3D radiomics predicts EGFR mutation, exon-19 deletion and exon-21 L858R mutation in lung adenocarcinoma

Comprehensive Potential of Artificial Intelligence for Predicting PD-L1 Expression and EGFR Mutations in Lung Cancer: A Systematic Review and Meta-Analysis

OBJECTIVE

To evaluate the methodological quality and the predictive performance of artificial intelligence (AI) for predicting programmed death ligand 1 (PD-L1) expression and epidermal growth factor receptors (EGFR) mutations in lung cancer (LC) based on systematic review and meta-analysis.

METHODS

AI studies based on PET/CT, CT, PET, and immunohistochemistry (IHC)-whole-slide image (WSI) were included to predict PD-L1 expression or EGFR mutations in LC. The modified Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool was used to evaluate the methodological quality. A comprehensive meta-analysis was conducted to analyze the overall area under the curve (AUC). The Cochrane diagnostic test and I2 statistics were used to assess the heterogeneity of the meta-analysis.

RESULTS

A total of 45 AI studies were included, of which 10 were used to predict PD-L1 expression and 35 were used to predict EGFR mutations. Based on the analysis using the QUADAS-2 tool, 37 studies achieved a high-quality score of 7. In the meta-analysis of PD-L1 expression levels, the overall AUCs for PET/CT, CT, and IHC-WSI were 0.80 (95% confidence interval [CI], 0.77-0.84), 0.74 (95% CI, 0.69-0.77), and 0.95 (95% CI, 0.93-0.97), respectively. For EGFR mutation status, the overall AUCs for PET/CT, CT, and PET were 0.85 (95% CI, 0.81-0.88), 0.83 (95% CI, 0.80-0.86), and 0.75 (95% CI, 0.71-0.79), respectively. The Cochrane Diagnostic Test revealed an I2 value exceeding 50%, indicating substantial heterogeneity in the PD-L1 and EGFR meta-analyses. When AI was combined with clinicopathological features, the enhancement in predicting PD-L1 expression was not substantial, whereas the prediction of EGFR mutations showed improvement compared to the CT and PET models, albeit not significantly so compared to the PET/CT models.

CONCLUSIONS

The overall performance of AI in predicting PD-L1 expression and EGFR mutations in LC has promising clinical implications.

Using Multi-phase CT Radiomics Features to Predict EGFR Mutation Status in Lung Adenocarcinoma Patients

RATIONALE AND OBJECTIVES

This study aimed to non-invasively predict epidermal growth factor receptor (EGFR) mutation status in patients with lung adenocarcinoma using multi-phase computed tomography (CT) radiomics features.

MATERIALS AND METHODS

A total of 424 patients with lung adenocarcinoma were recruited from two hospitals who underwent preoperative non-enhanced CT (NE-CT) and enhanced CT (including arterial phase CT [AP-CT], and venous phase CT [VP-CT]). Patients were divided into training (n = 297) and external validation (n = 127) cohorts according to hospital. Radiomics features were extracted from the NE-CT, AP-CT, and VP-CT images, respectively. The Wilcoxon test, correlation analysis, and simulated annealing were used for feature screening. A clinical model and eight radiomics models were established. Furthermore, a clinical-radiomics model was constructed by incorporating multi-phase CT features and clinical risk factors. Receiver operating characteristic curves were used to evaluate the predictive performance of the models.

RESULTS

The predictive performance of multi-phase CT radiomics model (AUC of 0.925 [95% CI, 0.879-0.971] in the validation cohort) was higher than that of NE-CT, AP-CT, VP-CT, and clinical models (AUCs of 0.860 [95% CI,0.794-0.927], 0.792 [95% CI, 0.713-0.871], 0.753 [95% CI, 0.669-0.838], and 0.706 [95% CI, 0.620-0.791] in the validation cohort, respectively) (all P < 0.05). The predictive performance of the clinical-radiomics model (AUC of 0.927 [95% CI, 0.882-0.971] in the validation cohort) was comparable to that of multi-phase CT radiomics model (P > 0.05).

CONCLUSION

Our multi-phase CT radiomics model showed good performance in identifying the EGFR mutation status in patients with lung adenocarcinoma, which may assist personalized treatment decisions.

Predicting EGFR Mutation Status in Non-Small Cell Lung Cancer Using Artificial Intelligence: A Systematic Review and Meta-Analysis

RATIONALE AND OBJECTIVES

Recent advancements in artificial intelligence (AI) render a substantial promise for epidermal growth factor receptor (EGFR) mutation status prediction in non-small cell lung cancer (NSCLC). We aimed to evaluate the performance and quality of AI algorithms that use radiomics features in predicting EGFR mutation status in patient with NSCLC.

MATERIALS AND METHODS

We searched PubMed (Medline), EMBASE, Web of Science, and IEEExplore for studies published up to February 28, 2022. Studies utilizing an AI algorithm (either conventional machine learning [cML] and deep learning [DL]) for predicting EGFR mutations in patients with NSLCL were included. We extracted binary diagnostic accuracy data and constructed a bivariate random-effects model to obtain pooled sensitivity, specificity, and 95% confidence interval. This study is registered with PROSPERO, CRD42021278738.

RESULTS

Our search identified 460 studies, of which 42 were included. Thirty-five studies were included in the meta-analysis. The AI algorithms exhibited an overall area under the curve (AUC) value of 0.789 and pooled sensitivity and specificity levels of 72.2% and 73.3%, respectively. The DL algorithms outperformed cML in terms of AUC (0.822 vs. 0.775) and sensitivity (80.1% vs. 71.1%), but had lower specificity (70.0% vs. 73.8%, p-value < 0.001) compared to cML. Subgroup analysis revealed that the use of positron-emission tomography/computed tomography, additional clinical information, deep feature extraction, and manual segmentation can improve diagnostic performance.

CONCLUSION

DL algorithms can serve as a novel method for increasing predictive accuracy and thus have considerable potential for use in predicting EGFR mutation status in patient with NSCLC. We also suggest that guidelines on using AI algorithms in medical image analysis should be developed with a focus on oncologic radiomics.

