Magnetic Resonance Imaging in Screening of Breast Cancer

Multigenic panels in breast cancer: Clinical utility and management of patients with pathogenic variants other than BRCA1/2

Multigene panels can analyze high and moderate/intermediate penetrance genes that predispose to breast cancer (BC), providing an opportunity to identify at-risk individuals within affected families. However, considering the complexity of different pathogenic variants and correlated clinical manifestations, a multidisciplinary team is needed to effectively manage BC. A classification of pathogenic variants included in multigene panels was presented in this narrative review to evaluate their clinical utility in BC. Clinical management was discussed for each category and focused on BC, including available evidence regarding the multidisciplinary and integrated management of patients with BC. The integration of both genetic testing and counseling is required for customized decisions in therapeutic strategies and preventative initiatives, as well as for a defined multidisciplinary approach, considering the continuous evolution of guidelines and research in the field.

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.

Improving signal-to-noise ratio by maximal convolution of longitudinal and transverse magnetization components in MRI: application to the breast cancer detection

PURPOSE

The extraction of information from images provided by medical imaging systems may be employed to obtain the specific objectives in the various fields. The quantity of signal to noise ratio (SNR) plays a crucial role in displaying the image details. The higher the SNR value, the more the information is available.

METHODS

In this study, a new function has been formulated using the appropriate suggestions on convolutional combination of the longitudinal and transverse magnetization components related to the relaxation times of T1 and T2 in MRI, where by introducing the distinct index on the maximum value of this function, the new maps are constructed toward the best SNR. Proposed functions were analytically simulated using Matlab software and evaluated with respect to various relaxation times. This proposed method can be applied to any medical images. For instance, the T1- and T2-weighted images of the breast indicated in the reference [35] were selected for modelling and construction of the full width at x maximum (FWxM) map at the different values of x-parameter from 0.01 to 0.955 at 0.035 and 0.015 intervals. The range of x-parameter is between zero and one. To determine the maximum value of the derived SNR, these intervals have been first chosen arbitrarily. However, the smaller this interval, the more precise the value of the x-parameter at which the signal to noise is maximum.

RESULTS

The results showed that at an index value of x = 0.325, the new map of FWxM (0.325) will be constructed with a maximum derived SNR of 22.7 compared to the SNR values of T1- and T2-maps by 14.53 and 17.47, respectively.

CONCLUSION

By convolving two orthogonal magnetization vectors, the qualified images with higher new SNR were created, which included the image with the best SNR. In other words, to optimize the adoption of MRI technique and enable the possibility of wider use, an optimal and cost-effective examination has been suggested. Our proposal aims to shorten the MRI examination to further reduce interpretation times while maintaining primary sensitivity.

SIGNIFICANCE

Our findings may help to quantitatively identify the primary sources of each type of solid and sequential cancer.

Effect of Phase Encoding Direction on Image Quality in Single-Shot EPI Diffusion-Weighted Imaging of the Breast

BACKGROUND

In breast diffusion-weighted imaging (DWI), distortion and physiologic artifacts affect clinical interpretation. Image quality can be optimized by addressing the effect of phase encoding (PE) direction on these artifacts.

PURPOSE

To compare distortion artifacts in breast DWI acquired with different PE directions and polarities, and to discuss their clinical implications.

STUDY TYPE

Prospective.

POPULATION

Eleven healthy volunteers (median age: 47 years old; range: 22-74 years old) and a breast phantom.

FIELD STRENGTH/SEQUENCE

Single-shot echo planar DWI and three-dimensional fast gradient echo sequences at 3 T.

ASSESSMENT

All DWI data were acquired with left-right, right-left, posterior-anterior, and anterior-posterior PE directions. In phantom data, displacement magnitude was evaluated by comparing the location of landmarks in anatomical and DWI images. Three breast radiologists (5, 17, and 23 years of experience) assessed the presence or absence of physiologic artifacts in volunteers' DWI datasets and indicated their PE-direction preference.

