ACR Appropriateness Criteria® Monitoring Response to Neoadjuvant Systemic Therapy for Breast Cancer

Time-Series MR Images Identifying Complete Response to Neoadjuvant Chemotherapy in Breast Cancer Using a Deep Learning Approach

BACKGROUND

Pathological complete response (pCR) is an essential criterion for adjusting follow-up treatment plans for patients with breast cancer (BC). The value of the visual geometry group and long short-term memory (VGG-LSTM) network using time-series dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) for pCR identification in BC is unclear.

PURPOSE

To identify pCR to neoadjuvant chemotherapy (NAC) using deep learning (DL) models based on the VGG-LSTM network.

STUDY TYPE

Retrospective.

POPULATION

Center A: 235 patients (47.7 ± 10.0 years) were divided 7:3 into training (n = 164) and validation set (n = 71). Center B: 150 patients (48.5 ± 10.4 years) were used as test set.

FIELD STRENGTH/SEQUENCE

3-T, T2-weighted spin-echo sequence imaging, and gradient echo DCE sequence imaging.

ASSESSMENT

Patients underwent MRI examinations at three sequential time points: pretreatment, after three cycles of treatment, and prior to surgery, with tumor regions of interest manually delineated. Histopathology was the gold standard. We used VGG-LSTM network to establish seven DL models using time-series DCE-MR images: pre-NAC images (t0 model), early NAC images (t1 model), post-NAC images (t2 model), pre-NAC and early NAC images (t0 + t1 model), pre-NAC and post-NAC images (t0 + t2 model), pre-NAC, early NAC and post-NAC images (t0 + t1 + t2 model), and the optimal model combined with the clinical features and imaging features (combined model). The models were trained and optimized on the training and validation set, and tested on the test set.

STATISTICAL TESTS

The DeLong, Student's t-test, Mann-Whitney U, Chi-squared, Fisher's exact, Hosmer-Lemeshow tests, decision curve analysis, and receiver operating characteristics analysis were performed. P < 0.05 was considered significant.

RESULTS

Compared with the other six models, the combined model achieved the best performance in the test set yielding an AUC of 0.927.

DATA CONCLUSION

The combined model that used time-series DCE-MR images, clinical features and imaging features shows promise for identifying pCR in BC.

LEVEL OF EVIDENCE: 4

TECHNICAL EFFICACY

Stage 4.

Performance of Diffusion-weighted Imaging-based Noncontrast MRI Protocols for Diagnosis of Breast Cancer: A Systematic Review and Meta-Analysis

Background Diffusion-weighted imaging (DWI) is increasingly recognized as a powerful diagnostic tool and tested alternative to contrast-enhanced (CE) breast MRI. Purpose To perform a systematic review and meta-analysis that assesses the diagnostic performance of DWI-based noncontrast MRI protocols (ncDWI) for the diagnosis of breast cancer. Materials and Methods A systematic literature search in PubMed for articles published from January 1985 to September 2023 was performed. Studies were excluded if they investigated malignant lesions or selected patients and/or lesions only, used DWI as an adjunct technique to CE MRI, or were technical studies. Statistical analysis included pooling of diagnostic accuracy and investigating between-study heterogeneity. Additional subgroup comparisons of ncDWI to CE MRI and standard mammography were performed. Results A total of 28 studies were included, with 4406 lesions (1676 malignant, 2730 benign) in 3787 patients. The pooled sensitivity and specificity of ncDWI were 86.5% (95% CI: 81.4, 90.4) and 83.5% (95% CI: 76.9, 88.6), and both measures presented with high between-study heterogeneity (I 2 = 81.6% and 91.6%, respectively; P < .001). CE MRI (18 studies) had higher sensitivity than ncDWI (95.1% [95% CI: 92.9, 96.7] vs 88.9% [95% CI: 82.4, 93.1], P = .004) at similar specificity (82.2% [95% CI: 75.0, 87.7] vs 82.0% [95% CI: 74.8, 87.5], P = .97). Compared with ncDWI, mammography (five studies) showed no evidence of a statistical difference for sensitivity (80.3% [95% CI: 56.3, 93.3] vs 56.7%; [95% CI: 41.9, 70.4], respectively; P = .09) or specificity (89.9% [95% CI: 85.5, 93.1] vs 90% [95% CI: 61.3, 98.1], respectively; P = .62), but ncDWI had a higher area under the summary receiver operating characteristic curve (0.93 [95% CI: 0.91, 0.95] vs 0.78 [95% CI: 0.74, 0.81], P < .001). Conclusion A direct comparison with CE MRI showed a modestly lower sensitivity at similar specificity for ncDWI, and higher diagnostic performance indexes for ncDWI than standard mammography. Heterogeneity was high, thus these results must be interpreted with caution. © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Kataoka and Iima in this issue.

Prediction of breast cancer and axillary positive-node response to neoadjuvant chemotherapy based on multi-parametric magnetic resonance imaging radiomics models

PURPOSE

Accurate identification of primary breast cancer and axillary positive-node response to neoadjuvant chemotherapy (NAC) is important for determining appropriate surgery strategies. We aimed to develop combining models based on breast multi-parametric magnetic resonance imaging and clinicopathologic characteristics for predicting therapeutic response of primary tumor and axillary positive-node prior to treatment.

MATERIALS AND METHODS

A total of 268 breast cancer patients who completed NAC and underwent surgery were enrolled. Radiomics features and clinicopathologic characteristics were analyzed through the analysis of variance and the least absolute shrinkage and selection operator algorithm. Finally, 24 and 28 optimal features were selected to construct machine learning models based on 6 algorithms for predicting each clinical outcome, respectively. The diagnostic performances of models were evaluated in the testing set by the area under the curve (AUC), sensitivity, specificity, and accuracy.

RESULTS

Of the 268 patients, 94 (35.1 %) achieved breast cancer pathological complete response (bpCR) and of the 240 patients with clinical positive-node, 120 (50.0 %) achieved axillary lymph node pathological complete response (apCR). The multi-layer perception (MLP) algorithm yielded the best diagnostic performances in predicting apCR with an AUC of 0.825 (95 % CI, 0.764-0.886) and an accuracy of 77.1 %. And MLP also outperformed other models in predicting bpCR with an AUC of 0.852 (95 % CI, 0.798-0.906) and an accuracy of 81.3 %.

CONCLUSIONS

Our study established non-invasive combining models to predict the therapeutic response of primary breast cancer and axillary positive-node prior to NAC, which may help to modify preoperative treatment and determine post-NAC surgery strategy.

Nomogram Based on US and Clinicopathologic Characteristics: Axillary Nodal Evaluation Following Neoadjuvant Chemotherapy in Patients With Node-Positive Breast Cancer

BACKGROUND

To develop a convenient modality to predict axillary response to neoadjuvant chemotherapy (NAC) in breast cancer patients.

MATERIALS AND METHODS

In this multi-center study, a total of 1019 breast cancer patients with biopsy-proven positive lymph node (LN) receiving NAC were randomly assigned to the training and validation groups at a ratio of 7:3. Clinicopathologic and ultrasound (US) characteristics of both primary tumors and LNs were used to develop corresponding prediction models, and a nomogram integrating clinicopathologic and US predictors was generated to predict the axillary response to NAC.

RESULTS

Axillary pathological complete response (pCR) was achieved in 47.79% of the patients. The expression of estrogen receptor, human epidermal growth factor receptor -2, Ki-67 score, and clinical nodal stage were independent predictors for nodal response to NAC. Location and radiological response of primary tumors, cortical thickness and shape of LNs on US were also significantly associated with nodal pCR. In the validation cohort, the discrimination of US model (area under the curve [AUC], 0.76) was superior to clinicopathologic model (AUC, 0.68); the combined model (AUC, 0.85) demonstrates strong discriminatory power in predicting nodal pCR. Calibration curves of the nomogram based on the combined model demonstrated that substantial agreement can be observed between the predictions and observations. This nomogram showed a false-negative rates of 16.67% in all patients and 10.53% in patients with triple negative breast cancer.

CONCLUSION

Nomogram incorporating routine clinicopathologic and US characteristics can predict nodal pCR and represents a tool to aid in treatment decisions for the axilla after NAC in breast cancer patients.

Radiomic model based on magnetic resonance imaging for predicting pathological complete response after neoadjuvant chemotherapy in breast cancer patients

Purpose

To establish a model combining radiomic and clinicopathological factors based on magnetic resonance imaging to predict pathological complete response (pCR) after neoadjuvant chemotherapy in breast cancer patients.

Method

MRI images and clinicopathologic data of 329 eligible breast cancer patients from the Affiliated Hospital of Qingdao University from August 2018 to August 2022 were included in this study. All patients received neoadjuvant chemotherapy (NAC), and imaging examinations were performed before and after NAC. A total of 329 patients were randomly allocated to a training set and a test set at a ratio of 7:3. We mainly studied the following three types of prediction models: radiomic models, clinical models, and clinical-radiomic models. All models were evaluated using subject operating characteristic curve analysis and area under the curve (AUC), decision curve analysis (DCA) and calibration curves.

