The role of magnetic resonance imaging in assessing residual disease and pathologic complete response in breast cancer patients receiving neoadjuvant chemotherapy: a systematic review

Deep-learning based discrimination of pathologic complete response using MRI in HER2-positive and triple-negative breast cancer

Distinguishing between pathologic complete response and residual cancer after neoadjuvant chemotherapy (NAC) is crucial for treatment decisions, but the current imaging methods face challenges. To address this, we developed deep-learning models using post-NAC dynamic contrast-enhanced MRI and clinical data. A total of 852 women with human epidermal growth factor receptor 2 (HER2)-positive or triple-negative breast cancer were randomly divided into a training set (n = 724) and a validation set (n = 128). A 3D convolutional neural network model was trained on the training set and validated independently. The main models were developed using cropped MRI images, but models using uncropped whole images were also explored. The delayed-phase model demonstrated superior performance compared to the early-phase model (area under the receiver operating characteristic curve [AUC] = 0.74 vs. 0.69, P = 0.013) and the combined model integrating multiple dynamic phases and clinical data (AUC = 0.74 vs. 0.70, P = 0.022). Deep-learning models using uncropped whole images exhibited inferior performance, with AUCs ranging from 0.45 to 0.54. Further refinement and external validation are necessary for enhanced accuracy.

Predictors of mastectomy in breast cancer patients with complete remission of primary tumor after neoadjuvant therapy: A retrospective study

INTRODUCTION

Neoadjuvant therapy (NAT) should increase the rate of breast-conserving surgery (BCS) in non-metastatic breast cancer (BC) patients, especially in those achieving tumor shrinkage. Still, the conversion from a pre-planned mastectomy to BCS in patients responding to NAT is not a widespread standard. We aimed to identify factors influencing surgical choices in this setting.

MATERIALS AND METHODS

We retrospectively collected data of BC patients with complete remission of primitive tumor (ypT0) after NAT, treated with BCS or mastectomy in two Italian breast units. Predictors of mastectomy were explored using logistic regression. Distant recurrence and event-free survival were assessed in the BCS and mastectomy cohort.

RESULTS

243 patients were included, 147 (60.5 %) treated with BCS and 96 (39.5 %) treated with mastectomy. In the mastectomy group, there were more centrally-located, multiple and larger tumors. At univariate regression analysis, central location, baseline tumor extension on ultrasound (US) and magnetic resonance imaging (MRI), multiple foci and clinical stage were significantly associated with the chance of receiving mastectomy. At multivariate analysis, only baseline focality on US and extension on MRI retained significance as predictors of mastectomy. Distant recurrence and event-free survival were significantly longer in patients undergoing BCS.

CONCLUSION

Baseline tumor extension and focality were the main predictors of mastectomy in patients with ypT0 after NAT. However, BCS did not negatively affect survival outcomes in our cohort. An effort should be made to avoid potentially unnecessary mastectomy in this population, aiming at minimizing surgery-associated toxicities and improving patients' quality of life.

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.

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.

Performance of Contrast-Enhanced Mammography (CEM) for Monitoring Neoadjuvant Chemotherapy Response among Different Breast Cancer Subtypes

Neoadjuvant chemotherapy (NAT) plays a crucial role in breast cancer (BC) treatment, both in advanced BC and in early-stage BC, with different rates of pathological complete response (pCR) among the different BC molecular subtypes. Imaging monitoring is mandatory to evaluate the NAT efficacy. This study evaluates the diagnostic performance of Contrast-Enhanced Mammography (CEM) in BC patients undergoing NAT. This retrospective two-center study included 174 patients. The breast lesions were classified based on the molecular subtypes in hormone receptor (HR+)/HER2-, HER2+, and triple-negative breast cancer (TNBC). The histopathological analysis performed following surgery was used as a reference standard for the pCR. Sensitivity, specificity, PPV, and NPV were measured overall and for the different subtypes. We enrolled 174 patients, 79/174 (46%) HR+/HER2-, 59/174 (33.9%) HER2+, and 35/174 (20.1%) TNBC; the pCR was found in 64/174 (36.8%), of which 57.1% were TNBCs. In the total population, the CEM sensitivity and specificity were 66.2% and 75.2%, with a PPV of 61.4% and an NPV of 78.8%. The highest specificity (80.9%) and NPV (91.7%) were found in HR+/HER2-, while the highest sensitivity (70%) and PPV appeared (73.7%) in TNBC. The results indicate that CEM is a valid tool to assess the pCR, with different performances among the subtypes of BC.

Comparative Evaluation of USG-Guided Single Tissue Marker Versus Multiple Tissue Marker Placements in Breast Malignancy Patients Undergoing Neoadjuvant Chemotherapy for Tumor Localization

Background Breast cancer remains one of the most common malignancies affecting women globally, contributing significantly to the disease burden. The advent of neoadjuvant chemotherapy (NAC) has revolutionized the treatment for locally advanced breast cancer, allowing tumors to be downstaged and making breast-conserving surgery (BCS) feasible. Accurate localization of the tumor bed post-NAC is crucial for successful surgical removal of residual disease. While traditional single tissue marker placement has been effective, recent advances suggest multiple markers might provide superior localization by comprehensively delineating the entire tumor area. This study aims to compare the effectiveness of single versus multiple tissue marker placements in breast malignancy patients undergoing NAC. Materials and methods A prospective study was conducted in the Department of Radio-diagnosis at Saveetha Medical College over 18 months, including 10 patients diagnosed with breast carcinoma, selected through convenience sampling. Inclusion criteria involved patients diagnosed with breast cancer via mammography, sonography, and histological confirmation, referred for clip placement before NAC. Exclusion criteria were patients unwilling to participate. The procedure involved placing one to two surgical clips within the tumor using a 14/16-gauge coaxial guiding needle under USG guidance, with additional clips for larger or multiple tumors. Data collection included pre-procedural USG, post-procedural mammography (MG1), pre-operative mammography (MG2)/USG, and gross specimen histopathological examination/specimen mammography. Statistical analysis Demographic data, clipping distribution, receptor status, localization methods, surgical outcomes, operation diagnoses, and correlation analysis were statistically analyzed. Mean age, standard deviation, and p-values were calculated to determine the significance of differences between single and multiple clip groups. Results The study included 10 patients with a mean age of 52.5 years. Of these, five (50%) had a single clip, and two (20%) had four clips. The average time from clipping to the second mammogram (MG2) was 106.3 days, and from clipping to operation was 111.0 days, with longer follow-up times for multiple clip patients. Six (60%) of the patients were estrogen receptor (ER) positive, and six (60%) were human epidermal growth factor receptor 2 (HER2) negative. Localization methods were similar between single and multiple clip groups. However, multiple clip patients tended to undergo more extensive surgeries like modified radical mastectomy (MRM). Imaging responses showed no preoperative ultrasound lesions in single clip patients, while multiple clip patients had higher inconsistent diagnoses (10 (100%)) suggesting that multiple clips provide better tumor localization but are linked to increased complexity and longer follow-up times. Conclusion Patients with multiple clips experienced significantly longer follow-up times, reflecting more complex clinical scenarios. Despite no significant differences in receptor status distributions, multiple clip patients required more extensive surgeries, emphasizing the need for tailored surgical planning. The study underscores the importance of considering the number of clips in clinical decision-making. Future research should focus on larger, prospective studies to validate these findings and explore underlying mechanisms.

