Dedicated Breast Positron Emission Tomography for the Evaluation of Early Response to Neoadjuvant Chemotherapy in Breast Cancer

Clinical Applications of Dedicated Breast Positron Emission Tomography

Breast-specific positron imaging systems provide higher sensitivity than whole-body PET for breast cancer detection. The clinical applications for breast-specific positron imaging are similar to breast MRI including preoperative local staging and neoadjuvant therapy response assessment. Breast-specific positron imaging may be an alternative for patients who cannot undergo breast MRI. Further research is needed in expanding the field-of-view for posterior breast lesions, increasing biopsy capability, and reducing radiation dose. Efforts are also necessary for developing appropriate use criteria, increasing availability, and advancing insurance coverage.

Diagnostic performance of dedicated breast positron emission tomography

Background

Dedicated breast positron emission tomography (dbPET) has been developed for detecting smaller breast cancer. We investigated the diagnostic performance of dbPET in patients with known breast cancer.

Methods

Eighty-two preoperative patients with breast cancer were included in the study (84 tumours: 11 ductal carcinomas in situ [DCIS], 73 invasive cancers). They underwent mammography (MMG), ultrasonography (US), and contrast-enhanced breast magnetic resonance imaging (MRI) before whole-body PET/MRI (WBPET/MRI) and dbPET. We evaluated the sensitivity of all modalities, and the association between the maximum standard uptake value (SUVmax) level and histopathological features.

Results

The sensitivities of MMG, US, MRI, WBPET/MRI and dbPET for all tumours were 81.2% (65/80), 98.8% (83/84), 98.6% (73/74), 86.9% (73/84), and 89.2% (75/84), respectively. For 11 DCIS and 22 small invasive cancers (≤ 2 cm), the sensitivity of dbPET (84.9%) tended to be higher than that of WBPET/MRI (69.7%) (p = 0.095). Seven tumours were detected by dbPET only, but not by WBPET/MRI. Five tumours were detected by only WBPET/MRI because of the blind area of dbPET detector, requiring a wider field of view. After making the mat of dbPET detector thinner, all 22 scanned tumours were depicted. The higher SUVmax of dbPET was significantly related to the negative oestrogen receptor status, higher nuclear grade, and higher Ki67 (p < 0.001).

Conclusions

The sensitivity of dbPET for early breast cancer was higher than that of WBPET/MRI. High SUVmax was related to aggressive features of tumours. Moreover, dbPET can be used for the diagnosis and oncological evaluation of breast cancer.

Development of High-Resolution Dedicated PET-Based Radiomics Machine Learning Model to Predict Axillary Lymph Node Status in Early-Stage Breast Cancer

Simple Summary

Accurate clinical axillary evaluation plays an important role in the diagnosis of and treatment planning for breast cancer (BC). This study aimed to develop a machine learning model integrating dedicated breast PET and clinical characteristics for prediction of axillary lymph node status in cT1-2N0-1M0 BC non-invasively. The performance of this integrating model in identifying pN0 and pN1 with the AUC was 0.94. We achieved an NPV of 96.88% in the cN0 and PPV of 92.73% in the cN1 subgroup. The higher true positive and true negative rate could delineate clinical subtypes and apply more precise treatment for patients with early-stage BC.

Abstract

Purpose of the Report: Accurate clinical axillary evaluation plays an important role in the diagnosis and treatment planning for early-stage breast cancer (BC). This study aimed to develop a scalable, non-invasive and robust machine learning model for predicting of the pathological node status using dedicated-PET integrating the clinical characteristics in early-stage BC. Materials and Methods: A total of 420 BC patients confirmed by postoperative pathology were retrospectively analyzed. 18F-fluorodeoxyglucose (¹⁸F-FDG) Mammi-PET, ultrasound, physical examination, Lymph-PET, and clinical characteristics were analyzed. The least absolute shrinkage and selection operator (LASSO) regression analysis were used in developing prediction models. The characteristic curve (ROC) of the area under receiver-operator (AUC) and DeLong test were used to evaluate and compare the performance of the models. The clinical utility of the models was determined via decision curve analysis (DCA). Then, a nomogram was developed based on the model with the best predictive efficiency and clinical utility and was validated using the calibration plots. Results: A total of 290 patients were enrolled in this study. The AUC of the integrated model diagnosed performance was 0.94 (95% confidence interval (CI), 0.91–0.97) in the training set (n = 203) and 0.93 (95% CI, 0.88–0.99) in the validation set (n = 87) (both p < 0.05). In clinical N0 subgroup, the negative predictive value reached 96.88%, and in clinical N1 subgroup, the positive predictive value reached 92.73%. Conclusions: The use of a machine learning integrated model can greatly improve the true positive and true negative rate of identifying clinical axillary lymph node status in early-stage BC.

