CT Gray-Level Texture Analysis as a Quantitative Imaging Biomarker of Epidermal Growth Factor Receptor Mutation Status in Adenocarcinoma of the Lung

EfficientNet-Based System for Detecting EGFR-Mutant Status and Predicting Prognosis of Tyrosine Kinase Inhibitors in Patients with NSCLC

We aimed to develop and validate a deep learning-based system using pre-therapy computed tomography (CT) images to detect epidermal growth factor receptor (EGFR)-mutant status in patients with non-small cell lung cancer (NSCLC) and predict the prognosis of advanced-stage patients with EGFR mutations treated with EGFR tyrosine kinase inhibitors (TKI). This retrospective, multicenter study included 485 patients with NSCLC from four hospitals. Of them, 339 patients from three centers were included in the training dataset to develop an EfficientNetV2-L-based model (EME) for predicting EGFR-mutant status, and the remaining patients were assigned to an independent test dataset. EME semantic features were extracted to construct an EME-prognostic model to stratify the prognosis of EGFR-mutant NSCLC patients receiving EGFR-TKI. A comparison of EME and radiomics was conducted. Additionally, we included patients from The Cancer Genome Atlas lung adenocarcinoma dataset with both CT images and RNA sequencing data to explore the biological associations between EME score and EGFR-related biological processes. EME obtained an area under the curve (AUC) of 0.907 (95% CI 0.840-0.926) on the test dataset, superior to the radiomics model (P = 0.007). The EME and radiomics fusion model showed better (AUC, 0.941) but not significantly increased performance (P = 0.895) compared with EME. In prognostic stratification, the EME-prognostic model achieved the best performance (C-index, 0.711). Moreover, the EME-prognostic score showed strong associations with biological pathways related to EGFR expression and EGFR-TKI efficacy. EME demonstrated a non-invasive and biologically interpretable approach to predict EGFR status, stratify survival prognosis, and correlate biological pathways in patients with NSCLC.

Computed Tomography Radiomics for Preoperative Prediction of Spread Through Air Spaces in the Early Stage of Surgically Resected Lung Adenocarcinomas

PURPOSE

To assess the added value of radiomics models from preoperative chest CT in predicting the presence of spread through air spaces (STAS) in the early stage of surgically resected lung adenocarcinomas using multiple validation datasets.

MATERIALS AND METHODS

This retrospective study included 550 early-stage surgically resected lung adenocarcinomas in 521 patients, classified into training, test, internal validation, and temporal validation sets (n=211, 90, 91, and 158, respectively). Radiomics features were extracted from the segmented tumors on preoperative chest CT, and a radiomics score (Rad-score) was calculated to predict the presence of STAS. Diagnostic performance of the conventional model and the combined model, based on a combination of conventional and radiomics features, for the diagnosis of the presence of STAS were compared using the area under the curve (AUC) of the receiver operating characteristic curve.

RESULTS

Rad-score was significantly higher in the STAS-positive group compared to the STAS-negative group in the training, test, internal, and temporal validation sets. The performance of the combined model was significantly higher than that of the conventional model in the training set {AUC: 0.784 [95% confidence interval (CI): 0.722-0.846] vs. AUC: 0.815 (95% CI: 0.759-0.872), p=0.042}. In the temporal validation set, the combined model showed a significantly higher AUC than that of the conventional model (p=0.001). The combined model showed a higher AUC than the conventional model in the test and internal validation sets, albeit with no statistical significance.

CONCLUSION

A quantitative CT radiomics model can assist in the non-invasive prediction of the presence of STAS in the early stage of lung adenocarcinomas.

Radiomics Based on Contrast-Enhanced CT for Recognizing c-Met-Positive Hepatocellular Carcinoma: a Noninvasive Approach to Predict the Outcome of Sorafenib Resistance

OBJECTIVES

The purpose of our project was to investigate the effectiveness of radiomic features based on contrast-enhanced computed tomography (CT) that can detect the expression of c-Met in hepatocellular carcinoma (HCC) and to validate its efficacy in predicting the outcome of sorafenib resistance.

MATERIALS AND METHODS

In total, 130 patients (median age, 60 years) with pathologically confirmed HCC who underwent contrast material-enhanced CT from October 2012 to July 2020 were randomly divided into a training set (n = 91) and a test set (n = 39). Radiomic features were extracted from arterial phase (AP), portal venous phase (VP) and delayed phase (DP) images of every participant's enhanced CT images.

RESULTS

The entire group comprised 39 Met-positive and 91 Met-negative patients. The combined model, which included the clinical factors and the radiomic features, performed well in the training (area under the curve [AUC] = 0.878) and validation (AUC = 0.851) cohorts. The nomogram, which relied on the combined model, fits well in the calibration curves. Decision curve analysis (DCA) further confirmed that the clinical valuation of the nomogram achieved comparable accuracy in c-Met prediction. Among another 20 patients with HCC who had received sorafenib, the predicted high-risk group had shorter overall survival (OS) than the predicted low-risk group (p < 0.05).

CONCLUSION

A multivariate model acquired from three phases (AP, VP and DP) of enhanced CT, HBV-DNA and γ glutamyl transpeptidase isoenzyme II (GGT-II) could be considered a satisfactory preoperative marker of the expression of c-Met in patients with HCC. This approach may help in overcoming sorafenib resistance in advanced HCC.

Computed tomography texture-based models for predicting KIT exon 11 mutation of gastrointestinal stromal tumors

Background

KIT exon 11 mutation in gastrointestinal stromal tumors (GISTs) is associated with treatment strategies. However, few studies have shown the role of imaging-based texture analysis in KIT exon 11 mutation in GISTs. In this study, we aimed to show the association between computed tomography (CT)-based texture features and KIT exon 11 mutation.

Methods

Ninety-five GISTs confirmed by surgery and identified with mutational genotype of KIT were included in this study. By amplifying the samples using over-sampling technique, a total of 183 region of interest (ROI) segments were extracted from 63 patients as training cohort. The 63 new ROI segments were extracted from the 63 patients as internal validation cohort. Thirty-two patients who underwent KIT exon 11 mutation test during 2021-2023 was selected as external validation cohort. The textural parameters were evaluated both in training cohort and validation cohort. Least absolute shrinkage and selection operator (LASSO) algorithms and logistic regression analysis were used to select the discriminant features.

Results

Three of textural features were obtained using LASSO analysis. Logistic regression analysis showed that patients' age, tumor location and radiomics features were significantly associated with KIT exon 11 mutation (p < 0.05). A nomogram was developed based on the associated factors. The area under the curve (AUC) of clinical features, radiomics features and their combination in training cohort was 0.687 (95 % CI: 0.604-0.771), 0.829 (95 % CI: 0.768-0.890) and 0.874 (95 % CI: 0.822-0.926), respectively. The AUC of radiomics features in internal validation cohort and external cohort was 0.880 (95 % CI: 0.796-0.964) and 0.827 (95%CI: 0.667-0.987), respectively.

Conclusion

The CT texture-based model can be used to predict KIT exon 11 mutation in GISTs.

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.

Prognostic analysis and risk stratification of lung adenocarcinoma undergoing EGFR-TKI therapy with time-serial CT-based radiomics signature

OBJECTIVES

To evaluate the value of time-serial CT radiomics features in predicting progression-free survival (PFS) for lung adenocarcinoma (LUAD) patients after epidermal growth factor receptor-tyrosine kinase inhibitors (EGFR-TKIs) therapy.

MATERIALS AND METHODS

LUAD patients treated with EGFR-TKIs were retrospectively included from three independent institutes and divided into training and validation cohorts. Intratumoral and peritumoral features were extracted from time-serial non-contrast chest CT (including pre-therapy and first follow-up images); moreover, the percentage variation per unit time (day) was introduced to adjust for the different follow-up periods of each patient. Test-retest was performed to exclude irreproducible features, while the Boruta algorithm was used to select critical radiomics features. Radiomics signatures were constructed with random forest survival models in the training cohort and compared against baseline clinical characteristics through Cox regression and nonparametric testing of concordance indices (C-indices).

