Development of an Ultrasound-based Nomogram for Predicting Pathologic Complete Response and Axillary Response in Node-Positive Patients with Triple- Negative Breast Cancer

BACKGROUND

The accurate prediction of pathological complete response (pCR) in the breast and axillary lymph nodes (ALN) before neoadjuvant chemotherapy (NAC) is of utmost importance for the development of treatment strategies. We aim to construct a nomogram on ultrasound (US) and clinical-pathologic factors to predict breast and ALN pCR in node-positive triple-negative breast cancers (TNBCs).

METHODS

Patients identified with TNBCs from institution 1 (n = 328) were used for training cohort and those from institution 2 (n = 192) were for validation cohort. US was conducted before and after NAC, and characteristics were obtained from medical records. Univariate and multivariate regression analysis were performed to identify US and clinical-pathologic factors associated with breast and ALN pCR in the training cohort. The assessment of predictive performance was conducted using the receiving operating characteristic curve (ROC), discrimination, and calibration.

RESULTS

Overall, 34.6% of patients achieved breast pCR and 48.1% of patients achieved ALN pCR. The nomogram 1 used for predicting pCR in the breast (AUC, 0.84; 95% CI: 0.79, 0.88) outperformed the clinical (AUC, 0.73; 95% CI: 0.68, 0.78) and US models (AUC, 0.79; 95% CI: 0.74, 0.83). The nomogram 2 used for predicting pCR in the axllia (AUC, 0.83; 95% CI: 0.78, 0.87) also outperformed the clinical (AUC, 0.64; 95% CI: 0.58, 0.69) and US models (AUC, 0.80; 95% CI: 0.75, 0.84). The calibration curve and discrimination curve indicate that the nomogram has good calibration performance and clinical applicability.

CONCLUSION

The nomogram showed promising predictive performance for predicting breast and ALN pCR in patients with TNBCs.

Assessment of Oxygen Saturation in Breast Lesions Using Photoacoustic Imaging: Correlation With Benign and Malignant Disease

BACKGROUND

Hypoxia is a hallmark of breast cancer (BC). Photoacoustic (PA) imaging, based on the use of laser-generated ultrasound (US), can detect oxygen saturation (So2) in the tissues of breast lesion patients.

PURPOSE

To measure the oxygenation status of tissue in and on both sides of the lesion in breast lesion participants using a multimodal Photoacoustic/ultrasound (PA/US) imaging system and to determine the correlation between So2 measured by PA imaging and benign or malignant disease.

MATERIALS AND METHODS

Multimodal PA/US imaging and gray-scale US (GSUS) of breast lesion was performed in consecutive breast lesion participants imaged in the US Outpatient Clinic between 2022 and 2023. Dual-wavelength PA imaging was used to measure the So2 value inside the lesion and on both sides of the tissue, and to distinguish benign from malignant lesions based on the So2 value. The ability of So2 to distinguish benign from malignant breast lesions was evaluated by the receiver operating characteristic curve (ROC) and the De-Long test.

RESULTS

A total of 120 breast lesion participants (median age, 42.5 years) were included in the study. The malignant lesions exhibited lower So2 levels compared to benign lesions (malignant: 71.30%; benign: 83.81%; P < .01). Moreover, PA/US imaging demonstrates superior diagnostic results compared to GSUS, with an area under the curve (AUC) of 0.89 versus 0.70, sensitivity of 89.58% versus 85.42%, and specificity of 86.11% versus 55.56% at the So2 cut-off value of 78.85 (P < .001). The false positive rate in GSUS reduced by 30.75%, and the false negative rate diminished by 4.16% with PA /US diagnosis. Finally, the So2 on both sides tissues of malignant lesions are lower than that of benign lesions (P < .01).

CONCLUSION

PA imaging allows for the assessment of So2 within the lesions of breast lesion patients, thereby facilitating a superior distinction between benign and malignant lesions.

MRI-based tumor shrinkage patterns after early neoadjuvant therapy in breast cancer: correlation with molecular subtypes and pathological response after therapy

BACKGROUND

MRI-based tumor shrinkage patterns (TSP) after neoadjuvant therapy (NAT) have been associated with pathological response. However, the understanding of TSP after early NAT remains limited. We aimed to analyze the relationship between TSP after early NAT and pathological response after therapy in different molecular subtypes.

METHODS

We prospectively enrolled participants with invasive ductal breast cancers who received NAT and performed pretreatment DCE-MRI from September 2020 to August 2022. Early-stage MRIs were performed after the first (1st-MRI) and/or second (2nd-MRI) cycle of NAT. Tumor shrinkage patterns were categorized into four groups: concentric shrinkage, diffuse decrease (DD), decrease of intensity only (DIO), and stable disease (SD). Logistic regression analysis was performed to identify independent variables associated with pathologic complete response (pCR), and stratified analysis according to tumor hormone receptor (HR)/human epidermal growth factor receptor 2 (HER2) disease subtype.

RESULTS

344 participants (mean age: 50 years, 113/345 [33%] pCR) with 345 tumors (1 bilateral) had evaluable 1st-MRI or 2nd-MRI to comprise the primary analysis cohort, of which 244 participants with 245 tumors had evaluable 1st-MRI (82/245 [33%] pCR) and 206 participants with 207 tumors had evaluable 2nd-MRI (69/207 [33%] pCR) to comprise the 1st- and 2nd-timepoint subgroup analysis cohorts, respectively. In the primary analysis, multivariate analysis showed that early DD pattern (OR = 12.08; 95% CI 3.34-43.75; p < 0.001) predicted pCR independently of the change in tumor size (OR = 1.37; 95% CI 0.94-2.01; p = 0.106) in HR+/HER2- subtype, and the change in tumor size was a strong pCR predictor in HER2+ (OR = 1.61; 95% CI 1.22-2.13; p = 0.001) and triple-negative breast cancer (TNBC, OR = 1.61; 95% CI 1.22-2.11; p = 0.001). Compared with the change in tumor size, the SD pattern achieved a higher negative predictive value in HER2+ and TNBC. The statistical significance of complete 1st-timepoint subgroup analysis was consistent with the primary analysis.

CONCLUSION

The diffuse decrease pattern in HR+/HER2- subtype and stable disease in HER2+ and TNBC after early NAT could serve as additional straightforward and comprehensible indicators of treatment response.

TRIAL REGISTRATION

Trial registration at https://www.chictr.org.cn/ .

REGISTRATION NUMBER

ChiCTR2000038578, registered September 24, 2020.