The Role of MRI in Breast Cancer and Breast Conservation Therapy

Contrast-enhanced breast MRI has an established role in aiding in the detection, evaluation, and management of breast cancer. This article discusses MRI sequences, the clinical utility of MRI, and how MRI has been evaluated for use in breast radiotherapy treatment planning. We highlight the contribution of MRI in the decision-making regarding selecting appropriate candidates for breast conservation therapy and review the emerging role of MRI-guided breast radiotherapy.

Machine learning approaches in the prediction of positive axillary lymph nodes post neoadjuvant chemotherapy using MRI, CT, or ultrasound: A systematic review

Background and objective

Neoadjuvant chemotherapy is a standard treatment approach for locally advanced breast cancer. Conventional imaging modalities, such as magnetic resonance imaging (MRI), computed tomography (CT), and ultrasound, have been used for axillary lymph node evaluation which is crucial for treatment planning and prognostication. This systematic review aims to comprehensively examine the current research on applying machine learning algorithms for predicting positive axillary lymph nodes following neoadjuvant chemotherapy utilizing imaging modalities, including MRI, CT, and ultrasound.

Methods

A systematic search was conducted across databases, including PubMed, Scopus, and Web of Science, to identify relevant studies published up to December 2023. Articles employing machine learning algorithms to predict positive axillary lymph nodes using MRI, CT, or ultrasound data after neoadjuvant chemotherapy were included. The review follows the preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines, encompassing data extraction and quality assessment.

Results

Seven studies were included, comprising 1502 patients. Four studies used MRI, two used CT, and one applied ultrasound. Two studies developed deep-learning models, while five used classic machine-learning models mainly based on multiple regression. Across the studies, the models showed high predictive accuracy, with the best-performing models combining radiomics and clinical data.

Conclusion

This systematic review demonstrated the potential of utilizing advanced data analysis techniques, such as deep learning radiomics, in improving the prediction of positive axillary lymph nodes in breast cancer patients following neoadjuvant chemotherapy.

Potential of MRI in Assessing Treatment Response After Neoadjuvant Radiation Therapy Treatment in Breast Cancer Patients: A Scoping Review

The objective of this scoping review is to evaluate the potential of Magnetic Resonance Imaging (MRI) and to determine which of the available MRI techniques reported in the literature are the most promising for assessing treatment response in breast cancer patients following neoadjuvant radiotherapy (NRT). Ovid Medline, Embase, CINAHL, and Cochrane databases were searched to identify relevant studies published from inception until March 13, 2023. After primary selection, 2 reviewers evaluated each study using a standardized data extraction template, guided by set inclusion and exclusion criteria. A total of 5 eligible studies were selected. The positive and negative predictive values for MRI predicting pathological complete response across the studies were 67% to 88% and 76% to 85%, respectively. MRI's potential in assessing postradiotherapy tumor sizes was greater for volume measurements than uni-dimensional longest diameter measurements; however, overestimation in surgical tumor sizes was observed. Apparent diffusion coefficient (ADC) values and Time to Enhance (TTE) was seen to increase post-NRT, with a notable difference between responders and nonresponders at 6 months, indicating a potential role in assessing treatment response. In conclusion, this review highlights tumor volume measurements, ADC, and TTE as promising MRI metrics for assessing treatment response post-NRT in breast cancer. However, further research with larger cohorts is needed to confirm their utility. If MRI can accurately identify responders from nonresponders to NRT, it could enable a more personalized and tailored treatment approach, potentially minimizing radiation therapy related toxicity and enhancing cosmetic outcomes.

Outcomes of sentinel node biopsy according to MRI response in an association with the subtypes in cN1-3 breast cancer after neoadjuvant systemic therapy, multicenter cohort study

BACKGROUND

This study investigated the feasibility of sentinel lymph node biopsy (SLNB) after neoadjuvant systemic therapy (NAST) in patients with initially high nodal burden.

METHODS

In the multicenter retrospective cohort, 388 individuals with cN1-3 breast cancer who underwent NAST and had SLNB followed by completion axillary lymph node dissection were included. In an external validation cohort, 267 patients with HER2+ or triple-negative breast cancer (TNBC) meeting similar inclusion criteria were included. Primary outcome was the false-negative rates (FNRs) of SLNB according to the MRI response and subtypes. We defined complete MRI responders as patients who experienced disappearance of suspicious features in the breast and axilla after NAST.

RESULTS

In the multicenter retrospective cohort, 130 (33.5%) of 388 patients were of cN2-3, and 55 (14.2%) of 388 patients showed complete MRI responses. In hormone receptor-positive HER2- (n = 207), complete and non-complete responders had a high FNRs (31.3% [95% CI 8.6-54.0] and 20.9% [95% CI 14.1-27.6], respectively). However, in HER2+ or TNBC (n = 181), the FNR of complete MRI responders was 0% (95% CI 0-0), whereas that of non-complete responders was 33.3% (95% CI 20.8-45.9). When we validated our findings in the external cohort with HER2+ or TNBC (n = 267), of which 34.2% were cN2-3, the FNRs of complete were 7.1% (95% CI 0-16.7).

