Tumor Heterogeneity in Lung Cancer: Assessment with Dynamic Contrast-enhanced MR Imaging

CT and MRI radiomic features of lung cancer (NSCLC): comparison and software consistency

BACKGROUND

Radiomics is a quantitative approach that allows the extraction of mineable data from medical images. Despite the growing clinical interest, radiomics studies are affected by variability stemming from analysis choices. We aimed to investigate the agreement between two open-source radiomics software for both contrast-enhanced computed tomography (CT) and contrast-enhanced magnetic resonance imaging (MRI) of lung cancers and to preliminarily evaluate the existence of radiomic features stable for both techniques.

METHODS

Contrast-enhanced CT and MRI images of 35 patients affected with non-small cell lung cancer (NSCLC) were manually segmented and preprocessed using three different methods. Sixty-six Image Biomarker Standardisation Initiative-compliant features common to the considered platforms, PyRadiomics and LIFEx, were extracted. The correlation among features with the same mathematical definition was analyzed by comparing PyRadiomics and LIFEx (at fixed imaging technique), and MRI with CT results (for the same software).

RESULTS

When assessing the agreement between LIFEx and PyRadiomics across the considered resampling, the maximum statistically significant correlations were observed to be 94% for CT features and 95% for MRI ones. When examining the correlation between features extracted from contrast-enhanced CT and MRI using the same software, higher significant correspondences were identified in 11% of features for both software.

CONCLUSIONS

Considering NSCLC, (i) for both imaging techniques, LIFEx and PyRadiomics agreed on average for 90% of features, with MRI being more affected by resampling and (ii) CT and MRI contained mostly non-redundant information, but there are shape features and, more importantly, texture features that can be singled out by both techniques.

RELEVANCE STATEMENT

Identifying and selecting features that are stable cross-modalities may be one of the strategies to pave the way for radiomics clinical translation.

KEY POINTS

• More than 90% of LIFEx and PyRadiomics features contain the same information. • Ten percent of features (shape, texture) are stable among contrast-enhanced CT and MRI. • Software compliance and cross-modalities stability features are impacted by the resampling method.

Correlation of MRI quantitative perfusion parameters with EGFR, VEGF and EGFR gene mutations in non-small cell cancer

To explore the relationship between quantitative perfusion histogram parameters of dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) with the expression of tumor tissue epidermal growth factor receptor (EGFR), vascular endothelial growth factor (VEGF) and EGFR gene mutations in non-small cell lung cancer (NSCLC). A total of 44 consecutive patients with known NSCLC were recruited from March 2018 to August 2021. Histogram parameters (mean, uniformity, skewness, energy, kurtosis, entropy, percentile) of each (Ktrans, Kep, Ve, Vp, Fp) were obtained by Omni Kinetics software. Immunohistochemistry staining was used in the detection of the expression of VEGF and EGFR protein, and the mutation of EGFR gene was detected by PCR. Corresponding statistical test was performed to compare the parameters and protein expression between squamous cell carcinoma (SCC) and adenocarcinoma (AC), as well as EGFR mutations and wild-type. Correlation analysis was used to evaluate the correlation between parameters with the expression of VEGF and EGFR protein. Fp (skewness, kurtosis, energy) were statistically significant between SCC and AC, and the area under the ROC curve were 0.733, 0.700 and 0.675, respectively. The expression of VEGF in AC was higher than in SCC. Fp (skewness, kurtosis, energy) were negatively correlated with VEGF (r =  - 0.527, - 0.428, - 0.342); Ktrans (Q50) was positively correlated with VEGF (r = 0.32); Kep (energy), Ktrans (skewness, kurtosis) were positively correlated with EGFR (r = 0.622, r = 0.375, 0.358), some histogram parameters of Kep, Ktrans (uniformity, entropy) and Ve (kurtosis) were negatively correlated with EGFR (r =  - 0.312 to - 0.644). Some perfusion histogram parameters were statistically significant between EGFR mutations and wild-type, they were higher in wild-type than mutated (P < 0.05). Quantitative perfusion histogram parameters of DCE-MRI have a certain value in the differential diagnosis of NSCLC, which have the potential to non-invasively evaluate the expression of cell signaling pathway-related protein.

A nomogram model based on pre-treatment and post-treatment MR imaging radiomics signatures: application to predict progression-free survival for nasopharyngeal carcinoma

BACKGROUND

To establish a novel model using radiomics analysis of pre-treatment and post-treatment magnetic resonance (MR) images for prediction of progression-free survival in the patients with stage II-IVA nasopharyngeal carcinoma (NPC) in South China.

METHODS

One hundred and twenty NPC patients who underwent chemoradiotherapy were enrolled (80 in the training cohort and 40 in the validation cohort). Acquiring data and screening features were performed successively. Totally 1133 radiomics features were extracted from the T2-weight images before and after treatment. Least absolute shrinkage and selection operator regression, recursive feature elimination algorithm, random forest, and minimum-redundancy maximum-relevancy (mRMR) method were used for feature selection. Nomogram discrimination and calibration were evaluated. Harrell's concordance index (C-index) and receiver operating characteristic (ROC) analyses were applied to appraise the prognostic performance of nomograms. Survival curves were plotted using Kaplan-Meier method.

RESULTS

Integrating independent clinical predictors with pre-treatment and post-treatment radiomics signatures which were calculated in conformity with radiomics features, we established a clinical-and-radiomics nomogram by multivariable Cox regression. Nomogram consisting of 14 pre-treatment and 7 post-treatment selected features has been proved to yield a reliable predictive performance in both training and validation groups. The C-index of clinical-and-radiomics nomogram was 0.953 (all P < 0.05), which was higher than that of clinical (0.861) or radiomics nomograms alone (based on pre-treatment statistics: 0.942; based on post-treatment statistics: 0.944). Moreover, we received Rad-score of pre-treatment named RS1 and post-treatment named RS2 and all were used as independent predictors to divide patients into high-risk and low-risk groups. Kaplan-Meier analysis showed that lower RS1 (less than cutoff value, - 1.488) and RS2 (less than cutoff value, - 0.180) were easier to avoid disease progression (all P < 0.01). It showed clinical benefit with decision curve analysis.

CONCLUSIONS

MR-based radiomics measured the burden on primary tumor before treatment and the tumor regression after chemoradiotherapy, and was used to build a model to predict progression-free survival (PFS) in the stage II-IVA NPC patients. It can also help to distinguish high-risk patients from low-risk patients, thus guiding personalized treatment decisions effectively.

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.

Dynamic contrast-enhanced MRI perfusion parameters are imaging biomarkers for angiogenesis in lung cancer

BACKGROUND

Quantitative parameters of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) may have the potential to reflect angiogenesis and proliferation of pulmonary neoplasms.

PURPOSE

To verify whether DCE-MRI can identify pulmonary neoplasm property and evaluate the correlation of DCE-MRI perfusion parameters with microvessel density (MVD) and Ki-67 in lung cancer.

MATERIAL AND METHODS

This study enrolled 65 patients with one pulmonary neoplasm who underwent computed tomography-guided percutaneous lung biopsy with pathological diagnosis (43 malignant, 22 benign; mean age = 59.71 ± 11.72 years). All patients did DCE-MRI before biopsy. Quantitative MRI parameters including endothelial transfer constant (K^trans), flux rate constant (K_ep), and fractional extravascular extracellular space (EES) volume (V_e) were calculated by extended Tofts linear model. MVD was evaluated by CD34-expressing tumor vessels. Proliferation was assessed by Ki-67 staining. The correlations of parameters with MVD and Ki-67 expression were analyzed.

RESULTS

K^trans and K_ep values were significantly increased in malignant lesions compared to benign lesions (P = 0.001 and 0.022, respectively), whereas no statistical difference in V_e was found. The CD34 expression was positively correlated to K^trans (r = 0.608; P = 0.004) and K_ep (r = 0.556; P = 0.001). Subsequent subtype analyses also showed positive correlations of K^trans and K_ep with MVD in adenocarcinoma group (r = 0.550 and 0.563; P = 0.012 and 0.015, respectively). No significant correlation was found between these parameters and Ki-67.

CONCLUSION

K^trans and K_ep may distinguish benign and malignant pulmonary neoplasm. K^trans and K_ep, with their positive correlation to MVD, can be used as non-invasive parameters reflecting lung cancer angiogenesis.

