Search Results (28)

Background/Objectives: This research presents a novel analytical method for breast tumor characterization and tissue classification by leveraging intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) combined with hyperspectral imaging techniques and deep learning. Traditionally, dynamic contrast-enhanced MRI (DCE-MRI) is employed for breast tumor diagnosis, but it involves gadolinium-based contrast agents, which carry potential health risks. IVIM imaging extends conventional diffusion-weighted imaging (DWI) by explicitly separating the signal decay into components representing true molecular diffusion ($D$) and microcirculation of capillary blood (pseudo-diffusion or $D*$). This separation allows for a more comprehensive, non-invasive assessment of tissue characteristics without the need for contrast agents, thereby offering a safer alternative for breast cancer diagnosis. The primary purpose of this study was to evaluate different methods for breast tumor characterization using IVIM-DWI data treated as hyperspectral image stacks. Dice similarity coefficients and Jaccard indices were specifically used to evaluate the spatial segmentation accuracy of tumor boundaries, confirmed by experienced physicians on dynamic contrast-enhanced MRI (DCE-MRI), emphasizing detailed tumor characterization rather than binary diagnosis of cancer. Methods: The data source for this study consisted of breast MRI scans obtained from 22 patients diagnosed with mass-type breast cancer, resulting in 22 distinct mass tumor cases analyzed. MR images were acquired using a 3T MRI system (Discovery MR750 3.0 Tesla, GE Healthcare, Chicago, IL, USA) with axial IVIM sequences and a bipolar pulsed gradient spin echo sequence. Multiple b-values ranging from 0 to 2500 s/mm² were utilized, specifically thirteen original b-values (0, 15, 30, 45, 60, 100, 200, 400, 600, 1000, 1500, 2000, and 2500 s/mm²), with the last four b-value images replicated once for a total of 17 bands used in the analysis. The methodology involved several steps: acquisition of multi-b-value IVIM-DWI images, image pre-processing, including correction for motion and intensity inhomogeneity, treating the multi-b-value data as hyperspectral image stacks, applying hyperspectral techniques like band expansion, and evaluating three tumor detection methods: kernel-based constrained energy minimization (KCEM), iterative KCEM (I-KCEM), and deep neural networks (DNNs). The comparisons were assessed by evaluating the similarity of the detection results from each method to ground truth tumor areas, which were manually drawn on DCE-MRI images and confirmed by experienced physicians. Similarity was quantitatively measured using the Dice similarity coefficient and the Jaccard index. Additionally, the performance of the detectors was evaluated using 3D-ROC analysis and its derived criteria (AUC_OD, AUC_TD, AUC_BS, AUC_TD−BS, AUC_ODP, AUC_SNPR). Results: The findings objectively demonstrated that the DNN method achieved superior performance in breast tumor detection compared to KCEM and I-KCEM. Specifically, the DNN yielded a Dice similarity coefficient of 86.56% and a Jaccard index of 76.30%, whereas KCEM achieved 78.49% (Dice) and 64.60% (Jaccard), and I-KCEM achieved 78.55% (Dice) and 61.37% (Jaccard). Evaluation using 3D-ROC analysis also indicated that the DNN was the best detector based on metrics like target detection rate and overall effectiveness. The DNN model further exhibited the capability to identify tumor heterogeneity, differentiating high- and low-cellularity regions. Quantitative parameters, including apparent diffusion coefficient ($ADC$), pure diffusion coefficient ($D$), pseudo-diffusion coefficient ($D*$), and perfusion fraction ($PF$), were calculated and analyzed, providing insights into the diffusion characteristics of different breast tissues. Analysis of signal intensity decay curves generated from these parameters further illustrated distinct diffusion patterns and confirmed that high cellularity tumor regions showed greater water molecule confinement compared to low cellularity regions. Conclusions: This study highlights the potential of combining IVIM-DWI, hyperspectral imaging techniques, and deep learning as a robust, safe, and effective non-invasive diagnostic tool for breast cancer, offering a valuable alternative to contrast-enhanced methods by providing detailed information about tissue microstructure and heterogeneity without the need for contrast agents. Full article

