Complete response on MR imaging after neoadjuvant chemotherapy in breast cancer patients: Factors of radiologic-pathologic discordance

The contrast-free diffusion MRI multiple index for the early prediction of pathological response to neoadjuvant chemotherapy in breast cancer

Early tumor response prediction can help avoid overtreatment with unnecessary chemotherapy sessions. It is important to determine whether multiple apparent diffusion coefficient indices (S index, ADC-diff) are effective in the early prediction of pathological response to neoadjuvant chemotherapy (NAC) in breast cancer (BC). Patients with stage II and III BCs who underwent T1WI, diffusion-weighted imaging (DWI), and dynamic contrast-enhanced MRI using a 3 T system were included. They were divided into two groups: major histological responders (MHRs, Miller-Payne G4/5) and nonmajor histological responders (nMHRs, Miller-Payne G1-3). Three b values were used for DWI to derive the S index; ADC-diff values were obtained using b = 0 and 1000 s/mm2. The different interquartile ranges of percentile S-index and ADC-diff values after treatment were calculated and compared. The assessment was performed at baseline and after two and four NAC cycles. A total of 59 patients were evaluated. There are some correlations of interquartile ranges of S-index parameters and ADC-diff values with histopathological prognostic factors (such as estrogen receptor and human epidermal growth factor receptor 2 expression, all p < 0.05), but no significant differences were found in some other interquartile ranges of S-index parameters or ADC-diff values between progesterone receptor positive and negative or for Ki-67 tumors (all P > 0.05). No differences were found in the dynamic contrast-enhanced MRI characteristics between the two groups. HER-2 expression and kurtosis of the S-index distribution were screened out as independent risk factors for predicting MHR group (p < 0.05, area under the curve (AUC) = 0.811) before NAC. After early NAC (two cycles), only the 10th percentile S index was statistically significant between the two groups (p < 0.05, AUC = 0.714). No significant differences were found in ADC-diff value at any time point of NAC between the two groups (P > 0.1). These findings demonstrate that the S-index value may be used as an early predictor of pathological response to NAC in BC; the value of ADC-diff as an imaging biomarker of NAC needs to be further confirmed by ongoing multicenter prospective trials.

Prediction model for individualized precision surgery in breast cancer patients with complete response on MRI and residual calcifications on mammography after neoadjuvant chemotherapy

BACKGROUND

Identifying whether there is residual carcinoma in remaining suspicious calcifications after neoadjuvant chemotherapy (NAC) in breast cancer patients can provide crucial information for surgeons in determining the most appropriate surgical approach. Therefore, we investigated factors predicting calcifications without residual carcinoma (ypCalc_0) or with residual carcinoma (ypCalc_ca) and aimed to develop a prediction model for patients exhibiting residual suspicious calcifications on mammography but complete response on MRI after NAC.

METHODS

This retrospective study included breast cancer patients undergoing NAC, showing residual suspicious mammographic calcifications but complete response on MRI between January 2019 and December 2020 (development set) and between January 2021 and December 2022 (validation set). Multivariable logistic regression analysis identified significant factors associated with ypCalc_0. The prediction model, developed using a decision tree and factors from logistic regression analysis, was validated in the validation set.

RESULTS

The development set included 134 women (mean age, 50.6 years; 91 with ypCalc_0 and 43 with ypCalc_ca) and validation set included 146 women (mean age, 51.0 years; 108 with ypCalc_0 and 38 with ypCalc_ca). Molecular subtype (P = .0002) and high Ki-67 (P = .02) emerged as significant independent factors associated with ypCalc_0 in the development set. The prediction model, incorporating hormone receptor (HR)-/human epidermal growth factor receptor 2 (HER2)+ with high Ki-67 as ypCalc_0 predictors, and HR+/HER2- cancers or HR+/HER2+ or triple-negative (TN) cancers with low Ki-67, as ypCalc_ca predictors, achieved an area under receiver operating characteristic curve of 0.844 (95% CI 0.774-0.914) in the validation set.

CONCLUSION

Minimized surgery may be considered for managing residual calcifications in HR-/HER2+ with high Ki-67 cancers, while complete excision is recommended for HR+/HER2- breast cancers or for HR+/HER2+or TN breast cancers with low Ki-67.