STATISTICAL TESTS

Analysis of variance with post-hoc tests were used to assess differences in displacement magnitude across DWI datasets and observers. A binomial test and a chi-squared test were used to evaluate if each in vivo DWI dataset had an equal probability (25%) of being preferred by radiologists. Inter-reader agreement was evaluated using Gwet's AC1 agreement coefficient. A P-value <0.05 was considered statistically significant.

RESULTS

In the phantom study, median displacement was the significantly largest in posterior-anterior data. While the displacement in the anterior-posterior and left-right data were equivalent (P = 0.545). In the in vivo data, there were no physiological artifacts observed in any dataset, regardless of PE direction. In the reader study, there was a significant preference for the posterior-anterior datasets which were selected 94% of the time. There was good agreement between readers (0.936).

DATA CONCLUSION

This study showed the impact of PE direction on distortion artifacts in breast DWI. In healthy volunteers, the posterior-to-anterior PE direction was preferred by readers.

LEVEL OF EVIDENCE

2 TECHNICAL EFFICACY: Stage 1.

[WITHDRAWN] Predicting breast cancer with AI for individual risk-adjusted MRI screening and early detection

This paper has been withdrawn by Lukas Hirsch. Major revisions and rewriting in progress.

Clinical and Imaging Features of MRI Screen-Detected Breast Cancer

BACKGROUND

Supplemental screening with breast MRI is recommended annually for patients who have greater than 20% lifetime risk for breast cancer. While there is robust data regarding features of mammographic screen-detected breast cancers, there is limited data regarding MRI-screen-detected cancers.

PATIENTS AND METHODS

Screening breast MRIs performed between August 1, 2016 and July 30, 2022 identified 50 screen-detected breast cancers in 47 patients. Clinical and imaging features of all eligible cancers were recorded.

RESULTS

During the study period, 50 MRI-screen detected cancers were identified in 47 patients. The majority of MRI-screen detected cancers (32/50, 64%) were invasive. Pathology revealed ductal carcinoma in situ (DCIS) in 36% (18/50), invasive ductal carcinoma (IDC) in 52% (26/50), invasive lobular carcinoma in 10% (5/50), and angiosarcoma in 2% (1/50). The majority of patients (43/47, 91%) were stage 0 or 1 at diagnosis and there were no breast cancer-related deaths during the follow-up periods. Cancers presented as masses in 50% (25/50), nonmass enhancement in 48% (25/50), and a focus in 2% (1/50). DCIS was more likely to present as nonmass enhancement (94.4%, 17/18), whereas invasive cancers were more likely to present as masses (75%, 24/32) (P < .001). All cancers that were stage 2 at diagnosis were detected either on a baseline exam or more than 4 years since the prior MRI exam.

CONCLUSION

MRI screen-detected breast cancers were most often invasive cancers. Cancers detected by MRI screening had an excellent prognosis in our study population. Invasive cancers most commonly presented as a mass.

Analysis of the indications for and results of breast cancer screening by magnetic resonance imaging at a cancer center in Brazil

Objective

To evaluate the indications for and results of magnetic resonance imaging (MRI) examinations for breast cancer screening at a cancer center in Brazil.

Materials and Methods

This was a retrospective observational study, based on electronic medical records, of patients undergoing MRI for breast cancer screening at a cancer center in Brazil.

Results

We included 597 patients between 19 and 82 years of age. The main indications for MRI screening were a personal history of breast cancer, in 354 patients (59.3%), a family history of breast cancer, in 102 (17.1%), and a confirmed genetic mutation, in 67 (11.2%). The MRI result was classified, in accordance with the categories defined in the Breast Imaging Reporting and Data System, as benign (category 1 or 2), in 425 patients (71.2%), probably benign (category 3), in 143 (24.0%), or suspicious (category 4 or 5), in 29 (4.9%). On MRI, 11 malignant tumors were identified, all of which were invasive carcinomas. Among those 11 carcinomas, six (54.5%) were categorized as minimal cancers (< 1 cm), and the axillary lymph nodes were negative in 10 (90.9%). The cancer detection rate was 18.4/1,000 examinations, and the positive predictive value for suspicious lesions submitted to biopsy was 37.9%.