RESULTS

The training cohort included 131 patients (74 women, 56.5%) from one institute and the validation cohort encompassed 41 patients (24 women, 58.5%) from two other institutes. The optimal signature contained 10 features and 7 were unit time feature variations. The comprehensive radiomics model outperformed the pre-therapy clinical characteristics in predicting PFS (training: 0.78, 95% CI: [0.72, 0.84] versus 0.55, 95% CI: [0.49, 0.62], p < 0.001; validation: 0.72, 95% CI: [0.60, 0.84] versus 0.54, 95% CI: [0.42, 0.66], p < 0.001).

CONCLUSION

Radiomics signature derived from time-serial CT images demonstrated optimal prognostic performance of disease progression. This dynamic imaging biomarker holds the promise of monitoring treatment response and achieving personalized management.

KEY POINTS

• The intrinsic tumor heterogeneity can be highly dynamic under the therapeutic effect of EGFR-TKI treatment, and the inevitable development of drug resistance may disrupt the duration of clinical benefit. Decision-making remained challenging in practice to detect the emergence of acquired resistance during the early response phase. • Time-serial CT-based radiomics signature integrating intra- and peritumoral features offered the potential to predict progression-free survival for LUAD patients treated with EGFR-TKIs. • The dynamic imaging signature allowed for prognostic risk stratification.

Predicting anaplastic lymphoma kinase rearrangement status in patients with non-small cell lung cancer using a machine learning algorithm that combines clinical features and CT images

PURPOSE

To develop an appropriate machine learning model for predicting anaplastic lymphoma kinase (ALK) rearrangement status in non-small cell lung cancer (NSCLC) patients using computed tomography (CT) images and clinical features.

METHOD AND MATERIALS

This study included 193 patients with NSCLC (154 in the training cohort, 39 in the validation cohort), 68 of whom tested positive for ALK rearrangements and 125 of whom tested negative. From the nonenhanced CT scans, 157 radiomic characteristics were extracted, and 8 clinical features were collected. Five machine learning (ML) models were assessed to find the best classification model for predicting ALK rearrangement status. A radiomic signature was developed using the least absolute shrinkage and selection operator (LASSO) algorithm. The predictive performance of the models based on radiomic features, clinical features, and their combination was assessed by receiver operating characteristic (ROC) curves.

RESULTS

The support vector machine (SVM) model had the highest AUC of 0.914 for classification. The clinical features model had an AUC=0.805 (95% CI 0.731-0.877) and an AUC=0.735 (95% CI 0.566-0.863) in the training and validation cohorts, respectively. The CT image-based ML model had an AUC=0.953 (95% CI 0.913-1.0) in the training cohort and an AUC=0.890 (95% CI 0.778-0.971) in the validation cohort. For predicting ALK rearrangement status, the ML model based on CT images and clinical features performed better than the model based on only clinical information or CT images, with an AUC of 0.965 (95% CI 0.826-0.882) in the primary cohort and an AUC of 0.914 (95% CI 0.804-0.893) in the validation cohort.

CONCLUSION

Our findings revealed that ALK rearrangement status could be accurately predicted using an ML-based classification model based on CT images and clinical data.

Radiomics/Radiogenomics in Lung Cancer: Basic Principles and Initial Clinical Results

Simple Summary

Radiogenomics is a promising new approach in cancer assessment, providing an evaluation of the molecular basis of imaging phenotypes after establishing associations between radiological features and molecular features at the genomic–transcriptomic–proteomic level. This review focuses on describing key aspects of radiogenomics while discussing limitations of translatability to the clinic and possible solutions to these challenges, providing the clinician with an up-to-date handbook on how to use this new tool.

Abstract

Lung cancer is the leading cause of cancer-related deaths worldwide, and elucidation of its complicated pathobiology has been traditionally targeted by studies incorporating genomic as well other high-throughput approaches. Recently, a collection of methods used for cancer imaging, supplemented by quantitative aspects leading towards imaging biomarker assessment termed “radiomics”, has introduced a novel dimension in cancer research. Integration of genomics and radiomics approaches, where identifying the biological basis of imaging phenotypes is feasible due to the establishment of associations between molecular features at the genomic–transcriptomic–proteomic level and radiological features, has recently emerged termed radiogenomics. This review article aims to briefly describe the main aspects of radiogenomics, while discussing its basic limitations related to lung cancer clinical applications for clinicians, researchers and patients.

Radiomic Signature as a Diagnostic Factor for Classification of Histologic Subtypes of Lung Cancer

<sec> <title>Objectives:</title> To discuss the application of radiomics using Computerized Tomography (CT) analysis, for improving its diagnostic efficacy in lung, specifically in distinguishing Squamous Cell Carcinoma (SCC), lung Adenocarcinoma (ADC), and Small Cell Lung Cancer (SCLC). </sec> <sec> <title>Methods:</title> The pathology of 189 identified cases of lung cancer was analyzed, retrospectively (60 patients with SCC, 69 patients with lung ADC and 60 patients with SCLC). A neural network was used to determine whether the pulmonary or mediastinal window was selected to extract effective radiomic features. The key features of radiomic signature were retrieved by a Least Absolute Shrinkage and Selection Operator (LASSO) multiple logistic regression model. Next, receiver operating characteristic curve and Area Under the Curve (AUC) analysis were used to evaluate the performance of the radiomic signature in both, training(129 patients) and validation cohorts (60 patients). </sec> <sec> <title>Results:</title> About 295 features were extracted from a manually outlined tumor region. Features extracted from mediastinal window CT scans had a better prognostic ability than pulmonary window scans. The average accuracy for mediastinal window scans was 0.933. Our analysis revealed that the radiomic features extracted from mediastinal window scans had the potential to build a prediction model for distinguishing between SCC, lung ADC, and SCLC. The performance of the radiomic signature to diagnose SCC and SCLC in validation cohorts proved effective, with AUC values of 0.869 and 0.859, respectively. </sec> <sec> <title>Conclusions:</title> A unique radiomic signature was constructed as a diagnostic factor for different histologic subtypes of lung cancer. Patients with lung cancer may benefit from this proposed radiomic signature. </sec>

Quantitative CT texture analysis in predicting PD-L1 expression in locally advanced or metastatic NSCLC patients

Purpose

The assessment of Programmed death-ligand 1 (PD-L1) expression has become a game changer in the treatment of patients with advanced non-small cell lung cancer (NSCLC). We aimed to investigate the ability of Radiomics applied to computed tomography (CT) in predicting PD-L1 expression in patients with advanced NSCLC.

Methods

By applying texture analysis, we retrospectively analyzed 72 patients with advanced NSCLC. The datasets were randomly split into a training cohort (2/3) and a validation cohort (1/3). Forty radiomic features were extracted by manually drawing tumor volumes of interest (VOIs) on baseline contrast-enhanced CT. After selecting features on the training cohort, two predictive models were created using binary logistic regression, one for PD-L1 values ≥ 50% and the other for values between 1 and 49%. The two models were analyzed with ROC curves and tested in the validation cohort.

Results

The Radiomic Score (Rad-Score) for PD-L1 values ≥ 50%, which consisted of Skewness and Low Gray-Level Zone Emphasis (GLZLM_LGZE), presented a cut-off value of − 0.745 with an area under the curve (AUC) of 0.811 and 0.789 in the training and validation cohort, respectively. The Rad-Score for PD-L1 values between 1 and 49% consisted of Sphericity, Skewness, Conv_Q3 and Gray Level Non-Uniformity (GLZLM_GLNU), showing a cut-off value of 0.111 with AUC of 0.763 and 0.806 in the two population, respectively.

Conclusion

Rad-Scores obtained from CT texture analysis could be useful for predicting PD-L1 expression and guiding the therapeutic choice in patients with advanced NSCLC.

Combining liquid biopsy and radiomics for personalized treatment of lung cancer patients. State of the art and new perspectives

Lung cancer has become a paradigm for precision medicine in oncology, and liquid biopsy (LB) together with radiomics may have a great potential in this scenario. They are both minimally invasive, easy to perform, and can be repeated during patient's follow-up. Also, increasing evidence suggest that LB and radiomics may provide an efficient way to screen and diagnose tumors at an early stage, including the monitoring of any change in the tumor molecular profile. This could allow treatment optimization, improvement of patients' quality of life, and healthcare-related costs reduction. Latest reports on lung cancer patients suggest a combination of these two strategies, along with cutting-edge data analysis, to decode valuable information regarding tumor type, aggressiveness, progression, and response to treatment. The approach seems more compatible with clinical practice than the current standard, and provides new diagnostic companions being able to suggest the best treatment strategy compared to conventional methods. To implement radiomics and liquid biopsy directly into clinical practice, an artificial intelligence (AI)-based system could help to link patients' clinical data together with tumor molecular profiles and imaging characteristics. AI could also solve problems and limitations related to LB and radiomics methodologies. Further work is needed, including new health policies and the access to large amounts of high-quality and well-organized data, allowing a complementary and synergistic combination of LB and imaging, to provide an attractive choice e in the personalized treatment of lung cancer.

