In search of biologic correlates for liver texture on portal-phase CT

Whole-liver enhanced CT radiomics analysis to predict metachronous liver metastases after rectal cancer surgery

Background

To develop a radiomics model based on pretreatment whole-liver portal venous phase (PVP) contrast-enhanced CT (CE-CT) images for predicting metachronous liver metastases (MLM) within 24 months after rectal cancer (RC) surgery.

Methods

This study retrospectively analyzed 112 RC patients without preoperative liver metastases who underwent rectal surgery between January 2015 and December 2017 at our institution. Volume of interest (VOI) segmentation of the whole-liver was performed on the PVP CE-CT images. All 1316 radiomics features were extracted automatically. The maximum-relevance and minimum-redundancy and least absolute shrinkage and selection operator methods were used for features selection and radiomics signature constructing. Three models based on radiomics features (radiomics model), clinical features (clinical model), and radiomics combined with clinical features (combined model) were built by multivariable logistic regression analysis. Receiver operating characteristic (ROC) curves were used to assess the diagnostic performance of models, and calibration curve and the decision curve analysis were performed to evaluate the clinical application value.

Results

In total, 52 patients in the MLM group and 60 patients in the non-MLM group were enrolled in this study. The radscore was built using 16 selected features and the corresponding coefficients. Both the radiomics model and the combined model showed higher diagnostic performance than clinical model (AUCs of training set: radiomics model 0.84 (95% CI, 0.76–0.93), clinical model 0.65 (95% CI, 0.55–0.75), combined model 0.85 (95% CI, 0.77–0.94); AUCs of validation set: radiomics model 0.84 (95% CI, 0.70–0.98), clinical model 0.58 (95% CI, 0.40–0.76), combined model 0.85 (95% CI, 0.71–0.99)). The calibration curves showed great consistency between the predicted value and actual event probability. The DCA showed that both the radiomics and combined models could add a net benefit on a large scale.

Conclusions

The radiomics model based on preoperative whole-liver PVP CE-CT could predict MLM within 24 months after RC surgery. Clinical features could not significantly improve the prediction efficiency of the radiomics model.

Supplementary Information

The online version contains supplementary material available at 10.1186/s40644-022-00485-z.

A systematic review of prognosis predictive role of radiomics in pancreatic cancer: heterogeneity markers or statistical tricks?

OBJECTIVES

We aimed to systematically evaluate the prognostic prediction accuracy of radiomics features extracted from pre-treatment imaging in patients with pancreatic ductal adenocarcinoma (PDAC).

METHODS

Radiomics literature on overall survival (OS) prediction of PDAC were all included in this systematic review. A further meta-analysis was performed on the effect size of first-order entropy. Methodological quality and risk of bias of the included studies were assessed by the radiomics quality score (RQS) and prediction model risk of bias assessment tool (PROBAST).

RESULTS

Twenty-three studies were finally identified in this review. Two (8.7%) studies compared prognosis prediction ability between radiomics model and TNM staging model by C-index, and both showed a better performance of the radiomics. Twenty-one (91.3%) studies reported significant predictive values of radiomics features. Nine (39.1%) studies were included in the meta-analysis, and it showed a significant correlation between first-order entropy and OS (HR 1.66, 95%CI 1.18-2.34). RQS assessment revealed validation was only performed in 5 (21.7%) studies on internal datasets and 2 (8.7%) studies on external datasets. PROBAST showed that 22 (95.7%) studies have a high risk of bias in participants because of the retrospective study design.

CONCLUSION

First-order entropy was significantly associated with OS and might improve the accuracy of PDAC prognosis prediction. Existing studies were poorly validated, and it should be noted in future studies. Modification of PROBAST for radiomics studies is necessary since the strict requirements of prospective study design may not be applicable to the demand for a large sample size in the model construction stage.

KEY POINTS

• Radiomics based on the primary lesion holds great potential for prognosis prediction. First-order entropy was significantly associated with the overall survival of PDAC and might improve the accuracy of current PDAC prognosis prediction. • We strongly recommend that at least an internal validation should be conducted in any radiomics study. Attention should be paid to the complex relationships between radiomics features. • Due to the close relationship between radiomics and big data, the strict requirement of prospective study design in PROABST may not be appropriate for radiomics studies. A balance between study types and sample sizes for radiomics studies needs to be found in the model construction stage.

Can the computed tomography texture analysis of colorectal liver metastases predict the response to first-line cytotoxic chemotherapy?

BACKGROUND

Artificial intelligence in radiology has the potential to assist with the diagnosis, prognostication and therapeutic response prediction of various cancers. A few studies have reported that texture analysis can be helpful in predicting the response to chemotherapy for colorectal liver metastases, however, the results have varied. Necrotic metastases were not clearly excluded in these studies and in most studies the full range of texture analysis features were not evaluated. This study was designed to determine if the computed tomography (CT) texture analysis results of non-necrotic colorectal liver metastases differ from previous reports. A larger range of texture features were also evaluated to identify potential new biomarkers.

AIM

To identify potential new imaging biomarkers with CT texture analysis which can predict the response to first-line cytotoxic chemotherapy in non-necrotic colorectal liver metastases (CRLMs).

METHODS

Patients who presented with CRLMs from 2012 to 2020 were retrospectively selected on the institutional radiology information system of our private radiology practice. The inclusion criteria were non-necrotic CRLMs with a minimum size of 10 mm (diagnosed on archived 1.25 mm portal venous phase CT scans) which were treated with standard first-line cytotoxic chemotherapy (FOLFOX, FOLFIRI, FOLFOXIRI, CAPE-OX, CAPE-IRI or capecitabine). The final study cohort consisted of 29 patients. The treatment response of the CRLMs was classified according to the RECIST 1.1 criteria. By means of CT texture analysis, various first and second order texture features were extracted from a single non-necrotic target CRLM in each responding and non-responding patient. Associations between features and response to chemotherapy were assessed by logistic regression models. The prognostic accuracy of selected features was evaluated by using the area under the curve.

RESULTS

There were 15 responders (partial response) and 14 non-responders (7 stable and 7 with progressive disease). The responders presented with a higher number of CRLMs (P = 0.05). In univariable analysis, eight texture features of the responding CRLMs were associated with treatment response, but due to strong correlations among some of the features, only two features, namely minimum histogram gradient intensity and long run low grey level emphasis, were included in the multiple analysis. The area under the receiver operating characteristic curve of the multiple model was 0.80 (95%CI: 0.64 to 0.96), with a sensitivity of 0.73 (95%CI: 0.48 to 0.89) and a specificity of 0.79 (95%CI: 0.52 to 0.92).

CONCLUSION

Eight first and second order texture features, but particularly minimum histogram gradient intensity and long run low grey level emphasis are significantly correlated with treatment response in non-necrotic CRLMs.

Improved precision of noise estimation in CT with a volume-based approach

Assessment of image noise is a relevant issue in computed tomography (CT). Noise is routinely measured by the standard deviation of density values (Hounsfield units, HU) within a circular region of interest (ROI). We explored the effect of a spherical volume of interest (VOI) on noise measurements. Forty-nine chronic obstructive pulmonary disease patients underwent CT with clinical protocol (regular dose [RD], volumetric CT dose index [CTDIvol] 3.04 mGy, 64-slice unit), and ultra-low dose (ULD) protocol (median CTDIvol 0.38 mGy, dual-source unit). Noise was measured in 27 1-cm² ROIs and 27 0.75-cm³ VOIs inside the trachea. Median true noise was 21 HU (range 17-29) for RD-CT and 33 HU (26-39) for ULD-CT. The VOI approach resulted in a lower mean distance between limits of agreement compared to ROI: 5.9 versus 10.0 HU for RD-CT (-40%); 4.7 versus 9.9 HU for ULD-CT (-53%). Mean systematic bias barely changed: -1.6 versus -0.9HU for RD-CT; 0.0 to 0.4HU for ULD-CT. The average measurement time was 6.8 s (ROI) versus 9.7 (VOI), independent of dose level. For chest CT, measuring noise with a VOI-based instead of a ROI-based approach reduces variability by 40-53%, without a relevant effect on systematic bias and measurement time.

