Search Results (100)

Oral squamous cell carcinoma (OSCC) is a malignancy that affects the oral mucosa and is characterized by indurated oral lesions. The RNAseq of formalin-fixed, paraffin-embedded (FFPE) samples is readily available in clinical settings. Such samples have long-term preservation and can provide highly accurate transcriptomic information regarding gene fusions, isoforms, and allele-specific expression. We determined differentially expressed genes using the transcriptomic profiles of oral potentially malignant disorder (OPMD) FFPE oral lesion samples of patients who developed OSCC over years. A technical comparison was completed comparing breast cancer (BC) FFPE publicly available data in this proof-of-concept pilot study. OSCC FFPE samples were collected from patients (N = 3) who developed OSCC 3 to 5 years following OPMD diagnosis (n = 3) and were analyzed using RNAseq. RNAseq sequences from the FFPE OSCC samples and publicly available FFPE samples of BC patients (n = 6) (Gene Expression Omnibus Database, GSE58135) aligned to human reference (GRCh38.p13). Genes were counted using the Spliced Transcripts Alignment to a Reference (STARv2.7.9a) software. Differential expression was determined in R using DESeq2v1.40.2 comparing OSCC to BC samples. Principal component analysis (PCA) plots were completed. Differential Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were determined via the Pathviewv.1.40.0 program. STRING v12.0 was used to determine protein–protein interactions between genes represented in more than one KEGG pathway. STARv2.7.9a identified 27,237 and 30,343 genes among the OSCC and BC groups, respectively. DESeq2v1.40.2 determined 9194 differentially expressed genes (DEPs), 4466 being upregulated (OSCC > BC) and 4728 being downregulated (BC > OSCC) (padj < 0.05). Most significant genes included KRT6B, SERPINB5, and DSC3 (5- to 10-fold change range; padj < 10 × 10−100). PCA showed that BC and OSCC samples clustered as separate groups. Pathviewv.1.40.0 identified 17 downregulated KEGG pathways in OSCC compared to the BC group. No upregulated KEGG pathways were identified. STRINGv12.0 determined Gene Ontology Biological Process enrichments for leukocytes and apoptosis in upregulated KEGG genes including multiple PIK3 genes and NIK/NF-kappaB signaling and metabolic responses from lipopolysaccharides in downregulated KEGG genes including CHUK and NFKB1. Using FFPE samples, we determined DEPs characteristic of OSCC and distinct from BC. KRT-family genes and lipopolysaccharide producing periodontal pathogens may be further investigated for their involvement in the OPMD to OSCC transition. 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

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: Artificial intelligence (AI)-based systems are increasingly being used to assist radiologists in detecting breast cancer on mammograms. However, applying fixed AI score thresholds across diverse lesion types may compromise diagnostic performance, especially in women with dense breasts. This study aimed to determine optimal category-specific AI thresholds and to analyze discrepancies between AI predictions and radiologist assessments, particularly for BI-RADS 4A versus 4B/4C lesions. Methods: We retrospectively analyzed 194 mammograms (76 BI-RADS 4A and 118 BI-RADS 4B/4C) using FDA-approved AI software. Lesion characteristics, breast density, AI scores, and pathology results were collected. A receiver operating characteristic (ROC) analysis was conducted to determine the optimal thresholds via Youden’s index. Discrepancy analysis focused on BI-RADS 4A lesions with AI scores of ≥35 and BI-RADS 4B/4C lesions with AI scores of <35. Results: AI scores were significantly higher in malignant versus benign cases (72.1 vs. 20.9; p < 0.001). The optimal AI threshold was 19 for BI-RADS 4A (AUC = 0.685) and 63 for BI-RADS 4B/4C (AUC = 0.908). In discordant cases, BI-RADS 4A lesions with scores of ≥35 had a malignancy rate of 43.8%, while BI-RADS 4B/4C lesions with scores of <35 had a malignancy rate of 19.5%. Conclusions: Using category-specific AI thresholds improves diagnostic accuracy and supports radiologist decision-making. However, limitations persist in BI-RADS 4A cases with overlapping scores, reinforcing the need for radiologist oversight and tailored AI integration strategies in clinical practice. Full article

