Role of quantitative magnetic resonance imaging parameters in the evaluation of treatment response in malignant tumors

Magnetic Resonance Imaging in the Clinical Care for Uveal Melanoma Patients-A Systematic Review from an Ophthalmic Perspective

Conversely to most tumour types, magnetic resonance imaging (MRI) was rarely used for eye tumours. As recent technical advances have increased ocular MRI's diagnostic value, various clinical applications have been proposed. This systematic review provides an overview of the current status of MRI in the clinical care of uveal melanoma (UM) patients, the most common eye tumour in adults. In total, 158 articles were included. Two- and three-dimensional anatomical scans and functional scans, which assess the tumour micro-biology, can be obtained in routine clinical setting. The radiological characteristics of the most common intra-ocular masses have been described extensively, enabling MRI to contribute to diagnoses. Additionally, MRI's ability to non-invasively probe the tissue's biological properties enables early detection of therapy response and potentially differentiates between high- and low-risk UM. MRI-based tumour dimensions are generally in agreement with conventional ultrasound (median absolute difference 0.5 mm), but MRI is considered more accurate in a subgroup of anteriorly located tumours. Although multiple studies propose that MRI's 3D tumour visualisation can improve therapy planning, an evaluation of its clinical benefit is lacking. In conclusion, MRI is a complementary imaging modality for UM of which the clinical benefit has been shown by multiple studies.

Effect of Matrix Size Reduction on Textural Information in Clinical Magnetic Resonance Imaging

The selection of the matrix size is an important element of the magnetic resonance imaging (MRI) process, and has a significant impact on the acquired image quality. Signal to noise ratio, often used to assess MR image quality, has its limitations. Thus, for this purpose we propose a novel approach: the use of texture analysis as an index of the image quality that is sensitive for the change of matrix size. Image texture in biomedical images represents tissue and organ structures visualized via medical imaging modalities such as MRI. The correlation between texture parameters determined for the same tissues visualized in images acquired with different matrix sizes is analyzed to aid in the assessment of the selection of the optimal matrix size. T2-weighted coronal images of shoulders were acquired using five different matrix sizes while maintaining the same field of view; three regions of interest (bone, fat, and muscle) were considered. Lin’s correlation coefficients were calculated for all possible pairs of the 310-element texture feature vectors evaluated for each matrix. The obtained results are discussed considering the image noise and blurring effect visible in images acquired with smaller matrices. Taking these phenomena into account, recommendations for the selection of the matrix size used for the MRI imaging were proposed.

Dynamic contrast-enhanced magnetic resonance imaging for evaluating early response to radiosurgery in patients with vestibular schwannoma

PURPOSE

This study aimed to use dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) to evaluate early treatment response in vestibular schwannoma (VS) patients after radiosurgery.

METHODS

Twenty-four VS patients who underwent gamma knife radiosurgery were prospectively followed up for at least four years. DCE-MRI sequences, in addition to standard MRI protocol, were obtained prior to radiosurgery, at 3 and 6 months. Conventionally, treatment responses based on tumor volume changes were classified as regression or stable (RS), transient tumor enlargement (TTE), and continuous tumor enlargement (CTE). DCE-MRI parameters, such as K^trans, K_ep and V_e, were compared according to follow-up periods and between groups. The diagnostic performance was tested using receiver operating characteristic (ROC) curves.

RESULTS

Changes in tumor volume were as follows at the last 48 months of follow-up: RS in 11 patients (45.8%), TTE in 10 patients (41.7%), and CTE in three patients (12.5%). The median time required to distinguish TTE from CTE using conventional MRI was 12 months (range 9-18). The K^trans and V_e were significantly decreased in patients with RS and TTE at 3 and 6 months, but did not differ significantly in patients with CTE. There were no significant differences in K^trans and V_e between patients with RS and TTE at 3 and 6 months. Both K^trans and V_e demonstrated high diagnostic performance in evaluating early treatment response to radiosurgery in patients with VS.

CONCLUSION

DCE-MRI may aid in the monitoring and early prediction of treatment response in patients with VS following radiosurgery.

