Contrast-enhanced MRI after neoadjuvant chemotherapy of breast cancer: lesion-to-background parenchymal signal enhancement ratio for discriminating pathological complete response from minimal residual tumour

Automated rating of background parenchymal enhancement in MRI of extremely dense breasts without compromising the association with breast cancer in the DENSE trial

OBJECTIVES

Background parenchymal enhancement (BPE) on dynamic contrast-enhanced MRI (DCE-MRI) as rated by radiologists is subject to inter- and intrareader variability. We aim to automate BPE category from DCE-MRI.

METHODS

This study represents a secondary analysis of the Dense Tissue and Early Breast Neoplasm Screening trial. 4553 women with extremely dense breasts who received supplemental breast MRI screening in eight hospitals were included. Minimal, mild, moderate and marked BPE rated by radiologists were used as reference. Fifteen quantitative MRI features of the fibroglandular tissue were extracted to predict BPE using Random Forest, Naïve Bayes, and KNN classifiers. Majority voting was used to combine the predictions. Internal-external validation was used for training and validation. The inverse-variance weighted mean accuracy was used to express mean performance across the eight hospitals. Cox regression was used to verify non inferiority of the association between automated rating and breast cancer occurrence compared to the association for manual rating.

RESULTS

The accuracy of majority voting ranged between 0.56 and 0.84 across the eight hospitals. The weighted mean prediction accuracy for the four BPE categories was 0.76. The hazard ratio (HR) of BPE for breast cancer occurrence was comparable between automated rating and manual rating (HR = 2.12 versus HR = 1.97, P = 0.65 for mild/moderate/marked BPE relative to minimal BPE).

CONCLUSION

It is feasible to rate BPE automatically in DCE-MRI of women with extremely dense breasts without compromising the underlying association between BPE and breast cancer occurrence. The accuracy for minimal BPE is superior to that for other BPE categories.

The features associated with mammography-occult MRI-detected newly diagnosed breast cancer analysed by comparing machine learning models with a logistic regression model

PURPOSE

To compare machine learning (ML) models with logistic regression model in order to identify the optimal factors associated with mammography-occult (i.e. false-negative mammographic findings) magnetic resonance imaging (MRI)-detected newly diagnosed breast cancer (BC).

MATERIAL AND METHODS

The present single-centre retrospective study included consecutive women with BC who underwent mammography and MRI (no more than 45 days apart) for breast cancer between January 2018 and May 2023. Various ML algorithms and binary logistic regression analysis were utilized to extract features linked to mammography-occult BC. These features were subsequently employed to create different models. The predictive value of these models was assessed using receiver operating characteristic curve analysis.

RESULTS

This study included 1957 malignant lesions from 1914 patients, with an average age of 51.64 ± 9.92 years and a range of 20-86 years. Among these lesions, there were 485 mammography-occult BCs. The optimal features of mammography-occult BC included calcification status, tumour size, mammographic density, age, lesion enhancement type on MRI, and histological type. Among the different ML models (ANN, L1-LR, RF, and SVM) and the LR-based combined model, the ANN model with RF features was found to be the optimal model. It demonstrated the best discriminative performance in predicting mammography false- negative findings, with an AUC of 0.912, an accuracy of 86.90%, a sensitivity of 85.85%, and a specificity of 84.18%.

CONCLUSION

Mammography-occult MRI-detected breast cancers have features that should be considered when performing breast MRI to improve the detection rate for breast cancer and aid in clinician management.

Omission of Breast Surgery in Predicted Pathologic Complete Response after Neoadjuvant Systemic Therapy: A Multicenter, Single-Arm, Non-inferiority Trial

PURPOSE

Advances in chemotherapeutic and targeted agents have increased pathologic complete response (pCR) rates after neoadjuvant systemic therapy (NST). Vacuum-assisted biopsy (VAB) has been suggested to accurately evaluate pCR. This study aims to confirm the non-inferiority of the 5-year disease-free survival of patients who omitted breast surgery when predicted to have a pCR based on breast magnetic resonance imaging (MRI) and VAB after NST, compared with patients with a pCR who had undergone breast surgery in previous studies.

