Breast MR with special focus on DW-MRI and DCE-MRI

Adding quantitative T1rho-weighted imaging to conventional MRI improves specificity and sensitivity for differentiating malignant from benign breast lesions

OBJECTIVES

To investigate the feasibility of T1rho-weighted imaging in differentiating malignant from benign breast lesions and to explore the additional value of T1rho to conventional MRI.

MATERIALS AND METHODS

We prospectively enrolled consecutive women with breast lesions who underwent preoperative T1rho-weighted imaging, diffusion-weighted imaging, and dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) between November 2021 and July 2023. The T1rho, apparent diffusion coefficient (ADC), and semi-quantitative parameters from DCE-MRI were obtained and compared between benign and malignant groups. The diagnostic performance was analyzed and compared using receiver operating characteristic (ROC) curves and the Delong Test.

RESULTS

This study included 113 patients (74 malignant and 39 benign lesions). The mean T1rho value in the benign group (92.61 ± 22.10 ms) was significantly higher than that in the malignant group (72.18 ± 16.37 ms) (P < 0.001). The ADC value and time to peak (TTP) value in the malignant group (1.13 ± 0.45 and 269.06 ± 106.01, respectively) were lower than those in the benign group (1.57 ± 0.45 and 388.30 ± 81.13, respectively) (all P < 0.001). T1rho combined with ADC and TTP showed good diagnostic performance with an area under the curve (AUC) of 0.896, a sensitivity of 81.0%, and a specificity of 87.1%. The specificity and sensitivity of the combination of T1rho, ADC, and TTP were significantly higher than those of the combination of ADC and TTP (87.1% vs. 84.6%, P < 0.005; 81.0% vs. 77.0%, P < 0.001).

CONCLUSION

T1rho-weighted imaging was a feasible MRI sequence for differentiating malignant from benign breast lesions. The combination of T1rho, ADC and TTP could achieve a favorable diagnostic performance with improved specificity and sensitivity, T1rho could serve as a supplementary approach to conventional MRI.

Hypofractionated proton therapy for benign tumors of the central nervous system: A systematic review of the literature

AIMS

Aim of the present analysis was to report results of a systematic review of the literature in the setting of patients treated with hypoF PT for benign lesions of the central nervous system (CNS).

METHODS

The methodology complied with the PRISMA recommendations. PubMed, EMBASE and Scopus databases were interrogated in September 2022.

RESULTS

Twelve papers have been selected including patients treated for base of the skull meningiomas (6 papers), vestibular schwannoma (3 papers) and pituitary adenomas (3 papers). Clinical outcomes were evaluated with both radiologic images and clinical parameters. Long-term toxicity was reported in all but one series with an incidence ranging from 2 % to 7 % in patients treated for base of skull meningioma and 1-9 % for schwannoma.

CONCLUSIONS

HypoF PT is a safe and effective treatment in selected benign tumors of the CNS. Further dosimetric and clinical comparisons are required to better refine the patients' selection criteria.

Machine learning for multi-parametric breast MRI: radiomics-based approaches for lesion classification

In the artificial intelligence era, machine learning (ML) techniques have gained more and more importance in the advanced analysis of medical images in several fields of modern medicine. Radiomics extracts a huge number of medical imaging features revealing key components of tumor phenotype that can be linked to genomic pathways. The multi-dimensional nature of radiomics requires highly accurate and reliable machine-learning methods to create predictive models for classification or therapy response assessment.

Multi-parametric breast magnetic resonance imaging (MRI) is routinely used for dense breast imaging as well for screening in high-risk patients and has shown its potential to improve clinical diagnosis of breast cancer. For this reason, the application of ML techniques to breast MRI, in particular to multi-parametric imaging, is rapidly expanding and enhancing both diagnostic and prognostic power. In this review we will focus on the recent literature related to the use of ML in multi-parametric breast MRI for tumor classification and differentiation of molecular subtypes. Indeed, at present, different models and approaches have been employed for this task, requiring a detailed description of the advantages and drawbacks of each technique and a general overview of their performances.

