Non-Small Cell Lung CancerDL-DriverBMNCT07373951
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
An observational study of Non-Small Cell Lung Cancer and Brain Metastases, run by Ming Yang at 1 site in China.
Trial 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. Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Non-Small Cell Lung Cancer, Brain Metastases
- How many people
- 380 participants
- Where
- 1 siteChina
- Study dates
- 1 Nov 2025to 1 May 2026
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
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).
Who may be able to join
- 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).
Who cannot join
- 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.
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
China1 site
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
About this study
Official title: 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.
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).
Read the detailed description
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).
Study 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.
Reference 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.
Imaging 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).
Model 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.
Outcomes 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.
Ethics 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.
- 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
- Medical subject headings
- Carcinoma, Non-Small-Cell Lung, Brain Neoplasms
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
| Study group | Role | What they receive |
|---|---|---|
| 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. |
What is being measured
The primary outcome is the main question the study is designed to answer.
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.
Measured: Retrospective analysis through data cutoff (May 1, 2026)
Secondary outcomes (6)
Sensitivity and specificity at prespecified thresholds (External Validation)
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.
Measured: Retrospective analysis through May 1, 2026
Predictive values (PPV/NPV) (External Validation)
PPV and NPV will be calculated in each external test cohort using the locked thresholds defined in the development cohort.
Measured: Retrospective analysis through May 1, 2026
Model calibration (External Validation)
Calibration of predicted probabilities will be evaluated in external test cohorts using calibration curves and Brier score (and/or calibration intercept/slope as applicable).
Measured: Retrospective analysis through May 1, 2026
Decision-curve analysis (Clinical utility)
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).
Measured: Retrospective analysis through May 1, 2026
Overall survival (OS) association (Exploratory)
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.
Measured: From date of brain metastasis surgery to death or last follow-up (up to May 1, 2026)
Progression-free survival (PFS) association (Exploratory)
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.
Measured: From date of brain metastasis surgery to progression/death or last follow-up (up to May 1, 2026)
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted28 Jan 2026
- Study start1 Nov 2025
- Primary completion1 Apr 2026 (estimated)
- Study completion1 May 2026 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (5)Papers the sponsor has linked to this study.
- Other trials for Non-Small Cell Lung CancerStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Retrospective
- Enrolment
- 380 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Ming Yang
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor-investigator, Ming Yang, Cancer Institute and Hospital, Chinese Academy of Medical Sciences
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07373951
- Sponsor's protocol ID
- NCC-EGFR/ALK-DL
- Acronym
- DL-DriverBM
- Status last verified
- Jan 2026
- First submitted
- 21 Jan 2026
- Last updated
- 29 Jan 2026
- Results posted
- No
- Data monitoring committee
- Yes
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Undecided: 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.
Publications
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
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.
PubMed 32154773Collins 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.
PubMed 38626948Mongan 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.
PubMed 33937821Clark 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.
PubMed 23884657
Study at a glance
From the registry record, last updated by the sponsor on 29 Jan 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- 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.
- ClinicalTrials.gov ID
- NCT07373951
- Acronym / sponsor ID
- DL-DriverBM / NCC-EGFR/ALK-DL
- 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
- Study type
- Observational
- Recruitment status
- Enrolling by invitation
- Enrolment
- 380 participants (estimated)
- Lead sponsor
- Ming YangUniversity, hospital or other
- Study start
- 1 Nov 2025 (actual)
- Primary completion
- 1 Apr 2026 (estimated)
- Study completion
- 1 May 2026 (estimated)
- First posted
- 28 Jan 2026 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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).
Detailed description
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).
Study 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.
Reference 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.
Imaging 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).
Model 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.
Outcomes 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.
Ethics 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.
Study design
- Observational model
- Cohort
- Time perspective
- Retrospective
- Number of arms
- 3
| Arm | Type | Receives |
|---|---|---|
| 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. |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- 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).
Key inclusion criteria (4 listed)
- 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).
Key exclusion criteria (3 listed)
- 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.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
Patient-level AUC for driver status (External Validation)
Time frame: Retrospective analysis through data cutoff (May 1, 2026)
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.
6 secondary outcomes
Sensitivity and specificity at prespecified thresholds (External Validation)
Time frame: Retrospective analysis through May 1, 2026
Predictive values (PPV/NPV) (External Validation)
Time frame: Retrospective analysis through May 1, 2026
Model calibration (External Validation)
Time frame: Retrospective analysis through May 1, 2026
Decision-curve analysis (Clinical utility)
Time frame: Retrospective analysis through May 1, 2026
Overall survival (OS) association (Exploratory)
Time frame: From date of brain metastasis surgery to death or last follow-up (up to May 1, 2026)
Progression-free survival (PFS) association (Exploratory)
Time frame: From date of brain metastasis surgery to progression/death or last follow-up (up to May 1, 2026)
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 29 Jan 2026.
- 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
- Background: PubMed 32154773Zwanenburg 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.
- Background: PubMed 38626948Collins 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.
- Background: PubMed 33937821Mongan 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.
- Background: PubMed 23884657Clark 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.
- Individual participant dataSharing planned: Undecided. 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.
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A Study Evaluating Sotorasib Platinum Doublet Combination Versus Pembrolizumab Platinum Doublet Combination as a Front-Line Therapy in Participants With Stage IV or Advanced Stage IIIB/C Nonsquamous Non-Small Cell Lung Cancers (CodeBreaK 202)
Non-Small Cell Lung Cancer (NSCLC)
Active, not recruitingPhase 2
Datopotamab Deruxtecan (Dato-DXd, DS-1062a) in Advanced and/or Unresectable Non-Small Cell Lung Cancer
Metastatic Lung Cancer, Advanced Non Small Cell Lung Cancer
Not yet recruitingPhase 1/2
STAR0602 With Nivolumab in Non Small Cell Lung Cancer and in Metastatic Castrate-Resistant Prostate Cancer, and STAR0602 Alone in Thymic Carcinoma
NSCLC, Non-Small Cell Lung Cancer
Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
The record lists no contact. Use the official record to reach the sponsor.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Non-Small Cell Lung Cancer and Brain Metastases, run by Ming Yang at 1 site in China.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 4 inclusion and 3 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: enrolling by invitation, as of the sponsor's last update on 29 Jan 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in China. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Patient-level AUC for driver status (External Validation) (time frame: Retrospective analysis through data cutoff (May 1, 2026)). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT07373951, which also lists 5 linked publications.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 29 Jan 2026.