METHODS

The Omission of breast surgery for PredicTed pCR patients wIth MRI and vacuum-assisted bIopsy in breaST cancer after neoadjuvant systemic therapy (OPTIMIST) trial is a prospective, multicenter, single-arm, non-inferiority study enrolling in 17 tertiary care hospitals in the Republic of Korea. Eligible patients must have a clip marker placed in the tumor and meet the MRI criteria suggesting complete clinical response (post-NST MRI size ≤ 1 cm and lesion-to-background signal enhancement ratio ≤ 1.6) after NST. Patients will undergo VAB, and breast surgery will be omitted for those with no residual tumor. Axillary surgery can also be omitted if the patient was clinically node-negative before and after NST and met the stringent criteria of MRI size ≤ 0.5 cm. Survival and efficacy outcomes are evaluated over five years.

DISCUSSION

This study seeks to establish evidence for the safe omission of breast surgery in exceptional responders to NST while minimizing patient burden. The trial will address concerns about potential undertreatment due to false-negative results and recurrence as well as improved patient-reported quality of life issues from the omission of surgery. Successful completion of this trial may reshape clinical practice for certain breast cancer subtypes and lead to a safe and less invasive approach for selected patients.

TRIAL REGISTRATION

ClinicalTrials.gov Identifier: NCT05505357. Registered on August 17, 2022. Clinical Research Information Service Identifier: KCT0007638. Registered on July 25, 2022.

Dual Nanoparticle Conjugates for Highly Sensitive and Versatile Sensing Using 19 F Magnetic Resonance Imaging

Fluorine magnetic resonance imaging (19 F MRI) has emerged as an attractive alternative to conventional 1 H MRI due to enhanced specificity deriving from negligible background signal in this modality. We report a dual nanoparticle conjugate (DNC) platform as an aptamer-based sensor for use in 19 F MRI. DNC consists of core-shell nanoparticles with a liquid perfluorocarbon core and a mesoporous silica shell (19 F-MSNs), which give a robust 19 F MR signal, and superparamagnetic iron oxide nanoparticles (SPIONs) as magnetic quenchers. Due to the strong magnetic quenching effects of SPIONs, this platform is uniquely sensitive and functions with a low concentration of SPIONs (4 equivalents) relative to 19 F-MSNs. The probe functions as a "turn-on" sensor using target-induced dissociation of DNA aptamers. The thrombin binding aptamer was incorporated as a proof-of-concept (DNCThr ), and we demonstrate a significant increase in 19 F MR signal intensity when DNCThr is incubated with human α-thrombin. This proof-of-concept probe is highly versatile and can be adapted to sense ATP and kanamycin as well. Importantly, DNCThr generates a robust 19 F MRI "hot-spot" signal in response to thrombin in live mice, establishing this platform as a practical, versatile, and biologically relevant molecular imaging probe.

Is contrast-enhanced mammography (CEM) an alternative to MRI in assessing the response to primary systemic therapy of breast cancer?

PURPOSE

To evaluate the accuracy of contrast-enhanced mammography (CEM) and magnetic resonance imaging (MRI) in the assessing radiological response to primary systemic therapy (PST).

METHOD

Prospective study between February 2021 and October 2022. Women with breast cancer and indication of PST were enrolled. CEM and MRI were performed before and after PST, and the findings, including size and radiological response pattern, were compared with the size of the residual lesion measured in surgical specimens and its Miller-Payne classification (considered the gold standard). Two of four independent radiologists, with 2 years of CEM experience and 10 years of MRI experience, reviewed the images while being blinded to the results of the other technique. The agreement between measurements was evaluated using the Pearson correlation coefficient (r) and Lin's coefficient.

RESULTS

Forty-eight women with breast cancer who required PST were enrolled in the study, with a mean age of 57.21 ± 10.14 years. A total of thirty-three participants (68.75 %) completed the study. The correlation between CEM and MRI measurements was high before PST (r: 0.97), and local staging was identical for 45 out of 48 patients. MRI demonstrated better accuracy in predicting residual tumor size than CEM, with Lin's coefficient 0.91 and 0.73, respectively. However, no significant differences were observed in predicting response to therapy. Both methods tended to overestimate the size and degree of response in our study, with mean overestimations of 2.87 mm in CEM and 0.51 mm in MRI.