Can quantitative analysis of multi-parametric MRI independently predict failure of focal salvage HIFU therapy in men with radio-recurrent prostate cancer?

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Quantitative mpMRI parameters predict failure of salvage HIFU in radiorecurrent prostate cancer

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Tumour microenvironment might produce heat-sinks which counter the effect of HIFU

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Ve value measured in the DCE sequence of the mpMRI is an independent predictor of treatment failure

Objectives

Focal salvage HIFU is a feasible therapeutic option in some men who have recurrence after primary radiotherapy for prostate cancer. We aimed to determine if multi-parametric quantitative parameters, in addition to clinical factors, might have a role in independently predicting focal salvage HIFU outcomes.

Methods

A retrospective registry analysis included 150 consecutive men who underwent focal salvage HIFU (Sonablate500) (2006-2015); 89 had mpMRI available. Metastatic disease was excluded by nodal assessment on pelvic MRI, a radioisotope bone-scan and/or choline or FDG PET/CT scan. All men had mpMRI and either transperineal template prostate mapping biopsy or targeted and systematic TRUS-biopsy. mpMRI included T2‐weighted, diffusion‐weighted and dynamic contrast‐enhancement. Pre-HIFU quantitative mpMRI data was obtained using Horos DICOM Viewer v3.3.5 for general MRI parameters and IB DCE v2.0 plug-in. Progression-free survival (PFS) was defined by biochemical failure and/or positive localized or distant imaging results and/or positive biopsy and/or systemic therapy and/or metastases/prostate cancer‐specific death. Potential predictors of PFS were analyzed by univariable and multivariable Cox-regression.

Results

Median age at focal salvage HIFU was 71 years (interquartile range [IQR] 65–74.5) and median PSA pre-focal salvage treatment was 5.8ng/ml (3.8-8). Median follow-up was 35 months (23-47) and median time to failure was 15 months (7.8–24.3). D-Amico low, intermediate and high-risk disease was present in 1% (1/89), 40% (36/89) and 43% (38/89) prior to focal salvage HIFU (16% missing data). 56% (50/89) failed by the composite outcome. A total of 22 factors were evaluated on univariable and 8 factors on multivariable analysis. The following quantitative parameters were included: Ktrans, Kep, Ve, Vp, IS, rTTP and TTP. On univariable analysis, PSA, prostate volume at time of radiotherapy failure and Ve (median) value were predictors for failure. Ve represents extracellular fraction of the whole tissue volume. On multivariable analysis, only Ve (median) value remained as an independent predictor.

Conclusions

One pharmacokinetic quantitative parameter based on DCE sequences seems to independently predict failure following focal salvage HIFU for radio-recurrent prostate cancer. This likely relates to the tumor microenvironment producing heat-sinks which counter the heating effect of HIFU. Further validation in larger datasets and evaluating mechanisms to reduce heat-sinks are required.

Radiomics: from qualitative to quantitative imaging

Historically, medical imaging has been a qualitative or semi-quantitative modality. It is difficult to quantify what can be seen in an image, and to turn it into valuable predictive outcomes. As a result of advances in both computational hardware and machine learning algorithms, computers are making great strides in obtaining quantitative information from imaging and correlating it with outcomes. Radiomics, in its two forms "handcrafted and deep," is an emerging field that translates medical images into quantitative data to yield biological information and enable radiologic phenotypic profiling for diagnosis, theragnosis, decision support, and monitoring. Handcrafted radiomics is a multistage process in which features based on shape, pixel intensities, and texture are extracted from radiographs. Within this review, we describe the steps: starting with quantitative imaging data, how it can be extracted, how to correlate it with clinical and biological outcomes, resulting in models that can be used to make predictions, such as survival, or for detection and classification used in diagnostics. The application of deep learning, the second arm of radiomics, and its place in the radiomics workflow is discussed, along with its advantages and disadvantages. To better illustrate the technologies being used, we provide real-world clinical applications of radiomics in oncology, showcasing research on the applications of radiomics, as well as covering its limitations and its future direction.