Optimizing surgical strategy in locally advanced breast cancer: a comparative analysis between preoperative MRI and postoperative pathology after neoadjuvant chemotherapy

PURPOSE

In the treatment of breast cancer, neo-adjuvant chemotherapy is often used as systemic treatment followed by tumor excision. In this context, planning the operation with regard to excision margins relies on tumor size measured by MRI. The actual tumor size can be determined through pathologic evaluation. The aim of this study is to investigate the correlation and agreement between pre-operative MRI and postoperative pathological evaluation.

METHODS

One hundred and ninety-three breast cancer patients that underwent neo-adjuvant chemotherapy and subsequent breast surgery were retrospectively included between January 2013 and July 2016. Preoperative tumor diameters determined with MRI were compared with postoperative tumor diameters determined by pathological analysis. Spearman correlation and Bland-Altman agreement methods were used. Results were subjected to subgroup analysis based on histological subtype (ER, HER2, ductal, lobular).

RESULTS

The correlation between tumor size at MRI and pathology was 0.63 for the whole group, 0.39 for subtype ER + /HER2-, 0.51 for ER + /HER2 + , 0.63 for ER-/HER2 +, and 0.85 for ER-/HER2-. The mean difference and limits of agreement (LoA) between tumor size measured MRI vs. pathological assessment was 4.6 mm (LoA -27.0-36.3 mm, n = 195). Mean differences and LoA for subtype ER + /HER2- was 7.6 mm (LoA -31.3-46.5 mm, n = 100), for ER + /HER2 + 0.9 mm (LoA -8.5-10.2 mm, n = 33), for ER-/HER2+ -1.2 mm (LoA -5.1-7.5 mm, n = 21), and for ER-/HER- -0.4 mm (LoA -8.6-7.7 mm, n = 41).

CONCLUSION

HER2 + and ER-/HER2- tumor subtypes showed clear correlation and agreement between preoperative MRI and postoperative pathological assessment of tumor size. This suggests that MRI evaluation could be a suitable predictor to guide the surgical approach. Conversely, correlation and agreement for ER + /HER2- and lobular tumors was poor, evidenced by a difference in tumor size of up to 5 cm. Hence, we demonstrate that histological tumor subtype should be taken into account when planning breast conserving surgery after NAC.

Neoadjuvant Chemotherapy and Neoadjuvant Chemotherapy With Immunotherapy Result in Different Tumor Shrinkage Patterns in Triple-Negative Breast Cancer

PURPOSE

This study aims to explore whether neoadjuvant chemotherapy with immunotherapy (NACI) leads to different tumor shrinkage patterns, based on magnetic resonance imaging (MRI), compared to neoadjuvant chemotherapy (NAC) alone in patients with triple-negative breast cancer (TNBC). Additionally, the study investigates the relationship between tumor shrinkage patterns and treatment efficacy was investigated.

METHODS

This retrospective study included patients with TNBC patients receiving NAC or NACI from January 2019 until July 2021 at our center. Pre- and post-treatment MRI results were obtained for each patient, and tumor shrinkage patterns were classified into three categories as follows: 1) concentric shrinkage (CS); 2) diffuse decrease; and 3) no change. Tumor shrinkage patterns were compared between the NAC and NACI groups, and the relevance of the patterns to treatment efficacy was assessed.

RESULTS

Of the 99 patients, 65 received NAC and 34 received NACI. The CS pattern was observed in 53% and 20% of patients in the NAC and NACI groups, respectively. Diffuse decrease pattern was observed in 36% and 68% of patients in the NAC and NACI groups. The association between the treatment regimens (NAC and NACI) and tumor shrinkage patterns was statistically significant (p = 0.004). The postoperative pathological complete response (pCR) rate was 45% and 82% in the NAC and NACI groups (p < 0.001), respectively. In the NACI group, 17% of patients with the CS pattern and 56% of those with the diffuse decrease pattern achieved pCR (p = 0.903). All tumor shrinkage patterns were associated with achieved a high pCR rate in the NACI group.

CONCLUSION

Our study demonstrates that the diffuse decrease pattern of tumor shrinkage is more common following NACI than that following NAC. Furthermore, our findings suggest that all tumor shrinkage patterns are associated with a high pCR rate in patients with TNBC treated with NACI.

TRIAL REGISTRATION

ClinicalTrials.gov Identifier: NCT04909554.

Exploring Neoadjuvant Chemotherapy, Predictive Models, Radiomic, and Pathological Markers in Breast Cancer: A Comprehensive Review

Breast cancer retains its position as the most prevalent form of malignancy among females on a global scale. The careful selection of appropriate treatment for each patient holds paramount importance in effectively managing breast cancer. Neoadjuvant chemotherapy (NACT) plays a pivotal role in the comprehensive treatment of this disease. Administering chemotherapy before surgery, NACT becomes a powerful tool in reducing tumor size, potentially enabling fewer invasive surgical procedures and even rendering initially inoperable tumors amenable to surgery. However, a significant challenge lies in the varying responses exhibited by different patients towards NACT. To address this challenge, researchers have focused on developing prediction models that can identify those who would benefit from NACT and those who would not. Such models have the potential to reduce treatment costs and contribute to a more efficient and accurate management of breast cancer. Therefore, this review has two objectives: first, to identify the most effective radiomic markers correlated with NACT response, and second, to explore whether integrating radiomic markers extracted from radiological images with pathological markers can enhance the predictive accuracy of NACT response. This review will delve into addressing these research questions and also shed light on the emerging research direction of leveraging artificial intelligence techniques for predicting NACT response, thereby shaping the future landscape of breast cancer treatment.

Exploring the added value of pretherapeutic MR descriptors in predicting breast cancer pathologic complete response to neoadjuvant chemotherapy

OBJECTIVES

To evaluate the association between pretreatment MRI descriptors and breast cancer (BC) pathological complete response (pCR) to neoadjuvant chemotherapy (NAC).

MATERIALS AND METHODS

Patients with BC treated by NAC with a breast MRI between 2016 and 2020 were included in this retrospective observational single-center study. MR studies were described using the standardized BI-RADS and breast edema score on T2-weighted MRI. Univariable and multivariable logistic regression analyses were performed to assess variables association with pCR according to residual cancer burden. Random forest classifiers were trained to predict pCR on a random split including 70% of the database and were validated on the remaining cases.

RESULTS

Among 129 BC, 59 (46%) achieved pCR after NAC (luminal (n = 7/37, 19%), triple negative (n = 30/55, 55%), HER2 + (n = 22/37, 59%)). Clinical and biological items associated with pCR were BC subtype (p < 0.001), T stage 0/I/II (p = 0.008), higher Ki67 (p = 0.005), and higher tumor-infiltrating lymphocytes levels (p = 0.016). Univariate analysis showed that the following MRI features, oval or round shape (p = 0.047), unifocality (p = 0.026), non-spiculated margins (p = 0.018), no associated non-mass enhancement (p = 0.024), and a lower MRI size (p = 0.031), were significantly associated with pCR. Unifocality and non-spiculated margins remained independently associated with pCR at multivariable analysis. Adding significant MRI features to clinicobiological variables in random forest classifiers significantly increased sensitivity (0.67 versus 0.62), specificity (0.69 versus 0.67), and precision (0.71 versus 0.67) for pCR prediction.