NOS3 and CTH gene mutations as new molecular markers for detection of lung adenocarcinoma

Gene mutations can contribute to lung adenocarcinoma (LUAD) development, metastasis, and therapy. This study aims to identify mutations in the endothelial nitric oxide synthase (eNOS or NOS3) and cystathionine γ-lyase (CSE or CTH) genes that are connected to LUAD symptoms. Two gene polymorphisms were identified using Sanger sequencing in 31 LUAD patients' formalin-fixed paraffin-embedded (FFPE) tissues. Epidermal growth factor receptor (EGFR) mutation and programmed death-ligand 1 (PD-L1) expression were examined in 110 LUAD patients using real-time polymerase chain reaction and immunohistochemistry. Mutations in the selected genes were retrieved from the gnomAD database for all cancer types and the Mutagene and COSMIC databases for LUAD patients. The GeneMANIA prediction server was used to predict the interaction between the studied genes. Poorly and moderately differentiated tumours predominated, with pT3 N2 Mx being the most prevalent stage. Polymorphism data showed 189 NOS3 gene mutations and 34 CTH gene mutations. In 110 LUAD patients, 14 (12.73%) were PD-L1 positive and expressed 50% or more protein. Eight (7.27%) samples included EGFR mutations, including two deletions and two point mutations in exon 19, four point mutations in exon 21. In gnomAD, 4012 NOS3 mutations and 1214 CTH mutations are present. In the Mutagene and COSMIC databases, the NOS3 gene had 295 and 93 mutations, whereas the CTH gene had 61 and 36. According to the GeneMANIA prediction server, 10 genes are related to NOS3, eight with CTH, 15 with EGFR, and 5 with PD-L1. This study is the first to identify several previously unknown mutations in LUAD patients' NOS3 and CTH genes, with potential therapeutic implications.

Research for accurate auxiliary diagnosis of lung cancer based on intracellular fluorescent fingerprint information

The distinctions in pathological types and genetic subtypes of lung cancer have a direct impact on the choice of treatment choices and clinical prognosis in clinical practice. This study used pathological histological sections of surgically removed or biopsied tumor tissue from 36 patients. Based on a small sample size, millions of spectral data points were extracted to investigate the feasibility of employing intracellular fluorescent fingerprint information to diagnose the pathological types and mutational status of lung cancer. The intracellular fluorescent fingerprint information revealed the EGFR gene mutation characteristics in lung cancer, and the area under the curve (AUC) value for the optimal model was 0.98. For the classification of lung cancer pathological types, the macro average AUC value for the ensemble-learning model was 0.97. Our research contributes new idea for pathological diagnosis of lung cancer and offers a quick, easy, and accurate auxiliary diagnostic approach.

Optimal 18F-FDG PET/CT radiomics model development for predicting EGFR mutation status and prognosis in lung adenocarcinoma: a multicentric study

Purpose

To develop and interpret optimal predictive models to identify epidermal growth factor receptor (EGFR) mutation status and subtypes in patients with lung adenocarcinoma based on multicentric ¹⁸F-FDG PET/CT data, and further construct a prognostic model to predict their clinical outcome.

Methods

The ¹⁸F-FDG PET/CT imaging and clinical characters of 767 patients with lung adenocarcinoma from 4 cohorts were collected. Seventy-six radiomics candidates using cross-combination method to identity EGFR mutation status and subtypes were built. Further, Shapley additive explanations and local interpretable model-agnostic explanations were used for optimal models' interpretation. Moreover, in order to predict the overall survival, a multivariate Cox proportional hazard model based on handcrafted radiomics features and clinical characteristics was constructed. The predictive performance and clinical net benefit of the models were evaluated via area under receiver operating characteristic (AUC), C-index and decision curve analysis.

Results

Among the 76 radiomics candidates, light gradient boosting machine classifier (LGBM) combined with recursive feature elimination wrapped LGBM feature selection method achieved best performance in predicting EGFR mutation status (AUC reached 0.80, 0.61, 0.71 in the internal test cohort and two external test cohorts, respectively). And extreme gradient boosting classifier combined with support vector machine feature selection method achieved best performance in predicting EGFR subtypes (AUC reached 0.76, 0.63, 0.61 in the internal test cohort and two external test cohorts, respectively). The C-index of the Cox proportional hazard model achieved 0.863.

Conclusions

The integration of cross-combination method and the external validation from multi-center data achieved a good prediction and generalization performance in predicting EGFR mutation status and its subtypes. The combination of handcrafted radiomics features and clinical factors achieved good performance in predicting prognosis. With the urgent needs of multicentric ¹⁸F-FDG PET/CT trails, robust and explainable radiomics models have great potential in decision making and prognosis prediction of lung adenocarcinoma.

Three-dimensional topological radiogenomics of epidermal growth factor receptor Del19 and L858R mutation subtypes on computed tomography images of lung cancer patients

OBJECTIVES

To elucidate a novel radiogenomics approach using three-dimensional (3D) topologically invariant Betti numbers (BNs) for topological characterization of epidermal growth factor receptor (EGFR) Del19 and L858R mutation subtypes.

METHODS

In total, 154 patients (wild-type EGFR, 72 patients; Del19 mutation, 45 patients; and L858R mutation, 37 patients) were retrospectively enrolled and randomly divided into 92 training and 62 test cases. Two support vector machine (SVM) models to distinguish between wild-type and mutant EGFR (mutation [M] classification) as well as between the Del19 and L858R subtypes (subtype [S] classification) were trained using 3DBN features. These features were computed from 3DBN maps by using histogram and texture analyses. The 3DBN maps were generated using computed tomography (CT) images based on the Čech complex constructed on sets of points in the images. These points were defined by coordinates of voxels with CT values higher than several threshold values. The M classification model was built using image features and demographic parameters of sex and smoking status. The SVM models were evaluated by determining their classification accuracies. The feasibility of the 3DBN model was compared with those of conventional radiomic models based on pseudo-3D BN (p3DBN), two-dimensional BN (2DBN), and CT and wavelet-decomposition (WD) images. The validation of the model was repeated with 100 times random sampling.

RESULTS

The mean test accuracies for M classification with 3DBN, p3DBN, 2DBN, CT, and WD images were 0.810, 0.733, 0.838, 0.782, and 0.799, respectively. The mean test accuracies for S classification with 3DBN, p3DBN, 2DBN, CT, and WD images were 0.773, 0.694, 0.657, 0.581, and 0.696, respectively.

CONCLUSION

3DBN features, which showed a radiogenomic association with the characteristics of the EGFR Del19/L858R mutation subtypes, yielded higher accuracy for subtype classifications in comparison with conventional features.

Comprehensive analysis of prediction of the EGFR mutation and subtypes based on the spinal metastasis from primary lung adenocarcinoma

Purpose

To investigate the use of multiparameter MRI-based radiomics in the in-depth prediction of epidermal growth factor receptor (EGFR) mutation and subtypes based on the spinal metastasis in patients with primary lung adenocarcinoma.

Methods

A primary cohort was conducted with 257 patients who pathologically confirmed spinal bone metastasis from the first center between Feb. 2016 and Oct. 2020. An external cohort was developed with 42 patients from the second center between Apr. 2017 and Jun. 2021. All patients underwent sagittal T1-weighted imaging (T1W) and sagittal fat-suppressed T2-weight imaging (T2FS) MRI imaging. Radiomics features were extracted and selected to build radiomics signatures (RSs). Machine learning classify with 5-fold cross-validation were used to establish radiomics models for predicting the EGFR mutation and subtypes. Clinical characteristics were analyzed with Mann-Whitney U and Chi-Square tests to identify the most important factors. Nomogram models were developed integrating the RSs and important clinical factors.

