Magnetic resonance imaging features of breast cancer according to intrinsic subtypes: correlations with neoadjuvant chemotherapy effects

The association of magnetic resonance imaging features with five molecular subtypes of breast cancer

Objective

To identify the association of magnetic resonance imaging (MRI) features with molecular subtypes of breast cancer (BC).

Materials and methods

A retrospective study was conducted on 112 invasive BC patients with preoperative breast MRI. The confirmed diagnosis and molecular subtypes of BC were based on the postoperative specimens. MRI features were collected by experienced radiologists. The association of MRI features of each subtype was compared to other molecular subtypes in univariate and multivariate logistic regression analyses.

Results

The proportions of luminal A, luminal B HER2-negative, luminal B HER2-positive, HER2-enriched, and triple-negative BC were 14.3 %, 52.7 %, 12.5 %, 10.7 %, and 9.8 %, respectively. Luminal A was associated with hypo-isointensityon T2-weighted images (OR=6.214, 95 % CI: 1.163-33.215) and non-restricted diffusion on DWI-ADC (OR=6.694, 95 % CI: 1.172-38.235). Luminal B HER2-negative was related to the presence of mass (OR=7.245, 95 % CI: 1.760-29.889) and slow/medium initial enhancement pattern (OR=3.654, 95 % CI: 1.588-8.407). There were no associations between MRI features and luminal B HER2-positive. HER2-enriched tended to present as non-mass enhancement lesions (OR=20.498, 95 % CI: 3.145-133.584) with fast uptake in the initial postcontrast phase (OR=9.788, 95 % CI: 1.689-56.740), and distortion (OR=11.471, 95 % CI: 2.250-58.493). Triple-negative were associated with unifocal (OR=7.877, 95 % CI: 1.180-52.589), hyperintensityon T2-weighted images (OR=14.496, 95 % CI: 1.303-161.328), rim-enhanced lesions (OR=18.706, 95 % CI: 1.915-182.764), and surrounding tissue edema (OR=5.768, 95 % CI: 1.040-31.987).

Conclusion

Each molecular subtype of BC has distinct features on breast MRI. These characteristics can serve as an adjunct to immunohistochemistry in diagnosing molecular subtypes, particularly in cases, where traditional methods yield equivocal results.

Intra- and peritumoral radiomics features based on multicenter automatic breast volume scanner for noninvasive and preoperative prediction of HER2 status in breast cancer: a model ensemble research

The aim to investigate the predictive efficacy of automatic breast volume scanner (ABVS), clinical and serological features alone or in combination at model level for predicting HER2 status. The model weighted combination method was developed to identify HER2 status compared with single data source model method and feature combination method. 271 patients with invasive breast cancer were included in the retrospective study, of which 174 patients in our center were randomized into the training and validation sets, and 97 patients in the external center were as the test set. Radiomics features extracted from the ABVS-based tumor, peritumoral 3 mm region, and peritumoral 5 mm region and clinical features were used to construct the four types of the optimal single data source models, Tumor, R3mm, R5mm, and Clinical model, respectively. Then, the model weighted combination and feature combination methods were performed to optimize the combination models. The proposed weighted combination models in predicting HER2 status achieved better performance both in validation set and test set. For the validation set, the single data source model, the feature combination model, and the weighted combination model achieved the highest area under the curve (AUC) of 0.803 (95% confidence interval [CI] 0.660-947), 0.739 (CI 0.556,0.921), and 0.826 (95% CI 0.689,0.962), respectively; with the sensitivity and specificity were 100%, 62.5%; 81.8%, 66.7%; 90.9%,75.0%; respectively. For the test set, the single data source model, the feature combination model, and the weighted combination model attained the best AUC of 0.695 (95% CI 0.583, 0.807), 0.668 (95% CI 0.555,0.782), and 0.700 (95% CI 0.590,0.811), respectively; with the sensitivity and specificity were 86.1%, 41.9%; 61.1%, 71.0%; 86.1%, 41.9%; respectively. The model weighted combination was a better method to construct a combination model. The optimized weighted combination models composed of ABVS-based intratumoral and peritumoral radiomics features and clinical features may be potential biomarkers for the noninvasive and preoperative prediction of HER2 status in breast cancer.

The growth pattern of liver metastases on MRI predicts early recurrence in patients with colorectal cancer: a multicenter study

OBJECTIVES

The multicenter study aimed to explore the relationship between the growth pattern of liver metastases on preoperative MRI and early recurrence in patients with colorectal cancer liver metastases (CRCLM) after surgery.