Current State of Breast Cancer Diagnosis, Treatment, and Theranostics

Breast cancer is one of the leading causes of cancer-related morbidity and mortality in women worldwide. Early diagnosis and effective treatment of all types of cancers are crucial for a positive prognosis. Patients with small tumor sizes at the time of their diagnosis have a significantly higher survival rate and a significantly reduced probability of the cancer being fatal. Therefore, many novel technologies are being developed for early detection of primary tumors, as well as distant metastases and recurrent disease, for effective breast cancer management. Theranostics has emerged as a new paradigm for the simultaneous diagnosis, imaging, and treatment of cancers. It has the potential to provide timely and improved patient care via personalized therapy. In nanotheranostics, cell-specific targeting moieties, imaging agents, and therapeutic agents can be embedded within a single formulation for effective treatment. In this review, we will highlight the different diagnosis techniques and treatment strategies for breast cancer management and explore recent advances in breast cancer theranostics. Our main focus will be to summarize recent trends and technologies in breast cancer diagnosis and treatment as reported in recent research papers and patents and discuss future perspectives for effective breast cancer therapy.

Preclinical Molecular Imaging for Precision Medicine in Breast Cancer Mouse Models

Precision and personalized medicine is gaining importance in modern clinical medicine, as it aims to improve diagnostic precision and to reduce consequent therapeutic failures. In this regard, prior to use in human trials, animal models can help evaluate novel imaging approaches and therapeutic strategies and can help discover new biomarkers. Breast cancer is the most common malignancy in women worldwide, accounting for 25% of cases of all cancers and is responsible for approximately 500,000 deaths per year. Thus, it is important to identify accurate biomarkers for precise stratification of affected patients and for early detection of responsiveness to the selected therapeutic protocol. This review aims to summarize the latest advancements in preclinical molecular imaging in breast cancer mouse models. Positron emission tomography (PET) imaging remains one of the most common preclinical techniques used to evaluate biomarker expression in vivo, whereas magnetic resonance imaging (MRI), particularly diffusion-weighted (DW) sequences, has been demonstrated as capable of distinguishing responders from nonresponders for both conventional and innovative chemo- and immune-therapies with high sensitivity and in a noninvasive manner. The ability to customize therapies is desirable, as this will enable early detection of diseases and tailoring of treatments to individual patient profiles. Animal models remain irreplaceable in the effort to understand the molecular mechanisms and patterns of oncologic diseases.

Differentiation of breast tuberculosis and breast cancer using diffusion-weighted, T2-weighted and dynamic contrast-enhanced magnetic resonance imaging

Background

The use of multi-parametric magnetic resonance imaging (MRI) in the evaluation of breast tuberculosis (BTB).

Objectives

To evaluate the value of diffusion-weighted imaging (DWI), T2-weighted (T2W) and dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) in differentiating breast cancer (BCA) from BTB.

Method

We retrospectively studied images of 17 patients with BCA who had undergone pre-operative MRI and 6 patients with pathologically proven BTB who underwent DCE-MRI during January 2014 to January 2015.

Results

All patients were female, with the age range of BTB patients being 23–43 years and the BCA patients being 31–74 years. Breast cancer patients had a statistically significant lower mean apparent diffusion coefficient (ADC) value (1072.10 ± 365.14), compared to the BTB group (1690.77 ± 624.05, p = 0.006). The mean T2-weighted signal intensity (T2SI) was lower for the BCA group (521.56 ± 233.73) than the BTB group (787.74 ± 196.04, p = 0.020). An ADC mean cut-off value of 1558.79 yielded 66% sensitivity and 94% specificity, whilst the T2SI cut-off value of 790.20 yielded 83% sensitivity and 83% specificity for differentiating between BTB and BCA. The homogeneous internal enhancement for focal mass was seen in BCA patients only.

Conclusion

Multi-parametric MRI incorporating the DWI, T2W and DCE-MRI may be a useful tool to differentiate BCA from BTB.

Retrospective analysis of the utility of multiparametric MRI for differentiating between benign and malignant breast lesions in women in China