CONCLUSION

CEM was found to be as accurate as MRI in predicting response to PST, indicating its potential as an alternative imaging technique, but further research is necessary.

Radiogenomic-based multiomic analysis reveals imaging intratumor heterogeneity phenotypes and therapeutic targets

Intratumor heterogeneity (ITH) profoundly affects therapeutic responses and clinical outcomes. However, the widespread methods for assessing ITH based on genomic sequencing or pathological slides, which rely on limited tissue samples, may lead to inaccuracies due to potential sampling biases. Using a newly established multicenter breast cancer radio-multiomic dataset (n = 1474) encompassing radiomic features extracted from dynamic contrast-enhanced magnetic resonance images, we formulated a noninvasive radiomics methodology to effectively investigate ITH. Imaging ITH (IITH) was associated with genomic and pathological ITH, predicting poor prognosis independently in breast cancer. Through multiomic analysis, we identified activated oncogenic pathways and metabolic dysregulation in high-IITH tumors. Integrated metabolomic and transcriptomic analyses highlighted ferroptosis as a vulnerability and potential therapeutic target of high-IITH tumors. Collectively, this work emphasizes the superiority of radiomics in capturing ITH. Furthermore, we provide insights into the biological basis of IITH and propose therapeutic targets for breast cancers with elevated IITH.

Contrast-enhanced ultrasonography for early prediction of response of neoadjuvant chemotherapy in breast cancer

Neoadjuvant chemotherapy (NAC) is widely accepted as a primary treatment for inoperable or locally advanced breast cancer before definitive surgery. However, not all advanced breast cancers are sensitive to NAC. Contrast-enhanced ultrasonography (CEUS) has been considered to assess tumor response to NAC as it can effectively reflect the condition of blood perfusion and lesion size. Therefore, this study aimed to evaluate the diagnostic performance of CEUS to predict early response in different regions of interest in breast tumors under NAC treatment. This prospective study included 82 patients with advanced breast cancer. Parameters of TIC (time-intensive curve) between baseline and after the first cycle of NAC were calculated for the rate of relative change (Δ), including Δpeak, ΔTTP (time to peak), ΔRBV (regional blood volume), ΔRBF (regional blood flow) and ΔMTT (mean transit time). The responders and non-responders were distinguished by the Miller-Payne Grading (MPG) system and parameters from different regions of tumors were compared in these two groups. For ROI 1(the greatest enhancement area in the central region of the tumor), there were significant differences in Δpeak1, ΔRBV1 and ΔRBF1 between responders and non-responders. For ROI 2 (the greatest enhancement area on edge of the tumor), there were significant differences in Δpeak2 and ΔRBF2 between the groups. The Δpeak1 and ΔRBF2 showed good prediction (AUC 0.798-0.820, p ≤ 0.02) after the first cycle of NAC. When the cut-off value was 0.115, the ΔRBF2 had the highest diagnostic accuracy and the maximum NPV. Quantitative TIC parameters could be effectively used to evaluate early response to NAC in advanced breast cancer.

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.

Breast Magnetic Resonance Imaging for Patients With Newly Diagnosed Breast Cancer: A Review

Despite the high sensitivity and widespread use of preoperative magnetic resonance imaging (MRI), the American Cancer Society and the National Comprehensive Cancer Network guidelines do not recommend the routine use of preoperative MRI owing to the conflicting results and lack of clear benefit to the surgical outcome (reoperation and mastectomy) and long-term clinical outcomes (local recurrence and metachronous contralateral breast cancer). Preoperative MRI detects additional cancers that are occult at mammography and ultrasound but increases the rate of mastectomy. Concerns about overdiagnosis and overtreatment of preoperative MRI might be mitigated by adjusting the confounding factors when conducting studies, using the state-of-the-art image-guided biopsy technique, applying the radiologists’ cumulative experiences in interpreting MRI findings, and performing multiple lumpectomies in patients with multicentric cancer. Among the various imaging methods, dynamic contrast-enhanced MRI has the highest accuracy in predicting pathologic complete response after neoadjuvant chemotherapy. Prospective trials aimed at applying the MRI information to the de-escalation of surgical or radiation treatments are underway. In this review, current studies on the clinical outcomes of preoperative breast MRI are updated, and circumstances in which MRI may be useful for surgical planning are discussed.

