Magnetic resonance imaging in breast cancer: A literature review and future perspectives

Employing Raman Spectroscopy and Machine Learning for the Identification of Breast Cancer

BACKGROUND

Breast cancer poses a significant health risk to women worldwide, with approximately 30% being diagnosed annually in the United States. The identification of cancerous mammary tissues from non-cancerous ones during surgery is crucial for the complete removal of tumors.

RESULTS

Our study innovatively utilized machine learning techniques (Random Forest (RF), Support Vector Machine (SVM), and Convolutional Neural Network (CNN)) alongside Raman spectroscopy to streamline and hasten the differentiation of normal and late-stage cancerous mammary tissues in mice. The classification accuracy rates achieved by these models were 94.47% for RF, 96.76% for SVM, and 97.58% for CNN, respectively. To our best knowledge, this study was the first effort in comparing the effectiveness of these three machine-learning techniques in classifying breast cancer tissues based on their Raman spectra. Moreover, we innovatively identified specific spectral peaks that contribute to the molecular characteristics of the murine cancerous and non-cancerous tissues.

CONCLUSIONS

Consequently, our integrated approach of machine learning and Raman spectroscopy presents a non-invasive, swift diagnostic tool for breast cancer, offering promising applications in intraoperative settings.

Abbreviated breast magnetic resonance imaging (MRI) or digital breast tomosynthesis for breast cancer detection in dense breasts? A retrospective preliminary study with comparable results

AIM

To compare the diagnostic performance of abbreviated breast magnetic resonance (AB-MR) imaging (MRI) and digital breast tomosynthesis (DBT) for breast cancer detection in Malaysian women with dense breasts, using histopathology as the reference standard.

MATERIALS AND METHODS

This was a single-centre cross-sectional study of 115 women with American College of Radiology (ACR) Breast Imaging-Reporting and Data System (BIRADS) breast density C and D on DBT with breast lesions who underwent AB-MR from June 2018 to December 2021. AB-MR was performed on a 3 T MRI system with an imaging protocol consisting of three sequences: axial T1 fat-saturated unenhanced; axial first contrast-enhanced; and subtracted first contrast-enhanced with maximum intensity projection (MIP). DBT and AB-MR images were evaluated by two radiologists blinded to the histopathology and patient outcomes. Diagnostic accuracy (sensitivity, specificity, positive predictive value [PPV] and negative predictive value [NPV]) was assessed.

RESULT

Of the 115 women, the mean age was 50.6 years. There were 48 (41.7%) Malay, 54 (47%) Chinese, and 12 (10.4%) Indian women. The majority (n=87, 75.7%) were from the diagnostic population. Sixty-one (53.1%) were premenopausal and 54 (46.9%) postmenopausal. Seventy-eight (72.4%) had an increased risk of developing breast cancer. Ninety-one (79.1%) women had density C and 24 (20.9%) had density D. There were 164 histopathology-proven lesions; 69 (42.1%) were malignant and 95 (57.9%) were benign. There were 62.8% (n=103/164) lesions detected at DBT. All the malignant lesions 100% (n=69) and 35.7% (n=34) of benign lesions were detected. Of the 61 lesions that were not detected, 46 (75.4%) were in density C, and 15 (24.6%) were in density D. The sensitivity, specificity, PPV, and NPV for DBT were 98.5%, 34.6%, 66.3%, and 94.7%, respectively. There were 65.2% (n=107/164) lesions detected on AB-MR, with 98.6% (n=68) malignant and 41.1% (39) benign lesions detected. The sensitivity, specificity, PPV, and NPV for AB-MR were 98.5%, 43.9%, 67.2%, and 96.2%, respectively. One malignant lesion (0.6%), which was a low-grade ductal carcinoma in-situ (DCIS), was missed on AB-MR.

CONCLUSION

The present findings suggest that both DBT and AB-MR have comparable effectiveness as an imaging method for detecting breast cancer and have high NPV for low-risk lesions in women with dense breasts.

Comparative Effectiveness of Mammography, Ultrasound, and MRI in the Detection of Breast Carcinoma in Dense Breast Tissue: A Systematic Review

This systematic review aimed to critically assess the effectiveness of mammography, ultrasound, and magnetic resonance imaging (MRI) in the detection of breast carcinoma within dense breast tissue. An exhaustive search of contemporary literature was undertaken, focusing on the diagnostic accuracy, false positive and negative rates, and clinical implications of the aforementioned imaging modalities. Each modality was assessed in isolation and side by side against the others to draw comparative inferences. While mammography remains a foundational imaging modality, its effectiveness waned within the context of dense breast tissue. Ultrasound demonstrated a strong differentiation prowess, especially among specific demographic cohorts. MRI, despite its exceptional precision and differentiation capabilities, exhibited a tendency for slightly elevated false positive rates. No single modality emerged as singularly superior for all cases. Instead, an integrated approach, combining the strengths of each modality based on individual patient profiles and clinical scenarios, is recommended. This tailored approach ensures optimized detection rates and minimizes diagnostic ambiguities, underscoring the significance of individualized patient care in the field of diagnostic radiology.

Evaluation of Features in Probably Benign and Malignant Nonmass Enhancement in Breast MRI

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is a highly sensitive breast imaging modality in detecting breast carcinoma. Nonmass enhancement (NME) is uniquely seen on MRI of the breast. The correlation between NME features and pathologic results has not been extensively explored. Our goal was to evaluate the characteristics of probably benign and suspicious NME lesions in MRI and determine which features are more associated with malignancy. We performed a retrospective research after approval by the hospital ethics committee on women who underwent breast MRI from March 2017 to March 2020 and identified 63 lesions of all 400 NME that were categorized as probably benign or suspicious according to the BI-RADS classification (version 2013). MRI features of NME findings including the location, size, distribution and enhancement pattern, kinetic curve, diffusion restriction, and also pathology result or 6-12-month follow-up MRI were evaluated and analyzed in each group (probably benign or suspicious NME). Vacuum-guided biopsies (VAB) were performed under mammographic or sonographic guidance and confirmed with MRI by visualization of the inserted clips. Segmental distribution and clustered ring internal enhancement were significantly associated with malignancy (p value<0.05), while linear distribution or homogeneous enhancement patterns were associated with benignity (p value <0.05). Additionally, the plateau and washout types in the dynamic curve were only seen in malignant lesions (p value <0.05). The presence of DWI restriction in NME lesions was also found to be a statistically important factor. Understanding the imaging findings of malignant NME is helpful to determine when biopsy is indicated. The correlation between NME features and pathologic results is critical in making appropriate management.

In vivo lymph node CEST-Dixon MRI in breast cancer patients with metastatic lymph node involvement

PURPOSE

Axillary lymph nodes (LNs) often present a reservoir for metastatic breast cancer, yet metastatic LN involvement cannot be discerned definitively using diagnostic imaging. This study investigated whether in vivo CEST may discriminate LNs with versus without metastatic involvement.

METHODS

3T MRI was performed in patients with breast cancer before clinically-indicated mastectomy or lumpectomy with LN removal, after which LN metastasic involvement was determined using histological evaluation. Non-contrast anatomical imaging, as well as B0 and B1 field maps, were acquired in sequence with three-point CEST-Dixon (3D turbo-gradient-echo; factor = 25; TR/TE1/ΔTE = 851/1.35/1.1 ms; spatial-resolution = 2.5 × 2.5 × 6 mm; slices = 10; four sinc-gauss pulses with duty-cycle = 0.5, total saturation duration = 701.7 ms; B1  = 1.5 μT; saturation offsets = -5.5 to +5.5 ppm; stepsize = 0.2 ppm; scan duration = 6 min 30 s). The mean z-spectrum from LNs with (n = 20) versus without (n = 22) metastatic involvement were analyzed and a Wilcoxon rank-sum test (significance: p < 0.05) was applied to evaluate differences in B0, B1 , and magnetization transfer ratio (MTR) in differing spectral regions of known proton exchange (nuclear Overhauser effect [NOE], amide, amine, and hydroxyl) between cohorts.

RESULTS

No difference in axillary B1 (p = 0.634) or B0 (p = 0.689) was observed between cohorts. Elevated MTR was observed for the NOE (-1.7 ppm; MTR = 0.285 ± 0.075 vs. 0.248 ± 0.039; p = 0.048), amine (+2.5 ppm; MTR = 0.284 ± 0.067 vs. 0.234 ± 0.31; p = 0.005), and hydroxyl (+1 ppm; MTR = 0.394 ± 0.075 vs. 0.329 ± 0.055; p = 0.002) protons in LNs from participants with versus without metastatic involvement.

CONCLUSIONS

Findings are consistent with a unique metastatic LN microenvironment detectable by CEST-Dixon and suggest that CEST MRI may have potential for mapping LN metastasis non-invasively in vivo.

Towards a fast PET/MRI protocol for breast cancer imaging: maintaining diagnostic confidence while reducing PET and MRI acquisition times

OBJECTIVES

To investigate the diagnostic feasibility of a shortened breast PET/MRI protocol in breast cancer patients.

