Blood Oxygenation Level Dependent Magnetic Resonance Imaging (MRI), Dynamic Contrast Enhanced MRI, and Diffusion Weighted MRI for Benign and Malignant Breast Cancer Discrimination: A Preliminary Experience

Non-Contrast-Enhanced Multiparametric MRI of the Hypoxic Tumor Microenvironment Allows Molecular Subtyping of Breast Cancer: A Pilot Study

Tumor neoangiogenesis is an important hallmark of cancer progression, triggered by alternating selective pressures from the hypoxic tumor microenvironment. Non-invasive, non-contrast-enhanced multiparametric MRI combining blood-oxygen-level-dependent (BOLD) MRI, which depicts blood oxygen saturation, and intravoxel-incoherent-motion (IVIM) MRI, which captures intravascular and extravascular diffusion, can provide insights into tumor oxygenation and neovascularization simultaneously. Our objective was to identify imaging markers that can predict hypoxia-induced angiogenesis and to validate our findings using multiplexed immunohistochemical analyses. We present an in vivo study involving 36 female athymic nude mice inoculated with luminal A, Her2+, and triple-negative breast cancer cells. We used a high-field 9.4-tesla MRI system for imaging and subsequently analyzed the tumors using multiplex immunohistochemistry for CD-31, PDGFR-β, and Hif1-α. We found that the hyperoxic-BOLD-MRI-derived parameter ΔR2* discriminated luminal A from Her2+ and triple-negative breast cancers, while the IVIM-derived parameter fIVIM discriminated luminal A and Her2+ from triple-negative breast cancers. A comprehensive analysis using principal-component analysis of both multiparametric MRI- and mpIHC-derived data highlighted the differences between triple-negative and luminal A breast cancers. We conclude that multiparametric MRI combining hyperoxic BOLD MRI and IVIM MRI, without the need for contrast agents, offers promising non-invasive markers for evaluating hypoxia-induced angiogenesis.

Radiomics to Differentiate Malignant and Benign Breast Lesions: A Systematic Review and Diagnostic Test Accuracy Meta-Analysis

Breast cancer is a prevalent global health concern, necessitating accurate diagnostic tools for effective management. Diagnostic imaging plays a pivotal role in breast cancer diagnosis, staging, treatment planning, and outcome evaluation. Radiomics is an emerging field of study in medical imaging that contains a broad set of computational methods to extract quantitative features from radiographic images. This can be utilized to guide diagnosis, treatment response, and prognosis in clinical settings.  A systematic review was performed in concordance with Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines and the Cochrane Handbook for Systematic Reviews of Diagnostic Test Accuracy. Quality was assessed using the radiomics quality score. Diagnostic sensitivity and specificity of radiomics analysis, with 95% confidence intervals (CIs), were included for meta-analysis. The area under the curve analysis was recorded. An extensive statistical analysis was performed following the Cochrane guidelines. Statistical significance was determined if p-values were less than 0.05. Statistical analyses were conducted using Review Manager (RevMan), Version 5.4.1. A total of 31 manuscripts involving 8,773 patients were included, with 17 contributing to the meta-analysis. The cohort comprised 56.2% malignant breast cancers and 43.8% benign breast lesions. MRI demonstrated a sensitivity of 0.91 (95% CI: 0.89-0.92) and a specificity of 0.84 (95% CI: 0.82-0.86) in differentiating between benign and malignant breast cancers. Mammography-based radiomic features predicted breast cancer subtype with a sensitivity of 0.79 (95% CI: 0.76-0.82) and a specificity of 0.81 (95% CI: 0.79-0.84). Ultrasound-based analysis yielded a sensitivity of 0.92 (95% CI: 0.90-0.94) and a specificity of 0.85 (95% CI: 0.83-0.88). Only one study reported the results of radiomic evaluation from CT, which had a sensitivity of 0.95 (95% CI: 0.88-0.99) and a specificity of 0.56 (95% CI: 0.45-0.67).  Across different imaging modalities, radiomics exhibited robust diagnostic accuracy in differentiating benign and malignant breast lesions. The results underscore the potential of radiomic assessment as a minimally invasive alternative or adjunctive diagnostic tool for breast cancer. This is pioneering data that reports on a novel diagnostic approach that is understudied and underreported. However, due to study limitations, the complexity of this technology, and the need for future development, biopsy still remains the current gold standard method of determining breast cancer type.