We explored the utility of time-resolved angiography with interleaved stochastic trajectories dynamic contrast-enhanced magnetic resonance imaging (TWIST DCE-MRI), readout segmentation of long variable echo-trains diffusion-weighted magnetic resonance imaging- diffusion-weighted magnetic resonance imaging (RESOLVE-DWI), and echo-planar imaging- diffusion-weighted magnetic resonance imaging (EPI-DWI) for distinguishing between malignant and benign breast lesions.This retrospective analysis included female patients with breast lesions seen at a single center in China between January 2016 and April 2016. Patients were allocated to a benign or malignant group based on pathologic diagnosis. All patients received routine MRI, RESOLVE-DWI, EPI-DWI, and TWIST DCE-T1WI. Variables measured included quantitative parameters (K, Kep, and Ve), semiquantitative parameters (rate of contrast enhancement for contrast agent inflow [W-in], rate of contrast decay for contrast agent outflow [W-out], and time-to-peak enhancement after contrast agent injection [TTP]) and apparent diffusion coefficient (ADC) values for RESOLVE-DWI (ADCr) and EPI-DWI (ADCe). Receiver-operating characteristic (ROC) curve analysis was used to evaluate the diagnostic utility of each parameter for differentiating malignant from benign breast lesions.A total of 87 patients were included (benign, n = 20; malignant, n = 67). Compared with the benign group, the malignant group had significantly higher K, Kep and W-in and significantly lower W-out, TTP, ADCe, and ADCr (all P < .05); Ve was not significantly different between groups. RESOLVE-DWI was superior to conventional EPI-DWI at illustrating lesion boundary and morphology, while ADCr was significantly lower than ADCe in all patients. Kep, W-out, ADCr, and ADCe showed the highest diagnostic efficiency (based on AUC value) for differentiating between benign and malignant lesions. Combining 3 parameters (Kep, W-out, and ADCr) had a higher diagnostic efficiency (AUC, 0.965) than any individual parameter and distinguished between benign and malignant lesions with high sensitivity (91.0%), specificity (95.0%), and accuracy (91.9%).An index combining Kep, W-out, and ADCr could potentially be used for the differential diagnosis of breast lesions.

Percentage change of primary tumor on 18F-FDG PET/CT as a prognostic factor for invasive ductal breast cancer with axillary lymph node metastasis: Comparison with MRI

We evaluated the prognostic value of quantitative parameters using dual time point (DTP) F-FDG PET/CT (PET/CT) in invasive ductal breast cancer (IDC) with metastatic axillary lymph nodes (ALN) as compared with dynamic contrast-enhanced (DCE) and diffusion-weighted (DW) MRI.Seventy patients with IDC and metastatic ALN were retrospectively registered. Static PET parameters including maximum standardized uptake value (SUVmax), metabolic tumor volume (MTV), total lesion glycolysis (TLG) of primary tumor, SUVmax of ALN (SUVALN), and percentage changes (Δ%) in those parameters were measured with DTP PET/CT. From DCE MRI, peak enhancement value, total tumor angio volume, and proportions of kinetic curve types on delayed-phases were investigated. The average apparent diffusion coefficient (ADCavg) was estimated on DWI. To demonstrate the prognostic value of quantitative imaging parameters for recurrence-free survival (RFS), univariate and multivariate analyses were performed using those parameters and clinicohistologic variables.All static PET parameters, %ΔSUVmax, %ΔMTV, and %ΔSUVALN on DTP PET/CT and ADCavg on DWI were significantly predictive for disease recurrence. Of clinicohistologic variables, pathologic tumor (pT) diameter, pathologic ALN stage, tumor grade, and hormonal status also were significantly prognostic. After multivariate analysis, %ΔSUVmax > 25.05 (P = .043), ADCavg ≤ 1016.55 (P = .020), pT diameter > 3 cm (P = .001), and ER negative status (P = .002) were independent prognostic factors for poor outcome.Only %ΔSUVmax of the primary tumor on PET/CT together with ADCavg, pT diameter, and ER status was an independent prognostic factor for predicting relapse in IDC with metastatic ALN. Percentage change of primary tumor on preoperative PET/CT may be a valuable imaging marker for selecting IDC patients that require adjunct treatment to prevent relapse.

Accuracy of diffusion kurtosis imaging in characterization of breast lesions

OBJECTIVE

The aim of this study was to evaluate the accuracy of diffusion kurtosis in the characterization and differentiation of breast lesions.

METHODS

49 females with 53 breast lesions underwent breast MRI. The MRI magnetic field is 1.5 T, and the protocol is standard MRI sequences, dynamic sequences pre- and post-contrast agent administration and diffusion images. Diffusion kurtosis imaging (DKI) was applied as part of our standard breast MRΙ protocol. Two experienced radiologists on breast MRI, blinded to the final diagnosis, reviewed the parametric maps and placed a volume of interest on all slices including each lesion. Kurtosis [K apparent (K_app)] and corrected apparent diffusion coefficient [D apparent (D_app)] median values were then calculated from the whole-lesion histogram analysis. Receiver-operating characteristic analysis was used to determine the most effective cut-off values for the differentiation between benign and malignant pathologies. Histological analysis of the breast lesions was performed, and further comparative analysis of the results was performed to investigate the accuracy of the method.