Terahertz Imaging for Breast Cancer Detection

Terahertz (THz) imaging has the potential to detect breast tumors during breast-conserving surgery accurately. Over the past decade, many research groups have extensively studied THz imaging and spectroscopy techniques for identifying breast tumors. This manuscript presents the recent development of THz imaging techniques for breast cancer detection. The dielectric properties of breast tissues in the THz range, THz imaging and spectroscopy systems, THz radiation sources, and THz breast imaging studies are discussed. In addition, numerous chemometrics methods applied to improve THz image resolution and data collection processing are summarized. Finally, challenges and future research directions of THz breast imaging are presented.

Evaluation of primary breast cancers using dedicated breast PET and whole-body PET

Metabolic imaging of the primary breast tumor with ¹⁸F-fluorodeoxyglucose ([¹⁸F]FDG) PET may assist in predicting treatment response in the neoadjuvant chemotherapy (NAC) setting. Dedicated breast PET (dbPET) is a high-resolution imaging modality with demonstrated ability in highlighting intratumoral heterogeneity and identifying small lesions in the breast volume. In this study, we characterized similarities and differences in the uptake of [¹⁸F]FDG in dbPET compared to whole-body PET (wbPET) in a cohort of ten patients with biopsy-confirmed, locally advanced breast cancer at the pre-treatment timepoint. Patients received bilateral dbPET and wbPET following administration of 186 MBq and 307 MBq [¹⁸F]FDG on separate days, respectively. [¹⁸F]FDG uptake measurements and 20 radiomic features based on morphology, tumor intensity, and texture were calculated and compared. There was a fivefold increase in SUL_peak for dbPET (median difference (95% CI): 4.0 mL⁻¹ (1.8–6.4 mL⁻¹), p = 0.006). Additionally, spatial heterogeneity features showed statistically significant differences between dbPET and wbPET. The higher [¹⁸F]FDG uptake in dbPET highlighted the dynamic range of this breast-specific imaging modality. Combining with the higher spatial resolution, dbPET may be able to detect treatment response in the primary tumor during NAC, and future studies with larger cohorts are warranted.

Current Landscape of Breast Cancer Imaging and Potential Quantitative Imaging Markers of Response in ER-Positive Breast Cancers Treated with Neoadjuvant Therapy

In recent years, neoadjuvant treatment trials have shown that breast cancer subtypes identified on the basis of genomic and/or molecular signatures exhibit different response rates and recurrence outcomes, with the implication that subtype-specific treatment approaches are needed. Estrogen receptor-positive (ER+) breast cancers present a unique set of challenges for determining optimal neoadjuvant treatment approaches. There is increased recognition that not all ER+ breast cancers benefit from chemotherapy, and that there may be a subset of ER+ breast cancers that can be treated effectively using endocrine therapies alone. With this uncertainty, there is a need to improve the assessment and to optimize the treatment of ER+ breast cancers. While pathology-based markers offer a snapshot of tumor response to neoadjuvant therapy, non-invasive imaging of the ER disease in response to treatment would provide broader insights into tumor heterogeneity, ER biology, and the timing of surrogate endpoint measurements. In this review, we provide an overview of the current landscape of breast imaging in neoadjuvant studies and highlight the technological advances in each imaging modality. We then further examine some potential imaging markers for neoadjuvant treatment response in ER+ breast cancers.