CONCLUSION

Non-spiculated margins and unifocality are independently associated with pCR and can increase models performance to predict BC response to NAC.

CLINICAL RELEVANCE STATEMENT

A multimodal approach integrating pretreatment MRI features with clinicobiological predictors, including tumor-infiltrating lymphocytes, could be employed to develop machine learning models for identifying patients at risk of non-response. This may enable consideration of alternative therapeutic strategies to optimize treatment outcomes.

KEY POINTS

• Unifocality and non-spiculated margins are independently associated with pCR at multivariable logistic regression analysis. • Breast edema score is associated with MR tumor size and TIL expression, not only in TN BC as previously reported, but also in luminal BC. • Adding significant MRI features to clinicobiological variables in machine learning classifiers significantly increased sensitivity, specificity, and precision for pCR prediction.

Triple-Negative Breast Cancer: Histopathologic Features, Genomics, and Treatment

Triple-negative breast cancer (TNBC) is a heterogeneous and aggressive group of tumors that are defined by the absence of estrogen and progesterone receptors and lack of ERBB2 (formerly HER2 or HER2/neu) overexpression. TNBC accounts for 8%-13% of breast cancers. In addition, it accounts for a higher proportion of breast cancers in younger women compared with those in older women, and it disproportionately affects non-Hispanic Black women. TNBC has high metastatic potential, and the risk of recurrence is highest during the 5 years after it is diagnosed. TNBC exhibits benign morphologic imaging features more frequently than do other breast cancer subtypes. Mammography can be suboptimal for early detection of TNBC owing to factors that include the fast growth of this cancer, increased mammographic density in young women, and lack of the typical features of malignancy at imaging. US is superior to mammography for TNBC detection, but benign-appearing features can lead to misdiagnosis. Breast MRI is the most sensitive modality for TNBC detection. Most cases of TNBC are treated with neoadjuvant chemotherapy, followed by surgery and radiation. MRI is the modality of choice for evaluating the response to neoadjuvant chemotherapy. Survival rates for individuals with TNBC are lower than those for persons with hormone receptor-positive and human epidermal growth factor receptor 2-positive cancers. The 5-year survival rates for patients with localized, regional, and distant disease at diagnosis are 91.3%, 65.8%, and 12.0%, respectively. The early success of immunotherapy has raised hope regarding the development of personalized strategies to treat TNBC. Imaging and tumor biomarkers are likely to play a crucial role in the prediction of TNBC treatment response and TNBC patient survival in the future. ©RSNA, 2023 Quiz questions for this article are available in the supplemental material.

Triple-Negative Breast Cancer: Multimodality Appearance

Purpose of Review

Triple-negative breast cancer (TNBC) represents about 15–20% of all breast cancers and often presents as an aggressive cancer with poor prognosis compared to other forms of breast cancer. This article will review the clinical manifestations, imaging features, pathology correlation, treatment and management, and prognosis of TNBC.

Recent Findings

While mammography and ultrasound can be used to diagnose TNBC, MRI is the most accurate and sensitive modality to detect TNBC at nearly 100% sensitivity. Contrast-enhanced breast MRI is the optimal imaging study for assessing response to neoadjuvant chemotherapy and can be used to tailor systemic therapy.

Summary

Understanding the imaging appearance of TNBC is imperative to diagnose TNBC accurately and to help guide management.

Preoperative Breast Magnetic Resonance Imaging as a Predictor of Response to Neoadjuvant Chemotherapy

Introduction:

The ability to accurately predict pathologic complete response (pCR) after neoadjuvant chemotherapy (NAC) in breast cancer would improve patient selection for specific treatment strategies, would provide important information for patients to aid in the treatment selection process, and could potentially avoid the need for more extensive surgery. The diagnostic performance of magnetic resonance imaging (MRI) in predicting pCR has previously been studied, with mixed results. Magnetic resonance imaging performance may also be influenced by tumour and patient factors.

Methods:

Eighty-seven breast cancer patients who underwent NAC were studied. Pre-NAC and post-NAC MRI findings were compared with pathologic findings postsurgical excision. The impact of patient and tumour characteristics on MRI accuracy was evaluated.

Results:

The mean (SD) age of participants was 48.7 (10.3) years. The rate of pCR based on post-NAC MRI was 19.5% overall (19/87). The sensitivity, specificity, positive predictive value (PPV), negative predictive value, and accuracy in predicting pCR were 52.9%, 77.1%, 36.0%, 87.1%, and 72.4%, respectively. Positive predictive value was the highest in nonluminal versus Luminal A disease (45.0% vs 25.0%, P < .001), with higher rates of false positivity in nonluminal subtypes (P = .002). Tumour grade, T category, and histological subtype were all independent predictors of MRI accuracy regarding post-NAC tumour size.

Conclusion:

Magnetic resonance imaging alone is insufficient to accurately predict pCR in breast cancer patients post-NAC. Magnetic resonance imaging predictions of pCR are more accurate in nonluminal subtypes. Tumour grade, T category, and histological subtype should be considered when evaluating post-NAC tumour sizes.

Meta-Analysis of Shrinkage Mode After Neoadjuvant Chemotherapy for Breast Cancers: Association With Hormonal Receptor

Background

Patients with concentric shrinkage mode after neoadjuvant chemotherapy (NAC) is considered to be ideal candidates for breast conserving treatment (BCT). While, what proportion of patients would represent CSM have not been well defined. This study was conducted to pool the rates of concentric shrinkage mode (CSM) in patients undergoing NAC, determine the impact of hormonal receptor on the shrinkage mode after NAC and estimate the rates of the CSM in various subgroups.

Methods

We conducted a systematic review following the guidelines for Meta-Analyses and Systematic reviews for the PRISMA guidelines. We systematically searched the literature about shrinkage mode after NAC from PubMed, Web of Science, Embase, The Cochrane Library, CNKI, Wanfang database published from January 2002 to June 2020 on breast cancer shrinkage mode after NAC and carefully screened the literature by using eligibility criteria: (1) patients with primary breast cancer treated with NAC; (2) publications with available data of shrinkage mode measured by magnetic resonance imaging (MRI), or data of pathology and hormonal receptor. The association between shrinkage mode and hormonal receptor was estimated using Stata 15.1 software.

