Search Results (323)

Background: Primary lymphoma of peripheral nerves (PLPN) is a rare extranodal non-Hodgkin lymphoma that mimics benign nerve conditions, leading to diagnostic delays. This systematic review evaluates the clinical, radiological, and pathological features of PLPN, alongside diagnostic and therapeutic strategies. Materials and Methods: A systematic search was conducted across PubMed, Scopus, and Web of Science, and identified 23 studies reporting 27 cases of PLPN. Data on demographics, clinical presentation, diagnostics, treatment, and outcomes were extracted and synthesized qualitatively due to study heterogeneity. Results: The sciatic nerve was most involved (48.15%), followed by the ulnar (18.5%) and radial nerves (18.5%). The median age at diagnosis was 58 years, with symptoms including motor deficits (88.9%), sensory disturbances (74.1%), and pain (70.4%). B-cell lymphomas accounted for 81.5% of cases, predominantly diffuse large B-cell lymphoma. MRI findings were non-specific; however, diffusion-weighted imaging (DWI) showed diagnostic potential. Treatments included combination therapies (51.9%), chemotherapy (25.9%), and surgery. Complete remission was achieved in 70.8%, with a 2-year survival rate of 83.3%. Conclusions: PLPN is rare but likely underdiagnosed. Early recognition requires multidisciplinary collaboration, advanced imaging, and standardized protocols. Future research should focus on molecular characterization, diagnostic criteria, and treatment optimization to improve outcomes for this challenging condition. Full article

Background: Thyroid nodules are highly prevalent, affecting up to 75% of the population, yet most are benign. The limited specificity of ultrasound-based workup leads to substantial overdiagnosis and overtreatment, underscoring the need for improved imaging-based classification. Diffusion-weighted MRI (DWI), quantified via the apparent diffusion coefficient (ADC), has emerged as a promising imaging biomarker. This meta-analysis updates pooled diagnostic performance metrics and systematically evaluates which DWI acquisition techniques, imaging parameters, and combinations with other MRI modalities are most promising for clinical translation. Methods: PubMed, Web of Science, Scopus, and ProQuest were systematically searched. Pooled sensitivity, specificity, and area under the curve (AUC) were calculated using bivariate random-effects models. The effects of b-value, magnetic field strength, echo time, and diffusion model on diagnostic accuracy and ADC values were examined through subgroup and meta-regression analyses. Results: Forty-six studies (3003 nodules) were included. Pooled sensitivity and specificity were 0.84 (95% CI: 0.81–0.86) and 0.88 (95% CI: 0.85–0.90), with an AUC of 0.912. Intravoxel incoherent motion and diffusion kurtosis imaging showed no added value over the mono-exponential model. For the mono-exponential model, a negative association between b-values and reported ADCs was observed, whereas no association was found between b-values and diagnostic accuracy. Magnetic field strength and echo time did not affect ADCs. Combining DWI with morphological imaging showed the potential to further enhance diagnostic performance. Conclusions: DWI holds strong potential to improve the diagnostic workup of thyroid nodules. Technical standardization, particularly of key acquisition parameters, should be pursued to enable clinical implementation. Full article

Apparent diffusion coefficient (ADC) values, derived from diffusion-weighted magnetic resonance imaging (DW-MRI), increase with age, reflecting microstructural changes in white matter integrity. However, factors beyond chronological aging may influence cerebral diffusion characteristics. We investigated whether anticoagulant use is associated with favorable white matter ADC profiles, suggesting preserved microvascular health. ADC values were analyzed in cerebral white matter across four age-defined adult cohorts (20–59 years). Minimum, mean, and maximum ADC values were extracted. Patients at the lowest and highest ends of the ADC spectrum within each group were identified. The prevalence of anticoagulant use was compared between groups, and a logistic regression model adjusted for age was used to assess the independent association between anticoagulant use and lower ADC values. Across all cohorts (n = 892), anticoagulated patients (n = 89) were significantly overrepresented among individuals with low ADC values consistent with younger diffusion profiles. Of the anticoagulated patients, 93.3% had ADC values below the lower cut-off limit. In contrast, only 30% of non-anticoagulated patients exhibited such profiles. Anticoagulant use was independently associated with low ADC values after adjusting for age (OR = 4.89, p < 0.0001). Anticoagulation is strongly associated with lower, more favorable ADC values in cerebral white matter, independent of age. These findings support the potential neuroprotective role of anticoagulants and suggest that diffusion MRI may serve as a surrogate marker for early microvascular brain health. Full article

