
==== Front
Discov Oncol
Discov Oncol
Discover Oncology
2730-6011
Springer US New York

1333
10.1007/s12672-024-01333-1
Research
Predicting the T790M mutation in non-small cell lung cancer (NSCLC) using brain metastasis MR radiomics: a study with an imbalanced dataset
Wu Wen-Feng 1
Lai Kuan-Ming 12
Chen Chia-Hung 12
Wang Bai-Chuan 3
Chen Yi-Jen 3
Shen Chia-Wei 3
Chen Kai-Yan 3
Lin Eugene C. cheel@ccu.edu.tw

34
Chen Chien-Chin hlmarkc@gmail.com

5678
1 https://ror.org/01em2mv62 grid.413878.1 0000 0004 0572 9327 Department of Radiology, Ditmanson Medical Foundation Chia-Yi Christian Hospital, Chiayi, 600 Taiwan
2 https://ror.org/03d4d3711 grid.411043.3 0000 0004 0639 2818 Central Taiwan University of Science and Technology Institute of Radiological Science, Taichung, 406 Taiwan
3 https://ror.org/0028v3876 grid.412047.4 0000 0004 0532 3650 Department of Chemistry and Biochemistry, National Chung Cheng University, 168 University Road, Min-Hsiung, Chiayi, 62102 Taiwan
4 https://ror.org/0028v3876 grid.412047.4 0000 0004 0532 3650 Center for Nano Bio-Detection, National Chung Cheng University, Chiayi, 621 Taiwan
5 https://ror.org/01em2mv62 grid.413878.1 0000 0004 0572 9327 Department of Pathology, Ditmanson Medical Foundation Chia-Yi Christian Hospital, No. 539, Zhongxiao Rd., East Dist., Chiayi City, 60002 Taiwan
6 grid.260542.7 0000 0004 0532 3749 Rong Hsing Research Center for Translational Medicine, National Chung Hsing University, Taichung, 402 Taiwan
7 https://ror.org/01b8kcc49 grid.64523.36 0000 0004 0532 3255 Department of Biotechnology and Bioindustry Sciences, College of Bioscience and Biotechnology, National Cheng Kung University, Tainan, 701 Taiwan
8 https://ror.org/02834m470 grid.411315.3 0000 0004 0634 2255 Department of Cosmetic Science, Chia Nan University of Pharmacy and Science, Tainan, 717 Taiwan
14 9 2024
14 9 2024
12 2024
15 4471 6 2024
10 9 2024
© The Author(s) 2024
2024
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Background

Early detection of T790M mutation in exon 20 of epidermal growth factor receptor (EGFR) in non-small cell lung cancer (NSCLC) patients with brain metastasis is crucial for optimizing treatment strategies. In this study, we developed radiomics models to distinguish NSCLC patients with T790M-positive mutations from those with T790M-negative mutations using multisequence MR images of brain metastasis despite an imbalanced dataset. Various resampling techniques and classifiers were employed to identify the most effective strategy.

Methods

Radiomic analyses were conducted on a dataset comprising 125 patients, consisting of 18 with EGFR T790M-positive mutations and 107 with T790M-negative mutations. Seventeen first- and second-order statistical features were selected from CET1WI, T2WI, T2FLAIR, and DWI images. Four classifiers (logistic regression, support vector machine, random forest [RF], and extreme gradient boosting [XGBoost]) were evaluated under 13 different resampling conditions.

Results

The area under the curve (AUC) value achieved was 0.89, using the SVM-SMOTE oversampling method in combination with the XGBoost classifier. This performance was measured against the AUC reported in the literature, serving as an upper-bound reference. Additionally, comparable results were observed with other oversampling methods paired with RF or XGBoost classifiers.

Conclusions

Our study demonstrates that, even when dealing with an imbalanced EGFR T790M dataset, reasonable predictive outcomes can be achieved by employing an appropriate combination of resampling techniques and classifiers. This approach has significant potential for enhancing T790M mutation detection in NSCLC patients with brain metastasis.