CONCLUSIONS

Our findings suggest that SLNB can be a reliable option for nodal status evaluation in selected patients who have responded well to NAST, especially in HER2+ and TNBC patients who show a complete MRI response.

CEM immediately after contrast-enhanced CT: a one-step staging of breast cancer

BACKGROUND

Contrast-enhanced mammography (CEM) is a promising technique. We evaluated the diagnostic potential of CEM performed immediately after contrast-enhanced computed tomography (CE-CT).

METHODS

Fifty patients with breast cancer underwent first CE-CT and then CEM without additional contrast material injection. Two independent radiologists evaluated CEM images. The sensitivity of CEM for detecting index and additional malignant lesions was compared with that of mammography/ultrasonography by the McNemar test, using histopathology as a reference standard. Interobserver agreement for detection of malignant lesions, for classifying index tumors, and for evaluating index tumor size and extent was assessed using Cohen κ. Pearson correlation was used for correlating index tumor size/extent at CEM or mammography/ultrasonography with histopathology.

RESULTS

Of the 50 patients, 30 (60%) had unifocal disease while 20 (40%) had multicentric or multifocal disease; 5 of 20 patients with multicentric disease (25%) had bilateral involvement, for a total of 78 malignant lesions, including 72 (92%) invasive ductal and 6 (8%) invasive lobular carcinomas. Sensitivity was 63/78 (81%, 95% confidence interval 70.27-88.82) for unenhanced breast imaging and 78/78 (100%, 95.38-100) for CEM (p < 0.001). The interobserver agreement for overall detection of malignant lesions, for classifying index tumor, and for evaluating index tumor size/extent were 0.94, 0.95, and 0.86 κ, respectively. For index tumor size/extent, correlation coefficients as compared with histological specimens were 0.50 for mammography/ultrasonography and 0.75 for CEM (p ≤ 0.010).

CONCLUSIONS

CEM acquired immediately after CE-CT without injection of additional contrast material showed a good performance for local staging of breast cancer.

RELEVANCE STATEMENT

When the CEM suite is near to the CE-CT acquisition room, CEM acquired immediately after, without injection of additional contrast material, could represent a way for local staging of breast cancer to be explored in larger prospective studies.

KEY POINTS

• CEM represents a new accurate tool in the field of breast imaging. • An intravenous injection of iodine-based contrast material is required for breast gland evaluation. • CEM after CE-CT could provide a one-stop tool for breast cancer staging.

Contrast-Enhanced Mammography-Guided Biopsy: Preliminary Results of a Single-Center Retrospective Experience

Background: CEM-guided breast biopsy is an advanced diagnostic procedure that takes advantage of the ability of CEM to enhance suspicious breast lesions. The aim pf this paper is to describe a single-center retrospective experience on CEM-guided breast biopsy in terms of procedural features and histological outcomes. Methods: 69 patients underwent the procedure. Patient age, breast density, presentation, dimensions, and lesion target enhancement were recorded. All the biopsy procedures were performed using a 7- or 10-gauge (G) vacuum-assisted biopsy needle. The procedural approach (horizontal or vertical) and the decubitus of the patient (lateral or in a sitting position) were noted. Results: A total of 69 patients underwent a CEM-guided biopsy. Suspicious lesions presented as mass enhancement in 35% of cases and non-mass enhancement in 65% of cases. The median size of the target lesions was 20 mm. The median procedural time for each biopsy was 10 ± 4 min. The patients were placed in a lateral decubitus position in 52% of cases and seated in 48% of cases. The most common approach was horizontal (57%). The mean AGD was 14.8 mGy. At histology, cancer detection rate was 28% (20/71). Conclusions: CEM-guided biopsy was feasible, with high procedure success rates and high tolerance by the patients.

Physicians' Hierarchy of Tumor Biomarkers for Optimizing Chemotherapy in Breast Cancer Care

BACKGROUND

Tumor biomarkers are regularly used to guide breast cancer treatment and clinical trial enrollment. However, there remains a lack of knowledge regarding physicians' perspectives towards biomarkers and their role in treatment optimization, where treatment intensity is reduced to minimize toxicity.

METHODS

Thirty-nine academic and community oncologists participated in semi-structured qualitative interviews, providing perspectives on optimization approaches to chemotherapy treatment. Interviews were audio-recorded, transcribed, and analyzed by 2 independent coders utilizing a constant comparative method in NVivo. Major themes and exemplary quotes were extracted. A framework outlining physicians' conception of biomarkers, and their comfortability with their use in treatment optimization, was developed.

RESULTS

In the hierarchal model of biomarkers, level 1 is comprised of standard-of-care (SoC) biomarkers, defined by a strong level of evidence, alignment with national guidelines, and widespread utilization. Level 2 includes SoC biomarkers used in alternative contexts, in which physicians expressed confidence, yet less certainty, due to a lack of data in certain subgroups. Level 3, or experimental, biomarkers created the most diverse concerns related to quality and quantity of evidence, with several additional modulators.

CONCLUSION

This study demonstrates that physicians conceptualize the use of biomarkers for treatment optimization in successive levels. This hierarchy can be used to guide trialists in the development of novel biomarkers and design of future trials.

The percentage of unnecessary mastectomy due to false size prediction using preoperative ultrasonography and MRI in breast cancer patients who underwent neoadjuvant chemotherapy: a prospective cohort study

BACKGROUND

Imaging-estimated tumour extent after neoadjuvant chemotherapy tends to be discordant with the pathological extent. The authors aimed to prospectively determine the proportion of decisions regarding total mastectomy for potential breast-conserving surgery candidates owing to false size prediction with imaging in neoadjuvant chemotherapy and non-neoadjuvant chemotherapy patients.

MATERIALS AND METHODS

The authors prospectively enroled clinical stage II or III breast cancer patients who are scheduled for total mastectomy between 2018 and 2021. This study was conducted at Seoul National University Hospital at South Korea. Before surgery, each surgeon recorded the hypothetical maximum tumour size at which the surgeon would have been able to attempt breast-conserving surgery if the patient had actually less than the size of the tumour at that location in the breast. After surgery, the hypothetical maximum tumour size was compared with the final pathologic total extent of the tumour, including invasive and in situ cancers.

