Breast Cancer Imaging Research Articles

Breast cancer image classification based on H&E staining using a causal attention graph neural network model

Breast cancer image classification based on H&E staining using a causal attention graph neural network model

FAP-targeted PET/CT imaging in patients with breast cancer from a prospective bi-center study: insights into diagnosis and clinic management.

To evaluate the diagnostic accuracy and clinical impact of fibroblast activation protein (FAP)-targeted PET/CT imaging in primary and metastatic breast cancer and compare the results with those of standard-of-care imaging (SCI) and [18F]FDG PET/CT. We prospectively analyzed patients with diagnosed or suspected breast cancer who underwent concomitant FAP-targeted PET/CT (radiotracers including either [68Ga]Ga-FAPI-46 or [18F]FAPI-42) and [18F]FDG PET/CT scans from June 2020 to January 2024 at two medical centers. Breast ultrasound (US) imaging was performed in all treatment-naïve patients as SCI. The SUVmax, tumor-to-background ratio (TBR), lesion detection rate, and tumor-node-metastasis (TNM) classifications between FAP-targeted and [18F]FDG PET/CT were evaluated and compared. Sixty-one female patients (median age, 52 y; range, 28-82 y) were included. Among them, 23 patients underwent evaluation for a definitive diagnosis of suspected breast lesions, 15 underwent initial staging, and 23 were evaluated for the detection of recurrence. The sensitivities of breast US, [18F]FDG, and FAP-targeted PET/CT for detecting primary breast tumors were 82%, 79%, and 100%, respectively. Regarding the diagnosis of recurrent/metastatic lesions, the lesion-based detection rate of FAP-targeted PET/CT was significantly higher than that of [18F]FDG, which included local and regional recurrence, neck lymph node (LN), abdomen LN, bone, and liver metastases. Compared with [18F]FDG PET/CT, FAP-targeted PET/CT altered thirteen patients' TNM staging/restaging (13/59, 22%) and nine patients' clinical management (9/59, 15%). Compared to SCI, FAPI changed fourteen patients' TNM staging/re-staging (14/59, 24%) and eleven patients' therapeutic regimens(11/59, 19%). There was no significant association between FAPI-derived SUVmax and receptor status/histologic type in both primary and metastatic lesions. FAP-targeted PET/CT was superior to [18F]FDG in diagnosing primary and metastatic breast cancer, with higher radiotracer uptake and TBR, especially in the detection of primary/recurrent tumors, abdominal LN metastases, liver, and bone metastases. FAP-targeted PET/CT is superior to [18F]FDG and SCI in TNM staging and may improve tumor staging, recurrence detection, and implementation of necessary treatment modifications.

Chitosan-Functionalized Fluorescent Calcium Carbonate Nanoparticle Loaded with Methotrexate: Future Theranostics for Triple Negative Breast Cancer.

Herein, fluorescent calcium carbonate nanoclusters encapsulated with methotrexate (Mtx) and surface functionalized with chitosan (25 nm) (@Calmat) have been developed for the imaging and treatment of triple-negative breast cancer (TNBC). These biocompatible, pH-sensitive nanoparticles demonstrate significant potential for targeted therapy and diagnostic applications. The efficacy of nanoparticles (NPs) was evaluated in MDA-MB-231 TNBC cell lines. The enhanced permeability and retention effect facilitated the accumulation of NPs, in tumor-bearing rats, as confirmed by in vivo fluorescence imaging. Treatment with @Calmat resulted in a marked reduction in pro-inflammatory cytokines, with levels of IL-6 (1225 ± 67 pg/mL), IL-1β (379 ± 69 pg/mL), and TNF-α (14.1 ± 2 pg/mL), in contrast to the diseased control group (IL-6: 2223 ± 99; IL-1β: 1632 ± 90; TNF-α: 40 ± 3 pg/mL). A similar trend was observed for liver and kidney function biomarkers. Mechanistic studies revealed that @Calmat treatment activates the Bax/Bcl-2 signaling pathway, leading to cell cycle arrest in the G1 phase and subsequent late-phase apoptosis. As a result, the tumor inhibition rate reached 88%, with 80% of treated rats surviving beyond 100 days. These findings highlight the strong potential of @Calmat as a dual-function theranostic agent for the management of TNBC.

