Assessment of tumor necrotic fraction by dynamic contrast-enhanced MRI: a preclinical study of human tumor xenografts with histopathologic correlation

Correlation of Perfusion Metrics with Ki-67 Proliferation Index and Axillary Involvement as a Prognostic Marker in Breast Carcinoma Cases: A Dynamic Contrast-Enhanced Perfusion MRI Study

Our study aims to reveal clinically helpful prognostic markers using quantitative radiologic data from perfusion magnetic resonance imaging for patients with locally advanced carcinoma, using the Ki-67 index as a surrogate. Patients who received a breast cancer diagnosis and had undergone dynamic contrast-enhanced magnetic resonance imaging of the breast for pretreatment evaluation and follow-up were searched retrospectively. We evaluated the MRI studies for perfusion parameters and various categories and compared them to the Ki-67 index. Axillary involvement was categorized as low (N0-N1) or high (N2-N3) according to clinical stage. A total sum of 60 patients' data was included in this study. Perfusion parameters and Ki-67 showed a significant correlation with the transfer constant (Ktrans) (ρ = 0.554 p = 0.00), reverse transfer constant (Kep) (ρ = 0.454 p = 0.00), and initial area under the gadolinium curve (IAUGC) (ρ = 0.619 p = 0.00). The IAUGC was also significantly different between axillary stage groups (Z = 2.478 p = 0.013). Outside of our primary hypothesis, associations between axillary stage and contrast enhancement (x2 = 8.023 p = 0.046) and filling patterns (x2 = 8.751 p = 0.013) were detected. In conclusion, these parameters are potential prognostic markers in patients with moderate Ki-67 indices, such as those in our study group. The relationship between axillary status and perfusion parameters also has the potential to determine patients who would benefit from limited axillary dissection.

New Cluster Analysis Method for Quantitative Dynamic Contrast-Enhanced MRI Assessing Tumor Heterogeneity Induced by a Tumor-Microenvironmental Ameliorator (E7130) Treatment to a Breast Cancer Mouse Model

BACKGROUND

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) can provide insight into tumor perfusion. However, a method that can quantitatively measure the intra-tumor distribution of tumor voxel clusters with a distinct range of K^trans and v_e values remains insufficiently explored.

HYPOTHESIS

Two-dimensional cluster analysis may quantify the distribution of a tumor voxel subregion with a distinct range of K^trans and v_e values in human breast cancer xenografts.

STUDY TYPE

Prospective longitudinal study.

ANIMAL MODEL

Twenty-two female athymic nude mice with MCF-7 xenograft, treated with E7130, a tumor-microenvironmental ameliorator, or saline.

FIELD STRENGTH/SEQUENCE

9.4 Tesla, turbo rapid acquisition with relaxation enhancement, and spoiled gradient-echo sequences.

ASSESSMENT

We performed two-dimensional k-means clustering to identify tumor voxel clusters with a distinct range of K^trans and v_e values on Days 0, 2, and 5 after treatment, calculated the ratio of the number of tumor voxels in each cluster to the total number of tumor voxels, and measured the normalized distances defined as the ratio of the distance between each tumor voxel and the nearest tumor margin to a tumor radius.

STATISTICAL TESTS

Unpaired t-tests, Dunnett's multiple comparison tests, and Chi-squared test were used.

RESULTS

The largest and second largest clusters constituted 44.4% and 27.5% of all tumor voxels with cluster centroid values of K^trans at 0.040 min^-1 and 0.116 min^-1 , and v_e at 0.131 and 0.201, respectively. At baseline (Day 0), the average normalized distances for the largest and second largest clusters were 0.33 and 0.24, respectively. E7130-treated group showed the normalized distance of the initial largest cluster decreasing to 0.25, while that of the second largest cluster increasing to 0.31. Saline-treated group showed no change.

DATA CONCLUSION

A two-dimensional cluster analysis might quantify the spatial distribution of a tumor subregion with a distinct range of K^trans and v_e values.

