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.

Application of Machine Learning Algorithm in Predicting Axillary Lymph Node Metastasis from Breast Cancer on Preoperative Chest CT

Axillary lymph node (ALN) status is one of the most critical prognostic factors in patients with breast cancer. However, ALN evaluation with contrast-enhanced CT (CECT) has been challenging. Machine learning (ML) is known to show excellent performance in image recognition tasks. The purpose of our study was to evaluate the performance of the ML algorithm for predicting ALN metastasis by combining preoperative CECT features of both ALN and primary tumor. This was a retrospective single-institutional study of a total of 266 patients with breast cancer who underwent preoperative chest CECT. Random forest (RF), extreme gradient boosting (XGBoost), and neural network (NN) algorithms were used. Statistical analysis and recursive feature elimination (RFE) were adopted as feature selection for ML. The best ML-based ALN prediction model for breast cancer was NN with RFE, which achieved an AUROC of 0.76 ± 0.11 and an accuracy of 0.74 ± 0.12. By comparing NN with RFE model performance with and without ALN features from CECT, NN with RFE model with ALN features showed better performance at all performance evaluations, which indicated the effect of ALN features. Through our study, we were able to demonstrate that the ML algorithm could effectively predict the final diagnosis of ALN metastases from CECT images of the primary tumor and ALN. This suggests that ML has the potential to differentiate between benign and malignant ALNs.

Deep learning model for predicting the presence of stromal invasion of breast cancer on digital breast tomosynthesis

To develop a deep learning (DL)-based algorithm to predict the presence of stromal invasion in breast cancer using digital breast tomosynthesis (DBT). Our institutional review board approved this retrospective study and waived the requirement for informed consent from the patients. Initially, 499 patients (mean age 50.5 years, age range, 29-90 years) who were referred to our hospital under the suspicion of breast cancer and who underwent DBT between March 1 and August 31, 2019, were enrolled in this study. Among the 499 patients, 140 who underwent surgery after being diagnosed with breast cancer were selected for the analysis. Based on the pathological reports, the 140 patients were classified into two groups: those with non-invasive cancer (n = 20) and those with invasive cancer (n = 120). VGG16, Resnet50, DenseNet121, and Xception architectures were used as DL models to differentiate non-invasive from invasive cancer. The diagnostic performance of the DL models was assessed based on the area under the receiver operating characteristic curve (AUC). The AUC for the four models were 0.56 [95% confidence intervals (95% CI) 0.49-0.62], 0.67 (95% CI 0.62-0.74), 0.71 (95% CI 0.65-0.75), and 0.75 (95% CI 0.69-0.81), respectively. Our proposed DL model trained on DBT images is useful for predicting the presence of stromal invasion in breast cancer.

Multimodal radiomics and nomogram-based prediction of axillary lymph node metastasis in breast cancer: An analysis considering optimal peritumoral region

PURPOSE

To explore the optimal peri-tumoral regions on ultrasound (US) images and investigate the performance of multimodal radiomics for predicting axillary lymph node metastasis (ALNM).

METHODS

This retrospective study included 326 patients (training cohort: n = 162, internal validation cohort: n = 74, external validation cohort: n = 90). Intra-tumoral region of interests (ROIs) were delineated on US and digital mammography (DM) images. Peri-tumoral ROI (PTR) on US images were gained by dilating actual 0.5, 1.0, 1.5, 2.0, 2.5, 3.0 and 3.5 mm radius surrounding the tumor. Support vector machine (SVM) method was used to calculate the importance of radiomics features and to pick the 10 most important. Recursive feature elimination-SVM was used to evaluate the efficacy of models with different feature numbers used.

RESULTS

The PTR0.5mm yielded a maximum AUC of 0.802 (95% confidence interval (CI): 0.676-0.901) within the validation cohort using SVM classifier. The multimodal radiomics (intra-tumoral US and DM and US-based PTR0.5mm radiomics model) achieved the highest predictive ability (AUC = 0.888/0.844/0.835 and 95% CI = 0.829-0.936/0.741-0.929/0.752-0.896 for training/internal validation/external validation cohort, respectively).

CONCLUSION

The PTR0.5mm could be the optimal area for predicting ALNM. A favorable predictive accuracy for predicting ALNM was achieved using multimodal radiomics and its based nomogram.

