Search Results (468)

Background/Objectives: To simplify the decision-making process in radiomics by employing RadiomiX, an algorithm designed to automatically identify the best model combination and validate them across multiple environments was developed, thus enhancing the reliability of results. Methods: RadiomiX systematically tests classifier and feature selection method combinations known to be suitable for radiomic datasets to determine the best-performing configuration across multiple train–test splits and K-fold cross-validation. The framework was validated on four public retrospective radiomics datasets including lung nodules, metastatic breast cancer, and hepatic encephalopathy using CT, PET/CT, and MRI modalities. Model performance was assessed using the area under the receiver-operating-characteristic curve (AUC) and accuracy metrics. Results: RadiomiX achieved superior performance across four datasets: LLN (AUC = 0.850 and accuracy = 0.785), SLN (AUC = 0.845 and accuracy = 0.754), MBC (AUC = 0.889 and accuracy = 0.833), and CHE (AUC = 0.837 and accuracy = 0.730), significantly outperforming original published models (p < 0.001 for LLN/SLN and p = 0.023 for MBC accuracy). When original published models were re-evaluated using ten-fold cross-validation, their performance decreased substantially: LLN (AUC = 0.783 and accuracy = 0.731), SLN (AUC = 0.748 and accuracy = 0.714), MBC (AUC = 0.764 and accuracy = 0.711), and CHE (AUC = 0.755 and accuracy = 0.677), further highlighting RadiomiX’s methodological advantages. Conclusions: Systematically testing model combinations using RadiomiX has led to significant improvements in performance. This emphasizes the potential of automated ML as a step towards better-performing and more reliable radiomic models. Full article

Introduction: Breast density is a well-recognized factor in breast cancer risk assessment, with higher density linked to increased malignancy risk and reduced sensitivity of conventional mammography. Background parenchymal enhancement (BPE), observed in contrast-enhanced imaging, reflects physiological contrast uptake in non-pathologic breast tissue. While extensively characterized in breast MRI, the role of BPE in contrast-enhanced mammography (CEM) remains uncertain due to inconsistent findings regarding its correlation with breast density and cancer risk. Unlike breast density—standardized through the ACR BI-RADS lexicon—BPE lacks a uniform classification system in CEM, leading to variability in clinical interpretation and research outcomes. To address this gap, we introduce the BPE-CEM Standard Scale (BCSS), a structured four-tiered classification system specifically tailored to the two-dimensional characteristics of CEM, aiming to improve consistency and diagnostic alignment in BPE evaluation. Materials and Methods: In this retrospective single-center study, 213 patients who underwent mammography (MG), ultrasound (US), and contrast-enhanced mammography (CEM) between May 2022 and June 2023 at the “A. Perrino” Hospital in Brindisi were included. Breast density was classified according to ACR BI-RADS (categories A–D). BPE was categorized into four levels: Minimal (< 10% enhancement), Light (10–25%), Moderate (25–50%), and Marked (> 50%). Three radiologists independently assessed BPE in a subset of 50 randomly selected cases to evaluate inter-observer agreement using Cohen’s kappa. Correlations between BPE, breast density, and age were examined through regression analysis. Results: BPE was Minimal in 57% of patients, Light in 31%, Moderate in 10%, and Marked in 2%. A significant positive association was found between higher breast density (BI-RADS C–D) and increased BPE (p < 0.05), whereas lower-density breasts (A–B) were predominantly associated with minimal or light BPE. Regression analysis confirmed a modest but statistically significant association between breast density and BPE (R² = 0.144), while age showed no significant effect. Inter-observer agreement for BPE categorization using the BCSS was excellent (κ = 0.85; 95% CI: 0.78–0.92), supporting its reproducibility. Conclusions: Our findings indicate that breast density is a key determinant of BPE in CEM. The proposed BCSS offers a reproducible, four-level framework for standardized BPE assessment tailored to the imaging characteristics of CEM. By reducing variability in interpretation, the BCSS has the potential to improve diagnostic consistency and facilitate integration of BPE into personalized breast cancer risk models. Further prospective multicenter studies are needed to validate this classification and assess its clinical impact. Full article