RESULTS

Benign (n = 19) and malignant lesions (n = 34) had mean diameters of 20.8 mm (10.1-31.5 mm) and 26.4 mm (10.5-42.3 mm), respectively. The lowest and the highest kurtosis values (K_app) of malignant lesions were significantly higher than those of benign lesions. A cut-off of 0.71 provided specificity of 93.7% and sensitivity 97.1%, and the area under the curve (AUC) was 0.976 (p < 0.0001). The lowest and the highest D_app values of malignant lesions were lower than those of benign lesions. A cut-off value of 1.57 × 10^-3 mm² s^-1 provided specificity of 93.7% and sensitivity of 91.2% with AUC of 0.949 (p < 0.0001).

CONCLUSION

DKI is an accurate additional tool for the characterization and differentiation of breast lesions with high K_app and D_app sensitivity and specificity rates. Advances in knowledge: DKI is able to distinguish benign from malignant breast pathologies. DKI increases the specificity of breast MRI.

PET and MRI: Is the Whole Greater than the Sum of Its Parts?

Over the past decades, imaging in oncology has been undergoing a "quiet" revolution to treat images as data, not as pictures. This revolution has been sparked by technological advances that enable capture of images that reflect not only anatomy, but also of tissue metabolism and physiology in situ Important advances along this path have been the increasing power of MRI, which can be used to measure spatially dependent differences in cell density, tissue organization, perfusion, and metabolism. In parallel, PET imaging allows quantitative assessment of the spatial localization of positron-emitting compounds, and it has also been constantly improving in the number of imageable tracers to measure metabolism and expression of macromolecules. Recent years have witnessed another technological advance, wherein these two powerful modalities have been physically merged into combined PET/MRI systems, appropriate for both preclinical or clinical imaging. As with all new enabling technologies driven by engineering physics, the full extent of potential applications is rarely known at the outset. In the work of Schmitz and colleagues, the authors have combined multiparametric MRI and PET imaging to address the important issue of intratumoral heterogeneity in breast cancer using both preclinical and clinical data. With combined PET and MRI and sophisticated machine-learning tools, they have been able identify multiple coexisting regions ("habitats") within living tumors and, in some cases, have been able to assign these habitats to known histologies. This work addresses an issue of fundamental importance to both cancer biology and cancer care. As with most new paradigm-shifting applications, it is not the last word on the subject and introduces a number of new avenues of investigation to pursue. Cancer Res; 76(21); 6163-6. ©2016 AACR.

Meta analysis of lymph node metastasis of breast cancer patients: Clinical value of DWI and ADC value

OBJECTIVES

To evaluate the diagnostic utility of DWI in the assessment of node metastases and investigate whether the ADC value could be used to discriminate between metastatic and non-metastatic lymph nodes in breast cancer patients.

MATERIALS AND METHODS

13 studies with a total of 676 metastatic and 811 non-metastatic lymph nodes were included.

RESULTS

(1) The pooled sensitivity, specificity, PPV and NPV of DWI were 0.83, 0.82, 0.83 and 0.85, respectively. The PLR and NLR were 4.95 and 0.23, respectively. The AUC and Q* index were 0.91 and 0.85, respectively. (2) The ADC value of metastatic lymph nodes was lower than non-metastatic lymph nodes (WMD=-0.213, 95% CI -0.349 to -0.076, Z=3.05, P<0.05). (3) Subgroup meta-analysis of the group of b(0800): The pooled sensitivity, specificity, PPV and NPV of DWI were 0.86, 0.86, 0.82 and 0.90, respectively. The PLR and NLR were 6.76 and 0.18, respectively. The AUC and Q* index were 0.93 and 0.87. The ADC value of metastatic lymph nodes was lower than non-metastatic lymph nodes(WMD=-0.267, 95% CI -0.348 to -0.185, Z=6.40, P<0.05).

CONCLUSIONS

DWI and ADC value appear to be a reliable method to differentiate metastatic and non-metastatic lymph nodes. The combination of b=0 and 800s/mm(2) resulted in higher diagnostic accuracy and more pronounced ADC value difference. If only a couple of b values are used, those of b=0 and 800s/mm(2) are recommended.

Qualitative and quantitative diffusion-weighted imaging of the breast at 3T - A useful adjunct to contrast-enhanced MRI in characterization of breast lesions

Objective:

To distinguish between benign and malignant breast lesions on the basis of their signal intensity on diffusion-weighted imaging and their apparent diffusion coefficient (ADC) values at 3 T MRI, along with histopathological correlation.

