Ignite Creation Date:
2026-03-26 @ 3:17 PM
Ignite Modification Date:
2026-03-31 @ 8:21 AM
Study NCT ID:
NCT07373951
Status:
ENROLLING_BY_INVITATION
Last Update Posted:
2026-01-29
First Post:
2026-01-21
Is NOT Gene Therapy:
True
Has Adverse Events:
False
Brief Title:
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
Sponsor:
Organization:
Raw JSON
{'hasResults': False, 'derivedSection': {'miscInfoModule': {'versionHolder': '2026-03-25'}, 'conditionBrowseModule': {'meshes': [{'id': 'D002289', 'term': 'Carcinoma, Non-Small-Cell Lung'}, {'id': 'D001932', 'term': 'Brain Neoplasms'}], 'ancestors': [{'id': 'D002283', 'term': 'Carcinoma, Bronchogenic'}, {'id': 'D001984', 'term': 'Bronchial Neoplasms'}, {'id': 'D008175', 'term': 'Lung Neoplasms'}, {'id': 'D012142', 'term': 'Respiratory Tract Neoplasms'}, {'id': 'D013899', 'term': 'Thoracic Neoplasms'}, {'id': 'D009371', 'term': 'Neoplasms by Site'}, {'id': 'D009369', 'term': 'Neoplasms'}, {'id': 'D008171', 'term': 'Lung Diseases'}, {'id': 'D012140', 'term': 'Respiratory Tract Diseases'}, {'id': 'D016543', 'term': 'Central Nervous System Neoplasms'}, {'id': 'D009423', 'term': 'Nervous System Neoplasms'}, {'id': 'D001927', 'term': 'Brain Diseases'}, {'id': 'D002493', 'term': 'Central Nervous System Diseases'}, {'id': 'D009422', 'term': 'Nervous System Diseases'}]}}, 'protocolSection': {'designModule': {'studyType': 'OBSERVATIONAL', 'designInfo': {'timePerspective': 'RETROSPECTIVE', 'observationalModel': 'COHORT'}, 'enrollmentInfo': {'type': 'ESTIMATED', 'count': 380}, 'patientRegistry': False}, 'statusModule': {'overallStatus': 'ENROLLING_BY_INVITATION', 'startDateStruct': {'date': '2025-11-01', 'type': 'ACTUAL'}, 'expandedAccessInfo': {'hasExpandedAccess': False}, 'statusVerifiedDate': '2026-01', 'completionDateStruct': {'date': '2026-05-01', 'type': 'ESTIMATED'}, 'lastUpdateSubmitDate': '2026-01-27', 'studyFirstSubmitDate': '2026-01-21', 'studyFirstSubmitQcDate': '2026-01-21', 'lastUpdatePostDateStruct': {'date': '2026-01-29', 'type': 'ACTUAL'}, 'studyFirstPostDateStruct': {'date': '2026-01-28', 'type': 'ACTUAL'}, 'primaryCompletionDateStruct': {'date': '2026-04-01', 'type': 'ESTIMATED'}}, 'outcomesModule': {'primaryOutcomes': [{'measure': 'Patient-level AUC for driver status (External Validation)', 'timeFrame': 'Retrospective analysis through data cutoff (May 1, 2026)', 'description': '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.'}], 'secondaryOutcomes': [{'measure': 'Sensitivity and specificity at prespecified thresholds (External Validation)', 'timeFrame': 'Retrospective analysis through May 1, 2026', 'description': 'Prespecified thresholds (e.g., screening-oriented high-sensitivity threshold and/or confirmation-oriented high-specificity threshold) will be determined using the National Cancer Center development cohort and then locked. Sensitivity and specificity will be reported in external test cohorts.'}, {'measure': 'Predictive values (PPV/NPV) (External Validation)', 'timeFrame': 'Retrospective analysis through May 1, 2026', 'description': 'PPV and NPV will be calculated in each external test cohort using the locked thresholds defined in the development cohort.'}, {'measure': 'Model calibration (External Validation)', 'timeFrame': 'Retrospective analysis through May 1, 2026', 'description': 'Calibration of predicted probabilities will be evaluated in external test cohorts using calibration curves and Brier score (and/or calibration intercept/slope as applicable).'}, {'measure': 'Decision-curve analysis (Clinical utility)', 'timeFrame': 'Retrospective analysis through May 1, 2026', 'description': 'Decision-curve analysis will be used to estimate clinical utility across a range of threshold probabilities, comparing model-guided triage strategies with default strategies (e.g., testing all vs testing none).'}, {'measure': 'Overall survival (OS) association (Exploratory)', 'timeFrame': 'From date of brain metastasis surgery to death or last follow-up (up to May 1, 2026)', 'description': 'OS will be defined from the date of brain metastasis surgery to death from any cause or last follow-up. Exploratory analyses will evaluate associations between model outputs (continuous probability and/or risk groups) and OS using Kaplan-Meier and Cox proportional hazards models in the subset with available follow-up data.'