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

Enhance registration precision of transmission breast images utilizing improved Levenberg-Marquardt optimization algorithm with normalized cross-correlation

Transmission imaging may become a possible advance for breast cancer screening with non-invasive, cost-effective, and radiation-free approaches for early detection. Frame accumulation can successfully eliminate the issue of low SNR, low grayscale and poor quality in transmission image. However, frame accumulation accuracy can be diminished because of inherent human body instability during image acquisition and the light absorption characteristics of breast tissue, resulting in distorted and misplaced image sequences. Therefore, improved Levenberg-Marquardt optimization algorithm with normalized cross-correlation is used as an innovative approach to rectify image sequences before frame accumulation processing. Two separate sets of data, showing breast images with and without markers, were collected using a halogen bulb and a mobile phone camera to validate the suggested method. The approach includes coarse registration utilizing normalized cross-correlation for initial value estimation, followed by fine registration using Levenberg-Marquardt algorithm. The results demonstrate a notable improvement in both accuracy of registration and frame accumulation quality. Specifically, the registration speed showed a remarkable increase, being 8.7 times faster, especially prominent in images that included markers. These images displayed normalized cross-correlation values reaching up to 0.99. The research emphasizes the future potential of the suggested method in overcoming the image quality challenges associated with breast transmission imaging, providing a significant milestone toward more accurate and efficient early breast cancer screening methods. Moreover, transmission imaging systems for the breast have been developed to verify the safety and effectiveness of the implemented technology.

Segmentation of breast lesion using fuzzy thresholding and deep learning

Breast cancer is a major cause of morbidity and mortality in women. In breast cancer screening, Dynamic Contrast Enhanced Magnetic Resonance Imaging (DCE-MRI) has shown promise as a technique, providing enhanced temporal patterns of breast tissues. This study proposes an enhanced segmentation method for identifying breast lesions. The proposed experiments are done using the breast DCE-MRI images of 123 slices from seven patients in The Cancer Image Archive database. The three methods proposed and tested to segment the lesions are: i) Fuzzy C-mean Thresholding (FCMTH) with morphological operations ii) deep learning networks trained with original images iii) deep learning networks trained with three types of preprocessed images: the core breast image, the filtered core breast image, and the fuzzy thresholded image. In this study, the deep learning networks are trained with preprocessed images generated from the FCMTH technique, resulting in higher segmentation accuracy. FCMTH achieves Dice and Jaccard coefficients of 0.8458 and 0.7471, while DeepLabv3+ with MobileNetv2 trained by preprocessed images achieves 0.9468 and 0.8990, respectively. Thus, the combination of deep learning and FCMTH techniques provides the best performance for lesion detection.

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.

Exploring the Potential of Sensing for Breast Cancer Detection

Breast cancer is a generalized global problem. Biomarkers are the active substances that have been considered as the signature of the existence and evolution of cancer. Early screening of different biomarkers associated with breast cancer can help doctors to design a treatment plan. However, each screening technique for breast cancer has some limitations. In most cases, a single technique can detect a single biomarker at a specific time. In this study, we address different types of biomarkers associated with breast cancer. This review article presents a detailed picture of different techniques and each technique’s associated mechanism, sensitivity, limit of detection, and linear range for breast cancer detection at early stages. The limitations of existing approaches require researchers to modify and develop new methods to identify cancer biomarkers at early stages.

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 cli