Invasive breast cancer: correlation of dynamic MR features with prognostic factors

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.

Breast masses with rim enhancement on contrast-enhanced mammography: morphological and enhancement features for diagnosis and differentiation of benign and malignant

OBJECTIVES

To investigate the imaging characteristics and clinicopathological features of rim enhancement of breast masses demonstrated on contrast-enhanced mammography (CEM).

METHODS

67 cases of breast lesions confirmed by pathology and showing rim enhancement on CEM examinations were analyzed. The lesions were divided into benign and malignant groups, and the morphological and enhanced features were described. Sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated separately for each morphology descriptor to evaluate the diagnostic ability of each indicator.

RESULTS

There were 35 (52.2%) malignant and 32 (47.8%) benign lesions. There are significant differences in the morphological and enhanced features between benign and malignant lesions. 29/35 (82.9%) malignant lesions exhibited irregular shapes, and 31/35 (88.6%) showed indistinct margins. 28/35 (80%) malignant lesions displayed strong enhancement on CEM, while 12/32 (37.5%) benign lesions exhibited weak enhancement (P = 0.001). Malignant lesions showed a higher incidence of unsmooth inner walls than benign lesions (28/35 vs 7/32; P <.001). Lesion margins showed high sensitivity of 88.57% and NPV of 81.8%. The presence of suspicious calcifications had the highest specificity of 100% and PPV of 100%. The diagnostic sensitivity, specificity, PPV, and NPV of the combined parameters were 97.14%, 93.15%, 94.44%, and 96.77%, respectively.

CONCLUSIONS

The assessment of morphological and enhanced features of breast lesions exhibiting rim enhancement on CEM can improve the differentiation between benign and malignant breast lesions.

ADVANCES IN KNOWLEDGE

This article provides a reference for the differential diagnosis of ring enhanced lesions on CEM.

The Correlation between Morpho-Dynamic Contrast-Enhanced Mammography (CEM) Features and Prognostic Factors in Breast Cancer: A Single-Center Retrospective Analysis

Breast cancer, a major contributor to female mortality globally, presents challenges in detection, prompting exploration beyond digital mammography. Contrast-Enhanced Mammography (CEM), integrating morphological and functional information, emerges as a promising alternative, offering advantages in cost-effectiveness and reduced anxiety compared to MRI. This study investigates CEM's correlation with breast cancer prognostic factors, encompassing histology, grade, and molecular markers. In a retrospective analysis involving 114 women, CEM revealed diverse lesion characteristics. Statistical analyses identified correlations between specific CEM features, such as spiculated margins and irregular shape, and prognostic factors like tumor grade and molecular markers. Notably, spiculated margins predicted lower grade and HER2 status, while irregular shape correlated with PgR and Ki-67 status. The study emphasizes CEM's potential in predicting breast cancer prognosis, shedding light on tumor behavior. Despite the limitations, including sample size and single-observer analysis, the findings advocate for CEM's role in stratifying breast cancers based on biological characteristics. CEM features, particularly spiculated margins, irregular shape, and enhancement dynamics, may serve as valuable indicators for personalized treatment decisions. Further research is crucial to validate these correlations and enhance CEM's clinical utility in breast cancer assessment.

Prediction of the MammaPrint Risk Group Using MRI Features in Women With Estrogen Receptor-Positive, HER2-Negative, and 1 to 3 Node-Positive Invasive Breast Cancer

BACKGROUND

MammaPrint assigns chemotherapeutic benefits to patients with estrogen receptor (ER)-positive, human epidermal growth factor receptor 2 (HER2)-negative, and 1 to 3 node-positive invasive breast cancer. However, its cost and time-consuming nature limit its use in certain clinical settings. We aimed to develop and validate the prediction models for the low MammaPrint risk group using clinicopathologic and MRI features.

PATIENTS AND METHODS

Overall, 352 women with ER-positive, HER2-negative, and 1 to 3 node-positive invasive breast cancer were retrospectively reviewed and assigned to development (n = 235) and validation sets (n = 117). Univariate and multivariate analyses identified features associated with the low MammaPrint risk group. The area under the receiver operating characteristic curves (AUROCs) of models based on clinicopathologic, MRI, and combined features were evaluated.

RESULTS

Development set multivariate analysis showed that clinicopathologic features including low histologic grade (odds ratio [OR], 5.29; P = .02), progesterone receptor-positivity (OR, 3.23; P = .01), and low Ki-67 (OR, 6.05; P < .001) and MRI features, including peritumoral edema absence (OR, 2.24; P = .04) and a high proportion of persistent components (OR, 1.15; P = .004) were significantly associated with the low MammaPrint risk group. The AUROCs of models based on clinicopathologic, MRI, and combined features were 0.77, 0.64, and 0.80 in the development and 0.66, 0.60, and 0.70 in the validation sets, respectively.

CONCLUSION

The combined model incorporating clinicopathologic and MRI features showed potential in predicting the low MammaPrint risk group, and may support decision-making in clinical settings with limited access to MammaPrint.

A Review of AI-Based Radiomics and Computational Pathology Approaches in Triple-Negative Breast Cancer: Current Applications and Perspectives

Breast cancer is one of the most common and deadly cancers worldwide. Approximately, 20% of all breast cancers are characterized as triple negative (TNBC). TNBC typically is associated with a poorer prognosis relative to other breast cancer subtypes. Due to its aggressiveness and lack of response to hormonal therapy, conventional cytotoxic chemotherapy is the usual treatment; however, this treatment is not always effective, and an important percentage of patients develop recurrence. More recently, immunotherapy has started to be used on some populations with TNBC showing promising results. Unfortunately, immunotherapy is only applicable to a minority of patients and responses in metastatic TNBC have overall been modest in comparison to other cancer types. This situation evidences the need for developing effective biomarkers that help to stratify and personalize patient management. Thanks to recent advances in artificial intelligence (AI), there has been an increasing interest in its use for medical applications aiming at supporting clinical decision making. Several works have used AI in combination with diagnostic medical imaging, more specifically radiology and digitized histopathological tissue samples, aiming to extract disease-specific information that is difficult to quantify by the human eye. These works have demonstrated that analysis of such images in the context of TNBC has great potential for (1) risk-stratifying patients to identify those patients who are more likely to experience disease recurrence or die from the disease and (2) predicting pathologic complete response. In this manuscript, we present an overview on AI and its integration with radiology and histopathological images for developing prognostic and predictive approaches for TNBC. We present state of the art approaches in the literature and discuss the opportunities and challenges with developing AI algorithms regarding further development and clinical deployment, including identifying those patients who may benefit from certain treatments (e.g., adjuvant chemotherapy) from those who may not and thereby should be directed toward other therapies, discovering potential differences between populations, and identifying disease subtypes.

Application of Machine Learning Algorithm in Predicting Axillary Lymph Node Metastasis from Breast Cancer on Preoperative Chest CT

Axillary lymph node (ALN) status is one of the most critical prognostic factors in patients with breast cancer. However, ALN evaluation with contrast-enhanced CT (CECT) has been challenging. Machine learning (ML) is known to show excellent performance in image recognition tasks. The purpose of our study was to evaluate the performance of the ML algorithm for predicting ALN metastasis by combining preoperative CECT features of both ALN and primary tumor. This was a retrospective single-institutional study of a total of 266 patients with breast cancer who underwent preoperative chest CECT. Random forest (RF), extreme gradient boosting (XGBoost), and neural network (NN) algorithms were used. Statistical analysis and recursive feature elimination (RFE) were adopted as feature selection for ML. The best ML-based ALN prediction model for breast cancer was NN with RFE, which achieved an AUROC of 0.76 ± 0.11 and an accuracy of 0.74 ± 0.12. By comparing NN with RFE model performance with and without ALN features from CECT, NN with RFE model with ALN features showed better performance at all performance evaluations, which indicated the effect of ALN features. Through our study, we were able to demonstrate that the ML algorithm could effectively predict the final diagnosis of ALN metastases from CECT images of the primary tumor and ALN. This suggests that ML has the potential to differentiate between benign and malignant ALNs.

