CT texture analysis of histologically proven benign and malignant lung lesions

Texture analysis based on CT for predicting the differentiation of esophageal squamous cancer: An observational study

To explore the feasibility and application value of texture analysis based on computed tomography (CT) for predicting the differentiation of esophageal squamous cell carcinoma (ESCC). Patients diagnosed with ESCC who underwent chest contrast-enhanced CT before treatment were selected. Based on the pathological results, the patients were stratified into poorly differentiated and moderately well-differentiated groups. FireVoxel software was used to analyze the region of interest based on venous phase CT images. Texture parameters including the mean, median, standard deviation (SD), inhomogeneity, skewness, kurtosis, and entropy were obtained automatically. Differences in the texture parameters and their relationship with the degree of differentiation between the 2 groups were analyzed. The value of CT texture parameters in identifying poor differentiation and moderate-well differentiation of esophageal cancer was analyzed using the ROC curve. A total of 48 patients with ESCC were included, including 24 patients in the poorly differentiated group and 24 patients in the moderate-well-differentiated group. There were negative correlations between SD, inhomogeneity, entropy, and the degree of differentiation of esophageal cancer (P < .05). The correlation of inhomogeneity was the highest (r = -0.505, P < .001). SD, inhomogeneity, and entropy could effectively distinguish between the poorly and moderately well-differentiated groups, with statistically significant differences between the 2 groups (P < .05). The best critical values for SD, inhomogeneity, and entropy were 17.538, 0.017, and 3.917, respectively. The areas under the ROC curve were 0.793, 0.792, and 0.729, respectively, with the SD and inhomogeneity being the best. The application of texture analysis on venous phase CT images holds promise as a method for forecasting the degree of differentiation in esophageal cancers, which could significantly contribute to the preoperative noninvasive evaluation of tumor differentiation.

Correlation of Radiomics with Treatment Response in Liver Metastases

RATIONALE AND OBJECTIVES

To assess differences in radiomics derived from semi-automatic segmentation of liver metastases for stable disease (SD), partial response (PR), and progressive disease (PD) based on RECIST1.1 and to assess if radiomics alone at baseline can predict response.

MATERIALS AND METHODS

Our IRB-approved study included 203 women (mean age 54 ± 11 years) with metastatic liver disease from breast cancer. All patients underwent contrast abdomen-pelvis CT in the portal venous phase at two points: baseline (pre-treatment) and follow-up (between 3 and 12 months following treatment). Patients were subcategorized into three subgroups based on RECIST 1.1 criteria (Response Evaluation Criteria in Solid Tumors version 1.1): 66 with SD, 69 with PR, and 68 with PD on follow-up CT. The deidentified baseline and follow-up CT images were exported to the radiomics prototype. The prototype enabled semi-automatic segmentation of the target liver lesions for the extraction of first and high order radiomics. Statistical analyses with logistic regression and random forest classifiers were performed to differentiate SD from PD and PR.

RESULTS

There was no significant difference between the radiomics on the baseline and follow-up CT images of patients with SD (area under the curve (AUC): 0.3). Random forest classifier differentiated patients with PR with an AUC of 0.845. The most relevant feature was the large dependence emphasis's high and low pass wavelet filter (derived gray level dependence matrix features). Random forest classifier differentiated PD with an AUC of 0.731, with the most relevant feature being the surface-to-volume ratio. There was no difference in radiomics among the three groups at baseline; therefore, a response could not be predicted.

CONCLUSION

Radiomics of liver metastases with semi-automatic segmentation demonstrate differences between SD from PR and PD.

SUMMARY STATEMENT

Semiautomatic segmentation and radiomics of metastatic liver disease demonstrate differences in SD from the PR and progressive metastatic on the baseline and follow-up CT. Despite substantial variations in the scanners, acquisition, and reconstruction parameters, radiomics had an AUC of 0.84-0.89 for differentiating stable hepatic metastases from decreasing and increasing metastatic disease.

An 18F-FDG-PET/CT-based radiomics signature for estimating malignance probability of solitary pulmonary nodule

BACKGROUND

Some solitary pulmonary nodules (SPNs) as early manifestations of lung cancer, it is difficult to determine its nature, which brings great trouble to clinical diagnosis and treatment. Radiomics can deeply explore the essence of images and provide clinical decision support for clinicians. The purpose of our study was to explore the effect of positron emission tomography (PET) with 2-deoxy-2-[fluorine-18] fluoro-d-glucose integrated with computed tomography (CT; 18F-FDG-PET/CT) combined with radiomics for predicting probability of malignancy of SPNs.

METHODS

We retrospectively enrolled 190 patients with SPNs confirmed by pathology from January 2013 to December 2019 in our hospital. SPNs were benign in 69 patients and malignant in 121 patients. Patients were randomly divided into a training or testing group at a ratio of 7:3. Three-dimensional regions of interest (ROIs) were manually outlined on PET and CT images, and radiomics features were extracted. Synthetic minority oversampling technique (SMOTE) method was used to balance benign and malignant samples to a ratio of 1:1. In the training group, least absolute shrinkage and selection operator (LASSO) regression analyses and Spearman correlation analyses were used to select the strongest radiomics features. Three models including PET model, CT model, and joint model were constructed using multivariate logistic regression analysis. Receiver operating characteristic (ROC) curves, calibration curves, and decision curves were plotted to evaluate diagnostic efficiency, calibration degree, and clinical usefulness of all models in training and testing groups.

RESULTS

The estimative effectiveness of the joint model was superior to the CT or PET model alone in the training and testing groups. For the joint model, CT model, and PET model, area under the ROC curve was 0.929, 0.819, 0.833 in the training group, and 0.844, 0.759, 0.748 in the testing group, respectively. Calibration and decision curves showed good fit and clinical usefulness for the joint model in both training and testing groups.

CONCLUSION

Radiomics models constructed by combining PET and CT radiomics features are valuable for distinguishing benign and malignant SPNs. The combined effect is superior to qualitative diagnoses with CT or PET radiomics models alone.

