Clinical trial · Observational
Retrospective Multicenter Study of Patient-level T1CE/FLAIR MRI Deep Learning to Predict EGFR/ALK Driver Status in NSCLC Brain Metastases With External Validation and Survival Analysis
Retrospective Multicenter Study: Patient-level Noninvasive Prediction of Non-small Cell Lung Cancer Brain Metastases Based on T1CE and FLAIR Multimodal MRI Deep Learning Models, With Targeted Drivers (EGFR or ALK), External Validation, and Survival Translation Assessment.
- Source
- ClinicalTrials.gov
- Retrieved
- Sep 8, 2026
- Layer
- normalized (units and labels harmonized; values unchanged)
- Run
- ING-CLINICALTRIALS-20260908-000001
Summary
Brief summary (as posted)
This retrospective multicenter observational study aims to develop and externally validate a noninvasive deep learning model based on routine brain MRI to identify actionable driver alterations in patients with non-small cell lung cancer (NSCLC) brain metastases. The model uses contrast-enhanced T1-weighted imaging (T1CE) and FLAIR sequences to classify patients as driver-positive (EGFR mutation and/or ALK rearrangement/fusion) versus driver-negative (EGFR-negative and ALK-negative), using brain metastasis tissue next-generation sequencing as the reference standard. The development and internal validation cohorts are from the National Cancer Center (China). Two independent external test cohorts are used: one from the First Affiliated Hospital of Anhui Medical University (China) and one from a public de-identified dataset hosted by The Cancer Imaging Archive (TCIA). The primary endpoint is the patient-level area under the receiver operating characteristic curve (AUC) in the external test cohorts. Secondary analyses include model calibration and decision-curve analysis to estimate clinical utility, comparisons of 2D/2.5D/3D modeling strategies and multimodal fusion approaches, and exploratory associations between model outputs and overall survival (OS) and progression-free survival (PFS), calculated from the date of brain metastasis surgery to the event or last follow-up (data cutoff: May 1, 2026).
Conditions
Conditions (2)
Free-text conditions as registered, with the CancerIndex entity they were reconciled to and the match type.
| Condition (as posted) | Mapped entity | Match | Confidence |
|---|---|---|---|
| Brain Metastases | Brain Neoplasm | PROBABILISTIC | 0.70 |
| Non-Small Cell Lung Cancer | Lung Non-Small Cell Carcinoma | ALIAS | 0.90 |
Interventions
Interventions (0)
Data not yet available
Design
Arms and outcomes
Arms (3)
- label
- National Cancer Center (NCC) Development Cohort
- description
- Retrospective cohort of NSCLC brain metastasis patients from the National Cancer Center (China) with preoperative brain MRI including T1CE and FLAIR and brain metastasis tissue NGS (NCG/NGS) results for EGFR and ALK. This cohort is used for model development and internal validation, including prespecified threshold selection.
- label
- Anhui Medical University 1st Affiliated Hospital External Test Cohort
- description
- Independent retrospective external validation cohort from the First Affiliated Hospital of Anhui Medical University (China) with preoperative T1CE and FLAIR MRI and brain metastasis tissue NGS results for EGFR and ALK. No model training or threshold tuning is performed in this cohort; it is used for locked external testing.
- label
- TCIA Public External Test Cohort
- description
- Independent external validation cohort obtained from The Cancer Imaging Archive (TCIA), consisting of de-identified public brain MRI data (including T1CE and FLAIR when available) from NSCLC brain metastasis patients. This cohort is used only for locked external testing and is not involved in any model training, tuning, or threshold selection.