METHODS

Altogether 90 women with newly diagnosed T1tumor-staged (T1ts) and T2tumor-staged (T2ts) breast cancer were included in this retrospective study. All underwent a dedicated comprehensive breast [18F]FDG-PET/MRI. List-mode PET data were retrospectively reconstructed with 20, 15, 10, and 5 min for each patient to simulate the effect of reduced PET acquisition times. The SUVmax/mean of all malign breast lesions was measured. Furthermore, breast PET data reconstructions were analyzed regarding image quality, lesion detectability, signal-to-noise ratio (SNR), and image noise (IN). The simultaneously acquired comprehensive MRI protocol was then shortened by retrospectively removing sequences from the protocol. Differences in malignant breast lesion detectability between the original and the fast breast MRI protocol were evaluated lesion-based. The 20-min PET reconstructions and the original MRI protocol served as reference.

RESULTS

In all PET reconstructions, 127 congruent breast lesions could be detected. Group comparison and T1ts vs. T2ts subgroup comparison revealed no significant difference of subjective image quality between 20, 15, 10, and 5 min acquisition times. SNR of qualitative image evaluation revealed no significant difference between different PET acquisition times. A slight but significant increase of IN with decreasing PET acquisition times could be detected. Lesion SUVmax group comparison between all PET acquisition times revealed no significant differences. Lesion-based evaluation revealed no significant difference in breast lesion detectability between original and fast breast MRI protocols.

CONCLUSIONS

Breast [18F]FDG-PET/MRI protocols can be shortened from 20 to below 10 min without losing essential diagnostic information.

KEY POINTS

• A highly accurate breast cancer evaluation is possible by the shortened breast [18F]FDG-PET/MRI examination protocol. • Significant time saving at breast [18F]FDG-PET/MRI protocol could increase patient satisfaction and patient throughput for breast cancer patients at PET/MRI.

MRI-Based Radiomics Approach Predicts Tumor Recurrence in ER + /HER2 - Early Breast Cancer Patients

Oncotype Dx Recurrence Score (RS) has been validated in patients with ER + /HER2 - invasive breast carcinoma to estimate patient risk of recurrence and guide the use of adjuvant chemotherapy. We investigated the role of MRI-based radiomics features extracted from the tumor and the peritumoral tissues to predict the risk of tumor recurrence. A total of 62 patients with biopsy-proved ER + /HER2 - breast cancer who underwent pre-treatment MRI and Oncotype Dx were included. An RS > 25 was considered discriminant between low-intermediate and high risk of tumor recurrence. Two readers segmented each tumor. Radiomics features were extracted from the tumor and the peritumoral tissues. Partial least square (PLS) regression was used as the multivariate machine learning algorithm. PLS β-weights of radiomics features included the 5% features with the largest β-weights in magnitude (top 5%). Leave-one-out nested cross-validation (nCV) was used to achieve hyperparameter optimization and evaluate the generalizable performance of the procedure. The diagnostic performance of the radiomics model was assessed through receiver operating characteristic (ROC) analysis. A null hypothesis probability threshold of 5% was chosen (p < 0.05). The exploratory analysis for the complete dataset revealed an average absolute correlation among features of 0.51. The nCV framework delivered an AUC of 0.76 (p = 1.1∙10-3). When combining "early" and "peak" DCE images of only T or TST, a tendency toward statistical significance was obtained for TST with an AUC of 0.61 (p = 0.05). The 47 features included in the top 5% were balanced between T and TST (23 and 24, respectively). Moreover, 33/47 (70%) were texture-related, and 25/47 (53%) were derived from high-resolution images (1 mm). A radiomics-based machine learning approach shows the potential to accurately predict the recurrence risk in early ER + /HER2 - breast cancer patients.

False-positive incidental lesions detected on contrast-enhanced breast MRI: clinical and imaging features

PURPOSE

To identify demographic and imaging features of MRI-detected enhancing lesions without clinical, ultrasound, and mammographic correlation associated with false-positive outcomes, impacting patient care.

MATERIALS AND METHODS

A retrospective multi-institutional study of imaging studies and patient's chart review of consecutive women with MRI-detected enhancing lesions without clinical, mammogram, or ultrasound correlation between January and December 2018, who underwent MRI-guided biopsy. According to the BI-RADS lexicon, lesions' frequency and imaging features were recorded. The demographic and imaging characteristics variables were correlated with histopathology as the gold standard and an uneventful follow-up of at least one year. Univariate logistic regression analysis was used to explore the correlation between the baseline variables such as age, genetic mutation, family history of breast cancer, personal history of breast cancer, MRI indication, background parenchymal enhancement, and MRI characteristic of the lesion with the false-positive results in main data and subgroup analysis.

RESULTS

Two hundred nineteen women (median age 49 years; range 26-85 years) with 219 MRI-detected enhancing lesions that underwent MRI-guided vacuum-assisted biopsy during the study period fulfilled the study criteria and formed the study cohort. Out of 219, 180 lesions (82.2%) yielded benign pathology results, including 137 benign outcomes (76%) and 43 high-risk lesions (24%). Most demographic and imaging characteristics variables did not help to differentiate malignant from benign lesions. The variables that showed statistically significant association with true-positive results in univariate analyses were age (OR 1.05; 95% CI 1.02-1.08; p = 0.0015), irregular mass-lesion shape when compared with oval/round mass lesion (OR 11.2; 95% CI 1.6-78.4; p = 0.015), and clumped and clustered ring of enhancement when compared with homogeneous (OR 3.22, 95% CI 1.40-7.40; p = 0.0058). For participants with mass breast lesion, the hyperintense signal on the T2-weighted sequence (compared to the normal fibroglandular signal) was significantly related to the false-positive result (OR 0.13; 95% CI 0.02-0.76; p = 0.024).

CONCLUSION

Young patients, oval/round mass-lesion shape, and homogeneous pattern of non-mass enhancement showed the strongest association with false-positive results of enhancing lesions depicted by MRI. For participants with mass breast lesion, T2-bright mass lesion showed significant association with false-positive result. It may impact the patient's management with a suggestion of follow-up rather than interventional procedure when these demographic and imaging parameters are present, consequently decreasing the patient's anxiety and health care costs.

Validation of Combined Deep Learning Triaging and Computer-Aided Diagnosis in 2901 Breast MRI Examinations From the Second Screening Round of the Dense Tissue and Early Breast Neoplasm Screening Trial

OBJECTIVES

Computer-aided triaging (CAT) and computer-aided diagnosis (CAD) of screening breast magnetic resonance imaging have shown potential to reduce the workload of radiologists in the context of dismissing normal breast scans and dismissing benign disease in women with extremely dense breasts. The aim of this study was to validate the potential of integrating CAT and CAD to reduce workload and workup on benign lesions in the second screening round of the DENSE trial, without missing cancer.

METHODS

We included 2901 breast magnetic resonance imaging scans, obtained from 8 hospitals in the Netherlands. Computer-aided triaging and CAD were previously developed on data from the first screening round. Computer-aided triaging dismissed examinations without lesions. Magnetic resonance imaging examinations triaged to radiological reading were counted and subsequently processed by CAD. The number of benign lesions correctly classified by CAD was recorded. The false-positive fraction of the CAD was compared with that of unassisted radiological reading in the second screening round. Receiver operating characteristics (ROC) analysis was performed and the generalizability of CAT and CAD was assessed by comparing results from first and second screening rounds.

RESULTS

Computer-aided triaging dismissed 950 of 2901 (32.7%) examinations with 49 lesions in total; none were malignant. Subsequent CAD classified 132 of 285 (46.3%) lesions as benign without misclassifying any malignant lesion. Together, CAT and CAD yielded significantly fewer false-positive lesions, 53 of 109 (48.6%) and 89 of 109 (78.9%), respectively ( P = 0.001), than radiological reading alone. Computer-aided triaging had a smaller area under the ROC curve in the second screening round compared with the first, 0.83 versus 0.76 ( P = 0.001), but this did not affect the negative predictive value at the 100% sensitivity operating threshold. Computer-aided diagnosis was not associated with significant differences in area under the ROC curve (0.857 vs 0.753, P = 0.08). At the operating thresholds, the specificities of CAT (39.7% vs 41.0%, P = 0.70) and CAD (41.0% vs 38.2%, P = 0.62) were successfully reproduced in the second round.

CONCLUSION

The combined application of CAT and CAD showed potential to reduce workload of radiologists and to reduce number of biopsies on benign lesions. Computer-aided triaging (CAT) correctly dismissed 950 of 2901 (32.7%) examinations with 49 lesions in total; none were malignant. Subsequent computer-aided diagnosis (CAD) classified 132 of 285 (46.3%) lesions as benign without misclassifying any malignant lesion. Together, CAT and CAD yielded significantly fewer false-positive lesions, 53 of 109 (48.6%) and 89 of 109 (78.9%), respectively ( P = 0.001), than radiological reading alone.

Magnetic particle based MRI thermometry at 0.2 T and 3 T

This study provides insight into the advantages and disadvantages of using ferrite particles embedded in agar gel phantoms as MRI temperature indicators for low-magnetic field scanners. We compare the temperature-dependent intensity of MR images at low-field (0.2 T) to those at high-field (3.0 T). Due to a shorter T₁ relaxation time at low-fields, MRI scanners operating at 0.2 T can use shorter repetition times and achieve a significant T₂^⁎ weighting, resulting in strong temperature-dependent changes of MR image brightness in short acquisition times. Although the signal-to-noise ratio for MR images at 0.2 T MR is much lower than at 3.0 T, it is sufficient to achieve a temperature measurement uncertainty of about ±1.0 °C at 37 °C for a 90 μg/mL concentration of magnetic particles.