An Effective Malignancy Prediction Model for Incidentally Detected Pulmonary Subsolid Nodules Based on Current and Prior CT Scans

INTRODUCTION

It is challenging to diagnose and manage incidentally detected pulmonary subsolid nodules due to their indolent nature and heterogeneity. The objective of this study is to construct a decision tree-based model to predict malignancy of a subsolid nodule based on radiomics features and evolution over time.

MATERIALS AND METHODS

We derived a training set (2947 subsolid nodules), a test set (280 subsolid nodules) from a cohort of outpatient CT scans, and a second test set (5171 subsolid nodules) from the National Lung Cancer Screening Trial (NLST). A Computer-Aided Diagnosis system (CADs) automatically extracted 28 preselected radiomics features, and we calculated the feature change rates as the change of the quantitative measure per time unit between the prior and current CT scans. We built classification models based on XGBoost and employed 5-fold cross validation to optimize the parameters.

RESULTS

The model that combined radiomics features with their change rates performed the best. The Areas Under Curve (AUCs) on the outpatient test set and on the NLST test set were 0.977 (95% CI, 0.958-0.996) and 0.955 (95% CI, 0.930-0.980), respectively. The model performed consistently well on subgroups stratified by nodule diameters, solid components, and CT scan intervals.

CONCLUSION

This decision tree-based model trained with the outpatient dataset gives promising predictive performance on the malignancy of pulmonary subsolid nodules. Additionally, it can assist clinicians to deliver more accurate diagnoses and formulate more in-depth follow-up strategies.

Surgical Resection of Benign Nodules in Lung Cancer Screening: Incidence and Features

Introduction

Interventions and surgical procedures are common for nonmalignant lung lesions detected on lung cancer screening (LCS). Inadvertent surgical resection of benign nodules with a clinical suspicion of lung cancer can occur, can be associated with complications, and adds to the cost of screening. The objective of this study is to assess the characteristics of surgically resected benign nodules detected on LCS computed tomography which were presumed to be lung cancers.

Methods

This retrospective study included 4798 patients who underwent LCS between June 2014 and January 2021. The benign lung nodules, surgically resected with a presumed cancer diagnosis, were identified from the LCS registry. Patient demographics, imaging characteristics, and pathologic diagnoses of benign nodules were analyzed.

Results

Of the 4798 patients who underwent LCS, 148 (3.1%) underwent surgical resection of a lung nodule, and of those who had a resection, 19 of 148 (12.8%) had a benign diagnosis (median age = 64 y, range: 56-77 y; F = 12 of 19, 63.2%; M = seven of 19, 36.8%). The median nodule size was 10 mm (range: 6-31 mm). Most nodules were solid (15 of 19, 78.9%), located in the upper lobes (11 of 19; 57.9%), and were peripheral (17 of 19, 89.5%). Most nodules (13 of 17; 76.5%) had interval growth, and four of 17 (23.5%) had increased fluorodeoxyglucose uptake. Of the 19 patients, 17 (89.5%) underwent sublobar resection (16 wedge resection and one segmentectomy), whereas two central nodules (10.5%) had lobectomies. Pathologies identified included focal areas of fibrosis or scarring (n = 8), necrotizing granulomatous inflammation (n = 3), other nonspecific inflammatory focus (n = 3), benign tumors (n = 3), reactive lymphoid hyperplasia (n = 1), and organizing pneumonia (n = 1).

Conclusions

Surgical resections of benign nodules that were presumed malignant are infrequent and may be unavoidable given overlapping imaging features of benign and malignant nodules. Knowledge of benign pathologies that can mimic malignancy may help reduce the incidence of unnecessary surgeries.

A novel nomogram model combining CT texture features and urine energy metabolism to differentiate single benign from malignant pulmonary nodule

Objective

To investigate a novel diagnostic model for benign and malignant pulmonary nodule diagnosis based on radiomic and clinical features, including urine energy metabolism index.

Methods

A total of 107 pulmonary nodules were prospectively recruited and pathologically confirmed as malignant in 86 cases and benign in 21 cases. A chest CT scan and urine energy metabolism test were performed in all cases. A nomogram model was established in combination with radiomic and clinical features, including urine energy metabolism levels. The nomogram model was compared with the radiomic model and the clinical feature model alone to test its diagnostic validity, and receiver operating characteristic (ROC) curves were plotted to assess diagnostic validity.

Results

The nomogram was established using a logistic regression algorithm to combine radiomic features and clinical characteristics including urine energy metabolism results. The predictive performance of the nomogram was evaluated using the area under the ROC and calibration curve, which showed the best performance, area under the curve (AUC) = 0.982, 95% CI = 0.940-1.000, compared to clinical and radiomic models in the testing cohort. The clinical benefit of the model was assessed using the decision curve analysis (DCA) and using the nomogram for benign and malignant pulmonary nodules, and preoperative prediction of benign and malignant pulmonary nodules using nomograms showed better clinical benefit.

Conclusion

This study shows that a coupled model combining CT imaging features and clinical features (including urine energy metabolism) in combination with the nomogram model has higher diagnostic performance than the radiomic and clinical models alone, suggesting that the combination of both methods is more advantageous in identifying benign and malignant pulmonary nodules.

