Angiogenic response of locally advanced breast cancer to neoadjuvant chemotherapy evaluated with parametric histogram from dynamic contrast-enhanced MRI

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.

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.

Radiomic analysis of HTR-DCE MR sequences improves diagnostic performance compared to BI-RADS analysis of breast MR lesions

PURPOSE

To assess the diagnostic performance of radiomic analysis using high temporal resolution (HTR)-dynamic contrast enhancement (DCE) MR sequences compared to BI-RADS analysis to distinguish benign from malignant breast lesions.

MATERIALS AND METHODS

We retrospectively analyzed data from consecutive women who underwent breast MRI including HTR-DCE MR sequencing for abnormal enhancing lesions and who had subsequent pathological analysis at our tertiary center. Semi-quantitative enhancement parameters and textural features were extracted. Temporal change across each phase of textural features in HTR-DCE MR sequences was calculated and called "kinetic textural parameters." Statistical analysis by LASSO logistic regression and cross validation was performed to build a model. The diagnostic performance of the radiomic model was compared to the results of BI-RADS MR score analysis.

RESULTS

We included 117 women with a mean age of 54 years (28-88). Of the 174 lesions analyzed, 75 were benign and 99 malignant. Seven semi-quantitative enhancement parameters and 57 textural features were extracted. Regression analysis selected 15 significant variables in a radiomic model (called "malignant probability score") which displayed an AUC = 0.876 (sensitivity = 0.98, specificity = 0.52, accuracy = 0.78). The performance of the malignant probability score to distinguish benign from malignant breast lesions (AUC = 0.876, 95%CI 0.825-0.925) was significantly better than that of BI-RADS analysis (AUC = 0.831, 95%CI 0.769-0.892). The radiomic model significantly reduced false positives (42%) with the same number of missed cancers (n = 2).

CONCLUSION

A radiomic model including kinetic textural features extracted from an HTR-DCE MR sequence improves diagnostic performance over BI-RADS analysis.

KEY POINTS

• Radiomic analysis using HTR-DCE is of better diagnostic performance (AUC = 0.876) than conventional breast MRI reading with BI-RADS (AUC = 0.831) (p < 0.001). • A radiomic malignant probability score under 19.5% gives a negative predictive value of 100% while a malignant probability score over 81% gives a positive predictive value of 100%. • Kinetic textural features extracted from HTR-DCE-MRI have a major role to play in distinguishing benign from malignant breast lesions.

Dynamic contrast-enhanced MRI histogram parameters predict progression-free survival in patients with advanced esophageal squamous carcinoma receiving concurrent chemoradiotherapy

BACKGROUND

There is increased interest in dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) for predicting the outcomes of patients with advanced esophageal cancer.

PURPOSE

To explore whether DCE-MRI histogram parameters can predict 12-month progression-free survival (PFS) in patients with advanced esophageal squamous carcinoma receiving concurrent chemoradiation therapy (CRT).

MATERIAL AND METHODS

This retrospective study enrolled 134 patients with advanced esophageal squamous carcinoma who were receiving CRT. The pre-CRT DCE-MRI histogram parameters (median, mean, SD, skewness, kurtosis, and 10th and 90th percentiles) of K^trans, K_ep, and V_e were collected. PFS analyses were performed using the Kaplan-Meier method and log-rank tests to compute the survival curves. The significant prognostic predictors among the data characteristics and DCE-MRI parameters were determined using multivariate Cox proportional hazards regression analyses.

RESULTS

There were 65 good responders (PFS ≥ 12 months) and 69 poor responders (PFS < 12 months). The median and mean values of K^trans were higher, and the kurtosis value of K^trans was lower in good responders. The median, mean, and 10th and 90th percentile values of K^trans were higher, and the kurtosis values of K^trans and V_e were lower in good responders. The PFS of patients aged ≥60 years, a CR effect, or a 10th percentile value of K^trans ≥0.13 was increased (P < 0.001, <0.001, and 0.014, respectively).

CONCLUSION

DCE-MRI histogram parameters can be used to evaluate the response to CRT in patients with advanced esophageal squamous carcinoma. The 10th percentile value of K^trans has significant prognostic value for 12-month PFS.

Textural Characteristics of Biopsy-proven Metastatic Axillary Nodes on Preoperative Breast MRI in Breast Cancer Patients: A Feasibility Study

OBJECTIVE

To determine the diagnostic accuracy of MRI textural analysis (TA) to differentiate malignant from benign axillary lymph nodes in patients with breast cancer.

METHODS

This was an institutional review board-approved retrospective study of axillary lymph nodes in women with breast cancer that underwent ultrasound-guided biopsy and contrast-enhanced (CE) breast MRI from January 2015 to December 2018. TA of axillary lymph nodes was performed on 3D dynamic CE T1-weighted fat-suppressed, 3D delayed CE T1-weighted fat-suppressed, and T2-weighted fat-suppressed MRI sequences. Quantitative parameters used to measure TA were compared with pathologic diagnoses. Areas under the curve (AUC) were calculated using receiver operating characteristic curve analysis to distinguish between malignant and benign lymph nodes.

RESULTS

Twenty-three biopsy-proven malignant lymph nodes and 24 benign lymph nodes were analyzed. The delayed CE T1-weighted fat-suppressed sequence had the greatest ability to differentiate malignant from benign outcome at all spatial scaling factors, with the highest AUC (0.84-0.93), sensitivity (0.78 [18/23] to 0.87 [20/23]), and specificity (0.76 [18/24] to 0.88 [21/24]). Kurtosis on the 3D delayed CE T1-weighted fat-suppressed sequence was the most prominent TA parameter differentiating malignant from benign lymph nodes (P < 0.0001).

CONCLUSION

This study suggests that MRI TA could be helpful in distinguishing malignant from benign axillary lymph nodes. Kurtosis has the greatest potential on 3D delayed CE T1-weighted fat-suppressed sequences to distinguish malignant and benign lymph nodes.

MRI Texture Analysis for Differentiating Nonfunctional Pancreatic Neuroendocrine Neoplasms From Solid Pseudopapillary Neoplasms of the Pancreas

RATIONALE AND OBJECTIVES

To evaluate the value of texture analysis on preoperative magnetic resonance imaging (MRI) for identifying nonfunctional pancreatic neuroendocrine neoplasms (NF-PNENs) and solid pseudopapillary neoplasms (SPNs).

MATERIALS AND METHODS

This retrospective study included 119 patients who underwent MRI, including T2-weighted imaging with fat-suppression, diffusion-weighted imaging (DWI), apparent diffusion coefficient, precontrast T1-weighted imaging with fat-suppression (T₁WI+fs), and dynamic contrast-enhanced (DCE)-T₁WI+fs. Raw data analysis, principal component analysis, linear discriminant analysis, and nonlinear discriminant analysis (NDA) were used to classify NF-PNENs and SPNs. The results are reported as misclassification rates. The images were simultaneously evaluated by an experienced senior radiologist without knowledge of the pathological results. The misclassification rate of the radiologist was compared to the MaZda (texture analysis software) results. Neural network classifier testing was used for validation. In addition, 30 textures for each MRI sequence were investigated.

RESULTS

The misclassification rate of NDA was lower than that of other analyses. In NDA, DWI obtained the lowest value of 7.92%, but there was no significant difference among the sequences. The misclassification rate of the radiologist (34.65%) was significantly higher than that of NDA for all sequences. The validation results were good in the arterial phase and delayed phase. In the training set, entropy and sum entropy were optimal texture features on DWI and precontrast T₁WI+fs, while the mean and percentile seemed to be the more discriminative features on DCE-T₁WI+fs.