Conclusion

In our sample, the main indication for breast MRI screening was a personal history of breast cancer. The results indicate that MRI is a highly accurate method for the early detection of breast neoplasms in this population.

Breast cancer: Epidemiology, risk factors and screening

Breast cancer is a global health concern with a significant impact on the well-being of women. Worldwide, the past several decades have witnessed changes in the incidence and mortality of breast cancer. Additionally, epidemiological data reveal distinct geographic and demographic disparities globally. A range of modifiable and non-modifiable risk factors are established as being associated with an increased risk of developing breast cancer. This review discusses genetic, hormonal, behavioral, environmental, and breast-related risk factors. Screening plays a critical role in the effective management of breast cancer. Various screening modalities, including mammography, ultrasound, magnetic resonance imaging (MRI), and physical examination, have different applications, and a combination of these modalities is applied in practice. Current screening recommendations are based on factors including age and risk, with a significant emphasis on minimizing potential harms to achieve an optimal benefits-to-harms ratio. This review provides a comprehensive insight into the epidemiology, risk factors, and screening of breast cancer. Understanding these elements is crucial for improving breast cancer management and reducing its burden on affected individuals and healthcare systems.

Breast and colorectal cancer risks among over 6,000 CHEK2 pathogenic variant carriers: A comparison of missense versus truncating variants

BACKGROUND AND AIMS

Heterozygous truncating pathogenic variants (PVs) in CHEK2 confer a 1.5 to 3-fold increased risk for breast cancer and may elevate colorectal cancer risks. Less is known regarding missense variants. Here we compared the cancer associations with truncating and missense PVs in CHEK2 across breast and colorectal cancer.

METHODS

This was a retrospective analysis of 705,797 patients who received single laboratory multigene panel testing between 2013 and 2020. Multivariable logistic regression models determined cancer risk associated with CHEK2 variants as odds ratios (ORs) and 95% confidence intervals (CIs) after adjusting for age at diagnosis, cancer history, and ancestry. Breast and colorectal cancer analyses were performed using 6255 CHEK2 PVs, including truncating PVs (N = 4505) and missense PVs (N = 1750).

RESULTS

CHEK2 PVs were associated with an increased risk of ductal invasive breast cancer (p < 0.001) and ductal carcinoma in situ (DCIS) (p < 0.001), with no statistically significant differences when truncating PVs (p < 0.001) and missense PVs (p < 0.001) were evaluated separately. All CHEK2 variants assessed conferred little to no risk of colorectal cancer.

CONCLUSIONS

In our large cohort, CHEK2 truncating and missense PVs conferred similar risks for breast cancer and did not seem to elevate risk for colorectal cancer.

Artificial intelligence to support person-centred care in breast imaging - A scoping review

AIM

To overview Artificial Intelligence (AI) developments and applications in breast imaging (BI) focused on providing person-centred care in diagnosis and treatment for breast pathologies.

METHODS

The scoping review was conducted in accordance with the Joanna Briggs Institute methodology. The search was conducted on MEDLINE, Embase, CINAHL, Web of science, IEEE explore and arxiv during July 2022 and included only studies published after 2016, in French and English. Combination of keywords and Medical Subject Headings terms (MeSH) related to breast imaging and AI were used. No keywords or MeSH terms related to patients, or the person-centred care (PCC) concept were included. Three independent reviewers screened all abstracts and titles, and all eligible full-text publications during a second stage.

