Overview of Breast Cancer Screening and Diagnosis

Proteomic and lipidomic analysis of low-density lipoprotein identifies potential biomarkers of early estrogen receptor-positive breast cancer

Estrogen receptor (ER)-positive breast cancer (BC) is a prevalent and fatal cancer among women, and there is a need to identify molecules involved in the disease pathophysiology which could also serve as biomarkers for early detection. Detection of cancer markers in whole plasma produces excessive information, and identifying important markers involved in cancer progression is challenging. We identified a BC-specific low-density lipoprotein (LDL) particle isolated by ultracentrifugation from the plasma of ER-positive BC patients. This LDL has an aberrant proteome and lipidome, significantly different from that of LDL from healthy women, including a high association with the pro-tumor chemokines CXCL4 and CXCL7, and an enrichment with the lipid subclasses phosphatidylethanolamine, ceramide, triglycerides, lysophosphatidylcholine, phosphatidylserine, phosphatidic acid, and sphingomyelin. In contrast, phosphatidylinositol species were significantly less abundant in LDL from tumor patients than in control. Moreover, BC-associated LDL has a distinct effect on macrophage phenotype, inducing an increased gene expression of IL1β, IL8 and CD206 and decreased gene expression of TNFα, a gene signature characteristic of tumor-associated macrophages (TAMs). This suggests that this formerly unrecognized form of LDL may represent LDL particles that are recruited by the tumor microenvironment to support tumor progression by inducing discrete subsets of TAMs. In conclusion, these data offer BC-associated LDL as an early biomarker detection platform for ER-positive BC. Furthermore, LDL-associated proteins and lipids that promote BC progression may also serve in the future as novel targets for BC therapies.

PET Imaging of Breast Cancer: Current Applications and Future Directions

As molecular imaging use expands for patients with breast cancer, it is important for breast radiologists to have a basic understanding of molecular imaging, including PET. Although breast radiologists may not directly interpret such studies, basic knowledge of molecular imaging will enable the radiologist to better direct diagnostic workup of patients as well as discuss diagnostic imaging with the patient and other treating physicians. Several new tracers are now available to complement imaging glucose metabolism with FDG. Because it provides a noninvasive assessment of disease status across the whole body, PET offers specific advantages over tissue-based assays. Paired with targeted therapy, molecular imaging has the potential to guide personalized treatment of breast cancer, including guiding dosing during drug trials as well as predicting and assessing clinical response. This review discusses the current established applications of FDG, which remains the most widely used PET radiotracer for malignancy, including breast cancer, and highlights potential areas for expanded use based on recent research. It also summarizes research to date on the U.S. Food and Drug Administration (FDA)-approved PET tracer 16α-18F-fluoro-17β-estradiol (FES), which targets ER, including the current guidelines from the Society of Nuclear Medicine and Molecular Imaging on the appropriate use of FES-PET/CT for breast cancer as well as areas of active investigation for other potential applications. Finally, the review highlights several of the most promising novel PET tracers that are poised for clinical translation in the near future.

Current status and prospects of breast cancer imaging-based diagnosis using artificial intelligence

Breast imaging has several modalities, each unique in terms of its imaging position, evaluation index, and imaging method. Breast diagnosis is made by combining a large number of past imaging features with the clinical course and histological findings. Artificial intelligence (AI), which extracts the features from image data and evaluates them based on comprehensive analysis, has been making rapid progress in this regard. Many previous studies have demonstrated the usefulness and development potential of AI, such as machine learning and deep learning, in breast imaging. However, despite studies showing the good performance of AI models, their overall utilization remains low, since a large amount of diverse imaging data is required, and prospective verification is necessary to prove its high reproducibility and robustness. Sharing information and collaborating with multiple institutions to collect and verify images of different conditions and backgrounds are vital. If image diagnosis using AI can indeed ensure a more detailed diagnosis, such as breast cancer subtypes or prognosis, it can help develop personalized medicine, which is urgently required. The positive results of AI research, using such image information, can make each modality more valuable than ever. The current review summarized the results of previous studies using AI in each evaluation field and discussed the related future prospects.