CONCLUSION

Texture analysis can sensitively distinguish between NF-PNENs and SPNs on MRI, and percentile and mean of DCE-T₁WI+fs images were informative for differentiation of neoplasms.

Efficient DCE-MRI Parameter and Uncertainty Estimation Using a Neural Network

Quantitative DCE-MRI provides voxel-wise estimates of tracer-kinetic parameters that are valuable in the assessment of health and disease. These maps suffer from many known sources of variability. This variability is expensive to compute using current methods, and is typically not reported. Here, we demonstrate a novel approach for simultaneous estimation of tracer-kinetic parameters and their uncertainty due to intrinsic characteristics of the tracer-kinetic model, with very low computation time. We train and use a neural network to estimate the approximate joint posterior distribution of tracer-kinetic parameters. Uncertainties are estimated for each voxel and are specific to the patient, exam, and lesion. We demonstrate the methods' ability to produce accurate tracer-kinetic maps. We compare predicted parameter ranges with uncertainties introduced by noise and by differences in post-processing in a digital reference object. The predicted parameter ranges correlate well with tracer-kinetic parameter ranges observed across different noise realizations and regression algorithms. We also demonstrate the value of this approach to differentiate significant from insignificant changes in brain tumor pharmacokinetics over time. This is achieved by enforcing consistency in resolving model singularities in the applied tracer-kinetic model.

Early evaluation using a radiomic signature of unresectable hepatic metastases to predict outcome in patients with colorectal cancer treated with FOLFIRI and bevacizumab

PURPOSE

The objective of this study was to build and validate a radiomic signature to predict early a poor outcome using baseline and 2-month evaluation CT and to compare it to the RECIST1·1 and morphological criteria defined by changes in homogeneity and borders.

METHODS

This study is an ancillary study from the PRODIGE-9 multicentre prospective study for which 491 patients with metastatic colorectal cancer (mCRC) treated by 5-fluorouracil, leucovorin and irinotecan (FOLFIRI) and bevacizumab had been analysed. In 230 patients, computed texture analysis was performed on the dominant liver lesion (DLL) at baseline and 2 months after chemotherapy. RECIST1·1 evaluation was performed at 6 months. A radiomic signature (Survival PrEdiction in patients treated by FOLFIRI and bevacizumab for mCRC using contrast-enhanced CT TextuRe Analysis (SPECTRA) Score) combining the significant predictive features was built using multivariable Cox analysis in 120 patients, then locked, and validated in 110 patients. Overall survival (OS) was estimated with the Kaplan-Meier method and compared between groups with the logrank test. An external validation was performed in another cohort of 40 patients from the PRODIGE 20 Trial.

RESULTS

In the training cohort, the significant predictive features for OS were: decrease in sum of the target liver lesions (STL), (adjusted hasard-ratio(aHR)=13·7, p=1·93×10^-7), decrease in kurtosis (ssf=4) (aHR=1·08, p=0·001) and high baseline density of DLL, (aHR=0·98, p<0·001). Patients with a SPECTRA Score >0·02 had a lower OS in the training cohort (p<0·0001), in the validation cohort (p<0·0008) and in the external validation cohort (p=0·0027). SPECTRA Score at 2 months had the same prognostic value as RECIST at 6 months, while non-response according to RECIST1·1 at 2 months was not associated with a lower OS in the validation cohort (p=0·238). Morphological response was not associated with OS (p=0·41).

CONCLUSION

A radiomic signature (combining decrease in STL, density and computed texture analysis of the DLL) at baseline and 2-month CT was able to predict OS, and identify good responders better than RECIST1.1 criteria in patients with mCRC treated by FOLFIRI and bevacizumab as a first-line treatment. This tool should now be validated by further prospective studies.

TRIAL REGISTRATION

Clinicaltrial.gov identifier of the PRODIGE 9 study: NCT00952029.Clinicaltrial.gov identifier of the PRODIGE 20 study: NCT01900717.

Differentiation between Luminal A and B Molecular Subtypes of Breast Cancer Using Pharmacokinetic Quantitative Parameters with Histogram and Texture Features on Preoperative Dynamic Contrast-Enhanced Magnetic Resonance Imaging

OBJECTIVE

The aim of the present study was to use pharmacokinetic quantitative parameters with histogram and texture features on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) to differentiate between the luminal A and luminal B molecular subtypes of breast cancer.

METHODS

We retrospectively reviewed the data of 94 patients with histopathologically proven breast cancer. The pharmacokinetic quantitative parameters (K^trans, K_ep, and V_e) with their corresponding histogram and texture features based on preoperative DCE-MRI were obtained. The parameters were compared using the Mann-Whitney U-test between the luminal A and luminal B groups, the human epidermal growth factor receptor-2 (HER2)-positive luminal B and HER2-negative luminal B groups, and the lymph node metastasis (LNM)-positive and LNM-negative groups. Receiver operating characteristic curves were generated for parameters that presented significant between-group differences.

RESULTS

The maximum values of K^trans, K_ep, and V_e, and the mean and 90th percentile values of V_e were significantly higher in the luminal B group than in the luminal A group. Among the texture features, only skewness of K^trans significantly differed between the luminal A and B groups. All histogram features of K^trans were higher in the HER2-positive luminal B group than in the HER2-negative luminal B group. However, no parameter differed between the LNM-positive and LNM-negative groups.

CONCLUSION

Pharmacokinetic quantitative parameters with histogram and texture features obtained from DCE-MRI are associated with the molecular subtypes of breast cancer, and may serve as potential imaging biomarkers to differentiate between the luminal A and luminal B molecular subtypes.

Histogram analysis of DCE-MRI for chemoradiotherapy response evaluation in locally advanced esophageal squamous cell carcinoma

AIMS

The aim of the study was to predict and assess treatment response by histogram analysis of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) to patients with locally advanced esophageal squamous cell carcinoma receiving chemoradiotherapy (CRT).

MATERIALS AND METHODS

Seventy-two patients with locally advanced esophageal squamous cell carcinoma who underwent DCE-MRI before and after chemoradiotherapy were enrolled and divided into the complete response (CR) group and the non-CR group based on RECIST. The histogram parameters (10th percentile, 90th percentile, median, mean, standard deviation, skewness, and kurtosis) of pre-CRT and post-CRT were compared using a paired Student's t test in the CR and non-CR groups, respectively. The histogram parameter differences between the CR and the non-CR groups were compared using an unpaired Student's t test. A receiver operating characteristic (ROC) analysis was performed to evaluate the diagnostic performance.

RESULTS

The histogram parameters of K^trans values were observed to have significantly decreased after chemoradiotherapy in the CR group. The CR responders showed significantly higher median, mean, and 10th and 90th percentile of pre-K^trans values than those of the non-CR group. The histogram analysis indicated the decreased heterogeneity in the CR group after CRT. Esophageal cancer with higher pre-K^trans and lower post-K^trans values indicated a good treatment response to CRT. Pre-K^trans-10th showed the best diagnostic performance in predicting the chemoradiotherapy response.

CONCLUSIONS

The histogram parameters of K^trans are useful in the assessment and prediction of the chemoradiotherapy response in patients with advanced esophageal squamous cell carcinoma. DCE-MRI could serve as an adjunctive imaging technique for treatment planning.

Differentiating the lymph node metastasis of breast cancer through dynamic contrast-enhanced magnetic resonance imaging

Objective:

Lymph node metastasis is an important trait of breast cancer, and tumors with different lymph node statuses require various clinical treatments. This study was designed to evaluate the lymph node metastasis of breast cancer through pharmacokinetic and histogram analysis via dynamic contrast-enhanced (DCE) MRI.