Diagnostic accuracy of intravoxel incoherent motion (IVIM) and dynamic contrast-enhanced (DCE) MRI to differentiate benign from malignant breast lesions: A systematic review and meta-analysis

PURPOSE

Magnetic resonance imaging (MRI) can reduce the need for unnecessary invasive diagnostic tests by nearly half. In this meta-analysis, we investigated the diagnostic accuracy of intravoxel incoherent motion modeling (IVIM) and dynamic contrast-enhanced (DCE) MRI in differentiating benign from malignant breast lesions.

METHOD

We systematically searched PubMed, EMBASE, and Scopus. We included English articles reporting diagnostic accuracy for both sequences in differentiating benign from malignant breast lesions. Articles were assessed by quality assessment of diagnostic accuracy studies-2 (QUADAS-2) questionnaire. We used a bivariate effects model for standardized mean difference (SMD) analysis and diagnostic test accuracy analysis.

RESULTS

Ten studies with 537 patients and 707 (435 malignant and 272 benign) lesions were included. The D, f, Ktrans, and Kep mean values significantly differ between benign and malignant lesions. The pooled sensitivity (95 % confidence interval) and specificity were 86.2 % (77.9 %-91.7 %) and 70.3 % (56.5 %-81.1 %) for IVIM, and 93.8 % (85.3 %-97.5 %) and 68.1 % (52.7 %-80.4 %) for DCE, respectively. Combined IVIM and DCE depicted the highest area under the curve of 0.94, with a sensitivity and specificity of 91.8 % (82.8 %-96.3 %) and 87.6 % (73.8 %-94.7 %), respectively.

CONCLUSIONS

Combined IVIM and DCE had the highest diagnostic accuracy, and multiparametric MRI may help reduce unnecessary benign breast biopsy.

Hyperoxic BOLD-MRI-Based Characterization of Breast Cancer Molecular Subtypes Is Independent of the Supplied Amount of Oxygen: A Preclinical Study

Hyperoxic BOLD-MRI targeting tumor hypoxia may provide imaging biomarkers that represent breast cancer molecular subtypes without the use of injected contrast agents. However, the diagnostic performance of hyperoxic BOLD-MRI using different levels of oxygen remains unclear. We hypothesized that molecular subtype characterization with hyperoxic BOLD-MRI is feasible independently of the amount of oxygen. Twenty-three nude mice that were inoculated into the flank with luminal A (n = 9), Her2+ (n = 5), and triple-negative (n = 9) human breast cancer cells were imaged using a 9.4 T Bruker BioSpin system. During BOLD-MRI, anesthesia was supplemented with four different levels of oxygen (normoxic: 21%; hyperoxic: 41%, 71%, 100%). The change in the spin-spin relaxation rate in relation to the normoxic state, ΔR2*, dependent on the amount of erythrocyte-bound oxygen, was calculated using in-house MATLAB code. ΔR2* was significantly different between luminal A and Her2+ as well as between luminal A and triple-negative breast cancer, reflective of the less aggressive luminal A breast cancer's ability to better deliver oxygen-rich hemoglobin to its tissue. Differences in ΔR2* between subtypes were independent of the amount of oxygen, with robust distinction already achieved with 41% oxygen. In conclusion, hyperoxic BOLD-MRI may be used as a biomarker for luminal A breast cancer identification without the use of exogenous contrast agents.

Recent Advances in Ultrasound Breast Imaging: From Industry to Clinical Practice

Breast ultrasound (US) has undergone dramatic technological improvement through recent decades, moving from a low spatial resolution, grayscale-limited technique to a highly performing, multiparametric modality. In this review, we first focus on the spectrum of technical tools that have become commercially available, including new microvasculature imaging modalities, high-frequency transducers, extended field-of-view scanning, elastography, contrast-enhanced US, MicroPure, 3D US, automated US, S-Detect, nomograms, images fusion, and virtual navigation. In the subsequent section, we discuss the broadened current application of US in breast clinical scenarios, distinguishing among primary US, complementary US, and second-look US. Finally, we mention the still ongoing limitations and the challenging aspects of breast US.

