Dynamic contrast-enhanced magnetic resonance imaging as an imaging biomarker

Model discovery approach enables noninvasive measurement of intra-tumoral fluid transport in dynamic MRI

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is a routine method to noninvasively quantify perfusion dynamics in tissues. The standard practice for analyzing DCE-MRI data is to fit an ordinary differential equation to each voxel. Recent advances in data science provide an opportunity to move beyond existing methods to obtain more accurate measurements of fluid properties. Here, we developed a localized convolutional function regression that enables simultaneous measurement of interstitial fluid velocity, diffusion, and perfusion in 3D. We validated the method computationally and experimentally, demonstrating accurate measurement of fluid dynamics in situ and in vivo. Applying the method to human MRIs, we observed tissue-specific differences in fluid dynamics, with an increased fluid velocity in breast cancer as compared to brain cancer. Overall, our method represents an improved strategy for studying interstitial flows and interstitial transport in tumors and patients. We expect that our method will contribute to the better understanding of cancer progression and therapeutic response.

Structural and practical identifiability of contrast transport models for DCE-MRI

Contrast transport models are widely used to quantify blood flow and transport in dynamic contrast-enhanced magnetic resonance imaging. These models analyze the time course of the contrast agent concentration, providing diagnostic and prognostic value for many biological systems. Thus, ensuring accuracy and repeatability of the model parameter estimation is a fundamental concern. In this work, we analyze the structural and practical identifiability of a class of nested compartment models pervasively used in analysis of MRI data. We combine artificial and real data to study the role of noise in model parameter estimation. We observe that although all the models are structurally identifiable, practical identifiability strongly depends on the data characteristics. We analyze the impact of increasing data noise on parameter identifiability and show how the latter can be recovered with increased data quality. To complete the analysis, we show that the results do not depend on specific tissue characteristics or the type of enhancement patterns of contrast agent signal.

Decoding Tumor Angiogenesis for Therapeutic Advancements: Mechanistic Insights

Tumor angiogenesis, the formation of new blood vessels within the tumor microenvironment, is considered a hallmark of cancer progression and represents a crucial target for therapeutic intervention. The tumor microenvironment is characterized by a complex interplay between proangiogenic and antiangiogenic factors, regulating the vascularization necessary for tumor growth and metastasis. The study of angiogenesis involves a spectrum of techniques, spanning from biomarker assessment to advanced imaging modalities. This comprehensive review aims to provide insights into the molecular intricacies, regulatory dynamics, and clinical implications of tumor angiogenesis. By delving into these aspects, we gain a deeper understanding of the processes driving vascularization in tumors, paving the way for the development of novel and effective antiangiogenic therapies in the fight against cancer.

Unbalanced regularized optimal mass transport with applications to fluid flows in the brain

As a generalization of the optimal mass transport (OMT) approach of Benamou and Brenier's, the regularized optimal mass transport (rOMT) formulates a transport problem from an initial mass configuration to another with the optimality defined by the total kinetic energy, but subject to an advection-diffusion constraint equation. Both rOMT and the Benamou and Brenier's formulation require the total initial and final masses to be equal; mass is preserved during the entire transport process. However, for many applications, e.g., in dynamic image tracking, this constraint is rarely if ever satisfied. Therefore, we propose to employ an unbalanced version of rOMT to remove this constraint together with a detailed numerical solution procedure and applications to analyzing fluid flows in the brain.

Structural and practical identifiability of contrast transport models for DCE-MRI

Compartment models are widely used to quantify blood flow and transport in dynamic contrast-enhanced magnetic resonance imaging. These models analyze the time course of the contrast agent concentration, providing diagnostic and prognostic value for many biological systems. Thus, ensuring accuracy and repeatability of the model parameter estimation is a fundamental concern. In this work, we analyze the structural and practical identifiability of a class of nested compartment models pervasively used in analysis of MRI data. We combine artificial and real data to study the role of noise in model parameter estimation. We observe that although all the models are structurally identifiable, practical identifiability strongly depends on the data characteristics. We analyze the impact of increasing data noise on parameter identifiability and show how the latter can be recovered with increased data quality. To complete the analysis, we show that the results do not depend on specific tissue characteristics or the type of enhancement patterns of contrast agent signal.

Dynamic contrast-enhanced magnetic resonance imaging parameter changes as an early biomarker of tumor responses following radiation therapy in patients with spinal metastases: a systematic review

PURPOSE

This systematic review aims to assess and summarize the clinical values of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) parameter changes as early biomarkers of tumor responses following radiation therapy (RT) in patients with spinal metastases.

MATERIALS AND METHODS

A systematic search was conducted on five electronic databases: PubMed, Scopus, Science Direct, Cochrane, and Embase. Studies were included if they mentioned DCE-MRI parameter changes before and after RT in patients with spinal metastases with a correlation to tumor responses based on clinical and imaging criteria. The Quality Assessment of Diagnostic Accuracy Studies 2 was used to assess study quality.

RESULTS

This systematic review included seven studies involving 107 patients. All seven studies evaluated the transfer constant (Ktrans), six studies evaluated the plasma volume fraction (Vp), three studies evaluated the extravascular extracellular space volume fraction, and two studies evaluated the rate constant. There were variations in the type of primary cancer, RT techniques used, post-treatment scan time, and median follow-up time. Despite the variations, however, the collected evidence generally suggested that significant differences could be detected in DCE-MRI parameters between before and after RT, which might reflect treatment success or failures in long-term follow-up. Responders showed higher reduction and lower values of Ktrans and Vp after RT. DCE-MRI parameters showed changes and detectable recurrences significantly earlier (up to 6 months) than conventional MRI with favorable diagnostic values.

CONCLUSION

The results of this systematic review suggested that DCE-MRI parameter changes in patients with spinal metastases could be a promising tool for treatment-response assessment following RT. Lower values and higher reduction of Ktrans and Vp after treatment demonstrated good prediction of local control. Compared to conventional MRI, DCE-MRI showed more rapid changes and earlier prediction of treatment failure.

A narrative review for radiation oncologists to implement preoperative partial breast irradiation

The strategy to anticipate radiotherapy (RT) before surgery, for breast cancer (BC) treatment, has recently generated a renewed interest. Historically, preoperative RT has remained confined either to highly selected patients, in the context of personalized therapy, or to clinical research protocols. Nevertheless, in the recent years, thanks to technological advances and increased tumor biology understanding, RT has undergone great changes that have also impacted the preoperative settings, embracing the modern approach to breast cancer. In particular, the reappraisal of preoperative RT can be viewed within the broader view of personalized and tailored medicine. In fact, preoperative accelerated partial breast irradiation (APBI) allows a more precise target delineation, with less variability in contouring among radiation oncologists, and a smaller treatment volume, possibly leading to lower toxicity and to dose escalation programs. The aim of the present review, which represents a benchmark study for the AIRC IG-23118, is to report available data on different technical aspects of preoperative RT including dosimetric studies, patient's selection and set-up, constraints, target delineation and clinical results. These data, along with the ones that will become available from ongoing studies, may inform the design of the future trials and representing a step toward a tailored APBI approach with the potential to challenge the current treatment paradigm in early-stage BC.Trial registration: The study is registered at clinicaltrials.gov (NCT04679454).

Synthesis of Multifunctional Mn3O4-Ag2S Janus Nanoparticles for Enhanced T1-Magnetic Resonance Imaging and Photo-Induced Tumor Therapy

The global burden of cancer is increasing rapidly, and nanomedicine offers promising prospects for enhancing the life expectancy of cancer patients. Janus nanoparticles (JNPs) have garnered considerable attention due to their asymmetric geometry, enabling multifunctionality in drug delivery and theranostics. However, achieving precise control over the self-assembly of JNPs in solution at the nanoscale level poses significant challenges. Herein, a low-temperature reversed-phase microemulsion system was used to obtain homogenous Mn3O4-Ag2S JNPs, which showed significant potential in cancer theranostics. Structural characterization revealed that the Ag2S (5-10 nm) part was uniformly deposited on a specific surface of Mn3O4 to form a Mn3O4-Ag2S Janus morphology. Compared to the single-component Mn3O4 and Ag2S particles, the fabricated Mn3O4-Ag2S JNPs exhibited satisfactory biocompatibility and therapeutic performance. Novel diagnostic and therapeutic nanoplatforms can be guided using the magnetic component in JNPs, which is revealed as an excellent T1 contrast enhancement agent in magnetic resonance imaging (MRI) with multiple functions, such as photo-induced regulation of the tumor microenvironment via producing reactive oxygen species and second near-infrared region (NIR-II) photothermal excitation for in vitro tumor-killing effects. The prime antibacterial and promising theranostics results demonstrate the extensive potential of the designed photo-responsive Mn3O4-Ag2S JNPs for biomedical applications.

Model discovery approach enables non-invasive measurement of intra-tumoral fluid transport in dynamic MRI

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is a routine method to non-invasively quantify perfusion dynamics in tissues. The standard practice for analyzing DCE-MRI data is to fit an ordinary differential equation to each voxel. Recent advances in data science provide an opportunity to move beyond existing methods to obtain more accurate measurements of fluid properties. Here, we developed a localized convolutional function regression that enables simultaneous measurement of interstitial fluid velocity, diffusion, and perfusion in 3D. We validated the method computationally and experimentally, demonstrating accurate measurement of fluid dynamics in situ and in vivo. Applying the method to human MRIs, we observed tissue-specific differences in fluid dynamics, with an increased fluid velocity in breast cancer as compared to brain cancer. Overall, our method represents an improved strategy for studying interstitial flows and interstitial transport in tumors and patients. We expect that it will contribute to the better understanding of cancer progression and therapeutic response.