Pre-operative MRI in evaluating pathologic complete response to neoadjuvant chemotherapy in patients with breast cancer: a study focused on influencing factors of baseline clinical-pathological and imaging features

Purpose

To investigate what pre-treatment clinical-pathological features and MRI characteristics influence the performance of breast MRI in assessing the pathologic complete response (pCR) of breast cancer patients to Neoadjuvant Chemotherapy (NAC).

Methods

A total of 225 patients with pathologically-confirmed breast cancer who underwent pre- and post-NAC breast MRI between January 2020 and April 2023 were retrospectively analyzed. All patients were categorized into radiologic complete response (rCR) and non-rCR groups based on pre-operative MRI. Univariable and multivariable logistic regression were used to identify independent clinicopathological and imaging features associated with imaging-pathological discordance. The performance of pre-operative MRI for predicting pCR to NAC was assessed according to the baseline characteristics of the clinicopathological data and pre-NAC MRI. In addition, the discrepancy between the pre-operative MRI and post-operative pathological findings was further analyzed by a case-control approach.

Results

Among 225 patients, 99 (44.0%) achieved pCR after NAC. MRI showed the overall sensitivity of 97.6%, specificity of 58.6%, accuracy of 80.4%, a positive predictive value (PPV) of 75.0%, and a negative predictive value (NPV) of 95.1% in identifying pCR. Of baseline features, presence of ductal carcinoma in situ (DCIS) (OR, 3.975 [95% CI: 1.448-10.908], p = 0.007), luminal B (OR, 5.076 [95% CI: 1.401-18.391], p = 0.013), HER2-enriched subtype (OR, 10.949 [95% CI: 3.262-36.747], p < 0.001), multifocal or multicentric lesions (OR, 2.467 [95% CI: 1.067-5.706], p = 0.035), segmental or regional distribution of NME (OR, 8.514 [95% CI: 1.049-69.098], p = 0.045) and rim enhancement of mass (OR, 4.261 [95% CI: 1.347-13.477], p = 0.014) were significantly associated with the discrepancy between MRI and pathology.

Conclusion

Presence of DCIS, luminal B or HER2-enriched subtype, multicentric or multifocal lesions, segmental or regional distribution of NME and rim enhancement of mass may lead to a decrease in diagnostic accuracy of MRI in patients of breast cancer treated with NAC.

Artificial Intelligence-Enhanced Breast MRI: Applications in Breast Cancer Primary Treatment Response Assessment and Prediction

ABSTRACT

Primary systemic therapy (PST) is the treatment of choice in patients with locally advanced breast cancer and is nowadays also often used in patients with early-stage breast cancer. Although imaging remains pivotal to assess response to PST accurately, the use of imaging to predict response to PST has the potential to not only better prognostication but also allow the de-escalation or omission of potentially toxic treatment with undesirable adverse effects, the accelerated implementation of new targeted therapies, and the mitigation of surgical delays in selected patients. In response to the limited ability of radiologists to predict response to PST via qualitative, subjective assessments of tumors on magnetic resonance imaging (MRI), artificial intelligence-enhanced MRI with classical machine learning, and in more recent times, deep learning, have been used with promising results to predict response, both before the start of PST and in the early stages of treatment. This review provides an overview of the current applications of artificial intelligence to MRI in assessing and predicting response to PST, and discusses the challenges and limitations of their clinical implementation.

Can breast MRI predict pathologic response following neoadjuvant chemotherapy for breast cancer? A retrospective cohort study

PURPOSE

For patients treated with neoadjuvant chemotherapy (NAC) for breast cancer, it is standard of care to perform pre- and post-NAC imaging to evaluate response to therapy prior to surgery. In this study we assess outcome metrics of magnetic resonance imaging (MRI) following NAC.

METHODS

We conducted a retrospective analysis of patients with invasive breast cancer who underwent a breast MRI before and after NAC between 2016 and 2021 at a single, multisite academic institution. All breast MRI studies were characterized as either radiologic complete response (rCR) or non-rCR. Corresponding surgical pathology reports were reviewed and categorized as pathologic complete response (pCR) or non-pCR. We defined a positive test as having residual enhancement on MRI (non-rCR) and a positive outcome as having residual disease on final surgical pathology (non-pCR).