LEVEL OF EVIDENCE

1 TECHNICAL EFFICACY: Stage 1.

Concordance of histopathological and radiological grading in soft tissue sarcomas

Aim: The grade of the tumor is essential for planning the treatment strategy in soft-tissue sarcomas (STS). The goal of this study is to determine magnetic resonance imaging features related to histopathological grade and aggressiveness of STS. Material and Method: This retrospective single-center study involved preoperative contrast-enhanced MRI examinations of 64 patients with STS. MRI findings evaluated were; heterogeneity, necrosis, hemorrhage, and relationship with surrounding tissue in T1-weighted (T1W), T2-weighted (T2W), and T1W post-contrast sequences of the lesion. Histological grade was determined with the Fédération Nationale des Centres de Lutte Contre Le Cancer (FNCLCC) grading system, and the aggressiveness of the lesion was measured with the Ki-67 index. Results: Sixty-four patients (mean age 45.5±21.6, M/F ratio 34/30) with STS were included. 33 (51.6%) patients graded as FNCLCC grade 3. On MRI examinations, the absence of necrosis was significantly associated with FNCLCC grade 1 and a low Ki-67 index (p

Comprehensive Imaging Characterization of Colorectal Liver Metastases

Colorectal liver metastases (CRLM) have heterogenous histopathological and immunohistochemical phenotypes, which are associated with variable responses to treatment and outcomes. However, this information is usually only available after resection, and therefore of limited value in treatment planning. Improved techniques for in vivo disease assessment, which can characterise the variable tumour biology, would support further personalization of management strategies. Advanced imaging of CRLM including multiparametric MRI and functional imaging techniques have the potential to provide clinically-actionable phenotypic characterisation. This includes assessment of the tumour-liver interface, internal tumour components and treatment response. Advanced analysis techniques, including radiomics and machine learning now have a growing role in assessment of imaging, providing high-dimensional imaging feature extraction which can be linked to clinical relevant tumour phenotypes, such as a the Consensus Molecular Subtypes (CMS). In this review, we outline how imaging techniques could reproducibly characterize the histopathological features of CRLM, with several matched imaging and histology examples to illustrate these features, and discuss the oncological relevance of these features. Finally, we discuss the future challenges and opportunities of CRLM imaging, with a focus on the potential value of advanced analytics including radiomics and artificial intelligence, to help inform future research in this rapidly moving field.

Management of interruptions in radiotherapy treatments: Adaptive implementation in high workload sites

Owing to predictable or unpredictable causes, interruptions may arise during therapy. On average, the extension of fractionated radiotherapy treatments is prone to be delayed by several weeks and interruptions can come up extending overall treatment time (OTT). Clonogenic cells of aggressive tumors might benefit from this situation, modifying local control (LC). Preserving treatment quality in radiotherapy is an essential issue for the treatment outcome, and our institution is increasingly concerned about this line of work. Establishing some objective criteria to schedule patients that have suffered interruptions along their treatments is of capital importance and not a trivial issue. Publications strongly encourage departments to minimize the effect of lag periods during treatments. Therefore, in July 2017, our facility implemented the so called 'Protocol to Manage Interruptions in Radiotherapy', based on a scoring system for patient categorization that considers not only histology but also associated comorbidity and sequence of the therapy.