Results

The RSs derived from T1W showed better performance for predicting the EGFR mutation and subtypes compared with those from T2FS in terms of AUC, accuracy and specificity. The nomogram models integrating RSs from combination of the two MRI sequences and important clinical factors achieved the best prediction capabilities in the training (AUCs, EGFR vs. Exon 19 vs. Exon 21, 0.829 vs. 0.885 vs.0.919), internal validation (AUCs, EGFR vs. Exon 19 vs. Exon 21, 0.760 vs. 0.777 vs.0.811), external validation (AUCs, EGFR vs. Exon 19 vs. Exon 21, 0.780 vs. 0.846 vs.0.818). DCA curves indicated potential clinical values of the radiomics models.

Conclusions

This study indicated potentials of multi-parametric MRI-based radiomics to assess the EGFR mutation and subtypes. The proposed clinical-radiomics nomogram models can be considered as non-invasive tools to assist clinicians in making individual treatment plans.

Feature selection methods and predictive models in CT lung cancer radiomics

Radiomics is a technique that extracts quantitative features from medical images using data-characterization algorithms. Radiomic features can be used to identify tissue characteristics and radiologic phenotyping that is not observable by clinicians. A typical workflow for a radiomics study includes cohort selection, radiomic feature extraction, feature and predictive model selection, and model training and validation. While there has been increasing attention given to radiomic feature extraction, standardization, and reproducibility, currently, there is a lack of rigorous evaluation of feature selection methods and predictive models. Herein, we review the published radiomics investigations in CT lung cancer and provide an overview of the commonly used radiomic feature selection methods and predictive models. We also compare limitations of various methods in clinical applications and present sources of uncertainty associated with those methods. This review is expected to help raise awareness of the impact of radiomic feature and model selection methods on the integrity of radiomics studies.

Combined Radiomics-Clinical Model to Predict Radiotherapy Response in Inoperable Stage III and IV Non-Small-Cell Lung Cancer

Purpose: Radiotherapy is a promising treatment option for lung cancer, but patients' responses vary. The purpose of the study was to investigate the potential of radiomics and clinical signature for predicting the radiotherapy sensitivity and overall survival of inoperable stage III and IV non-small-cell lung cancer (NSCLC) patients. Materials: This retrospective study collected 104 inoperable stage III and IV NSCLC patients at the Yunnan Cancer Hospital from October 2016 to September 2020. They were divided into radiation-sensitive and non-sensitive groups. We used analysis of variance (ANOVA) to select features and support vector machine (SVM) to build the radiomic model. Furthermore, the logistic regression method was used to screen out clinically relevant predictive factors and construct the combined model of radiomics-clinical features. Finally, survival was estimated using the Kaplan-Meier method. Results: There were 40 patients in the radiation-sensitive group and 64 in the non-sensitive group. These patients were divided into training set (73 cases) and testing set (31 cases) according to the ratio of 7:3. Nine radiomics features and one clinical feature were significantly associated with radiotherapy sensitivity. Both the radiomics model and combined model have good predictive performance (the areas under the curve (AUC) values of the testing set were 0.864 (95% confidence interval [CI]: 0.683-0.996) and 0.868 (95% CI: 0.689-1.000), respectively). Only platelet level status was associated with overall survival. Conclusion: The combined model constructed based on radiomics and clinical features can effectively identify the radiation-sensitive population and provide valuable clinical information. Patients with higher platelet levels may have a poor prognosis.

Role of intratumoral and peritumoral CT radiomics for the prediction of EGFR gene mutation in primary lung cancer

Objectives

To determine the added value of combining intratumoral and peritumoral CT radiomics for the prediction of epidermal growth factor receptor (EGFR) gene mutations in primary lung cancer (PLC).

Methods

This study included 478 patients with PLC (348 adenocarcinomas and 130 other histological types) who underwent surgical resection and EGFR gene testing. Two radiologists performed segmentation of tumors and peritumoral regions using precontrast high-resolution CT images, and 398 radiomic features (212 intra- and 186 peritumoral features) were extracted. The peritumoral region was defined as the lung parenchyma within a distance of 3 mm from the tumor border. Model performance was estimated using Random Forest, a machine-learning algorithm.

Results

EGFR mutations were found in 162 tumors; 161 adenocarcinomas, and one pleomorphic carcinoma. After exclusion of poorly reproducible and redundant features, 32 radiomic features remained (14 intra- and 18 peritumoral features) and were included in the model building. For predicting EGFR mutations, combining intra- and peritumoral radiomics significantly improved the performance compared to intratumoral radiomics alone (AUC [area under the receiver operating characteristic curve], 0.774 vs 0.730; p < 0.001). Even in adenocarcinomas only, adding peritumoral radiomics significantly increased performance (AUC, 0.687 vs 0.630; p < 0.001). The predictive performance using radiomics and clinical features was significantly higher than that of clinical features alone (AUC, 0.826 vs 0.777; p = 0.005).

Conclusions

Combining intra- and peritumoral radiomics improves the predictive accuracy of EGFR mutations and could be used to aid in decision-making of whether to perform biopsy for gene tests.

Advances in knowledge

Adding peritumoral to intratumoral radiomics yields greater accuracy than intratumoral radiomics alone in predicting EGFR mutations and may serve as a non-invasive method of predicting of the gene status in PLC.

Development and validation of MRI-based radiomics signatures as new markers for preoperative assessment of EGFR mutation and subtypes from bone metastases

Background

This study aimed to develop and externally validate contrast-enhanced (CE) T1-weighted MRI-based radiomics for the identification of epidermal growth factor receptor (EGFR) mutation, exon-19 deletion and exon-21 L858R mutation from MR imaging of spinal bone metastasis from primary lung adenocarcinoma.

Methods

A total of 159 patients from our hospital between January 2017 and September 2021 formed a primary set, and 24 patients from another center between January 2017 and October 2021 formed an independent validation set. Radiomics features were extracted from the CET1 MRI using the Pyradiomics method. The least absolute shrinkage and selection operator (LASSO) regression was applied for selecting the most predictive features. Radiomics signatures (RSs) were developed based on the primary training set to predict EGFR mutations and differentiate between exon-19 deletion and exon-21 L858R. The RSs were validated on the internal and external validation sets using the Receiver Operating Characteristic (ROC) curve analysis.