Preoperative prediction of invasive behavior of pancreatic solid pseudopapillary neoplasm by MRI-based multiparametric radiomics models

OBJECTIVE

A log-combined model was developed to predict the invasive behavior of pancreatic solid pseudopapillary neoplasm (pSPN) based on clinical and radiomic features extracted from multiparametric magnetic resonance imaging (MRI).

MATERIALS AND METHODS

A total of 111 patients with pathologically confirmed pSPN who underwent preoperative plain and contrast-enhanced MRI were included, and divided into an invasive group (n = 34) and non-invasive group (n = 77). Clinical features and laboratory data related to pSPN invasive behavior were analyzed. Regions of interest were delineated based on T1-weighted imaging (T1WI), T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI), and contrast-enhanced T1WI (CE-T1WI) to extract radiomic features. Correlation analysis was performed for these features, followed by L1_based feature selection (C = 0.15). A logistic regression algorithm was used to construct models based on each of the four sequences and a log-combined model was used to integrate the sequences. A receiver operating characteristic (ROC) curve was plotted to evaluate the model performance, and the Brier score was used to assess the overall accuracy of the model predictions.

RESULTS

The area under the ROC curve was 0.68, 0.73, 0.71, and 0.49 for Log-T1WI, Log-T2WI, Log-DWI, and Log-CE models, respectively, and 0.81 for the log-combined model. The accuracy, precision, sensitivity, and specificity of the log-combined model were 0.77, 0.88, 0.75, and 0.78, respectively. The best performance was obtained with the log-combined model with a Brier score of 0.18. Tumor location was identified as a significant clinical feature in comparison between the two groups (p < 0.05), and invasive pSPN was more frequent in the tail of the pancreas.

CONCLUSION

The log-combined model based on multiparametric MRI and clinical features can be used as a non-invasive diagnostic tool for preoperative prediction of pSPN invasive behavior and to facilitate the development of individualized treatment strategies and monitoring management plans.

Not only lymphadenopathy: case of chest lymphangitis assessed with MRI after COVID 19 vaccine

BACKGROUND

To date, no paper reports cases of lymphangitis after COVID 19 vaccination. We present a case of lymphangitis after vaccination from COVID 19, in a patient with colorectal liver metastases.

METHODS

We described the case of a 56-year-old woman with history of a surgical resection of colorectal cancer and liver metastases, without any kind of drug therapy for about a month. In addition, a recent administration (2 days ago) of Spikevax (mRNA-1273, Moderna vaccine), as a booster dose, on the right arm was reported.

RESULTS

The magnetic resonance (MR) examination showed the effects of the previous surgical resection and five new hepatic metastases, located in the VIII, VI, V, IV and II hepatic segments. As an accessory finding the presence of lymphadenopathy in the axillary area and lymphangitis of the right breast and chest were identified. The computed tomography scan performed a week earlier, and re-evaluated in light of the MR data, did not identify the presence of lymphadenopathy in the axillary area and lymphangitis signs.

CONCLUSIONS

Lymphangitis could occur after COVID 19 vaccine and it is important to know this data to avoid alarmism in patients and clinicians and economic waste linked to the execution of various radiological investigations for the search for a tumour that probably does not exist.

TRIAL REGISTRATION

Not applicable.

Automatic PI-RADS assignment by means of formal methods

INTRODUCTION AND OBJECTIVES

The Prostate Imaging Reporting and Data System (PI-RADS) version 2 emerged as standard in prostate magnetic resonance imaging examination. The Pi-RADS scores are assigned by radiologists and indicate the likelihood of a clinically significant cancer. The aim of this paper is to propose a methodology to automatically mark a magnetic resonance imaging with its related PI-RADS.

MATERIALS AND METHODS

We collected a dataset from two different institutions composed by DWI ADC MRI for 91 patients marked by expert radiologists with different PI-RADS score. A formal model is generated starting from a prostate magnetic resonance imaging, and a set of properties related to the different PI-RADS scores are formulated with the help of expert radiologists and pathologists.

RESULTS

Our methodology relies on the adoption of formal methods and radiomic features, and in the experimental analysis, we obtain a specificity and sensitivity equal to 1.

Q CONCLUSIONS

The proposed methodology is able to assign the PI-RADS score by analyzing prostate magnetic resonance imaging with a very high accuracy.