Bone metastases represent a critical complication in oncology, frequently indicating advanced malignancy and substantially reducing patient quality of life. This review provides a comprehensive analysis of the complex interactions between tumor cells and the bone microenvironment, emphasizing the relevance of the “seed and soil” hypothesis, the RANK/RANKL/OPG signaling axis, and Wnt signaling pathways that collectively drive metastatic progression. The molecular and cellular mechanisms underlying the formation of osteolytic and osteoblastic lesions are examined in detail, with a particular focus on their implications for bone metastases associated with breast, prostate, lung, and other cancers. A central component of this review is the categorization of pathological biomarkers into four types: diagnostic, prognostic, predictive, and monitoring. We provide a comprehensive evaluation of circulating tumor cells (CTCs), bone turnover markers (such as TRACP-5b and CTX), advanced imaging biomarkers (including PET/CT and MRI), and novel genomic signatures. These biomarkers offer valuable insights for early detection, enhanced risk stratification, and optimized therapeutic decision-making. Furthermore, emerging strategies in immunotherapy and bone-targeted treatments are discussed, highlighting the potential of biomarker-guided precision medicine to enhance personalized patient care. The distinctiveness of this review lies in its integrative approach, combining fundamental pathophysiological insights with the latest developments in biomarker discovery and therapeutic innovation. By synthesizing evidence across various cancer types and biomarker categories, we provide a cohesive framework aimed at advancing both the scientific understanding and clinical management of bone metastases. Full article

This paper investigates the categorization of mammogram images into benign, malignant and normal categories, providing novel approaches based on Deep Convolutional Neural Networks to the early identification and classification of breast lesions. Multiple DCNN models were tested to see how well deep learning worked for difficult, multi-class categorization problems. These models were trained on pre-processed datasets with optimized hyperparameters (e.g., the batch size, learning rate, and dropout) which increased the precision of classification. Evaluation measures like confusion matrices, accuracy, and loss demonstrated their great classification efficiency with low overfitting and the validation results well aligned with the training. DenseNet-201 and MobileNet-V3 Large displayed significant generalization skills, whilst EfficientNetV2-B3 and NASNet Mobile struck the optimum mix of accuracy and efficiency, making them suitable for practical applications. The use of data augmentation also improved the management of data imbalances, resulting in more accurate large-scale detection. Unlike prior approaches, the combination of the architectures, pre-processing approaches, and data augmentation improved the system’s accuracy, indicating that these models are suitable for medical imaging tasks that require transfer learning. The results have shown precise and accurate classifications in terms of dealing with class imbalances and dataset poor quality. In particular, we have not defined a new framework for computer-aided diagnosis here, but we have reviewed a variety of promising solutions for future developments in this field. Full article

Background: Breast cancer is a heterogeneous disease with distinct molecular subtypes, each requiring tailored therapeutic strategies. Accurate classification of these subtypes is crucial for optimizing treatment and improving patient outcomes. While immunohistochemistry remains the gold standard for subtyping, it is invasive and may not fully capture tumor heterogeneity. Artificial Intelligence (AI), particularly Deep Learning (DL), offers a promising non-invasive alternative by analyzing medical imaging data. Methods: In this study, we propose a multimodal DL model that integrates mammography images with clinical metadata to classify breast lesions into five categories: benign, luminal A, luminal B, HER2-enriched, and triple-negative. Using the publicly available Chinese Mammography Database (CMMD), our model was trained and evaluated on a dataset of 4056 images from 1775 patients. Results: The proposed multimodal approach significantly outperformed a unimodal model based solely on mammography images, achieving an AUC of 88.87% for multiclass classification of these five categories, compared to 61.3% AUC for the unimodal model. Conclusions: These findings highlight the potential of multimodal AI-driven approaches for non-invasive breast cancer subtype classification, paving the way for improved diagnostic precision and personalized treatment strategies. Full article