Primary outcomes (1)
Eligibility
Eligibility (as posted)
- Sex
- All
- Minimum age
- 18 Years
Show eligibility criteria text
Inclusion Criteria: Age ≥ 18 years at the time of brain metastasis surgery. Histologically confirmed non-small cell lung cancer (NSCLC). Brain metastasis treated with surgical resection (index date for survival analyses). Preoperative brain MRI is available, including, at minimum, contrast-enhanced T1-weighted imaging (T1CE) and FLAIR. EGFR and ALK status are available from next-generation sequencing (NCG/NGS) performed on resected brain metastasis tissue (+/-). MRI quality sufficient for analysis (adequate brain coverage and no severe artifacts). Exclusion Criteria: Missing required MRI sequences (T1CE or FLAIR) or non-diagnostic image quality due to severe artifacts/motion. Missing or unverifiable molecular testing results for EGFR and/or ALK from brain metastasis tissue. Uncertain primary tumor origin or non-NSCLC histology. Prior intracranial therapy that substantially alters lesion appearance before the index MRI and cannot be reliably ascertained or adjusted for (e.g., radiotherapy immediately before the MRI), as determined by study investigators.
References
Publications (5)
- BACKGROUNDChadha, S., Sritharan, D., Dolezal, D., Chande, S., Hager, T., Bousabarah, K., Aboian, M., chiang, v., Lin, M., Nguyen, D., Aneja, S. (2025). MR Imaging and Segmentations with Matched Brain Biopsy Pathology Slides from Patients with Brain Metastases from Primary Lung Cancer (Brain-Mets-Lung-MRI-Path-Segs) (Version 2) [dataset]. The Cancer Imaging Archive. https://doi.org/10.7937/k0sm-y874
- BACKGROUNDZwanenburg A, Vallieres M, Abdalah MA, Aerts HJWL, Andrearczyk V, Apte A, Ashrafinia S, Bakas S, Beukinga RJ, Boellaard R, Bogowicz M, Boldrini L, Buvat I, Cook GJR, Davatzikos C, Depeursinge A, Desseroit MC, Dinapoli N, Dinh CV, Echegaray S, El Naqa I, Fedorov AY, Gatta R, Gillies RJ, Goh V, Gotz M, Guckenberger M, Ha SM, Hatt M, Isensee F, Lambin P, Leger S, Leijenaar RTH, Lenkowicz J, Lippert F, Losnegard A, Maier-Hein KH, Morin O, Muller H, Napel S, Nioche C, Orlhac F, Pati S, Pfaehler EAG, Rahmim A, Rao AUK, Scherer J, Siddique MM, Sijtsema NM, Socarras Fernandez J, Spezi E, Steenbakkers RJHM, Tanadini-Lang S, Thorwarth D, Troost EGC, Upadhaya T, Valentini V, van Dijk LV, van Griethuysen J, van Velden FHP, Whybra P, Richter C, Lock S. The Image Biomarker Standardization Initiative: Standardized Quantitative Radiomics for High-Throughput Image-based Phenotyping. Radiology. 2020 May;295(2):328-338. doi: 10.1148/radiol.2020191145. Epub 2020 Mar 10. PMID 32154773
- BACKGROUNDCollins GS, Moons KGM, Dhiman P, Riley RD, Beam AL, Van Calster B, Ghassemi M, Liu X, Reitsma JB, van Smeden M, Boulesteix AL, Camaradou JC, Celi LA, Denaxas S, Denniston AK, Glocker B, Golub RM, Harvey H, Heinze G, Hoffman MM, Kengne AP, Lam E, Lee N, Loder EW, Maier-Hein L, Mateen BA, McCradden MD, Oakden-Rayner L, Ordish J, Parnell R, Rose S, Singh K, Wynants L, Logullo P. TRIPOD+AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods. BMJ. 2024 Apr 16;385:e078378. doi: 10.1136/bmj-2023-078378. PMID 38626948
- BACKGROUNDMongan J, Moy L, Kahn CE Jr. Checklist for Artificial Intelligence in Medical Imaging (CLAIM): A Guide for Authors and Reviewers. Radiol Artif Intell. 2020 Mar 25;2(2):e200029. doi: 10.1148/ryai.2020200029. eCollection 2020 Mar. No abstract available. PMID 33937821