Computed Tomography Image Texture under Feature Extraction Algorithm in the Diagnosis of Effect of Specific Nursing Intervention on Mycoplasma Pneumonia in Children

To evaluate the effect of specific nursing intervention in children with mycoplasma pneumonia (MP), a feature extraction algorithm based on gray level co-occurrence matrix (GLCM) was proposed and combined with computed tomography (CT) image texture features. Then, 98 children with MP were rolled into the observation group with 49 cases (specific nursing) and the control group with 49 cases (routine nursing). CT images based on feature extraction algorithm of optimized GLCM were used to examine the children before and after nursing intervention, and the recovery of the two groups of children was discussed. The results showed that the proportion of lung texture increase, rope shadow, ground glass shadow, atelectasis, and pleural effusion in the observation group (24.11%, 3.86%, 8.53%, 15.03%, and 3.74%) was significantly lower than that in the control group (28.53%, 10.23%, 13.34%, 21.15%, and 8.13%) after nursing (P < 0.05). There were no significant differences in the proportion of small patchy shadows, large patchy consolidation shadows, and bronchiectasis between the observation group and the control group (P > 0.05). In the course of nursing intervention, in the observation group, the disappearance time of cough, normal temperature, disappearance time of lung rales, and absorption time of lung shadow (2.15 ± 0.86 days, 4.81 ± 1.14 days, 3.64 ± 0.55 days, and 5.96 ± 0.62 days) were significantly shorter than those in the control group (2.87 ± 0.95 days, 3.95 ± 1.06 days, 4.51 ± 1.02 days, and 8.14 ± 1.35 days) (P < 0.05). After nursing intervention, the proportion of satisfaction and total satisfaction in the experimental group (67.08% and 28.66%) was significantly higher than that in the control group (40.21% and 47.39%), while the proportion of dissatisfaction (4.26%) was significantly lower than that in the control group (12.4%) (P < 0.05). To sum up, specific nursing intervention was more beneficial to improve the progress of characterization recovery and the overall recovery effect of children with MP relative to conventional nursing. CT image based on feature extraction algorithm of optimized GLCM was of good adoption value in the diagnosis and treatment of MP in children.

Texture Analysis of Computed Tomography Images in the Lung of Patients With Breast Cancer

OBJECTIVE

The aim of this study was to investigate whether the texture features of lung computed tomography images were altered by primary breast cancer without pulmonary metastasis.

METHODS

Texture analysis was performed on the regions of interest of lung computed tomography images from 36 patients with breast cancer and 36 healthy controls. Texture parameters between subjects with different clinical stages and hormone receptor (HR) statuses in patients with breast cancer were analyzed.

RESULTS

Three texture parameters (mean, SD, and variance) were significantly different between patients with breast cancer and healthy controls and between preoperative and postoperative stages in patients with breast cancer. All 3 parameters showed an increasing trend under the tumor-bearing state. These parameters were significantly higher in the stage III + IV group than in the stage I + II group. The variance parameter was significantly higher in the HR-negative group than in the HR-positive group.

CONCLUSIONS

Texture analysis may serve as a novel additional tool for discovering conventionally invisible changes in the lung tissue of patients with breast cancer.

Radiomics and functional imaging in lung cancer: the importance of radiological heterogeneity beyond FDG PET/CT and lung biopsy

PURPOSE

[¹⁸F]-Fluorodeoxyglucose Positron Emission Tomography-Computed Tomography (FDG PET/CT) has a central role in the lung nodules' characterization even if, with SUV < 2.5, percutaneous CT-guided Lung Biopsy (CTLB) is needed to assess nodule nature. In that scenario, CT Texture Analysis (CTTA) could be a non-invasive imaging biomarker. Our purpose is to test CTTA ability in differentiating malignant from benign nodules.

METHOD

Patients that underwent FDG PET/CT followed by CTLB between January 2013 and December 2018 were retrospectively enrolled. Were included patients with lung nodule SUV < 2.5 and histological diagnosis.

EXCLUSION CRITERIA

nodules SUV > 2.5, patients who refused CTLB or received oncological treatment before CTLB, indeterminate pathology report, CT motion artifacts. Two radiologists in consensus performed CTTA, drawing a volumetric Region of Interest of nodule with a dedicated first order TA software with and without spatial scaling filters, on preliminary CT performed for CTLB. Statistics included a comparison between malignant and benign neoplasms distribution (2-tailed T-test or Mann-Whitney test according to normal/non-normal data distribution), P-values < 0.05 were considered statistically significant. CTTA accuracy was tested with Receiver Operating Characteristics (ROC) curve.

RESULTS

Form an initial population of 1178, 46 patients encountered inclusion criteria. Pathologist reported 27/46 (59%) malignant and 19/46 (41%) benign nodules. In malignant lesions CTTA showed lower Kurtosis' and higher Skewness' values (all P ≤ 0.0013 and all filtered TA P < 0.024, respectively). ROC curve showed significant Area Under the Curve for Kurtosis and Skewness (0.654 and 0.642, P < 0.001) at medium filtration.

CONCLUSIONS

CTTA is a promising radiological tool to characterize benign and malignant lung nodules, even in those cases without an altered glucose metabolism.

Texture analysis of iodine maps and conventional images for k-nearest neighbor classification of benign and metastatic lung nodules

BACKGROUND

The purpose of this study was to analyze if the use of texture analysis on spectral detector CT (SDCT)-derived iodine maps (IM) in addition to conventional images (CI) improves lung nodule differentiation, when being applied to a k-nearest neighbor (KNN) classifier.

METHODS

183 cancer patients who underwent contrast-enhanced, venous phase SDCT of the chest were included: 85 patients with 146 benign lung nodules (BLN) confirmed by either prior/follow-up CT or histopathology and 98 patients with 425 lung metastases (LM) verified by histopathology, 18F-FDG-PET-CT or unequivocal change during treatment. Semi-automatic 3D segmentation of BLN/LM was performed, and volumetric HU attenuation and iodine concentration were acquired. For conventional images and iodine maps, average, standard deviation, entropy, kurtosis, mean of the positive pixels (MPP), skewness, uniformity and uniformity of the positive pixels (UPP) within the volumes of interests were calculated. All acquired parameters were transferred to a KNN classifier.

RESULTS

Differentiation between BLN and LM was most accurate, when using all CI-derived features combined with the most significant IM-derived feature, entropy (Accuracy:0.87; F1/Dice:0.92). However, differentiation accuracy based on the 4 most powerful CI-derived features performed only slightly inferior (Accuracy:0.84; F1/Dice:0.89, p=0.125). Mono-parametric lung nodule differentiation based on either feature alone (i.e. attenuation or iodine concentration) was poor (AUC=0.65, 0.58, respectively).

