ESMO Real World Data and Digital Oncology最新文献

{"title":"Editoiral Board","authors":"","doi":"10.1016/S2949-8201(26)00118-9","DOIUrl":"10.1016/S2949-8201(26)00118-9","url":null,"abstract":"","PeriodicalId":100491,"journal":{"name":"ESMO Real World Data and Digital Oncology","volume":"13 ","pages":"Article 100799"},"PeriodicalIF":2.6,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149043818","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":"Editoiral Board","authors":"","doi":"10.1016/S2949-8201(26)00060-3","DOIUrl":"10.1016/S2949-8201(26)00060-3","url":null,"abstract":"","PeriodicalId":100491,"journal":{"name":"ESMO Real World Data and Digital Oncology","volume":"12 ","pages":"Article 100741"},"PeriodicalIF":2.6,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148539541","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":"Evolving phase I oncology trials in the precision medicine era: design, outcomes, and geographic access","authors":"Y. Katsuya,&nbsp;T. Koyama,&nbsp;J. Sato,&nbsp;M. Okada,&nbsp;N. Yamamoto","doi":"10.1016/j.esmorw.2026.100692","DOIUrl":"10.1016/j.esmorw.2026.100692","url":null,"abstract":"<div><h3>Background</h3><div>Over the past decade, early phase oncology trials have evolved from safety-focused designs to precision-oriented strategies. This study evaluated longitudinal trends, focusing on clinical outcomes, trial design evolution, and the impact of comprehensive genomic profiling (CGP) following its national insurance coverage in Japan in 2019.</div></div><div><h3>Methods</h3><div>This retrospective observational study included patients enrolled in early phase oncology trials at National Cancer Center Hospital (NCCH) between 2016 and 2023. Clinical, molecular, and geographic data were extracted from medical records. Categorical outcomes were compared using chi-square tests. Variables were compared between the early period (2016-2019) and the recent period (2020-2023).</div></div><div><h3>Results</h3><div>Over 8 years, 1243 patients were enrolled across 1642 early phase trials. The proportion of patients enrolled in genomically matched trials increased significantly from 10.5% in the early period to 28.4% in the recent period (<em>P</em> &lt; 0.0001), in parallel with expanded CGP implementation, which rose from 44.4% to 82.8% (<em>P</em> &lt; 0.0001). The overall objective response rate (ORR) was numerically higher in the recent period (12.6% to 15.9%), with an ORR of 29.1% observed in genomically matched trials. Small-molecule therapies demonstrated a numerical increase in ORR (9.8% to 18.2%), whereas antibody–drug conjugates maintained high ORRs (30.8% to 30.5%). The median travel distance to NCCH increased from 26.6 km to 32.7 km (<em>P</em> &lt; 0.0001), indicating expanded nationwide referral.</div></div><div><h3>Conclusions</h3><div>The evolving early phase oncology trials, characterized by increased adoption of genomically matched trial designs, broader access to CGP under universal health coverage, and expanded nationwide referral networks, highlight a shift toward more precision in early drug development.</div></div>","PeriodicalId":100491,"journal":{"name":"ESMO Real World Data and Digital Oncology","volume":"11 ","pages":"Article 100692"},"PeriodicalIF":0.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147421108","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":"Editoiral Board","authors":"","doi":"10.1016/S2949-8201(26)00029-9","DOIUrl":"10.1016/S2949-8201(26)00029-9","url":null,"abstract":"","PeriodicalId":100491,"journal":{"name":"ESMO Real World Data and Digital Oncology","volume":"11 ","pages":"Article 100710"},"PeriodicalIF":0.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147538504","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":"Predicting efficacy in patients with locally advanced/metastatic urothelial carcinoma (mUC) treated with immunotherapy using explainable machine learning approaches: the SamUR-AI trial on behalf of the Meet-URO group","authors":"A. Rametta ,&nbsp;S. Ferri ,&nbsp;M. Maruzzo ,&nbsp;F. Pierantoni ,&nbsp;L. Antonuzzo ,&nbsp;V. Rossi ,&nbsp;S. Buti ,&nbsp;I. Testi ,&nbsp;P. Zucali ,&nbsp;C. Mucciarini ,&nbsp;M. Stellato ,&nbsp;L. Provenzano ,&nbsp;D. Raggi ,&nbsp;E. Farè ,&nbsp;A.L.G. Pedrocchi ,&nbsp;A. Prelaj ,&nbsp;A. Necchi ,&nbsp;G. Procopio ,&nbsp;V. Miskovic ,&nbsp;P. Giannatempo","doi":"10.1016/j.esmorw.2025.100681","DOIUrl":"10.1016/j.esmorw.2025.100681","url":null,"abstract":"<div><h3>Background</h3><div>Immune checkpoint inhibitors (ICIs) have reshaped the treatment landscape for metastatic urothelial carcinoma (mUC), yet reliable predictive biomarkers remain limited. The SamUR-AI study was designed to evaluate whether machine learning (ML) and explainable artificial intelligence (XAI) approaches could improve prediction of clinical outcomes in patients with mUC treated with ICIs.