Background/Objectives: To develop a DL-based model predicting recurrence risk in HER2-low breast cancer patients and to compare performance of the MRI-alone, clinicopathologic-alone, and combined models. Methods: We analyzed 453 patients with HER2-low breast cancer who underwent surgery and preoperative breast MRI between May 2018 and April 2022. Patients were randomly assigned to either a training cohort (n = 331) or a test cohort (n = 122). Imaging features were extracted from DCE-MRI and ADC maps, with regions of interest manually annotated by radiologists. Clinicopathological features included tumor size, nodal status, histological grade, and hormone receptor status. Three DL prediction models were developed: a CNN-based MRI-alone model, a clinicopathologic-alone model based on a multi-layer perceptron (MLP) and a combined model integrating CNN-extracted MRI features with clinicopathological data via MLP. Model performance was evaluated using AUC, sensitivity, specificity, and F1-score. Results: The MRI-alone model achieved an AUC of 0.69 (95% CI, 0.68–0.69), with a sensitivity of 37.6% (95% CI, 35.7–39.4), specificity of 87.5% (95% CI, 86.9–88.2), and F1-score of 0.34 (95% CI, 0.33–0.35). The clinicopathologic-alone model yielded the highest AUC of 0.92 (95% CI, 0.92–0.92) and sensitivity of 93.6% (95% CI, 93.4–93.8), but showed the lowest specificity (72.3%, 95% CI, 71.8–72.8) and F1-score of 0.50 (95% CI, 0.49–0.50). The combined model demonstrated the most balanced performance, achieving an AUC of 0.90 (95% CI, 0.89–0.91), sensitivity of 80.0% (95% CI, 78.7–81.3), specificity of 83.2% (95% CI: 82.7–83.6), and the highest F1-score of 0.55 (95% CI, 0.54–0.57). Conclusions: The DL-based model combining MRI and clinicopathological features showed superior performance in predicting recurrence in HER2-low breast cancer. This multimodal approach offers a framework for individualized risk assessment and may aid in refining follow-up strategies. Full article

Objective: The aim of this study was to determine whether preoperative MRI has an impact on surgical planning in breast cancer patients. Tumor extent and molecular breast cancer subtypes were evaluated. Methods: This was a single-center study including 137 female patients with a first diagnosis of invasive breast cancer. Each patient had a standard clinical preoperative workup and an additional breast MRI. The interdisciplinary tumor board made written recommendations regarding the surgical therapy of each patient with and without the knowledge of the MRI findings. Results: The addition of MRI led to changes in surgical recommendations in 32 (23%) of the 137 patients. The highest rate of change in surgical therapy recommendations was observed in patients with multifocal tumors (53%). Molecular subtype had no influence on the changes in surgical therapy recommendations (p = 0.8). Conclusions: Patients with multifocal breast tumors were more likely to have a change in surgical therapy following MRI. Full article

Background: Pathological complete response (pCR) serves as a prognostic surrogate endpoint for long-term clinical outcomes in breast cancer patients receiving neoadjuvant systemic therapy (NAST). This study aims to develop and evaluate machine learning-based biomarkers for predicting pCR and recurrence-free survival (RFS). Methods: This retrospective monocentric study included 235 women (mean age 46 ± 11 years) with non-metastatic breast cancer treated with NAST. We developed various machine learning models using clinical features (age, genetic mutations, TNM stage, hormonal receptor expression, HER2 status, and histological grade), along with morphological features (size, T2 signal, and surrounding edema) and radiomics data extracted from pre-treatment MRI. Patients were divided into training and test groups with different MRI models. A customized machine learning pipeline was implemented to handle these diverse data types, consisting of feature selection and classification components. Results: The models demonstrated superior prediction ability using radiomics features, with the best model achieving an AUC of 0.72. Subgroup analysis revealed optimal performance in triple-negative breast cancer (AUC of 0.80) and HER2-positive subgroups (AUC of 0.65). Conclusion: Machine learning models incorporating clinical, qualitative, and radiomics data from pre-treatment MRI can effectively predict pCR in breast cancer patients receiving NAST, particularly among triple-negative and HER2-positive breast cancer subgroups. Full article