CONCLUSIONS

First-order texture feature analysis of contrast-enhanced staging SDCT scans of the chest yield accurate differentiation between benign and metastatic lung nodules. In our study cohort, the most powerful iodine map-derived feature slightly, yet insignificantly increased classification accuracy  compared to classification based on conventional image features only.

CT texture analysis of mediastinal lymphadenopathy: Combining with US-based elastographic parameter and discrimination between sarcoidosis and lymph node metastasis from small cell lung cancer

Objectives

To investigate the potential of computed tomography (CT)-based texture analysis and elastographic data provided by endobronchial ultrasonography (EBUS) for differentiating the mediastinal lymphadenopathy by sarcoidosis and small cell lung cancer (SCLC) metastasis.

Methods

Sixteen patients with sarcoidosis and 14 with SCLC were enrolled. On CT images showing the largest mediastinal lymph node, a fixed region of interest was drawn on the node, and texture features were automatically measured. Among the 30 patients, 19 (12 sarcoidosis and 7 SCLC) underwent endobronchial ultrasound transbronchial needle aspiration, and the fat-to-lesion strain ratio (FLR) was recorded. Texture features and FLRs were compared between the 2 patient groups. Logistic regression analysis was performed to evaluate the diagnostic accuracy of these measurements.

Results

Of the 31 texture features, the differences between 11 texture features of CT ROIs in the patients with sarcoidosis versus patients with SCLC were significant. Among them, the grey-level run length matrix with high gray-level run emphasis (GLRLM-HGRE) showed the greatest difference (P<0.01). Differences between FLRs were significant (P<0.05). Logistic regression analysis together with receiver operating characteristic curve analysis demonstrated that the FLR combined with the GLRLM-HGRE showed a high diagnostic accuracy (100% sensitivity, 92% specificity, 0.988 area under the curve) for discriminating between sarcoidosis and SCLC.

Conclusion

Texture analysis, particularly combined with the FLR, is useful for discriminating between mediastinal lymphadenopathy caused by sarcoidosis from that caused by metastasis from SCLC.

Review of Value of CT Texture Analysis and Machine Learning in Differentiating Fat-Poor Renal Angiomyolipoma from Renal Cell Carcinoma

The diagnosis of patients with suspected angiomyolipoma relies on the detection of abundant macroscopic intralesional fat, which is always of no use to differentiate fat-poor angiomyolipoma (fp-AML) from renal cell carcinoma and diagnosis of fp-AML excessively depends on individual experience. Texture analysis was proven to be a potentially useful biomarker for distinguishing between benign and malignant tumors because of its capability of providing objective and quantitative assessment of lesions by analyzing features that are not visible to the human eye. This review aimed to summarize the literature on the use of texture analysis to diagnose patients with fat-poor angiomyolipoma vs those with renal cell carcinoma and to evaluate its current application, limitations, and future challenges in order to avoid unnecessary surgical resection.

Performance improvement of mediastinal lymph node severity detection using GAN and Inception network

BACKGROUND AND OBJECTIVE

In lung cancer, the determination of mediastinal lymph node (MLN) status as benign or malignant influence treatment planning and survival rate. Invasive pathological tests for the classification of MLNs into benign and malignant have various shortcomings like painfulness, the risk associated with anesthesia, and depends to a large extent on skillset and preferences of the surgeon performing the test. Hence, computer-aided system for MLNs severity detection has been explored widely by the researchers. Very recently, in our earlier concluded work on non-invasive method for MLNs differential diagnosis in computed tomography (CT) images, combination of different data augmentation approaches and state-of-art fully convolutional network (FCN) were implemented to enhance the performance of malignancy detection. However, the performance of FCN network were highly depended on the selection of appropriate data augmentation approach and control of their hyperparameters. Moreover, a standard practice to get hierarchical features in convolutional neural network (CNN) models requires deeper stacking of layers. This leads to an increase in number of trainable parameters which prone to overfitting of the network.

METHODS

In view of the above mention limitations, in this paper, authors have proposed an approach that includes: 1) Generative Adversarial Network (GAN) for data augmentation, and 2) Inception network for malignancy detection. Unlike conventional data augmentation strategy, GAN based augmentation approach generates data that correlates to original data distribution. In the case of Inception based model, it uses multiple size kernels with factorized convolution for hierarchical feature extraction. This helps to a significant reduction in trainable parameters and the problem of overfitting.

RESULTS

In this paper, experiments with different GAN approaches, as well as with different Inception architectures, are conducted to evaluate and justify the selection of appropriate GAN and Inception architecture, respectively for MLNs severity detection. The proposed approach achieves superior results with an average accuracy, sensitivity, specificity, and area under curve of 94.95%, 93.65%, 96.67%, and 95%, respectively.

CONCLUSION

The obtained results validate the usefulness of GANs for data augmentation in the differential diagnosis of benign and malignant MLNs. The proposed Inception network based classifier for malignancy detection shows promising results compared to all investigated methods presented in various literature.

Enhancing the differentiation of pulmonary lymphoma and fungal pneumonia in hematological patients using texture analysis in 3-T MRI

Objectives

To evaluate texture analysis in nonenhanced 3-T MRI for differentiating pulmonary fungal infiltrates and lymphoma manifestations in hematological patients and to compare the diagnostic performance with that of signal intensity quotients (“nonenhanced imaging characterization quotients,” NICQs).

Methods

MR scans were performed using a speed-optimized imaging protocol without an intravenous contrast medium including axial T2-weighted (T2w) single-shot fast spin-echo and T1-weighted (T1w) gradient-echo sequences. ROIs were drawn within the lesions to extract first-order statistics from original images using HeterogeneityCAD and PyRadiomics. NICQs were calculated using signal intensities of the lesions, muscle, and fat. The standard of reference was histology or clinical diagnosis in follow-up. Statistical testing included ROC analysis, clustered ROC analysis, and DeLong test. Intra- and interrater reliability was tested using intraclass correlation coefficients (ICC).