Assessing dynamic metabolic heterogeneity in non-small cell lung cancer patients via ultra-high sensitivity total-body [18F]FDG PET/CT imaging: quantitative analysis of [18F]FDG uptake in primary tumors and metastatic lymph nodes

PURPOSE

This study aimed to quantitatively assess [¹⁸F]FDG uptake in primary tumor (PT) and metastatic lymph node (mLN) in newly diagnosed non-small cell lung cancer (NSCLC) using the total-body [¹⁸F]FDG PET/CT and to characterize the dynamic metabolic heterogeneity of NSCLC.

METHODS

The 60-min dynamic total-body [¹⁸F]FDG PET/CT was performed before treatment. The PTs and mLNs were manually delineated. An unsupervised K-means classification method was used to cluster patients based on the imaging features of PTs. The metabolic features, including Patlak-Ki, Patlak-Intercept, SUV_mean, metabolic tumor volume (MTV), total lesion glycolysis (TLG), and textural features, were extracted from PTs and mLNs. The targeted next-generation sequencing of tumor-associated genes was performed. The expression of Ki67, CD3, CD8, CD34, CD68, and CD163 in PTs was determined by immunohistochemistry.

RESULTS

A total of 30 patients with stage IIIA-IV NSCLC were enrolled. Patients were divided into fast dynamic FDG metabolic group (F-DFM) and slow dynamic FDG metabolic group (S-DFM) by the unsupervised K-means classification of PTs. The F-DFM group showed significantly higher Patlak-Ki (P < 0.001) and SUV_mean (P < 0.001) of PTs compared with the S-DFM group, while no significant difference was observed in Patlak-Ki and SUV_mean of mLNs between the two groups. The texture analysis indicated that PTs in the S-DFM group were more heterogeneous in FDG uptake than those in the F-DFM group. Higher T cells (CD3⁺/CD8⁺) and macrophages (CD68⁺/CD163⁺) infiltration in the PTs were observed in the F-DFM group. No significant difference was observed in tumor mutational burden between the two groups.

CONCLUSION

The dynamic total-body [¹⁸F]FDG PET/CT stratified NSCLC patients into the F-DFM and S-DFM groups, based on Patlak-Ki and SUV_mean of PTs. PTs in the F-DFM group seemed to be more homogenous in terms of [¹⁸F]FDG uptake than those in the S-DFM group. The higher infiltrations of T cells and macrophages were observed in the F-DFM group, which suggested a potential benefit from immunotherapy.

Volumetric Analysis of Intravoxel Incoherent Motion Diffusion-Weighted Imaging for Predicting the Response to Chemotherapy in Patients With Locally Advanced Non-Small Cell Lung Cancer

OBJECTIVE

We aimed to prospectively investigate intravoxel incoherent motion parameters to predict the response to chemotherapy in locally advanced non-small cell lung cancer (NSCLC) patients.

METHODS

From July 2016 to March 2018, 30 advanced NSCLC patients were enrolled and underwent chest intravoxel incoherent motion-diffusion-weighted imaging at Siemens 3T magnetic resonance imaging before and at the end of the first cycle of chemotherapy. Regions of interest were drawn including the whole tumor volume to derive the apparent diffusion coefficient value, D, D*, and f, respectively. Time-dependent receiver operating characteristic curves were generated to evaluate the cutoff values of continuous variables. A Cox proportional hazards model was used to assess the independent predictors of progression-free survival (PFS) and overall survival (OS). Kaplan-Meier curves and log-rank test were generated.

RESULTS

Among the 30 patients, 28 cases (93.3%) died and 2 cases (6.7%) survived till the closeout date. Univariate Cox regression analyses revealed that the significant predictors of PFS and OS were the tumor size reduction rate, the change rates of D and apparent diffusion coefficient values, and the D value before therapy (PFS: P = 0.015, hazard ratio [HR] = 2.841; P < 0.001, HR = 5.840; P = 0.044, HR = 2.457; and P = 0.027, HR = 2.715; OS: P = 0.008, HR = 2.987; P < 0.001, HR = 4.357; P = 0.006, HR = 3.313; and P = 0.013, HR = 2.941, respectively). Multivariate Cox regression analysis suggested that △D% was identified as independent predictors of both PFS and OS (P = 0.003, HR = 9.200 and P = 0.016, HR = 4.617). In addition, the cutoff value of △D% was 21.06% calculated by receiver operating characteristic curve analysis. In the Kaplan-Meier analysis, the PFS and OS were significantly greater in the group of patients with △D% larger than 21.06% (log-rank test, χ2 = 16.453, P < 0.001; χ2 = 13.952, P < 0.001).

CONCLUSIONS

Intravoxel incoherent motion-diffusion-weighted imaging was preferred for predicting the prognosis of advanced NSCLC patients treated with chemotherapy. A D increase more than 21.06% at 1 month was associated with a lower rate of disease progression and death.

Differentiating solitary brain metastases from glioblastoma by radiomics features derived from MRI and 18F-FDG-PET and the combined application of multiple models

This study aimed to explore the ability of radiomics derived from both MRI and 18F-fluorodeoxyglucose positron emission tomography (18F-FDG-PET) images to differentiate glioblastoma (GBM) from solitary brain metastases (SBM) and to investigate the combined application of multiple models. The imaging data of 100 patients with brain tumours (50 GBMs and 50 SBMs) were retrospectively analysed. Three model sets were built on MRI, 18F-FDG-PET, and MRI combined with 18F-FDG-PET using five feature selection methods and five classification algorithms. The model set with the highest average AUC value was selected, in which some models were selected and divided into Groups A, B, and C. Individual and joint voting predictions were performed in each group for the entire data. The model set based on MRI combined with 18F-FDG-PET had the highest average AUC compared with isolated MRI or 18F-FDG-PET. Joint voting prediction showed better performance than the individual prediction when all models reached an agreement. In conclusion, radiomics derived from MRI and 18F-FDG-PET could help differentiate GBM from SBM preoperatively. The combined application of multiple models can provide greater benefits.

Features from MRI texture analysis associated with survival outcomes in triple-negative breast cancer patients

PURPOSE

The purpose of the study is to evaluate the associations between intratumoral or peritumoral textural features derived from pretreatment magnetic resonance imaging (MRI) and recurrence-free survival (RFS) in triple-negative breast cancer (TNBC) patients.

METHODS

Forty-three patients with TNBC who underwent preoperative MRI between February 2008 and March 2014 were included. We performed two-dimensional texture analysis on the intratumoral or peritumoral region of interest (ROI) on axial of T2-weighted image (T2WI), dynamic contrast-enhanced (DCE)-MRI and DCE-MRI subtraction images. We also analyzed histopathological data. Cox proportional hazards models were used to investigate associations with survival outcomes.

RESULTS

Twelve of the 43 patients (27.9%) had recurrence disease, at a median of 32.5 months follow-up (1.4-61.5 months). In univariate analysis, nine texture features in T2WI and DCE-MRI subtraction images were significantly associated with RFS. In multivariate analysis, intratumoral difference entropy in DCE-MRI subtraction images in the initial phase (hazard ratio 11.71; 95% confidence interval (CI) [1.41, 97.00]; p value 0.023) and, peritumoral difference variance in DCE-MRI subtraction images in the delayed phase (hazard ratio 9.60; 95% CI [1.98, 46.51]; p value 0.005), were both independently associated with RFS. Moreover, multivariate analysis revealed the presence of lymphovascular invasion as independently associated with RFS (hazard ratio 8.13; 95% CI [2.16, 30.30]; p value 0.002).

CONCLUSIONS

At pretreatment MRI, an intratumoral and peritumoral quantitative approach using texture analysis has the potential to serve as a prognostic marker in patients with TNBC.