Value of contrast-enhanced CT texture analysis in predicting IDH mutation status of intrahepatic cholangiocarcinoma

To explore the value of contrast-enhanced CT texture analysis in predicting isocitrate dehydrogenase (IDH) mutation status of intrahepatic cholangiocarcinomas (ICCs). Institutional review board approved this study. Contrast-enhanced CT images of 138 ICC patients (21 with IDH mutation and 117 without IDH mutation) were retrospectively reviewed. Texture analysis was performed for each lesion and compared between ICCs with and without IDH mutation. All textural features in each phase and combinations of textural features (p < 0.05) by Mann-Whitney U tests were separately used to train multiple support vector machine (SVM) classifiers. The classification generalizability and performance were evaluated using a tenfold cross-validation scheme. Among plain, arterial phase (AP), portal venous phase (VP), equilibrium phase (EP) and Sig classifiers, VP classifier showed the highest accuracy of 0.863 (sensitivity, 0.727; specificity, 0.885), with a mean area under the receiver operating characteristic curve of 0.813 in predicting IDH mutation in validation cohort. Texture features of CT images in portal venous phase could predict IDH mutation status of ICCs with SVM classifier preoperatively.

Radiomics in immuno-oncology

With the ongoing advances in imaging techniques, increasing volumes of anatomical and functional data are being generated as part of the routine clinical workflow. This surge of available imaging data coincides with increasing research in quantitative imaging, particularly in the domain of imaging features. An important and novel approach is radiomics, where high-dimensional image properties are extracted from routine medical images. The fundamental principle of radiomics is the hypothesis that biomedical images contain predictive information, not discernible to the human eye, that can be mined through quantitative image analysis. In this review, a general outline of radiomics and artificial intelligence (AI) will be provided, along with prominent use cases in immunotherapy (e.g. response and adverse event prediction) and targeted therapy (i.e. radiogenomics). While the increased use and development of radiomics and AI in immuno-oncology is highly promising, the technology is still in its early stages, and different challenges still need to be overcome. Nevertheless, novel AI algorithms are being constructed with an ever-increasing scope of applications.

Clinical and radiological predictors of epidermal growth factor receptor mutation in nonsmall cell lung cancer

Purpose

To determine the prognostic factors of epidermal growth factor receptor (EGFR) mutation status in a group of patients with nonsmall cell lung cancer (NSCLC) by analyzing their clinical and radiological features.

Materials and methods

Patients with NSCLC who underwent EGFR mutation detection between 2014 and 2017 were included. Clinical features and general imaging features were collected, and radiomic features were extracted from CT data by 3D Slicer software. Prognostic factors of EGFR mutation status were selected by least absolute shrinkage and selection operator (LASSO) logistic regression analysis, and receiver operating characteristic (ROC) curves were drawn for each prediction model of EGFR mutation.

Results

A total of 118 patients were enrolled in this study. The smoking index (P = 0.028), pleural retraction (P = 0.041), and three radiomic features were significantly associated with EGFR mutation status. The areas under the ROC curve (AUCs) for prediction models of clinical features, general imaging features, and radiomic features were 0.284, 0.703, and 0.815, respectively, and the AUC for the combined prediction model of the three models was 0.894. Finally, a nomogram was established for individualized EGFR mutation prediction.

Conclusions

The combination of radiomic features with clinical features and general imaging features can enable discrimination of EGFR mutation status better than the use of any group of features alone. Our study may help develop a noninvasive biomarker to identify EGFR mutation status by using a combination of the three group features.

CT Radiomics in Predicting EGFR Mutation in Non-small Cell Lung Cancer: A Single Institutional Study

Objective: To evaluate the value of CT radiomics in predicting the epidermal growth factor receptor (EGFR) mutation of patients with non-small cell lung cancer (NSCLC), and combing with the clinical characteristic to construct the prediction model.

Methods: Sixty-seven cases of NSCLC confirmed by pathology were enrolled. The pre-treatment chest CT enhanced images were used in Radiomics analysis. Two experienced radiologists delineated the region of interest (ROI) on open source software 3D-Slicer. The feature of ROI was extracted by Pyradiomics software package and a total of 849 features were extracted. By calculating Pearson correlation coefficient between pair-wise features and LASSO method for feature screening. The prediction model was constructed by logical regression, diagnostic efficacy of the model by the area under the receiver operating characteristic (ROC) curve was calculated.

Results: Based on clinical model and the radiomics model, the AUC under the ROC was 0.8387 and 0.8815, respectively. The model combining clinical and radiomics features perfect best, the AUC under the ROC was 0.9724, the sensitivity and specificity were 85.3 and 90.9%, respectively.

Conclusions: Compared with clinical features or radiomics features alone, the model constructed by combining clinical and pre-treatment chest enhanced CT features may show more utility for improved patient stratification in EGFR mutation and EGFR wild.

Beyond tissue biopsy: a diagnostic framework to address tumor heterogeneity in lung cancer

PURPOSE OF REVIEW

The objective of this review is to discuss the strength and limitations of tissue and liquid biopsy and functional imaging to capture spatial and temporal tumor heterogeneity either alone or as part of a diagnostic framework in non-small cell lung cancer (NSCLC).

RECENT FINDINGS

NSCLC displays genetic and phenotypic heterogeneity - a detailed knowledge of which is crucial to personalize treatment. Tissue biopsy often lacks spatial and temporal resolution. Thus, NSCLC needs to be characterized by complementary diagnostic methods to resolve heterogeneity. Liquid biopsy offers detection of tumor biomarkers and for example, the classification and monitoring of EGFR mutations in NSCLC. It allows repeated sampling, and therefore, appears promising to address temporal aspects of tumor heterogeneity. Functional imaging methods and emerging image analytic tools, such as radiomics capture temporal and spatial heterogeneity. Further standardization of radiomics is required to allow introduction into clinical routine.

SUMMARY

To augment the potential of precision therapy, improved diagnostic characterization of tumors is pivotal. We suggest a comprehensive diagnostic framework combining tissue and liquid biopsy and functional imaging to address the known aspects of spatial and temporal tumor heterogeneity on the example of NSCLC. We envision how this framework might be implemented in clinical practice.

Computed tomography texture analysis of response to second-line nivolumab in metastatic non-small cell lung cancer

Objectives:

Assess computed tomography texture analysis of patients likely to benefit from nivolumab.

Materials & methods:

Texture analysis was used to quantify heterogeneity within the largest tumor before immunotherapy. Histogram analysis was classified as hyperdense (positive skewness) or hypodense (negative skewness) and subclassified on median standard deviation value or entropy measurement.

Results:

47 patients were included. At a median follow-up of 18 months, statistical significant differences in progression-free survival were observed when stratified by positive skewness with low entropy, hazard ratio: 0.43 (0.19–0.95); p = 0.036, and positive skewness with low standard deviation, hazard ratio: 0.42 (0.18–0.96); p = 0.04.

Conclusion:

Patients who derive a clinical benefit to Nivolumab show a computed tomography texture of a hyperdense yet homogenous tumor.

Predicting EGFR mutation subtypes in lung adenocarcinoma using 18F-FDG PET/CT radiomic features

Background

Identification of epidermal growth factor receptor (EGFR) mutation types is crucial before tyrosine kinase inhibitors (TKIs) treatment. Radiomics is a new strategy to noninvasively predict the genetic status of cancer. In this study, we aimed to develop a predictive model based on ¹⁸F-fluorodeoxyglucose positron emission tomography-computed tomography (¹⁸F-FDG PET/CT) radiomic features to identify the specific EGFR mutation subtypes.