Dynamic characterization of breast cancer response to neoadjuvant therapy using biophysical metrics of spatial proliferation

Current tools to assess breast cancer response to neoadjuvant chemotherapy cannot reliably predict disease eradication, which if possible, could allow early cessation of therapy. In this work, we assessed the ability of an image data-driven mathematical modeling approach for dynamic characterization of breast cancer response to neoadjuvant therapy. We retrospectively analyzed patients enrolled in the I-SPY 2 TRIAL at the Atrium Health Wake Forest Baptist Comprehensive Cancer Center. Patients enrolled on the study received four MR imaging examinations during neoadjuvant therapy with acquisitions at baseline (T₀), 3-weeks/early-treatment (T₁), 12-weeks/mid-treatment (T₂), and completion of therapy prior to surgery (T₃). We use a biophysical mathematical model of tumor growth to generate spatial estimates of tumor proliferation to characterize the dynamics of treatment response. Using histogram summary metrics to quantify estimated tumor proliferation maps, we found strong correlation of mathematical model-estimated tumor proliferation with residual cancer burden, with Pearson correlation coefficients ranging from 0.88 and 0.97 between T₀ and T₂, representing a significant improvement from conventional assessment methods of change in mean apparent diffusion coefficient and functional tumor volume. This data shows the significant promise of imaging-based biophysical mathematical modeling methods for dynamic characterization of patient-specific response to neoadjuvant therapy with correlation to residual disease outcomes.

Microcalcifications and Peritumoral Edema Predict Survival Outcome in Luminal Breast Cancer Treated with Neoadjuvant Chemotherapy

Background Little is known regarding findings at imaging associated with survival in patients with luminal breast cancer treated with neoadjuvant chemotherapy (NAC). Purpose To determine the relationship between imaging (MRI, US, and mammography) and clinical-pathologic variables in predicting distant metastasis-free survival (DMFS) and overall survival (OS) in patients with luminal breast cancer treated with NAC. Materials and Methods In this retrospective study, consecutive women with luminal breast cancer who underwent NAC followed by surgery were identified from the breast cancer registries of two hospitals. Women from one hospital between January 2003 and July 2015 were classified into the development cohort, and women from the other hospital between January 2007 and July 2015 were classified into the validation cohort. MRI scans, US scans, and mammograms before and after NAC (hereafter, referred to as pre- and post-NAC, respectively) and clinical-pathologic data were reviewed. Peritumoral edema was defined as the water-like high signal intensity surrounding the tumor on T2-weighted MRI scans. The prediction model was developed in the development cohort by using Cox regression and then tested in the validation cohort. Results The development cohort consisted of 318 women (68 distant metastases, 54 deaths) and the validation cohort consisted of 165 women (37 distant metastases, 14 deaths) (median age, 46 years in both cohorts). Post-NAC MRI peritumoral edema, age younger than 40 years, clinical N2 or N3, and lymphovascular invasion were associated with worse DMFS (all, P < .05). Pre-NAC mammographic microcalcifications, post-NAC MRI peritumoral edema, age older than 60 years, and clinical T3 or T4 were associated with worse OS (all, P < .05). The prediction model showed good discrimination ability (C index, 0.67-0.75 for DMFS and 0.70-0.77 for OS) and stratified prognosis into low-risk and high-risk groups (10-year DMFS rates, 79% vs 21%, respectively; and 10-year OS rates, 95%-96% vs 63%-67%, respectively) in the validation cohort. Conclusion MRI features and clinical-pathologic variables were identified that were associated with prolonged survival of patients with luminal breast cancer treated with neoadjuvant chemotherapy. © RSNA, 2022 Online supplemental material is available for this article. See also the editorial by Kataoka in this issue.