Predicting the Response to FOLFOX-Based Chemotherapy Regimen from Untreated Liver Metastases on Baseline CT: a Deep Neural Network Approach

In developed countries, colorectal cancer is the second cause of cancer-related mortality. Chemotherapy is considered a standard treatment for colorectal liver metastases (CLM). Among patients who develop CLM, the assessment of patient response to chemotherapy is often required to determine the need for second-line chemotherapy and eligibility for surgery. However, while FOLFOX-based regimens are typically used for CLM treatment, the identification of responsive patients remains elusive. Computer-aided diagnosis systems may provide insight in the classification of liver metastases identified on diagnostic images. In this paper, we propose a fully automated framework based on deep convolutional neural networks (DCNN) which first differentiates treated and untreated lesions to identify new lesions appearing on CT scans, followed by a fully connected neural networks to predict from untreated lesions in pre-treatment computed tomography (CT) for patients with CLM undergoing chemotherapy, their response to a FOLFOX with Bevacizumab regimen as first-line of treatment. The ground truth for assessment of treatment response was histopathology-determined tumor regression grade. Our DCNN approach trained on 444 lesions from 202 patients achieved accuracies of 91% for differentiating treated and untreated lesions, and 78% for predicting the response to FOLFOX-based chemotherapy regimen. Experimental results showed that our method outperformed traditional machine learning algorithms and may allow for the early detection of non-responsive patients.

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.

Quantitative tumor heterogeneity MRI profiling improves machine learning-based prognostication in patients with metastatic colon cancer

OBJECTIVES

Intra-tumor heterogeneity has been previously shown to be an independent predictor of patient survival. The goal of this study is to assess the role of quantitative MRI-based measures of intra-tumor heterogeneity as predictors of survival in patients with metastatic colorectal cancer.

METHODS

In this IRB-approved retrospective study, we identified 55 patients with stage 4 colon cancer with known hepatic metastasis on MRI. Ninety-four metastatic hepatic lesions were identified on post-contrast images and manually volumetrically segmented. A heterogeneity phenotype vector was extracted from each lesion. Univariate regression analysis was used to assess the contribution of 110 extracted features to survival prediction. A random forest-based machine learning technique was applied to the feature vector and to the standard prognostic clinical and pathologic variables. The dataset was divided into a training and test set at a ratio of 4:1. ROC analysis and confusion matrix analysis were used to assess classification performance.

RESULTS

Mean survival time was 39 ± 3.9 months for the study population. A total of 22 texture features were associated with patient survival (p < 0.05). The trained random forest machine learning model that included standard clinical and pathological prognostic variables resulted in an area under the ROC curve of 0.83. A model that adds imaging-based heterogeneity features to the clinical and pathological variables resulted in improved model performance for survival prediction with an AUC of 0.94.

CONCLUSIONS

MRI-based texture features are associated with patient outcomes and improve the performance of standard clinical and pathological variables for predicting patient survival in metastatic colorectal cancer.

KEY POINTS

• MRI-based tumor heterogeneity texture features are associated with patient survival outcomes. • MRI-based tumor texture features complement standard clinical and pathological variables for prognosis prediction in metastatic colorectal cancer. • Agglomerative hierarchical clustering shows that patient survival outcomes are associated with different MRI tumor profiles.

Diameter of Superior Rectal Vein - CT Predictor of KRAS Mutation in Rectal Carcinoma

Background

The purpose of this study was to investigate the feasibility of CT parameters to predict the presence of KRAS mutations in rectal cancer patients. The relationship between the presence of a KRAS mutation and pathological findings was evaluated simultaneously.

Methods

Eighty-nine patients (29 females, 60 males, age 27–90, mean 59.7±12 years) with pathologically proven rectal cancer were enrolled. A KRAS mutation test was completed following surgery. Parameters evaluated on CT included the tumor location, the diameter of the superior rectal vein (SRV) and inferior mesenteric vein (IMV), the presence of calcification, ulceration, lymph node enlargement (LNE), distant metastasis, tumor shape (intraluminal polypoid mass, infiltrative mass, or bulky), circumferential extent (C0–C1/4, C1/4–C1/2, C1/2–C3/4, or C3/4–C1), enhanced pattern (homogeneous or heterogeneous), CT ratio, and the length of the tumor (LOT). Pathological findings included lymphovascular emboli, signet ring cell, peripheral fat interval infiltration, focal ulcer, lymph node metastasis, tumor pathological type, and differentiation extent. The correlations between KRAS status and CT parameters, and KRAS status and pathological findings were investigated. The accuracy of CT characteristics for predicting KRAS mutation was evaluated.

Results

A KRAS mutation was detected in 42 cases. On CT image, the diameter of the SRV was significantly increased in the KRAS mutation group compared to in the KRAS wild-type group (4.6±0.9 mm vs 4.2±0.9 mm, p=0.02), and LNE was more likely to occur in the KRAS mutation group (73.3% vs 26.7%, p=0.03). There was no significant difference between the KRAS mutation group and the KRAS wild-type group on the other CT parameters (location, IMV, calcification, ulcer, distant metastasis, tumor shape, enhanced pattern, circumferential extent, CT ratio, and LOT). In the pathological findings, a KRAS mutation was more likely to occur in the middle differentiation group (p=0.03). No significant difference was found between the KRAS mutation group and the KRAS wild-type group in the presence of lymphovascular emboli, signet ring cell, peripheral fat interval infiltration, focal ulcer, lymph node metastasis, and tumor pathological type. With the best cut-off value of 4.07 mm, the AUC of the SRV to predict a KRAS mutation was 0.63 with a sensitivity of 76.2% and a specificity of 48.9%.

Conclusion

It was feasible to use the diameter of the SRV to predict a KRAS mutation in rectal cancer patients, and LNE also can be regarded as an important clue on preoperative CT images.

Computer vision in autism spectrum disorder research: a systematic review of published studies from 2009 to 2019

The current state of computer vision methods applied to autism spectrum disorder (ASD) research has not been well established. Increasing evidence suggests that computer vision techniques have a strong impact on autism research. The primary objective of this systematic review is to examine how computer vision analysis has been useful in ASD diagnosis, therapy and autism research in general. A systematic review of publications indexed on PubMed, IEEE Xplore and ACM Digital Library was conducted from 2009 to 2019. Search terms included ['autis*' AND ('computer vision' OR 'behavio* imaging' OR 'behavio* analysis' OR 'affective computing')]. Results are reported according to PRISMA statement. A total of 94 studies are included in the analysis. Eligible papers are categorised based on the potential biological/behavioural markers quantified in each study. Then, different computer vision approaches that were employed in the included papers are described. Different publicly available datasets are also reviewed in order to rapidly familiarise researchers with datasets applicable to their field and to accelerate both new behavioural and technological work on autism research. Finally, future research directions are outlined. The findings in this review suggest that computer vision analysis is useful for the quantification of behavioural/biological markers which can further lead to a more objective analysis in autism research.

Radiomics Texture Features in Advanced Colorectal Cancer: Correlation with BRAF Mutation and 5-year Overall Survival

PURPOSE

To explore the potential of radiomics texture features as potential biomarkers to enable detection of the presence of BRAF mutation and prediction of 5-year overall survival (OS) in stage IV colorectal cancer (CRC).