Results

The AUCs of the clinical prediction model, independent imaging model and clinical combined imaging model in the training set were 0.864 0.968 and 0.984, and those in the test set were 0.724, 0.754 and 0.877, respectively. According to DCA and calibration curves, the clinical-radiomic model showed good predictive performance in both the training set and the test set, and we found that we had developed a more concise clinical-radiomic nomogram.

Conclusion

We have developed a clinical-radiomic model by integrating radiomic features and clinical factors to predict pCR after NAC in breast cancer patients, thereby contributing to the personalized treatment of patients.

Quantification of Hepatic Steatosis on Dual-Energy CT in Comparison With MRI mDIXON-Quant Sequence in Breast Cancer

OBJECTIVE

The study aimed to evaluate the correlation and diagnostic value of liver fat quantification in unenhanced dual-energy CT (DECT) using quantitative magnetic resonance imaging (MRI) mDIXON-Quant sequence as reference standard in patients with breast cancer.

METHODS

Patients with breast cancer were prospectively recruited between June 2018 and April 2020. Each patient underwent liver DECT and MRI mDIXON-Quant examination. The DECT-fat volume fraction (FVF) and liver-spleen attenuation differences were compared with the MRI-proton density fat fraction using scatterplots, Bland-Altman plots, and concordance correlation coefficient. Receiver operating characteristic curves were established to determine the diagnostic accuracy of hepatic steatosis by DECT.

RESULTS

A total of 216 patients with breast cancer (mean age, 50.08 ± 9.33 years) were evaluated. The DECT-FVF correlated well with MRI-proton density fat fraction ( r2 = 0.902; P < 0.001), which was higher than the difference in liver-spleen attenuation ( r2 = 0.728; P < 0.001). Bland-Altman analysis revealed slight positive bias; the mean difference was 3.986. The DECT-FVF yielded an average concordance correlation coefficient of 0.677, which was higher than the difference of liver-spleen attenuation (-0.544). The DECT-FVF and the difference in liver-spleen attenuation both lead to mild overestimation of hepatic steatosis. The areas under the curve of DECT-FVF (0.956) were higher than the difference in liver-spleen attenuation (0.807) in identifying hepatic steatosis ( P < 0.001).

CONCLUSIONS

Dual-energy CT-FVF may serve as a reliable screening and quantitative tool for hepatic steatosis in patients with breast cancer.

Combined conventional ultrasonography with clinicopathological features to predict axillary status after neoadjuvant therapy for breast cancer: A case-control study

OBJECTIVES

This study aimed to evaluate the value of a model combining conventional ultrasonography and clinicopathologic features for predicting axillary status after neoadjuvant therapy in breast cancer.

METHODS

This retrospective study included 329 patients with lymph node-positive who underwent neoadjuvant systemic treatment (NST) from June 2019 to March 2022. Ultrasound and clinicopathological characteristics of breast lesions and axillary lymph nodes were analyzed before and after NST. The diagnostic efficacy of ultrasound, clinicopathological characteristics, and combined model were evaluated using multivariate logistic regression and receiver operator characteristic curve (ROC) analyses.

RESULTS

The area under ROC (AUC) for the ability of the combined model to predict the axillary pathological complete response (pCR) after NST was 0.882, that diagnostic effectiveness was significantly better than that of the clinicopathological model (AUC of 0.807) and the ultrasound feature model (AUC of 0.795). In addition, eight features were screened as independent predictors of axillary pCR, including clinical N stage, ERBB2 status, Ki-67, and after NST the maximum diameter reduction rate and margins of breast lesions, the short diameter, cortical thickness, and fatty hilum of lymph nodes.

CONCLUSIONS

The combined model constructed from ultrasound and clinicopathological features for predicting axillary pCR has favorable diagnostic results, which allowed more accurate identification of BC patients who had received axillary pCR after NST.

ADVANCES IN KNOWLEDGE

A combined model incorporated ultrasound and clinicopathological characteristics of breast lesions and axillary lymph nodes demonstrated favorable performance in evaluating axillary pCR preoperatively and non-invasively.

Use of Pretreatment Multiparametric MRI to Predict Tumor Regression Pattern to Neoadjuvant Chemotherapy in Breast Cancer

RATIONALE AND OBJECTIVES

To develop an easy-to-use model by combining pretreatment MRI and clinicopathologic features for early prediction of tumor regression pattern to neoadjuvant chemotherapy (NAC) in breast cancer.

MATERIALS AND METHODS

We retrospectively analyzed 420 patients who received NAC and underwent definitive surgery in our hospital from February 2012 to August 2020. Pathologic findings of surgical specimens were used as the gold standard to classify tumor regression patterns into concentric and non-concentric shrinkage. Morphologic and kinetic MRI features were both analyzed. Univariable and multivariable analyses were performed to select the key clinicopathologic and MRI features for pretreatment prediction of regression pattern. Logistic regression and six machine learning methods were used to construct prediction models, and their performance were evaluated with receiver operating characteristic curve.

RESULTS

Two clinicopathologic variables and three MRI features were selected as independent predictors to construct prediction models. The apparent area under the curve (AUC) of seven prediction models were in the range of 0.669-0.740. The logistic regression model yielded an AUC of 0.708 (95% confidence interval [CI]: 0.658-0.759), and the decision tree model achieved the highest AUC of 0.740 (95% CI: 0.691-0.787). For internal validation, the optimism-corrected AUCs of seven models were in the range of 0.592-0.684. There was no significant difference between the AUCs of the logistic regression model and that of each machine learning model.

CONCLUSION

Prediction models combining pretreatment MRI and clinicopathologic features are useful for predicting tumor regression pattern in breast cancer, which can assist to select patients who can benefit from NAC for de-escalation of breast surgery and modify treatment strategy.

Targeted axillary dissection using Radioguided Occult Lesion Localization technique in the clinically negative marked lymph node after neoadjuvant treatment in breast cancer patients

PURPOSE

To be aware of the feasibility of targeted axillary dissection (TAD) injecting 99mTechnetium-labeled macroaggregated albumin (99mTc-MAA) preoperatively into the clipped lymph node of patients with axillary complete clinical response (ycN0), after neoadjuvant chemotherapy (NAC) for breast cancer.

PATIENTS AND METHODS

A retrospective observational study was performed on N1 patients with a clipped positive node and a clinically negative axilla (ycN0) after NAC in one center. The pretreatment positive lymph node was injected with 99mTc-MAA the day before surgery and identified intraoperatively with a radioguided occult lesion localization (ROLL) technique. Patients were subjected to a TAD with the intent of identifying the clipped node and other/s sentinel nodes through a standard sentinel lymph node biopsy (SLNB).

RESULTS

54 patients and 55 axillary clipped nodes were included. The clip was intraoperatively encountered in every patient, accomplishing a 100% detection rate, although in one case no lymphatic tissue could be found in the intraoperative frozen section. An axillary lymph node dissection (ALND) was avoided in 62.9% of the cases (34/54).

CONCLUSION

The use of the ROLL technique is a highly valuable tool since it allows a 100% success rate in retrieving the marker (and a 98.1% rate in detecting the clipped lymph node) in ycN0 breast cancer patients.

Biomarkers in Breast Cancer: An Old Story with a New End

Breast cancer is the second most frequent cancer in the world. It is a heterogeneous disease and the leading cause of cancer mortality in women. Advances in molecular technologies allowed for the identification of new and more specifics biomarkers for breast cancer diagnosis, prognosis, and risk prediction, enabling personalized treatments, improving therapy, and preventing overtreatment, undertreatment, and incorrect treatment. Several breast cancer biomarkers have been identified and, along with traditional biomarkers, they can assist physicians throughout treatment plan and increase therapy success. Despite the need of more data to improve specificity and determine the real clinical utility of some biomarkers, others are already established and can be used as a guide to make treatment decisions. In this review, we summarize the available traditional, novel, and potential biomarkers while also including gene expression profiles, breast cancer single-cell and polyploid giant cancer cells. We hope to help physicians understand tumor specific characteristics and support decision-making in patient-personalized clinical management, consequently improving treatment outcome.

Omitting Sentinel Lymph Node Biopsy after Neoadjuvant Systemic Therapy for Clinically Node Negative HER2 Positive and Triple Negative Breast Cancer: A Pooled Analysis

Recent advances in systemic treatment for breast cancer have been underpinned by recognising and exploiting subtype-specific vulnerabilities to achieve higher rates of pathologic complete response (pCR) after neo-adjuvant systemic therapy (NAST). This down-staging of disease has permitted safe surgical de-escalation in patients who respond well. Triple-negative (TNBC) or HER2-positive breast cancer is most likely to achieve complete radiological response (rCR) and pCR after NAST. Hence, for selected patients, particularly those who are clinically node-negative (cN0) at diagnosis, the probability of disease in the sentinel node after NAST could be low enough to justify omitting axillary surgery. The aim of this pooled analysis was to determine the rate of sentinel node positivity (ypN+) in patients with TNBC or HER2-positive breast cancer who were initially cN0, achieving rCR and/or pCR in the breast after NAST. MedLine was searched using appropriate search terms. Five studies (N = 3834) were included in the pooled analysis, yielding a pooled ypN+ rate of 2.16% (95% CI: 1.70-2.63). This is significantly lower than the acceptable false negative rate of sentinel lymph node biopsy (SLNB) and supports consideration of omission of SLNB in this subset of patients.

