Evaluating pathologic response of breast cancer to neoadjuvant chemotherapy with computer-extracted features from contrast-enhanced ultrasound videos

Application of Contrast-Enhanced Ultrasound Parameters of Metastatic Axillary Lymph Nodes in Breast Cancer Patients in Predicting the Efficacy of Neoadjuvant Chemotherapy in Early Stage

BACKGROUND

To investigate the performance of contrast-enhanced ultrasound(CEUS) parameters of metastatic axillary lymph nodes (ALNs) before and after two courses of neoadjuvant chemotherapy (NAC) in breast cancer patients in predicting the efficacy of NAC.

METHODS

A total of 41 postoperative breast cancer patients were selected. All patients underwent NAC, and ALN biopsy was positive before chemotherapy. Metastatic ALN was examined by CEUS before and after two courses of NAC. The CEUS parameters of metastatic ALNs before and after two courses of NAC were analyzed to determine the performance of CEUS parameters in predicting the efficacy of NAC in early stage.

RESULTS

The NAC was effective for 28 cases and ineffective for 13 cases. There were no statistically significant differences in the CEUS parameters between effective NAC and ineffective NAC individuals before and after two courses of NAC. But, there were statistically significant differences in long diameter (LD), short diameter (SD), Peak intensity (Peak%) and area under the curve (AUC) between the effective and ineffective NAC patients after two courses of NAC. Receiver operating characteristic curve (ROC) analysis suggested the drop-out value of LD, SD, Peak% and AUC after two courses of NAC can be used as important indicators to evaluate the efficacy of NAC (p < 0.05).

CONCLUSIONS

CEUS parameters of metastatic axillary lymph nodes (ALNs) before and after two courses of neoadjuvant chemotherapy (NAC) in breast cancer patients can predict the efficacy of NAC in early stage.

Current status and future perspectives of contrast-enhanced ultrasound diagnosis of breast lesions

Advances in various imaging modalities for breast lesions have improved diagnostic capabilities not only for tumors but also for non-tumorous lesions. Contrast-enhanced ultrasound (CEUS) plays a crucial role not only in the differential diagnosis of breast lesions, identification of sentinel lymph nodes, and diagnosis of lymph node metastasis but also in assessing the therapeutic effects of neoadjuvant chemotherapy (NAC). In CEUS, two image interpretation approaches, i.e., qualitative analysis and quantitative analysis, are employed and applied in various clinical settings. In this paper, we review CEUS for breast lesions, including its various applications.

The Clinical Application of Artificial Intelligence Assisted Contrast-Enhanced Ultrasound on BI-RADS Category 4 Breast Lesions

RATIONALE AND OBJECTIVES

To propose a novel deep learning method incorporating multiple regions based on contrast-enhanced ultrasound and grayscale ultrasound, evaluate its performance in reducing false positives for Breast Imaging Reporting and Data System (BI-RADS) category 4 lesions, and compare its diagnostic performance with that of ultrasound experts.

MATERIALS AND METHODS

This study enrolled 163 breast lesions in 161 women from November 2018 to March 2021. Contrast-enhanced ultrasound and conventional ultrasound were performed before surgery or biopsy. A novel deep learning model incorporating multiple regions based on contrast-enhanced ultrasound and grayscale ultrasound was proposed for minimizing the number of false-positive biopsies. The area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and accuracy were compared between the deep learning model and ultrasound experts.

RESULTS

The AUC, sensitivity, specificity, and accuracy of the deep learning model in BI-RADS category 4 lesions were 0.910, 91.5%, 90.5%, and 90.8%, respectively, compared with those of ultrasound experts were 0.869, 89.4%, 84.5%, and 85.9%, respectively.

CONCLUSION

The novel deep learning model we proposed had a diagnostic accuracy comparable to that of ultrasound experts, showing the potential to be clinically useful in minimizing the number of false-positive biopsies.

Early Prediction of Response to Neoadjuvant Chemotherapy Using Quantitative Parameters on Automated Breast Ultrasound Combined with Contrast-Enhanced Ultrasound in Breast Cancer

OBJECTIVE

This prospective study was aimed at evaluating the role of automated breast ultrasound (ABUS) and contrast-enhanced ultrasound (CEUS) in the early prediction of treatment response to neoadjuvant chemotherapy (NAC) in patients with breast cancer.