68Ga-FAPI PET/CT as an Alternative to 18F-FDG PET/CT in the Imaging of Invasive Lobular Breast Carcinoma

Accurate staging of invasive lobular carcinoma (ILC), a subtype of breast cancer, is vital for effective clinical management. Although 18F-FDG PET/CT is a commonly used tool, its efficacy varies across different histologic subtypes. To mitigate this challenge, our investigation delves into the potential utility of 68Ga-fibroblast activation protein inhibitor (FAPI) PET/CT as an alternative for staging ILC, aiming to address a significant research gap using a more expansive patient cohort than the smaller samples commonly found in the existing literature. Methods: In this retrospective analysis, women diagnosed with primary ILC of the breast underwent both 18F-FDG PET/CT and 68Ga-FAPI PET/CT. Both modalities were compared across all lesion locations with the used reference standard. The interval between scans was 1 wk, without any intervening treatments. Lesions were categorized visually, and tracer activity was analyzed using SUVmax, tumor-to-background uptake ratio, and uptake ratios. Both modalities were compared across various parameters, and statistical analysis was performed using SPSS 22.0. A P value of less than 0.05 was chosen to determine statistical significance. Results: The study included 23 female ILC patients (mean age, 51 y) with hormone-positive, human epidermal growth factor receptor type 2-negative tumors. Most (65%) had the luminal A subtype. 68Ga-FAPI PET/CT outperformed 18F-FDG PET/CT, with higher tumoral activity and tumor-to-background uptake ratios (P < 0.001). Primary tumors showed significantly increased uptake with 68Ga-FAPI PET/CT (P < 0.001), detecting additional foci, including multicentric cancer. Axillary lymph node metastases were more frequent and had higher uptake values with 68Ga-FAPI PET/CT (P = 0.012). Moreover, 68Ga-FAPI PET/CT identified more lesions, including bone and liver metastases. Pathologic features did not significantly correlate with imaging modalities, but a positive correlation was observed between peritumoral lymphocyte ratio and 68Ga-FAPI PET/CT-to-18F-FDG PET/CT uptake ratios (P = 0.026). Conclusion: This study underscores 68Ga-FAPI PET/CT's superiority over 18F-FDG PET/CT for ILC. 68Ga-FAPI PET/CT excels in detecting primary breast masses, axillary lymph nodes, and distant metastases; can complement 18F-FDG PET/CT in ILC; and holds potential as an alternative imaging method in future studies.

Quarter-Century Transformation of Oncology: Positron Emission Tomography for Patients with Breast Cancer

PET radiotracers have become indispensable in the care of patients with breast cancer. 18F-fluorodeoxyglucose has become the preferred method of many oncologists for systemic staging of breast cancer at initial diagnosis, detecting recurrent disease, and for measuring treatment response after therapy. 18F-Sodium Fluoride is valuable for detection of osseous metastases. 18F-fluoroestradiol is now FDA-approved with multiple appropriate clinical uses. There are multiple PET radiotracers in clinical trials, which may add utility of PET imaging for patients with breast cancer in the future. This article will describe the advances during the last quarter century in PET for patients with breast cancer.

Women's Health Update: Growing Role of PET for Patients with Breast Cancer

Positron Emission Tomography (PET) has been growing in usage for patients with breast cancer, due to an increased number of FDA-approved PET radiotracers pertinent to patients with breast cancer as well as increased prospective evidence for the value of these agents. The leading PET radiotracer for patients with breast cancer is 18F-fluorodeoxyglucose (18F-FDG), which measures glucose metabolism. There is prospective evidence for the use of 18F-FDG PET in systemic staging of newly diagnosed locally advanced breast cancer (stages IIB-IIIC), monitoring breast cancer treatment response, and detecting breast cancer recurrence, particularly in no special type (NST) breast cancer. 16α-18F-fluoro-17β-Fluoroestradiol (18F-FES) is a radiolabeled estrogen which evaluates estrogen receptor (ER) accessible for estrogen binding. There is prospective evidence supporting 18F-FES PET as a predictive biomarker for selecting patients with metastatic breast cancer for endocrine therapies. 18F-FES PET has also been shown to be valuable in the evaluation of ER status of lesions which are difficult to biopsy, for evaluation of ER status in lesions that are equivocal on other imaging modalities, and for selecting optimal dosage of novel ER-targeted systemic therapies in early clinical trials. Multiple investigators have suggested 18F-FES PET will have an increasing role for patients with invasive lobular breast cancer (ILC), which is less optimally evaluated by 18F-FDG PET. Sodium 18F-Fluoride (18F-NaF) evaluates bone turnover and has been effective in evaluation of malignancies which commonly metastasize to bone. In patients with metastatic breast cancer, 18F-NaF PET/CT has demonstrated superior sensitivity for osseous metastases than 99mTc-MDP or CT. In addition to these three FDA-approved PET radiotracers, there are multiple novel radiotracers currently in clinical trials with potential to further increase PET usage for patients with breast cancer.

