Hepatic entropy and uniformity: additional parameters that can potentially increase the effectiveness of contrast enhancement during abdominal CT

18 F-FDG PET/MRI of Primary Hepatic Malignancies: Differential Diagnosis and Histologic Grading

BACKGROUND

Distinguishing between IHCC and HCC is important because of their differences in treatment and prognosis. The hybrid Positron Emission Tomography/magnetic Resonance Imaging (PET/MRI) system has become more widely accessible, with oncological imaging becoming one of its most promising applications.

OBJECTIVE

The objective of this study was to see how well 18F-fluorodeoxyglucose (18F-FDG) PET/MRI could be used for differential diagnosis and histologic grading of primary hepatic malignancies.

METHODS

We retrospectively evaluated 64 patients (53 patients with HCC, 11 patients with IHCC) with histologically proven primary hepatic malignancies using 18F-FDG/MRI. The Apparent Diffusion Coefficient (ADC), Coefficient of Variance (CV) of the ADC, and standardized uptake value (SUV) were calculated.

RESULTS

The mean SUVmax value was higher for IHCC (7.7 ± 3.4) than for HCC (5.2 ± 3.1) (p = 0.019). The area under the curve (AUC) was 0.737, an optimal 6.98 cut-off value providing 72% sensitivity and 79% specificity. The ADCcv value in IHCC was statistically significantly higher than in HCC (p=0.014). ADC mean values in HCCs were significantly higher in low-grade tumors than in high-grade tumors. The AUC value was 0.73, and the optimal cut-off point was 1.20x10-6 mm2/s, giving 62% sensitivity and 72% specificity. The SUVmax value was also found to be statistically significantly higher in the high-grade group. The ADCcv value in the HCC low-grade group was found to be lower than in the highgrade group (p=0.036).

CONCLUSION

18F FDG PET/MRI is a novel imaging technique that can aid in the differentiation of primary hepatic neoplasms as well as tumor-grade estimation.

Preoperative predicting invasiveness of lung adenocarcinoma manifesting as ground-glass nodules based on multimodal images of dual-layer spectral detector CT radiomics models

OBJECTIVE

To construct and validate conventional and radiomics models based on dual-layer spectral CT radiomics for preoperative prediction of lung ground glass nodules (GGNs) invasiveness.

MATERIALS AND METHODS

A retrospective study was conducted on 176 GGNs patients who underwent chest non-contrast enhancement scan on dual-layer spectral detector CT at our hospital within 2 weeks before surgery. Patients were randomized into the training cohort and testing cohort. Clinical features, imaging features and spectral quantitative parameters were collected to establish a conventional model. Radiomics models were established by extracting 1781 radiomics features form regions of interest of each spectral image [120 kVp poly energetic images (PI), 60 keV images and electron density maps], respectively. After selecting the optimal radiomic features and integrating multiple machine learning models, the conventional model, PI model, 60 keV model, electron density (ED) model and combined model based on multimodal spectral images were finally established. The performance of these models was assessed through the evaluation of discrimination, calibration, and clinical application.

RESULTS

In the conventional model, age, vacuole sign, 60 keV and ED were independent risk factors of invasiveness. The combined model using logistic regression-least absolute shrinkage and selection operator classifiers was the optimal model with a higher area under the curve of the training (0.961, 95% confidence interval, CI: 0.932-0.991) and testing set (0.944, 0.890-0.999).

CONCLUSION

The combined models are helpful to predict the invasiveness of GGNs before surgery and guide the individualized treatment of patients.

Radiomics model based on contrast-enhanced CT texture features for pretreatment prediction of overall survival in esophageal neuroendocrine carcinoma

Background

Limited studies have observed the prognostic value of CT images for esophageal neuroendocrine carcinoma (NEC) due to rare incidence and low treatment experience in clinical. In this study, the pretreatment enhanced CT texture features and clinical characteristics were investigated to predict the overall survival of esophageal NEC.

Methods

This retrospective study included 89 patients with esophageal NEC. The training and testing cohorts comprised 61 (70%) and 28 (30%) patients, respectively. A total of 402 radiomics features were extracted from the tumor region that segmented pretreatment venous phase CT images. The least absolute shrinkage and selection operator (LASSO) Cox regression was applied to feature dimension reduction, feature selection, and radiomics signature construction. A radiomics nomogram was constructed based on the radiomics signature and clinical risk factors using a multivariable Cox proportional regression. The performance of the nomogram for the pretreatment prediction of overall survival (OS) was evaluated for discrimination and calibration.

Results

Only the enhancement degree was an independent factor in clinical variable influenced OS. The radiomics signatures demonstrated good predictability for prognostic status discrimination. The radiomics nomogram integrating texture signatures was slightly superior to the nomogram derived from the combined model with a C-index of 0.844 (95%CI: 0.783-0.905) and 0.847 (95% CI: 0.782-0.912) in the training set, and 0.805 (95%CI: 0.707-0.903) and 0.745 (95% CI: 0.639-0.851) in the testing set, respectively.

Conclusion

The radiomics nomogram based on pretreatment CT radiomics signature had better prognostic power and predictability of the overall survival in patients with esophageal NEC than the model using combined variables.

Association of Multi-Phasic MR-Based Radiomic and Dosimetric Features with Treatment Response in Unresectable Hepatocellular Carcinoma Patients following Novel Sequential TACE-SBRT-Immunotherapy

This study aims to investigate the association of pre-treatment multi-phasic MR-based radiomics and dosimetric features with treatment response to a novel sequential trans-arterial chemoembolization (TACE) plus stereotactic body radiotherapy (SBRT) plus immunotherapy regimen in unresectable Hepatocellular Carcinoma (HCC) sub-population. Twenty-six patients with unresectable HCC were retrospectively analyzed. Radiomic features were extracted from 42 lesions on arterial phase (AP) and portal-venous phase (PVP) MR images. Delta-phase (DeltaP) radiomic features were calculated as AP-to-PVP ratio. Dosimetric data of the tumor was extracted from dose-volume-histograms. A two-sided independent Mann-Whitney U test was used to assess the clinical association of each feature, and the classification performance of each significant independent feature was assessed using logistic regression. For the 3-month timepoint, four DeltaP-derived radiomics that characterize the temporal change in intratumoral randomness and uniformity were the only contributors to the treatment response association (p-value = 0.038-0.063, AUC = 0.690-0.766). For the 6-month timepoint, DeltaP-derived radiomic features (n = 4) maintained strong clinical associations with the treatment response (p-value = 0.047-0.070, AUC = 0.699-0.788), additional AP-derived radiomic features (n = 4) that reflect baseline tumoral arterial-enhanced signal pattern and tumor morphology (n = 1) that denotes initial tumor burden were shown to have strong associations with treatment response (p-value = 0.028-0.074, AUC = 0.719-0.773). This pilot study successfully demonstrated associations of pre-treatment multi-phasic MR-based radiomics with tumor response to the novel treatment regimen.

Radiomics models based on CT at different phases predicting lymph node metastasis of esophageal squamous cell carcinoma (GASTO-1089)

PURPOSE

To investigate the value of radiomics models based on CT at different phases (non-contrast-enhanced and contrast-enhanced images) in predicting lymph node (LN) metastasis in esophageal squamous cell carcinoma (ESCC).

METHODS AND MATERIALS

Two hundred and seventy-four eligible patients with ESCC were divided into a training set (n =193) and a validation set (n =81). The least absolute shrinkage and selection operator algorithm (LASSO) was used to select radiomics features. The predictive models were constructed with radiomics features and clinical factors through multivariate logistic regression analysis. The predictive performance and clinical application value of the models were evaluated by area under receiver operating characteristic curve (AUC) and decision curve analysis (DCA). The Delong Test was used to evaluate the differences in AUC among models.

RESULTS

Sixteen and eighteen features were respectively selected from non-contrast-enhanced CT (NECT) and contrast-enhanced CT (CECT) images. The model established using only clinical factors (Model 1) has an AUC value of 0.655 (95%CI 0.552-0.759) with a sensitivity of 0.585, a specificity of 0.725 and an accuracy of 0.654. The models contained clinical factors with radiomics features of NECT or/and CECT (Model 2,3,4) have significantly improved prediction performance. The values of AUC of Model 2,3,4 were 0.766, 0.811 and 0.809, respectively. It also achieved a great AUC of 0.800 in the model built with only radiomics features derived from NECT and CECT (Model 5). DCA suggested the potential clinical benefit of model prediction of LN metastasis of ESCC. A comparison of the receiver operating characteristic (ROC) curves using the Delong test indicated that Models 2, 3, 4, and 5 were superior to Model 1(P< 0.05), and no difference was found among Model 2, 3, 4 and Model 5(P > 0.05).