METHODS

A total of 348 CRCLM patients from 3 independent centers were enrolled, including 130 patients with 339 liver metastases in the primary cohort and 218 patients in validation cohorts. Referring to the gross classification of hepatocellular carcinoma (HCC), the growth pattern of each liver metastasis on MRI was classified into four types: rough, smooth, focal extranodular protuberant (FEP), and nodular confluent (NC). Disease-free survival (DFS) curve was constructed using the Kaplan-Meier method.

RESULTS

In primary cohort, 42 (12.4%) of the 339 liver metastases were rough type, 237 (69.9%) were smooth type, 29 (8.6%) were FEP type, and 31 (9.1%) were NC type. Those patients with FEP- and/or NC-type liver metastases had shorter DFS than those without such metastases (p < 0.05). However, there were no significant differences in DFS between patients with rough- and smooth-type liver metastases and those without such metastases. The patients with FEP- and/or NC-type liver metastases also had shorter DFS than those without such metastases in two external validation cohorts. In addition, 40.5% of high-risk-type (FEP and NC) liver metastases converted to low-risk types (rough and smooth) after neoadjuvant chemotherapy.

CONCLUSION

The FEP- and NC-type liver metastases were associated with early recurrence, which may facilitate the clinical treatment of CRCLM patients.

KEY POINTS

• In the primary cohort, patients with FEP- and NC-type metastases had shorter disease-free survival (DFS) and a higher intrahepatic recurrence rate than patients without such metastases in the liver. • In the primary cohort, there were no significant differences in DFS or intrahepatic recurrence rate between patients with rough- and smooth-type metastases and those without such metastases in the liver. • High-risk patients had shorter DFS and a higher intrahepatic recurrence rate than low-risk patients in primary and external validation cohorts.

Sigmoid model analysis of breast dynamic contrast-enhanced MRI: Distinguishing between benign and malignant breast masses and breast cancer subtype prediction

Dynamic contrast‐enhanced magnetic resonance imaging (DCE‐MRI) is performed to distinguish between benign and malignant lesions by evaluating the changes in signal intensity of the acquired image (kinetic curve). This study aimed to verify whether the existing breast DCE‐MRI analyzed by the sigmoid model can accurately distinguish between benign and invasive ductal carcinoma (IDC) and predict the subtype.

A total of 154 patients who underwent breast MRI for detailed breast mass examinations were included in this study (38 with benign masses and 116 with IDC. The sigmoid model involved the acquisition of images at seven timepoints in 1‐min intervals to determine the change in signal intensity before and after contrast injection. From this curve, the magnitude of the increase in signal intensity in the early phase, the time to reach the maximum increase, and the slopes in the early and late phases were calculated. The Mann–Whitney U‐test was used for the statistical analysis.

The IDC group exhibited a significantly larger and faster signal increase in the early phase and a significantly smaller rate of increase in the late phase than the benign group (P < 0.001). The luminal A‐like group demonstrated a significantly longer time to reach the maximum signal increase rate than other IDC subtypes (P < 0.05).

The sigmoid model analysis of breast DCE‐MRI can distinguish between benign lesions and IDC and may also help in predicting luminal A‐like breast cancer.

Breast Cancer Subtypes and Quantitative Magnetic Resonance Imaging: A Systemic Review

Magnetic resonance imaging (MRI) is the most sensitive imaging modality for breast cancer detection. This systematic review investigated the role of quantitative MRI features in classifying molecular subtypes of breast cancer. We performed a literature search of articles published on the application of quantitative MRI features in invasive breast cancer molecular subtype classification in PubMed from 1 January 2002 to 30 September 2021. Of the 1275 studies identified, 106 studies with a total of 12,989 patients fulfilled the inclusion criteria. Bias was assessed based using the Quality Assessment of Diagnostic Studies. All studies were case-controlled and research-based. Most studies assessed quantitative MRI features using dynamic contrast-enhanced (DCE) kinetic features and apparent diffusion coefficient (ADC) values. We present a summary of the quantitative MRI features and their correlations with breast cancer subtypes. In DCE studies, conflicting results have been reported; therefore, we performed a meta-analysis. Significant differences in the time intensity curve patterns were observed between receptor statuses. In 10 studies, including a total of 1276 lesions, the pooled difference in proportions of type Ⅲ curves (wash-out) between oestrogen receptor-positive and -negative cancers was not significant (95% confidence interval (CI): [−0.10, 0.03]). In nine studies, including a total of 1070 lesions, the pooled difference in proportions of type 3 curves between human epidermal growth factor receptor 2-positive and -negative cancers was significant (95% CI: [0.01, 0.14]). In six studies including a total of 622 lesions, the pooled difference in proportions of type 3 curves between the high and low Ki-67 groups was significant (95% CI: [0.17, 0.44]). However, the type 3 curve itself is a nonspecific finding in breast cancer. Many studies have examined the relationship between mean ADC and breast cancer subtypes; however, the ADC values overlapped significantly between subtypes. The heterogeneity of ADC using kurtosis or difference, diffusion tensor imaging parameters, and relaxation time was reported recently with promising results; however, current evidence is limited, and further studies are required to explore these potential applications.