Probabilistic classification of tumour habitats in soft tissue sarcoma

The purpose of this work is to propose a method to characterize tumour heterogeneity on MRI, using probabilistic classification based on a reference tissue. The method uses maps of the apparent diffusion coefficient (ADC), T₂ relaxation, and a calculated map representing high-b-value diffusion-weighted MRI (denoted simDWI) to identify up to five habitats (i.e. sub-regions) of tumours. In this classification method, the parameter values (ADC, T₂ , and simDWI) from each tumour voxel are compared against the corresponding parameter probability distributions in a reference tissue. The probability that a tumour voxel belongs to a specific habitat is the joint probability for all parameters. The classification can be visualized using a custom colour scheme. The proposed method was applied to data from seven patients with biopsy-confirmed soft tissue sarcoma, at three time-points over the course of pre-operative radiotherapy. Fast-spin-echo images with two different echo times and diffusion MRI with three b-values were obtained and used as inputs to the method. Imaging findings were compared with pathology reports from pre-radiotherapy biopsy and post-surgical resection. Regions of hypercellularity, high-T₂ proteinaceous fluid, necrosis, collagenous stroma, and fibrosis were identified within soft tissue sarcoma. The classifications were qualitatively consistent with pathological observations. The percentage of necrosis on imaging correlated strongly with necrosis estimated from FDG-PET before radiotherapy (R²  = 0.97) and after radiotherapy (R²  = 0.96). The probabilistic classification method identifies realistic habitats and reflects the complex microenvironment of tumours, as demonstrated in soft tissue sarcoma.

Understanding K trans: a simulation study based on a multiple-pathway model

The transfer constant K ^trans is commonly employed in dynamic contrast-enhanced MRI studies, but the utility and interpretation of K ^trans as a potential biomarker of tumor vasculature remains unclear. In this study, computer simulations based on a comprehensive tracer kinetic model with multiple pathways was used to provide clarification on the interpretation and application of K ^trans. Tissue concentration-time curves pertaining to a wide range of transport conditions were simulated using the multiple-pathway (MP) model and fitted using the generalized kinetic (GK) and extended GK models. Relationships between K ^trans and plasma flow F _p, vessel permeability PS and extraction rate EF _p under various transport conditions were assessed by correlation and regression analysis. Results show that the MP model provides an alternative two-tier interpretation of K ^trans based on the vascular transit time. K ^trans is primarily associated with F _p and EF _p respectively, in the slow and rapid vascular transit states, independent of the magnitude of PS. The relative magnitudes of PS and F _p only serve as secondary constraints for which K ^trans can be further associated with EF _p and PS in the slow and rapid transit states, respectively.

Cluster analysis of DCE-MRI data identifies regional tracer-kinetic changes after tumor treatment with high intensity focused ultrasound

Evaluation of high intensity focused ultrasound (HIFU) treatment with MRI is generally based on assessment of the non-perfused volume from contrast-enhanced T1-weighted images. However, the vascular status of tissue surrounding the non-perfused volume has not been extensively investigated with MRI. In this study, cluster analysis of the transfer constant K(trans) and extravascular extracellular volume fraction ve , derived from dynamic contrast-enhanced MRI (DCE-MRI) data, was performed in tumor tissue surrounding the non-perfused volume to identify tumor subregions with distinct contrast agent uptake kinetics. DCE-MRI was performed in CT26.WT colon carcinoma-bearing BALB/c mice before (n = 12), directly after (n = 12) and 3 days after (n = 6) partial tumor treatment with HIFU. In addition, a non-treated control group (n = 6) was included. The non-perfused volume was identified based on the level of contrast enhancement. Quantitative comparison between non-perfused tumor fractions and non-viable tumor fractions derived from NADH-diaphorase histology showed a stronger agreement between these fractions 3 days after treatment (R(2) to line of identity = 0.91) compared with directly after treatment (R(2) = 0.74). Next, k-means clustering with four clusters was applied to K(trans) and ve parameter values of all significantly enhanced pixels. The fraction of pixels within two clusters, characterized by a low K(trans) and either a low or high ve , significantly increased after HIFU. Changes in composition of these clusters were considered to be HIFU induced. Qualitative H&E histology showed that HIFU-induced alterations in these clusters may be associated with hemorrhage and structural tissue disruption. Combined microvasculature and hypoxia staining suggested that these tissue changes may affect blood vessel functionality and thereby tumor oxygenation. In conclusion, it was demonstrated that, in addition to assessment of the non-perfused tumor volume, the presented methodology gives further insight into HIFU-induced effects on tumor vascular status. This method may aid in assessment of the consequences of vascular alterations for the fate of the tissue.