Tumor nano-lysate activates dendritic cells to evoke a preventative immune response

A tumor nano-lysate "TNL" vaccine comprised of sonicated 4T1 cells was developed, characterized and implemented for the prevention of triple-negative breast cancer. This study aimed to gain a better understanding of the immune response behind the success of the vaccine in vivo, through use of ex vivo and in vivo assays. Here, we analyze the activation of various immune cells isolated from healthy mouse spleens and find that antigen-presenting cells (APCs) such as dendritic cells (DCs) are being activated following 24 h incubation with 1:10 mg TNL/mg splenocytes. These cells were further explored to determine the pathway by which activation is occurring, and it was observed that TNL are phagocytosed by DCs to activate NF-kB and c-Fos pathways, resulting in enhanced cytokine release after 24 h. An in vivo temporal analysis was performed in mice to understand the immune response at 1, 3, 7 and 10 days after one 100 μL dose of TNL consisting of 105 sonicated 4T1 cells via cardiac puncture and splenocyte and peripheral blood mononuclear cell (PBMC) analysis. Changes were observed for up to one week. A multiple dose study was performed comparing mice that were vaccinated with one dose of TNL administered every ten days for 3 doses total, as well as a PBS vehicle control. Survival for TNL-vaccinated mice was enhanced compared to the PBS control, and there was an average delay of 10 days in the onset of metastasis. The differences between the groups at the end of the study demonstrate the potential for TNL as a preventative therapeutic.

Quantifying changes in shoulder orientation between the prone and supine positions from magnetic resonance imaging

BACKGROUND

Predicting breast tissue motion using biomechanical models can provide navigational guidance during breast cancer treatment procedures. These models typically do not account for changes in posture between procedures. Difference in shoulder position can alter the shape of the pectoral muscles and breast. A greater understanding of the differences in the shoulder orientation between prone and supine could improve the accuracy of breast biomechanical models.

METHODS

19 landmarks were placed on the sternum, clavicle, scapula, and humerus of the shoulder girdle in prone and supine breast MRIs (N = 10). These landmarks were used in an optimization framework to fit subject-specific skeletal models and compare joint angles of the shoulder girdle between these positions.

FINDINGS

The mean Euclidean distance between joint locations from the fitted skeletal model and the manually identified joint locations was 15.7 mm ± 2.7 mm. Significant differences were observed between prone and supine. Compared to supine position, the shoulder girdle in the prone position had the lateral end of the clavicle in more anterior translation (i.e., scapula more protracted) (P < 0.05), the scapula in more protraction (P < 0.01), the scapula in more upward rotation (associated with humerus elevation) (P < 0.05); and the humerus more elevated (P < 0.05) for both the left and right sides.

INTERPRETATION

Shoulder girdle orientation was found to be different between prone and supine. These differences would affect the shape of multiple pectoral muscles, which would affect breast shape and the accuracy of biomechanical models.