Introduction: A potential prognostic biomarker for predicting the response to immunotherapy in breast cancer (BC) is tumor-infiltrating lymphocytes (TILs). The purpose of this research is to examine if preoperative characteristics of breast magnetic resonance imaging (MRI) may be used to predict TIL levels in a group of BC patients. In addition, we aimed to assess any potential relationship between the various tumor biology subgroups and MR imaging characteristics. Materials and Methods: This retrospective analysis comprised 145 participants with histologically confirmed BC who had preoperative DCE MRI. We collected and examined patient information as well as tumor MRI features, such as size and shape, edema, necrosis, multifocality/multicentricity, background parenchymal enhancement (BPE), and apparent diffusion coefficient (ADC) values. We divided patients into two groups based on their TIL levels: low-TIL (<10%) and high-TIL groups (≥10%). Following core needle biopsy, tumors were categorized as Luminal A, Luminal B, HER2+, and Triple Negative using immunohistochemical analysis. TIL levels were correlated with tumor biological profiles and MRI features using both parametric and non-parametric tests. Results: Patients were categorized as having a high TIL level (≥10%; 54/145 patients) and a low TIL level (<10%; 91/145 patients) based on the median TIL level of 10%. Of the lesions, 13 were HER2-positive, 16 were Triple Negative, 49 were Luminal A, and 67 were Luminal B. Higher TIL levels were statistically correlated with TNBC (11/16 individuals, p: 0.007). ADC values (p = 0.01), BPE levels (p = 0.008), and TIL levels were all significantly negatively correlated. Significantly more homogenous enhancement was seen in tumors with elevated TIL levels (p = 0.001). The ADC values and the enhancing characteristics were the most important factors in predicting TIL levels, according to logistic regression analysis, and when combined, they demonstrated the strongest ability to distinguish between the two groups (AUC = 0.744). Conclusions: MRI features, particularly ADC values and enhancement characteristics, may play a pivotal role in the assessment of TIL levels in BC before surgery. This could help patients to better customize treatments to the features of their tumors. Full article

Background/Objectives: Intraneural tumors (INTs) pose a diagnostic challenge, owing to their varied origins within nerve fascicles and their wide spectrum, which includes both benign and malignant forms. Accurate diagnosis and management of these tumors depends upon the skills of the radiologist in identifying key imaging features and correlating them with the patient’s clinical symptoms and examination findings. Methods: This comprehensive review systematically analyzes the various imaging features in the diagnosis of intraneural tumors, ranging from basic MR to advanced MR imaging techniques such as MR neurography (MRN), diffusion tensor imaging (DTI), and dynamic contrast-enhanced (DCE) MRI. Results: The article emphasizes the differentiation of benign from malignant lesions using characteristic MRI features, such as the “target sign” and “split-fat sign” for tumor characterization. The role of advanced multiparametric MRI in improving biopsy planning, guiding surgical mapping, and enhancing post-treatment monitoring is also highlighted. The review also underlines the importance of common diagnostic pitfalls and highlights the need for a multi-disciplinary approach to achieve an accurate diagnosis, appropriate treatment strategy, and post-therapy surveillance planning. Conclusions: In this review, we illustrate the main imaging findings of intraneural tumors, focusing on specific MR imaging features that are crucial for an accurate diagnosis and the differentiation between benign and malignant lesions. Full article