The effectiveness of an ultrafast breast MRI protocol in the differentiation of benign and malignant breast lesions

AIM

To evaluate the effectiveness of an ultrafast breast magnetic resonance imaging (MRI) protocol in differentiating benign and malignant breast lesions.

MATERIALS AND METHODS

Fifty-four patients with Breast Imaging Reporting and Data System (BI-RADS) 4 or 5 lesions were recruited between July 2020 to May 2021. A standard breast MRI was performed with the inclusion of the ultrafast protocol between the unenhanced sequence and the first contrast-enhanced sequence. Three radiologists performed image interpretation in consensus. Ultrafast kinetic parameters analysed included the maximum slope (MS), time to enhancement (TTE), and arteriovenous index (AVI). These parameters were compared using receiver operating characteristics with p-values of <0.05 considered to indicate statistical significance.

RESULTS

Eighty-three histopathological proven lesions from 54 patients (mean age 53.87 years, SD 12.34, range 26-78 years) were analysed. Forty-one per cent (n=34) were benign and 59% (n=49) were malignant. All malignant and 38.2% (n=13) benign lesions were visualised on the ultrafast protocol. Of the malignant lesions, 77.6% (n=53) were invasive ductal carcinoma (IDC) and 18.4% (n=9) were ductal carcinoma in situ (DCIS). The MS for malignant lesions (13.27%/s) were significantly larger than for benign (5.45%/s; p<0.0001). No significant differences were seen for TTE and AVI. The area under the ROC curve (AUC) for the MS, TTE, and AVI were 0.836, 0.647, and 0.684, respectively. Different types of invasive carcinoma had similar MS and TTE. The MS of high-grade DCIS was also similar to that of IDC. Lower MS values were observed for low-grade (5.3%/s) compared to high-grade DCIS (14.8%/s) but the results were not significant statistically.

CONCLUSION

The ultrafast protocol showed potential to discriminate between malignant and benign breast lesions with high accuracy using MS.

A Hybrid Machine Learning Approach to Screen Optimal Predictors for the Classification of Primary Breast Tumors from Gene Expression Microarray Data

The high dimensionality and sparsity of the microarray gene expression data make it challenging to analyze and screen the optimal subset of genes as predictors of breast cancer (BC). The authors in the present study propose a novel hybrid Feature Selection (FS) sequential framework involving minimum Redundancy-Maximum Relevance (mRMR), a two-tailed unpaired t-test, and meta-heuristics to screen the most optimal set of gene biomarkers as predictors for BC. The proposed framework identified a set of three most optimal gene biomarkers, namely, MAPK 1, APOBEC3B, and ENAH. In addition, the state-of-the-art supervised Machine Learning (ML) algorithms, namely Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Neural Net (NN), Naïve Bayes (NB), Decision Tree (DT), eXtreme Gradient Boosting (XGBoost), and Logistic Regression (LR) were used to test the predictive capability of the selected gene biomarkers and select the most effective breast cancer diagnostic model with higher values of performance matrices. Our study found that the XGBoost-based model was the superior performer with an accuracy of 0.976 ± 0.027, an F1-Score of 0.974 ± 0.030, and an AUC value of 0.961 ± 0.035 when tested on an independent test dataset. The screened gene biomarkers-based classification system efficiently detects primary breast tumors from normal breast samples.

Tumor-Attentive Segmentation-Guided GAN for Synthesizing Breast Contrast-Enhanced MRI Without Contrast Agents

OBJECTIVE

Breast dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is a sensitive imaging technique critical for breast cancer diagnosis. However, the administration of contrast agents poses a potential risk. This can be avoided if contrast-enhanced MRI can be obtained without using contrast agents. Thus, we aimed to generate T1-weighted contrast-enhanced MRI (ceT1) images from pre-contrast T1 weighted MRI (preT1) images in the breast.

METHODS

We proposed a generative adversarial network to synthesize ceT1 from preT1 breast images that adopted a local discriminator and segmentation task network to focus specifically on the tumor region in addition to the whole breast. The segmentation network performed a related task of segmentation of the tumor region, which allowed important tumor-related information to be enhanced. In addition, edge maps were included to provide explicit shape and structural information. Our approach was evaluated and compared with other methods in the local (n = 306) and external validation (n = 140) cohorts. Four evaluation metrics of normalized mean squared error (NRMSE), Pearson cross-correlation coefficients (CC), peak signal-to-noise ratio (PSNR), and structural similarity index map (SSIM) for the whole breast and tumor region were measured. An ablation study was performed to evaluate the incremental benefits of various components in our approach.

RESULTS

Our approach performed the best with an NRMSE 25.65, PSNR 54.80 dB, SSIM 0.91, and CC 0.88 on average, in the local test set.

CONCLUSION

Performance gains were replicated in the validation cohort.

SIGNIFICANCE

We hope that our method will help patients avoid potentially harmful contrast agents. Clinical and Translational Impact Statement-Contrast agents are necessary to obtain DCE-MRI which is essential in breast cancer diagnosis. However, administration of contrast agents may cause side effects such as nephrogenic systemic fibrosis and risk of toxic residue deposits. Our approach can generate DCE-MRI without contrast agents using a generative deep neural network. Thus, our approach could help patients avoid potentially harmful contrast agents resulting in an improved diagnosis and treatment workflow for breast cancer.

Quantitative spatial mapping of tissue water and lipid content using spatial frequency domain imaging in the 900- to 1000-nm wavelength region

Significance

Water and lipid are key participants of many biological processes, but there are few label-free, non-contact optical methods that can spatially map these components in-vivo. Shortwave infrared meso-patterned imaging (SWIR-MPI) is an emerging technique that successfully addresses this need. However, it requires a dedicated SWIR camera to probe the 900- to 1300-nm wavelength region, which hinders practical translation of the technology.

Aim

Compared with SWIR-MPI, we aim to develop a new technique that can dramatically reduce the cost in detector while maintaining high accuracy for the quantification of tissue water and lipid content.

Approach

By utilizing water and lipid absorption features in the 900- to 1000-nm wavelength region as well as optimal wavelength and spatial frequency combinations, we develop a new imaging technique based on spatial frequency domain imaging to quantitatively map tissue water and lipid content using a regular silicon-based camera.

Results

The proposed method is validated with a phantom study, which shows average error of 0.9 ± 1.2 % for water content estimation, and -0.4 ± 0.7 % for lipid content estimation, respectively. The proposed method is also demonstrated for ex vivo porcine tissue lipid mapping as well as in-vivo longitudinal water content monitoring.

Conclusions

The proposed technique enables spatial mapping of tissue water and lipid content with the cost in detector reduced by two orders of magnitude compared with SWIR-MPI while maintaining high accuracy. The experimental results highlight the potential of this technique for substantial impact in both scientific and industrial applications.

Clinical Value of Contrast-Enhanced Ultrasound in Breast Cancer Diagnosis

Breast cancer (BC) ranks first in morbidity and mortality among female malignant tumors worldwide. This study is aimed at clarifying clinical value of contrast-enhanced ultrasound (CEUS) in the diagnosis and differentiation of BC. A total of 108 BC patients admitted to our hospital from January 2019 to December 2021 were enrolled. All patients underwent conventional color Doppler ultrasound and CEUS imaging examination. All ultrasound images were analyzed by a senior (5+ years) sonographer. The lesion location, echo, size, and color Doppler flow imaging (CDFI) blood flow distribution of benign and malignant BC were assessed. The transverse and longitudinal diameters of malignant BC presented significant elevation compared with the control group (P < 0.05). CEUS is more reliable than conventional ultrasound in the differentiation of benign and malignant breast lesions, and CEUS has the best reliability. The comparison of CEUS observation indicators between benign and malignant groups demonstrated that CEUS enhancement patterns (time and intensity) and morphological features (lesion boundary, shape, range, homogeneity, and filling defect) presented statistical significance (P < 0.01). Irregular shape and range expansion were high-specificity indicators (all >90.00%); fast-forward, high enhancement, clear boundary, and range expansion were high-sensitivity (all >90.00%); and fast-forward, high enhancement, and clear boundary were low-specificity indicators (all <50.00%); moderate sensitivity is as follows: homogeneous enhancement and range expansion (all >80.00%). The area under curve of CEUS (0.735 ± 0.053) presented elevation relative to conventional ultrasound (0.901 ± 0.024), with statistical significance (Z1 = 2.462, P < 0.05). Relative to conventional ultrasound, the specificity and positive predictive value of CEUS presented elevation (P < 0.05). In conclusion, in the differentiation of benign and malignant breast lesions, CEUS has better diagnostic accuracy and reliability than conventional ultrasound. The diagnostic advantages of CEUS are to elevate the diagnostic specificity and positive predictive value and reduce the misdiagnosis rate.