Impact of Imaging Biomarkers and AI on Breast Cancer Management: A Brief Review

Breast cancer stands out as the most frequently identified malignancy, ranking as the fifth leading cause of global cancer-related deaths. The American College of Radiology (ACR) introduced the Breast Imaging Reporting and Data System (BI-RADS) as a standard terminology facilitating communication between radiologists and clinicians; however, an update is now imperative to encompass the latest imaging modalities developed subsequent to the 5th edition of BI-RADS. Within this review article, we provide a concise history of BI-RADS, delve into advanced mammography techniques, ultrasonography (US), magnetic resonance imaging (MRI), PET/CT images, and microwave breast imaging, and subsequently furnish comprehensive, updated insights into Molecular Breast Imaging (MBI), diagnostic imaging biomarkers, and the assessment of treatment responses. This endeavor aims to enhance radiologists' proficiency in catering to the personalized needs of breast cancer patients. Lastly, we explore the augmented benefits of artificial intelligence (AI), machine learning (ML), and deep learning (DL) applications in segmenting, detecting, and diagnosing breast cancer, as well as the early prediction of the response of tumors to neoadjuvant chemotherapy (NAC). By assimilating state-of-the-art computer algorithms capable of deciphering intricate imaging data and aiding radiologists in rendering precise and effective diagnoses, AI has profoundly revolutionized the landscape of breast cancer radiology. Its vast potential holds the promise of bolstering radiologists' capabilities and ameliorating patient outcomes in the realm of breast cancer management.

Artificial intelligence for non-mass breast lesions detection and classification on ultrasound images: a comparative study

BACKGROUND

This retrospective study aims to validate the effectiveness of artificial intelligence (AI) to detect and classify non-mass breast lesions (NMLs) on ultrasound (US) images.

METHODS

A total of 228 patients with NMLs and 596 volunteers without breast lesions on US images were enrolled in the study from January 2020 to December 2022. The pathological results served as the gold standard for NMLs. Two AI models were developed to accurately detect and classify NMLs on US images, including DenseNet121_448 and MobileNet_448. To evaluate and compare the diagnostic performance of AI models, the area under the curve (AUC), accuracy, specificity and sensitivity was employed.

RESULTS

A total of 228 NMLs patients confirmed by postoperative pathology with 870 US images and 596 volunteers with 1003 US images were enrolled. In the detection experiment, the MobileNet_448 achieved the good performance in the testing set, with the AUC, accuracy, sensitivity, and specificity were 0.999 (95%CI: 0.997-1.000),96.5%,96.9% and 96.1%, respectively. It was no statistically significant compared to DenseNet121_448. In the classification experiment, the MobileNet_448 model achieved the highest diagnostic performance in the testing set, with the AUC, accuracy, sensitivity, and specificity were 0.837 (95%CI: 0.990-1.000), 70.5%, 80.3% and 74.6%, respectively.

CONCLUSIONS

This study suggests that the AI models, particularly MobileNet_448, can effectively detect and classify NMLs in US images. This technique has the potential to improve early diagnostic accuracy for NMLs.

Reproducibility assessment of uptake on dedicated breast PET for noise discrimination

OBJECTIVES

Dedicated breast PET (dbPET) systems have improved the detection of small breast cancers but have increased false-positive diagnoses due to an increased chance of noise detection. This study examined whether reproducibility assessment using paired images helped to improve noise discrimination and diagnostic performance in dbPET.

METHODS

This study included 21 patients with newly diagnosed breast cancer who underwent [¹⁸F]FDG-dbPET and contrast-enhanced breast MRI. A 10-min dbPET data scan was acquired per breast, and two sets of reconstructed images were generated (named dbPET-1 and dbPET-2, respectively), each of which consisted of randomly allocated 5-min data from the 10-min data. Uptake spots higher than the background were indexed for the study with visual assessment. All indexed uptakes on dbPET-1 were evaluated using dbPET-2 for reproducibility. MRI findings based on the Breast Imaging-Reporting and Data System (BI-RADS) 2013 were used as the gold standard. Uptake spots that corresponded to BI-RADS 1 on MRI were considered noise, while those with BI-RADS 4b-6 were considered malignancies. The diagnostic performance of dbPET for malignancy was evaluated using four different criteria: any uptake on dbPET-1 regarded as positive (criterion A), a subjective visual assessment of dbPET-1 (criterion B), reproducibility assessment between dbPET-1 and dbPET-2 (criterion C), and a combination of B and C (criterion D).