METHODS

Forty-three patients with pathologically confirmed invasive breast cancer treated with NAC were included. The standard for evaluation of response to NAC was based on surgery within 21 d of completing treatment. The patients were classified as having a pathological complete response (pCR) and a non-pCR. All patients underwent CEUS and ABUS 1 wk before receiving NAC and after two treatment cycles. The rising time (RT), time to peak (TTP), peak intensity (PI), wash-in slope (WIS) and wash-in area under the curve (Wi-AUC) were measured on the CEUS images before and after NAC. The maximum tumor diameters in the coronal and sagittal planes were measured on ABUS, and the tumor volume (V) was calculated. The difference (∆) in each parameter between the two treatment time points was compared. Binary logistic regression analysis was used to identify the predictive value of each parameter.

RESULTS

∆V, ∆TTP and ∆PI were independent predictors of pCR. The CEUS-ABUS model achieved the highest AUC (0.950), followed by those based on CEUS (0.918) and ABUS (0.891) alone.

CONCLUSION

The CEUS-ABUS model could be used clinically to optimize the treatment of patients with breast cancer.

Contrast Enhanced EUS for Predicting Solid Pancreatic Neuroendocrine Tumor Grade and Aggressiveness

Pancreatic neuroendocrine tumor (PNET) behavior assessment is a daily challenge for physicians. Modern PNET management varies from a watch-and-wait strategy to surgery depending on tumor aggressiveness. Therefore, the aggressiveness definition plays a pivotal role in the PNET work-up. The aggressiveness of PNETs is mainly based on the dimensions and histological grading, with sometimes a lack of specificity and sensibility. In the last twenty years, EUS has become a cornerstone in the diagnostic phase of PNET management for its high diagnostic yield and the possibility of obtaining a histological specimen. The number of EUS applications in the PNET work-up has been rapidly increasing with new and powerful possibilities. The application of contrast has led to an important step in PNET detection; in recent years, it has been gaining interesting applications in aggressiveness assessment. In this review, we underline the latest experiences and opportunities in the behavior assessment of PNETs using contact-enhanced EUS and contested enhanced harmonic EUS with a particular focus on the future application and possibility that these techniques could provide.

Four-quadrant fast compressive tracking of breast ultrasound videos for computer-aided response evaluation of neoadjuvant chemotherapy in mice

BACKGROUND AND OBJECTIVE

Neoadjuvant chemotherapy (NAC) is a valuable treatment approach for locally advanced breast cancer. Contrast-enhanced ultrasound (CEUS) potentially enables the assessment of therapeutic response to NAC. In order to evaluate the response accurately, quantitatively and objectively, a method that can effectively compensate motions of breast cancer in CEUS videos is urgently needed.

METHODS

We proposed the four-quadrant fast compressive tracking (FQFCT) approach to automatically perform CEUS video tracking and compensation for mice undergoing NAC. The FQFCT divided a tracking window into four smaller windows at four quadrants of a breast lesion and formulated the tracking at each quadrant as a binary classification task. After the FQFCT of breast cancer videos, the quantitative features of CEUS including the mean transit time (MTT) were computed. All mice showed a pathological response to NAC. The features between pre- (day 1) and post-treatment (day 3 and day 5) in these responders were statistically compared.

RESULTS

When we tracked the CEUS videos of mice with the FQFCT, the average tracking error of FQFCT was 0.65 mm, reduced by 46.72% compared with the classic fast compressive tracking method (1.22 mm). After compensation with the FQFCT, the MTT on day 5 of the NAC was significantly different from the MTT before NAC (day 1) (p = 0.013).

CONCLUSIONS

The FQFCT improves the accuracy of CEUS video tracking and contributes to the computer-aided response evaluation of NAC for breast cancer in mice.

Application of conventional ultrasonography combined with contrast-enhanced ultrasonography in the axillary lymph nodes and evaluation of the efficacy of neoadjuvant chemotherapy in breast cancer patients

Objective:

Axillary lymph node status assessment has always been an important issue in clinical treatment of breast cancer. However, there has been no effective method to accurately predict the pathological complete response (pCR) of axillary lymph node after neoadjuvant chemotherapy (NAC). The objective of our study was to investigate whether conventional ultrasonography combined with contrast-enhanced ultrasonography (CEUS) can be used to evaluate axillary lymph node status of breast cancer patients after NAC.

Methods:

A total of 74 patients who underwent NAC were recruited for the present study. Prior to and after NAC, examinations of conventional ultrasonography and CEUS were performed. After evaluating the images of conventional ultrasonography, four characteristics were recorded: lymph node medulla boundary, cortex of lymph node, lymph node hilus, and lymph node aspect ratio. Two additional imaging characteristics of CEUS were analyzed: CEUS way and CEUS pattern. Receiver operating characteristiccurve analysis was applied to evaluate their diagnostic performance.