The association between breast cancer and non-Hodgkin lymphoma of the spleen is extremely rare, with very few cases documented in the medical literature. We present the case of a 39-year-old woman in good health but with a family history of breast cancer, who, in 2017, developed invasive lobular carcinoma in her right breast, which was treated with mastectomy followed by hormonal therapy. In 2024, she presented with a suspicious right axillary mass, suspected of recurrence, which was confirmed by fine-needle aspiration biopsy. The patient received neoadjuvant chemotherapy, followed by axillary lymph node dissection and bilateral adnexectomy. CT and PET scans showed suspicious splenic lesions suggestive of metastases. Infectious and hematological tests were negative, leading to the decision to perform laparoscopic splenectomy. Histological examination revealed follicular B-cell non-Hodgkin lymphoma. The patient is now in good general condition and is on a biannual follow-up. The case highlights the diagnostic complexity of tumor recurrences and the need to consider alternative diagnoses other than metastasis in oncological patients. Full article

Background: Breast cancer is the second leading cause of cancer-related mortality among women, accounting for 12% of cases. Early diagnosis, based on the identification of radiological features, such as masses and microcalcifications in mammograms, is crucial for reducing mortality rates. However, manual interpretation by radiologists is complex and subject to variability, emphasizing the need for automated diagnostic tools to enhance accuracy and efficiency. This study compares a radiomics workflow based on machine learning (ML) with a deep learning (DL) approach for classifying breast lesions as benign or malignant. Methods: matRadiomics was used to extract radiomics features from mammographic images of 1219 patients from the CBIS-DDSM public database, including 581 cases of microcalcifications and 638 of masses. Among the ML models, a linear discriminant analysis (LDA) demonstrated the best performance for both lesion types. External validation was conducted on a private dataset of 222 images to evaluate generalizability to an independent cohort. Additionally, a deep learning approach based on the EfficientNetB6 model was employed for comparison. Results: The LDA model achieved a mean validation AUC of 68.28% for microcalcifications and 61.53% for masses. In the external validation, AUC values of 66.9% and 61.5% were obtained, respectively. In contrast, the EfficientNetB6 model demonstrated superior performance, achieving an AUC of 81.52% for microcalcifications and 76.24% for masses, highlighting the potential of DL for improved diagnostic accuracy. Conclusions: This study underscores the limitations of ML-based radiomics in breast cancer diagnosis. Deep learning proves to be a more effective approach, offering enhanced accuracy and supporting clinicians in improving patient management. Full article

Metastases to the breast from non-mammary malignancies are rare, accounting for 0.1–5% of all breast malignancies. Neuroendocrine tumors (NETs) rarely metastasize to the breast. PET-CT somatostatin receptor imaging plays a pivotal role in the staging and follow-up of NETs, leveraging tracers like 68Ga-DOTATOC that bind to somatostatin receptors (SSTRs) expressed on tumor cells. While both primary and metastatic NETs express SSTRs, primary breast tumors may also exhibit an uptake of 68Ga-somatostatin analogs, making the differential diagnosis between primary breast tumors and neuroendocrine metastases challenging. Additionally, imaging characteristics of breast metastases from NETs are poorly documented in the literature, posing a diagnostic challenge that extends to pathology, particularly when in the absence of clinical suspicion. Misdiagnosis in such cases can lead to inappropriate therapeutic interventions. We report the case of a 75-year-old female patient with a history of pancreatic NET who presented to our breast clinic for further evaluation of a breast mass after a PET-CT scan revealed moderate 68Ga-DOTATOC uptake. Multimodality breast examination, including mammography and multiparametric US with B-mode, Color Doppler, Strain Elastography (SE), Shear Wave Elastography (SWE), and contrast-enhanced US (CEUS), was performed. Following a core biopsy, the lesion underwent surgical excision, revealing the diagnosis of NET metastasis. This case highlights a rare instance of neuroendocrine tumor metastasis to the breast, assessed using various ultrasound techniques, with detailed imaging and quantitative analysis. The comprehensive multimodal assessment contributes to the limited body of literature and provides elements for the differential diagnosis of a rare breast lesion that should always be considered in the presence of a known primary NET. Full article