RESULTS

Among the 360 enroled patients, 130 underwent neoadjuvant chemotherapy, and 230 did not undergo neoadjuvant chemotherapy. Of the total of each group, 47.7% in the neoadjuvant chemotherapy group and 21.3% in the non-neoadjuvant chemotherapy group had a smaller pathologic tumour extent than the pre-recorded hypothetical maximum tumour size (P<0.001). Further analyses were conducted for the neoadjuvant chemotherapy group. The proportions of total mastectomy with false size prediction were higher in HER2-positive (63.3%) and triple-negative (57.6%) patients compared with ER-positive/HER2-negative (25.0%) patients (P<0.001). Both magnetic resonance imaging-pathology and ultrasonography-pathology size discrepancies were significantly associated with false decisions for total mastectomy (both P<0.001). Without magnetic resonance imaging, the false decision may be reduced by 21.5%.

CONCLUSION

A total of 47.7% of patients who received total mastectomy after neoadjuvant chemotherapy were breast-conserving surgery eligible, which was significantly higher than that of non-neoadjuvant chemotherapy patients. Magnetic resonance imaging contributed the most to false size predictions.

Abbreviated breast MRI for evaluating breast cancer before initiation of neoadjuvant chemotherapy: A cross-sectional study

Background

Although, there are accumulating evidence about diagnostic role of abbreviated breast magnetic resonance imaging (MRI) in screening setting, the implementation of abbreviated MRI in staging of breast cancer has been poorly elucidated.

Objective

To evaluate the diagnostic performance of abbreviated breast MRI in estimating extent of disease before initiation of neoadjuvant chemotherapy.

Methods

A total of 54 patients with biopsy-proven main lesion referred to evaluate by standard protocol breast MRI before initiation of neoadjuvant chemotherapy were retrospectively enrolled. From a standard protocol, a data set of abbreviated protocol consisting fat-saturated T1-weighted (T1W) pre-contrast and first two fat-saturated T1W post-contrast series with reconstruction of their subtraction including maximum intensity projection (MIP) were obtained and interpreted. The concordance rate of abbreviated with standard protocol (as a reference standard) were compared. Diagnostic accuracy, sensitivity, specificity, and positive and negative predictive value were calculated, as well.

Results

The maximum size of the main mass was 38.6 ± 17.3 and 40.7 ± 17.9 for abbreviated and standard protocol, respectively. All of the main mass was detected by abbreviated protocol with 100% concordance. Concordance was 98.1% and 94.4% in terms of multifocal/multicentric status and for estimating of NME, respectively. The abbreviated protocol has high sensitivity and specificity with more than 90% value regarding main mass detection, measurement of the maximum size of the main mass, determination of multifocal/multicenter status and NAC involvement.

Conclusion

Abbreviated protocol may be a reliable surrogate for standard protocol breast MRI in evaluating extent of breast cancer.

Value of mammographic microcalcifications and MRI-enhanced lesions in the evaluation of residual disease after neoadjuvant therapy for breast cancer

Background

Microcalcifications persist even if a patient with breast cancer achieves pathologic complete response (pCR) as confirmed by surgery after neoadjuvant treatment (NAT). In practice, surgeons tend to remove all the microcalcifications. This study aimed to explore the correlation between changes in the extent of microcalcification after NAT and pathological tumor response and compare the accuracy of mammography (MG) and magnetic resonance imaging (MRI) in predicting the size of residual tumors.

Methods

This was a retrospective study which included a consecutive series of patients in Guangdong Provincial People's Hospital. Between January 2010 and January 2020, 127 patients with breast cancer and Breast Imaging Reporting and Data System (BI-RADS) 4-5 microcalcifications were included in this study. The maximum diameter of the microcalcifications on MG and lesion enhancement on MRI pre- and post-NAT were measured. The correlations between the changes in residual microcalcifications on MG and pCR were analyzed. Intraclass correlation coefficients (ICCs) were computed between the extent of the residual microcalcifications, residual enhancement, and residual tumor size.

Results

There were no statistically significant differences in the changes in microcalcifications after NAT according to the RECIST criteria on MRI (P=0.09) and Miller-Payne grade (P=0.14). MRI showed a higher agreement than did residual microcalcifications on MG in predicting residual tumor size (ICC: 0.771 vs. 0.097).

Conclusions

MRI is more accurate for evaluating residual tumor size in breast cancer. In our study, the extent of microcalcifications on MG after NAT had nearly no correlation with the pathological size of the residual tumor. Therefore, residual tumors with microcalcifications may not necessarily be a contraindication to breast-conserving surgery.

Imaging findings for response evaluation of ductal carcinoma in situ in breast cancer patients treated with neoadjuvant systemic therapy: a systematic review and meta-analysis

OBJECTIVES

In approximately 45% of invasive breast cancer (IBC) patients treated with neoadjuvant systemic therapy (NST), ductal carcinoma in situ (DCIS) is present. Recent studies suggest response of DCIS to NST. The aim of this systematic review and meta-analysis was to summarise and examine the current literature on imaging findings for different imaging modalities evaluating DCIS response to NST. More specifically, imaging findings of DCIS pre- and post-NST, and the effect of different pathological complete response (pCR) definitions, will be evaluated on mammography, breast MRI, and contrast-enhanced mammography (CEM).

METHODS

PubMed and Embase databases were searched for studies investigating NST response of IBC, including information on DCIS. Imaging findings and response evaluation of DCIS were assessed for mammography, breast MRI, and CEM. A meta-analysis was conducted per imaging modality to calculate pooled sensitivity and specificity for detecting residual disease between pCR definition no residual invasive disease (ypT0/is) and no residual invasive or in situ disease (ypT0).

RESULTS

Thirty-one studies were included. Calcifications on mammography are related to DCIS, but can persist despite complete response of DCIS. In 20 breast MRI studies, an average of 57% of residual DCIS showed enhancement. A meta-analysis of 17 breast MRI studies confirmed higher pooled sensitivity (0.86 versus 0.82) and lower pooled specificity (0.61 versus 0.68) for detection of residual disease when DCIS is considered pCR (ypT0/is). Three CEM studies suggest the potential benefit of simultaneous evaluation of calcifications and enhancement.