Initial Results of 68Ga-FAPI-46 PET/MRI to Assess Response to Neoadjuvant Chemotherapy in Breast Cancer

Improving imaging-based response after neoadjuvant chemotherapy (NAC) in breast cancer assessment could obviate histologic confirmation of pathologic complete response (pCR) and facilitate deescalation of chemotherapy or surgery. Fibroblast activation protein inhibitor (FAPI) PET/MRI is a promising novel molecular imaging agent for the tumor microenvironment with intense uptake in breast cancer. We assessed the diagnostic performance of follow-up breast 68Ga-FAPI-46 (68Ga-FAPI) PET/MRI in classifying the response status of local breast cancer and lymph node metastases after completion of NAC and validated this approach immunohistochemically. Methods: In women who completed NAC for invasive breast cancer, follow-up 68Ga-FAPI PET/MRI and corresponding fibroblast activation protein (FAP) immunostainings were retrospectively analyzed. Metrics of 68Ga-FAPI uptake and FAP immunoreactivity in women with or without pCR were compared using the Mann-Whitney U test. Diagnostic performance to detect remnant invasive cancer was calculated for tracer uptake metrics using receiver-operating-characteristic curves and for masked readers' visual assessment categories of PET/MRI and MRI alone. Results: Thirteen women (mean age ± SD, 47 ± 9 y) were evaluated. Seven of the 13 achieved pCR in the breast and 6 in the axilla. FAP immunoreactivity was significantly associated with response status. The 68Ga-FAPI PET/MRI mean breast tumor-to-background ratio was 0.9 (range, 0.6-1.2) for pCR and 2.1 (range, 1.4-3.1) for no pCR (P = 0.001). Integrated PET/MRI could classify breast response correctly in all 13 women based on readers' visual assessment or tumor-to-background ratio. Evaluation of MRI alone resulted in at least 2 false-positives. For lymph nodes, PET/MRI readers had at least 2 false-negative classifications, whereas MRI alone resulted in 2 false-negatives and 1 false-positive. Conclusion: To our knowledge, this was the first analysis of 68Ga-FAPI PET/MRI for response assessment after NAC for breast cancer. The diagnostic performance of PET/MRI in a small study sample trended toward a gain over MRI alone, clearly supporting future prospective studies.

Application of triple evaluation method in predicting the efficacy of neoadjuvant therapy for breast cancer

OBJECTIVE

To analyze the factors related to the efficacy of neoadjuvant therapy for breast cancer and find appropriate evaluation methods for evaluating the efficacy of neoadjuvant therapy METHODS: A total of 143 patients with breast cancer treated by neoadjuvant chemotherapy at Baotou Cancer Hospital were retrospectively analyzed. The chemotherapy regimen was mainly paclitaxel combined with carboplatin for 1 week, docetaxel combined with carboplatin for 3 weeks, and was replaced with epirubicin combined with cyclophosphamide after evaluation of disease progression. All HER2-positive patients were treated with simultaneous targeted therapy, including trastuzumab single-target therapy and trastuzumab combined with pertuzumab double-target therapy. Combined with physical examination, color Doppler ultrasound, and magnetic resonance imaging (MRI), a systematic evaluation system was initially established-the "triple evaluation method." A baseline evaluation was conducted before treatment. The efficacy was evaluated by physical examination and color Doppler every cycle, and the efficacy was evaluated by physical examination, color Doppler, and MRI every two cycles.

RESULTS

The increase in ultrasonic blood flow after treatment could affect the efficacy of monitoring. The presence of two preoperative time-signal intensity curves is a therapeutically effective protective factor for inflow. The triple evaluation determined by physical examination, color Doppler ultrasound, and MRI in determining clinical efficacy is consistent with the effectiveness of the pathological gold standard.

CONCLUSION

The therapeutic effect of neoadjuvant therapy can be better evaluated by combining clinical physical examination, color ultrasound, and nuclear magnetic resonance evaluation. The three methods complement each other to avoid the insufficient evaluation of a single method, which is convenient for most prefecty-level hospitals. Additionally, this method is simple, feasible, and suitable for promotion.

A radiomics model development via the associations with genomics features in predicting axillary lymph node metastasis of breast cancer: a study based on a public database and single-centre verification

AIM

To evaluate the predictive performance of the radiomics model in predicting axillary lymph node (ALN) metastasis through the associations between radiomics features and genomic features in patients with breast cancer.

MATERIALS AND METHODS

Patients with breast cancer were enrolled retrospectively from a public database (111 patients as training group) and one hospital (15 patients as external validation group). The genomics features from transcriptome data and radiomics features from dynamic contrast-enhanced magnetic resonance imaging (MRI) were collected. Firstly, overlapping genes were identified using the Kyoto Encyclopedia of Genes and Genomes and differentially expressed gene analysis, while radiomics features were reduced using a data-driven method. Then, the associations between overlapping genes and retained radiomics features were assessed to obtain key pairs of radiomics-genomics features. Furthermore, the least absolute shrinkage and selection operator (LASSO) algorithm was used to detect the key-pairs features. Finally, radiomics and genomics models were constructed to predict ALN metastasis.

RESULTS

After using the hybrid data- and gene-driven selection method, key pairs of features were detected, which consisted of six radiomic features associated with four genomic features. The radiomics model exhibited comparable performance to the genomics model in predicting ALN metastasis (radiomic model: area under the curve [AUC] = 0.71, sensitivity = 77%, specificity = 56%; genomic model: AUC = 0.72, sensitivity = 85%, specificity = 74%). The four genomic features were enriched in six pathways and related to metabolism and human diseases.

CONCLUSION

The radiomics model established using the gene-driven hybrid selection method could predict ALN metastasis in breast cancer, which showed comparable performance to the genomics model.

Surgical Planning after Neoadjuvant Treatment in Breast Cancer: A Multimodality Imaging-Based Approach Focused on MRI

Neoadjuvant chemotherapy (NACT) today represents a cornerstone in the treatment of locally advanced breast cancer and highly chemo-sensitive tumors at early stages, increasing the possibilities of performing more conservative treatments and improving long term outcomes. Imaging has a fundamental role in the staging and prediction of the response to NACT, thus aiding surgical planning and avoiding overtreatment. In this review, we first examine and compare the role of conventional and advanced imaging techniques in preoperative T Staging after NACT and in the evaluation of lymph node involvement. In the second part, we analyze the different surgical approaches, discussing the role of axillary surgery, as well as the possibility of non-operative management after-NACT, which has been the subject of recent trials. Finally, we focus on emerging techniques that will change the diagnostic assessment of breast cancer in the near future.

Axillary ultrasound after neoadjuvant therapy reduces the false-negative rate of sentinel lymph node biopsy in patients with cytologically node-positive breast cancer

OBJECTIVES

This study aimed to determine whether post-neoadjuvant therapy (NAT) axillary ultrasound (AUS) could reduce the false-negative rate (FNR) of sentinel lymph node biopsy (SLNB). We also performed subgroup analyses to identify the appropriate patient for SLNB.

METHODS

A total of 220 patients with cytologically proven axillary node-positive breast cancer who underwent both SLNB and axillary lymph node dissection (ALND) after NAT were included. We calculated the FNR of SLNB. In the case of post-NAT AUS results available, AUS was classified as negative or positive. Then the FNR of post-NAT AUS combined with SLNB was evaluated. Subgroup analyses based on the number of sentinel lymph nodes removed, molecular subtypes, and the clinical N stage were also performed.

RESULTS

The overall axillary lymph node pathological complete response rate was 45.5% (100/220). The FNR of SLNB alone was 15.8% (95%CI: 9.2 to 22.5%). Post-NAT AUS results were available for 181 patients. When combined negative post-NAT AUS results and SLNB, the FNR was reduced to 7.5% (95%CI: 2.4 to 12.7%). Subgroup analyses of the FNR for SLNB alone and negative post-NAT AUS combined with SLNB were shown as follows: in cases patients with less than three sentinel lymph nodes (SLNs) and at least three SLNs removed, the FNR was decreased from 24.5 to 13.2%, and 9.0 to 5.0%, respectively. The FNR was decreased from 20.8 to 10.5% in HR+/HER2+subgroup, 21.4 to 16.7% in HR-/HER2+subgroup, 15.9 to 7.0% in HR+/HER2- subgroup, and 0% in HR-/HER2- subgroup, respectively. For cN1 patients, the FNR was decreased from 18.1 to 12.1% while 17.1 to 3.6% for cN2 patients and 0% for cN3 patients.

