Imaging Challenges in Diagnosing Triple-Negative Breast Cancer

USPSTF recommends biennial mammography for breast cancer screening in women aged 40 to 74 y

SOURCE CITATION

US Preventive Services Task Force; Nicholson WK, Silverstein M, Wong JB, et al. Screening for breast cancer: US Preventive Services Task Force recommendation statement. JAMA. 2024;331:1918-1930. 38687503.

A near-infrared fluorescent probe with assembly/aggregation-induced retention effect for specific diagnosis of metastasis and image-guided surgery in breast cancer

Image-guided surgery is crucial for achieving complete tumor resection, reducing postoperative recurrence and improving patient survival. However, current clinical near-infrared fluorescent probes, such as indocyanine green (ICG), face two main limitations: 1) lack of active tumor targeting, and 2) short retention time in tumors, which restricts real-time imaging during surgery. To address these issues, we developed a near-infrared fluorescent probe capable of in situ nanofiber formation within tumor lesions. This probe actively targets the integrin αvβ3 receptors overexpressed on breast cancer cells and exhibits assembly/aggregation-induced retention effects at the tumor site, significantly extending the imaging time window. Additionally, we found that the probe's fluorescence intensity can be enhanced under receptor induction. Due to its excellent tumor specificity and sensitivity, 1FCG-FFGRGD not only identifies primary breast cancer but also precisely locates smaller lymph node metastases and detects sub-millimeter peritoneal metastases. In summary, this near-infrared probe, leveraging assembly/aggregation-induced retention effects, holds substantial potential for various biomedical applications.

The Landscape of Breast Cancer Molecular and Histologic Subtypes in Canada

Purpose: To characterize the histologic and molecular subtype distribution of, and survival from, breast cancer (BC) among Canadian women overall, and by stage and age at diagnosis. Methods: Invasive BC cases from the Canadian Cancer Registry for women aged 15-99 years between 2012 and 2017 in Canada, excluding Quebec, were examined using pre-existing mortality linkages. Stage at diagnosis, molecular, and histologic subtypes, and 5-year net survival (NS) by age, subtype, and stage were determined. Results: 107,271 women with BC were included. Luminal A was the most common subtype, present in increasing proportions as women aged, up to a maximum of 55% of cases in 70-74. Ductal and luminal A were most likely to be diagnosed at stage I, while HER2+ had the highest proportion of diagnosis at stage III; triple negative (TN) and unknown had the highest proportion of stage IV. For all stages combined, luminal A had a five-year NS of 98%, while TN was 74%. NS for stage I BC was 99-100% for all subtypes, excepting TN, which was 96%. Survival decreased with advancing stage, most markedly for TN, for which stage III was 47% and stage IV 7%. Survival by equivalent stage and subtype was comparable across age groups but declined in older age categories. Conclusions: The varying natural histories of BC subtypes and histologies can inform prognoses, health system economics, and screening practices. The NS of 96% or greater for stage I, regardless of subtype, highlights the importance of early detection for all subtypes of BC, especially in aggressive subtypes.

Enhancing the Kaiser score for lesion characterization in unenhanced breast MRI

PURPOSE

To adapt the methodology of the Kaiser score, a clinical decision rule for lesion characterization in breast MRI, for unenhanced protocols.

METHOD

In this retrospective IRB-approved cross-sectional study, we included 93 consecutive patients who underwent breast MRI between 2021 and 2023 for further work-up of BI-RADS 0, 3-5 in conventional imaging or for staging purposes (BI-RADS 6). All patients underwent biopsy for histologic verification or were followed for a minimum of 12 months. MRI scans were conducted using 1.5 T or 3 T scanners using dedicated breast coils and a protocol in line with international recommendations including DWI and ADC. Lesion characterization relied solely on T2w and DWI/ADC-derived features (such as lesion type, margins, shape, internal signal, surrounding tissue findings, ADC value). Statistical analysis was done using decision tree analysis aiming to distinguish benign (histology/follow-up) from malignant outcomes.

RESULTS

We analyzed a total of 161 lesions (81 of them non-mass) with a malignancy rate of 40%. Lesion margins (spiculated, irregular, or circumscribed) were identified as the most important criterion within the decision tree, followed by the ADC value as second most important criterion. The resulting score demonstrated a strong diagnostic performance with an AUC of 0.840, providing both rule-in and rule-out criteria. In an independent test set of 65 lesions the diagnostic performance was verified by two readers (AUC 0.77 and 0.87, kappa: 0.62).

CONCLUSIONS

We developed a clinical decision rule for unenhanced breast MRI including lesion margins and ADC value as the most important criteria, achieving high diagnostic accuracy.

