ESMO Real World Data and Digital Oncology最新文献

{"title":"DIPSS and DIPSS Plus risk scoring in myelofibrosis utilizing automated, electronic health record-integrated decision system","authors":"A. Mervaala-Muroke ,&nbsp;M. Lehto ,&nbsp;K. Porkka ,&nbsp;O. Brück","doi":"10.1016/j.esmorw.2025.100196","DOIUrl":"10.1016/j.esmorw.2025.100196","url":null,"abstract":"<div><h3>Background</h3><div>Automated risk scoring could reduce human errors and enhance consistency. The aim of the study was to investigate whether automation of myelofibrosis Dynamic Prognostic Scoring System (DIPSS) and DIPSS Plus scores could improve their prognostic accuracy.</div></div><div><h3>Materials and methods</h3><div>We built an automated, electronic health record (EHR)-integrated decision system, extracting risk score covariates from tabular source databases and patient journals using text mining. Physician-defined scores were obtained through manual chart review (DIPSS 12%, DIPSS Plus 21%) or manual calculations (DIPSS 88%, DIPSS Plus 79%) using the reported risk score covariates. We compared automated scores with physician-defined scores by their ability to predict overall survival, using Cox regression, C-index, and time-dependent area under the receiver operating characteristic (AUROC) values.</div></div><div><h3>Results</h3><div>We included real-world data of patients with myelofibrosis (<em>n</em> = 251) from the Helsinki University Hospital district, Finland, at the time of their diagnosis. Cox regression analyses demonstrated C-indices of 0.72/0.72 (DIPSS/DIPSS Plus) for automated scoring and 0.69/0.71 for physician-defined scoring. Yearly time-dependent AUROC values for 10-year overall survival varied 0.75-0.82/0.74-0.84 for automated scoring and 0.71-0.79/0.74-0.82 for physician-defined scoring. We validated the feasibility and performance (C-indices: 0.68/70 for automated scoring versus 0.66/67 for physician-defined scoring, AUROC ranges: 0.67-0.76/0.67-0.87 for automated scoring versus 0.65-0.74/0.65-0.79 for physician-defined scoring) of the automated model in an external dataset (<em>n</em> = 120 patients).</div></div><div><h3>Conclusions</h3><div>We present the first automated, EHR-integrated decision system for calculating DIPSS and DIPSS Plus scores. The accuracy of the scores was aligned with the physician-defined scores, but the availability of the scores was significantly improved, highlighting the need for machine-assisted scoring.</div></div>","PeriodicalId":100491,"journal":{"name":"ESMO Real World Data and Digital Oncology","volume":"10 ","pages":"Article 100196"},"PeriodicalIF":0.0,"publicationDate":"2025-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145268342","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-driven phase recognition in lung surgery: a single-centre pilot study☆","authors":"S.J.-Y. Ohtani-Kim ,&nbsp;J. Samejima ,&nbsp;M. Wakabayashi ,&nbsp;M. Tada ,&nbsp;T. Miyoshi ,&nbsp;Y. Matsumura ,&nbsp;K. Tane ,&nbsp;K. Aokage ,&nbsp;Y. Ishikawa ,&nbsp;K. Hayashi ,&nbsp;T. Ogane ,&nbsp;K. Sasaki ,&nbsp;S. Takenaka ,&nbsp;Y. Kinebuchi ,&nbsp;M. Ito ,&nbsp;M. Tsuboi","doi":"10.1016/j.esmorw.2025.100194","DOIUrl":"10.1016/j.esmorw.2025.100194","url":null,"abstract":"<div><h3>Introduction</h3><div>Effective surgical specimen management during lung resection is crucial for accurate analyses and treatment. Minimally invasive techniques complicate workflows; thus, artificial intelligence (AI)-based solutions are needed to improve their safety and efficiency. We assessed the feasibility of AI for the automated classification of surgical phases in thoracoscopic wedge resection, examining the link between classification accuracy and surgical complexity, particularly during specimen extraction.</div></div><div><h3>Patients and methods</h3><div>This single-centre retrospective observational study from Japan included 73 video recordings of video-assisted thoracic surgery lung wedge resections with extraction of a single specimen carried out from January 2021 to December 2023. A Swin Transformer AI model was used to classify five distinct surgical phases: preparatory actions, lesion identification, resection, and specimen extraction. Pre- and postprocessing techniques improved model performance across different phases. The primary outcome was AI model performance in classifying surgical phases using metrics such as accuracy, precision, recall, and F1 score.