Performance of 18F-FDG PET/CT Radiomics for Predicting EGFR Mutation Status in Patients With Non-Small Cell Lung Cancer

Prognostic value of 18F-FDG PET radiomics and sarcopenia in patients with oral squamous cell carcinoma

BACKGROUND

Oral cancer is one of the most common malignancies in the head and neck region. Approximately 90% of oral cancers are oral squamous cell carcinomas (OSCC). 18F-FDG PET/CT has been used in OSCC patients for its high value in detecting metastatic lymph nodes and distant metastases. PET radiomics and sarcopenia can be measured on the PET and CT components of 18F-FDG PET/CT.

PURPOSE

This study aimed to investigate the prognostic value of radiomics and sarcopenia measured on the PET and CT components of pre-operation 18F-FDG PET/CT in OSCC.

METHODS

A total of 116 patients eventually enrolled in our study were randomly divided into two cohorts: training cohort (n = 58) and validation cohort (n = 58). The Cox model combined with the least absolute shrinkage and selection operator (LASSO) algorithm was applied to construct the radiomics score (Rad_score). The third lumber skeletal muscle index (L3 SMI) was calculated to identify sarcopenia. Univariate and multivariate Cox regression analyses were performed to identify the independent prognostic factors. Based on the clinical factors, the clinical model was constructed, and the combined model was developed through the combination of the clinical model and Rad_score. C index, time-dependent C-index curves, receiver operating characteristic (ROC) curve, calibration curves, and decision curve analysis were used to evaluate the performance of prediction models.

RESULTS

Three radiomics features constitute the Rad_score for overall survival (OS) and progression-free survival (PFS), respectively. Multivariate Cox regression analysis revealed that Rad_score was an independent prognostic factor, whereas sarcopenia was not. The combined models showed satisfactory performance in both the training cohort (C-index: OS:0.836, PFS:0.776) and the validation cohort (C-index: OS:0.744, PFS:0.712). The combined models were visualized as nomograms. Nomogram scores can realize the risk stratification of OSCC patients. Lower nomogram score is significantly related to the poorer OS (training cohort: p < 0.0001, validation cohort: p < 0.0001, overall cohort: p < 0.0001) and PFS (training cohort: p < 0.0001, validation cohort: p = 0.00017, overall cohort: p < 0.0001).

CONCLUSIONS

Rad_score, but not sarcopenia, was an independent prognostic factor for patients with OSCC. The nomograms had a satisfactory performance, which might be helpful for OSCC patients and clinicians in personalized prognostic prediction and treatment decision-making.

Value of multi-center 18F-FDG PET/CT radiomics in predicting EGFR mutation status in lung adenocarcinoma

BACKGROUND

Accurate, noninvasive, and reliable assessment of epidermal growth factor receptor (EGFR) mutation status and EGFR molecular subtypes is essential for treatment plan selection and individualized therapy in lung adenocarcinoma (LUAD). Radiomics models based on 18F-FDG PET/CT have great potential in identifying EGFR mutation status and EGFR subtypes in patients with LUAD. The validation of multi-center data, model visualization, and interpretation are significantly important for the management, application and trust of machine learning predictive models. However, few EGFR-related research involved model visualization and interpretation, and multi-center trial.

PURPOSE

To develop explainable optimal predictive models based on handcrafted radiomics features (HRFs) extracted from multi-center 18F-FDG PET/CT to predict EGFR mutation status and molecular subtypes in LUAD.

METHODS

Baseline 18F-FDG PET/CT images of 383 LUAD patients from three hospitals and one public data set were collected. Further, 1808 HRFs were extracted from the primary tumor regions using Pyradiomics. Predictive models were built based on cross-combination of seven feature selection methods and seven machine learning algorithms. Yellowbrick and explainable artificial intelligence technology were used for model visualization and interpretation. Receiver operating characteristic curve, classification report and confusion matrix were used for model performance evaluation. Clinical applicability of the optimal models was assessed by decision curve analysis.

RESULTS

STACK feature selection method combined with light gradient boosting machine (LGBM) reached optimal performance in identifying EGFR mutation status ([area under the curve] AUC = 0.81 in the internal test cohort; AUC = 0.62 in the external test cohort). Random forest feature selection method combined with LGBM reached optimal performance in predicting EGFR mutation molecular subtypes (AUC = 0.89 in the internal test cohort; AUC = 0.61 in the external test cohort).