Results

Eight, three, and five most predictive features were selected to build RS-EGFR, RS-19, and RS-21 for predicting EGFR mutation, exon-19 deletion and exon-21 L858R, respectively. The RSs generated favorable prediction efficacies for the primary (AUCs, RS-EGFR vs. RS-19 vs. RS-21, 0.851 vs. 0.816 vs. 0.814) and external validation (AUCs, RS-EGFR vs. RS-19 vs. RS-21, 0.807 vs. 0.742 vs. 0.792) sets.

Conclusions

Radiomics features from the CE MRI could be used to detect the EGFR mutation, increasing the certainty of identifying exon-19 deletion and exon-21 L858R mutations based on spinal metastasis MR imaging.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-022-09985-4.

Ranking the most influential predictors of CT-based radiomics feature values in metastatic lung adenocarcinoma

PURPOSE

To investigate which acquisition, post-processing, tumor, and patient characteristics contribute the most to the value of radiomics features (RFs) in lung adenocarcinoma in order to better understand and order the potential sources of bias in radiomics studies in a multivariate setting.

METHODS

This single-center retrospective study included all consecutive patients with newly-diagnosed lung adenocarcinoma treated between December 2016 and September 2018 who had pre-treatment contrast-enhanced CT-scan showing ≥ 2 target lesions per response evaluation criteria in solid tumors (RECIST) v1.1. All measurable lesions were manually segmented; 49 RFs were extracted using LIFEx v7.0.0. Afterwards, we reverted the usual radiomics approach (i.e., predicting a clinical outcome base on multiple RFs). To do so, for each RF, random forests and linear regression algorithms were trained using cross-validation to predict the RF value depending on the following variables: patient, mutational status, phase of CT-scan acquisition, discretization (binsize), lesion location, lesion volume, and best response obtained during the first line of treatment (partial response per RECIST vs other). The most important contributors to the value of reproducible RFs (intra-class correlation coefficient > 0.80) according to the best random forests model (selected via R-squared) were ranked.

RESULTS

101 patients (median age: 62.3) were included, with a median of 5 target lesions per patient (range: 2-10) providing 466 segmented lesions. Twenty-nine RFs were reproducible. The most important predictors of the reproducible RFs values were, in order: tumor volume, binsize, tumor location, CT-scan phase, KRAS mutation, and treatment response (average importance: 61.7%, 57.4%, 8.1%, 3.3%, 3%, and 2.7%, respectively). The treatment response and KRAS and EGFR/ROS1/ALK mutational status remained independently correlated with the RF value for 64.3%, 32.1%, and 50% reproducible RFs, respectively.

CONCLUSION

Tumor volume, location, acquisition and post-processing parameters should systematically be incorporated in radiomics-based modeling; however, most reproducible RFs do have significant relationships with mutational status and treatment response.

Radiomics for Detection of the EGFR Mutation in Liver Metastatic NSCLC

RATIONALE AND OBJECTIVES

The research aims to investigate whether MRI radiomics on hepatic metastasis from primary nonsmall cell lung cancer (NSCLC) can be used to differentiate patients with epidermal growth factor receptor (EGFR) mutations from those with EGFR wild-type, and develop a prediction model based on combination of primary tumor and the metastasis.

MATERIALS AND METHODS

A total of 130 patients were enrolled between Aug. 2017 and Dec. 2021, all pathologically confirmed harboring hepatic metastasis from primary NSCLC. The pyradiomics was used to extract radiomics features from intra- and peritumoral areas of both primary tumor and metastasis. The least absolute shrinkage and selection operator (LASSO) regression was applied to identify most predictive features and to develop radiomics signatures (RSs) for prediction of the EGFR mutation status. The receiver operating characteristic (ROC) curve analysis was performed to assess the prediction capability of the developed RSs.

RESULTS

A RS-Primary and a RS-Metastasis were derived from the primary tumor and metastasis, respectively. The RS-Combine by combination of the primary tumor and metastasis achieved the highest prediction performance in the training (AUCs, RS-Primary vs. RS-Metastasis vs. RS-Combine, 0.826 vs. 0.821 vs. 0.908) and testing (AUCs, RS-Primary vs. RS-Metastasis vs. RS-Combine, 0.760 vs. 0.791 vs. 0.884) set. The smoking status showed significant difference between EGFR mutant and wild-type groups (p < 0.05) in the training set.

CONCLUSION

The study indicates that hepatic metastasis-based radiomics can be used to detect the EGFR mutation. The developed multiorgan combined radiomics signature may be helpful to guide individual treatment strategies for patients with metastatic NSCLC.

Development and externally validate MRI-based nomogram to assess EGFR and T790M mutations in patients with metastatic lung adenocarcinoma

OBJECTIVES

This study aims to explore values of multi-parametric MRI-based radiomics for detecting the epidermal growth factor receptor (EGFR) mutation and resistance (T790M) mutation in lung adenocarcinoma (LA) patients with spinal metastasis.

METHODS

This study enrolled a group of 160 LA patients from our hospital (between Jan. 2017 and Feb. 2021) to build a primary cohort. An external cohort was developed with 32 patients from another hospital (between Jan. 2017 and Jan. 2021). All patients underwent spinal MRI (including T1-weighted (T1W) and T2-weighted fat-suppressed (T2FS)) scans. Radiomics features were extracted from the metastasis for each patient and selected to develop radiomics signatures (RSs) for detecting the EGFR and T790M mutations. The clinical-radiomics nomogram models were constructed with RSs and important clinical parameters. The receiver operating characteristics (ROC) curve was used to evaluate the predication capabilities of each model. Calibration and decision curve analyses (DCA) were constructed to verify the performance of the models.

RESULTS

For detecting the EGFR and T790M mutation, the developed RSs comprised 9 and 4 most important features, respectively. The constructed nomogram models incorporating RSs and smoking status showed favorite prediction efficacy, with AUCs of 0.849 (Sen = 0.685, Spe = 0.885), 0.828 (Sen = 0.964, Spe = 0.692), and 0.778 (Sen = 0.611, Spe = 0.929) in the training, internal validation, and external validation sets for detecting the EGFR mutation, respectively, and with AUCs of 0.0.842 (Sen = 0.750, Spe = 0.867), 0.823 (Sen = 0.667, Spe = 0.938), and 0.800 (Sen = 0.875, Spe = 0.800) in the training, internal validation, and external validation sets for detecting the T790M mutation, respectively.

CONCLUSIONS

Radiomics features from the spinal metastasis were predictive on both EGFR and T790M mutations. The constructed nomogram models can be potentially considered as new markers to guild treatment management in LA patients with spinal metastasis.

KEY POINTS

• To our knowledge, this study was the first approach to detect the EGFR T790M mutation based on spinal metastasis in patients with lung adenocarcinoma. • We identified 13 MRI features that were strongly associated with the EGFR T790M mutation. • The proposed nomogram models can be considered as potential new markers for detecting EGFR and T790M mutations based on spinal metastasis.