Radiomics-based neural network predicts recurrence patterns in glioblastoma using dynamic susceptibility contrast-enhanced MRI

Glioblastoma remains the most devastating brain tumor despite optimal treatment, because of the high rate of recurrence. Distant recurrence has distinct genomic alterations compared to local recurrence, which requires different treatment planning both in clinical practice and trials. To date, perfusion-weighted MRI has revealed that perfusional characteristics of tumor are associated with prognosis. However, not much research has focused on recurrence patterns in glioblastoma: namely, local and distant recurrence. Here, we propose two different neural network models to predict the recurrence patterns in glioblastoma that utilizes high-dimensional radiomic profiles based on perfusion MRI: area under the curve (AUC) (95% confidence interval), 0.969 (0.903-1.000) for local recurrence; 0.864 (0.726-0.976) for distant recurrence for each patient in the validation set. This creates an opportunity to provide personalized medicine in contrast to studies investigating only group differences. Moreover, interpretable deep learning identified that salient radiomic features for each recurrence pattern are related to perfusional intratumoral heterogeneity. We also demonstrated that the combined salient radiomic features, or "radiomic risk score", increased risk of recurrence/progression (hazard ratio, 1.61; p = 0.03) in multivariate Cox regression on progression-free survival.

Correlation between quantitative perfusion histogram parameters of DCE-MRI and PTEN, P-Akt and m-TOR in different pathological types of lung cancer

Background

To explore if the quantitative perfusion histogram parameters of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) correlates with the expression of PTEN, P-Akt and m-TOR protein in lung cancer.

Methods

Thirty‐three patients with 33 lesions who had been diagnosed with lung cancer were enrolled in this study. They were divided into three groups: squamous cell carcinoma (SCC, 15 cases), adenocarcinoma (AC, 12 cases) and small cell lung cancer (SCLC, 6 cases). Preoperative imaging (conventional imaging and DCE-MRI) was performed on all patients. The Exchange model was used to measure the phar- macokinetic parameters, including K^trans, V_p, K_ep, V_e and F_p, and then the histogram parameters meanvalue, skewness, kurtosis, uniformity, energy, entropy, quantile of above five parameters were analyzed. The expression of PTEN, P-Akt and m-TOR were assessed by immunohistochemistry. Spearman correlation analysis was used to compare the correlation between the quantitative perfusion histogram parameters and the expression of PTEN, P-Akt and m-TOR in different pathological subtypes of lung cancer.

Results

The expression of m-TOR (P = 0.013) and P-Akt (P = 0.002) in AC was significantly higher than those in SCC. V_p (uniformity) in SCC group, K^trans (uniformity), V_e (kurtosis, Q10, Q25) in AC group, F_p (skewness, kurtosis, energy), V_e (Q75, Q90, Q95) in SCLC group was positively correlated with PTEN, and F_p (entropy) in the SCLC group was negatively correlated with PTEN (P < 0.05); K_ep (Q5, Q10) in the SCLC group was positively correlated with P-Akt, and K_ep (energy) in the SCLC group was negatively correlated with P-Akt (P < 0.05); K_ep (Q5) in SCC group and V_p (meanvalue, Q75, Q90, Q95) in SCLC group was positively correlated with m-TOR, and V_e (meanvalue) in SCC group was negatively correlated with m-TOR (P < 0.05).

Conclusions

The quantitative perfusion histogram parameters of DCE-MRI was correlated with the expression of PTEN, P-Akt and m-TOR in different pathological types of lung cancer, which may be used to indirectly evaluate the activation status of PI3K/Akt/mTOR signal pathway gene in lung cancer, and provide important reference for clinical treatment.

Flexibility and Adaptation of Cancer Cells in a Heterogenous Metabolic Microenvironment

The metabolic microenvironment, comprising all soluble and insoluble nutrients and co-factors in the extracellular milieu, has a major impact on cancer cell proliferation and survival. A large body of evidence from recent studies suggests that tumor cells show a high degree of metabolic flexibility and adapt to variations in nutrient availability. Insufficient vascular networks and an imbalance of supply and demand shape the metabolic tumor microenvironment, which typically contains a lower concentration of glucose compared to normal tissues. The present review sheds light on the recent literature on adaptive responses in cancer cells to nutrient deprivation. It focuses on the utilization of alternative nutrients in anabolic metabolic pathways in cancer cells, including soluble metabolites and macromolecules and outlines the role of central metabolic enzymes conferring metabolic flexibility, like gluconeogenesis enzymes. Moreover, a conceptual framework for potential therapies targeting metabolically flexible cancer cells is presented.

Whole-Lesion DCE-MRI Intensity Histogram Analysis for Diagnosis in Patients with Suspected Lung Cancer

RATIONALE AND OBJECTIVES

To explore the diagnostic value of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) intensity histogram metrics, relative to time intensity curve (TIC)-derived metrics, in patients with suspected lung cancer.

MATERIALS AND METHODS

This retrospective study enrolled 49 patients with suspected lung cancer on routine CT imaging who underwent DCE-MRI scans and had final histopathologic diagnosis. Three TIC-derived metrics (maximum enhancement ratio, peak time [T_max] and slope) and eight intensity histogram metrics (volume, integral, maximum, minimum, median, coefficient of variation [CoV], skewness, and kurtosis) were extracted from DCE-MRI images. TIC-derived and intensity histogram metrics were compared between benignity versus malignancy using the Wilcoxon rank-sum test. Associations between imaging metrics and malignancy risk were assessed by univariate and multivariate logistic regression odds ratios (ORs).

RESULTS

There were 33 malignant lesions and 16 benign lesions based on histopathology. Lower CoV (OR = 0.2 per 1-SD increase, p = 0.0006), lower T_max (OR = 0.4 per 1-SD increase, p = 0.005), and steeper slope (OR = 2.4 per 1-SD increase, p = 0.010) were significantly associated with increased risk of malignancy. Under multivariate analysis, CoV was significantly independently associated with malignancy likelihood after accounting for either T_max (OR = 0.3 per 1-SD increase, p = 0.007) or slope (OR = 0.3 per 1-SD increase, p = 0.011).

CONCLUSION

This initial study found that DCE-MRI CoV was independently associated with malignancy in patients with suspected lung cancer. CoV has the potential to help diagnose indeterminate pulmonary lesions and may complement TIC-derived DCE-MRI metrics. Further studies are warranted to validate the diagnostic value of DCE-MRI intensity histogram analysis.

Evaluating Heterogeneity of Primary Lung Tumor Using Clinical Routine Magnetic Resonance Imaging and a Tumor Heterogeneity Index

Objective

To improve the assessment of primary tumor heterogeneity in magnetic resonance imaging (MRI) of non-small cell lung cancer (NSCLC), we proposed a method using basic measurements from T1- and T2-weighted MRI.

Methods

One hundred and four NSCLC patients with different T stages were studied. Fifty-two patients were analyzed as training group and another 52 as testing group. The ratios of standard deviation (SD)/mean signal value of primary tumor from T1-weighted (T1WI), T1-enhanced (T1C), T2-weighted (T2WI), and T2 fat suppression (T2fs) images were calculated. In the training group, correlation analyses were performed between the ratios and T stages. Then an ordinal regression model was built to generate the tumor heterogeneous index (THI) for evaluating the heterogeneity of tumor. The model was validated in the testing group.

Results

There were 11, 32, 40, and 21 patients with T1, T2, T3, and T4 disease, respectively. In the training group, the median SD/mean on T1WI, T1C, T2WI, and T2fs sequences was 0.11, 0.19, 0.16, and 0.15 respectively. The SD/mean on T1C (p=0.003), T2WI (p=0.000), and T2fs sequences (p=0.002) correlated significantly with T stages. Patients with more advanced T stage showed higher SD/mean on T2-weighted, T2fs, and T1C sequences. The median THI in the training group was 2.15. THI correlated with T stage significantly (p=0.000). In the testing group, THI was also significantly related to T stages (p=0.001). Higher THI had relevance to more advanced T stage.

Conclusions

The proposed ratio measurements and THI based on MRI can serve as functional radiomic markers that correlated with T stages for evaluating heterogeneity of lung tumors.

Exploring MRI based radiomics analysis of intratumoral spatial heterogeneity in locally advanced nasopharyngeal carcinoma treated with intensity modulated radiotherapy

Background

We hypothesized that spatial heterogeneity exists between recurrent and non-recurrent regions within a tumor. The aim of this study was to determine if there is a difference between radiomics features derived from recurrent versus non recurrent regions within the tumor based on pre-treatment MRI.

Methods

A total of 14 T4NxM0 NPC patients with histologically proven “in field” recurrence in the post nasal space following curative intent IMRT were included in this study. Pretreatment MRI were co-registered with MRI at the time of recurrence for the delineation of gross tumor volume at diagnosis(GTV) and at recurrence(GTVr). A total of 7 histogram features and 40 texture features were computed from the recurrent(GTVr) and non-recurrent region(GTV-GTVr). Paired t-tests and Wilcoxon signed-rank tests were carried out on the 47 quantified radiomics features.