Deep Learning Mechanism for Predicting the Axillary Lymph Node Metastasis in Patients with Primary Breast Cancer

The second largest cause of mortality worldwide is breast cancer, and it mostly occurs in women. Early diagnosis has improved further treatments and reduced the level of mortality. A unique deep learning algorithm is presented for predicting breast cancer in its early stages. This method utilizes numerous layers to retrieve significantly greater amounts of information from the source inputs. It could perform automatic quantitative evaluation of complicated image properties in the medical field and give greater precision and reliability during the diagnosis. The dataset of axillary lymph nodes from the breast cancer patients was collected from Erasmus Medical Center. A total of 1050 images were studied from the 850 patients during the years 2018 to 2021. For the independent test, data samples were collected for 100 images from 95 patients at national cancer institute. The existence of axillary lymph nodes was confirmed by pathologic examination. The feed forward, radial basis function, and Kohonen self-organizing are the artificial neural networks (ANNs) which are used to train 84% of the Erasmus Medical Center dataset and test the remaining 16% of the independent dataset. The proposed model performance was determined in terms of accuracy (Ac), sensitivity (Sn), specificity (Sf), and the outcome of the receiver operating curve (Roc), which was compared to the other four radiologists' mechanism. The result of the study shows that the proposed mechanism achieves 95% sensitivity, 96% specificity, and 98% accuracy, which is higher than the radiologists' models (90% sensitivity, 92% specificity, and 94% accuracy). Deep learning algorithms could accurately predict the clinical negativity of axillary lymph node metastases by utilizing images of initial breast cancer patients. This method provides an earlier diagnostic technique for axillary lymph node metastases in patients with medically negative changes in axillary lymph nodes.

Effect of Neoadjuvant Chemotherapy on Angiogenesis and Cell Proliferation of Breast Cancer Evaluated by Dynamic Enhanced Magnetic Resonance Imaging

Background. Breast cancer is the uncontrolled proliferation of breast epithelial cells under the action of various carcinogenic factors. The evaluation of early efficacy of neoadjuvant chemotherapy for breast cancer is helpful to change the treatment plan in time. On this basis, dynamic contrast enhancement magnetic resonance imaging (DCE-MRI) was used to evaluate the effects of neoadjuvant chemotherapy on angiogenesis and cell proliferation in breast cancer. Objective. To evaluate the effect of neoadjuvant chemotherapy on angiogenesis and cell proliferation of breast cancer by dynamic enhanced DCE-MRI. Method. 80 breast cancer patients were divided into the routine chemotherapy group (3 cycles) and neoadjuvant chemotherapy groups (3 cycles) of 40 cases each from January 2018 to June 2021. Based on conventional imaging, DCE-MRI was performed with Intera Achieva 3.0 TMR superconducting MR scanner before and after treatment. The quantitative indexes, MRI parameters, cell proliferation expression, and DCE-MRI angiogenesis were compared between the two groups. Result. The inhibition rate, Vepost, Ktranspre, ADC, Bax, Alexi, and Aurora in the neoadjuvant chemotherapy group were significantly higher than those in the conventional chemotherapy group ( $P<0.05$ ), while Kep, Ktrans, and Nek2 were significantly lower than those in the conventional chemotherapy group ( $P<0.05$ ). Vepre (cm3), Ktranspre (ml/min/100 cm3), and Ve had no significant difference ( $P>0.05$ ). Conclusion. The quantitative parameters, MRI parameters, proliferation, and expression of DCE-MRI in breast cancer patients with different chemotherapy regimens are quite different. They can be applied to the diagnosis of neoadjuvant chemotherapy in breast cancer patients with angiogenesis and cell proliferation.

Clinical experience of tensor-valued diffusion encoding for microstructure imaging by diffusional variance decomposition in patients with breast cancer

BACKGROUND

Diffusion-weighted imaging plays a key role in magnetic resonance imaging (MRI) of breast tumors. However, it remains unclear how to interpret single diffusion encoding with respect to its link with tissue microstructure. The purpose of this retrospective cross-sectional study was to use tensor-valued diffusion encoding to investigate the underlying microstructure of invasive ductal carcinoma (IDC) and evaluate its potential value in a clinical setting.

METHODS

We retrospectively reviewed biopsy-proven breast cancer patients who underwent preoperative breast MRI examination from July 2020 to March 2021. We reviewed the MRI of 29 patients with 30 IDCs, including analysis by diffusional variance decomposition enabled by tensor-valued diffusion encoding. The diffusion parameters of mean diffusivity (MD), total mean kurtosis (MKT), anisotropic mean kurtosis (MKA), isotropic mean kurtosis (MKI), macroscopic fractional anisotropy (FA), and microscopic fractional anisotropy (µFA) were estimated. The parameter differences were compared between IDC and normal fibroglandular breast tissue (FGBT), as well as the association between the diffusion parameters and histopathologic items.