CONCLUSION

Using negative post-NAT AUS may help to decrease the FNR and improve patient selection for SLNB.

Assessment of diffusion-weighted MRI in predicting response to neoadjuvant chemotherapy in breast cancer patients

To compare region of interest (ROI)-apparent diffusion coefficient (ADC) on diffusion-weighted imaging (DWI) measurements and Ki-67 proliferation index before and after neoadjuvant chemotherapy (NACT) for breast cancer. 55 women were enrolled in this prospective single-center study, with a final population of 47 women (49 cases of invasive breast cancer). ROI-ADC measurements were obtained on MRI before and after NACT and were compared to histological findings, including the Ki-67 index in the whole study population and in subgroups of "pathologic complete response" (pCR) and non-pCR. Nineteen percent of women experienced pCR. There was a significant inverse correlation between Ki-67 index and ROI-ADC before NACT (r = - 0.443, p = 0.001) and after NACT (r = - 0.614, p < 0.001). The mean Ki-67 index decreased from 45.8% before NACT to 18.0% after NACT (p < 0.001), whereas the mean ROI-ADC increased from 0.883 × 10^-3 mm²/s before NACT to 1.533 × 10^-3 mm²/s after NACT (p < 0.001). The model for the prediction of Ki67 index variations included patient age, hormonal receptor status, human epidermal growth factor receptor 2 status, Scarff-Bloom-Richardson grade 2, and ROI-ADC variations (p = 0.006). After NACT, a significant increase in breast cancer ROI-ADC on diffusion-weighted imaging was observed and a significant decrease in the Ki-67 index was predicted. Clinical trial registration number: clinicaltrial.gov NCT02798484, date: 14/06/2016.

Diffusion-Weighted MRI in the Evaluation of Early-Stage Breast Cancer Treated with a Short Preoperative Radiotherapy: Preliminary Results

Objective

To assess tumor response with diffusion-weighted MRI (DW-MRI) after a short preoperative radiotherapy in early-stage breast cancer (BCa).

Materials and Methods

This was a prospective, single-center pilot study. 3T-MRI were performed before and after radiotherapy. The longest diameter (LD) and the apparent diffusion coefficient (ADC) value of a region of interest (ROI) of the tumors were recorded. Histopathology and immunohistochemistry, including the Ki-67 index of the core biopsy and of the surgical specimen, were the reference standards.

Results

Nineteen patients with 22 early-stage BCa were included. The mean ROI ADC value was 1.093 ± 0.278 × 10^-3 mm²/s before radiotherapy and 1.490 ± 0.429 × 10^-3 mm²/s (p-value < 0.001) after radiotherapy. The Ki-67 index was 9.2 ± 9.1% at the percutaneous biopsy before radiotherapy and 4.9 ± 7.5% (p-value = 0.005) after radiotherapy at the surgical specimen. After neoadjuvant radiotherapy, a 4.7% decrease in LD and a 36.3% increase in ROI-ADC of the tumors were measured at MRI and a 46.7% decrease in Ki-67 index was observed at histology of the surgical specimen in comparison with the percutaneous core biopsy.

Conclusion

In early-stage BCa, a significant increase in ROI-ADC at DWI and a significant decrease in Ki-67 index were observed after a short preoperative radiotherapy, suggesting early tumor response.

Development and validation of a radiogenomics model to predict axillary lymph node metastasis in breast cancer integrating MRI with transcriptome data: A multicohort study

Introduction

To develop and validate a radiogenomics model for predicting axillary lymph node metastasis (ALNM) in breast cancer compared to a genomics and radiomics model.

Methods

This retrospective study integrated transcriptomic data from The Cancer Genome Atlas with matched MRI data from The Cancer Imaging Archive for the same set of 111 patients with breast cancer, which were used as the training and testing groups. Fifteen patients from one hospital were enrolled as the external validation group. Radiomics features were extracted from dynamic contrast-enhanced (DCE)-MRI of breast cancer, and genomics features were derived from differentially expressed gene analysis of transcriptome data. Boruta was used for genomics and radiomics data dimension reduction and feature selection. Logistic regression was applied to develop genomics, radiomics, and radiogenomics models to predict ALNM. The performance of the three models was assessed by receiver operating characteristic curves and compared by the Delong test.

Results

The genomics model was established by nine genomics features, and the radiomics model was established by three radiomics features. The two models showed good discrimination performance in predicting ALNM in breast cancer, with areas under the curves (AUCs) of 0.80, 0.67, and 0.52 for the genomics model and 0.72, 0.68, and 0.71 for the radiomics model in the training, testing and external validation groups, respectively. The radiogenomics model integrated with five genomics features and three radiomics features had a better performance, with AUCs of 0.84, 0.75, and 0.82 in the three groups, respectively, which was higher than the AUC of the radiomics model in the training group and the genomics model in the external validation group (both P < 0.05).

Conclusion

The radiogenomics model combining radiomics features and genomics features improved the performance to predict ALNM in breast cancer.

Beyond N Staging in Breast Cancer: Importance of MRI and Ultrasound-based Imaging

The correct N-staging in breast cancer is crucial to tailor treatment and stratify the prognosis. N-staging is based on the number and the localization of suspicious regional nodes on physical examination and/or imaging. Since clinical examination of the axillary cavity is associated with a high false negative rate, imaging modalities play a central role. In the presence of a T1 or T2 tumor and 0–2 suspicious nodes, on imaging at the axillary level I or II, a patient should undergo sentinel lymph node biopsy (SLNB), whereas in the presence of three or more suspicious nodes at the axillary level I or II confirmed by biopsy, they should undergo axillary lymph node dissection (ALND) or neoadjuvant chemotherapy according to a multidisciplinary approach, as well as in the case of internal mammary, supraclavicular, or level III axillary involved lymph nodes. In this scenario, radiological assessment of lymph nodes at the time of diagnosis must be accurate. False positives may preclude a sentinel lymph node in an otherwise eligible woman; in contrast, false negatives may lead to an unnecessary SLNB and the need for a second surgical procedure. In this review, we aim to describe the anatomy of the axilla and breast regional lymph node, and their diagnostic features to discriminate between normal and pathological nodes at Ultrasound (US) and Magnetic Resonance Imaging (MRI). Moreover, the technical aspects, the advantage and limitations of MRI versus US, and the possible future perspectives are also analyzed, through the analysis of the recent literature.

A Novel Combined Nomogram Model for Predicting the Pathological Complete Response to Neoadjuvant Chemotherapy in Invasive Breast Carcinoma of No Specific Type: Real-World Study

Objective

To explore the value of a predictive model combining the multiparametric magnetic resonance imaging (mpMRI) radiomics score (RAD-score), clinicopathologic features, and morphologic features for the pathological complete response (pCR) to neoadjuvant chemotherapy (NAC) in invasive breast carcinoma of no specific type (IBC-NST).

Methods

We enrolled, retrospectively and consecutively, 206 women with IBC-NST who underwent surgery after NAC and obtained pathological results from August 2018 to October 2021. Four RAD-scores were constructed for predicting the pCR based on fat-suppression T2-weighted imaging (FS-T2WI), diffusion-weighted imaging (DWI), contrast-enhanced T1-weighted imaging (T1WI+C) and their combination, which was called mpMRI. The best RAD-score was combined with clinicopathologic and morphologic features to establish a nomogram model through binary logistic regression. The predictive performance of the nomogram was evaluated using the area under receiver operator characteristic (ROC) curve (AUC) and calibration curve. The clinical net benefit of the model was evaluated using decision curve analysis (DCA).

Results

The mpMRI RAD-score had the highest diagnostic performance, with AUC of 0.848 among the four RAD-scores. T stage, human epidermal growth factor receptor-2 (HER2) status, RAD-score, and roundness were independent factors for predicting the pCR (P < 0.05 for all). The combined nomogram model based on these factors achieved AUCs of 0.930 and 0.895 in the training cohort and validation cohort, respectively, higher than other models (P < 0.05 for all). The calibration curve showed that the predicted probabilities of the nomogram were in good agreement with the actual probabilities, and DCA indicated that it provided more net benefit than the treat-none or treat-all scheme by decision curve analysis in both training and validation datasets.

Conclusion

The combined nomogram model based on the mpMRI RAD-score combined with clinicopathologic and morphologic features may improve the predictive performance for the pCR of NAC in patients with IBC-NST.