The value of dynamic contrast-enhanced magnetic resonance imaging combined with apparent diffusion coefficient in the differentiation of benign and malignant diseases of the breast

BACKGROUND

The value of combined dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and apparent diffusion coefficient (ADC) histogram analysis for the diagnosis of breast cancer has not been evaluated in previous studies.

PURPOSE

To investigate the diagnostic value of DCE-MRI combined with ADC in benign and malignant breast lesions.

MATERIAL AND METHODS

The clinicopathological imaging data included 168 patients (177 lesions) with breast lesions who underwent convention breast MRI, DCE-MRI, and diffusion-weighted imaging (DWI); they were divided into the benign lesion group (n = 39) and malignant lesion group (n = 129) based on pathology.

RESULTS

Using the type III outflow curve as a diagnostic criterion for malignant breast lesions, the diagnostic sensitivity was 76.9%, the specificity was 80%, the correct rate was 72.2%, and its area under the curve (AUC) was 0.823. Using an enhancement ratio > 100% as a diagnostic criterion for malignant breast lesions, the sensitivity was 61.5%, specificity was 80%, and AUC was 0.723. Using > 3 ipsilateral vessels as a diagnostic criterion for malignant lesions in the breast resulted in a diagnostic sensitivity of 81.6%, a specificity of 80.8%, and an AUC of 0.805.

CONCLUSION

The type of time intensity curve DCE-MRI, the early enhancement rate in the first phase, the number of ipsilateral vessels, and the ADC full volume histogram of the blood supply score and DWI are valuable in the diagnosis of benign and malignant breast lesions.

Diagnostic role of dynamic contrast-enhanced magnetic resonance imaging in differentiating breast lesions

OBJECTIVE

This study aimed to assess the diagnostic role of perfusion weighted image (DCE-PWI) to differentiate benign from malignant breast lesions.

PATIENTS AND METHODS

The study comprised 32 women who had mammography and/or breast ultrasonography findings that were clinically questionable. All patients were fasting during the magnetic resonance imaging (MRI) test to avoid nausea or dynamic contrast-enhanced vomiting from the contrast medium.

RESULT

In this study, we observed the form of the dynamic curve (time and signal intensity curve) type I (persistent curve) was noted in 12 lesions (37.5%): 10 lesions were benign and two lesions were malignant; type II (plateau curve) was noted in eight lesions (25%): three lesions were benign and five lesions were malignant, and type III (washout curve) noted in 12 lesions (37.5%): one lesion was benign and 11 lesions were malignant.

CONCLUSIONS

The dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) perfusion technique plays an important role in differentiating benign and malignant tumors in breast cancer.

Volatile Organic Compounds Analysis as a Potential Novel Screening Tool for Breast Cancer: A Systematic Review

Introduction

An early diagnosis is crucial in reducing mortality among people who have breast cancer (BC). There is a shortfall of characteristic early clinical symptoms in BC patients, highlighting the importance of investigating new methods for its early detection. A promising novel approach is the analysis of volatile organic compounds (VOCs) produced and emitted through the metabolism of cancer cells.

Methods

The purpose of this systematic review is to outline the published research regarding BC-associated VOCs. For this, headspace analysis of VOCs was explored in patient-derived body fluids, animal model-derived fluids, and BC cell lines to identify BC-specific VOCs. A systematic search in PubMed and Web of Science databases was conducted according to the PRISMA guidelines.

Results

Thirty-two studies met the criteria for inclusion in this review. Results highlight that VOC analysis can be promising as a potential novel screening tool. However, results of in vivo, in vitro and case-control studies have delivered inconsistent results leading to a lack of inter-matrix consensus between different VOC sampling methods.

Discussion

Discrepant VOC results among BC studies have been obtained, highly due to methodological discrepancies. Therefore, methodological issues leading to disparities have been reviewed and recommendations have been made on the standardisation of VOC collection and analysis methods for BC screening, thereby improving future VOC clinical validation studies.

Extracellular matrix in cancer progression and therapy

The tumor ecosystem with heterogeneous cellular compositions and the tumor microenvironment has increasingly become the focus of cancer research in recent years. The extracellular matrix (ECM), the major component of the tumor microenvironment, and its interactions with the tumor cells and stromal cells have also enjoyed tremendously increased attention. Like the other components of the tumor microenvironment, the ECM in solid tumors differs significantly from that in normal organs and tissues. We review recent studies of the complex roles the tumor ECM plays in cancer progression, from tumor initiation, growth to angiogenesis and invasion. We highlight that the biomolecular, biophysical, and mechanochemical interactions between the ECM and cells not only regulate the steps of cancer progression, but also affect the efficacy of systemic cancer treatment. We further discuss the strategies to target and modify the tumor ECM to improve cancer therapy.

A modified source-detector configuration for the discrimination between normal and diseased human breast based on the continuous-wave diffuse optical imaging approach: a simulation study

Breast tumors are among the most common types of tumors that can affect both genders. It may spread to the whole breast without any symptoms. Therefore, the early detection and accurate diagnosis of breast tumors are significantly important. Current approaches for breast cancer screening such as positron emission tomography (PET) and magnetic resonance imaging (MRI) have some limitations of being time and money-consuming. In addition, mammography screening is not recommended for women under forty. Consequently, optical techniques have been introduced as safe and functional alternatives. Diffuse optical imaging is a non-invasive imaging technique that utilizes near-infrared light to examine biological tissues based on measuring the optical transmission and/or reflection at various locations on the tissue surface. In this paper, we propose a modified arrangement between the laser source and the detectors for distinguishing tumors from normal breast tissue. A three-dimensional model of the normal human breast with three types of tumors is developed using a COMSOL simulation software based on the finite element solution of Helmholtz equation to estimate optical fluence distribution. The breast model consists of four layers: skin, fat, glandular, and muscle, where the tumor is included in the glandular layer. Different wavelengths were used to determine the most proper wavelength for the discrimination between the normal tissue and tumor. The obtained results were verified using the receiver operating characteristic (ROC) method. The resultant fluence images show different features between normal breast and breast with tumor especially using 600-nm incident laser as demonstrated by the obtained ROC curves.

A literature review on the imaging methods for breast cancer

Breast cancer will be easier and more effective to treat if detected early. Breast cancer is assessed and detected using imaging as a primary approach. The capacity to diagnose breast cancers is continually improving thanks to developments in imaging technologies. However, some of these enhancements have been linked to delays in the initiation of treatment procedures of breast cancer. Overall, cancer management relies heavily on imaging procedures such as screening and symptomatic disease management. Mammography, which is considered the gold standard, and breast ultrasonography are employed as routine imaging modalities. Previous research has shown that, despite recent developments, no single imaging modality can detect and characterizing majority of breast lesions. Various imaging methods and their uses in diagnosing and caring the breast cancer are discussed in this study.

Halftone spatial frequency domain imaging enables kilohertz high-speed label-free non-contact quantitative mapping of optical properties for strongly turbid media

The ability to quantify optical properties (i.e., absorption and scattering) of strongly turbid media has major implications on the characterization of biological tissues, fluid fields, and many others. However, there are few methods that can provide wide-field quantification of optical properties, and none is able to perform quantitative optical property imaging with high-speed (e.g., kilohertz) capabilities. Here we develop a new imaging modality termed halftone spatial frequency domain imaging (halftone-SFDI), which is approximately two orders of magnitude faster than the state-of-the-art, and provides kilohertz high-speed, label-free, non-contact, wide-field quantification for the optical properties of strongly turbid media. This method utilizes halftone binary patterned illumination to target the spatial frequency response of turbid media, which is then mapped to optical properties using model-based analysis. We validate the halftone-SFDI on an array of phantoms with a wide range of optical properties as well as in vivo human tissue. We demonstrate with an in vivo rat brain cortex imaging study, and show that halftone-SFDI can longitudinally monitor the absolute concentration as well as spatial distribution of functional chromophores in tissue. We also show that halftone-SFDI can spatially map dual-wavelength optical properties of a highly dynamic flow field at kilohertz speed. Together, these results highlight the potential of halftone-SFDI to enable new capabilities in fundamental research and translational studies including brain science and fluid dynamics.

Association between lesion enhancement and breast cancer in contrast-enhanced spectral mammography

BACKGROUND

Contrast-enhanced spectral mammography (CESM) may help to determine the malignancy potential of lesions according to the degree of enhancement.

PURPOSE

To investigate the correlation between the degree of contrast enhancement of the lesions in contrast-enhanced spectral mammography (CESM) and the final histopathological diagnosis in patients with BI-RADS 4 and 5 lesions.

MATERIAL AND METHODS

CESM was performed in 128 patients who had BI-RADS 4 and 5 lesions on mammography and underwent histopathological examination. A total of 128 index lesions were scored using a 4-point scale regarding the degree of contrast enhancement (0 = no contrast enhancement, 1 = minimal, 2 = moderate, 3 = marked), a score of 2 and 3 was accepted as suggestive of malignancy. The study was approved in our institutional scientific committee.

RESULTS

In total, 76 (59.4%) of the lesions had benign histopathological results, whereas 52 of them had malignant results. Contrast enhancement was not observed in 22.7% of the lesions while 24.2% had minimal enhancement, 18.8% had moderate enhancement, and 34.4% had marked enhancement in CESM. The sensitivity of the degree of contrast enhancement in CESM was 98.1%, when the specificity was 77.6%, positive predictive value was 75%, negative predictive value was 98.3%, and accuracy was 85.9%.