Preclinical evaluation of zirconium-89 labeled anti-Trop2 antibody-drug conjugate (Trodelvy) for imaging in gastric cancer and triple-negative breast cancer.

Trophoblast cell-surface antigen 2 (Trop2) is overexpressed in various solid tumors and contributes to tumor progression, while its expression remains low in normal tissues. Trop2-targeting antibody-drug conjugate (ADC), sacituzumab govitecan-hziy (Trodelvy), has shown efficacy in targeting this antigen. Leveraging the enhanced specificity of ADCs, we conducted the first immunoPET imaging study of Trop2 expression in gastric cancer (GC) and triple-negative breast cancer (TNBC) models using 89Zr-labeled Trodelvy ([89Zr]Zr-DFO-Trodelvy). This approach enables preclinical screening to identify patients who may benefit from targeted therapies. Trop2 expression levels in GC and TNBC cell lines (NCI-N87, HGC-27, MDST8, and MDA-MB-468) were assessed via flow cytometry and immunofluorescence staining. Labeling of DFO-Trodelvy with 89Zr was performed in Na2CO3 buffer at pH 7 (37°C, 1.5h). In vitro stability was analyzed using radio-thin layer chromatography. Biological properties were evaluated through cell uptake, saturation binding assays, and biodistribution experiments. ImmunoPET imaging with [89Zr]Zr-DFO-Trodelvy was performed at various time points to confirm its in vivo targeting. Immunohistochemical and immunofluorescence analyses were conducted on tumor tissues from tumor-bearing mice. The radiochemical yield of [89Zr]Zr-DFO-Trodelvy exceeded 90%, with a radiochemical purity (RCP) greater than 99%. Trop2 expression was high in MDA-MB-468 and NCI-N87 cells, while it was low in MDST8 and HGC-27 cells. The KD values of [89Zr]Zr-DFO-Trodelvy were 9.44nM for MDA-MB-468 and 3.51nM for NCI-N87 cells. ImmunoPET imaging with [89Zr]Zr-DFO-Trodelvy provided clear visualization of tumor morphology in MDA-MB-468 and NCI-N87 models (n = 3) as early as 6h post-injection. Tumor uptake of [89Zr]Zr-DFO-Trodelvy increased over time, peaking at 48h (MDA-MB-468: 10.03 ± 1.26%ID/g; NCI-N87: 14.30 ± 2.09%ID/g), and was significantly higher than in the MDST8 (5.27 ± 0.71%ID/g) and HGC-27 (4.37 ± 0.54%ID/g) models. Co-injection with 2mg of unlabeled Trodelvy significantly reduced uptake in NCI-N87 and MDA-MB-468 tumors (P < 0.001). A high target-to-non-target ratio was observed at 48h, showing specific tumor uptake and minimal off-target accumulation. Fluorescence imaging further confirmed higher tumor uptake in the IRDye800CW-Trodelvy group compared to the IRDye800CW-Trodelvy-blocking group (P < 0.001). [89Zr]Zr-DFO-Trodelvy for immunoPET imaging in TNBC and GC tumor models demonstrated specific, rapid, and sustained accumulation in tumors with high Trop2 expression, allowing for noninvasive monitoring of Trop2 status. The increased tumor-to-background ratio observed in immunoPET imaging suggests strong potential for clinical translation. Additionally, optical imaging, with its superior spatial resolution compared to PET, was employed to aid in precise probe localization and potentially enhancing differentiation between healthy and malignant tissues during surgical procedures.

Usefulness of four-dimensional noise reduction filtering using a similarity algorithm in low-dose dynamic computed tomography for the evaluation of breast cancer: a preliminary study.