Background/Objectives: This study investigates the utility of multiparametric PET/MRI in delineating changes in physiologically distinct intratumoral habitats during trastuzumab-induced alterations in a preclinical HER2+ breast cancer model. Methods: By integrating diffusion-weighted MRI, dynamic contrast-enhanced MRI, [¹⁸F]Fluorodeoxyglucose- and [¹⁸F]Fluorothymidine-PET, voxel-wise parametric maps were generated capturing cellular density, vascularity, metabolism, and proliferation. BT-474 tumor-bearing mice have high expression of HER2 and, in response to trastuzumab, an anti-HER2 antibody, effectively show changes in proliferation and tumor microenvironment alterations that result in decreases in tumor volume through time. Results: Single imaging metrics and changes in metrics were incapable of identifying treatment-induced alterations early in the course of therapy (day 4) prior to changes in tumor volume. Hierarchical clustering identified five distinct tumor habitats, which enabled longitudinal assessment of early treatment response. Tumor habitats were defined based on imaging metrics related to biology and categorized as highly vascular (HV), hypoxic responding (HRSP), transitional zone (TZ), active tumor (ATMR) and responding (RSP). The HRSP cluster volume significantly decreased in trastuzumab-treated tumors compared to controls by day 4 (p = 0.015). The volume of ATMR cluster was significantly different at baseline between cohorts (p = 0.03). The TZ cluster, indicative of regions transitioning more to necrosis, significantly decreased in treated tumors (p = 0.031), suggesting regions had already transitioned. Multiparametric image clustering showed a significant positive linear correlation with histological multiparametric mapping, with R² values of 0.56 (HRSP, p = 0.013, 0.64 (ATMR, p = 0.0055), and 0.49 (responding cluster, p = 0.024), confirming the biological relevance of imaging-derived clusters. Conclusions: These findings highlight the potential utility of multiparametric PET/MRI to capture biological alterations prior to any single imaging metric which has potential for better understanding longitudinal changes in biology, stratifying tumors based on those changes, optimizing therapeutic monitoring and advancing precision oncology. Full article

Accurate preoperative distinction between in situ and invasive Breast Cancer (BC) is critical for clinical decision-making and treatment planning. Radiomics and Machine Learning (ML) have shown promise in enhancing diagnostic performance from breast MRI, yet their application to this specific task remains underexplored. The aim of this study was to evaluate the performance of several ML classifiers, trained on radiomic features extracted from DCE–MRI and supported by basic clinical information, for the classification of in situ versus invasive BC lesions. In this study, we retrospectively analysed 71 post-contrast DCE–MRI scans (24 in situ, 47 invasive cases). Radiomic features were extracted from manually segmented tumour regions using the PyRadiomics library, and a limited set of basic clinical variables was also included. Several ML classifiers were evaluated in a Leave-One-Out Cross-Validation (LOOCV) scheme. Feature selection was performed using two different strategies: Minimum Redundancy Maximum Relevance (MRMR), mutual information. Axial 3D rotation was used for data augmentation. Support Vector Machine (SVM), K Nearest Neighbors (KNN), Random Forest (RF), and Extreme Gradient Boosting (XGBoost) were the best-performing models, with an Area Under the Curve (AUC) ranging from 0.77 to 0.81. Notably, KNN achieved the best balance between sensitivity and specificity without the need for data augmentation. Our findings confirm that radiomic features extracted from DCE–MRI, combined with well-validated ML models, can effectively support the differentiation of in situ vs. invasive breast cancer. This approach is quite robust even in small datasets and may aid in improving preoperative planning. Further validation on larger cohorts and integration with additional imaging or clinical data are recommended. Full article