PET/CT Radiomic Features: A Potential Biomarker for EGFR Mutation Status and Survival Outcome Prediction in NSCLC Patients Treated With TKIs

Backgrounds

Epidermal growth factor receptor (EGFR) mutation profiles play a vital role in treatment strategy decisions for non–small cell lung cancer (NSCLC). The purpose of this study was to evaluate the predictive efficacy of baseline ¹⁸F-FDG PET/CT-based radiomics analysis for EGFR mutation status, mutation site, and the survival benefit of targeted therapy.

Methods

A sum of 313 NSCLC patients with pre-treatment ¹⁸F-FDG PET/CT scans and genetic mutations detection were retrospectively studied. Clinical and PET metabolic parameters were incorporated into independent predictors of determining mutation status and mutation site. The dataset was randomly allocated into the training and the validation sets in a 7:3 ratio. Three-dimensional (3D) radiomics features were extracted from each PET- and CT-volume of interests (VOI) singularly, and then a radiomics signature (RS) associated with EGFR mutation profiles is built by feature selection. Three different prediction models based on support vector machine (SVM), decision tree (DT), and random forest (RF) classifiers were established. Furthermore, nomograms for estimation of overall survival (OS) and progression-free survival (PFS) were established by integrating PET/CT radiomics score (Rad-score), metabolic parameters, and clinical factors. Predictive performance was assessed by the receiver operating characteristic (ROC) analysis and the calibration curve analysis. The decision curve analysis (DCA) was applied to estimate and compare the clinical usefulness of nomograms.

Results

Three hundred thirteen NSCLC patients were classified into a training set (n=218) and a validation set (n=95). Multivariate analysis demonstrated that SUVmax and sex were independent indicators of EGFR mutation status and mutation site. Eight CT-derived RS, six PET-derived RS, and two clinical factors were retained to develop integrated models, which exhibited excellent ability to distinguish between EGFR wild type (EGFR-WT), EGFR 19 mutation type (EGFR-19-MT), and EGFR 21 mutation type (EGFR-21-MT). The SVM model outperformed the RF model and the DT model, yielding training area under the curves (AUC) of EGFR-WT, EGFR-19-WT, and EGFR-21-WT, with 0.881, 0.851, and 0.849, respectively, and validation AUCs of 0.926, 0.805 and 0.859, respectively. For prediction of OS, the integrated nomogram is superior to the clinical nomogram and the radiomics nomogram, with C-indexes of 0.80 in the training set and 0.83 in the validation set, respectively.

Conclusions

The PET/CT-based radiomics analysis might provide a novel approach to predict EGFR mutation status and mutation site in NSCLC patients and could serve as useful predictors for the patients’ survival outcome of targeted therapy in clinical practice.

Radiomics evaluates the EGFR mutation status from the brain metastasis: a multi-center study

Objective. To develop and externally validate habitat-based MRI radiomics for preoperative prediction of the EGFR mutation status based on brain metastasis (BM) from primary lung adenocarcinoma (LA). Approach. We retrospectively reviewed 150 and 38 patients from hospital 1 and hospital 2 between January 2017 and December 2021 to form a primary and an external validation cohort, respectively. Radiomics features were calculated from the whole tumor (W), tumor active area (TAA) and peritumoral oedema area (POA) in the contrast-enhanced T1-weighted (T1CE) and T2-weighted (T2W) MRI image. The least absolute shrinkage and selection operator was applied to select the most important features and to develop radiomics signatures (RSs) based on W (RS-W), TAA (RS-TAA), POA (RS-POA) and in combination (RS-Com). The area under receiver operating characteristic curve (AUC) and accuracy analysis were performed to assess the performance of radiomics models. Main results. RS-TAA and RS-POA outperformed RS-W in terms of AUC, ACC and sensitivity. The multi-region combined RS-Com showed the best prediction performance in the primary validation (AUCs, RS-Com versus RS-W versus RS-TAA versus RS-POA, 0.901 versus 0.699 versus 0.812 versus 0.883) and external validation (AUCs, RS-Com versus RS-W versus RS-TAA versus RS-POA, 0.900 versus 0.637 versus 0.814 versus 0.842) cohort. Significance. The developed habitat-based radiomics models can accurately detect the EGFR mutation in patients with BM from primary LA, and may provide a preoperative basis for personal treatment planning.

EGFR Mutation Status and Subtypes Predicted by CT-Based 3D Radiomic Features in Lung Adenocarcinoma

Objective

In this study, we aim to establish a non-invasive tool to predict epidermal growth factor receptor (EGFR) mutation status and subtypes based on radiomic features of computed tomography (CT).

Methods

A total of 233 lung adenocarcinoma patients were investigated and randomly divided into the training and test cohorts. In this study, 2300 radiomic features were extracted from original and filtered (Exponential, Laplacian of Gaussian, Logarithm, Gabor, Wavelet) CT images. The radiomic features were divided into four categories, including histogram, volumetric, morphologic, and texture features. An RF-BFE algorithm was developed to select the features for building the prediction models. Clinicopathological features (including age, gender, smoking status, TNM staging, maximum diameter, location, and growth pattern) were combined to establish an integrated model with radiomic features. ROC curve and AUC quantified the effectiveness of the predictor of EGFR mutation status and subtypes.

Results

A set of 10 features were selected to predict EGFR mutation status between EGFR mutant and wild type, while 9 selected features were used to predict mutation subtypes between exon 19 deletion and exon 21 L858R mutation. To predict the EGFR mutation status, the AUC of the training cohort was 0.778 and the AUC of the test cohort was 0.765. To predict the EGFR mutation subtypes, the AUC of training cohort was 0.725 and the AUC of test cohort was 0.657. The integrated model showed the most optimal predictive performance with EGFR mutation status (AUC = 0.870 and 0.759) and subtypes (AUC = 0.797 and 0.554) in the training and test cohorts.

Conclusion

CT-based radiomic features can extract information on tumor heterogeneity in lung adenocarcinoma. In addition, we have established a radiomic model and an integrated model to non-invasively predict the EGFR mutation status and subtypes of lung adenocarcinoma, which is conducive to saving clinical costs and guiding targeted therapy.

Development of a Nomogram Based on 3D CT Radiomics Signature to Predict the Mutation Status of EGFR Molecular Subtypes in Lung Adenocarcinoma: A Multicenter Study

Background

This study aimed to noninvasively predict the mutation status of epidermal growth factor receptor (EGFR) molecular subtype in lung adenocarcinoma based on CT radiomics features.

Methods

In total, 728 patients with lung adenocarcinoma were included, and divided into three groups according to EGFR mutation subtypes. 1727 radiomics features were extracted from the three-dimensional images of each patient. Wilcoxon test, least absolute shrinkage and selection operator regression, and multiple logistic regression were used for feature selection. ROC curve was used to evaluate the predictive performance of the model. Nomogram was constructed by combining radiomics features and clinical risk factors. Calibration curve was used to evaluate the goodness of fit of the model. Decision curve analysis was used to evaluate the clinical applicability of the model.

