Associations between the ultrasound features of invasive breast cancer and breast cancer specific survival

Prediction of disease-free survival using strain elastography and diffuse optical tomography in patients with T1 breast cancer: a 10-year follow-up study

BACKGROUND

Early-stage breast cancer (BC) presents a certain risk of recurrence, leading to variable prognoses and complicating individualized management. Yet, preoperative noninvasive tools for accurate prediction of disease-free survival (DFS) are lacking. This study assessed the potential of strain elastography (SE) and diffuse optical tomography (DOT) for non-invasive preoperative prediction of recurrence in T1 BC and developed a prediction model for estimating the probability of DFS.

METHODS

A total of 565 eligible patients with T1 invasive BC were enrolled prospectively and followed to investigate the recurrence. The associations between imaging features and DFS were evaluated and a best-prediction model for DFS was developed and validated.

RESULTS

During the median follow-up period of 10.8 years, 77 patients (13.6%) developed recurrences. The fully adjusted Cox proportional hazards model showed a significant trend between an increasing strain ratio (SR) (P < 0.001 for trend) and the total hemoglobin concentration (TTHC) (P = 0.001 for trend) and DFS. In the subgroup analysis, an intensified association between SR and DFS was observed among women who were progesterone receptor (PR)-positive, lower Ki-67 expression, HER2 negative, and without adjuvant chemotherapy and without Herceptin treatment (all P < 0.05 for interaction). Significant interactions between TTHC status and the lymphovascular invasion, estrogen receptor (ER) status, PR status, HER2 status, and Herceptin treatment were found for DFS(P < 0.05).The imaging-clinical combined model (TTHC + SR + clinicopathological variables) proved to be the best prediction model (AUC = 0.829, 95% CI = 0.786-0.872) and was identified as a potential risk stratification tool to discriminate the risk probability of recurrence.

CONCLUSION

The combined imaging-clinical model we developed outperformed traditional clinical prognostic indicators, providing a non-invasive, reliable tool for preoperative DFS risk stratification and personalized therapeutic strategies in T1 BC. These findings underscore the importance of integrating advanced imaging techniques into clinical practice and offer support for future research to validate and expand on these predictive methodologies.

Correlation between Baseline Conventional Ultrasounds, Shear-Wave Elastography Indicators, and Neoadjuvant Therapy Efficacy in Triple-Negative Breast Cancer

In patients with triple-negative breast cancer (TNBC)-the subtype with the poorest prognosis among breast cancers-it is crucial to assess the response to the currently widely employed neoadjuvant treatment (NAT) approaches. This study investigates the correlation between baseline conventional ultrasound (US) and shear-wave elastography (SWE) indicators and the pathological response of TNBC following NAT, with a specific focus on assessing predictive capability in the baseline state. This retrospective analysis was conducted by extracting baseline US features and SWE parameters, categorizing patients based on postoperative pathological grading. A univariate analysis was employed to determine the relationship between ultrasound indicators and pathological reactions. Additionally, we employed a receiver operating characteristic (ROC) curve analysis and multivariate logistic regression methods to evaluate the predictive potential of the baseline US indicators. This study comprised 106 TNBC patients, with 30 (28.30%) in a nonmajor histological response (NMHR) group and 76 (71.70%) in a major histological response (MHR) group. Following the univariate analysis, we found that T staging, dmax values, volumes, margin changes, skin alterations (i.e., thickening and invasion), retromammary space invasions, and supraclavicular lymph node abnormalities were significantly associated with pathological efficacy (p < 0.05). Combining clinical information with either US or SWE independently yielded baseline predictive abilities, with AUCs of 0.816 and 0.734, respectively. Notably, the combined model demonstrated an improved AUC of 0.827, with an accuracy of 76.41%, a sensitivity of 90.47%, a specificity of 55.81%, and statistical significance (p < 0.01). The baseline US and SWE indicators for TNBC exhibited a strong relationship with NAT response, offering predictive insights before treatment initiation, to a considerable extent.