To evaluate the effects of four-dimensional noise reduction filtering using a similarity algorithm (4D-SF) on the image quality and tumor visibility of low-dose dynamic computed tomography (CT) in evaluating breast cancer. Thirty-four patients with 38 lesions who underwent low-dose dynamic breast CT and were pathologically diagnosed with breast cancer were enrolled. Dynamic CT images were reconstructed using iterative reconstruction alone or in combination with 4D-SF. We selected the peak enhancement phase image of breast cancer for each patient for quantitative and qualitative evaluations of image quality and measurement of the maximum diameter of breast cancer. The signal-to-noise and contrast-to-noise ratios were calculated for quantitative evaluation. The maximum diameters of the breast cancer were measured from the images obtained with and without 4D-SF (4D-SF ±) (size-4D-SF + and size-4D-SF-) and for the pathological specimen (size-PS) and compared. The median and interquartile ranges of the signal-to-noise ratio [4D-SF-: 3.03 (2.54-4.17) vs 4D-SF + : 5.52 (4.75-6.66)] and contrast-to-noise ratio [4D-SF-: 2.88 (2.00-3.60) vs 4D-SF + : 7.84 (4.65-10.35)] were significantly higher for 4D-SF + than for 4D-SF- (p < 0.001). The overall image quality (Observer 1, p < 0.001; Observer 2, p < 0.001) and tumor margin sharpness scores (Observer 1, p = 0.003; Observer 2, p < 0.001) were significantly higher for 4D-SF + than for 4D-SF-. The tumor contrast scores for 4D-SF + and 4D-SF- were not significantly different (Observers 1, 2; p = 0.083). Size-4D-SF- was significantly smaller than size-PS (p < 0.001); size-4D-SF + was also smaller than size-PS, but the difference was not significant (p = 0.088). The Spearman's rank correlation coefficient was 0.65 for size-PS and size-4D-SF- and 0.77 for size-PS and size-4D-SF + . The 4D-SF can improve the image quality and tumor visibility of low-dose dynamic CT in evaluating breast cancer extent due to noise reduction.

Preoperative comprehensive risk estimation for axillary lymph node metastasis in breast cancer: development and verification of a network-based prediction model

To prevent the overaggressive treatment of axillary lymph nodes (ALNs) in breast cancer, it is necessary to develop a convenient analysis method that accurately and comprehensively reflects whether ALNs are metastatic or nonmetastatic. We retrospectively analyzed data from patients who underwent surgery for breast cancer at the Weifang Hospital of Traditional Chinese Medicine between January 2019 and June 2023. Binary logistic regression analysis was used to predict the metastasis status of ALNs. The developmental data set included 531 patients (January 2019–June 2023). The validation set included separate data points (n = 178, January 2019–June 2023). Multivariate analysis revealed that positive findings on breast physical examination, ultrasound grades of ALNs, lymphovascular invasion, and Her-2 status had significant predictive value for metastatic ALNs. Based on these findings, a 5-grade risk scoring system and 3-level management recommendations were developed. The risk of metastasis ranged from 11.25 to 93.46%, which was positively correlated with an increase in risk grade. The areas under the curve of the development and validation sets were 0.895 and 0.865, respectively. Ultimately, a convenient, accurate and comprehensive web-based predictive model was constructed using various breast cancer clinical, imaging and pathological criteria to stratify ALNs according to the metastasis probability.

Tumour volume analysis applied to imaging and histological examinations in breast cancer.