PET/MRI of hypoxia and vascular function in ER-positive breast cancer: correlations with immunohistochemistry

OBJECTIVES

To explore the relationship between indices of hypoxia and vascular function from 18F-fluoromisonidazole ([18F]-FMISO)-PET/MRI with immunohistochemical markers of hypoxia and vascularity in oestrogen receptor-positive (ER +) breast cancer.

METHODS

Women aged  > 18 years with biopsy-confirmed, treatment-naïve primary ER + breast cancer underwent [18F]-FMISO-PET/MRI prior to surgery. Parameters of vascular function were derived from DCE-MRI using the extended Tofts model, whilst hypoxia was assessed using the [18F]-FMISO influx rate constant, Ki. Histological tumour sections were stained with CD31, hypoxia-inducible factor (HIF)-1α, and carbonic anhydrase IX (CAIX). The number of tumour microvessels, median vessel diameter, and microvessel density (MVD) were obtained from CD31 immunohistochemistry. HIF-1α and CAIX expression were assessed using histoscores obtained by multiplying the percentage of positive cells stained by the staining intensity. Regression analysis was used to study associations between imaging and immunohistochemistry variables.

RESULTS

Of the lesions examined, 14/22 (64%) were ductal cancers, grade 2 or 3 (19/22; 86%), with 17/22 (77%) HER2-negative. [18F]-FMISO Ki associated negatively with vessel diameter (p = 0.03), MVD (p = 0.02), and CAIX expression (p = 0.002), whilst no significant relationships were found between DCE-MRI pharmacokinetic parameters and immunohistochemical variables. HIF-1α did not significantly associate with any PET/MR imaging indices.

CONCLUSION

Hypoxia measured by [18F]-FMISO-PET was associated with increased CAIX expression, low MVD, and smaller vessel diameters in ER + breast cancer, further corroborating the link between inadequate vascularity and hypoxia in ER + breast cancer.

KEY POINTS

• Hypoxia, measured by [18F]-FMISO-PET, was associated with low microvessel density and small vessel diameters, corroborating the link between inadequate vascularity and hypoxia in ER + breast cancer. • Increased CAIX expression was associated with higher levels of hypoxia measured by [18F]-FMISO-PET. • Morphologic and functional abnormalities of the tumour microvasculature are the major determinants of hypoxia in cancers and support the previously reported perfusion-driven character of hypoxia in breast carcinomas.

Magnetic Resonance Imaging of Mouse Cerebral Cavernomas Reveal Differential Lesion Progression and Variable Permeability to Gadolinium

BACKGROUND

Cerebral cavernous malformations, also known as cavernous angiomas, are blood vessel abnormalities comprised of clusters of grossly enlarged and hemorrhage-prone capillaries. The prevalence in the general population, including asymptomatic cases, is estimated to be 0.5%. Some patients develop severe symptoms, including seizures and focal neurological deficits, whereas others remain asymptomatic. The causes of this remarkable presentation heterogeneity within a primarily monogenic disease remain poorly understood.

METHODS

We established a chronic mouse model of cerebral cavernous malformations, induced by postnatal ablation of Krit1 with Pdgfb-CreERT2, and examined lesion progression in these mice with T2-weighted 7T magnetic resonance imaging (MRI). We also established a modified protocol for dynamic contrast-enhanced MRI and produced quantitative maps of gadolinium tracer gadobenate dimeglumine. After terminal imaging, brain slices were stained with antibodies against microglia, astrocytes, and endothelial cells.

RESULTS

These mice develop cerebral cavernous malformations lesions gradually over 4 to 5 months of age throughout the brain. Precise volumetric analysis of individual lesions revealed nonmonotonous behavior, with some lesions temporarily growing smaller. However, the cumulative lesional volume invariably increased over time and after about 2 months followed a power trend. Using dynamic contrast-enhanced MRI, we produced quantitative maps of gadolinium in the lesions, indicating a high degree of heterogeneity in lesional permeability. MRI properties of the lesions were correlated with cellular markers for endothelial cells, astrocytes, and microglia. Multivariate comparisons of MRI properties of the lesions with cellular markers for endothelial and glial cells revealed that increased cell density surrounding lesions correlates with stability, whereas denser vasculature within and surrounding the lesions may correlate with high permeability.

CONCLUSIONS

Our results lay a foundation for better understanding individual lesion properties and provide a comprehensive preclinical platform for testing new drug and gene therapies for controlling cerebral cavernous malformations.

Nanoparticles in the diagnosis and treatment of cancer metastases: Current and future perspectives

Metastasis accounts for greater than 90% of cancer-related deaths. Despite recent advancements in conventional chemotherapy, immunotherapy, targeted therapy, and their rational combinations, metastatic cancers remain essentially untreatable. The distinct obstacles to treat metastases include their small size, high multiplicity, redundancy, therapeutic resistance, and dissemination to multiple organs. Recent advancements in nanotechnology provide the numerous applications in the diagnosis and prophylaxis of metastatic diseases, including the small particle size to penetrate cell membrane and blood vessels and their capacity to transport complex molecular 'cargo' particles to various metastatic regions such as bones, brain, liver, lungs, and lymph nodes. Indeed, nanoparticles (NPs) have demonstrated a significant ability to target specific cells within these organs. In this regard, the purpose of this review is to summarize the present state of nanotechnology in terms of its application in the diagnosis and treatment of metastatic cancer. We intensively reviewed applications of NPs in fluorescent imaging, PET scanning, MRI, and photoacoustic imaging to detect metastasis in various cancer models. The use of targeted NPs for cancer ablation in conjunction with chemotherapy, photothermal treatment, immuno therapy, and combination therapy is thoroughly discussed. The current review also highlights the research opportunities and challenges of leveraging engineering technologies with cancer cell biology and pharmacology to fabricate nanoscience-based tools for treating metastases.

Clinical utility of combined assessments of 4D volumetric perfusion CT, diffusion-weighted MRI and 18F-FDG PET-CT for the prediction of outcomes of head and neck squamous cell carcinoma treated with chemoradiotherapy

BACKGROUND

Multiparametric imaging has been seen as a route to improved prediction of chemoradiotherapy treatment outcomes. Four-dimensional volumetric perfusion CT (4D PCT) is useful for whole-organ perfusion measurement, as it reflects the heterogeneity of the tumor and its perfusion parameters. However, there has been no study using multiparametric imaging including 4D PCT for the prognostic prediction of chemoradiotherapy. The purpose of this study was to determine whether combining assessments of 4D PCT with diffusion-weighted MRI (DWI) and ¹⁸F-fluorodeoxyglucose PET-CT could enhance prognostic accuracy in head and neck squamous cell carcinoma (HNSCC) patients treated with chemoradiotherapy.

METHODS

We examined 53 patients with HNSCC who underwent 4D PCT, DWI and PET-CT before chemoradiotherapy. The imaging and clinical parameters were assessed the relations to locoregional control (LRC) and progression-free survival (PFS) by logistic regression analyses. A receiver operating characteristic (ROC) analysis was performed to assess the accuracy of the significant parameters identified by the multivariate analysis for the prediction of LRC and PFS. We additionally assessed using the scoring system whether these independent parameters could have a complementary role for the prognostic prediction.

RESULTS

The median follow-up was 30 months. In multivariate analysis, blood flow (BF; p = 0.02) and blood volume (BV; p = 0.04) were significant prognostic factors for LRC, and BF (p = 0.03) and skewness of the ADC histogram (p = 0.02) were significant prognostic factors for PFS. A significant positive correlation was found between BF and BV (ρ = 0.6, p < 0.001) and between BF and skewness (ρ = 0.46, p < 0.01). The ROC analysis showed that prognostic accuracy for LRC of BF, BV, and combination of BF and BV were 77.8%, 70%, and 92.9%, and that for PFS of BF, skewness, and combination of BF and skewness were 55.6%, 63.2%, and 77.5%, respectively. The scoring system demonstrated that the combination of higher BF and higher BV was significantly associated with better LRC (p = 0.04), and the combination of lower BF and lower skewness was significantly associated with worse PFS (p = 0.004).

CONCLUSION

A combination of parameters derived from 4DPCT and ADC histograms may enhance prognostic accuracy in HNSCC patients treated with chemoradiotherapy.