Magnetic Resonance Imaging Phenotypes of Breast Cancer Molecular Subtypes: A Systematic Review

OBJECTIVE

Magnetic resonance imaging (MRI) is the most sensitive imaging modality in detecting breast cancer. The purpose of this systematic review is to investigate the role of human extracted MRI phenotypes in classifying molecular subtypes of breast cancer.

METHODS

We performed a literature search of published articles on the application of MRI phenotypic features in invasive breast cancer molecular subtype classifications by radiologists' interpretation on Medline Complete, Pubmed, and Google scholar from 1st January 2000 to 31st March 2021. Of the 1453 literature identified, 42 fulfilled the inclusion criteria.

RESULTS

All studies were case-controlled, retrospective study and research-based. The majority of the studies assessed the MRI features using American College of Radiology- Breast Imaging Reporting and Data System (ACR-BIRADS) classification and using dynamic contrast-enhanced (DCE) kinetic features, Apparent Diffusion Coefficient (ADC) values, and T2 sequence. Most studies divided invasive breast cancer into 4 main subtypes, luminal A, luminal B, HER2, and triple-negative (TN) cancers, and used 2 readers. We present a summary of the radiologists' extracted breast MRI phenotypical features and their correlating breast cancer subtypes classifications. The characteristic features are morphology, enhancement kinetics, and T2 signal intensity. We found that the TN subtype has the most distinctive MRI features compared to the other subtypes and luminal A and B have many similar features.

CONCLUSION

The MRI features which are predictive of each subtype are the morphology, internal enhancement features, and T2 signal intensity, predominantly between TN and the rest. Radiologists' visual interpretation of some of MRI features may offer insight into the respective invasive breast cancer molecular subtype. However, current evidence are still limited to "suggestive" features instead of a diagnostic standard.  Further research is recommended to explore this potential application, for example, by augmentation of radiologists' visual interpretation by artificial intelligence.

Intratumoral and Peritumoral Radiomics Based on Functional Parametric Maps from Breast DCE-MRI for Prediction of HER-2 and Ki-67 Status

BACKGROUND

Radiomics has been applied to breast magnetic resonance imaging (MRI) for gene status prediction. However, the features of peritumoral regions were not thoroughly investigated.

PURPOSE

To evaluate the use of intratumoral and peritumoral regions from functional parametric maps based on breast dynamic contrast-enhanced MRI (DCE-MRI) for prediction of HER-2 and Ki-67 status.

STUDY TYPE

Retrospective.

POPULATION

A total of 351 female patients (average age, 51 years) with pathologically confirmed breast cancer were assigned to the training (n = 243) and validation (n = 108) cohorts.

FIELD STRENGTH/SEQUENCE

3.0T, T₁ gradient echo.

ASSESSMENT

Radiomic features were extracted from intratumoral and peritumoral regions on six functional parametric maps calculated using time-intensity curves of DCE-MRI. The intraclass correlation coefficients (ICCs) were used to determine the reproducibility of feature extraction. Based on the intratumoral, peritumoral, and combined intra- and peritumoral regions, three radiomics signatures (RSs) were built using the least absolute shrinkage and selection operator (LASSO) logistic regression model, respectively.

STATISTICAL TESTS

Wilcoxon rank-sum test, minimum redundancy maximum relevance, LASSO, receiver operating characteristic curve (ROC) analysis, and DeLong test.