Background/Objectives: This study aimed to evaluate the diagnostic performance of simplified intravoxel incoherent motion (IVIM) diffusion-weighted imaging (DWI) parameters in distinguishing malignant from benign breast lesions, and to explore their association with clinicopathological features. Methods: This retrospective study included 108 women who underwent breast MRI with multi-b-value DWI (0, 20, 200, 500, 800 s/mm²). Of those 108 women, 73 had pathologically confirmed malignant lesions. IVIM maps (ADC_map, D, D*, and perfusion fraction f) were generated using IB-Diffusion™ software version 21.12. Lesions were manually segmented by radiologists, and clinicopathological data including receptor status, Ki-67 index, cancer type, histologic grade, and molecular subtype were extracted from medical records. Nonparametric tests and ROC analysis were used to assess group differences and diagnostic performance. Additionally, a binary logistic regression model combining D, D*, and f was developed to evaluate their joint diagnostic utility, with ROC analysis applied to the model’s predicted probabilities. Results: Malignant lesions demonstrated significantly lower diffusion parameters compared to benign lesions, including ADC_map (p = 0.004), D (p = 0.009), and D* (p = 0.016), indicating restricted diffusion in cancerous tissue. In contrast, the perfusion fraction (f) did not show a significant difference (p = 0.202). ROC analysis revealed moderate diagnostic accuracy for ADC_map (AUC = 0.671), D (AUC = 0.657), and D* (AUC = 0.644), while f showed poor discrimination (AUC = 0.576, p = 0.186). A combined logistic regression model using D, D*, and f significantly improved diagnostic performance, achieving an AUC of 0.725 (p < 0.001), with 67.1% sensitivity and 74.3% specificity. ADC_map achieved the highest sensitivity (100%) but had low specificity (11.4%). Among clinicopathological features, only histologic grade was significantly associated with IVIM metrics, with higher-grade tumors showing lower ADC_map and D* values (p = 0.042 and p = 0.046, respectively). No significant associations were found between IVIM parameters and ER, PR, HER2 status, Ki-67 index, cancer type, or molecular subtype. Conclusions: Simplified IVIM DWI offers moderate accuracy in distinguishing malignant from benign breast lesions, with diffusion-related parameters (ADC_map, D, D*) showing the strongest diagnostic value. Incorporating D, D*, and f into a combined model enhanced diagnostic performance compared to individual IVIM metrics, supporting the potential of multivariate IVIM analysis in breast lesion characterization. Tumor grade was the only clinicopathological feature consistently associated with diffusion metrics, suggesting that IVIM may reflect underlying tumor differentiation but has limited utility for molecular subtype classification. Full article