RESULTS

The mean value of MD in IDCs was significantly lower than that of normal FGBT (1.07±0.27 vs. 1.34±0.29, P<0.001); however, MKT, MKA, MKI, FA, and µFA were significantly higher (P<0.005). Among all the diffusion parameters, MKI was positively correlated with the tumor size on both MRI and pathological specimen (rs=0.38, P<0.05 vs. rs=0.54, P<0.01), whereas MKT had a positive correlation with the tumor size in the pathological specimen only (rs=0.47, P<0.02). In addition, the lymph node (LN) metastasis group had significantly higher MKT, MKA, and µFA compared to the metastasis negative group (P<0.05).

CONCLUSIONS

Tensor-valued diffusion encoding enables a useful non-invasive method for characterizing breast cancers with information on tissue microstructures. Particularly, µFA could be a potential imaging biomarker for evaluating breast cancers prior to surgery or chemotherapy.

Lymph Node Metastasis Prediction from Primary Breast Cancer US Images Using Deep Learning

Background Deep learning (DL) algorithms are gaining extensive attention for their excellent performance in image recognition tasks. DL models can automatically make a quantitative assessment of complex medical image characteristics and achieve increased accuracy in diagnosis with higher efficiency. Purpose To determine the feasibility of using a DL approach to predict clinically negative axillary lymph node metastasis from US images in patients with primary breast cancer. Materials and Methods A data set of US images in patients with primary breast cancer with clinically negative axillary lymph nodes from Tongji Hospital (974 imaging studies from 2016 to 2018, 756 patients) and an independent test set from Hubei Cancer Hospital (81 imaging studies from 2018 to 2019, 78 patients) were collected. Axillary lymph node status was confirmed with pathologic examination. Three different convolutional neural networks (CNNs) of Inception V3, Inception-ResNet V2, and ResNet-101 architectures were trained on 90% of the Tongji Hospital data set and tested on the remaining 10%, as well as on the independent test set. The performance of the models was compared with that of five radiologists. The models' performance was analyzed in terms of accuracy, sensitivity, specificity, receiver operating characteristic curves, areas under the receiver operating characteristic curve (AUCs), and heat maps. Results The best-performing CNN model, Inception V3, achieved an AUC of 0.89 (95% confidence interval [CI]: 0.83, 0.95) in the prediction of the final clinical diagnosis of axillary lymph node metastasis in the independent test set. The model achieved 85% sensitivity (35 of 41 images; 95% CI: 70%, 94%) and 73% specificity (29 of 40 images; 95% CI: 56%, 85%), and the radiologists achieved 73% sensitivity (30 of 41 images; 95% CI: 57%, 85%; P = .17) and 63% specificity (25 of 40 images; 95% CI: 46%, 77%; P = .34). Conclusion Using US images from patients with primary breast cancer, deep learning models can effectively predict clinically negative axillary lymph node metastasis. Artificial intelligence may provide an early diagnostic strategy for lymph node metastasis in patients with breast cancer with clinically negative lymph nodes. Published under a CC BY 4.0 license. Online supplemental material is available for this article. See also the editorial by Bae in this issue.

Cancer metabolism in a snapshot: MRS(I)

The contribution of MRS(I) to the in vivo evaluation of cancer-metabolism-derived metrics, mostly since 2016, is reviewed here. Increased carbon consumption by tumour cells, which are highly glycolytic, is now being sampled by ¹³ C magnetic resonance spectroscopic imaging (MRSI) following the injection of hyperpolarized [1-¹³ C] pyruvate (Pyr). Hot-spots of, mostly, increased lactate dehydrogenase activity or flow between Pyr and lactate (Lac) have been seen with cancer progression in prostate (preclinical and in humans), brain and pancreas (both preclinical) tumours. Therapy response is usually signalled by decreased Lac/Pyr ¹³ C-labelled ratio with respect to untreated or non-responding tumour. For therapeutic agents inducing tumour hypoxia, the ¹³ C-labelled Lac/bicarbonate ratio may be a better metric than the Lac/Pyr ratio. ³¹ P MRSI may sample intracellular pH changes from brain tumours (acidification upon antiangiogenic treatment, basification at fast proliferation and relapse). The steady state tumour metabolome pattern is still in use for cancer evaluation. Metrics used for this range from quantification of single oncometabolites (such as 2-hydroxyglutarate in mutant IDH1 glial brain tumours) to selected metabolite ratios (such as total choline to N-acetylaspartate (plain ratio or CNI index)) or the whole ¹ H MRSI(I) pattern through pattern recognition analysis. These approaches have been applied to address different questions such as tumour subtype definition, following/predicting the response to therapy or defining better resection or radiosurgery limits.