Methods

We retrospectively studied ¹⁸F-FDG PET/CT images of 148 patients with isolated lung lesions, which were scanned in two hospitals with different CT scan setting (slice thickness: 3 and 5 mm, respectively). The tumor regions were manually segmented on PET/CT images, and 1,570 radiomic features (1,470 from CT and 100 from PET) were extracted from the tumor regions. Seven hundred and ninety-four radiomic features insensitive to different CT settings were first selected using the Mann white U test, and collinear features were further removed from them by recursively calculating the variation inflation factor. Then, multiple supervised machine learning models were applied to identify prognostic radiomic features through: (I) a multi-variate random forest to select features of high importance in discriminating different EGFR mutation status; (II) a logistic regression model to select features of the highest predictive value of the EGFR subtypes. The EGFR mutation predicting model was constructed from prognostic radiomic features using the popular Xgboost machine-learning algorithm and validated using 3-fold cross-validation. The performance of predicting model was analyzed using the receiver operating characteristic curve (ROC) and measured with the area under the curve (AUC).

Results

Two sets of prognostic radiomic features were found for specific EGFR mutation subtypes: 5 radiomic features for EGFR exon 19 deletions, and 5 radiomic features for EGFR exon 21 L858R missense. The corresponding radiomic predictors achieved the prediction accuracies of 0.77 and 0.92 in terms of AUC, respectively. Combing these two predictors, the overall model for predicting EGFR mutation positivity was also constructed, and the AUC was 0.87.

Conclusions

In our study, we established predictive models based on radiomic analysis of 18F-FDG PET/CT images. And it achieved a satisfying prediction power in the identification of EGFR mutation status as well as the certain EGFR mutation subtypes in lung cancer.

A novel radiomic nomogram for predicting epidermal growth factor receptor mutation in peripheral lung adenocarcinoma

To predict the epidermal growth factor receptor (EGFR) mutation status in patients with lung adenocarcinoma using quantitative radiomic biomarkers and semantic features. We analyzed the computed tomography (CT) images and medical record data of 104 patients with lung adenocarcinoma who underwent surgical excision and EGFR mutation detection from 2016 to 2018 at our center. CT radiomic and semantic features that reflect the tumors' heterogeneity and phenotype were extracted from preoperative non-enhanced CT scans. The least absolute shrinkage and selection operator method was applied to select the most distinguishable features. Three logistic regression models were built to predict the EGFR mutation status by combining the CT semantic with clinicopathological characteristics, using the radiomic features alone, and by combining the radiomic and clinicopathological features. Receiver operating characteristic (ROC) curve analysis was performed using five-fold cross-validation and the mean area under the curve (AUC) values were calculated and compared between the models to obtain the optimal model for predicting EGFR mutation. Furthermore, radiomic nomograms were constructed to demonstrate the performance of the model. In total, 1025 radiomic features were extracted and reduced to 13 features as the most important predictors to build the radiomic signature. The combined radiomic and clinicopathological features model was developed based on the radiomic signature, sex, smoking, vascular infiltration, and pathohistological type. The AUC was 0.90  ±  0.02 for the training, 0.88  ±  0.11 for the verification, and 0.894 for the test dataset. This model was superior to the other prediction models that used the combined CT semantic and clinicopathological features (AUC for the test dataset: 0.768) and radiomic features alone (AUC for the test dataset: 0.837). The prediction model built by radiomic biomarkers and clinicopathological features, including the radiomic signature, sex, smoking, vascular infiltration, and pathological type, outperformed the other two models and could effectively predict the EGFR mutation status in patients with peripheral lung adenocarcinoma. The radiomic nomogram of this model is expected to become an effective biomarker for patients with lung adenocarcinoma requiring adjuvant targeted treatment.

Radiogenomic Models Using Machine Learning Techniques to Predict EGFR Mutations in Non-Small Cell Lung Cancer

BACKGROUND

The purpose of this study was to build radiogenomics models from texture signatures derived from computed tomography (CT) and ¹⁸F-FDG PET-CT (FDG PET-CT) images of non-small cell lung cancer (NSCLC) with and without epidermal growth factor receptor (EGFR) mutations.

METHODS

Fifty patients diagnosed with NSCLC between 2011 and 2015 and with known EGFR mutation status were retrospectively identified. Texture features extracted from pretreatment CT and FDG PET-CT images by manual contouring of the primary tumor were used to develop multivariate logistic regression (LR) models to predict EGFR mutations in exon 19 and exon 20.

RESULTS

An LR model evaluating FDG PET-texture features was able to differentiate EGFR mutant from wild type with an area under the curve (AUC), sensitivity, specificity, and accuracy of 0.87, 0.76, 0.66, and 0.71, respectively. The model derived from CT texture features had an AUC, sensitivity, specificity, and accuracy of 0.83, 0.84, 0.73, and 0.78, respectively. FDG PET-texture features that could discriminate between mutations in EGFR exon 19 and 21 demonstrated AUC, sensitivity, specificity, and accuracy of 0.86, 0.84, 0.73, and 0.78, respectively. Based on CT texture features, the AUC, sensitivity, specificity, and accuracy were 0.75, 0.81, 0.69, and 0.75, respectively.

CONCLUSION

Non-small cell lung cancer texture analysis using FGD-PET and CT images can identify tumors with mutations in EGFR. Imaging signatures could be valuable for pretreatment assessment and prognosis in precision therapy.

Utility of CT radiomics for prediction of PD-L1 expression in advanced lung adenocarcinomas

Background

We aimed to assess if quantitative radiomic features can predict programmed death ligand 1 (PD‐L1) expression in advanced stage lung adenocarcinoma.

Methods

This retrospective study included 153 patients who had advanced stage (>IIIA by TNM classification) lung adenocarcinoma with pretreatment thin section computed tomography (CT) images and PD‐L1 expression test results in their pathology reports. Clinicopathological data were collected from electronic medical records. Visual analysis and radiomic feature extraction of the tumor from pretreatment CT were performed. We constructed two models for multivariate logistic regression analysis (one based on clinical variables, and the other based on a combination of clinical variables and radiomic features), and compared c‐statistics of the receiver operating characteristic curves of each model to identify the model with the higher predictability.

Results

Among 153 patients, 53 patients were classified as PD‐L1 positive and 100 patients as PD‐L1 negative. There was no significant difference in clinical characteristics or imaging findings on visual analysis between the two groups (P > 0.05 for all). Rad‐score by radiomic analysis was higher in the PD‐L1 positive group than in the PD‐L1 negative group with a statistical significance (−0.378 ± 1.537 vs. −1.171 ± 0.822, P = 0.0008). A prediction model that uses clinical variables and CT radiomic features showed higher performance compared to a prediction model that uses clinical variables only (c‐statistic = 0.646 vs. 0.550, P = 0.0299).

Conclusions

Quantitative CT radiomic features can predict PD‐L1 expression in advanced stage lung adenocarcinoma. A prediction model composed of clinical variables and CT radiomic features may facilitate noninvasive assessment of PD‐L1 expression.

Key points

Significant findings of the study

Quantitative CT radiomic features can help predict PD‐L1 expression, whereas none of the qualitative imaging findings is associated with PD‐L1 positivity.

What this study adds

A prediction model composed of clinical variables and CT radiomic features may facilitate noninvasive assessment of PD‐L1 expression.

The Diagnostic Value of Radiomics-Based Machine Learning in Predicting the Grade of Meningiomas Using Conventional Magnetic Resonance Imaging: A Preliminary Study

Objective: The purpose of the current study is to investigate whether texture analysis-based machine learning algorithms could help devise a non-invasive imaging biomarker for accurate classification of meningiomas using machine learning algorithms.

Method: The study cohort was established from the hospital database by reviewing the medical records. Patients were selected if they underwent meningioma resection in the neurosurgery department between January 2015 and December 2018. A total number of 40 texture parameters were extracted from pretreatment postcontrast T1-weighted (T1C) images based on six matrixes. Three feature selection methods were adopted, namely, distance correlation, least absolute shrinkage and selection operator (LASSO), and gradient boosting decision tree (GBDT). Multiclass classification methods of linear discriminant analysis (LDA) and support vector machine (SVM) algorithms were employed to establish the classification models. The diagnostic performances of models were evaluated with confusion matrix based on which the areas under the curve, accuracy, and Kappa value of models were calculated.

