Preoperative MRI in breast cancer: effect of breast density on biopsy rate and yield

MM-3D Unet: development of a lightweight breast cancer tumor segmentation network utilizing multi-task and depthwise separable convolution

Background and objectives

This paper introduces a novel lightweight MM-3DUNet (Multi-task Mobile 3D UNet) network designed for efficient and accurate segmentation of breast cancer tumors masses from MRI images, which leverages depth-wise separable convolutions, channel expansion units, and auxiliary classification tasks to enhance feature representation and computational efficiency.

Methods

We propose a 3D depth-wise separable convolution, and construct channel expansional convolution (CEC) unit and inverted residual block (IRB) to reduce the parameter count and computational load, making the network more suitable for use in resource-constrained environments. In addition, an auxiliary classification task (ACT) is introduced in the proposed architecture to provide additional supervisory signals for the main task of segmentation. The network architecture features a contracting path for downsampling and an expanding path for precise localization, enhanced by skip connections that integrate multi-level semantic information.

Results

The network was evaluated using a dataset of Dynamic Contrast Enhanced MRI (DCE-MRI) breast cancer images, and the results show that compared to the classical 3DU-Net, MM-3DUNet could significantly reduce model parameters by 63.16% and computational demands by 80.90%, while increasing segmentation accuracy by 1.30% in IoU (Intersection over Union).

Conclusions

MM-3DUNet offers a substantial reduction in computational requirements of breast cancer mass segmentation network. This network not only enhances diagnostic precision but also supports deployment in diverse clinical settings, potentially improving early detection and treatment outcomes for breast cancer patients.

Pre-Treatment Breast MRI: Clinical Indications, Outcomes, and Future Directions

Breast MRI is the most sensitive modality for assessing the extent of disease in patients with newly-diagnosed breast cancer because it identifies clinically- and mammographically-occult breast cancers. Though highly sensitive, breast MRI has lower specificity that may result in false positive findings and potential overestimation of disease if additional MRI findings are not biopsied prior to surgery. It had been anticipated that the superior cancer detection rate of pre-treatment MRI would translate to improved immediate (surgical re-excision) and long-term patient outcomes such as breast cancer recurrence and survival rates, but studies have not necessarily supported this assumption. In this review, current recommendations and utilization of breast MRI for pre-treatment local staging of breast cancer will be presented, with an emphasis on specific clinical scenarios for patient selection and its impact on short- and long-term patient clinical outcomes. We will also present new evidence that pre-treatment MRI may support de-escalation of treatment and discuss emerging advanced MRI techniques that may improve diagnostic performance.

Subsequent percutaneous breast biopsies after initial atypia diagnosis: The patient burden of long-term follow up

BACKGROUND

Breast atypia increases overall breast cancer risk, potentially necessitating future interventions. This study examines the frequency and outcomes of additional percutaneous biopsies after an atypia diagnosis.

METHODS

Adult patients with breast atypia (atypical ductal hyperplasia, atypical lobular hyperplasia, lobular carcinoma in situ) at a single institution were reviewed for subsequent core needle biopsies (CNBs) and corresponding malignant outcomes.

RESULTS

Among 432 patients, median age at diagnosis was 54.8 ​y. Seventy-one (71/432, 16.4 ​%) patients developed a breast malignancy. During a median follow-up of 7.4 ​y, 113 patients underwent 149 additional CNBs. Twenty-six patients (26/113, 23.0 ​%) had >2 additional CNBs. Approximately half (79/149, 53.0 ​%) of all additional CNBs occurred within 5 years after breast atypia diagnosis.

CONCLUSION

A considerable number of patients with breast atypia undergo additional percutaneous biopsies, especially within 5 years post-atypia diagnosis. Our study highlights the significant burden of surveillance and the need for tailored follow-up strategies.