Supplementary Information

The online version contains supplementary material available at 10.1007/s12672-024-01333-1.

Keywords

Radiomics
Machine learning
Imbalanced data
Magnetic resonance imaging
Non-small cell lung cancer
EGFR
T790M
Brain metastases
http://dx.doi.org/10.13039/501100012537 Ditmanson Medical Foundation Chia-Yi Christian Hospital R111-63 http://dx.doi.org/10.13039/100020595 National Science and Technology Council 112-2113-M-194-007 issue-copyright-statement© Springer Science+Business Media, LLC 2024
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pmcIntroduction

Patients diagnosed with lung cancer and concomitant brain metastasis often face a grim prognosis. The incidence of brain metastasis in lung cancer patients at the time of initial presentation is alarmingly high, ranging from 77 to 88%, according to population-based studies [1]. In the case of non-small cell lung cancer (NSCLC) patients, a spectrum of therapeutic strategies has been devised to manage intracranial disease, encompassing both localized and systemic interventions. However, it is essential to acknowledge that localized treatments alone, such as neurosurgery and radiation therapy, may not uniformly improve the clinical outlook for every afflicted patient [2]. Conversely, systemic therapeutic options comprise chemotherapy, targeted therapy, and immunotherapy, with targeted therapy being particularly relevant for individuals harboring epidermal growth factor receptor (EGFR) mutations [3–5]. EGFR mutations are detected in 7% to 76% of NSCLC patients, with a notably higher prevalence in the Asia–Pacific region [6]. These mutations primarily manifest as activating alterations within the EGFR intracellular kinase domain, mainly occurring at exons 18 to 21. These genetic events set off downstream signaling pathways that potentiate NSCLC tumorigenesis [7]. The frequency of EGFR kinase domain mutations in NSCLC includes 5% for nucleotide substitutions in exon 18, 45% for in-frame deletions in exon 19, 5% for in-frame insertions in exon 20, and 40–45% for L858R substitutions in exon 21 [7].

Studies have unequivocally demonstrated that the development and utilization of tyrosine kinase inhibitors (TKIs) across three generations have significantly enhanced progression-free survival (PFS) in treatment-naive patients with EGFR-mutated advanced NSCLC [4], subsequently translating into improved overall survival (OS) for those with stage IV EGFR mutation-positive NSCLC [5]. First and second-generation EGFR-TKIs have become the gold standard for first-line treatment in EGFR mutation-sensitive NSCLC patients (exon 19 deletion or L858R substitution), consistently yielding response rates ranging from 56% to a remarkable 84.6%, nearly doubling the efficacy observed with conventional chemotherapy [8]. Nevertheless, resistance to first-line EGFR-TKIs has been documented, often mediated by the emergence of the T790M mutation [8]. The T790M mutation, characterized by the substitution of threonine with methionine at residue 790 [8–10], has demonstrated a strong association with the development of brain metastasis in patients with EGFR mutations undergoing first- or second-generation EGFR-TKI therapy [11]. The advent of osimertinib, a third-generation EGFR-TKI, has proven to be a game-changer, improving median PFS in T790M-positive NSCLC patients [12, 13]. Consequently, in clinical practice, it becomes imperative to ascertain EGFR mutation status and promptly detect the presence of the T790M mutation, particularly during disease progression, utilizing a non-invasive methodology to make informed decisions regarding EGFR-TKI therapy or combination treatments.

While previous research has predominantly concentrated on utilizing radiomics derived from chest CT [14–16], lung PET/CT [17, 18], or chest MR [19] images to predict EGFR mutation status in lung cancer, there has been a recent expansion of this approach to investigate EGFR mutation status in brain metastases of lung cancer patients through radiomic analyses [20–28]. These studies have employed various feature selection methods and classification algorithms to construct predictive models. The discrepancy of EGFR expression or mutation status between brain metastases and the matched primary NSCLC [29–35] implies the need to select brain metastases rather than their corresponding primary tumors for further specific targeted therapies [29].