MATERIALS AND METHODS

In this retrospective study, a total of 145 patients (mean age, 61 years ± 14 [standard deviation {SD}]; 68 female patients and 77 male patients) with stage IV CRC who underwent molecular profiling and pretreatment contrast material-enhanced CT scans between 2004 and 2018 were included. Tumor radiomics texture features, including the mean, the SD, the mean value of positive pixels (MPP), skewness, kurtosis, and entropy, were extracted from regions of interest on CT images after applying three Laplacian-of-Gaussian filters known as spatial scaling factors (SSFs) (SSF = 2, fine; SSF = 4, medium; SSF = 6, coarse) by using specialized software; values of these parameters were also obtained without filtration (SSF = 0). The Wilcoxon rank sum test was used to assess differences between mutated versus wild-type BRAF tumors. Associations between radiomics texture features and 5-year OS were determined by using Kaplan-Meier estimators using the log-rank test and multivariate Cox proportional-hazards regression analysis.

RESULTS

The SDs and MPPs of radiomic texture features were significantly lower in BRAF mutant tumors than in wild-type BRAF tumors at SSFs of 0, 4, and 6 (P = .006, P = .007, and P = .005, respectively). Patients with skewness less than or equal to -0.75 at an SSF of 0 and a mean of greater than or equal to 17.76 at an SSF of 2 showed better 5-year OS (hazard ratio [HR], 0.53 [95% confidence interval {CI}: 0.29, 0.94]; HR, 0.40 [95% CI: 0.22, 0.71]; log-rank P = .025 and P = .002, respectively). Tumor location (right colon vs left colon vs rectum) had no significant impact on the clinical outcome (log-rank P = .53).

CONCLUSION

Radiomics texture features can serve as potential biomarkers for determining BRAF mutation status and as predictors of 5-year OS in patients with advanced-stage CRC.Keywords: Abdomen/GI, CT, Comparative Studies, Large BowelSupplemental material is available for this article.© RSNA, 2020.

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.

Feasibility of computed tomography texture analysis of hepatic fibrosis using dual-energy spectral detector computed tomography

PURPOSE

To evaluate feasibility of computer tomography texture analysis (CTTA) at different energy level using dual-energy spectral detector CT for liver fibrosis.

MATERIALS AND METHODS

Eighty-seven patients who underwent a spectral CT examination and had a reference standard of liver fibrosis (histopathologic findings, n = 61, or clinical findings for normal, n = 26) were included. Mean gray-level intensity, mean number of positive pixels (MPP), entropy, skewness, and kurtosis using commercially available software (TexRAD) were compared at different energy levels. Optimal CTTA parameter cutoffs to diagnose liver fibrosis were evaluated. CTTA parameters at different energy levels correlated with liver fibrosis. The association of CTTA parameters with energy level was evaluated.

RESULTS

Mean gray-level intensity, skewness, kurtosis, and entropy showed significant differences between patients with and without clinically significant hepatic fibrosis (P < 0.05). Mean gray-level intensity at 50 keV was significantly positively correlated with liver fibrosis (ρ = 0.502, P < 0.001). To diagnose stages F2-F4, entropy and mean gray-level intensity at low keV level showed the largest area under the curve (AUC; 0.79 and 0.79). Estimated marginal means (EMMs) of mean gray-level intensity showed prominent differences at low energy levels.

CONCLUSION

CTTA parameters from different keV levels demonstrated meaningful accuracy for diagnosis of liver fibrosis or clinically significant hepatic fibrosis.

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.

Discrimination of HPV status using CT texture analysis: tumour heterogeneity in oropharyngeal squamous cell carcinomas

PURPOSE

To evaluate the diagnostic performance of texture analysis for discriminating human papillomavirus (HPV) status in patients with oropharyngeal squamous cell carcinoma (OPSCC) in the primary tumours and metastatic lymph nodes.

METHODS

Ninety-five patients with primary tumour and 91 with metastatic lymph nodes with confirmed HPV status, who underwent pretreatment contrast-enhanced CT (CECT), were included as the discovery population. CT texture analysis was performed using commercially available software. Differences between HPV-positive and HPV-negative groups were analysed using the χ² test (or Mann-Whitney U test) and independent t test (or Fisher's exact test). ROC curve analysis was performed to discriminate HPV status according to heterogeneity parameters. Diagnostic accuracy was evaluated in the separate validation population (n = 36) from an outside hospital.

RESULTS

HPV positivity was 52.6% for primary tumours and 56.0% for metastatic lymph nodes. The entropy and standard deviation (SD) values in the HPV-positive group were significantly lower. Entropy using the medium filter was the best discriminator between HPV-positive and HPV-negative primary OPSCCs (AUC, 0.85) and SD without the filter for metastatic lymph nodes (AUC, 0.82). Diagnostic accuracy of entropy for the primary tumour was 80.0% in the discovery group and 75.0% in the validation group. In cases of metastatic lymph node, the accuracy of SD was 79.1% and 78.8%, respectively.

CONCLUSION

Significant differences were found in heterogeneity parameters from texture analysis of pretreatment CECT, according to HPV status. Texture analysis could be used as an adjunctive tool for diagnosis of HPV status in clinical practice.

Hepatocellular carcinoma: preoperative gadoxetic acid-enhanced MR imaging can predict early recurrence after curative resection using image features and texture analysis

PURPOSE

To investigate whether pre-operative gadoxetic acid-enhanced MRI can predict early recurrence after curative resection of single HCC using image features and texture analysis.

MATERIALS AND METHODS

179 patients with single HCC and who underwent pre-operative MRI were included. Two reviewers analyzed MR findings, including the tumor margin, peritumoral enhancement, peritumoral hypointensity on the hepatobiliary phase (HBP), diffusion restriction, capsule, tumoral fat, washout, portal-vein thrombus, signal intensity on HBP, and satellite nodule. Texture analysis on the HBP was also quantified. A multivariate analysis was used to identify predictive factors for early recurrence, microvascular invasion (MVI), and the tumor grade.

RESULTS

For early recurrence, satellite nodule, peritumoral hypointensity, absence of capsule, and GLCM ASM were predictors (P < 0.05). For MVI, satellite nodule, peritumoral hypointensity, washout, and sphericity were predictors (P < 0.05). Satellite nodules, peritumoral hypointensity, diffusion restriction, and iso to high signal intensity on HBP were predictor for higher tumor grade (P < 0.05). Satellite nodules and peritumoral hypointensity were important showed common predictors for early recurrence, MVI, and grade (P < 0.05). The sensitivity and specificity for satellite nodule were 47.36% and 96.25%. When added texture variables to MRI findings, the diagnostic performance for predicting early recurrence is improved from 0.7 (SD 0.604-0.790) to 0.83 (SD 0.787-0.894).

CONCLUSION

MR finding, including satellite nodule and peritumoral hypointensity on the HBP, as well as the texture parameters are useful to predict not only early recurrence, but also MVI and higher grade.

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.

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.

[Radiomics: Big Data Instead of Biopsies in the Future?]

Precision medicine is increasingly pushed forward, also with respect to upcoming new targeted therapies. Individual characterization of diseases on the basis of biomarkers is a prerequisite for this development. So far, biomarkers are characterized clinically, histologically or on a molecular level. The implementation of broad screening methods (“Omics”) and the analysis of big data – in addition to single markers – allow to define biomarker signatures. Next to “Genomics”, “Proteomics”, and “Metabolicis”, “Radiomics” gained increasing interest during the last years. Based on radiologic imaging, multiple radiomic markers are extracted with the help of specific algorithms. These are correlated with clinical, (immuno-) histopathological, or genomic data. Underlying structural differences are based on the imaging metadata and are often not visible and therefore not detectable without specific software. Radiomics are depicted numerically or by graphs. The fact that radiomic information can be extracted from routinely performed imaging adds a specific appeal to this method. Radiomics could potentially replace biopsies and additional investigations. Alternatively, radiomics could complement other biomarkers and thus lead to a more precise, multimodal prediction. Until now, radiomics are primarily used to investigate solid tumors. Some promising studies in head and neck cancer have already been published.