Response Evaluation Criteria in Solid Tumours (RECIST) determines partial response (PR) and progressive disease (PD) as a 30% reduction and 20% increase in the longest diameter (LD), respectively. Tumour volume analysis (TVA) utilises three diameters to calculate response parameters. We conducted a pilot investigation of patients who underwent neoadjuvant breast cancer treatment and evaluation using RECIST with LD measurements and TVA with three diametric measurements, using the parameters PR (>30% tumour regression), PD (>20% tumour growth), and intermediate stable disease (SD). According to TVA, RECIST miscategorised 7 of 28 patients (25%). We evaluated 145 patients who underwent baseline breast magnetic resonance imaging (MRI), neoadjuvant chemotherapy, presurgical MRI, and surgery and calculated LD and volume from all MRI examinations. Of the 173 patients, 157 had measurable disease at baseline and treatment completion, and 32 were miscategorised (20.4%). The number of patients with a PR increased from 123 to 150 after TVA. The sensitivity of RECIST-measured responses (95% confidence interval: 97-100%) was 100% for TVA. This altered the staging, as 32 of 157 (20.4%) patients were allocated to another response group, with fewer cases of SD: 26 patients moved from SD to PR and 6 patients from SD to PD. Measuring a solid mass using LD is fundamentally flawed, as the lesser axes considerably affect the volume, leading to inaccurate response categorisation, with implications for patient management. TVA is a novel method that increases accuracy of tumour size measurement and response to therapy.

Clinical feasibility of a deep learning approach for conventional and synthetic diffusion-weighted imaging in breast cancer: Qualitative and quantitative analyses

PurposeIn this study, we aimed to investigate the clinical feasibility of deep learning (DL)-based reconstruction applied to conventional diffusion-weighted imaging (cDWI) and synthetic diffusion-weighted imaging (sDWI) by comparing the DL reconstructions to cDWIs and sDWIs in patients with various breast malignancies. MethodsWe retrospectively analyzed 115 patients with biopsy-proven breast malignancies who underwent breast magnetic resonance imaging from July 2022 to June 2023, including conventional DWI with b-value of 800 s/mm2 (cDWI800), sDWI with b-value of 1500 s/mm2 (sDWI1500), DWI using DL-based reconstruction (DL-DWI) with b-value of 800 s/mm2, and synthetic DL-DWI with b-value of 1500 s/mm2 (DL-DWI800 and sDL-DWI1500). Two radiologists independently performed the qualitative analyses using a 5-point Likert scale for all DWI sets. The quantitative analyses were also conducted for signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and cancer-to-parenchyma contrast ratio (CPR). ResultsDL-DWI800 and sDL-DWI1500 provided better lesion conspicuity and thoracic muscle and rib delineation than cDWI800 and sDWI1500 (all P < 0.05). DL-DWI800 and sDL-DWI1500 showed comparable normal parenchymal signals to those of cDWI800 and sDWI1500 (all P > 0.05). sDL-DWI1500 and sDWI1500 showed no significant differences in SNR and CNR (P = 0.908, and P = 0.081, respectively). DL-DWI800 and cDWI800 were not significantly different between SNR, CNR, and CPR (all P > 0.05). ConclusionsDL-DWI outperformed cDWI and sDWI in both qualitative and quantitative analyses at the high b-values, while also achieving a shorter acquisition time.

Deep Learning Predicts Subtype Heterogeneity and Outcomes in Luminal A Breast Cancer Using Routinely Stained Whole-Slide Images.

A deep learning model, trained using transcriptomic data, inexpensively quantifies and fine-maps ITH due to subtype admixture in routine images of LumA breast cancer, the most favorable subtype. This new approach could facilitate exploration of the mechanisms behind such heterogeneity and its impact on selection of therapy for individual patients.