Crohn’s Disease (CD) is a chronic inflammatory bowel disease affecting the gastrointestinal tract. The search continues for new markers for assessing the activity of CD. Among them, pro-inflammatory and anti-inflammatory cytokines appear promising. We performed the analysis of cytokine concentrations in blood serum using the Bio-Plex Multiplex system (Bio-Rad), and their correlations with radiological parameters were assessed by magnetic resonance enterography (MRE), and fecal calprotectin levels were measured quantitatively by ELISA and clinical evaluation according to the Crohn’s Disease Activity Index (CDAI). Our study found that measuring cytokine serum concentrations can be a valuable tool in the diagnosis and treatment of CD. Positive correlations were reported between contrast enhancement on DCE-MRE and the concentrations of PDGF-BB and RANTES. Also, a positive correlation was found between the delayed-phase of DCE and IL-10 concentration, a strong negative correlation between the delayed-phase of DCE and IL-12 concentration, and a strong positive correlation between the delayed-phase of DCE and RANTES concentrations. A strong positive correlation was also observed between the thickness of the intestinal wall on T2-weighted images and RANTES concentration. Therefore, concentrations of PDGF-BB, RANTES, IL-10 and IL-12 are promising markers of CD activity. The study also demonstrated significant correlations between the severity of disease activity assessed by the CDAI and the concentrations of IL-5, IL-8 and IL-9, as well as positive correlations between the levels of fecal calprotectin and the concentrations of IL-1RA and VEGF. Therefore, the levels of IL-5, IL-8, IL-9, VEGF and IL-1RA may be useful markers in the diagnosis and clinical assessment of disease activity. Full article

Background: The aim of this study was to determine the differences in diffusion-weighted imaging (DWI) and dynamic contrast enhancement (DCE) parameters between patients with peritoneal high-grade serous ovarian cancer (HGSOC) recurrence with BRCA mutations (BRCAmut) or BRCA wild type (BRCAwt). Materials and Methods: We retrospectively analyzed the abdominal and pelvic magnetic resonance (MR) images of 43 patients suspected of having recurrent HGSOC, of whom 18 had BRCA1/2 gene mutations. Patients underwent MRI examination via a 1.5 T MRI scanner, and the analyzed parameters were as follows: apparent diffusion coefficient (ADC), time to peak (TTP) and perfusion maximum enhancement (Perf. Max. En.). Results: The mean ADC in patients with BRCAwt was lower than that in patients with BRCAmut: 788.7 (SD: 139.5) vs. 977.3 (SD: 103), p-value = 0.00002. The average TTP value for patients with BRCAwt was greater than that for patients with mutations: 256.3 (SD: 50) vs. 160.6 (SD: 35.5), p-value < 0.01. The Perf. Max. En. value was lower in the BRCAwt group: 148.6 (SD: 12.3) vs. 233.6 (SD: 29.2), p-value < 0.01. Conclusion: Our study revealed a statistically significant correlation between DWI and DCE parameters in examinations of peritoneal metastasis in patients with BRCA1/2 mutations. Adding DCE perfusion to the MRI protocol for ovarian cancer recurrence in patients with BRCAmut may be a valuable tool. Full article

Mild traumatic brain injury (mTBI) is a common occurrence around the world, associated with a variety of blunt force and torsion injuries affecting all age groups. Most never reach medical attention, and the identification of acute injury and later clearance to return to usual activities is relegated to clinical evaluation—particularly in sports injuries. Advanced structural imaging is rarely performed due to the usual absence of associated acute anatomic/hemorrhagic changes. This review targets physiologic imaging techniques available to identify subtle blood–brain barrier dysfunction and white matter tract shear injury and their association with chronic traumatic encephalopathy. These techniques provide needed objective measures to assure recovery from injury in those patients with persistent cognitive/emotional symptoms and in the face of repetitive mTBI. Full article

The role of magnetic resonance imaging (MRI) in rectal cancer management has significantly increased over the last decade, in line with more personalized treatment approaches. Total neoadjuvant treatment (TNT) plays a pivotal role in the shift from traditional surgical approach to non-surgical approaches such as ‘watch-and-wait’. MRI plays a central role in this evolving landscape, providing essential morphological and functional data that support clinical decision-making. Key MRI-based biomarkers, including circumferential resection margin (CRM), extramural venous invasion (EMVI), tumour deposits, diffusion-weighted imaging (DWI), and MRI tumour regression grade (mrTRG), have proven valuable for staging, response assessment, and patient prognosis. Functional imaging techniques, such as dynamic contrast-enhanced MRI (DCE-MRI), alongside emerging biomarkers derived from radiomics and artificial intelligence (AI) have the potential to transform rectal cancer management offering data that enhance T and N staging, histopathological characterization, prediction of treatment response, recurrence detection, and identification of genomic features. This review outlines validated morphological and functional MRI-derived biomarkers with both prognostic and predictive significance, while also exploring the potential of radiomics and artificial intelligence in rectal cancer management. Furthermore, we discuss the role of rectal MRI in the ‘watch-and-wait’ approach, highlighting important practical aspects in selecting patients for non-surgical management. Full article