RESULTS

The intratumoral and peritumoral RSs for prediction of HER-2 and Ki-67 status achieved areas under the ROC (AUCs) of 0.683 (95% confidence interval [CI], 0.574-0.793) and 0.690 (95% CI, 0.577-0.804), and 0.714 (95% CI, 0.616-0.812) and 0.692 (95% CI, 0.590-0.794) in the validation cohort, respectively. The combined RSs yielded AUCs of 0.713 (95% CI, 0.604-0.823) and 0.749 (95% CI, 0.656-0.841), respectively. There were no significant differences in prediction performance among intratumoral, peritumoral, and combined RSs. Most (69.7%) of the features had good agreement (ICCs >0.8).

DATA CONCLUSION

Radiomic features of intratumoral and peritumoral regions on functional parametric maps based on breast DCE-MRI had the potential to identify HER-2 and Ki-67 status.

LEVEL OF EVIDENCE

3 Technical Efficacy Stage: 2.

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.

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

BACKGROUND/AIM

The aim of the study was to evaluate whether residual tumor assessment by magnetic resonance imaging (MRI) after neoadjuvant chemotherapy (NACT) is fundamental for a successive surgical strategy.

PATIENTS AND METHODS

We collected 55 MRIs performed after NACT.

RESULTS

Pathological response rate was 20%. MRI's sensitivity, specificity, PPV and NPV were 50%, 88%, 54% and 86%, respectively. We observed a high variability between the different subgroups, with high number of false positives in luminal A/B tumors. Triple negative and HER2+ tumors had almost the same specificity and sensitivity (81% and 50%). Nevertheless, in the HER2+ group, PPV was greater than that in the triple negative group (71% and 33% respectively) and the NPV of the triple negative group was greater than that of the HER2+ one (90% and 64%, respectively). Statistical analysis showed a weak but significant correlation between MRI and pathological assessment of residual tumor dimension.

CONCLUSION

The present study, confirms literature data about MRI accuracy in diagnosing HER2+ and triple negative tumors, but suggests caution in case of luminal tumors' evaluation.

The value of dynamic contrast-enhanced MRI in the diagnosis and management of triple-negative breast cancer

Background

Breast cancer is undoubtedly the world’s most frequent cancer among women. Triple-negative breast cancer (TNBC) is a subtype of breast cancer that does not express estrogen, progesterone, or human epidermal growth factor receptors, yet its imaging is considered a challenge to radiologists having imprecise features.

In this study, we aimed at defining the MRI characteristics of triple-negative breast cancer to validate its impact on management.

Results

Most of the TNBCs in this study showed malignant pattern kinetic curves (types II and III), 87/104 (83.7%), and P value 0.673 (not significant), and regarding the type of lesion enhancement, 104/172 cases (60.5%) showed mass enhancement compared to 52/172 (30.2%) non-mass enhancement and 16/172 (9.3%) focus enhancement, P value 0.185 (not significant). As for the internal enhancement pattern of mass lesions, rim internal enhancement was predominant in TNBC mass lesions, 48/104 (46.2%), as well as intratumoral bright signal intensity on T2-weighted images, 108/172 (62.8%), P value 0.001 (highly significant).

Conclusion

Triple-negative breast cancers (TNBC) are larger, better defined, and more necrotic than conventional cancers. On MRI, necrosis yields high T2-weighted signal intensity.

Imaging and Clinicopathologic Features Associated With Pathologic Complete Response in HER2-positive Breast Cancer Receiving Neoadjuvant Chemotherapy With Dual HER2 Blockade

BACKGROUND

In human epidermal growth factor receptor 2-positive (HER2⁺) breast cancer, the incorporation of a dual HER2 blockade into neoadjuvant chemotherapy (NAC) has been shown to induce a higher rate of pathologic complete response (pCR). The purpose of this study was to investigate whether pretreatment imaging and clinicopathologic features show any association with pCR in HER2⁺ breast cancer receiving NAC plus dual blockade.

MATERIALS AND METHODS

This retrospective study evaluated 94 consecutive patients (mean age, 49.8 ± 9.9 years) with HER2⁺ breast cancer who underwent NAC plus dual blockade with trastuzumab and pertuzumab between April 2016 and June 2018. All patients underwent mammography, ultrasound, and magnetic resonance imaging prior to NAC. Clinicopathologic and imaging features acquired before NAC were evaluated for their ability to predict the pathologic response after surgery. Multivariate analysis was used to identify independent predictors of pCR.