</div></div><div><h3>Results</h3><div>The modified AI model achieved an overall accuracy of 0.778, with phase-specific accuracies ranging from 0.574 to 0.911. Significant improvements were observed in critical phases for specimen management (accuracy: 0.816). Clinical factors, including the number of access ports and phase duration, were key determinants of accuracy.</div></div><div><h3>Conclusions</h3><div>Our AI-driven phase recognition model for thoracoscopic lung surgery videos shows potential for optimizing operating room workflow, enhancing real-time decision-making, and improving efficiency by automating surgical phase classification.</div></div>","PeriodicalId":100491,"journal":{"name":"ESMO Real World Data and Digital Oncology","volume":"10 ","pages":"Article 100194"},"PeriodicalIF":0.0,"publicationDate":"2025-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145268341","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":"Outcomes in relapsed/refractory diffuse large B-cell lymphoma in an Asian tertiary cancer center: real-world interventions as benchmark for novel therapy","authors":"N.-A. Lim ,&nbsp;Y. Sun ,&nbsp;J.Y. Tan ,&nbsp;R.M.H. Lim ,&nbsp;Y.H. Tan ,&nbsp;L.C.K. Ng ,&nbsp;F.L.W.I. Lim ,&nbsp;Y.T. Goh ,&nbsp;J.T.M. Hoe ,&nbsp;J. Chiang ,&nbsp;E.W.Y. Chang ,&nbsp;E.Y.L. Poon ,&nbsp;N. Somasundaram ,&nbsp;M. Tao ,&nbsp;S.T. Lim ,&nbsp;E.S. Mulvihill ,&nbsp;A. Hanna-Elias ,&nbsp;J.Y. Chan","doi":"10.1016/j.esmorw.2025.100195","DOIUrl":"10.1016/j.esmorw.2025.100195","url":null,"abstract":"<div><h3>Background</h3><div>Recent advances have led to the approval of several new therapies for relapsed/refractory diffuse large B-cell lymphoma (R/R DLBCL). However, real-world data for treatment patterns and outcomes in R/R DLBCL in Asian countries are lacking, limiting the benchmarking of novel treatments.</div></div><div><h3>Patients and methods</h3><div>We conducted a retrospective single-center cohort study including 227 patients with R/R DLBCL diagnosed from 2010 to 2022. Outcomes including progression-free survival (PFS) and overall survival (OS) were assessed.</div></div><div><h3>Results</h3><div>In the overall cohort, median OS was 13.5 months and 5-year OS was 29.8% from the time of first relapse. Median time to first relapse from completion of first-line immunochemotherapy was 7.03 months (range 0-148.4 months). Time to first relapse was significantly associated with OS outcomes. Patients who had earlier relapse, defined as 12 months or less from first-line treatment, had worse OS at 8.6 months, as compared with patients who relapsed &gt;12 months after first-line treatment, who had OS of 34.2 months (hazard ratio 1.73, 95% confidence interval 1.26-2.37, <em>P</em> = 0.007). Median PFS was 3.2 months as compared with 15.3 months, respectively. The majority (<em>n</em> = 121, 53.3%) were transplant-ineligible and only 44 (19.4%) were transplanted. The median PFS for non-transplant recipients was 3.5 months versus 34.4 months for transplant recipients. The median OS for non-transplant recipients was 8.5 months versus 114.1 months for transplant recipients.</div></div><div><h3>Conclusion</h3><div>Poor outcomes remain for patients with R/R DLBCL. Novel therapeutics including bispecifics and CAR-T may improve outcomes of these patients.</div></div>","PeriodicalId":100491,"journal":{"name":"ESMO Real World Data and Digital Oncology","volume":"10 ","pages":"Article 100195"},"PeriodicalIF":0.0,"publicationDate":"2025-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145268340","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":"Clinical outcomes of systemic anticancer therapies in solid cancer patients with liver and kidney transplant: an observational cross-sectional study","authors":"E. Chen ,&nbsp;N. Belkaid ,&nbsp;C. Duvoux ,&nbsp;D. Sahali ,&nbsp;P. Grimbert ,&nbsp;M.-B. Matignon ,&nbsp;A. Hulin ,&nbsp;S. Babai ,&nbsp;C. Chouaïd ,&nbsp;J.B. Assié ,&nbsp;M. Carvalho ,&nbsp;C. Tournigand ,&nbsp;E. Kempf","doi":"10.1016/j.esmorw.2025.100185","DOIUrl":"10.1016/j.esmorw.2025.100185","url":null,"abstract":"<div><h3>Background</h3><div>Solid cancer patients with liver transplant (LT) or kidney transplant (KT) receiving systemic anticancer therapies (SACT) are likely to present an increased risk of treatment-related infections. We aimed to describe their clinical outcomes.