MR-based radiomics signature in differentiating ocular adnexal lymphoma from idiopathic orbital inflammation

OBJECTIVES

To assess the value of the MR-based radiomics signature in differentiating ocular adnexal lymphoma (OAL) and idiopathic orbital inflammation (IOI).

METHODS

One hundred fifty-seven patients with pathology-proven OAL (84 patients) and IOI (73 patients) were divided into primary and validation cohorts. Eight hundred six radiomics features were extracted from morphological MR images. The least absolute shrinkage and selection operator (LASSO) procedure and linear combination were used to select features and build radiomics signature for discriminating OAL from IOI. Discriminating performance was assessed by the area under the receiver-operating characteristic curve (AUC). The predictive results were compared with the assessment of radiologists by chi-square test.

RESULTS

Five radiomics features were included in the radiomics signature, which differentiated OAL from IOI with an AUC of 0.74 and 0.73 in the primary and validation cohorts respectively. There was a significant difference between the classification results of the radiomics signature and those of a radiology resident (p < 0.05), although there was no significant difference between the results of the radiomics signature and those of a more experienced radiologist (p > 0.05).

CONCLUSIONS

Radiomics features have the potential to differentiate OAL from IOI.

KEY POINTS

• Clinical and imaging findings of OAL and IOI often overlap, which makes diagnosis difficult. • Radiomics features can potentially differentiate OAL from IOI non invasively. • The radiomics signature discriminates OAL from IOI at the same level as an experienced radiologist.

CT texture features of liver parenchyma for predicting development of metastatic disease and overall survival in patients with colorectal cancer

OBJECTIVES

To determine if identifiable hepatic textural features are present at abdominal CT in patients with colorectal cancer (CRC) prior to the development of CT-detectable hepatic metastases.

METHODS

Four filtration-histogram texture features (standard deviation, skewness, entropy and kurtosis) were extracted from the liver parenchyma on portal venous phase CT images at staging and post-treatment surveillance. Surveillance scans corresponded to the last scan prior to the development of CT-detectable CRC liver metastases in 29 patients (median time interval, 6 months), and these were compared with interval-matched surveillance scans in 60 CRC patients who did not develop liver metastases. Predictive models of liver metastasis-free survival and overall survival were built using regularised Cox proportional hazards regression.

RESULTS

Texture features did not significantly differ between cases and controls. For Cox models using all features as predictors, all coefficients were shrunk to zero, suggesting no association between any CT texture features and outcomes. Prognostic indices derived from entropy features at surveillance CT incorrectly classified patients into risk groups for future liver metastases (p < 0.001).

CONCLUSIONS

On surveillance CT scans immediately prior to the development of CRC liver metastases, we found no evidence suggesting that changes in identifiable hepatic texture features were predictive of their development.

KEY POINTS

• No correlation between liver texture features and metastasis-free survival was observed. • Liver texture features incorrectly classified patients into risk groups for liver metastases. • Standardised texture analysis workflows need to be developed to improve research reproducibility.

Quantitative Imaging Features and Postoperative Hepatic Insufficiency: A Multi-Institutional Expanded Cohort

BACKGROUND

Post-hepatectomy liver insufficiency (PHLI) is a significant cause of morbidity and mortality after liver resection. Quantitative imaging analysis using CT scans measures variations in pixel intensity related to perfusion. A preliminary study demonstrated a correlation between quantitative imaging features of the future liver remnant (FLR) parenchyma from preoperative CT scans and PHLI. The objective of this study was to explore the potential application of quantitative imaging analysis in PHLI in an expanded, multi-institutional cohort.

STUDY DESIGN

We retrospectively identified patients from 5 high-volume academic centers who developed PHLI after major hepatectomy, and matched them to control patients without PHLI (by extent of resection, preoperative chemotherapy treatment, age [±5 years], and sex). Quantitative imaging features were extracted from the FLR in the preoperative CT scan, and the most discriminatory features were identified using conditional logistic regression. Percent remnant liver volume (RLV) was defined as follows: (FLR volume)/(total liver volume) × 100. Significant clinical and imaging features were combined in a multivariate analysis using conditional logistic regression.

RESULTS

From 2000 to 2015, 74 patients with PHLI and 74 matched controls were identified. The most common indications for surgery were colorectal liver metastases (53%), hepatocellular carcinoma (37%), and cholangiocarcinoma (9%). Two CT imaging features (FD1_4: image complexity; ACM1_10: spatial distribution of pixel intensity) were strongly associated with PHLI and remained associated with PHLI on multivariate analysis (p = 0.018 and p = 0.023, respectively), independent of clinical variables, including preoperative bilirubin and %RLV.

CONCLUSIONS

Quantitative imaging features are independently associated with PHLI and are a promising preoperative risk stratification tool.

Translational Radiomics: Defining the Strategy Pipeline and Considerations for Application-Part 2: From Clinical Implementation to Enterprise

Enterprise imaging has channeled various technological innovations to the field of clinical radiology, ranging from advanced imaging equipment and postacquisition iterative reconstruction tools to image analysis and computer-aided detection tools. More recently, the advancement in the field of quantitative image analysis coupled with machine learning-based data analytics, classification, and integration has ushered in the era of radiomics, a paradigm shift that holds tremendous potential in clinical decision support as well as drug discovery. However, there are important issues to consider to incorporate radiomics into a clinically applicable system and a commercially viable solution. In this two-part series, we offer insights into the development of the translational pipeline for radiomics from methodology to clinical implementation (Part 1) and from that point to enterprise development (Part 2). In Part 2 of this two-part series, we study the components of the strategy pipeline, from clinical implementation to building enterprise solutions.

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.

Whole-volume vs. segmental CT texture analysis of the liver to assess metachronous colorectal liver metastases

PURPOSE

It is unclear whether changes in liver texture in patients with colorectal cancer are caused by diffuse (e.g., perfusional) changes throughout the liver or rather based on focal changes (e.g., presence of occult metastases). The aim of this study is to compare a whole-liver approach to a segmental (Couinaud) approach for measuring the CT texture at the time of primary staging in patients who later develop metachronous metastases and evaluate whether assessing CT texture on a segmental level is of added benefit.

METHODS

46 Patients were included: 27 patients without metastases (follow-up >2 years) and 19 patients who developed metachronous metastases within 24 months after diagnosis. Volumes of interest covering the whole liver were drawn on primary staging portal-phase CT. In addition, each liver segment was delineated separately. Mean gray-level intensity, entropy (E), and uniformity (U) were derived with different filters (σ0.5-2.5). Patients/segments without metastases and patients/segments that later developed metachronous metastases were compared using independent samples t tests.

RESULTS

Absolute differences in entropy and uniformity between the group without metastases and the group with metachronous metastases group were consistently smaller for the segmental approach compared to the whole-liver approach. No statistically significant differences were found in the texture measurements between both groups.

CONCLUSIONS

In this small patient cohort, we could not demonstrate a clear predictive value to identify patients at risk of developing metachronous metastases within 2 years. Segmental CT texture analysis of the liver probably has no additional benefit over whole-liver texture analysis.

Texture analysis of the liver at MDCT for assessing hepatic fibrosis

PURPOSE

To evaluate CT texture analysis (CTTA) for staging of hepatic fibrosis (stages F0-F4) METHODS: Quantitative texture analysis (QTA) of the liver was performed on abdominal MDCT scans using commercially available software (TexRAD), which uses a filtration-histogram statistic-based technique. Single-slice ROI measurements of the total liver, Couinaud segments IV-VIII, and segments I-III were obtained. CTTA parameters were correlated against fibrosis stage (F0-F4), with biopsy performed within one year for all cases with intermediate fibrosis (F1-F3).