Background and Objectives: The aim of our study was to assess the diagnostic accuracy of four imaging modalities—digital mammography (DM), digital breast tomosynthesis (DBT), ultrasound (US), and breast magnetic resonance imaging (MRI)—applied individually and in combination in early cancer detection in women with dense breasts. Methods: This single-center retrospective study was conducted from January 2021 to September 2024 at the Oncology Institute of Vojvodina in Serbia and included 168 asymptomatic and symptomatic women with dense breasts. Based on the exclusion criteria, the final number of women who were screened with all four imaging methods was 156. The reference standard for checking the diagnostic accuracy of these methods is the result of a histopathological examination, if a biopsy is performed, or a stable radiological finding in the next 12–24 months. Results: The findings underscore the superior diagnostic performance of breast MRI with the highest sensitivity (95.1%), specificity (78.7%), and overall accuracy (87.2%). In contrast, DM showed the lowest sensitivity (87.7%) and low specificity (49.3%). While the combination of DM + DBT + US demonstrated improved sensitivity to 96.3%, its specificity drastically decreased to 32%, illustrating as ensitivity–specificity trade-off. Notably, the integration of all four modalities increased sensitivity to 97.5% but decreased specificity to 29.3%, suggesting an overdiagnosis risk. DBT significantly improved performance over DM alone, likely due to enhanced tissue differentiation. US proved valuable in dense breast tissue but was associated with a high false-positive rate. Breast MRI, even when used alone, confirmed its status as the gold standard for dense breast imaging. However, its widespread use is constrained by economic and logistical barriers. ROC curve analysis further emphasized MRI’s diagnostic superiority (AUC = 0.958) compared with US (0.863), DBT (0.828), and DM (0.820). Conclusions: This study provides a unique, comprehensive comparison of all four imaging modalities within the same patient cohort, offering a rare model for optimizing diagnostic pathways in women with dense breasts. The findings support the strategic integration of complementary imaging approaches to improve early cancer detection while highlighting the risk of increased false-positive rates. In settings where MRI is not readily accessible, a combined DM + DBT + US protocol may serve as a pragmatic alternative, though its limitations in specificity must be carefully considered. Full article

Background/Objectives: Assessment of a novel deep-learning (DL)-based T1w volumetric interpolated breath-hold (VIBE_DL) sequence in breast MRI in comparison with standard VIBE (VIBE_Std) for image quality evaluation. Methods: Prospective study of 52 breast cancer patients examined at 1.5T breast MRI with T1w VIBE_Std and T1 VIBE_DL sequence. T1w VIBE_DL was integrated as an additional early non-contrast and a delayed post-contrast scan. Two radiologists independently scored T1w VIBE _Std/DL sequences both pre- and post-contrast and their calculated subtractions (SUBs) for image quality, sharpness, (motion)–artifacts, perceived signal-to-noise and diagnostic confidence with a Likert-scale from 1: Non-diagnostic to 5: Excellent. Lesion diameter was evaluated on the SUB for T1w VIBE_Std/_DL. All lesions were visually evaluated in T1w VIBE_Std/_DL pre- and post-contrast and their subtractions. Statistics included correlation analyses and paired t-tests. Results: Significantly higher Likert scale values were detected in the pre-contrast T1w VIBE_DL compared to the T1w VIBE_Std for image quality (each p < 0.001), image sharpness (p < 0.001), SNR (p < 0.001), and diagnostic confidence (p < 0.010). Significantly higher values for image quality (p < 0.001 in each case), image sharpness (p < 0.001), SNR (p < 0.001), and artifacts (p < 0.001) were detected in the post-contrast T1w VIBE_DL and in the SUB. SUB_DL provided superior diagnostic certainty compared to SUB_Std in one reader (p = 0.083 or p = 0.004). Conclusions: Deep learning-enhanced T1w VIBE_DL at 1.5T breast MRI offers superior image quality compared to T1w VIBE_Std. Full article

Carbon nanodots have recently attracted attention as fluorescence imaging probes and magnetic resonance imaging (MRI) contrast agents in diagnostic and therapeutic applications due to their unique optical properties. In this work we report the synthesis of biocompatible Mn (II)-doped carbon nanodots and their performance as fluorescence and MRI contrast agents in in vitro assays. The thermal decomposition of a Diphenylhydantoin–Mn(II) complex assured the incorporation of manganese (II) ions in the carbon dots. The obtained materials display a favorable spin density for MRI applications. The synthesized Mn(II)-CNDs also displayed remarkable photoluminescence, with a bright blue emission and good response in in vitro fluorescence imaging. Cytotoxicity investigations revealed good cell viability on malignant melanoma cell lines in a large concentration range. A cytotoxic effect was observed for MG-63 osteosarcoma and breast adenocarcinoma cell lines. The in vitro MRI assays demonstrated the potentialities of the Mn(II)-CNDs as T2 contrast agents at low dosages, with relaxivity values higher than those of commercial ones. Due to the simplicity of their synthetic pathway and their low cytotoxicity, the prepared Mn(II)-CNDs are potential alternatives to currently used contrast agents based on gadolinium complexes. Full article