CONCLUSIONS AND CLINICAL RELEVANCE

Calcifications on mammography can remain despite complete response of DCIS, and residual DCIS does not always show enhancement on breast MRI and CEM. Moreover, pCR definition effects diagnostic performance of breast MRI. Given the lack of evidence on imaging findings of response of the DCIS component to NST, further research is demanded.

KEY POINTS

• Ductal carcinoma in situ has shown to be responsive to neoadjuvant systemic therapy, but imaging studies mainly focus on response of the invasive tumour. • The 31 included studies demonstrate that after neoadjuvant systemic therapy, calcifications on mammography can remain despite complete response of DCIS and residual DCIS does not always show enhancement on MRI and contrast-enhanced mammography. • The definition of pCR has impact on the diagnostic performance of MRI in detecting residual disease, and when DCIS is considered pCR, pooled sensitivity was slightly higher and pooled specificity slightly lower.

The evaluation of the delayed swollen breast in patients with a history of breast implants

Breast implants, whether placed for reconstructive or cosmetic purposes, are rarely lifetime devices. Rupture, resulting from compromised implant shell integrity, and capsular contracture caused by constriction of the specialized scar tissue that normally forms around breast implants, have long been recognized, and remain the leading causes of implant failure. It is apparent, however, that women with breast implants may also experience delayed breast swelling due to a range of etiologic factors. While a majority of delayed seromas associated with breast implants have a benign etiology, this presentation cannot be ignored without an adequate workup as malignancies such as breast implant associated anaplastic large cell lymphoma (BIA-ALCL), breast implant associated diffuse large B-cell lymphoma (BIA-DLBCL), and breast implant associated squamous cell carcinoma (BIA-SCC) can have a similar clinical presentation. Since these malignancies occur with sufficient frequency, and with sometimes lethal consequences, their existence must be recognized, and an appropriate diagnostic approach implemented. A multidisciplinary team that involves a plastic surgeon, radiologist, pathologist, and, as required, surgical and medical oncologists can expedite judicious care. Herein we review and further characterize conditions that can lead to delayed swelling around breast implants.

Multimodal Spatiotemporal Deep Learning Framework to Predict Response of Breast Cancer to Neoadjuvant Systemic Therapy

Current approaches to breast cancer therapy include neoadjuvant systemic therapy (NST). The efficacy of NST is measured by pathologic complete response (pCR). A patient who attains pCR has significantly enhanced disease-free survival progress. The accurate prediction of pCR in response to a given treatment regimen could increase the likelihood of achieving pCR and prevent toxicities caused by treatments that are not effective. Th early prediction of response to NST can increase the likelihood of survival and help with decisions regarding breast-conserving surgery. An automated NST prediction framework that is able to precisely predict which patient undergoing NST will achieve a pathological complete response (pCR) at an early stage of treatment is needed. Here, we propose an end-to-end efficient multimodal spatiotemporal deep learning framework (deep-NST) framework to predict the outcome of NST prior or at an early stage of treatment. The deep-NST model incorporates imaging data captured at different timestamps of NST regimens, a tumor's molecular data, and a patient's demographic data. The efficacy of the proposed work is validated on the publicly available ISPY-1 dataset, in terms of accuracy, area under the curve (AUC), and computational complexity. In addition, seven ablation experiments were carried out to evaluate the impact of each design module in the proposed work. The experimental results show that the proposed framework performs significantly better than other recent methods.

Effect of Longitudinal Variation in Tumor Volume Estimation for MRI-guided Personalization of Breast Cancer Neoadjuvant Treatment

Purpose To investigate the impact of longitudinal variation in functional tumor volume (FTV) underestimation and overestimation in predicting pathologic complete response (pCR) after neoadjuvant chemotherapy (NAC). Materials and Methods Women with breast cancer who were enrolled in the prospective I-SPY 2 TRIAL (Investigation of Serial Studies to Predict Your Therapeutic Response with Imaging and Molecular Analysis 2) from May 2010 to November 2016 were eligible for this retrospective analysis. Participants underwent four MRI examinations during NAC treatment. FTV was calculated based on automated segmentation. Baseline FTV before treatment (FTV0) and the percentage of FTV change at early treatment and inter-regimen time points relative to baseline (∆FTV1 and ∆FTV2, respectively) were classified into high-standard or standard groups based on visual assessment of FTV under- and overestimation. Logistic regression models predicting pCR using single predictors (FTV0, ∆FTV1, and ∆FTV2) and multiple predictors (all three) were developed using bootstrap resampling with out-of-sample data evaluation with the area under the receiver operating characteristic curve (AUC) independently in each group. Results This study included 432 women (mean age, 49.0 years ± 10.6 [SD]). In the FTV0 model, the high-standard and standard groups showed similar AUCs (0.61 vs 0.62). The high-standard group had a higher estimated AUC compared with the standard group in the ∆FTV1 (0.74 vs 0.63), ∆FTV2 (0.79 vs 0.62), and multiple predictor models (0.85 vs 0.64), with a statistically significant difference for the latter two models (P = .03 and P = .01, respectively). Conclusion The findings in this study suggest that longitudinal variation in FTV estimation needs to be considered when using early FTV change as an MRI-based criterion for breast cancer treatment personalization. Keywords: Breast, Cancer, Dynamic Contrast-enhanced, MRI, Tumor Response ClinicalTrials.gov registration no. NCT01042379 Supplemental material is available for this article. © RSNA, 2023 See also the commentary by Ram in this issue.

Predicting the response to neoadjuvant chemotherapy. Can the addition of tomosynthesis improve the accuracy of contrast-enhanced spectral mammography? A comparison with breast MRI

OBJECTIVES

Image monitoring is essential to monitor response to neoadjuvant chemotherapy (NACT). Whilst breast MRI is the gold-standard technique, evidence suggests contrast-enhanced spectral mammography (CESM) is comparable. We investigate whether the addition of digital breast tomosynthesis (DBT) to CESM increases the accuracy of response prediction.

METHODS

Women receiving NACT for breast cancer were included. Imaging with CESM+DBT and MRI was performed post-NACT. Imaging appearance was compared with pathological specimens. Accuracy for predicting pathological complete response (pCR) and concordance with size of residual disease was calculated.