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) measures microvascular perfusion by capturing the temporal changes of an MRI contrast agent in a target tissue, and it provides valuable information for the diagnosis and prognosis of a wide range of tumors. Quantitative DCE-MRI analysis commonly relies on the nonlinear least square (NLLS) fitting of a pharmacokinetic (PK) model to concentration curves. However, the voxel-wise application of such nonlinear curve fitting is highly time-consuming. The arterial input function (AIF) needs to be utilized in quantitative DCE-MRI analysis. and in practice, a population-based arterial AIF is often used in PK modeling. The contribution of intravascular dispersion to the measured signal enhancement is assumed to be negligible. The MR dispersion imaging (MRDI) model was recently proposed to account for intravascular dispersion, enabling more accurate PK modeling. However, the complexity of the MRDI hinders its practical usability and makes quantitative PK modeling even more time-consuming. In this paper, we propose fast MR dispersion imaging (fMRDI) to effectively represent the intravascular dispersion and highly accelerated PK parameter estimation. We also propose a deep learning-based, two-stage framework to accelerate PK parameter estimation. We used a deep neural network (NN) to estimate PK parameters directly from enhancement curves. The estimation from NN was further refined using several steps of NLLS, which is significantly faster than performing NLLS from random initializations. A data synthesis module is proposed to generate synthetic training data for the NN. Two data-processing modules were introduced to improve the model’s stability against noise and variations. Experiments on our in-house clinical prostate MRI dataset demonstrated that our method significantly reduces the processing time, produces a better distinction between normal and clinically significant prostate cancer (csPCa) lesions, and is more robust against noise than conventional DCE-MRI analysis methods. Full article

There is a need to develop user-friendly imaging tools estimating robust quantitative biomarkers (QIBs) from multiparametric (mp)MRI for clinical applications in oncology. Quantitative metrics derived from (mp)MRI can monitor and predict early responses to treatment, often prior to anatomical changes. We have developed a vendor-agnostic, flexible, and user-friendly MATLAB-based toolkit, MRI-Quantitative Analysis and Multiparametric Evaluation Routines (“MRI-QAMPER”, current release v3.0), for the estimation of quantitative metrics from dynamic contrast-enhanced (DCE) and multi-b value diffusion-weighted (DW) MR and MR relaxometry. MRI-QAMPER’s functionality includes generating numerical parametric maps from these methods reflecting tumor permeability, cellularity, and tissue morphology. MRI-QAMPER routines were validated using digital reference objects (DROs) for DCE and DW MRI, serving as initial approval stages in the National Cancer Institute Quantitative Imaging Network (NCI/QIN) software benchmark. MRI-QAMPER has participated in DCE and DW MRI Collaborative Challenge Projects (CCPs), which are key technical stages in the NCI/QIN benchmark. In a DCE CCP, QAMPER presented the best repeatability coefficient (RC = 0.56) across test–retest brain metastasis data, out of ten participating DCE software packages. In a DW CCP, QAMPER ranked among the top five (out of fourteen) tools with the highest area under the curve (AUC) for prostate cancer detection. This platform can seamlessly process mpMRI data from brain, head and neck, thyroid, prostate, pancreas, and bladder cancer. MRI-QAMPER prospectively analyzes dose de-escalation trial data for oropharyngeal cancer, which has earned it advanced NCI/QIN approval for expanded usage and applications in wider clinical trials. Full article