Deep Transfer Learning for Breast Cancer Classification

According to GLOBOCAN 2020, there were 10.3 million cancer-related deaths and 19.3 million new cases of the disease worldwide. Female breast cancer represented 11.7% of all cases, or 2.26 million cases, globally [10, 31]. In Africa, year 2020, 85 787 women died from breast cancer out of 186598 new cases, 12.5% of world’s death by breast cancer [31]. In the same year, we diagnosed 2262419 new cases of breast cancer in the world [31]. Through the use of computer-aided procedures, the accuracy of diagnosis may be significantly boosted. One of the most efficient methods to detect breast cancer is a pathologist’s examination of histopathological pictures under a microscope [9, 12]. Since the invention of digital image scanners, picture identification has improved [3]. The practice of digital pathology has advanced significantly, in part because whole slide image (WSI) scanners enable quick and affordable diagnosis [8]. Breast cancer of the Invasive Ductal Carcinoma (IDC) kind comes from a dysfunction of milk duct’s cells of the breast and eventually spreads to the breast tissue nearby. It is an example of cancer type where early detection and accurate classification can significantly improve patient outcomes. The results for patients can be considerably improved by IDC early identification and precise categorization. On the basis of histopathological scans, IDCs have been accurately classified using machine learning approaches [1, 26]. Diagnosing pathological imaging is difficult and necessitates manual skills and a microscope. Due to the limitations of human cognition, this time-consuming approach frequently results in mistakes. One might anticipate time savings and decreased mistake rates with an automated system [26]. One of the main challenges in analyzing cancer images using deep learning algorithms is the limited availability of data [23, 29]. Deep learning algorithms require large amounts of data to train effectively, and the lack of data can lead to overfitting or underfitting of the model [23]. Many models have been created to detect malignant cells for quite some time, and deep learning models have been used in that regard. [24]. But, one can ask that: can deep learning algorithms accurately predict the presence of cancer tumor in image data? In this paper, we are going to use Vision Transformers, VGG, and ResNets to classify images of breast cancer.

A Linear system of reflection coefficients for tomographic imaging of breast cancer

A Linear system of reflection coefficients for tomographic imaging of breast cancer

Breast Cancer Image Classification Based on Attention Mechanisms

Convolutional neural networks have been frequently utilized in computer-aided diagnosis (CAD). Breast cancer image classification is one of the vital applications of CAD. The purpose of this study was to explore the role of attention mechanism in breast cancer image classification. Specifically, this study introduces attention mechanism into the classical image classification deep learning network and constructs a new breast cancer image classification model. Test results indicate that convolutional neural networks perform better in classification when an attention mechanism is added, and they also perform better in terms of training loss and accuracy.

Novel Molecular Classification of Breast Cancer with PET Imaging.

Breast cancer is a heterogeneous disease characterized by a wide range of biomarker expressions, resulting in varied progression, behavior, and prognosis. While traditional biopsy-based molecular classification is the gold standard, it is invasive and limited in capturing tumor heterogeneity, especially in deep or metastatic lesions. Molecular imaging, particularly positron emission tomography (PET) imaging, offering a non-invasive alternative, potentially plays a crucial role in the classification and management of breast cancer by providing detailed information about tumor location, heterogeneity, and progression. This narrative review, which focuses on both clinical patients and preclinical studies, explores the latest advancements in PET imaging for breast cancer, emphasizing the development of new tracers targeting hormone receptors such as the estrogen alpha receptor, progesterone receptor, androgen receptor, estrogen beta receptor, as well as the ErbB family of receptors, VEGF/VEGFR, PARP1, PD-L1, and markers for indirectly assessing Ki-67. These innovative radiopharmaceuticals have the potential to guide personalized treatment approaches based on the unique tumor profiles of individual patients. Additionally, they may improve the assessment of treatment efficacy, ultimately leading to better outcomes for those diagnosed with breast cancer.

Evaluation of functional magnetic resonance APT and DKI imaging for breast cancer