RESULTS

Sixteen cancers in 14 patients were included, 10 demonstrated pCR. Greatest accuracy for predicting pCR was with CESM enhancement (accuracy: 81.3%, sensitivity: 100%, specificity: 57.1%), followed by MRI (accuracy: 62.5%, sensitivity: 44.4%, specificity: 85.7%). Concordance with invasive tumour size was greater for CESM enhancement than MRI, concordance-coefficients 0.70 vs 0.66 respectively. MRI demonstrated greatest concordance with whole tumour size followed by CESM+microcalcification, concordance coefficients 0.86 vs 0.69. DBT did not improve accuracy for prediction of pCR or residual disease size. CESM+DBT underestimated size of residual disease, MRI overestimated but no significant differences were seen (p>0.05).

CONCLUSIONS

CESM is similar to MRI for predicting residual disease post-NACT. Size of enhancement alone demonstrates best concordance with invasive disease. Inclusion of residual microcalcification improves concordance with ductal carcinoma in situ. The addition of DBT to CESM does not improve accuracy.

ADVANCES IN KNOWLEDGE

The addition ofDBT to CESM does not improve NACT response prediction.CESM enhancement has greatest accuracy for residual invasive disease, CESM+calcification has greater accuracy for residual in situ disease.

Preoperative Partial Breast Irradiation in Patients with Low-Risk Breast Cancer: A Systematic Review of Literature

BACKGROUND

Preoperative instead of standard postoperative partial breast irradiation (PBI) after breast-conserving surgery (BCS) has the advantage of reducing the irradiated breast volume, toxicity, and number of radiotherapy sessions and can allow tumor downstaging. In this review, we assessed tumor response and clinical outcomes after preoperative PBI.

PATIENTS AND METHODS

We conducted a systematic review of studies on preoperative PBI in patients with low-risk breast cancer using the databases Ovid Medline, Embase.com, Web of Science (Core Collection), and Scopus (PROSPERO registration CRD42022301435). References of eligible manuscripts were checked for other relevant manuscripts. The primary outcome measure was pathologic complete response (pCR).

RESULTS

A total of eight prospective and one retrospective cohort study were identified (n = 359). In up to 42% of the patients, pCR was obtained and this increased after a longer interval between radiotherapy and BCS (0.5-8 months). After a maximum median follow-up of 5.0 years, three studies on external beam radiotherapy reported low local recurrence rates (0-3%) and overall survival of 97-100%. Acute toxicity consisted mainly of grade 1 skin toxicity (0-34%) and seroma (0-31%). Late toxicity was predominantly fibrosis grade 1 (46-100%) and grade 2 (10-11%). Cosmetic outcome was good to excellent in 78-100% of the patients.

CONCLUSIONS

Preoperative PBI showed a higher pCR rate after a longer interval between radiotherapy and BCS. Mild late toxicity and good oncological and cosmetic outcomes were reported. In the ongoing ABLATIVE-2 trial, BCS is performed at a longer interval of 12 months after preoperative PBI aiming to achieve a higher pCR rate.

Prediction of pathologic complete response after single-dose MR-guided partial breast irradiation in low-risk breast cancer patients: the ABLATIVE-2 trial-a study protocol

BACKGROUND

Partial breast irradiation (PBI) is standard of care in low-risk breast cancer patients after breast-conserving surgery (BCS). Pre-operative PBI can result in tumor downstaging and more precise target definition possibly resulting in less treatment-related toxicity. This study aims to assess the pathologic complete response (pCR) rate one year after MR-guided single-dose pre-operative PBI in low-risk breast cancer patients.

METHODS

The ABLATIVE-2 trial is a multicenter prospective single-arm trial using single-dose ablative PBI in low-risk breast cancer patients. Patients ≥ 50 years with non-lobular invasive breast cancer ≤ 2 cm, grade 1 or 2, estrogen receptor-positive, HER2-negative, and tumor-negative sentinel node procedure are eligible. A total of 100 patients will be enrolled. PBI treatment planning will be performed using a radiotherapy planning CT and -MRI in treatment position. The treatment delivery will take place on a conventional or MR-guided linear accelerator. The prescribed radiotherapy dose is a single dose of 20 Gy to the tumor, and 15 Gy to the 2 cm of breast tissue surrounding the tumor. Follow-up MRIs, scheduled at baseline, 2 weeks, 3, 6, 9, and 12 months after PBI, are combined with liquid biopsies to identify biomarkers for pCR prediction. BCS will be performed 12 months after radiotherapy or after 6 months, if MRI does not show a radiologic complete response. The primary endpoint is the pCR rate after PBI. Secondary endpoints are radiologic response, toxicity, quality of life, cosmetic outcome, patient distress, oncological outcomes, and the evaluation of biomarkers in liquid biopsies and tumor tissue. Patients will be followed up to 10 years after radiation therapy.

DISCUSSION

This trial will investigate the pathological tumor response after pre-operative single-dose PBI after 12 months in patients with low-risk breast cancer. In comparison with previous trial outcomes, a longer interval between PBI and BCS of 12 months is expected to increase the pCR rate of 42% after 6-8 months. In addition, response monitoring using MRI and biomarkers will help to predict pCR. Accurate pCR prediction will allow omission of surgery in future patients.

TRIAL REGISTRATION

The trial was registered prospectively on April 28th 2022 at clinicaltrials.gov (NCT05350722).

ACR Appropriateness Criteria® Monitoring Response to Neoadjuvant Systemic Therapy for Breast Cancer: 2022 Update

Imaging plays a vital role in managing patients undergoing neoadjuvant chemotherapy, as treatment decisions rely heavily on accurate assessment of response to therapy. This document provides evidence-based guidelines for imaging breast cancer before, during, and after initiation of neoadjuvant chemotherapy. 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 process support the systematic analysis of the medical literature from peer reviewed journals. Established methodology principles such as Grading of Recommendations Assessment, Development, and Evaluation or GRADE are adapted to evaluate the evidence. The RAND/UCLA Appropriateness Method User Manual provides the methodology to determine the appropriateness of imaging and treatment procedures for specific clinical scenarios. In those instances where peer reviewed literature is lacking or equivocal, experts may be the primary evidentiary source available to formulate a recommendation.