</div></div><div><h3>Patients and methods</h3><div>This retrospective study included SACT-treated patients from two university medical oncology centers between 2000 and 2023, identified through automated extraction and manual record review. We excluded patients treated with immune checkpoint inhibitors. We manually collected immunosuppressant and SACT administration, patient SACT grade 3 to 5 toxicities, cause of death, and overall survival.</div></div><div><h3>Results</h3><div>Fifty-three patients were included: 39 (74%) men, 33 (62%) LT, and 20 (38%) KT; median age was 62 years (interquartile range 32-82 years); 19 (58%) and 15 (75%) LT and KT patients, respectively, had stage IV diseases. Primary cancer was liver (<em>n</em> = 16; 45%) and digestive (<em>n</em> = 10; 30%) for LT patients; digestive (<em>n</em> = 7; 35%), kidney, breast, and lung (all three <em>n</em> = 3; 15%) for KT patients. From the time before cancer diagnosis to SACT administration, the immunosuppressant regimen was changed in both subgroups. Overall, 29 (88%) LT patients and 17 (85%) KT patients were exposed at least once to standard cytotoxic drugs, and 8 (24%) LT patients and 3 (15%) KT patients to tyrosine kinase inhibitors. Thirty-three patients had grade 3-4 toxicities, 52% LT patients and 30% KT patients. Twenty-four patients had grade 5 toxicities (79% LT and 86% KT patients treatment-related infections, respectively). One-year overall survival rates were 52% for LT patients and 50% for KT patients. After treatment-related infections, death was due to tumor progression for 9 LT patients (32%) and 5 (42%) KT patients, respectively.</div></div><div><h3>Conclusions</h3><div>Treatment-related infections drive the prognosis of LT and KT patients undergoing SACT.</div></div>","PeriodicalId":100491,"journal":{"name":"ESMO Real World Data and Digital Oncology","volume":"10 ","pages":"Article 100185"},"PeriodicalIF":0.0,"publicationDate":"2025-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145220928","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":"Barriers to career development and gender-related challenges in oncology: the results of the JSMO and JSCO surveys in Japan","authors":"M. Ono ,&nbsp;I. Takahashi ,&nbsp;T. Kudo ,&nbsp;N. Miyazaki ,&nbsp;T. Azuma ,&nbsp;H. Horinouchi ,&nbsp;I. Kinoshita ,&nbsp;T.E. Nakajima ,&nbsp;T. Yoshino ,&nbsp;H. Minami","doi":"10.1016/j.esmorw.2025.100186","DOIUrl":"10.1016/j.esmorw.2025.100186","url":null,"abstract":"<div><h3>Background</h3><div>As the number of female physicians in Japan increases, so does the number of female oncologists. This study investigates gender-related challenges and identifies factors influencing career progression among oncology professionals in Japan.</div></div><div><h3>Materials and methods</h3><div>A survey was distributed to members of the Japanese Society of Medical Oncology (JSMO) and the Japan Society of Clinical Oncology (JSCO), based on the European Society for Medical Oncology (ESMO) Women for Oncology questionnaire. It covered demographics, professional environment, work–life balance, personal choices, and career-related challenges.</div></div><div><h3>Results</h3><div>A total of 612 responses (47.5% women, 52.5% men) were analyzed; 58.9% were &lt;50 years of age. Female respondents were significantly more likely to report that their careers impacted marital status, childbearing decisions, and time for childcare. Women also reported a greater influence of life choices—such as marriage, childbirth, and reduced working hours—on their careers than men. Notably, 66.4% of women perceived career barriers compared with 40.6% of men. Major obstacles included difficulty balancing work and life, limited opportunities for clinical research, and childcare responsibilities. Multivariate analysis showed that gender and age were significant predictors of perceived career barriers.