Objectives: To evaluate the agreement between AI-CAD-guided mammographic and MRI measurements of tumor size and T stage in breast cancer patients being considered for neoadjuvant chemotherapy (NAC). Methods: This retrospective study included 144 women (mean age, 52 ± 11 years) with invasive breast cancer who subsequently received NAC and underwent both AI-CAD mammography (score ≥ 10) and pre-treatment MRI. Tumor sizes from AI-CAD contours were compared with MRI using Pearson correlation, intraclass correlation coefficients (ICCs), and Bland–Altman analysis. Concordance was defined as a ±0.5 cm difference. The contour showing the highest agreement was used to compare T stage with MRI using weighted kappa. Results: The mean AI-CAD abnormality score was 86.3 ± 22.2. Tumor sizes on mammography were 3.0 ± 1.2 cm (inner), 3.8 ± 1.5 cm (middle), and 4.8 ± 2.2 cm (outer), while the MRI-measured tumor size was 4.0 ± 1.9 cm. The middle contour showed the strongest correlation with MRI (r = 0.897; ICC = 0.866), the smallest mean difference (–0.19 cm; limits of agreement, –1.87 to 1.49), and the highest concordance (61.1%). Agreement was higher in mass-only lesions than in NME-involved lesions (ICC = 0.883 vs. 0.775; concordance, 70.9% vs. 46.6%). T stage comparison using the middle contour showed substantial agreement with MRI (κ = 0.743 [95% CI, 0.634–0.852]; agreement, 88.2%), with higher concordance in mass-only lesions (93.0%) than NME-involved lesions (81.0%) and more frequent understaging in the latter (17.2% vs. 2.3%). Conclusions: AI-CAD-guided mammographic assessment using the middle contour demonstrated good agreement with MRI for tumor size and T stage, indicating its value as a supportive tool for clinical staging in MRI-limited settings. 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

Background/Objectives: Abbreviated breast MRI protocols have been proposed as a faster and more cost-effective alternative to standard full protocols for breast cancer detection. This study aimed to compare the diagnostic accuracy of an abbreviated protocol with that of a full protocol in identifying lobular breast carcinoma using Breast Imaging Reporting and Data System (BI-RADS) classification. The diagnostic performance was evaluated against a gold standard comprising biopsy-proven lobular carcinoma or negative follow-up imaging, using Receiver Operating Characteristic (ROC) analysis and performance metrics such as sensitivity and specificity. Methods: A retrospective analysis was conducted on 35 breast MRI examinations performed between January 2019 and December 2021. Of these, 20 cases had biopsy-confirmed lobular carcinoma, and 15 were determined to be normal based on at least 12 months of negative follow-up imaging. Two radiologists independently reviewed the images using only the abbreviated protocol, blinded to the original reports. Their findings were then compared with the initial full-protocol MRI reports. BI-RADS categories 1 and 2 were considered negative for malignancy, while BI-RADS categories 3, 4, and 5 were considered positive. Results: The area under the ROC curve (AUC) was 1.0 for the full protocol and 0.920 and 0.922 for Radiologists A and B, respectively, using the abbreviated protocol. All malignant lesions were correctly identified by both radiologists across both protocols, resulting in a sensitivity of 100%. However, the abbreviated protocol demonstrated significantly lower specificity (73.3% for Radiologist A and 53.5% for Radiologist B) compared to 100% specificity with the full protocol (p < 0.05). Lymph node involvement was correctly identified in 6–7 of 7 cases, though Radiologist A reported four false positives. Lesion laterality and count matched histopathology in 75–90% of cancer cases depending on protocol. Lesion localization was accurate in 60–80% of cases using the abbreviated protocol, though size comparisons were limited due to the incomplete radiological documentation of dimensions. Conclusions: While the abbreviated MRI protocol achieved diagnostic accuracy and sensitivity comparably to the full protocol, it demonstrated reduced specificity. These findings suggest that abbreviated MRI breast protocol may be a viable screening tool, although the higher false-positive rate should be considered in clinical decision-making. Full article