A Two-Step Feature Selection Radiomic Approach to Predict Molecular Outcomes in Breast Cancer

Breast Cancer (BC) is the most common cancer among women worldwide and is characterized by intra- and inter-tumor heterogeneity that strongly contributes towards its poor prognosis. The Estrogen Receptor (ER), Progesterone Receptor (PR), Human Epidermal Growth Factor Receptor 2 (HER2), and Ki67 antigen are the most examined markers depicting BC heterogeneity and have been shown to have a strong impact on BC prognosis. Radiomics can noninvasively predict BC heterogeneity through the quantitative evaluation of medical images, such as Magnetic Resonance Imaging (MRI), which has become increasingly important in the detection and characterization of BC. However, the lack of comprehensive BC datasets in terms of molecular outcomes and MRI modalities, and the absence of a general methodology to build and compare feature selection approaches and predictive models, limit the routine use of radiomics in the BC clinical practice. In this work, a new radiomic approach based on a two-step feature selection process was proposed to build predictors for ER, PR, HER2, and Ki67 markers. An in-house dataset was used, containing 92 multiparametric MRIs of patients with histologically proven BC and all four relevant biomarkers available. Thousands of radiomic features were extracted from post-contrast and subtracted Dynamic Contrast-Enanched (DCE) MRI images, Apparent Diffusion Coefficient (ADC) maps, and T2-weighted (T2) images. The two-step feature selection approach was used to identify significant radiomic features properly and then to build the final prediction models. They showed remarkable results in terms of F1-score for all the biomarkers: 84%, 63%, 90%, and 72% for ER, HER2, Ki67, and PR, respectively. When possible, the models were validated on the TCGA/TCIA Breast Cancer dataset, returning promising results (F1-score = 88% for the ER+/ER- classification task). The developed approach efficiently characterized BC heterogeneity according to the examined molecular biomarkers.

Radiomic Models Predict Tumor Microenvironment Using Artificial Intelligence-the Novel Biomarkers in Breast Cancer Immune Microenvironment

Breast cancer is the most common malignancy in women, and some subtypes are associated with a poor prognosis with a lack of efficacious therapy. Moreover, immunotherapy and the use of other novel antibody‒drug conjugates have been rapidly incorporated into the standard management of advanced breast cancer. To extract more benefit from these therapies, clarifying and monitoring the tumor microenvironment (TME) status is critical, but this is difficult to accomplish based on conventional approaches. Radiomics is a method wherein radiological image features are comprehensively collected and assessed to build connections with disease diagnosis, prognosis, therapy efficacy, the TME, etc In recent years, studies focused on predicting the TME using radiomics have increasingly emerged, most of which demonstrate meaningful results and show better capability than conventional methods in some aspects. Beyond predicting tumor-infiltrating lymphocytes, immunophenotypes, cytokines, infiltrating inflammatory factors, and other stromal components, radiomic models have the potential to provide a completely new approach to deciphering the TME and facilitating tumor management by physicians.

DCE-MRI and DWI can differentiate benign from malignant prostate tumors when serum PSA is ≥10 ng/ml

Background

This study investigated the diagnostic utility of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and diffusion-weighted imaging (DWI) parameters for distinguishing between benign and malignant prostate tumors when serum prostate-specific antigen (PSA) level is ≥10 ng/ml.

Methods

Patients with prostate cancer (PCa) and benign prostatic hyperplasia (BPH) with serum PSA ≥10 ng/ml before treatment were recruited. Transrectal ultrasound-guided biopsy or surgery was performed for tumor classification and patients were stratified accordingly into PCa and BPH groups. Patients underwent DCE-MRI and DWI scanning and the transfer constant (K_trans), rate constant (K_ep), fractional volume of the extravascular extracellular space, plasma volume (V_p), and Prostate Imaging Reporting and Data System Version 2 (PI-RADS v2) score were determined. The apparent diffusion coefficient (ADC) was calculated from DWI. The diagnostic performance of these parameters was assessed by receiver operating characteristic (ROC) curve analysis, and those showing a significant difference between the PCa and BPH groups were combined into a multivariate logistic regression model for PCa diagnosis. Spearman's correlation was used to analyze the relationship between Gleason score and imaging parameters.

Results

The study enrolled 65 patients including 32 with PCa and 33 with BPH. Ktrans (P=0.006), Kep (P=0.001), and Vp (P=0.009) from DCE-MRI and ADC (P<0.001) from DWI could distinguish between the 2 groups when PSA was ≥10 ng/ml. PI-RADS score (area under the ROC curve [AUC]=0.705), Ktrans (AUC=0.700), Kep (AUC=0.737), Vp (AUC=0.688), and ADC (AUC=0.999) showed high diagnostic performance for discriminating PCa from BPH. A combined model based on PI-RADS score, Ktrans, Kep, Vp, and ADC had a higher AUC (1.000), with a sensitivity of 0.998 and specificity of 0.999. Imaging markers showed no significant correlation with Gleason score in PCa.

Conclusion

DCE-MRI and DWI parameters can distinguish between benign and malignant prostate tumors in patients with serum PSA ≥10 ng/ml.

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.

Diagnostic Performance of Dynamic Contrast-Enhanced MRI and 18F-FDG PET/CT for Evaluation of Soft Tissue Tumors and Correlation with Pathology Parameters

RATIONALE AND OBJECTIVES

To assess the diagnostic performance of dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) and fluorine-18-fluorodeoxyglucose (¹⁸F-FDG) positron-emission tomography/computed tomography (PET/CT) parameters in evaluating the biological behavior of soft tissue tumors.

MATERIALS AND METHODS

We retrospectively analyzed DCE-MRI and ¹⁸F-FDG PET/CT parameters in 78 patients with pathology-confirmed soft tissue tumors. A total of 78 patients had undergone DCE-MRI examination, while 24 patients with malignant soft tissue tumor had undergone ¹⁸F-FDG PET/CT examination. Microvessel density (MVD) and the Ki-67 labeling index (LI) were detected using immunohistochemistry. Differences in parameters (Ktrans, Kep, Ve, MVD, and Ki-67 LI) between benign and malignant tumors were compared. Differences in parameters (Ktrans, Kep, Ve, MVD, and SUV_max) between high- and low-proliferation malignant tumors (grouped by Ki-67 LI) were compared. Correlation of the DCE-MRI and ¹⁸F-FDG PET/CT parameters with MVD and Ki-67 LI was analyzed.

RESULTS

Only the Ktrans, Kep, MVD, and Ki-67 LI differed significantly between the benign and malignant soft tissue tumors (all p < 0.001). Only Kep (p = 0.033) and SUV_max (p = 0.001) differed significantly between high- and low-proliferation malignant soft tissue tumors. Ktrans, Kep, and SUV_max correlated positively with MVD (r = 0.805, 0.778, 0.730, respectively; all p < 0.001), and with Ki-67 LI (r = 0.721, 0.685, 0.655, respectively; all p < 0.001).

CONCLUSION

DCE-MRI and ¹⁸F-FDG PET/CT parameters indicate soft tissue tumor biological behavior and can be used to differentiate between benign and malignant soft tissue tumors and between high- and low-proliferation malignant soft tissue tumors.

Optimization of the Contrast Agent Injection Protocol for Carotid Artery Dynamic Contrast-Enhanced Magnetic Resonance Imaging

BACKGROUND

The injection protocol used in previous carotid artery dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) studies varied.

PURPOSE

To investigate the effect of contrast injection protocol and optimize this protocol for carotid artery DCE-MRI.

STUDY TYPE

Prospective.

SUBJECTS

Digital phantom and seven patients with carotid atherosclerosis.

FIELD STRENGTH/SEQUENCE

3 T, spoiled gradient recalled echo sequence.

ASSESSMENT

Different injection doses (0.01-0.3 mmol/kg) and effective injection rates (0.01-1 mmol/sec) were tested using a digital carotid plaque phantom considering the contrast pharmacokinetics, DCE-MRI imaging, contrast variation and flow-related imaging artifacts, random time delay between the contrast injection and image acquisition, and pharmacokinetic analysis process. For each injection protocol, combining the root mean square relative error (RMSRE) of the measured K trans and v P maps within the adventitial vasa vasorum from 10 tested time delays by the root mean square produced RMSRE_overall-vv which was used to measure the overall accuracy of the pharmacokinetic parameters. In vivo validation was performed on seven patients with carotid atherosclerosis by imaging them twice using the traditional commonly used protocol and the recommended protocol found by simulation.

STATISTICAL TEST

Student's t-test, chi-square test, and paired t-test, P < 0.05 was considered statistically significant.

RESULTS

A low region of RMSRE_overall-vv with the combination of medium injection dose and low effective injection rate was found. The protocol with injection dose of 0.07 mmol/kg and effective injection rate of 0.06 mmol/sec achieved the minimal RMSRE_overall-vv (4.29%), thus was recommended, which showed more accurate arterial input function. Coinciding with the simulation results, this recommended protocol in in vivo experiments produced significantly fewer image artifacts, lower K trans and v P (P all <0.05) than traditional protocol which overestimated these parameters in simulation.

DATA CONCLUSION

The contrast injection protocol influenced the accuracy of the pharmacokinetics parameter estimation in carotid artery DCE-MRI. The injection protocol with injection dose of 0.07 mmol/kg and effective injection rate of 0.06 mmol/sec was recommended.

LEVEL OF EVIDENCE

2 TECHNICAL EFFICACY STAGE: 1.

Evaluation of tracer kinetic parameters in cervical cancer using dynamic contrast-enhanced MRI as biomarkers in terms of biological relevance, diagnostic performance and inter-center variability

Objectives

This study assessed the clinical value of parameters derived from dynamic contrast-enhanced (DCE) MRI with respect to correlation with angiogenesis and proliferation of cervical cancer, performance of diagnosis and reproducibility of DCE-MRI parameters across MRI scanners.

Materials and Methods

A total of 113 patients with cervical carcinoma from two centers were included in this retrospective study. The DCE data were centralized and processed using five tracer kinetic models (TKMs) (Tofts, Ex-Tofts, ATH, SC, and DP), yielding the following parameters: volume transfer constant (Ktrans), extravascular extracellular volume (Ve), fractional volume of vascular space (Vp), blood flow (Fp), and permeability surface area product (PS). CD34 counts and Ki-67 PI (proliferation index) of cervical cancer and normal cervix tissue were obtained using immunohistochemical staining in Center 1.

