Defining radiologic complete response using a correlation of presurgical ultrasound and mammographic localization findings with pathological complete response following neoadjuvant chemotherapy in breast cancer

Breast Mass Detection and Classification Using Machine Learning Approaches on Two-Dimensional Mammogram: A Review

Breast cancer is a leading cause of mortality among women, both in India and globally. The prevalence of breast masses is notably common in women aged 20 to 60. These breast masses are classified, according to the breast imaging-reporting and data systems (BI-RADS) standard, into categories such as fibroadenoma, breast cysts, benign, and malignant masses. To aid in the diagnosis of breast disorders, imaging plays a vital role, with mammography being the most widely used modality for detecting breast abnormalities over the years. However, the process of identifying breast diseases through mammograms can be time-consuming, requiring experienced radiologists to review a significant volume of images. Early detection of breast masses is crucial for effective disease management, ultimately reducing mortality rates. To address this challenge, advancements in image processing techniques, specifically utilizing artificial intelligence (AI) and machine learning (ML), have tiled the way for the development of decision support systems. These systems assist radiologists in the accurate identification and classification of breast disorders. This paper presents a review of various studies where diverse machine learning approaches have been applied to digital mammograms. These approaches aim to identify breast masses and classify them into distinct subclasses such as normal, benign and malignant. Additionally, the paper highlights both the advantages and limitations of existing techniques, offering valuable insights for the benefit of future research endeavors in this critical area of medical imaging and breast health.

Evaluation of Treatment Response in Patients with Breast Cancer

Breast cancer (BC) remains one of the leading causes of death among women. The management and outcome in BC are strongly influenced by a multidisciplinary approach, which includes available treatment options and different imaging modalities for accurate response assessment. Among breast imaging modalities, MR imaging is the modality of choice in evaluating response to neoadjuvant therapy, whereas F-18 Fluorodeoxyglucose positron emission tomography, conventional computed tomography (CT), and bone scan play a vital role in assessing response to therapy in metastatic BC. There is an unmet need for a standardized patient-centric approach to use different imaging methods for response assessment.

Construction and validation of a personalized nomogram of ultrasound for pretreatment prediction of breast cancer patients sensitive to neoadjuvant chemotherapy

OBJECTIVE

To construct a combined radiomics model based on pre-treatment ultrasound for predicting of advanced breast cancers sensitive to neoadjuvant chemotherapy (NAC).

METHODS

A total of 288 eligible breast cancer patients who underwent NAC before surgery were enrolled in the retrospective study cohort. Radiomics features reflecting the phenotype of the pre-NAC tumors were extracted. With features selected using the least absolute shrinkage and selection operator (LASSO) regression, radiomics signature (Rad-score) was established based on the pre-NAC ultrasound. Then, radiomics nomogram of ultrasound (RU) was established on the basis of the best radiomic signature incorporating independent clinical features. The performance of RU was evaluated in terms of calibration curve, area under the curve (AUC), and decision curve analysis (DCA).

RESULTS

Nine features were selected to construct the radiomics signature in the training cohort. Combined with independent clinical characteristics, the performance of RU for identifying Grade 4-5 patients was significantly superior than the clinical model and Rad-score alone (p < 0.05, as per the Delong test), which achieved an AUC of 0.863 (95% CI, 0.814-0.963) in the training group and 0.854 (95% CI, 0.776-0.931) in the validation group. DCA showed that this model satisfactory clinical utility, suggesting its robustness as a response predictor.

CONCLUSION

This study demonstrated that RU has a potential role in predicting drug-sensitive breast cancers.

ADVANCES IN KNOWLEDGE

Aiming at early detection of Grade 4-5 breast cancer patients, the radiomics nomogram based on ultrasound has been approved as a promising indicator with high clinical utility. It is the first application of ultrasound-based radiomics nomogram to distinguish drug-sensitive breast cancers.