CONCLUSION

This study demonstrated that the degree of contrast enhancement of the lesions in CESM may be used in daily practice with easily performing a visual scale in predicting the malignancy potential of the lesions.

The additive role of dynamic contrast-enhanced and diffusion-weighted MR imaging in preoperative staging of breast cancer

Background

In women with diagnosed breast cancer, accurate loco-regional staging and preoperative examination are of utmost importance for optimal patient management decisions. MRI may be warranted for correct preoperative staging as recommended from international guidelines. DWI-MRI can be combined with CE-MRI to assess more functional data. So we aimed to evaluate the performance of CE-MRI and qualitative DWI-MRI in preoperative loco-regional staging of malignant breast lesions as regards the local extension of the disease and axillary lymph node status, beyond standard assessment with mammography and ultrasound. This prospective study included 50 female patients with pathologically proven malignant breast lesions (BIRADS VI) coming for preoperative staging. Full-field digital mammography (FFDM) and ultrasound, CE-MRI, and DWI-MRI findings were compared for all patients, and the findings were evaluated independently. Results were then correlated to postoperative histopathology.

Results

Fifty women with pathologically proven malignant breast lesions (BIRADS VI) were enrolled in this study; the mean age of this study population was 43.25 years. The 50 patients were divided into 2 groups: 37/50 (74%) underwent upfront surgery and 13/50 (26%) received neoadjuvant therapy before surgery. All patients performed DCE and DWI-MRI breast. Among patients who underwent upfront surgery, DCE-MRI showed the highest correlation with the postoperative pathology size and the overall sensitivity regarding multiplicity. Regarding patients who received neoadjuvant therapy, DCE-MRI was found to have the highest correlation with the postoperative pathology concerning lesion size and multiplicity after completion of the neoadjuvant chemotherapy cycles.

Conclusion

CE-MRI can accurately map lesion extension and detect multifocality/multicentricity, thus tailor surgical management options (either conservative surgery or mastectomy). Qualitative DWI can be combined with ultrasonography for better evaluation of the axillary nodal status.

Is Elevated Choline on Magnetic Resonance Spectroscopy a Reliable Marker of Breast Lesion Malignancy?

Background

Contemporary magnetic resonance imaging (MRI) of the breast represents a powerful diagnostic modality for cancer detection, with excellent sensitivity and high specificity. Magnetic resonance spectroscopy (MRS) is being explored as an additional tool for improving specificity in breast cancer detection, using multiparametric MRI. The aim of this study was to examine the possibility of 1H-MRS to discriminate malignant from benign breast lesions, using elevated choline (Cho) peak as an imaging biomarker.

Methods

A total of 60 patients were included in this prospective study: 30 with malignant (average age, 55.2 years; average lesion size, 35 mm) and 30 with benign breast lesions (average age, 44.8 years; average lesion size, 20 mm), who underwent multiparametric MRI with multivoxel 3D ¹H-MRS on a 1.5-T scanner in a 3-year period. Three patients with benign breast lesions were excluded from the study. All lesions were histologically verified. Peaks identified on ¹H-MRS were lipid (0.9, 2.3, 2.8, and 5.2 ppm), choline (3.2 ppm), and water peaks (4.7 ppm). Sensitivity and specificity, as well as positive and negative predictive values, were defined using ROC curves. Cohen's Kappa test of inter-test reliability was performed [testing the agreement between ¹H-MRS and histologic finding, and ¹H-MRS and MR mammography (MRM)].

Results

Choline peak was elevated in 24/30 malignant lesions and in 20/27 benign breast lesions. The sensitivity of ¹H-MRS was 0.8, specificity was 0.741, positive predictive value was 0.774, and negative predictive value was 0.769. Area under ROC was 0.77 (CI 0.640-0.871). Inter-test reliability between ¹H-MRS and histologic finding was 0.543 (moderate agreement) and that between ¹H-MRS and MRM was 0.573 (moderate agreement). False-negative findings were most frequently observed in invasive lobular cancers, while false-positive findings were most frequently observed in adenoid fibroadenomas.

Conclusion

Although elevation of the choline peak has a good sensitivity and specificity in breast cancer detection, both are significantly lower than those of multiparametric MRM. Inclusion of spectra located on tumor margins as well as analysis of lipid peaks could aid both sensitivity and specificity. An important ratio of false-positive and false-negative findings in specific types of breast lesions (lobular cancer and adenoid fibroadenoma) suggests interpreting these lesions with a caveat.

Is radiomic MRI a feasible alternative to OncotypeDX® recurrence score testing? A systematic review and meta-analysis

BACKGROUND

OncotypeDX® recurrence score (RS) aids therapeutic decision-making in oestrogen-receptor-positive (ER+) breast cancer. Radiomics is an evolving field that aims to examine the relationship between radiological features and the underlying genomic landscape of disease processes. The aim of this study was to perform a systematic review of current evidence evaluating the comparability of radiomics and RS.

METHODS

A systematic review was performed as per PRISMA guidelines. Studies comparing radiomic MRI tumour analyses and RS were identified. Sensitivity, specificity and area under curve (AUC) delineating low risk (RS less than 18) versus intermediate-high risk (equal to or greater than 18) and low-intermediate risk (RS less than 30) and high risk (RS greater than 30) were recorded. Log rate ratios (lnRR) and standard error were determined from AUC and 95 per cent confidence intervals.

RESULTS

Nine studies including 1216 patients met inclusion criteria; the mean age at diagnosis was 52.9 years. Mean RS was 16 (range 0-75); 401 patients with RS less than 18, 287 patients with RS 18-30 and 100 patients with RS greater than 30. Radiomic analysis and RS were comparable for differentiating RS less than 18 versus RS 18 or greater (RR 0.93 (95 per cent c.i. 0.85 to 1.01); P = 0.010, heterogeneity (I2)=0%) as well as RS less than 30 versus RS 30 or greater (RR 0.76 (95 per cent c.i. 0.70 to 0.83); P < 0.001, I2=0%). MRI sensitivity and specificity for RS less than 18 versus 18 or greater was 0.89 (95 per cent c.i. 0.85 to 0.93) and 0.72 (95 per cent c.i. 0.66 to 0.78) respectively, and 0.79 (95 per cent c.i. 0.72 to 0.86) and 0.74 (95 per cent c.i. 0.68 to 0.80) for RS less than 30 versus 30 or greater.

CONCLUSION

Radiomic tumour analysis is comparable to RS in differentiating patients into clinically relevant subgroups. For patients requiring MRI, radiomics may complement and enhance RS for prognostication and therapeutic decision making in ER+ breast cancer.

Reducing False-Positive Screening MRI Rate in Women with Extremely Dense Breasts Using Prediction Models Based on Data from the DENSE Trial

Background High breast density increases breast cancer risk and lowers mammographic sensitivity. Supplemental MRI screening improves cancer detection but increases the number of false-positive screenings. Thus, methods to distinguish true-positive MRI screening results from false-positive ones are needed. Purpose To build prediction models based on clinical characteristics and MRI findings to reduce the rate of false-positive screening MRI findings in women with extremely dense breasts. Materials and Methods Clinical characteristics and MRI findings in Dutch breast cancer screening participants (age range, 50-75 years) with positive first-round MRI screening results (Breast Imaging Reporting and Data System 3, 4, or 5) after a normal screening mammography with extremely dense breasts (Volpara density category 4) were prospectively collected within the randomized controlled Dense Tissue and Early Breast Neoplasm Screening (DENSE) trial from December 2011 through November 2015. In this secondary analysis, prediction models were built using multivariable logistic regression analysis to distinguish true-positive MRI screening findings from false-positive ones. Results Among 454 women (median age, 52 years; interquartile range, 50-57 years) with a positive MRI result in a first supplemental MRI screening round, 79 were diagnosed with breast cancer (true-positive findings), and 375 had false-positive MRI results. The full prediction model (area under the receiver operating characteristics curve [AUC], 0.88; 95% CI: 0.84, 0.92), based on all collected clinical characteristics and MRI findings, could have prevented 45.5% (95% CI: 39.6, 51.5) of false-positive recalls and 21.3% (95% CI: 15.7, 28.3) of benign biopsies without missing any cancers. The model solely based on readily available MRI findings and age had a comparable performance (AUC, 0.84; 95% CI: 0.79, 0.88; P = .15) and could have prevented 35.5% (95% CI: 30.4, 41.1) of false-positive MRI screening results and 13.0% (95% CI: 8.8, 18.6) of benign biopsies. Conclusion Prediction models based on clinical characteristics and MRI findings may be useful to reduce the false-positive first-round screening MRI rate and benign biopsy rate in women with extremely dense breasts. Clinical trial registration no. NCT01315015 © RSNA, 2021 Online supplemental material is available for this article. See also the editorial by Imbriaco in this issue.