CONCLUSION

Radiomics models based on CT at different phases could accurately predict the lymph node metastasis in patients with ESCC, and their predictive efficiency was better than the clinical model based on tumor size criteria. NECT-based radiomics model could be a reasonable option for ESCC patients due to its lower price and availability for renal failure or allergic patients.

Quantifying Tumor Heterogeneity from Multiparametric Magnetic Resonance Imaging of Prostate Using Texture Analysis

Simple Summary

Prostate cancer (PCa) occurs in males at a rate of 21.8%, predominantly at the customary primary site. High cure rates are possible through early detection and therapy when the tumor is still restricted to the prostate. These tumors do not grow rapidly, allowing for periods of up to 20 years between diagnosis and death. Multiparametric MRI (mp-MRI) is used as a non-invasive approach to diagnose PCa in subjects. This imaging method uses MR imaging with at least one functional MRI sequence to detect and characterize PCa. The use of multiparametric magnetic resonance imaging has refined the diagnosis of prostate cancer in radiology. Malignancy-modified critical features in tissue composition, such as heterogeneity, are associated with adverse tumor biology. Heterogeneity can be quantified through texture analysis, an effective technique for reviewing tumor images acquired in routine clinical practice. This study focused on identifying and quantifying tumor heterogeneity from prostate mp-MRI utilizing texture analysis.

Abstract

(1) Background: Multiparametric MRI (mp-MRI) is used to manage patients with PCa. Tumor identification via irregular sampling or biopsy is problematic and does not allow the comprehensive detection of the phenotypic and genetic alterations in a tumor. A non-invasive technique to clinically assess tumor heterogeneity is also in demand. We aimed to identify tumor heterogeneity from multiparametric magnetic resonance images using texture analysis (TA). (2) Methods: Eighteen patients with prostate cancer underwent mp-MRI scans before prostatectomy. A single radiologist matched the histopathology report to single axial slices that best depicted tumor and non-tumor regions to generate regions of interest (ROIs). First-order statistics based on the histogram analysis, including skewness, kurtosis, and entropy, were used to quantify tumor heterogeneity. We compared non-tumor regions with significant tumors, employing the two-tailed Mann–Whitney U test. Analysis of the area under the receiver operating characteristic curve (ROC-AUC) was used to determine diagnostic accuracy. (3) Results: ADC skewness for a 6 × 6 px filter was significantly lower with an ROC-AUC of 0.82 (p = 0.001). The skewness of the ADC for a 9 × 9 px filter had the second-highest result, with an ROC-AUC of 0.66; however, this was not statistically significant (p = 0.08). Furthermore, there were no substantial distinctions between pixel filter size groups from the histogram analysis, including entropy and kurtosis. (4) Conclusions: For all filter sizes, there was poor performance in terms of entropy and kurtosis histogram analyses for cancer diagnosis. Significant prostate cancer may be distinguished using a textural feature derived from ADC skewness with a 6 × 6 px filter size.

Variation in textural parameters of hepatic lesions during contrast medium injection

BACKGROUND

Textural parameters extracted using quantitative imaging techniques have been shown to have prognostic value for hepatocellular carcinoma (HCC).

PURPOSE

To evaluate whether the contrast medium timing of the image acquisition affects the reproducibility of textural parameters in HCC and hepatic tissue.

MATERIAL AND METHODS

This retrospective study included 17 patients with 37 HCC lesions. Perfusion computed tomography (CT) was obtained after 50 mL contrast medium injection. HCC lesions were segmented for analysis. The gray-level co-occurrence (GLCM) textural analysis parameters, homogeneity, energy, entropy, inertia, and correlation were calculated. Variation was quantified by calculating the SD of each parameter during respective perfusion series and the inter lesion variation as the SD among the lesions.

RESULTS

The average change in texture parameters in both HCC and hepatic tissue per second after injection was 0.01% to 0.3% of the respective texture parameter. In HCC, the average variation in homogeneity, energy, and entropy within each lesion after contrast medium injection was significantly less than the variation observed among the lesions (23% to 74%, P < 0.001). Significant differences in energy, entropy, inertia, and correlation between hepatic tissue and HCC were observed. However, when considering the intra-individual variation of hepatic tissue over time, only the HCC parameter energy was significantly outside that 95% confidence interval (P < 0.02).

CONCLUSION

The contrast medium timing does not affect the reproducibility of textural parameters in HCC and hepatic tissue. Thus, contrast medium timing should not be an issue at CT texture analysis of HCC.

Texture analysis of dorsal striatum in functional neurological (conversion) disorder

In this study, it was aimed to evaluate the dorsal striatum nuclei of patients diagnosed with Functional Neurological Disorder by texture analysis method from magnetic resonance imaging images and to compare them with healthy controls. Study groups consisted of 20 female patients and 20 healthy women. The brains of patients and controls were scanned for high-resolution images with a 1.5T scanner using the sagittal plane and 3D spiral fast spin echo sequence. Using the texture analysis method, mean, standard deviation, minimum, maximum, median, variance, entropy, size %L, size %U, size %M, kurtosis, skewness and homogeneity values of the dorsal striatum nuclei were calculated from the images. The data were compared with comparison tests according to Kolmogorov-Smirnov test results. There was no statistically significant difference between paired regions in terms of texture analysis findings in the cross-sectional images of the participants. In patients, mean, standard deviation, minimum, maximum, median, variance and entropy values for the putamen nucleus, and mean, standard deviation, minimum, maximum, median, variance, entropy and kurtosis values for the caudate nucleus were found significantly higher than controls. Additional receiver operating characteristic curve and logistic regression analyzes were performed. The implications of the results of the study are that there are significant microstructural changes in the dorsal striatum nuclei of patients and their reflection on brain images. Texture analysis is a useful technique to show tissue changes in the dorsal striatum of patients using images. It is highly recommended to use texture analysis to identify and evaluate potentially affected areas of the brain in new studies.

Contrast Administration Impacts CT-Based Radiomics of Colorectal Liver Metastases and Non-Tumoral Liver Parenchyma Revealing the "Radiological" Tumour Microenvironment

The impact of the contrast medium on the radiomic textural features (TF) extracted from the CT scan is unclear. We investigated the modification of TFs of colorectal liver metastases (CLM), peritumoral tissue, and liver parenchyma. One hundred and sixty-two patients with 409 CLMs undergoing resection (2017–2020) into a single institution were considered. We analyzed the following volumes of interest (VOIs): The CLM (Tumor-VOI); a 5-mm parenchyma rim around the CLM (Margin-VOI); and a 2-mL sample of parenchyma distant from CLM (Liver-VOI). Forty-five TFs were extracted from each VOI (LIFEx^®®). Contrast enhancement affected most TFs of the Tumor-VOI (71%) and Margin-VOI (62%), and part of those of the Liver-VOI (44%, p = 0.010). After contrast administration, entropy increased and energy decreased in the Tumor-VOI (0.93 ± 0.10 vs. 0.85 ± 0.14 in pre-contrast; 0.14 ± 0.03 vs. 0.18 ± 0.04, p < 0.001) and Margin-VOI (0.89 ± 0.11 vs. 0.85 ± 0.12; 0.16 ± 0.04 vs. 0.18 ± 0.04, p < 0.001), while remaining stable in the Liver-VOI. Comparing the VOIs, pre-contrast Tumor and Margin-VOI had similar entropy and energy (0.85/0.18 for both), while Liver-VOI had lower values (0.76/0.21, p < 0.001). In the portal phase, a gradient was observed (entropy: Tumor > Margin > Liver; energy: Tumor < Margin < Liver, p < 0.001). Contrast enhancement affected TFs of CLM, while it did not modify entropy and energy of parenchyma. TFs of the peritumoral tissue had modifications similar to the Tumor-VOI despite its radiological aspect being equal to non-tumoral parenchyma.

Magnetic resonance imaging histogram analysis of corpus callosum in a functional neurological disorder

Background/aim

The aim of the present study was to examine and compare the corpus callosum (CC) via histogram analysis (HA) on T1-weighted MR images of patients diagnosed with Functional Neurological Disorder (FND) and healthy controls.

Materials and methods

The study group included 19 female patients diagnosed with FND, and the control group included 20 healthy subjects. All participants were scanned with a 1.5 T MR scanner. A high-resolution structural image of the entire brain was obtained with sagittal 3D spiral fast spin echo T1-weighted images. Gray level intensity, standard deviation of the histogram, entropy, uniformity, skewness, and kurtosis values were determined with texture analysis. A student’s t-test was used to compare the group data. P < 0.05 was accepted as statistically significant.

Results

It was determined that the mean gray level intensity, standard deviation of the histogram, entropy calculated by the maximum, median and variance and size M percentage values were higher in patients with FND. Kurtosis and size U percentages values were lower in patients with FND.