RESULTS

3417 results were identified by the search and 106 studies were included for meeting all criteria. Six themes relating to the AI-enabled PCC in BI were identified: individualised risk prediction/growth and prediction/false negative reduction (44.3%), treatment assessment (32.1%), tumour type prediction (11.3%), unnecessary biopsies reduction (5.7%), patients' preferences (2.8%) and other issues (3.8%). The main BI modalities explored in the included studies were magnetic resonance imaging (MRI) (31.1%), mammography (27.4%) and ultrasound (23.6%). The studies were predominantly retrospective, and some variations (age range, data source, race, medical imaging) were present in the datasets used.

CONCLUSIONS

The AI tools for person-centred care are mainly designed for risk and cancer prediction and disease management to identify the most suitable treatment. However, further studies are needed for image acquisition optimisation for different patient groups, improvement and customisation of patient experience and for communicating to patients the options and pathways of disease management.

Breast Cancer Screening for Women at Higher-Than-Average Risk: Updated Recommendations From the ACR

Early detection decreases breast cancer death. The ACR recommends annual screening beginning at age 40 for women of average risk and earlier and/or more intensive screening for women at higher-than-average risk. For most women at higher-than-average risk, the supplemental screening method of choice is breast MRI. Women with genetics-based increased risk, those with a calculated lifetime risk of 20% or more, and those exposed to chest radiation at young ages are recommended to undergo MRI surveillance starting at ages 25 to 30 and annual mammography (with a variable starting age between 25 and 40, depending on the type of risk). Mutation carriers can delay mammographic screening until age 40 if annual screening breast MRI is performed as recommended. Women diagnosed with breast cancer before age 50 or with personal histories of breast cancer and dense breasts should undergo annual supplemental breast MRI. Others with personal histories, and those with atypia at biopsy, should strongly consider MRI screening, especially if other risk factors are present. For women with dense breasts who desire supplemental screening, breast MRI is recommended. For those who qualify for but cannot undergo breast MRI, contrast-enhanced mammography or ultrasound could be considered. All women should undergo risk assessment by age 25, especially Black women and women of Ashkenazi Jewish heritage, so that those at higher-than-average risk can be identified and appropriate screening initiated.

Breast Cancer Diagnosis Using Texture and Shape Features in MRI

Contrast-enhanced magnetic resonance (MR) breast imaging represents a tool with great potential for the detection, evaluation and diagnosis of breast cancer (BC). Due to its high sensitivity and in combination with medical imaging biomarkers, it can overcome setbacks and limitations manifested in other diagnostic modalities such as mammography or ultrasound. In order to aid and assist clinicians in the diagnosis of BC, a methodology based on the extraction of 2D texture and 3D shape features in MR images is proposed. To categorize breast tumor malignancy, we considered its location in the coronal plane, divided into 4 quadrants (UOQ, UIQ, LOQ and LOQ), and the tumor type according to its genetic information (positive HER2 and Luminal B with negative HER2). In this regard, six different studies were conducted: one per feature type (texture and shape), as well as the combination of both features (texture + shape) for each of the two covariables (tumor type and location in the coronal plane). A dataset of 43 BC patients were considered. A radiomics approach was implemented extracting 43 texture and 17 shape features and using to train 5 different predictive models (Linear SVM, Gaussian SVM, Bagged Tree, KNN and Naïve Bayes). The highest precision result for the tumor type study (74.04% in terms of AUC) was obtained with 43 texture features. Whereas for the quadrant localization study, the highest precision result (67.99% AUC) was obtained as a combination of 3 textures and shape features. Both results were achieved with the SVM with Linear Kernel classification model.Clinical Relevance- This work emphasizes the use of quantitative biomarkers as texture and shape features in combination with machine learning techniques to aid in breast tumor malignancy diagnosis on MR imaging. Moreover, considering the location of the tumor in the coronal plane and its type according to its genetic information may improve the selection of appropriate treatments, survival rate, and quality of life for breast cancer patients.