</div></div><div><h3>Conclusions</h3><div>Gender disparities remain a major issue for female oncologists in Japan, rooted in societal expectations, unequal childcare burdens, and institutional limitations. Systemic changes—such as promoting task sharing, expanding mentorship, and implementing inclusive workplace policies—are essential to foster equitable career advancement in oncology.</div></div>","PeriodicalId":100491,"journal":{"name":"ESMO Real World Data and Digital Oncology","volume":"10 ","pages":"Article 100186"},"PeriodicalIF":0.0,"publicationDate":"2025-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145159279","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":"Integrating radiomics and real-world data to predict immune checkpoint inhibitor efficacy in advanced non-small-cell lung cancer☆","authors":"L. Provenzano ,&nbsp;M. Favali ,&nbsp;L. Mazzeo ,&nbsp;A. Spagnoletti ,&nbsp;M. Ruggirello ,&nbsp;G. Calareso ,&nbsp;F.G. Greco ,&nbsp;R. Vigorito ,&nbsp;A. Quarta ,&nbsp;F. Calimeri ,&nbsp;M. Monteleone ,&nbsp;G. Baselli ,&nbsp;E. De Momi ,&nbsp;B. Guirges ,&nbsp;A. Di Lello ,&nbsp;A. Zec ,&nbsp;A. Ferrarin ,&nbsp;C. Giani ,&nbsp;C. Silvestri ,&nbsp;M. Occhipinti ,&nbsp;A. Prelaj","doi":"10.1016/j.esmorw.2025.100182","DOIUrl":"10.1016/j.esmorw.2025.100182","url":null,"abstract":"<div><h3>Background</h3><div>Immunotherapy (IO) revolutionized the prognosis of patients with non-small-cell lung cancer (NSCLC). However, identifying optimal candidates for this treatment remains challenging. Based on previous studies suggesting the potential power of radiomics in predicting clinical outcomes in different clinical settings, we aimed to assess its capability in predicting IO efficacy in advanced NSCLC patients.</div></div><div><h3>Materials and methods</h3><div>A total of 375 advanced NSCLC patients treated with IO-based regimens from April 2013 to May 2022 were enrolled. Primary lung lesions were segmented and radiomic features extracted. Using clinical benefit rate and overall survival status at 6 and 24 months (OS6 and OS24) as endpoints, machine learning classifiers were trained and then evaluated on a test set.</div></div><div><h3>Results</h3><div>Model achieving the highest performance predicting long-term survival (OS24) reached an accuracy of 0.71 and area under the curve of 0.79 on the test set, using the combination of radiomic features and real-world data (RWD) as input. Combining radiomics with RWD consistently allowed to outperform predictions obtained using the current standard predictive biomarker, i.e. programmed death-ligand 1 expression, for most of the outcomes.</div></div><div><h3>Conclusions</h3><div>We explored a radiomics-based signature with potential utility in predicting the prognosis of NSCLC patients undergoing IO. Further validation is required to confirm its clinical applicability and to support oncologists in making prognostic assessments.</div></div>","PeriodicalId":100491,"journal":{"name":"ESMO Real World Data and Digital Oncology","volume":"10 ","pages":"Article 100182"},"PeriodicalIF":0.0,"publicationDate":"2025-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145109180","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":"Comparison of machine learning and deep learning models for survival prediction in early-stage hormone receptor-positive/HER2-negative breast cancer receiving neoadjuvant chemotherapy","authors":"L. Mastrantoni ,&nbsp;G. Garufi ,&nbsp;G. Giordano ,&nbsp;N. Maliziola ,&nbsp;E. Di Monte ,&nbsp;G. Arcuri ,&nbsp;V. Frescura ,&nbsp;A. Rotondi ,&nbsp;A. Orlandi ,&nbsp;L. Carbognin ,&nbsp;A. Palazzo ,&nbsp;L. Pontolillo ,&nbsp;A. Fabi ,&nbsp;S. Pannunzio ,&nbsp;I. Paris ,&nbsp;F. Marazzi ,&nbsp;A. Franco ,&nbsp;G. Franceschini ,&nbsp;G. Scambia ,&nbsp;D. Giannarelli ,&nbsp;E. Bria","doi":"10.1016/j.esmorw.2025.100184","DOIUrl":"10.1016/j.esmorw.2025.100184","url":null,"abstract":"<div><h3>Background</h3><div>We compared machine learning (ML) and deep learning (DL) models to predict disease-free survival (DFS) and overall survival (OS) in patients with hormone receptor (HR)-positive/human epidermal growth factor receptor 2 (HER2)-negative breast cancer (BC) receiving neoadjuvant chemotherapy (NACT), using routine clinicopathological features before and after surgery.