Prediction of pathologic complete response on MRI in patients with breast cancer receiving neoadjuvant chemotherapy according to molecular subtypes

OBJECTIVES

This study aimed to investigate the predictability of breast MRI for pathologic complete response (pCR) by molecular subtype in patients with breast cancer receiving neoadjuvant chemotherapy (NAC) and investigate the MRI findings that can mimic residual malignancy.

METHODS

A total of 506 patients with breast cancer who underwent MRI after NAC and underwent surgery between January and December 2018 were included. Two breast radiologists dichotomized the post-NAC MRI findings as radiologic complete response (rCR) and no-rCR. The diagnostic performance of MRI predicting pCR was evaluated. pCR was determined based on the final pathology reports. Tumors were divided according to hormone receptor (HR) and human epidermal growth factor receptor (HER) 2. Residual lesions on post-NAC MRI were divided into overt and subtle which classified as nodularity or delayed enhancement. Pearson's χ² and Wilcoxon rank-sum tests were used for MRI findings causing false-negative pCR.

RESULTS

The overall pCR rate was 30.04%. The overall accuracy for predicting pCR using MRI was 76.68%. The accuracy was significantly different by subtypes (p < 0.001), as follows in descending order: HR - /HER2 - (85.63%), HR + /HER2 - (82.84%), HR + /HER2 + (69.37%), and HR - /HER2 + (62.38%). MRI in the HR - /HER2 + type showed the highest false-negative rate (18.81%) for predicting pCR. The subtle residual enhancement observed only in the delayed phase was associated with false-negative findings (76.2%, p = 0.016).

CONCLUSIONS

The diagnostic accuracy of MRI for predicting pCR differed by molecular subtypes. When the residual enhancement on MRI after NAC is subtle and seen only in the delayed phase, overinterpretation of residual tumors should be performed with caution.

KEY POINTS

• In patients with breast cancer after completion of neoadjuvant chemotherapy, the diagnostic accuracy of MRI for predicting pathologic complete response (pCR) differed according to molecular subtype. • When residual enhancement on MRI is subtle and seen only in the delayed phase, this finding could be associated with false-negative pCR results.

Background Parenchymal Enhancement on Breast MRI: Assessment and Clinical Implications

Purpose of Review

To present recent literature regarding the assessment and clinical implications of background parenchymal enhancement on breast MRI.

Recent Findings

The qualitative assessment of BPE remains variable within the literature, as well as in clinical practice. Several different quantitative approaches have been investigated in recent years, most commonly region of interest-based and segmentation-based assessments. However, quantitative assessment has not become standard in clinical practice to date. Numerous studies have demonstrated a clear association between higher BPE and future breast cancer risk. While higher BPE does not appear to significantly impact cancer detection, it may result in a higher abnormal interpretation rate. BPE is also likely a marker of pathologic complete response after neoadjuvant chemotherapy, with decreases in BPE during and after neoadjuvant chemotherapy correlated with pCR. In contrast, pre-treatment BPE does not appear to be predictive of pCR. The association between BPE and prognosis is less clear, with heterogeneous results in the literature.

Summary

Assessment of BPE continues to evolve, with heterogeneity in approaches to both qualitative and quantitative assessment. The level of BPE has important clinical implications, with associations with future breast cancer risk and treatment response. BPE may also be an imaging marker of prognosis, but future research is needed on this topic.