Results

CD34 count and Ki-67 PI in cervical cancer were significantly higher than in normal cervix tissue (p<0.05). Parameter Ve from each TKM was significantly smaller in cervical cancer tissue than in normal cervix tissue (p<0.05), indicating the higher proliferation of cervical cancer cells. Ve of each TKM attained the largest AUC to diagnose cervical cancer. The distributions of DCE parameters for both cervical cancer and normal cervix tissue were not significantly different between two centers (P>0.05).

Conclusion

Parameter Ve was similar to the expression of Ki-67 in revealing the proliferation of tissue cells, attained good performance in diagnosis of cervical cancer, and demonstrated consistent findings on measured values across centers.

Invasive ductal breast cancer molecular subtype prediction by MRI radiomic and clinical features based on machine learning

Background

Most studies of molecular subtype prediction in breast cancer were mainly based on two-dimensional MRI images, the predictive value of three-dimensional volumetric features from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) for predicting breast cancer molecular subtypes has not been thoroughly investigated. This study aimed to look into the role of features derived from DCE-MRI and how they could be combined with clinical data to predict invasive ductal breast cancer molecular subtypes.

Methods

From January 2019 to December 2021, 190 Chinese women with invasive ductal breast cancer were studied (32 triple-negative, 59 HER2-enriched, and 99 luminal lesions) in this institutional review board-approved retrospective cohort study. The image processing software extracted 1130 quantitative radiomic features from the segmented lesion area, including shape-based, first-order statistical, texture, and wavelet features. Three binary classifications of the subtypes were performed: triple-negative vs. non-triple-negative, HER2-overexpressed vs. non-HER2-overexpressed, and luminal (A + B) vs. non-luminal. For the classification, five machine learning methods (random forest, logistic regression, support vector machine, naïve Bayes, and eXtreme Gradient Boosting) were employed. The classifiers were chosen using the least absolute shrinkage and selection operator method. The area evaluated classification performance under the receiver operating characteristic curve, sensitivity, specificity, accuracy, F1-Score, false positive rate, precision, and geometric mean.

Results

EXtreme Gradient Boosting model showed the best performance in luminal and non-luminal groups, with AUC, sensitivity, specificity, accuracy, F1-Score, false positive rate, precision, and geometric mean of 0.8282, 0.7524, 0.6542, 0.6964, 0.6086, 0.3458, 0.8524 and 0.7016, respectively. Meanwhile, the random forest model showed the best performance in HER2-overexpressed and non-HER2-overexpressed groups, with AUC, sensitivity, specificity, accuracy, F1-Score, false positive rate, precision, and geometric mean of 0.8054, 0.2941, 0.9744, 0.7679, 0.4348, 0.0256, 0.8333 and 0.5353, respectively. Furthermore, eXtreme Gradient Boosting model showed the best performance in the triple-negative and non-triple-negative groups, with AUC, sensitivity, specificity, accuracy, F1-Score, false positive rate, precision, and geometric mean of 0.9031, 0.9362, 0.4444, 0.8571, 0.9167, 0.5556, 0.8980 and 0.6450.

Conclusion

Clinical data and three-dimension imaging features from DCE-MRI were identified as potential biomarkers for distinguishing between three molecular subtypes of invasive ductal carcinomas breast cancer. In the future, more extensive studies will be required to evaluate the findings.

Amide proton transfer weighted imaging combined with dynamic contrast-enhanced MRI in predicting lymphovascular space invasion and deep stromal invasion of IB1-IIA1 cervical cancer

Objectives

To investigate the value of amide proton transfer weighted (APTw) imaging combined with dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) in predicting intermediate-risk factors of deep stromal invasion (DSI) and lymphovascular vascular space invasion (LVSI) in cervical cancer.

Methods

Seventy patients with cervical cancer who underwent MRI before operation from July 2019 to February 2022 were retrospectively included in this study. Clinical information including age, histologic subtype etc. were recorded for patients. ATPw imaging parameter APT_mean and DCE-MRI parameters K^trans, K_ep and V_e were measured and analyzed. The independent-sample t-test, Mann-Whitney U test, or Chi-square test was used to compare the differences of parameters between DSI/LVSI positive and negative groups. Logistic analysis was used to develop a combined predictive model. The receiver operating characteristic curve was for predictive performance. ANOVA and Kruskal-Wallis test were used to compare the differences of consecutive parameters among multiple groups.

Results

K^trans and SCC-Ag were independent factors in predicting DSI; K^trans+SCC-Ag had the highest AUC 0.819 with sensitivity and specificity of 71.74% and 91.67%, respectively. APT_mean and K^trans were independent factors in predicting LVSI; APT_mean+K^trans had the highest AUC 0.874 with sensitivity and specificity of 92.86% and 75.00%, respectively. K^trans and Ve could discriminate coexistence of DSI and LVSI from presence of single one, APT_mean could discriminate the presence of DSI or LVSI from no risk factor presence.

Conclusion

The combination of APTw and DCE-MRI is valuable in predicting intermediate-risk factors of DSI and LVSI in cervical cancer.

MRI techniques for immunotherapy monitoring

MRI is a widely available clinical tool for cancer diagnosis and treatment monitoring. MRI provides excellent soft tissue imaging, using a wide range of contrast mechanisms, and can non-invasively detect tissue metabolites. These approaches can be used to distinguish cancer from normal tissues, to stratify tumor aggressiveness, and to identify changes within both the tumor and its microenvironment in response to therapy. In this review, the role of MRI in immunotherapy monitoring will be discussed and how it could be utilized in the future to address some of the unique clinical questions that arise from immunotherapy. For example, MRI could play a role in identifying pseudoprogression, mixed response, T cell infiltration, cell tracking, and some of the characteristic immune-related adverse events associated with these agents. The factors to be considered when developing MRI imaging biomarkers for immunotherapy will be reviewed. Finally, the advantages and limitations of each approach will be discussed, as well as the challenges for future clinical translation into routine clinical care. Given the increasing use of immunotherapy in a wide range of cancers and the ability of MRI to detect the microstructural and functional changes associated with successful response to immunotherapy, the technique has great potential for more widespread and routine use in the future for these applications.

ADC and kinetic parameter of primary tumor: Surrogate imaging markers for fertility-sparing vaginal radical trachelectomy in patients with stage IB cervical cancer

PURPOSE

To investigate the role of ADC and kinetic parameters derived from DCE-MRI in selecting eligible candidates for fertility-sparing vaginal radical trachelectomy (VRT).

METHOD

Female patients with FIGO stage IB cervical cancers between March 2019 and January 2022 were retrospectively included. All patients underwent hysterectomy and bilateral lymphadenectomy. According to the surgical pathology, the study population was divided into VRT-eligible group and VRT-ineligible group. ADC, semi-quantitative and quantitative kinetic parameters of the primary tumor were compared between the two groups. Logistic regression analysis was used to determine the independent predictors for VRT eligibility and ROC curve was used to evaluate the predictive performance.

RESULTS

19 patients were deemed eligible for VRT and 50 were ineligible. Compared with VRT-eligible group, time to peak and ADC were significantly lower in VRT-ineligible group (P = 0.004 and 0.001, respectively) while volume fraction of plasma (V_p) was higher in VRT ineligible group (P = 0.001). ADC and V_p were independent predictors for VRT eligibility. Combining V_p and ADC yielded the highest area under the ROC curve of 0.853 compared with that of 0.766 for V_p and 0.764 for ADC, though marginal differences were found (P = 0.109 and 0.078, respectively).

CONCLUSIONS

ADC and the kinetic DCE-MRI parameter V_p can be used as surrogate markers to select eligible candidates for fertility-sparing VRT.

MRI Radiogenomics in Precision Oncology: New Diagnosis and Treatment Method

Precision medicine for cancer affords a new way for the most accurate and effective treatment to each individual cancer. Given the high time-evolving intertumor and intratumor heterogeneity features of personal medicine, there are still several obstacles hindering its diagnosis and treatment in clinical practice regardless of extensive exploration on it over the past years. This paper is to investigate radiogenomics methods in the literature for precision medicine for cancer focusing on the heterogeneity analysis of tumors. Based on integrative analysis of multimodal (parametric) imaging and molecular data in bulk tumors, a comprehensive analysis and discussion involving the characterization of tumor heterogeneity in imaging and molecular expression are conducted. These investigations are intended to (i) fully excavate the multidimensional spatial, temporal, and semantic related information regarding high-dimensional breast magnetic resonance imaging data, with integration of the highly specific structured data of genomics and combination of the diagnosis and cognitive process of doctors, and (ii) establish a radiogenomics data representation model based on multidimensional consistency analysis with multilevel spatial-temporal correlations.

Multi-parametric PET/MRI for enhanced tumor characterization of patients with cervical cancer

Aim

The concept of personalized medicine has brought increased awareness to the importance of inter- and intra-tumor heterogeneity for cancer treatment. The aim of this study was to explore simultaneous multi-parametric PET/MRI prior to chemoradiotherapy for cervical cancer for characterization of tumors and tumor heterogeneity.