}, {'measure': 'Progression-free survival (PFS) association (Exploratory)', 'timeFrame': 'From date of brain metastasis surgery to progression/death or last follow-up (up to May 1, 2026)', 'description': 'PFS will be defined from the date of brain metastasis surgery to radiographic or clinical progression, death, or last follow-up. Exploratory analyses will evaluate associations between model outputs (continuous probability and/or risk groups) and PFS using Kaplan-Meier and Cox models in the subset with available follow-up data.'}]}, 'oversightModule': {'oversightHasDmc': True, 'isFdaRegulatedDrug': False, 'isFdaRegulatedDevice': False}, 'conditionsModule': {'keywords': ['Magnetic Resonance Imaging', 'Deep Learning', 'Multimodal MRI', 'T1-weighted Contrast-Enhanced (T1CE)', 'FLAIR', 'Transformer', 'EGFR', 'ALK'], 'conditions': ['Non-Small Cell Lung Cancer', 'Brain Metastases']}, 'referencesModule': {'references': [{'type': 'BACKGROUND', 'citation': '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'}, {'pmid': '32154773', 'type': 'BACKGROUND', 'citation': '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': '38626948', 'type': 'BACKGROUND', 'citation': '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': '33937821', 'type': 'BACKGROUND', 'citation': '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': '23884657', 'type': 'BACKGROUND', 'citation': '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.'}]}, 'descriptionModule': {'briefSummary': '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).', 'detailedDescription': 'Rationale and Objectives Actionable driver alterations such as EGFR mutations and ALK rearrangements/fusions are key determinants of treatment selection in NSCLC. In patients with brain metastases, tissue acquisition may be limited by surgical risk, lesion location, and time constraints. Routine brain MRI provides rich phenotypic information that may capture imaging correlates of molecular drivers. This study is designed to develop and externally validate a patient-level deep learning model that leverages multimodal MRI (T1CE and FLAIR) to noninvasively identify driver-positive status (EGFR mutation and/or ALK rearrangement/fusion) versus driver-negative status (EGFR-negative and ALK-negative).\n\nStudy Design and Data Sources This is a retrospective multicenter observational cohort study. Model development (training and internal validation) will be performed using data from the National Cancer Center (China). External validation will be conducted in two independent cohorts: (1) a clinical cohort from the First Affiliated Hospital of Anhui Medical University (China) and (2) a public de-identified cohort obtained from The Cancer Imaging Archive (TCIA). The TCIA cohort is used as an independent test set and is not involved in model training, hyperparameter tuning, or threshold selection.\n\nReference Standard and Driver Definition Driver status will be determined by next-generation sequencing performed on resected brain metastasis tissue. Driver-positive is defined as EGFR mutation and/or ALK rearrangement/fusion detected on brain metastasis tissue testing. Driver-negative is defined as both EGFR-negative and ALK-negative.\n\nImaging Inputs and Preprocessing Eligible patients must have preoperative brain MRI including at minimum T1CE and FLAIR sequences with acceptable image quality. Imaging data will be de-identified and standardized for analysis. Preprocessing will include harmonized spatial resampling to a common voxel spacing, intensity normalization, and co-registration between modalities when needed. Lesion localization/segmentation will be performed using manual, semi-automated, or automated approaches with quality control by trained reviewers, depending on data availability. For patients with multiple brain metastases, lesion-level representations will be aggregated to produce a patient-level prediction using a predefined pooling strategy (e.g., attention pooling or multiple-instance learning).