The 2021 WHO classification of brain tumours revolutionised the oncological field by emphasising the role of molecular, genetic and pathogenetic advances in classifying brain tumours. In this context, incidental gliomas have been increasingly identified due to the widespread performance of standard and advanced MRI sequences and represent a diagnostic and therapeutic challenge. The impactful decision to perform a surgical procedure deeply relies on the non-invasive identification of features or parameters that may correlate with brain tumour genetic profile and grading. Therefore, it is paramount to reach an early and proper diagnosis through neuroradiological techniques, such as MRI. Standard MRI sequences are the cornerstone of diagnosis, while consolidated and emerging roles have been awarded to advanced sequences such as Diffusion-Weighted Imaging/Apparent Diffusion Coefficient (DWI/ADC), Perfusion-Weighted Imaging (PWI), Magnetic Resonance Spectroscopy (MRS), Diffusion Tensor Imaging (DTI) and functional MRI (fMRI). The current novelty relies on the application of AI in brain neuro-oncology, mainly based on radiomics and radiogenomics models, which enhance standard and advanced MRI sequences in predicting glioma genetic status by identifying the mutation of multiple key biomarkers deeply impacting patients’ diagnosis, prognosis and treatment, such as IDH, EGFR, TERT, MGMT promoter, p53, H3-K27M, ATRX, Ki67 and 1p19. AI-driven models demonstrated high accuracy in glioma detection, grading, prognostication, and pre-surgical planning and appear to be a promising frontier in the neuroradiological field. On the other hand, standardisation challenges in image acquisition, segmentation and feature extraction variability, data scarcity and single-omics analysis, model reproducibility and generalizability, the black box nature and interpretability concerns, as well as ethical and privacy challenges remain key issues to address. Future directions, rooted in enhanced standardisation and multi-institutional validation, advancements in multi-omics integration, and explainable AI and federated learning, may effectively overcome these challenges and promote efficient AI-based models in glioma management. The aims of our multidisciplinary review are to: (1) extensively present the role of standard and advanced MRI sequences in the differential diagnosis of iLGGs as compared to HGGs (High-Grade Gliomas); (2) give an overview of the current and main applications of AI tools in the differential diagnosis of iLGGs as compared to HGGs (High-Grade Gliomas); (3) show the role of MRI, radiomics and radiogenomics in unravelling glioma genetic profiles. Standard and advanced MRI, radiomics and radiogenomics are key to unveiling the grading and genetic profile of gliomas and supporting the pre-operative planning, with significant impact on patients’ differential diagnosis, prognosis prediction and treatment strategies. Today, neuroradiologists are called to efficiently use AI tools for the in vivo, non-invasive, and comprehensive assessment of gliomas in the path towards patients’ personalised medicine. Full article

Pelvic inflammatory disease (PID) encompasses a broad range of infection-induced inflammatory disorders of the female upper genital tract, commonly caused by ascending sexually transmitted infections. Diagnosis is often challenging because of nonspecific or absent symptoms and the overlap with other pelvic pathologies. While clinical and laboratory assessments are essential, cross-sectional imaging plays a pivotal role, especially in complicated, atypical, or equivocal cases. This review focuses on the typical and atypical imaging features of PID and highlights the crucial roles of computed tomography (CT) and magnetic resonance imaging (MRI) in its diagnostic evaluation. CT is frequently employed in emergency settings because of its widespread availability and ability to detect acute complications such as tubo-ovarian abscesses (TOA), peritonitis, or Fitz-Hugh–Curtis syndrome. However, it is limited by ionizing radiation and suboptimal soft-tissue contrast. MRI provides superior tissue characterization and multiplanar imaging without radiation exposure. When combined with diffusion-weighted imaging (DWI), MRI achieves high diagnostic accuracy, particularly in differentiating PID from other entities such as endometriosis, adnexal tumors, and gastrointestinal or urinary tract diseases. This review also addresses PID in specific clinical contexts, including post-partum infection, post-assisted reproductive technologies (ART), intrauterine device (IUD) use, and chronic or recurrent forms. A comprehensive, multimodal imaging approach integrated with clinical findings is essential for timely diagnosis, effective treatment, and prevention of severe reproductive sequelae. Full article