Result: Confusion matrix showed that the LDA-based models represented better diagnostic performances than SVM-based models. The highest accuracy among LDA-based models was 75.6%, shown in the combination of Lasso + LDA. The optimal models for SVM-based models was Lasso+SVM, with accuracy of 59.0% in the testing group. One of the SVM-based models, GBDT+SVM, was overfitting, suggesting that this model was not suitable for application.

Conclusion: Machine learning algorithms with texture features extracted from T1C images could potentially serve as the assistant imaging biomarkers for presurgically grading meningiomas.

Value of pre-therapy 18F-FDG PET/CT radiomics in predicting EGFR mutation status in patients with non-small cell lung cancer

PURPOSE

To assess the predictive power of pre-therapy ¹⁸F-FDG PET/CT-based radiomic features for epidermal growth factor receptor (EGFR) mutation status in non-small cell lung cancer.

METHODS

Two hundred and forty-eight lung cancer patients underwent pre-therapy diagnostic ¹⁸F-FDG PET/CT scans and were tested for genetic mutations. The LIFEx package was used to extract 47 PET and 45 CT radiomic features reflecting tumor heterogeneity and phenotype. The least absolute shrinkage and selection operator (LASSO) algorithm was used to select radiomic features and develop a radiomics signature. We compared the predictive performance of models established by radiomics signature, clinical variables, and their combinations using receiver operating curves (ROCs). In addition, a nomogram based on the radiomics signature score (rad-score) and clinical variables was developed.

RESULTS

The patients were divided into a training set (n = 175) and a validation set (n = 73). Ten radiomic features were selected to build the radiomics signature model. The model showed a significant ability to discriminate between EGFR mutation and EGFR wild type, with area under the ROC curve (AUC) equal to 0.79 in the training set, and 0.85 in the validation set, compared with 0.75 and 0.69 for the clinical model. When clinical variables and radiomics signature were combined, the AUC increased to 0.86 (95% CI [0.80-0.91]) in the training set and 0.87 (95% CI [0.79-0.95]) in the validation set, thus showing better performance in the prediction of EGFR mutations.

CONCLUSION

The PET/CT-based radiomic features showed good performance in predicting EGFR mutation in non-small cell lung cancer, providing a useful method for the choice of targeted therapy in a clinical setting.

Usefulness of CT texture analysis in differentiating benign and malignant renal tumours

AIM

To elucidate visually imperceptible differences between benign and malignant renal tumours using computed tomography texture analysis (CTTA) using filtration histogram based parameters.

MATERIALS AND METHODS

A retrospective study was performed by texture analysis of pretreatment contrast-enhanced CT examinations in 354 histopathologically confirmed renal cell carcinomas (RCCs) and 147 benign renal tumours. A region-of-interest was drawn encompassing the largest cross-section of the tumour on venous phase axial CT. CTTA features of entropy, kurtosis, mean positive pixel density, and skewness at different spatial filters were calculated and compared in an attempt to differentiate benign lesions from malignancy.

RESULTS

Entropy with fine spatial filter was significantly higher in RCC than benign renal tumours (p=0.022). Entropy with fine and medium filters was higher in RCC than lipid-poor angiomyolipoma (p=0.050 and 0.052, respectively). Entropy >5.62 had high specificity of 85.7%, but low sensitivity of 31.3%, respectively, for predicting RCC.

CONCLUSIONS

Differences in entropy were helpful in differentiating RCC from lipid-poor angiomyolipoma, and chromophobe RCC from oncocytoma. This technique may be useful to differentiate lesions that appear equivocal on visual assessment or alter management in poor surgical candidates.

CT radiomics and PET radiomics: ready for clinical implementation?

INTRODUCTION

Today, rapid technical and clinical developments result in an increasing number of treatment options for oncological diseases. Thus, decision support systems are needed to offer the right treatment to the right patient. Imaging biomarkers hold great promise in patient-individual treatment guidance. Routinely performed for diagnosis and staging, imaging datasets are expected to hold more information than used in the clinical practice. Radiomics describes the extraction of a large number of meaningful quantitative features from medical images, such as computed tomography (CT) and positron emission tomography (PET). Due to the non-invasive nature and ability to capture 3D image-based heterogeneity, radiomic features are potential surrogate markers of the cancer phenotype. Several radiomic studies are published per day, owing to encouraging results of many radiomics-based patient outcome models. Despite this comparably large number of studies, radiomics is mainly studied in proof of principle concept. Hence, a translation of radiomics from a hot topic research field into an essential clinical decision-making tool is lacking, but of high clinical interest.

EVIDENCE ACQUISITION

Herein, we present a literature review addressing the clinical evidence of CT and PET radiomics. An extensive literature review was conducted in PubMed, including papers on robustness and clinical applications.

EVIDENCE SYNTHESIS

We summarize image-modality related influences on the robustness of radiomic features and provide an overview of clinical evidence reported in the literature. Today, more evidence has been provided for CT imaging, however, PET imaging offers the promise of direct imaging of biological processes and functions. We provide a summary of future research directions, which needs to be addressed in order to successfully introduce radiomics into clinical medicine. In comparison to CT, more focus should be directed towards harmonization of PET acquisition and reconstruction protocols, which is important for transferable modelling.

CONCLUSIONS

Both CT and PET radiomics are promising pre-treatment and intra-treatment biomarkers for outcome prediction. Most studies are performed in retrospective setting, however their validation in prospective data collections is ongoing.

Association of radiomic features with epidermal growth factor receptor mutation status in non-small cell lung cancer and survival treated with tyrosine kinase inhibitors

Since the discovery of the fact that tyrosine kinase inhibitors could improve progression-free survival for patients with advanced non-small cell lung cancer compared with traditional chemotherapy, it has been extremely important to identify epidermal growth factor receptor mutation status in treatment stratification. Although lack of sufficient biopsy samples limit the precise detection of epidermal growth factor receptor mutation status in clinical practice, and it is difficult to identify the sensitive patients who confer favorable response to tyrosine kinase inhibitors. An increasing number of scholars tried to deal with these problems using methods based on the non-invasive imaging including computed tomography and PET to find the association with epidermal growth factor receptor mutation status and survival treated with tyrosine kinase inhibitor in non-small cell lung cancer. Although the conclusions have not reached a consensus, quantitative and high-throughput radiomics have brought us a new direction and might successfully help identify patients undergoing tyrosine kinase inhibitors who could get significant benefits.

CT imaging-based histogram features for prediction of EGFR mutation status of bone metastases in patients with primary lung adenocarcinoma

OBJECTIVE

To identify imaging markers that reflect the epidermal growth factor receptor (EGFR) mutation status by comparing computed tomography (CT) imaging-based histogram features between bone metastases with and without EGFR mutation in patients with primary lung adenocarcinoma.

MATERIALS AND METHODS

This retrospective study included 57 patients, with pathologically confirmed bone metastasis of primary lung adenocarcinoma. EGFR mutation status of bone metastases was confirmed by gene detection. The CT imaging of the metastatic bone lesions which were obtained between June 2014 and December 2017 were collected and analyzed. A total of 42 CT imaging-based histogram features were automatically extracted. Feature selection was conducted using Student's t-test, Mann-Whitney U test, single-factor logistic regression analysis and Spearman correlation analysis. A receiver operating characteristic (ROC) curve was plotted to compare the effectiveness of features in distinguishing between EGFR(+) and EGFR(-) groups. DeLong's test was used to analyze the differences between the area under the curve (AUC) values.

RESULTS

Three histogram features, namely range, skewness, and quantile 0.975 were significantly associated with EGFR mutation status. After combining these three features and combining range and skewness, we obtained the same AUC values, sensitivity and specificity. Meanwhile, the highest AUC value was achieved (AUC 0.783), which also had a higher sensitivity (0.708) and specificity (0.788). The differences between AUC values of the three features and their various combinations were statistically insignificant.

CONCLUSION

CT imaging-based histogram features of bone metastases with and without EGFR mutation in patients with primary lung adenocarcinoma were identified, and they may contribute to diagnosis and prediction of EGFR mutation status.