Results

A total of 7 features were significantly different between recurrent and non-recurrent regions. Other than the variance from intensity-based histogram, the remaining six significant features were either from the gray-level size zone matrix (GLSZM) or the neighbourhood gray-tone difference matrix (NGTDM).

Conclusions

The radiomic features extracted from pre-treatment MRI can potentially reflect the difference between recurrent and non-recurrent regions within a tumor and has a potential role in pre-treatment identification of intra-tumoral radio-resistance for selective dose escalation.

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

PURPOSE OF REVIEW

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

RECENT FINDINGS

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

SUMMARY

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

Classification Models for Skin Tumor Detection Using Texture Analysis in Medical Images

Medical images have made a great contribution to early diagnosis. In this study, a new strategy is presented for analyzing medical images of skin with melanoma and nevus to model, classify and identify lesions on the skin. Machine learning applied to the data generated by first and second order statistics features, Gray Level Co-occurrence Matrix (GLCM), keypoints and color channel information-Red, Green, Blue and grayscale images of the skin were used to characterize decisive information for the classification of the images. This work proposes a strategy for the analysis of skin images, aiming to choose the best mathematical classifier model, for the identification of melanoma, with the objective of assisting the dermatologist in the identification of melanomas, especially towards an early diagnosis.

A Single Enhanced Dual-Energy CT Scan May Distinguish Lung Squamous Cell Carcinoma From Adenocarcinoma During the Venous phase

RATIONALE AND OBJECTIVES

To investigate whether iodine quantification extracted from enhanced dual energy-computed tomography (DE-CT) is useful for distinguishing lung squamous cell carcinoma from adenocarcinoma and to evaluate whether a single scan evaluated during the venous phase (VP) can be substituted for scans evaluated during other phases.

MATERIALS AND METHODS

Sixty-two patients with lung cancer (32 squamous cell carcinomas; 30 adenocarcinomas) underwent enhanced dual-phase DE-CT scans, including an arterial phase and VP. The iodine concentration (IC), normalized iodine concentration (NIC), and slope of the curve (K) in lesions were measured during two scanning phases in two different pathological types of lung cancers. The differences in parameters (IC, NIC, and K) between these two types of lung cancers were statistically analyzed. In addition, the receiver operating characteristic curves of these parameters were performed to discriminate squamous cell carcinoma from adenocarcinoma.

RESULTS

The mean IC, NIC, and K in adenocarcinomas were all higher than those in squamous cell carcinomas during the two scanning phases. However, the differences in these parameters between the two types of cancers were significant only during the VP, not during the arterial phase. Receiver operating characteristic analysis demonstrated that the optimal thresholds of the IC, NIC, and K for discriminating squamous cell carcinoma from adenocarcinoma were 1.550, 0.227, and 1.608, respectively. In addition, the sensitivity, specificity, and area under the curve were 81.2%, 83.3%, and 0.871 for the IC; 56.2%, 93.3%, and 0.800 for the NIC; and 65.6%, 80%, and 0.720 for the K; 81.3%, 83.3%, and 0.874 for the IC + NIC; 68.8%, 93.3%, and 0.891 for the IC + NIC + K, respectively. The "IC + NIC + K" had the highest diagnostic efficiency for discriminating two types of lung cancers, but with low sensitivity. Whereas, "IC"and "IC + NIC" had the similar lower diagnostic efficiency, but with high sensitivity and specificity.

CONCLUSION

The iodine quantification parameters derived from enhanced DE-CT during the VP may be useful for distinguishing lung squamous cell carcinoma from adenocarcinoma.

Gradient-based generation of intermediate images for heterogeneous tumor segmentation within hybrid PET/MRI scans

Segmentation of tumors from hybrid PET/MRI scans plays an essential role in accurate diagnosis and treatment planning. However, when treating tumors, several challenges, notably heterogeneity and the problem of leaking into surrounding tissues with similar high uptake, have to be considered. To address these issues, we propose an automated method for accurate delineation of tumors in hybrid PET/MRI scans. The method is mainly based on creating intermediate images. In fact, an automatic detection technique that determines a preliminary Interesting Uptake Region (IUR) is firstly performed. To overcome the leakage problem, a separation technique is adopted to generate the final IUR. Then, smart seeds are provided for the Graph Cut (GC) technique to obtain the tumor map. To create intermediate images that tend to reduce heterogeneity faced on the original images, the tumor map gradient is combined with the gradient image. Lastly, segmentation based on the GC_sum^max technique is applied to the generated images. The proposed method has been validated on PET phantoms as well as on real-world PET/MRI scans of prostate, liver and pancreatic tumors. Experimental comparison revealed the superiority of the proposed method over state-of-the-art methods. This confirms the crucial role of automatically creating intermediate images in addressing the problem of wrongly estimating arc weights for heterogeneous targets.

Advanced Imaging of Brain Metastases: From Augmenting Visualization and Improving Diagnosis to Evaluating Treatment Response

Early detection of brain metastases and differentiation from other neuropathologies is crucial. Although biopsy is often required for definitive diagnosis, imaging can provide useful information. After treatment commences, imaging is also performed to assess the efficacy of treatment. Contrast-enhanced magnetic resonance imaging (MRI) is the traditional imaging method for the evaluation of brain metastases, as it provides information about lesion size, morphology, and macroscopic properties. Newer MRI sequences have been developed to increase the conspicuity of detecting enhancing metastases. Other advanced MRI techniques, that have the capability to probe beyond the anatomic structure, are available to characterize micro-structures, cellularity, physiology, perfusion, and metabolism. Artificial intelligence provides powerful computational tools for detection, segmentation, classification, prediction, and prognosis. We highlight and review a few advanced MRI techniques for the assessment of brain metastases-specifically for (1) diagnosis, including differentiating between malignancy types and (2) evaluation of treatment response, including the differentiation between radiation necrosis and disease progression.

Spatial heterogeneity of oxygenation and haemodynamics in breast cancer resolved in vivo by conical multispectral optoacoustic mesoscopy

The characteristics of tumour development and metastasis relate not only to genomic heterogeneity but also to spatial heterogeneity, associated with variations in the intratumoural arrangement of cell populations, vascular morphology and oxygen and nutrient supply. While optical (photonic) microscopy is commonly employed to visualize the tumour microenvironment, it assesses only a few hundred cubic microns of tissue. Therefore, it is not suitable for investigating biological processes at the level of the entire tumour, which can be at least four orders of magnitude larger. In this study, we aimed to extend optical visualization and resolve spatial heterogeneity throughout the entire tumour volume. We developed an optoacoustic (photoacoustic) mesoscope adapted to solid tumour imaging and, in a pilot study, offer the first insights into cancer optical contrast heterogeneity in vivo at an unprecedented resolution of <50 μm throughout the entire tumour mass. Using spectral methods, we resolve unknown patterns of oxygenation, vasculature and perfusion in three types of breast cancer and showcase different levels of structural and functional organization. To our knowledge, these results are the most detailed insights of optical signatures reported throughout entire tumours in vivo, and they position optoacoustic mesoscopy as a unique investigational tool linking microscopic and macroscopic observations.

Computed Tomography-Based Tumor Heterogeneity Analysis Reveals Differences in a Cohort with Advanced Pancreatic Carcinoma under Palliative Chemotherapy

Purpose

Imaging in pancreatic cancer is a challenge, especially regarding therapy response evaluation. Tumor size, attenuation, and perfusion are widely used as parameters for computed tomography (CT) examinations, but are often limited due to blurry tumor borders and missing qualitative parameters. To improve monitoring of therapy response, we tested a new CT-based approach of tumor heterogeneity feature analysis.

Methods

A total of 13 patients with pancreatic adenocarcinoma undergoing abdominal CT according to standard as baseline imaging with clinical follow-up and imaging (median time span 64 days) under systematic therapy (FOLFIRINOX/gemcitabine) were retrospectively analyzed. Progression was defined as new lesions and local tumor spread. Tumor heterogeneity analysis was performed using mintLesion®. Seven different image features referring to image heterogeneity were analyzed. Statistical analysis was performed with Spearman's rank correlation and Mann-Whitney U test.