Upconversion and NIR-II luminescent rare earth nanoparticles combined with machine learning for cancer theranostics

How to develop contrast agents for cancer theranostics is a meaningful and challenging endeavor, and rare earth nanoparticles (RENPs) may provide a possible solution. In this study, we initially modified RENPs through the application of photodynamic agents (ZnPc) and targeted the bevacizumab antibody for cancer theranostics, which was aimed at improving the therapeutic targeting and efficacy. Subsequently, we amalgamated anthocyanin with the modified RENPs, creating a potential cancer diagnosis platform. When the spectral data were obtained from the composite of cells, the crucial information was extracted through a competitive adaptive reweighted sampling feature algorithm. Then, we employed a machine learning classification model and classified both the individual spectral data and fused spectral data to accurately predict distinctions between breast cancer and normal tissue. The results indicate that the amalgamation of fusion techniques with machine learning algorithms provides highly precise predictions for molecular-level breast cancer detection. Finally, in vitro and in vivo experiments were carried out to validate the near-infrared luminescence and therapeutic effectiveness of the modified nanomedicine. This research not only underscores the targeted effects of nanomedicine but also demonstrates the potent synergy between optical spectral technology and machine learning. This innovative approach offers a comprehensive strategy for the integrated treatment of breast cancer.

Locoregional Management of Multiple Ipsilateral Breast Cancers: A Review

The incidence of preoperatively diagnosed multiple ipsilateral breast cancer (MIBC) is increasing due to improved sensitivity of screening and preoperative staging modalities including digital breast tomosynthesis (3D breast mammography) and magnetic resonance imaging (MRI). The surgical management of MIBC remains controversial. Many surgeons continue to recommend mastectomy due to high local recurrence rates in patients with MIBC undergoing breast conservation therapy reported in historic, retrospective studies. More recent retrospective studies report acceptable rates of local recurrence. Yet concerns persist due to a paucity of prospective data regarding recurrence as well as concerns for margin positivity, cosmetic outcomes and the feasibility of adequate and safe delivery of radiation following breast conserving surgery. Breast conservation has emerged as the preferred surgical strategy for eligible patients with unifocal disease. Benefits include improved quality of life, body image and sexual function and lower surgical complication rates. A recent prospective clinical trial has corroborated a large body of retrospective data confirming the safety of breast conserving therapy and adjuvant radiation in women with MIBC with good oncologic control, low rates of conversion to mastectomy and satisfactory patient-reported cosmetic outcomes. With the current rise in MIBC diagnoses, it is imperative that surgeons understand the existent evidence in order to guide shared decision-making conversations with patients diagnosed with MIBC. This comprehensive review synthesizes the best available data and offers current recommendations for management of both the primary sites of disease as well as management of the axilla in patients with MIBC.

Imaging the dense breast

The sensitivity of mammography reduces as breast density increases, which impacts breast screening and locoregional staging in breast cancer. Supplementary imaging with other modalities can offer improved cancer detection, but this often comes at the cost of more false positives. Magnetic resonance imaging and contrast-enhanced mammography, which assess tumour enhancement following contrast administration, are more sensitive than digital breast tomosynthesis and ultrasound, which predominantly rely on the assessment of tumour morphology.

A Multi-institutional Analysis of Factors Influencing the Rate of Positive MRI Biopsy Among Women with Early-Stage Breast Cancer

BACKGROUND

The use of preoperative magnetic resonance imaging (MRI) for early-stage breast cancer (ESBC) is increasing, but its utility in detecting additional malignancy is unclear and delays surgical management (Jatoi and Benson in Future Oncol 9:347-353, 2013. https://doi.org/10.2217/fon.12.186 , Bleicher et al. J Am Coll Surg 209:180-187, 2009. https://doi.org/10.1016/j.jamcollsurg.2009.04.010 , Borowsky et al. J Surg Res 280:114-122, 2022. https://doi.org/10.1016/j.jss.2022.06.066 ). The present study sought to identify ESBC patients most likely to benefit from preoperative MRI by assessing the positive predictive values (PPVs) of ipsilateral and contralateral biopsies.