Staging Breast Cancer with MRI, the T. A Key Role in the Neoadjuvant Setting

Breast cancer (BC) is the most common cancer among women worldwide. Neoadjuvant chemotherapy (NACT) indications have expanded from inoperable locally advanced to early-stage breast cancer. Achieving a pathological complete response (pCR) has been proven to be an excellent prognostic marker leading to better disease-free survival (DFS) and overall survival (OS). Although diagnostic accuracy of MRI has been shown repeatedly to be superior to conventional methods in assessing the extent of breast disease there are still controversies regarding the indication of MRI in this setting. We intended to review the complex literature concerning the tumor size in staging, response and surgical planning in patients with early breast cancer receiving NACT, in order to clarify the role of MRI. Morphological and functional MRI techniques are making headway in the assessment of the tumor size in the staging, residual tumor assessment and prediction of response. Radiomics and radiogenomics MRI applications in the setting of the prediction of response to NACT in breast cancer are continuously increasing. Tailored therapy strategies allow considerations of treatment de-escalation in excellent responders and avoiding or at least postponing breast surgery in selected patients.

Machine learning for multi-parametric breast MRI: radiomics-based approaches for lesion classification

In the artificial intelligence era, machine learning (ML) techniques have gained more and more importance in the advanced analysis of medical images in several fields of modern medicine. Radiomics extracts a huge number of medical imaging features revealing key components of tumor phenotype that can be linked to genomic pathways. The multi-dimensional nature of radiomics requires highly accurate and reliable machine-learning methods to create predictive models for classification or therapy response assessment.

Multi-parametric breast magnetic resonance imaging (MRI) is routinely used for dense breast imaging as well for screening in high-risk patients and has shown its potential to improve clinical diagnosis of breast cancer. For this reason, the application of ML techniques to breast MRI, in particular to multi-parametric imaging, is rapidly expanding and enhancing both diagnostic and prognostic power. In this review we will focus on the recent literature related to the use of ML in multi-parametric breast MRI for tumor classification and differentiation of molecular subtypes. Indeed, at present, different models and approaches have been employed for this task, requiring a detailed description of the advantages and drawbacks of each technique and a general overview of their performances.

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.

Radioproteomics in Breast Cancer: Prediction of Ki-67 Expression With MRI-based Radiomic Models

RATIONALE AND OBJECTIVES

We aimed to investigate the value of magnetic resonance image (MRI)-based radiomics in predicting Ki-67 expression of breast cancer.

METHODS

In this retrospective study, 159 lesions from 154 patients were included. Radiomic features were extracted from contrast-enhanced T1-weighted MRI (C+MRI) and apparent diffusion coefficient (ADC) maps, with open-source software. Dimension reduction was done with reliability analysis, collinearity analysis, and feature selection. Two different Ki-67 expression cut-off values (14% vs 20%) were studied as reference standard for the classifications. Input for the models were radiomic features from individual MRI sequences or their combination. Classifications were performed using a generalized linear model.

RESULTS

Considering Ki-67 cut-off value of 14%, training and testing AUC values were 0.785 (standard deviation [SD], 0.193) and 0.849 for ADC; 0.696 (SD, 0.150) and 0.695 for C+MRI; 0.755 (SD, 0.171) and 0.635 for the combination of both sequences, respectively. Regarding Ki-67 cut-off value of 20%, training and testing AUC values were 0.744 (SD, 0.197) and 0.617 for ADC; 0.629 (SD, 0.251) and 0.741 for C+MRI; 0.761 (SD, 0.207) and 0.618 for the combination of both sequences, respectively.

CONCLUSION

ADC map-based selected radiomic features coupled with generalized linear modeling might be a promising non-invasive method to determine the Ki-67 expression level of breast cancer.

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.

Preoperative Rim Enhancement on Magnetic Resonance Imaging Indicates Larger Tumor Size and Poor Prognosis in Chinese Basal-Like Breast Cancer Patients

Background: This study was to investigate the prevalence of preoperative rim enhancement, and its association with clinicopathological features, relapse, and survival profiles in Chinese basal-like breast cancer (BC) patients. Materials and Methods: The preoperative breast magnetic resonance imaging images of 145 basal-like BC patients who underwent surgical excision were obtained to determine rim enhancement. Besides, based on disease status and survival status during follow-up, the 1-year relapse rate/mortality, 3-year relapse rate/mortality, 5-year relapse rate/mortality were calculated; disease-free survival (DFS) and overall survival (OS) were determined. Results: There were 51 (35.2%) patients with rim enhancement and 94 (64.8%) patients without rim enhancement. Furthermore, rim enhancement was associated with larger tumor size and advanced T stage, whereas it did not associate with age, pathological differentiation, N stage, or TNM stage. In addition, rim enhancement was associated with higher 1-, 3-, and 5-year relapse rate and shorter DFS; meanwhile, rim enhancement was associated with increased 1-, 3-, and 5-year mortality rate and decreased OS. By multivariate Cox's regression analyses, rim enhancement, pathological differentiation, and N stage independently predicted reduced DFS; T stage independently predicted declined OS. Conclusion: Preoperative rim enhancement on MRI might be a possible noninvasive indicator for guiding personalized treatment strategies and improving prognosis in Chinese basal-like BC patients.

Ultrafast Dynamic Contrast-Enhanced MRI Using Compressed Sensing: Associations of Early Kinetic Parameters With Prognostic Factors of Breast Cancer

OBJECTIVE. The purpose of this study was to investigate whether early kinetic parameters derived from ultrafast dynamic contrast-enhanced MRI (DCE-MRI) using compressed sensing are associated with prognostic factors for breast cancer. MATERIALS AND METHODS. We evaluated 201 consecutive women (mean age, 54.6 years) with breast cancer (168 invasive, 33 ductal carcinoma in situ) who underwent both ultrafast DCE-MRI using compressed sensing (temporal resolution, 4.7 seconds; spatial resolution, 0.8 × 1.1 × 0.9 mm) and surgery between 2018 and 2019. Early kinetic parameters (time to enhancement [TTE] and maximum slope [MS]) were measured in breast lesions by two radiologists using a software program and were correlated with histopathologic prognostic factors. The Mann-Whitney U test and linear regression analysis were used. RESULTS. The median TTE and MS values for breast cancer were 11.9 seconds and 7.7%/s, respectively. The median MS was significantly larger in invasive cancer lesions than in ductal carcinoma in situ lesions (8.4%/s vs 4.7%/s, p < .001). In women with invasive cancer, multivariate linear regression analyses showed that a larger tumor size (> 2 cm) (p = .048) and estrogen receptor-negative status (p < .001) were significantly associated with a shorter TTE. A higher histologic grade (grade 3) (p = .01) was significantly associated with a larger MS. We observed excellent interobserver agreement between two readers in the measurements of TTE and MS (intraclass correlation coefficients, 0.943 and 0.890, respectively). CONCLUSION. Ultrafast MRI-derived early enhancement parameters, such as TTE and MS, are associated with histopathologic prognostic factors in women with breast cancer.

Dynamic contrast enhanced-MRI and diffusion-weighted image as predictors of lymphovascular invasion in node-negative invasive breast cancer

BACKGROUND

Lymphovascular invasion (LVI) is an important risk factor for prognosis of breast cancer and an unfavorable prognostic factor in node-negative invasive breast cancer patients. The purpose of this study was to evaluate the association between LVI and pre-operative features of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and diffusion-weighted imaging (DWI) in node-negative invasive breast cancer.