Prediction of clinical response to neoadjuvant therapy in advanced breast cancer by baseline B-mode ultrasound, shear-wave elastography, and pathological information

Background

Neoadjuvant therapy (NAT) is the preferred treatment for advanced breast cancer nowadays. The early prediction of its responses is important for personalized treatment. This study aimed at using baseline shear wave elastography (SWE) ultrasound combined with clinical and pathological information to predict the clinical response to therapy in advanced breast cancer.

Methods

This retrospective study included 217 patients with advanced breast cancer who were treated in West China Hospital of Sichuan University from April 2020 to June 2022. The features of ultrasonic images were collected according to the Breast imaging reporting and data system (BI-RADS), and the stiffness value was measured at the same time. The changes were measured according to the Response evaluation criteria in solid tumors (RECIST1.1) by MRI and clinical situation. The relevant indicators of clinical response were obtained through univariate analysis and incorporated into a logistic regression analysis to establish the prediction model. The receiver operating characteristic (ROC) curve was used to evaluate the performance of the prediction models.

Results

All patients were divided into a test set and a validation set in a 7:3 ratio. A total of 152 patients in the test set, with 41 patients (27.00%) in the non-responders group and 111 patients (73.00%) in the responders group, were finally included in this study. Among all unitary and combined mode models, the Pathology + B-mode + SWE model performed best, with the highest AUC of 0.808 (accuracy 72.37%, sensitivity 68.47%, specificity 82.93%, P<0.001). HER2+, Skin invasion, Post mammary space invasion, Myometrial invasion and Emax were the factors with a significant predictive value (P<0.05). 65 patients were used as an external validation set. There was no statistical difference in ROC between the test set and the validation set (P>0.05).

Conclusion

As the non-invasive imaging biomarkers, baseline SWE ultrasound combined with clinical and pathological information can be used to predict the clinical response to therapy in advanced breast cancer.

A National Chinese Survey on Ultrasound Feature Interpretation and Risk Assessment of Breast Masses Under ACR BI-RADS

Purpose

Through this nationwide survey on ACR BI-RADS including ultrasound images of 10 selected breast lesions, we aimed to learn about consistency in feature interpretation and assessment categories and to identify factors that might contribute to inconsistencies, thereby promoting the application of BI-RADS in China.

Materials and Methods

The survey was delivered through a self-developed website about blinded image interpretation and was released to the public through online platforms and social media. A total of 10 representative lesions were selected by an experienced radiologist to gather information about the general practice of BI-RADS lexicons and categories. The Kappa statistic, the chi-squared test, and descriptive statistics were used for data analysis.

Results

Nine hundred ultrasound workers completed the questionnaire, coming from all provinces and major cities in China. They had different positions, grades of work organization, and seniority. The interrater agreement of BI-RADS features was fair to substantial (kappa value: 0.37–0.66). For BI-RADS categories, the highest agreement was observed in the typical benign group (average constituent rate = 74.78%), and generally lower agreement was observed in the typical malignant (average constituent rate = 36.03%) and suspicious groups (average constituent rate = 39.02%).

Conclusion

We found inconsistencies in BI-RADS applications, providing direction for image feature research using big data. Therefore, we call for more efforts to improve the consistency of BI-RADS application and provide an evidence-based basis for identifying benign and malignant lesions by sonographic features.

A pre-operative prognostic model predicting all cause and cause specific mortality for women presenting with invasive breast cancer

PURPOSE

The aim of this study is to develop a pre-operative prognostic model based on known pre-operative factors.

METHODS

A database of ultrasound (US) lesions undergoing biopsy documented US lesion size, stiffness, and patient source prospectively. Women with invasive cancer presenting between 2010 and 2015 were the study group. Breast and axillary core results and ER, PR and HER receptor status were collected prospectively. Assessment of US skin thickening, US distal enhancement and presence of chronic kidney disease (CKD) was performed retrospectively. Patient survival and cause of death were ascertained from computer records. Predictive models for (i) all-cause mortality (ACM) and (ii) breast cancer death (BCD) were built and then validated using bootstrap k-fold cross-validation. A comparison of predictive performance was made between a full cause-specific Cox model, a sub cause-specific Cox model, and a full Fine-Gray sub-distribution hazard model.