Results

During follow-up, tumor volume did not significantly change between our groups with overall progression (local and systemic) and progression-free patients (p = 0.661). Mean positivity of pixel values were significantly higher in patients without progression compared to patients with progression (p = 0.030). There was a significant negative correlation between changes in kurtosis and time to local tumor spread (p = 0.008) or systemic progression (p = 0.017).

Conclusions

Results suggest that analysis of tumor heterogeneity might provide valuable information from routine-acquired images regarding therapy response evaluation. This might help adjusting therapy regimes and could be easily integrated in clinical workflows. Furthermore, this procedure might possibly predict therapy response and, hence could lead the way to find a potential marker for progression-free survival.

CT Radiomics Signature of Tumor and Peritumoral Lung Parenchyma to Predict Nonsmall Cell Lung Cancer Postsurgical Recurrence Risk

RATIONALE AND OBJECTIVES

To estimate recurrence risk after surgery in nonsmall cell lung cancer (NSCLC) patients by employing tumoral and peritumoral radiomics analysis.

MATERIALS AND METHODS

One-hundred twenty-four surgically treated stage IA-IIB NSCLC patients' data from 2008 to 2013 were retrospectively collected. Patient outcome was defined as local recurrence (LR), distant metastasis (DM), and (sum of LR and DM) total recurrence (TR) at follow-up. Volumetric region of interests (ROIs) were drawn for the tumor, peritumoral lung parenchyma (2 cm around the tumor) and involved lobe on CT images. Ninety-four (morphological, first-order, textural, fractal-based) radiomics features were extracted from the ROIs and datasets were created from single or combined ROIs. Predictive models were built with radiomics signature (RS) and clinicopathological data, and the area under the curve (AUC) was used to evaluate the performance. Radiomics score was calculated with the best models' feature coefficients, low- and high-risk groups of patients defined accordingly. Kaplan-Meier curves were built, and the log-rank test was used for comparison among low- and high-risk groups. Differences in recurrence risk among the two risk groups were calculated (chi-square test).

RESULTS

Fifty-six patients developed TR (25 LR, 31 DM). The tumor-node-metastasis (TNM) stage recurrence predictability (AUC_TR 0.680; AUC_DM 0.672; AUC_LR 0.580) was substantially improved when RS was added to the predictive model (AUC_TR 0.760; AUC_DM 0.759; AUC_LR 0.750). Seventy-five percent of high-risk patients developed TR. Recurrence risk of the high-risk group was 16-fold higher than that of the low-risk group (p < 0.001).

CONCLUSION

Combination of the tumoral and peritumoral RS with TNM staging system outperformed TNM staging alone in individualized recurrence risk estimation of patients with surgically treated NSCLC.

Correction for Magnetic Field Inhomogeneities and Normalization of Voxel Values Are Needed to Better Reveal the Potential of MR Radiomic Features in Lung Cancer

Purpose: To design and validate a preprocessing procedure dedicated to T2-weighted MR images of lung cancers so as to improve the ability of radiomic features to distinguish between adenocarcinoma and other histological types.

Materials and Methods: A discovery set of 52 patients with advanced lung cancer who underwent T2-weighted MR imaging at 3 Tesla in a single center study from August 2017 to May 2019 was used. Findings were then validated using a validation set of 19 additional patients included from May to October 2019. Tumor type was obtained from the pathology report after trans-thoracic needle biopsy, metastatic lymph node or metastasis samples, or surgical excisions. MR images were preprocessed using N4ITK bias field correction and by normalizing voxel intensities with fat as a reference region. Segmentation and extraction of radiomic features were performed with LIFEx software on the raw images, on the N4ITK-corrected images and on the fully preprocessed images. Two analyses were conducted where radiomic features were extracted: (1) from the whole tumor volume (3D analysis); (2) from all slices encompassing the tumor (2D analysis). Receiver operating characteristic (ROC) analysis was used to identify features that could distinguish between adenocarcinoma and other histological types. Sham experiments were also designed to control the number of false positive findings.

Results: There were 31 (12) adenocarcinomas and 21 (7) other histological types in the discovery (validation) set. In 2D, preprocessing increased the number of discriminant radiomic features from 8 without preprocessing to 22 with preprocessing. 2D analysis yielded more features able to identify adenocarcinoma than 3D analysis (12 discriminant radiomic features after preprocessing in 3D). Preprocessing did not increase false positive findings as no discriminant features were identified in any of the sham experiments. The greatest sensitivity of the 2D analysis applied to preprocessed data was confirmed in the validation set.

Conclusion: Correction for magnetic field inhomogeneities and normalization of voxel values are essential to reveal the full potential of radiomic features to identify the tumor histological type from MR T2-weighted images, with classification performance similar to those reported in PET/CT and in multiphase CT in lung cancers.

High-Grade Soft-Tissue Sarcomas: Can Optimizing Dynamic Contrast-Enhanced MRI Postprocessing Improve Prognostic Radiomics Models?

BACKGROUND

Heterogeneity on pretreatment dynamic contrast-enhanced (DCE)-MRI of sarcomas may be prognostic, but the best technique to capture this characteristic remains unknown.

PURPOSE

To investigate the best method to extract prognostic data from baseline DCE-MRI.

STUDY TYPE

Retrospective, single-center.

POPULATION

Fifty consecutive uniformly-treated adults with nonmetastatic high-grade sarcomas.

FIELD STRENGTH/SEQUENCE

1.5T; T₂ -weighted-imaging, fat-suppressed fast spoiled gradient echo DCE-MRI.

ASSESSMENT

Ninety-two radiomics features (RFs) were extracted at each DCE-MRI phase (11, from t = 0-88 sec). Relative changes in RFs (rRFs) since the acquisition baseline were calculated (11 × 92 rRFs). Curves of rRF as function of time postinjection were integrated (92 integrated-rRFs [irRFs]). K^trans and area under the time-intensity curve at 88-sec parametric maps were computed and 2 × 92 parametric-RFs (pRFs) were extracted. Five DCE-MRI-based radiomics models were built on: an RFs subset (32 sec, 64 sec, 88 sec); all rRFs; all irRFs; and all pRFs. Two models were elaborated as reference, on: conventional radiological features; and T₂ -WI RFs.

STATISTICAL TESTS

A common machine-learning approach was applied to radiomics models. Features with P < 0.05 at univariate analysis were entered in a LASSO-penalized Cox regression including bootstrapped 10-fold cross-validation. The resulting radiomics scores (RScores) were dichotomized per their median and entered in multivariate Cox models for predicting metastatic relapse-free survival. Models were compared with integrative area under the curve (AUC) and concordance index.

RESULTS

Only dichotomized RScores from models based on rRFs subset, all rRFS and irRFS correlated with prognostic (P = 0.0107-0.0377). The models including all rRFs and irRFs had the highest c-index (0.83), followed by the radiological model. The radiological model had the highest integrative AUC (0.87), followed by models including all rRFs and irRFs. The radiological and full rRFs models were significantly better than the T₂ -based radiomics model (P = 0.02).

DATA CONCLUSION

The initial DCE-MRI of STS contains prognostic information. It seems more relevant to make predictions on rRFs instead of pRFs. Evidence Level: 3 Technical Efficacy: 3 J. Magn. Reson. Imaging 2020;52:282-297.

Measurement Variability in Treatment Response Determination for Non-Small Cell Lung Cancer: Improvements Using Radiomics

Multimodality imaging measurements of treatment response are critical for clinical practice, oncology trials, and the evaluation of new treatment modalities. The current standard for determining treatment response in non-small cell lung cancer (NSCLC) is based on tumor size using the RECIST criteria. Molecular targeted agents and immunotherapies often cause morphological change without reduction of tumor size. Therefore, it is difficult to evaluate therapeutic response by conventional methods. Radiomics is the study of cancer imaging features that are extracted using machine learning and other semantic features. This method can provide comprehensive information on tumor phenotypes and can be used to assess therapeutic response in this new age of immunotherapy. Delta radiomics, which evaluates the longitudinal changes in radiomics features, shows potential in gauging treatment response in NSCLC. It is well known that quantitative measurement methods may be subject to substantial variability due to differences in technical factors and require standardization. In this review, we describe measurement variability in the evaluation of NSCLC and the emerging role of radiomics.