RESULTS

A total of 213 indexed uptake spots were identified on dbPET-1, including 152, 15, 6, 6, and 34 lesions classified as BI-RADS MRI categories 1, 2, 4b, 4c, and 5, respectively. Overall, 31.9% of the index uptake values were reproducible. All malignant lesions were reproducible, whereas 93.4% of noise was not reproducible. The sensitivities for malignancy for criteria A, B, C, and D were 100%, 91.3%, 100%, and 91.3%, respectively, with positive predictive values (PPVs) of 21.4%, 68.9%, 67.6%, and 82.4%, respectively.

CONCLUSIONS

Our results demonstrated that reproducibility assessment helped reduce false-positive findings caused by noise on dbPET without lowering the sensitivity for malignancy. While subjective visual assessment was also efficient in increasing PPV, it occasionally missed malignant uptake.

Imaging for Radiation Planning in Breast Cancer

Radiation therapy is an integral component of the treatment of breast cancer. The indications and type of radiation therapy vary depending on disease invasiveness and stage. Imaging is the cornerstone for radiation therapy planning. While conventional imaging with CT remains the primary modality for radiation treatment planning locally in the breast, molecular imaging with [¹⁸F]FDG-PET/CT identifies additional occult disease that may help alter the local radiation therapy plan or treat oligometastatic disease. The ultimate effects on long-term outcomes remain to be determined. This article reviews the role of imaging in radiation planning for breast cancer.

Multiple microRNAs as biomarkers for early breast cancer diagnosis

MicroRNA (miRNA or miR) is stably present in plasma. It has been reported that miRNA could be used for detecting cancer. Circulating miRNAs are being increasingly recognized as powerful biomarkers in a number of different pathologies, including in breast cancer. The aim of the current study was to establish and validate miRNA sets that are useful for the early diagnosis of breast cancer. Specifically, the current study intended to determine whether miRNA biomarkers were tumor-specific and to statistically verify whether circulating miRNA analysis could be used for breast cancer diagnosis. In the present study, a total of nine candidate miRNA biomarkers were selected by examining reference miRNAs associated with the generation and progression of breast cancer to identify novel miRNAs that could be used to detect early breast cancer. A total of 226 plasma samples from patients with breast cancer were used. In addition, 146 plasma healthy samples were used as non-cancer controls. These samples were divided into training and validation cohorts. The training cohort was used to identify a combination of miRNA that could detect breast cancer. The validation cohort was used to validate this combination of miRNA. Total RNAs were isolated from collected samples. A total of 9 miRNAs were quantified using reverse-transcription quantitative PCR. A total of nine candidate miRNA expression levels were compared between patients with breast cancer and healthy controls. It was indicated that combinations of two or more of the nine miRNAs could detect breast cancer with higher accuracy than the use of a single biomarker. As a representative example, combinations of four miRNAs (miR-1246+miR-206+miR-24+miR-373) of the nine miRNAs had a sensitivity of 98%, a specificity of 96% and an accuracy of 97% for breast cancer detection in the validation cohort. The results of the present study suggest that multiple miRNAs could be used as potential biomarkers for early diagnosis of breast cancer. These biomarkers are expected to overcome limitations of mammography when used as an auxiliary diagnosis of mammography.

Modes of Early Detection of Breast Cancer in Katowice Region, Poland

Background: Our 2004 survey of breast cancer survivors in the Katowice region (Poland) showed that the detection of the disease was triggered by self-examination in 58.9%, mammography in 19.2%, and clinical examination in 19.7% of cases. The purpose of the current study (2019/2020) was to determine if the implementation of national screening (mammography) in 2007 resulted in an increase of the relative contribution of mammography to detection of cancer. Methods: Subjects were 215 breast cancer patients, members of self-support groups in Katowice region. The questionnaire included questions on early detection of breast cancer, participation in screening, and socio-economic status. Results: Early detection of cancer was initiated by self-examination in 63.7%, mammography in 22.8%, clinical examination in 13.5% of subjects. Age at detection depended on the method (p < 0.001): 54.1 ± 10.8 years for self-examination, 60.0 ± 7.6 years for mammography, and 58.7 ± 10.8 years for clinical examination. Conclusions: Both in 2004 and 2020 self-examination is the most frequent method of early detection of breast cancer in the study area. The contribution of mammography remains on a low level (23%). This finding could be explained by a low participation in screening and by age of 50 years used as the entry criterion to national screening of breast cancer in Poland.