Longitudinal MRI-based fusion novel model predicts pathological complete response in breast cancer treated with neoadjuvant chemotherapy: a multicenter, retrospective study

Background

Accurate identification of pCR to neoadjuvant chemotherapy (NAC) is essential for determining appropriate surgery strategy and guiding resection extent in breast cancer. However, a non-invasive tool to predict pCR accurately is lacking. Our study aims to develop ensemble learning models using longitudinal multiparametric MRI to predict pCR in breast cancer.

Methods

From July 2015 to December 2021, we collected pre-NAC and post-NAC multiparametric MRI sequences per patient. We then extracted 14,676 radiomics and 4096 deep learning features and calculated additional delta-value features. In the primary cohort (n = 409), the inter-class correlation coefficient test, U-test, Boruta and the least absolute shrinkage and selection operator regression were used to select the most significant features for each subtype of breast cancer. Five machine learning classifiers were then developed to predict pCR accurately for each subtype. The ensemble learning strategy was used to integrate the single-modality models. The diagnostic performances of models were evaluated in the three external cohorts (n = 343, 170 and 340, respectively).

Findings

A total of 1262 patients with breast cancer from four centers were enrolled in this study, and pCR rates were 10.6% (52/491), 54.3% (323/595) and 37.5% (66/176) in HR+/HER2-, HER2+ and TNBC subtype, respectively. Finally, 20, 15 and 13 features were selected to construct the machine learning models in HR+/HER2-, HER2+ and TNBC subtypes, respectively. The multi-Layer Perception (MLP) yields the best diagnostic performances in all subtypes. For the three subtypes, the stacking model integrating pre-, post- and delta-models yielded the highest AUCs of 0.959, 0.974 and 0.958 in the primary cohort, and AUCs of 0.882-0.908, 0.896-0.929 and 0.837-0.901 in the external validation cohorts, respectively. The stacking model had accuracies of 85.0%-88.9%, sensitivities of 80.0%-86.3%, and specificities of 87.4%-91.5% in the external validation cohorts.

Interpretation

Our study established a novel tool to predict the responses of breast cancer to NAC and achieve excellent performance. The models could help to determine post-NAC surgery strategy for breast cancer.

Funding

This study is supported by grants from the National Natural Science Foundation of China (82171898, 82103093), the Deng Feng project of high-level hospital construction (DFJHBF202109), the Guangdong Basic and Applied Basic Research Foundation (grant number, 2020A1515010346, 2022A1515012277), the Science and Technology Planning Project of Guangzhou City (202002030236), the Beijing Medical Award Foundation (YXJL-2020-0941-0758), and the Beijing Science and Technology Innovation Medical Development Foundation (KC2022-ZZ-0091-5). Funding sources were not involved in the study design, data collection, analysis and interpretation, writing of the report, or decision to submit the article for publication.

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.

Comparison of automated breast volume scanning with conventional ultrasonography, mammography, and MRI to assess residual breast cancer after neoadjuvant therapy by molecular type

AIM

To compare the accuracy of hand-held ultrasonography (US), mammography (MG), magnetic resonance imaging (MRI), and automated breast volume scanning (ABVS) in defining residual breast cancer tumour size after neoadjuvant therapy (NAT).

MATERIALS AND METHODS

Patients diagnosed breast cancer and who received NAT at the Breast Center, Peking University People's Hospital, were enrolled prospectively. Imaging was performed after the last cycle of NAT. The residual tumour size, intraclass correlation coefficients (ICCs), and receiver operating characteristic (ROC) to predict pathological complete response (pCR) were analysed.

RESULTS

A total of 156 patients with 159 tumours were analysed. ABVS had a moderate correlation with histopathology residual tumour size (ICC = 0.666), and showed high agreement among triple-positive tumours (ICC = 0.797). With 5 mm as the threshold, the coincidence rate reached 64.7% between ABVS and pathological size, which was significantly higher than that between US, MG, MRI, and pathological size (50%, 45.1%, 41.4%; p=0.009, p=0.001, p<0.001, respectively). For ROC analysis, ABVS demonstrated a higher area under the ROC curve, but with no statistical difference, except for MG (0.855, 0.816, 0.819, and 0.788, respectively; p=0.183 for US, p=0.044 for MG, and p=0.397 for MRI, with ABVS as the reference).

CONCLUSIONS

The longest tumour diameter on ABVS had a moderate correlation with pathological residual invasive tumour size. ABVS was shown to have good ability to predict pCR and would appear to be a potential useful tool for the assessment after NAT for breast cancer.

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.

Assessment of low-cost surgical metallic clip placement for tumor localization in BIRDAS VI breast cancer patients undergoing neoadjuvant chemotherapy

Background

Neoadjunvant chemotherapy has become a challenging connotation for both surgeons and radiologists due to the high clinical response up to dramatic pathological complete response (pCR) that may hinder proper localization of any residual tumoral tissue. So the radiopaque markers implantation at the tumor bed became a reliable and recommended method for tumor localization before surgical intervention or NAC. Many types of commercial clips and markers are available; however they are relatively of high cost and represent a considerable burden on the governments and the heath institute that made the researchers study cheaper alternatives as standard titanium based cholecystectomy surgical clips for tumor localization.

Results

The study was conducted on 45 patients where 57 clips were inserted corresponding to number of lesions found in the total number of the patients. The response to Neoadjunvant chemotherapy was recorded and showed that 6 patients (about 13.3%) had complete radiological response after NAC, while 27 patients (60%) had regressive course after the treatment. The low cost surgical clips were evaluated by using sono-mammography and magnetic resonance imaging, and complications that occurred were recorded. Our study showed that in only 2 patients (3.5%) there was difficulty in clip visualization by Ultrasound during post-treatment follow up. In 45 patients, all the inserted clips (100%) were well visualized as small signal void on MRI at both T1WIs and T2WIs sequences, and the primary malignancy was easily visualized on both MRI and sono-mammography not interfering with the image interpretation and judgment. As regards the reported complications, our results revealed that in only 2 patients (3.5%) there was evidence of positive clip migration, while only 2 patients (3.5%) developed hematoma during the procedure as shown by ultrasound, Also 4 patients (7%) complained of pain only shortly after clip insertion. No other significant complications like infection or heat sensation developed either during the procedure or during MRI. The total price of the surgical clips was calculated with average cost of the needle about 10 US$ equivalent to 170 LE Egyptian pounds and the clip about 1.3 US$ or 20 Egyptian pounds, which is considered of lower cost when compared to the commercial breast markers of different companies with an estimated price range for clip = 75–200 US$ (average 90 US$). So insertion of surgical clips saved about 1135 Egyptian pounds equivalent to 73–75 US$ per clip placement.

