastatic castration-resistant prostate cancer (mCRPC). Secondary objectives were to describe factors associated with the receipt of testing.</p><p><strong>Methods: </strong>This retrospective cohort study included a cohort of Veterans diagnosed with mCRPC from January 2016 to December 2021. Receipt of genetic testing was identified using structured and unstructured data. Time to testing, age at testing, and testing rate were analyzed. Sociodemographic and clinical factors associated with receipt of hereditary cancer genetic testing were identified including race, marital status, rurality, Charlson comorbidity index (CCI), and genetic counseling.</p><p><strong>Results: </strong>Among 9,703 Veterans with mCRPC who did not decline testing, 16% received genetic testing, with nearly half of the tests occurring in 2020-2021. Factors positively associated with genetic testing included receipt of genetic counseling (adjusted odds ratio [aOR], 11.07 [95% CI, 3.66 to 33.51]), enrollment in clinical trial (aOR, 7.42 [95% CI, 5.59 to 9.84]), and treatment at a Prostate Cancer Foundation-Veterans Affairs Center of Excellence (aOR, 1.43 [95% CI, 1.04 to 1.95]). Negative associations included older age (aOR, 0.95 [95% CI, 0.93 to 0.97]) and severe CCI score (aOR, 0.82 [95% CI, 0.71 to 0.94]). Trends revealed that time to testing decreased per diagnosis year while median age at testing increased per year.</p><p><strong>Conclusion: </strong>Although testing rates are still suboptimal, they have increased steadily since 2016. Educating Veterans about the benefits of genetic testing may further improve testing rates.</p>","PeriodicalId":51626,"journal":{"name":"JCO Clinical Cancer Informatics","volume":"9 ","pages":"e2400189"},"PeriodicalIF":3.3,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11834961/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143392532","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":"Advancements in Interoperability: Achieving Anatomic Pathology Reports That Adhere to International Standards and Are Both Human-Readable and Readily Computable.","authors":"Walter S Campbell, Brian A Rous, Stefan Dubois, Paul A Seegers, Rajesh C Dash, Thomas Rüdiger, Suzanne Santamaria, Elaine Wooler, James Case, Lazslo Igali, Mary E Edgerton, Ross W Simpson, Ekaterina Bazyleva, George Birdsong, Richard Moldwin, Timothy R Helliwell, Peter Paul Yu, John Srigley","doi":"10.1200/CCI-24-00180","DOIUrl":"https://doi.org/10.1200/CCI-24-00180","url":null,"abstract":"<p><strong>Purpose: </strong>Over the past 50 years, multiple pathology organizations worldwide have evolved in cancer histopathology reporting from subjective, narrative assessments to structured, synoptic formats using controlled vocabulary. These reporting protocols include the required data elements that represent the minimum set of evidence-based, clinically actionable parameters necessary to convey the diagnostic, prognostic, and predictive information essential for patient care. Despite these advances, the synoptic reporting protocols were not harmonized across the various pathology organizations. Cancer pathology continues to be widely reported and stored in free-text format, or without encoded data such that it is neither computable nor interoperable across organizations.</p><p><strong>Methods: </strong>In 2020, SNOMED International created the Cancer Synoptic Reporting Working Group (CSRWG). This resulted in international collaboration across multiple pathology organizations. CCRWG's mission was to use SNOMED Clinical Terms (CT) concepts to represent the required content within the College of American Pathologists (CAP) and International Collaboration on Cancer Reporting (ICCR) published pathology reporting protocols.</p><p><strong>Results: </strong>In late 2023, the CSRWG published over 1,300 new or revised SNOMED CT concepts to represent all required pathology cancer data elements for adult and pediatric solid tumors in both CAP and ICCR using the semantic principles of the SNOMED-CT concept model. Thus, computability and interoperability would be broadly established.</p><p><strong>Conclusion: </strong>This work brings to fruition the longstanding desire for an international, interoperable, human- and machine-readable cancer pathology report for use in patient care, health care quality improvement, population health, public health surveillance, and translational and clinical trial research. The following report describes the project, its methods, and applications in the stated use cases.</p>","PeriodicalId":51626,"journal":{"name":"JCO Clinical Cancer Informatics","volume":"9 ","pages":"e2400180"},"PeriodicalIF":3.3,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143257239","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":"Erratum: 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-25-00009","DOIUrl":"https://doi.org/10.1200/CCI-25-00009","url":null,"abstract":"","PeriodicalId":51626,"journal":{"name":"JCO Clinical Cancer Informatics","volume":"9 ","pages":"e2500009"},"PeriodicalIF":3.3,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143442956","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":"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}