Conclusion

In the present study, analysis of CC with T1-weighted MR image HA demonstrated significant differences between FND patients and healthy controls. The study findings indicated that HA is a beneficial technique for demonstrating textural variations between the CCs of patients with FND and healthy controls using MR images.

A radiomics nomogram for the prediction of overall survival in patients with hepatocellular carcinoma after hepatectomy

Background

Hepatocellular carcinoma (HCC) is associated with a dismal prognosis, and prediction of the prognosis of HCC can assist in therapeutic decision-makings. An increasing number of studies have shown that the texture parameters of images can reflect the heterogeneity of tumors, and may have the potential to predict the prognosis of patients with HCC after surgical resection. The aim of this study was to investigate the prognostic value of computed tomography (CT) texture parameters in patients with HCC after hepatectomy and to develop a radiomics nomogram by combining clinicopathological factors and the radiomics signature.

Methods

In all, 544 eligible patients were enrolled in this retrospective study and were randomly divided into the training cohort (n = 381) and the validation cohort (n = 163). The tumor regions of interest (ROIs) were delineated, and the corresponding texture parameters were extracted. The texture parameters were selected by using the least absolute shrinkage and selection operator (LASSO) Cox model in the training cohort, and a radiomics signature was established. Then, the radiomics signature was further validated as an independent risk factor for overall survival (OS). The radiomics nomogram was established based on the Cox regression model. The concordance index (C-index), calibration plot and decision curve analysis (DCA) were used to evaluate the performance of the radiomics nomogram.

Results

The radiomics signature was formulated based on 7 OS-related texture parameters, which were selected in the training cohort. In addition, the radiomics nomogram was developed based on the following five variables: α-fetoprotein (AFP), platelet-to-lymphocyte ratio (PLR), largest tumor size, microvascular invasion (MVI) and radiomics score (Rad-score). The nomogram displayed good accuracy in predicting OS (C-index = 0.747) in the training cohort and was confirmed in the validation cohort (C-index = 0.777). The calibration plots also showed excellent agreement between the actual and predicted survival probabilities. The DCA indicated that the radiomics nomogram showed better clinical utility than the clinicopathologic nomogram.

Conclusion

The radiomics signature is a potential prognostic biomarker of HCC after hepatectomy. The radiomics nomogram that integrated the radiomics signature can provide a more accurate estimation of OS than the clinicopathologic nomogram for HCC patients after hepatectomy.

Supplementary Information

The online version contains supplementary material available at 10.1186/s40644-020-00360-9.

Entropy and uniformity as additional parameters to optimize the effectiveness of bone CT in the evaluation of acromegalic patients

PURPOSE

Acromegaly is considered an important cause of secondary osteoporosis. However, studies on bone mineral density (BMD) have yielded conflicting results and there are few studies that evaluate an accurate imaging method for early diagnosis of osteoporosis in these patients. The objective of this study was to assess whether entropy and uniformity on computed tomography (CT) scans are useful parameters for optimization of assessment of bone fragility in patients with acromegaly.

METHODS

We included 34 patients and 36 controls matched for age and sex in a cross-sectional study. Patients and controls underwent CT scan of the lumbosacral spine, dual-energy x-ray absorptiometry (DXA) and blood tests. A software was developed to calculate the entropy and uniformity by a region of interest (ROI) of the trabecular bone of the first lumbar vertebra (L1).

RESULTS

The acromegalic group presented higher mean bone entropy (6.87 ± 0.98 vs. 6.03 ± 1.68, p = 0.013) and lower mean bone uniformity (0.035 ± 0.704 vs. 0.113 ± 0.205, p = 0.035) than control group. Analyzing only acromegalics, mean bone entropy was higher and bone uniformity was lower in patients with hypogonadism than patients without hypogonadism (7.28 ± 0.36 vs. 6.74 ± 1.08, p = 0.038 and 0.008 ± 0.002 vs. 0.043 ± 0.079, p = 0.031) respectively. Patients with acromegaly presented higher BMD and Z-score in the femoral neck than control group (1.156 ± 0.108 vs. 0.925 ± 0.326 g/cm², p = 0.043 and 0.6 ± 0.6 vs. -0.05 ± 0.8, p = 0.041, respectively). Entropy was negatively correlated with T-score of the lumbar spine (r_p = -0.357, p = 0.033) in control group and uniformity was positively correlated with T-score of the lumbar spine, neck, and total hip, respectively (r_p = 0.371, p = 0.031; r_p = 0.348, p = 0.043 and r_p = 0.341, p = 0.049) in acromegalic group.

CONCLUSIONS

The study identified that entropy and uniformity are a relevant parameters data in bone fragility assessment in acromegalic patients.

Predicting ventricular tachyarrhythmia in patients with systolic heart failure based on texture features of the gray zone from contrast-enhanced magnetic resonance imaging

BACKGROUND

Previous research showed that gray zone detected by late gadolinium enhancement cardiovascular magnetic resonance (LGE-CMR) imaging could help identify high-risk patients. In this study, we investigated whether LGE-CMR gray zone heterogeneity measured by image texture features could predict cardiovascular events in patients with heart failure (HF).

METHOD

This is a retrospective cohort study. Patients with systolic HF undergoing CMR imaging were enrolled. Cine and LGE images were analyzed to derive left ventricular (LV) function and scar characteristics. Entropy and uniformity of gray zones were derived by texture analysis.

RESULTS

A total of 82 systolic HF patients were enrolled. After a median 1021 (25%-75% quartiles, 205-2066) days of follow-up, the entropy (0.60 ± 0.260 vs. 0.87 ± 0.28, p = 0.013) was significantly increased while the uniformity (0.68 ± 0.14 vs. 0.53±0.15, p = 0.016) was significantly decreased in patients with ventricular tachycardia or ventricular fibrillation (VT/VF). The percentage of core scar (21.9 ± 10.6 vs. 30.6 ± 10.4, p = 0.029) was higher in cardiac mortality group than survival group while the uniformity (0.55 ± 0.17 vs. 0.67 ± 0.14, p = 0.018) was lower in cardiac mortality group than survival group. A multivariate Cox regression model showed that higher percentage of gray zone area (HR = 8.805, 1.620-47.84, p = 0.045), higher entropy (>0.85) (HR = 1.391, 1.092-1.772, p = 0.024) and lower uniformity (≦0.54) (HR = 0.535, 0.340-0.842, p = 0.022) were associated with VT/VF attacks. Also, higher percentage of gray zone area (HR = 5.716, 1.379-23.68, p = 0.017), core scar zone (HR = 1.939, 1.056-3.561, p = 0.025), entropy (>0.85) (HR = 1.434, 1.076-1.911, p = 0.008) and lower uniformity (≦0.54) (HR = 0.513, 0.296-0.888, p = 0.009) were associated with cardiac mortality during follow-up.

CONCLUSIONS

Gray zone heterogeneity by texture analysis method could provide additional prognostic value to traditional LGE-CMR substrate analysis method.

Predicting outcome in childhood diffuse midline gliomas using magnetic resonance imaging based texture analysis

BACKGROUND

Diffuse midline gliomas (DMG) are aggressive brain tumours, previously known as diffuse intrinsic pontine gliomas (DIPG), with 10% overall survival (OS) at 18 months. Predicting OS will help refine treatment strategy in this patient group. MRI based texture analysis (MRTA) is novel image analysis technique that provides objective information about spatial arrangement of MRI signal intensity (heterogeneity) and has potential to be imaging biomarker.

OBJECTIVES

To investigate MRTA in predicting OS in childhood DMG.

METHODS

Retrospective study of patients diagnosed with DMG, based on radiological features, treated at our institution 2007-2017. MRIs were acquired at diagnosis and 6 weeks after radiotherapy (54Gy in 30 fractions). MRTA was performed using commercial available TexRAD research software on T2W sequence and Apparent Diffusion Coefficient (ADC) maps encapsulating tumour in the largest single axial plane. MRTA comprised filtration-histogram technique using statistical and histogram metrics for quantification of texture. Kaplan-Meier survival analysis determined association of MRI texture parameters with OS.

RESULTS

In all, 32 children 2-14 years (median 7 years) were included. MRTA was undertaken on T2W (n=32) and ADC (n=22). T2W-MRTA parameters were better at prognosticating than ADC-MRTA. Children with homogenous tumour texture, at medium scale on diagnostic T2W MRI, had worse prognosis (Mean of Positive Pixels (MPP): P=0.005, mean: P=0.009, SD: P=0.011, kurtosis: P=0.037, entropy: P=0.042). Best predictor MPP was able to stratify patients into poor and good prognostic groups with median survival of 7.5 months versus 17.5 months, respectively.