CONCLUSIONS

Explainable machine learning models combined with radiomics features extracted from multi-center/scanner 18F-FDG PET/CT have certain potential to identify EGFR mutation status and subtypes in LUAD, which might be helpful to the treatment of LUAD.

Efficient 18F-fluorodeoxyglucose positron emission tomography/computed tomography-based machine learning model for predicting epidermal growth factor receptor mutations in non-small cell lung cancer

BACKGROUND

Beyond the human eye's limitations, radiomics provides more information that can be used for diagnosis. We develop a personalized and efficient model based on 18F-fluorodeoxyglucose (18F-FDG) positron emission tomography/computed tomography (PET/CT) to predict epidermal growth factor receptor (EGFR) mutations to help identify which non-small cell cancer (NSCLC) patients are candidates for EGFR-tyrosine kinase inhibitors (TKIs) therapy.

METHODS

We retrospectively included 100 patients with NSCLC and randomized them according to 70 patients in the training group and 30 patients in the validation group. The least absolute shrinkage and selection operator logistic regression (LLR) algorithm and support vector machine (SVM) classifier were used to build the models and predict whether EGFR is mutated or not. The predictive efficacy of the LLR algorithm-based model and the SVM classifier-based model was evaluated by plotting the receiver operating characteristic (ROC) curves and calculating the area under the curve (AUC).

RESULTS

The AUC, sensitivity and specificity of our radiomics model by LLR algorithm were 0.792, 0.967, and 0.600 for the training group and 0.643, 1.00, and 0.378 for the validation group, respectively, in predicting EGFR mutations. The AUC was 0.838 for the training group and 0.696 for the validation group after combining radiomics features with clinical features. The prediction results based on the SVM classifier showed that the validation group had the best performance when based on radial kernel function with AUC, sensitivity, and specificity of 0.741, 0.667, and 0.825, respectively.

CONCLUSIONS

Radiomics models based on 18F-FDG PET/CT modeled with different machine learning algorithms can improve the predictive efficacy of the models. Models that combine clinical features are more clinically valuable.

Predictive value of 18F-FDG PET/CT radiomics for EGFR mutation status in non-small cell lung cancer: a systematic review and meta-analysis

Objective

This study aimed to evaluate the value of 18F-FDG PET/CT radiomics in predicting EGFR gene mutations in non-small cell lung cancer by meta-analysis.

Methods

The PubMed, Embase, Cochrane Library, Web of Science, and CNKI databases were searched from the earliest available date to June 30, 2023. The meta-analysis was performed using the Stata 15.0 software. The methodological quality and risk of bias of included studies were assessed using the Quality Assessment of Diagnostic Accuracy Studies 2 and Radiomics Quality Score criteria. The possible causes of heterogeneity were analyzed by meta-regression.

Results

A total of 17 studies involving 3763 non-small cell lung cancer patients were finally included. We analyzed 17 training cohorts and 10 validation cohorts independently. Within the training cohort, the application of 18F-FDG PET/CT radiomics in predicting EGFR mutations in NSCLC demonstrated a sensitivity of 0.76 (95% CI: 0.70-0.81) and a specificity of 0.78 (95% CI: 0.74-0.82), accompanied by a positive likelihood ratio of 3.5 (95% CI:3.0-4.2), a negative likelihood ratio of 0.31 (95% CI: 0.24-0.39), a diagnostic odds ratio of 11.0 (95% CI: 8.0-16.0), and an area under the curve (AUC) of 0.84 (95% CI: 0.80-0.87). In the validation cohort, the values included a sensitivity of 0.76 (95% CI: 0.67-0.83), a specificity of 0.75 (95% CI: 0.68-0.80), a positive likelihood ratio of 3.0 (95% CI:2.4-3.8), a negative likelihood ratio of 0.32 (95% CI: 0.24-0.44), a diagnostic odds ratio of 9 (95% CI: 6-15), and an AUC of 0.82 (95% CI: 0.78-0.85). The average Radiomics Quality Score (RQS) across studies was 10.47 ± 4.72. Meta-regression analysis identifies the application of deep learning and regions as sources of heterogeneity.

Conclusion

18F-FDG PET/CT radiomics may be useful in predicting mutation status of the EGFR gene in non-small cell lung cancer.

Systematic review registration

https://www.crd.york.ac.uk/PROSPERO, identifier CRD42022385364.