</div></div><div><h3>Materials and methods</h3><div>We conducted a multicenter retrospective analysis including 438 patients treated with ICIs across 34 Italian institutions from the Meet-URO network. Baseline clinical and laboratory features were analyzed using ML and XAI methodologies to predict objective response rate (ORR), progression-free survival (PFS), and overall survival (OS).</div></div><div><h3>Results</h3><div>Classification models showed suboptimal performance in predicting ORR (best test F1-score: 0.61), likely due to class imbalance and overfitting. In contrast, survival models achieved moderate predictive accuracy, with the extra survival trees model yielding a concordance index (C-index) of 0.67 for OS. SHapley Additive exPlanations-based explainability identified key prognostic factors, including Eastern Cooperative Oncology Group performance status, line of immunotherapy and treatment combinations, liver and lung metastases, neutrophil count, and hemoglobin level.</div></div><div><h3>Conclusions</h3><div>Although further validation is needed, our findings highlight the potential of XAI-enhanced ML to identify clinically relevant features and to support personalized treatment strategies in patients with mUC.</div></div>","PeriodicalId":100491,"journal":{"name":"ESMO Real World Data and Digital Oncology","volume":"11 ","pages":"Article 100681"},"PeriodicalIF":0.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146077862","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":"Final results of the INFINITY precision oncology registry: non-standard targeted treatments in patients with advanced cancers in routine care☆","authors":"L. Sellmann ,&nbsp;D. Marschner ,&nbsp;U.M. Martens ,&nbsp;J. Schröder ,&nbsp;T. Decker ,&nbsp;M. Schuler ,&nbsp;A. Schneeweiss ,&nbsp;M. Reiser ,&nbsp;M.K. Schuler ,&nbsp;C. Vannier ,&nbsp;M. Looß ,&nbsp;S.M. Woerner ,&nbsp;S. Grebhardt ,&nbsp;P.R. Wright ,&nbsp;S. Dille ,&nbsp;B. Kasenda ,&nbsp;S. Busies ,&nbsp;K. Potthoff","doi":"10.1016/j.esmorw.2025.100667","DOIUrl":"10.1016/j.esmorw.2025.100667","url":null,"abstract":"<div><h3>Background</h3><div>The INFINITY precision oncology registry evaluated biomarker-driven non-standard targeted treatment (NSTT) in a community oncology setting in Germany. We present final results including clinical outcome and patient benefit from NSTTs.</div></div><div><h3>Patients and methods</h3><div>INFINITY (NCT04389541) was a multicenter, non-interventional cohort study using routinely collected health data of patients with advanced solid or hematological malignancies, who had no standard therapy options and therefore received biomarker-informed NSTT. Endpoints were overall response rate (ORR), progression-free survival (PFS), and overall survival. The PFS ratio was calculated to quantify the potential benefit from the NSTT (cut-off ratio ≥1.3).</div></div><div><h3>Results</h3><div>From April 2020 to November 2022, 499 eligible patients (median age 62.6 years, 49.9% female) were enrolled. More than 43 different cancers have been included with 93.2% (<em>n</em> = 465) solid tumors, including colorectal (16.0%, <em>n</em> = 80), biliary tract (8.2%, <em>n</em> = 41), and lung cancers (7.0%, <em>n</em> = 35). Patients had received a median of two prior systemic therapy lines before starting NSTT. Immunohistochemistry was the most commonly carried out biomarker testing method (68.3%), followed by next-generation sequencing (49.9%). Key decisive biomarkers were PD-(L)1 expression (30.7%), <em>HER2</em> aberrations (9.8%), and <em>BRAF</em> mutations (9.0%). ORR was 27.3%, and 31.7% of patients had a PFS ratio ≥1.3.</div></div><div><h3>Conclusions</h3><div>In a tumor-agnostic approach, biomarker-informed NSTT was associated with clinical benefit in 31.7% of the patients, with checkpoint inhibitors being the most common treatment. The INFINITY project demonstrates the feasibility of a large-scale precision oncology project in a community oncology setting and thus supports the implementation of precision oncology into routine clinical practice in Germany.</div></div>","PeriodicalId":100491,"journal":{"name":"ESMO Real World Data and Digital Oncology","volume":"11 ","pages":"Article 100667"},"PeriodicalIF":0.