RESULTS

The study cohort consisted of 289 adults (158M/131W; mean age, 51 years), including healthy controls (F0, n = 77), and patients with increasing stages of fibrosis (F1, n = 42; F2 n = 37; F3 n = 53; F4 n = 80). Mean gray-level intensity increased with fibrosis stage, demonstrating an ROC AUC of 0.78 at medium filtration for F0 vs F1-4, with sensitivity and specificity of 74% and 74% at cutoff 0.18. For significant fibrosis (≥F2), mean showed AUCs ranging from 0.71-0.73 across medium- and coarse- filtered textures with sensitivity and specificity of 71% and 68% at cutoff of 0.3, with similar performance also observed for advanced fibrosis (≥F3). Entropy showed a similar trend. Conversely, kurtosis and skewness decreased with increasing fibrosis, particularly in cirrhotic patients. For cirrhosis (≥F4), kurtosis and skewness showed AUCs of 0.86 and 0.87, respectively, at coarse-filtered scale, with skewness showing a sensitivity and specificity of 84% and 75% at cutoff of 1.3.

CONCLUSION

CTTA may be helpful in detecting the presence of hepatic fibrosis and discriminating between stages of fibrosis, particularly at advanced levels.

Whole liver CT texture analysis to predict the development of colorectal liver metastases-A multicentre study

OBJECTIVES

CT texture analysis has shown promise to differentiate colorectal cancer patients with/without hepatic metastases.

AIM

To investigate whether whole-liver CT texture analysis can also predict the development of colorectal liver metastases.

MATERIAL AND METHODS

Retrospective multicentre study (n=165). Three subgroups were assessed: patients [A] without metastases (n=57), [B] with synchronous metastases (n=54) and [C] who developed metastases within ≤24 months (n=54). Whole-liver texture analysis was performed on primary staging CT. Mean grey-level intensity, entropy and uniformity were derived with different filters (σ0.5-2.5). Univariable logistic regression (group A vs. B) identified potentially predictive parameters, which were tested in multivariable analyses to predict development of metastases (group A vs. C), including subgroup analyses for early (≤6 months), intermediate (7-12 months) and late (13-24 months) metastases.

RESULTS

Univariable analysis identified uniformity (σ0.5), sex, tumour site, nodal stage and carcinoembryonic antigen as potential predictors. Uniformity remained a significant predictor in multivariable analysis to predict early metastases (OR 0.56). None of the parameters could predict intermediate/late metastases.

CONCLUSIONS

Whole-liver CT-texture analysis has potential to predict patients at risk of developing early liver metastases ≤6 months, but is not robust enough to identify patients at risk of developing metastases at later stage.

Multi-scale radiomic analysis of sub-cortical regions in MRI related to autism, gender and age

We propose using multi-scale image textures to investigate links between neuroanatomical regions and clinical variables in MRI. Texture features are derived at multiple scales of resolution based on the Laplacian-of-Gaussian (LoG) filter. Three quantifier functions (Average, Standard Deviation and Entropy) are used to summarize texture statistics within standard, automatically segmented neuroanatomical regions. Significance tests are performed to identify regional texture differences between ASD vs. TDC and male vs. female groups, as well as correlations with age (corrected p < 0.05). The open-access brain imaging data exchange (ABIDE) brain MRI dataset is used to evaluate texture features derived from 31 brain regions from 1112 subjects including 573 typically developing control (TDC, 99 females, 474 males) and 539 Autism spectrum disorder (ASD, 65 female and 474 male) subjects. Statistically significant texture differences between ASD vs. TDC groups are identified asymmetrically in the right hippocampus, left choroid-plexus and corpus callosum (CC), and symmetrically in the cerebellar white matter. Sex-related texture differences in TDC subjects are found in primarily in the left amygdala, left cerebellar white matter, and brain stem. Correlations between age and texture in TDC subjects are found in the thalamus-proper, caudate and pallidum, most exhibiting bilateral symmetry.

Prediction of the therapeutic response after FOLFOX and FOLFIRI treatment for patients with liver metastasis from colorectal cancer using computerized CT texture analysis

PURPOSE

To determine whether baseline CT texture analysis of hepatic metastasis from colorectal cancer (CRC) is predictive of therapeutic response after cytotoxic chemotherapy.

MATERIALS AND METHODS

235 patients with liver metastasis from CRC who underwent CT and cytotoxic chemotherapy using FOLFOX and FOLFIRI were divided into derivation cohort (n=145) and validation cohort (n=90). The CT texture of the hepatic metastasis was quantified using baseline CT. We analyzed the independent predictor for the response from derivation cohort and validated it using validation cohort. We also compared texture features between included four CT scanners.

RESULTS

89 responding and 146 non-responding patients were evaluated. In the derivation cohort, lower skewness (OR, 6.739) in 2D, higher mean attenuation (OR, 2.587), and narrower standard deviation (SD) (OR, 3.163) in 3D were independently associated with response to chemotherapy. However, only lower skewness (P=0.213) on 2D and narrower SD on 3D analysis (P=0.097) did not show a significant difference on either CT scanner. When applied to the validation set, the lower skewness on 2D (AUC=0.797) and narrower SD on 3D (AUC=0.785) showed good performance.

CONCLUSION

CT texture analysis is useful for prediction of therapeutic response after cytotoxic chemotherapy in patients with liver metastasis from colorectal cancer.

Radiomics: a new application from established techniques

The increasing use of biomarkers in cancer have led to the concept of personalized medicine for patients. Personalized medicine provides better diagnosis and treatment options available to clinicians. Radiological imaging techniques provide an opportunity to deliver unique data on different types of tissue. However, obtaining useful information from all radiological data is challenging in the era of "big data". Recent advances in computational power and the use of genomics have generated a new area of research termed Radiomics. Radiomics is defined as the high throughput extraction of quantitative imaging features or texture (radiomics) from imaging to decode tissue pathology and creating a high dimensional data set for feature extraction. Radiomic features provide information about the gray-scale patterns, inter-pixel relationships. In addition, shape and spectral properties can be extracted within the same regions of interest on radiological images. Moreover, these features can be further used to develop computational models using advanced machine learning algorithms that may serve as a tool for personalized diagnosis and treatment guidance.

Multi Texture Analysis of Colorectal Cancer Continuum Using Multispectral Imagery

PURPOSE

This paper proposes to characterize the continuum of colorectal cancer (CRC) using multiple texture features extracted from multispectral optical microscopy images. Three types of pathological tissues (PT) are considered: benign hyperplasia, intraepithelial neoplasia and carcinoma.

MATERIALS AND METHODS

In the proposed approach, the region of interest containing PT is first extracted from multispectral images using active contour segmentation. This region is then encoded using texture features based on the Laplacian-of-Gaussian (LoG) filter, discrete wavelets (DW) and gray level co-occurrence matrices (GLCM). To assess the significance of textural differences between PT types, a statistical analysis based on the Kruskal-Wallis test is performed. The usefulness of texture features is then evaluated quantitatively in terms of their ability to predict PT types using various classifier models.

RESULTS

Preliminary results show significant texture differences between PT types, for all texture features (p-value < 0.01). Individually, GLCM texture features outperform LoG and DW features in terms of PT type prediction. However, a higher performance can be achieved by combining all texture features, resulting in a mean classification accuracy of 98.92%, sensitivity of 98.12%, and specificity of 99.67%.

CONCLUSIONS

These results demonstrate the efficiency and effectiveness of combining multiple texture features for characterizing the continuum of CRC and discriminating between pathological tissues in multispectral images.

Preliminary data using computed tomography texture analysis for the classification of hypervascular liver lesions: generation of a predictive model on the basis of quantitative spatial frequency measurements--a work in progress

OBJECTIVE

Computed tomography texture analysis (CTTA) is a method of quantifying lesion heterogeneity based on distribution of pixel intensities within a region of interest. This study investigates the ability of CTTA to distinguish different hypervascular liver lesions and compares CTTA parameters by creating a proof-of-concept model to distinguish between different lesions.