Methods and materials:

A retrospective analysis was conducted to quantitatively evaluate the lymph node statuses of patients with breast cancer. A total of 75 patients, i.e. 34 patients with lymph node metastasis and 41 patients without lymph node metastasis, were involved in this research. Of the patients with lymph node metastases, 19 had sentinel lymph node metastasis, and 15 had axillary lymph node metastasis. MRI was conducted using a 3.0 T imaging device. Segmentation was carried out on the regions of interest (ROIs) in breast tumors under DCE-MRI, and pharmacokinetic and histogram parameters were calculated from the same ROIs. Mann–Whitney U test was performed, and receiver operating characteristic curves for the parameters of the two groups were constructed to determine their diagnostic values.

Results:

Pharmacokinetic parameters, including Ktrans, Kep, area under the curve of time–concentration, and time to peak, which were derived from the extended Tofts linear model for DCE-MRI, could highlight the tumor areas in the breast and reveal the increased perfusion. Conversely, the pharmacokinetic parameters showed no significant difference between the patients with and without lymph node metastases. By contrast, the parameters from the histogram analysis yielded promising results. The entropy of the ROIs exhibited the best diagnostic ability between patients with and without lymph node metastases (p < 0.01, area under the curve of receiver operating characteristic = 0.765, specificity = 0.706, sensitivity = 0.780).

Conclusion:

In comparison with the pharmacokinetic parameters, the histogram analysis of the MR images could reveal the differences between patients with and without lymph node metastases. The entropy from the histogram indicated that the diagnostic ability was highly sensitive and specific.

Advances in knowledge:

This research gave out a promising result on the differentiating lymph node metastases through histogram analysis on tumors in DCE-MR images. Histogram could reveal the tumors heterogenicity between patients with different lymph node status.

Analysis of DCE-MRI for Early Prediction of Breast Cancer Therapy Response

Positive response to neoadjuvant chemotherapy (NACT) has been correlated to better long-term outcomes in breast cancer treatment. Early prediction of response to NACT can help modify the regimen for non-responding patients, sparing them of potential toxicities of ineffective therapies. It has been observed that tumor functions such as vascularization and vascular permeability change even before noticeable changes occur in the tumor size in response to the treatment. Therefore, it is essential to have reliable imaging based features to measure these changes. Texture analysis on parametric maps from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) has shown to be a good predictor of breast cancer response to NACT at an early stage. But hand crafted texture features might not be able to capture the rich spatio-temporal information in the parametric maps. In this work, we studied the ability of convolutional neural networks in predicting the response to NACT at an early stage.

Functional evaluation of therapeutic response of HCC827 lung cancer to bevacizumab and erlotinib targeted therapy using dynamic contrast-enhanced and diffusion-weighted MRI

This study aimed to investigate the therapeutic responses of lung cancer mice models with adenocarcinoma HCC827 (gefitinib sensitive) and HCC827R (gefitinib resistant) to the epidermal growth factor receptor-tyrosine kinase inhibitor erlotinib alone and in combination with the anti-angiogenesis agent bevacizumab using dynamic contrast enhanced (DCE) and diffusion-weighted MRI. In the HCC827 model, temporal changes in DCE-MRI derived parameters (K^trans, k_ep, and iAUC₉₀) and apparent diffusion coefficient (ADC) were significantly correlated with tumor size. K^trans and iAUC₉₀ significantly decreased at week 2 in the groups receiving erlotinib alone and in combination with bevacizumab, whereas k_ep decreased at week 1 and 2 in both treatment groups. In addition, there was a significant difference in iAUC₉₀ between the treatment groups at week 1. Compared to the control group of HCC827, there was a significant reduction in microvessel density and increased tumor apoptosis in the two treatment group. ADC value increased in the erlotinib alone group at week 1 and week 2, and in the erlotinib combined with bevacizumab group at week 2. Enlarged areas of central tumor necrosis were associated with a higher ADC value. However, progressive enlargement of the tumors but no significant differences in DCE parameters or ADC were noted in the HCC827R model. These results showed that both erlotinib alone and in combination with bevacizumab could effectively inhibit tumor growth in the gefitinib-sensitive lung cancer mice model, and that this was associated with decreased vascular perfusion, increased ADC percentage, decreased microvessel density, and increased tumor apoptosis with a two-week treatment cycle.

Features from Computerized Texture Analysis of Breast Cancers at Pretreatment MR Imaging Are Associated with Response to Neoadjuvant Chemotherapy

Purpose To evaluate whether features from texture analysis of breast cancers were associated with pathologic complete response (pCR) after neoadjuvant chemotherapy and to explore the association between texture features and tumor subtypes at pretreatment magnetic resonance (MR) imaging. Materials and Methods Institutional review board approval was obtained. This retrospective study included 85 patients with 85 breast cancers who underwent breast MR imaging before neoadjuvant chemotherapy between April 10, 2008, and March 12, 2015. Two-dimensional texture analysis was performed by using software at T2-weighted MR imaging and contrast material-enhanced T1-weighted MR imaging. Quantitative parameters were compared between patients with pCR and those with non-pCR and between patients with triple-negative breast cancer and those with non-triple-negative cancer. Multiple logistic regression analysis was used to determine independent parameters. Results Eighteen tumors (22%) were triple-negative breast cancers. pCR was achieved in 30 of the 85 tumors (35%). At univariate analysis, mean pixel intensity with spatial scaling factor (SSF) of 2 and 4 on T2-weighted images and kurtosis on contrast-enhanced T1-weighted images showed a significant difference between triple-negative breast cancer and non-triple-negative breast cancer (P = .009, .003, and .001, respectively). Kurtosis (SSF, 2) on T2-weighted images showed a significant difference between pCR and non-pCR (P = .015). At multiple logistic regression, kurtosis on T2-weighted images was independently associated with pCR in non-triple-negative breast cancer (P = .033). A multivariate model incorporating T2-weighted and contrast-enhanced T1-weighted kurtosis showed good performance for the identification of triple-negative breast cancer (area under the receiver operating characteristic curve, 0.834). Conclusion At pretreatment MR imaging, kurtosis appears to be associated with pCR to neoadjuvant chemotherapy in non-triple-negative breast cancer and may be a promising biomarker for the identification of triple-negative breast cancer. ^© RSNA, 2017.

Dynamic fractal signature dissimilarity analysis for therapeutic response assessment using dynamic contrast-enhanced MRI

PURPOSE

To develop a dynamic fractal signature dissimilarity (FSD) method as a novel image texture analysis technique for the quantification of tumor heterogeneity information for better therapeutic response assessment with dynamic contrast-enhanced (DCE)-MRI.

METHODS

A small animal antiangiogenesis drug treatment experiment was used to demonstrate the proposed method. Sixteen LS-174T implanted mice were randomly assigned into treatment and control groups (n = 8/group). All mice received bevacizumab (treatment) or saline (control) three times in two weeks, and one pretreatment and two post-treatment DCE-MRI scans were performed. In the proposed dynamic FSD method, a dynamic FSD curve was generated to characterize the heterogeneity evolution during the contrast agent uptake, and the area under FSD curve (AUCFSD) and the maximum enhancement (MEFSD) were selected as representative parameters. As for comparison, the pharmacokinetic parameter K(trans) map and area under MR intensity enhancement curve AUCMR map were calculated. Besides the tumor's mean value and coefficient of variation, the kurtosis, skewness, and classic Rényi dimensions d1 and d2 of K(trans) and AUCMR maps were evaluated for heterogeneity assessment for comparison. For post-treatment scans, the Mann-Whitney U-test was used to assess the differences of the investigated parameters between treatment/control groups. The support vector machine (SVM) was applied to classify treatment/control groups using the investigated parameters at each post-treatment scan day.