A deep learning model integrating multisequence MRI to predict EGFR mutation subtype in brain metastases from non-small cell lung cancer

BACKGROUND

To establish a predictive model based on multisequence magnetic resonance imaging (MRI) using deep learning to identify wild-type (WT) epidermal growth factor receptor (EGFR), EGFR exon 19 deletion (19Del), and EGFR exon 21-point mutation (21L858R) simultaneously.

METHODS

A total of 399 patients with proven brain metastases of non-small cell lung cancer (NSCLC) were retrospectively enrolled and divided into training (n = 306) and testing (n = 93) cohorts separately based on two timepoints. All patients underwent 3.0-T brain MRI including T2-weighted, T2-weighted fluid-attenuated inversion recovery, diffusion-weighted imaging, and contrast-enhanced T1-weighted sequences. Radiomics features were extracted from each lesion based on four sequences. An algorithm combining radiomics approach with graph convolutional networks architecture (Radio-GCN) was designed for the prediction of EGFR mutation status and subtype. The area under the curve (AUC) at receiver operating characteristic analysis was used to evaluate the predication capabilities of each model.

RESULTS

We extracted 1,290 radiomics features from each MRI sequence. The AUCs of the Radio-GCN model for identifying EGFR 19Del, 21L858R, and WT for the lesion-wise analysis were 0.996 ± 0.004, 0.971 ± 0.013, and 1.000 ± 0.000 on the independent testing cohort separately. It also yielded AUCs of 1.000 ± 0.000, 0.991 ± 0.009, and 1.000 ± 0.000 for predicting EGFR mutations respectively for the patient-wise analysis. The κ coefficients were 0.735 and 0.812, respectively.

CONCLUSIONS

The constructed Radio-GCN model is a new potential tool to predict the EGFR mutation status and subtype in NSCLC patients with brain metastases.

RELEVANCE STATEMENT

The study demonstrated that a deep learning approach based on multisequence MRI can help to predict the EGFR mutation status in NSCLC patients with brain metastases, which is beneficial to guide a personalized treatment.

KEY POINTS

• This is the first study to predict the EGFR mutation subtype simultaneously. • The Radio-GCN model holds the potential to be used as a diagnostic tool. • This study provides an imaging surrogate for identifying the EGFR mutation subtype.

Clinical Applications of Radiomics in Nuclear Medicine

Radiomics is an emerging field of artificial intelligence that focuses on the extraction and analysis of quantitative features such as intensity, shape, texture and spatial relationships from medical images. These features, often imperceptible to the human eye, can reveal complex patterns and biological insights. They can also be combined with clinical data to create predictive models using machine learning to improve disease characterization in nuclear medicine. This review article examines the current state of radiomics in nuclear medicine and shows its potential to improve patient care. Selected clinical applications for diseases such as cancer, neurodegenerative diseases, cardiovascular problems and thyroid diseases are examined. The article concludes with a brief classification in terms of future perspectives and strategies for linking research findings to clinical practice.

Prediction of EGFR mutation status in lung adenocarcinoma based on 18F-FDG PET/CT radiomic features

The earlier identification of EGFR mutation status in lung adenocarcinoma patients is crucial for treatment decision-making. Radiomics, which involves high-throughput extraction of imaging features from medical images for quantitative analysis, can quantify tumor heterogeneity and assess tumor biology non-invasively. This field has gained attention from researchers in recent years. The aim of this study is to establish a model based on 18F-FDG PET/CT radiomic features to predict the epidermal growth factor receptor (EGFR) mutation status of lung adenocarcinoma and evaluate its performance. 155 patients with lung adenocarcinoma who underwent 18F-FDG PET/CT scans and EGFR gene detection before treatment were retrospectively analyzed. The LIFEx packages was used to perform 3D volume of interest (VOI) segmentation manually on DICOM images and extract 128 radiomic features. The Wilcoxon rank sum test and least absolute shrinkage and selection operator (LASSO) regression algorithm were applied to filter the radiomic features and establish models. The performance of the models was evaluated by the receiver operating characteristic (ROC) curve and the area under the curve (AUC). Among the models we have built, the radiomic model based on 18F-FDG PET/CT has the best prediction performance for EGFR gene mutation status, with an AUC of 0.90 (95% CI 0.84~0.96) in the training set and 0.79 (95% CI 0.64~0.94) in the test set. In conclusion, we have established a radiomics model based on 18F-FDG PET/CT, which has good predictive performance in identifying EGFR gene mutation status in lung adenocarcinoma patients.