Results:

After 6~8 cycles of NAC, 46 (71.9%) patients had negative axillary lymph node, and 18 (28.1%) patients turned out non-pCR. According to statistical analysis, lymph node medulla, lymph node aspect ratio and CEUS way were independently associated with pCR of axillary lymph node after NAC. The area under the curve of the prediction model with three imaging characteristics was 0.882 (95% confidence interval: 0.608–0.958), and the accuracy to predict the patients’ lymph node status was 78.1% (p < 0.01).

Conclusions:

Conventional ultrasonography combined with CEUS technology can accurately predict axillary lymph nodes status of breast cancer patients after NAC.

Advances in knowledge:

The usefulness of CEUS technology in predicting pCR after neoadjuvant chemotherapy is highlighted.

Prediction of treatment responses to neoadjuvant chemotherapy in breast cancer using contrast-enhanced ultrasound

Background

Elucidation the efficacy of neoadjuvant chemotherapy (NAC) in breast cancer is important for informing therapeutic decisions. This study aimed at evaluating the potential value of contrast-enhanced ultrasound (CEUS) parameters in predicting breast cancer responses to NAC.

Methods

We performed CEUS examinations before and after two cycles of NAC. Quantitative CEUS parameters [maximum intensity (IMAX), rise time (RT), time to peak (TTP), and mean transit time (mTT)], tumor diameter, and their changes were measured and compared to histopathological responses, according to the Miller-Payne Grading (MPG) system (score 1, 2, or 3: minor response; score 4 or 5: good response). Prediction models for good response were developed by multiple logistic regression analysis and internally validated through bootstrap analysis. The receiver operating characteristic (ROC) curve was used to evaluate the performance of prediction models.

Results

A total of 143 patients were enrolled in this study among whom 98 (68.5%) achieved a good response and while 45 (31.5%) exhibited a minor response. Several imaging variables including diameter, IMAX, changes in diameter (Δdiameter), IMAX (ΔIMAX) and TTP (ΔTTP) were found to be significantly associated with good therapeutic responses (P<0.05). The areas under the curve (AUC) increased from 0.748 to 0.841 in the multivariate model that combined CEUS parameters and molecular subtypes with a sensitivity and specificity of 0.786, 0.745, respectively. Tumor molecular subtype was the primary predictor of primary endpoint.

Conclusions

CEUS is a potential tool for predicting responses to NAC in locally advanced breast cancer patients. Compared to the other molecular subtypes, triple negative and HER2+/ER- subtypes are more likely to exhibit a good response to NAC.

Contrast-enhanced harmonic endoscopic ultrasound using time-intensity curve analysis predicts pathological grade of pancreatic neuroendocrine neoplasm

PURPOSE

Histological grading is important for the treatment algorithm in pancreatic neuroendocrine neoplasms (PNEN). The present study examined the efficacy of contrast-enhanced harmonic endoscopic ultrasound (CH-EUS) and time-intensity curve (TIC) analysis of PNEN diagnosis and grading.

METHODS

TIC analysis was performed in 30 patients using data obtained from CH-EUS, and a histopathological diagnosis was made via EUS-guided fine-needle aspiration or surgical resection. The TIC parameters were analyzed by dividing them into G1/G2 and G3/NEC groups. Then, patients were classified into non-aggressive and aggressive groups and evaluated.

RESULTS

Twenty-six patients were classified as G1/G2, and four as G3/NEC. From the TIC analysis, five parameters were obtained (I: echo intensity change, II: time for peak enhancement, III: speed of contrast, IV: decrease rate for enhancement, and V: enhancement ratio for node/pancreatic parenchyma). Three of these parameters (I, IV, and V) showed high diagnostic performance. Using the cutoff value obtained from the receiver-operating characteristic (ROC) analysis, the correct diagnostic rates of parameters I, IV, and V were 96.7%, 100%, and 100%, respectively, between G1/G2 and G3/NEC. A total of 21 patients were classified into the non-aggressive group, and nine into the aggressive group. Using the cutoff value obtained from the ROC analysis, the accurate diagnostic rates of I, IV, and V were 86.7%, 86.7%, and 88.5%, respectively, between the non-aggressive and aggressive groups.

CONCLUSION

CH-EUS and TIC analysis showed high diagnostic accuracy for grade diagnosis of PNEN. Quantitative perfusion analysis is useful to predict PNEN grade diagnosis preoperatively.