Methods

Ten patients with histologically proven primary cervical cancer were examined with multi-parametric 68Ga-NODAGA-E[c(RGDyK)]2-PET/MRI for radiation treatment planning after diagnostic 18F-FDG-PET/CT. Standardized uptake values (SUV) of RGD and FDG, diffusion weighted MRI and the derived apparent diffusion coefficient (ADC), and pharmacokinetic maps obtained from dynamic contrast-enhanced MRI with the Tofts model (iAUC60, Ktrans, ve, and kep) were included in the analysis. The spatial relation between functional imaging parameters in tumors was examined by a correlation analysis and joint histograms at the voxel level. The ability of multi-parametric imaging to identify tumor tissue classes was explored using an unsupervised 3D Gaussian mixture model-based cluster analysis.

Results

Functional MRI and PET of cervical cancers appeared heterogeneous both between patients and spatially within the tumors, and the relations between parameters varied strongly within the patient cohort. The strongest spatial correlation was observed between FDG uptake and ADC (median r =  − 0.7). There was moderate voxel-wise correlation between RGD and FDG uptake, and weak correlations between all other modalities. Distinct relations between the ADC and RGD uptake as well as the ADC and FDG uptake were apparent in joint histograms. A cluster analysis using the combination of ADC, FDG and RGD uptake suggested tissue classes which could potentially relate to tumor sub-volumes.

Conclusion

A multi-parametric PET/MRI examination of patients with cervical cancer integrated with treatment planning and including estimation of angiogenesis and glucose metabolism as well as MRI diffusion and perfusion parameters is feasible. A combined analysis of functional imaging parameters indicates a potential of multi-parametric PET/MRI to contribute to a better characterization of tumor heterogeneity than the modalities alone. However, the study is based on small patient numbers and further studies are needed prior to the future design of individually adapted treatment approaches based on multi-parametric functional imaging.

Interstitial Fluid Behavior and Diseases

Living things comprise a typical hierarchical and porous medium, and their most fundamental logical architectures are interstitial structures encapsulating parenchymal structures. The recent discovery of the efficient transport mechanisms of interstitial streams has provided a new understanding of these complex activities. The substance transport of interstitial streams follows mesoscopic fluid behavior dynamics, which is intimately associated with material transfer in nanoconfined spaces and a unique signal transmission. Accordingly, the evaluation of interstitial stream transport behavior at the mesoscopic scale is essential. In this review, recent advances in physical and chemical properties, the substance transport model, and the characterization methods of interstitial streams at the mesoscopic scale, as well as the relationships between interstitial streams and disease are summarized. Interstitial stream transport can be used as a basis to fully mine hierarchal behavior in images to expand imaging behavior into an omics field. By starting from the perspective of soft matter, a new understanding can be gained of health and disease and quantitative physical markers for research, clinical diagnosis, and treatment can be provided, as well as prognosis evaluation in complex diseases such as cancer and Alzheimer's disease. This will provide a foundation for the development of medicine of soft matter.

Added-value of dynamic contrast-enhanced MRI on prediction of tumor recurrence in locally advanced cervical cancer treated with chemoradiotherapy

OBJECTIVES

To evaluate whether the DCE-MRI derived parameters integrated into clinical and conventional imaging variables may improve the prediction of tumor recurrence for locally advanced cervical cancer (LACC) patients following concurrent chemoradiotherapy (CCRT).

METHODS

Between March 2014 and November 2019, 79 consecutive LACC patients who underwent pelvic MRI examinations with DCE-MRI sequence before treatment were prospectively enrolled. The primary outcome was disease-free survival (DFS). DCE-MRI derived parameters, conventional imaging, and clinical factors were collected. Univariate and multivariate Cox hazard regression analyses were performed to evaluate these parameters in the prediction of DFS. The independent and prognostic interested variables were combined to build a prediction model compared with the clinical International Federation of Gynecological (FIGO) staging system.

RESULTS

Lymph node metastasis (LNM) and the mean value of v_e (v_{e_mean}) were independently associated with tumor recurrence (all p < 0.05). The prediction model based on T stage, LNM, and v_{e_mean} demonstrated a moderate predictive capability in identifying LACC patients with a high risk of tumor recurrence; the model was more accurate than the FIGO staging system alone (c-index: 0.735 vs. 0.661) and the combination of v_{e_mean} and the FIGO staging system (c-index: 0.735 vs. 0.688). Moreover, patients were grouped into low-, medial-, and high-risk levels based on the advanced T stage, positive LNM, and v_{e_mean} < 0.361, with which the 2-year DFS was significantly stratified (p < 0.001).

CONCLUSIONS

The v_{e_mean} from DCE-MRI could be used as a useful biomarker to predict DFS in LACC patients treated with CCRT as an assistant of LNM and T stage.

KEY POINTS

Lower v_{e_mean} is an independent predictor of poor prognosis for disease-free survival in locally advanced cervical cancer patients treated with concurrent chemoradiotherapy (hazard ratio [HR]: 0.016, p<0.023). A combined prediction model based on advanced T stage, LNM, and v_{e_mean} performed better than the FIGO staging system alone.

Assessment of quantitative dynamic contrast-enhanced MRI in distinguishing different histologic grades of breast phyllode tumor

OBJECTIVES

To investigate whether quantitative DCE-MRI (qDCE-MRI) could help distinguish breast phyllodes tumor (PT) grades.

MATERIALS AND METHODS

This retrospective study included 67 breast PTs (26 benign lesions, 25 borderline lesions, and 16 malignant lesions) from April 2016 to July 2020. MRI was performed with a 1.5-T MR system. Perfusion parameters (K^trans, k_ep, v_e, iAUC60) derived from qDCE-MRI, tumor size, and the mean ADC value were correlated with histologic grades using Spearman's rank correlation coefficient. K^trans, k_ep, v_e, and iAUC60 of three histologic grades were also calculated and compared.

RESULTS

The Spearman correlation coefficient with histologic grade of the tumor size was 0.578 (p < 0.001); the ADC value was not correlated with histologic grades of breast PT (p = 0.059). The K^trans, k_ep, v_e, and iAUC60 of benign breast PTs were significantly lower than those of borderline breast PTs (p < 0.001) and lower than those of malignant breast PTs (p < 0.001). In comparison, the K^trans, v_e, and iAUC60 of borderline breast PTs were significantly lower than those of malignant breast PTs (p < 0.001, p < 0.001, p = 0.007, respectively). For ROC analysis, AUCs of K^trans, v_e, and iAUC60 were higher than tumor size and ADC value for differentiating three PT grades.

CONCLUSION

Quantitative and semi-quantitative perfusion parameters (K^trans, v_e, and iAUC60, especially K^trans) derived from qDCE-MRI showed better diagnosis efficiency than tumor size and ADC for grading breast PTs. Therefore, qDCE-MRI may be helpful for preoperative differentiating breast PT grades.

KEY POINTS

• Quantitative dynamic contrast-enhanced MRI can be used as a complementary noninvasive method to improve the differential diagnosis of breast PT. • K^trans, v_e, and iAUC60 derived from qDCE-MRI showed better diagnosis efficiency than tumor size and ADC for grading breast PTs.

Multiparametric MRI of early tumor response to immune checkpoint blockade in metastatic melanoma

BACKGROUND

Immune checkpoint inhibitors are now standard of care treatment for many cancers. Treatment failure in metastatic melanoma is often due to tumor heterogeneity, which is not easily captured by conventional CT or tumor biopsy. The aim of this prospective study was to investigate early microstructural and functional changes within melanoma metastases following immune checkpoint blockade using multiparametric MRI.

METHODS

Fifteen treatment-naïve metastatic melanoma patients (total 27 measurable target lesions) were imaged at baseline and following 3 and 12 weeks of treatment on immune checkpoint inhibitors using: T2-weighted imaging, diffusion kurtosis imaging, and dynamic contrast-enhanced MRI. Treatment timepoint changes in tumor cellularity, vascularity, and heterogeneity within individual metastases were evaluated and correlated to the clinical outcome in each patient based on Response Evaluation Criteria in Solid Tumors V.1.1 at 1 year.

RESULTS

Differential tumor growth kinetics in response to immune checkpoint blockade were measured in individual metastases within the same patient, demonstrating significant intertumoral heterogeneity in some patients. Early detection of tumor cell death or cell loss measured by a significant increase in the apparent diffusivity (Dapp) (p<0.05) was observed in both responding and pseudoprogressive lesions after 3 weeks of treatment. Tumor heterogeneity, as measured by apparent diffusional kurtosis (Kapp), was consistently higher in the pseudoprogressive and true progressive lesions, compared with the responding lesions throughout the first 12 weeks of treatment. These preceded tumor regression and significant tumor vascularity changes (Ktrans, ve, and vp) detected after 12 weeks of immunotherapy (p<0.05).

CONCLUSIONS

Multiparametric MRI demonstrated potential for early detection of successful response to immune checkpoint inhibitors in metastatic melanoma.

