JCO Clinical Cancer Informatics最新文献

{"title":"Methodologic Approach to Defining Comorbidities in a Cohort of Patients With Cancer: An Example in the Optimal Breast Cancer Chemotherapy Dosing Study.","authors":"Peng Wang, Kelli O'Connell, Jenna Bhimani, Victoria Blinder, Rachael Burganowski, Isaac J Ergas, Grace B Gallagher, Jennifer J Griggs, Narre Heon, Tatjana Kolevska, Yuriy Kotsurovskyy, Candyce H Kroenke, Cecile A Laurent, Raymond Liu, Kanichi G Nakata, Sonia Persaud, Donna R Rivera, Janise M Roh, Sara Tabatabai, Emily Valice, Elisa V Bandera, Lawrence H Kushi, Erin J Aiello Bowles, Elizabeth D Kantor","doi":"10.1200/CCI-24-00231","DOIUrl":"https://doi.org/10.1200/CCI-24-00231","url":null,"abstract":"<p><strong>Purpose: </strong>We evaluated the definitions of five comorbidities (renal and hepatic impairments, anemia, neutropenia, and thrombocytopenia) in women with breast cancer using data from electronic health records.</p><p><strong>Methods: </strong>In 11,097 women receiving adjuvant chemotherapy for stage I-IIIA breast cancer at Kaiser Permanente Northern California or Kaiser Permanente Washington, we assessed comorbidity definitions in two commonly used lookback windows (1 year before diagnosis, T1; and extending until chemotherapy initiation, T1-2). Within each, we assessed data availability and agreement between International Classification of Diseases (ICD)-defined and lab-defined comorbidities of varying severity. To assess how different pieces of information may affect providers in making treatment decisions, we used multivariable logistic regression to evaluate four-category (with comorbidity indicated by both ICD and lab, ICD-only, lab-only, or neither) and collapsed binary (comorbidity indicated by either ICD or lab <i>v</i> neither) definitions in relation to first cycle chemotherapy dose reduction (FCDR).</p><p><strong>Results: </strong>Extending the lookback period to chemotherapy initiation increased laboratory data availability (missingness ≤4.1% in T1-2 <i>v</i> >40% in T1). Assessment of agreement guided selection of laboratory cutpoints. In both time periods, the four-category and binary comorbidity variables were associated with use of FCDR, but binary variables had larger cell sizes and more stability of regression models. Ultimately, the comorbidity variables in T1 were defined by a combination of either ICD/qualifying laboratory values. Results were similar in T1-2, except laboratory data alone outperformed the combination variable for renal and hepatic comorbidity.</p><p><strong>Conclusion: </strong>Leveraging both ICD and lab data and extending the lookback period to include postdiagnosis but prechemotherapy initiation may provide better capture of comorbidity. Future studies may consider validating our findings with a gold-standard comorbidity status and in other community health care settings.</p>","PeriodicalId":51626,"journal":{"name":"JCO Clinical Cancer Informatics","volume":"9 ","pages":"e2400231"},"PeriodicalIF":3.3,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143417054","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":"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":"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":"https://doi.org/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":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143392532","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":"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":"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":"https://doi.org/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":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143411480","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":"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":"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":"https://doi.org/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":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142985624","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":"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}