RESULTS

There were 225 patients included in the study (mean age 52 ± 12 years). Breast cancer receptor distribution was HR+/HER2- (n = 71, 32%), HR+/HER2+ (n = 51, 23%), HR-/HER2- (n = 72, 32%), and HR-/HER2+ (n = 31, 14%). In total, 78 (35%) had rCR and 77 (34%) had pCR; 43 (19%) had both rCR and pCR. The overall accuracy rate was 69% (156/225), sensitivity 76% (113/148), specificity 56% (43/77), positive predictive value 77% (113/147), and negative predictive value 55% (43/78). The PPV was significantly associated with receptor status (p = 0.004). No patient or imaging characteristics were associated with sensitivity.

CONCLUSION

Breast MRI only moderately predicts pathologic response for invasive breast cancer treated with NAC (overall accuracy 69%). PPV is significantly associated with receptor status.

Multimodality Imaging Review of HER2-positive Breast Cancer and Response to Neoadjuvant Chemotherapy

Human epidermal growth factor receptor 2 (HER2/neu or ErbB2)-positive breast cancers comprise 15%-20% of all breast cancers. The most common manifestation of HER2-positive breast cancer at mammography or US is an irregular mass with spiculated margins that often contains calcifications; at MRI, HER2-positive breast cancer may appear as a mass or as nonmass enhancement. HER2-positive breast cancers are often of intermediate to high nuclear grade at histopathologic analysis, with increased risk of local recurrence and metastases and poorer overall prognosis. However, treatment with targeted monoclonal antibody therapies such as trastuzumab and pertuzumab provides better local-regional control and leads to improved survival outcome. With neoadjuvant treatments, including monoclonal antibodies, taxanes, and anthracyclines, women are now potentially able to undergo breast conservation therapy and sentinel lymph node biopsy versus mastectomy and axillary lymph node dissection. Thus, the radiologist's role in assessing the extent of local-regional disease and response to neoadjuvant treatment at imaging is important to inform surgical planning and adjuvant treatment. However, assessment of treatment response remains difficult, with the potential for different imaging modalities to result in underestimation or overestimation of disease to varying degrees when compared with surgical pathologic analysis. In particular, the presence of calcifications at mammography is especially difficult to correlate with the results of pathologic analysis after chemotherapy. Breast MRI findings remain the best predictor of pathologic response. The authors review the initial manifestations of HER2-positive tumors, the varied responses to neoadjuvant chemotherapy, and the challenges in assessing residual cancer burden through a multimodality imaging review with pathologic correlation. ^© RSNA, 2023 Quiz questions for this article are available through the Online Learning Center.

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.

Diagnostic performance of digital breast tomosynthesis for predicting response to neoadjuvant systemic therapy in breast cancer patients: A comparison with magnetic resonance imaging, ultrasound, and full-field digital mammography

Background

The goals of neoadjuvant systemic therapy (NST) are to reduce tumor volume and to provide a prognostic indicator in assessing treatment response. Digital breast tomosynthesis (DBT) was developed and has increased interest in clinical settings due to its higher sensitivity for breast cancer detection compared to full-field digital mammography (FFDM).

Purpose

To evaluate the accuracy of DBT in assessing response to NST compared to FFDM, ultrasound (US), and magnetic resonance imaging (MRI) in breast cancer patients.

Material and Methods

In this retrospective study, 95 stages II–III breast cancer patients undergoing NST and subsequent surgeries were enrolled. After NST, the longest diameter of residual tumor measured by DBT, FFDM, US, and MRI was compared with pathology. Agreements and correlations of tumor size were assessed, and the diagnostic performance for predicting pathologic complete response (pCR) was evaluated.

Results

Mean residual tumor size after NST was 19.9 mm for DBT, 18.7 mm for FFDM, 16.0 mm for US, and 18.4 mm for MRI, compared with 17.9 mm on pathology. DBT and MRI correlated better with pathology than that of FFDM and US. The ICC values were 0.85, 0.87, 0.74, and 0.77, respectively. Twenty-five patients (26.3%) achieved pCR after NST. For predicting pCR, area under the receiver operating characteristic (ROC) curve for DBT, FFDM, US, and MRI were 0.79, 0.66, 0.68, and 0.77, respectively.

Conclusion

DBT has good correlation with histopathology for measuring residual tumor size after NST. DBT was comparable to MRI in assessing tumor response after completion of NST.

Pre-treatment MRI tumor features and post-treatment mammographic findings: may they contribute to refining the prediction of pathologic complete response in post-neoadjuvant breast cancer patients with radiologic complete response on MRI?