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

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

Radiomics of Tumor Heterogeneity in Longitudinal Dynamic Contrast-Enhanced Magnetic Resonance Imaging for Predicting Response to Neoadjuvant Chemotherapy in Breast Cancer

Breast tumor morphological and vascular characteristics can be changed during neoadjuvant chemotherapy (NACT). The early changes in tumor heterogeneity can be quantitatively modeled by longitudinal dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI), which is useful in predicting responses to NACT in breast cancer. In this retrospective analysis, 114 female patients with unilateral unifocal primary breast cancer who received NACT were included in a development (n = 61) dataset and a testing dataset (n = 53). DCE-MRI was performed for each patient before and after treatment (two cycles of NACT) to generate baseline and early follow-up images, respectively. Feature-level changes (delta) of the entire tumor were evaluated by calculating the relative net feature change (deltaRAD) between baseline and follow-up images. The voxel-level change inside the tumor was evaluated, which yielded a Jacobian map by registering the follow-up image to the baseline image. Clinical information and the radiomic features were fused to enhance the predictive performance. The area under the curve (AUC) values were assessed to evaluate the prediction performance. Predictive models using radiomics based on pre- and post-treatment images, Jacobian maps and deltaRAD showed AUC values of 0.568, 0.767, 0.630 and 0.726, respectively. When features from these images were fused, the predictive model generated an AUC value of 0.771. After adding the molecular subtype information in the fused model, the performance was increased to an AUC of 0.809 (sensitivity of 0.826 and specificity of 0.800), which is significantly higher than that of the baseline imaging- and Jacobian map-based predictive models (p = 0.028 and 0.019, respectively). The level of tumor heterogeneity reduction (evaluated by texture feature) is higher in the NACT responders than in the nonresponders. The results suggested that changes in DCE-MRI features that reflect a reduction in tumor heterogeneity following NACT could provide early prediction of breast tumor response. The prediction was improved when the molecular subtype information was combined into the model.

Criteria for identifying residual tumours after neoadjuvant chemotherapy of breast cancers: a magnetic resonance imaging study

We investigated magnetic resonance imaging (MRI) criteria identifying residual tumours in patients with triple-negative and human epidermal growth factor receptor type 2-positive (HER2+) breast cancer following neoadjuvant chemotherapy. Retrospectively, 290 patients were included who had undergone neoadjuvant chemotherapy and definitive surgery. Clinicopathological features, as well as lesion size and lesion-to-background parenchymal signal enhancement ratio (SER) in early- and late-phase MRIs, were analysed. Receiver operating characteristic (ROC) analyses evaluated diagnostic performances. Maximal MRI values showing over 90% sensitivity and negative predictive value (NPV) were set as cut-off points. Identified MRI criteria were prospectively applied to 13 patients with hormone receptor-negative (HR-) tumours. The lesion size in HR-HER2-tumours had the highest area under the ROC curve value (0.92), whereas this parameter in HR + HER2 + tumours was generally low (≤ 0.75). For HR-tumours, both sensitivity and NPV exceeded the 90% threshold for early size > 0.2 cm (HR-HER2-) or > 0.1 cm (HR-HER2 +), late size > 0.4 cm, and early SER > 1.3. In the prospective pilot cohort, the criteria size and early SER did not find false negative cases, but one case was false negative with late SER. Distinguishing residual tumours based on MRI is feasible in selected triple-negative and HER2 + breast cancer patients.

Applications for Transition-Metal Chemistry in Contrast-Enhanced Magnetic Resonance Imaging

Contrast-enhanced magnetic resonance imaging (MRI) is an indispensable tool for diagnostic medicine. However, safety concerns related to gadolinium in commercial MRI contrast agents have emerged in recent years. For patients suffering from severe renal impairment, there is an important unmet medical need to perform contrast-enhanced MRI without gadolinium. There are also concerns over the long-term effects of retained gadolinium within the general patient population. Demand for gadolinium-free MRI contrast agents is driving a new wave of inorganic chemistry innovation as researchers explore paramagnetic transition-metal complexes as potential alternatives. Furthermore, advances in personalized care making use of molecular-level information have motivated inorganic chemists to develop MRI contrast agents that can detect pathologic changes at the molecular level. Recent studies have highlighted how reaction-based modulation of transition-metal paramagnetism offers a highly effective mechanism to achieve MRI contrast enhancement that is specific to biochemical processes. This Viewpoint highlights how recent advances in transition-metal chemistry are leading the way for a new generation of MRI contrast agents.