Ocular flutter is an uncommon ophthalmic finding that may indicate paraneoplastic phenomena, and it is clinically characterized by intermittent bursts of conjugate, horizontal saccades without an intersaccadic interval. Ocular flutter must be differentiated from opsoclonus, which, although also characteristic of certain paraneoplastic syndromes, is instead defined by multidirectional saccades on both the horizontal and vertical planes. This report describes a very rare presentation of anti-Ri syndrome in a patient with an undiagnosed breast cancer, presenting with ocular flutter, dizziness, blurred vision, photophobia, and vomiting. Comprehensive evaluations, including contrast-enhanced brain Magnetic Resonance Imaging (MRI), brain Computed Tomography (CT) scan, ophthalmological assessment, viral serology, complete blood count and thyroid, renal coagulation, hepatic function assessments, vitamin D and B12 levels, were all normal. Upon excluding other potential etiologies for the neurological symptoms, a paraneoplastic origin was considered. Serological tests confirmed the presence of anti-Ri onconeural antibodies, and a whole-body CT scan identified nodules in the right breast. Despite surgical excision of the primary tumor and subsequent medical therapy, there was no improvement in the neurological symptoms. Follow-up evaluations at 2 months, 6 months, 1 year and 2 years revealed persistent vestibular and neurological symptoms, with serum tests remaining positive for anti-Ri antibodies and no clinical or radiological evidence of neoplastic recurrence. Full article

In this article, we present a case of a 49-year-old woman presenting with a recurrent metastatic neuroendocrine tumor. Background: Insulinomas are neuroendocrine tumors derived from beta cells of the pancreas that secrete insulin. Usually, they are benign tumors; however, metastatic insulinomas are an extremely rare malignant form of these tumors, carrying a significantly worse prognosis. Case Presentation: A 49-year-old woman, a patient in the University Hospital in Wroclaw in the Department of Endocrinology, Diabetes and Isotope Therapy, first presented with abdominal pain in 2009, when ultrasound and further examination led to the diagnosis of a tumor in the pancreas (a solid pseudopapillary tumor of the pancreas—meta NET G2), and the patient underwent distal pancreatectomy with splenectomy. For ten years, she was under observation, and her symptoms, such as abdominal pain, nausea, weight loss, and general weakness, reappeared in 2019. Then, magnetic resonance imaging (MRI) showed a lesion in the liver, and further histopathology revealed neuroendocrine tumor (NET) metastasis to the liver. In 2022, the patient presented with loss of consciousness and convulsion, loss of weight, and hypoglycemia after meals. In April 2022, the daily glycemic profile was recorded and a 72 h fasting test was performed; however, their results excluded insulinoma. Positron emission tomography–computed tomography (PET-CT) with 18F-fluorodeoxyglucose (18F-FDG) and PET with gallium-68-DOTA-(Tyr3)-octreotate (68Ga-DOTA-TATE) showed a metastatic proliferative process in the liver. Persistent hypoglycemia led to another hospitalization in May 2022, and repeated tests allowed for the diagnosis of insulinoma. Treatment with somatostatin analogs and diazoxide was started. A CT scan in November 2022 and a PET scan in January 2023 showed new metastases to the liver, bones, and cervical lymph nodes, and it was decided to intensify the treatment. In May 2023, the patient was qualified for Lutathera treatment for insulinoma at the University Clinical Hospital in Poznań. In June 2023, another disturbing symptom was reported by the patient, a painful lump in the breast. During diagnostics, metastases with high proliferation markers were found in both breasts. Two months later, in August 2023, the patient received another dose of Lutathera. In October 2023, significant progression of liver lesions, metastases to bones of the spine, ribs, and pelvis, and periaortic and pelvic lymphadenopathy were found as well as elevated values of neuron-specific enolase and calcitonin. The patient was also referred to the Palliative Medicine Home Hospice. In consultation with the Lower Silesian Cancer Center, the decision was made to forgo further treatment with PRRT and initiate systemic chemotherapy. Despite the chosen treatment, the patient died on 27/DEC/2023. Conclusions: This case report can serve clinicians, as it presents a case of an extremely rare and insidious tumor, metastatic insulinoma. Full article