METHODS

Following institutional review board approval, CTTA software (TexRAD Ltd) was used to retrospectively analyze 17 cases of focal nodular hyperplasia, 19 hepatic adenomas, 25 hepatocellular carcinomas, and 19 cases of normal liver parenchyma using arterial phase scans. Two radiologists read the same image series used by the CTTA software and reported their best guess diagnosis. Computed tomography texture analysis parameters were computed from regions of interest using spatial band-pass filters to quantify heterogeneity. Random-forest method was used to construct a predictive model from these parameters, and a separate regression model was created using a subset of parameters.

RESULTS

The random-forest model successfully distinguished the 3 lesion types and normal liver with predicted classification performance accuracy for 91.2% for adenoma, 94.4% for focal nodular hyperplasia, and 98.6% for hepatocellular carcinoma. This error prediction was generated using a subset of data points not used in generation of the model, but not on discrete prospective cases. In contrast, the 2 human readers using the same image series data analyzed by the CTTA software had lower accuracies, of 72.2% and 65.6%, respectively. The explicit regression model with a subset of image parameters had intermediate overall accuracy of 84.9%.

CONCLUSIONS

Computed tomography texture analysis may prove valuable in lesion characterization. Differentiation between common hypervascular lesion types could be aided by the judicious incorporation of texture parameters into clinical analysis.

Whole-liver CT texture analysis in colorectal cancer: Does the presence of liver metastases affect the texture of the remaining liver?

BACKGROUND

Liver metastases limit survival in colorectal cancer. Earlier detection of (occult) metastatic disease may benefit treatment and survival.

OBJECTIVE

The objective of this article is to evaluate the potential of whole-liver CT texture analysis of apparently disease-free liver parenchyma for discriminating between colorectal cancer (CRC) patients with and without hepatic metastases.

METHODS

The primary staging CT examinations of 29 CRC patients were retrospectively analysed. Patients were divided into three groups: patients without liver metastases (n = 15), with synchronous liver metastases (n = 10) and metachronous liver metastases within 18 months following primary staging (n = 4). Whole-liver texture analysis was performed by delineation of the apparently non-diseased liver parenchyma (excluding metastases or other focal liver lesions) on portal phase images. Mean grey-level intensity (M), entropy (E) and uniformity (U) were derived with no filtration and different filter widths (0.5 = fine, 1.5 = medium, 2.5 = coarse).

RESULTS

Mean E1.5 and E2.5 for the whole liver in patients with synchronous metastases were significantly higher compared with the non-metastatic patients (p = 0.02 and p = 0.01). Mean U1.5 and U2.5 were significantly lower in the synchronous metastases group compared with the non-metastatic group (p = 0.04 and p = 0.02). Texture parameters for the metachronous metastases group were not significantly different from the non-metastatic group or synchronous metastases group (p > 0.05), although - similar to the synchronous metastases group - there was a subtle trend towards increased E1.5, E2.5 and decreased U1.5, U2.5 values. Areas under the ROC curve for the diagnosis of synchronous metastatic disease based on the texture parameters E1.5,2.5 and U1.5,2.5 ranged between 0.73 and 0.78.

CONCLUSION

Texture analysis of the apparently non-diseased liver holds promise to differentiate between CRC patients with and without metastatic liver disease. Further research is required to determine whether these findings may be used to benefit the prediction of metachronous liver disease.

CT texture analysis of renal masses: pilot study using random forest classification for prediction of pathology

RATIONALE AND OBJECTIVES

Computed tomography texture analysis (CTTA) allows quantification of heterogeneity within a region of interest. This study investigates the possibility of distinguishing between several common renal masses using CTTA-derived parameters by developing and validating a predictive model.

MATERIALS AND METHODS

CTTA software was used to analyze 20 clear cell renal cell carcinomas (RCCs), 20 papillary RCCs, 20 oncocytomas, and 20 renal cysts. Regions of interest were drawn around each mass on multiple slices in the arterial, venous, and delayed phases on renal mass protocol CT scans. Unfiltered images and spatial band-pass filtered images were analyzed to quantify heterogeneity. Random forest method was used to construct a predictive model to classify lesions using quantitative parameters. The model was externally validated on a separate set of 19 unknown cases.

RESULTS

The random forest model correctly categorized oncocytomas in 89% of cases (sensitivity = 89%, specificity = 99%), clear cell RCCs in 91% of cases (sensitivity = 91%, specificity = 97%), cysts in 100% of cases (sensitivity = 100%, specificity = 100%), and papillary RCCs in 100% of cases (sensitivity = 100%, specificity = 98%).

CONCLUSIONS

CTTA, in conjunction with random forest modeling, demonstrates promise as a tool to characterize lesions. Various renal masses were accurately classified using quantitative information derived from routine scans.

Evaluation of clinical outcomes of antiangiogenic-targeted therapy in patients with pulmonary metastatic renal cell carcinoma using non-contrast-enhanced computed tomography

RATIONALE AND OBJECTIVES

The objective of this study was to assess whether changes to radiographic parameters before and after treatment with antiangiogenic drugs would improve performance in predicting tumor response with non-contrast-enhanced computed tomography (NCECT) compared to Response Evaluation Criteria in Solid Tumors (RECIST).

MATERIAL AND METHODS

The exploration sample group and the validation sample group consisted of 58 and 25 patients, respectively, who had pulmonary metastatic renal cell carcinoma and were receiving antiangiogenic drugs. All patients underwent NCECT scans at baseline and at first evaluation (after two cycles of treatment) with the same scan protocol. Tumor diameter, attenuation value, entropy, and uniformity of the exploration sample group were examined by receiver operating characteristic (ROC) analysis and stepwise discriminant analysis. The threshold value derived from ROC analysis and discriminant function of the exploration sample group were also used for the validation sample group and were compared to RECIST using Kaplan-Meier survival curves.

RESULTS

According to the model obtained from the exploration group, Kaplan-Meier curves for patients without disease progression were significantly different for the discriminant analysis of the validation sample group (P = .04) and better than individually using RECIST (P = .08), percentage change for attenuation value (P = .49), entropy (P = .47, .89, .72, .73, and .58), and uniformity (P = .53, .72, .51, .39, and .16; without filtration, at scale values of 1.0, 1.5, 2.0, and 2.5, respectively).

CONCLUSIONS

Combined with changes to imaging parameters, including size, attenuation value, and uniformity between pre- and post-treatment, discrimination analysis can help predict biologic response to antiangiogenic drugs and provide a more accurate response assessment than RECIST criteria.

The use of molecular imaging combined with genomic techniques to understand the heterogeneity in cancer metastasis

Tumour heterogeneity has, in recent times, come to play a vital role in how we understand and treat cancers; however, the clinical translation of this has lagged behind advances in research. Although significant advancements in oncological management have been made, personalized care remains an elusive goal. Inter- and intratumour heterogeneity, particularly in the clinical setting, has been difficult to quantify and therefore to treat. The histological quantification of heterogeneity of tumours can be a logistical and clinical challenge. The ability to examine not just the whole tumour but also all the molecular variations of metastatic disease in a patient is obviously difficult with current histological techniques. Advances in imaging techniques and novel applications, alongside our understanding of tumour heterogeneity, have opened up a plethora of non-invasive biomarker potential to examine tumours, their heterogeneity and the clinical translation. This review will focus on how various imaging methods that allow for quantification of metastatic tumour heterogeneity, along with the potential of developing imaging, integrated with other in vitro diagnostic approaches such as genomics and exosome analyses, have the potential role as a non-invasive biomarker for guiding the treatment algorithm.

Multifunctional imaging signature for V-KI-RAS2 Kirsten rat sarcoma viral oncogene homolog (KRAS) mutations in colorectal cancer

This study explores the potential for multifunctional imaging to provide a signature for V-KI-RAS2 Kirsten rat sarcoma viral oncogene homolog (KRAS) gene mutations in colorectal cancer.