Results

This analysis included a total of 2434 tumors from 23 papers. The included studies were heterogeneous (I2 = 89.4%, P<0.01). Random effects model was used to estimate the overall rates of CSM: 56.6% [95%CI (50.5%, 62.7%)]. According to the analysis of hormonal receptor, 10 of the paper was included for HR+ (hormone receptor positive) type analysis and the rate of CSM for HR+ type was 45.7% [95%CI (36.4%, 55.0%)]; 9 of the paper was used for HR- type (hormone receptor negative) analysis and the incidence of HR-CSM is 63.1% [95%CI (50.0%, 76.1%)]; with HR+ type as the control, the OR of the HR- CSM rate is 2.32 (1.32, 4.08) folds of HR+ type. From subgroup analyses, the CSM% of luminal A, luminal B, Her2+, and triple negative were 29.7% (16.5%, 42.8%); 47.2% (19.1%, 75.3%); 59.0% (39.7%, 78.3%); 66.2% (52.8%, 79.6%), respectively.

Conclusions

Breast cancer patients undergoing NAC did not get an ideal odds ratio of CSM. The incidence of CSM in breast cancer after NAC is associated with hormonal receptor. Patients with triple-negative breast cancers have the highest rates of CSM after NAC. More care should be taken to select patients with the luminal subtypes for BCT throughout NAC.

Prediction of Tumor Shrinkage Pattern to Neoadjuvant Chemotherapy Using a Multiparametric MRI-Based Machine Learning Model in Patients With Breast Cancer

Aim: After neoadjuvant chemotherapy (NACT), tumor shrinkage pattern is a more reasonable outcome to decide a possible breast-conserving surgery (BCS) than pathological complete response (pCR). The aim of this article was to establish a machine learning model combining radiomics features from multiparametric MRI (mpMRI) and clinicopathologic characteristics, for early prediction of tumor shrinkage pattern prior to NACT in breast cancer.

Materials and Methods: This study included 199 patients with breast cancer who successfully completed NACT and underwent following breast surgery. For each patient, 4,198 radiomics features were extracted from the segmented 3D regions of interest (ROI) in mpMRI sequences such as T1-weighted dynamic contrast-enhanced imaging (T1-DCE), fat-suppressed T2-weighted imaging (T2WI), and apparent diffusion coefficient (ADC) map. The feature selection and supervised machine learning algorithms were used to identify the predictors correlated with tumor shrinkage pattern as follows: (1) reducing the feature dimension by using ANOVA and the least absolute shrinkage and selection operator (LASSO) with 10-fold cross-validation, (2) splitting the dataset into a training dataset and testing dataset, and constructing prediction models using 12 classification algorithms, and (3) assessing the model performance through an area under the curve (AUC), accuracy, sensitivity, and specificity. We also compared the most discriminative model in different molecular subtypes of breast cancer.

Results: The Multilayer Perception (MLP) neural network achieved higher AUC and accuracy than other classifiers. The radiomics model achieved a mean AUC of 0.975 (accuracy = 0.912) on the training dataset and 0.900 (accuracy = 0.828) on the testing dataset with 30-round 6-fold cross-validation. When incorporating clinicopathologic characteristics, the mean AUC was 0.985 (accuracy = 0.930) on the training dataset and 0.939 (accuracy = 0.870) on the testing dataset. The model further achieved good AUC on the testing dataset with 30-round 5-fold cross-validation in three molecular subtypes of breast cancer as following: (1) HR+/HER2–: 0.901 (accuracy = 0.816), (2) HER2+: 0.940 (accuracy = 0.865), and (3) TN: 0.837 (accuracy = 0.811).

Conclusions: It is feasible that our machine learning model combining radiomics features and clinical characteristics could provide a potential tool to predict tumor shrinkage patterns prior to NACT. Our prediction model will be valuable in guiding NACT and surgical treatment in breast cancer.

Early prediction of response to neoadjuvant chemotherapy using contrast-enhanced ultrasound in breast cancer

Early prediction of non-response is essential in order to avoid inefficient treatments. The objective of this study was to determine the contrast-enhanced ultrasound (CEUS) for early predicting pathologic complete response (pCR) to neoadjuvant chemotherapy (NAC) in breast cancer patients.Between March 2018 and October 2019, 93 consecutive patients with histologically proven breast cancer scheduled for NAC were enrolled. Conventional ultrasound and CEUS imaging were performed before NAC and after two cycles of NAC. CEUS parameters were compared with pathologic response. Multiple logistic regression analyses were utilized to explore CEUS parameters to predict pCR, and receiver operating characteristic analysis was used to evaluate the predictive ability.Therapeutic response was obtained from 25 (27%) patients with pCR and 68 (73%) with non-pCR. Compared to non-pCR, pCR cases have a significantly higher proportion of homogeneous enhancement feature (56% vs 14%, P < .001) and centripetal enhancement (52% vs 23%, P = .012). A significant decrease in peak intensity (PI) was observed after two cycles of NAC. Compared with non-pCR patients, the kinetic parameters PI change (PI%) was higher in pCR patients (P < .001). Multiple logistic regression demonstrated two independent predictors of pCR: internal homogeneity (odds ratio, 4.85; 95% confidence interval: 1.20-19.65; P = .027) and PI% (odds ratio, 1.08; 95% confidence interval: 1.02-1.15; P = .007). In receiver operating characteristic curve analysis, internal homogeneity and PI%, with area under curve of 0.71 and 0.84, predicted pCR with sensitivity (56%, 95%) and specificity (85%, 70%), respectively.Internal homogeneity and PI% of CEUS may be useful in the noninvasive early prediction of pCR in patients with breast cancer.

Early ultrasound evaluation identifies excellent responders to neoadjuvant systemic therapy among patients with triple-negative breast cancer

BACKGROUND

Heterogeneity exists in the response of triple-negative breast cancer (TNBC) to standard anthracycline (AC)/taxane-based neoadjuvant systemic therapy (NAST), with 40% to 50% of patients having a pathologic complete response (pCR) to therapy. Early assessment of the imaging response during NAST may identify a subset of TNBCs that are likely to have a pCR upon completion of treatment. The authors aimed to evaluate the performance of early ultrasound (US) after 2 cycles of neoadjuvant NAST in identifying excellent responders to NAST among patients with TNBC.

METHODS

Two hundred fifteen patients with TNBC were enrolled in the ongoing ARTEMIS (A Robust TNBC Evaluation Framework to Improve Survival) clinical trial. The patients were divided into a discovery cohort (n = 107) and a validation cohort (n = 108). A receiver operating characteristic analysis with 95% confidence intervals (CIs) and a multivariate logistic regression analysis were performed to model the probability of a pCR on the basis of the tumor volume reduction (TVR) percentage by US from the baseline to after 2 cycles of AC.