MRI in the Setting of Neoadjuvant Treatment of Breast Cancer

Neoadjuvant therapy may reduce tumor burden preoperatively, allowing breast conservation treatment for tumors previously unresectable or requiring mastectomy without reducing disease-free survival. Oncologists can also use the response of the tumor to neoadjuvant chemotherapy (NAC) to identify treatment likely to be successful against any unknown potential distant metastasis. Accurate preoperative estimations of tumor size are necessary to guide appropriate treatment with minimal delays and can provide prognostic information. Clinical breast examination and mammography are inaccurate methods for measuring tumor size after NAC and can over- and underestimate residual disease. While US is commonly used to measure changes in tumor size during NAC due to its availability and low cost, MRI remains more accurate and simultaneously images the entire breast and axilla. No method is sufficiently accurate at predicting complete pathological response that would obviate the need for surgery. Diffusion-weighted MRI, MR spectroscopy, and MRI-based radiomics are emerging fields that potentially increase the predictive accuracy of tumor response to NAC.

Role of shear wave elastography as an adjunct to axillary ultrasonography in predicting nodal metastasis in breast cancer patients with suspicious nodes

Objective:

To evaluate the role of shear wave elastography (SWE) of suspicious axillary lymph nodes and its combination with B-mode USG in predicting nodal metastasis in breast cancer patients.

Methods:

Prospective observational study was performed from June 2018 to August 2020 on breast cancer patients with suspicious axillary nodes on USG. B-mode features (cortical thickness, effacement of fatty hilum, non-hilar blood flow and round shape) and SWE parameters (Emax, Emin, Emean and ESD) of the node with the thickest cortex were evaluated. Diagnostic performances of USG, SWE and their combination were estimated using pathological status of the node on biopsy as the gold standard.

Results:

Of the 54 patients evaluated, optimal elasticity maps were obtained in 49 nodes of 49 patients (mean age, 46.3 ± 12.1 years; 48/49 (98%) females). On biopsy, 38 nodes (77.6%) had metastasis, while 11 (22.4%) had reactive hyperplasia. Emax, Emin, Emean and ESD of both cortex and hilum were significantly higher in metastatic than reactive nodes. Emax (cortex) ≥14.9 kPa had the best diagnostic performance (sensitivity, 73.7%; specificity, 81.8%). Cortical thickness ≥6.7 mm had the best diagnostic performance among B-mode features (sensitivity, 89.5%; specificity, 72.7%). Combining cortical thickness with effacement of fatty hilum and/or non-hilar blood flow yielded sensitivity of 89.5% and specificity of 90.9%. Addition of Emax (cortex) to cortical thickness and combination of ≥2 B-mode features increased their specificities to 90.9 and 100%, respectively.

Conclusions:

Metastatic axillary nodes are stiffer than reactive nodes on SWE in breast cancer patients. Emax (cortex) has the best diagnostic performance in differentiating between reactive hyperplasia and nodal metastasis. Combination of Emax (cortex) and cortical thickness increases the specificity for diagnosing metastasis, especially in nodes showing only cortical thickening.

Advances in knowledge:

Combination of SWE and B-mode USG is highly specific for differentiating metastasis from reactive hyperplasia in suspicious nodes of breast carcinoma patients, especially in nodes with only cortical thickening.

Advances in Imaging in Evaluating the Efficacy of Neoadjuvant Chemotherapy for Breast Cancer

Neoadjuvant chemotherapy (NAC) is increasingly widely used in breast cancer treatment, and accurate evaluation of its response provides essential information for treatment and prognosis. Thus, the imaging tools used to quantify the disease response are critical in evaluating and managing patients treated with NAC. We discussed the recent progress, advantages, and disadvantages of common imaging methods in assessing the efficacy of NAC for breast cancer.

ACR Appropriateness Criteria® Imaging of the Axilla

This publication reviews the current evidence supporting the imaging approach of the axilla in various scenarios with broad differential diagnosis ranging from inflammatory to malignant etiologies. Controversies on the management of axillary adenopathy results in disagreement on the appropriate axillary imaging tests. Ultrasound is often the appropriate initial imaging test in several clinical scenarios. Clinical information (such as age, physical examinations, risk factors) and concurrent complete breast evaluation with mammogram, tomosynthesis, or MRI impact the type of initial imaging test for the axilla. Several impactful clinical trials demonstrated that selected patient's population can received sentinel lymph node biopsy instead of axillary lymph node dissection with similar overall survival, and axillary lymph node dissection is a safe alternative as the nodal staging procedure for clinically node negative patients or even for some node positive patients with limited nodal tumor burden. This approach is not universally accepted, which adversely affect the type of imaging tests considered appropriate for axilla. This document is focused on the initial imaging of the axilla in various scenarios, with the understanding that concurrent or subsequent additional tests may also be performed for the breast. The American College of Radiology Appropriateness Criteria are evidence-based guidelines for specific clinical conditions that are reviewed annually by a multidisciplinary expert panel. The guideline development and revision include an extensive analysis of current medical literature from peer reviewed journals and the application of well-established methodologies (RAND/UCLA Appropriateness Method and Grading of Recommendations Assessment, Development, and Evaluation or GRADE) to rate the appropriateness of imaging and treatment procedures for specific clinical scenarios. In those instances where evidence is lacking or equivocal, expert opinion may supplement the available evidence to recommend imaging or treatment.

Combining conventional ultrasound and sonoelastography to predict axillary status after neoadjuvant chemotherapy for breast cancer

OBJECTIVE

To determine the ability of conventional ultrasound (US) combined with shear wave elastography (SWE) to reveal axillary status after neoadjuvant chemotherapy (NAC) in breast cancer patients.

METHODS

From September 2016 to December 2021, 201 patients with node-positive breast cancer who underwent NAC were enrolled in this prospective study. Conventional US features of axillary lymph nodes and SWE characteristics of breast lesions after NAC were analyzed. The diagnostic performances of US, SWE, and their combination were assessed using multivariate logistic regression and receiver operator characteristic curve (ROC) analyses.

RESULTS

The area under the ROC curve (AUC) for the ability of conventional US features to determine axillary status after NAC was 0.82, with a sensitivity of 85.23%, a specificity of 67.39%, and an accuracy of 76.11%. Shear wave velocity (SWV) displayed moderate performance for predicting axilla status after NAC with SWV_mean demonstrating an AUC of 0.85. Cortical thickness and shape of axillary nodes and SWV_mean of breast tumors were independently associated with axillary nodal metastasis after NAC. Compared to conventional US, the combination of conventional US of axillary lymph nodes with SWE of breast lesions achieved a significantly higher AUC (0.90 vs 0.82, p < 0.01, Delong's test) with a sensitivity of 87.50%, improved specificity of 82.61% and accuracy of 85.00%.

CONCLUSIONS

Breast SWE was independently associated with residual metastasis of axillary node after NAC in patients with initially diagnosed positive axilla. Combining SWE with conventional US showed good diagnostic performance for axillary node disease after NAC.

KEY POINTS

• Breast SWE can serve as a supplement to axilla US for the evaluation of the axilla after NAC. • The combination of axilla US with breast SWE may be a promising method to facilitate less-invasive treatment in patients receiving NAC.

Value of diffusion-weighted MRI in predicting early response to neoadjuvant chemotherapy of breast cancer: comparison between ROI-ADC and whole-lesion-ADC measurements

OBJECTIVE

The aim of the study was to assess DWI with ROI-ADC and WL-ADC measurements in early response after NAC in breast cancer.

METHODS

Between January 2016 and December 2019, 55 women were enrolled in this prospective single-center study. MRI was performed at three time points for each patient: before treatment (MRI 1: DW and DCE MRI), after one cycle of NAC (MRI 2: noncontrast DW MRI), and after completion of NAC before surgery (MRI 3: DW and DCE MRI). ROI-ADC and WL-ADC measurements were obtained on MRI and were compared to histology findings and to the RCB class. Patients were categorized as having pCR or non-pCR.

RESULTS

Among 48 patients, 9 experienced pCR. An increase of ROI-ADC between MRI 1 and 2 of more than 47.5% had a sensitivity of 88.9% and a specificity of 63.4% in predicting pCR, whereas WL-ADC did not predict pCR. An increase of ROI-ADC between MRI 1 and 2 of more than 47.5% had a sensitivity of 83.3% and a specificity of 64.9% in predicting radiologic complete response. An increase of WL-ADC between MRI 1 and 2 of more than 25.5% had a sensitivity of 83.3% and a specificity of 75.5% in predicting radiologic complete response.

CONCLUSION

After one cycle of NAC, a significant increase in breast tumor ROI-ADC at DWI predicted complete pathologic and radiologic responses.

KEY POINTS

• An increase of WL-ADC between MRI 1 and 2 of more than 25.5% had a sensitivity of 83.3% and a specificity of 75.5% in predicting radiologic complete response. • An increase of ROI-ADC between MRI 1 and 2 of more than 47.5% had a sensitivity of 88.9% and a specificity of 63.4% in predicting pCR, and a sensitivity of 83.3% and a specificity of 64.9% in predicting radiologic complete response. • A significant increase in breast tumor ROI-ADC at DWI predicted complete pathologic and radiologic responses.