In previous pioneering studies [20–27] aiming to identify EGFR mutations or distinguish between EGFR T790M-positive and negative cases, models were trained to achieve a balanced ratio between positive and negative classes, typically ranging from 0.69 to 1.17. Maintaining this equilibrium is essential during model training to prevent bias towards the majority class. However, attaining such balance can be challenging, particularly when the positive class is relatively rare. To address this challenge, various resampling techniques have been developed, including oversampling methods that generate additional data for the minority class and undersampling methods that reduce the data volume of the majority class. The hybrid approach combines elements of both oversampling and undersampling, optimizing the dataset for improved classification performance, especially for rare conditions [36–38].

In the current study, our primary objective is to identify the EGFR T790M mutation in brain metastases of NSCLC patients using MR radiomic features. To achieve this goal, we systematically explore different resampling techniques and machine-learning classifiers. This research aims to enhance our understanding of the most effective strategies for accurate classification within the context of an imbalanced dataset.

Materials and methods

Patient enrollment

A retrospective analysis was conducted on a consecutive cohort of 1679 patients diagnosed with NSCLC. These patients underwent gadolinium-enhanced brain MRI between 2010 and 2019 under the approval of the local Institutional Review Board (IRB2022059, approved on 14th July 2022) at Ditmanson Medical Foundation Chia-Yi Christian Hospital. According to the approved IRB protocol (IRB2022059), the requirement for informed consent was waived. This decision was made because the study utilized anonymized brain MRI images and corresponding molecular data, ensuring patient confidentiality through a complete de-linking process. The waiver was granted in accordance with existing policies and IRB approval. The data analysis was conducted in accordance with the approved guidelines (IRB2022059) by the Institutional Review Board at Ditmanson Medical Foundation Chia-Yi Christian Hospital.

The inclusion criteria encompassed the following: (1) Pathologically confirmed diagnosis of NSCLC; (2) Diagnosis of brain metastasis via brain MRI; and (3) MR imaging before treatment. Exclusion criteria comprised: (1) Brain metastasis originating from sources other than lung cancer; (2) Clinical lung cancer stage I, II, or III; (3) Extra-brain parenchymal metastasis, including calvarium, pachymeninges, leptomeninges, liver, adrenal, or bone; (4) No testing results of EGFR mutation status; (5) Artifacts or missing sequences in brain MR examinations. The flowchart of patient selection is shown in Fig. 1. Adhering to these criteria, a total of 125 patients were enrolled in the final study cohort, with 18 patients classified as T790M-positive and 107 as T790M-negative. The clinical and MRI characteristics of the enrolled patients are provided in Table 1.Fig. 1 The flowchart of patient selection

Table 1 The characteristics of the enrolled patients

	T790M positive (N = 18)	T790M negative (N = 107)	p value	
Clinical characteristics		
Gender	1.00	
 Male	7	43		
 Female	11	64		
Age	60.7 ± 10.8	65.2 ± 11.3	0.11	
Race	1.00	
 Asian	18	107		
 Non-Asian	0	0		
Smoking status	1.00	
 Yes	5a	27a		
 No	13	79		
Histology	1.00	
 Adenocarcinoma	18	106		
 Squamous cell carcinoma	0	1		
Clinical stage		
 IV	18	107	1.00	
 III	0	0		
 II	0	0		
 I	0	0		
MRI characteristics		
 Number of BMs per patient	8.77 ± 14.56	9.32 ± 29.17	0.93	
Number of BMs for size per patient		
  ≤ 10 mm	8.11 ± 14.78	8.34 ± 29.02	0.93	
  > 10 mm	0.66 ± 1.32	0.98 ± 1.67	0.64	
Number of BMs for localization per patient		
 Supratentorial	6.61 ± 12.79	8.22 ± 27.70	0.51	
 Infratentorial	2.16 ± 4.20	1.10 ± 2.52	0.20	
BM: brain metastasis