</div></div><div><h3>Materials and methods</h3><div>In this retrospective cohort, 572 patients with stage I-III HR-positive/HER2-negative BC treated with anthracycline/taxane-based NACT and surgery were analyzed. Data were split into training (<em>n</em> = 463) and validation (<em>n</em> = 109) sets. Five ML models (random survival forest, extra survival tree, gradient boosting machine, support vector machine, regularized Cox) and four neural networks (DeepSurv, DeepHit, logistic hazard, multi-task logistic regression) were trained via five-fold cross-validation. Performance was assessed on the validation cohort by the C-index and integrated Brier score (iBS).</div></div><div><h3>Results</h3><div>Median age was 49 years and pathological complete response (pCR) rate was 15%. Median DFS was 103 months [95% confidence interval (CI) 84.4 months-not estimable (NE)], and 5-year OS was 78.6% (95% CI 74.8% to 82.5%). DeepSurv yielded the best overall performance, with a C-index of 0.70 (95% CI 0.60-0.78, iBS 0.22) for DFS and 0.68 (95% CI 0.56-0.79, iBS 0.17) for OS. The top ML model achieved C-indices of 0.64 (DFS) and 0.68 (OS). Key predictors were nodal status, estrogen receptor/progesterone receptor expression, tumor size, Ki-67 and pCR.</div></div><div><h3>Conclusions</h3><div>Both ML and DL models predicted survival post-NACT in HR-positive/HER2-negative BC, suggesting that simple models can perform as well as DL architectures in small datasets. The marginally higher discrimination of DL models should be weighed against computational demands and lower interpretability compared with ML methods.</div></div>","PeriodicalId":100491,"journal":{"name":"ESMO Real World Data and Digital Oncology","volume":"10 ","pages":"Article 100184"},"PeriodicalIF":0.0,"publicationDate":"2025-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145097935","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":"Prevalence, treatment response, and survival in a real-world NTRK gene fusion-positive microsatellite instability-high metastatic colorectal cancer cohort","authors":"S.J. Schraa ,&nbsp;M.M. Laclé ,&nbsp;K. Zwart ,&nbsp;E.H. Gort ,&nbsp;W.W. de Leng ,&nbsp;M. Koopman ,&nbsp;G.R. Vink ,&nbsp;G.M. Bol ,&nbsp;PLCRC Working Group","doi":"10.1016/j.esmorw.2025.100180","DOIUrl":"10.1016/j.esmorw.2025.100180","url":null,"abstract":"<div><h3>Background</h3><div>Tropomyosin receptor kinase (TRK) inhibitors are approved for patients with neurotrophic tyrosine receptor kinase (<em>NTRK</em>) fusion-positive solid tumors, but clinical benefit in metastatic colorectal cancer (mCRC) is uncertain. We aimed to determine the prevalence, treatment response, and overall survival of <em>NTRK</em> fusion-positive, microsatellite instability-high (MSI-H), or mismatch repair-deficient (dMMR) mCRC patients in a real-world cohort.</div></div><div><h3>Materials and methods</h3><div>Tumor tissue was collected for all patients diagnosed with MSI-H/dMMR mCRC between 2015 and 2021 in the Netherlands. Pan-TRK immunohistochemistry was carried out, and positive cases were assessed for <em>NTRK</em> fusions by RNA-based next-generation sequencing. Clinical data on treatment duration and overall survival were obtained from the Netherlands Cancer Registry.</div></div><div><h3>Results</h3><div>Nine out of 268 MSI-H/dMMR colorectal tumors (3.4%) harbored an <em>NTRK</em> fusion. All <em>NTRK</em> fusion-positive tumors were <em>RAS</em>/<em>BRAF</em>^{<em>V600E</em>} wild-type. In the subgroup of <em>RAS</em>/<em>BRAF</em>^{<em>V600E</em>} wild-type MSI-H/dMMR patients, the prevalence of <em>NTRK</em> fusions was 22%. The benefit of chemotherapy (2/7) and anti-epidermal growth factor receptor (EGFR) therapy (0/2) in <em>NTRK</em> fusion-positive patients was limited. All four patients treated with programmed death-(ligand) 1 [PD-(L)1] inhibitors had sustained responses and a minimum overall survival of 36 months.</div></div><div><h3>Conclusions</h3><div>In this real-world cohort study, <em>NTRK</em> fusions are most prevalent in <em>RAS</em>/<em>BRAF</em>^{<em>V600E</em>} wild-type MSI-H/dMMR mCRC patients. This is the first study to suggest resistance to chemotherapy and anti-EGFR therapy in <em>NTRK</em> fusion-positive tumors, leading to impaired overall survival. In contrast, PD-(L)1 inhibitors seem effective in this population. Therapeutic strategies regarding TRK inhibitors versus PD-(L)1 inhibitors need investigation. We recommend early testing for <em>NTRK</em> fusions in <em>RAS</em> and <em>BRAF</em>^{<em>V600E</em>} wild-type MSI-H/dMMR mCRC patients.