Materials and Methods:

A retrospective analysis of 500 patients who underwent 3 T MRI between August 2011 and May 2013 was done. Of these, 226 patients with 232 lesions that were proved by histopathology were included in the study. ADC values were calculated at b values of 0, 1000, and 1500 s/mm² after identification on contrast-enhanced images and appropriate ROI(Region of interest) placement. ADC value and histopathology correlation was analyzed.

Results:

Out of 232 lesions, 168 lesions were histologically malignant and 64 were histologically benign. With an ADC cut-off value of 1.1 ×10⁻³ mm²/s for malignant lesions, a sensitivity of 92.80% and specificity of 80.23% was obtained. Out of 12/232 false-negative lesions, 6 were mucinous carcinoma in which a high ADC value of 1.8-1.9 ×10⁻³ mm²/s was obtained. Purely DCIS (Ductal carcinoma in situ) lesions presenting as non-mass-like enhancement had a high ADC value of 1.2-1.5 ×10⁻³ mm²/s, thereby reducing specificity.

Conclusion:

Diffusion-weighted Imaging and quantitative assessment by ADC values may act as an effective parameter in increasing the diagnostic accuracy and specificity of contrast-enhanced breast MRI in characterization of breast lesions.

Apparent diffusion coefficient measurement by diffusion weighted magnetic resonance imaging is a useful tool in differentiating renal tumors

BACKGROUND

To determine the clinical value of apparent diffusion coefficient (ADC) measurement by diffusion weighted magnetic resonance imaging (DW-MRI) in differentiating renal tumors.

METHODS

Electronic databases were searched using combinations of keywords and free words relating to renal tumor, ADC and DW-MRI. Based on carefully selected inclusion and exclusion criteria, relevant case-control studies were identified and the related clinical data was acquired. Statistical analyses were performed using STATA 12.0 (Stata Corporation, College station, TX).

RESULTS

Sixteen case-control studies were ultimately included in the present meta-analysis. These 16 high quality studies contained a combined total of 438 normal renal tissues and 832 renal tumor lesions (597 malignant and 235 benign). The results revealed that ADC values of malignant renal tumor tissues were markedly lower than normal renal tissues and benign renal tumor tissues. ADC values of benign renal tumor tissues were also significantly lower than normal renal tissue.

CONCLUSIONS

ADC measurement by DW-MRI provided clinically useful information on the internal structure of renal tumors and could be an important radiographic index for differentiation of malignant renal tumors from benign renal tumors.

Characterization of breast tumors using diffusion kurtosis imaging (DKI)

Aim

The aim of this study was to investigate and evaluate the role of magnetic resonance (MR) diffusion kurtosis imaging (DKI) in characterizing breast lesions.

Materials and Methods

One hundred and twenty-four lesions in 103 patients (mean age: 57±14 years) were evaluated by MR DKI performed with 7 b-values of 0, 250, 500, 750, 1,000, 1,500, 2,000 s/mm² and dynamic contrast-enhanced (DCE) MR imaging. Breast lesions were histologically characterized and DKI related parameters—mean diffusivity (MD) and mean kurtosis (MK)—were measured. The MD and MK in normal fibroglandular breast tissue, benign and malignant lesions were compared by One-way analysis of variance (ANOVA) with Tukey's multiple comparison test. Receiver operating characteristic (ROC) analysis was performed to assess the sensitivity and specificity of MD and MK in the diagnosis of breast lesions.

Results

The benign lesions (n = 42) and malignant lesions (n = 82) had mean diameters of 11.4±3.4 mm and 35.8±20.1 mm, respectively. The MK for malignant lesions (0.88±0.17) was significantly higher than that for benign lesions (0.47±0.14) (P<0.001), and, in contrast, MD for benign lesions (1.97±0.35 (10⁻³ mm²/s)) was higher than that for malignant lesions (1.20±0.31 (10⁻³ mm²/s)) (P<0.001). At a cutoff MD/MK 1.58 (10⁻³ mm²/s)/0.69, sensitivity and specificity of MD/MK for the diagnosis of malignant were 79.3%/84.2% and 92.9%/92.9%, respectively. The area under the curve (AUC) is 0.86/0.92 for MD/MK.

Conclusions

DKI could provide valuable information on the diffusion properties related to tumor microenvironment and increase diagnostic confidence of breast tumors.