CONCLUSIONS

DMG with more homogeneous texture on diagnostic MRI is associated with worse prognosis. Texture parameter MPP is the most predictive marker of OS in childhood DMG.

White paper on pancreatic ductal adenocarcinoma from society of abdominal radiology's disease-focused panel for pancreatic ductal adenocarcinoma: Part II, update on imaging techniques and screening of pancreatic cancer in high-risk individuals

Pancreatic ductal adenocarcinoma (PDAC) is an aggressive gastrointestinal malignancy with a poor 5-year survival rate. Its high mortality rate is attributed to its aggressive biology and frequently late presentation. While surgical resection remains the only potentially curative treatment, only 10-20% of patients will present with surgically resectable disease. Over the past several years, development of vascular bypass graft techniques and introduction of neoadjuvant treatment regimens have increased the number of patients who can undergo resection with a curative intent. While the role of conventional imaging in the detection, characterization, and staging of patients with PDAC is well established, its role in monitoring treatment response, particularly following neoadjuvant therapy remains challenging because of the complex anatomic and histological nature of PDAC. Novel morphologic and functional imaging techniques (such as DECT, DW-MRI, and PET/MRI) are being investigated to improve the diagnostic accuracy and the ability to measure response to therapy. There is also a growing interest to detect PDAC and its precursor lesions at an early stage in asymptomatic patients to increase the likelihood of achieving cure. This has led to the development of pancreatic cancer screening programs. This article will review recent updates in imaging techniques and the current status of screening and surveillance of individuals at a high risk of developing PDAC.

Pulmonary adenocarcinoma appearing as ground-glass opacity nodules identified using non-enhanced and contrast-enhanced CT texture analysis: A retrospective analysis

The present study aimed to investigate the ability of CT-based texture analysis to differentiate invasive adenocarcinoma (IA) from pre-invasive lesions (PIL) or minimally IA (MIA) appearing as ground-glass opacity (GGO) nodules, and to further compare the performance of non-enhanced CT (NECT) images with that of contrast-enhanced CT (CECT) images. A total of 77 patients with GGO nodules and surgically confirmed pulmonary adenocarcinoma were included in the present retrospective study. Each GGO nodule was manually segmented and its texture features were extracted from NECT and CECT images using in-house developed software coded in MATLAB (MathWorks). The independent-samples t-test was used to select the texture features with statistically significant differences between IA and MIA/PIL. Multivariate logistic regression and receiver operating characteristics (ROC) curve analyses were performed to identify predictive features. Of the 77 GGO nodules, 12 were atypical adenomatous hyperplasia or adenocarcinoma in situ (15.6%), 36 were MIA (46.8%) and 29 were IA (37.7%). IA and MIA/PIL exhibited significant differences in most histogram features and gray-level co-occurrence matrix features (P<0.05). Multivariate logistic regression and ROC curve analyses revealed that smaller energy and higher entropy were significant differentiators of IA from MIA and PIL, irrespective of whether NECT images [area under the curve (AUC): 0.839, 0.859] or CECT images (AUC: 0.818, 0.820) are used. Texture analysis of CT images, regardless of whether NECT or CECT is used, has the potential to distinguish IA from PIL or MIA, particularly the parameters of energy and entropy. Furthermore, NECT images were simpler to obtain and no contrast agent was required; thus, analysis with NECT may be a preferred choice.

Texture Analysis of Magnetic Resonance Enterography Contrast Enhancement Can Detect Fibrosis in Crohn Disease Strictures

OBJECTIVE

The aim of the study was to investigate if texture analysis of contrast-enhanced magnetic resonance enterography (MRE) images can determine Crohn disease (CD) stricture histologic type.

MATERIALS AND METHODS

A radiology report database query identified 25 pediatric patients with established CD who underwent MRE followed by bowel resection within 30 days. MRE images were reviewed to identify strictures on enteric phase T1-weighted fat-suppressed images, that were matched with sites of histologic sectioning. Regions of interest were drawn over the bowel wall and texture analysis was performed using TexRAD software (Cambridge, UK), with skewness, mean, entropy and standard deviation parameters assessed. A pathologist reviewed all stricture histology specimens to assess for active mucosal inflammation and mural fibrosis. Multivariate logistic regression and analysis of variance were performed to identify texture features associated with stricture fibrosis.

RESULTS

Sixty-four bowel segments from 25 patients (mean age 16 ± 2 years) with imaging-histologic correlation were included. Of note, all strictures included had undergone surgical resection with MRE imaging available within 30 days. The histologic distribution of these bowel segments included 9 segments that showed active inflammation without fibrosis, 23 segments that showed only fibrosis, and 32 mixed segments with concomitant active inflammation and fibrosis. Bivariate regression analysis demonstrated that skewness, standard deviation, entropy, and mean texture analysis features are independently associated with stricture fibrosis. Stepwise logistic regression showed that the combination of mean, skewness, and entropy texture predicted stricture fibrosis with a goodness-of-fit value of 0.995. A combination of threshold values for these 3 texture analysis parameters was able to correctly classify 100% of the strictures in the study cohort for presence (55/55) and absence (9/9) of fibrosis.

CONCLUSIONS

MRE texture analysis (MRE-TA) texture features can differentiate CD stricture types and accurately detect fibrosis.

Differentiation of intrapancreatic accessory spleen from small hypervascular neuroendocrine tumor of the pancreas: textural analysis on contrast-enhanced computed tomography

BACKGROUND

Intrapancreatic accessory spleens (IPASs) are usually misdiagnosed as pancreatic neuroendocrine tumors (PNETs). Texture analysis is valuable in tumor detection, diagnosis, and staging.

PURPOSE

To identify the potential of texture features in differentiating IPASs from small hypervascular PNETs.

MATERIAL AND METHODS

Twenty-one patients with PNETs and 13 individuals with IPASs who underwent pretreatment dynamic contrast-enhanced computed tomography (CT) were retrospectively analyzed. The routine imaging features-such as location, size, margin, cystic or solid appearance, enhancement degree and pattern, and lymph node enlargement-were recorded. Texture features, such as entropy, skewness, kurtosis, and uniformity, on contrast-enhanced images were analyzed. Receiver operating characteristic (ROC) analysis was performed to differentiate IPASs from PNETs.

RESULTS

No significant differences were observed in margin, enhancement degree (arterial and portal phase), lymph node enlargement, or size between PNETs and IPASs (all P > 0.05). However, IPASs usually showed heterogeneous enhancement at the arterial phase and the same degree of enhancement as the spleen at the portal phase, both of which were greater than those of PNETs (69% vs. 35%, P = 0.06; 100% vs. 29%, P = 0.04). Entropy and uniformity were significantly different between IPASs and PNETs at moderate (1.5) and high sigma values (2.5) (both P < 0.01). ROC analysis showed that uniformity at moderate and high sigma had the highest area under the curve (0.82 and 0.89) with better sensitivity (85.0-95.0%) and acceptable specificity (75.0-83.3%) for differentiating IPASs from PNETs.

CONCLUSIONS

Texture parameters have potential in differentiating IPASs from PNETs.

Diagnostic value of MR-based texture analysis for the assessment of hepatic fibrosis in patients with nonalcoholic fatty liver disease (NAFLD)

PURPOSE

To investigate the performance of MR-based texture analysis (TA) for the assessment of hepatic fibrosis in patients with nonalcoholic fatty liver disease (NAFLD).

METHODS

Fifty-four adult patients (33 females, 21 males, mean age 49.8 ± 13.5 years) with biopsy-proven NAFLD were enrolled and underwent MR imaging on a 1.5 T system. TA parameters were extracted on axial noncontrast 3D-GRE T1W images (slice thickness = 4.6 mm) using a commercially available research software (TexRAD). Receiver operating curves (ROC), areas under the ROC (AUROC) and 95% confidence intervals (CI) were calculated to assess the accuracy of each TA parameter for the diagnosis of significant (F ≥ 2) and advanced fibrosis (F ≥ 3). The correlation between TA and histopathological features of nonalcoholic steatohepatitis (NASH) was tested calculating the Spearman's rank correlation coefficient (ρ).

RESULTS

Thirty-seven (68%) subjects had significant fibrosis and 20 (37%) had advanced fibrosis. The TA parameters with the best performance were standard deviation (SD) and entropy, respectively, with AUROC 0.755 (95% CI 0.619-0.862, p ≤ 0.0002) and 0.769 (95% CI 0.634-0.873, p < 0.0001) for significant fibrosis and AUROC 0.746 (95% CI 0.609-0.854, p ≤ 0.0004) and 0.754 (95% CI 0.618-0.861, p ≤ 0.0002) for advanced fibrosis. SD and entropy demonstrated a moderate correlation with the degree of fibrosis (ρ = 0.457 and 0.480; p < 0.01). No significant correlation was found between TA parameters and other histopathological features of NASH.