Results

Thirty-three fungal infiltrates in 16 patients and 38 pulmonary lymphoma manifestations in 19 patients were included. Considering the leading lesion in each patient, diagnostic performance was excellent for T1w entropy (AUC 80.2%; p < 0.005) and slightly inferior for T2w energy (79.9%; p < 0.005), T1w uniformity (79.6%; p < 0.005), and T1w energy (77.0%; p < 0.01); the best AUC for NICQs was 72.0% for T2NICQmean (p < 0.05). Intra- and interrater reliability was good to excellent (ICC > 0.81) for these parameters except for moderate intrarater reliability of T1w energy (ICC = 0.64).

Conclusions

T1w entropy, uniformity, and energy and T2w energy showed the best performances for differentiating pulmonary lymphoma and fungal pneumonia and outperformed NICQs. Results of the texture analysis should be checked for their intrinsic consistency to identify possible incongruities of single parameters.

Key Points

• Texture analysis in nonenhanced pulmonary MRI improves the differentiation of pulmonary lymphoma and fungal pneumonia compared with signal intensity quotients.

• T1w entropy, uniformity, and energy along with T2w energy show the best performances for differentiating pulmonary lymphoma from fungal pneumonia.

• The results of the texture analysis should be checked for their intrinsic consistency to identify possible incongruities of single parameters.

Radiomic features of primary tumor by lung cancer stage: analysis in BRAF mutated non-small cell lung cancer

Background

The clinical features and traditional semantic imaging characteristics of BRAF-mutated non-small cell lung cancer (NSCLC) have been previously reported. The radiomic features of BRAF-mutated NSCLC and their role in predicting cancer stage, however, have yet to be investigated. This study’s goal is to assess the differences in CT radiomic features of primary NSCLC driven by BRAF mutation and stratified by tumor-node-metastasis (TNM) staging.

Methods

Our IRB approved study included 62 patients with BRAF mutations (V600 in 27 and non-V600 in 35 patients), who underwent contrast-enhanced chest CT. Tumor stage was determined based on the 8^th edition of TNM staging. Two thoracic radiologists assessed the primary tumor imaging features such, including tumor size (maximum and minimum dimensions) and density (Hounsfield units, HU). De-identified transverse CT images (DICOM) were processed with 3D slicer (Version 4.7) for manual lesion segmentation and estimation of radiomic features. Descriptive statistics, multivariate logistic regression, and receiver operating characteristics (ROC) were performed.

Results

There were significant differences in the radiomic features based on cancer stages I-IV with the most significant differences between stage IV and stage I lesions [AUC 0.94 (95% CI: 0.86–0.99), P<0.04]. There were also significant differences in radiomic features between stage IV and combined stages I-III [40/113 radiomic features; AUC 0.71 (95% CI: 0.59–0.85); P<0.04–0.0001]. None of the clinical (0/6) or imaging (0/3) features were significantly different between stage IV and combined stages I–III.

Conclusions

The radiomic features of primary tumor in BRAF driven NSCLC significantly vary with cancer stage, independent of standard imaging and clinical features.

Differentiating hypovascular pancreatic neuroendocrine tumors from pancreatic ductal adenocarcinoma based on CT texture analysis

Background

Hypovascular pancreatic neuroendocrine tumor is usually misdiagnosed as pancreatic ductal adenocarcinoma.

Purpose

To investigate the value of texture analysis in differentiating hypovascular pancreatic neuroendocrine tumors from pancreatic ductal adenocarcinoma on contrast-enhanced computed tomography (CT) images.

Material and Methods

Twenty-one patients with hypovascular pancreatic neuroendocrine tumors and 63 patients with pancreatic ductal adenocarcinomas were included in this study. All patients underwent preoperative unenhanced and dynamic contrast-enhanced CT examinations. Two radiologists independently and manually contoured the region of interest of each lesion using texture analysis software on pancreatic parenchymal and portal phase CT images. Multivariate logistic regression analysis was performed to identify significant features to differentiate hypovascular pancreatic neuroendocrine tumors from pancreatic ductal adenocarcinomas. Receiver operating characteristic curve analysis was performed to ascertain diagnostic ability.

Results

The following CT texture features were obtained to differentiate hypovascular pancreatic neuroendocrine tumors from pancreatic ductal adenocarcinomas: RMS (root mean square) (odds ratio [OR] = 0.50, P<0.001), Quantile50 (OR = 1.83, P<0.001), and sumAverage (OR = 0.92, P=0.007) in parenchymal images and “contrast” in portal phase images (OR = 6.08, P<0.001). The areas under the curves were 0.76 for RMS (sensitivity = 0.75, specificity = 0.67), 0.73 for Quantile50 (sensitivity = 0.60, specificity = 0.77), 0.70 for sumAverage (sensitivity = 0.65, specificity = 0.82), 0.85 for the combined texture features (sensitivity = 0.77, specificity = 0.85).

Conclusion

CT texture analysis may be helpful to differentiate hypovascular pancreatic neuroendocrine tumors from pancreatic ductal adenocarcinomas. The three combined texture features showed acceptable diagnostic performance.

Histogram analysis with computed tomography angiography for discriminating soft tissue sarcoma from benign soft tissue tumor