Contrast-enhanced mammography (CEM) combines morphological and functional imaging, enhancing breast cancer (BC) diagnosis. This study investigates the relationship between CEM morphodynamic features and histopathological characteristics of BC. In this prospective study, 50 female patients (mean age: 57.2 ± 13.7 years) with BI-RADS 4–5 lesions underwent CEM followed by surgical excision between December 2022 and May 2024. Low-energy and recombined CEM images were analyzed for breast composition, lesion characteristics, and enhancement patterns, while histopathological evaluation included tumor size, histotype, grade, lymphovascular invasion, and immunophenotype. Spearman rank correlation and multivariable regression analysis were used to evaluate the relationship between CEM findings and histopathological characteristics. Tumor size on CEM strongly correlated with histopathological tumor size (ρ = 0.788, p < 0.001) and was associated with high-grade lesions (p = 0.017). Non-circumscribed margins were linked to a Luminal-B subtype (p = 0.001), while high lesion conspicuity was associated with Luminal-B and triple-negative BC (p = 0.001) and correlated with larger tumors (ρ = 0.517, p < 0.001). Background parenchymal enhancement was negatively correlated with age (ρ = −0.286, p = 0.049). CEM provides critical insights into BC, demonstrating significant relationship between imaging features and histopathological characteristics. These findings highlight CEM’s potential as a reliable tool for tumor size estimation, subtype characterization, and prognostic assessment, suggesting its role as an alternative to MRI, particularly for patients with contraindications. Full article

Background: The discovery of novel antibody–drug conjugates for low-expression human epidermal growth factor receptor 2 (HER2-low) breast cancer highlights the inadequacy of the conventional binary classification of HER2 status as either negative or positive. Identification of HER2-low breast cancer is crucial for selecting patients who may benefit from targeted therapies. This study aims to determine whether qualitative and quantitative magnetic resonance imaging (MRI) features can effectively reflect low-HER2-expression breast cancer. Methods: Pre-treatment breast MRI images from 232 patients with pathologically confirmed breast cancer were retrospectively analyzed. Both clinicopathologic and MRI features were recorded. Qualitative MRI features included Breast Imaging Reporting and Data System (BI-RADS) descriptors from dynamic contrast-enhanced MRI (DCE-MRI), as well as intratumoral T2 hyperintensity and peritumoral edema observed in T2-weighted imaging (T2WI). Quantitative features were derived from diffusion kurtosis imaging (DKI) using multiple b-values and included statistics such as mean, median, 5th and 95th percentiles, skewness, kurtosis, and entropy from apparent diffusion coefficient (ADC), D_app, and K_app histograms. Differences in clinicopathologic, qualitative, and quantitative MRI features were compared across groups, with multivariable logistic regression used to identify significant independent predictors of HER2-low breast cancer. The discriminative power of MRI features was assessed using receiver operating characteristic (ROC) curves. Results: HER2 status was categorized as HER2-zero (n = 60), HER2-low (n = 91), and HER2-overexpressed (n = 81). Clinically, estrogen receptor (ER), progesterone receptor (PR), hormone receptor (HR), and Ki-67 levels significantly differed between the HER2-low group and others (all p < 0.001). In MRI analyses, intratumoral T2 hyperintensity was more prevalent in HER2-low cases (p = 0.009, p = 0.008). Mass lesions were more common in the HER2-zero group than in the HER2-low group (p = 0.038), and mass shape (p < 0.001) and margin (p < 0.001) significantly varied between the HER2 groups, with mass shape emerging as an independent predictive factor (HER2-low vs. HER2-zero: p = 0.010, HER2-low vs. HER2-over: p = 0.012). Qualitative MRI features demonstrated an area under the curve (AUC) of 0.763 (95% confidence interval [CI]: 0.667–0.859) for distinguishing HER2-low from HER2-zero status. Quantitative features showed distinct differences between HER2-low and HER2-overexpression groups, particularly in non-mass enhancement (NME) lesions. Combined variables achieved the highest predictive accuracy for HER2-low status, with an AUC of 0.802 (95% CI: 0.701–0.903). Conclusions: Qualitative and quantitative MRI features offer valuable insights into low-HER2-expression breast cancer. While qualitative features are more effective for mass lesions, quantitative features are more suitable for NME lesions. These findings provide a more accessible and cost-effective approach to noninvasively identifying patients who may benefit from targeted therapy. Full article