CONCLUSIONS

Entropy and SD extracted on T1-weighted MR images have fair accuracy for the diagnosis of significant and advanced hepatic fibrosis in patients with NAFLD.

Evaluation of texture analysis for the differential diagnosis of focal nodular hyperplasia from hepatocellular adenoma on contrast-enhanced CT images

PURPOSE

To explore the value of CT texture analysis (CTTA) for differentiation of focal nodular hyperplasia (FNH) from hepatocellular adenoma (HCA) on contrast-enhanced CT (CECT).

METHODS

This is a retrospective, IRB-approved study conducted in a single institution. A search of the medical records between 2008 and 2017 revealed 48 patients with 70 HCA and 50 patients with 62 FNH. All lesions were histologically proven and with available pre-operative CECT imaging. Hepatic arterial phase (HAP) and portal venous phase (PVP) were used for CTTA. Textural features were extracted using a commercially available research software (TexRAD). The differences between textural parameters of FNH and HCA were assessed using the Mann-Whitney U test and the AUROC were calculated. CTTA parameters showing significant difference in rank sum test were used for binary logistic regression analysis. A p value < 0.05 was considered statistically significant.

RESULTS

On HAP images, mean, mpp, and skewness were significantly higher in FNH than in HCA on unfiltered images (p ≤ 0.007); SD, entropy, and mpp on filtered analysis (p ≤ 0.006). On PVP, mean, mpp, and skewness in FNH were significantly different from HCA (p ≤ 0.001) on unfiltered images, while entropy and kurtosis were significantly higher in FNH on filtered images (p ≤ 0.018). The multivariate logistic regression analysis indicated that the mean, mpp, and entropy of medium-level and coarse-level filtered images on HAP were independent predictors for the diagnosis of HCA and a model based on all these parameters showed the largest AUROC (0.824).

CONCLUSIONS

Multiple explored CTTA parameters are significantly different between FNH and HCA on CECT.

Texture analysis on diffusion tensor imaging: discriminating glioblastoma from single brain metastasis

BACKGROUND

Texture analysis has been done on several radiological modalities to stage, differentiate, and predict prognosis in many oncologic tumors.

PURPOSE

To determine the diagnostic accuracy of discriminating glioblastoma (GBM) from single brain metastasis (MET) by assessing the heterogeneity of both the solid tumor and the peritumoral edema with magnetic resonance imaging (MRI) texture analysis (MRTA).

MATERIAL AND METHODS

Preoperative MRI examinations done on a 3-T scanner of 43 patients were included: 22 GBM and 21 MET. MRTA was performed on diffusion tensor imaging (DTI) in a representative region of interest (ROI). The MRTA was assessed using a commercially available research software program (TexRAD) which applies a filtration histogram technique for characterizing tumor and peritumoral heterogeneity. The filtration step selectively filters and extracts texture features at different anatomical scales varying from 2 mm (fine) to 6 mm (coarse). Heterogeneity quantification was obtained by the statistical parameter entropy. A threshold value to differentiate GBM from MET with sensitivity and specificity was calculated by receiver operating characteristic (ROC) analysis.

RESULTS

Quantifying the heterogeneity of the solid part of the tumor showed no significant difference between GBM and MET. However, the heterogeneity of the GBMs peritumoral edema was significantly higher than the edema surrounding MET, differentiating them with a sensitivity of 80% and specificity of 90%.

CONCLUSION

Assessing the peritumoral heterogeneity can increase the radiological diagnostic accuracy when discriminating GBM and MET. This will facilitate the medical staging and optimize the planning for surgical resection of the tumor and postoperative management.

Textural analysis on contrast-enhanced CT in pancreatic neuroendocrine neoplasms: association with WHO grade

PURPOSE

Grades of pancreatic neuroendocrine neoplasms (PNENs) are associated with the choice of treatment strategies. Texture analysis has been used in tumor diagnosis and staging evaluation. In this study, we aim to evaluate the potential ability of texture parameters in differentiation of PNENs grades.

MATERIALS AND METHODS

37 patients with histologically proven PNENs and underwent pretreatment dynamic contrast-enhanced computed tomography examinations were retrospectively analyzed. Imaging features and texture features at contrast-enhanced images were evaluated. Receiver operating characteristic curves were used to determine the cut-off values and the sensitivity and specificity of prediction.

RESULTS

There were significant differences in tumor margin, pancreatic duct dilatation, lymph nodes invasion, size, portal enhancement ratio (PER), arterial enhancement ratio (AER), mean grey-level intensity, kurtosis, entropy, and uniformity among G1, G2, and pancreatic neuroendocrine carcinoma (PNEC) G3 (p < 0.01). Similar results were found between pancreatic neuroendocrine tumors (PNETs) G1/G2 and PNEC G3. AER and PER showed the best sensitivity (0.86-0.94) and specificity (0.92-1.0) for differentiating PNEC G3 from PNETs G1/G2. Mean grey-level intensity, entropy, and uniformity also showed acceptable sensitivity (0.73-0.91) and specificity (0.85-1.0). Mean grey-level intensity was also showed acceptable sensitivity (91% to 100%) and specificity (82% to 91%) in differentiating PNET G1 from PNET G2.

CONCLUSIONS

Our data indicated that texture parameters have potential in grading PNENs, in particular in differentiating PNEC G3 from PNETs G1/G2.

CT texture analysis of pancreatic cancer

OBJECTIVES

We investigated the value of CT texture analysis (CTTA) in predicting prognosis of unresectable pancreatic cancer.

METHODS

Sixty patients with unresectable pancreatic cancers at presentation were enrolled for post-processing with CTTA using commercially available software (TexRAD Ltd, Cambridge, UK). The largest cross-section of the tumour on axial CT was chosen to draw a region-of-interest. CTTA parameters (mean value of positive pixels (MPP), kurtosis, entropy, skewness), arterial and venous invasion, metastatic disease and tumour size were correlated with overall and progression-free survivals.

RESULTS

The median overall and progression-free survivals of cohort were 13.3 and 7.8 months, respectively. On multivariate Cox proportional hazard regression analysis, presence of metastatic disease at presentation had the highest association with overall survival (p = 0.003-0.05) and progression-free survival (p < 0.001 to p = 0.004). MPP at medium spatial filter was significantly associated with poor overall survival (p = 0.04). On Kaplan-Meier survival analysis of CTTA parameters at medium spatial filter, MPP of more than 31.625 and kurtosis of more than 0.565 had significantly worse overall survival (p = 0.036 and 0.028, respectively).

CONCLUSIONS

CTTA features were significantly associated with overall survival in pancreas cancer, particularly in patients with non-metastatic, locally advanced disease.

KEY POINTS

• CT texture analysis is easy to perform on contrast-enhanced CT. • CT texture analysis can determine prognosis in patients with unresectable pancreas cancer. • The best predictors of poor prognosis were high kurtosis and MPP.

Could texture features from preoperative CT image be used for predicting occult peritoneal carcinomatosis in patients with advanced gastric cancer?

Purpose

To retrospectively investigate whether texture features obtained from preoperative CT images of advanced gastric cancer (AGC) patients could be used for the prediction of occult peritoneal carcinomatosis (PC) detected during operation.

Materials and methods

51 AGC patients with occult PC detected during operation from January 2009 to December 2012 were included as occult PC group. For the control group, other 51 AGC patients without evidence of distant metastasis including PC, and whose clinical T and N stage could be matched to those of the patients of the occult PC group, were selected from the period of January 2011 to July 2012. Each group was divided into test (n = 41) and validation cohort (n = 10). Demographic and clinical data of these patients were acquired from the hospital database. Texture features including average, standard deviation, kurtosis, skewness, entropy, correlation, and contrast were obtained from manually drawn region of interest (ROI) over the omentum on the axial CT image showing the omentum at its largest cross sectional area. After using Fisher's exact and Wilcoxon signed-rank test for comparison of the clinical and texture features between the two groups of the test cohort, conditional logistic regression analysis was performed to determine significant independent predictor for occult PC. Using the optimal cut-off value from receiver operating characteristic (ROC) analysis for the significant variables, diagnostic sensitivity and specificity were determined in the test cohort. The cut-off value of the significant variables obtained from the test cohort was then applied to the validation cohort. Bonferroni correction was used to adjust P value for multiple comparisons.

Results

Between the two groups, there was no significant difference in the clinical features. Regarding the texture features, the occult PC group showed significantly higher average, entropy, standard deviation, and significantly lower correlation (P value < 0.004 for all). Conditional logistic regression analysis demonstrated that entropy was significant independent predictor for occult PC. When the cut-off value of entropy (> 7.141) was applied to the validation cohort, sensitivity and specificity for the prediction of occult PC were 80% and 90%, respectively.

