ESMO Real World Data and Digital Oncology最新文献

{"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":"2026-01-30","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":"Predictors of acute lymphopenia after radiotherapy for prostate cancer including pelvic node irradiation: results of a real-world prospective multi-centric study","authors":"V. Vavassori ,&nbsp;M. Pavarini ,&nbsp;L. Alborghetti ,&nbsp;S. Aimonetto ,&nbsp;A. Maggio ,&nbsp;V. Landoni ,&nbsp;P. Ferrari ,&nbsp;A. Bianculli ,&nbsp;E. Petrucci ,&nbsp;A. Cicchetti ,&nbsp;B. Farina ,&nbsp;M.G. Ubeira-Gabellini ,&nbsp;P. Salmoiraghi ,&nbsp;E. Moretti ,&nbsp;B. Avuzzi ,&nbsp;T. Giandini ,&nbsp;F. Munoz ,&nbsp;A. Magli ,&nbsp;B. Noris Chiorda ,&nbsp;G. Sanguineti ,&nbsp;C. Cozzarini","doi":"10.1016/j.esmorw.2025.100680","DOIUrl":"10.1016/j.esmorw.2025.100680","url":null,"abstract":"<div><h3>Background</h3><div>Acute lymphopenia (AL) is a clinically relevant concern in patients undergoing pelvic lymph-node irradiation (PNI) for prostate cancer (PCa) and reliable predictive models are not available. The purpose of the current analysis was to develop a predictive model of AL after PNI for PCa combining dosimetric and clinical information in a large, prospectively followed cohort.</div></div><div><h3>Materials and methods</h3><div>Clinical/dosimetry/blood test data from a multi-centric prospective study were available, including absolute lymphocyte count (ALC) at baseline, mid-point and radiotherapy (RT) end. Dose–volume histograms (DVHs) of the body and of pelvic bones were extracted, as well as the integral dose (ID) to the body. Lymph-nodal planning target volume (LN-PTV) and its cranial limit were also recovered. The current analysis focused on acute CTCAEv4.03 grade ≥ 3 (G3+) lymphopenia (ALC &lt; 500/μl), defined as the lowest count between baseline and mid-point or RT end. The patient population was split into training and validation cohorts, and a multivariable logistic regression model combining DVHs and clinical information was trained and validated.</div></div><div><h3>Results</h3><div>700/887 patients with full 3D planning data and available baseline, mid-point and RT end counts were considered. 290 patients (41.4%) experienced acute G3+ lymphopenia. Both ID and pelvic bone DVH parameters were significantly associated with the endpoint. The two best resulting models included baseline ALC (OR = 0.999, <em>P</em> &lt; 0.001) and ID (Gy∗l) (OR = 1.003, <em>P</em> = 0.012) or baseline ALC, cranial LN-PTV limit (OR = 1.019, <em>P</em> &lt; 0.001) and EQD2 to LN-PTV (OR = 1.095, <em>P</em> = 0.002), when replacing ID with the cranial LN-PTV limit.</div></div><div><h3>Conclusions</h3><div>Severe AL after PNI for PCa is largely modulated by baseline ALC, with an independent role of the LN-PTV cranial limit or, alternatively, of ID, with the risk increasing by 5%-10% per 10² Gy∗l.</div></div>","PeriodicalId":100491,"journal":{"name":"ESMO Real World Data and Digital Oncology","volume":"11 ","pages":"Article 100680"},"PeriodicalIF":0.0,"publicationDate":"2026-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146077772","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":"How to bring generative AI to oncology practice","authors":"D. Truhn ,&nbsp;J.N. Kather","doi":"10.1016/j.esmorw.2025.100679","DOIUrl":"10.1016/j.esmorw.2025.100679","url":null,"abstract":"<div><div>Generative artificial intelligence (AI) is entering oncology. Large language models are the near-term workhorse because oncology runs on narrative text and structured tables. We review current adoption and outline a practical path from stand-alone chat models to retrieval-augmented systems and, ultimately, agentic assistants that plan tasks, call domain tools, and integrate multimodal data within the electronic health record. Concrete uses include molecular tumor board synthesis with transparent evidence, grading along guidelines, synoptic radiology and pathology drafting, and computable trial matching. We also map the constraints: fragmented hospital information technology, privacy and provenance requirements, domain shift across sites, and persistent hallucinations. We envision that evaluation must move beyond leaderboards toward multicenter, prospective designs with endpoints that reflect clinical utility, such as faithfulness to cited sources, extraction accuracy, time to task completion, plan correctness, recovery after tool failure, and silent clinical studies before exposure. Finally, we sketch an adoption trajectory. Institutions will replace <em>ad hoc</em> use of public tools with sanctioned drafting assistants, then embed retrieval and calculators inside the record, and only later enable event-driven agents that propose context-aware actions. The destination is augmentation, not automation: a learning assistant that shows its work, improves routine care, and leaves clinical judgment with clinicians.