Dynamic Contrast-Enhanced MRI Evaluation of Pathologic Complete Response in Human Epidermal Growth Factor Receptor 2 (HER2)-Positive Breast Cancer After HER2-Targeted Therapy

RATIONALE AND OBJECTIVES

Pathologic complete response (pCR) in patients with human epidermal growth factor receptor 2 (HER2)-positive breast cancer after HER2-targeted therapy correlates increased disease-free survival and decreased mastectomy rates. The aim of this study was to explore tumor shrinkage patterns and initial tumor enhancement with pCR in HER2-positive breast cancer.

MATERIALS AND METHODS

This was an institutional review board-approved retrospective analysis of 51 HER2 positive breast cancer patients with breast MRI both pre- and post-HER2-targeted therapy. Initial enhancement ratio (IER, initial enhancement percentage over baseline at first postcontrast imaging), pattern of tumor shrinkage, and Dynamic contrast enhanced (DCE)-MRI imaging features were assessed. Wilcoxon rank, Spearman correlation, Fisher's exact, and Mann-Whitney tests were used to correlate MRI imaging features with pCR. IER reader agreement was evaluated by intraclass correlation. Binary logistic regression was used to evaluate multivariate associations with pCR.

RESULTS

56.9% (29/51) of patients had pCR at surgery. Concentric tumor shrinkage pattern was associated with pCR (p = 0.001, Area under the curve (AUC) 0.778): accuracy 80.4%, specificity 96.6%, and sensitivity of 59.1%. There was no association with pCR and imaging response as defined by RECIST criteria (p = 0.169), pretreatment IER (Reader 1 (R1) p = 0.665, Reader 2 (R2) p = 0.766), or lesion size (p = 0.69). IER was associated with axillary metastases (R1 p = 0.016, R2 < 0.001) and ki-67 (R1 r = 0.52, p = 0.008, R2 r = -0.44, p = 0.028).

CONCLUSION

The shrinkage pattern of HER2-positive tumors after targeted therapy may be associated with pCR. There was no association between IER and pCR. Future studies evaluating the correlation of shrinkage patterns to texture radiomics are of interest.

MRI Performance in Detecting pCR After Neoadjuvant Chemotherapy by Molecular Subtype of Breast Cancer

BACKGROUND

MRI performance in detecting pathologic complete response (pCR) post-neoadjuvant chemotherapy (NAC) in breast cancer has been previously explored. However, since tumor response varies by molecular subtype, it is plausible that imaging performance also varies. Therefore, we performed a literature review on subtype-specific MRI performance in detecting pCR post-NAC.

METHODS

Two reviewers searched Cochrane, PubMed, and EMBASE for articles published between 2013 and 2018 that examined MRI performance in detecting pCR post-NAC. After filtering, ten primary research articles were included. Statistical metrics, such as sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV), were extracted per study for triple negative, HR+/HER2-, and HER2+ patients.

RESULTS

Ten studies involving 2310 patients were included. In triple negative breast cancer, MRI showed NPV (58-100%) and PPV (72.7-94.7%) across 446 patients and sensitivity (45.5-100%) and specificity (49-94.4%) in 375 patients. In HR+/HER2- breast cancer patients, MRI showed NPV (29.4-100%) and PPV (21.4-95.1%) across 851 patients and sensitivity (43-100%) and specificity (45-93%) across 780 patients. In HER2+-enriched subtype, MRI showed NPV (62-94.6%) and PPV (34.9-72%) in 243 patients and sensitivity (36.2-83%) and specificity (47-90%) in 255 patients.

CONCLUSION

MRI accuracy in detecting pCR post-NAC by subtype is not as consistent, nor as high, as individual studies suggest. Larger studies using standardized pCR definition with appropriate timing of surgery and MRI need to be conducted. This study has shown that MRI is in fact not an accurate prediction of pCR, and thus, clinicians may need to rely on other approaches such as biopsies of the tumor bed.