Quantitative Measurement of Breast Tumors Using Intravoxel Incoherent Motion (IVIM) MR Images

Breast magnetic resonance imaging (MRI) is currently a widely used clinical examination tool. Recently, MR diffusion-related technologies, such as intravoxel incoherent motion diffusion weighted imaging (IVIM-DWI), have been extensively studied by breast cancer researchers and gradually adopted in clinical practice. In this study, we explored automatic tumor detection by IVIM-DWI. We considered the acquired IVIM-DWI data as a hyperspectral image cube and used a well-known hyperspectral subpixel target detection technique: constrained energy minimization (CEM). Two extended CEM methods—kernel CEM (K-CEM) and iterative CEM (I-CEM)—were employed to detect breast tumors. The K-means and fuzzy C-means clustering algorithms were also evaluated. The quantitative measurement results were compared to dynamic contrast-enhanced T1-MR imaging as ground truth. All four methods were successful in detecting tumors for all the patients studied. The clustering methods were found to be faster, but the CEM methods demonstrated better performance according to both the Dice and Jaccard metrics. These unsupervised tumor detection methods have the advantage of potentially eliminating operator variability. The quantitative results can be measured by using ADC, signal attenuation slope, D*, D, and PF parameters to classify tumors of mass, non-mass, cyst, and fibroadenoma types.

Discrimination of Malignant and Benign Breast Lesions Using Quantitative Multiparametric MRI: A Preliminary Study

We aimed to compare diagnostic performance in discriminating malignant and benign breast lesions between two intravoxel incoherent motion (IVIM) analysis methods for diffusion-weighted magnetic resonance imaging (DW-MRI) data and between DW- and dynamic contrast-enhanced (DCE)-MRI, and to determine if combining DW- and DCE-MRI further improves diagnostic accuracy. DW-MRI with 12 b-values and DCE-MRI were performed on 26 patients with 28 suspicious breast lesions before biopsies. The traditional biexponential fitting and a 3-b-value method were used for independent IVIM analysis of the DW-MRI data. Simulations were performed to evaluate errors in IVIM parameter estimations by the two methods across a range of signal-to-noise ratio (SNR). Pharmacokinetic modeling of DCE-MRI data was performed. Conventional radiological MRI reading yielded 86% sensitivity and 21% specificity in breast cancer diagnosis. At the same sensitivity, specificity of individual DCE- and DW-MRI markers improved to 36%–57% and that of combined DCE- or combined DW-MRI markers to 57%–71%, with DCE-MRI markers showing better diagnostic performance. The combination of DCE- and DW-MRI markers further improved specificity to 86%–93% and the improvements in diagnostic accuracy were statistically significant (P < .05) when compared with standard clinical MRI reading and most individual markers. At low breast DW-MRI SNR values (<50), like those typically seen in clinical studies, the 3-b-value approach for IVIM analysis generates markers with smaller errors and with comparable or better diagnostic performances compared with biexponential fitting. This suggests that the 3-b-value method could be an optimal IVIM-MRI method to be combined with DCE-MRI for improved diagnostic accuracy.

Preoperative breast magnetic resonance imaging in patients with ductal carcinoma in situ: a systematic review for the European Commission Initiative on Breast Cancer (ECIBC)

Objective

To evaluate the impact of preoperative MRI in the management of Ductal carcinoma in situ (DCIS).

Methods

We searched the PubMed, EMBASE and Cochrane Library databases to identify randomised clinical trials (RCTs) or cohort studies assessing the impact of preoperative breast MRI in surgical outcomes, treatment change or loco-regional recurrence. We provided pooled estimates for odds ratios (OR), relative risks (RR) and proportions and assessed the certainty of the evidence using the GRADE approach.

Results

We included 3 RCTs and 23 observational cohorts, corresponding to 20,415 patients. For initial breast-conserving surgery (BCS), the RCTs showed that MRI may result in little to no difference (RR 0.95, 95% CI 0.90 to 1.00) (low certainty); observational studies showed that MRI may have no difference in the odds of re-operation after BCS (OR 0.96; 95% CI 0.36 to 2.61) (low certainty); and uncertain evidence from RCTs suggests little to no difference with respect to total mastectomy rate (RR 0.91; 95% CI 0.65 to 1.27) (very low certainty). We also found that MRI may change the initial treatment plans in 17% (95% CI 12 to 24%) of cases, but with little to no effect on locoregional recurrence (aHR = 1.18; 95% CI 0.79 to 1.76) (very low certainty).

Conclusion

We found evidence of low to very low certainty which may suggest there is no improvement of surgical outcomes with pre-operative MRI assessment of women with DCIS lesions. There is a need for large rigorously conducted RCTs to evaluate the role of preoperative MRI in this population.

Key Points

• Evidence of low to very low certainty may suggest there is no improvement in surgical outcomes with pre-operative MRI.

• There is a need for large rigorously conducted RCTs evaluating the role of preoperative MRI to improve treatment planning for DCIS.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00330-021-07873-2.

Magnetic Resonance Imaging Features in Different Types of Invasive Breast Cancer: A Systematic Review of the Literature

Breast cancer is the most common malignancy affecting women worldwide, and early diagnosis of breast cancer is the key to its successful and effective treatment. Traditional imaging techniques such as mammography and ultrasound are used to detect and configure breast abnormalities; unfortunately, these modalities have low sensitivity and specificity, particularly in young patients with dense breast tissue, breast implants, or post-surgical scar/architecture distortions. Therefore, breast magnetic resonance imaging (MRI) has been superior in the characterization and detection of breast cancer, especially that with invasive features. This review article explores the importance of breast MRI in the early detection of invasive breast cancer versus traditional tools, including mammography and ultrasound, while also analyzing the use of MRI as a screening tool for high-risk women. We will also discuss the different MRI features for invasive ductal carcinoma and lobular carcinoma and the role of breast MRI in the detection of ductal carcinoma in situ with a focus on the utilization of new techniques, including MR spectroscopy and diffusion-weighted imaging.

Diagnostic accuracy of MRI textural analysis in the classification of breast tumors

OBJECTIVE

To investigate whether textural analysis (TA) of MRI heterogeneity may play a role in the clinical assessment and classification of breast tumors.

MATERIALS AND METHODS

For this retrospective study, patients with breast masses ≥1 cm on contrast-enhanced MRI were obtained in 69 women (mean age: 51 years; range 21-78 years) with 77 masses (38 benign, 39 malignant) from 2006 to 2018. The selected single slice sagittal peak post-contrast T1-weighted image was analyzed with commercially available TA software [TexRAD Ltd., UK]. Eight histogram TA parameters were evaluated at various spatial scaling factors (SSF) including mean pixel intensity, standard deviation of the pixel histogram (SD), entropy, mean of the positive pixels (MPP), skewness, kurtosis, sigma, and Tx_sigma. Additional statistical tests were used to determine their predictiveness.

RESULTS

Entropy showed a significant difference between benign and malignant tumors at all textural scales (p < 0.0001) and kurtosis was significant at SSF = 0-5 (p = 0.0026-0.0241). The single best predictor was entropy at SSF = 4 with AUC = 0.80, giving a sensitivity of 95% and specificity of 53%. An AUC of 0.91 was found using a model combining entropy with sigma, which yielded better performance with a sensitivity of 92% and specificity of 79%.

CONCLUSION

TA of breast masses has the potential to assist radiologists in categorizing tumors as benign or malignant on MRI. Measurements of entropy, kurtosis, and entropy combined with sigma may provide the best predictability.

Histopathologic Correlates of Nonmass Enhancement Detected by Breast Magnetic Resonance Imaging

CONTEXT.—

Dynamic, contrast-enhanced magnetic resonance imaging (MRI) is a highly sensitive imaging modality used for screening and diagnostic purposes. Nonmass enhancement (NME) is commonly seen on MRI of the breast. However, the pathologic correlates of NME have not been extensively explored. Consequently, concordance between MRI and pathologic findings in such cases may be uncertain and this uncertainty may cause the need for additional procedures.

OBJECTIVE.—

To examine the histologic alterations that correspond to NME on MRI.

DESIGN.—

We performed a retrospective search for women who underwent breast MRI between March 2014 and December 2016 and identified 130 NME lesions resulting in biopsy. The MRI findings and pathology slides for all cases were reviewed. The follow-up findings on any subsequent excisions were also noted.

RESULTS.—

Among the 130 cases, the core needle biopsy showed 1 or more benign lesions without atypia in 80 cases (62%), atypical lesions in 21 (16%), ductal carcinoma in situ in 22 (17%), and invasive carcinoma in 7 (5%). Review of the imaging features demonstrated some statistically significant differences in lesions that corresponded to malignant lesions as compared with benign alterations, including homogeneous or clumped internal enhancement, type 3 kinetics, and T2 dark signal; however, there was considerable overlap of features between benign and malignant lesions overall. Of 130 cases, 54 (41.5%) underwent subsequent excision with only 6 cases showing a worse lesion on excision.

CONCLUSIONS.—

This study illustrates that NME can be associated with benign, atypical, and/or malignant pathology and biopsy remains indicated given the overlap of radiologic features.

Five-dimensional quantitative low-dose Multitasking dynamic contrast- enhanced MRI: Preliminary study on breast cancer

PURPOSE

To develop a low-dose Multitasking DCE technique (LD-MT-DCE) for breast imaging, enabling dynamic T₁ mapping-based quantitative characterization of tumor blood flow and vascular properties with whole-breast coverage, a spatial resolution of 0.9 × 0.9 × 1.1 mm³ , and a temporal resolution of 1.4 seconds using a 20% gadolinium dose (0.02 mmol/kg).