Role of MRI in predicting response to neo-adjuvant systemic therapy (NAST) in breast cancer

BACKGROUND AND OBJECTIVES

MRI is generally performed to assess response to Neo-adjuvant systemic therapy (NAST) in breast cancer.

OBJECTIVE

To assess role of MRI in determining the probability of having residual disease in patients undergoing NAST. We also evaluated synchronous cancers diagnosed following MRI.

METHODS

This is a retrospective study which included all patients who had pre-and post-NAST MRI between June 2014 and December 2019. Data on demographics, tumour characteristics and pathology were collected and analysed. Pre- and post-MRI probability were calculated and depicted on nomograms.

RESULTS

The study included 205 patients. Overall pre-MRI probability of having residual disease was 55% (OR:1.2). The post-MRI probability was 78% (95% CI 72-83%; OR:3.5) if MRI showed residual disease and 23% (95% CI 16-31%, OR:0.3) if imaging showed complete response. The absolute benefit was higher in TNBC and HR-HER2. Additional cancers were identified in 8.78% of patients.

CONCLUSION

MRI is beneficial in evaluating response to NAST specifically in TNBC and HR-HER2 cancers. Pre- and post-MRI probabilities of residual disease depicted on nomograms are a useful tool for clinicians. MRI can potentially impact the treatment decisions by identification of synchronous cancers.

ACR Appropriateness Criteria® Plexopathy: 2021 Update

Plexopathy may be caused by diverse pathologies, including trauma, nerve entrapment, neoplasm, inflammation, infection, autoimmune disease, hereditary disease, and idiopathic etiologies. For patients presenting with brachial or lumbosacral plexopathy, dedicated plexus MRI is the most appropriate initial imaging modality for all clinical scenarios and can identify processes both intrinsic and extrinsic to the nerves. Other imaging tests may be appropriate for initial imaging depending on the clinical scenario. This document addresses initial imaging strategies for brachial and lumbosacral plexopathy in the following clinical situations: nontraumatic plexopathy with no known malignancy, traumatic plexopathy (not perinatal), and plexopathy occurring in the context of a known malignancy or posttreatment syndrome. The American College of Radiology Appropriateness Criteria are evidence-based guidelines for specific clinical conditions that are reviewed annually by a multidisciplinary expert panel. The guideline development and revision include an extensive analysis of current medical literature from peer reviewed journals and the application of well-established methodologies (RAND/UCLA Appropriateness Method and Grading of Recommendations Assessment, Development, and Evaluation or GRADE) to rate the appropriateness of imaging and treatment procedures for specific clinical scenarios. In those instances where evidence is lacking or equivocal, expert opinion may supplement the available evidence to recommend imaging or treatment.

Analysis of mammograms using artificial intelligence to predict response to neoadjuvant chemotherapy in breast cancer patients: proof of concept

Objectives

In this proof of concept study, a deep learning–based method for automatic analysis of digital mammograms (DM) as a tool to aid in assessment of neoadjuvant chemotherapy (NACT) treatment response in breast cancer (BC) was examined.

Methods

Baseline DM from 453 patients receiving NACT between 2005 and 2019 were included in the study cohort. A deep learning system, using the aforementioned baseline DM, was developed to predict pathological complete response (pCR) in the surgical specimen after completion of NACT. Two image patches, one extracted around the detected tumour and the other from the corresponding position in the reference image, were fed into a classification network. For training and validation, 1485 images obtained from 400 patients were used, and the model was ultimately applied to a test set consisting of 53 patients.

Results

A total of 95 patients (21%) achieved pCR. The median patient age was 52.5 years (interquartile range 43.7–62.1), and 255 (56%) were premenopausal. The artificial intelligence (AI) model predicted the pCR as represented by the area under the curve of 0.71 (95% confidence interval 0.53–0.90; p = 0.035). The sensitivity was 46% at a fixed specificity of 90%.

Conclusions

Our study describes an AI platform using baseline DM to predict BC patients’ responses to NACT. The initial AI performance indicated the potential to aid in clinical decision-making. In order to continue exploring the clinical utility of AI in predicting responses to NACT for BC, further research, including refining the methodology and a larger sample size, is warranted.

Key Points

• We aimed to answer the following question: Prior to initiation of neoadjuvant chemotherapy, can artificial intelligence (AI) applied to digital mammograms (DM) predict breast tumour response?

• DMs contain information that AI can make use of for predicting pathological complete (pCR) response after neoadjuvant chemotherapy for breast cancer.

• By developing an AI system designed to focus on relevant parts of the DM, fully automatic pCR prediction can be done well enough to potentially aid in clinical decision-making.

Imaging Updates to Breast Cancer Lymph Node Management

Metastatic lymph node involvement in breast cancer is a key determinant of the overall stage of disease and prognosis. Historically, lymph node status was determined by surgery first, with adjuvant treatments determined based on the results of the final surgical pathologic analysis. While this sequence is still applicable in many cases, neoadjuvant systemic treatment (NST) is increasingly being administered as the initial treatment. In cases that demonstrate good therapeutic response to drug therapies, NST may permit the option to perform less radical surgeries subsequently. Current breast cancer treatment has become multidisciplinary, with overlapping roles from the different disciplines. As surgery may be postponed, imaging and image-guided lymph node interventions have gained importance as the primary means of lymph node assessment. Imaging enables evaluation of all regional nodal basins, including locations where surgery is not usually performed. By differentiating limited versus extensive nodal involvement, imaging findings help determine whether initial treatment should be surgical or medical. If medical treatment with NST is indicated, imaging is performed to monitor the in vivo nodal response to drug therapy and ultimately to help determine the surgical technique to perform on the basis of the final imaging findings after NST. The authors discuss the imaging features of nodal metastases and the indications and techniques for the various image-guided procedures. The relative usefulness and shortcomings of the various imaging examinations are reviewed to discuss how they can be applied when biopsy results are not available. The role of imaging in the multidisciplinary team approach is emphasized based on past clinical trials of lymph node management and recent evolving knowledge of breast cancer staging. Online supplemental material is available for this article. ^©RSNA, 2021.

PET Imaging for Breast Cancer

Imaging plays an integral role in the clinical care of patients with breast cancer. This review article focuses on the use of PET imaging for breast cancer, highlighting the clinical indications and limitations of 2-deoxy-2-[¹⁸F]fluoro-d-glucose (FDG) PET/CT, the potential use of PET/MRI, and 16α-[¹⁸F]fluoroestradiol (FES), a newly approved radiopharmaceutical for estrogen receptor imaging.

What's Hot in Breast MRI

Several articles in the literature have demonstrated a promising role for breast MRI techniques that are more economic in total exam time than others when used as supplement to mammography for detection and diagnosis of breast cancer. There are many technical factors that must be considered in the shortened breast MRI protocols to cut down time of standard ones, including using optimal fat suppression, gadolinium-chelates intravascular contrast administrations for dynamic imaging with post processing subtractions and maximum intensity projections (MIP) high spatial and temporal resolution among others. Multiparametric breast MRI that includes both gadolinium-dependent, i.e., dynamic contrast-enhanced (DCE-MRI) and gadolinium-free techniques, i.e., diffusion-weighted/diffusion-tensor magnetic resonance imaging (DWI/DTI) are shown by several investigators that can provide extremely high sensitivity and specificity for detection of breast cancer. This article provides an overview of the proven indications for breast MRI including breast cancer screening for higher than average risk, determining chemotherapy induced tumor response, detecting residual tumor after incomplete surgical excision, detecting occult cancer in patients presenting with axillary node metastasis, detecting residual tumor after incomplete breast cancer surgical excision, detecting cancer when results of conventional imaging are equivocal, as well patients suspicious of having breast implant rupture. Despite having the highest sensitivity for breast cancer detection, there are pitfalls, however, secondary to false positive and false negative contrast enhancement and contrast-free MRI techniques. Awareness of the strengths and limitations of different approaches to obtain state of the art MR images of the breast will facilitate the work-up of patients with suspicious breast lesions.

Breast cancer subtypes affect the ultrasound performance for axillary lymph node status evaluation after neoadjuvant chemotherapy: a retrospective analysis

PURPOSE

The aim of our study was to investigate the effect of breast cancer subtypes on the diagnostic value of axillary ultrasound for node status evaluation after neoadjuvant chemotherapy.

PATIENTS AND METHODS

Pathologic node-positive breast cancer patients underwent axillary ultrasound imaging after neoadjuvant chemotherapy were retrospectively reviewed. The enrolled patients were classified into four subtypes: Luminal A, Luminal B, human epidermal growth factor receptor 2-enriched and triple-negative. Ultrasound images of axillary nodes were reviewed and were evaluated as normal or abnormal and were associated with final pathologic results. Diagnostic value of axillary ultrasound was assessed in four subtypes based on sensitivity, specificity, positive predictive value and negative predictive value. The diagnostic value of axillary ultrasound as well as clinical and pathological characteristics was compared between four breast cancer subtypes using chi-square test or fisher's exact test.