Ultrasound-based comparative analysis and nomogram development for predicting triple-negative and non-triple-negative breast cancer: a 4-year institutional study in Quanzhou First Hospital

OBJECTIVE

The objective of this study was to compare ultrasound features and establish a predictive nomogram for distinguishing between triple-negative breast cancer (TNBC) and non-TNBC.

DESIGN

A retrospective cohort study.

SETTING

This study was conducted at Quanzhou First Hospital, a grade A tertiary hospital in Quanzhou, China, with the research data set covering the period from September 2019 to August 2023.

PARTICIPANTS

The study included a total of 205 female patients with confirmed TNBC and 574 female patients with non-TNBC, who were randomly divided into a training set and a validation set at a ratio of 7:3.

MAIN OUTCOME MEASURES

All patients underwent ultrasound examination and received a confirmatory pathological diagnosis. Nodules were classified according to the Breast Imaging-Reporting and Data System standard. Subsequently, the study conducted a comparative analysis of clinical characteristics and ultrasonic features.

RESULTS

A statistically significant difference was observed in multiple clinical and ultrasonic features between TNBC and non-TNBC. Specifically, in the logistic regression analysis conducted on the training set, indicators such as posterior echo, lesion size, presence of clinical symptoms, margin characteristics, internal blood flow signals, halo and microcalcification were found to be statistically significant (p<0.05). These significant indicators were then effectively incorporated into a static and dynamic nomogram model, demonstrating high predictive performance in distinguishing TNBC from non-TNBC.

CONCLUSION

The results of our study demonstrated that ultrasound features can be valuable in distinguishing between TNBC and non-TNBC. The presence of posterior echo, size, clinical symptoms, margin, internal flow, halo and microcalcification was identified as predictive factors for this differentiation. Microcalcification, hyperechoic halo, internal flow and clinical symptoms emerged as the strongest predictive factors, indicating their potential as reliable indicators for identifying TNBC and non-TNBC.

Evolving paradigms in breast cancer screening: Balancing efficacy, personalization, and equity

Breast cancer remains a significant global health challenge, with projections indicating a troubling increase in incidence. Breast cancer screening programs have long been hailed as life-saving initiatives, yet their true impact on mortality rates is a subject of ongoing debate. Screening poses the risk of false positives and the detection of indolent tumors, potentially leading to overtreatment. Bias factors, including lead time, length time, and selection biases, further complicate the assessment of screening efficacy. Recent studies suggest that AI-driven image analysis may revolutionize breast cancer screening, maintaining diagnostic accuracy while reducing radiologists' workload. However, the generalizability of these findings to diverse populations is a critical consideration. Personalized screening approaches and equitable access to advanced technologies are essential to mitigate disparities. In conclusion, the breast cancer screening landscape is evolving, emphasizing the need for risk stratification, appropriate imaging modalities, and a personalized approach to reduce overdiagnosis and focus on cancers with the potential to impact lives while prioritizing patient-centered care.

Predictive value of triple negative breast cancer based on DCE-MRI multi-phase full-volume ROI clinical radiomics model

BACKGROUND

Since no studies compared the value of radiomics features of distinct phases of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) for predicting triple-negative breast cancer (TNBC).

PURPOSE

To identify the optimal phase of DCE-MRI for diagnosing TNBC and, in combination with clinical factors, to develop a clinical-radiomics model to well predict TNBC.

MATERIAL AND METHODS

This retrospective study included 158 patients with pathology-confirmed breast cancer, including 38 cases of TNBC. The patients were randomly divided into the training and validation set (7:3). Eight radiomics models were built based on eight DCE-MR phases, and their performances were evaluated using receiver operating characteristic curve (ROC) and DeLong's test. The Radscore derived from the best radiomics model was integrated with independent clinical risk factors to construct a clinical-radiomics predictive model, and evaluate its performance using ROC analysis, calibration, and decision curve analyses.

RESULTS

WHO classification, margin, and T2-weighted (T2W) imaging signals were significantly correlated with TNBC and independent risk factors for TNBC (P<0.05). The clinical model yielded areas under the curve (AUCs) of 0.867 and 0.843 in the training and validation sets, respectively. The radiomics model based on DCEphase7 achieved the highest efficacy, with an AUC of 0.818 and 0.777. The AUC of the clinical-radiomics model was 0.936 and 0.886 in the training and validation sets, respectively. The decision curve showed the clinical utility of the clinical-radiomics model.

CONCLUSION

The radiomics features of DCE-MRI had the potential to predict TNBC and could improve the performance of clinical risk factors for preoperative personalized prediction of TNBC.