To investigate the feasibility of histogram analysis with computed tomography angiography (CTA) in distinguishing between soft tissue sarcomas and benign soft tissue tumors. Fourty nine patients (23 men, mean age = 44.3 years, age range = 25-64) with pathologically-confirmed soft tissue sarcoma (n = 24) or benign soft tissue tumors (n = 25) in the lower extremities undergoing CTA for tumor evaluation were retrospectively analyzed. Two radiologists separately performed histogram analyses of CT density with CTA images by drawing a region of interest (ROI). The 10th (P10), 25th (P25), 50th (P50), 75th (P75), 90th percentiles (P90), mean, and standard deviations (SD) of measured tumor density were obtained along with measurements of the absolute value of kurtosis (AVK), absolute value of skewness (AVS), and inhomogeneity for each tumor. Intra-class correlation coefficients (ICC) were calculated to determine inter- and intra-reader variability in parameter measurements. The Mann-Whitney U test was used to compare histogram parameters between soft tissue sarcomas and benign soft tissue tumors. Receiver operator characteristic (ROC) curves were constructed to evaluate the accuracy of tumor discrimination. ICC was greater than 0.7 for AVS, AVK, and inhomogeneity, and >0.9 for mean, SD, and all percentile measures. There was no significant difference in P10, P25, P50, P75, P90, mean, or SD between soft tissue sarcomas and benign tumors (P > .05). AVS, AVK, and inhomogeneity were significantly higher in soft tissue sarcomas (P < .05). Areas under the curve (AUC) were 0.81, 0.83, and 0.84 for AVS, AVK, and inhomogeneity respectively. AUC were below 0.6 for mean, SD, and all percentiles.Skewness, kurtosis, and inhomogeneity measurements derived from histogram analysis from CTA distinguish between soft tissue sarcomas and benign soft tissue tumors.

Combined Qualitative and Quantitative Assessment of Low-Attenuation Renal Lesions Improves Identification of Renal Malignancy on Noncontrast Computed Tomography

OBJECTIVE

The objective of this study was to assess renal lesions measuring less than 20 Hounsfield units (HU) on noncontrast computed tomography (NCT).

METHODS

Twenty-one (18.1%) of 116 consecutive pathologically proven renal cell carcinomas measured less than 20 HU on NCT and were compared with 40 confirmed benign cysts also measuring less than 20 HU. All lesions were assessed qualitatively (heterogeneous or homogenous) by 3 blinded readers and quantitatively with commercially available textural analysis software. Finally, a combined assessment was performed.

RESULTS

Qualitative assessment performed well (sensitivity, 76%-90%; specificity, 70%-88%). Quantitative assessment revealed mean positive pixels as having the highest performance (area under the curve, 0.912; sensitivity, 90%; specificity, 80% at a cutoff value of 21). The combined assessment, using the mean positive pixel cutoff, improved the sensitivity (reader 1, 100%; reader 2, 95%; and reader 3, 95%).

CONCLUSION

Qualitative and quantitative assessments have relatively good performance, but the combination can nearly eliminate renal cell carcinomas being missed on NCT.

Elevating pancreatic cystic lesion stratification: Current and future pancreatic cancer biomarker(s)

Pancreatic ductal adenocarcinoma (PDAC) is an incredibly deadly disease with a 5-year survival rate of 9%. The presence of pancreatic cystic lesions (PCLs) confers an increased likelihood of future pancreatic cancer in patients placing them in a high-risk category. Discerning concurrent malignancy and risk of future PCL progression to cancer must be carefully and accurately determined to improve survival outcomes and avoid unnecessary morbidity of pancreatic resection. Unfortunately, current image-based guidelines are inadequate to distinguish benign from malignant lesions. There continues to be a need for accurate molecular and imaging biomarker(s) capable of identifying malignant PCLs and predicting the malignant potential of PCLs to enable risk stratification and effective intervention management. This review provides an update on the current status of biomarkers from pancreatic cystic fluid, pancreatic juice, and seromic molecular analyses and discusses the potential of radiomics for differentiating PCLs harboring cancer from those that do not.

Application of Radiomics in Predicting the Malignancy of Pulmonary Nodules in Different Sizes

OBJECTIVE. The purpose of this study was to investigate the utility of radiomics for predicting the malignancy of pulmonary nodules (PNs) of different sizes using unenhanced, thin-section CT images. MATERIALS AND METHODS. Patients with a single PN (n = 373) who underwent a preoperative chest CT were recruited retrospectively at Beijing Friendship Hospital from March 2015 to March 2018. Of the 373 PNs studied, 192 were benign and 181 were malignant. The lesions were classified into three groups (T1a, T1b, or T1c according to the 8th edition of the TNM staging system for lung cancer) on the basis of lesion diameters: T1a (diameter, 0-1 cm), T1b (1 cm < diameter ≤ 2 cm) and T1c (2 cm < diameter ≤ 3 cm). A total of 1160 radiomic features were extracted from PN segmentation on unenhanced CT images. We developed three radiomic models to predict PN malignancy in each group on the basis of the extracted radiomic features. Fivefold cross-validation was used to estimate AUC, accuracy, sensitivity, and specificity for indicating the performance of prediction models. RESULTS. The AUC, accuracy, sensitivity, and specificity for predicting PN malignancy in each group were 0.84, 0.77, 0.89, and 0.74 with the T1a model; 0.78, 0.73, 0.74, and 0.71 with the T1b model, and 0.79, 0.76, 0.77, and 0.73 with the T1c model, respectively. The most contributive radiomic features for predicting PN malignancy for groups T1a, T1b, and T1c were LoG_X_Uniformity, Intensity_Minimum, and Shape_SI9, respectively. CONCLUSION. Radiomic features based on unenhanced CT images can be used to predict the malignancy of pulmonary nodules. The radiomic T1a model showed superior prediction performance to the T1b and T1c models, and the best performance in terms of AUC and sensitivity was found for predicting the malignancy of T1a PN.

Feasibility of using computed tomography texture analysis parameters as imaging biomarkers for predicting risk grade of gastrointestinal stromal tumors: comparison with visual inspection

PURPOSE

To evaluate the feasibility of using computed tomography texture analysis (CTTA) parameters for predicting malignant risk grade and mitosis index of gastrointestinal stromal tumors (GISTs), compared with visual inspection.

METHOD AND MATERIALS

CTTA was performed on portal phase CT images of 145 surgically confirmed GISTs (mean size: 42.9 ± 37.5 mm), using TexRAD software. Mean, standard deviation, entropy, mean of positive pixels (MPP), skewness, and kurtosis of CTTA parameters, on spatial scaling factor (SSF), 2-6 were compared by risk grade, mitosis rate, and the presence or absence of necrosis on visual inspection. CTTA parameters were correlated with risk grade. Diagnostic performance was evaluated with receiver operating characteristic curve analysis. Enhancement pattern, necrosis, heterogeneity, calcification, growth pattern, and mucosal ulceration were subjectively evaluated by two observers.