METHODS

Data were collected retrospectively from 132 cases who had undergone pre-operative MRI and had invasive breast carcinoma confirmed on the last surgical pathology report. MRI and DWI data were analyzed for the size of tumor, mass shape, margin, internal enhancement pattern, kinetic enhancement curve, high intratumoral T2-weighted signal intensity, peritumoral edema, DWI rim sign, and apparent diffusion coefficient (ADC) values. We calculated the relationship between presence of LVI and various prognostic factors and MRI features.

RESULTS

Pathologic tumor size, mass margin, internal enhancement pattern, kinetic enhancement curve, DWI rim sign, and the difference between maximum and minimum ADC were significantly correlated with LVI (p < 0.05).

CONCLUSIONS

We suggest that DCE-MRI with DWI would assist in predicting LVI status in node-negative invasive breast cancer patients.

Association between Oncotype DX recurrence score and dynamic contrast-enhanced MRI features in patients with estrogen receptor-positive HER2-negative invasive breast cancer

BACKGROUND

Oncotype DX is a multigene assay used in breast cancer, and the result provided as a 'recurrence score (RS)' corresponds to the risk of a cancer recurrence and the chemotherapeutic benefit in estrogen receptor (ER)-positive human epidermal growth factor receptor (HER)2-negative invasive breast cancer. However, its accessibility is limited.

PURPOSE

To evaluate whether magnetic resonance imaging (MRI) could be used to predict Oncotype DX RS in patients with ER-positive HER2-negative invasive breast cancer.

MATERIAL AND METHODS

We enrolled 473 patients with ER-positive HER2-negative invasive breast cancer who underwent a preoperative MRI and Oncotype DX assay between January 2015 and December 2018. The MRI was reviewed and associations between Oncotype DX RS values were evaluated. Logistic regression analysis was used to identify independent predictors of high and low RS.

RESULTS

Of the 485 cancers, 288 (59.4%) had low (<18), 155 (31.9%) had intermediate (18-30), and 42 (8.7%) had high (≥31) RS. Multiple logistic regression analysis revealed that a round shape (odds ratio [OR] = 2.554, P = 0.089) and low proportion of washout component (OR = 1.011, P = 0.014) were associated with low RS and that heterogeneously dense (OR = 3.205, P = 0.007) or scattered fibroglandular (OR = 3.776, P = 0.005) breast tissue, a non-spiculated margin (OR = 5.435, P = 0.007), and low proportion of persistent component (OR = 1.012, P = 0.036) were associated with high RS.

CONCLUSION

MRI features showed the potential for the discrimination of Oncotype DX RS in patients with ER-positive HER2-negative invasive breast cancer.

Histopathologic Correlates of Nonmass Enhancement Detected by Breast Magnetic Resonance Imaging

CONTEXT.—

Dynamic, contrast-enhanced magnetic resonance imaging (MRI) is a highly sensitive imaging modality used for screening and diagnostic purposes. Nonmass enhancement (NME) is commonly seen on MRI of the breast. However, the pathologic correlates of NME have not been extensively explored. Consequently, concordance between MRI and pathologic findings in such cases may be uncertain and this uncertainty may cause the need for additional procedures.

OBJECTIVE.—

To examine the histologic alterations that correspond to NME on MRI.

DESIGN.—

We performed a retrospective search for women who underwent breast MRI between March 2014 and December 2016 and identified 130 NME lesions resulting in biopsy. The MRI findings and pathology slides for all cases were reviewed. The follow-up findings on any subsequent excisions were also noted.

RESULTS.—

Among the 130 cases, the core needle biopsy showed 1 or more benign lesions without atypia in 80 cases (62%), atypical lesions in 21 (16%), ductal carcinoma in situ in 22 (17%), and invasive carcinoma in 7 (5%). Review of the imaging features demonstrated some statistically significant differences in lesions that corresponded to malignant lesions as compared with benign alterations, including homogeneous or clumped internal enhancement, type 3 kinetics, and T2 dark signal; however, there was considerable overlap of features between benign and malignant lesions overall. Of 130 cases, 54 (41.5%) underwent subsequent excision with only 6 cases showing a worse lesion on excision.

CONCLUSIONS.—

This study illustrates that NME can be associated with benign, atypical, and/or malignant pathology and biopsy remains indicated given the overlap of radiologic features.

Accurate Estimation of Breast Tumor Size: A Comparison Between Ultrasonography, Mammography, Magnetic Resonance Imaging, and Associated Contributing Factors

Objective

This study aimed to provide further evidence on the accuracy of tumor size estimates and influencing factors.

Materials and Methods

In this cross-sectional study, patients with a biopsy-proven diagnosis of breast cancer referred to our hospital to obtain a preoperative magnetic resonance imaging (MRI) between 2015 and 2016 were included. Data from 76 breast cancer patients with 84 lesions were collected. All participants underwent ultrasonography and MRI, and their mammograms (MGMs) were reevaluated for tumor size estimation. Measurements by the three imaging modalities were compared with the pathologically determined tumor size to assess their accuracy. Influencing factors such as surgical management, molecular and histopathological subtypes, and Breast Imaging Reporting and Data System enhancement types in MRI were also assessed.

Results

The rates of concordance with the gold standard were 64.3%, 76.2%, and 82.1% for MGM, ultrasound (US), and MRI measurements, respectively. Therefore, the highest concordance rate was observed in MRI-based estimates. Among the discordant cases, US and MGM underestimation were more prevalent (70%); nevertheless, MRI showed significant overestimation (80%). Tumor size estimates in patients whose MRIs presented with either non-mass enhancement [p=0.030; odds ratio (OR)=17.2; 95% confidence interval (CI): 1.3-225.9] or mass lesion with non-mass enhancement (p=0.001; OR=51.0; 95% CI: 5.0-518.4) were more likely to be discordant with pathological measurements compared with those in cases with only mass lesion on their MRIs.

Conclusion

MRI was more accurate than either US or MGM in estimating breast tumor size but had the highest overestimation rate. Therefore, caution should be practiced in interpreting data obtained from subjects whose MRIs present with non-mass enhancement or mass lesion with non-mass enhancement.

Correlation of prognostic factors with MRI findings in malignant breast lesions

Background

Magnetic resonance imaging (MRI) of the breast represents the most sensitive imaging modality in the detection of breast cancer, with a reported sensitivity between 94 and 100%. We aim to detect the correlation between MRI findings and pathologically detected prognostic factors in malignant breast lesions.

Breast parenchymal density distribution, background parenchymal enhancement pattern, lesion’s morphologic features, T2WI signal characteristics, contrast enhancement, time/signal intensity curves, and lesions localizations in breast were evaluated using dynamic MRI images. Histopathological diagnosis, maximum measurements of the lesion, histological grade, presence of estrogen and/or progesterone receptors, c-erb B2, and Ki-67 parameters were noted as prognostic factors.

Results

We cannot detect any relationship between the breast parenchymal density and prognostic factors. Mild background breast enhancement is related with ER presence, a good prognostic factor. Histopathological grade of the lesions augmented with the increase in the lesion diameters. ADC values are not related with prognostic factors.

Conclusion

A mild background enhancement, an intermediate signal intensity on T2WI, a high tpeak value, and absence of pathological axillary lymph node are found to be related with good prognostic factors. An irregular contour, a huge diameter, having a type III kinetic curve, a high slopei value, and presence of pathological axillary lymph node are found to be related with poor prognostic factors. MRI can be used to predict prognostic factors in breast cancer cases.

CT-based radiomics model to distinguish necrotic hepatocellular carcinoma from pyogenic liver abscess

AIM

To investigate the feasibility of a computed tomography (CT)-based radiomics model in distinguishing necrotic hepatocellular carcinoma (nHCC) from pyogenic liver abscess (PLA).