Conclusion

For AGC patients whose PC cannot be detected with routine imaging such as CT, texture analysis may be a useful adjunct for the prediction of occult PC.

Pancreatic adenocarcinoma: cross-sectional imaging techniques

Pancreatic adenocarcinoma is a common malignancy that has a poor prognosis. Imaging is vital in its detection, staging, and management. Although a variety of imaging techniques are available, MDCT is the preferred imaging modality for staging and assessing the resectability of pancreatic adenocarcinoma. MR also has an important adjunct role, and may be used in addition to CT or as a problem-solving tool. A dedicated pancreatic protocol should be acquired as a biphasic technique optimized for the detection of pancreatic adenocarcinoma and to allow accurate local and distant disease staging. Emerging techniques like dual-energy CT and texture analysis of CT and MR images have a great potential in improving lesion detection, characterization, and treatment monitoring.

Computed Tomography Image Texture: A Noninvasive Prognostic Marker of Hepatic Recurrence After Hepatectomy for Metastatic Colorectal Cancer

BACKGROUND

Recurrence after resection of colorectal liver metastases (CRLMs) occurs in up to 75% of patients. Preoperative prediction of hepatic recurrence may inform therapeutic strategies at the time of initial resection. Texture analysis (TA) is an established technique that quantifies pixel intensity variations (heterogeneity) on cross-sectional imaging. We hypothesized that tumoral and parenchymal changes that are predictive of overall survival (OS) and recurrence in the future liver remnant (FLR) can be detected using TA on preoperative computed tomography (CT) images.

METHODS

Patients who underwent resection for CRLM between 2003 and 2007 with appropriate preoperative CT scans were included (n = 198) in this retrospective study. Texture features extracted from the tumor and FLR, and clinicopathologic variables, were incorporated into a multivariable survival model.

RESULTS

Quantitative imaging features of the FLR were an independent predictor of both OS and hepatic disease-free survival (HDFS). Tumor texture showed significant association with OS. TA of the FLR allowed patient stratification into two groups, with significantly different risks of hepatic recurrence (hazard ratio 2.09, 95% confidence interval 1.33-3.28; p = 0.001). Patients with homogeneous parenchyma had approximately twice the risk of hepatic recurrence (41 vs. 20%).

CONCLUSION

TA of the tumor and FLR are independently associated with OS, and TA of the FLR is independently associated with HDFS. Patients with homogeneous parenchyma had a significantly higher risk of hepatic recurrence. Preoperative TA of the liver represents a potential biomarker to identify patients at risk of liver recurrence after resection for CRLM.

Perfusion parameters as potential imaging biomarkers for the early prediction of radiotherapy response in a rat tumor model

PURPOSE

We aimed to compare various tumor-related radiologic morphometric changes and computed tomography (CT) perfusion parameters before and after treatment, and to determine the optimal imaging assessment technique for the prediction of early response in a rat tumor model treated with radiotherapy.

METHODS

Among paired tumors of FN13762 murine breast cancer cells implanted bilaterally in the necks of eight Fischer rats, tumors on the right side were treated with a single 20 Gy dose of radiotherapy. Perfusion CT studies were performed on day 0 before radiotherapy, and on days 1 and 5 after radiotherapy. Variables based on the size, including the longest diameter, tumor area, and volume, were measured. Quantitative perfusion analysis was performed for the whole tumor volume and permeabilities and blood volumes (BVs) were obtained. The area under the curve (AUC) difference in the histograms of perfusion parameters and texture analyses of uniformity and entropy were quantified. Apoptotic cell density was measured on pathology specimens immediately after perfusion imaging on day 5.

RESULTS

On day 1 after radiotherapy, differences in size between the irradiated and nonirradiated tumors were not significant. In terms of percent changes in the uniformity of permeabilities between tumors before irradiation and on day 1 after radiotherapy, the changes were significantly higher in the irradiated tumors than in the nonirradiated tumors (0.085 [-0.417, 0.331] vs. -0.131 [-0.536, 0.261], respectively; P = 0.042). The differences in AUCs of the histogram of voxel-by-voxel vascular permeability and BV in tumors between day 0 and day 1 were significantly higher in treated tumors compared with the control group (permeability, 21.4 [-2.2, 37.5] vs. 9.5 [-8.9, 33.8], respectively, P = 0.030; BV, 52.9 [-6186.0, 419.2] vs. 11.9 [-198.3, 346.7], respectively, P = 0.049). Apoptotic cell density showed a significantly positive correlation with the AUC difference of BV, the percent change of uniformity in permeability and BV (r=0.202, r=0.644, and r=0.706, respectively).

CONCLUSION

By enabling earlier tumor response prediction than morphometric evaluation, the histogram analysis of CT perfusion parameters appears to have a potential in providing prognostic predictive information in an irradiated rat model.

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.

Magnetic resonance based texture parameters as potential imaging biomarkers for predicting long-term survival in locally advanced rectal cancer treated by chemoradiotherapy

AIM

The study aimed to investigate whether textural features of rectal cancer on MRI can predict long-term survival in patients treated with long-course chemoradiotherapy.

METHOD

Textural analysis (TA) using a filtration-histogram technique of T2-weighted pre- and 6-week post-chemoradiotherapy MRI was undertaken using TexRAD, a proprietary software algorithm. Regions of interest enclosing the largest cross-sectional area of the tumour were manually delineated on the axial images and the filtration step extracted features at different anatomical scales (fine, medium and coarse) followed by quantification of statistical features [mean intensity, standard deviation, entropy, skewness, kurtosis and mean of positive pixels (MPP)] using histogram analysis. Cox multiple regression analysis determined which univariate features including textural, radiological and histological independently predicted overall survival (OS), disease-free survival (DFS) and recurrence-free survival (RFS).

RESULTS

MPP [fine texture, hazard ratio (HR) 6.9, 95% CI: 2.43-19.55, P < 0.001], mean (medium texture, HR 5.6, 95% CI: 1.4-21.7, P = 0.007) and extramural venous invasion (EMVI) on MRI (HR 2.96, 95% CI: 1.04-8.37, P = 0.041) independently predicted OS while mean (medium texture, HR 4.53, 95% CI: 1.58-12.94, P = 0.003), MPP (fine texture, HR 3.36, 95% CI: 1.36-8.31, P = 0.008) and threatened circumferential resection margin (CRM) on MRI (HR 3.1, 95% CI: 1.01-9.46, P = 0.046) predicted DFS. For OS, EMVI on MRI (HR 4.23, 95% CI: 1.41-12.69, P = 0.01) and for DFS kurtosis (medium texture, HR 3.97, 95% CI: 1.44-10.94, P = 0.007) and CRM involvement on MRI (HR 3.36, 95% CI: 1.21-9.32, P = 0.02) were the independent post-treatment factors. Only TA independently predicted RFS on pre- or post-treatment analyses.

CONCLUSION

MR based TA of rectal cancers can predict outcome before undergoing surgery and could potentially select patients for individualized therapy.

PET Radiomics in NSCLC: state of the art and a proposal for harmonization of methodology

Imaging with positron emission tomography (PET)/computed tomography (CT) is crucial in the management of cancer because of its value in tumor staging, response assessment, restaging, prognosis and treatment responsiveness prediction. In the last years, interest has grown in texture analysis which provides an "in-vivo" lesion characterization, and predictive information in several malignances including NSCLC; however several drawbacks and limitations affect these studies, especially because of lack of standardization in features calculation, definitions and methodology reporting. The present paper provides a comprehensive review of literature describing the state-of-the-art of FDG-PET/CT texture analysis in NSCLC, suggesting a proposal for harmonization of methodology.

Intratumoural heterogeneity measured using FDG PET and MRI is associated with tumour-stroma ratio and clinical outcome in head and neck squamous cell carcinoma

AIM

To evaluate the association between the tumour-stroma ratio and intratumoural heterogeneity measured using 2-[¹⁸F]-fluoro-2-deoxy-d-glucose (FDG) positron-emission tomography (PET) and magnetic resonance imaging (MRI), and further investigate the prognostic significance of imaging biomarkers in head and neck squamous cell carcinoma (HNSCC).

MATERIALS AND METHODS

Textural-based imaging parameters of the primary tumour were extracted in 44 patients. In addition, the difference between the minimum and maximum apparent diffusion coefficient (ADC) values (ADCdiff) was calculated on MRI. The relationships between the tumour-stroma ratio and imaging parameters were evaluated. The associations between imaging parameters and recurrence-free survival (RFS) were assessed using Cox proportional hazard regression models.