Detection of the gene mutation of epidermal growth factor receptor in lung adenocarcinoma by radiomic features from a small amount of PET data

OBJECTIVE

The purpose of this work was to identify the potential mutation of epidermal growth factor receptor in nonsmall cell adenocarcinoma by noninvasive method, and to explore whether the same or better effect can be achieved using a small amount of single-mode PET image data.

METHOD

A total of 115 patients were recruited and the results of their 18F-FDG PET images and gene detection after resection were obtained; 117 original radiation features and 744 wavelet transform features were extracted from PET images. Several methods were used to reduce the dimension of the data, and four classifier models were established to classify it. The above process was repeated to reduce the total amount of data and the area under the receiver operating characteristic curve (AUC) value that changed with the reduction of the data and the stability of the results was recorded.

RESULTS

The classifier with the best comprehensive performance under this dataset was logistic regression, whose AUC value is 0.843. And similar results can be obtained from only 30 cases of data.

CONCLUSION

A similar or better result could be achieved using a small number of single-mode PET images. In addition, significant results could be obtained using only the PET images of 30 patients.

FDG PET texture indices as imaging biomarkers for epidermal growth factor receptor mutation status in lung adenocarcinoma

Identifying the epidermal growth factor receptor (EGFR) mutation status is important for the optimal treatment of patients with EGFR mutations. We investigated the relationship between ¹⁸F-fluorodeoxyglucose (FDG) positron emission tomography (PET) texture indices and EGFR mutation status in patients with newly diagnosed lung adenocarcinoma. We retrospectively analyzed data of patients with newly diagnosed lung adenocarcinoma who underwent pretreatment FDG PET/computed tomography and EGFR mutation testing between August 2014 and November 2020. Patients were divided into mutated EGFR and wild-type EGFR groups. The maximum standardized uptake value (SUVmax) and 31 texture indices for the primary tumor were calculated from PET images and compared between the two groups. Of the 66 patients included, 22 had mutated EGFR and 44 had wild-type EGFR. The SUVmax did not significantly differ between the two groups. Among the 31 evaluated texture indices, the following five showed a statistically significant difference between the groups: correlation (P = 0.003), gray-level nonuniformity for run (P = 0.042), run length nonuniformity (P = 0.02), coarseness (P = 0.006), and gray-level nonuniformity for zone (P = 0.04). Based on the preliminary results of this study in a small patient population, FDG PET texture indices may be potential imaging biomarkers for the EGFR mutation status in patients with newly diagnosed lung adenocarcinoma.

The predictive value of [18F]FDG PET/CT radiomics combined with clinical features for EGFR mutation status in different clinical staging of lung adenocarcinoma

BACKGROUND

This study aims to construct radiomics models based on [¹⁸F]FDG PET/CT using multiple machine learning methods to predict the EGFR mutation status of lung adenocarcinoma and evaluate whether incorporating clinical parameters can improve the performance of radiomics models.

METHODS

A total of 515 patients were retrospectively collected and divided into a training set (n = 404) and an independent testing set (n = 111) according to their examination time. After semi-automatic segmentation of PET/CT images, the radiomics features were extracted, and the best feature sets of CT, PET, and PET/CT modalities were screened out. Nine radiomics models were constructed using logistic regression (LR), random forest (RF), and support vector machine (SVM) methods. According to the performance in the testing set, the best model of the three modalities was kept, and its radiomics score (Rad-score) was calculated. Furthermore, combined with the valuable clinical parameters (gender, smoking history, nodule type, CEA, SCC-Ag), a joint radiomics model was built.

RESULTS

Compared with LR and SVM, the RF Rad-score showed the best performance among the three radiomics models of CT, PET, and PET/CT (training and testing sets AUC: 0.688, 0.666, and 0.698 vs. 0.726, 0.678, and 0.704). Among the three joint models, the PET/CT joint model performed the best (training and testing sets AUC: 0.760 vs. 0.730). The further stratified analysis found that CT_RF had the best prediction effect for stage I-II lesions (training set and testing set AUC: 0.791 vs. 0.797), while PET/CT joint model had the best prediction effect for stage III-IV lesions (training and testing sets AUC: 0.722 vs. 0.723).