RESULTS

The tumor mean K(trans) and its heterogeneity measurements d1 and d2 values showed significant differences between treatment/control groups in the second post-treatment scan. In contrast, the relative values (in reference to the pretreatment value) of AUCFSD and MEFSD in both post-treatment scans showed significant differences between treatment/control groups. When using AUCFSD and MEFSD as SVM input for treatment/control classification, the achieved accuracies were 93.8% and 93.8% at first and second post-treatment scan days, respectively. In comparison, the classification accuracies using d1 and d2 of K(trans) map were 87.5% and 100% at first and second post-treatment scan days, respectively.

CONCLUSIONS

As quantitative metrics of tumor contrast agent uptake heterogeneity, the selected parameters from the dynamic FSD method accurately captured the therapeutic response in the experiment. The potential application of the proposed method is promising, and its addition to the existing DCE-MRI techniques could improve DCE-MRI performance in early assessment of treatment response.

Analysis of factors influencing the degree of detectability on diffusion-weighted MRI and diffusion background signals in patients with invasive breast cancer

To determine the factors influencing the degree of detectability of lesions and diffusion background signals on magnetic resonance diffusion-weighted imaging (DWI) in invasive breast cancer.Institutional review board approval was obtained and patient consent was waived. Patients with newly diagnosed invasive ductal carcinoma, who underwent preoperative breast magnetic resonance imaging with DWI were included in this study (n = 167). Lesion detectability on DWI and contrast-enhanced subtracted T1-weighted images, the degree of background parenchymal enhancement (BPE), and diffusion background signal were qualitatively rated. Detectability of lesions on DWI was compared with clinicopathological findings including menopausal status, mammographic density, and molecular subtype of breast cancer. Multivariate linear regression analysis was performed to determine variables independently associated with detectability of lesions on DWI and diffusion background signals.Univariate analysis showed that the detectability of lesions on DWI was significantly associated with lesion size (P = 0.001), diffuse background signal (P < 0.0001), and higher detectability scores for contrast-enhanced T1-weighted subtraction images (P = 0.000). The degree of diffusion background signal was significantly affected by age (P < 0.0001), BPE (P < 0.0001), mammographic density (P = 0.002), and menopausal status (P < 0.0001). On multivariate analysis, the diffusion background signal (P < 0.0001) and histologic grade (P < 0.0001) were correlated with the detectability on DWI of invasive breast cancer. Only BPE was correlated with the amount of diffusion background signal on DWI (P < 0.0001).For invasive breast cancers, detectability on DWI was significantly affected by the diffusion background signal. BPE, menopausal status, menstrual cycle, or mammographic density did not show statistically significant correlation with the diffusion detectability of lesions on DWI.

Evaluation of the effect of transcytolemmal water exchange analysis for therapeutic response assessment using DCE-MRI: a comparison study

This study compares the shutter-speed (SS) and the Tofts models as used in assessing therapeutic response in a longitudinal DCE-MRI experiment. Sixteen nu/nu mice with implanted colorectal adenocarcinoma cell line (LS-174T) were randomly assigned into treatment/control groups (n  =  8/group) and received bevacizumab/saline twice weekly (Day1/Day4/Day8). All mice were scanned at one pre- (Day0) and two post-treatment (Day2/Day9) time points using a high spatiotemporal resolution DCE-MRI pulse sequence. The CA extravasation rate constant [Formula: see text] from the Tofts/SS model and the mean intracellular water residence time [Formula: see text] from the SS model were analyzed. A biological subvolume (BV) within the tumor was identified based on the [Formula: see text] intensity distribution, and the SS model parameters within the BV ([Formula: see text] and [Formula: see text]) were analyzed. It is found that [Formula: see text] and [Formula: see text] have a similar spatial distribution in the tumor volume. The Bayesian information criterion results show that the SS model was a better fit for all scans. At Day9, the treatment group had significantly higher tumor mean [Formula: see text] (p  =  0.021), [Formula: see text] (p  =  0.021) and [Formula: see text] (p  = 0.045). When BV from transcytolemmal water exchange analysis was adopted, the treatment group had higher mean [Formula: see text] at both Day2 (p  =  0.038) and Day9 (p  =  0.007). Additionally, at Day9, the treatment group had higher mean [Formula: see text] (p  =  0.045) and higher [Formula: see text] spatial heterogeneity indices (Rényi dimensions) d 1 (p  = 0.010) and d 2 (p  = 0.021). When mean [Formula: see text] and its coefficient of variation (CV) were used to separate treatment/control group samples using supporting vector machine, the accuracy of treatment/control classification was 68.8% at Day2 and 87.5% at Day9; in contrast, the Day2/Day9 accuracy were 62.5%/87.5% using tumor mean [Formula: see text] and its CV and were 50.0%/87.5% using tumor mean [Formula: see text] and its CV, respectively. These results suggest that the SS model parameters outperformed the Tofts model parameters in terms of capturing bevacizumab therapeutic effect in this longitudinal experiment.

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.

Tumor Vascular Permeability Pattern Is Associated With Complete Response in Immunocompetent Patients With Newly Diagnosed Primary Central Nervous System Lymphoma: Retrospective Cohort Study

A dynamic contrast-enhanced MR imaging (DCE-MRI) could provide the information about tumor drug delivery efficacy. We investigated the potential utility of the permeability pattern of DCE-MRI for predicting tumor response to high dose-methotrexate treatment and progression-free survival (PFS) in patients with primary CNS lymphoma (PCNSL). Clinical and conventional imaging parameters were assessed as potential predictors of tumor response in 48 immunocompetent PCNSL patients in a preliminary study. Fifty additional immunocompetent patients (27 men and 23 women; mean age, 60.6 years) with PCNSL underwent DCE-MRI before starting first-line treatment with high dose-methotrexate. The DCE-MRI pattern was categorized as diffuse or nondiffuse. After 4 courses of high dose methotrexate, patients underwent follow-up brain MR imaging to identify their complete response (CR). Predictors of CR and PFS were analyzed using clinical parameters, conventional MRI, and DCE-MRI. CR was noted in 20 (74.1%) of 27 patients with diffuse DCE-MRI pattern and in 4 (17.4%) of 23 patients with nondiffuse DCE-MRI pattern. The diffuse DCE-MRI pattern showed a significantly higher association with CR than the nondiffuse pattern (P < 0.001). Multivariate Cox proportional hazards model revealed that the DCE-MRI pattern (hazard ratio = 0.70; P = 0.045), age (hazard ratio = 1.47; P = 0.041), and adjuvant autologous stem-cell transplantation (hazard ratio = 6.97; P = 0.003) tended to be associated with a PFS. The pretreatment diffuse DCE-MRI pattern can be used as a potential imaging biomarker for predicting CR and a longer PFS in patients with newly diagnosed PCNSLs.

Histogram analysis of iodine maps from dual energy computed tomography for monitoring targeted therapy of melanoma patients

AIM

Iodine quantification with dual energy computed tomography (DECT) enables quantitative assessment of contrast medium uptake. Our purpose was to investigate patterns of enhancement under BRAF inhibitor therapy by performing histogram analyses (HAs) of iodine maps.

MATERIALS & METHODS

A total of 11 stage IV melanoma patients (32 metastases) underwent DECT at baseline and at least one follow up. Iodine uptake and HAs including maximum HU value (MAX), mean HU value (MEAN) and standard deviation (STD) was calculated.