</div></div>","PeriodicalId":100491,"journal":{"name":"ESMO Real World Data and Digital Oncology","volume":"11 ","pages":"Article 100679"},"PeriodicalIF":0.0,"publicationDate":"2026-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146077837","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":"Artificial intelligence for clinical trial design, conduct, and analysis: a narrative review","authors":"D.G. Knapen,&nbsp;M. van Kruchten,&nbsp;D.J.A. de Groot,&nbsp;K.E. Broekman,&nbsp;R.S.N. Fehrmann","doi":"10.1016/j.esmorw.2026.100682","DOIUrl":"10.1016/j.esmorw.2026.100682","url":null,"abstract":"<div><div>Drug development in oncology is facing rising complexity, prolonged timelines, and increasing costs, with many trials failing to reach completion or secure regulatory approval for the investigated treatment. Against this backdrop, artificial intelligence (AI) and real-world data have emerged as promising tools to improve the efficiency and quality of clinical research. This narrative review explores how AI can support clinical trial design, conduct, and analysis. AI-driven methods can streamline information gathering, optimize eligibility criteria, and predict trial success, thereby reducing costly failures. Patient recruitment and retention, often the most challenging aspects of oncology trials, may benefit from AI-supported matching algorithms, digital health technologies, and personalized interactions. During trial conduct, natural language processing and sensor-based monitoring offer opportunities to reduce administrative burden and capture real-world, patient-centred outcomes. For trial analyses, AI enhances radiology, digital pathology, pharmacometrics, and multimodal modelling, enabling more accurate prognostic and predictive insights. In addition, AI-based simulations, such as digital twins and <em>in silico</em> trials, hold potential to complement conventional trial designs. However, despite rapid technical advances, evidence supporting clinical utility remains limited. Most applications are tested retrospectively or in single-centre settings, limiting generalizability. Regulatory frameworks, including the European Union Artificial Intelligence Act and emerging ESMO standards for AI-based biomarkers, emphasize transparency, reproducibility, and prospective validation. Ultimately, the successful integration of AI into oncology trials will depend less on technical capacity than on rigorous evaluation, harmonized regulation, and adoption of shared quality standards.</div></div>","PeriodicalId":100491,"journal":{"name":"ESMO Real World Data and Digital Oncology","volume":"11 ","pages":"Article 100682"},"PeriodicalIF":0.0,"publicationDate":"2026-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146077771","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":"Digital symptom monitoring and activity tracking in chemoradiation—lessons from the CAM experience","authors":"J. Fu ,&nbsp;A. Pepin ,&nbsp;C. Hollawell ,&nbsp;K. Tang ,&nbsp;J.S. Bryer ,&nbsp;A. Munter ,&nbsp;S. Sinha ,&nbsp;A. Goel","doi":"10.1016/j.esmorw.2026.100683","DOIUrl":"10.1016/j.esmorw.2026.100683","url":null,"abstract":"<div><div>Artificial intelligence (AI) is increasingly being explored as a way to improve supportive care during chemoradiotherapy (CRT). Through our CAM 1.0 and CAM 2.0 studies, we examined how AI-supported remote patient monitoring could be integrated into CRT care, revealing both its potential benefits and its challenges in real-world implementation. We observed how AI can facilitate remote patient monitoring beyond routine visits, yet also how it may create additional tasks for providers, magnify digital inequities, and leave patients still seeking human connection. These experiences underscore that AI’s impact in oncology will be defined not only by its technical capabilities