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145977531","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":"AI for clinical trials in oncology","authors":"E. Cerami ,&nbsp;I.B. Riaz ,&nbsp;K.L. Kehl","doi":"10.1016/j.esmorw.2025.100658","DOIUrl":"10.1016/j.esmorw.2025.100658","url":null,"abstract":"<div><div>In this perspective, we summarize recent developments in artificial intelligence (AI) applications for oncology clinical trials, divided into four key areas: (1) AI-driven drug design; (2) trial risk assessment, or predicting whether a novel therapy candidate will be safe and efficacious; (3) trial matching—that is, retrieval of reasonable trial options for specific patients, or patients for specific trials; and (4) trial eligibility screening or pre-screening, given a candidate patient–trial match.</div></div>","PeriodicalId":100491,"journal":{"name":"ESMO Real World Data and Digital Oncology","volume":"11 ","pages":"Article 100658"},"PeriodicalIF":0.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145705823","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 the statistical utility and information loss in the transformation of a real-world oncology database from CDISC-SDTM to OMOP-CDM","authors":"A. Lambert ,&nbsp;C. Castagne ,&nbsp;D. Pau ,&nbsp;J. Chmiel ,&nbsp;A. Labarga ,&nbsp;E. Boernert ,&nbsp;F. Margraff ,&nbsp;C. Bachot ,&nbsp;L. Kaczmarek ,&nbsp;D. Toshev ,&nbsp;J. Martinez G. ,&nbsp;T. Stone","doi":"10.1016/j.esmorw.2025.100655","DOIUrl":"10.1016/j.esmorw.2025.100655","url":null,"abstract":"<div><h3>Background</h3><div>This project documented the conversion of a French real-world oncology study database from Clinical Data Interchange Standards Consortium (CDISC) Study Data Tabulation Model (SDTM) to the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM). The goal was to measure syntactic and semantic information loss resulting from this transformation.</div></div><div><h3>Patients and methods</h3><div>Source data originated from a retrospective observational study of HER2-positive early breast cancer patients. Initially in CDISC-SDTM standards, the data included 73 variables detailing patient demographics, disease characteristics, surgery, and follow-up information such as adjuvant treatment. Data conversion to OMOP-CDM utilized extract, load and transform (ELT) procedures with Observational Health Data Sciences and Informatics (OHDSI) tools and the data build tool (dbt), encountering challenges in mapping specific variables and maintaining data granularity. Information loss assessments involved statistical analyses.</div></div><div><h3>Results</h3><div>The source database was successfully mapped to OMOP-CDM and standardized terminologies. Statistical results from the OMOP-transformed database were consistent with those from the original SDTM database, achieving 100% concordance across all tested equality criteria for univariate, bivariate, logistic, survival, and correlation analyses at a 95% confidence interval or respective <em>P</em> value significance levels. Information loss (&lt;1%) during conversion varied based on the original database’s detail and mapping approach.</div></div><div><h3>Conclusions</h3><div>Statistical utility of the real-world oncology dataset was maintained after transformation to OMOP-CDM, ensuring reproducibility of statistical analyses. Information loss during conversion is significantly dependent on the intrinsic characteristics and level of standardization of the source database, particularly if not originally adhering to standard vocabularies.</div></div>","PeriodicalId":100491,"journal":{"name":"ESMO Real World Data and Digital Oncology","volume":"11 ","pages":"Article 100655"},"PeriodicalIF":0.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145791774","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 extraction of temporalized tumor evolution from oncology EMRs using natural language processing","authors":"C. Vinot ,&nbsp;C. Ferté ,&nbsp;T. Gaboriaud ,&nbsp;A. Minvielle-Sebastia ,&nbsp;A. Dubois ,&nbsp;R. Schwob ,&nbsp;S. Mallah ,&nbsp;M. Pajiep ,&nbsp;J.M. Alliot ,&nbsp;A. Ferreira ,&nbsp;E. Pons-Tostivint ,&nbsp;J. Mazieres ,&nbsp;A. Yazigi","doi":"10.1016/j.esmorw.2025.100660","DOIUrl":"10.1016/j.esmorw.2025.100660","url":null,"abstract":"<div><h3>Background</h3><div>Extracting temporally sensitive outcomes such as tumor progression from unstructured electronic medical records (EMRs) remains a major challenge in oncology. This study evaluates a solution with a domain-adapted natural language processing (NLP) pipeline designed to extract structured, temporally anchored clinical outcomes from narrative EMR data.