RESULTS

For BRAF-responders MEAN, MAX and STD decreased significantly (p < 0.05). Nonresponder showed increasing MAX and STD for six out of seven lesions, while MEAN and Iodine uptake decreased (four) and increased (three).

CONCLUSION

HA based on DECT enables a quantitative and functional criterion and contributes to accurate response assessment for promising targeted therapies.

Evaluation of the treatment response to neoadjuvant chemotherapy in locally advanced breast cancer using combined magnetic resonance vascular maps and apparent diffusion coefficient

PURPOSE

To evaluate the treatment response of locally advanced breast cancer (LABC) to neoadjuvant chemotherapy using magnetic resonance (MR) vascular maps and apparent diffusion coefficient (ADC) at 3T. Materials and Methods Thirty-one patients with LABC who underwent breast MR studies before, after the first course, and after completing neoadjuvant chemotherapy were enrolled. Vascular morphology was retrieved via Hessian matrix and the voxels of the vessels and volume of vessels were measured automatically. Whole tumor mean ADC values were calculated. Clinical responders were defined as >50% tumor reduction in the final MR studies. Pathologically complete responders were also recorded.

RESULTS

There were 21 clinical responders and 10 nonresponders. Compared to the nonresponders after the first course, the responders were characterized by more vascular reduction of the breast lesion and decreased bilateral vascular discrepancy (voxels and volume), and increments in the ADC value and ADC percentage of the lesions (all P < 0.05). There were three pathological complete responders who showed more apparent early vascular reduction of the lesion breast (voxels and volume) and increments in the ADC value than others (P = 0.02, 0.01 and 0.02, respectively).

CONCLUSION

The early changes of MR vascular maps and ADC are associated with the final treatment response of LABC.

Cluster analysis of quantitative parametric maps from DCE-MRI: application in evaluating heterogeneity of tumor response to antiangiogenic treatment

PURPOSE

The objective of this study was to compare a clustering approach to conventional analysis methods for assessing changes in pharmacokinetic parameters obtained from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) during antiangiogenic treatment in a breast cancer model.

MATERIALS AND METHODS

BALB/c mice bearing established transplantable her2+ tumors were treated with a DNA-based antiangiogenic vaccine or with an empty plasmid (untreated group). DCE-MRI was carried out by administering a dose of 0.05 mmol/kg of Gadocoletic acid trisodium salt, a Gd-based blood pool contrast agent (CA) at 1T. Changes in pharmacokinetic estimates (K(trans) and vp) in a nine-day interval were compared between treated and untreated groups on a voxel-by-voxel analysis. The tumor response to therapy was assessed by a clustering approach and compared with conventional summary statistics, with sub-regions analysis and with histogram analysis.

RESULTS

Both the K(trans) and vp estimates, following blood-pool CA injection, showed marked and spatial heterogeneous changes with antiangiogenic treatment. Averaged values for the whole tumor region, as well as from the rim/core sub-regions analysis were unable to assess the antiangiogenic response. Histogram analysis resulted in significant changes only in the vp estimates (p<0.05). The proposed clustering approach depicted marked changes in both the K(trans) and vp estimates, with significant spatial heterogeneity in vp maps in response to treatment (p<0.05), provided that DCE-MRI data are properly clustered in three or four sub-regions.

CONCLUSIONS

This study demonstrated the value of cluster analysis applied to pharmacokinetic DCE-MRI parametric maps for assessing tumor response to antiangiogenic therapy.

Textural differences in apparent diffusion coefficient between low- and high-stage clear cell renal cell carcinoma

OBJECTIVE

The purpose of this article is to evaluate differences in texture measures on apparent diffusion coefficient (ADC) maps between low- and high-stage clear cell renal cell carcinomas (RCCs).

MATERIALS AND METHODS

In this retrospective study, 61 patients with clear cell RCC at pathologic examination and who underwent preoperative MRI with diffusion-weighted imaging were included. Clear cell RCCs were clinically staged on review of preoperative MRI by a board-certified radiologist blinded to the pathologic findings. Whole lesions were segmented on ADC maps by two readers independently, from which first-order texture features (i.e., mean and skewness) and second-order texture features (i.e., cooccurrence matrix measures) were calculated. Texture metrics were compared between low- and high-stage clear cell RCC.

RESULTS

In 61 patients, there were 62 clear cell RCCs (33 low stage [stages I and II] and 29 high stage [stages III and IV]) at pathologic examination. Staging accuracy of qualitative interpretation was 100% for low-stage lesions and 37.9% (11/29) for high-stage lesions. There was no statistically significant difference in mean ADC between high- and low-stage clear cell RCCs (1.77×10(-3) vs 1.80×10(-3) mm2/s; p=0.7). However, high-stage clear cell RCCs were larger (6.96±2.93 vs 3.49±1.57 cm; p<0.0001) and had statistically significantly (p≤0.0001) higher ADC skewness (0.02±0.33 vs -0.52±0.65) and cooccurrence matrix correlation (0.64±0.11 vs 0.49±0.13). Multivariate logistic regression identified size, skewness, and cooccurrence matrix correlation as significant independent predictors of high stage (AUC=0.92). Interreader correlation in texture metrics ranged from 0.82 to 0.89.

CONCLUSION

First- and second-order ADC texture metrics differ between low- and high-stage clear cell RCCs. A model that includes size and ADC texture measures may help to stage clear cell RCCs noninvasively.

Improving tumour heterogeneity MRI assessment with histograms

By definition, tumours are heterogeneous. They are defined by marked differences in cells, microenvironmental factors (oxygenation levels, pH, VEGF, VPF and TGF-α) metabolism, vasculature, structure and function that in turn translate into heterogeneous drug delivery and therapeutic outcome. Ways to estimate quantitatively tumour heterogeneity can improve drug discovery, treatment planning and therapeutic responses. It is therefore of paramount importance to have reliable and reproducible biomarkers of cancerous lesions' heterogeneity. During the past decade, the number of studies using histogram approaches increased drastically with various magnetic resonance imaging (MRI) techniques (DCE-MRI, DWI, SWI etc.) although information on tumour heterogeneity remains poorly exploited. This fact can be attributed to a poor knowledge of the available metrics and of their specific meaning as well as to the lack of literature references to standardised histogram methods with which surrogate markers of heterogeneity can be compared. This review highlights the current knowledge and critical advances needed to investigate and quantify tumour heterogeneity. The key role of imaging techniques and in particular the key role of MRI for an accurate investigation of tumour heterogeneity is reviewed with a particular emphasis on histogram approaches and derived methods.

Heterogeneity of tumor vasculature and antiangiogenic intervention: insights from MR angiography and DCE-MRI

PURPOSE

Solid tumor vasculature is highly heterogeneous, which presents challenges to antiangiogenic intervention as well as the evaluation of its therapeutic efficacy. The aim of this study is to evaluate the spatial tumor vascular changes due to bevacizumab/paclitaxel therapy using a combination approach of MR angiography and DCE-MRI method.

EXPERIMENTAL DESIGN

Tumor vasculature of MCF-7 breast tumor mouse xenografts was studied by a combination of MR angiography and DCE-MRI with albumin-Gd-DTPA. Tumor macroscopic vasculature was extracted from the early enhanced images. Tumor microvascular parameters were obtained from the pharmacokinetic modeling of the DCE-MRI data. A spatial analysis of the microvascular parameters based on the macroscopic vasculature was used to evaluate the changes of the heterogeneous vasculature induced by a 12 day bevacizumab/paclitaxel treatment in mice bearing MCF-7 breast tumor.