Radiogenomics: bridging imaging and genomics

From diagnostics to prognosis to response prediction, new applications for radiomics are rapidly being developed. One of the fastest evolving branches involves linking imaging phenotypes to the tumor genetic profile, a field commonly referred to as "radiogenomics." In this review, a general outline of radiogenomic literature concerning prominent mutations across different tumor sites will be provided. The field of radiogenomics originates from image processing techniques developed decades ago; however, many technical and clinical challenges still need to be addressed. Nevertheless, increasingly accurate and robust radiogenomic models are being presented and the future appears to be bright.

Radiology and Enterprise Medical Imaging Extensions (REMIX)

Radiology and Enterprise Medical Imaging Extensions (REMIX) is a platform originally designed to both support the medical imaging-driven clinical and clinical research operational needs of Department of Radiology of The Ohio State University Wexner Medical Center. REMIX accommodates the storage and handling of “big imaging data,” as needed for large multi-disciplinary cancer-focused programs. The evolving REMIX platform contains an array of integrated tools/software packages for the following: (1) server and storage management; (2) image reconstruction; (3) digital pathology; (4) de-identification; (5) business intelligence; (6) texture analysis; and (7) artificial intelligence. These capabilities, along with documentation and guidance, explaining how to interact with a commercial system (e.g., PACS, EHR, commercial database) that currently exists in clinical environments, are to be made freely available.

Texture analysis of cardiovascular magnetic resonance cine images differentiates aetiologies of left ventricular hypertrophy

AIM

To investigate whether unenhanced cardiovascular magnetic resonance (CMR) balanced steady state free precession (bSSFP) cine images could be analysed using textural analysis (TA) software to differentiate different aetiologies of disease causing increased myocardial wall thickness (left ventricular hypertrophy [LVH]) and indicate the severity of myocardial tissue abnormality.

MATERIALS AND METHODS

A mid short axis unenhanced cine frame of 216 patients comprising 50 cases of hypertrophic cardiomyopathy (HCM; predominantly Left ventricular outflow tract obstruction [LVOTO] subtype), 52 cases of cardiac amyloid (CA; predominantly AL: light chain subtype), 68 cases of aortic stenosis (AS), 15 hypertensive patients with LVH (HTN+LVH), and 31 healthy volunteers (HV) underwent TA of the CMR cine images (CMRTA) using TexRAD (TexRAD Ltd, Cambridge, UK). Among the HV, 16/31 were scanned twice to form a test-retest reproducibility cohort. CMRTA comprised a filtration-histogram technique to extract and quantify features using six parameters.

RESULTS

Test-retest analysis in the HV showed a medium filter (3 mm) was the most reproducible (intra-class correlation of 0.9 for kurtosis and skewness and 0.8 for mean and SD). Disease cohorts were statistically different (p<0.001) to HV for all parameters. Pairwise comparisons of CMRTA parameters showed kurtosis and skewness was consistently significant in ranking the degree of difference from HV (greatest to least): CA, HCM, LVH+HTN, AS (p<0.001). Similarly, mean, standard deviation, entropy, and mean positive pixel (MPP) were consistent in ranking degree of difference from HV: HCM, CA, AS and HTN+LVH.

CONCLUSION

Radiomic features of bSSFP CMR data sets derived using TA show promise in discriminating between the aetiologies of LVH.

CT Slice Thickness and Convolution Kernel Affect Performance of a Radiomic Model for Predicting EGFR Status in Non-Small Cell Lung Cancer: A Preliminary Study

We evaluated whether the optimal selection of CT reconstruction settings enables the construction of a radiomics model to predict epidermal growth factor receptor (EGFR) mutation status in primary lung adenocarcinoma (LAC) using standard of care CT images. Fifty-one patients (EGFR:wildtype = 23:28) with LACs of clinical stage I/II/IIIA were included in the analysis. The LACs were segmented in four conditions, two slice thicknesses (Thin: 1 mm; Thick: 5 mm) and two convolution kernels (Sharp: B70f/B70s; Smooth: B30f/B31f/B31s), which constituted four groups: (1) Thin-Sharp, (2) Thin-Smooth, (3) Thick-Sharp, and (4) Thick-Smooth. Machine learning algorithms selected and combined 1,695 quantitative image features to build prediction models. The performance of prediction models was assessed by calculating the area under the curve (AUC). The best prediction model yielded AUC (95%CI) = 0.83 (0.68, 0.92) using the Thin-Smooth reconstruction setting. The AUC of models using thick slices was significantly lower than that of thin slices (P < 10-3), whereas the impact of reconstruction kernel was not significant. Our study showed that the optimal prediction of EGFR mutational status in early stage LACs was achieved by using thin CT-scan slices, independently of convolution kernels. Results from the prediction model suggest that tumor heterogeneity is associated with EGFR mutation.

CT texture analysis of lung adenocarcinoma: can Radiomic features be surrogate biomarkers for EGFR mutation statuses

Objective

To investigate whether radiomic features can be surrogate biomarkers for epidermal growth factor receptor (EGFR) mutation statuses.

Materials and methods

Two hundred ninety six consecutive patients, who underwent CT examinations before operation within 3 months and had EGFR mutations tested, were enrolled in this retrospective study. CT texture features were extracted using an open-source software with whole volume segmentation. The association between CT texture features and EGFR mutation statuses were analyzed.

Results

In the 296 patients, there were 151 patients with EGFR mutations (51%). Logistic analysis identified that lower age (Odds Ratio[OR]: 0.968,95% confidence interval [CI]:0.946~0.990, p = 0.005) and a radiomic feature named GreyLevelNonuniformityNormalized (OR: 0.012, 95% CI:0.000~0.352, p = 0.01) were predictors for exon 19 mutation; higher age (OR: 1.027, 95%CI:1.003~1.052,p = 0.025), female sex (OR: 2.189, 95%CI:1.264~3.791, p = 0.005) and a radiomic feature named Maximum2DDiameterColumn (OR: 0.968, 95%CI:0.946~0.990], p = 0.005) for exon 21 mutation; and female sex (OR: 1.883,95%CI:1.064~3.329, p = 0.030), non-smoking status (OR: 2.070, 95%CI:1.090~3.929, p = 0.026) and a radiomic feature termed SizeZone NonUniformityNormalized (OR: 0.010, 95% CI:0.0001~0.852, p = 0.042) for EGFR mutations. Areas under the curve (AUCs) of combination with clinical and radiomic features to predict exon 19 mutation, exon 21 mutation and EGFR mutations were 0.655, 0.675 and 0.664, respectively.

Conclusion

Several radiomic features are associated with EGFR mutation statuses of lung adenocarcinoma. Combination with clinical files, moderate diagnostic performance can be obtained to predict EGFR mutation status of lung adenocarcinoma. Radiomic features might harbor potential surrogate biomarkers for identification of EGRF mutation statuses.

CT reconstruction algorithms affect histogram and texture analysis: evidence for liver parenchyma, focal solid liver lesions, and renal cysts

PURPOSE

To determine the effects of different reconstruction algorithms on histogram and texture features in different targets.

MATERIALS AND METHODS

Among 3620 patients, 480 had normal liver parenchyma, 494 had focal solid liver lesions (metastases = 259; hepatocellular carcinoma = 99; hemangioma = 78; abscess = 32; and cholangiocarcinoma = 26), and 488 had renal cysts. CT images were reconstructed with filtered back-projection (FBP), hybrid iterative reconstruction (HIR), and iterative model reconstruction (IMR) algorithms. Computerized histogram and texture analyses were performed by extracting 11 features.

RESULTS

Different reconstruction algorithms had distinct, significant effects. IMR had a greater effect than HIR. For instance, IMR had a significant effect on five features of liver parenchyma, nine features of focal liver lesions, and four features of renal cysts on portal-phase scans and four, eight, and four features, respectively, on precontrast scans (p < 0.05). Meanwhile, different algorithms had a greater effect on focal liver lesions (six in HIR and nine in IMR on portal-phase, three in HIR, and eight in IMR on precontrast scans) than on liver parenchyma or cysts. The mean attenuation and standard deviation were not affected by the reconstruction algorithm (p > .05). Most parameters showed good or excellent intra- and interobserver agreement, with intraclass correlation coefficients ranging from 0.634 to 0.972.