The Role of Deep Learning in Advancing Breast Cancer Detection Using Different Imaging Modalities: A Systematic Review

Breast cancer is among the most common and fatal diseases for women, and no permanent treatment has been discovered. Thus, early detection is a crucial step to control and cure breast cancer that can save the lives of millions of women. For example, in 2020, more than 65% of breast cancer patients were diagnosed in an early stage of cancer, from which all survived. Although early detection is the most effective approach for cancer treatment, breast cancer screening conducted by radiologists is very expensive and time-consuming. More importantly, conventional methods of analyzing breast cancer images suffer from high false-detection rates. Different breast cancer imaging modalities are used to extract and analyze the key features affecting the diagnosis and treatment of breast cancer. These imaging modalities can be divided into subgroups such as mammograms, ultrasound, magnetic resonance imaging, histopathological images, or any combination of them. Radiologists or pathologists analyze images produced by these methods manually, which leads to an increase in the risk of wrong decisions for cancer detection. Thus, the utilization of new automatic methods to analyze all kinds of breast screening images to assist radiologists to interpret images is required. Recently, artificial intelligence (AI) has been widely utilized to automatically improve the early detection and treatment of different types of cancer, specifically breast cancer, thereby enhancing the survival chance of patients. Advances in AI algorithms, such as deep learning, and the availability of datasets obtained from various imaging modalities have opened an opportunity to surpass the limitations of current breast cancer analysis methods. In this article, we first review breast cancer imaging modalities, and their strengths and limitations. Then, we explore and summarize the most recent studies that employed AI in breast cancer detection using various breast imaging modalities. In addition, we report available datasets on the breast-cancer imaging modalities which are important in developing AI-based algorithms and training deep learning models. In conclusion, this review paper tries to provide a comprehensive resource to help researchers working in breast cancer imaging analysis.

Efficacy and Safety of Albumin-Bound Paclitaxel Compared to Docetaxel as Neoadjuvant Chemotherapy for HER2-Negative Breast Cancer

Background

Nanoparticle albumin-bound paclitaxel (nab-paclitaxel) as neoadjuvant chemotherapy (NAC) for breast cancer remains controversial. We conducted a retrospective study to compare the efficacy and safety of nab-paclitaxel with those of docetaxel as neoadjuvant regimens for HER2-negative breast cancer.

Methods

In this retrospective analysis, a total of 159 HER2-negative breast cancer patients who had undergone operation after NAC were consecutively analyzed from May 2016 to April 2018. Patients were classified into the nab-paclitaxel group (n = 79, nab-paclitaxel 260 mg/m², epirubicin 75 mg/m², and cyclophosphamide 500 mg/m²) and the docetaxel group (n = 80, docetaxel 75 mg/m², epirubicin 75 mg/m², and cyclophosphamide 500 mg/m²) according to the drug they received for neoadjuvant treatment. The efficacy and adverse events were evaluated in the two groups.

Results

The pathological complete response (pCR)(ypT0/isN0) rate was significantly higher in the nab-paclitaxel group than in the docetaxel group (36.71% vs 20.00%; P = 0.031). The multivariate analysis revealed that therapeutic drugs, lymph node status, and tumor subtype were the most significant factor influencing treatment outcome. At a median follow-up of 47 months, disease-free survival (DFS) was not significantly different in those assigned to nab-paclitaxel compared with docetaxel (82.28% vs 76.25%; P = 0.331). The incidence of peripheral sensory neuropathy in the nab-paclitaxel group was higher than that in the docetaxel group (60.76% vs 36.25%; P = 0.008), while the incidence of arthralgia was observed more frequently in the docetaxel group (57.50% vs 39.97%; P = 0.047).

Conclusions

Compared with docetaxel, nab-paclitaxel achieved a higher pCR rate, especially those patients with triple-negative breast cancer or lymph node negative breast cancer. However, there was no significant difference in DFS between the two groups. This study provides a valuable reference for the management of patients with HER2-negative breast cancer.

Assessment and Prediction of Response to Neoadjuvant Chemotherapy in Breast Cancer: A Comparison of Imaging Modalities and Future Perspectives

Neoadjuvant chemotherapy (NAC) is becoming the standard of care for locally advanced breast cancer, aiming to reduce tumor size before surgery. Unfortunately, less than 30% of patients generally achieve a pathological complete response and approximately 5% of patients show disease progression while receiving NAC. Accurate assessment of the response to NAC is crucial for subsequent surgical planning. Furthermore, early prediction of tumor response could avoid patients being overtreated with useless chemotherapy sections, which are not free from side effects and psychological implications. In this review, we first analyze and compare the accuracy of conventional and advanced imaging techniques as well as discuss the application of artificial intelligence tools in the assessment of tumor response after NAC. Thereafter, the role of advanced imaging techniques, such as MRI, nuclear medicine, and new hybrid PET/MRI imaging in the prediction of the response to NAC is described in the second part of the review. Finally, future perspectives in NAC response prediction, represented by AI applications, are discussed.