but by how it fits into workflows, addresses barriers to access, and preserves the relational aspects of care. This perspectives piece aims to highlight the practical insights from CAM 1.0 and 2.0 that may inform how oncologists, developers, and health systems approach future AI integration into oncology workflows, particularly in the context of CRT. NCT: <span><span>NCT05318027</span><svg><path></path></svg></span></div></div>","PeriodicalId":100491,"journal":{"name":"ESMO Real World Data and Digital Oncology","volume":"11 ","pages":"Article 100683"},"PeriodicalIF":0.0,"publicationDate":"2026-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146037818","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":"Real-world demographics, clinical characteristics, and testing and treatment patterns in patients with non-small-cell lung cancer and extensive-stage small-cell lung cancer in Belgium: the AIBED study","authors":"K.M. Muylle ,&nbsp;L. Decoster ,&nbsp;D. Verleyen ,&nbsp;P.R. Germonpre ,&nbsp;I. Masuy ,&nbsp;B. Verheyden ,&nbsp;C.L. Oeste ,&nbsp;E. Callewaert ,&nbsp;S. Derijcke ,&nbsp;E. Wauters ,&nbsp;A. Janssens ,&nbsp;A. Verbiest","doi":"10.1016/j.esmorw.2025.100668","DOIUrl":"10.1016/j.esmorw.2025.100668","url":null,"abstract":"<div><h3>Background</h3><div>Real-world evidence is essential to understand the implementation of lung cancer treatments. This study describes the characteristics, testing patterns, and treatment of lung cancer patients in Belgium.</div></div><div><h3>Methods</h3><div>This multicenter study used routinely collected longitudinal electronic health record data from five Belgian hospitals (2019-2021). Natural language processing was used to process unstructured data, which was combined with structured data to generate an Observational Medical Outcomes Partnership (OMOP) common data model (OMOP-CDM) data warehouse. We generated a lung cancer cohort and subcohorts of special interest: stage IB-IIIB resected non-small-cell lung cancer (NSCLC), unresected stage III NSCLC, (EGFR-mutated) stage IV NSCLC, and extensive-stage small-cell lung cancer (ES-SCLC).</div></div><div><h3>Results</h3><div>We included 1952 patients, of whom 87% (1699) had NSCLC and 13% (253) SCLC (86.6% ES-SCLC). Stage IB-IIIB resected NSCLC included 17.3%, unresected stage III NSCLC 12.3%, and stage IV NSCLC 45.0% of NSCLC patients. Brain metastasis around diagnosis was reported in 16.4% of stage IV NSCLC (22.3% in EGFR mutated) and 15.1% of ES-SCLC patients. Across NSCLC subcohorts, 53.9% of patients had a programmed death-ligand 1 (PD-L1)- positive tumor (≥1%) and 12.7% an EGFR mutation-positive tumor. In stage IB-IIIB resected NSCLC patients, resection status was retrieved for 83.0% of patients, of whom 93.0% had complete resection. Among 327 stage III NSCLC patients, 63.9% (<em>n</em> = 209) were unresected, 36.7% (<em>n</em> = 120) had a PD-L1-positive tumor, 18.3% (<em>n</em> = 60) completed chemoradiotherapy, and 11.3% (<em>n</em> = 37) initiated durvalumab.</div></div><div><h3>Conclusions</h3><div>This is the first Belgian study to provide real-world insights into patient trajectories in lung cancer patients leveraging artificial intelligence technologies.</div></div>","PeriodicalId":100491,"journal":{"name":"ESMO Real World Data and Digital Oncology","volume":"11 ","pages":"Article 100668"},"PeriodicalIF":0.0,"publicationDate":"2026-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146037819","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":"Histotype-specific incidence and survival of urothelial carcinoma—an analysis of the German North Rhine-Westphalia Cancer Registry","authors":"C. Darr ,&nbsp;L. Möller ,&nbsp;A. Szentkiralyi ,&nbsp;K. Claassen ,&nbsp;N. Schürger ,&nbsp;H. Reis ,&nbsp;T. Hilser ,&nbsp;A. Stang ,&nbsp;B.A. Hadaschik ,&nbsp;H. Kajüter ,&nbsp;V. Grünwald","doi":"10.1016/j.esmorw.2025.100678","DOIUrl":"10.1016/j.esmorw.2025.100678","url":null,"abstract":"<div><h3>Background</h3><div>The aim of this study was to evaluate the incidence and survival of urothelial carcinoma (UC) and non-urothelial tumor types. The primary objective was to define the incidence and survival of pure UC and non-UC tumor types.