RESULT

Luminal A subtype had highest positive predictive value (92.1%), lowest sensitivity (43.8%) and lowest negative predictive value (11.8%). Triple-negative subtype had lowest positive predictive value (73.2%), highest sensitivity (76.9%) and highest negative predictive value (59.1%) (P < 0.05). Luminal B and human epidermal growth factor receptor 2-enriched subtypes had medium sensitivity, positive predictive value and negative predictive value.

CONCLUSION

The diagnostic value of axillary ultrasound for node residue disease assessment after neoadjuvant chemotherapy is different between four breast cancer subtypes.

Predicting pathological axillary lymph node status with ultrasound following neoadjuvant therapy for breast cancer

Purpose

High-performing imaging and predictive markers are warranted to minimize surgical overtreatment of the axilla in breast cancer (BC) patients receiving neoadjuvant chemotherapy (NACT). Here we have investigated whether axillary ultrasound (AUS) could identify axillary lymph node (ALN) metastasis (ALNM) pre-NACT and post-NACT for BC. The association of tumor, AUS features and mammographic density (MD) with axillary-pathological complete response (axillary-pCR) post-NACT was also assessed.

Methods

The NeoDense-study cohort (N = 202, NACT during 2014–2019), constituted a pre-NACT cohort, whereas patients whom had a cytology verified ALNM pre-NACT and an axillary dissection performed (N = 114) defined a post-NACT cohort. AUS characteristics were prospectively collected pre- and post-NACT. The diagnostic accuracy of AUS was evaluated and stratified by histological subtype and body mass index (BMI). Predictors of axillary-pCR were analyzed, including MD, using simple and multivariable logistic regression models.

Results

AUS demonstrated superior performance for prediction of ALNM pre-NACT in comparison to post-NACT, as reflected by the positive predictive value (PPV) 0.94 (95% CI 0.89–0.97) and PPV 0.76 (95% CI 0.62–0.87), respectively. We found no difference in AUS performance according to neither BMI nor histological subtype. Independent predictors of axillary-pCR were: premenopausal status, ER-negativity, HER2-overexpression, and high MD.

Conclusion

Baseline AUS could, to a large extent, identify ALNM; however, post-NACT, AUS was insufficient to determine remaining ALNM. Thus, our results support the surgical staging of the axilla post-NACT. Baseline tumor biomarkers and patient characteristics were predictive of axillary-pCR. Larger, multicenter studies are needed to evaluate the performance of AUS post-NACT.

Supplementary Information

The online version contains supplementary material available at 10.1007/s10549-021-06283-8.

Current and future cancer staging after neoadjuvant treatment for solid tumors

Until recently, cancer registries have only collected cancer clinical stage at diagnosis, before any therapy, and pathological stage after surgical resection, provided no treatment has been given before the surgery, but they have not collected stage data after neoadjuvant therapy (NAT). Because NAT is increasingly being used to treat a variety of tumors, it has become important to make the distinction between both the clinical and the pathological assessment without NAT and the assessment after NAT to avoid any misunderstanding of the significance of the clinical and pathological findings. It also is important that cancer registries collect data after NAT to assess response and effectiveness of this treatment approach on a population basis. The prefix y is used to denote stage after NAT. Currently, cancer registries of the American College of Surgeons' Commission on Cancer only partially collect y stage data, and data on the clinical response to NAT (yc or posttherapy clinical information) are not collected or recorded in a standardized fashion. In addition to NAT, nonoperative management after radiation and chemotherapy is being used with increasing frequency in rectal cancer and may be expanded to other treatment sites. Using examples from breast, rectal, and esophageal cancers, the pathological and imaging changes seen after NAT are reviewed to demonstrate appropriate staging.

Neoadjuvant Chemotherapy, Endocrine Therapy, and Targeted Therapy for Breast Cancer: ASCO Guideline

PURPOSE

To develop guideline recommendations concerning optimal neoadjuvant therapy for breast cancer.

METHODS

ASCO convened an Expert Panel to conduct a systematic review of the literature on neoadjuvant therapy for breast cancer and provide recommended care options.

RESULTS

A total of 41 articles met eligibility criteria and form the evidentiary basis for the guideline recommendations.

RECOMMENDATIONS

Patients undergoing neoadjuvant therapy should be managed by a multidisciplinary care team. Appropriate candidates for neoadjuvant therapy include patients with inflammatory breast cancer and those in whom residual disease may prompt a change in therapy. Neoadjuvant therapy can also be used to reduce the extent of local therapy or reduce delays in initiating therapy. Although tumor histology, grade, stage, and estrogen, progesterone, and human epidermal growth factor receptor 2 (HER2) expression should routinely be used to guide clinical decisions, there is insufficient evidence to support the use of other markers or genomic profiles. Patients with triple-negative breast cancer (TNBC) who have clinically node-positive and/or at least T1c disease should be offered an anthracycline- and taxane-containing regimen; those with cT1a or cT1bN0 TNBC should not routinely be offered neoadjuvant therapy. Carboplatin may be offered to patients with TNBC to increase pathologic complete response. There is currently insufficient evidence to support adding immune checkpoint inhibitors to standard chemotherapy. In patients with hormone receptor (HR)-positive (HR-positive), HER2-negative tumors, neoadjuvant chemotherapy can be used when a treatment decision can be made without surgical information. Among postmenopausal patients with HR-positive, HER2-negative disease, hormone therapy can be used to downstage disease. Patients with node-positive or high-risk node-negative, HER2-positive disease should be offered neoadjuvant therapy in combination with anti-HER2-positive therapy. Patients with T1aN0 and T1bN0, HER2-positive disease should not be routinely offered neoadjuvant therapy.Additional information is available at www.asco.org/breast-cancer-guidelines.

Radiomic Nomogram for Pretreatment Prediction of Pathologic Complete Response to Neoadjuvant Therapy in Breast Cancer: Predictive Value of Staging Contrast-enhanced CT

INTRODUCTION

The purpose of this study was to predict pathologic complete response (pCR) to neoadjuvant therapy in breast cancer using radiomics based on pretreatment staging contrast-enhanced computed tomography (CECT).

PATIENTS AND METHODS

A total of 215 patients were retrospectively analyzed. Based on the intratumoral and peritumoral regions of CECT images, radiomic features were extracted and selected, respectively, to develop an intratumoral signature and a peritumoral signature with logistic regression in a training dataset (138 patients from November 2015 to October 2017). We also developed a clinical model with the molecular characterization of the tumor. A radiomic nomogram was further constructed by incorporating the intratumoral and peritumoral signatures with molecular characterization. The performance of the nomogram was validated in terms of discrimination, calibration, and clinical utility in an independent validation dataset (77 patients from November 2017 to December 2018). Stratified analysis was performed to develop a subtype-specific radiomic signature for each subgroup.

RESULTS

Compared with the clinical model (area under the curve [AUC], 0.756), the radiomic nomogram (AUC, 0.818) achieved better performance for pCR prediction in the validation dataset with continuous net reclassification improvement of 0.787 and good calibration. Decision curve analysis suggested the nomogram was clinically useful. Subtype-specific radiomic signatures showed improved AUCs (luminal subgroup, 0.936; human epidermal growth factor receptor 2-positive subgroup, 0.825; and triple negative subgroup, 0.858) for pCR prediction.

CONCLUSION

This study has revealed a predictive value of pretreatment staging-CECT and successfully developed and validated a radiomic nomogram for individualized prediction of pCR to neoadjuvant therapy in breast cancer, which could assist clinical decision-making and improve patient outcome.

Neoadjuvant breast cancer treatment response; tumor size evaluation through different conventional imaging modalities in the NeoDense study

BACKGROUND

Neoadjuvant chemotherapy (NACT) is offered to an increasing number of breast cancer (BC) patients, and comprehensive monitoring of treatment response is of utmost importance. Several imaging modalities are available to follow tumor response, although likely to provide different clinical information. We aimed to examine the association between early radiological response by three conventional imaging modalities and pathological complete response (pCR). Further, we investigated the agreement between these modalities pre-, during, and post-NACT, and the accuracy of predicting pathological residual tumor burden by these imaging modalities post-NACT.

MATERIAL AND METHODS

This prospective Swedish cohort study included 202 BC patients assigned to NACT (2014-2019). Breast imaging with clinically used modalities: mammography, ultrasound, and tomosynthesis was performed pre-, during, and post-NACT. We investigated the agreement of tumor size by the different imaging modalities, and their accuracy of tumor size estimation. Patients with a radiological complete response or radiological partial response (≥30% decrease in tumor diameter) during NACT were classified as radiological early responders.

RESULTS

Patients with an early radiological response by ultrasound had 2.9 times higher chance of pCR than early radiological non-responders; the corresponding relative chance for mammography and tomosynthesis tumor size measures was 1.8 and 2.8, respectively. Post-NACT, each modality, separately, could accurately estimate tumor size (within 5 mm margin compared to pathological evaluation) in 43-46% of all tumors. The diagnostic precision in predicting pCR post-NACT was similar between the three imaging modalities; however, tomosynthesis had slightly higher specificity and positive predictive values.