CONCLUSIONS

Different reconstruction algorithms affect histogram and texture features. Reconstruction algorithms showed stronger effects in focal liver lesions than in liver parenchyma or renal cysts.

KEY POINTS

• Imaging heterogeneities influenced the quantification of image features. • Different reconstruction algorithms had a significant effect on histogram and texture features. • Solid liver lesions were more affected than liver parenchyma or cysts.

Virtual monoenergetic imaging in rapid kVp-switching dual-energy CT (DECT) of the abdomen: impact on CT texture analysis

PURPOSE

To study the impact of keV levels of virtual monoenergetic images generated from rapid kVp-switching dual-energy CT (rsDECT) on CT texture analysis (CTTA).

METHODS

This study included 30 consecutive patients (59.3 ± 12 years; range 34-77 years; 17M:13F) who underwent portal venous phase abdominal CT on a rsDECT scanner. Axial 5-mm monoenergetic images at 5 energy levels (40/50/60/70/80 keV) were created and CTTA of liver was performed. CTTA comprised a filtration-histogram technique with different spatial scale filter (SSF) values (0-6). CTTA quantification at each SSF value included histogram-based statistical parameters such as mean intensity, standard deviation (SD), entropy, mean of positive pixels (MPP), skewness, and kurtosis. The values were compared using repeated measures ANOVA.

RESULTS

Among the different CTTA metrics, mean intensity (at SSF > 0), skewness, and kurtosis did not show variability whereas entropy, MPP, and SD varied with different keV levels. There was no change in skewness and kurtosis values for all 6 filters (p > 0.05). Mean intensity showed no change for filters 2-6 (p > 0.05). Mean intensity at SSF = 0 i.e., mean attenuations were 91.2 ± 2.9, 108.7 ± 3.6, 136.1 ± 4.7, 179.8 ± 6.9, and 250.5 ± 10.1 HU for 80, 70, 60, 50, and 40 keV images, respectively demonstrating significant variability (decrease) with increasing keV levels (p < 0.001). Entropy, MPP, and SD values showed a statistically significant decrease with increasing keV of monoenergetic images on all 6 filters (p < 0.001).

CONCLUSION

The energy levels of monoenergetic images have variable impact on the different CTTA parameters, with no significant change in skewness, kurtosis, and filtered mean intensity whereas significant decrease in mean attenuation, entropy, MPP, and SD values with increasing energy levels.

CT textural analysis of gastric cancer: correlations with immunohistochemical biomarkers

To investigate the ability of CT texture analysis to assess and predict the expression statuses of E-cadherin, Ki67, VEGFR2 and EGFR in gastric cancers, the enhanced CT images of 139 patients with gastric cancer were retrospectively reviewed. The region of interest was manually drawn along the margin of the lesion on the largest slice in the arterial and venous phases, which yielded a series of texture parameters. Our results showed that the standard deviation, width, entropy, entropy (H), correlation and contrast from the arterial and venous phases were significantly correlated with the E-cadherin expression level in gastric cancers (all P < 0.05). The skewness from the arterial phase and the mean and autocorrelation from the venous phase were negatively correlated with the Ki67 expression level in gastric cancers (all P < 0.05). The width, entropy and contrast from the venous phase were positively correlated with the VEGFR2 expression level in gastric cancers (all P < 0.05). No significant correlation was found between the texture features and EGFR expression level. CT texture analysis, which had areas under the receiver operating characteristic curve (AUCs) ranging from 0.612 to 0.715, holds promise in predicting E-cadherin, Ki67 and VEGFR2 expression levels in gastric cancers.

CT texture analysis can be a potential tool to differentiate gastrointestinal stromal tumors without KIT exon 11 mutation

OBJECTIVE

To evaluate CT texture analysis as a tool to differentiate gastrointestinal stromal tumors (GISTs) without KIT exon 11 mutation.

MATERIALS AND METHODS

This study consisted of a study group of 69 GISTs and a validation group of 17 GISTs. Clinical information of the patients were collected and analyzed. Two-dimensional and three-dimensional texture analysis was performed. The textural parameters were evaluated in the study group and were validated in the validation group. The repeatability of the textural parameters on the single region of interest (single-ROI), double-ROI, and whole volume of interest (whole-VOI) was analyzed. The independent predictor for the GIST genotypes was analyzed with logistic regression models. The support vector machine (SVM) classifiers were also trained and 6-fold cross validation ROC curves were computed. Subjective heterogeneity scores of each lesion on enhanced CT images were given by radiologists and the corresponding difference of the heterogeneity rating was evaluated.

RESULTS

The non-gastric location, lower CD34_stain level and higher textural parameter standard Deviation (stdDeviation) were associated with the GISTs without KIT exon 11 mutation in the study group. The cross validation SVM classifiers achieved with combination of stdDeviation, anatomic location and CD34_stain level demonstrated medium to good prediction efficiency (AUC = 0.864-0.904) regarding the GIST genotypes. The stdDeviation was an independent predictor of GISTs without KIT exon 11 mutation, and had a medium correlation with the GIST genotypes in the study group (AUC = 0.726-0.750). The stdDeviation showed good performance (AUC = 0.904-0.962) when validated in the validation group. The double-ROIs improved the performances of single-ROIs, decreasing the variances of single-ROIs brought by section-selection, and demonstrating excellent agreements between ROIs and whole-VOI. Subjective heterogeneity scores had no statistically significant differences between GIST genotypes.

CONCLUSION

CT texture analysis can potentially help to differentiate GISTs without KIT exon 11 mutation from those GISTs with KIT exon 11 mutation on enhanced CT images.

Computed tomography characteristics of lung adenocarcinomas with epidermal growth factor receptor mutation: A propensity score matching study

OBJECTIVES

We investigated the relationship between computed tomography (CT) characteristics and epidermal growth factor receptor (EGFR) mutations in a large Asian cohort who received surgical resection of invasive lung adenocarcinoma.

MATERIALS AND METHODS

We retrospectively included 864 patients (524 with EGFR mutation and 340 with EGFR wild-type) who received surgical resections for invasive lung adenocarcinomas. After applying propensity score matching, 312 patients with mutated EGFR were matched with 312 patients with wild-type EGFR. CT characteristics, predominant histologic subtype, and CT measurement parameters (volume and estimated diameter of the total tumor and inner solid portion and ground-glass opacity [GGO] proportion) were compared within matched pairs.

RESULTS

Tumors in the EGFR mutation group showed higher proportions of pure ground-glass nodules (4.1% vs 1.3%), GGO-predominant (23.7% vs 14.7%), and solid-predominant part-solid nodules (37.2% vs 31.7%) CT characteristics, whereas EGFR wild-type tumors predominantly presented as pure solid nodules (34.6% vs 52.2%, P < 0.0001). EGFR mutation tumors more frequently had a lepidic-predominant subtype than did EGFR wild-type tumors (20.2% and 11.9%; P < 0.0001), and showed a smaller whole tumor size and solid portion (P < 0.0001) with a higher GGO proportion (P < 0.0001). Tumors with exon 21 missense mutations showed the highest GGO proportion and the smallest inner solid portion size, followed by tumors harboring an exon 19 deletion, compared with EGFR wild-type tumors (posthoc P < 0.01).

CONCLUSION

Adenocarcinomas with EGFR mutations had a higher GGO proportion than those with wild-type EGFR after matching of clinical variables. Lesions with an exon 21 mutation had a higher GGO proportion than lesions with other mutations.

Deciphering Clinicoradiologic Phenotype for Thymidylate Synthase Expression Status in Patients with Advanced Lung Adenocarcinoma Using a Radiomics Approach

We aimed to identify predictive clinicoradiologic characteristics of thymidylate synthase (TS) expression status in advanced non-squamous non-small cell lung cancer patients. We reviewed clinicoradiologic features of 169 patients stratified into TS-negative (n = 84) and TS-positive (n = 85) groups, including quantitative CT radiomic features of both primary lung and metastatic lesions from initial CT and PET. Clinical factors including age and smoking history were significantly associated with TS as well as radiomic features. The predictive performance for dichotomizing TS expression status was slightly higher when imaging features of primary lung lesions were added compared to the model based solely on the clinical features, but without statistical significance (10-fold cross-validated AUC = 0.619 and 0.581, respectively; P = 0.425). The predictive performance of clinicoradiologic parameters slightly increased with primary lung lesions only compared to the inclusion of metastatic lesions, but without statistical significance (10-fold cross-validated AUC = 0.619 and 0.554, respectively; P = 0.203). Overall survival was prolonged in the TS-negative group compared to the TS-positive group (P = 0.001). TS-negativity is a potential prognostic biomarker, and our study presents that although CT radiomic features have potential for predicting TS expression status, clinical significance is uncertain. The addition of radiomic features to clinical factors did not show significant improvement in predicting TS-negativity.