aThe smoking record is absent for one of the patients

MRI research protocol

Brain MR images were retrieved from the picture archiving and communication systems at Ditmanson Medical Foundation Chia-Yi Christian Hospital. Specifically, 58 T790M-negative and 8 T790M-positive cases were conducted using a 1.5 T Signa™ HDxt scanner (GE Healthcare, Milwaukee, WI) equipped with an eight-channel neurovascular array GE coil and the software of HD 16.0_V03_1638.a. Additionally, 49 T790M-negative and 10 T790M-positive cases were performed using a 1.5 T Optima™ MR450w (GE Healthcare) scanner with a 16-channel GE head-and-neck unit coil and the software of DV25.1_R05_2131.a.

The brain MR examination protocols included various sequences

These sequences consisted of fast spin echo (FSE) T1-weighted image (T1WI) with the following parameters: TE = 24 ms, rephasing RF pulse = 160°, FOV = 220 mm × 220 mm, section thickness = 6 mm, gap = 0.6 mm, and matrix = 320 × 224. Additionally, FSE T2-weighted image (T2WI) was included with TE = 106 ms, rephasing RF pulse = 160°, FOV = 220 mm × 220 mm, section thickness = 6 mm, gap = 0.6 mm, and matrix = 320 mm × 192 mm. The TR values for T1WI and T2WI were automatically determined based on the slice number using the scanner vendor's operating software. Typically, TR values for T1WI were shorter than 700 ms, while those for T2WI were longer than 2000 ms. The examination also featured FSE T2 fluid-attenuated inversion recovery (T2FLAIR) with TR/TE = 9000/140 ms, inversion time (TI) of 2200 ms, rephasing RF pulse 160°, FOV = 220 mm × 220 mm, section thickness 6 mm, gap 0.6 mm, and matrix = 320 × 224. Single-shot echo-planar imaging diffusion-weighted imaging (DWI) was conducted with TR/TE = 8000/76.6 ms, FOV = 240 mm × 240 mm, section thickness = 6 mm, gap = 0.6 mm, matrix = 128 × 128, and a b value of 1000 s/mm2. Furthermore, gadolinium-enhanced T1-weighted imaging (CET1WI) was performed. The CET1WI sequence was acquired after the intravenous administration of gadobutrol (Gadovist®, 0.1 mmol/kg body weight). For the DWI sequences, the array spatial sensitivity encoding technique was implemented to accelerate image acquisition. It's important to note that all acquired MR images were in two-dimensional (2D) format.

Imaging processing

All the images were resized to a matrix size of 450 × 450. Two radiographers (K.-M. L. and C.-H. C.) independently selected regions of interest (ROIs) based on CET1WI images. The ROI with the largest area within each patient was chosen as the representative ROI for subsequent analysis. Other sequence images (T1WI, T2WI, T2FLAIR, and DWI) were then aligned to the corresponding CET1WI images using a six-parameter rigid body transformation and mutual information algorithm. Image resizing, ROI selection, and image alignment were conducted using the Multimodal Radiomics Platform (V5.0) [39] under the environment of MATLAB 2021a (The MathWorks, Inc., Natick, MA). The similarity of the ROIs from the two radiologists was evaluated using the Dice score and Jaccard index with Pillow (9.4.0) in the Python environment (3.10.9). The images were discretized into 256 levels [40]. Subsequently, the image information was extracted, encompassing 19 first-order statistical features, 94 s-order features (including gray level co-occurrence matrix [GLCM], gray level run length matrix [GLRLM], gray level size zone matrix [GLSZM], neighboring gray-tone difference matrix [NGTDM], and gray level dependence matrix [GLDM]) for each imaging sequence, and 10 2D shape features for each ROI. Consequently, a total of 480 features were extracted from each patient (i.e., 5 imaging sequences × 94 imaging features + 10 ROI features). The image discretization and feature extraction were using PyRadiomics (v3.1.0).