Results

There were three, two, and one clinical factor and fourteen, thirteen, and four radiomics features, respectively, which were significantly related to each EGFR molecular subtype. Compared with the clinical and radiomics models, the combined model had the highest predictive performance in predicting EGFR molecular subtypes [Del-19 mutation vs. wild-type, AUC=0.838 (95% CI, 0.799-0.877); L858R mutation vs. wild-type, AUC=0.855 (95% CI, 0.817-0.894); and Del-19 mutation vs. L858R mutation, AUC=0.906 (95% CI, 0.869-0.943), respectively], and it has a stable performance in the validation set [AUC was 0.813 (95% CI, 0.740-0.886), 0.852 (95% CI, 0.790-0.913), and 0.875 (95% CI, 0.781-0.929), respectively].

Conclusion

Our combined model showed good performance in predicting EGFR molecular subtypes in patients with lung adenocarcinoma. This model can be applied to patients with lung adenocarcinoma.

MRI-Based Radiomics Nomogram as a Potential Biomarker to Predict the EGFR Mutations in Exon 19 and 21 Based on Thoracic Spinal Metastases in Lung Adenocarcinoma

RATIONALE AND OBJECTIVES

Preoperative identifications of epidermal growth factor receptor (EGFR) mutation subtypes based on the MRI image of spinal metastases are needed to provide individualized therapy, but has not been previously investigated. This study aims to develop and evaluate an MRI-based radiomics nomogram for differentiating the exon 19 and 21 in EGFR mutation from spinal bone metastases in patients with primary lung adenocarcinoma.

MATERIALS AND METHODS

A total of 76 patients underwent T1-weighted and T2-weighted fat-suppressed MRI scans were enrolled in this study, 38 were positive for EGFR mutation in exon 19 and 38 were in exon 21.MRI imaging features were extracted and selected from each MRI pulse sequence, and used to form the radiomics signature. A radiomics nomogram was developed integrating the radiomics signature and important clinical factors with receiver operating characteristic, calibration and decision curve analysis to assess the nomogram. Clinical characteristics were analyzed with Mann-Whitney U and Chi-Square tests to identify the most important factors.

RESULTS

A total of 6 features were selected as the most discriminative predictors from the two MRI pulse sequences. The nomogram integrating the combined radiomics signature, age and CEA level generated good prediction performance in the training (AUCs, nomogram vs. combined radiomics signature vs. clinical model, 0.90 vs. 0.87 vs. 0.59) and validation (AUCs, nomogram vs. combined radiomics signature vs. clinical model, 0.88 vs. 0.86 vs. 0.72) cohort. DCA analysis confirmed the potential clinical utility of the nomogram.

CONCLUSION

This study demonstrated that MRI features from spinal bone metastases can be used to prognosticate EGFR mutation subtypes in exon 19 and 21. The developed pre-treatment nomogram can potentially guide treatments for lung adenocarcinoma patients.

Clinical characteristics of non-small cell lung cancer patients with EGFR mutations and ALK&ROS1 fusions

OBJECTIVE

To study the relationship between clinical characteristics and anaplastic lymphoma kinase (ALK) fusions, c-ros oncogene 1, receptor tyrosine kinase (ROS1) gene fusions, and epidermic growth factor receptor (EGFR) mutations in non-small cell lung cancer (NSCLC) patients to distinguish these different types.

METHODS

Both ALK, ROS1 gene rearrangements and EGFR mutations testing were performed. The clinical characteristics and associated pulmonary abnormalities were investigated.

RESULTS

Four hundred fifty-three NSCLC patients were included for analysis. One hundred seventy (37.5%), 32 (7.1%), and 9 cases (2.0%) with EGFR mutations, ALK gene fusions, and ROS1 gene fusions were identified, respectively. The EGFR-positive and ALK&ROS1-positive were more common in female (χ2  = 61.934, P < 0.001 and χ2  = 28.152, P < 0.001), non-smoking (χ2  = 59.315, P < 0.001 and χ2  = 11.080, P = 0.001), and adenocarcinoma (χ2  = 44.864, P < 0.001 and χ2  = 12.318, P = 0.002) patients; proportion of patients with emphysema was lower (χ2  = 35.494, P < 0.001 and χ2  = 15.770, P < 0.001) than the wild-type patients. The results of logistic regression analysis indicated that female (adjusted odds ratio [OR] 1.834, 95% confidence interval [CI] 1.069-3.144, P = 0.028), non-smoking (adjusted OR 2.504, 95% CI 1.456-4.306, P = 0.001), lung adenocarcinoma (adjusted OR 4.512, 95% CI 2.465-8.260, P < 0.001), stage III-IV (adjusted OR 2.232, 95% CI 1.066-4.676, P = 0.033), and no symptoms of emphysema (adjusted OR 2.139, 95% CI 1.221-3.747, P = 0.008) were independent variables associated with EGFR mutations. Young (adjusted OR 3.947, 95% CI 1.873-8.314, P < 0.001) and lung adenocarcinoma (adjusted OR 2.950, 95% CI 0.998-8.719, P = 0.050) were associated with ALK/ROS1 fusions.

CONCLUSIONS

EGFR mutations were more likely to occur in non-smoking, stage III-IV, and female patients with lung adenocarcinoma, whereas ALK&ROS1 gene fusions were more likely to occur in young patients with lung adenocarcinoma. Emphysema was less common in patients with EGFR mutations.

Molecular typing of lung adenocarcinoma with computed tomography and CT image-based radiomics: a narrative review of research progress and prospects

Objective

The purpose of this paper was to perform a narrative review of current research evidence on conventional computed tomography (CT) imaging features and CT image-based radiomic features for predicting gene mutations in lung adenocarcinoma and discuss how to translate the research findings to guide future practice.

Background

Lung cancer, especially lung adenocarcinoma, is the leading cause of cancer-related deaths. With advances in the diagnosis and treatment of lung adenocarcinoma with the emergence of molecular testing, the prediction of oncogenes and even drug resistance gene mutations have become key to individualized and precise clinical treatment in order to prolong survival and improve quality of life. The progress of imageological examination includes the development of CT and radiomics are promising quantitative methods for predicting different gene mutations in lung adenocarcinoma, especially common mutations, such as epidermal growth factor receptor (EGFR) mutation, anaplastic lymphoma kinase (ALK) mutation and Kirsten rat sarcoma viral oncogene (KRAS) mutation.