Kanakugiol, a Compound Isolated from Lindera erythrocarpa, Promotes Cell Death by Inducing Mitotic Catastrophe after Cell Cycle Arrest

A novel compound named 'kanakugiol' was recently isolated from Lindera erythrocarpa and showed free radical-scavenging and antifungal activities. However, the details of the anticancer effect of kanakugiol on breast cancer cells remain unclear. We investigated the effect of kanakugiol on the growth of MCF-7 human breast cancer cells. Kanakugiol affected cell cycle progression, and decreased cell viability in MCF-7 cells in a dose-dependent manner. It also enhanced PARP cleavage (50 kDa), whereas DNA laddering was not induced. FACS analysis with annexin V-FITC/PI staining showed necrosis induction in kanakugiol-treated cells. Caspase-9 cleavage was also induced. Expression of death receptors was not altered. However, Bcl-2 expression was suppressed, and mitochondrial membrane potential collapsed, indicating limited apoptosis induction by kanakugiol. Immunofluorescence analysis using α-tubulin staining revealed mitotic exit without cytokinesis (4N cells with two nuclei) due to kanakugiol treatment, suggesting that mitotic catastrophe may have been induced via microtubule destabilization. Furthermore, cell cycle analysis results also indicated mitotic catastrophe after cell cycle arrest in MCF-7 cells due to kanakugiol treatment. These findings suggest that kanakugiol inhibits cell proliferation and promotes cell death by inducing mitotic catastrophe after cell cycle arrest. Thus, kanakugiol shows potential for use as a drug in the treatment of human breast cancer.

Initial experience of dedicated breast PET imaging of ER+ breast cancers using [F-18]fluoroestradiol

Dedicated breast positron emission tomography (dbPET) is an emerging technology with high sensitivity and spatial resolution that enables detection of sub-centimeter lesions and depiction of intratumoral heterogeneity. In this study, we report our initial experience with dbPET using [F-18]fluoroestradiol (FES) in assessing ER+ primary breast cancers. Six patients with >90% ER+ and HER2- breast cancers were imaged with dbPET and breast MRI. Two patients had ILC, three had IDC, and one had an unknown primary tumor. One ILC patient was treated with letrozole, and another patient with IDC was treated with neoadjuvant chemotherapy without endocrine treatment. In this small cohort, we observed FES uptake in ER+ primary breast tumors with specificity to ER demonstrated in a case with tamoxifen blockade. FES uptake in ILC had a diffused pattern compared to the distinct circumscribed pattern in IDC. In evaluating treatment response, the reduction of SUVmax was observed with residual disease in an ILC patient treated with letrozole, and an IDC patient treated with chemotherapy. Future study is critical to understand the change in FES SUVmax after endocrine therapy and to consider other tracer uptake metrics with SUVmax to describe ER-rich breast cancer. Limitations include variations of FES uptake in different ER+ breast cancer diseases and exclusion of posterior tissues and axillary regions. However, FES-dbPET has a high potential for clinical utility, especially in measuring response to neoadjuvant endocrine treatment. Further development to improve the field of view and studies with a larger cohort of ER+ breast cancer patients are warranted.

Microwave Sensors for Breast Cancer Detection

Breast cancer is the leading cause of death among females, early diagnostic methods with suitable treatments improve the 5-year survival rates significantly. Microwave breast imaging has been reported as the most potential to become the alternative or additional tool to the current gold standard X-ray mammography for detecting breast cancer. The microwave breast image quality is affected by the microwave sensor, sensor array, the number of sensors in the array and the size of the sensor. In fact, microwave sensor array and sensor play an important role in the microwave breast imaging system. Numerous microwave biosensors have been developed for biomedical applications, with particular focus on breast tumor detection. Compared to the conventional medical imaging and biosensor techniques, these microwave sensors not only enable better cancer detection and improve the image resolution, but also provide attractive features such as label-free detection. This paper aims to provide an overview of recent important achievements in microwave sensors for biomedical imaging applications, with particular focus on breast cancer detection. The electric properties of biological tissues at microwave spectrum, microwave imaging approaches, microwave biosensors, current challenges and future works are also discussed in the manuscript.