CONCLUSIONS

Combining with clinical parameters can improve the predictive performance of PET/CT radiomics model, especially for patients with advanced lung adenocarcinoma.

Recent and current advances in PET/CT imaging in the field of predicting epidermal growth factor receptor mutations in non-small cell lung cancer

Tyrosine kinase inhibitors (TKIs) are a significant treatment strategy for the management of non-small cell lung cancer (NSCLC) with epidermal growth factor receptor (EGFR) mutation status. Currently, EGFR mutation status is established based on tumor tissue acquired by biopsy or resection, so there is a compelling need to develop non-invasive, rapid, and accurate gene mutation detection methods. Non-invasive molecular imaging, such as positron emission tomography/computed tomography (PET/CT), has been widely applied to obtain the tumor molecular and genomic features for NSCLC treatment. Recent studies have shown that PET/CT can precisely quantify EGFR mutation status in NSCLC patients for precision therapy. This review article discusses PET/CT advances in predicting EGFR mutation status in NSCLC and their clinical usefulness.

Combination of 18F-Fluorodeoxyglucose PET/CT Radiomics and Clinical Features for Predicting Epidermal Growth Factor Receptor Mutations in Lung Adenocarcinoma

OBJECTIVE

To identify epidermal growth factor receptor (EGFR) mutations in lung adenocarcinoma based on ¹⁸F-fluorodeoxyglucose (FDG) PET/CT radiomics and clinical features and to distinguish EGFR exon 19 deletion (19 del) and exon 21 L858R missense (21 L858R) mutations using FDG PET/CT radiomics.

MATERIALS AND METHODS

We retrospectively analyzed 179 patients with lung adenocarcinoma. They were randomly assigned to training (n = 125) and testing (n = 54) cohorts in a 7:3 ratio. A total of 2632 radiomics features were extracted from the tumor region of interest from the PET (1316) and CT (1316) images. Six PET/CT radiomics features that remained after the feature selection step were used to calculate the radiomics model score (rad-score). Subsequently, a combined clinical and radiomics model was constructed based on sex, smoking history, tumor diameter, and rad-score. The performance of the combined model in identifying EGFR mutations was assessed using a receiver operating characteristic (ROC) curve. Furthermore, in a subsample of 99 patients, a PET/CT radiomics model for distinguishing 19 del and 21 L858R EGFR mutational subtypes was established, and its performance was evaluated.

RESULTS

The area under the ROC curve (AUROC) and accuracy of the combined clinical and PET/CT radiomics models were 0.882 and 81.6%, respectively, in the training cohort and 0.837 and 74.1%, respectively, in the testing cohort. The AUROC and accuracy of the radiomics model for distinguishing between 19 del and 21 L858R EGFR mutational subtypes were 0.708 and 66.7%, respectively, in the training cohort and 0.652 and 56.7%, respectively, in the testing cohort.

CONCLUSION

The combined clinical and PET/CT radiomics model could identify the EGFR mutational status in lung adenocarcinoma with moderate accuracy. However, distinguishing between EGFR 19 del and 21 L858R mutational subtypes was more challenging using PET/CT radiomics.

IMPORTANCE of PRETREATMENT 18F-FDG PET/CT TEXTURE ANALYSIS in PREDICTING EGFR and ALK MUTATION in PATIENTS with NON-SMALL CELL LUNG CANCER

OBJECTIVE

Identification of anaplastic lymphoma kinase (ALK) and epidermal growth factor receptor (EGFR) mutation types is of great importance before treatment with tyrosine kinase inhibitors (TKIs). Radiomics is a new strategy for noninvasively predicting the genetic status of cancer. We aimed to evaluate the predictive power of 18F-FDG PET/CT-based radiomic features for mutational status before treatment in non-small cell lung cancer (NSCLC) and to develop a predictive model based on radiomic features.

METHODS

Images of patients who underwent 18F-FDG PET/CT for initial staging with the diagnosis of NSCLC between January 2015 and July 2020 were evaluated using LIFEx software. The region of interest (ROI) of the primary tumor was established and volumetric and textural features were obtained. Clinical data and radiomic data were evaluated with machine learning (ML) algorithms to create a model.