RESULTS

Fifty patients (53.2%) showed pCR and 44 (46.8%) did not. According to a univariate analysis, fine pleomorphic/fine linear or linear-branching calcification morphology on mammography, parallel orientation on ultrasound, intratumoral high signal intensity on T2-weighted magnetic resonance imaging, progesterone receptor negativity, and high levels of tumor-infiltrating lymphocytes were associated with pCR. On multivariate analysis, fine pleomorphic/fine linear or linear-branching calcification morphology on mammography (odds ratio [OR], 7.23), progesterone receptor negativity (OR, 6.76), and a high tumor-infiltrating lymphocyte level (OR, 5.92) remained significant independent factors associated with pCR.

CONCLUSION

Several pretreatment imaging and clinicopathologic features were shown to be independent variables predicting pCR in patients with HER2⁺ breast cancer receiving NAC with dual blockade.

Association of Peritumoral Radiomics With Tumor Biology and Pathologic Response to Preoperative Targeted Therapy for HER2 (ERBB2)-Positive Breast Cancer

IMPORTANCE

There has been significant recent interest in understanding the utility of quantitative imaging to delineate breast cancer intrinsic biological factors and therapeutic response. No clinically accepted biomarkers are as yet available for estimation of response to human epidermal growth factor receptor 2 (currently known as ERBB2, but referred to as HER2 in this study)-targeted therapy in breast cancer.

OBJECTIVE

To determine whether imaging signatures on clinical breast magnetic resonance imaging (MRI) could noninvasively characterize HER2-positive tumor biological factors and estimate response to HER2-targeted neoadjuvant therapy.

DESIGN, SETTING, AND PARTICIPANTS

In a retrospective diagnostic study encompassing 209 patients with breast cancer, textural imaging features extracted within the tumor and annular peritumoral tissue regions on MRI were examined as a means to identify increasingly granular breast cancer subgroups relevant to therapeutic approach and response. First, among a cohort of 117 patients who received an MRI prior to neoadjuvant chemotherapy (NAC) at a single institution from April 27, 2012, through September 4, 2015, imaging features that distinguished HER2+ tumors from other receptor subtypes were identified. Next, among a cohort of 42 patients with HER2+ breast cancers with available MRI and RNaseq data accumulated from a multicenter, preoperative clinical trial (BrUOG 211B), a signature of the response-associated HER2-enriched (HER2-E) molecular subtype within HER2+ tumors (n = 42) was identified. The association of this signature with pathologic complete response was explored in 2 patient cohorts from different institutions, where all patients received HER2-targeted NAC (n = 28, n = 50). Finally, the association between significant peritumoral features and lymphocyte distribution was explored in patients within the BrUOG 211B trial who had corresponding biopsy hematoxylin-eosin-stained slide images. Data analysis was conducted from January 15, 2017, to February 14, 2019.

MAIN OUTCOMES AND MEASURES

Evaluation of imaging signatures by the area under the receiver operating characteristic curve (AUC) in identifying HER2+ molecular subtypes and distinguishing pathologic complete response (ypT0/is) to NAC with HER2-targeting.

RESULTS

In the 209 patients included (mean [SD] age, 51.1 [11.7] years), features from the peritumoral regions better discriminated HER2-E tumors (maximum AUC, 0.85; 95% CI, 0.79-0.90; 9-12 mm from the tumor) compared with intratumoral features (AUC, 0.76; 95% CI, 0.69-0.84). A classifier combining peritumoral and intratumoral features identified the HER2-E subtype (AUC, 0.89; 95% CI, 0.84-0.93) and was significantly associated with response to HER2-targeted therapy in both validation cohorts (AUC, 0.80; 95% CI, 0.61-0.98 and AUC, 0.69; 95% CI, 0.53-0.84). Features from the 0- to 3-mm peritumoral region were significantly associated with the density of tumor-infiltrating lymphocytes (R2 = 0.57; 95% CI, 0.39-0.75; P = .002).

CONCLUSIONS AND RELEVANCE

A combination of peritumoral and intratumoral characteristics appears to identify intrinsic molecular subtypes of HER2+ breast cancers from imaging, offering insights into immune response within the peritumoral environment and suggesting potential benefit for treatment guidance.