Multi-parametric MRI (mpMRI) for treatment response assessment of radiation therapy

Magnetic resonance imaging (MRI) plays an important role in the modern radiation therapy (RT) workflow. In comparison with computed tomography (CT) imaging, which is the dominant imaging modality in RT, MRI possesses excellent soft-tissue contrast for radiographic evaluation. Based on quantitative models, MRI can be used to assess tissue functional and physiological information. With the developments of scanner design, acquisition strategy, advanced data analysis, and modeling, multiparametric MRI (mpMRI), a combination of morphologic and functional imaging modalities, has been increasingly adopted for disease detection, localization, and characterization. Integration of mpMRI techniques into RT enriches the opportunities to individualize RT. In particular, RT response assessment using mpMRI allows for accurate characterization of both tissue anatomical and biochemical changes to support decision-making in monotherapy of radiation treatment and/or systematic cancer management. In recent years, accumulating evidence have, indeed, demonstrated the potentials of mpMRI in RT response assessment regarding patient stratification, trial benchmarking, early treatment intervention, and outcome modeling. Clinical application of mpMRI for treatment response assessment in routine radiation oncology workflow, however, is more complex than implementing an additional imaging protocol; mpMRI requires additional focus on optimal study design, practice standardization, and unified statistical reporting strategy to realize its full potential in the context of RT. In this article, the mpMRI theories, including image mechanism, protocol design, and data analysis, will be reviewed with a focus on the radiation oncology field. Representative works will be discussed to demonstrate how mpMRI can be used for RT response assessment. Additionally, issues and limits of current works, as well as challenges and potential future research directions, will also be discussed.

Early Imaging and Molecular Changes with Neoadjuvant Bevacizumab in Stage II/III Breast Cancer

This prospective, phase II study evaluated novel biomarkers as predictors of response to bevacizumab in patients with breast cancer (BC), using serial imaging methods and gene expression analysis. Patients with primary stage II/III BC received bevacizumab 15 mg/kg (cycle 1; C1), then four cycles of neoadjuvant docetaxel doxorubicin, and bevacizumab every 3 weeks (C2-C5). Tumour proliferation and hypoxic status were evaluated using 18F-fluoro-3'-deoxy-3'-L-fluorothymidine (FLT)- and 18F-fluoromisonidazole (FMISO)-positron emission tomography (PET) at baseline, and during C1 and C5. Pre- and post-bevacizumab vascular changes were evaluated using dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). Molecular biomarkers were assessed using microarray analysis. A total of 70 patients were assessed for treatment efficacy. Significant decreases from baseline in tumour proliferation (FLT-PET), vascularity, and perfusion (DCE-MRI) were observed during C1 (p ≤ 0.001), independent of tumour subtype. Bevacizumab treatment did not affect hypoxic tumour status (FMISO-PET). Significant changes in the expression of 28 genes were observed after C1. Changes in vascular endothelial growth factor receptor (VEGFR)-2p levels were observed in 65 patients, with a > 20% decrease in VEGFR-2p observed in 13/65. Serial imaging techniques and molecular gene profiling identified several potentially predictive biomarkers that may predict response to neoadjuvant bevacizumab therapy in BC patients.

Comparison of Segmentation Methods in Assessing Background Parenchymal Enhancement as a Biomarker for Response to Neoadjuvant Therapy

Breast parenchymal enhancement (BPE) has shown association with breast cancer risk and response to neoadjuvant treatment. However, BPE quantification is challenging, and there is no standardized segmentation method for measurement. We investigated the use of a fully automated breast fibroglandular tissue segmentation method to calculate BPE from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) for use as a predictor of pathologic complete response (pCR) following neoadjuvant treatment in the I-SPY 2 TRIAL. In this trial, patients had DCE-MRI at baseline (T0), after 3 weeks of treatment (T1), after 12 weeks of treatment and between drug regimens (T2), and after completion of treatment (T3). A retrospective analysis of 2 cohorts was performed: one with 735 patients and another with a final cohort of 340 patients, meeting a high-quality benchmark for segmentation. We evaluated 3 subvolumes of interest segmented from bilateral T1-weighted axial breast DCE-MRI: full stack (all axial slices), half stack (center 50% of slices), and center 5 slices. The differences between methods were assessed, and a univariate logistic regression model was implemented to determine the predictive performance of each segmentation method. The results showed that the half stack method provided the best compromise between sampling error from too little tissue and inclusion of incorrectly segmented tissues from extreme superior and inferior regions. Our results indicate that BPE calculated using the half stack segmentation approach has potential as an early biomarker for response to treatment in the hormone receptor–negative and human epidermal growth factor receptor 2–positive subtype.

7T dynamic contrast-enhanced MRI for the detection of subtle blood-brain barrier leakage

Background and Purpose

Dynamic contrast‐enhanced MRI (DCE‐MRI) can be employed to assess the blood–brain barrier (BBB) integrity. Detection of BBB leakage at lower field strengths (≤3T) is cumbersome as the signal is noisy, while leakage can be subtle. Utilizing the increased signal‐to‐noise ratio at higher field strengths, we explored the application of 7T DCE‐MRI for assessing BBB leakage.

Methods

A dual‐time resolution DCE‐MRI method was implemented at 7T and a slow injection rate (0.3 ml/s) and low dose (3 mmol) served to obtain signal changes linearly related to the gadolinium concentration, that is, minimized for T₂^* degradation effects. With the Patlak graphical approach, the leakage rate (K_i) and blood plasma volume fraction (v_p) were calculated. The method was evaluated in 10 controls, an ischemic stroke patient, and a patient with a transient ischemic attack.

Results

K_i and v_p were significantly higher in gray matter compared to white matter of all participants. These K_i values were higher in both patients compared to the control subjects. Finally, for the lesion identified in the ischemic stroke patient, higher leakage values were observed compared to normal‐appearing tissue.

Conclusion

We demonstrate how a dual‐time resolution DCE‐MRI protocol at 7T, with administration of half the clinically used contrast agent dose, can be used for assessing subtle BBB leakage. Although the feasibility of DCE‐MRI for assessing the BBB integrity at 3T is well known, we showed that a continuous sampling DCE‐MRI method tailored for 7T is also capable of assessing leakage with a high sensitivity over a range of K_i values.

Quantitative kinetic parameters of primary tumor can be used to predict pelvic lymph node metastasis in early-stage cervical cancer

PURPOSE

To investigate the role of kinetic parameters of primary tumor derived from dynamic contrast-enhanced MRI (DCE-MRI) in predicting pelvic lymph node metastasis (PLNM) in patients with cervical cancer.

METHODS

66 women with newly diagnosed cervical cancer were included between July 2017 and August 2019. All patients had a FIGO stage IB-IIA cancer and treated with hysterectomy and bilateral lymphadenectomy. Kinetic parameters of the primary tumor were derived from DCE-MRI data. The tumor diameter, ADC value, kinetic parameters, and nodal short-axis diameter were compared between patients with or without PLNM. Logistic regression analysis was used to determine the independent predictors for PLNM and receiver operator characteristic curve was used to evaluate the predictive performance.

RESULTS

There were 20 patients with PLNM and 46 patients without PLNM. Tumor diameter, the efflux rate constant (K_ep), and nodal short-axis diameter were significantly higher in patients with PLNM (P < 0.01). Multivariate logistic regression analysis showed that K_ep and short-axis diameter were independent predictors for PLNM. Combining K_ep and nodal short-axis diameter yielded the highest area under the curve (AUC) of 0.839. Combined with K_ep, the sensitivity, specificity, negative predictive value, and positive predictive value of nodal short-axis diameter increased from 0.500, 0.957, 0.815, and 0.833 to 0.600, 0.978, 0.923, and 0.849, respectively. With 1.113 min^-1 as threshold, the sensitivity and specificity values of K_ep in predicting PLNM in patients with normal-sized lymph nodes were 0.909 and 0.667, respectively.

CONCLUSIONS

K_ep of primary tumor can be used as a surrogate marker to predict PLNM in cervical cancer.

Association between Oncotype DX recurrence score and dynamic contrast-enhanced MRI features in patients with estrogen receptor-positive HER2-negative invasive breast cancer

BACKGROUND

Oncotype DX is a multigene assay used in breast cancer, and the result provided as a 'recurrence score (RS)' corresponds to the risk of a cancer recurrence and the chemotherapeutic benefit in estrogen receptor (ER)-positive human epidermal growth factor receptor (HER)2-negative invasive breast cancer. However, its accessibility is limited.

PURPOSE

To evaluate whether magnetic resonance imaging (MRI) could be used to predict Oncotype DX RS in patients with ER-positive HER2-negative invasive breast cancer.

MATERIAL AND METHODS

We enrolled 473 patients with ER-positive HER2-negative invasive breast cancer who underwent a preoperative MRI and Oncotype DX assay between January 2015 and December 2018. The MRI was reviewed and associations between Oncotype DX RS values were evaluated. Logistic regression analysis was used to identify independent predictors of high and low RS.

RESULTS

Of the 485 cancers, 288 (59.4%) had low (<18), 155 (31.9%) had intermediate (18-30), and 42 (8.7%) had high (≥31) RS. Multiple logistic regression analysis revealed that a round shape (odds ratio [OR] = 2.554, P = 0.089) and low proportion of washout component (OR = 1.011, P = 0.014) were associated with low RS and that heterogeneously dense (OR = 3.205, P = 0.007) or scattered fibroglandular (OR = 3.776, P = 0.005) breast tissue, a non-spiculated margin (OR = 5.435, P = 0.007), and low proportion of persistent component (OR = 1.012, P = 0.036) were associated with high RS.

CONCLUSION

MRI features showed the potential for the discrimination of Oncotype DX RS in patients with ER-positive HER2-negative invasive breast cancer.