Methods

The PubMed electronic database was searched along with a set of terms specific to lung adenocarcinoma, radiomics (including texture analysis), CT, computed tomography, EGFR, ALK, KRAS, rearranging transfection (RET) rearrangement and c-ros oncogene 1 (ROS-1), v-raf murine sarcoma viral oncogene homolog B1 (BRAF), and human epidermal growth factor receptor 2 (HER2) mutations et al. This review has been reported in compliance with the Narrative Review checklist guidelines. From each full-text article, information was extracted regarding a set of terms above.

Conclusions

Research on the application of conventional CT features and CT image-based radiomic features for predicting the gene mutation status of lung adenocarcinoma is still in a preliminary stage. Noninvasively determination of mutation status in lung adenocarcinoma before targeted therapy with conventional CT features and CT image-based radiomic features remains both hopes and challenges. Before radiomics could be applied in clinical practice, more work needs to be done.

Using contrast-enhanced CT and non-contrast-enhanced CT to predict EGFR mutation status in NSCLC patients-a radiomics nomogram analysis

Objectives

To develop and validate a general radiomics nomogram capable of identifying EGFR mutation status in non-small cell lung cancer (NSCLC) patients, regardless of patient with either contrast-enhanced CT (CE-CT) or non-contrast-enhanced CT (NE-CT).

Methods

A total of 412 NSCLC patients were retrospectively enrolled in this study. Patients’ radiomics features not significantly different between NE-CT and CE-CT were defined as general features, and were further used to construct the general radiomics signature. Fivefold cross-validation was used to select the best machine learning algorithm. Finally, a general radiomics nomogram was developed using general radiomics signature, and clinical and radiological characteristics. Two groups of data collected at different time periods were used as two test sets to access the discrimination and clinical usefulness. Area under the receiver operating characteristic curve (ROC-AUC) was applied to performance evaluation.

Result

The general radiomics signature yielded the highest AUC of 0.756 and 0.739 in the two test sets, respectively. When applying to same type of CT, the performance of general radiomics signature was always similar to or higher than that of models built using only NE-CT or CE-CT features. The general radiomics nomogram combining general radiomics signature, smoking history, emphysema, and ILD achieved higher performance whether applying to NE-CT or CE-CT (test set 1, AUC = 0.833 and 0.842; test set 2, AUC = 0.839 and 0.850).

Conclusions

Our work demonstrated that using general features to construct radiomics signature and nomogram could help identify EGFR mutation status of NSCLC patients and expand its scope of clinical application.

Key Points

• General features were proposed to construct general radiomics signature using different types of CT of different patients at the same time to identify EGFR mutation status of NSCLC patients.

• The general radiomics nomogram based on general radiomics signature, and clinical and radiological characteristics could identify EGFR mutation status of patients with NSCLC and outperformed the general radiomics signature.

• The general radiomics nomogram had a wider scope of clinical application; no matter which of NE-CT and CE-CT the patient has, its EGFR mutation status could be predicted.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00330-021-08366-y.

Deep learning predicts epidermal growth factor receptor mutation subtypes in lung adenocarcinoma

PURPOSE

This study aimed to explore the predictive ability of deep learning (DL) for the common epidermal growth factor receptor (EGFR) mutation subtypes in patients with lung adenocarcinoma.

METHODS

A total of 665 patients with lung adenocarcinoma (528/137) were recruited from two different institutions. In the training set, an 18-layer convolutional neural network (CNN) and fivefold cross-validation strategy were used to establish a CNN model. Subsequently, an independent external validation cohort from the other institution was used to evaluate the predictive efficacy of the CNN model. Grad-weighted class activation mapping (Grad-CAM) technology was used for the visual interpretation of the CNN model. In addition, this study also compared the prediction abilities of the radiomics and CNN models. Receiver operating characteristic (ROC) curves, accuracy and precision values, and recall and F1-score were used to evaluate the effectiveness of the CNN model and compare its performance with that of the radiomics model.

RESULTS

In the validation set, the micro- and macroaverage values of the area under the ROC curve of the CNN model to identify the three EGFR subtypes were 0.78 and 0.79, respectively. All evaluation indicators of the CNN model were better than those of the radiomics model.

CONCLUSIONS

Our study confirmed the potential of DL for predicting the EGFR mutation status in lung adenocarcinoma. The imaging phenotypes of the three mutation subtypes were found to be different, which can provide a basis for choosing more accurate and personalized treatment in patients with lung adenocarcinoma.

Radiomics is feasible for prediction of spread through air spaces in patients with nonsmall cell lung cancer

Tumor spread through air spaces (STAS) in non-small-cell lung cancer (NSCLC) is known to influence a poor patient outcome, even in patients presenting with early-stage disease. However, the pre-operative diagnosis of STAS remains challenging. With the progress of radiomics-based analyses several attempts have been made to predict STAS based on radiological findings. In the present study, patients with NSCLC which is located peripherally and tumors ≤ 2 cm in size on computed tomography (CT) that were potential candidates for sublobar resection were enrolled in this study. The radiologic features of the targeted tumors on thin-section CT were extracted using the PyRadiomics v3.0 software package, and a predictive model for STAS was built using the t-test and XGBoost. Thirty-five out of 226 patients had a STAS histology. The predictive model of STAS indicated an area under the receiver-operator characteristic curve (AUC) of 0.77. There was no significant difference in the overall survival (OS) for lobectomy between the predicted-STAS (+) and (−) groups (p = 0.19), but an unfavorable OS for sublobar resection was indicated in the predicted-STAS (+) group (p < 0.01). These results suggest that radiomics with machine-learning helped to develop a favorable model of STAS (+) NSCLC, which might be useful for the proper selection of candidates who should undergo sublobar resection.

Machine-Learning-Derived Nomogram Based on 3D Radiomic Features and Clinical Factors Predicts Progression-Free Survival in Lung Adenocarcinoma

Background

To establish a machine-learning-derived nomogram based on radiomic features and clinical factors to predict post-surgical 2-year progression-free survival (PFS) in patients with lung adenocarcinoma.

Methods

Patients with >2 years post-surgical prognosis results of lung adenocarcinoma were included in Hospital-1 for model training (n = 100) and internal validation (n = 50), and in Hospital-2 for external testing (n = 50). A total of 1,672 radiomic features were extracted from 3D segmented CT images. The Rad-score was established using random survival forest by accumulating and weighting the top-20 imaging features contributive to PFS. A nomogram for predicting PFS was established, which comprised the Rad-score and clinical factors highly relevant to PFS.

Results

In the training, internal validation, and external test groups, 69/100 (69%), 37/50 (74%) and 36/50 (72%) patients were progression-free at two years, respectively. According to the Rad-score, the integral of area under the curve (iAUC) for discriminating high and low risk of progression was 0.92 (95%CI: 0.77-1.0), 0.70 (0.41-0.98) and 0.90 (0.65-1.0), respectively. The C-index of Rad-score was 0.781 and 0.860 in the training and external test groups, higher than 0.707 and 0.606 for TNM stage, respectively. The nomogram integrating Rad-score and clinical factors (lung nodule type, cM stage and histological type) achieved a C-index of 0.845 and 0.837 to predict 2-year PFS, respectively, significantly higher than by only radiomic features (all p < 0.01).