A method for cranial target delineation in radiotherapy treatment planning aided by single-voxel magnetic resonance spectroscopy: evaluation using a custom-designed gel-based phantom and simulations

OBJECTIVE

Magnetic resonance spectroscopy (MRS) has been useful in radiotherapy treatment planning (RTP) especially in tumor delineation. Routinely, 2D/3D MRSI data are used for this application. However, not all centers have access to 2D/3D MRSI. The objective of this study was to introduce a method of using single-voxel spectroscopy (SVS) data in target delineation and assess its reliability.

METHODS

A gel-based phantom containing Creatine (Cr), N-acetyl-l-aspartic-acid (NAA), and Choline (Cho) was designed and built. The metabolite ratios simulate the normal and tumoral part of the brain. The jMRUI software (v. 6.0) was used to simulate a 1.5 T GE MRI scanner. The metabolite spectra provided by different time of echos (TE)s of the Point-RESolved Spectroscopy pulse-sequence (PRESS), different data-points, and post-processings were quantized by jMRUI. PseudoMRSI maps of Cho/Cr, NAA/Cr, and Cho + Cr/NAA were created. A conformity index (CI) was used to determine which metabolite-ratio isolines are more appropriate for tumor delineation.

RESULTS

The simulation accuracy was verified. There were no differences > 4% between the measured and simulated spectra in peak regions. The pseudoMRSI map of Cho + Cr/NAA smoothly followed the complicated geometry of the tumor inside the gel-based phantom. The results showed that the single-voxel spectra produced by the PRESS pulse sequence with the TE of 144 ms, 512 data-points, and minimum post-processings of water suppression, eddy current correction, and baseline correction can be used for target delineation.

CONCLUSION

This study suggests that SVS data can be used to aid target delineation by using a mathematical approach. This can enable a wider use of MR-derived information in radiotherapy.

ADVANCES IN KNOWLEDGE

To the best of our knowledge, until now, 2D or 3D MRSI data provided from 3T MRI scanners have been used for MRS-based radiotherapy treatment planning. However, there are a lot of centers that are equipped to 1.5 T MRI scanners and some of them just equipped to SVS. This study introduces a mathematical approach to help these centers to take the benefits of MRS-based treatment planning.

Optoacoustic imaging of the breast: correlation with histopathology and histopathologic biomarkers

Aim

This study was conducted in order to investigate the role of gray-scale ultrasound (US) and optoacoustic imaging combined with gray-scale ultrasound (OA/US) to better differentiate between breast cancer molecular subtypes.

Materials and methods

All 67 malignant masses included in the Maestro trial were retrospectively reviewed to compare US and OA/US feature scores and histopathological findings. Kruskal–Wallis tests were used to analyze the relationship between US and OA/US features and molecular subtypes of breast cancer. If a significant relationship was found, additional Wilcoxon–Mann–Whitney tests were used to identify the differences between molecular subtype groups.

Results

US sound transmission helped to differentiate between LUMA and LUMB, LUMB and TNBC, and LUMB and all other molecular subtypes combined (p values < 0.05). Regarding OA/US features, the sum of internal features helped to differentiate between TNBC and HER2-enriched subtypes (p = 0.049). Internal vessels (p = 0.025), sum of all internal features (p = 0.019), and sum of internal and external features (p = 0.028) helped to differentiate between LUMA and LUMB. All internal features, the sum of all internal features, the sum of all internal and external features, and the ratio of internal and external features helped to differentiate between LUMA and TNBC. The same features also helped to differentiate between LUMA and TNBC from other molecular subtypes (p values < 0.05).

Conclusions

The use of OA/US might help radiologists to better differentiate between breast cancer molecular subtypes. Further studies need to be carried out in order to validate these results.

Key Points

• The combination of functional and morphologic information provided by optoacoustic imaging (OA) combined with gray-scale US helped to differentiate between breast cancer molecular subtypes.

Electronic supplementary material

The online version of this article (10.1007/s00330-019-06262-0) contains supplementary material, which is available to authorized users.