RESULTS

Overall, 39.3% of the patients (42 of 107) achieved a pCR. A positive predictive value (PPV) analysis identified a cutoff point of 80% TVR after 2 cycles; the pCR rate was 77% (17 of 22) in patients with a TVR ≥ 80%, and the area under the curve (AUC) was 0.84 (95% CI, 0.77-0.92; P < .0001). In the validation cohort, the pCR rate was 44%. The PPV for pCR with a TVR ≥ 80% after 2 cycles was 76% (95% CI, 55%-91%), and the AUC was 0.79 (95% CI, 0.70-0.87; P < .0001).

CONCLUSIONS

The TVR percentage by US evaluation after 2 cycles of NAST may be a cost-effective early imaging biomarker for a pCR to AC/taxane-based NAST.

Can breast MRI and adjunctive Doppler ultrasound improve the accuracy of predicting pathological complete response after neoadjuvant chemotherapy?

BACKGROUND

To examine the accuracy of MRI and Doppler ultrasound (US) for detecting residual tumor after neoadjuvant chemotherapy (NAC) for breast cancer and evaluate whether adjunctive Doppler US improves the MRI accuracy.

METHODS

We reviewed 276 invasive breast cancer cases treated with NAC. Tumors were classified into four subtypes based on estrogen receptor and HER2 status. Response to NAC was evaluated using contrast-enhanced MRI and Doppler US. Residual Doppler flow was assumed to indicate a residual tumor. MRI and Doppler findings were compared with the histopathological findings of resected specimens. Pathological complete response (pCR) was defined as neither in situ nor invasive cancer left.

RESULTS

Of the 276 tumors, imaging complete responses were observed using MRI and Doppler US in 62 (22%) and 111 (40%), respectively, whereas pCR was achieved in 44 (16%). MRI and Doppler US predicted residual tumor with 88% and 69% sensitivity, 80% and 91% specificity, 87% and 73% accuracy, 96% and 98% PPV, and 56% and 36% NPV, respectively. The accuracies of MRI and Doppler US were significantly higher for HER2-negative than HER2-positive tumors (p < 0.001 and p = 0.043, respectively). Seven (26%) of 27 false-negative cases identified by MRI were correctly diagnosed as positives with adjunctive Doppler US.

CONCLUSIONS

Although MRI accurately detected residual tumor with 87% accuracy, this was still not sufficient to meet clinical demands and differed with tumor subtype. Adjunctive Doppler US in cases that appear to show a complete response on MRI might reduce chances of false negatives and increase the NPV of MRI for predicting residual tumor.

Photoacoustic Computed Tomography of Breast Cancer in Response to Neoadjuvant Chemotherapy

Neoadjuvant chemotherapy (NAC) has contributed to improving breast cancer outcomes, and it would ideally reduce the need for definitive breast surgery in patients who have no residual cancer after NAC treatment. However, there is no reliable noninvasive imaging modality accepted as the routine method to assess response to NAC. Because of the inability to detect complete response, post-NAC surgery remains the standard of care. To overcome this limitation, a single-breath-hold photoacoustic computed tomography (SBH-PACT) system is developed to provide contrast similar to that of contrast-enhanced magnetic resonance imaging, but with much higher spatial and temporal resolution and without injection of contrast chemicals. SBH-PACT images breast cancer patients at three time points: before, during, and after NAC. The analysis of tumor size, blood vascular density, and irregularity in the distribution and morphology of the blood vessels on SBH-PACT accurately identifies response to NAC as confirmed by the histopathological diagnosis. SBH-PACT shows its near-term potential as a diagnostic tool for assessing breast cancer response to systemic treatment by noninvasively measuring the changes in cancer-associated angiogenesis. Further development of SBH-PACT may also enable serial imaging, rather than the use of current invasive biopsies, to diagnose and follow indeterminate breast lesions.

Factors Affecting Pathologic Complete Response Following Neoadjuvant Chemotherapy in Breast Cancer: Development and Validation of a Predictive Nomogram

Background There is an increasing need to develop a more accurate prediction model for pathologic complete response (pCR) after neoadjuvant chemotherapy (NAC) in breast cancer. Purpose To develop a nomogram based on MRI and clinical-pathologic variables to predict pCR. Materials and Methods In this single-center retrospective study, consecutive women with stage II-III breast cancer who underwent NAC followed by surgery between January 2011 and December 2017 were considered for inclusion. The women were divided into a development cohort between January 2011 and September 2015 and a validation cohort between October 2015 and December 2017. Clinical-pathologic data were collected, and mammograms and MRI scans obtained before and after NAC were analyzed. Logistic regression analyses were performed to identify independent variables associated with pCR in the development cohort from which the nomogram was created. Nomogram performance was assessed with the area under the receiver operating characteristic curve (AUC) and calibration slope. Results A total of 359 women (mean age, 49 years ± 10 [standard deviation]) were in the development cohort and 351 (49 years ± 10) in the validation cohort. Hormone receptor negativity (odds ratio [OR], 3.1; 95% CI: 1.4, 7.1; P = .006), high Ki-67 index (OR, 1.05; 95% CI: 1.03, 1.07; P < .001), and post-NAC MRI variables, including small tumor size (OR, 0.6; 95% CI: 0.4, 0.9; P = .03), low lesion-to-background parenchymal signal enhancement ratio (OR, 0.2; 95% CI: 0.1, 0.6; P = .004), and absence of enhancement in the tumor bed (OR, 3.8; 95% CI: 1.4, 10.5; P = .009) were independently associated with pCR. The nomogram incorporating these variables showed good discrimination (AUC, 0.90; 95% CI: 0.86, 0.94) and calibration abilities (calibration slope, 0.91; 95% CI: 0.69, 1.13) in the independent validation cohort. Conclusion A nomogram incorporating hormone receptor status, Ki-67 index, and MRI variables showed good discrimination and calibration abilities in predicting pathologic complete response. © RSNA, 2021 Online supplemental material is available for this article. See also the editorial by Imbriaco and Ponsiglione in this issue.

Evaluation of Post-Neoadjuvant Chemotherapy Pathologic Complete Response and Residual Tumor Size of Breast Cancer: Analysis on Accuracy of MRI and Affecting Factors

목적

신보강화학요법을 시행한 유방암 환자에서 병리학적 관해와 잔류 암의 크기를 평가하는 데 있어 유방자기공명영상의 정확도를 분석하고 이에 영향을 미치는 인자들이 무엇인지 알아본다.

대상과 방법

2010년부터 2017년까지 본원에서 신보강화학요법 후 수술을 시행한 88명의 유방암 환자를 대상으로 하였다. 병리학적 관해는 수술 병리 결과에서 침윤성 유방암이 발견되지 않는 것으로 정의하였고 자기공명영상과 병리 조직의 잔류 암 크기 차이는 최대 직경으로 비교하였다. 병리학적 관해 및 자기공명영상과 병리 조직에서의 잔류 암 크기 차이에 영향을 미치는 인자를 알아보기 위해 통계분석을 시행하였다.