99mTC-sestamibi breast imaging: Current status, new ideas and future perspectives

Here we proposed the most recent innovations in the use of Breast Specific Gamma Imaging with ⁹⁹mTc-sestamibi for the management of breast cancer patients. To this end, we reported the recent discoveries concerning: a) the implementation of both instrumental devices and software, b) the biological mechanisms involved in the ⁹⁹mTc-sestamibi uptake in breast cancer cells, c) the evaluation of Breast Specific Gamma Imaging with ⁹⁹mTc-sestamibi as predictive markers of metastatic diseases. In this last case, we also reported preliminary data about the capability of Breast Specific Gamma Imaging with ⁹⁹mTc-sestamibi to identify breast cancer lesions with high propensity to form bone metastatic lesions due to the presence of Breast Osteoblast-Like Cells.

Reversal of the hanging protocol of Contrast Enhanced Mammography leads to similar diagnostic performance yet decreased reading times

OBJECTIVES

Contrast-enhanced mammography (CEM) was found superior to Full-Field Digital Mammography (FFDM) for breast cancer detection. Current hanging protocols show low-energy (LE, similar to FFDM) images first, followed by recombined (RC) images. However, evidence regarding which hanging protocol leads to the most efficient reading process and highest diagnostic performance is lacking. This study investigates the effects of hanging-protocol ordering on the reading process and diagnostic performance of breast radiologists using eye-tracking methodology. Furthermore, it investigates differences in reading processes and diagnostic performance between LE, RC and FFDM images.

MATERIALS AND METHODS

Twenty-seven breast radiologists were randomized into three reading groups: LE-RC (commonly used hangings), RC-LE (reversed hangings) and FFDM. Thirty cases (nine malignant) were used. Fixation count, net dwell time and time-to-first fixation on malignancies as visual search measures were registered by the eye-tracker. Reading time per image was measured. Participants clicked on suspicious lesions to determine sensitivity and specificity. Area-under-the-ROC-curve (AUC) values were calculated.

RESULTS

RC-LE scored identical on visual search measures, t(16)= -1.45, p =  .17 or higher-p values, decreased reading time with 31%, t(16)= -2.20, p = .04, while scoring similar diagnostic performance compared to LE-RC, t(13.2) = -1.39, p - .20 or higher p-values. The reading process was more efficient on RC compared to LE. Diagnostic performance of CEM was superior to FFDM; F (2,26) = 16.1, p <  .001. Average reading time did not differ between the three groups, F (2,25) = 3.15, p =  .06.

CONCLUSION

The reversed CEM hanging protocol (RC-LE) scored similar on diagnostic performance compared to LE-RC, while reading time was a third faster. Abnormalities were interpreted quicker on RC images. A RC-LE hanging protocol is therefore recommended for clinical practice and training. Diagnostic performance of CEM was (again) superior to FFDM.

Why the Gold Standard Approach by Mammography Demands Extension by Multiomics? Application of Liquid Biopsy miRNA Profiles to Breast Cancer Disease Management

In the global context, the epidemic of breast cancer (BC) is evident for the early 21st century. Evidence shows that national mammography screening programs have sufficiently reduced BC related mortality. Therefore, the great utility of the mammography-based screening is not an issue. However, both false positive and false negative BC diagnosis, excessive biopsies, and irradiation linked to mammography application, as well as sub-optimal mammography-based screening, such as in the case of high-dense breast tissue in young females, altogether increase awareness among the experts regarding the limitations of mammography-based screening. Severe concerns regarding the mammography as the “golden standard” approach demanding complementary tools to cover the evident deficits led the authors to present innovative strategies, which would sufficiently improve the quality of the BC management and services to the patient. Contextually, this article provides insights into mammography deficits and current clinical data demonstrating the great potential of non-invasive diagnostic tools utilizing circulating miRNA profiles as an adjunct to conventional mammography for the population screening and personalization of BC management.