Multi-physics modeling to study the influence of tissue compression and cold stress on enhancing breast tumor detection using microwave radiometry

The influence of tissue compression and external thermal modulation on passive detection of breast tumors using medical microwave radiometry was investigated using multi-physics numerical modeling. A three-dimensional numerical model of the pendant breast with 10 and 6 mm diameter tumors at varying depths (15 mm, 30 mm) was analyzed at thermodynamic equilibrium using a circular waveguide as the receive antenna. The contrast in the brightness temperature, ΔT_B , between the unhealthy and healthy breasts was found to be significantly more for breast compression alone, compared to thermal modulation of the tissue surface, irrespective of tissue composition, tumor size, and depth. The study also concludes that small deep-seated tumor with very low metabolic activity that is not detectable by a radiometer with 0.1 °C sensitivity could be detected under breast compression and short duration cold stress. Thus, detection of deep-seated breast tumors can be significantly improved under controlled tissue compression with an optional cold stress. Bioelectromagnetics. © 2019 Bioelectromagnetics Society.

Magnetic Resonance Spectroscopy and its Clinical Applications: A Review

In vivo NMR spectroscopy is known as magnetic resonance spectroscopy (MRS). MRS has been applied as both a research and a clinical tool in order to detect visible or nonvisible abnormalities. The adaptability of MRS allows a technique that can probe a wide variety of metabolic uses across different tissues. Although MRS is mostly applied for brain tissue, it can be used for detection, localization, staging, tumour aggressiveness evaluation, and tumour response assessment of breast, prostate, hepatic, and other cancers. In this article, the medical applications of MRS in the brain, including tumours, neural and psychiatric disorder studies, breast, prostate, hepatic, gastrointestinal, and genitourinary investigations have been reviewed.

Reliability of 18F-FDG PET Metabolic Parameters Derived Using Simultaneous PET/MRI and Correlation With Prognostic Factors of Invasive Ductal Carcinoma: A Feasibility Study

OBJECTIVE

The objective of our study was to correlate semiquantitative PET parameters-standardized uptake value (SUV) and total lesion glycolysis (TLG)-derived in simultaneous PET/MRI using MRI-based attenuation correction with clinical and histopathologic prognostic factors in patients with breast cancer.

MATERIALS AND METHODS

Eighty-two invasive ductal carcinomas in 69 women were included in the study. All the subjects underwent whole-body (WB) PET/MRI (supine WB mode) and dedicated PET/MRI of the breast (prone breast imaging mode) for staging on a simultaneous PET/MRI system. The SUV and TLG values were calculated from ¹⁸F-FDG PET data using MRI-based attenuation correction (2-point Dixon sequence for tissue segmentation). Relationships between SUV and TLG values and clinical and histopathologic parameters (i.e., tumor size, tumor grade, Ki-67 status, and hormonal receptor expression status) were evaluated using Spearman correlation coefficient analysis.

RESULTS

A significant correlation was observed between mean SUV (SUV_mean) and maximum SUV (SUV_max) values derived with WB PET and regional PET of the breasts performed simultaneously with MRI (r = 0.88 and 0.89, respectively). A significant difference (p < 0.05) was observed in SUV_mean, SUV_max, and TLG values between the grades and molecular subtypes of breast cancer. High SUV_mean, SUV_max, and TLG values were found to correlate with larger tumor size (p < 0.01), higher proliferation index (p < 0.05), higher grade (p < 0.01), and triple-negative hormonal receptor status (p < 0.01, p < 0.05).

CONCLUSION

Semiquantitative FDG parameters derived with MRI-based attenuation correction in simultaneous PET/MRI are reliable and correlate with clinicopathologic features such as grade as well as subtype and thus could be used in the prognostication of breast cancer.

Association of pharmacokinetic and metabolic parameters derived using simultaneous PET/MRI: Initial findings and impact on response evaluation in breast cancer

PURPOSE

To study relationships among pharmacokinetic and ¹⁸F-fluorodeoxyglucose (¹⁸F-FDG) PET parameters obtained through simultaneous PET/MRI in breast cancer patients and evaluate their combined potential for response evaluation.

METHODS

The study included 41 breast cancer patients for correlation study and 9 patients (pre and post therapy) for response evaluation. All patients underwent simultaneous PET/MRI with dedicated breast imaging. Pharmacokinetic parameters and PET parameters for tumor were derived using an in- house developed and vendor provided softwares respectively. Relationships between SUV and pharmacokinetic parameters and clinical as well as histopathologic parameters were evaluated using Spearman correlation analysis. Response to chemotherapy was derived as percentage reduction in size and in parameters post therapy.

RESULTS

Significant correlations were observed between SUVmean, max, peak, TLG with K^trans (ρ=0.446, 0.417, 0.491, 0.430; p≤0.01); with Kep(ρ=0.303, ρ=0.315, ρ=0.319; p≤0.05); and with iAUC(ρ=0.401, ρ=0.410, ρ=0.379; p≤0.05, p≤0.01). The ratio of ve/iAUC showed significant negative correlation to SUVmean, max, peak and TLG (ρ=0.420, 0.446, 0.443, 0.426; p≤0.01). Ability of SUV as well as pharmacokinetic parameters to predict response to therapy matched the RECIST criteria in 9 out of 11 lesions in 9 patients. Maximum post therapy quantitative reduction was observed in SUVpeak, TLG and K^trans.