Measuring Glucose Uptake in Primary Invasive Breast Cancer Using Simultaneous Time-of-Flight Breast PET/MRI: A Method Comparison Study with Prone PET/CT

Purpose

To compare the measurement of glucose uptake in primary invasive breast cancer using simultaneous, time-of-flight breast PET/MRI with prone time-of-flight PET/CT.

Materials and Methods

In this prospective study, women with biopsy-proven invasive breast cancer undergoing preoperative breast MRI from 2016 to 2018 were eligible. Participants who had fasted underwent prone PET/CT of the breasts approximately 60 minutes after injection of 370 MBq (10 mCi) fluorine 18 fluorodeoxyglucose (18F-FDG) followed by prone PET/MRI using standard clinical breast MRI sequences performed simultaneously with PET acquisition. Volumes of interest were drawn for tumors and contralateral normal breast fibroglandular tissue to calculate standardized uptake values (SUVs). Spearman correlation, Wilcoxon signed ranked test, Mann-Whitney test, and Bland-Altman analyses were performed.

Results

Twenty-three women (mean age, 50 years; range, 33-70 years) were included. Correlation between tumor uptake values measured with PET/MRI and PET/CT was strong (r s = 0.95-0.98). No difference existed between modalities for tumor maximum SUV (SUVmax) normalized to normal breast tissue SUVmean (normSUVmax) (P = .58). The least amount of measurement bias was observed with normSUVmax, +3.86% (95% limits of agreement: -28.92, +36.64).

Conclusion

These results demonstrate measurement agreement between PET/CT, the current reference standard for tumor glucose uptake quantification, and simultaneous time-of-flight breast 18F-FDG PET/MRI.Keywords: Breast, Comparative Studies, PET/CT, PET/MR Supplemental material is available for this article. © RSNA, 2021See also the commentary by Mankoff and Surti in this issue.

MR Image-Based Attenuation Correction of Brain PET Imaging: Review of Literature on Machine Learning Approaches for Segmentation

Recent emerging hybrid technology of positron emission tomography/magnetic resonance (PET/MR) imaging has generated a great need for an accurate MR image-based PET attenuation correction. MR image segmentation, as a robust and simple method for PET attenuation correction, has been clinically adopted in commercial PET/MR scanners. The general approach in this method is to segment the MR image into different tissue types, each assigned an attenuation constant as in an X-ray CT image. Machine learning techniques such as clustering, classification and deep networks are extensively used for brain MR image segmentation. However, only limited work has been reported on using deep learning in brain PET attenuation correction. In addition, there is a lack of clinical evaluation of machine learning methods in this application. The aim of this review is to study the use of machine learning methods for MR image segmentation and its application in attenuation correction for PET brain imaging. Furthermore, challenges and future opportunities in MR image-based PET attenuation correction are discussed.

Preclinical Applications of Magnetic Resonance Imaging in Oncology

The evolving possibilities of molecular imaging (MI) are fundamentally changing the way we look at cancer, with imaging paradigms now shifting away from basic morphological measures toward the longitudinal assessment of functional, metabolic, cellular, and molecular information in vivo. Recent developments of imaging methodology and probe molecules utilizing the vast number of novel animal models of human cancers have enhanced our ability to non-invasively characterize neoplastic tissue and follow anticancer treatments. While preclinical molecular imaging offers a whole palette of excellent methodology to choose from, we will focus on magnetic resonance imaging (MRI) techniques, since they provide excellent molecular imaging capabilities and bear high potential for clinical translation. Prerequisites and consequences of using animal models as surrogates of human cancers in preclinical molecular imaging are outlined. We present physical principles, values, and limitations of MRI as molecular imaging modality and comment on its high potential to non-invasively assess information on metabolism, hypoxia, angiogenesis, and cell trafficking in preclinical cancer research.

Clinical Pharmacokinetics and Pharmacodynamics of Nintedanib

Nintedanib is an oral, small-molecule tyrosine kinase inhibitor approved for the treatment of idiopathic pulmonary fibrosis and patients with advanced non-small cell cancer of adenocarcinoma tumour histology. Nintedanib competitively binds to the kinase domains of vascular endothelial growth factor (VEGF), platelet-derived growth factor (PDGF) and fibroblast growth factor (FGF). Studies in healthy volunteers and in patients with advanced cancer have shown that nintedanib has time-independent pharmacokinetic characteristics. Maximum plasma concentrations of nintedanib are reached approximately 2–4 h after oral administration and thereafter decline at least bi-exponentially. Over the investigated dose range of 50–450 mg once daily and 150–300 mg twice daily, nintedanib exposure increases are dose proportional. Nintedanib is metabolised via hydrolytic ester cleavage, resulting in the formation of the free acid moiety that is subsequently glucuronidated and excreted in the faeces. Less than 1% of drug-related radioactivity is eliminated in urine. The terminal elimination half-life of nintedanib is about 10–15 h. Accumulation after repeated twice-daily dosing is negligible. Sex and renal function have no influence on nintedanib pharmacokinetics, while effects of ethnicity, low body weight, older age and smoking are within the inter-patient variability range of nintedanib exposure and no dose adjustments are required. Administration of nintedanib in patients with moderate or severe hepatic impairment is not recommended, and patients with mild hepatic impairment should be monitored closely and the dose adjusted accordingly. Nintedanib has a low potential for drug–drug interactions, especially with drugs metabolised by cytochrome P450 enzymes. Concomitant treatment with potent inhibitors or inducers of the P-glycoprotein transporter can affect the pharmacokinetics of nintedanib. At an investigated dose of 200 mg twice daily, nintedanib does not have proarrhythmic potential.

Assessment of pelvic lymph node metastasis in FIGO IB and IIA cervical cancer using quantitative dynamic contrast-enhanced MRI parameters

PURPOSE

We prospectively determined whether the quantitative parameters derived from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) are useful for predicting pelvic lymph node (LN) status in cervical cancer through node-by-node pathologic validation of images.

METHODS

Overall, 182 LNs harvested from 200 consecutive patients with 2018 FIGO stage IB-IIA cervical cancer (82 metastatic and 100 nonmetastatic) were used for node-by-node assessment. Each LN was quantitatively assessed using Ktrans, Ve, and Kep values. The short-axis diameter, ratio of the long-axis to short-axis diameter, and long-axis diameter were also assessed. Data on metastatic LNs were divided into four groups according to the FIGO staging system. Receiver operating characteristic (ROC) curve analysis was performed to evaluate statistically significant parameters derived from DCE-MRI for the differentiation of metastatic LNs from nonmetastatic LNs.

RESULTS

The mean short-axis diameter of metastatic LNs was significantly larger than that of nonmetastatic LNs (all P < 0.05) despite several overlaps. In comparison with nonmetastatic LNs, metastatic LNs showed a significantly lower Ktrans (all P < 0.05); however, Kep and Ve were not significantly different (all P > 0.05). For IB3 and IIA2 cervical cancer, Ktrans had moderate diagnostic ability for differentiating metastatic LNs from nonmetastatic LNs (for IB3: area under the curve [AUC] 0.740, 95% CI 0.657-0.838, 61.7% sensitivity, 80.2% specificity, P = 0.007; for IIA2: AUC 0.786, 95% CI 0.650-0.846, 60.2% sensitivity, 81.8% specificity, P = 0.008).

CONCLUSION

Ktrans appears to be a useful parameter for detecting metastatic LNs, especially for IB3 and IIA2 cervical cancer.

CAD and AI for breast cancer-recent development and challenges

Computer-aided diagnosis (CAD) has been a popular area of research and development in the past few decades. In CAD, machine learning methods and multidisciplinary knowledge and techniques are used to analyze the patient information and the results can be used to assist clinicians in their decision making process. CAD may analyze imaging information alone or in combination with other clinical data. It may provide the analyzed information directly to the clinician or correlate the analyzed results with the likelihood of certain diseases based on statistical modeling of the past cases in the population. CAD systems can be developed to provide decision support for many applications in the patient care processes, such as lesion detection, characterization, cancer staging, treatment planning and response assessment, recurrence and prognosis prediction. The new state-of-the-art machine learning technique, known as deep learning (DL), has revolutionized speech and text recognition as well as computer vision. The potential of major breakthrough by DL in medical image analysis and other CAD applications for patient care has brought about unprecedented excitement of applying CAD, or artificial intelligence (AI), to medicine in general and to radiology in particular. In this paper, we will provide an overview of the recent developments of CAD using DL in breast imaging and discuss some challenges and practical issues that may impact the advancement of artificial intelligence and its integration into clinical workflow.

SLIC-supervoxels-based response evaluation of osteosarcoma treated with neoadjuvant chemotherapy using multi-parametric MR imaging

OBJECTIVE

Histopathological examination (HPE) is the current gold standard for assessing chemotherapy response to tumor, but it is possible only after surgery. The purpose of the study was to develop a noninvasive, imaging-based robust method to delineate, visualize, and quantify the proportions of necrosis and viable tissue present within the tumor along with peritumoral edema before and after neoadjuvant chemotherapy (NACT) and to evaluate treatment response with correlation to HPE necrosis after surgery.