Conclusion

The nomogram comprising CT-derived radiomic features and risk factors showed a high performance in predicting post-surgical 2-year PFS of patients with lung adenocarcinoma, which may help personalize the treatment decisions.

Prognostic Value of Pre-Treatment CT Radiomics and Clinical Factors for the Overall Survival of Advanced (IIIB-IV) Lung Adenocarcinoma Patients

Purpose

The purpose of this study was to investigate the prognostic value of pre-treatment CT radiomics and clinical factors for the overall survival (OS) of advanced (IIIB–IV) lung adenocarcinoma patients.

Methods

This study involved 165 patients with advanced lung adenocarcinoma. The Lasso–Cox regression model was used for feature selection and radiomics signature building. Then a clinical model was built based on clinical factors; a combined model in the form of nomogram was constructed with both clinical factors and the radiomics signature. Harrell’s concordance index (C-Index) and Receiver operating characteristic (ROC) curves at cut-off time points of 1-, 2-, and 3- year were used to estimate and compare the predictive ability of all three models. Finally, the discriminatory ability and calibration of the nomogram were analyzed.

Results

Thirteen significant features were selected to build the radiomics signature whose C-indexes were 0.746 (95% CI, 0.699 to 0.792) in the training cohort and 0.677 (95% CI, 0.597 to 0.766) in the validation cohort. The C-indexes of combined model achieved 0.799 (95% CI, 0.757 to 0.84) in the training cohort and 0.733 (95% CI, 0.656 to 0.81) in the validation cohort, which outperformed the clinical model and radiomics signature. Moreover, the areas under the curve (AUCs) of the radiomic signature for 2-year prediction was superior to that of the clinical model. The combined model had the best AUCs for 2- and 3-year predictions.

Conclusions

Radiomic signatures and clinical factors have prognostic value for OS in advanced (IIIB–IV) lung adenocarcinoma patients. The optimal model should be selected according to different cut-off time points in clinical application.

Combining radiomic phenotypes of non-small cell lung cancer with liquid biopsy data may improve prediction of response to EGFR inhibitors

Among non-small cell lung cancer (NSCLC) patients with therapeutically targetable tumor mutations in epidermal growth factor receptor (EGFR), not all patients respond to targeted therapy. Combining circulating-tumor DNA (ctDNA), clinical variables, and radiomic phenotypes may improve prediction of EGFR-targeted therapy outcomes for NSCLC. This single-center retrospective study included 40 EGFR-mutant advanced NSCLC patients treated with EGFR-targeted therapy. ctDNA data included number of mutations and detection of EGFR T790M. Clinical data included age, smoking status, and ECOG performance status. Baseline chest CT scans were analyzed to extract 429 radiomic features from each primary tumor. Unsupervised hierarchical clustering was used to group tumors into phenotypes. Kaplan–Meier (K–M) curves and Cox proportional hazards regression were modeled for progression-free survival (PFS) and overall survival (OS). Likelihood ratio test (LRT) was used to compare fit between models. Among 40 patients (73% women, median age 62 years), consensus clustering identified two radiomic phenotypes. For PFS, the model combining radiomic phenotypes with ctDNA and clinical variables had c-statistic of 0.77 and a better fit (LRT p = 0.01) than the model with clinical and ctDNA variables alone with a c-statistic of 0.73. For OS, adding radiomic phenotypes resulted in c-statistic of 0.83 versus 0.80 when using clinical and ctDNA variables (LRT p = 0.08). Both models showed separation of K–M curves dichotomized by median prognostic score (p < 0.005). Combining radiomic phenotypes, ctDNA, and clinical variables may enhance precision oncology approaches to managing advanced non-small cell lung cancer with EGFR mutations.

Simultaneous Identification of EGFR,KRAS,ERBB2, and TP53 Mutations in Patients with Non-Small Cell Lung Cancer by Machine Learning-Derived Three-Dimensional Radiomics

Simple Summary

Multiple genetic mutations are associated with the outcomes of patients with non-small cell lung cancer (NSCLC) after using tyrosine kinase inhibitor, but the cost for detecting multiple genetic mutations is high. Few studies have investigated whether multiple genetic mutations can be simultaneously detected based on image features in patients with NSCLC. We developed a machine learning-derived radiomics approach that can simultaneously discriminate the presence of EGFR, KRAS, ERBB2, and TP53 mutations on CT images in patients with NSCLC. These findings suggest that machine learning-derived radiomics may become a noninvasive and low-cost method to screen for multiple genetic mutations in patients with NSCLC before using next-generation sequencing tests, which can help to improve individualized targeted therapies.

Abstract

Purpose: To develop a machine learning-derived radiomics approach to simultaneously discriminate epidermal growth factor receptor (EGFR), Kirsten rat sarcoma viral oncogene (KRAS), Erb-B2 receptor tyrosine kinase 2 (ERBB2), and tumor protein 53 (TP53) genetic mutations in patients with non-small cell lung cancer (NSCLC). Methods: This study included consecutive patients from April 2018 to June 2020 who had histologically confirmed NSCLC, and underwent pre-surgical contrast-enhanced CT and post-surgical next-generation sequencing (NGS) tests to determine the presence of EGFR, KRAS, ERBB2, and TP53 mutations. A dedicated radiomics analysis package extracted 1672 radiomic features in three dimensions. Discriminative models were established using the least absolute shrinkage and selection operator to determine the presence of EGFR, KRAS, ERBB2, and TP53 mutations, based on radiomic features and relevant clinical factors. Results: In 134 patients (63.6 ± 8.9 years), the 20 most relevant radiomic features (13 for KRAS) to mutations were selected to construct models. The areas under the curve (AUCs) of the combined model (radiomic features and relevant clinical factors) for discriminating EGFR, KRAS, ERBB2, and TP53 mutations were 0.78 (95% CI: 0.70–0.86), 0.81 (0.69–0.93), 0.87 (0.78–0.95), and 0.84 (0.78–0.91), respectively. In particular, the specificity to exclude EGFR mutations was 0.96 (0.87–0.99). The sensitivity to determine KRAS, ERBB2, and TP53 mutations ranged from 0.82 (0.69–90) to 0.92 (0.62–0.99). Conclusions: Machine learning-derived 3D radiomics can simultaneously discriminate the presence of EGFR, KRAS, ERBB2, and TP53 mutations in patients with NSCLC. This noninvasive and low-cost approach may be helpful in screening patients before invasive sampling and NGS testing.