CONCLUSION

Simultaneous PET/MRI enables illustration of close interactions between glucose metabolism and pharmacokinetic parameters in breast cancer patients and potential of their simultaneity in response assessment to therapy.

Can positron emission mammography help to identify clinically significant breast cancer in women with suspicious calcifications on mammography?

OBJECTIVE

To evaluate the diagnostic accuracy of positron emission mammography (PEM) for identifying malignant lesions in patients with suspicious microcalcifications detected on mammography.

METHODS

A prospective, single-centre study that evaluated 40 patients with suspicious calcifications at mammography and indication for percutaneous or surgical biopsy, with mean age of 56.4 years (range: 28-81 years). Patients who agreed to participate in the study underwent PEM with 18F-fluorodeoxyglucose before the final histological evaluation. PEM findings were compared with mammography and histological findings.

RESULTS

Most calcifications (n = 34; 85.0 %) were classified as BIRADS 4. On histology, there were 25 (62.5 %) benign and 15 (37.5 %) malignant lesions, including 11 (27.5 %) ductal carcinoma in situ (DCIS) and 4 (10 %) invasive carcinomas. On subjective analysis, PEM was positive in 15 cases (37.5 %) and most of these cases (n = 14; 93.3 %) were confirmed as malignant on histology. There was one false-positive result, which corresponded to a fibroadenoma, and one false negative, which corresponded to an intermediate-grade DCIS. PEM had a sensitivity of 93.3 %, specificity of 96.0 % and accuracy of 95 %.

CONCLUSION

PEM was able to identify all invasive carcinomas and high-grade DCIS (nuclear grade 3) in the presented sample, suggesting that this method may be useful for further evaluation of patients with suspected microcalcifications.

KEY POINTS

• Many patients with suspicious microcalcifications at mammography have benign results at biopsy. • PEM may help to identify invasive carcinomas and high-grade DCIS. • Management of patients with suspicious calcifications can be improved.

Functional imaging of the angiogenic switch in a transgenic mouse model of human breast cancer by dynamic contrast enhanced magnetic resonance imaging

Tumour progression depends on several sequential events that include the microenvironment remodelling processes and the switch to the angiogenic phenotype, leading to new blood vessels recruitment. Non-invasive imaging techniques allow the monitoring of functional alterations in tumour vascularity and cellularity. The aim of this work was to detect functional changes in vascularisation and cellularity through Dynamic Contrast Enhanced (DCE) and Diffusion Weighted (DW) Magnetic Resonance Imaging (MRI) modalities during breast cancer initiation and progression of a transgenic mouse model (BALB-neuT mice). Histological examination showed that BALB-neuT mammary glands undergo a slow neoplastic progression from simple hyperplasia to invasive carcinoma, still preserving normal parts of mammary glands. DCE-MRI results highlighted marked functional changes in terms of vessel permeability (K(trans) , volume transfer constant) and vascularisation (vp , vascular volume fraction) in BALB-neuT hyperplastic mammary glands if compared to BALB/c ones. When breast tissue progressed from simple to atypical hyperplasia, a strong increase in DCE-MRI biomarkers was observed in BALB-neuT in comparison to BALB/c mice (K(trans)  = 5.3 ± 0.7E-4 and 3.1 ± 0.5E-4; vp  = 7.4 ± 0.8E-2 and 4.7 ± 0.6E-2 for BALB-neuT and BALB/c, respectively) that remained constant during the successive steps of the neoplastic transformation. Consistent with DCE-MRI observations, microvessel counting revealed a significant increase in tumour vessels. Our study showed that DCE-MRI estimates can accurately detect the angiogenic switch at early step of breast cancer carcinogenesis. These results support the view that this imaging approach is an excellent tool to characterize microvasculature changes, despite only small portions of the mammary glands developed neoplastic lesions in a transgenic mouse model.