METHODS

Magnetic resonance Multitasking was used to reconstruct 5D images with three spatial dimensions, one T₁ recovery dimension for dynamic T₁ quantification, and one DCE dimension for contrast kinetics. Kinetic parameters F p , v p , K trans , and v e were estimated from dynamic T₁ maps using the two-compartment exchange model. The LD-MT-DCE repeatability and agreement against standard-dose MT-DCE were evaluated in 20 healthy subjects. In 7 patients with triple-negative breast cancer, LD-MT-DCE image quality and diagnostic results were compared with that of standard-dose clinical DCE in the same imaging session. One-way unbalanced analysis of variance with Tukey test was performed to evaluate the statistical significance of the kinetic parameters between control and patient groups.

RESULTS

The LD-MT-DCE technique was repeatable, agreed with standard-dose MT-DCE, and showed excellent image quality. The diagnosis using LD-MT-DCE matched well with clinical results. The values of F p , v p , and K trans were significantly different between malignant tumors and normal breast tissue (P < .001, < .001, and < .001, respectively), and between malignant and benign tumors (P = .020, .003, and < .001, respectively).

CONCLUSION

The LD-MT-DCE technique was repeatable and showed excellent image quality and equivalent diagnosis compared with standard-dose clinical DCE. The estimated kinetic parameters were capable of differentiating between normal breast tissue and benign and malignant tumors.

Metamaterial inspired wireless coil for clinical breast imaging

In this work, we propose an application of a metamaterial inspired volumetric wireless coil (WLC) based on coupled split-loop resonators for targeted breast MRI at 1.5 T. Due to strong electromagnetic coupling with the body coil, the metamaterial inspired WLC locally focuses radiofrequency (RF) magnetic flux in the target region, thus improving both transmit and receive performance of the external body coil. This leads to substantial enhancement in local transmit efficiency and improvement of RF safety. Phantom images showed a tenfold increase of signal-to-noise ratio (SNR) in the region-of-interest (ROI) and, at the same time, an almost 50-fold reduction in transmit power relative to the same body coil used alone.

Prospective evaluation of whole-body MRI and 18F-FDG PET/MRI in N and M staging of primary breast cancer patients

OBJECTIVES

To evaluate and compare the diagnostic potential of whole-body MRI and whole-body 18F-FDG PET/MRI for N and M staging in newly diagnosed, histopathologically proven breast cancer.

MATERIAL AND METHODS

A total of 104 patients (age 53.4 ± 12.5) with newly diagnosed, histopathologically proven breast cancer were enrolled in this study prospectively. All patients underwent a whole-body 18F-FDG PET/MRI. MRI and 18F-FDG PET/MRI datasets were evaluated separately regarding lesion count, lesion localization, and lesion characterization (malignant/benign) as well as the diagnostic confidence (5-point ordinal scale, 1-5). The N and M stages were assessed according to the eighth edition of the American Joint Committee on Cancer staging manual in MRI datasets alone and in 18F-FDG PET/MRI datasets, respectively. In the majority of lesions histopathology served as the reference standard. The remaining lesions were followed-up by imaging and clinical examination. Separately for nodal-positive and nodal-negative women, a McNemar chi2 test was performed to compare sensitivity and specificity of the N and M stages between 18F-FDG PET/MRI and MRI. Differences in diagnostic confidence scores were assessed by Wilcoxon signed rank test.

RESULTS

MRI determined the N stage correctly in 78 of 104 (75%) patients with a sensitivity of 62.3% (95% CI: 0.48-0.75), a specificity of 88.2% (95% CI: 0.76-0.96), a PPV (positive predictive value) of 84.6% % (95% CI: 69.5-0.94), and a NPV (negative predictive value) of 69.2% (95% CI: 0.57-0.8). Corresponding results for 18F-FDG PET/MRI were 87/104 (83.7%), 75.5% (95% CI: 0.62-0.86), 92.2% (0.81-0.98), 90% (0.78-0.97), and 78.3% (0.66-0.88), showing a significantly better sensitivity of 18F-FDG PET/MRI determining malignant lymph nodes (p = 0.008). The M stage was identified correctly in MRI and 18F-FDG PET/MRI in 100 of 104 patients (96.2%). Both modalities correctly staged all 7 patients with distant metastases, leading to false-positive findings in 4 patients in each modality (3.8%). In a lesion-based analysis, 18F-FDG PET/MRI showed a significantly better performance in correctly determining malignant lesions (85.8% vs. 67.1%, difference 18.7% (95% CI: 0.13-0.26), p < 0.0001) and offered a superior diagnostic confidence compared with MRI alone (4.1 ± 0.7 vs. 3.4 ± 0.7, p < 0.0001).

CONCLUSION

18F-FDG PET/MRI has a better diagnostic accuracy for N staging in primary breast cancer patients and provides a significantly higher diagnostic confidence in lesion characterization than MRI alone. But both modalities bear the risk to overestimate the M stage.

Shortwave-infrared meso-patterned imaging enables label-free mapping of tissue water and lipid content

Water and lipids are key participants in many biological processes, but there are few non-invasive methods that provide quantification of these components in vivo, and none that can isolate and quantify lipids in the blood. Here we develop a new imaging modality termed shortwave infrared meso-patterned imaging (SWIR-MPI) to provide label-free, non-contact, spatial mapping of water and lipid concentrations in tissue. The method utilizes patterned hyperspectral illumination to target chromophore absorption bands in the 900-1,300 nm wavelength range. We use SWIR-MPI to monitor clinically important physiological processes including edema, inflammation, and tumor lipid heterogeneity in preclinical models. We also show that SWIR-MPI can spatially map blood-lipids in humans, representing an example of non-invasive and contact-free measurements of in vivo blood lipids. Together, these results highlight the potential of SWIR-MPI to enable new capabilities in fundamental studies and clinical monitoring of major conditions including obesity, cancer, and cardiovascular disease.

Applications of Computational Methods in Biomedical Breast Cancer Imaging Diagnostics: A Review

With the exponential increase in new cases coupled with an increased mortality rate, cancer has ranked as the second most prevalent cause of death in the world. Early detection is paramount for suitable diagnosis and effective treatment of different kinds of cancers, but this is limited to the accuracy and sensitivity of available diagnostic imaging methods. Breast cancer is the most widely diagnosed cancer among women across the globe with a high percentage of total cancer deaths requiring an intensive, accurate, and sensitive imaging approach. Indeed, it is treatable when detected at an early stage. Hence, the use of state of the art computational approaches has been proposed as a potential alternative approach for the design and development of novel diagnostic imaging methods for breast cancer. Thus, this review provides a concise overview of past and present conventional diagnostics approaches in breast cancer detection. Further, we gave an account of several computational models (machine learning, deep learning, and robotics), which have been developed and can serve as alternative techniques for breast cancer diagnostics imaging. This review will be helpful to academia, medical practitioners, and others for further study in this area to improve the biomedical breast cancer imaging diagnosis.

MR Elastography of the Breast: Evolution of Technique, Case Examples, and Future Directions

Recognizing that breast cancers present as firm, stiff lesions, the foundation of breast magnetic resonance elastography (MRE) is to combine tissue stiffness parameters with sensitive breast MR contrast-enhanced imaging. Breast MRE is a non-ionizing, cross-sectional MR imaging technique that provides for quantitative viscoelastic properties, including tissue stiffness, elasticity, and viscosity, of breast tissues. Currently, the technique continues to evolve as research surrounding the use of MRE in breast tissue is still developing. In the setting of a newly diagnosed cancer, associated desmoplasia, stiffening of the surrounding stroma, and necrosis are known to be prognostic factors that can add diagnostic information to patient treatment algorithms. In fact, mechanical properties of the tissue might also influence breast cancer risk. For these reasons, exploration of breast MRE has great clinical value. In this review, we will: (1) address the evolution of the various MRE techniques; (2) provide a brief overview of the current clinical studies in breast MRE with interspersed case examples; and (3) suggest directions for future research.

Accuracy of breast MRI in patients receiving neoadjuvant endocrine therapy: comprehensive imaging analysis and correlation with clinical and pathological assessments

Purpose

To assess the accuracy of magnetic resonance imaging (MRI) measurements in locally advanced oestrogen receptor-positive and human epidermal growth factor receptor 2-negative breast tumours before, during and after neoadjuvant endocrine treatment (NET) for evaluation of tumour response in comparison with clinical and pathological assessments.

Methods

This prospective study enrolled postmenopausal patients treated neoadjuvant with letrozole and exemestane given sequentially in an intra-patient cross-over regimen. Fifty-four patients were initially recruited, but only 35 fulfilled the inclusion criteria and confirmed to participate with a median age of 77. Tumours were scanned with MRI prior to treatment, during the eighth week of treatment and prior to surgery. Additionally, changes in longest diameter on clinical examination (CE) and tumour size at pathology were determined. Pre- and post-operative measurements of tumour size were compared in order to evaluate tumour response.