Practical no-gold-standard evaluation framework for quantitative imaging methods: application to lesion segmentation in positron emission tomography

Recently, a class of no-gold-standard (NGS) techniques have been proposed to evaluate quantitative imaging methods using patient data. These techniques provide figures of merit (FoMs) quantifying the precision of the estimated quantitative value without requiring repeated measurements and without requiring a gold standard. However, applying these techniques to patient data presents several practical difficulties including assessing the underlying assumptions, accounting for patient-sampling-related uncertainty, and assessing the reliability of the estimated FoMs. To address these issues, we propose statistical tests that provide confidence in the underlying assumptions and in the reliability of the estimated FoMs. Furthermore, the NGS technique is integrated within a bootstrap-based methodology to account for patient-sampling-related uncertainty. The developed NGS framework was applied to evaluate four methods for segmenting lesions from F-Fluoro-2-deoxyglucose positron emission tomography images of patients with head-and-neck cancer on the task of precisely measuring the metabolic tumor volume. The NGS technique consistently predicted the same segmentation method as the most precise method. The proposed framework provided confidence in these results, even when gold-standard data were not available. The bootstrap-based methodology indicated improved performance of the NGS technique with larger numbers of patient studies, as was expected, and yielded consistent results as long as data from more than 80 lesions were available for the analysis.

No-gold-standard evaluation of image-acquisition methods using patient data

Several new and improved modalities, scanners, and protocols, together referred to as image-acquisition methods (IAMs), are being developed to provide reliable quantitative imaging. Objective evaluation of these IAMs on the clinically relevant quantitative tasks is highly desirable. Such evaluation is most reliable and clinically decisive when performed with patient data, but that requires the availability of a gold standard, which is often rare. While no-gold-standard (NGS) techniques have been developed to clinically evaluate quantitative imaging methods, these techniques require that each of the patients be scanned using all the IAMs, which is expensive, time consuming, and could lead to increased radiation dose. A more clinically practical scenario is where different set of patients are scanned using different IAMs. We have developed an NGS technique that uses patient data where different patient sets are imaged using different IAMs to compare the different IAMs. The technique posits a linear relationship, characterized by a slope, bias, and noise standard-deviation term, between the true and measured quantitative values. Under the assumption that the true quantitative values have been sampled from a unimodal distribution, a maximum-likelihood procedure was developed that estimates these linear relationship parameters for the different IAMs. Figures of merit can be estimated using these linear relationship parameters to evaluate the IAMs on the basis of accuracy, precision, and overall reliability. The proposed technique has several potential applications such as in protocol optimization, quantifying difference in system performance, and system harmonization using patient data.

Simultaneous PET/MRI assessment of response to cytotoxic and hormone neo-adjuvant chemotherapy in breast cancer: a preliminary report

The aim of this study was to assess the response to cytotoxic and hormone neo-adjuvant chemotherapy in four patients with locally advanced breast cancer by simultaneous PET/MRI. Four patients with locally advanced breast cancer underwent simultaneous PET/MRI of the breast using a 3 T Biograph mMR before and after neo-adjuvant chemotherapy (two patients were treated with hormone-therapy and two patients were treated with cytotoxic chemotherapy). Morpho-structural tumoral features and tumor size were assessed; area value, metabolic (SUV and MTV) and functional (ADC, K _trans, V _e, k _ep and iAUC) data were obtained by positioning regions of interest. A comparison of all parameters between the pre- and post-treatment PET/MRI examinations and between the two different therapeutic schedules was assessed. In patients treated with cytotoxic chemotherapy and classified as PR, there was a significant reduction of post-treatment morphological, metabolic and functional parameters. In a patient treated with hormone therapy, classified as SD, there was an increase of all post-treatment perfusion parameters, a substantially stable ADC value and a poor reduction of lesion size and of maximum SUV (SUV_max) values; the last patient, treated with hormone therapy and classified as PR, showed a significant reduction of lesion size and SUV_max values with a reduction of perfusion parameters and substantially stable ADC values. Multiparametric evaluation with simultaneous PET/MRI could be a useful tool to assess the response to cytotoxic and hormone neo-adjuvant chemotherapy in patients with breast cancer. Future studies in a larger cohort of patients are warranted to confirm the results of this preliminary study.