Robustness Analysis of DCE-MRI-Derived Radiomic Features in Breast Masses: Assessing Quantization Levels and Segmentation Agreement

Featured Application The use of highly robust radiomic features is fundamental to reduce intrinsic dependencies and to provide reliable predictive models. This work presents a study on breast tumor DCE-MRI considering the radiomic feature robustness against the quantization settings and segmentation methods. Machine learning models based on radiomic features allow us to obtain biomarkers that are capable of modeling the disease and that are able to support the clinical routine. Recent studies have shown that it is fundamental that the computed features are robust and reproducible. Although several initiatives to standardize the definition and extraction process of biomarkers are ongoing, there is a lack of comprehensive guidelines. Therefore, no standardized procedures are available for ROI selection, feature extraction, and processing, with the risk of undermining the effective use of radiomic models in clinical routine. In this study, we aim to assess the impact that the different segmentation methods and the quantization level (defined by means of the number of bins used in the feature-extraction phase) may have on the robustness of the radiomic features. In particular, the robustness of texture features extracted by PyRadiomics, and belonging to five categories-GLCM, GLRLM, GLSZM, GLDM, and NGTDM-was evaluated using the intra-class correlation coefficient (ICC) and mean differences between segmentation raters. In addition to the robustness of each single feature, an overall index for each feature category was quantified. The analysis showed that the level of quantization (i.e., the 'bincount' parameter) plays a key role in defining robust features: in fact, in our study focused on a dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) dataset of 111 breast masses, sets with cardinality varying between 34 and 43 robust features were obtained with 'binCount' values equal to 256 and 32, respectively. Moreover, both manual segmentation methods demonstrated good reliability and agreement, while automated segmentation achieved lower ICC values. Considering the dependence on the quantization level, taking into account only the intersection subset among all the values of 'binCount' could be the best selection strategy. Among radiomic feature categories, GLCM, GLRLM, and GLDM showed the best overall robustness with varying segmentation methods

Transcranial Magnetic Resonance Imaging-Guided Focused Ultrasound Treatment at 1.5 T: A Retrospective Study on Treatment- and Patient-Related Parameters Obtained From 52 Procedures

Objective: To present a retrospective analysis of patient- and sonication-related parameters of a group of patients treated with a transcranial magnetic resonance imaging (MRI)-guided focused ultrasound (tcMRgFUS) system integrated with a 1.5-T MRI unit. Methods: The data obtained from 59 patients, who underwent the tcMRgFUS procedure from January 2015 to April 2019, were retrospectively reviewed for this study. The following data, among others, were mainly collected: skull density ratio (SDR), skull area (SA), number of available transducer elements (Tx), and estimated focal power at target (FP). For each of the four different treatment stages, we calculated the number of sonication processes (S-n), user-defined sonication power (S-p), effective measured power (S-mp), sonication duration (S-d), user-defined energy (E), effective measured energy (E-m), maximum temperature (T-max), and MR thermometry plane orientation. Furthermore, the time delay between each sonication (S-t) and the total treatment time (T-t) were recorded. Results: Fifty-two patients (40 males and 12 females; age 64.51 +/- SD 11.90 years; range 26-86 years), who underwent unilateral Vim thalamotomy (left = 50, 96.15%; right = 2, 3.85%) for medication-refractory essential tremor (n = 39; 78%) or Parkinson tremor (n = 13; 22%) were considered. A total of 1,068 (95.10%) sonication processes were included in our final analysis (average S-n per treatment: 20.65 +/- 6.18; range 13-41). The energy released onto the planned target was found to decrease with the SDR for all temperature ranges. A positive correlation was observed between the slope of T-max vs. E-m plot and the SDR (R-2 = 0.765; p < 0.001). In addition, the T-max was positively correlated with SDR (R-2 = 0.398; p < 0.005). On the contrary, no significant correlation was found between SDR and SA or Tx. An analysis of the MR thermometry scanning plane indicated that, at our site, the axial and the coronal planes were used (on average) 10.4 (SD +/- 3.8) and 7.7 (SD +/- 3.0) times, respectively, whereas the sagittal plane was used only 2.5 (SD +/- 3.0) times per treatment. Conclusion: Our results confirm the factors that significantly influence the course of a tcMRgFUS procedure even when a 1.5-T MRI scanner is used for procedure guidance. The experience we gained in this study indicates that the SDR remains one of the most significant technical parameters to be considered in a tcMRgFUS procedure. The possibility of prospectively setting the sonication energy according to the presented curves of energy delivery as a function of SDR for each treatment stage could provide a further understanding and a greater awareness of this emerging technology