Results

The correlation between post-treatment MRI size and pathology was moderate and higher with a correlation coefficient (r) 0.64 compared to the correlation between CE and pathology r = 0.25. Post-treatment MRI and clinical results had a negligible bias towards underestimation of lesion size. Tumour size on MRI and CE had 0.82 cm and 0.52 cm lower mean size than tumour size measured by pathology, respectively.

Conclusions

The higher correlation between measurements of residual disease obtained on MRI and those obtained with pathology validates the accuracy of imaging assessment during NET. MRI was found to be more accurate for estimating complete responses than clinical assessments and warrants further investigation in larger cohorts to validate this finding.

Electronic supplementary material

The online version of this article (10.1007/s10549-020-05852-7) contains supplementary material, which is available to authorized users.

Radiomics methodology for breast cancer diagnosis using multiparametric magnetic resonance imaging

Purpose: This study aims to develop and compare human-engineered radiomics methodologies that use multiparametric magnetic resonance imaging (mpMRI) to diagnose breast cancer. Approach: The dataset comprises clinical multiparametric MR images of 852 unique lesions from 612 patients. Each MR study included a dynamic contrast-enhanced (DCE)-MRI sequence and a T2-weighted (T2w) MRI sequence, and a subset of 389 lesions were also imaged with a diffusion-weighted imaging (DWI) sequence. Lesions were automatically segmented using the fuzzy C-means algorithm. Radiomic features were extracted from each MRI sequence. Two approaches, feature fusion and classifier fusion, to utilizing multiparametric information were investigated. A support vector machine classifier was trained for each method to differentiate between benign and malignant lesions. Area under the receiver operating characteristic curve (AUC) was used to evaluate and compare diagnostic performance. Analyses were first performed on the entire dataset and then on the subset that was imaged using the three-sequence protocol. Results: When using the full dataset, the single-parametric classifiers yielded the following AUCs and 95% confidence intervals:AUC DCE = 0.84 [0.82, 0.87],AUC T 2 w = 0.83 [0.80, 0.86], andAUC DWI = 0.69 [0.62, 0.75]. The two multiparametric classifiers both yielded AUCs of 0.87 [0.84, 0.89] and significantly outperformed all single-parametric methods classifiers. When using the three-sequence subset, the mpMRI classifiers' performances significantly decreased. Conclusions: The proposed mpMRI radiomics methods can improve the performance of computer-aided diagnostics for breast cancer and handle missing sequences in the imaging protocol.

A deep learning methodology for improved breast cancer diagnosis using multiparametric MRI

Multiparametric magnetic resonance imaging (mpMRI) has been shown to improve radiologists’ performance in the clinical diagnosis of breast cancer. This machine learning study develops a deep transfer learning computer-aided diagnosis (CADx) methodology to diagnose breast cancer using mpMRI. The retrospective study included clinical MR images of 927 unique lesions from 616 women. Each MR study included a dynamic contrast-enhanced (DCE)-MRI sequence and a T2-weighted (T2w) MRI sequence. A pretrained convolutional neural network (CNN) was used to extract features from the DCE and T2w sequences, and support vector machine classifiers were trained on the CNN features to distinguish between benign and malignant lesions. Three methods that integrate the sequences at different levels (image fusion, feature fusion, and classifier fusion) were investigated. Classification performance was evaluated using the receiver operating characteristic (ROC) curve and compared using the DeLong test. The single-sequence classifiers yielded areas under the ROC curves (AUCs) [95% confidence intervals] of AUC_DCE = 0.85 [0.82, 0.88] and AUC_T2w = 0.78 [0.75, 0.81]. The multiparametric schemes yielded AUC_ImageFusion = 0.85 [0.82, 0.88], AUC_{FeatureFusion} = 0.87 [0.84, 0.89], and AUC_{ClassifierFusion} = 0.86 [0.83, 0.88]. The feature fusion method statistically significantly outperformed using DCE alone (P < 0.001). In conclusion, the proposed deep transfer learning CADx method for mpMRI may improve diagnostic performance by reducing the false positive rate and improving the positive predictive value in breast imaging interpretation.

Breast dynamic contrast-enhanced-magnetic resonance imaging and radiomics: State of art

Breast cancer represents the most common malignancy in women, being one of the most frequent cause of cancer-related mortality. Ultrasound, mammography, and magnetic resonance imaging (MRI) play a pivotal role in the diagnosis of breast lesions, with different levels of accuracy. Particularly, dynamic contrast-enhanced MRI has shown high diagnostic value in detecting multifocal, multicentric, or contralateral breast cancers. Radiomics is emerging as a promising tool for quantitative tumor evaluation, allowing the extraction of additional quantitative data from radiological imaging acquired with different modalities. Radiomics analysis may provide novel information through the quantification of lesions heterogeneity, that may be relevant in clinical practice for the characterization of breast lesions, prediction of tumor response to systemic therapies and evaluation of prognosis in patients with breast cancers. Several published studies have explored the value of radiomics with good-to-excellent diagnostic and prognostic performances for the evaluation of breast lesions. Particularly, the integrations of radiomics data with other clinical and histopathological parameters have demonstrated to improve the prediction of tumor aggressiveness with high accuracy and provided precise models that will help to guide clinical decisions and patients management. The purpose of this article in to describe the current application of radiomics in breast dynamic contrast-enhanced MRI.

Lipid-weighted intraoperative photoacoustic tomography of breast tumors: Volumetric comparison to preoperative MRI

With a lifetime risk of 1 in 8, breast cancer continues to be a major concern for women and their physicians. The optimal treatment of the disease depends on the stage of the cancer at diagnosis, which is typically assessed using medical imaging. However, currently employed imaging systems for breast tumor measurement rarely agree perfectly. Our group developed an Intraoperative Photoacoustic Screening (iPAS) soft tissue scanner featuring high bulk tissue sensitivity, a clinically compatible scan-time of 6 min, imaging depths greater than 2 cm and the capability to visualize whole breast tumors based on their lipid, rather than hemoglobin, profile. Here, we report on the first clinical experience with breast cancer patients by comparing tumor-measurement using iPAS, preoperative dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) and gold-standard pathology. Tumor size was measured volumetrically for iPAS and DCE-MRI, and separately using maximum diameters for pathology, DCE-MRI and iPAS. Comparisons were performed using Pearson's correlation coefficients, and the non-parametric Wilcoxon signed-rank test. Twelve consecutive patients were included in the study, contingent on pathologically documented invasive carcinoma. iPAS volumetric tumor size was positively correlated to DCE-MRI (Pearson's r = 0.78, p = 0.003) and not significantly different (Wilcoxon, p = 0.97). In comparison to pathology, tumor diameters given by iPAS were positively correlated (Pearson's r = 0.87, p = 0.0002) and significantly different (Wilcoxon, p = 0.0015). The results indicated that volumetric-measurement of invasive breast tumors with iPAS is similar to that of DCE-MRI. On the other hand, tumor diameter measurements were less reliable. Beyond enhancing surgical specimen examination, an extension of this technology to diagnostic imaging promises a new perspective on tumor assessment, potentially improving our current understanding and treatment of breast cancer.

Diffusion-weighted imaging or dynamic contrast-enhanced curve: a retrospective analysis of contrast-enhanced magnetic resonance imaging-based differential diagnoses of benign and malignant breast lesions

OBJECTIVE

To compare the diagnostic performance of models based on a combination of contrast-enhanced (CE) magnetic resonance imaging (MRI) with diffusion-weighted imaging (DWI) or time-intensity curves (TIC) in diagnosing malignancies of breast lesions.

METHODS

A double-blind retrospective study was conducted in 328 patients (254 for training and the following 74 for validation) who underwent dynamic contrast-enhanced MRI (DCE-MRI) of the breast with pathological results. Two score models, the DWI model (apparent diffusion coefficient (ADC) + morphology + enhanced information) and the TIC model (TIC + morphology + enhanced information), were established with binary logistic regression for mass and non-mass enhancements (NMEs) in the training set. The sensitivity, specificity, and area under the curve (AUC) were compared between the two models (DWI model vs. TIC model); p < 0.05 was considered as statistically different. External validation was used.

RESULTS

In the training set, the sensitivities, specificities, and AUCs of the DWI/TIC model were 95.2%/95.8%, 70.8%/47.9%, and 0.932/0.891 for masses, and 94.2%/90.4%, 47.4%/47.4%, and 0.798 (95% CI, 0.686-0.884)/0.802 (95% CI, 0.691-0.887) for NMEs, respectively. The AUC of the DWI model was significantly higher than that of the TIC model (p < 0.05) for masses. In the validation set, the AUCs of the DWI/TIC model were 0.896/0.861 for masses (p < 0.05) and 0.936/0.836 for NMEs (p > 0.05).

CONCLUSIONS

Combined with CE MRI, the DWI model was superior or equal to the TIC model in differentiating benign and malignant breast lesions.

KEY POINTS

• Diffusion magnetic resonance imaging played an important role in the diagnosis of breast neoplasms. • On the basis of contrast-enhanced MRI, the DWI model had significantly higher diagnostic ability than the TIC model in distinguishing benign and malignant masses. • It would be reasonable to replace the time-consuming TIC with DWI for less scan time and similar diagnostic efficiency.