METHODS

This prospective study approved by the institutional review board comprised 33 patients undergoing PET/CT before surgery for proven primary colorectal cancer. Tumor tissue was examined histologically for presence of the KRAS mutations and for expression of hypoxia-inducible factor-1 (HIF-1) and minichromosome maintenance protein 2 (mcm2). The following imaging parameters were derived for each tumor: (18)F-FDG uptake ((18)F-FDG maximum standardized uptake value [SUVmax]), CT texture (expressed as mean of positive pixels [MPP]), and blood flow measured by dynamic contrast-enhanced CT. A recursive decision tree was developed in which the imaging investigations were applied sequentially to identify tumors with KRAS mutations. Monte Carlo analysis provided mean values and 95% confidence intervals for sensitivity, specificity, and accuracy.

RESULTS

The final decision tree comprised 4 decision nodes and 5 terminal nodes, 2 of which identified KRAS mutants. The true-positive rate, false-positive rate, and accuracy (95% confidence intervals) of the decision tree were 82.4% (63.9%-93.9%), 0% (0%-10.4%), and 90.1% (79.2%-96.0%), respectively. KRAS mutants with high (18)F-FDG SUVmax and low MPP showed greater frequency of HIF-1 expression (P = 0.032). KRAS mutants with low (18)F-FDG SUV(max), high MPP, and high blood flow expressed mcm2 (P = 0.036).

CONCLUSION

Multifunctional imaging with PET/CT and recursive decision-tree analysis to combine measurements of tumor (18)F-FDG uptake, CT texture, and perfusion has the potential to identify imaging signatures for colorectal cancers with KRAS mutations exhibiting hypoxic or proliferative phenotypes.

Measurements of heterogeneity in gliomas on computed tomography relationship to tumour grade

To undertake a preliminary study that uses CT texture analysis (CTTA) to quantify heterogeneity in gliomas on contrast-enhanced CT and to assess the relationship between tumour heterogeneity and grade. Retrospective analysis of contrast enhanced CT images was performed in 44 patients with histologically proven cerebral glioma between 2007 and 2010. 11 tumours were low grade (Grade I = 3; Grade II, = 8) and 33 high grade (Grade III = 10, Grade IV = 23). CTTA assessment of tumour heterogeneity was performed using a proprietary software algorithm (TexRAD) that selectively filters and extracts textures at different anatomical scales between filter values 1.0 (fine detail) and 2.5 (coarse features). Heterogeneity was quantified as standard deviation (SD) with or without filtration. Tumour heterogeneity, size and attenuation were correlated with tumour grade. For each parameter, receiver operating characteristics characterised the diagnostic performance for discrimination of high grade from low grade glioma and of grade III tumours from grade IV. Further the CTTA was compared to the radiological diagnosis. Tumour heterogeneity correlated significantly with grade (SD without filtration rs = 0.664, p < 0.001, SD with coarse filtration (rs = 0.714, p < 0.001). Tumour size and attenuation showed only moderate correlations with tumour grade (rs = 0.426, p = 0.004 and rs = 0.447, p = 0.002 respectively). Coarse texture was the best discriminator between high and low grade tumours (AUC 0.832, p < 0.0001) and between grade III and grade IV gliomas (AUC = 0.878 p = 0.0001). Compared to the radiological diagnosis, CTTA further characterised the indetermined cases. By quantifying tumour heterogeneity, CTTA has the potential to provide a marker of tumour grade for patients with cerebral glioma. By differentiating between high and low grade tumours, CTTA could possibly assist clinical management.

Assessment of tumor heterogeneity by CT texture analysis: can the largest cross-sectional area be used as an alternative to whole tumor analysis?

OBJECTIVE

To determine if there is a difference between contrast enhanced CT texture features from the largest cross-sectional area versus the whole tumor, and its effect on clinical outcome prediction.

METHODS

Entropy (E) and uniformity (U) were derived for different filter values (1.0-2.5: fine to coarse textures) for the largest primary tumor cross-sectional area and the whole tumor of the staging contrast enhanced CT in 55 patients with primary colorectal cancer. Parameters were compared using non-parametric Wilcoxon test. Kaplan-Meier analysis was performed to determine the relationship between CT texture and 5-year overall survival.

RESULTS

E was higher and U lower for the whole tumor indicating greater heterogeneity at all filter levels (1.0-2.5): median (range) for E and U for whole tumor versus largest cross-sectional area of 7.89 (7.43-8.31) versus 7.62 (6.94-8.08) and 0.005 (0.004-0.01) versus 0.006 (0.005-0.01) for filter 1.0; 7.88 (7.22-8.48) versus 7.54 (6.86-8.1) and 0.005 (0.003-0.01) versus 0.007 (0.004-0.01) for filter 1.5; 7.88 (7.17-8.54) versus 7.48 (5.84-8.25) and 0.005 (0.003-0.01) versus 0.007 (0.004-0.02) for filter 2.0; and 7.83 (7.03-8.57) versus 7.42 (5.19-8.26) and 0.005 (0.003-0.01) versus 0.006 (0.004-0.03) for filter 2.5 respectively (p ≤ 0.001). Kaplan-Meier analysis demonstrated better separation of E and U for whole tumor analysis for 5-year overall survival.

CONCLUSION

Whole tumor analysis appears more representative of tumor heterogeneity.

Assessment of tumor heterogeneity: an emerging imaging tool for clinical practice?

Background

Tumor spatial heterogeneity is an important prognostic factor, which may be reflected in medical images

Methods

Image texture analysis is an approach of quantifying heterogeneity that may not be appreciated by the naked eye. Different methods can be applied including statistical-, model-, and transform-based methods.

Results

Early evidence suggests that texture analysis has the potential to augment diagnosis and characterization as well as improve tumor staging and therapy response assessment in oncological practice.

Conclusion

This review provides an overview of the application of texture analysis with different imaging modalities, CT, MRI, and PET, to date and describes the technical challenges that have limited its widespread clinical implementation so far. With further efforts to refine its application, image texture analysis has the potential to develop into a valuable clinical tool for oncologic imaging.

Teaching Points

• Tumor spatial heterogeneity is an important prognostic factor.

• Image texture analysis is an approach of quantifying heterogeneity.

• Different methods can be applied, including statistical-, model-, and transform-based methods.

• Texture analysis could improve the diagnosis, tumor staging, and therapy response assessment.

Multiparametric PET/CT in oncology

The standardized uptake value (SUV) and other measurements of tumour uptake of fluorodeoxyglucose (FDG) on positron emission tomography (PET) can potentially be supplemented by additional imaging parameters derived either from the PET images or from the computed tomography (CT) component of integrated PET/CT examinations including tumour size, CT attenuation, texture (reflecting tumour heterogeneity) and blood flow. This article illustrates the emerging benefits of such a multiparametric approach. Example benefits include greater diagnostic accuracy in characterization of adrenal masses achieved by using both the SUV and measured CT attenuation. Tumour size combined with the SUV can potentially improve the prognostic information available from PET/CT in oesophageal and lung cancer. However, greater improvements may be realized through using CT measurements of texture instead of size. Studies in breast and lung cancer suggest that combined PET/CT measurements of glucose metabolism and blood flow provide correlates for tumour proliferation and angiogenesis, respectively. These combined measurements can be utilized to determine vascular-metabolic phenotypes, which vary with tumour type. Uncoupling of blood flow and metabolism suggests a poor prognosis for larger more advanced tumours, high-grade lesions and tumours responding poorly to treatment. Vascular-metabolic imaging also has the potential to subclassify tumour response to treatment. The additional biomarkers described can be readily incorporated in existing FDG-PET examinations thereby improving the ability of PET/CT to depict tumour biology, characterize potentially malignant lesions, and assess prognosis and therapeutic response.