MATERIAL AND METHODS

One hundred-four enrolled patients with nHCC (n=56) and PLA (n=48) were divided randomly into a training cohort (n=62) and validation cohort (n=42). ROI (region of interest) of the wall (ROI-wall) and ROI of the necrotic cavity (ROI-necrotic cavity) of the lesion were delineated from each arterial phase (AP) and portal venous phase (PP) image. The least absolute shrinkage and the selection operator logistic regression method was used to select radiomics features, and radiomics scores (R-scores) were calculated. Four radiomics models, including R-score (ROI-wall) in the AP, R-score (ROI-necrotic cavity) in the AP, R-score (ROI-wall) in the PP and R-score (ROI-necrotic cavity) in the PP, were constructed and evaluated by area under the curve (AUC) of receiver operating characteristic curve.

RESULTS

The AUCs of R-score (ROI-wall) in the AP, R-score (ROI-necrotic cavity) in the AP, R-score (ROI-wall) in the PP, and R-score (ROI-necrotic cavity) in the PP were 0.935 and 0.917, 0.906 and 0.824, 0.985 and 0.928, 0.899 and 0.850, in the training and validation cohorts, respectively. In the training cohort, the AUC of R-score (ROI-wall) in the PP was higher than that of R-score (ROI-wall) in the AP (p=0.024) or R-score (ROI-necrotic cavity) in the AP (p=0.046) or R-score (ROI-necrotic cavity) in the PP (p=0.044).

CONCLUSION

CT-based radiomics models can be used to distinguish nHCC from PLA.

Heterogeneity of enhancement kinetics in dynamic contrast-enhanced MRI and implication of distant metastasis in invasive breast cancer

AIM

To investigate the heterogeneity of enhancement kinetics for breast tumour in order to demonstrate the predictive power of dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) features for distant metastasis (DM) in invasive breast cancer.

MATERIALS AND METHODS

Time-signal intensity curve (TIC) patterns from 128 patients with invasive breast cancer were analysed by a pixel-based DCE-MRI analysis. This MRI technique enabled pixels with varying TIC patterns (persistent, plateau, washout and non-enhancement) to be categorised semi-automatically and the percentage of different TIC patterns in each breast tumour to be calculated. The percentage of TIC patterns was compared between the DM and non-DM groups. DM-free survival was estimated using Kaplan-Meier survival analysis.

RESULTS

This study demonstrated a larger percentage of persistent TIC and non-enhancement TIC was associated with DM in invasive breast cancer. The cut-off values of persistent TIC and non-enhancement TIC were 22.5% and 2.5%. Combining TIC patterns and traditional predictors (tumour size and axillary lymph node status) can improve the prediction efficiency. The multivariable model yielded an area under the receiver operating characteristic curve (AUC) of 0.87 with 0.70 sensitivity and 0.87 specificity in leave-one-out cross-validation (LOOCV). These predictors showed significant differences in DM-free survival by Kaplan-Meier analysis.

CONCLUSION

This study shows that breast tumours with higher heterogeneity are more likely to metastasise, and pixel-based TIC analysis has utility in predicting distant metastasis of invasive breast cancer.

Time-to-enhancement at ultrafast breast DCE-MRI: potential imaging biomarker of tumour aggressiveness

OBJECTIVES

This study was conducted in order to investigate whether there is a correlation between the time-to-enhancement (TTE) in ultrafast MRI and histopathological characteristics of breast cancers.

METHODS

Between January and August 2017, 274 consecutive breast cancer patients (mean age, 53.5 years; range, 25-80 years) who underwent ultrafast MRI and subsequent surgery were included for analysis. Ultrafast MRI scans were acquired using TWIST-VIBE or 4D TRAK-3D TFE sequences. TTE and maximum slope (MS) were derived from the ultrafast MRI. The repeated measures ANOVA, Mann-Whitney U test and Kruskal-Wallis H test were performed to compare the median TTE, MS and SER according to histologic type, histologic grade, ER/PR/HER2 positivity, level of Ki-67 and tumour subtype. For TTE calculation, intraclass correlation coefficient (ICC) was used to evaluate interobserver variability.

RESULTS

The median TTE of invasive cancers was shorter than that of in situ cancers (p < 0.001). In invasive cancers, large tumours showed shorter TTE than small tumours (p = 0.001). High histologic/nuclear grade cancers had shorter TTE than low to intermediate grade cancers (p < 0.001 and p < 0.001). HER2-positive cancers showed shorter TTE than HER2-negative cancers (p = 0.001). The median TTE of cancers with high Ki-67 was shorter than that of cancers with low Ki-67 (p < 0.001). ICC between two readers showed moderate agreement (0.516). No difference was found in the median MS or SER values according to the clinicopathologic features.

CONCLUSIONS

The median TTE of breast cancer in ultrafast MRI was shorter in invasive or aggressive tumours than in in situ cancer or less aggressive tumours, respectively.

KEY POINTS

• Invasive breast tumours show a shorter TTE in ultrafast DCE-MRI than in situ cancers. • A shorter TTE in ultrafast DCE-MRI is associated with breast tumours of a large size, high histologic or nuclear grade, PR negativity, HER2 positivity and high Ki-67 level.

Imaging Phenotypes of Breast Cancer Heterogeneity in Preoperative Breast Dynamic Contrast Enhanced Magnetic Resonance Imaging (DCE-MRI) Scans Predict 10-Year Recurrence

PURPOSE

Identifying imaging phenotypes and understanding their relationship with prognostic markers and patient outcomes can allow for a noninvasive assessment of cancer. The purpose of this study was to identify and validate intrinsic imaging phenotypes of breast cancer heterogeneity in preoperative breast dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) scans and evaluate their prognostic performance in predicting 10 years recurrence.

EXPERIMENTAL DESIGN

Pretreatment DCE-MRI scans of 95 women with primary invasive breast cancer with at least 10 years of follow-up from a clinical trial at our institution (2002-2006) were retrospectively analyzed. For each woman, a signal enhancement ratio (SER) map was generated for the entire segmented primary lesion volume from which 60 radiomic features of texture and morphology were extracted. Intrinsic phenotypes of tumor heterogeneity were identified via unsupervised hierarchical clustering of the extracted features. An independent sample of 163 women diagnosed with primary invasive breast cancer (2002-2006), publicly available via The Cancer Imaging Archive, was used to validate phenotype reproducibility.

RESULTS

Three significant phenotypes of low, medium, and high heterogeneity were identified in the discovery cohort and reproduced in the validation cohort (P < 0.01). Kaplan-Meier curves showed statistically significant differences (P < 0.05) in recurrence-free survival (RFS) across phenotypes. Radiomic phenotypes demonstrated added prognostic value (c = 0.73) predicting RFS.

CONCLUSIONS

Intrinsic imaging phenotypes of breast cancer tumor heterogeneity at primary diagnosis can predict 10-year recurrence. The independent and additional prognostic value of imaging heterogeneity phenotypes suggests that radiomic phenotypes can provide a noninvasive characterization of tumor heterogeneity to augment personalized prognosis and treatment.

Apparent diffusion coefficient cannot predict molecular subtype and lymph node metastases in invasive breast cancer: a multicenter analysis

Background

Radiological imaging plays a central role in the diagnosis of breast cancer (BC). Some studies suggest MRI techniques like diffusion weighted imaging (DWI) may provide further prognostic value by discriminating between tumors with different biologic characteristics including receptor status and molecular subtype. However, there is much contradictory reported data regarding such associations in the literature. The purpose of the present study was to provide evident data regarding relationships between quantitative apparent diffusion coefficient (ADC) values on DWI and pathologic prognostic factors in BC.

Methods

Data from 5 centers (661 female patients, mean age, 51.4 ± 10.5 years) were acquired. Invasive ductal carcinoma (IDC) was diagnosed in 625 patients (94.6%) and invasive lobular carcinoma in 36 cases (5.4%). Luminal A carcinomas were diagnosed in 177 patients (28.0%), luminal B carcinomas in 279 patients (44.1%), HER 2+ carcinomas in 66 cases (10.4%), and triple negative carcinomas in 111 patients (17.5%). The identified lesions were staged as T1 in 51.3%, T2 in 43.0%, T3 in 4.2%, and as T4 in 1.5% of the cases. N0 was found in 61.3%, N1 in 33.1%, N2 in 2.9%, and N3 in 2.7%. ADC values between different groups were compared using the Mann–Whitney U test and by the Kruskal-Wallis H test. The association between ADC and Ki 67 values was calculated by Spearman’s rank correlation coefficient.