Role of MR Imaging in Neoadjuvant Therapy Monitoring

Neoadjuvant chemotherapy (NAC) has become an important treatment approach for stage II/III breast cancers to downsize tumor and enable breast-conserving surgery for patients that may otherwise undergo mastectomy. MR imaging has the potential to identify early response or disease progression, enabling potential modification to NAC regimens. Detection of size and morphologic changes is better appreciated with MR imaging than other modalities and is different between molecular subtypes of breast cancer. The combination of DCE-MR imaging and DWI provides the highest sensitivity and specificity. Other new modalities such as FDG PET/MR imaging and molecular breast imaging are still undergoing research.

Computer-aided evaluation of the correlation between MRI morphology and immunohistochemical biomarkers or molecular subtypes in breast cancer

Studies using tumor circularity (TC), a quantitative MRI morphologic index, to evaluate breast cancer are scarce. The purpose of this study is to evaluate the correlation between TC and immunohistochemical biomarkers or molecular subtypes in breast cancer. 146 patients with 150 breast cancers were selected. All tumors were confirmed by histopathology and examined by 3.0T MRI. TC was calculated by computer-aided software. The associations between TC and patient age, tumor size, histological grade, molecular subtypes, and immunohistochemical biomarkers including estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor 2 (HER2), and Ki67 were analyzed. TC correlated inversely with tumor size (r = -0.224, P < 0.001), ER (r = -0.490, P < 0.001) and PR (r = -0.484, P < 0.001). However, TC correlated positively with Ki67 (r = 0.332, P < 0.001) and histological grade (r = 0.309, P < 0.001). In multiple linear regression analysis, tumor size, ER, PR and Ki67 were independent influential factors of TC. Compared with HER2-overexpressed (61.6%), luminal A (54.7%) and luminal B (52.3%) subtypes, triple-negative breast cancer (TNBC) showed the highest score of TC (70.8%, P < 0.001). Our study suggests that TC can be used as an imaging biomarker to predict the aggressiveness of newly diagnosed breast cancers. TNBC seems to present as an orbicular appearance when comparing with other subtypes.

Features from Computerized Texture Analysis of Breast Cancers at Pretreatment MR Imaging Are Associated with Response to Neoadjuvant Chemotherapy

Purpose To evaluate whether features from texture analysis of breast cancers were associated with pathologic complete response (pCR) after neoadjuvant chemotherapy and to explore the association between texture features and tumor subtypes at pretreatment magnetic resonance (MR) imaging. Materials and Methods Institutional review board approval was obtained. This retrospective study included 85 patients with 85 breast cancers who underwent breast MR imaging before neoadjuvant chemotherapy between April 10, 2008, and March 12, 2015. Two-dimensional texture analysis was performed by using software at T2-weighted MR imaging and contrast material-enhanced T1-weighted MR imaging. Quantitative parameters were compared between patients with pCR and those with non-pCR and between patients with triple-negative breast cancer and those with non-triple-negative cancer. Multiple logistic regression analysis was used to determine independent parameters. Results Eighteen tumors (22%) were triple-negative breast cancers. pCR was achieved in 30 of the 85 tumors (35%). At univariate analysis, mean pixel intensity with spatial scaling factor (SSF) of 2 and 4 on T2-weighted images and kurtosis on contrast-enhanced T1-weighted images showed a significant difference between triple-negative breast cancer and non-triple-negative breast cancer (P = .009, .003, and .001, respectively). Kurtosis (SSF, 2) on T2-weighted images showed a significant difference between pCR and non-pCR (P = .015). At multiple logistic regression, kurtosis on T2-weighted images was independently associated with pCR in non-triple-negative breast cancer (P = .033). A multivariate model incorporating T2-weighted and contrast-enhanced T1-weighted kurtosis showed good performance for the identification of triple-negative breast cancer (area under the receiver operating characteristic curve, 0.834). Conclusion At pretreatment MR imaging, kurtosis appears to be associated with pCR to neoadjuvant chemotherapy in non-triple-negative breast cancer and may be a promising biomarker for the identification of triple-negative breast cancer. ^© RSNA, 2017.