Imaging analysis

The selection of ROI features was performed using backward elimination with a p-value < 0.05. Feature selection was applied to ROIs delineated independently by the two observers, and the intersection of selected features from both observers was utilized for classification. To mitigate potential bias in results stemming from the imbalanced dataset, two categories of approaches were employed. First, we utilized class weights, which adjust the weights of the loss function to emphasize the minority class and avoid biased results toward the majority class. Second, we resampled selected features using various techniques, including 5 oversampling methods (random oversampling [ROS], synthetic minority oversampling technique [SMOTE], adaptive synthetic [ADASYN], borderline SMOTE [bSMOTE], and support vector machine SMOTE [SVM-SMOTE]), 4 undersampling methods (random undersampling [RUS], cluster centroids, Tomek’s links [TL], and near miss), and 2 hybrid sampling approaches that combine oversampling and undersampling techniques (SMOTE-ENN [edited nearest neighbor] and SMOTE-TL). All resampling techniques were applied with their default parameters. For ROS, we introduced additional perturbations to the distribution of synthetic data by setting the “shrinkage” parameter to 1 and 3, denoted as ROS1 and ROS3, respectively, in addition to the default setting. Resampled features were subsequently classified using logistic regression (LR), SVM, random forest (RF), and extreme gradient boosting (XGBoost) with threefold and fivefold cross-validations. RF and XGBoost necessitated hyperparameter fine-tuning to optimize performance, with surveyed hyperparameters listed in Table S1. To avoid evaluation bias toward the majority (non-T790M mutant) class, macro-averaged precision, recall, and F1 scores were calculated in addition to the area under the receiver operating characteristic (ROC) curve (AUC). The macro-averaged F1 score provided a comprehensive evaluation of classification performance, equally considering recall and precision while mitigating the influence of imbalanced data by treating the F1 score from each class equally. We also employed SHAP values to understand the importance of the selected features in the classification. All the aforementioned procedures were implemented using scikit-learn (1.21), imbalanced-learning (0.10.1), xgboost (1.73), and shap (0.42.1) in the Python environment.

Results

The selected CET1WI images of brain metastases from NSCLC patients are shown in Fig. 2. There are no visually distinguishable characteristics between T790M-positive (Fig. 2A and B) and T790M-negative (Fig. 2C and D) cases. The Dice scores and Jaccard indices for the two observers are 0.88 ± 0.10 and 0.80 ± 0.13, respectively. The ROIs with high similarity selected by the two observers are shown in Fig. 2A and C, while those with low similarity are shown in Fig. 2B and D, where discrepancies usually occur in the prepheral regions of the enhancing rumor part.Fig. 2 Selected CET1WI images of T790M-positive (A and B) and T790M-negative (C and D) cases. The ROIs selected by observers 1 and 2 are outlined in blue and red, respectively. High similarity examples are shown in (A and C) while low similarity examples are shown in (B and D)

Using backward elimination, the number of image features was significantly reduced from 480 to 20 for each of the two observers, as shown in Table S1. Twelve features were common to both independent processes. Among these common features, 2 were from T1WI, 10 from T2WI, and 1 from T2FLAIR. Specifically, the common features included 2 first-order features (energy and total energy) from T2WI and 11 s-order features across the sequences. These included 1 GLDM and 1 GLRLM feature from T1WI, 1 GLCM, 4 GLDM and 3 GLSZM features from T2WI, and 1 GLDM feature from T2FLAIR. Notably, the feature Gray Level Non-Uniformity of GLDM appeared repeatedly across all three sequences.