Results

ADC values of different tumor subtypes overlapped significantly. Luminal B carcinomas had statistically significant lower ADC values compared with luminal A (p = 0.003) and HER 2+ (p = 0.007) lesions. No significant differences of ADC values were observed between luminal A, HER 2+ and triple negative tumors. There were no statistically significant differences of ADC values between different T or N stages of the tumors. Weak statistically significant correlation between ADC and Ki 67 was observed in luminal B carcinoma (r = − 0.130, p = 0.03). In luminal A, HER 2+ and triple negative tumors there were no significant correlations between ADC and Ki 67.

Conclusion

ADC was not able to discriminate molecular subtypes of BC, and cannot be used as a surrogate marker for disease stage or proliferation activity.

Predictive factors affecting axillary lymph node involvement in patients with breast cancer in Duhok: Cross-sectional study

Background

Breast cancer is the most common type of cancer affecting women during their life, there are many histological types of breast cancer that have different biological behaviors. Tumors have different genetic and molecular differences which affect the expression of various hormone receptors.

Patients and methods

The aim of this study is to show the factors that determine the axillary lymph node involvement in patients with breast cancer in Duhok city.

A total number of 479 female patients with breast cancer of various histological types, immunohistochemical characteristics, and clinical stages were included in this study. These patients underwent modified radical mastectomy and axillary lymph node dissection.

Results

The mean age of our patients was 48.24 years, the mean number of the positive lymph nodes were 2 lymph nodes, the median size of the tumor was 30 mm. A significant correlation was found with the size of the tumor and the estrogen receptor status (P values (0.000 and 0.042) respectively, while there was no significant correlation with other factors such as the age, stage of the tumor, grade of the tumor, tumor necrosis, progesterone and HER-2 receptors status.

Conclusion

Size of the tumor and the estrogen receptor status were the most common factors that determined axillary lymph node involvement in patients with breast cancer in our study.

•

Breast cancer is the commonest type of cancer affecting women during their life.

•

Early diagnosis is the most important step in the management.

•

Axillary lymph node involvement is one of the most important factors that affects the long term survival.

Kinetic Features of Invasive Breast Cancers on Computer-Aided Diagnosis Using 3T MRI Data: Correlation with Clinical and Pathologic Prognostic Factors

OBJECTIVE

To investigate the correlation of kinetic features of breast cancers on computer-aided diagnosis (CAD) of preoperative 3T magnetic resonance imaging (MRI) data and clinical-pathologic factors in breast cancer patients.

MATERIALS AND METHODS

Between July 2016 and March 2017, 85 patients (mean age, 54 years; age range, 35-81 years) with invasive breast cancers (mean, 1.8 cm; range, 0.8-4.8 cm) who had undergone MRI and surgery were retrospectively enrolled. All magnetic resonance images were processed using CAD, and kinetic features of tumors were acquired. The relationships between kinetic features and clinical-pathologic factors were assessed using Spearman correlation test and binary logistic regression analysis.

RESULTS

Peak enhancement and angio-volume were significantly correlated with histologic grade, Ki-67 index, and tumor size: r = 0.355 (p = 0.001), r = 0.330 (p = 0.002), and r = 0.231 (p = 0.033) for peak enhancement, r = 0.410 (p = 0.005), r = 0.341 (p < 0.001), and r = 0.505 (p < 0.001) for angio-volume. Delayed-plateau component was correlated with Ki-67 (r = 0.255 [p = 0.019]). In regression analysis, higher peak enhancement was associated with higher histologic grade (odds ratio [OR] = 1.004; 95% confidence interval [CI]: 1.001-1.008; p = 0.024), and higher delayed-plateau component and angio-volume were associated with higher Ki-67 (Or = 1.051; 95% CI: 1.011-1.094; p = 0.013 for delayed-plateau component, OR = 1.178; 95% CI: 1.023-1.356; p = 0.023 for angio-volume).

CONCLUSION

Of the CAD-assessed kinetic features, higher peak enhancement may correlate with higher histologic grade, and higher delayed-plateau component and angio-volume correlate with higher Ki-67 index. These results support the clinical application of kinetic features in prognosis assessment.

Correlation between apparent diffusion coefficient values in breast magnetic resonance imaging and prognostic factors of breast invasive ductal carcinoma

BACKGROUND

We wanted to examine whether the apparent diffusion coefficient values obtained by diffusion-weighted imaging techniques could indicate an early prognostic assessment for patients with Invasive Ductal Carcinoma and, therefore, influence the treatment decision making.

OBJECTIVE

The main objective was to evaluate the correlation between the apparent diffusion coefficient values obtained by diffusion-weighted imaging and the key prognostic factors in breast invasive ductal carcinoma. Secondary objectives were to analyze the eventual correlations between magnetic resonance imaging findings and prognostic factors in breast cancer; and to perform a comparison between results in 1.5 and 3.0 T scanners.

METHODS

Breast magnetic resonance imaging with diffusion-weighted imaging sequence was performed on 100 patients, who were proven histopathologically to have breast invasive ductal carcinoma. We compared the apparent diffusion coefficient values, obtained previous to biopsy, with the main prognostic factors in breast cancer: tumor size, histologic grade, hormonal receptors, Ki67 index, human epidermal growth factor receptor type 2, and axillary lymph node status. The Mann-Whitney U test and the Kruskal-Wallis analysis were used to establish these correlations.

RESULTS

The mean apparent diffusion coefficient value was inferior in the estrogen receptor-positive group than in the estrogen receptor-negative group (1.04 vs 1.17 × 10-3 mm2/s, P = .004). Higher histologic grade related to larger tumor size (P = .002). We found association between spiculated margins and positive axillary lymph node status [odds ratio = 4.35 (1.49-12.71)]. There were no differences in apparent diffusion coefficient measurements between 1.5 and 3.0 T magnetic resonance imaging scanners (P = .513).

CONCLUSIONS

Low apparent diffusion coefficient values are related with positive expression of estrogen receptor. Larger tumors and spiculated margins are associated to worse prognosis. Rim enhancement is more frequently observed in estrogen receptor-negative tumors. There are no differences in apparent diffusion coefficient measurements between different magnetic resonance imaging scanners.

Is there a correlation between 3T multiparametric MRI and molecular subtypes of breast cancer?

OBJECTIVES

To test whether 3 T multiparametric magnetic resonance imaging (mMRI) provides information related to molecular subtypes of breast cancer.

METHODS

Women with mammographic or US findings of breast lesions (BI-RADS 4-5) underwent 3 T mMRI (DCE, DWI and MR spectroscopy). The histological type of breast cancer was assessed. Estrogen-receptor (ER), progesterone-receptor (PgR), Ki-67 status and HER-2 expression, assessed by immunohistochemistry (IHC), defined four molecular subtypes: Luminal-A, Luminal-B, HER2-enriched and triple-negative. Non-parametric tests (Kruskal-Wallis, k-sample equality of medians, and Mann-Whitney), logistic regression or ANOVA, and a multivariate analysis were performed to investigate correlations between the four molecular subtypes and mMRI (lesion volume, margins or distribution, enhancement pattern, ADC, type of kinetic curve, and total choline (tCho) signal-to-noise-ratio (SNR)). A ROC analysis was finally performed to test the diagnostic power of a multivariate logistic regression model.

RESULTS

433 patients (453 lesions) were considered. Volume was smaller in Luminal-B and larger in triple-negative tumours compared to the other subtypes combined. Margins were significantly correlated to Luminal-A and Luminal-B. The type of curve was significantly correlated to Luminal-A. ADC values were higher in Luminal-A. tCho SNR was higher in triple-negative tumours. The ROC analysis showed that the area under the curve (AUC) significantly improved when multiple MRI features were used compared to individual parameters.