RESULTS

For EGFR mutation prediction, the most successful machine learning algorithm obtained with GLZLM_GLNU and clinical data was Naive Bayes (AUC: 0.751, MCC: 0.347, acc: 71.4%). For ALK rearrangement prediction, the most successful machine learning algorithm obtained with GLCM_correlation, GLZLM_LZHGE and clinical data was evaluated as Naive Bayes (AUC: 0.682, MCC: 0.221, acc: 77.4%).

CONCLUSIONS

In our study, we created prediction models based on radiomic analysis of 18F-FDG PET/CT images. Tissue analysis with ML algorithms are non-invasive methods for predicting ALK rearrangement and EGFR mutation status in NSCLC, which may be useful for targeted therapy selection in a clinical setting.

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 in Oncological PET Imaging: A Systematic Review—Part 1, Supradiaphragmatic Cancers

Radiomics is an upcoming field in nuclear oncology, both promising and technically challenging. To summarize the already undertaken work on supradiaphragmatic neoplasia and assess its quality, we performed a literature search in the PubMed database up to 18 February 2022. Inclusion criteria were: studies based on human data; at least one specified tumor type; supradiaphragmatic malignancy; performing radiomics on PET imaging. Exclusion criteria were: studies only based on phantom or animal data; technical articles without a clinically oriented question; fewer than 30 patients in the training cohort. A review database containing PMID, year of publication, cancer type, and quality criteria (number of patients, retrospective or prospective nature, independent validation cohort) was constructed. A total of 220 studies met the inclusion criteria. Among them, 119 (54.1%) studies included more than 100 patients, 21 studies (9.5%) were based on prospectively acquired data, and 91 (41.4%) used an independent validation set. Most studies focused on prognostic and treatment response objectives. Because the textural parameters and methods employed are very different from one article to another, it is complicated to aggregate and compare articles. New contributions and radiomics guidelines tend to help improving quality of the reported studies over the years.

PET/CT Based EGFR Mutation Status Classification of NSCLC Using Deep Learning Features and Radiomics Features

Purpose: This study aimed to compare the performance of radiomics and deep learning in predicting EGFR mutation status in patients with lung cancer based on PET/CT images, and tried to explore a model with excellent prediction performance to accurately predict EGFR mutation status in patients with non-small cell lung cancer (NSCLC). Method: PET/CT images of 194 NSCLC patients from Xijing Hospital were collected and divided into a training set and a validation set according to the ratio of 7:3. Statistics were made on patients' clinical characteristics, and a large number of features were extracted based on their PET/CT images (4306 radiomics features and 2048 deep learning features per person) with the pyradiomics toolkit and 3D convolutional neural network. Then a radiomics model (RM), a deep learning model (DLM), and a hybrid model (HM) were established. The performance of the three models was compared by receiver operating characteristic (ROC) curves, sensitivity, specificity, accuracy, calibration curves, and decision curves. In addition, a nomogram based on a deep learning score (DS) and the most significant clinical characteristic was plotted. Result: In the training set composed of 138 patients (64 with EGFR mutation and 74 without EGFR mutation), the area under the ROC curve (AUC) of HM (0.91, 95% CI: 0.86-0.96) was higher than that of RM (0.82, 95% CI: 0.75-0.89) and DLM (0.90, 95% CI: 0.85-0.95). In the validation set composed of 57 patients (32 with EGFR mutation and 25 without EGFR mutation), the AUC of HM (0.85, 95% CI: 0.77-0.93) was also higher than that of RM (0.68, 95% CI: 0.52-0.84) and DLM (0.79, 95% CI: 0.67-0.91). In all, HM achieved better diagnostic performance in predicting EGFR mutation status in NSCLC patients than two other models. Conclusion: Our study showed that the deep learning model based on PET/CT images had better performance than radiomics model in diagnosing EGFR mutation status of NSCLC patients based on PET/CT images. Combined with the most statistically significant clinical characteristic (smoking) and deep learning features, our hybrid model had better performance in predicting EGFR mutation types of patients than two other models, which could enable NSCLC patients to choose more personalized treatment schemes.