결과

전체 환자의 10%가 병리학적 관해에 도달하였다. 자기공명영상으로 관해를 예측할 때의 정확도와 곡선하부면적은 각각 90.91%, 0.8017이었다. 신보강화학요법 시행 후 유방자기공명영상과 병리 조직에서 측정한 잔류 암의 크기는 매우 강한 연관성을 보였고(r = 0.9, p < 0.001), 특히 영상에서 단일 종괴로 보였던 병변에서(p = 0.047) 그러하였다. 자기공명영상과 병리 조직 간의 잔류 암 크기는 내강형(p = 0.023), 그리고 자기공명영상에서 다초점 종괴 및 비종괴성 조영증강을 보인(p = 0.047) 환자군에서 유의미하게 큰 차이를 보였다.

결론

자기공명영상은 유방암의 병리학적 완전 관해와 잔류 암 크기의 평가에 있어서 정확도가 높은 검사이다. 유방암 아형과 병변의 영상의학적 소견이 자기공명영상의 정확도에 영향을 미친다.

Construction of Nomograms for Predicting Pathological Complete Response and Tumor Shrinkage Size in Breast Cancer

Purpose

Pathological complete response (pCR) is the goal of neoadjuvant chemotherapy (NAC) for the HER2-positive and triple-negative subtypes of breast cancer and is related to survival benefit; however, luminal breast cancer is not sensitive to NAC, and the size of tumor shrinkage is a more meaningful clinical indicator for the luminal breast cancer subtype. We wanted to use a nomogram or formula to develop and implement a series of prediction models for pCR or tumor shrinkage size.

Patients and Methods

We developed a prediction model in a primary cohort consisting of 498 patients with invasive breast cancer, and the data were gathered from July 2016 to September 2018. The endpoint was pCR and tumor shrinkage size. In the primary cohort, the HER2-positive cohort, and the triple-negative cohort, multivariate logistic regression analysis was used to screen the significant clinical features and clinicopathological features to develop nomograms. In the luminal group, multivariate linear regression analysis was used to test the risk factors that affect tumor shrinkage size. The area under the receiver operating characteristic curve (AUC) and calibration curves were adopted to evaluate and analyze the discrimination and calibration ability of nomograms. Furthermore, we also performed internal validation and independent validation in the primary cohort.

Results

ER status, KI67 status, HER2 status, number of NAC cycles, and tumor size were independent predictive factors of pCR in the primary cohort. These indicators had good discrimination and calibration in the primary and validation cohorts (AUC: 0.873, 0.820). The nomogram for HER2-positive and triple-negative breast cancer (TNBC) had an AUC of 0.820 and 0.785, respectively. Both the HER2 positive and TNBC nomogram calibration curves indicated significant agreement. Moreover, the luminal subtype prediction model was Y (tumor shrinkage size) = -0.576 × (age at diagnosis) + 2.158 × (number of NAC cycles) + 0.233 × (pre-NAC tumor size) + 51.662.

Conclusion

Utilizing this predictive model will enable us to identify patients at high probability for pCR after NAC. Clinicians can stratify these patients and make individualized and personalized recommendations for therapy.

Evaluation of the Efficacy of Neoadjuvant Chemotherapy for Breast Cancer

Neoadjuvant chemotherapy is increasingly used in breast cancer, especially for downstaging the primary tumor in the breast and the metastatic axillary lymph node. Accurate evaluations of the response to neoadjuvant chemotherapy provide important information on the impact of systemic therapies on breast cancer biology, prognosis, and guidance for further therapy. Moreover, pathologic complete response is a validated and valuable surrogate prognostic factor of survival after therapy. Evaluations of neoadjuvant chemotherapy response are very important in clinical work and basic research. In this review, we will elaborate on evaluations of the efficacy of neoadjuvant chemotherapy in breast cancer and provide a clinical evaluation procedure for neoadjuvant chemotherapy.

Correlation Between Mammographic Radiomics Features and the Level of Tumor-Infiltrating Lymphocytes in Patients With Triple-Negative Breast Cancer

Objectives: Tumor-infiltrating lymphocytes (TILs) have been identified as a significant prognostic indicator of response to neoadjuvant therapy and immunotherapy for triple-negative breast cancer (TNBC) patients. Herein, we aim to assess the association between TIL levels and mammographic features in TNBC patients. Methods: Forty-three patients with surgically proven TNBC who underwent preoperative mammography from January 2018 to December 2018 were recruited. Pyradiomics software was used to extract 204 quantitative radiomics features, including morphologic, grayscale, and textural features, from the segmented lesion areas. The correlation between radiological characteristics and TIL levels was evaluated by screening the most statistically significant radiological features using Mann-Whitney U-test and Pearson correlation coefficient. The patients were divided into two groups based on tumor TIL levels: patients with TIL levels <50% and those with TIL levels ≥50%. The correlation between TIL levels and clinicopathological characteristics was assessed using the chi-square test or Fisher's exact test. Mann-Whitney U-test and Pearson correlation coefficient were used to analyze the statistical significance and Pearson correlation coefficient of clinical pathological features, age, and radiological features. Results: Of 43 patients, 32 (74.4%) had low TIL levels and 11 (25.6%) had high TIL levels. The histological grade of the low TIL group was higher than that of the high TIL group (p = 0.043). The high TIL group had a more negative threshold Ki-67 level (<14%) than the low TIL group (p = 0.017). The six most important radiomics features [uniformity, variance, grayscale symbiosis matrix (GLCM) correlation, GLCM autocorrelation, gray level difference matrix (GLDM) low gray level emphasis, and neighborhood gray-tone difference matrix (NGTDM) contrast], representing qualitative mammographic image characteristics, were statistically different (p < 0.05) among the low and high TIL groups. Tumors in the high TIL group had a more non-uniform density and a smoother gradient of the tumor pattern than the low TIL group. The changes in Ki-67, age, epidermal growth factor receptor, radiomic characteristics, and Pearson correlation coefficient were statistically significant (p < 0.05). Conclusion: Mammography features not only distinguish high and low TIL levels in TNBC patients but also can act as imaging biomarkers to enhance diagnosis and the response of patients to neoadjuvant therapies and immunotherapies.

WITHDRAWN: Risks and Benefits of Gadolinium-Based Contrast Enhanced MRI

The Publisher regrets that this article is an accidental duplication of an article that has already been published in [Seminars in Ultrasound, CT, and MRI, 41/2 (2020) 170–182], https://dx.doi.org/10.1053/j.sult.2019.12.005. The duplicate article has therefore been withdrawn. The full Elsevier Policy on Article Withdrawal can be found at https://www.elsevier.com/about/our-business/policies/article-withdrawal

Breast Conservation After Neoadjuvant Chemotherapy for Triple-Negative Breast Cancer: Surgical Results From the BrighTNess Randomized Clinical Trial

Importance

Neoadjuvant systemic therapy (NST) is often administered to enable breast-conserving therapy (BCT) in stages II to III breast cancer.

