Evidence map›Paper›PMID 41647340›Full record

ArticleESMO real world data and digital oncology2025

Data analytics for real-world data integration in TKI-treated NSCLC patients using electronic health records.

L Mazzeo, F Corso, P Baili, F Scotti, V Torri, M Ganzinelli, V Mišković, R Leporati, L Provenzano, A Spagnoletti and 20 more

Abstract read
In one paragraph

Article in ESMO real world data and digital oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Review
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

30 authors.

L MazzeoDepartment of Medical Oncology, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy.
F CorsoDepartment of Medical Oncology, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy.
P BailiDepartment of Epidemiology and Data Science, Data Science Unit, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy.
F ScottiDepartment of Management, Economics and Industrial Engineering, Politecnico di Milano, Milan, Italy.
V TorriMOX - Modelling and Scientific Computing, Department of Mathematics, Politecnico di Milano, Milan, Italy.
M GanzinelliDepartment of Medical Oncology, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy.
V MiškovićDepartment of Medical Oncology, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy.
R LeporatiDepartment of Medical Oncology, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy.
L ProvenzanoDepartment of Medical Oncology, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy.
A SpagnolettiDepartment of Medical Oncology, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy.
C SilvestriDepartment of Medical Oncology, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy.
C GianiDepartment of Medical Oncology, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy.
C CavalliDepartment of Medical Oncology, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy.
R M di MauroDepartment of Medical Oncology, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy.
M Meazza PrinaDepartment of Medical Oncology, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy.
C ProtoDepartment of Medical Oncology, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy.
M BrambillaDepartment of Medical Oncology, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy.
M OcchipintiDepartment of Medical Oncology, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy.
S ManglavitiOncology Unit, ASST Ospedale Maggiore di Crema, Crema, Italy.
T BeninatoDepartment of Medical Oncology, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy.
D MilizianoDepartment of Medical Oncology, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy.
A D DumitrascuDepartment of Medical Oncology, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy.
G Di LibertiDepartment of Medical Oncology, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy.
T S CassanoDepartment of Medical Oncology, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy.
F G M de BraudDepartment of Medical Oncology, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy.
Giuseppe Lo RussoDepartment of Medical Oncology, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy.
A CappozzoDepartment of Economics, Management and Quantitative Methods, Università degli Studi di Milano, Milan, Italy.
A M PaganoniMOX - Modelling and Scientific Computing, Department of Mathematics, Politecnico di Milano, Milan, Italy.
F IevaMOX - Modelling and Scientific Computing, Department of Mathematics, Politecnico di Milano, Milan, Italy.
A PrelajDepartment of Medical Oncology, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Real-world data (RWD) are routinely collected in clinical practice during therapeutic interventions. Data warehouses (DWHs) represent the primary source of RWD in which electronic health records (EHRs) can be rapidly analyzed via natural language processing. This study illustrates an analytic framework that systematically exploits RWD and methods to generate real-world evidence (RWE) about innovative cancer drugs. The framework has been applied to investigate real-world treatment patterns and clinical outcomes of patients with advanced non-small-cell lung cancer (aNSCLC) treated with tyrosine kinase inhibitors (TKIs). Materials and methods: Data from a cohort of 190 epidermal growth factor receptor-positive mutation (EGFRm) patients with aNSCLC were retrospectively collected in an Italian cancer institute between 2014 and 2022. Patients were treated in first-line (1L) with osimertinib or other TKIs (non-osimertinib). A text-mining algorithm was implemented to retrieve RWD from EHRs. Survival endpoints were median time to treatment discontinuation (mTTD) and median overall survival (mOS) estimated with Kaplan-Meier curves. Time-dependent multivariate Cox analysis was carried out to overcome immortal time bias. Results: Approximately 38% of patients received 1L osimertinib, while the remaining 62% received previous-generation TKIs. Longer mTTD [15 months; 95% confidence interval (CI) 11.9-26.4 months] was found for patients treated with 1L osimertinib compared with non-osimertinib (10 months; 95% CI 7.9-13.1 months). In multivariate analysis, osimertinib was an independent protective factor regardless of bone and brain metastases and local radiotherapy. mOS was 27 months (95% CI 21.4-39.5 months) for osimertinib versus 20.2 months (95% CI 17.6-23.1 months) for non-osimertinib. Conclusions: Data analytics frameworks are useful tools to integrate RWE in cancer research and data-driven models are suitable to process large amounts of RWD. This study demonstrates that real-world treatment patterns and outcomes of TKIs are comparable with those found in both clinical trials and other real-world studies. RWE studies can support clinicians in investigating the best treatment strategy and decision makers to drive new health policies.

Indexed as

non-small cell lung cancerosimertinibreal-world datasurvival analysistext-miningtyrosine kinase inhibitors

Identifiers

PMID41647340
PMCPMC12836734

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.