A Review of the Correlation Between Epidermal Growth Factor Receptor Mutation Status and 18F-FDG Metabolic Activity in Non-Small Cell Lung Cancer

PET/CT with 18F-2-fluoro-2-deoxyglucose (18F-FDG) has been proposed as a promising modality for diagnosing and monitoring treatment response and evaluating prognosis for patients with non-small cell lung cancer (NSCLC). The status of epidermal growth factor receptor (EGFR) mutation is a critical signal for the treatment strategies of patients with NSCLC. Higher response rates and prolonged progression-free survival could be obtained in patients with NSCLC harboring EGFR mutations treated with tyrosine kinase inhibitors (TKIs) when compared with traditional cytotoxic chemotherapy. However, patients with EGFR mutation treated with TKIs inevitably develop drug resistance, so predicting the duration of resistance is of great importance for selecting individual treatment strategies. Several semiquantitative metabolic parameters, e.g., maximum standard uptake value (SUVmax), metabolic tumor volume (MTV), and total lesion glycolysis (TLG), measured by PET/CT to reflect 18F-FDG metabolic activity, have been demonstrated to be powerful in predicting the status of EGFR mutation, monitoring treatment response of TKIs, and assessing the outcome of patients with NSCLC. In this review, we summarize the biological and clinical correlations between EGFR mutation status and 18F-FDG metabolic activity in NSCLC. The metabolic activity of 18F-FDG, as an extrinsic manifestation of NSCLC, could reflect the mutation status of intrinsic factor EGFR. Both of them play a critical role in guiding the implementation of treatment modalities and evaluating therapy efficacy and outcome for patients with NSCLC.

Noninvasive Method for Predicting the Expression of Ki67 and Prognosis in Non-Small-Cell Lung Cancer Patients: Radiomics

Purpose

In this study, we aimed to develop and validate a noninvasive method based on radiomics to evaluate the expression of Ki67 and prognosis of patients with non-small-cell lung cancer (NSCLC). Patients and Methods. A total of 120 patients with NSCLC were enrolled in this retrospective study. All patients were randomly assigned to a training dataset (n = 85) and test dataset (n = 35). According to the preprocessed F-FDG PET/CT image of each patient, a total of 384 radiomics features were extracted from the segmentation of regions of interest (ROIs). The Spearman correlation test and least absolute shrinkage and selection operator (LASSO), after normalization on the features matrix, were applied to reduce the dimensionality of the features. Furthermore, multivariable logistic regression analysis was used to propose a model for predicting Ki67. The survival curve was used to explore the prognostic significance of radiomics features.

Results

A total of 62 Ki67 positive patients and 58 Ki67 negative patients formed the training set and test training dataset and test dataset. Radiomics signatures showed good performance in predicting the expression of Ki67 with AUCs of 0.86 (training dataset) and 0.85 (test dataset). Validation and calibration showed that the radiomics had a strong predictive power in patients with NSCLC survival, which was significantly close to the effect of Ki67 expression on the survival of patients with NSCLC.

Conclusion

Radiomics signatures based on preoperative F-FDG PET/CT could distinguish the expression of Ki67, which also had a strong predictive performance for the survival outcome.

Correlation of epidermal growth factor receptor mutation status and PD-L1 expression with [18F]FDG PET using volume-based parameters in non-small cell lung cancer

OBJECTIVE

We investigated the relationship between 2-deoxy-2-[18F]fluoro-D-glucose (FDG) PET using volume-based parameters and epidermal growth factor receptor (EGFR) mutation status, programmed death-ligand-1 (PD-L1) expression level, and their combination, in pretreated non-small cell lung cancer (NSCLC).

METHODS

FDG PET findings and EGFR mutation status and PD-L1 expression level were investigated retrospectively in 93 patients with newly diagnosed NSCLC (77 adenocarcinomas, 16 squamous cell carcinomas). Tumors were divided into six groups: EGFR mutant/negative PD-L1, EGFR mutant/low PD-L1, EGFR mutant/high PD-L1, EGFR wild/negative PD-L1, EGFR wild/low PD-L1, and EGFR wild/high PD-L1. The maximum standardized uptake value (SUVmax), metabolic tumor volume (MTV), and total lesion glycolysis (TLG) for primary tumor were measured from PET images. The EGFR mutation status and PD-L1 expression level were estimated in tumor tissue specimens and compared with the PET parameters.

RESULTS

None of the PET parameters differed significantly between EGFR-mutated and wild-type EGFR. According to the PD-L1 level, significant differences were detected in SUVmax (P = 0.001) and TLG (P = 0.016), but not MTV. Comparing all six groups, significant difference was detected in only SUVmax (P = 0.011).

