Several studies have identified various factors that can predict the recurrence of breast cancer ranging from clinical characteristics, immunohistochemical assays to gene expression levels. Clinically, the most reliable prognostic markers include nodal status and tumor size. Various other clinical factors such as tumor grade, patient age, and treatment type have been added to build a Clinical Treatment Score (CTS), which provides a recurrence risk estimate for ER + breast cancer. In the past two decades, there has been a range of genetic markers to predict the risk of cancer recurrence, primarily for EBC. [6, 27, 28]. Some studies have compared the genetic analysis with imaging features showing good correlations. In a study by Sutton et al. (2015), the association between the gene assay recurrence score and texture-based image features extracted from magnetic resonance imaging (MRI) was investigated.. Similarly, a study by Woodard et al. (2018) showed equivalent efficacy of the gene assay and Breast Imaging and Reporting Data System (BI-RADS) mammography and MR images in the prediction of recurrence. 
The role of ultrasound is widely recognized in the screening and diagnosis of breast masses. Applications include assessing morphological tumor details (spiculated, rounded, with necrosis, microcalcification), anatomic relationships of masses to their surrounding tissues, and regional lymph nodal involvement. However, recent clinical applications of ultrasound have expanded in the management of breast cancer. For example, ultrasound elastography techniques can be used to differentiate benign from malignant breast lesions, contrast-enhanced ultrasound can characterize tissues with different vascularity, and three-dimensional ultrasound improves the characterization of breast lesions. QUS Techniques have been applied in characterizing tumor masses and predicting treatment response in patients with LABC.[17, 17, 32]
In the past, it has been demonstrated in preclinical studies that QUS can evaluate cell death in response to different treatment modalities.[33, 34] The intracellular mechanisms resulting in phenotypical cell changes differ according to the mode of cell death. For example, mitotic arrest results in cell swelling, whereas apoptotic death leads to cell shrinkage, chromatin condensation, and nuclear fragmentation. These events form the basis for differential change in QUS spectral and textural parameters for cell death monitoring. These observations have been interpreted in clinical studies to predict treatment response within 4 weeks of initiation of NAC.
The work presented in this study aimed to investigate the effectiveness of QUS texture derivatives in predicting recurrence risk rather than the local response in breast cancer within weeks of initiation of systemic therapy. The improvement in AUC performance with the addition of texture derivatives, as well as from week 0 to week 4 in the k-NN classifier, signifies the importance of higher-order derivatives and continuous monitoring. It was interesting to observe that the best features chosen by the k-NN model to classify patients in the two groups mainly were texture derivatives (texture of texture features). The features were a combination of ACEW0, AAC-CON-CONW0, and ∆ASD-CON-CON, at week 4 (Week 0 + ∆Week 4). Amongst the three features, the two parameters (ACEW0 & AAC-CON-CONW0) are related to tissue composition and microstructural organization of cells. The selection of texture derivative 'AAC-CON-CONW0' technically represents the summation of other sub-variables and possibly depicts heterogeneity within a tumor at a more advanced level. Other parameters were selected from the pretreatment (week 0) data. This suggests that the spatial organization of the tissue as reflected in QUS parameters relates to tumor biology and may have a role in recurrence prediction to some extent before initiation of treatment. In previous studies, ACE W0 was found to be a significant predictive parameter to distinguish tissues of different types, supporting the findings in this study.[36, 37]
The other texture derivative selected was ∆ASD-CON-CON, where ASD signifies microstructural size and may be related to lobular diameter, and the contrast derivatives (contrast of contrast), which measure the intensity differences. This was found to be significant after the initiation of treatment. As mentioned previously, changes occurring at the molecular level in tumor cells in response to the treatment can be captured by the QUS technique. Hence, it is postulated that as treatment initiates, ASD and its derivatives have a potential role in predicting recurrence risk and differentiating the two groups. -free survival.
We estimated the survival outcomes based on a prediction by the two machine learning classifiers. Out of the two, the k-NN classifier using texture derivatives (week 4) was able to closely approximate the curve obtained from the patient's clinical details on recurrence, thus displaying the efficacy of the classifier in recurrence prediction.
One of the limitations of the study here was the relatively small number of patients who presented with recurrence. The inclusion of more patients will likely improve the performance and robustness of the classifiers. In the future, with a larger cohort, it may also be worthwhile to combine clinical features and molecular subgroups with a QUS-radiomics model. The promising results obtained in the study here emphasize the importance of QUS parameters as a valuable tool for the timely identification of patients whose tumors have a strong tendency towards recurrence. An early prediction of recurrence risk can potentially assist oncologists to make decisions in regards to selecting systemic agents for treatment or changing a less effective treatment to more effective therapy or maintenance therapies. It may provide an insight into an earlier shift to surgery or towards an intensification of systemic therapy before missing the "therapeutic window" for benefit.