A systematic investigation was carried out, exploring various pairs of four different classifiers (LR, SVM, RF, and XGBoost) combined with a range of resampling techniques to address the issue of imbalanced data, utilizing the 13 selected features. Classification performance metrics, including macro-averaged F1, recall, precision, and AUC, were remarkably consistent between the two observers (Figures S1 and S2). The average performance of these two observers is presented in Figure S3. In general, RF and XGBoost demonstrated superior performance compared to LR and SVM [36, 37]. Without employing any resampling technique, the performance metrics hovered around 0.5, with slight improvement observed with the utilization of undersampling. In contrast, both oversampling and hybrid-sampling methods yielded significantly improved classification results. Macro-averaged F1 scores ranged from 0.81 to 0.94 with RF and from 0.79 to 0.93 with XGBoost when these resampling methods were employed.

We selected ROC curves of no-sampling with LR and SVM-SMOTE with XGBoost to represent the improvement by resampling in Fig. 3A, where the corresponding AUCs are 0.89 and 0.47, respectively. In addition to one T1WI feature, the SHAP values in Fig. 3B indicate that the T2WI features are essential for distinguishing between T790M-positive and T790M-negative cases.Fig. 3 A The ROC curves of two representative combinations of resampling technique and classifier, including no-sampling + LR and SVM-SMOTE + XGBoost. B The SHAP values of SVM-SMOTE + XGBoost

Furthermore, an attempt was made to utilize only the 10 features from T2WI features, with the aim of minimizing scanner time in clinical practice. The performance with these reduced features exhibited consistency between the two observers (not shown), and the averaged performance is illustrated in Figure S4. Notably, the classification performance using features from only T2WI images closely resembled the performance achieved with all the features.

Discussion

The emergence of resistance to first-line EGFR-TKIs typically occurs within 10 to 14 months of treatment, primarily due to secondary resistance caused by the EGFR T790M mutation [3], approximately 50 to 60% among the cases [9], making it crucial to detect this resistant mutation early through a non-invasive approach for tailored precision therapy.

Radiomic identification of the EGFR T790M mutation has predominantly relied on chest images [14–19]. However, recent studies have ventured into recognizing EGFR [20–25] or EGFR T790M [26, 27] mutations through MR images of brain metastases, yielding reasonable performance (Table 2). The AUC for detecting EGFR mutation in these studies ranged from 0.73 to 0.99 [20–25]. To the best of our knowledge, only two studies have attempted to differentiate between T790M-positive and T790M-negative cases, achieving AUCs of 0.81 and 0.89 [26, 27].Table 2 The summary of the relevant studies

Classification	Patient number	Class ratioa	Sequences	Classifier	AUCb	resampling	References	
EGFR mutation/wild type	29/32	0.91	CET1WI	RF	0.89	–	Ahn, 2020 [20]	
EGFR mutation/wild type	28/24	1.17	CET1WI, T2WI, T2FLAIR, DWI	LASSO	0.99	–	Wang, 2021 [21]	
EGFR mutation/wild type	42/57	0.74	CET1WI, ADC, FA	RF	0.73	–	Park, 2021 [22]	
EGFR mutation/wild type	92/96	0.96	CET1WI, T2WI	LASSO	0.90	–	Cao, 2022 [23]	
EGFR mutation/wild type	70/92	0.76	CET1WI, T2WI, T2FLAIR	LR	0.85	–	Zhen, 2022 [24]	
EGFR/ALK mutations	96/90	1.06	CET1WI, T2WI, T2FLAIR	RF	0.99	–	Li, 2022 [25]	
EGFR/ALK/KRAS mutations	75/21/15	0.28/0.2	CET1WI, T2FLAIR	RF	0.91	SMOTE	Chen, 2020 [28]	
T790M positive/negative	95/138	0.69	CET1WI, T2WI, DWI	RF	0.89	SMOTE	Li, 2023 [26]	
T790M positive/negative	45/65	0.69	CET1WI, T2WI	LASSO	0.81	–	Fan, 2023 [27]	
EGFR: epidermal growth factor receptor; ALK: anaplastic lymphoma kinase; KRAS: Kirsten rat sarcoma virus; AUC: the area under the receiver operating characteristic curve; T2WI: T2-weighted image; DWI: diffusion-weighted imaging; CET1WI: gadolinium-enhanced T1-weighted image; T2FLAIR: T2 fluid-attenuated inversion recovery; ADC: apparent diffusion coefficient; FA: fractional anisotropy; LR: logistic regression; RF: random forest; LASSO: least absolute shrinkage and selection operator; SMOTE: synthetic minority oversampling technique