CONCLUSION

Based on the preliminary results of this study, FDG PET may help in the prediction of PD-L1 expression level, but not EGFR mutation status, in patients with newly diagnosed NSCLC. The SUVmax rather than MTV or TLG, may be of value in predicting the six groups according to the combination of EGFR mutation status and PD-L1 expression level.

Deep Learning to Predict EGFR Mutation and PD-L1 Expression Status in Non-Small-Cell Lung Cancer on Computed Tomography Images

OBJECTIVE

The detection of epidermal growth factor receptor (EGFR) mutation and programmed death ligand-1 (PD-L1) expression status is crucial to determine the treatment strategies for patients with non-small-cell lung cancer (NSCLC). Recently, the rapid development of radiomics including but not limited to deep learning techniques has indicated the potential role of medical images in the diagnosis and treatment of diseases.

METHODS

Eligible patients diagnosed/treated at the West China Hospital of Sichuan University from January 2013 to April 2019 were identified retrospectively. The preoperative CT images were obtained, as well as the gene status regarding EGFR mutation and PD-L1 expression. Tumor region of interest (ROI) was delineated manually by experienced respiratory specialists. We used 3D convolutional neural network (CNN) with ROI information as input to construct a classification model and established a prognostic model combining deep learning features and clinical features to stratify survival risk of lung cancer patients.

RESULTS

The whole cohort (N = 1262) was divided into a training set (N = 882, 70%), validation set (N = 125, 10%), and test set (N = 255, 20%). We used a 3D convolutional neural network (CNN) to construct a prediction model, with AUCs of 0.96 (95% CI: 0.94-0.98), 0.80 (95% CI: 0.72-0.88), and 0.73 (95% CI: 0.63-0.83) in the training, validation, and test cohorts, respectively. The combined prognostic model showed a good performance on survival prediction in NSCLC patients (C-index: 0.71).

CONCLUSION

In this study, a noninvasive and effective model was proposed to predict EGFR mutation and PD-L1 expression status as a clinical decision support tool. Additionally, the combination of deep learning features with clinical features demonstrated great stratification capabilities in the prognostic model. Our team would continue to explore the application of imaging markers for treatment selection of lung cancer patients.

Prediction of EGFR Mutation Status Based on 18F-FDG PET/CT Imaging Using Deep Learning-Based Model in Lung Adenocarcinoma

Objective

The purpose of this study was to develop a deep learning-based system to automatically predict epidermal growth factor receptor (EGFR) mutant lung adenocarcinoma in ¹⁸F-fluorodeoxyglucose (FDG) positron emission tomography/computed tomography (PET/CT).

Methods

Three hundred and one lung adenocarcinoma patients with EGFR mutation status were enrolled in this study. Two deep learning models (SE_CT and SE_PET) were developed with Squeeze-and-Excitation Residual Network (SE-ResNet) module for the prediction of EGFR mutation with CT and PET images, respectively. The deep learning models were trained with a training data set of 198 patients and tested with a testing data set of 103 patients. Stacked generalization was used to integrate the results of SE_CT and SE_PET.

Results

The AUCs of the SE_CT and SE_PET were 0.72 (95% CI, 0.62–0.80) and 0.74 (95% CI, 0.65–0.82) in the testing data set, respectively. After integrating SE_CT and SE_PET with stacked generalization, the AUC was further improved to 0.84 (95% CI, 0.75–0.90), significantly higher than SE_CT (p<0.05).

Conclusion

The stacking model based on ¹⁸F-FDG PET/CT images is capable to predict EGFR mutation status of patients with lung adenocarcinoma automatically and non-invasively. The proposed model in this study showed the potential to help clinicians identify suitable advanced patients with lung adenocarcinoma for EGFR‐targeted therapy.

Artificial Intelligence Applications to Improve the Treatment of Locally Advanced Non-Small Cell Lung Cancers

Locally advanced non-small cell lung cancer patients represent around one third of newly diagnosed lung cancer patients. There remains a large unmet need to find treatment strategies that can improve the survival of these patients while minimizing therapeutical side effects. Increasing the availability of patients' data (imaging, electronic health records, patients' reported outcomes, and genomics) will enable the application of AI algorithms to improve therapy selections. In this review, we discuss how artificial intelligence (AI) can be integral to improving clinical decision support systems. To realize this, a roadmap for AI must be defined. We define six milestones involving a broad spectrum of stakeholders, from physicians to patients, that we feel are necessary for an optimal transition of AI into the clinic.

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.