CONCLUSIONS

A significant correlation was found between some MRI features and molecular subtypes of breast tumours. A multiparametric approach improved the diagnostic power of MRI. However, further research is needed in order to predict the molecular subtype on the sole basis of mMRI.

Can imaging kinetic parameters of dynamic contrast-enhanced magnetic resonance imaging be valuable in predicting clinicopathological prognostic factors of invasive breast cancer?

Background Intrinsic molecular profiling of breast cancer provides clinically relevant information that helps tailor therapy directed to the specific tumor subtype. We hypothesized that dynamic contrast-enhanced MRI (DCE-MRI) derived quantitative kinetic parameters (CD-QKPs) may help predict molecular tumor profiles non-invasively. Purpose To determine the association between DCE-MRI (CD-QKPs) and breast cancer clinicopathological prognostic factors. Material and Methods Clinicopathological factors in consecutive women with biopsy-confirmed invasive breast cancer who underwent breast DCE-MRI were retrospectively reviewed. Analysis of variance was used to examine associations between prognostic factors and CD-QKPs. Fisher's exact test was used to investigate the relationship between kinetic curve type and prognostic factors. Results A total of 198 women with invasive breast cancer were included. High-grade and HER2+ tumors were more likely to have a washout type curve while luminal A tumors were less likely. High-grade was significantly associated with increased peak enhancement (PE; P = 0.01), enhancement maximum slope (MS; P = 0.03), and mean enhancement ( ME, P = 0.03), while high clinical lymph node stage (cN3) was significantly associated with increased MS and time to peak (tP; P = 0.01). HER2+ tumors were associated with a higher PE ( P = 0.03) and ME ( P = 0.06) than HER2- counterparts, and ER-/HER2+ tumors showed higher PE and ME values than ER+/HER2- tumors ( P = 0.06). Conclusion DCE-MRI time-intensity CD-QKPs are associated with high tumor grade, advanced nodal stage, and HER2+ status, indicating their utility as imaging biomarkers.

Can diffusion-weighted imaging predict tumor grade and expression of Ki-67 in breast cancer? A multicenter analysis

Background

Numerous studies have analyzed associations between apparent diffusion coefficient (ADC) and histopathological features such as Ki-67 proliferation index in breast cancer (BC), with mixed results. The purpose of this study was to perform a multicenter analysis to determine relationships between ADC and expression of Ki-67 and tumor grade in BC.

Methods

For this study, data from six centers were acquired. The sample comprises 870 patients (all female; mean age, 52.6 ± 10.8 years). In every case, breast magnetic resonance imaging with diffusion-weighted imaging was performed. The comparison of ADC values in groups was performed by Mann-Whitney U test where the p values are adjusted for multiple testing (Bonferroni correction). The association between ADC and Ki-67 values was calculated by Spearman’s rank correlation coefficient. Sensitivity, specificity, negative and positive predictive values, accuracy, and AUC were calculated for the diagnostic procedures. ADC thresholds were chosen to maximize the Youden index.

Results

Overall, data of 870 patients were acquired for this study. The mean ADC value of the tumors was 0.98 ± 0.22 × 10^− 3 mm² s^− 1. ROC analysis showed that it is impossible to differentiate high/moderate grade tumors from grade 1 lesions using ADC values. Youden index identified a threshold ADC value of 1.03 with a sensitivity of 56.2% and specificity of 67.9%. The positive predictive value was 18.2%, and the negative predictive value was 92.4%. The level of the Ki-67 proliferation index was available for 845 patients. The mean value was 12.33 ± 21.77%. ADC correlated with weak statistical significant with expression of Ki-67 (p = − 0.202, p < 0.001). ROC analysis was performed to distinguish tumors with high proliferative potential from tumors with low expression of Ki-67 using ADC values. Youden index identified a threshold ADC value of 0.91 (sensitivity 64%, specificity 50%, positive predictive value 67.7%, negative predictive value 45.0%).

Conclusions

ADC cannot be used as a surrogate marker for proliferation activity and/or for tumor grade in breast cancer.

The role of MRI in predicting Ki-67 in breast cancer: preliminary results from a prospective study

Purpose:

In the last decade contrast-enhanced magnetic resonance imaging (MRI) has gained a growing role as a complementary tool for breast cancer diagnosis. Currently the relationship between the kinetic features of a breast lesion and pathologic prognostic factors has become a popular field of research. Our aim is to verify whether breast MRI could be considered a useful tool to predict Ki-67 score, thus resulting as a breast cancer prognosis indicator.

Methods:

From June to December 2014, we enrolled patients with breast cancer who underwent preoperative dynamic contrast-enhanced MRI at the local health agency. We analyzed the time-signal intensity curves calculating the mean values of the following parameters: the basal enhancement (Ebase), the enhancement ratio (ENHratio), the maximum enhancement (Emax), and the steepest slope of the contrast enhancement curve (Smax). Scatterplots and Pearson correlation test were used to investigate the eventual associations among these parameters.

Results:

A total of 27 patients underwent breast MRI during the study period. The mean ± SD Ki-67 percentage was 27.03 ± 16.8; the mean Emax, Smax, Ebase, and ENHratio were 433.9 ± 120.2, 267.3 ± 96.8, 165.5 ± 77.1, and 187.1 ± 94.8, respectively. Scatterplots suggest a positive correlation between Ki-67 and both Emax and Smax. The correlation tests between Ki-67 and Emax, Ki-67 and Smax showed statistical significance.

Conclusions:

Our preliminary data suggest that enhancement pattern is closely linked to breast cancer proliferation, thus proving the relationship between more proliferating tumors and more rapidly enhanced lesions. This is hypothesis-generating for further studies aimed at promoting breast MRI in the early estimation of cancer prognosis and tumor in vivo response to chemotherapy.

Morphological MR features and quantitative ADC evaluation in invasive breast cancer: Correlation with prognostic factors

OBJECTIVE

Assess the correlation between MRI characteristics of invasive breast cancer and tumor prognostic features.

MATERIALS AND METHODS

95 women with invasive breast cancer underwent pre-treatment MR. Morphological findings and quantitative ADC were retrospectively evaluated.

RESULTS

Smaller size, round shape, spiculated margins and homogeneous internal enhancement pattern on dynamic MRI were independently associated with established predictors of good prognosis, while larger size and rim enhancement pattern were related to predictors of poor prognosis. A positive correlation was observed between ADC value and clinical stage.

CONCLUSIONS

MRI may be a useful tool for breast cancer aggressiveness prediction and for guiding subsequent clinical-therapeutic management.

MRI texture analysis in differentiating luminal A and luminal B breast cancer molecular subtypes - a feasibility study

Background

The aim of this study was to use texture analysis (TA) of breast magnetic resonance (MR) images to assist in differentiating estrogen receptor (ER) positive breast cancer molecular subtypes.

Methods

Twenty-seven patients with histopathologically proven invasive ductal breast cancer were selected in preliminary study. Tumors were classified into molecular subtypes: luminal A (ER-positive and/or progesterone receptor (PR)-positive, human epidermal growth factor receptor type 2 (HER2) -negative, proliferation marker Ki-67 < 20 and low grade (I)) and luminal B (ER-positive and/or PR-positive, HER2-positive or HER2-negative with high Ki-67 ≥ 20 and higher grade (II or III)). Co-occurrence matrix -based texture features were extracted from each tumor on T1-weighted non fat saturated pre- and postcontrast MR images using TA software MaZda. Texture parameters and tumour volumes were correlated with tumour prognostic factors.