Background/Objectives: This study aimed to examine the changes in brain metabolites and water molecule diffusion using chemical exchange saturation transfer (CEST) imaging and diffusion-weighted imaging (DWI) after 15 Gy of X-ray irradiation in a rat model of glioma. Methods: The glioma-derived cell line, C6, was implanted into the striatum of the right brain of 7-week-old male Wistar rats. CEST imaging and DWI were performed on days 8, 10, and 17 after implantation using a 7T-magnetic resonance imaging. X-ray irradiation (15 Gy) was performed on day 9. Magnetization transfer ratio (MTR) and apparent diffusion coefficient (ADC) values were calculated for CEST and DWI, respectively. Results: On day 17, the MTR values at 1.2 ppm, 1.5 ppm, 1.8 ppm, 2.1 ppm, and 2.4 ppm in the irradiated group decreased significantly compared with those of the control group. The standard deviation for the ADC values on a pixel-by-pixel basis increased from day 8 to day 17 (0.6 ± 0.06 → 0.8 ± 0.17 (×10⁻³ mm²/s)) in the control group, whereas it remained nearly unchanged (0.6 ± 0.06 → 0.8 ± 0.11 (×10⁻³ mm²/s)) in the irradiated group. Conclusions: This study revealed the effects of 15 Gy X-ray irradiation in a rat model of glioma using CEST imaging and DWI. Full article

Background/Objectives: Brainstem ischemic strokes comprise 10% of ischemic strokes and are challenging to diagnose due to small lesion size and complex presentations. Diffusion-weighted imaging (DWI) is crucial for detecting ischemia, yet it can miss small lesions, especially when only axial slices are employed. This study investigated whether ischemic lesions visible in a single imaging plane correspond to smaller volumes and whether coronal DWI enhances detection compared to axial DWI alone. Methods: This retrospective single-center study examined 134 patients with brainstem ischemic strokes between December 2018 and November 2023. All patients underwent axial and coronal DWI. Clinical data, NIH Stroke Scale (NIHSS) scores, and modified Rankin Scale (mRS) scores were recorded. Diffusion-restricted lesion volumes were calculated using multiple models (planimetric, ellipsoid, and spherical), and lesion visibility per imaging plane was analyzed. Results: Brainstem ischemic strokes were detected in 85.8% of patients. Coronal DWI alone identified 6% of lesions that were undetectable on axial DWI; meanwhile, axial DWI alone identified 6.7%. Combining both improved overall sensitivity to 86.6%. Ischemic lesions visible in only one plane were significantly smaller across all volume models. Higher NIHSS scores were strongly correlated with larger diffusion-restricted lesion volumes. Coronal DWI correlated better with clinical severity than axial DWI, especially in the midbrain and medulla. Conclusions: Coronal DWI significantly improves the detection of small brainstem infarcts and should be incorporated into routine stroke imaging protocols. Infarcts visible in only one plane are typically smaller, yet still clinically relevant. Combined imaging enhances diagnostic accuracy and supports early and precise intervention in posterior circulation strokes. Full article

Purpose: Predicting colorectal cancer liver metastasis (CRLM) is essential for prognostic assessment. This study aims to develop and validate an interpretable multimodal machine learning framework based on multiparametric MRI for predicting CRLM, and to enhance the clinical interpretability of the model through SHapley Additive exPlanations (SHAP) analysis and deep learning visualization. Methods: This multicenter retrospective study included 463 patients with pathologically confirmed colorectal cancer from two institutions, divided into training (n = 256), internal testing (n = 111), and external validation (n = 96) sets. Radiomics features were extracted from manually segmented regions on axial T2-weighted imaging (T2WI) and diffusion-weighted imaging (DWI). Deep learning features were obtained from a pretrained ResNet101 network using the same MRI inputs. A least absolute shrinkage and selection operator (LASSO) logistic regression classifier was developed for clinical, radiomics, deep learning, and combined models. Model performance was evaluated by AUC, sensitivity, specificity, and F1-score. SHAP was used to assess feature contributions, and Grad-CAM was applied to visualize deep feature attention. Results: The combined model integrating features across the three modalities achieved the highest performance across all datasets, with AUCs of 0.889 (training), 0.838 (internal test), and 0.822 (external validation), outperforming single-modality models. Decision curve analysis (DCA) revealed enhanced clinical net benefit from the integrated model, while calibration curves confirmed its good predictive consistency. SHAP analysis revealed that radiomic features related to T2WI texture (e.g., LargeDependenceLowGrayLevelEmphasis) and clinical biomarkers (e.g., CA19-9) were among the most predictive for CRLM. Grad-CAM visualizations confirmed that the deep learning model focused on tumor regions consistent with radiological interpretation. Conclusions: This study presents a robust and interpretable multiparametric MRI-based model for noninvasively predicting liver metastasis in colorectal cancer patients. By integrating handcrafted radiomics and deep learning features, and enhancing transparency through SHAP and Grad-CAM, the model provides both high predictive performance and clinically meaningful explanations. These findings highlight its potential value as a decision-support tool for individualized risk assessment and treatment planning in the management of colorectal cancer. Full article