Tumour heterogeneity in non-small cell lung carcinoma assessed by CT texture analysis: a potential marker of survival

PURPOSE

To establish the potential for tumour heterogeneity in non-small cell lung cancer (NSCLC) as assessed by CT texture analysis (CTTA) to provide an independent marker of survival for patients with NSCLC.

MATERIALS AND METHODS

Tumour heterogeneity was assessed by CTTA of unenhanced images of primary pulmonary lesions from 54 patients undergoing (18)F-fluorodeoxyglucose (FDG) PET-CT for staging of NSCLC. CTTA comprised image filtration to extract fine, medium and coarse features with quantification of the distribution of pixel values (uniformity) within the filtered images. Receiver operating characteristics identified thresholds for PET and CTTA parameters that were related to patient survival using Kaplan-Meier analysis.

RESULTS

The median (range) survival was 29.5 (1-38) months. 24, 10, 14 and 6 patients had tumour stages I, II, III and IV respectively. PET stage and tumour heterogeneity assessed by CTTA were significant independent predictors of survival (PET stage: Odds ratio 3.85, 95% confidence limits 0.9-8.09, P = 0.002; CTTA: Odds ratio 56.4, 95% confidence limits 4.79-666, p = 0.001). SUV was not a significantly associated with survival.

CONCLUSION

Assessment of tumour heterogeneity by CTTA of non-contrast enhanced images has the potential for to provide a novel, independent predictor of survival for patients with NSCLC.

KEY POINTS

Computed tomography is a routine staging procedure in non-small cell lung cancer. CT texture analysis (CTTA) can quantify heterogeneity within these lung tumours. CTTA seems to offer a novel independent predictor of survival for NSCLC. CTTA could contribute to disease risk-stratification for patients with NSCLC.

Heterogeneity of focal breast lesions and surrounding tissue assessed by mammographic texture analysis: preliminary evidence of an association with tumor invasion and estrogen receptor status

AIM

This pilot study investigates whether heterogeneity in focal breast lesions and surrounding tissue assessed on mammography is potentially related to cancer invasion and hormone receptor status.

MATERIALS AND METHODS

Texture analysis (TA) assessed the heterogeneity of focal lesions and their surrounding tissues in digitized mammograms from 11 patients randomly selected from an imaging archive [ductal carcinoma in situ (DCIS) only, n = 4; invasive carcinoma (IC) with DCIS, n = 3; IC only, n = 4]. TA utilized band-pass image filtration to highlight image features at different spatial frequencies (filter values: 1.0-2.5) from fine to coarse texture. The distribution of features in the derived images was quantified using uniformity.

RESULTS

Significant differences in uniformity were observed between patient groups for all filter values. With medium scale filtration (filter value = 1.5) pure DCIS was more uniform (median = 0.281) than either DCIS with IC (median = 0.246, p = 0.0102) or IC (median = 0.249, p = 0.0021). Lesions with high levels of estrogen receptor expression were more uniform, most notably with coarse filtration (filter values 2.0 and 2.5, r(s) = 0.812, p = 0.002). Comparison of uniformity values in focal lesions and surrounding tissue showed significant differences between DCIS with or without IC versus IC (p = 0.0009).

CONCLUSION

This pilot study shows the potential for computer-based assessments of heterogeneity within focal mammographic lesions and surrounding tissue to identify adverse pathological features in mammographic lesions. The technique warrants further investigation as a possible adjunct to existing computer aided diagnosis systems.

Dynamic contrast-enhanced texture analysis of the liver: initial assessment in colorectal cancer

PURPOSE

To undertake an initial assessment of the potential utility of dynamic contrast-enhanced texture analysis (DCE-TA) of the liver in patients with colorectal cancer.

MATERIALS AND METHODS

TA comprised measurement of mean gray-level intensity, entropy, and uniformity with and without selective-scale filtration using a band-pass filter to highlight different spatial frequencies reflecting fine, medium, and coarse textures. An initial phantom study assessed the sensitivity of each texture qualifier to computed tomography (CT) acquisition parameters. Texture was analyzed in DCE-CT series from 27 colorectal cancer patients having apparently normal hepatic morphology (node-negative: n = 8, node-positive: n = 19). Averaged changes in hepatic texture induced by contrast material were assessed qualitatively and quantitatively by using kinetic modeling to calculate hepatic perfusion indices following fine, medium, and coarse image filtration.

RESULTS

All texture qualifiers were less sensitive to changes in CT acquisition parameters than measurement of CT attenuation. Temporal changes in hepatic texture were qualitatively different from changes in enhancement. Statistically significant differences between node-negative and node-positive patients were observed for at least 1 time period for measurements of hepatic enhancement and for all texture parameters. The differences were most statistically significant and occurred over the greatest number of time periods for fine texture quantified as mean gray-level intensity (5 time periods, minimum P value: 0.006) followed by fine texture quantified as entropy (4 time points, minimum P value: 0.006). There was no difference in hepatic perfusion indices for the 2 groups.

CONCLUSIONS

DCE-TA is a potentially useful adjunct to DCE-CT warranting further investigation.

Possible role of the scintigraphic estimation of the relative liver perfusion in the diagnosis and therapy of liver carcinomas

BACKGROUND

The aim of the study is evaluation of the possible role of the scintigraphic estimation of the relative liver perfusion in diagnosis and the choice of treatment of liver carcinomas.

MATERIAL AND METHODS

Hepatic perfusion index was obtained by dynamic scintigraphy in 126 patients.

RESULTS

In the control group values did not differ from the value in the patients with benign tumors (p > 0.05). However, in hepatocellular carcinoma and liver metastases of different tumors, HPI values were significantly decreased in comparison to controls and benign tumors (p < 0.01), but they didn't differ between themselves (p > 0.05). The values were especially low in the patients with malignant diseases in the liver accosciated with vascular disturbances in the portal system.

CONCLUSION

HRA could be easily done during the different conventional nuclear medicine methods. It can be an useful method for the assessment of different degrees of hemodynamic alterations in portal system, for differential diagnosis of benign and malignant liver tumors, as well as for assessment of the liver tissue and tumor perfusion, which might be helpful in the decision making for the undertaking of intraarterial (radionuclide, chemotherapy etc.) therapy.

Texture analysis of non-small cell lung cancer on unenhanced computed tomography: initial evidence for a relationship with tumour glucose metabolism and stage

The aim was to undertake an initial study of the relationship between texture features in computed tomography (CT) images of non-small cell lung cancer (NSCLC) and tumour glucose metabolism and stage. This retrospective pilot study comprised 17 patients with 18 pathologically confirmed NSCLC. Non-contrast-enhanced CT images of the primary pulmonary lesions underwent texture analysis in 2 stages as follows: (a) image filtration using Laplacian of Gaussian filter to differentially highlight fine to coarse textures, followed by (b) texture quantification using mean grey intensity (MGI), entropy (E) and uniformity (U) parameters. Texture parameters were compared with tumour fluorodeoxyglucose (FDG) uptake (standardised uptake value (SUV)) and stage as determined by the clinical report of the CT and FDG-positron emission tomography imaging. Tumour SUVs ranged between 2.8 and 10.4. The number of NSCLC with tumour stages I, II, III and IV were 4, 4, 4 and 6, respectively. Coarse texture features correlated with tumour SUV (E: r = 0.51, p = 0.03; U: r = −0.52, p = 0.03), whereas fine texture features correlated with tumour stage (MGI: r_s = 0.71, p = 0.001; E: r_s = 0.55, p = 0.02; U: r_s = −0.49, p = 0.04). Fine texture predicted tumour stage with a kappa of 0.7, demonstrating 100% sensitivity and 87.5% specificity for detecting tumours above stage II ( p = 0.0001). This study provides initial evidence for a relationship between texture features in NSCLC on non-contrast-enhanced CT and tumour metabolism and stage. Texture analysis warrants further investigation as a potential method for obtaining prognostic information for patients with NSCLC undergoing CT.