RESULTS

Coarseness (r=-0.382) on PET and ADCdiff (r=0.534) on MRI were significantly correlated with the proportion of stroma. The best imaging biomarkers for the 2-year RFS prediction were coarseness (AUC=0.741) and ADCdiff (AUC=0.779). Multivariate analysis showed that coarseness (hazard ratio=10.549, 95% confidence interval=2.544-43.748, p=0.001) was an independent prognostic factor for RFS.

CONCLUSION

Heterogeneity imaging parameters are significantly associated with the tumour-stroma ratio. These imaging biomarkers may help to facilitate the risk stratification for tumour recurrence in HNSCC.

Influence of gray level and space discretization on brain tumor heterogeneity measures obtained from magnetic resonance images

PURPOSE

Tumor heterogeneity in medical imaging is a current research trend due to its potential relationship with tumor malignancy. The aim of this study is to analyze the effect of dynamic range and matrix size changes on the results of different heterogeneity measures.

MATERIALS AND METHODS

Four patients harboring three glioblastomas and one metastasis were considered. Sixteen textural heterogeneity measures were computed for each patient, with a configuration including co-occurrence matrices (CM) features (local heterogeneity) and run-length matrices (RLM) features (regional heterogeneity). The coefficient of variation measured agreement between the textural measures in two types of experiments: (i) fixing the matrix size and changing the dynamic range and (ii) fixing the dynamic range and changing the matrix size.

RESULTS

None of the measures considered were robust under dynamic range changes. The CM Entropy and the RLM high gray-level run emphasis (HGRE) were the outstanding textural features due to their robustness under matrix size changes. Also, the RLM low gray-level run emphasis (LGRE) provided robust results when the dynamic range considered was sufficiently high (more than 8 levels). All of the remaining textural features were not robust.

CONCLUSION

Tumor texture studies based on images with different characteristics (e.g. multi-center studies) should first fix the dynamic range to be considered. For studies involving images of different resolutions either (i) only robust measures should be used (in our study CM entropy, RLM HGRE and/or RLM LGRE) or (ii) images should be resampled to match those of the lowest resolution before computing the textural features.

Assessment of Heterogeneity Difference Between Edge and Core by Using Texture Analysis: Differentiation of Malignant From Inflammatory Pulmonary Nodules and Masses

RATIONALE AND OBJECTIVES

This study aimed to test the hypothesis that the heterogeneity difference between edge and core of lesions by using intensity and entropy features obtained from whole-lesion texture analysis on contrast-enhanced computed tomography (CT) may be useful for differentiation of malignant from inflammatory pulmonary nodules and masses.

MATERIALS AND METHODS

In all, 48 single pulmonary nodules and masses were retrospectively evaluated. All lesions were histologically or clinically confirmed (malignancy: inflammation = 24:20). We utilized a newly introduced texture analysis method based on contrast-enhanced CT (first described by Grove et al.) that automatically divided the whole lesion volume into two regions: edge and core. Mean attenuation value (AV) and entropy of each region and also the whole lesion were evaluated separately. Each texture metric (absolute value for each region, and difference value defined as difference between edge and core) of malignant and inflammatory lesions were compared using Mann-Whitney U test. Individual image parameters were combined by using linear discriminant analysis. Receiver operating characteristic curves were generated to assess each texture metric and their combination for discriminating between the two entities.

RESULTS

Mean AV difference and entropy difference were significantly higher in malignant lesions than in inflammatory lesions (4.71 HU ± 5.06 vs -1.53 HU ± 5.05, P < .001; 0.45 ± 0.23 vs 0.18 ± 0.30, P = .001). Receiver operating characteristic curves for individual mean AV difference and entropy difference provided relatively high values for the area under the curve (0.836 and 0.795, respectively). The combination of mean AV difference, entropy difference, and lesion volume improved the area under the curve to 0.864.

CONCLUSION

Heterogeneity difference between edge and core by using whole-lesion texture analysis on contrast-enhanced CT may be a promising tool for estimating the probability of malignancy in pulmonary nodules and masses.

Tumor heterogeneity measured on F-18 fluorodeoxyglucose positron emission tomography/computed tomography combined with plasma Epstein-Barr Virus load predicts prognosis in patients with primary nasopharyngeal carcinoma

OBJECTIVES/HYPOTHESIS

Plasma Epstein-Barr virus (EBV) DNA concentrations predict prognosis in patients with nasopharyngeal carcinoma (NPC). Recent evidence also indicates that intratumor heterogeneity on F-18 fluorodeoxyglucose positron emission tomography (¹⁸ F-FDG PET) scans is predictive of treatment outcomes in different solid malignancies. Here, we sought to investigate the prognostic value of heterogeneity parameters in patients with primary NPC.

STUDY DESIGN

Retrospective cohort study.

METHODS

We examined 101 patients with primary NPC who underwent pretreatment ¹⁸ F-FDG PET/computed tomography. Circulating levels of EBV DNA were measured in all participants. The following PET heterogeneity parameters were collected: histogram-based heterogeneity parameters, second-order texture features (uniformity, contrast, entropy, homogeneity, dissimilarity, inverse difference moment), and higher-order (coarseness, contrast, busyness, complexity, strength) texture features.

RESULTS

The median follow-up time was 5.14 years. Total lesion glycolysis (TLG), tumor heterogeneity measured by histogram-based parameter skewness, and the majority of second-order or higher-order texture features were significantly associated with overall survival (OS) and/or recurrence-free survival (RFS). In multivariate analysis, age (P =.005), EBV DNA load (P = .0002), and uniformity (P = .001) independently predicted OS. Only skewness retained the independent prognostic significance for RFS. Tumor stage, standardized uptake value, or TLG did not show an independent association with survival endpoints. The combination of uniformity, EBV DNA load, and age resulted in a more reliable prognostic stratification (P < .001).

CONCLUSIONS

Tumor heterogeneity is superior to traditional PET parameters for predicting outcomes in primary NPC. The combination of uniformity with EBV DNA load can improve prognostic stratification in this clinical entity.

LEVEL OF EVIDENCE

4 Laryngoscope, 127:E22-E28, 2017.

Measuring Computed Tomography Scanner Variability of Radiomics Features

OBJECTIVES

The purpose of this study was to determine the significance of interscanner variability in CT image radiomics studies.

MATERIALS AND METHODS

We compared the radiomics features calculated for non-small cell lung cancer (NSCLC) tumors from 20 patients with those calculated for 17 scans of a specially designed radiomics phantom. The phantom comprised 10 cartridges, each filled with different materials to produce a wide range of radiomics feature values. The scans were acquired using General Electric, Philips, Siemens, and Toshiba scanners from 4 medical centers using their routine thoracic imaging protocol. The radiomics feature studied included the mean and standard deviations of the CT numbers as well as textures derived from the neighborhood gray-tone difference matrix. To quantify the significance of the interscanner variability, we introduced the metric feature noise. To look for patterns in the scans, we performed hierarchical clustering for each cartridge.

RESULTS

The mean CT numbers for the 17 CT scans of the phantom cartridges spanned from -864 to 652 Hounsfield units compared with a span of -186 to 35 Hounsfield units for the CT scans of the NSCLC tumors, showing that the phantom's dynamic range includes that of the tumors. The interscanner variability of the feature values depended on both the cartridge material and the feature, and the variability was large relative to the interpatient variability in the NSCLC tumors for some features. The feature interscanner noise was greatest for busyness and least for texture strength. Hierarchical clustering produced different clusters of the phantom scans for each cartridge, although there was some consistent clustering by scanner manufacturer.

CONCLUSIONS

The variability in the values of radiomics features calculated on CT images from different CT scanners can be comparable to the variability in these features found in CT images of NSCLC tumors. These interscanner differences should be considered, and their effects should be minimized in future radiomics studies.

Tumour heterogeneity in glioblastoma assessed by MRI texture analysis: a potential marker of survival

OBJECTIVE:

The main objective of this retrospective work was the study of three-dimensional (3D) heterogeneity measures of post-contrast pre-operative MR images acquired with T₁ weighted sequences of patients with glioblastoma (GBM) as predictors of clinical outcome.

METHODS:

79 patients from 3 hospitals were included in the study. 16 3D textural heterogeneity measures were computed including run-length matrix (RLM) features (regional heterogeneity) and co-occurrence matrix (CM) features (local heterogeneity). The significance of the results was studied using Kaplan-Meier curves and Cox proportional hazards analysis. Correlation between the variables of the study was assessed using the Spearman's correlation coefficient.

RESULTS:

Kaplan-Meyer survival analysis showed that 4 of the 11 RLM features and 4 of the 5 CM features considered were robust predictors of survival. The median survival differences in the most significant cases were of over 6 months.

CONCLUSION:

Heterogeneity measures computed on the post-contrast pre-operative T₁ weighted MR images of patients with GBM are predictors of survival.

