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

NCT07373951 | Enrolling By Invitation DMC

• 1 other identifier: NCC-EGFR/ALK-DL
• Study Type: observational
• Target: 380
• Locations: 1 country
• Sites: 1

Brief Summary

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

Non-Small Cell Lung Cancer; Brain Metastases

Keywords

Magnetic Resonance Imaging; Deep Learning; Multimodal MRI; T1-weighted Contrast-Enhanced (T1CE); FLAIR; Transformer; EGFR; ALK

Outcome Measures

Primary Outcomes (1)

• Patient-level AUC for driver status (External Validation)

  The deep learning model uses preoperative brain MRI (T1CE and FLAIR) to output a patient-level probability of driver-positive status. Performance will be evaluated primarily in two independent external test cohorts (First Affiliated Hospital of Anhui Medical University and TCIA). AUC with 95% confidence intervals will be reported.

  Retrospective analysis through data cutoff (May 1, 2026)

Secondary Outcomes (6)

• Sensitivity and specificity at prespecified thresholds (External Validation)

  Retrospective analysis through May 1, 2026

• Predictive values (PPV/NPV) (External Validation)

  Retrospective analysis through May 1, 2026

• Model calibration (External Validation)

  Retrospective analysis through May 1, 2026

• Decision-curve analysis (Clinical utility)

  Retrospective analysis through May 1, 2026

• Overall survival (OS) association (Exploratory)

  From date of brain metastasis surgery to death or last follow-up (up to May 1, 2026)

Study Arms (3)

National Cancer Center (NCC) Development Cohort

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.

Anhui Medical University 1st Affiliated Hospital External Test Cohort

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.

TCIA Public External Test Cohort

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.

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

Retrospective multicenter cohorts of adult patients with non-small cell lung cancer (NSCLC) brain metastases who underwent brain metastasis surgery and had preoperative brain MRI including T1CE and FLAIR. EGFR and ALK status are determined by next-generation sequencing (NCG/NGS) performed on resected brain metastasis tissue. The development/internal validation cohort is from the National Cancer Center (China). Two independent external test cohorts are from the First Affiliated Hospital of Anhui Medical University (China) and a de-identified public dataset from The Cancer Imaging Archive (TCIA). Overall survival and progression-free survival are calculated from the date of brain metastasis surgery to the event or last follow-up (data cutoff: May 1, 2026).

You may qualify if:

• 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).

You may not qualify if:

• 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.

Sponsors & Collaborators

• Ming Yang: lead

Study Sites (1)

National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College

Beijing, Beijing Municipality, 100021, China

Location

Related Publications (5)

• Chadha, 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

  BACKGROUND

• Zwanenburg 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 BACKGROUND

• Collins 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 BACKGROUND

• Mongan 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 BACKGROUND

• Clark K, Vendt B, Smith K, Freymann J, Kirby J, Koppel P, Moore S, Phillips S, Maffitt D, Pringle M, Tarbox L, Prior F. The Cancer Imaging Archive (TCIA): maintaining and operating a public information repository. J Digit Imaging. 2013 Dec;26(6):1045-57. doi: 10.1007/s10278-013-9622-7.

  PMID: 23884657 BACKGROUND

MeSH Terms

Conditions

Carcinoma, Non-Small-Cell Lung; Brain Neoplasms

Condition Hierarchy (Ancestors)

Carcinoma, Bronchogenic; Bronchial Neoplasms; Lung Neoplasms; Respiratory Tract Neoplasms; Thoracic Neoplasms; Neoplasms by Site; Neoplasms; Lung Diseases; Respiratory Tract Diseases; Central Nervous System Neoplasms; Nervous System Neoplasms; Brain Diseases; Central Nervous System Diseases; Nervous System Diseases

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: RETROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: SPONSOR INVESTIGATOR
• PI Title: Clinical Professor

Study Record Dates

• First Submitted: January 21, 2026
• First Posted: January 28, 2026
• Study Start: November 1, 2025
• Primary Completion: April 1, 2026
• Study Completion: May 1, 2026
• Last Updated: January 29, 2026

Record last verified: 2026-01

Locations

CN (1)