METHODS

A retrospective cohort study included patients with cTis-T2N0-N1 breast cancer from two institutions during 2016-2021. A "positive" biopsy result was defined as additional cancer (PositiveCancer) or cancer with histology often excised (PositiveSurg). The PPV of MRI biopsies was calculated with respect to age, family history, breast density, and histology. Uni- and multivariate logistic regression determined whether combinations of age younger than 50 years, dense breasts, family history, and pure ductal carcinoma in situ (DCIS) histology led to higher biopsy yield.

RESULTS

Of the included patients, 447 received preoperative MRI and 131 underwent 149 MRI-guided biopsies (96 ipsilateral, 53 contralateral [18 bilateral]). PositiveCancer for ipsilateral biopsy was 54.2%, and PositiveCancer for contralateral biopsy was 17.0%. PositiveSurg for ipsilateral biopsy was 62.5%, and PositiveSurg for contralateral biopsy was 24.5%. Among the contralateral MRI biopsies, patients younger than 50 years were less likely to have PositiveSurg (odds ratio, 0.02; 95% confidence interval, 0.00-0.84; p = 0.041). The combinations of age, density, family history, and histology did not lead to a higher biopsy yield.

CONCLUSION

Historically accepted factors for recommending preoperative MRI did not appear to confer a higher MRI biopsy yield. To prevent delays to surgical management, MRI should be carefully selected for individual patients most likely to benefit from additional imaging.

The Impact of Preoperative Breast MRI on Timing of Surgical Management in Newly Diagnosed Breast Cancer

Purpose: Preoperative breast MRI has been recommended at our center since 2016 for invasive lobular carcinoma and cancers in dense breasts. This study examined how preoperative breast MRI impacted surgical timing and outcomes for patients with newly diagnosed breast cancer. Methods: Retrospective single-center study of consecutive women diagnosed with new breast cancer between June 1, 2019, and March 1, 2021, in whom preoperative breast MRI was recommended. MRI, tumor histology, breast density, post-MRI biopsy, positive predictive value of biopsy (PPV3), surgery, and margin status were recorded. Time from diagnosis to surgery was compared using t-tests. Results: There were 1054 patients reviewed, and 356 were included (mean age 60.9). Of these, 44.4% (158/356) underwent preoperative breast MRI, and 55.6% (198/356) did not. MRI referral was more likely for invasive lobular carcinoma, multifocal disease, and younger patients. Following preoperative MRI, 29.1% (46/158) patients required additional breast biopsies before surgery, for a PPV3 of 37% (17/46). The time between biopsy and surgery was 55.8 ± 21.4 days for patients with the MRI, compared to 42.8 ± 20.3 days for those without (P < .00001). MRI was not associated with the type of surgery (mastectomy vs breastconserving surgery) (P = .44) or rate of positive surgical margins (P = .52). Conclusion: Among patients who underwent preoperative breast MRI, we observed significant delays to surgery by almost 2 weeks. When preoperative MRI is requested, efforts should be made to mitigate associated delays.

Breast density and the likelihood of malignant MRI-detected lesions in women diagnosed with breast cancer

OBJECTIVES

To assess whether mammographic breast density in women diagnosed with breast cancer correlates with the total number of incidental magnetic resonance imaging (MRI)-detected lesions and the likelihood of the lesions being malignant.

METHODS

Patients diagnosed with breast cancer meeting the EUSOBI and EUSOMA criteria for preoperative breast MRI routinely undergo mammography and ultrasound before MRI at our institution. Incidental suspicious breast lesions detected in MRI are biopsied. We included patients diagnosed with invasive breast cancers between 2014 and 2019 who underwent preoperative breast MRI. One reader retrospectively determined breast density categories according to the 5th edition of the BI-RADS lexicon.