Value of Computed Tomography Radiomic Features for Differentiation of Periprosthetic Mass in Patients With Suspected Prosthetic Valve Obstruction

Background:

We aimed to determine whether quantitative computed tomography radiomic features can aid in differentiating between the causes of prosthetic valve obstruction (PVO) in patients who had undergone prosthetic valve replacement.

Methods:

This retrospective study included 39 periprosthetic masses in 34 patients who underwent cardiac computed tomography scan from January 2014 to August 2017 and were clinically suspected as PVO. The cause of PVO was assessed by redo-surgery and follow-up imaging as standard reference, and classified as pannus, thrombus, or vegetation. Visual analysis was performed to assess the possible cause of PVO on axial and valve-dedicated views. Computed tomography radiomic analysis of periprosthetic masses was performed and radiomic features were extracted. The advantage of radiomic score compared with visual analysis for differentiation of pannus from other abnormalities was assessed.

Results:

Of 39 masses, there were 20 cases of pannus, 11 of thrombus, and 8 of vegetation on final diagnosis. The radiomic score was significantly higher in the pannus group compared with nonpannus group (mean, −0.156±0.422 versus −0.883±0.474; P <0.001). The area under the curve of radiomic score for diagnosis of pannus was 0.876 (95% CI, 0.731–0.960). Combination of radiomic score and visual analysis showed a better performance for the differentiation of pannus than visual analysis alone.

Conclusions:

Compared with visual analysis, computed tomography radiomic features may have added value for differentiating pannus from thrombus or vegetation in patients with suspected PVO.

Computational quantitative MR image features - a potential useful tool in differentiating glioblastoma from solitary brain metastasis

PURPOSE

Glioblastomas (GBM) and metastases are the most frequent malignant brain tumors in the adult population. Their presentation on conventional MRI is quite similar, but treatment strategy and prognosis are substantially different. Even with advanced MR techniques, in some cases diagnostic uncertainty remains. The main objective of this study was to determine whether fractal, texture, or both MR image analyses could aid in differentiating glioblastoma from solitary brain metastasis.

METHOD

In a retrospective study of 55 patients (30 glioblastomas and 25 solitary metastases) who underwent T2W/SWI/CET1 MRI, quantitative parameters of fractal and texture analysis were estimated, using box-counting and gray level co-occurrence matrix (GLCM) methods.

RESULTS

All five GLCM parameters obtained from T2W images showed significant difference between glioblastomas and solitary metastases, as well as on CET1 images except correlation (S_COR), contrary to SWI images which showed different values of two parameters (angular second moment-S_ASM and contrast-S_CON). Only three fractal features (binary box dimension-D_bin, normalized box dimension-D_norm and lacunarity-λ) measured on T2W and D_norm measured on CET1 images significantly differed GBMs from solitary metastases. The highest sensitivity and specificity were obtained from inverse difference moment (S_IDM) on T2W and S_IDM on CET1 images, respectively. Combination of several GLCM parameters yielded better results. The processing of T2W images provided the most significantly different parameters between the groups, followed by CET1 and SWI images.

CONCLUSIONS

Computational-aided quantitative image analysis may potentially improve diagnostic accuracy. According to our results texture features are more significant than fractal-based features in differentiation glioblastoma from solitary metastasis.

AI-based applications in hybrid imaging: how to build smart and truly multi-parametric decision models for radiomics

INTRODUCTION

The quantitative imaging features (radiomics) that can be obtained from the different modalities of current-generation hybrid imaging can give complementary information with regard to the tumour environment, as they measure different morphologic and functional imaging properties. These multi-parametric image descriptors can be combined with artificial intelligence applications into predictive models. It is now the time for hybrid PET/CT and PET/MRI to take the advantage offered by radiomics to assess the added clinical benefit of using multi-parametric models for the personalized diagnosis and prognosis of different disease phenotypes.

OBJECTIVE

The aim of the paper is to provide an overview of current challenges and available solutions to translate radiomics into hybrid PET-CT and PET-MRI imaging for a smart and truly multi-parametric decision model.

Histogram analysis of dynamic contrast-enhanced magnetic resonance imaging for differentiating malignant from benign orbital lymphproliferative disorders

BACKGROUND

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) has been used for assessing orbital lymphoproliferative disorders (OLPDs). However, only the mean values of quantitative parameters were obtained in previous studies and tumor heterogeneity was ignored.

PURPOSE

To assess the value of DCE-MRI derived histogram parameters in differentiating malignant from benign OLPDs.

MATERIAL AND METHODS

Forty-eight OLPDs patients (25 malignant and 23 benign) who had undergone DCE-MRI for pre-treatment evaluation were retrospectively included. Histogram parameters of K^trans, k_ep, and v_e were calculated and compared between two groups using the independent sample's t-test. Receiver operating characteristic (ROC) curve analyses were used to determine the diagnostic value of each significant parameter. Multivariate stepwise logistic regression analysis was used to identify the independent predictors of malignant OLPDs.

RESULTS

Tenth k_ep, mean k_ep, median k_ep, and 90th k_ep were significantly higher in the malignant OLPD group than in the benign OLPD group. Tenth v_e was significantly lower in the malignant OLPD group than in the benign OLPD group. Ninetieth k_ep was the only independent predictor of malignant OLPDs ( P = 0.019), with an area under ROC curve of 0.828, a sensitivity of 92.00%, and a specificity of 78.26% at a cut-off value of 1.057 min^-1.

CONCLUSION

Histogram analysis of DCE-MRI derived parameters may help to differentiate malignant from benign OLPDs. The 90th k_ep hold the potential as an independent predictor for malignant OLPDs.

Predictive value of pretreatment MRI texture analysis in patients with primary nasopharyngeal carcinoma

Objectives

To determine the predictive value of pretreatment MRI texture analysis for progression-free survival (PFS) in patients with primary nasopharyngeal carcinoma (NPC).

Methods

Ethical approval by the institutional review board was obtained for this retrospective analysis. In 79 patients with primary NPC, texture analysis of the primary tumour was performed on pretreatment T2 and contrast-enhanced T1-weighted images (T2WIs and CE-T1WIs). The Cox proportional hazards model was used to determine the association of texture features, tumour volume and the tumour-node-metastasis (TNM) stage with PFS. Survival curves were plotted using the Kaplan-Meier method. The prognostic performance was evaluated with the receiver operating characteristic (ROC) analyses and C-index.

Results

Tumour volume (hazard ratio, 1.054; 95% confidence interval [CI], 1.016–1.093) and CE-T1WI-based uniformity (hazard ratio, 0; 95% CI, 0–0.001) were identified as independent predictors for PFS (p < 0.05). Kaplan-Meier analysis showed that smaller tumour volume (less than the cut-off value, 11.699 cm³) and higher CE-T1WI-based uniformity (greater than the cut-off value, 0.856) were associated with improved PFS (p < 0.05). The combination of CE-T1WI-based uniformity with tumour volume and the overall stage predicted PFS better (area under the curve [AUC], 0.825; Cindex, 0.794) than the tumour volume (AUC, 0.659; C-index, 0.616) or the overall stage (AUC, 0.636; C-index, 0.627) did (p < 0.05).

Conclusions

A texture parameter of pretreatment CE-T1WI-based uniformity improves the prediction of PFS in NPC patients.

Key Points

• Higher CE-T1WI-based uniformity and smaller tumour volume are predictive of improved PFS in NPC patients.

• The combination of CE-T1WI-based uniformity with tumour volume and the overall stage has a better predictive ability for PFS than the tumour volume or the overall stage alone.

• Pretreatment MRI texture analysis has a prognostic value for NPC patients.

Electronic supplementary material

The online version of this article (10.1007/s00330-018-5961-6) contains supplementary material, which is available to authorized users.

Prognostic value of radiomic analysis of iodine overlay maps from dual-energy computed tomography in patients with resectable lung cancer

OBJECTIVES

To investigate whether radiomics on iodine overlay maps from dual-energy computed tomography (DECT) can predict survival outcomes in patients with resectable lung cancer.

METHODS

Ninety-three lung cancer patients eligible for curative surgery were examined with DECT at the time of diagnosis. The median follow-up was 60.4 months. Radiomic features of the entire primary tumour were extracted from iodine overlay maps generated by DECT. A Cox proportional hazards regression model was used to determine independent predictors of overall survival (OS) and disease-free survival (DFS), respectively.