Objectives

To prospectively evaluate the role of NST in conversion from BCT ineligibility to BCT eligibility and to assess the association of response to NST, germline BRCA (gBRCA) status, and region of treatment with surgical choice in women with triple-negative breast cancer (TNBC).

Design, Setting, and Participants

This prespecified secondary analysis of a multicentered, phase 3, double-blind, randomized clinical trial (BrighTNess) enrolled 634 eligible women across 145 centers in 15 countries in North America, Europe, and Asia. Women with operable, clinical stages II to III TNBC who underwent gBRCA mutation testing before initiating NST were eligible to participate. Data were collected from April 1, 2014, to December 8, 2016. This preplanned analysis was performed from January 5, 2018, to October 28, 2019.

Interventions

Study participants were randomized to receive 12 weeks of weekly paclitaxel alone or with the addition of carboplatin and/or veliparib, followed by 4 cycles of doxorubicin hydrochloride and cyclophosphamide.

Main Outcomes and Measures

Surgeons assessed BCT candidacy by clinical and radiographic criteria before and after NST. Surgical choices and whether BCT eligibility was associated with the likelihood of pathologic complete response were then analyzed.

Results

Among the 634 randomized patients (median age, 51 [range, 22-78] years), pre- and post-NST assessments were available for 604 patients. Of 141 patients deemed BCT ineligible at baseline, 75 (53.2%) converted to BCT eligible. Overall, 342 (68.1%) of 502 patients deemed BCT eligible after NST underwent BCT, including 42 (56.0%) of the 75 who converted to BCT eligible. Patients treated in Europe and Asia were more likely to undergo BCT (odds ratio, 2.66; 95% CI, 1.84-3.84) compared with those treated in North America. Among patients without gBRCA mutation undergoing mastectomy, those treated in North America were more likely to undergo contralateral prophylactic mastectomy (57 of 81 [70.4%] vs 6 of 30 [20.0%]; P < .001). Rates of pathologic complete response were similar between patients deemed BCT eligible at baseline and those who were BCT ineligible but converted to BCT eligibility after NST (55.3 [235 of 425] vs 49.3% [37 of 75]; P = .38).

Conclusions and Relevance

This prospective analysis of NST and BCT eligibility in TNBC demonstrates a conversion from BCT ineligibility to BCT eligibility of 53.2%. Lower BCT rates among eligible patients and higher bilateral mastectomy rates among patients without gBRCA mutation in North America merit investigation.

Trial Registration

ClinicalTrials.gov identifier: NCT02032277.

Texture Analysis of Dynamic Contrast-Enhanced MRI in Evaluating Pathologic Complete Response (pCR) of Mass-Like Breast Cancer after Neoadjuvant Therapy

Objectives

MRI is the standard imaging method in evaluating treatment response of breast cancer after neoadjuvant therapy (NAT), while identification of pathologic complete response (pCR) remains challenging. Texture analysis (TA) on post-NAT dynamic contrast-enhanced (DCE) MRI was explored to assess the existence of pCR in mass-like cancer.

Materials and Methods

A primary cohort of 112 consecutive patients (40 pCR and 72 non-pCR) with mass-like breast cancers who received preoperative NAT were retrospectively enrolled. On post-NAT MRI, volumes of the residual-enhanced areas and TA first-order features (19 for each sequence) of the corresponding areas were achieved for both early- and late-phase DCE using an in-house radiomics software. Groups were divided according to the operational pathology. Receiver operating characteristic curves and binary logistic regression analysis were used to select features and achieve a predicting formula. Overall diagnostic abilities were compared between TA and radiologists' subjective judgments. Validation was performed on a time-independent cohort of 39 consecutive patients.

Results

TA features with high consistency (Cronbach's alpha >0.9) between 2 observers showed significant differences between pCR and non-pCR groups. Logistic regression using features selected by ROC curves generated a synthesized formula containing 3 variables (volume of residual enhancement, entropy, and robust mean absolute deviation from early-phase) to yield AUC = 0.81, higher than that of using radiologists' subjective judgment (AUC = 0.72), and entropy was an independent risk factor (P < 0.001). Accuracy and sensitivity for identifying pCR were 83.93% and 70.00%. AUC of the validation cohort was 0.80.

Conclusions

TA may help to improve the diagnostic ability of post-NAT MRI in identifying pCR in mass-like breast cancer. Entropy, as a first-order feature to depict residual tumor heterogeneity, is an important factor.

Early assessment with magnetic resonance imaging for prediction of pathologic response to neoadjuvant chemotherapy in triple-negative breast cancer: Results from the phase III BrighTNess trial

INTRODUCTION

The ability of breast magnetic resonance imaging (MRI) to predict pathologic complete response (pCR) to neoadjuvant systemic therapy (NST) varies across biological subtypes. We sought to determine how well breast MRI findings following initial treatment on the phase III BrighTNess trial correlated with pCR in patients with triple negative breast cancer (TNBC).

METHODS

Baseline and mid-treatment imaging and pathologic response data were available in 519 patients with stage II-III TNBC who underwent NST as per protocol. MRI complete response (mCR) was defined as disappearance of all target lesion(s) and MRI partial response (mPR) as a ≥50% reduction in the largest tumor diameter.

RESULTS

Overall, mCR was demonstrated in 116 patients (22%), whereas 166 (32%) had mPR and 237 (46%) had stable/progressive disease (SD/PD). The positive predictive value (PPV), negative predictive value, and overall accuracy of the mid-treatment MRI for pCR were 78%, 56%, and 61%, respectively; accuracy did not differ significantly between gBRCA mutation carriers and non-carriers (52% vs. 63%, p = 0.10). When compared to patients with SD/PD, those with mPR or mCR were 3.35-fold (95% CI 2.07-5.41) more likely to have pCR at surgery. MRI response during NST was significantly associated with eligibility for breast-conserving surgery following completion of treatment (93.1% for mCR vs. 81.6% for SD/PD, p < 0.001).

CONCLUSIONS

Complete response on mid-treatment MRI in the BrighTNess trial had a PPV of 78% for demonstration of pCR after completion of NST in TNBC. However, a substantial proportion of patients with mPR or SD/PD also achieved a pCR.

CLINICAL TRIAL REGISTRATION

NCT02032277.

Evaluation of MRI accuracy after primary systemic therapy in breast cancer patients considering tumor biology: optimizing the surgical planning

Background

We analyzed the accuracy of magnetic resonance imaging (MRI) after primary systemic therapy (PST) according to tumor subtype.