Prostate cancer (PCa) is the most common malignancy in men. Precise grading is crucial for the effective treatment approaches of PCa. Machine learning (ML) applied to biparametric Magnetic Resonance Imaging (bpMRI) radiomics holds promise for improving PCa diagnosis and prognosis. This study investigated the efficiency of seven ML models to diagnose the different PCa grades, changing the input variables. Our studied sample comprised 214 men who underwent bpMRI in different imaging centers. Seven ML algorithms were compared using radiomic features extracted from T2-weighted (T2W) and diffusion-weighted (DWI) MRI, with and without the inclusion of Prostate-Specific Antigen (PSA) values. The performance of the models was evaluated using the receiver operating characteristic curve analysis. The models’ performance was strongly dependent on the input parameters. Radiomic features derived from T2WI and DWI, whether used independently or in combination, demonstrated limited clinical utility, with AUC values ranging from 0.703 to 0.807. However, incorporating the PSA index significantly improved the models’ efficiency, regardless of lesion location or degree of malignancy, resulting in AUC values ranging from 0.784 to 1.00. There is evidence that ML methods, in combination with radiomic analysis, can contribute to solving differential diagnostic problems of prostate cancers. Also, optimization of the analysis method is critical, according to the results of our study. Full article

Background: This study aimed at evaluating apparent diffusion coefficient (ADC) values of nasopharyngeal carcinoma (NPC) in the pre-treatment stages of NPC for establishing comparative quantitative parameters between children and adolescents compared to adults. Methods: A retrospective multicentric imaging study was conducted in three medical centers by collecting patient data over a 5-year timeframe. Patients were included in the study based on the following criteria: histopathologically proven carcinoma of the nasopharynx with all available medical records. The total sample included 20 patients (6 pediatric patients and 14 adults). A quantitative analysis of the ADC maps was performed. Two radiologists manually drew the region of interest (ROI) on ADC maps using the whole tumor on all magnetic resonance imaging (MRI) slices. The mean ADC was extracted for each patient and each radiologist’s evaluation. Differences in ADC values between pediatric and adult patients were evaluated using an independent samples t-test, with normality and variance assumptions tested via the Shapiro–Wilk and Levene’s tests, respectively. p-values less than 0.05 were considered statistically significant. Results: The mean ADC values extracted from the initial pre-treatment diffusion-weighted imaging (DWI) data from magnetic resonance imaging (MRI) in children were 712.22 × 10⁻⁶ mm²/s, compared to adults in whom the mean ADC values were 877.34 × 10⁻⁶ mm²/s. We found a statistically significant difference between the mean ADC values of pediatric patients and adult patients, t (17.44) = −3.15, p = 0.006, with the mean ADC values of pediatric patients (M = 712.22, standard deviation [SD] = 57.03) being lower, on average, than the mean ADC values of adult patients (M = 877.34, SD = 175.25). Conclusions: Our results showed significantly lower ADC values in pediatric patients than in adults, independent of tumor T-stage. Additionally, early-stage tumors, particularly in children, tended to exhibit even lower ADC values, suggesting potential biological distinctions across age groups. Full article