aThe class ratio is relative to the first class

bWe only list AUC from the training set of best strategy for a fair comparison among the studies

This study included a dataset of 125 cases comparable to the sample sizes in related literature (ranging from 52 to 233, Table 2). To minimize observer biases, two radiographers independently selected ROIs, with the areas of selected ROIs showing high Dice score (0.88 ± 0.10) and Jaccard index (0.80 ± 0.13) between observers. A total of 480 image features were extracted based on 2D shape, first-order, and second-order statistical features. Feature selection using backward elimination from both sets of ROIs yielded highly similar results, with 13 common features (Table 2), suggesting the analyses were based on reliable ROIs.

Out of the 13 common features, 11 were second-order statistical features, which aligns with findings in other studies where second-order features predominated [21, 22, 28]. This indicates the importance of the complex relationships between adjacent voxels in differentiating T790M-positive and T790M-negative cases, which may not be visually discernible. Some studies have highlighted the potential benefits of including diffusion sequences in classification [22, 26], but the DWI features were not selected by the backward elimination in our study.

A main challenge in this study was the exceptionally low ratio of T790M-positive to T790M-negative cases, standing at only 0.17. This ratio posed a notable contrast to similar studies (Table 2) and necessitated the application of resampling techniques to address the dataset's severe class imbalance [26, 28, 36–38]. Consequently, we conducted a systematic exploration of classification methods, considering the combined impact of resampling approaches and classifiers. Our study leveraged four widely recognized classifiers: LR, SVM, RF, and XGBoost. Previous research has indicated that RF and XGBoost often outperform other methods in similar tasks [20, 22, 26, 28, 36, 37]. However, it is important to note that both of these methods require meticulous parameter optimization to achieve their best performance. To this end, we conducted a thorough parameter tuning process, as detailed in Table S2, ensuring that the classifiers were operating at their optimal settings to address the unique challenges posed by our imbalanced dataset. The imbalanced data could also be addressed by increasing the weights of the loss function for the minority class. However, we did not find a substantial benefit from this approach. Regardless of the classifier used, performance metrics (Figs. 3 and S1-S4) were approximately 0.5 without resampling, suggesting a bias toward the T790M-negative class. Interestingly, under the undersampling technique, simpler classifiers (LR and SVM) performed better than complex ones (RF and XGBoost), indicating that simpler classifiers may be more efficient with smaller sample sizes. However, the metrics obtained with undersampling were only slightly improved compared to those without resampling. In general, RF and XGBoost exhibited good performance with oversampling or hybrid-sampling methods. Among the oversampling methods, ROS with RF and XGBoost yielded unusually high macro-averaged F1 scores and AUCs, which decreased with more perturbation (ROS1 and ROS3). Similarly, the AUCs for hybrid sampling (especially SMOTE-ENN) with RF and XGBoost tended to be higher. Considering the upper bound of AUC from the literature is 0.89 [26], SVM-SMOTE with XGBoost from two observers does not surpass this boundary, which might be the best model in our survey. Although the AUCs are higher than those reported in the literature, combinations of XGBoost or RF with other oversampling techniques could be promising. However, these combinations still need to be validated with a larger dataset.