CONCLUSION

Breast imaging modalities correctly estimated pathological tumor size in less than half of the tumors. Based on this finding, predicting residual tumor size post-NACT is challenging using conventional imaging. Patients with early radiological non-response might need improved monitoring during NACT and be considered for changed treatment plans.

Breast cancer during pregnancy: matched study of diagnostic approach, tumor characteristics, and prognostic factors

INTRODUCTION

Breast cancer is one of the most frequently occurring cancers during pregnancy and its incidence is increasing. Many studies have shown poor outcomes, the causes of which remain unclear.

OBJECTIVES

To analyze radiologic characteristics, histology, and prognosis factors of breast cancer during pregnancy.

METHODS

A total of 42 patients with breast cancer diagnosed during pregnancy (BCP) were matched with 84 patients with breast cancer of similar age who were not pregnant. Sensitivity of radiology, tumor characteristics, prognosis factors, disease-free survival, and overall survival were analyzed.

RESULTS

The sensitivity of breast ultrasound was higher than that of mammography for both groups. Ultrasound sensitivity for cancer was 95.7% in patients with BCP versus 98% in the not pregnant group, with non-statistically significant differences. Mammography sensitivity for cancer was 56.5% in patients with BCP versus 61% in the not pregnant group, with non-statistically significant differences. The stage at diagnosis according to the TNM staging system was significantly higher in patients with BCP with stage IV cancer: 16.7% in patients with BCP versus 3.7% in the not pregnant group (p = 0.03). No statistically significant differences were observed in histologic grade, Ki-67 index, or molecular subtype. Disease-free survival and overall survival were significantly lower in patients with BCP (p = 0.002 and p = 0.04). Multivariate analysis showed no difference when adjusting for stage and surrogate molecular subtype.

CONCLUSION

Breast ultrasound shows a high sensitivity to detect breast cancer during pregnancy. BCP is diagnosed at a higher stage than in nonpregnant women. In our series, patients with BCP had poorer outcomes than the not pregnant group. These results were not observed when adjusting for stage.

Axillary Nodal Evaluation in Breast Cancer: State of the Art

Axillary lymph node (LN) metastasis is the most important predictor of overall recurrence and survival in patients with breast cancer, and accurate assessment of axillary LN involvement is an essential component in staging breast cancer. Axillary management in patients with breast cancer has become much less invasive and individualized with the introduction of sentinel LN biopsy (SLNB). Emerging evidence indicates that axillary LN dissection may be avoided in selected patients with node-positive as well as node-negative cancer. Thus, assessment of nodal disease burden to guide multidisciplinary treatment decision making is now considered to be a critical role of axillary imaging and can be achieved with axillary US, MRI, and US-guided biopsy. For the node-positive patients treated with neoadjuvant chemotherapy, restaging of the axilla with US and MRI and targeted axillary dissection in addition to SLNB is highly recommended to minimize the false-negative rate of SLNB. Efforts continue to develop prediction models that incorporate imaging features to predict nodal disease burden and to select proper candidates for SLNB. As methods of axillary nodal evaluation evolve, breast radiologists and surgeons must work closely to maximize the potential role of imaging and to provide the most optimized treatment for patients.

Predicting Axillary Response to Neoadjuvant Chemotherapy: Breast MRI and US in Patients with Node-Positive Breast Cancer

Background In patients who are expected to achieve axillary pathologic complete response (pCR) after neoadjuvant chemotherapy (NAC), omission of axillary lymph node (LN) dissection could prevent morbidity and complications. Purpose To develop a clinical model to predict residual axillary LN metastasis in patients with clinically node-positive breast cancer after NAC by using MRI and US. Materials and Methods In this retrospective study, women with clinically node-positive breast cancer who were treated with NAC following surgery between January 2015 and September 2017 were included. The patients were randomly assigned to a test and validation set (7:3 ratio). Univariable and multivariable logistic regression analyses were performed to evaluate the independent predictors of residual axillary LN metastasis in the test set. A prediction risk score was developed based on the odds ratios from the multivariable analysis and validated in both sets. Results A total of 408 women were included (mean age ± standard deviation, 47.9 years ± 9.6). The axillary pCR rate was 56.6% (231 of 408). Independent predictors of residual axillary LN metastasis were clinical stage N2 or N3, presence of axillary lymphadenopathy at US after NAC, tumor size reduction less than 50% at MRI, Ki-67 negativity, hormone receptor positivity, and human epidermal growth factor receptor 2 negativity (all, P < .05). In a model using these predictors, the area under the receiver operating characteristic curve in the test and validation sets was 0.84 (95% confidence interval: 0.79, 0.88) and 0.78 (95% confidence interval: 0.70, 0.87), respectively. When the patients had a simplified risk score of 1, the false-negative rates ranged between 5%-10%. Conclusion A prediction model incorporating nodal status stage, US finding, MRI response, and molecular receptor status shows good diagnostic performance for residual axillary lymph node metastasis after neoadjuvant chemotherapy in patients with clinically node-positive breast cancer. © RSNA, 2019 Online supplemental material is available for this article. See also the editorial by Whitman in this issue.

Transcatheter arterial chemoembolization improves the resectability of malignant breast phyllodes tumor with angiosarcoma component: a case report

Background

A giant phyllodes tumor of the breast is a rare fibroepithelial lesion, and its treatment is controversial. Many case reports have reported performing skin graft reconstruction after tumor excision. Chest wall resection may be required if the tumor has invaded the chest muscle layer. We speculated that transcatheter arterial chemoembolization (TACE) can improve the resectability of malignant phyllodes tumor of the breast without requiring skin grafting. The English literature contains only one case report similar to our experience.

Case presentation

We report a rare case of a 51-year-old woman who had a giant malignant phyllodes tumor with heterologous sarcomatous differentiation in her right breast. The tumor was 19.43 × 12.98 × 21.47 cm. Whole-body computed tomography (CT) and bone scan did not reveal distant metastasis. Chest magnetic resonance imaging showed chest wall tumor invasion. Considering that skin defects after mastectomy can be extensive, we administered four courses of chemoembolization in the 5 weeks before surgery (30 mg of epirubicin and embozene microspheres [400, 500, and 700 μm]/week). Each process was well tolerated, with no serious complications. Only fever and local pain at the tumor site were noted, and these symptoms resolved with time. The follow-up CT scan showed a 45% reduction in tumor volume. Therefore, simple mastectomy was performed without skin grafting reconstruction. Wound healing was satisfactory, and the patient was discharged 1 week after surgery. Pathological and immunohistochemistry (IHC) findings showed a malignant phyllodes tumor with an angiosarcoma component. Because of tumor invasion of the chest wall, we recommended the patient receive radiotherapy, but she refused. Two months after surgery, recurrence of the malignant phyllodes tumor with right axillary lymph node involvement and lung metastasis was confirmed.

Conclusion

Initial surgical resection of giant phyllodes tumors is often challenging. For initial presentation with unresectable giant phyllodes tumor, we recommend to perform TACE prior to surgery. In our patient, preoperative TACE was effective and safe. If the tumor has invaded the chest wall, early radiotherapy after surgery may be recommended for preventing recurrence.

Use of Contrast-Enhanced MRI in Management of Discordant Core Biopsy Results

OBJECTIVE. Evaluating concordance between core biopsy results and imaging findings is an integral component of breast intervention. Pathologic results deemed benign discordant reflect concern that a malignancy may have been incorrectly sampled. Standard of care currently is surgical excision, although a large percentage of these lesions will be benign at final pathologic analysis. The purpose of this study was to determine whether inclusion of contrast-enhanced MRI would optimize patient care. MATERIALS AND METHODS. Forty-five patients with 46 lesions were identified who underwent contrast-enhanced MRI after receiving discordant ultrasound or stereotactic biopsy results between 2012 and mid 2018. These findings were classified BI-RADS category 4 at diagnostic imaging. Disease-positive was defined as all malignancies and borderline lesions. RESULTS. Fourteen patients had suspicious MRI findings; 31 patients did not. Negative or benign MRI findings were validated by stability at imaging follow-up of at least 1 year in 27 patients (28 lesions) and at least 6 months in four patients. Eight of the total of 46 discordant lesions were ultimately malignant, a rate of 17.3%, an expected result for BI-RADS 4 lesions. Sensitivity, specificity, positive predictive value, and negative predictive value of MRI calculated in the group of 41 patients (42 lesions) with documented stability for at least 1 year were 100%, 93.3%, 85.7%, and 100%. The false-negative rate of MRI was 0%; the false-positive rate was 2 of 30 (6.7%). CONCLUSION. In the management of discordant benign core biopsy results, contrast-enhanced MRI facilitated successful triage of patients to surgery; 31 of the original 45 patients (68.9%) avoided surgery.