RESULTS

Macroscopic vessels that feed the tumors were not affected by the bevacizumab/paclitaxel combination therapy. A higher portion of the tumors was within close proximity of these macroscopic vessels after the treatment, concomitant with tumor growth retardation. There was a significant decrease in microvascular permeability and vascular volume in the tumor regions near these vessels.

CONCLUSION

Bevacizumab/paclitaxel combination therapy did not block the blood supply to the MCF-7 breast tumor. Such finding is consistent with the modest survival benefits of adding bevacizumab to current treatment regimens for some types of cancers.

Dynamic contrast-enhanced MRI-based biomarkers of therapeutic response in triple-negative breast cancer

OBJECTIVE

To predict the response of breast cancer patients to neoadjuvant chemotherapy (NAC) using features derived from dynamic contrast-enhanced (DCE) MRI.

MATERIALS AND METHODS

60 patients with triple-negative early-stage breast cancer receiving NAC were evaluated. Features assessed included clinical data, patterns of tumor response to treatment determined by DCE-MRI, MRI breast imaging-reporting and data system descriptors, and quantitative lesion kinetic texture derived from the gray-level co-occurrence matrix (GLCM). All features except for patterns of response were derived before chemotherapy; GLCM features were determined before and after chemotherapy. Treatment response was defined by the presence of residual invasive tumor and/or positive lymph nodes after chemotherapy. Statistical modeling was performed using Lasso logistic regression.

RESULTS

Pre-chemotherapy imaging features predicted all measures of response except for residual tumor. Feature sets varied in effectiveness at predicting different definitions of treatment response, but in general, pre-chemotherapy imaging features were able to predict pathological complete response with area under the curve (AUC)=0.68, residual lymph node metastases with AUC=0.84 and residual tumor with lymph node metastases with AUC=0.83. Imaging features assessed after chemotherapy yielded significantly improved model performance over those assessed before chemotherapy for predicting residual tumor, but no other outcomes.

CONCLUSIONS

DCE-MRI features can be used to predict whether triple-negative breast cancer patients will respond to NAC. Models such as the ones presented could help to identify patients not likely to respond to treatment and to direct them towards alternative therapies.

Assessment of dynamic contrast-enhanced magnetic resonance imaging in the differentiation of malignant from benign orbital masses

OBJECTIVE

Dynamic contrast enhanced MR imaging (DCE-MRI) allows imaging of the physiology of the microcirculation. The purpose of this study was to determine the diagnostic efficacy of time intensity curve (TIC) and DCE parameters for characterization of orbital masses.

METHODS

Fifty-nine patients with untreated orbital lesions underwent DCE-MRI before surgery. For each lesion, peak height (PH), maximum enhancement ratio (ERmax), time of peak enhancement (Tpeak) and maximum rise slope (Slopemax) were plotted and calculated. Receiver operator characteristics (ROC) analysis was conducted to assess the appropriate cut-off value.

RESULTS

All 26 lesions that demonstrated persistent pattern (type-I) TICs were benign. Most of the masses with the washout pattern (type-III) TIC were malignant (10/14), including lymphoma (n=6) and melanoma (n=4). The Slopemax of benign lesions was statistically lower than malignant ones, while the ERmax and Tpeak values of benign lesions were significantly higher. No statistical difference was found in PH (P=0.121). The AUC for ERmax, Tpeak and Slopemax in differentiating benign orbital lesions from malignant ones were 0.683, 0.837 and 0.738, respectively. In the three DCE parameters, Slopemax cut-off value of 1.10 provided the highest sensitivity of 93.8%; however, the corresponding specificity was low (58.1%). The ERmax cut-off value of 1.37 and Tpeak cut-off value of 35.14 respectively offered the best diagnostic performances.

CONCLUSION

DCE-MRI, especially the qualitative TIC pattern and quantitative value of Slopemax, ERmax and Tpeak, could be a complementary investigation in distinguishing malignant orbital tumor from benign ones.

Analysis of parametric histogram from dynamic contrast-enhanced MRI: application in evaluating brain tumor response to radiotherapy

Dynamic contrast-enhanced MRI (DCE MRI) has been used to study tumor response to treatment for many years. In this study, the modified full width at half-maximum (mFWHM), calculated from the wash-in slope histogram, is proposed as a parameter for the evaluation of changes in tumor heterogeneity which respond to radiotherapy. Twenty-five patients with brain tumors were evaluated and divided into the nonresponder group (n = 11) and the responder group (n = 14) according to the Response Evaluation Criteria in Solid Tumors (RECIST). All selected tumors were evaluated by mFWHM ratios of post- to pre-therapy (the ratio was defined as the therapeutic mFWHM ratio, TMR). The changes in kurtosis of the histograms and the averaged K(trans) within a tumor were also calculated for comparison. The receiver operating characteristic analysis and Kaplan-Meier curves were used to examine the diagnosis ability. The TMR values were significantly higher in nonresponders than in responders (p < 0.001). When compared with the other two parameters, the proposed method also demonstrated better sensitivity and specificity. When adopting the TMR for the estimation of prognosis after therapy, there was a significant difference between the population survival curves. In conclusion, the derived mFWHM reflects tumor heterogeneity, and the ability to depict patient survival probability from TMR corresponds well with that from RECIST. The results reveal that, in brain tumors, progression may be exhibited not only by tumor size, but also by tumor heterogeneity.

Histogram analysis of whole-lesion enhancement in differentiating clear cell from papillary subtype of renal cell cancer

PURPOSE

To compare histogram analysis of voxel-based whole-lesion (WL) enhancement to qualitative assessment and region-of-interest (ROI)-based enhancement analysis in discriminating the renal cell cancer (RCC) subtype clear cell RCC (ccRCC) from papillary RCC (pRCC).

MATERIALS AND METHODS

In this institutional review board-approved, HIPAA-compliant retrospective study, 73 patients underwent magnetic resonance (MR) imaging prior to surgery for RCC between January 2007 and January 2010. Three-dimensional fat-suppressed T1-weighted gradient-echo corticomedullary phase acquisitions, obtained before and after contrast agent administration, were transferred to a workstation at which automated registration followed by semiautomated segmentation of the RCC was performed. Percent enhancement was computed on a per-voxel basis: (SI(post) - SI(pre))/SI(pre) .100, where SI(pre) and SI(post) indicate signal intensity before and after contrast enhancement, respectively. The WL quantitative parameters of mean, median, and third quartile enhancement and histogram distribution parameters kurtosis and skewness were computed for each lesion. WL enhancement parameters were compared with ROI-based analysis and qualitative assessment with regards to diagnostic accuracy and interreader agreement in differentiating ccRCC from pRCC.

RESULTS

There were 19 pRCCs and 55 ccRCCs at pathologic examination. ccRCC had significantly higher WL mean, median, and third quartile enhancement compared with pRCC and hade significantly lower kurtosis and skewness (all P < .001). Third quartile enhancement had the highest accuracy (94.6%; area under the curve, 0.980) in discriminating ccRCC from pRCC, which was significantly higher than the accuracy of qualitative assessment (86.0%; P = .04) but not significantly higher than that of ROI enhancement (89.2%; P = .52). WL enhancement parameters had higher interreader agreement (κ = 0.91-1.0) compared with ROI enhancement or qualitative assessment (κ = 0.83 and 0.7, respectively) in discriminating ccRCC from pRCC.

CONCLUSION

WL enhancement histogram analysis is feasible and can potentially be used to differentiate ccRCC from pRCC with high accuracy.