PURPOSE

Radiologic complete response (rCR) in breast cancer patients after neoadjuvant chemotherapy (NAC) does not necessarily correlate with pathologic complete response (pCR), a marker traditionally associated with better outcomes. We sought to verify if data extracted from two important steps of the imaging workup (tumor features at pre-treatment MRI and post-treatment mammographic findings) might assist in refining the prediction of pCR in post-NAC patients showing rCR.

METHODS

A total of 115 post-NAC women with rCR on MRI (2010-2016) were retrospectively assessed. Pre-treatment MRI (lesion morphology, size, and distribution) and post-treatment mammographic findings (calcification, asymmetry, mass, architectural distortion) were assessed, as well as clinical and molecular variables. Bivariate and multivariate analyses evaluated correlation between such variables and pCR. Post-NAC mammographic findings and their correlation with ductal in situ carcinoma (DCIS) were evaluated using Pearson's correlation.

RESULTS

Tumor distribution at pre-treatment MRI was the only significant predictive imaging feature on multivariate analysis, with multicentric lesions having lower odds of pCR (p = 0.035). There was no significant association between tumor size and morphology with pCR. Mammographic residual calcifications were associated with DCIS (p = 0.009). The receptor subtype remained as a significant predictor, with HR-HER2 + and triple-negative status demonstrating higher odds of pCR on multivariate analyses.

CONCLUSIONS

Multicentric lesions on pre-NAC MRI were associated with a lower chance of pCR in post-NAC rCR patients. The receptor subtype remained a reliable predictor of pCR. Residual mammographic calcifications correlated with higher odds of malignancy, making the correlation between mammography and MRI essential for surgical planning. Key Points • The presence of a multicentric lesion on pre-NAC MRI, even though the patient reaches a radiologic complete response on MRI, is associated with a lower chance of pCR. • Molecular status of the tumor remained the only significant predictor of pathologic complete response in such patients in the present study. • Post-neoadjuvant residual calcifications found on mammography were related to higher odds of residual malignancy, making the correlation between mammography and MRI essential for surgical planning.

Breast MRI for Evaluation of Response to Neoadjuvant Therapy

Neoadjuvant therapy is increasingly being used to treat early-stage triple-negative and human epidermal growth factor 2-overexpressing breast cancers, as well as locally advanced and inflammatory breast cancers. The rationales for neoadjuvant therapy are to shrink tumor size and potentially decrease the extent of surgery, to serve as an in vivo test of response to therapy, and to reveal prognostic information for the patient. MRI is the most accurate modality to demonstrate response to therapy and to help ensure accurate presurgical planning. Changes in lesion diameter, volume, and enhancement are used to predict complete response, partial response, or nonresponse to therapy. However, residual disease may be overestimated or underestimated at MRI. Fibrosis, necrotic tumors, and residual benign masses may be causes of overestimation of residual disease. Nonmass lesions, invasive lobular carcinoma, hormone receptor-positive tumors, nonconcentric shrinkage patterns, the use of antiangiogenic therapy, and late-enhancing foci may be causes of underestimation of residual disease. In patients with known axillary lymph node metastasis, neoadjuvant therapy may be followed by targeted axillary dissection to avoid the potential morbidity associated with an axillary lymph node dissection. Diffusion-weighted imaging, radiomics, machine learning, and deep learning methods are under investigation to improve MRI accuracy in predicting treatment response.^©RSNA, 2021.

Accuracy and Reproducibility of Contrast-Enhanced Mammography in the Assessment of Response to Neoadjuvant Chemotherapy in Breast Cancer Patients with Calcifications in the Tumor Bed

This study aimed to evaluate contrast-enhanced mammography (CEM) accuracy and reproducibility in the detection and measurement of residual tumor after neoadjuvant chemotherapy (NAC) in breast cancer (BC) patients with calcifications, using surgical specimen pathology as the reference. Pre- and post-NAC CEM images of 36 consecutive BC patients receiving NAC in 2012–2020, with calcifications in the tumor bed at diagnosis, were retrospectively reviewed by two radiologists; described were absence/presence and size of residual disease based on contrast enhancement (CE) only and CE plus calcifications. Twenty-eight patients (77.8%) had invasive and 5 (13.9%) in situ-only residual disease at surgical specimen pathology. Considering CE plus calcifications instead of CE only, CEM sensitivity for invasive residual tumor increased from 85.7% (95% CI = 67.3–96%) to 96.4% (95% CI = 81.7–99.9%) and specificity decreased from 5/8 (62.5%; 95% CI = 24.5–91.5%) to 1/8 (14.3%; 95% CI = 0.4–57.9%). For in situ-only residual disease, false negatives decreased from 3 to 0 and false positives increased from 1 to 2. CEM pathology concordance in residual disease measurement increased (R squared from 0.38 to 0.45); inter-reader concordance decreased (R squared from 0.79 to 0.66). Considering CE plus calcifications to evaluate NAC response in BC patients increases sensitivity in detection and accuracy in measurement of residual disease but increases false positives.