A new predictive scoring system based on clinical data and computed tomography features for diagnosing EGFR-mutated lung adenocarcinoma

Background

We aimed to develop a new EGFR mutation-predictive scoring system to use in screening for EGFR-mutated lung adenocarcinomas (lacs).

Methods

The study enrolled 279 patients with lac, including 121 patients with EGFR wild-type tumours and 158 with EGFR-mutated tumours. The Student t-test, chi-square test, or Fisher exact test was applied to discriminate clinical and computed tomography (ct) features between the two groups. Using a principal component analysis (pca) model, we derived predictive coefficients for the presence of EGFR mutation in lac.

Results

The EGFR mutation-predictive score includes sex, smoking history, homogeneity, ground-glass opacity (ggo) on imaging, and the presence of pericardial effusion. The pca predictive model took this form: [Formula: see text]Model scores ranged from 79 to 147. The area under the receiver operating characteristic curve was 0.752 [95% confidence interval (ci): 0.697 to 0.801] in the lac population at the optimal cut-off value of 109, and the sensitivity and specificity were 68.4% (95% ci: 60.5% to 75.5%) and 74.4% (95% ci: 65.6% to 81.9%) respectively.

Conclusions

The EGFR mutation risk scoring system based on clinical data and ct features is noninvasive and user-friendly. The model appears to frame a positive predictive value and was able to determine the value of repeating a biopsy if tissue is limited.

Non-invasive tumor genotyping using radiogenomic biomarkers, a systematic review and oncology-wide pathway analysis

With targeted treatments playing an increasing role in oncology, the need arises for fast non-invasive genotyping in clinical practice. Radiogenomics is a rapidly evolving field of research aimed at identifying imaging biomarkers useful for non-invasive genotyping. Radiogenomic genotyping has the advantage that it can capture tumor heterogeneity, can be performed repeatedly for treatment monitoring, and can be performed in malignancies for which biopsy is not available. In this systematic review of 187 included articles, we compiled a database of radiogenomic associations and unraveled networks of imaging groups and gene pathways oncology-wide. Results indicated that ill-defined tumor margins and tumor heterogeneity can potentially be used as imaging biomarkers for 1p/19q codeletion in glioma, relevant for prognosis and disease profiling. In non-small cell lung cancer, FDG-PET uptake and CT-ground-glass-opacity features were associated with treatment-informing traits including EGFR-mutations and ALK-rearrangements. Oncology-wide gene pathway analysis revealed an association between contrast enhancement (imaging) and the targetable VEGF-signalling pathway. Although the need of independent validation remains a concern, radiogenomic biomarkers showed potential for prognosis prediction and targeted treatment selection. Quantitative imaging enhanced the potential of multiparametric radiogenomic models. A wealth of data has been compiled for guiding future research towards robust non-invasive genomic profiling.

Imaging genotyping of functional signaling pathways in lung squamous cell carcinoma using a radiomics approach

Imaging features can be useful for identifying distinct genomic differences and have predictive power for certain phenotypes attributed to genomic mutations. We aimed to identify predictive imaging biomarkers that underpin genomic alterations and clinical outcomes in lung squamous cell carcinoma (SQCC) using a radiomics approach. In 57 patients with lung SQCC who underwent preoperative computed tomography (CT) and whole-exome DNA sequencing, 63 quantitative imaging features were extracted from CT and 73 clinicoradiological features including imaging features were classified into 8 categories: clinical, global, histogram-based, lung cancer-specific, shape, local, regional, and emphysema. Mutational profiles for core signaling pathways of lung SQCC were classified into five categories: redox stress, apoptosis, proliferation, differentiation, and chromatin remodelers. Range and right lung volume was significantly associated with alternation of apoptosis and proliferation pathway (p = 0.03, and p = 0.03). Energy was associated with the redox stress pathway (p = 0.06). None of the clinicoradiological features showed any significant association with the alteration of differentiation and chromatin remodelers pathway. This study showed that radiomic features indicating five different functional pathways of lung SQCC were different form one another. Radiomics approaches to lung SQCC have the potential to noninvasively predict alterations in core signaling pathways and clinical outcome.

CT Texture Analysis: Definitions, Applications, Biologic Correlates, and Challenges

This review discusses potential oncologic and nononcologic applications of CT texture analysis ( CTTA CT texture analysis ), an emerging area of "radiomics" that extracts, analyzes, and interprets quantitative imaging features. CTTA CT texture analysis allows objective assessment of lesion and organ heterogeneity beyond what is possible with subjective visual interpretation and may reflect information about the tissue microenvironment. CTTA CT texture analysis has shown promise in lesion characterization, such as differentiating benign from malignant or more biologically aggressive lesions. Pretreatment CT texture features are associated with histopathologic correlates such as tumor grade, tumor cellular processes such as hypoxia or angiogenesis, and genetic features such as KRAS or epidermal growth factor receptor (EGFR) mutation status. In addition, and likely as a result, these CT texture features have been linked to prognosis and clinical outcomes in some tumor types. CTTA CT texture analysis has also been used to assess response to therapy, with decreases in tumor heterogeneity generally associated with pathologic response and improved outcomes. A variety of nononcologic applications of CTTA CT texture analysis are emerging, particularly quantifying fibrosis in the liver and lung. Although CTTA CT texture analysis seems to be a promising imaging biomarker, there is marked variability in methods, parameters reported, and strength of associations with biologic correlates. Before CTTA CT texture analysis can be considered for widespread clinical implementation, standardization of tumor segmentation and measurement techniques, image filtration and postprocessing techniques, and methods for mathematically handling multiple tumors and time points is needed, in addition to identification of key texture parameters among hundreds of potential candidates, continued investigation and external validation of histopathologic correlates, and structured reporting of findings. ^©RSNA, 2017.

Quantitative Biomarkers for Prediction of Epidermal Growth Factor Receptor Mutation in Non-Small Cell Lung Cancer

OBJECTIVES

To predict epidermal growth factor receptor (EGFR) mutation status using quantitative radiomic biomarkers and representative clinical variables.

METHODS

The study included 180 patients diagnosed as of non-small cell lung cancer (NSCLC) with their pre-therapy computed tomography (CT) scans. Using a radiomic method, 485 features that reflect the heterogeneity and phenotype of tumors were extracted. Afterwards, these radiomic features were used for predicting epidermal growth factor receptor (EGFR) mutation status by a least absolute shrinkage and selection operator (LASSO) based on multivariable logistic regression. As a result, we found that radiomic features have prognostic ability in EGFR mutation status prediction. In addition, we used radiomic nomogram and calibration curve to test the performance of the model.

RESULTS

Multivariate analysis revealed that the radiomic features had the potential to build a prediction model for EGFR mutation. The area under the receiver operating characteristic curve (AUC) for the training cohort was 0.8618, and the AUC for the validation cohort was 0.8725, which were superior to prediction model that used clinical variables alone.

CONCLUSION

Radiomic features are better predictors of EGFR mutation status than conventional semantic CT image features or clinical variables to help doctors to decide who need EGFR tyrosine kinase inhibitor (TKI) treatment.

Small-scale noise-like moiré pattern caused by detector sensitivity inhomogeneity in computed tomography

We report a new type of moiré pattern caused by inhomogeneous detector sensitivity in computed tomography. Defects in one or a few detector bins or miscalibrated detectors induce well-known ring artifacts. When detector sensitivity is not homogenous over all detector bins, these ring artifacts occur everywhere as distributed rings in reconstructed images and may cause a moiré pattern when combined with insufficient view sampling, which induces a noise-like pattern or a subtle texture in the reconstructed images. Complete correction of the inhomogeneity in detectors can remove the pattern and improve image quality. This paper describes several properties of moiré patterns caused by detector sensitivity inhomogeneity.