'Earlier than Early' Detection of Breast Cancer in Israeli BRCA Mutation Carriers Applying AI-Based Analysis to Consecutive MRI Scans

Female BRCA1/BRCA2 (=BRCA) pathogenic variants (PVs) carriers are at a substantially higher risk for developing breast cancer (BC) compared with the average risk population. Detection of BC at an early stage significantly improves prognosis. To facilitate early BC detection, a surveillance scheme is offered to BRCA PV carriers from age 25-30 years that includes annual MRI based breast imaging. Indeed, adherence to the recommended scheme has been shown to be associated with earlier disease stages at BC diagnosis, more in-situ pathology, smaller tumors, and less axillary involvement. While MRI is the most sensitive modality for BC detection in BRCA PV carriers, there are a significant number of overlooked or misinterpreted radiological lesions (mostly enhancing foci), leading to a delayed BC diagnosis at a more advanced stage. In this study we developed an artificial intelligence (AI)-network, aimed at a more accurate classification of enhancing foci, in MRIs of BRCA PV carriers, thus reducing false-negative interpretations. Retrospectively identified foci in prior MRIs that were either diagnosed as BC or benign/normal in a subsequent MRI were manually segmented and served as input for a convolutional network architecture. The model was successful in classification of 65% of the cancerous foci, most of them triple-negative BC. If validated, applying this scheme routinely may facilitate 'earlier than early' BC diagnosis in BRCA PV carriers.

The Future of Breast Cancer Research in the Survivorship Field

Prevalence of survivors of breast cancer has been steadily increasing in the last 20 years. Currently, more than 90% of women diagnosed with early-stage breast cancer are expected to be alive at 5 years from diagnosis thanks to early detection and breakthrough innovations in multimodal treatment strategies. Alongside this advancement in clinical outcomes, survivors of breast cancer might experience several specific challenges and present with unique needs. Survivorship trajectories after diagnosis and treatment of breast cancer can be significantly impacted by long-lasting and severe treatment-related side effects, including physical problems, psychological distress, fertility issues in young women, and impaired social and work reintegration, which add up to patients' individual risk of cancer recurrence and second primary malignancies. Alongside cancer-specific sequelae, survivors still present with general health needs, including management of chronic preexisting or ensuing conditions. Survivorship care should implement high-quality, evidence-based strategies to promptly screen, identify, and address survivors' needs in a comprehensive way and minimize the impact of severe treatment sequelae, preexisting comorbidities, unhealthy lifestyles, and risk of recurrence on quality of life. This narrative review focuses on core areas of survivorship care and discuss the state of the art and future research perspectives in key domains including selected long-term side effects, surveillance for recurrences and second cancers, well-being promotion, and specific survivors' needs.

Breast Cancer Screening Utilization and Outcomes in Women With Neurofibromatosis Type 1

INTRODUCTION

Women with neurofibromatosis type 1 (NF1) have up to a 5-fold increased risk for breast cancer before age 50 and a 3.5-fold increased risk of breast cancer overall. The purpose of our study was to assess breast cancer screening utilization and outcomes in this population.

PATIENTS AND METHODS

This IRB approved HIPAA compliant study retrospectively assessed consecutive NF1 patients (January 2012-December 2021) with recorded clinical visits and/or breast imaging. Patient demographics, risk factors, and screening mammogram and breast magnetic resonance imaging (MRI) outcomes were recorded. Descriptive statistics were obtained and standard breast screening measures were calculated.

RESULTS

One hundred and eleven women (median age 43, range 30-82) were eligible for screening based on current NCCN guidelines. A total of 86% (95/111) of all patients and 80% (24/30) of patients under age 40 had at least 1 mammogram. In contrast, 28% (31/111) of all patients and 33% (25/76) of patients ages 30 to 50 had at least 1 screening MRI. Of 368 screening mammograms performed, 38 of 368 (10%) resulted in the recall, and 22 of 368 (6%) resulted in a biopsy. Of 48 screening MRIs performed, 19 of 48 (40%) short-term follow-ups and 12 of 48 (25%) biopsies were recommended. All 6 screen-detected cancers in our cohort were detected initially on screening mammograms.