Results

Textural differences were observed mainly in precontrast images. The two most discriminative texture parameters to differentiate luminal A and luminal B subtypes were sum entropy and sum variance (p = 0.003). The AUCs were 0.828 for sum entropy (p = 0.004), and 0.833 for sum variance (p = 0.003), and 0.878 for the model combining texture features sum entropy, sum variance (p = 0.001). In the LOOCV, the AUC for model combining features sum entropy and sum variance was 0.876. Sum entropy and sum variance showed positive correlation with higher Ki-67 index. Luminal B types were larger in volume and moderate correlation between larger tumour volume and higher Ki-67 index was also observed (r = 0.499, p = 0.008).

Conclusions

Texture features which measure randomness, heterogeneity or smoothness and homogeneity may either directly or indirectly reflect underlying growth patterns of breast tumours. TA and volumetric analysis may provide a way to evaluate the biologic aggressiveness of breast tumours and provide aid in decisions regarding therapeutic efficacy.

Value of contrast-enhanced ultrasound and PET/CT in assessment of extramedullary lymphoma

OBJECTIVE

The aim of the study was to evaluate clinical value of contrast-enhanced ultrasonography (CEUS) and PET/CT for assessment of extramedullary lymphoma, using histopathology as reference standard.

METHOD

A total of 63 patients with histopathologically-confirmed extramedullary lymphoma who had underwent CEUS and PET/CT examinations of suspicious lymph nodes included in the study. CEUS patterns and parameters (arrival time, peak time and intensity, base intensity, area under the time-intensity curve, ascending and descending slopes) and PET/CT parameters including maximum standardized uptake value, mean standardized uptake value, and metabolic tumor volume (MTV) were evaluated. Patients were classified into Hodgkin lymphomas (HL), non-Hodgkin lymphomas (NHL), early (stage I and II) and advanced (stage III and IV) lymphoma, B cells and T cells lymphoma, and aggressive and indolent lymphoma. The differences between the two independent samples were compared using non-parametric rank and inspection, P < 0.05 was considered statistically significant. The optimal cut-off value for parameters was used to predict the staging and pathology using Receiver Operating Characteristic (ROC) curve analysis.

RESULT

In the early and advanced group, the differences between △T and ascending slope (AS) were statistically significant (p = 0.010, 0.024 < 0.05). Hodgkin lymphomas (HL) or non-Hodgkin lymphomas (NHL) results were determined by optimal cut-off value of AT and TP (p = 0.001, 0.001 < 0.05). Aggressive or indolent lymphoma were determined by optimal cut-off values of Color Doppler flow resistance index (P = 0.001 < 0.05) and SUVmax (p = 0.001 < 0.05). There was no statistically significant difference between B and T cell lymphoma. And there was no statistically significant difference among the qualitative indexes. The optimal cutoff value for statistically significant indicators was calculated by ROC.

CONCLUSION

The quantitative parameters of CEUS and SUVmax of PET/CT are proven useful in assessment of different clinical and pathologic patterns of extramedullary lymphoma.

Feasibility analysis of early temporal kinetics as a surrogate marker for breast tumor type, grade, and aggressiveness

BACKGROUND

Screening breast MRI has been shown to preferentially detect high-grade ductal carcinoma in situ (DCIS) and invasive carcinoma, likely due to increased angiogenesis resulting in early initial uptake of contrast. As interest grows in abbreviated screening breast MRI (AB-MRI), markers of early contrast washin that can predict tumor grade and potential aggressiveness are of clinical interest.

PURPOSE

To evaluate the feasibility of using the initial enhancement ratio (IER) as a surrogate marker for tumor grade, hormone receptor status, and prognostic markers, as an initial step to being incorporated into AB-MRI.

STUDY TYPE

Retrospective.

SUBJECTS

In all, 162 women (mean 55.0 years, range 32.8-87.7 years) with 187 malignancies imaged January 2012-November 2015.

FIELD STRENGTH/SEQUENCE

Images were acquired at 3.0T with a T₁ -weighted gradient echo fat-suppressed-volume interpolated breath-hold sequence.

ASSESSMENT

Subjects underwent dynamic contrast-enhanced breast MRI with a 7-channel breast coil. IER (% signal increase over baseline at the first postcontrast acquisition) was assessed and correlated with background parenchymal enhancement, washout curves, stage, and final pathology.

STATISTICAL TESTS

Chi-square test, Spearman rank correlation, Mann-Whitney U-tests, Bland-Altman analysis, and receiver operating characteristic curve analysis.

RESULTS

IER was higher for invasive cancer than for DCIS (R1/R2, P < 0.001). IER increased with tumor grade (R1: r = 0.56, P < 0.001, R2: r = 0.50, P < 0.001), as ki-67 increased (R1: r = 0.35, P < 0.001; R2 r = 0.35, P < 0.001), and for node-positive disease (R1/R2, P = 0.001). IER was higher for human epidermal growth factor receptor two-positive and triple negative cancers than for estrogen receptor-positive / progesterone receptor-positive tumors (R1 P < 0.001-0.002; R2 P = 0.0.001-0.011). IER had higher sensitivity (80.6% vs. 75.5%) and specificity (55.8% vs. 48.1%) than washout curves for positive nodes, higher specificity (48.1% vs. 36.5%) and positive predictive value (70.2% vs. 66.7%) for high ki-67, and excellent interobserver agreement (intraclass correlation coefficient = 0.82).

DATA CONCLUSION

IER, a measurement of early contrast washin, is associated with higher-grade malignancies and tumor aggressiveness and might be potentially incorporated into an AB-MRI protocol.

LEVEL OF EVIDENCE

3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018;47:1692-1700.

[Correlations between apparent diffusion coefficient in diffusion?weighted magnetic resonance imaging and molecular subtypes of invasive breast cancer masses]

OBJECTIVE

To study the correlation of apparent diffusion coefficient (ADC) measured by diffusion-weighted magnetic resonance imaging (MRI) with the molecular subtypes and biological prognostic factors of invasive breast cancer masses.

METHODS

Breast MRI data (including dynamic enhanced and diffusion-weighted imaging) were collected from 64 patients with pathologically confirmed invasive breast cancer masses (a total of 69 lesions). The mean ADC values of the lesions were calculated and their correlations were analyzed with the 5 molecular subtypes of invasive breast cancer and the biological prognostic factors including estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor 2 (HER2), and Ki-67 index.

RESULTS

The ADC values did not differ significantly among the 5 molecular subtypes of invasive breast cancer masses (P>0.05) or among lesions with different ER, PR, or HER2 status (P>0.05). The mean ADC values were significantly higher in Ki-67-positive lesions than in the negative lesions (P=0.023 and negatively correlated with the expressions of Ki-67 (r=-0.249).

CONCLUSION

ADC value can not be used to identify the molecular subtypes of invasive breast cancer masses or to evaluate the biological prognosis of the lesions, but its correlation with Ki-67 expression may help in prognostic evaluation and guiding clinical therapy of the tumors.

Correlation of MRI features and pathohistological prognostic factors in invasive ductal breast carcinoma

OBJECTIVE

The aim of this study was to correlate magnetic resonance imaging (MRI) features of invasive ductal carcinomas (IDC) with pathohistological prognostic factors. Such an association, if present, could have significant translational implications for early identification of aggressive types of breast cancer.

MATERIALS AND METHODS

One hundred and fourteen consecutive women with IDC who underwent breast MRI within one month prior to surgery were included in this retrospective study. MRI features were analyzed and then interpreted with a Göttingen score (GS) that included morphological (shape, margins, and pattern of enhancement) and kinetic characteristics (initial signal increase and post-initial behavior of the time-signal intensity curve). Histological specimens were analyzed for tumor size, axillary lymph node status, histological grade, estrogen receptors (ER), progesterone receptors (PR), HER2, and Ki-67.

RESULTS

By multivariate analysis, a smooth margin was a significant, independent predictor of a larger tumor size (p = 0.041), lymph node invasion (p = 0.013), and lower expression of ER (p = 0.022). High GS was a significant, independent predictor of a higher histological grade (p = 0.022) while round or oval shape of lesion was independent predictor of a higher PR expression (p = 0.027).