MR and mammographic imaging features of HER2-positive breast cancers according to hormone receptor status: a retrospective comparative study

Background Human epidermal growth factor receptor 2-positive (HER2+) breast cancer has two distinct subtypes according to hormone receptor (HR) status. Survival, pattern of recurrence, and treatment response differ between HR-/HER2+ and HR+/HER2+ cancers. Purpose To investigate imaging and clinicopathologic features of HER2+ cancers and their correlation with HR expression. Material and Methods Between 2011 and 2013, 252 consecutive patients with 252 surgically confirmed HER2+ cancers (125 HR- and 127 HR+) were included. Two experienced breast radiologists blinded to the clinicopathologic findings reviewed the mammograms and magnetic resonance (MR) images using the BI-RADS lexicon. Tumor kinetic features were acquired by computer-aided detection (CAD). The imaging and clinicopathologic features of 125 HR-/HER2+ cancers were compared with those of 127 HR+/HER2+ cancers. Association between the HR status and each feature was assessed. Results Multiple logistic regression analysis showed that circumscribed mass margin (odds ratio [OR], 4.73; P < 0.001), associated non-mass enhancement (NME) on MR images (OR, 3.29; P = 0.001), high histologic grade (OR, 3.89; P = 0.002), high Ki-67 index (OR, 3.06; P = 0.003), and older age (OR, 2.43; P = 0.006) remained independent indicators associated with HR-/HER2+ cancers. Between the two HER2+ subtypes, there were no differences in mammographic imaging presentations and calcification features and MR kinetic features by a CAD. Conclusion HER2+ breast cancers have different MR imaging (MRI) phenotypes and clinicopathologic feature according to HR status. MRI features related to HR and HER2 status have the potential to be used for the diagnosis and treatment decisions in HER2+ breast cancer patients.

Intratumoral and peritumoral radiomics for the pretreatment prediction of pathological complete response to neoadjuvant chemotherapy based on breast DCE-MRI

Background

In this study, we evaluated the ability of radiomic textural analysis of intratumoral and peritumoral regions on pretreatment breast cancer dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) to predict pathological complete response (pCR) to neoadjuvant chemotherapy (NAC).

Methods

A total of 117 patients who had received NAC were retrospectively analyzed. Within the intratumoral and peritumoral regions of T1-weighted contrast-enhanced MRI scans, a total of 99 radiomic textural features were computed at multiple phases. Feature selection was used to identify a set of top pCR-associated features from within a training set (n = 78), which were then used to train multiple machine learning classifiers to predict the likelihood of pCR for a given patient. Classifiers were then independently tested on 39 patients. Experiments were repeated separately among hormone receptor-positive and human epidermal growth factor receptor 2-negative (HR⁺, HER2⁻) and triple-negative or HER2⁺ (TN/HER2⁺) tumors via threefold cross-validation to determine whether receptor status-specific analysis could improve classification performance.

Results

Among all patients, a combined intratumoral and peritumoral radiomic feature set yielded a maximum AUC of 0.78 ± 0.030 within the training set and 0.74 within the independent testing set using a diagonal linear discriminant analysis (DLDA) classifier. Receptor status-specific feature discovery and classification enabled improved prediction of pCR, yielding maximum AUCs of 0.83 ± 0.025 within the HR⁺, HER2⁻ group using DLDA and 0.93 ± 0.018 within the TN/HER2⁺ group using a naive Bayes classifier. In HR⁺, HER2⁻ breast cancers, non-pCR was characterized by elevated peritumoral heterogeneity during initial contrast enhancement. However, TN/HER2⁺ tumors were best characterized by a speckled enhancement pattern within the peritumoral region of nonresponders. Radiomic features were found to strongly predict pCR independent of choice of classifier, suggesting their robustness as response predictors.

Conclusions

Through a combined intratumoral and peritumoral radiomics approach, we could successfully predict pCR to NAC from pretreatment breast DCE-MRI, both with and without a priori knowledge of receptor status. Further, our findings suggest that the radiomic features most predictive of response vary across different receptor subtypes.

Electronic supplementary material

The online version of this article (doi:10.1186/s13058-017-0846-1) contains supplementary material, which is available to authorized users.

Reliability of MRI in measuring the response to neoadjuvant chemotherapy in breast cancer patients and its therapeutic implications

Aim: Neoadjuvant chemotherapy (NAC) has recently been applied in treatment of operable breast cancers to enable breast conservation. We aimed to evaluate the accuracy of MRI in delineating residual tumor and pathological complete response (pCR). Patients & methods: 69 cases treated with NAC were monitored using breast MRI, findings were recorded and compared with histopathology. Results: MRI showed radiological complete response in 19 (27.5%), which correlated with pCR in 12 (63%) cases. However, five (7.3%) patients who achieved pCR were missed. Overall, the sensitivity was 70.6%, specificity 86.5%, positive predictive value 63.2% and negative predictive value of 90.0%. Conclusion: MRI showed promising results for evaluating response to NAC and predicting pCR, results need validation in larger trial.