CONCLUSION

Results confirm the utility and performance of screening mammography in the NF1 population. The low utilization of MRI in our cohort limits the evaluation of outcomes via this modality and suggests there may be an education or interest gap among referrers and patients regarding supplemental screening recommendations.

Efficacy Evaluation of Neoadjuvant Chemotherapy in Breast Cancer by MRI

Breast cancer is a highly harmful malignancy, which often causes great distress to patients and seriously affects their physical and mental health. Breast cancer causes patients to experience decreased appetite, decreased eating, and indigestion, which in turn leads to malnutrition, body wasting, resistance, immune compromise, progressive anemia, cachexia, and, as a result, severe secondary infections. To investigate the efficacy evaluation of neoadjuvant chemotherapy in breast cancer by MRI, forty-eight subjects treated at the hospital from June 2014 to August 2019 were recruited. After the neoadjuvant chemotherapy, the patients were divided into two groups based on the results of histopathological examination, namely, the ineffective group (n = 14) and the effective group (n = 34). Changes in MRI indicators were compared between the two groups before and after the neoadjuvant chemotherapy. The maximum diameter of lesions decreased significantly after the neoadjuvant chemotherapy than before. The apparent diffusion coefficient (ADC) increased considerably, and the time-intensity curve (TIC) showed a transition from type III to type II/I and from type II to type I. MRI can indicate the maximum diameter of the breast cancer lesion, ADC, and TIC type. Therefore, it can be used to evaluate the efficacy of neoadjuvant chemotherapy for breast cancer and be widely applied in clinical practice.

Breast Lesion Classification with Multiparametric Breast MRI Using Radiomics and Machine Learning: A Comparison with Radiologists' Performance

Simple Summary

Currently, breast contrast-enhanced MRI is the most sensitive imaging technique for breast cancer detection; however, its specificity is low given the common characteristics shared by benign breast lesions and some cancers. This leads to a high number of false-positive cases and, therefore, unnecessary biopsies. Multiparametric MRI including diffusion-weighted imaging assists in this task by increasing the specificity for breast lesion discrimination. Nevertheless, interpretation of breast MRI is still highly dependent on the reader’s level of experience. Our work combines radiomic features extracted from multiparametric MRI to generate predictive models for breast cancer differentiation. Additionally, decision support models were compared with the performance of two breast dedicated radiologists for lesion differentiation. Our work proves the potential of multiparametric radiomics coupled with machine learning to be implemented in clinical practice for lesion differentiation on breast MRI. AI algorithms show value to assist less experienced readers, improving the accuracy for breast lesion discrimination.

Abstract

This multicenter retrospective study compared the performance of radiomics analysis coupled with machine learning (ML) with that of radiologists for the classification of breast tumors. A total of 93 consecutive women (mean age: 49 ± 12 years) with 104 histopathologically verified enhancing lesions (mean size: 22.8 ± 15.1 mm), classified as suspicious on multiparametric breast MRIs were included. Two experienced breast radiologists assessed all of the lesions, assigning a Breast Imaging Reporting and Database System (BI-RADS) suspicion category, providing a diffusion-weighted imaging (DWI) score based on lesion signal intensity, and determining the apparent diffusion coefficient (ADC). Ten predictive models for breast lesion discrimination were generated using radiomic features extracted from the multiparametric MRI. The area under the receiver operating curve (AUC) and the accuracy were compared using McNemar’s test. Multiparametric radiomics with DWI score and BI-RADS (accuracy = 88.5%; AUC = 0.93) and multiparametric radiomics with ADC values and BI-RADS (accuracy= 88.5%; AUC = 0.96) models showed significant improvements in diagnostic accuracy compared to the multiparametric radiomics (DWI + DCE data) model (p = 0.01 and p = 0.02, respectively), but performed similarly compared to the multiparametric assessment by radiologists (accuracy = 85.6%; AUC = 0.03; p = 0.39). In conclusion, radiomics analysis coupled with the ML of multiparametric MRI could assist in breast lesion discrimination, especially for less experienced readers of breast MRIs.