Patients and methods

Two-hundred and four breast cancer patients treated with PST were studied. MRI findings after PST were compared with pathologic findings, and results were stratified based on tumor subtype.

Results

Of the two-hundred and four breast cancer patients, eighty-four (41.2%) achieved a pathologic complete response (pCR) in the breast. The MRI accuracy for predicting pCR was highest in triple-negative (TN) and HER2-positive (non-luminal) breast cancer (83.9 and 80.9%, respectively). The mean size discrepancy between MRI-measured and pathologic residual tumor size was lowest in TN breast cancer and highest in luminal B-like (HER2-negative) breast cancer (0.45cm vs. 0.98 cm, respectively; p = 0.003). After breast conserving surgery (BCS), we found a lower rate of positive margins in TN breast cancer and a higher rate of positive margins in luminal B-like (HER2-negative) breast cancer (2.4% vs. 23.6%, respectively).

Conclusions

If tumor response after PST is assessed by MRI, tumor subtype should be considered when BCS is planned. The accuracy of MRI is highest in TN breast cancer.

TNBC Challenge: Oligonucleotide Aptamers for New Imaging and Therapy Modalities

Compared to other breast cancers, triple-negative breast cancer (TNBC) usually affects younger patients, is larger in size, of higher grade and is biologically more aggressive. To date, conventional cytotoxic chemotherapy remains the only available treatment for TNBC because it lacks expression of the estrogen receptor (ER), progesterone receptor (PR) and epidermal growth factor receptor 2 (HER2), and no alternative targetable molecules have been identified so far. The high biological and clinical heterogeneity adds a further challenge to TNBC management and requires the identification of new biomarkers to improve detection by imaging, thus allowing the specific treatment of each individual TNBC subtype. The Systematic Evolution of Ligands by EXponential enrichment (SELEX) technique holds great promise to the search for novel targetable biomarkers, and aptamer-based molecular approaches have the potential to overcome obstacles of current imaging and therapy modalities. In this review, we highlight recent advances in oligonucleotide aptamers used as imaging and/or therapeutic agents in TNBC, discussing the potential options to discover, image and hit new actionable targets in TNBC.

Multivariate machine learning models for prediction of pathologic response to neoadjuvant therapy in breast cancer using MRI features: a study using an independent validation set

PURPOSE

To determine whether a multivariate machine learning-based model using computer-extracted features of pre-treatment dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) can predict pathologic complete response (pCR) to neoadjuvant therapy (NAT) in breast cancer patients.

METHODS

Institutional review board approval was obtained for this retrospective study of 288 breast cancer patients at our institution who received NAT and had a pre-treatment breast MRI. A comprehensive set of 529 radiomic features was extracted from each patient's pre-treatment MRI. The patients were divided into equal groups to form a training set and an independent test set. Two multivariate machine learning models (logistic regression and a support vector machine) based on imaging features were trained to predict pCR in (a) all patients with NAT, (b) patients with neoadjuvant chemotherapy (NACT), and (c) triple-negative or human epidermal growth factor receptor 2-positive (TN/HER2+) patients who had NAT. The multivariate models were tested using the independent test set, and the area under the receiver operating characteristics (ROC) curve (AUC) was calculated.

RESULTS

Out of the 288 patients, 64 achieved pCR. The AUC values for predicting pCR in TN/HER+ patients who received NAT were significant (0.707, 95% CI 0.582-0.833, p < 0.002).

CONCLUSIONS

The multivariate models based on pre-treatment MRI features were able to predict pCR in TN/HER2+ patients.

Apparent diffusion coefficient measurement in luminal breast cancer: will tumour shrinkage patterns affect its efficacy of evaluating the pathological response?

AIM

To determine which region of interest (ROI) placement method of apparent diffusion coefficient (ADC) measurement has the best performance for predicting pathological complete response (PCR) at two cycles of neoadjuvant chemotherapy (NAC) according to different tumour shrinkage patterns of luminal breast cancer and to assess the evaluative accuracy of ADC value combined with other clinicopathological indicators.

MATERIALS AND METHODS

Sixty-one patients who underwent NAC for histopathologically confirmed breast cancer were enrolled in this retrospective study. The ADC values of different shrinkage patterns (concentric shrinkage, nest or dendritic shrinkage, and mixed shrinkage) for tumours shown by diffusion-weighted imaging (DWI) were measured independently using three ROI placement methods (single-round, three-round, and freehand). Intraclass correlation coefficients (ICCs) were used to assess the interobserver variability in the ADC values. Multivariate logistic regression analysis was performed to identify the independent predictors of PCR.

RESULTS

The best placement method found was single-round ROI in all the patients (AUC=0.863). When analysed separately, the effectiveness results differed: the single-round method was optimal for concentrically shrinking tumours (AUC=0.970); the freehand method was optimal for nest or dendritically shrinking tumours (AUC=0.714); and the three-round method was optimal for mixed shrinking tumours (AUC=0.975). Multivariate logistic analysis showed that oestrogen receptor (ER), ΔADC% and tumour diameter reduction (ΔD%) were independent factors in evaluating the PCR.

CONCLUSION

The methods for measuring ADC values vary across different shrinkage patterns of luminal tumours. ΔADC%, ER and ΔD% were independent factors for evaluating the PCR.

Pretreatment prediction of pathologic complete response to neoadjuvant chemotherapy in breast cancer: Perfusion metrics of dynamic contrast enhanced MRI

The purpose of this study was to investigate imaging parameters predicting pathologic complete response (pCR) in pretreatment dynamic contrast enhanced-magnetic resonance imaging (DCE-MRI) in breast cancer patients who were treated with neoadjuvant chemotherapy (NAC). A total of 74 patients who received NAC followed by surgery were retrospectively reviewed. All patients underwent breast MRI before NAC. Perfusion parameters including Ktrans, Kep and Ve of tumor were measured three-dimensionally. These perfusion parameters of background parenchyma of contralateral breasts were analyzed two-dimensionally. Receiver-operating characteristic (ROC) analysis and multivariable logistic regression analysis were performed to compare the ability of perfusion parameters to predict pCR. Of 74 patients, 13 achieved pCR in final pathology. The fiftieth percentile and skewness of each perfusion parameter - Ktrans, Kep, and Ve of tumor were associated with pCR. Perfusion parameters of contralateral breast parenchyma in 2D analysis also showed predictive ability for pCR. The model combining perfusion parameters of contralateral breast background parenchyma and those of the tumor had higher predictive value than each single parameter. Thus, perfusion parameters of tumor, background parenchyma of contralateral breast and their combinations in pretreatment breast MRI allow early prediction for pCR of breast cancer.