</div></div>","PeriodicalId":100491,"journal":{"name":"ESMO Real World Data and Digital Oncology","volume":"10 ","pages":"Article 100180"},"PeriodicalIF":0.0,"publicationDate":"2025-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145049867","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":"Clinical evaluation of an automated pan-organ combined PD-L1 scoring using artificial intelligence on immunostained whole-slide images","authors":"C. Bossard ,&nbsp;C. Magois ,&nbsp;H. Roussel ,&nbsp;F. Thomas ,&nbsp;B. Cormier ,&nbsp;A. Collin ,&nbsp;V. Lemerle ,&nbsp;I. Chokri ,&nbsp;L. Lambros ,&nbsp;F. Jossic ,&nbsp;J.-F. Jazeron ,&nbsp;C. Eymerit-Morin ,&nbsp;A. Dhouibi ,&nbsp;N. Labaied ,&nbsp;A. Mensah ,&nbsp;B. Gourdin ,&nbsp;F. Leclair ,&nbsp;D. Pommeret ,&nbsp;Y. Salhi ,&nbsp;M. Cecchini ,&nbsp;J. Chetritt","doi":"10.1016/j.esmorw.2025.100181","DOIUrl":"10.1016/j.esmorw.2025.100181","url":null,"abstract":"<div><h3>Background</h3><div>Programmed death-ligand 1 (PD-L1) inhibitors have shown remarkable results in oncology; however, many patients fail to respond, highlighting the need for reliable assessment of PD-L1 expression for patient selection. PD-L1 scoring, especially the combined positive score (CPS), is hindered by inter- and intraobserver variability, complex staining patterns, and technical discrepancies, all of which can impact therapeutic decisions. Artificial intelligence (AI) offers a solution by standardizing PD-L1 evaluation. This study evaluates a PD-L1 CPS AI, designed for reproducible and robust PD-L1 scoring across various tumor types and conditions.</div></div><div><h3>Materials and methods</h3><div>AI performance was validated on 142 samples spanning multiple tumor types (gastrointestinal, head and neck, breast, and uterine cervix) and sourced from four centers, reflecting diverse staining protocols. Routine scores were available. A gold standard was established through independent retrospective scoring by three senior pathologists enabling the assessment of variability. The scoring process was followed by collegial discussions to resolve discordant cases and ensure medical consensus. After a washout period, cases were reassessed with AI assistance. AI and routine manual scores were compared with the gold standard using organ-specific cut-offs.</div></div><div><h3>Results</h3><div>AI assistance improved interobserver agreement among pathologists, increasing intraclass correlation coefficient (ICC) from 62% to 74%, with a particularly pronounced effect in challenging cases with CPS &lt; 20 (<em>n</em> = 91), where ICC improved from 19% to 62%, underscoring the value of AI in reducing variability near clinical decision thresholds. Based on clinical cut-offs, AI-based scoring outperformed routine manual scoring in accuracy (88% versus 75%) and sensitivity (96% versus 78%), while maintaining a comparable positive predictive value (88% versus 87%), indicating an improved ability to detect true-positive cases.</div></div><div><h3>Conclusions</h3><div>This study highlights the potential of an AI-driven tool—DiaKwant PD-L1 algorithm—to improve PD-L1 scoring accuracy and reduce observer variability, particularly near clinical thresholds, across various solid carcinomas, independently of pre-analytical and digitization platforms. Its integration into clinical workflows could enhance efficiency and optimize patient eligibility for immunotherapy.</div></div>","PeriodicalId":100491,"journal":{"name":"ESMO Real World Data and Digital Oncology","volume":"10 ","pages":"Article 100181"},"PeriodicalIF":0.0,"publicationDate":"2025-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145049851","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":"The digital shift in oncology EMRs in LMICs","authors":"N.H. Rajput","doi":"10.1016/j.esmorw.2025.100183","DOIUrl":"10.1016/j.esmorw.2025.100183","url":null,"abstract":"","PeriodicalId":100491,"journal":{"name":"ESMO Real World Data and Digital Oncology","volume":"10 ","pages":"Article 100183"},"PeriodicalIF":0.0,"publicationDate":"2025-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145020670","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}