CONCLUSION

A smooth margin of breast cancer on breast MRI was able to predict positive axillary lymph nodes, larger tumor size, and lower expression of ER. Except for a higher histological grade, GS was not able to predict other unfavorable prognostic factors, probably due to the fact that smooth margins were assigned fewer points than spiculated margins.

Cone-beam Breast Computed Tomography: CT Density Does Not Reflect Proliferation Potential and Receptor Expression of Breast Carcinoma

PURPOSE: Recently, cone-beam breast computed tomography (CBCT) is established for the breast investigation. The purpose of the present study was to investigate possible associations between CBCT findings and histopathological features in breast cancer. METHODS: Overall, 59 female patients, mean age of 64.6 years with histological proven breast cancer were included into the study. In all cases, non-contrast CBCT examination was done. The diagnosis of the identified lesions was confirmed histologically by biopsy. Immunohistochemical staining against estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor 2 (HER2) and Ki-67 was performed for every lesion. Collected data were evaluated by means of descriptive statistics. Spearman's correlation coefficient was used to analyze the association between CT density and Ki-67 values. P values <0.05 were taken to indicate statistical significance in all instances. RESULTS: The size of the lesion varied from 2.7 to 90.0, mean size, 15.88 ± 13.0 mm. The mean value of CT density of the lesions was 63.95 ± 38.18 HU. The density tended to be higher in tubular carcinoma. Correlation analysis identified no significant correlations between CT density and Ki-67 level (r = −0.031, P = .784). There were no statistically significant differences of CT density between tumors with different receptor status. CONCLUSIONS: No significant associations between CT density and receptor status in breast cancer. Tubular carcinoma tended to have higher CT density in comparison to other subtypes of breast carcinomas.

PET/MR in invasive ductal breast cancer: correlation between imaging markers and histological phenotype

BACKGROUND

Differences in genetics and receptor expression (phenotypes) of invasive ductal breast cancer (IDC) impact on prognosis and treatment response. Immunohistochemistry (IHC), the most used technique for IDC phenotyping, has some limitations including its invasiveness. We explored the possibility of contrast-enhanced positron emission tomography magnetic resonance (CE-FDG PET/MR) to discriminate IDC phenotypes.

METHODS

21 IDC patients with IHC assessment of oestrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor-2 (HER2), and antigen Ki-67 (Ki67) underwent CE-FDG PET/MR. Magnetic resonance-perfusion biomarkers, apparent diffusion coefficient (ADC), and standard uptake value (SUV) were compared with IHC markers and phenotypes, using a Student's t-test and one-way ANOVA.

RESULTS

ER/PR- tumours demonstrated higher Kepmean and SUVmax than ER or PR+ tumours. HER2- tumours displayed higher ADCmean, Kepmean, and SUVmax than HER2+tumours. Only ADCmean discriminated Ki67⩽14% tumours (lower ADCmean) from Ki67>14% tumours. PET/MR biomarkers correlated with IHC phenotype in 13 out of 21 patients (62%; P=0.001).

CONCLUSIONS

Positron emission tomography magnetic resonance might non-invasively help discriminate IDC phenotypes, helping to optimise individual therapy options.

Comparison of conventional DCE-MRI and a novel golden-angle radial multicoil compressed sensing method for the evaluation of breast lesion conspicuity

PURPOSE

To compare a novel multicoil compressed sensing technique with flexible temporal resolution, golden-angle radial sparse parallel (GRASP), to conventional fat-suppressed spoiled three-dimensional (3D) gradient-echo (volumetric interpolated breath-hold examination, VIBE) MRI in evaluating the conspicuity of benign and malignant breast lesions.

MATERIALS AND METHODS

Between March and August 2015, 121 women (24-84 years; mean, 49.7 years) with 180 biopsy-proven benign and malignant lesions were imaged consecutively at 3.0 Tesla in a dynamic contrast-enhanced (DCE) MRI exam using sagittal T1-weighted fat-suppressed 3D VIBE in this Health Insurance Portability and Accountability Act-compliant, retrospective study. Subjects underwent MRI-guided breast biopsy (mean, 13 days [1-95 days]) using GRASP DCE-MRI, a fat-suppressed radial "stack-of-stars" 3D FLASH sequence with golden-angle ordering. Three readers independently evaluated breast lesions on both sequences. Statistical analysis included mixed models with generalized estimating equations, kappa-weighted coefficients and Fisher's exact test.

RESULTS

All lesions demonstrated good conspicuity on VIBE and GRASP sequences (4.28 ± 0.81 versus 3.65 ± 1.22), with no significant difference in lesion detection (P = 0.248). VIBE had slightly higher lesion conspicuity than GRASP for all lesions, with VIBE 12.6% (0.63/5.0) more conspicuous (P < 0.001). Masses and nonmass enhancement (NME) were more conspicuous on VIBE (P < 0.001), with a larger difference for NME (14.2% versus 9.4% more conspicuous). Malignant lesions were more conspicuous than benign lesions (P < 0.001) on both sequences.

CONCLUSION

GRASP DCE-MRI, a multicoil compressed sensing technique with high spatial resolution and flexible temporal resolution, has near-comparable performance to conventional VIBE imaging for breast lesion evaluation.

LEVEL OF EVIDENCE

3 Technical Efficacy: Stage 3 J. MAGN. RESON. IMAGING 2017;45:1746-1752.

The Use of Breast Magnetic Resonance Imaging Parameters to Identify Possible Signaling Pathways of a Serum Biomarker, HE4

OBJECTIVES

This study aimed to investigate the relationship between breast magnetic resonance imaging (MRI) parameters; clinical features such as age, tumor diameter, N, T, and TNM stages; and serum human epididymis protein 4 (HE4) levels in patients with breast carcinoma and use this as a means of estimating possible signaling pathways of the biomarker, HE4.

METHODS

Thirty-seven patients with breast cancer were evaluated by breast MRI and serum HE4 levels before therapy. Correlations between parameters including age, tumor diameter T and N, dynamic curve type, enhancement ratio (ER), slope washin (S-WI), time to peak (TTP), slope washout (S-WO), and the serum level of HE4 were investigated statistically. Human epididymis protein 4 levels of early and advanced stage of disease were also compared statistically.

RESULTS

Breast MRI parameters showed correlation to serum HE4 levels and correlations were statistically significant. Of these MRI parameters, S-WI had higher correlation coefficient than the others. Human epididymis protein 4 levels were not statistically different in early and advanced stage of disease.

CONCLUSIONS

High correlation with MRI parameters related to neoangiogenesis may indicate signaling pathway of HE4.

Association between dynamic contrast enhanced MRI imaging features and WHO histopathological grade in patients with invasive ductal breast cancer

The present study aimed to investigate the dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) and World Health Organization (WHO) histopathological grade in patients with invasive ductal breast cancer. A retrospective analysis on the results of DCE-MRI of 92 patients, who were diagnosed with invasive ductal breast cancer following surgery or biopsy, and these results were correlated with WHO histopathological grade. The statistical analysis demonstrated that the tumor size, shape and characteristics of early enhancement were associated with the WHO histopathological grade: The larger the lesion's long diameter, the higher the WHO histopathological grade; the WHO histopathological grades of round and oval masses were relatively lower, while those of lobulated and irregular masses were higher; and tumors with heterogeneous and ring-like enhancement exhibited higher WHO histopathological grades, while those of homogeneous enhancement were lower. The lesion's margin shape was not associated with the WHO histopathological grade. The present study demonstrates that features of DCE-MRI and WHO histopathological grade in patients with invasive ductal breast cancer are correlated, and these MRI features could be used to evaluate the biological behavior and prognosis of lesions.

Utilization of magnetic resonance imaging in breast cancer screening

Early detection of malignancy through breast cancer screening has contributed significantly to the decline in cancer-related mortality. [...]