We further explored the use of features from the minimum required imaging sequences to reduce scanning time. A total of 10 T2WI features were chosen for further classification (Table S1). The performance with the reduced features (Figure S4) was slightly reduced but still comparable to that with the full set of selected features (Figs. 3 and S3). This is supported by the SHAP values (Fig. 3B), which indicate that T2WI features play an important role in the classification.

Although numerous resampling techniques have been developed to address imbalanced data, there is no gold standard to date [36, 37]. Conducting a systematic survey may indeed be the most effective approach to comprehensively assess the performance of these resampling techniques. However, it's crucial to note that solely pursuing high-performance metrics can potentially lead to overfitting due to the generation of synthetic data. As a precaution, in our evaluation, we considered the AUC reported in the literature as the upper boundary [26]. The resampling procedure can be implemented before or after feature selection. In this study, we only considered the latter procedure. It's worth noting that different sets of features might be chosen when resampling is performed prior to feature selection, which can make it challenging to evaluate the intricate interplay between resampling techniques and classifiers. Due to the small data size in our study, model estimations with fivefold cross-validation could also result in overfitting. Therefore, we also employed threefold cross-validation for each condition (data not shown). The results from both threefold and fivefold cross-validation were comparable.

This study is accompanied by several notable limitations. Firstly, the EGFR T790M mutation status for this cohort was obtained from primary NSCLC specimens. Consequently, it was assumed that brain metastatic lesions would possess the same mutation status as their corresponding primary tumor sites. While previous research has reported low overall discordance rates in EGFR mutation status between primary lung cancer and corresponding brain metastases [41], the potential for discordance cannot be entirely ruled out. Indeed, previous studies have demonstrated discordance in EGFR expression or mutation status between primary lung cancer and corresponding metastatic sites [29–31, 42, 43]. Although there is no definitive discordance rate for T790M mutation status in primary NSCLC and brain metastases, reported rates of EGFR discordance between lung cancer and corresponding brain metastatic sites in patients have ranged from 6.7 to 32% [30, 32–35, 44]. Secondly, this study is retrospective in nature and relies on data collected over a 10-year period from a database. This extended timeframe introduces various inherent confounding variables, including differences in patient characteristics, variations in MR scanners used, and changes in imaging parameters. Finally, it is important to acknowledge that the data utilized in this study was sourced from a single institution, and external validation was not conducted. Moreover, the limited number of positive cases further constrained our analysis of the imbalanced dataset. As a result, our findings and the performance of our approach can only be assessed by comparing them to existing literature [26, 27].

Conclusion

This study aimed to detect the EGFR T790M mutation in lung cancer using MR images of brain metastases within an imbalanced dataset, where the ratio of T790M-positive to T790M-negative cases was only 0.17. When considering the highest reported AUC from the literature as an upper bound (0.89), our results demonstrated that SVM-SMOTE paired with XGBoost provided the highest AUC at 0.89. However, other oversampling methods in combination with RF or XGBoost could also yield comparable performance. This study showcases that it is possible to achieve a level of performance comparable to existing literature even with an imbalanced EGFR T790M dataset. Our findings contribute to the growing body of evidence addressing imbalanced datasets in scientific research.

Supplementary Information

Supplementary file 1

Acknowledgements

This study was supported by the National Science and Technology Council (NSTC, Taiwan): 112-2113-M-194-007, and Ditmanson Medical Foundation Chia-Yi Christian Hospital: R111-63.

Author contributions

K.-M.L. and C.-H.C. selected regions of interest. B.-C.W., Y.-J.C., C.-W.S, and K.-Y.C. analyzed the data. W.-F.W., E.C.L., and C.-C.C. designed and directed the project. W.-F.W. and E.C.L. contributed to the interpretation of the results. W.-F.W. and E.C.L. wrote the manuscript with input from all authors.

Data availability

Data is available on request due to ethical restrictions.

Declarations

Competing interests

The authors declare no competing interests.

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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