On unsupervised methods for medical image segmentation: Investigating classic approaches in breast cancer dce-mri

Unsupervised segmentation techniques, which do not require labeled data for training and can be more easily integrated into the clinical routine, represent a valid solution especially from a clinical feasibility perspective. Indeed, large-scale annotated datasets are not always available, undermining their immediate implementation and use in the clinic. Breast cancer is the most common cause of cancer death in women worldwide. In this study, breast lesion delineation in Dynamic Contrast Enhanced MRI (DCE-MRI) series was addressed by means of four popular unsupervised segmentation approaches: Split-and-Merge combined with Region Growing (SMRG), k-means, Fuzzy C-Means (FCM), and spatial FCM (sFCM). They represent well-established pattern recognition techniques that are still widely used in clinical research. Starting from the basic versions of these segmentation approaches, during our analysis, we identified the shortcomings of each of them, proposing improved versions, as well as developing ad hoc pre- and post-processing steps. The obtained experimental results, in terms of area-based—namely, Dice Index (DI), Jaccard Index (JI), Sensitivity, Specificity, False Positive Ratio (FPR), False Negative Ratio (FNR)—and distance-based metrics—Mean Absolute Distance (MAD), Maximum Distance (MaxD), Hausdorff Distance (HD)—encourage the use of unsupervised machine learning techniques in medical image segmentation. In particular, fuzzy clustering approaches (namely, FCM and sFCM) achieved the best performance. In fact, for area-based metrics, they obtained DI = 78.23% ± 6.50 (sFCM), JI = 65.90% ± 8.14 (sFCM), sensitivity = 77.84% ± 8.72 (FCM), specificity = 87.10% ± 8.24 (sFCM), FPR = 0.14 ± 0.12 (sFCM), and FNR = 0.22 ± 0.09 (sFCM). Concerning distance-based metrics, they obtained MAD = 1.37 ± 0.90 (sFCM), MaxD = 4.04 ± 2.87 (sFCM), and HD = 2.21 ± 0.43 (FCM). These experimental findings suggest that further research would be useful for advanced fuzzy logic techniques specifically tailored to medical image segmentation.</jats:p

The Effect of Contrast Medium SonoVue® on the Electric Charge Density of Blood Cells

The effect of contrast medium SonoVue® on the electric charge density of blood cells (erythrocytes and thrombocytes) was measured using a microelectrophoretic method. We examined the effect of adsorbed H+ and OH− ions on the surface charge of erythrocytes or thrombocytes. Surface charge density values were determined from electrophoretic mobility measurements of blood cells performed at various pH levels. The interaction between solution ions and the erythrocyte’s or thrombocyte’s surface was described by a four-component equilibrium model. The agreement between the experimental and theoretical charge variation curves of the erythrocytes and thrombocytes was good at pH 2–9. The deviation observed at a higher pH may be caused by disregarding interactions between the functional groups of blood cells

Design and implementation of a World Wide Web teaching files database on diagnostic radiology

INTRODUCTION: The aim of this project was to demonstrate the feasibility and effectiveness of a teaching files database on diagnostic radiology accessible via the Internet and the World Wide Web. The Italian experience in developing a national database named "RadioDB" is presented. MATERIALS AND METHODS: "RadioDB" is sponsored by the Italian Society of Radiology (SIRM) and is based on a multicenter academic collaboration. It constitutes a collection of multimedia case record and teaching files. "RadioDB" is based on a Pentium workstation running a Structured Query Language (SQL) database and a World Wide Web server. Each case record contains at least one radiological image and a text file for case comment with key words and codes from the American College of Radiology (ACR). In most cases, a complete teaching file is also available. Peer reviewed teaching files available on this server are contributed via anonymous file transfer protocol (FTP) through the Internet from various Italian academic Departments of Radiology. RESULTS: Worldwide users can easily access and search "RadioDB" database through a World Wide Web query interface and retrieve on-the-fly case record and teaching files. Users can search the database by author name, description, key words, free text or ACR codes. CONCLUSIONS: "RadioDB" enlarges the base of current off-line educational materials offering and it allows radiologists to improve their medical education