SUPPLEMENTAL MATERIAL

http://radiology.rsna.org/lookup/suppl/doi:10.1148/radiol.12111281/-/DC1.

Multifunctional magnetic resonance imaging probes

Magnetic resonance imaging (MRI) is a key imaging modality in cancer diagnostics and therapy monitoring. MRI-based tumor detection and characterization is commonly achieved by exploiting the compositional, metabolic, cellular, and vascular differences between malignant and healthy tissue. Contrast agents are frequently applied to enhance this contrast. The last decade has witnessed an increasing interest in novel multifunctional MRI probes. These multifunctional constructs, often of nanoparticle design, allow the incorporation of multiple imaging agents for complementary imaging modalities as well as anti-cancer drugs for therapeutic purposes. The composition, size, and surface properties of such constructs can be tailored as to improve biodistribution and ensure optimal delivery to the tumor microenvironment by passive or targeted mechanisms. Multifunctional MRI probes hold great promise to facilitate more specific tumor diagnosis, patient-specific treatment planning, the monitoring of local drug delivery, and the early evaluation of therapy. This chapter reviews the state-of-the-art and new developments in the application of multifunctional MRI probes in oncology.

Quantitative assessment of tumor responses after radiation therapy in a DLD-1 colon cancer mouse model using serial dynamic contrast-enhanced magnetic resonance imaging

PURPOSE

The purpose of this study was to investigate the predictability of pretreatment values including Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) derived parameters (K(trans), K(ep) and V(e)), early changes in parameters (K(trans), tumor volume), and heterogeneity (standard deviation of K(trans)) for radiation therapy responses via a human colorectal cancer xenograft model.

MATERIALS AND METHODS

A human colorectal cancer xenograft model with DLD-1 cancer cells was produced in the right hind limbs of five mice. Tumors were irradiated with 3 fractions of 3 Gy each for 3 weeks. Baseline and follow up DCE-MRI were performed. Quantitative parameters (K(trans), K(ep) and V(e)) were calculated based on the Tofts model. Early changes in K(trans), standard deviation (SD) of K(trans), and tumor volume were also calculated. Tumor responses were evaluated based on histology. With a cut-off value of 0.4 for necrotic factor, a comparison between good and poor responses was conducted.

RESULTS

The good response group (mice #1 and 2) exhibited higher pretreatment K(trans) than the poor response group (mice #3, 4, and 5). The good response group tended to show lower pretreatment K(ep), higher pretreatment V(e), and larger baseline tumor volume than the poor response group. All the mice in the good response group demonstrated marked reductions in K(trans) and SD value after the first radiation. All tumors showed increased volume after the first radiation therapy.

CONCLUSION

The good response after radiation therapy group in the DLD-1 colon cancer xenograft nude mouse model exhibited a higher pretreatment K(trans) and showed an early reduction in K(trans), demonstrating a more homogenous distribution.

Quantifying heterogeneity in human tumours using MRI and PET

Most tumours, even those of the same histological type and grade, demonstrate considerable biological heterogeneity. Variations in genomic subtype, growth factor expression and local microenvironmental factors can result in regional variations within individual tumours. For example, localised variations in tumour cell proliferation, cell death, metabolic activity and vascular structure will be accompanied by variations in oxygenation status, pH and drug delivery that may directly affect therapeutic response. Documenting and quantifying regional heterogeneity within the tumour requires histological or imaging techniques. There is increasing evidence that quantitative imaging biomarkers can be used in vivo to provide important, reproducible and repeatable estimates of tumoural heterogeneity. In this article we review the imaging methods available to provide appropriate biomarkers of tumour structure and function. We also discuss the significant technical issues involved in the quantitative estimation of heterogeneity and the range of descriptive metrics that can be derived. Finally, we have reviewed the existing clinical evidence that heterogeneity metrics provide additional useful information in drug discovery and development and in clinical practice.

Parametric histogram analysis of dynamic contrast-enhanced MRI in multiple myeloma: a technique to evaluate angiogenic response to therapy?

RATIONALE AND OBJECTIVES

From dynamic contrast-enhanced magnetic resonance imaging, it is known that microcirculation patterns in multiple myeloma differ depending on the infiltration pattern. The purpose of this study was to evaluate histogram analysis of dynamic contrast-enhanced magnetic resonance imaging in MM to monitor early treatment response on the basis of microcirculation patterns.

MATERIALS AND METHODS

A total of 51 patients with multiple myeloma requiring therapy were examined. Dynamic contrast-enhanced magnetic resonance imaging of the lumbar spine was performed before and after conventional or high-dose chemotherapy with autologous stem cell transplantation. Statistical analysis included 245 vertebrae and dynamic microcirculation parameters as displayed in histograms. Resulting parameters (amplitude, exchange rate constant, skewness, kurtosis, and left shift) were correlated with therapeutic response.

RESULTS

More than 70% of histograms derived from the microcirculation parameters showed a difference between the maximum peak before and after therapy (left shift). However, there was no significant difference between the particular treatment. Significantly different skewness of amplitude in 98% and kurtosis of exchange rate constant (94.1% and 98%) were seen in the patients who responded to treatment (P for each < .05).

CONCLUSIONS

Histogram analysis revealed early changes after therapy resulting in a shift toward more (kurtosis) and lower values (skewness) of microcirculation parameters. Therefore, histogram analysis can determine and describe if a chosen therapy works at all. However, there were no differences between the chosen therapies. This needs to be reevaluated in a larger number of treated patients. Histogram analysis can also be an adjunct to a subjective visual analysis but is hampered by heterogeneous infiltration pattern seen in multiple myeloma.

Statistical comparison of dynamic contrast-enhanced MRI pharmacokinetic models in human breast cancer

By fitting dynamic contrast-enhanced MRI data to an appropriate pharmacokinetic model, quantitative physiological parameters can be estimated. In this study, we compare four different models by applying four statistical measures to assess their ability to describe dynamic contrast-enhanced MRI data obtained in 28 human breast cancer patient sets: the chi-square test (χ(2)), Durbin-Watson statistic, Akaike information criterion, and Bayesian information criterion. The pharmacokinetic models include the fast exchange limit model with (FXL_v(p)) and without (FXL) a plasma component, and the fast and slow exchange regime models (FXR and SXR, respectively). The results show that the FXL_v(p) and FXR models yielded the smallest χ(2) in 45.64 and 47.53% of the voxels, respectively; they also had the smallest number of voxels showing serial correlation with 0.71 and 2.33%, respectively. The Akaike information criterion indicated that the FXL_v(p) and FXR models were preferred in 42.84 and 46.59% of the voxels, respectively. The Bayesian information criterion also indicated the FXL_v(p) and FXR models were preferred in 39.39 and 45.25% of the voxels, respectively. Thus, these four metrics indicate that the FXL_v(p) and the FXR models provide the most complete statistical description of dynamic contrast-enhanced MRI time courses for the patients selected in this study.

MRI in breast cancer therapy monitoring

Breast MRI has several roles in the clinical management of breast cancer, including as a screening method for high-risk women, as a diagnostic tool used as an adjunct to mammography and ultrasound, and for the staging of disease extent prior to treatment. In addition to these uses, MRI is also employed to track small changes in tumor size and microenvironment. MRI has produced several early indicators of treatment response in clinical trials over the last 10  years, including initial lesion pattern, changes in lesion size, kinetic parameters, apparent diffusion coefficient and T(2) value; the related technique of (1) H MRS has also shown that choline concentration, T(2) value and water-to-fat ratio are response indicators. In addition to measuring anatomical changes in the lesion size, as performed in traditional radiology, MRI has the ability to track vascular and cellular changes using dynamic contrast-enhanced MRI and diffusion-weighted MRI, respectively. By adding (1) H MRS to MRI examinations, metabolic changes can also be determined. These functional imaging techniques allow studies to focus on early time points relative to neoadjuvant treatment. Early treatment response predictors may allow therapy to be tailored to individual patients and thus aid in the realization of the goal of personalized medicine.