RESULTS

Three to four parameters at different scales were significantly different according to the risk grade, mitosis rate, and the presence or absence of necrosis (p < 0.041). MPP at fine or medium scale (r = - 0.547 to - 393) and kurtosis at coarse scale (r = 0.424-0.454) correlated significantly with risk grade (p < 0.001). HG-GIST was best differentiated from LG-GIST by MPP at SSF 2 (AUC, 0.782), and kurtosis at SSF 4 (AUC, 0.779) (all p < 0.001). CT features predictive of HG-GIST were density lower than or equal to that of the erector spinae muscles on enhanced images (OR 2.1; p = 0.037; AUC, 0.59), necrosis (OR, 6.1; p < 0.001; AUC, 0.70), heterogeneity (OR, 4.3; p < 0.001; AUC, 0.67), and mucosal ulceration (OR, 3.3; p = 0.002; AUC, 0.62).

CONCLUSION

Using TexRAD, MPP and kurtosis are feasible in predicting risk grade and mitosis index of GISTs. CTTA demonstrated meaningful accuracy in preoperative risk stratification of GISTs.

Reliability of CT-based texture features: Phantom study

Objective

To determine the intra‐, inter‐ and test‐retest variability of CT‐based texture analysis (CTTA) metrics.

Materials and methods

In this study, we conducted a series of CT imaging experiments using a texture phantom to evaluate the performance of a CTTA panel on routine abdominal imaging protocols. The phantom comprises of three different regions with various textures found in tumors. The phantom was scanned on two CT scanners viz. the Philips Brilliance 64 CT and Toshiba Aquilion Prime 160 CT scanners. The intra‐scanner variability of the CTTA metrics was evaluated across imaging parameters such as slice thickness, field of view, post‐reconstruction filtering, tube voltage, and tube current. For each scanner and scanning parameter combination, we evaluated the performance of eight different types of texture quantification techniques on a predetermined region of interest (ROI) within the phantom image using 235 different texture metrics. We conducted the repeatability (test‐retest) and robustness (intra‐scanner) test on both the scanners and the reproducibility test was conducted by comparing the inter‐scanner differences in the repeatability and robustness to identify reliable CTTA metrics. Reliable metrics are those metrics that are repeatable, reproducible and robust.

Results

As expected, the robustness, repeatability and reproducibility of CTTA metrics are variably sensitive to various scanner and scanning parameters. Entropy of Fast Fourier Transform‐based texture metrics was overall most reliable across the two scanners and scanning conditions. Post‐processing techniques that reduce image noise while preserving the underlying edges associated with true anatomy or pathology bring about significant differences in radiomic reliability compared to when they were not used.

Conclusion

Following large‐scale validation, identification of reliable CTTA metrics can aid in conducting large‐scale multicenter CTTA analysis using sample sets acquired using different imaging protocols, scanners etc.

Quantitative computed tomography texture analysis: can it improve diagnostic accuracy to differentiate malignant lymph nodes?

Background and objective

Mediastinal lymph node (LN) staging in individuals with non-small-cell lung cancer plays an important role in staging and treatment planning. This study aimed to assess the accuracy of computed tomography (CT) texture analysis (CTTA) in differentiating benign and malignant mediastinal LNs.

Methods

Pathologically confirmed malignant and benign mediastinal LN samples, obtained using endobronchial ultrasound-guided transbronchial needle aspiration (EBUS-TBNA), were retrospectively reviewed, in addition to chest CT and 18-fluorodeoxyglucose (FDG) uptake positron emission tomography (PET) data. For each LN, CTTA was performed using “AVIEW” software (Coreline Soft, Republic of Korea) by drawing a region of interest.

Results

A total of 132 LNs from 80 patients were included and classified into two groups according to pathology results: malignant (n = 61) and benign (n = 71). In EBUS, size > 1 cm, round shape, heterogeneous echogenicity, and presence of coagulation necrosis sign were more prevalent in malignant than in benign LNs; length was the only feature that distinguished the two groups. Among CTTA features, compactness and normalized standard deviation (SD) showed differences between the two groups. The ability to distinguish malignant LNs was higher using high standard uptake value (SUV) on FDG PET/CT (SUV_max ≥ 5) and normalized SD on CTTA (area under the receiver operating characteristic curve 0.739 versus 0.742, respectively); however, normalized SD demonstrated very low sensitivity despite high specificity.

Conclusions

CTTA may be helpful in distinguishing between benign and malignant LNs; however, the diagnostic value was not high. Therefore, integrated evaluation with other imaging modalities is needed.

Electronic supplementary material

The online version of this article (10.1186/s40644-019-0214-8) contains supplementary material, which is available to authorized users.

Potential role of CT-textural features for differentiation between viral interstitial pneumonias, pneumocystis jirovecii pneumonia and diffuse alveolar hemorrhage in early stages of disease: a proof of principle

Background

Pulmonary involvement is common in several infectious and non-infectious diagnostic settings. Imaging findings consistently overlap and are therefore difficult to differentiate by chest-CT. The aim of this study was to evaluate the role of CT-textural features(CTTA) for discrimination between atypical viral (respiratory-syncitial-virus(RSV) and herpes-simplex-1-virus (HSV1)), fungal (pneumocystis-jirovecii-pneumonia(PJP)) interstitial pneumonias and alveolar hemorrhage.

Methods

By retrospective single-centre analysis we identified 46 consecutive patients (29 m) with RSV(n = 5), HSV1(n = 6), PJP(n = 21) and lung hemorrhage(n = 14) who underwent unenhanced chest CTs in early stages of the disease between 01/2016 and 02/2017. All cases were confirmed by microbiologic direct analysis of bronchial lavage. On chest-CT-scans, the presence of imaging features like ground-glass opacity(GGO), crazy-paving, air-space consolidation, reticulation, bronchial wall thickening and centrilobular nodules were described. A representative large area was chosen in both lungs and used for CTTA-parameters (included heterogeneity, intensity, average, deviation, skewness).