Novos métodos funcionais na avaliação de lesões mamárias

O câncer de mama é um dos mais prevalentes nas mulheres. A mamografia é um excelente método com impacto comprovado na redução da mortalidade pelo câncer de mama. Porém, não é um método perfeito, apresentando alguns pontos fracos, principalmente no rastreamento de mulheres com mamas densas, estadiamento e avaliação de tratamento. A ressonância magnética mamária mostrou a necessidade e importância da avaliação funcional mamária. Descrevemos dois métodos de avaliação funcional mamária e demonstramos nossa experiência com a mamografia digital com contraste e com a imagem molecular mamária realizada em gama-câmara específica. Estes dois métodos já estão disponíveis em nosso meio e apresentam resultados promissores na detecção de lesões mamográficas ocultas, confirmação de lesões suspeitas e redução de biópsias desnecessárias, podendo assim melhorar o estudo mamário, principalmente nos pontos falhos da mamografia.

Diagnostic Performance of Breast-Specific Gamma Imaging (BSGI) for Breast Cancer: Usefulness of Dual-Phase Imaging with (99m)Tc-sestamibi

PURPOSE

The aim of this study was to investigate the usefulness of breast-specific gamma imaging (BSGI) with dual-phase imaging for increasing diagnostic performance and interpreter confidence.

METHODS

We studied 76 consecutive patients (mean age: 49.3 years, range: 33-61 years) who received 925 MBq (25 mCi) (99m)Tc-sestamibi intravenously. Craniocaudal and mediolateral oblique planar images were acquired for all patients. Delayed images were obtained from all patients 1 h after tracer injection, except for patients with no definite abnormal uptake. All images were classified into four categories: group 1 (definite negative) = no definite abnormal uptake; group 2 (possible negative) = symmetrically diffuse and amorphous uptake; group 3 (possible positive) = asymmetrically mild and nodular uptake; group 4 (definite positive) = asymmetrically intense and nodular uptake. To evaluate diagnostic performance, the BSGI studies were classified as positive (group 3 or 4) or negative (group 1 or 2) for malignancy according to a visual analysis. The final diagnoses were derived from histopathological confirmation and/or imaging follow-up after at least 6 months (range: 6-14 months) by both ultrasonography and mammography.

RESULTS

The patients' ages ranged from 33 to 61 years, with an average of 49.3 years. Thirteen patients were diagnosed with malignancy, and 63 patients were diagnosed as negative for malignancy. Using early images, 43 patients were classified as group 1, 12 as group 2, 10 as group 3 and 11 as group 4. Based on early images, the sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and accuracy of BSGI were 77 %, 83 %, 48 %, 95 % and 82 %, respectively. Dual-phase BSGI had a sensitivity, specificity, PPV, NPV and accuracy of 69 %, 95 %, 75 %, 94 % and 91 %, respectively. The BSGI specificity was significantly higher with dual-phase imaging than with single-phase imaging (p = 0.0078), but the sensitivity did not differ significantly (p = 1.0). Based on dual-phase imaging, the sensitivity, specificity, positive predictive value, negative predictive value and accuracy of BSGI for the evaluation of US BI-RADS 4 lesions were 60 %, 86 %, 67 %, 83 % and 78 %, respectively.

CONCLUSION

Dual-phase imaging in BSGI showed good diagnostic performance and would be useful for increasing interpreter diagnostic confidence, with higher specificity, positive predictive value and accuracy for breast cancer screening as well as the differential diagnosis of breast disease compared with single-phase imaging.

Combining a PI3K inhibitor with a PARP inhibitor provides an effective therapy for BRCA1-related breast cancer

There is a need to improve treatments for metastatic breast cancer. Here, we show the activation of the phosphoinositide 3-kinase (PI3K) and mitogen-activated protein kinase (MAPK) pathways in a MMTV-CreBrca1(f/f)Trp53(+/-) mouse model of breast cancer. When treated with the pan-class IA PI3K inhibitor NVP-BKM120, tumor doubling was delayed from 5 to 26 days. NVP-BKM120 reduced AKT phosphorylation, tumor cell proliferation, and angiogenesis. Resistant tumors maintained suppression of AKT phosphorylation but exhibited activation of the MAPK pathway at the "pushing margin." Surprisingly, PI3K inhibition increased indicators of DNA damage, poly-ADP-ribosylation (PAR), and γ-H2AX, but decreased Rad51 focus formation, suggesting a critical role of PI3K activity for Rad51 recruitment. The PARP inhibitor olaparib alone attenuated tumor growth modestly; however, the combination of NVP-BKM120 and olaparib delayed tumor doubling to more than 70 days in the mouse model and more than 50 days in xenotransplants from human BRCA1-related tumors, suggesting that combined PI3K and PARP inhibition might be an effective treatment of BRCA1-related tumors.