RESULTS

1136 patients were included in the study. The median follow-up time was 6.2 years. 125 (11%) women died from breast cancer and 155 (14%) died from other causes. For the prediction of BCD, the cause-specific Cox sub-model performed the best. The time dependent AUC begins above 0.91 in year one to 3 reducing to 0.83 in year 6. The factors included in the Cox sub model were tumour size, skin thickening, source of detection, tumour grade, ER status, pre-operative nodal metastasis and CKD.

CONCLUSION

We have shown that a model based on preoperative factors can predict BCD. Such prediction if externally validated and incorporating treatment data could be useful for treatment planning and patient counselling.

The value of prognostic ultrasound features of breast cancer in different molecular subtypes with a focus on triple negative disease

The ultrasound (US) features of breast cancer have recently been shown to have prognostic significance. We aim to assess these features according to molecular subtype. 1140 consecutive US visible invasive breast cancers had US size and mean stiffness by shearwave elastography (SWE) recorded prospectively. Skin thickening (> 2.5 mm) overlying the cancer on US and the presence of posterior echo enhancement were assessed retrospectively while blinded to outcomes. Cancers were classified as luminal, triple negative (TN) or HER2 + ve based on immunohistochemistry and florescent in-situ hybridization. The relationship between US parameters and breast cancer specific survival (BCSS) was ascertained using Kaplan–Meier survival curves and ROC analysis. At median follow-up 6.3 year, there were 117 breast cancer (10%) and 132 non-breast deaths (12%). US size was significantly associated with BCSS all groups (area under the curve (AUC) 0.74 in luminal cancers, 0.64 for TN and 0.65 for HER2 + ve cancers). US skin thickening was associated most strongly with poor prognosis in TN cancers (53% vs. 80% 6 year survival, p = 0.0004). Posterior echo enhancement was associated with a poor BCSS in TN cancers (63% vs. 82% 6 year survival, p = 0.02). Mean stiffness at SWE was prognostic in the luminal and HER2 positive groups (AUC 0.69 and 0.63, respectively). In the subgroup of patients with TN cancers receiving neo-adjuvant chemotherapy posterior enhancement and skin thickening were not associated with response. US skin thickening is a poor prognostic indicator is all 3 subtypes studied, while posterior enhancement was associated with poor outcome in TN cancers

A retrospective review of MRI features associated with metastasis-free survival in women with breast cancer: focusing on skin thickening and skin enhancement

OBJECTIVES

To identify associations between MRI-detected skin thickening and enhancement and metastasis-free survival (MFS) given recent reports of skin thickening on ultrasound being a poorer prognostic indicator.

METHODS

Interrogation of a prospectively collected database of ultrasound-visible breast lesions showed 214 lesions with pre-treatment MRIs available for analysis in a single centre. Data on MFS was prospectively collected. Retrospective MRI review was performed blinded to outcome. Imaging factors recorded were presence of skin thickening and enhancement, non-mass-enhancement (NME) and abnormal nodes, mass characteristics, perilesional oedema and background parenchymal enhancement. Statistical analysis used chi-squared test, Kaplan-Meier survival curves, the Log-rank test and receiver-operator characteristic (ROC) curves.

RESULTS

During a median follow-up period of 5.6 years, 21 (10%) of 212 patients developed distant metastases. Skin thickening [24 of 30 (80%) vs 169 of 184 (92%), p = 0.043] and skin enhancement [15 of 20 (75%) vs 178 of 194 (92%), p = 0.016] were associated with poorer MFS. Large index lesion size [p < 0.001, AUC 0.823], large sum of masses [p < 0.001, AUC 0.813], increasing total lesion extent including NME [p < 0.001, AUC 0.749] and abnormal axillary nodes [55 of 66 (83%) vs 138 of 148 (93%), p = 0.024] were also associated with poorer MFS.