Results

Discriminatory CTTA-features were found between alveolar hemorrhage and PJP consisting of differences in mean heterogeneity(p < 0.015) and uniformity of skewness(p < 0.006). There was no difference between CT-textural features of diffuse alveolar hemorrhage and viral pneumonia or PJP and viral pneumonia. Visual HRCT-assessment yielded great overlap of imaging findings with predominance of GGO for PJP and airspace consolidation for pneumonia/alveolar hemorrhage. Significant correlations between HRCT-based imaging findings and CT-textural features were found for all three disease groups.

Conclusion

CT-textural features showed significant differences in mean heterogeneity and uniformity of skewness. HRCT-based imaging findings correlated with certain CT-textural features showing that the latter have the potential to characterize structural properties of lung parenchyma and related abnormalities.

Electronic supplementary material

The online version of this article (10.1186/s12880-019-0338-0) contains supplementary material, which is available to authorized users.

Texture Analysis of Imaging: What Radiologists Need to Know

OBJECTIVE

Radiologic texture is the variation in image intensities within an image and is an important part of radiomics. The objective of this article is to discuss some parameters that affect the performance of texture metrics and propose recommendations that can guide both the design and evaluation of future radiomics studies.

CONCLUSION

A variety of texture-extraction techniques are used to assess clinical imaging data. Currently, no consensus exists regarding workflow, including acquisition, extraction, or reporting of variable settings leading to poor reproducibility.

Predictive models composed by radiomic features extracted from multi-detector computed tomography images for predicting low- and high- grade clear cell renal cell carcinoma: A STARD-compliant article

To evaluate the values of conventional image features (CIFs) and radiomic features (RFs) extracted from multi-detector computed tomography (MDCT) images for predicting low- and high-grade clear cell renal cell carcinoma (ccRCC).Two hundred twenty-seven patients with ccRCC were retrospectively recruited. Five hundred seventy features including 14 CIFs and 556 RFs were extracted from MDCT images of each ccRCC. The CIFs were extracted manually and RFs by the free software-MaZda. Least absolute shrinkage and selection operator (Lasso) was applied to shrink the high-dimensional data set and select the features. Five predictive models for predicting low- and high-grade ccRCC were constructed by the selected CIFs and RFs. The 5 models were as follows: model of minimum mean squared error (minMSE) of CIFs (CIF-minMSE), minMSE of cortico-medullary phase (CMP) of kidney (CMP-minMSE), minMSE of parenchyma phase (PP) of kidney (PP-minMSE), the combined model of CIF-minMSE and CMP-minMSE (CIF-CMP-minMSE), and the combined model of CIF-minMSE and PP-minMSE (CIF-PP-minMSE). The Lasso regression equation of each model was constructed, and the predictive values were calculated. The receiver operating characteristic (ROC) curves of predictive values of the 5 models were drawn by SPSS19.0, and the areas under the curves (AUCs) were calculated.According to Lasso regression, 12, 19 and 10 features were respectively selected from the CIFs, RFs of CMP image and that of PP images to construct the 5 predictive models. The models ordered by their AUCs from large to small were CIF-CMP-minMSE (AUC: 0.986), CIF-PP-minMSE (AUC: 0.981), CIF-minMSE (AUC: 0.980), CMP-minMSE (AUC: 0.975), and PP-minMSE (AUC: 0.963). The maximum diameter of the largest axial section of ccRCC had a maximum weight in predicting the grade of ccRCC among all the features, and its cutoff value was 6.15 cm with a sensitivity of 0.901, a specificity of 0.963, and an AUC of 0.975.When combined with CIFs, RFs extracted from MDCT images contributed to the larger AUC of the predictive model, but were less valuable than CIFs when used alone. The CIF-CMP-minMSE was the optimal predictive model. The maximum diameter of the largest axial section of ccRCC had the largest weight in all features.

Can CT radiomic analysis in NSCLC predict histology and EGFR mutation status?

To assess the role of radiomic features in distinguishing squamous and adenocarcinoma subtypes of nonsmall cell lung cancers (NSCLC) and predict EGFR mutations.Institution Review Board-approved study included chest CT scans of 93 consecutive patients (43 men, 50 women, mean age 60 ± 11 years) with biopsy-proven squamous and adenocarcinoma lung cancers greater than 1 cm. All cancers were evaluated for epidermal growth factor receptor (EGFR) mutation. The clinical parameters such as age, sex, and smoking history and standard morphology-based CT imaging features such as target lesion longest diameter (LD), longest perpendicular diameter (LPD), density, and presence of cavity were recorded. The radiomics data was obtained using commercial CT texture analysis (CTTA) software. The CTTA was performed on a single image of the dominant lung lesion. The predictive value of clinical history, standard imaging features, and radiomics was assessed with multivariable logistic regression and receiver operating characteristic (ROC) analyses.Between adenocarcinoma and squamous cell carcinomas, ROC analysis showed significant difference in 3/11 radiomic features (entropy, normalized SD, total) [AUC 0.686-0.744, P = .006 to <.0001], 1/3 clinical features (smoking) [AUC 0.732, P = .001], and 2/3 imaging features (LD and LPD) [AUC 0.646-0658, P = .020 to .032]. ROC analysis for probability variables showed higher values for radiomics (AUC 0.800, P < .0001) than clinical (AUC 0.676, P = .017) and standard imaging (AUC 0.708, P < .0001). Between EGFR mutant and wild-type adenocarcinoma, ROC analysis showed significant difference in 2/11 radiomic features (kurtosis, K2) [AUC 0.656-0.713, P = .03 to .003], 1/3 clinical features (smoking) [AUC 0.758, P < .0001]. The combined probability variable for radiomics, clinical and imaging features was higher (AUC 0.890, P < .0001) than independent probability variables.The radiomics evaluation adds incremental value to clinical history and standard imaging features in predicting histology and EGFR mutations.