ObjectiveThis study aimed to compare the performance of amide proton transfer–weighted imaging (APTWI) and diffusion kurtosis imaging (DKI) in differentiating benign from malignant breast lesions, evaluate molecular subtypes of breast cancer, and determine the diagnostic efficacy of the quantitative magnetic resonance imaging (qMRI) parameters in differentiating benign from malignant breast diseases.MethodsThe study included 168 women who underwent breast APTWI and DKI at Yunnan Cancer Hospital between December 2022 and July 2023. The APT signal intensity (SI), apparent kurtosis coefficient (Kapp), non-Gaussian diffusion coefficient (Dapp), and apparent diffusion coefficient (ADC) values were measured before surgery. The differences in the aforementioned qMRI parameters in molecular subtypes of breast cancer were analyzed using one-way analysis of variance. The efficacy of each quantitative parameter in differentiating benign from malignant breast diseases was evaluated using the receiver-operating characteristic curve.ResultsSignificant differences in qMRI parameters were noted between benign and malignant breast lesions. The Kapp (P < .0001) and APT (P < .05) values were higher for malignant tumors than for benign lesions. Conversely, the ADC (P < .0001) and Dapp (P < .0001) values were lower for malignant tumors than for benign lesions. The diagnostic performance was assessed using the area under the curve (AUC) for various parameter combinations. The AUC of Kapp was 0.871, Dapp was 0.872, APT SI was 0.643, DKI + APT was 0.893, DKI + ADC was 0.936, APT + ADC was 0.925, and DKI + APT + ADC was 0.933. Additionally, ADC values (P = .01) demonstrated superior diagnostic performance compared to Kapp (P = .03), Dapp (P = .03), and APT values (P = .06) in distinguishing between different molecular subtypes of breast cancer.ConclusionsAPTWI distinguished benign from malignant breast disease and enhanced the utility of diffusion-weighted MRI. However, it was not superior to DKI and DWI in identifying the molecular subtypes of breast cancer.

Breast cancer image classification by using HCNN and LeNet5

Medical data from many sectors has greatly increased during the last 10 years. One of the main causes of death and illness among women worldwide is breast cancer. Breast cancer (BC) is one of the most common cancers in women; the death rate is high and holds the second position next to lung cancer. Breast cancer develops when cells in the mammary glands and the ducts that transfer milk to the nipple grow out of control. This is the first step in the progression of the disease. However, the time complexity of the available techniques is immense due to the many processes. Additionally, the current research attempts to improve the computation time involved in the detection process. Therefore, an effective hybrid deep learning model is introduced to improve the prediction performance and reduce the time consumption compared to the machine learning model. The breast cancer dataset, obtained from Kaggle, is used as the input data. A Wiener filter preprocessing technique is applied to enhance the image quality, with an active Wiener filter employed for this purpose. The segmentation step is achieved using a Modified Watershed Algorithm, which isolates the region of interest within the images. Finally, classification is performed using a hybrid deep learning model. This model combines a Convolutional Neural Network (CNN) with an Enhanced Recurrent Neural Network (ERNN), leveraging the strengths of both architectures. According to experimental results, the proposed Hybrid Convolutional Neural Network (HCNN) model achieves an accuracy of 96.12%, a precision of 96.99%, a recall of 97.52%, and an F-measure of 97.25%, outperforming other existing models.

A Combined MobileNetV2 and CBAM Model to Improve Classifying the Breast Cancer Ultrasound Images

Breast cancer is the main cause of death in women throughout the world. Early detection using ultrasound is very necessary to reduce cases of breast cancer. However, the ultrasound analysis process requires a lot of time and medical personnel because classification is difficult due to noise, complex texture, and subjective assessment. Previous studies were successful in ultrasound classification of breast cancer but required large computations and complex models. This research aims to overcome these shortcomings by using a lighter but more accurate model. We integrated the CBAM attention module into the MobileNetV2 model to improve breast cancer detection accuracy, speed up diagnosis, and reduce computational requirements. Gradient Weighted Class Activation Mapping (Grad-CAM) is used to improve classification explanations. Ultrasound images from two databases were combined to train, validate, and test this model. The test results show that MobileNetV2-CBAM achieves a test accuracy of 93%, higher than the complex models VGG-16 (80%), VGG-19 (82%), InceptionV3 (80%), and ResNet-50 (84%). CBAM is proven to improve MobileNetV2 performance with an 11% increase in accuracy. Grad-CAM visualization shows that MobileNetV2-CBAM can better focus on localizing important regions in breast cancer images, providing clearer explanations and assisting medical personnel in diagnosis.

Preclinical Comparison of [111In]In- and [225Ac]Ac-DOTA-Trastuzumab IgG, F(ab')2 and Fab for Theranostic SPECT/CT Imaging and α-Particle Radioimmunotherapy of HER2-Positive Human Breast Cancer.