Background: B3 lesions of the breast, for which vacuum-assisted biopsy (VABB) represents the standard tissue sampling approach, have different risks of upgrade to malignancy at surgery and/or follow-up. This study aimed to investigate if complete or partial lesion removal during VABB of B3 lesions presenting as microcalcifications influences their subsequent upgrade rate. Methods: For this retrospective single-center study, we retrieved 165 lesions diagnosed as B3 at VABB that presented solely as microcalcifications categorized as Breast Imaging Reporting & Data System (BI-RADS) 4 or 5 at mammography between January 2016 and December 2020. Surgical pathology or at least 3-year follow-up were obtained to determine potential lesion upgrade to malignancy. χ², Fisher’s, and Mantel–Haenszel tests were performed to assess if complete lesion removal influenced upgrade rates overall and among different B3 subtypes. Results: Complete lesion removal was achieved in 99/165 cases (60.0%) and did not differ among B3 subtypes (p = 0.092). The overall upgrade rate was 8.5% (95% confidence interval [CI] 5.1–13.7%, 14/165), without statistically significant differences among B3 subtypes (p = 0.562). Conversely, completely removed lesions (4.0%, 95% CI 1.6–9.9%) had a statistically significant lower upgrade rate compared to partially removed lesions (15.2%, 95% CI 8.4–25.7%, p = 0.019). According to stratified analysis according to B3 subtypes, the odds ratio of upgrade among completely and partially removed flat epithelial atypia (0.13, 95% CI 0.00–1.45) was lower (Mantel-Haenszel test p = 0.016) than those of atypical ductal hyperplasia (0.31, 95% CI 0.02–3.17) and of lobular neoplasia (0.73, 95% CI 0.01–60.62). Conclusions: The upgrade rate of B3 lesions is significantly influenced by complete lesion removal, both overall and among different B3 subtypes. Full article