ADVANCES IN KNOWLEDGE:

Texture analysis to assess tumour heterogeneity has been widely studied. However, most works develop a two-dimensional analysis, focusing only on one MRI slice to state tumour heterogeneity. The study of fully 3D heterogeneity textural features as predictors of clinical outcome is more robust and is not dependent on the selected slice of the tumour.

Prognostic value of computed tomography texture features in non-small cell lung cancers treated with definitive concomitant chemoradiotherapy

OBJECTIVES

The aim of this study was to investigate whether the computed tomography (CT) texture features of primary tumors are associated with the overall survival (OS) of non-small cell lung cancer (NSCLC) patients undergoing definitive concomitant chemoradiotherapy (CCRT).

MATERIALS AND METHODS

In this retrospective study, 98 patients (83 men and 15 women; mean age, 61.9 ± 8.0 years) with unresectable NSCLCs (stage IIIA, 45; stage IIIB, 53) underwent definitive CCRT at our institution from January 2006 to December 2011. Patients were followed up for 3 years or until death. The CT texture parameters of primary tumors were extracted from contrast-enhanced CT images taken before CCRT using an in-house software program. Each texture parameter was dichotomized based on their optimal cutoff values obtained from receiver operating characteristics curve analysis. Three-year OS was compared between the dichotomized subgroups using Kaplan-Meier analysis and the log-rank test. Multivariate Cox regression analysis was performed to determine significant prognostic factors.

RESULTS

The 3-year cumulative survival rate was 0.51. The mean 3-year OS was 24.0 months (95% confidence interval, 21.5-26.6 months). There were no significant differences in 3-year OS according to tumor stage or histologic subtypes. However, entropy (P = 0.030), skewness (P = 0.021), and mean attenuation (P = 0.030) were shown to be significantly associated with 3-year OS. Multivariate Cox regression analysis revealed that higher entropy (adjusted hazard ratio [HR],2.31; P = 0.040), higher skewness (adjusted HR,1.92; P = 0.046), and higher mean attenuation (adjusted HR,1.93; P = 0.028) were independent predictors of decreased 3-year OS.

CONCLUSIONS

Computed tomography texture features have the potential to be used as prognostic biomarkers in unresectable NSCLC patients undergoing definitive CCRT.

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.

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.

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.

Glioma: application of whole-tumor texture analysis of diffusion-weighted imaging for the evaluation of tumor heterogeneity

BACKGROUND AND PURPOSE

To apply a texture analysis of apparent diffusion coefficient (ADC) maps to evaluate glioma heterogeneity, which was correlated with tumor grade.

MATERIALS AND METHODS

Forty patients with glioma (WHO grade II (n = 8), grade III (n = 10) and grade IV (n = 22)) underwent diffusion-weighted imaging (DWI), and the corresponding ADC maps were obtained. Regions of interest containing the lesions were drawn on every section of the ADC map containing the tumor, and volume-based data of the entire tumor were constructed. Texture and first order features including entropy, skewness and kurtosis were derived from the ADC map using in-house software. A histogram analysis of the ADC map was also performed. The texture and histogram parameters were compared between low-grade and high-grade gliomas using an unpaired student's t-test. Additionally, a one-way analysis of variance analysis with a post-hoc test was performed to compare the parameters of each grade.

RESULTS

Entropy was observed to be significantly higher in high-grade gliomas than low-grade tumors (6.861±0.539 vs. 6.261±0.412, P  = 0.006). The fifth percentiles of the ADC cumulative histogram also showed a significant difference between high and low grade gliomas (836±235 vs. 1030±185, P = 0.037). Only entropy proved to be significantly different between grades III and IV (6.295±0.4963 vs. 7.119±0.3165, P<0.001). The diagnostic accuracy of ADC entropy was significantly higher than that of the fifth percentile of the ADC histogram (P = 0.0034) in distinguishing high- from low-grade glioma.

CONCLUSION

A texture analysis of the ADC map based on the entire tumor volume can be useful for evaluating glioma grade, which provides tumor heterogeneity.

Computerized texture analysis of persistent part-solid ground-glass nodules: differentiation of preinvasive lesions from invasive pulmonary adenocarcinomas

PURPOSE

To retrospectively investigate the value of computerized three-dimensional texture analysis for differentiation of preinvasive lesions from invasive pulmonary adenocarcinomas (IPAs) that manifest as part-solid ground-glass nodules (GGNs).

MATERIALS AND METHODS

The institutional review board approved this retrospective study with a waiver of patients' informed consent. The study consisted of 86 patients with 86 pathologic analysis-confirmed part-solid GGNs (mean size, 16 mm ± 5.4 [standard deviation]) who had undergone computed tomographic (CT) imaging between January 2005 and October 2011. Each part-solid GGN was manually segmented and its computerized texture features were quantitatively extracted by using an in-house software program. Multivariate logistic regression analysis was performed to investigate the differentiating factors of preinvasive lesions from IPAs. Three-layered artificial neural networks (ANNs) with a back-propagation algorithm and receiver operating characteristic curve analysis were used to build a discriminating model with texture features and to evaluate its discriminating performance.

RESULTS

Pathologic analysis confirmed 58 IPAs (seven minimally invasive adenocarcinomas and 51 invasive adenocarcinomas) and 28 preinvasive lesions (four atypical adenomatous hyperplasias and 24 adenocarcinomas in situ). IPAs and preinvasive lesions exhibited significant differences in various histograms and volumetric parameters (P < .05). Multivariate analysis revealed that smaller mass (adjusted odds ratio, 0.092) and higher kurtosis (adjusted odds ratio, 3.319) are significant differentiators of preinvasive lesions from IPAs (P < .05). With mean attenuation, standard deviation of attenuation, mass, kurtosis, and entropy, the ANNs model showed excellent accuracy in differentiation of preinvasive lesions from IPAs (area under the curve, 0.981).

CONCLUSION

In part-solid GGNs, higher kurtosis and smaller mass are significant differentiators of preinvasive lesions from IPAs, and preinvasive lesions can be accurately differentiated from IPAs by using computerized texture analysis. Online supplemental material is available for this article.

The role of texture analysis in imaging as an outcome predictor and potential tool in radiotherapy treatment planning

Predicting a tumour's response to radiotherapy prior to the start of treatment could enhance clinical care management by enabling the personalization of treatment plans based on predicted outcome. In recent years, there has been accumulating evidence relating tumour texture to patient survival and response to treatment. Tumour texture could be measured from medical images that provide a non-invasive method of capturing intratumoural heterogeneity and hence could potentially enable a prior assessment of a patient's predicted response to treatment. In this article, work presented in the literature regarding texture analysis in radiotherapy in relation to survival and outcome is discussed. Challenges facing integrating texture analysis in radiotherapy planning are highlighted and recommendations for future directions in research are suggested.

Changes in primary breast cancer heterogeneity may augment midtreatment MR imaging assessment of response to neoadjuvant chemotherapy

PURPOSE

To evaluate whether changes in magnetic resonance (MR) imaging heterogeneity may aid assessment for pathologic complete response (pCR) to neoadjuvant chemotherapy (NACT) in primary breast cancer and to compare pCR with standard Response Evaluation Criteria in Solid Tumors response.

MATERIALS AND METHODS

Institutional review board approval, with waiver of informed consent, was obtained for this retrospective analysis of 36 consecutive female patients, with unilateral unifocal primary breast cancer larger than 2 cm in diameter who were receiving sequential anthracycline-taxane NACT between October 2008 and October 2012. T2- and T1-weighted dynamic contrast material-enhanced MR imaging was performed before, at midtreatment (after three cycles), and after NACT. Changes in tumor entropy (irregularity) and uniformity (gray-level distribution) were determined before and after MR image filtration (for different-sized features). Entropy and uniformity for pathologic complete responders and nonresponders were compared by using the Mann-Whitney U test and receiver operating characteristic analysis.

RESULTS

With NACT, there was an increase in uniformity and a decrease in entropy on T2-weighted and contrast-enhanced subtracted T1-weighted MR images for all filters (uniformity: 23.45% and 22.62%; entropy: -19.15% and -19.26%, respectively). There were eight complete pathologic responders. An area under the curve of 0.84 for T2-weighted MR imaging entropy and uniformity (P = .004 and .003) and 0.66 for size (P = .183) for pCR was found, giving a sensitivity and specificity of 87.5% and 82.1% for entropy and 87.5% and 78.6% for uniformity compared with 50% and 82.1%, respectively, for tumor size change for association with pCR.

CONCLUSION

Tumors become more homogeneous with treatment. An increase in T2-weighted MR imaging uniformity and a decrease in T2-weighted MR imaging entropy following NACT may provide an earlier indication of pCR than tumor size change.