Radioimmunotherapy (RIT) with α-particle-emitting, 225Ac complexed to trastuzumab may offer an alternative treatment for patients who progress on HER2-targeted therapies. Moreover, RIT with [225Ac]Ac-DOTA-trastuzumab could be combined with SPECT/CT imaging with [111In]In-DOTA-trastuzumab in a theranostic approach. In this study, we compared DOTA-conjugated trastuzumab IgG, F(ab')2 or Fab complexed to 111In or 225Ac for SPECT/CT imaging and α-particle RIT of subcutaneous (s.c.) HER2-positive 164/8-1B/H2N.luc+ human BC tumors in NRG mice. SPECT/CT imaging and tumor and normal tissue uptake were compared in NRG or NOD-SCID mice coinjected i.v. with [111In]In-DOTA-trastuzumab IgG, F(ab')2 or Fab and [225Ac]Ac-DOTA-trastuzumab IgG, F(ab')2 or Fab. Radiation absorbed doses in the tumor and normal organs for [225Ac]Ac-DOTA-trastuzumab IgG, F(ab')2 or Fab were estimated based on the biodistribution of the [111In]In-DOTA-trastuzumab IgG, F(ab')2 or Fab. Normal tissue toxicity was assessed by hematology and blood biochemistry analyses and monitoring body weight in NRG mice injected i.v. with 2 and 4 kBq of [225Ac]Ac-DOTA-trastuzumab IgG, F(ab')2 or Fab separated by 8 d. RIT studies were performed in NRG mice with s.c. 164/8-1B/H2N.luc+ tumors injected i.v. with 2 kBq and 4 kBq of [225Ac]Ac-DOTA-trastuzumab IgG, F(ab')2 or Fab separated by 8 d or irrelevant [225Ac]Ac-DOTA-IgG1, two doses of unlabeled trastuzumab IgG or 0.9% NaCl. A tumor growth index (TGI) was plotted vs time (d) and Kaplan-Meier median survival estimated. [111In]In-DOTA-trastuzumab IgG or F(ab')2 exhibited 4.1-fold and 3.3-fold significantly greater tumor uptake at 2 d postinjection (p.i.) than Fab at 24 h p.i. However, spleen uptake at 2 d p.i. for [111In]In-DOTA-trastuzumab IgG was 3.3-fold significantly higher than F(ab')2 and 13.2-fold higher than Fab at 24 h p.i. [111In]In-DOTA-trastuzumab F(ab')2 and Fab exhibited higher kidney uptake than IgG. Tumors were imaged by SPECT/CT with [111In]In-DOTA-trastuzumab IgG and F(ab')2 but were not well-visualized with [111In]In-DOTA-trastuzumab Fab. The absorbed dose in the tumor was 2.2-fold greater for [225Ac]Ac-DOTA-trastuzumab F(ab')2 than IgG and 3.4-fold greater than Fab. Hematological toxicity was observed for [225Ac]Ac-DOTA-trastuzumab IgG but not for [225Ac]Ac-DOTA-trastuzumab F(ab')2 or Fab. No kidney or liver toxicity or decreased body weight was observed for any RIT agent. Tumor growth was significantly inhibited by [225Ac]Ac-DOTA-trastuzumab IgG, F(ab')2 or Fab but [225Ac]Ac-DOTA-trastuzumab F(ab')2 was most effective for increasing median survival (46 d vs 22 d for IgG and 29 d for Fab). We conclude that [111In]In- and [225Ac]Ac-DOTA-trastuzumab F(ab')2 exhibited superior properties for theranostic imaging and α-particle RIT of HER2-positive human BC xenografts in NRG mice.