Breast-Conserving Surgery after Neoadjuvant Chemotherapy Using a Three-Dimensional-Printed Surgical Guide Based on Supine Magnetic Resonance Imaging: A Case Report

Tumor localization in patients receiving neoadjuvant chemotherapy (NACT) is challenging because substantial therapeutic remission of the original tumor after NACT is often noted. Currently, there is no guidance device that allows for an accurate estimation of the resection range in breast-conserving surgery after NACT. To increase the accuracy of tumor resection, we used a 3-dimensional-printed breast surgical guide based on magnetic resonance imaging (MRI) in the supine position for a breast cancer patient who underwent breast-conserving surgery after NACT. Using this device, the breast tumor with apparent therapeutic changes after NACT on imaging was successfully removed with clear resection margins by identifying the original tumor site in the affected breast. Irrespective of whether the residual tumor area after NACT is well defined, it is possible to confirm and target the tumor area on pre-NACT MRI using this device.

Modern surgical treatment of breast cancer

Breast cancer is the most frequent cancer in women all over the world. The prognosis is generally good, with a five-year overall survival rate above 90% for all stages. It is still the second leading cause of cancer-related death among women. Surgical treatment of breast cancer has changed dramatically over the years. Initially, treatment involved major surgery with long hospitalization, but it is now mostly accomplished as an outpatient procedure with a quick recovery. Thanks to well-designed retrospective and randomly controlled prospective studies, guidelines are continually changing. We are presently in an era where safely de-escalating surgery is increasingly emphasized. Breast cancer is a heterogenous disease, where a "one-size-fits-all" treatment approach is not appropriate. There is often more than one surgical solution carrying equal oncological safety for an individual patient. In these situations, it is important to include the patient in the treatment decision-making process through well informed consent. For this to be optimal, the physician must be fully updated on the surgical options. A consequence of an improved prognosis is more breast cancer survivors, and therefore physical appearance and quality of life is more in focus. Modern breast cancer treatment is increasingly personalized from a surgical point of view but is dependent on a multidisciplinary approach. Detailed algorithms for surgery of the breast and the axilla are required for optimal treatment and quality control. This review illustrates how breast cancer treatment has changed over the years and how the current standard is based on high quality scientific research.

Breast cancer phenotype influences MRI response evaluation after neoadjuvant chemotherapy

PURPOSE

To evaluate which factors may influence magnetic resonance imaging (MRI) performance in the detection of pathologic complete response (pCR) after neoadjuvant chemotherapy (NAC).

METHOD

This retrospective study included 219 patients diagnosed with invasive breast carcinoma who underwent breast MRI before and after NAC. The MRI findings were compared to gold standard pathological examinations. Resolution of invasive breast disease was defined as pCR.

RESULTS

The mean age of our cohort was 48 years (range: 20-85). The molecular subtypes included: Luminal B/Her-2 negative (n = 89; 40%), triple-negative (n = 69; 32%), Luminal B/Her-2 positive (n = 43; 20%), and Her-2 overexpression (n = 18; 8%). MRI analysis after NAC showed complete response in 76 cases (35%), while pathological analysis of surgical specimens after NAC detected pCR in 85 cases (39%). The accuracy of MRI in diagnosing pCR was 80%, with 69% sensitivity, 87% specificity, and positive and negative predictive values of 78% and 82%, respectively. The only factor statistically associated with a higher discordance rate between MRI and pathologic response was the presence of non-mass enhancement at pre-treatment MRI (p = 0.003).

CONCLUSIONS

MRI demonstrated good accuracy in predicting pCR after NAC among the breast cancer patients examined. However, non-mass enhancement at pre-treatment MRI negatively affected the diagnostic performance of MRI in assessing treatment response after NAC.