RESULTS

Of 946 patients with 973 malignant primary breast tumors, 166 (17.5%) had a total of 175 (18.0%) incidental MRI-detected lesions (82 (46.9%) malignant and 93 (53.1%) benign). High breast density according to BI-RADS was associated with higher incidence of all incidental enhancing lesions in preoperative breast MRIs: 2.66 (95% confidence interval: 1.03-6.86) higher for BI-RADS density category B, 2.68 (1.04-6.92) for category C, and 3.67 (1.36-9.93) for category D compared to category A (p < 0.05). However, high breast density did not predict higher incidence of malignant incidental lesions (p = 0.741). Incidental MRI-detected lesions in the contralateral breast were more likely benign (p < 0.001): 18 (27.3%)/48 (72.7%) vs. 64 (58.7%)/45 (41.3%) malignant/benign incidental lesions in contralateral vs. ipsilateral breasts.

CONCLUSION

Women diagnosed with breast cancer who have dense breasts have more incidental MRI-detected lesions, but higher breast density does not translate to increased likelihood of malignant incidental lesions.

CLINICAL RELEVANCE STATEMENT

Dense breasts should not be considered as an indication for preoperative breast MRI in women diagnosed with breast cancer.

KEY POINTS

• The role of preoperative MRI of patients with dense breasts diagnosed with breast cancer is under debate. • Women with denser breasts have a higher incidence of all MRI-detected incidental breast lesions, but the incidence of malignant MRI-detected incidental lesions is not higher than in women with fatty breasts. • High breast density alone should not indicate preoperative breast MRI.

Preoperative Breast MRI: Current Evidence and Patient Selection

Breast MRI is the most sensitive imaging modality for the assessment of newly diagnosed breast cancer extent and can detect additional mammographically and clinically occult breast cancers in the ipsilateral and contralateral breasts. Nonetheless, appropriate use of breast MRI in the setting of newly diagnosed breast cancer remains debated. Though highly sensitive, MRI is less specific and may result in false positives and overestimation of disease when MRI findings are not biopsied prior to surgical excision. Furthermore, improved anatomic depiction of breast cancer on MRI has not consistently translated to improved clinical outcomes, such as lower rates of re-excision or breast cancer recurrence, though there is a paucity of well-designed studies examining these issues. In addition, current treatment paradigms have been developed in the absence of this more accurate depiction of disease span, which likely has limited the value of MRI. These issues have led to inconsistent and variable utilization of preoperative MRI across practice settings and providers. In this review, we discuss the history of breast MRI and its current use and recommendations with a focus on the preoperative setting. We review the evidence surrounding the use of preoperative MRI in the evaluation of breast malignancies and discuss the data on breast MRI in the setting of specific patient factors often used to determine breast MRI eligibility, such as age, index tumor phenotype, and breast density. Finally, we review the impact of breast MRI on surgical outcomes (re-excision and mastectomy rates) and long-term breast recurrence and survival outcomes.

Chimp Optimization Algorithm Influenced Type-2 Intuitionistic Fuzzy C-Means Clustering-Based Breast Cancer Detection System

In recent years, breast cancer detection is an important area of concentration towards curative image dispensation and exploration. Detection of a disease at an early stage is an important factor in taking it to the next level of treatment. Accuracy plays an important role in the detection of disease. COA-T2FCM (Chimp Optimization Algorithm Based Type-2 Intuitionistic Fuzzy C-Means Clustering) is constructed for detection of such malignancy with the highest accuracy in this paper. The proposed detection process is designed with the combination of type-2 intuitionistic fuzzy c-means clustering in addition to oppositional function. In the type-2 intuitionistic fuzzy c-means clustering, the efficient cluster center can be preferred using the chimp optimization algorithm. Initially, the objective function of the type-2 intuitionistic fuzzy c-means clustering is considered. The chimp optimization algorithm is utilized to optimize the cluster center and fuzzifier in the clustering method. The projected technique is implemented, and in addition, performance metrics such as specificity, sensitivity, accuracy, Jaccard Similarity Index (JSI), and Dice Similarity Coefficient (DSC) are assessed. The projected technique is compared with the conventional technique such as fuzzy c means clustering and k mean clustering methods. The resulting method was also compared with existing methods to ensure the accuracy in the proposed method. The proposed algorithm is tested for its effectiveness on the mammogram images of the three different datasets collected from the Mini-Mammographic Image Analysis Society (Mini-MIAS), the Digital Database for Screening Mammography (DDSM), and Inbreast. The accuracy and Jaccard index score are generally used to measure the similarity between the proposed output and the actual cancer affected regions from the image considered. On an average the proposed method achieved an accuracy of 97.29% and JSI of 95.