Importance of parametric and molecular imaging for therapeutic management of breast cancer

In recent years, various magnetic resonance (MRI) and positron emission tomography (PET) parameters have been investigated in breast cancer. Parametric imaging focuses on the visualization and quantification of biological, physiological, and pathological processes at the cellular and molecular level. It therefore provides important insights into the key processes in carcinogenesis and tumor progression. This article aims to illustrate the importance for the management of breast cancer. Based on the current literature, an overview of the current state of parametric breast imaging and its importance in therapy management is given. Moreover, future opportunities and challenges are highlighted. Parametric imaging in breast cancer includes MRI, nuclear medicine procedures such as PET, the combination of different techniques (PET-CT, PET-MRI) and the use of specific radiotracers. Parametric MRI of the breast mainly uses T2 and diffusion-weighted imaging (DWI) as well as dynamic contrast-enhanced MRI (CM-MRI). Quantitative and qualitative imaging biomarkers provide insights into tumor biology and allow conclusions to be drawn about the molecular subtype or prognosis. Recently, parametric imaging has become established in breast diagnostics. It is constantly evolving and will continue to gain in importance in the forthcoming years. It offers the opportunity to improve the diagnosis and treatment management of breast cancer.

Diagnostic value of mammography combined with ultrasound shear wave elastography and magnetic resonance imaging in breast cancer.

Breast cancer is one of the most common malignancies affecting women worldwide, and an early diagnosis is critical for improving prognosis. The present study aimed to investigate the diagnostic value of mammography (MG) combined with ultrasound shear wave elastography (SWE) and magnetic resonance imaging (MRI) for the early screening of breast cancer. Patients with breast tumors who underwent lumpectomy at a single hospital between December 2021 and January 2023 were selected and categorized into a benign or malignant group based on pathological findings. All patients had undergone examinations with MG, SWE and MRI. Imaging parameters were subsequently compared between the two groups. A total of 93 patients with breast tumors were included in the study, comprising 37 individuals in the benign group and 56 in the malignant group. MG findings revealed that patients in the malignant group exhibited significantly higher incidences of high breast glandular density, irregular mass margins, unclear mass borders and axillary lymph node involvement compared with those in the benign group. SWE results indicated that the elasticity ratio of the lesion to fat, and the mean and maximum values of the elastic modulus were significantly lower in the benign group than in the malignant group. Additionally, MRI findings demonstrated that the MRI-measured maximum diameter was larger, and the prevalence of irregular lesion morphology, irregular mass margins, signal enhancement and type III time-signal intensity curves was greater in the malignant group compared with the benign group. The diagnostic sensitivity, specificity, positive predictive value and negative predictive value of MG + SWE + MRI were 94.6, 86.5, 91.4 and 91.4%, respectively. Furthermore, the diagnostic efficacy of this combination surpassed that of MG + SWE, MG + MRI and SWE + MRI (area under the curve, 0.906 vs. 0.767, 0.758 and 0.763, respectively). In conclusion, the combination of MG with SWE and MRI exhibits a superior performance in the early diagnosis of breast cancer, exhibiting higher diagnostic accuracy and reliability compared with pairwise combinations.

Graph regularized least squares regression for automated breast ultrasound imaging

Breast cancer, recognized as one of the most pervasive malignancies affecting females, manifests a perpetual escalation in its worldwide morbidity. Timely screening offers patients interventions that are not only more expeditious but also more efficacious. ABUS, as an automated breast ultrasound imaging technology, has the advantages of high resolution, reproducible results, and strong objectivity. However, manual interpretation of ABUS images can be highly tedious and laborious when handling huge amounts of data from images, may leading to the risk of missed diagnosis. In practical clinical diagnosis, the incorporation of Computer-Aided Diagnosis (CAD) systems emerges as a more favorable option. In consideration of the holistic factors encompassing model construction efficiency, training cost and overall performance, we provide a unique subspace clustering approach known as Graph regularized Least Squares Regression (GLSR) in this paper. Firstly, we encode the data's intrinsic geometry into the framework of least squares regression to respect local and global correlation structures at the same time. Secondly, we construct a nearest neighbor graph to model the smooth structure of the representation, which may lead to a more accurate representation. Finally, a nonnegative constraint is introduced into our framework so that the representation obtained by GLSR has clearer physical meaning. We provide an optimization approach for solving such a framework based on iterative updates of four blocks. Extensive experimental results indicate the effectiveness of the proposed method compared with state-of-the-art approaches on real-world breast cancer images and other biological data.