CONCLUSIONS

Skin thickening and enhancement on breast MRI are associated with poorer MFS. These findings should be taken into account when managing patients with invasive breast cancer.

ADVANCES IN KNOWLEDGE

Skin enhancement on breast MRI at diagnosis is associated with metastases development. Skin thickening on breast MRI is associated with future metastatic disease.

Ultrasound Image Features under Deep Learning in Breast Conservation Surgery for Breast Cancer

This study was to analyze the effect of the combined application of deep learning technology and ultrasound imaging on the effect of breast-conserving surgery for breast cancer. A deep label distribution learning (LDL) model was designed, and the semiautomatic segmentation algorithm based on the region growing and active contour technology (RA) and the segmentation model based on optimized nearest neighbors (ON) were introduced for comparison. The designed algorithm was applied to the breast-conserving surgery of breast cancer patients. According to the difference in intraoperative guidance methods, 102 female patients with early breast cancer were divided into three groups: 34 cases in W1 group (ultrasound guidance based on deep learning segmentation model), 34 cases in W2 group (ultrasound guidance), and 34 cases in W3 group (palpation guidance). The results revealed that the tumor area segmented by the LDL algorithm constructed in this study was closer to the real tumor area; the segmentation accuracy (AC), Jaccard, and true-positive (TP) values of the LDL algorithm were obviously greater than those of the RA and ON algorithms, while the false-positive (FP) value was significantly lower in contrast to the RA and ON algorithms, showing statistically observable differences (P < 0.05); the actual resection volume of the patients in the W1 group was the closest to the ideal resection volume, which was much smaller in contrast to that of the patients in the W2 and W3 groups, showing statistical differences (P < 0.05); the positive margins of the patients in the W1 group were statistically lower than those in the W2 and W3 groups (P < 0.05). In addition, 1 patient in the W1 group was not satisfied with the cosmetic effect, 3 patients in the W2 group were not satisfied with the cosmetic effect, and 9 patients in the W3 group were not satisfied with the cosmetic effect. Finally, it was found that the ultrasound image based on the deep LDL model effectively improved the AC of tumor resection and negative margins, reduced the probability of normal tissue being removed, and improved the postoperative cosmetic effect of breast.

Are baseline mammographic and ultrasound features associated with metastasis free survival in women receiving neoadjuvant chemotherapy for invasive breast cancer?

OBJECTIVES

To identify associations between baseline ultrasound (US) and mammographic features and metastasis free survival (MFS) in women receiving neo-adjuvant chemotherapy (NACT) for breast cancer.

METHODS

The data were collected as part of an ethically approved prospective study. Women with invasive breast cancer receiving NACT who were metastasis free at diagnosis were included. Baseline US and mammography were performed. Imaging was assessed by an experienced breast radiologist who was blinded to outcomes. US imaging features documented included posterior effect, skin thickening, size and stiffness using shear wave elastography (SWE). The mammographic features documented were spiculation and microcalcification. The development of metastatic disease was ascertained from computer records. Statistical analysis was performed using Kaplan Meier survival curves and Receiver Operator Characteristic (ROC) analysis.

RESULTS

171 women with 172 cancers were included in the study and 55 developed metastatic disease. Mean follow-up was 6.0 years. Women with mammographic calcification had significantly poorer metastasis free survival (MFS) compared to women without calcification (p = 0.043, 6 yr MFS 50 % vs 69 %). Women bearing cancer with distal shadowing had poorer MFS than women without shadowing (p = 0.025, 6 yr MFS 47 % vs. 73 %). Women with US skin thickening had poorer MFS compared to women without skin thickening (p = 0.032, 6 yr MFS 52 % vs. 68 %). Mammographic spiculation, US size and stiffness at SWE had no significant association with MFS.

CONCLUSION

We have identified mammographic and US features associated with MFS in women receiving NACT. Such information may be useful when counselling patients about the benefits and risks of NACT.