Decision quality and regret with treatment decisions in women with breast cancer: Pre-operative breast MRI and breast density

PURPOSE

We evaluated self-report of decision quality and regret with breast cancer surgical treatment by pre-operative breast MRI use in women recently diagnosed with breast cancer.

METHODS

We conducted a survey with 957 women aged 18 + with stage 0-III breast cancer identified in the Breast Cancer Surveillance Consortium. Participants self-reported receipt of pre-operative breast MRI. Primary outcomes were process measures in the Breast Cancer Surgery Decision Quality Instrument (BCS-DQI) (continuous outcome) and Decision Regret Scale (dichotomized outcome as any/none). Generalized estimating equations with linear and logit link were used to estimate adjusted associations between breast MRI and primary outcomes. All analyses were also stratified by breast density.

RESULTS

Survey participation rate was 27.9% (957/3430). Study population was primarily > 60 years, White, college educated, and diagnosed with early-stage breast cancer. Pre-operative breast MRI was reported in 46% of women. A higher proportion of women who were younger age (< 50 years), commercially insured, and self-detected their breast cancer reported pre-operative breast MRI use. In adjusted analysis, pre-operative breast MRI use compared with no use was associated with a small but statistically significantly higher decision quality scores (69.5 vs 64.7, p-value = 0.043). Decision regret did not significantly differ in women who reported pre-operative breast MRI use compared with no use (54.2% v. 48.7%, respectively, p-value = 0.11). Study results did not vary when stratified by breast density for either primary outcome.

CONCLUSIONS AND RELEVANCE

Breast MRI use in the diagnostic work-up of breast cancer does not negatively alter women's perceptions of surgical treatment decisions in early survivorship.

CLINICAL TRIALS REGISTRATION NUMBER

NCT03029286.

Management of Non-Mass Enhancement at Breast Magnetic Resonance in Screening Settings Referred for Magnetic Resonance-Guided Biopsy

Rationale and Objectives

According to the Breast Imaging and Reporting Data System (BI-RADS), one of the main limitations of MRI is diagnosing the non-mass enhancement (NME). The NME lesion is challenging since it is unique to the MRI lexicon. This study aims to report our experience with NME lesions diagnosed by MRI referred for MRI-guided biopsies and discuss the management and follow-up of these lesions.

Materials and Methods

We retrospectively evaluated all MRI-guide breast biopsies. We included all patients referred for NME breast MRI-guided biopsy in screening settings. All patients had a negative second-look mammography or ultrasonography. We correlated the distribution and internal enhancement pattern (IEP) of the NME lesions with histology. Invasive ductal carcinomas (IDC) of no special type and ductal carcinoma in situ (DCIS) were considered malignant lesions.

Results

From January-2018 to July-2021, we included 96 women with a total of 96 lesions in the study. There were 90 benign and 6 malignant lesions with DCIS prevalence (5/6 cancers). The most frequent benign lesion type was fibrocystic changes. There were no NME lesions with diffuse or multiple area distribution features referred to MRI-guided biopsy. The positive-predictive values (PPV) were respectively 0.0%, 2.5%, 9.0%, and 11.0% for linear, focal, regional, and segmental distribution describers, and 0.0, 3.0%, 7.9%, and 50% for homogenous, heterogeneous, clumped, and clustered-ring enhancement patterns.

Conclusion

We observe the high potential risk for malignancy in the clustered-ring enhancement followed by the clumped pattern. Segmental distribution presented the highest predictive-positive values.