RESULTS

Forty-two patients (45.2%) had disease recurrence and 39 patients (41.9%) died during the follow-up period. The mean DFS was 49.8 months and OS was 55.2 months. Univariate analysis revealed that significant predictors of both OS and DFS were stage and radiomic parameters, including histogram energy, histogram entropy, grey-level co-occurrence matrix (GLCM) angular second moment, GLCM entropy and homogeneity. The multivariate analysis identified stage and entropy as independent risk factors predicting both OS (stage, hazard ratio (HR) = 2.020 [95% CI 1.014-4.026], p = 0.046; entropy, HR = 1.543 [95% CI 1.069-2.228], p = 0.021) and DFS (stage, HR = 2.132 [95% CI 1.060-4.287], p = 0.034; entropy, HR = 1.497 [95% CI 1.031-2.173], p = 0.034). The C-index showed that adding entropy improved prediction of OS compared to stage only (0.720 and 0.667, respectively; p = 0.048).

CONCLUSIONS

Radiomic features extracted from iodine overlay map reflecting heterogeneity of tumour perfusion can add prognostic information for patients with resectable lung cancer.

KEY POINTS

• Radiomic feature (histogram entropy) from DECT iodine overlay maps was an independent risk factor predicting both overall survival and disease-free survival. • Adding histogram entropy to clinical stage improved prediction of overall survival compared to stage only (0.720 and 0.667, respectively; p = 0.048). • DECT can be a good option for comprehensive pre-operative evaluation in cases of resectable lung cancer.

Pre-TACE kurtosis of ADCtotal derived from histogram analysis for diffusion-weighted imaging is the best independent predictor of prognosis in hepatocellular carcinoma

PURPOSE

To determine the feasibility of pre-TACE IVIM imaging based on histogram analysis for predicting prognosis in the treatment of unresectable hepatocellular carcinoma (HCC).

MATERIALS AND METHODS

Fifty-five patients prospectively underwent 1.5T MRI 1 week before TACE. Histogram metrics for IVIM parameters and ADCs maps between responders and non-responders with mRECIST assessment were compared. Kaplan-Meier, log-rank tests and Cox proportional hazard regression model were used to correlate variables with time to progression (TTP).

RESULTS

Mean (p = 0.022), median (p = 0.043), and 25^th percentile (p < 0.001) of perfusion fraction (PF), mean (p < 0.001), median (p < 0.001), 25^th percentile (p < 0.001) and 75^th percentile (p = 0.001) of ADC_(0,500), mean (p = 0.005), median (p = 0.008) and 25^th percentile (p = 0.039) of ADC_total were higher, while skewness and kurtosis of PF (p = 0.001, p = 0.005, respectively), kurtosis of ADC_(0,500) and ADC_total (p = 0.005, p = 0.001, respectively) were lower in responders compared to non-responders. Multivariable analysis demonstrated that mRECIST was associated with TTP independently, and kurtosis of ADC_total had the best predictive performance for disease progression.

CONCLUSION

Pre-TACE kurtosis of ADC_total is the best independent predictor for TTP.

KEY POINTS

• mRECIST was associated with TTP independently. • Lower kurtosis and higher mean for ADCs tend to have good response. • Pre-TACE kurtosis of ADC _total is the best independent predictor for TTP.

LGE-CMR-derived texture features reflect poor prognosis in hypertrophic cardiomyopathy patients with systolic dysfunction: preliminary results

OBJECTIVES

To evaluate the prognostic value of texture features based on late gadolinium enhancement cardiac magnetic resonance (LGE-CMR) images in hypertrophic cardiomyopathy (HCM) patients with systolic dysfunction.

METHODS

67 HCM patients with systolic dysfunction (41 male and 26 female, mean age ± standard deviation, 46.20 years ± 13.38) were enrolled. All patients underwent 1.5 T CMR cine and LGE imaging. Texture features were extracted from LGE images. Cox proportional hazard analysis and Kaplan-Meier analysis were used to determine the association of texture features and traditional parameters with event free survival.

RESULTS

Family history (hazard ratio [HR]=2.558, 95 % confidence interval [CI]=1.060-6.180), NYHA III-IV (HR=5.627, CI=1.652-19.173), left ventricular ejection fraction (HR=0.945, CI=0.902-0.991), left ventricular end-diastolic volume index (HR=1.006, CI=1.000-1.012), LGE extent (HR=1.911, CI=1.348-2.709) and three texture parameters [X0_H_skewness (HR=0.783, CI=0.691-0.889), X0_GLCM_cluster_tendency (HR=0.735, CI=0.616-0.877) and X0_GLRLM_energy (HR=1.344, CI=1.173-1.540)] were significantly associated with event free survival in univariate analysis (p<0.05). The HR of LGE extent (HR=1.548 [CI=1.046-2.293], 1.650 [CI=1.122-2.428] and 1.586 [CI=1.044-2.409] per 10 % increase, p<0.05) remained significant when adjusted by one of the three texture features.

CONCLUSION

Increased LGE heterogeneity (higher X0_GLRLM_energy, lower X0_H_skewness and lower X0_GLCM_cluster_tendency) was associated with adverse events in HCM patients with systolic dysfunction.

KEY POINTS

• Textural analysis from CMR can be applied in HCM. • Texture features derived from LGE images can capture fibrosis heterogeneity. • CMR texture analysis provides prognostic information in HCM patients.

Advanced Hepatocellular Carcinoma: Pretreatment Contrast-enhanced CT Texture Parameters as Predictive Biomarkers of Survival in Patients Treated with Sorafenib

Purpose To determine whether texture features on pretreatment contrast material-enhanced computed tomographic (CT) images can help predict overall survival (OS) and time to progression (TTP) in patients with advanced hepatocellular carcinoma (HCC) treated with sorafenib. Materials and Methods This retrospective study included 92 patients with advanced HCC treated with sorafenib between January 2009 and April 2015 at two independent university hospitals. Sixty-four of the 92 patients (70%) (six women, 58 men; median age, 66 years) were included from institution 1 and constituted a training cohort; 28 patients (30%) (five women, 23 men; median age, 64 years) were included from institution 2 and constituted a validation cohort. Pretreatment CT texture analysis was performed on late arterial and portal venous phase HCC images. Mean gray-level intensity, entropy, kurtosis, skewness, and standard deviation values were derived from the pixel distribution histogram before and after spatial filtration at different anatomic scales ranging from fine to coarse texture. Lesion heterogeneity was also visually graded on a 4-point scale. Correlations between visual analysis and texture parameters were assessed with the Spearman rank correlation. Univariate Kaplan-Meier and multivariate Cox proportional hazards regression analyses were performed in the training cohort to identify independent predictors of OS and TTP. Their predictive capacity was tested on the validation cohort by using Kaplan-Meier analysis. Results Visual analysis of tumor heterogeneity correlated with entropy at both arterial (P = .012) and portal venous (P = .038) phases. Portal phase-derived entropy at fine (hazard ratio [HR], 5.08; P = .0033), medium (HR, 2.23; P = .019), and coarse (HR, 2.26; P = .0032) texture scales was identified as an independent predictor of OS and confirmed in the validation cohort (P < .05). The difference in median survival between patients in the validation cohort with entropy values below and above the identified threshold was 272 days (with fine texture) and 741 days (with medium and coarse textures). Arterial phase-derived texture parameters (P > .085) and visual analysis (P > .11) were not associated with changes in survival. Conclusion Pretreatment portal venous phase-derived tumor entropy may be a predictor of survival in patients with advanced HCC treated with sorafenib.

Standard diffusion-weighted, diffusion kurtosis and intravoxel incoherent motion MR imaging of sinonasal malignancies: correlations with Ki-67 proliferation status

OBJECTIVES

To explore the correlations of parameters derived from standard diffusion-weighted imaging (DWI), diffusion kurtosis imaging (DKI) and intravoxel incoherent motion (IVIM) with the Ki-67 proliferation status.

METHODS

Seventy-five patients with histologically proven sinonasal malignancies who underwent standard DWI, DKI and IVIM were retrospectively reviewed. The mean, minimum, maximum and whole standard DWI [apparent diffusion coefficient (ADC)], DKI [diffusion kurtosis (K) and diffusion coefficient (Dk)] and IVIM [pure diffusion coefficient (D), pseudo-diffusion coefficient (D*) and perfusion fraction (f)] parameters were measured and correlated with the Ki-67 labelling index (LI). The Ki-67 LI was categorised as high (> 50%) or low (≤ 50%).