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

Objectives

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

Materials and Methods

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

Results

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

Conclusions

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

Radiologic complete response (rCR) in contrast-enhanced magnetic resonance imaging (CE-MRI) after neoadjuvant chemotherapy for early breast cancer predicts recurrence-free survival but not pathologic complete response (pCR)

Background

Patients with early breast cancer (EBC) achieving pathologic complete response (pCR) after neoadjuvant chemotherapy (NACT) have a favorable prognosis. Breast surgery might be avoided in patients in whom the presence of residual tumor can be ruled out with high confidence. Here, we investigated the diagnostic accuracy of contrast-enhanced MRI (CE-MRI) in predicting pCR and long-term outcome after NACT.

Methods

Patients with EBC, including patients with locally advanced disease, who had undergone CE-MRI after NACT, were retrospectively analyzed (n = 246). Three radiologists, blinded to clinicopathologic data, reevaluated all MRI scans regarding to the absence (radiologic complete remission; rCR) or presence (no-rCR) of residual contrast enhancement. Clinical and pathologic responses were compared categorically using Cohen’s kappa statistic. The Kaplan-Meier method was used to estimate recurrence-free survival (RFS) and overall survival (OS).

Results

Overall rCR and pCR (no invasive tumor in the breast and axilla (ypT0/is N0)) rates were 45% (111/246) and 29% (71/246), respectively. Only 48% (53/111; 95% CI 38–57%) of rCR corresponded to a pCR (= positive predictive value - PPV). Conversely, in 87% (117/135; 95% CI 79–92%) of patients, residual tumor observed on MRI was pathologically confirmed (= negative predictive value - NPV). Sensitivity to detect a pCR was 75% (53/71; 95% CI 63–84%), while specificity to detect residual tumor and accuracy were 67% (117/175; 95% CI 59–74%) and 69% (170/246; 95% CI 63–75%), respectively. The PPV was significantly lower in hormone-receptor (HR)-positive compared to HR-negative tumors (17/52 = 33% vs. 36/59 = 61%; P = 0.004). The concordance between rCR and pCR was low (Cohen’s kappa − 0.1), however in multivariate analysis both assessments were significantly associated with RFS (rCR P = 0.037; pCR P = 0.033) and OS (rCR P = 0.033; pCR P = 0.043).

Conclusion

Preoperative CE-MRI did not accurately predict pCR after NACT for EBC, especially not in HR-positive tumors. However, rCR was strongly associated with favorable RFS and OS.

Electronic supplementary material

The online version of this article (10.1186/s13058-018-1091-y) contains supplementary material, which is available to authorized users.

Factors affecting the concordance of radiologic and pathologic tumor size in breast carcinoma

Background

Radiologic assessment of tumor size is an integral part of the work-up for breast carcinoma. With improved radiologic equipment, surgical decision relies profoundly upon radiologic/clinical stage. We wanted to see the concordance between radiologic and pathologic tumor size to infer how accurate radiologic/clinical staging is.

Materials and methods

The surgical pathology and ultrasonography reports of patients with breast carcinoma were reviewed. Data were collected for 406 cases. Concordance was defined as a size difference within ±2 mm.

Results

The difference between radiologic and pathologic tumor size was within ±2 mm in 40.4% cases. The mean radiologic size was 1.73 ± 1.06 cm. The mean pathologic size was 1.84 ± 1.24 cm. A paired t-test showed a significant mean difference between radiologic and pathologic measurements (0.12 ± 1.03 cm, p = 0.03). Despite the size difference, stage classification was the same in 59.9% of cases. Radiologic size overestimated stage in 14.5% of cases and underestimated stage in 25.6% of cases. The concordance rate was significantly higher for tumors ≤2 cm (pT1) (51.1%) as compared to those greater than 2 cm (≥pT2) (19.7%) (p < 0.0001). Significantly more lumpectomy specimens (47.5%) had concordance when compared to mastectomy specimens (29.8%) (p < 0.0001). Invasive ductal carcinoma had better concordance compared to other tumors (p = 0.02).

Conclusion

Mean pathologic tumor size was significantly different from mean radiologic tumor size. Concordance was in just over 40% of cases and the stage classification was the same in about 60% of cases only. Therefore, surgical decision of lumpectomy versus mastectomy based on radiologic tumor size may not always be accurate.