Background: Uterine fibroids are the most common tumors in gynecology, detected in up to 80% of patients at various points in their lives. Uterine sarcomas account for 3% to 7% of all uterine cancers. The diagnosis of uterine fibroids is possible through ultrasonography (US), but this method has many limitations. More accurate examinations include magnetic resonance imaging (MRI) and positron emission tomography (PET) scans. Methods: This study evaluates MRI and PET in differentiating uterine fibroids from sarcomas. MRI uses T2-weighted and diffusion-weighted imaging (DWI), while PET assesses metabolism and estrogen receptor activity using [18F] fluorodeoxyglucose (FDG) and 16α-[18F]-fluoro-17β-estradiol (FES). Results: MRI allows for the identification of uterine fibroids when they exhibit good delineation and low intensity in T2-weighted images and DWI. Uterine sarcoma is characterized by moderate to high signal intensity on T2-weighted imaging, irregular borders, high signal intensity at high DWI values, and a decreased apparent diffusion coefficient. PET imaging with FDG and FES is a useful tool in differentiating uterine fibroids from sarcomas. Uterine sarcomas exhibit greater FDG uptake than smooth muscle fibroids, although cases of similar uptake do occur. On the other hand, FES provides information about estrogen receptors (ERs). Conclusions: Future research should focus on conducting standardized imaging studies, which would facilitate the inclusion of larger patient cohorts. This, in turn, would enable the development of specific diagnostic guidelines, ultimately leading to more accurate diagnoses and reducing the difficulty of differentiating these tumors through imaging. Full article

Objectives: This study systematically evaluates the diagnostic accuracy of dynamic contrast-enhanced MRI (DCE-MRI), diffusion-weighted imaging (DWI), and apparent diffusion coefficient (ADC) values. Methods: The literature search started and ended on 10 June 2024. We searched MEDLINE, Cochrane Library, Pubmed, Science Direct, and Google Scholar. Our research question could be formulated as “In women with NME detected by MRI, how accurate are DCE and DWI in ruling in and ruling out malignancy when the diagnosis is compared to histopathology analysis with or without a clinical follow-up?”. The meta-analysis was conducted using the STATA 17 software with the “midas” commands. The study protocol has been registered in the International Prospective Register of Systematic Reviews (PROSPERO) database. Results: Fifty-four studies involving 6121 NME lesions were analyzed. The combined use of DCE-MRI and DWI demonstrated the highest diagnostic accuracy (AUC: 0.91; 95% CI: 0.88–0.93), followed by DWI alone (AUC: 0.85; 95% CI: 0.81–0.87) and ADC (AUC: 0.77; 95% CI: 0.74–0.81). DCE-MRI alone showed the lowest performance (AUC: 0.68; 95% CI: 0.64–0.72). Significant heterogeneity was observed across all modalities, with I² values exceeding 95% in several analyses. The likelihood ratio scattergram indicated that no modality reliably confirmed or excluded malignancy. Conclusions: While the combination of DCE-MRI and DWI achieves the highest diagnostic accuracy, no modality can reliably differentiate benign from malignant NME lesions. Standardized imaging protocols and refined diagnostic descriptors are needed for clinical improvement. Full article

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: Diffusion-weighted imaging (DWI) is a non-invasive technique for acquiring liver pathology data and characterizing liver lesions. This modality shows promise for applications in the initial diagnosis and monitoring of liver diseases, providing valuable insights for clinical assessment and treatment strategies. Intravoxel incoherent motion (IVIM), diffusion kurtosis imaging (DKI), and diffusion tensor imaging (DTI) are advanced forms of DWI. These techniques have proven effective for assessing liver lesions, including liver tumors and fibrosis. However, the results can be inconsistent. Thus, it is essential to summarize the current applications of these methods in liver fibrosis, identify existing limitations, and suggest future directions for development. Methods: This review assessed studies concerning liver DWI and its applications published in the PubMed database over the last nine years. It presents these techniques’ fundamental principles and key factors before discussing their application in liver fibrosis. Results and conclusions: It has been observed that advanced DWI sequences remain unreliable in ensuring the robustness and reproducibility of measurements when assessing liver fibrosis grades, due to inconsistent results and significant overlap among these techniques across different stages of fibrotic conditions. Full article