DCE-MRI biomarkers of tumour heterogeneity predict CRC liver metastasis shrinkage following bevacizumab and FOLFOX-6

BACKGROUND

There is limited evidence that imaging biomarkers can predict subsequent response to therapy. Such prognostic and/or predictive biomarkers would facilitate development of personalised medicine. We hypothesised that pre-treatment measurement of the heterogeneity of tumour vascular enhancement could predict clinical outcome following combination anti-angiogenic and cytotoxic chemotherapy in colorectal cancer (CRC) liver metastases.

METHODS

Ten patients with 26 CRC liver metastases had two dynamic contrast-enhanced MRI (DCE-MRI) examinations before starting first-line bevacizumab and FOLFOX-6. Pre-treatment biomarkers of tumour microvasculature were computed and a regression analysis was performed against the post-treatment change in tumour volume after five cycles of therapy. The ability of the resulting linear model to predict tumour shrinkage was evaluated using leave-one-out validation. Robustness to inter-visit variation was investigated using data from a second baseline scan.

RESULTS

In all, 86% of the variance in post-treatment tumour shrinkage was explained by the median extravascular extracellular volume (v(e)), tumour enhancing fraction (E(F)), and microvascular uniformity (assessed with the fractal measure box dimension, d(0)) (R(2)=0.86, P<0.00005). Other variables, including baseline volume were not statistically significant. Median prediction error was 12%. Equivalent results were obtained from the second scan.

CONCLUSION

Traditional image analyses may over-simplify tumour biology. Measuring microvascular heterogeneity may yield important prognostic and/or predictive biomarkers.

Magnetic resonance imaging: a potential tool in assessing the addition of hyperthermia to neoadjuvant therapy in patients with locally advanced breast cancer

The poor overall survival for patients with locally advanced breast cancers has led over the past decade to the introduction of numerous neoadjuvant combined therapy regimens to down-stage the disease before surgery. At the same time, more evidence suggests the need for treatment individualisation with a wide variety of new targets for cancer therapeutics and also multi modality therapies. In this context, early determination of whether the patient will fail to respond can enable the use of alternative therapies that can be more beneficial. The purpose of this review is to examine the potential role of magnetic resonance imaging (MRI) in early prediction of treatment response and prognosis of overall survival in locally advanced breast cancer patients enrolled on multi modality therapy trials that include hyperthermia. The material is organised with a review of dynamic contrast (DCE)-MRI and diffusion weighted (DW)-MRI for characterisation of phenomenological parameters of tumour physiology and their potential role in estimating therapy response. Most of the work published in this field has focused on responses to neoadjuvant chemotherapy regimens alone, so the emphasis will be there, however the available data that involves the addition of hyperthermia to the regimen will be discussed The review will also include future directions that include the potential use of MRI imaging techniques in establishing the role of hyperthermia alone in modifying breast tumour microenvironment, together with specific challenges related to performing such studies.

Breast MR imaging: current indications and advanced imaging techniques

Breast cancer is the most common solid tumor diagnosed in women. In the past decades, great strides have been made in breast cancer screening. While multiple screening trials have shown the benefits of screening mammography, there are limitations to x-ray mammography. Given these inherent limitations, efforts have been made to develop adjunctive imaging techniques, including screening ultrasonography, gamma-specific breast imaging, breast tomosynthesis, dedicated breast computed tomography, and breast magnetic resonance (MR) imaging. This article addresses the current indications and advanced imaging applications of breast MR imaging.

DCE-MRI parameters have potential to predict response of locally advanced breast cancer patients to neoadjuvant chemotherapy and hyperthermia: a pilot study

Combined therapies represent a staple of modern medicine. For women treated with neoadjuvant chemotherapy (NA ChT) for locally advanced breast cancer (LABC), early determination of whether the patient will fail to respond can enable the use of alternative, more beneficial therapies. This is even more desirable when the combined therapy includes hyperthermia (HT), an efficient way to improve drug delivery, however, more costly and time consuming. There is data showing that this goal can be achieved using magnetic resonance imaging (MRI) with contrast agent (CA) enhancement. This work for the first time proposes combining the information extracted from pre-treatment MR imaging into a morpho-physiological tumour score (MPTS) with the hypothesis that this score will increase the prognostic efficacy, compared to each of its MR-derived components: morphological (derived from the shape of the tumour enhancement) and physiological (derived from the CA enhancement variance dynamics parameters). The MPTS was correlated with response as determined by both pathologic residual tumour and MRI imaging, and was shown to have potential to predict response. The MPTS was extracted from pre-treatment MRI parameters, so independent of the combined therapy used.

PURPOSE

To use a novel morpho-physiological tumour score (MPTS) generated from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) to predict response to treatment.

MATERIALS AND METHODS

A protocol was designed to acquire DCE-MRI images of 20 locally advanced breast cancer (LABC) patients treated with neoadjuvant chemotherapy (NA ChT) and hyperthermia (HT). Imaging was done over 30 min following bolus injection of gadopentetate-based contrast agent. Parametric maps were generated by fitting the signal intensity to a double exponential curve and were used to derive a morphological characterisation of the lesions. Enhancement-variance dynamics parameters, wash-in and wash-out parameters (WiP, WoP), were extracted. The morphological characterisation and the WiP and WoP were combined into a MPTS with the intent of achieving better prognostic efficacy. The MPTS was correlated with response to NA therapy as determined by pathological residual tumour and MRI imaging.

RESULTS

The contrast agent in all tumours typically peaked in the first 1-4 min. The tumours' WiP and WoP varied considerably. The MPTS was highly correlated with whether the patients had a pathological response. This scoring system has a specificity of 78% and a sensitivity of 91% for predicting response to NA chemotherapy. The kappa was 0.69 with a 95% confidence interval of [0.38, 1] and a p-value of 0.002.

CONCLUSIONS

This pilot study shows that the MPTS derived using pre-treatment MRI images has the potential to predict response to NA ChT and HT in LABC patients. Further prospective studies are needed to confirm the validity of these results.

Quantifying spatial heterogeneity in dynamic contrast-enhanced MRI parameter maps

Dynamic contrast-enhanced MRI is becoming a standard tool for imaging-based trials of anti-vascular/angiogenic agents in cancer. So far, however, biomarkers derived from DCE-MRI parameter maps have largely neglected the fact that the maps have spatial structure and instead focussed on distributional summary statistics. Such statistics-e.g., biomarkers based on median values-neglect the spatial arrangement of parameters, which may carry important diagnostic and prognostic information. This article describes two types of heterogeneity biomarker that are sensitive to both parameter values and their spatial arrangement. Methods based on Rényi fractal dimensions and geometrical properties are developed, both of which attempt to describe the complexity of DCE-MRI parameter maps. Experiments using simulated data show that the proposed biomarkers are sensitive to changes that distribution-based summary statistics cannot detect and demonstrate that heterogeneity biomarkers could be applied in the drug trial setting. An experiment using 23 DCE-MRI parameter maps of gliomas-a class of tumour that is graded on the basis of heterogeneity-shows that the proposed heterogeneity biomarkers are able to differentiate between low- and high-grade tumours.