METHODS

The MRI dataset of 30 patients (N = 30; male:female = 24:6; age = 17.6 ± 2.7 years) with osteosarcoma was acquired using 1.5 T Philips Achieva MRI scanner before (baseline) and after 3 cycles of NACT (follow-up). After NACT, all patients underwent surgical resection followed by HPE. Simple linear iterative clustering supervoxels and Otsu multithresholding were combined to develop the proposed method-SLICs+MTh-to subsegment and quantify viable and nonviable regions within tumor using multiparametric MRI. Manually drawn ground-truth ROIs and SLICs+MTh-based segmentation of tumor, edema, and necrosis were compared using Jacquard index (JI), Dice coefficient (DC), precision (P), and recall (R). Postcontrast T1W images (PC-T1W) were used to validate the SLICs+MTh-based necrosis. SLICs+MTh-based necrosis volume at follow-up was compared with HPE necrosis using paired t test (p ≤ 0.05).

RESULTS

Active tumor, necrosis, and edema were segmented with moderate to satisfactory accuracy (JI = 62-78%; DC = 72-87%; P = 67-87%; R = 63-88%). Qualitatively and quantitatively (DC = 74 ± 9%), the SLICs+MTh-based necrosis area correlated well with the hypointense necrosis areas in PC-T1W. No significant difference (paired t test, p = 0.26; Bland-Altman plot, bias = 2.47) between SLICs+MTh-based necrosis at follow-up and HPE necrosis was observed.

CONCLUSION

The proposed multiparametric MRI-based SLICs+MTh method performs noninvasive assessment of NACT response in osteosarcoma that may improve cancer treatment monitoring, planning, and overall prognosis.

KEY POINTS

• The simple linear iterative clustering supervoxels and Otsu multithresholding-based technique (SLICs+MTh) successfully estimates the proportion of necrosis, viable tumor, and edema in osteosarcoma in the course of chemotherapy. • The proposed technique is noninvasive and uses multiparametric MRI to measure necrosis as an indication of anticancer treatment response. • SLICs+MTh-based necrosis was in satisfactory agreement with histological necrosis after surgery.

Benign or Malignant Characterization of Soft-Tissue Tumors by Using Semiquantitative and Quantitative Parameters of Dynamic Contrast-Enhanced Magnetic Resonance Imaging

PURPOSE

To evaluate the efficacy of the semiquantitative and quantitative parameters of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) in differentiating between benign and malignant soft-tissue tumors.

METHODS

A total of 45 patients with pathologically confirmed soft-tissue tumors (15 benign and 30 malignant tumors) underwent DCE-MRI. The semiquantitative parameters assessed were as follows: time to peak (TTP), maximum concentration (MAX Conc), area under the curve of time-concentration curve (AUC-TC), and maximum rise slope (MAX Slope). Quantitative DCE-MRI was analyzed with the extended Tofts-Kety model to assess the following quantitative parameters: volume transfer constant (Ktrans), microvascular permeability reflux constant (Kep), and distribute volume per unit tissue volume (Ve). Data were evaluated using the independent t test or Mann-Whitney U test and receiver operating characteristic (ROC) curves.

RESULTS

The TTP (P = .0035), MAX Conc (P = .0018), AUC-TC (P = .0018), MAX Slope (P = .0018), Ktrans (P = .0018), and Kep (P = .0035) were significantly different between the benign and malignant soft-tissue tumors. The AUC of the ROC curve demonstrated the diagnostic potential of TTP (0.778), MAX Conc (0.849), AUC-TC (0.831), MAX Slope (0.847), Ktrans (0.836), Kep (0.778), and Ve (0.638).

CONCLUSIONS

The use of semiquantitative and quantitative parameters of DCE-MRI enabled differentiation between benign and malignant soft-tissue tumors. The values of TTP were lower, while those of MAX Conc, AUC-TC, MAX Slope, Ktrans, and Kep were higher in malignant than in benign tumors.

Deformable Registration for Longitudinal Breast MRI Screening

MRI screening of high-risk patients for breast cancer provides very high sensitivity, but with a high recall rate and negative biopsies. Comparing the current exam to prior exams reduces the number of follow-up procedures requested by radiologists. Such comparison, however, can be challenging due to the highly deformable nature of breast tissues. Automated co-registration of multiple scans has the potential to aid diagnosis by providing 3D images for side-by-side comparison and also for use in CAD systems. Although many deformable registration techniques exist, they generally have a large number of parameters that need to be optimized and validated for each new application. Here, we propose a framework for such optimization and also identify the optimal input parameter set for registration of 3D T₁-weighted MRI of breast using Elastix, a widely used and freely available registration tool. A numerical simulation study was first conducted to model the breast tissue and its deformation through finite element (FE) modeling. This model generated the ground truth for evaluating the registration accuracy by providing the deformation of each voxel in the breast volume. An exhaustive search was performed over various values of 7 registration parameters (4050 different combinations of parameters were assessed) and the optimum parameter set was determined. This study showed that there was a large variation in the registration accuracy of different parameter sets ranging from 0.29 mm to 2.50 mm in median registration error and 3.71 mm to 8.90 mm in 95 percentile of the registration error. Mean registration errors of 0.32 mm, 0.29 mm, and 0.30 mm and 95 percentile errors of 3.71 mm, 5.02 mm, and 4.70 mm were obtained by the three best parameter sets. The optimal parameter set was applied to consecutive breast MRI scans of 13 patients. A radiologist identified 113 landmark pairs (~ 11 per patient) which were used to assess registration accuracy. The results demonstrated that using the optimal registration parameter set, a registration accuracy (in mm) of 3.4 [1.8 6.8] was achieved.

On the potential use of dynamic contrast-enhanced (DCE) MRI parameters as radiomic features of cervical cancer

PURPOSE

To evaluate whether the analysis of high-temporal resolution DCE-MRI by various tracer kinetic models could yield useful radiomic features in discriminating cervix carcinoma and normal cervix tissue.

METHODS

Forty-three patients (median age 51 yr; range 26-78 yr) diagnosed with cervical cancer based on postoperative pathology were enrolled in this study with informed consent. DCE-MRI data with temporal resolution of 2 s were acquired and analyzed using the Tofts (TOFTS), extended Tofts (EXTOFTS), conventional two-compartment (CC), adiabatic tissue homogeneity (ATH), and distributed parameter (DP) models. Ability of all kinetic parameters in distinguishing tumor from normal tissue was assessed using Mann-Whitney U test and receiver operating characteristic (ROC) curves. Repeatability of parameter estimates due to sampling of arterial input functions (AIFs) was also studied using intraclass correlation (ICC) analysis.

RESULTS

Fractional extravascular, extracellular volume (Ve) of all models were significantly smaller in cervix carcinoma than normal cervix tissue, and were associated with large values of area under ROC curve (AUC 0.884-0.961). Capillary permeability PS derived from the ATH, CC, and DP models also yielded large AUC values (0.730, 0.860, and 0.797). Transfer constant Ktrans derived from TOFTS and EXTOFTS models yielded smaller AUC (0.587 and 0.701). Repeatability of parameters derived from all models was robust to AIF sampling, with ICC coefficients typically larger than 0.80.

CONCLUSIONS

With the use of high-temporal resolution DCE-MRI, all tracer kinetic models could reflect pathophysiological differences between cervix carcinoma and normal tissue (with significant differences in Ve and PS) and potentially yield radiomic features with diagnostic value.

Digital Analysis in Breast Imaging

Breast imaging is a multimodal approach that plays an essential role in the diagnosis of breast cancer. Mammography, sonography, magnetic resonance, and image-guided biopsy are imaging techniques used to search for malignant changes in the breast or precursors of malignant changes in, e.g., screening programs or follow-ups after breast cancer treatment. However, these methods still have some disadvantages such as interobserver variability and the mammography sensitivity in women with radiologically dense breasts. In order to overcome these difficulties and decrease the number of false positive findings, improvements in imaging analysis with the help of artificial intelligence are constantly being developed and tested. In addition, the extraction and correlation of imaging features with special tumor characteristics and genetics of the patients in order to get more information about treatment response, prognosis, and also cancer risk are coming more and more in focus. The aim of this review is to address recent developments in digital analysis of images and demonstrate their potential value in multimodal breast imaging.

Brain SPECT and perfusion MRI: do they provide complementary information about the tumour lesion and its grading?

AIM

To evaluate the relative and combined utility of ^99mTc-tetrofosmin (^99mTc-TF) brain single-photon-emission computed tomography (SPECT) and dynamic susceptibility contrast (DSC) perfusion magnetic resonance imaging (MRI) in grading brain gliomas.

MATERIALS AND METHODS

Thirty-six patients with clinically suspected brain tumours were assessed by ^99mTc-TF SPECT and DSC-MRI. Brain tumour malignancy was confirmed in all patients at histopathology. On both techniques brain lesions were evaluated via visual and semi-quantitative analysis methods (deriving tetrofosmin index [T-index] and relative cerebral blood volume [rCBV] ratios, respectively).

RESULTS

^99mTc-TF SPECT showed abnormally elevated tracer uptake in 31/36 patients whereas MRI detected the brain tumour in all patients. Optimal cut-off values of each index for discriminating between low- and high-grade gliomas were obtained through receiver operating characteristic (ROC) analyses. A T-index cut-off of 6.35 ensured 82% sensitivity and 71% specificity for discriminating between high- and low-grade gliomas, whereas a relative rCBV ratio cut-off of 1.80 achieved 91% sensitivity and 100% specificity. Requiring a positive result on either technique to characterise a high-grade glioma was associated with similar specificity and slightly increased sensitivity.

CONCLUSION

Both imaging techniques, ^99mTF SPECT and DSC MRI, may provide complementary indices of tumour grade and have an independent diagnostic value for high-risk tumours.