Background. CXCR4 is a chemokine receptor that is frequently overexpressed in invasive breast cancer and plays a major role in tumor proliferation, aggressiveness and metastasis. The aim of this prospective study was to establish the value of CXCR4-directed PET imaging in patients with breast cancer using the novel CXCR4-targeted PET probe ⁶⁸Ga-Pentixafor by comparing it with ¹⁸F-FDG PET/CT (n = 40). Materials and methods. In this prospective cross-sectional study, fifty-one patients with breast cancer aged 36–81 (median (Q1-Q3) 51 (42.5–63)), n = 47 (92%) with initially diagnosed and n = 4 (8%) patients with recurrent breast cancer, underwent CXCR4-targeted PET imaging using ⁶⁸Ga-Pentixafor. Maximum standardized uptake values (SUVmax), total lesion glycolysis (TLG) or total lesion uptake (TLU), metabolic tumor volume (MTV) and tumor-to-background ratios (TBR) of tumor lesions were measured and correlated with pathological prognostic factors, molecular subtypes and CXCR4 immunohistochemistry (IHC) staining. ¹⁸F-FDG PET/CT images were available in 40 of 51 cases (82%) and were compared semi-quantitatively. The patients were followed up for a median of 11 months (range 4–80 months) to determine whether CXCR4 expression correlated with survival. Results. ⁶⁸Ga-Pentixafor-PET/CT was visually positive in 49/51 (96%) of the cases; in addition, [¹⁸F]FDG demonstrated a higher SUVmax compared to ⁶⁸Ga-Pentixafor. The mean SUVmax was 7.26 ± 2.84 and 18.8 ± 9.1 for ⁶⁸Ga-Pentixafor and [¹⁸F]FDG, respectively. Thirty-seven percent (18/51) of patients had triple-negative breast cancer and 25/51 (49%) had estrogen receptor (ER+) disease. There was a statistically significant correlation between tumor grade, proliferative index (Ki-67) and SUVmax obtained from ⁶⁸Ga-Pentixafor PET p = 0.002. There was no correlation between the SUVmax obtained from ⁶⁸Ga-Pentixafor and PET molecular subtypes, estrogen receptor (ER), progesterone receptor (PR) or human epidermal growth factor receptor 2 (HER2) status; however, triple-negative breast cancers had more avid ⁶⁸Ga-Pentixafor accumulation compared to luminals A and B. The median (Q1–Q3) ⁶⁸Ga-Pentixafor TLU was significantly higher in HIV-positive (376 (219–881)) compared to HIV-negative (174 (105–557)) breast cancer patients. Conclusions. In conclusion, ⁶⁸Ga-Pentixafor had a sensitivity of 96% and a specificity of 100% for detecting primary breast cancer; in addition, ⁶⁸Ga-Pentixafor exhibited significantly higher uptake in patients with higher tumor grade, high proliferative index and triple-negative breast cancer (TNBC), as well as HIV-infected breast cancer patients, highlighting the potential clinical utility and prognostic role of CXCR4-targeted PET imaging in aggressive breast cancer. Notably, ⁶⁸Ga-Pentixafor complements ¹⁸F-FDG by detecting more metastasis in the brain and the skull where FDG has limitations, while ¹⁸F-FDG remains superior for detecting skeletal metastasis. Future research should further explore the potential of CXCR4-targeted PET imaging in selecting patients with triple-negative breast cancer and high-grade breast cancer who may benefit from CXCR4-targeted therapies, particularly in the context of HIV co-infection. Full article

Background/Objectives: Vacuum-assisted excision (VAE) is a minimally invasive technique for breast tumor treatment, offering precision, comfort, and quick recovery. It is widely used for benign breast lesions and is playing an increasingly important role in the therapeutic management of non-surgical patients or patients who refuse surgery. Optimal outcomes require an understanding of device features to tailor treatment to each lesion. The Mammotome^® Elite 10G operates in a fixed mode, while the Mammotome^® Revolve EX 8G offers multiple aspiration levels and aperture windows for greater versatility. This study analyzed the specimen features (weight and length), comparing the weight obtained from two different VAE systems to aid the appropriate selection of a device based on the clinical setting. It also determined the number of specimens needed to achieve the 4 g diagnostic threshold. Methods: The Mammotome^® Elite 10G and the Mammotome^® Revolve EX were evaluated under controlled conditions. For Mammotome^® Revolve EX, combinations of five aspiration levels and three aperture lengths (12 mm, 18 mm, and 25 mm) were tested. Twelve samples were collected from a chicken breast phantom for each setting. Specimen weights and the minimum excisions required to reach the 4 g threshold were analyzed. Results: The mean weight per sample for the Mammotome^® Elite 10G was 0.16 ± 0.04 g. For the Mammotome^® Revolve EX, the weights increased with aperture size and aspiration level, ranging from a minimum of 0.132 ± 0.028 g (a window length of 12 mm and aspiration level 1) to a maximum of 0.407 ± 0.055 g (a window length of 25 mm and aspiration level 5). The 25 mm window at aspiration level 5 achieved the 4 g threshold in as few as 10 samples. By comparison, the Mammotome^® Elite required up to 26 samples. Conclusions: Compared to the Mammotome Elite, Mammotome^® Revolve EX offers superior versatility and efficiency, reducing patient discomfort by minimizing the required samples. Its technical advantages make it a valuable tool for both diagnostic and therapeutic applications. Full article