This paper investigates the effect of anisotropic resolution on the image textural features of pharmacokinetic (PK) parameters of a murine glioma model using dynamic contrast-enhanced (DCE) MR images acquired with an isotropic resolution at 7T with pre-contrast T1 mapping. The PK parameter maps of whole tumors at isotropic resolution were generated using the two-compartment exchange model combined with the three-site-two-exchange model. The textural features of these isotropic images were compared with those of simulated, thick-slice, anisotropic images to assess the influence of anisotropic voxel resolution on the textural features of tumors. The isotropic images and parameter maps captured distributions of high pixel intensity that were absent in the corresponding anisotropic images with thick slices. A significant difference was observed in 33% of the histogram and textural features extracted from anisotropic images and parameter maps, compared to those extracted from corresponding isotropic images. Anisotropic images in different orthogonal orientations demonstrated 42.1% of the histogram and textural features to be significantly different from those of isotropic images. This study demonstrates that the anisotropy of voxel resolution needs to be carefully considered when comparing the textual features of tumor PK parameters and contrast-enhanced images. Full article

Radiomics and artificial intelligence have been increasingly applied in breast MRI. However, the advantages of using radiomics to evaluate lesions amenable to MR-guided vacuum-assisted breast biopsy (MR-VABB) are unclear. This study includes patients scheduled for MR-VABB, corresponding to subjects with MRI-only visible lesions, i.e., with a negative second-look ultrasound. The first acquisition of the multiphase dynamic contrast-enhanced MRI (DCE-MRI) sequence was selected for image segmentation and radiomics analysis. A total of 80 patients with a mean age of 55.8 years ± 11.8 (SD) were included. The dataset was then split into a training set (50 patients) and a validation set (30 patients). Twenty out of the 30 patients with a positive histology for cancer were in the training set, while the remaining 10 patients with a positive histology were included in the test set. Logistic regression on the training set provided seven features with significant p values (<0.05): (1) ‘AverageIntensity’, (2) ‘Autocorrelation’, (3) ‘Contrast’, (4) ‘Compactness’, (5) ‘StandardDeviation’, (6) ‘MeanAbsoluteDeviation’ and (7) ‘InterquartileRange’. AUC values of 0.86 (95% C.I. 0.73–0.94) for the training set and 0.73 (95% C.I. 0.54–0.87) for the test set were obtained for the radiomics model. Radiological evaluation of the same lesions scheduled for MR-VABB had AUC values of 0.42 (95% C.I. 0.28–0.57) for the training set and 0.4 (0.23–0.59) for the test set. In this study, a radiomics logistic regression model applied to DCE-MRI images increased the diagnostic accuracy of standard radiological evaluation of MRI suspicious findings in women scheduled for MR-VABB. Confirming this performance in large multicentric trials would imply that using radiomics in the assessment of patients scheduled for MR-VABB has the potential to reduce the number of biopsies, in suspicious breast lesions where MR-VABB is required, with clear advantages for patients and healthcare resources. Full article

Background: In this prospective study, we hypothesized that magnetic resonance imaging (MRI) may represent not only the tumor but also the microenvironment, reflecting the heterogeneity and microstructural complexity of neoplasms. We investigated the correlation between both diffusion kurtosis imaging (DKI) and dynamic contrast-enhanced (DCE)-MRI with the pathological factors in oral cavity squamous cell carcinomas (OSCCs). Methods: A total of 37 patients with newly diagnosed OSCCs underwent an MR examination on a 3T system. The diffusion coefficient (D), the kurtosis parameter (K), the transfer constants K^trans and K_ep and the volume of extravascular extracellular space v_e were quantified. A histogram-based approach was proposed to investigate the associations between the imaging and the pathological factors based on the histology and immunochemistry. Results: Significant differences in the DCE-MRI and DKI parameters were found in relation to the inflammatory infiltrate, tumor grading, keratinization and desmoplastic reaction. Relevant relationships emerged between tumor-infiltrating lymphocytes (TILs) and DKI, with lower D and higher K values being associated with increased TILs. Conclusion: Although a further investigation is needed, these findings provide a more comprehensive biological characterization of OSCCs and may contribute to a better understanding of DKI-derived parameters, whose biophysical meaning is still not well-defined. Full article