Ultrasound and magnetic resonance imaging of cyclic arginine glycine aspartic acid-gadopentetic acid-polylactic acid in human breast cancer by targeting αvβ3 in xenograft-bearing nude mice

Effective early detection shows the potential to reduce breast cancer mortality. This study aimed to establish a targeted contrast agent for Magnetic Resonance Imaging (MRI)/ultrasound dual-modality molecular radiography for breast cancer. The cyclic arginine-glycine-aspartate-gadopentetic acid-polylactic acid (cRGD and Gd-DTPA) coated by multi-functional blank poly (lactic-co-glycolic acid) (PLGA) nanoparticles) was successfully constructed by chemical synthesis method with high stability. The safety of cRGD-Gd-DTPA-PLGA was demonstrated in vitro and in vivo, and their affinity to breast cancer cells was revealed. Moreover, MRI/ultrasound dual-modality molecular radiography in vitro showed that as the concentration of contrast agent increased, the echo enhancement and signal intensity of MRI imaging were also elevated. The mouse models of human breast cancer also indicated significant target enhancements of cRGD-Gd-DTPA-PLGA magnetic nanoparticles in the mouse tumor. Thus, cRGD-Gd-DTPA-PLGA magnetic nanoparticles were suggested as qualified MRI/ultrasound dual-modality molecular radiography contrast agent. We further explored the targeting mechanism of cRGD-Gd-DTPA-PLGA in breast cancer. The results showed that αvβ3 was highly expressed in breast cancer tissues, and cRGD-Gd-DTPA-PLGA used for MRI/ultrasound dual-modality molecular radiography by targeting αvβ3. Additionally, we found that the signal-to-noise ratio of MRI was positively correlated with microvessel density (MVD). The cRGD-Gd-DTPA-PLGA dynamicly and quantitatively monitored breast cancer by monitoring the state of neovascularization. In conclusion, in the present study, we successfully constructed the cRGD-Gd-DTPA-PLGA magnetic nanoparticles for MRI/ultrasound dual-modality molecular radiography. The cRGD-Gd-DTPA-PLGA showed potential in early detection and diagnosis of metastasis, and dynamic evaluation of the efficacy of molecular targeted therapy of integrin αvβ3.

Diagnostic Value of 1H NMR-Based Metabolomics in Acute Lymphoblastic Leukemia, Acute Myeloid Leukemia, and Breast Cancer

Cancer refers to a massive number of diseases distinguished by the development of abnormal cells that divide uncontrollably and have the capability of infiltration and destroying the normal body tissue. It is critical to detect biomarkers that are early detectable and noninvasive to save millions of lives. The aim of the present work is to use NMR as a noninvasive diagnostic tool for cancer diseases. This study included 30 plasma and 21 urine samples of patients diagnosed with acute lymphoblastic leukemia (ALL) and acute myeloid leukemia (AML), 25 plasma and 17 urine samples of patients diagnosed with breast cancer (BC), and 9 plasma and urine samples obtained from healthy individuals as controls. They were prepared for NMR measurements; then, the metabolites were identified and the data were analyzed using multivariate statistical procedures. The OPLS-DA score plots clearly discriminated ALL, AML, and BC from healthy controls. Plots of the PLS-DA loadings and S-line plots showed that all metabolites in plasma were greater in BC than in the healthy controls, whereas lactate, O-acetylcarnitine, pyruvate, trimethylamine-N-oxide (TMAO), and glucose were higher in healthy controls than in ALL and AML. On the other hand, urine samples showed lower amounts of lactate, melatonin, pyruvate, and succinate in all of the studied types of cancer when compared to those of healthy controls. ¹H NMR can be a successful and noninvasive tool for the diagnosis of different types of cancer.