</div></div><div><h3>Materials and methods</h3><div>Malignant invasive urinary cancers of the urothelial tract diagnosed between 2008 and 2022 were identified via the North Rhine-Westphalia Cancer Registry and classified according to the 2016 World Health Organization (WHO) classification (fourth edition). Evaluation focused on pure tumor types: UC, papillary invasive UC (PIUC), squamous-cell carcinoma (SCC), adenocarcinoma (ADC), rarer tumor types and mixed histologies grouped under other specific tumor types (OSTT), and unspecified tumor types (UTT). The primary outcomes were age-standardized incidence rate with estimated annual percentage changes, and relative survival.</div></div><div><h3>Results</h3><div>UC and PIUC were the most common histology types, accounting for 57.9% and 26.2% (<em>n</em> = 73 751), respectively. The age-standardized incidence of PIUC and UTT decreased over time in both sexes, while OSTT incidence increased over time. Incidence rate declined among men with SCC and among women with ADC. Relative survival was notably poor for SCC (32.3%) and OSTT (23.8%), in contrast to a more favorable outcome for PIUC (72.0%). Decreasing survival with advancing T-stages was noted, particularly for UC, PIUC, ADC, and UTT; T1 in PIUC showed the best survival (81.0%) and T3-4 in OSTT had the poorest survival (16.4%).</div></div><div><h3>Conclusion</h3><div>Relative survival was most favorable for PIUC, UC, and ADC. In contrast, SSC and OSTT exhibited poorer survival outcomes, highlighting a pressing medical need for enhanced treatment options for these subgroups.</div></div>","PeriodicalId":100491,"journal":{"name":"ESMO Real World Data and Digital Oncology","volume":"11 ","pages":"Article 100678"},"PeriodicalIF":0.0,"publicationDate":"2026-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146037817","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":"2026-01-14","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":"Liquid biopsies using circulating tumor DNA for surveillance of gastrointestinal cancers in Hispanics: first real-world data report","authors":"C. Cardona-De Jesús ,&nbsp;H. Centeno-Girona ,&nbsp;F. Garau-Rolón ,&nbsp;H. Melero-Rodríguez ,&nbsp;I. Alicea ,&nbsp;M. Cruz-Correa","doi":"10.1016/j.esmorw.2025.100652","DOIUrl":"10.1016/j.esmorw.2025.100652","url":null,"abstract":"<div><h3>Background</h3><div>Malignant tumors release circulating tumor DNA (ctDNA) into the bloodstream, providing insights into tumor-specific mutations and pathways driving cancer progression. ctDNA testing is currently approved as a type of liquid biopsy to monitor disease burden and detect minimal residual disease (MRD). This study aimed to evaluate the adoption of ctDNA testing in a community oncology practice and assess the overall diagnostic performance of ctDNA and its association with disease progression in stage IV colorectal cancer (CRC), as determined by imaging studies.</div></div><div><h3>Patients and methods</h3><div>This retrospective study analyzed the medical records of 88 patients with gastrointestinal cancers (80 CRC, 5 gastric, 3 esophageal) who underwent ctDNA molecular testing between January 2020 and April 2022. Electronic medical records from patients aged ≥21 years who had two or more ctDNA tests with concurrent imaging studies or a pathology-confirmed CRC, gastric cancer, or esophageal cancer diagnosis were evaluated.</div></div><div><h3>Results</h3><div>At baseline, 47 (53.4%) patients had negative and 41 (46.6%) had positive results. Most patients had CRC (90.1%). In stage IV CRC, ctDNA was increasing before radiologic progression in all documented cases (100%), with a median lead time of 2.5 months (range 0.5-15 months). In early-stage CRC (I-III), ctDNA preceded radiologic progression in 40% of cases, with a median lead time of 6 months (range 6-10 months).</div></div><div><h3>Conclusions</h3><div>Using real-world data, we report the first-time results of the ctDNA testing adoption in a community oncology setting among patients with gastrointestinal cancers, predominantly CRC. Our findings suggest that integration of ctDNA testing may support disease monitoring in routine clinical practice.</div></div>","PeriodicalId":100491,"journal":{"name":"ESMO Real World Data and Digital Oncology","volume":"11 ","pages":"Article 100652"},"PeriodicalIF":0.0,"publicationDate":"2026-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145977532","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":"2026-01-09","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}