Role of Magnetic Resonance Imaging in the Evaluation of Breast Cancer Response to Neoadjuvant Chemotherapy

Involvement of N4BP2L1, PLEKHA4, and BEGAIN genes in breast cancer and muscle cell development

Patients with breast cancer show altered expression of genes within the pectoralis major skeletal muscle cells of the breast. Through analyses of The Cancer Genome Atlas (TCGA)-breast cancer (BRCA), we identified three previously uncharacterized putative novel tumor suppressor genes expressed in normal muscle cells, whose expression was downregulated in breast tumors. We found that NEDD4 binding protein 2-like 1 (N4BP2L1), pleckstrin homology domain-containing family A member 4 (PLEKHA4), and brain-enriched guanylate kinase-associated protein (BEGAIN) that are normally highly expressed in breast myoepithelial cells and smooth muscle cells were significantly downregulated in breast tumor tissues of a cohort of 50 patients with this cancer. Our data revealed that the low expression of PLEKHA4 in patients with menopause below 50 years correlated with a higher risk of breast cancer. Moreover, we identified N4BP2L1 and BEGAIN as potential biomarkers of HER2-positive breast cancer. Furthermore, low BEGAIN expression in breast cancer patients with blood fat, heart problems, and diabetes correlated with a higher risk of this cancer. In addition, protein and RNA expression analysis of TCGA-BRCA revealed N4BP2L1 as a promising diagnostic protein biomarker in breast cancer. In addition, the in silico data of scRNA-seq showed high expression of these genes in several cell types of normal breast tissue, including breast myoepithelial cells and smooth muscle cells. Thus, our results suggest their possible tumor-suppressive function in breast cancer and muscle development.

Overview of Artificial Intelligence in Breast Cancer Medical Imaging

The heavy global burden and mortality of breast cancer emphasize the importance of early diagnosis and treatment. Imaging detection is one of the main tools used in clinical practice for screening, diagnosis, and treatment efficacy evaluation, and can visualize changes in tumor size and texture before and after treatment. The overwhelming number of images, which lead to a heavy workload for radiologists and a sluggish reporting period, suggests the need for computer-aid detection techniques and platform. In addition, complex and changeable image features, heterogeneous quality of images, and inconsistent interpretation by different radiologists and medical institutions constitute the primary difficulties in breast cancer screening and imaging diagnosis. The advancement of imaging-based artificial intelligence (AI)-assisted tumor diagnosis is an ideal strategy for improving imaging diagnosis efficient and accuracy. By learning from image data input and constructing algorithm models, AI is able to recognize, segment, and diagnose tumor lesion automatically, showing promising application prospects. Furthermore, the rapid advancement of "omics" promotes a deeper and more comprehensive recognition of the nature of cancer. The fascinating relationship between tumor image and molecular characteristics has attracted attention to the radiomic and radiogenomics, which allow us to perform analysis and detection on the molecular level with no need for invasive operations. In this review, we integrate the current developments in AI-assisted imaging diagnosis and discuss the advances of AI-based breast cancer precise diagnosis from a clinical point of view. Although AI-assisted imaging breast cancer screening and detection is an emerging field and draws much attention, the clinical application of AI in tumor lesion recognition, segmentation, and diagnosis is still limited to research or in limited patients' cohort. Randomized clinical trials based on large and high-quality cohort are lacking. This review aims to describe the progress of the imaging-based AI application in breast cancer screening and diagnosis for clinicians.

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.

Neoadjuvant chemotherapy in hormone receptor-positive/HER2-negative early breast cancer: When, why and what?

Indication for neoadjuvant chemotherapy (NACT) in HR+/HER2-negative tumors is controversial. Pathological complete response (pCR) rates range from 0 to 18 % while breast-conserving surgery (BCS) is achievable in up to 60 % of tumors. No pathological feature definitely predicts pCR; lobular and molecular luminal A tumors are less likely to achieve pCR although experiencing better outcomes. Luminal B subtype, high proliferation, lack of progesterone receptor, high tumor-infiltrating lymphocytes are positively associated with increased pCR rates but worse outcomes and the prognostic role of pCR is inconsistent across studies. Molecular intrinsic subtyping and genomic signatures appear as more accurate predictors of benefit from NACT, but larger studies are needed. Anthracycline and taxane-based chemotherapy remains the standard NACT; however, CDK 4/6 inhibitors and immune checkpoint inhibitors are under evaluation. In conclusion, NACT may be proposed for luminal tumors requiring downsizing for BCS after multidisciplinary evaluation, provided that other contraindications to BCS are excluded.

Breast cancer abutting the pectoralis major muscle on breast MRI: what are the clinical implications?

OBJECTIVES

Defining the posterior extent of breast cancer prior to surgery has clinical implications. However, there are limited data available to guide the interpretation of breast cancers seen on MRI that abut the pectoralis muscle but lack associated muscle enhancement.

METHODS

In this retrospective study of breast MRIs performed between May 2008 and July 2019, 43 female patients demonstrated breast cancers abutting the pectoralis muscle without enhancement of the muscle itself. Imaging features of the cancers as well as pathologic and clinical outcomes were recorded. Statistical analyses of associations between imaging findings and clinical outcomes were performed using Fisher's exact test, logistic regression, a Mann-Whitney U test and/or Student's t-test.

RESULTS

The pectoralis major muscle was pathologically invaded by carcinoma in 4/43 (9.3%). There was no significant association between pectoralis muscle invasion and any MR imaging feature of the breast cancer. Tumors causing deformation of the muscle contour by MRI, tumors larger in size, tumors with a larger extent abutting the muscle and tumors in which the imaging feature abutting the muscle was a mass or non-mass enhancement (rather than a spicule) were more commonly seen in patients with muscle invasion, although these did not reach statistical significance (p > 0.05).

CONCLUSION

In this study, a lack of pectoralis muscle enhancement by MRI did not exclude pathologic muscle invasion by breast cancers abutting the muscle.

ADVANCES IN KNOWLEDGE

Knowledge of the likelihood of pectoralis muscle involvement for breast cancers abutting the pectoralis muscle on MRI may guide accurate interpretation and definition of the posterior extent of disease.