The hidden breast lesions: A case report of bilateral breast cancer

Bilateral breast cancer (BBC) is rare and is associated with an unfavorable prognosis. Consequently it is crucial to improve diagnostic performance of breast cancer in the clinical setting. We report a case of BBC in a 66-year-old woman and describe the imaging findings, including mammography, hand-held ultrasound, automated breast ultrasound, anatomical intelligence for breast ultrasound (AI-breast), and magnetic resonance imaging. Only AI-breast ultrasound successfully located the two tumors, while other imaging examinations failed to detect the tumor in the right breast.

Fabrication of a dual stimuli-responsive magnetic nanohydrogel for delivery of anticancer drugs

A dual stimuli-responsive magnetic nanohydrogel was fabricated as a potent drug delivery system (DDS) for 'smart' treatment of cancer by chemo/hyperthermia approach. For this objective, Fe₃O₄ nanoparticles (NPs) were produced via a co-precipitation approach and then modified by 3-(trimethoxysilyl) propylmethacrylate (MPS) moiety. The modified NPs were copolymerized with N,N'-(dimethylamino)ethyl methacrylate (DMAEMA), and maleic anhydride (MA) monomers by a free radical polymerization approach to afford a Fe₃O₄@P(DMAEMA-co-MA) core-shell NPs. Afterward, the NPs were shell crosslinked by the reaction of anhydride unites with neutralized cystamine (Cys). The fabricated pH- and reduction-responsive magnetic nanohydrogel was physically loaded with methotrexate (MTX), as an anticancer drug, and its drug loading efficiency (LE) was calculated as 64 ± 2.7%. The developed nanohydrogel/MTX exhibited proper stimuli-triggered drug release behavior that qualified it as an efficient DDS according to the abnormal micro-environment of cancerous tumors. The anticancer activity investigation using chemo/hyperthermia therapy approach by MTT-assay revealed that the nanohydrogel/MTX might show better clinical outcomes than those of the free MTX.

Radiomics and Machine Learning with Multiparametric Breast MRI for Improved Diagnostic Accuracy in Breast Cancer Diagnosis

The purpose of this multicenter retrospective study was to evaluate radiomics analysis coupled with machine learning (ML) of dynamic contrast-enhanced (DCE) and diffusion-weighted imaging (DWI) radiomics models separately and combined as multiparametric MRI for improved breast cancer detection. Consecutive patients (Memorial Sloan Kettering Cancer Center, January 2018-March 2020; Medical University Vienna, from January 2011-August 2014) with a suspicious enhancing breast tumor on breast MRI categorized as BI-RADS 4 and who subsequently underwent image-guided biopsy were included. In 93 patients (mean age: 49 years ± 12 years; 100% women), there were 104 lesions (mean size: 22.8 mm; range: 7-99 mm), 46 malignant and 58 benign. Radiomics features were calculated. Subsequently, the five most significant features were fitted into multivariable modeling to produce a robust ML model for discriminating between benign and malignant lesions. A medium Gaussian support vector machine (SVM) model with five-fold cross validation was developed for each modality. A model based on DWI-extracted features achieved an AUC of 0.79 (95% CI: 0.70-0.88), whereas a model based on DCE-extracted features yielded an AUC of 0.83 (95% CI: 0.75-0.91). A multiparametric radiomics model combining DCE- and DWI-extracted features showed the best AUC (0.85; 95% CI: 0.77-0.92) and diagnostic accuracy (81.7%; 95% CI: 73.0-88.6). In conclusion, radiomics analysis coupled with ML of multiparametric MRI allows an improved evaluation of suspicious enhancing breast tumors recommended for biopsy on clinical breast MRI, facilitating accurate breast cancer diagnosis while reducing unnecessary benign breast biopsies.