RESULTS

The K and f values were positively correlated with the Ki-67 LI (rho = 0.295~0.532), whereas the ADC, Dk and D values were negatively correlated with the Ki-67 LI (rho = -0.443~-0.277). The ADC, Dk and D values were lower, whereas the K value was higher in sinonasal malignancies with a high Ki-67 LI than in those in a low Ki-67 LI (all p < 0.05). A higher maximum K value (K_max > 0.977) independently predicted a high Ki-67 status [odds ratio (OR) = 7.614; 95% confidence interval (CI) = 2.197-38.674; p = 0.017].

CONCLUSION

ADC, Dk, K, D and f are correlated with Ki-67 LI. K_max is the strongest independent factor for predicting Ki-67 status.

KEY POINTS

• DWI-derived parameters from different models are capable of providing different pathophysiological information. • DWI, DKI and IVIM parameters are associated with Ki-67 proliferation status. • K _max derived from DKI is the strongest independent factor for the prediction of Ki-67 proliferation status.

The prognostic value of CT radiomic features for patients with pulmonary adenocarcinoma treated with EGFR tyrosine kinase inhibitors

PURPOSE

To determine if the radiomic features on CT can predict progression-free survival (PFS) in epidermal growth factor receptor (EGFR) mutant adenocarcinoma patients treated with first-line EGFR tyrosine kinase inhibitors (TKIs) and to identify the incremental value of radiomic features over conventional clinical factors in PFS prediction.

METHODS

In this institutional review board-approved retrospective study, pretreatment contrast-enhanced CT and first follow-up CT after initiation of TKIs were analyzed in 48 patients (M:F = 23:25; median age: 61 years). Radiomic features at baseline, at 1st first follow-up, and the percentage change between the two were determined. A Cox regression model was used to predict PFS with nonredundant radiomic features and clinical factors, respectively. The incremental value of radiomic features over the clinical factors in PFS prediction was also assessed by way of a concordance index.

RESULTS

Roundness (HR: 3.91; 95% CI: 1.72, 8.90; P = 0.001) and grey-level nonuniformity (HR: 3.60; 95% CI: 1.80, 7.18; P<0.001) were independent predictors of PFS. For clinical factors, patient age (HR: 2.11; 95% CI: 1.01, 4.39; P = 0.046), baseline tumor diameter (HR: 1.03; 95% CI: 1.01, 1.05; P = 0.002), and treatment response (HR: 0.46; 95% CI: 0.24, 0.87; P = 0.017) were independent predictors. The addition of radiomic features to clinical factors significantly improved predictive performance (concordance index; combined model = 0.77, clinical-only model = 0.69, P<0.001).

CONCLUSIONS

Radiomic features enable PFS estimation in EGFR mutant adenocarcinoma patients treated with first-line EGFR TKIs. Radiomic features combined with clinical factors provide significant improvement in prognostic performance compared with using only clinical factors.

Clinical applications of textural analysis in non-small cell lung cancer

Lung cancer is the leading cause of cancer mortality worldwide. Treatment pathways include regular cross-sectional imaging, generating large data sets which present intriguing possibilities for exploitation beyond standard visual interpretation. This additional data mining has been termed "radiomics" and includes semantic and agnostic approaches. Textural analysis (TA) is an example of the latter, and uses a range of mathematically derived features to describe an image or region of an image. Often TA is used to describe a suspected or known tumour. TA is an attractive tool as large existing image sets can be submitted to diverse techniques for data processing, presentation, interpretation and hypothesis testing with annotated clinical outcomes. There is a growing anthology of published data using different TA techniques to differentiate between benign and malignant lung nodules, differentiate tissue subtypes of lung cancer, prognosticate and predict outcome and treatment response, as well as predict treatment side effects and potentially aid radiotherapy planning. The aim of this systematic review is to summarize the current published data and understand the potential future role of TA in managing lung cancer.

Relationship between Glioblastoma Heterogeneity and Survival Time: An MR Imaging Texture Analysis

BACKGROUND AND PURPOSE

The heterogeneity of glioblastoma contributes to the poor and variant prognosis. The aim of this retrospective study was to assess the glioblastoma heterogeneity with MR imaging textures and to evaluate its impact on survival time.

MATERIALS AND METHODS

A total of 133 patients with primary glioblastoma who underwent postcontrast T1-weighted imaging (acquired before treatment) and whose data were filed with the survival times were selected from the Cancer Genome Atlas. On the basis of overall survival, the patients were divided into 2 groups: long-term (≥12 months, n = 67) and short-term (<12 months, n = 66) survival. To measure heterogeneity, we extracted 3 types of textures, co-occurrence matrix, run-length matrix, and histogram, reflecting local, regional, and global spatial variations, respectively. Then the support vector machine classification was used to determine how different texture types perform in differentiating the 2 groups, both alone and in combination. Finally, a recursive feature-elimination method was used to find an optimal feature subset with the best differentiation performance.

RESULTS

When used alone, the co-occurrence matrix performed best, while all the features combined obtained the best survival stratification. According to feature selection and ranking, 43 top-ranked features were selected as the optimal subset. Among them, the top 10 features included 7 run-length matrix and 3 co-occurrence matrix features, in which all 6 regional run-length matrix features emphasizing high gray-levels ranked in the top 7.

CONCLUSIONS

The results suggest that local and regional heterogeneity may play an important role in the survival stratification of patients with glioblastoma.

Quantification of hepatocellular carcinoma heterogeneity with multiparametric magnetic resonance imaging

Tumour heterogeneity poses a significant challenge for treatment stratification. The goals of this study were to quantify heterogeneity in hepatocellular carcinoma (HCC) using multiparametric magnetic resonance imaging (mpMRI), and to report preliminary data correlating quantitative MRI parameters with advanced histopathology and gene expression in a patient subset. Thirty-two HCC patients with 39 HCC lesions underwent mpMRI including diffusion-weighted imaging (DWI), blood-oxygenation-level-dependent (BOLD), tissue-oxygenation-level-dependent (TOLD) and dynamic contrast-enhanced (DCE)-MRI. Histogram characteristics [central tendency (mean, median) and heterogeneity (standard deviation, kurtosis, skewness) MRI parameters] in HCC and liver parenchyma were compared using Wilcoxon signed-rank tests. Histogram data was correlated between MRI methods in all patients and with histopathology and gene expression in 14 patients. HCCs exhibited significantly higher intra-tissue heterogeneity vs. liver with all MRI methods (P < 0.030). Although central tendency parameters showed significant correlations between MRI methods and with each of histopathology and gene expression, heterogeneity parameters exhibited additional complementary correlations between BOLD and DCE-MRI and with histopathologic hypoxia marker HIF1α and gene expression of Wnt target GLUL, pharmacological target FGFR4, stemness markers EPCAM and KRT19 and immune checkpoint PDCD1. Histogram analysis combining central tendency and heterogeneity mpMRI features is promising for non-invasive HCC characterization on the imaging, histologic and genomics levels.

Characterization of PET/CT images using texture analysis: the past, the present… any future?

After seminal papers over the period 2009 - 2011, the use of texture analysis of PET/CT images for quantification of intratumour uptake heterogeneity has received increasing attention in the last 4 years. Results are difficult to compare due to the heterogeneity of studies and lack of standardization. There are also numerous challenges to address. In this review we provide critical insights into the recent development of texture analysis for quantifying the heterogeneity in PET/CT images, identify issues and challenges, and offer recommendations for the use of texture analysis in clinical research. Numerous potentially confounding issues have been identified, related to the complex workflow for the calculation of textural features, and the dependency of features on various factors such as acquisition, image reconstruction, preprocessing, functional volume segmentation, and methods of establishing and quantifying correspondences with genomic and clinical metrics of interest. A lack of understanding of what the features may represent in terms of the underlying pathophysiological processes and the variability of technical implementation practices makes comparing results in the literature challenging, if not impossible. Since progress as a field requires pooling results, there is an urgent need for standardization and recommendations/guidelines to enable the field to move forward. We provide a list of correct formulae for usual features and recommendations regarding implementation. Studies on larger cohorts with robust statistical analysis and machine learning approaches are promising directions to evaluate the potential of this approach.