</div></div><div><h3>Patients and methods</h3><div>Patients with oncogene-addicted advanced or metastatic non-small-cell lung cancer (NSCLC) treated with oral targeted therapies between January 2020 and June 2023 at a French academic hospital were included. Extracted Facts were benchmarked against expert annotations. All outputs were mapped to Observational Medical Outcome Partnership vocabularies. F1-scores were calculated for the correct Concept detection without and with their Temporality. Real-world progression-free survival (rwPFS) was estimated based on retrieved clinical outcomes.</div></div><div><h3>Results</h3><div>Among 1030 NSCLC patients treated between 2020 and 2023, 112 were confirmed to have advanced or metastatic disease with an oncogenic driver mutation, primarily EGFR (<em>n</em> = 66), ALK (<em>n</em> = 23), and KRAS (<em>n</em> = 16). The NLP pipeline achieved high accuracy in extracting clinical concepts, with an F1-score of 79.7% for tumor evolution concepts and 62.0% when temporality was included. Overall performance across all domains reached F1-scores of 76.5% for concept extraction and 63.7% with temporality. Median rwPFS was 21.9 months for EGFR-mutated, 52.4 months for ALK-translocated, and 5.0 months for KRAS-mutant tumors, in line with published benchmarks. Reviewing automatically collected data was 5.8 times faster compared with manual collection.</div></div><div><h3>Conclusions</h3><div>Our solution demonstrates robust performance for extracting temporally structured tumor outcomes from EMRs and supports the reconstruction of real-world endpoints in oncology.</div></div>","PeriodicalId":100491,"journal":{"name":"ESMO Real World Data and Digital Oncology","volume":"11 ","pages":"Article 100660"},"PeriodicalIF":0.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145926649","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":"Prognostic impact of clinical factors in MSI-H/dMMR gastrointestinal tumors treated with immune checkpoint inhibitors: results from a multicenter real-world study","authors":"N. Martinez-Lago ,&nbsp;A. Cousillas ,&nbsp;P. Jara ,&nbsp;M. Reboredo ,&nbsp;P.G. Villarroel ,&nbsp;M. Covela ,&nbsp;R. Jimenez ,&nbsp;S. Agraso ,&nbsp;P. Sampedro ,&nbsp;A. Carral ,&nbsp;M. Perez ,&nbsp;A. Garrido ,&nbsp;J. de la Cámara Gómez ,&nbsp;N. De Dios Alvarez ,&nbsp;A. Fernandez-Montes ,&nbsp;C. Lopez","doi":"10.1016/j.esmorw.2025.100676","DOIUrl":"10.1016/j.esmorw.2025.100676","url":null,"abstract":"<div><h3>Background</h3><div>Immune checkpoint inhibitors (ICIs) provide durable benefit in high microsatellite instability (MSI-H)/mismatch repair deficiency (dMMR) gastrointestinal (GI) tumors, but real-world evidence across colorectal cancer (CRC) and non-CRC primaries remains limited.</div></div><div><h3>Materials and methods</h3><div>We conducted a multicenter retrospective study including 122 patients with advanced MSI-H/dMMR GI tumors treated with ICIs in eight Spanish university hospitals. The primary endpoint was overall survival (OS). Secondary endpoints included progression-free survival (PFS), response, and safety.</div></div><div><h3>Results</h3><div>Median age at ICI initiation was 70.4 years; 79.5% had an Eastern Cooperative Oncology Group (ECOG) performance status (PS) of 0-1. CRC accounted for 54.9% of cases, gastroesophageal adenocarcinoma (GEA) for 34.4%, and other GI tumors for 10.7%. At a median follow-up of 33 months, median PFS was 46.0 months [95% confidence interval (CI) 30.6-61.4 months] and median OS was 53.2 months (95% CI 42.6-63.7 months), with 36-month rates of 57.1% for PFS and 62.8% for OS. The objective response rate (ORR) was 77.7% and the disease control rate (DCR) was 91.1%, with comparable efficacy across CRC, GEA, and other GI tumor subgroups. In multivariable analysis, ECOG PS was the only independent prognostic factor for both OS and PFS. Treatment was generally well tolerated; 47.5% of patients experienced adverse events, grade ≥3 in 10.7%, with no treatment-related deaths.</div></div><div><h3>Conclusions</h3><div>In this multicenter European real-world cohort, ICIs demonstrated clinically meaningful and durable benefit in advanced MSI-H/dMMR GI tumors, confirming pivotal trial results in routine practice. ECOG PS emerged as the main independent prognostic factor, underscoring its central role in patient selection.</div></div>","PeriodicalId":100491,"journal":{"name":"ESMO Real World Data and Digital Oncology","volume":"11 ","pages":"Article 100676"},"PeriodicalIF":0.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145926647","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}