\n\nModel Development and External Validation The primary model will use multimodal inputs (T1CE + FLAIR) and a fusion strategy (including transformer-based fusion as a prespecified approach). Comparative analyses will evaluate 2D, 2.5D, and 3D modeling strategies and alternative fusion schemes (e.g., early vs late fusion) under a consistent evaluation framework. All model selection and threshold determination will be completed using the National Cancer Center development data. The finalized model and prespecified thresholds will then be locked and evaluated once in each external cohort without any additional training or recalibration.\n\nOutcomes and Statistical Analysis The primary endpoint is discrimination performance assessed by patient-level AUC in the external test cohorts, with 95% confidence intervals. Secondary endpoints include sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), calibration (calibration curves and Brier score), and decision-curve analysis to estimate net benefit across clinically relevant thresholds. Subgroup analyses may be performed by center, imaging acquisition characteristics, and single versus multiple metastases. In a subset with follow-up data, exploratory analyses will evaluate associations between model outputs and OS/PFS using Kaplan-Meier methods and Cox proportional hazards models. OS and PFS will be calculated from the date of brain metastasis surgery to death/progression or last follow-up; the data cutoff date is May 1, 2026.\n\nEthics and Privacy This study uses retrospective clinical data that will be de-identified prior to analysis. Institutional review board approval and/or waiver of informed consent will be obtained as required by participating institutions. The TCIA cohort consists of public de-identified data and does not involve direct participant contact.'}, 'eligibilityModule': {'sex': 'ALL', 'stdAges': ['ADULT', 'OLDER_ADULT'], 'minimumAge': '18 Years', 'samplingMethod': 'NON_PROBABILITY_SAMPLE', 'studyPopulation': '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).', 'healthyVolunteers': False, 'eligibilityCriteria': 'Inclusion Criteria:\n\nAge ≥ 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).\n\nPreoperative brain MRI is available, including, at minimum, contrast-enhanced T1-weighted imaging (T1CE) and FLAIR.\n\nEGFR and ALK status are available from next-generation sequencing (NCG/NGS) performed on resected brain metastasis tissue (+/-).\n\nMRI quality sufficient for analysis (adequate brain coverage and no severe artifacts).\n\nExclusion Criteria:\n\nMissing required MRI sequences (T1CE or FLAIR) or non-diagnostic image quality due to severe artifacts/motion.\n\nMissing or unverifiable molecular testing results for EGFR and/or ALK from brain metastasis tissue.\n\nUncertain 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.'}, 'identificationModule': {'nctId': 'NCT07373951', 'acronym': 'DL-DriverBM', 'briefTitle': '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', 'organization': {'class': 'OTHER', 'fullName': 'Cancer Institute and Hospital, Chinese Academy of Medical Sciences'}, 'officialTitle': '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.', 'orgStudyIdInfo': {'id': 'NCC-EGFR/ALK-DL'}}, 'armsInterventionsModule': {'armGroups': [{'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.'}]}, 'contactsLocationsModule': {'locations': [{'zip': '100021', 'city': 'Beijing', 'state': 'Beijing Municipality', 'country': 'China', 'facility': 'National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College', 'geoPoint': {'lat': 39.9075, 'lon': 116.39723}}]}, 'ipdSharingStatementModule': {'ipdSharing': 'UNDECIDED', 'description': "A definitive IPD sharing plan has not yet been determined at the time of registration. Because the study involves retrospective multi-institutional clinical imaging, tissue-based molecular testing, and survival follow-up, any data sharing must comply with IRB approvals, privacy regulations, and participating institutions' data governance policies. The study team anticipates evaluating controlled-access sharing of de-identified derived datasets and model outputs after publication, subject to appropriate agreements and approvals."}, 'sponsorCollaboratorsModule': {'leadSponsor': {'name': 'Ming Yang', 'class': 'OTHER'}, 'responsibleParty': {'type': 'SPONSOR_INVESTIGATOR', 'investigatorTitle': 'Clinical Professor', 'investigatorFullName': 'Ming Yang', 'investigatorAffiliation': 'Cancer Institute and Hospital, Chinese Academy of Medical Sciences'}}}}