Diagnostic assessment by dynamic contrast-enhanced and diffusion-weighted magnetic resonance in differentiation of breast lesions under different imaging protocols

BACKGROUND

The apparent diffusion coefficient (ADC) is a highly diagnostic factor in discriminating malignant and benign breast masses in diffusion-weighted magnetic resonance imaging (DW-MRI). The combination of ADC and other pictorial characteristics has improved lesion type identification accuracy. The objective of this study was to reassess the findings on an independent patient group by changing the magnetic field from 1.5-Tesla to 3.0-Tesla.

METHODS

This retrospective study consisted of a training group of 234 female patients, including 85 benign and 149 malignant lesions, imaged using 1.5-Tesla MRI, and a test group of 95 female patients, including 19 benign and 85 malignant lesions, imaged using 3.0-Tesla MRI. The lesion of interest was segmented from the raw image and four sets of measurements describing the morphology, kinetics, DW-MRI, and texture of the pictorial properties of each lesion were obtained. Each lesion was characterized by 28 features in total. Three classical machine-learning algorithms were used to build prediction models on the training group, which evaluated the prognostic performance of the multi-sided features in three scenarios. To reduce information redundancy, five highly diagnostic factors were selected to obtain a compact yet informative characterization of the lesion status.

RESULTS

Three classification models were built on the training of 1.5-Tesla patients and were tested on the independent 3.0-Tesla test group. The following results were found. i) Characterization of breast masses in a multi-sided way dramatically increased prediction performance. The usage of all features gave a higher performance in both sensitivity and specificity than any individual feature groups or their combinations. ii) ADC was a highly effective factor in improving the sensitivity in discriminating malignant from benign masses. iii) Five features, namely ADC, Sum Average, Entropy, Elongation, and Sum Variance, were selected to achieve the highest performance in diagnosis of the 3.0-Tesla patient group.

CONCLUSIONS

The combination of ADC and other multi-sided characteristics can increase the capability of discriminating malignant and benign breast lesions, even under different imaging protocols. The selected compact feature subsets achieved a high diagnostic performance and thus are promising in clinical applications for discriminating lesion type and for personalized treatment planning.

The potential of hypoxia markers as target for breast molecular imaging--a systematic review and meta-analysis of human marker expression

Background

Molecular imaging of breast cancer is a promising emerging technology, potentially able to improve clinical care. Valid imaging targets for molecular imaging tracer development are membrane-bound hypoxia-related proteins, expressed when tumor growth outpaces neo-angiogenesis. We performed a systematic literature review and meta-analysis of such hypoxia marker expression rates in human breast cancer to evaluate their potential as clinically relevant molecular imaging targets.

Methods

We searched MEDLINE and EMBASE for articles describing membrane-bound proteins that are related to hypoxia inducible factor 1α (HIF-1α), the key regulator of the hypoxia response. We extracted expression rates of carbonic anhydrase-IX (CAIX), glucose transporter-1 (GLUT1), C-X-C chemokine receptor type-4 (CXCR4), or insulin-like growth factor-1 receptor (IGF1R) in human breast disease, evaluated by immunohistochemistry. We pooled study results using random-effects models and applied meta-regression to identify associations with clinicopathological variables.

Results

Of 1,705 identified articles, 117 matched our selection criteria, totaling 30,216 immunohistochemistry results. We found substantial between-study variability in expression rates. Invasive cancer showed pooled expression rates of 35% for CAIX (95% confidence interval (CI): 26-46%), 51% for GLUT1 (CI: 40-61%), 46% for CXCR4 (CI: 33-59%), and 46% for IGF1R (CI: 35-70%). Expression rates increased with tumor grade for GLUT1, CAIX, and CXCR4 (all p < 0.001), but decreased for IGF1R (p < 0.001). GLUT1 showed the highest expression rate in grade III cancers with 58% (45-69%). CXCR4 showed the highest expression rate in small T1 tumors with 48% (CI: 28-69%), but associations with size were only significant for CAIX (p < 0.001; positive association) and IGF1R (p = 0.047; negative association). Although based on few studies, CAIX, GLUT1, and CXCR4 showed profound lower expression rates in normal breast tissue and benign breast disease (p < 0.001), and high rates in carcinoma in situ. Invasive lobular carcinoma consistently showed lower expression rates (p < 0.001).

Conclusions

Our results support the potential of hypoxia-related markers as breast cancer molecular imaging targets. Although specificity is promising, combining targets would be necessary for optimal sensitivity. These data could help guide the choice of imaging targets for tracer development depending on the envisioned clinical application.