Breast MR is the most sensitive imaging modality, but there are cases of malignant tumors that are not detected in MR. This study evaluated the frequency and main causes of malignant breast lesions not detected in dynamic contrast-enhanced (DCE) MR. A total of 1707 cases of preoperative breast MR performed between 2020 and 2021 were included. Three radiologists individually reviewed the DCE MRs and found not-detected malignancy cases in the MRs. The final cases were decided through consensus. For the selected cases, images other than DCE MRIs, such as mammography, ultrasounds, diffusion-weighted MRs, and, if possible, contrast-enhanced chest CTs, were analyzed. In the final sample, 12 cases were not detected in DCE MR, and the frequency was 0.7% (12/1707). Six cases were not detected due to known non-enhancing histologic features. In four cases, tumors were located in the breast periphery and showed no enhancement in MR. In the remaining two cases, malignant lesions were not identified due to underlying marked levels of BPE. The frequency of not-detected malignancy in DCE MR is rare. Knowing the causes of each case and correlating it with other imaging modalities could be helpful in the diagnosis of breast malignancy in DCE MR. Full article

Although convection-enhanced delivery can successfully bypass the blood-brain barrier, its clinical performance remains disappointing. This is primarily attributed to the heterogeneous intratumoral environment, particularly the tumor microvasculature. This study investigates the combined convection-enhanced delivery of antiangiogenic drugs and liposomal cytotoxic drugs in a heterogeneous brain tumor environment using a transport-based mathematical model. The patient-specific 3D brain tumor geometry and the tumor’s heterogeneous tissue properties, including microvascular density, porosity and cell density, are extracted from dynamic contrast-enhanced magnetic resonance imaging data. Results show that antiangiogenic drugs can effectively reduce the tumor microvascular density. This change in tissue structure would inhibit the fluid loss from the blood to prevent drug concentration from dilution, and also reduce the drug loss by blood drainage. The comparisons between different dosing regimens demonstrate that the co-infusion of liposomal cytotoxic drugs and antiangiogenic drugs has the advantages of homogenizing drug distribution, increasing drug accumulation, and enlarging the volume where tumor cells can be effectively killed. The delivery outcomes are susceptible to the location of the infusion site. This combination treatment can be improved by infusing drugs at higher microvascular density sites. In contrast, infusion at a site with high cell density would lower the treatment effectiveness of the whole brain tumor. Results obtained from this study can deepen the understanding of this combination therapy and provide a reference for treatment design and optimization that can further improve survival and patient quality of life. Full article

The purpose of this study was to investigate the diagnostic performance of ultrafast DCE (UF-DCE) MRI after the completion of neoadjuvant systemic therapy (NST) in breast cancer. In this study, MR examinations of 55 post-NST breast cancers were retrospectively analyzed. Residual tumor sizes were measured in the 20th phase of UF-DCE MRI, early and delayed phases of conventional DCE MRI, and high spatial-resolution CE MRI (UF, early, delayed, and HR, respectively). The diagnostic performance for the detection of residual invasive cancer was calculated by ROC analysis. The size difference between MRI and pathological findings was analyzed using the Wilcoxon signed-rank test with the Bonferroni correction. The overall AUC was highest for UF (0.86 and 0.88 for readers 1 and 2, respectively). The difference in imaging and pathological sizes for UF (5.7 ± 8.2 mm) was significantly smaller than those for early, delayed, and HR (p < 0.01). For luminal subtype breast cancer, the size difference was significantly smaller for UF and early than for delayed (p < 0.01). UF-DCE MRI demonstrated higher AUC and specificity for the more accurate detection of residual cancer and the visualization of tumor extent than conventional DCE MRI. Full article