SIGNIFICANCE

Current treatment options for triple-negative breast cancer are limited to chemotherapeutic regimens that have considerable toxicity and are not curative. We report here that the combination of a PI3K inhibitor with a PARP inhibitor provides in vivo synergy for treatment of an endogenous mouse model for BRCA1-related breast cancers, making this a candidate combination to be tested in human clinical trials.

Fluorescence-guided optical coherence tomography imaging for colon cancer screening: a preliminary mouse study

A new concept for cancer screening has been preliminarily investigated. A cancer targeting agent loaded with a near-infrared (NIR) dye was topically applied on the tissue to highlight cancer-suspect locations and guide optical coherence tomography (OCT) imaging, which was used to further investigate tissue morphology at the micron scale. A pilot study on ApcMin mice has been performed to preliminarily test this new cancer screening approach. As a cancer-targeting agent, poly(epsilon-caprolactone) microparticles (PCLMPs), labeled with a NIR dye and functionalized with an RGD (argenine-glycine-aspartic acid) peptide, were used. This agent recognizes the α(ν)β(3) integrin receptor (ABIR), which is over-expressed by epithelial cancer cells. The contrast agent was administered topically in vivo in mouse colon. After incubation, the animals were sacrificed and fluorescence-guided high resolution optical coherence tomography (OCT) imaging was used to visualize colon morphology. The preliminary results show preferential staining of the abnormal tissue, as indicated by both microscopy and laser-induced fluorescence imaging, and OCT's capability to differentiate between normal mucosal areas, early dysplasia, and adenocarcinoma. Although very preliminary, the results of this study suggest that fluorescence-guided OCT imaging might be a suitable approach for cancer screening. If successful, this approach could be used by clinicians to more reliably diagnose early stage cancers in vivo.

Noninvasive detection of breast cancer lymph node metastasis using carbonic anhydrases IX and XII targeted imaging probes

PURPOSE

To develop targeted molecular imaging probes for the noninvasive detection of breast cancer lymph node metastasis.

EXPERIMENTAL DESIGN

Six cell surface or secreted markers were identified by expression profiling and from the literature as being highly expressed in breast cancer lymph node metastases. Two of these markers were cell surface carbonic anhydrase isozymes (CAIX and/or CAXII) and were validated for protein expression by immunohistochemistry of patient tissue samples on a breast cancer tissue microarray containing 47 normal breast tissue samples, 42 ductal carcinoma in situ, 43 invasive ductal carcinomas without metastasis, 46 invasive ductal carcinomas with metastasis, and 49 lymph node macrometastases of breast carcinoma. Targeted probes were developed by conjugation of CAIX- and CAXII-specific monoclonal antibodies to a near-infrared fluorescent dye.

RESULTS

Together, these two markers were expressed in 100% of the lymph node metastases surveyed. Selectivity of the imaging probes were confirmed by intravenous injection into nude mice-bearing mammary fat pad tumors of marker-expressing cells and nonexpressing cells or by preinjection of unlabeled antibody. Imaging of lymph node metastases showed that peritumorally injected probes detected nodes harboring metastatic tumor cells. As few as 1,000 cells were detected, as determined by implanting, under ultrasound guidance, a range in number of CAIX- and CAXII-expressing cells into the axillary lymph nodes.

CONCLUSION

These imaging probes have potential for noninvasive staging of breast cancer in the clinic and elimination of unneeded surgery, which is costly and associated with morbidities.

A mammaglobin-A targeting agent for noninvasive detection of breast cancer metastasis in lymph nodes

Pathologic axillary lymph node (ALN) status is an important prognostic factor for staging breast cancer. Currently, status is determined by histopathology following surgical excision of sentinel lymph node(s), which is an invasive, time consuming, and costly procedure with potential morbidity to the patient. Here, we describe an imaging platform for noninvasive assessment of ALN status, eliminating the need for surgical examination of patients to rule out nodal involvement. A targeted imaging probe (MamAb-680) was developed by conjugation of a mammaglobin-A-specific monoclonal antibody to a near-infrared fluorescent dye. Using DNA and tissue microarray, mammaglobin-A was validated as a cell-surface target that is expressed in ALN-positive patient samples but is not expressed in normal lymph nodes. In vivo selectivity was determined by i.v. injection of MamAb-680 into mice with mammaglobin-A-positive and -negative mammary fat pad (MFP) tumors; and by peritumoral MFP injection of the targeted imaging probe in mice with spontaneous ALN metastases. Fluorescence imaging showed that probe was only retained in positive tumors and metastases. As few as 1,000 cells that endogenously express mammaglobin-A were detected in ALN, indicating high sensitivity of this method. Translation of this approach offers considerable potential as a noninvasive clinical strategy to stage breast cancer.