Conclusion

We concluded from our study that the use of breast markers are mandatory before NAC where Surgical clips can safely substitute the commercial tissue markers as tumor localizers as they are effective, safe, well tolerated, easily visualized on imaging and do not interfere with assessment of the treatment response, with no evidence of complications and are of low cost compared with the commercial breast clips.

Contrast-Enhanced Mammography Versus MRI in the Evaluation of Neoadjuvant Therapy Response in Patients With Breast Cancer: A Prospective Study

BACKGROUND. Contrast-enhanced mammography (CEM) is rapidly expanding as a credible alternative to MRI in various clinical settings. OBJECTIVE. The purpose of this study was to compare CEM and MRI for neoadjuvant therapy (NAT) response assessment in patients with breast cancer. METHODS. This prospective study included 51 patients (mean age, 46 ± 11 [SD] years) with biopsy-proven breast cancer who were candidates for NAT from May 2015 to April 2018. Patients underwent both CEM and MRI before, during, and after NAT (pre-NAT, mid-NAT, and post-NAT, respectively). Post-NAT CEM included a 6-minute delayed acquisition. One breast radiologist with experience in CEM reviewed CEM examinations; one breast radiologist with experience in MRI reviewed MRI examinations. The radiologists assessed for the presence of an enhancing lesion; if an enhancing lesion was detected, its size was measured. RECIST version 1.1 response assessment categories were derived. Pathologic complete response (pCR) was defined as absence of both invasive cancer and ductal carcinoma in situ (DCIS). RESULTS. Of 51 patients, 16 achieved pCR. CEM yielded systematically lower size measurements compared with MRI (mean difference, -0.2 mm for pre-NAT, -0.7 mm for mid-NAT, and -0.3 mm for post-NAT). All post-NAT imaging tests yielded systematically larger size measurements compared with pathology (mean difference, 0.8 mm for CEM, 1.2 mm for MRI, and 1.9 mm for delayed CEM). Of 12 patients with residual DCIS, an enhancing lesion was detected in seven on post-NAT CEM, eight on post-NAT MRI, and nine on post-NAT delayed CEM. Agreement of RECIST response categories between CEM and MRI, expressed as kappa coefficient, was 0.791 at mid-NAT and 0.871 at post-NAT. For detecting pCR by post-NAT imaging, sensitivity and specificity were 81% and 83% for CEM, 100% and 86% for MRI, and 81% and 89% for delayed CEM. Sensitivity was significantly higher for MRI than CEM (p = .001) and delayed CEM (p = .002); remaining comparisons were not significant (p > .05). CONCLUSION. After NAT for breast cancer, CEM and MRI yielded comparable assessments of lesion size (both slightly overestimated vs pathology) and RECIST categories and showed no significant difference in specificity for pCR. MRI had higher sensitivity for pCR. Delayed CEM acquisition may help detect residual DCIS. CLINICAL IMPACT. Although MRI remains the preferred test for NAT response monitoring, the findings support CEM as a useful alternative when MRI is contraindicated or not tolerated.

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

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

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

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

Preoperative Breast Magnetic Resonance Imaging as a Predictor of Response to Neoadjuvant Chemotherapy

Introduction:

The ability to accurately predict pathologic complete response (pCR) after neoadjuvant chemotherapy (NAC) in breast cancer would improve patient selection for specific treatment strategies, would provide important information for patients to aid in the treatment selection process, and could potentially avoid the need for more extensive surgery. The diagnostic performance of magnetic resonance imaging (MRI) in predicting pCR has previously been studied, with mixed results. Magnetic resonance imaging performance may also be influenced by tumour and patient factors.

Methods:

Eighty-seven breast cancer patients who underwent NAC were studied. Pre-NAC and post-NAC MRI findings were compared with pathologic findings postsurgical excision. The impact of patient and tumour characteristics on MRI accuracy was evaluated.

Results:

The mean (SD) age of participants was 48.7 (10.3) years. The rate of pCR based on post-NAC MRI was 19.5% overall (19/87). The sensitivity, specificity, positive predictive value (PPV), negative predictive value, and accuracy in predicting pCR were 52.9%, 77.1%, 36.0%, 87.1%, and 72.4%, respectively. Positive predictive value was the highest in nonluminal versus Luminal A disease (45.0% vs 25.0%, P < .001), with higher rates of false positivity in nonluminal subtypes (P = .002). Tumour grade, T category, and histological subtype were all independent predictors of MRI accuracy regarding post-NAC tumour size.

Conclusion:

Magnetic resonance imaging alone is insufficient to accurately predict pCR in breast cancer patients post-NAC. Magnetic resonance imaging predictions of pCR are more accurate in nonluminal subtypes. Tumour grade, T category, and histological subtype should be considered when evaluating post-NAC tumour sizes.

The Influence of Data-Driven Compressed Sensing Reconstruction on Quantitative Pharmacokinetic Analysis in Breast DCE MRI

Radial acquisition with MOCCO reconstruction has been previously proposed for high spatial and temporal resolution breast DCE imaging. In this work, we characterize MOCCO across a wide range of temporal contrast enhancement in a digital reference object (DRO). Time-resolved radial data was simulated using a DRO with lesions in different PK parameters. The under sampled data were reconstructed at 5 s temporal resolution using the data-driven low-rank temporal model for MOCCO, compressed sensing with temporal total variation (CS-TV) and more conventional low-rank reconstruction (PCB). Our results demonstrated that MOCCO was able to recover curves with K^trans values ranging from 0.01 to 0.8 min⁻¹ and fixed V_e = 0.3, where the fitted results are within a 10% bias error range. MOCCO reconstruction showed less impact on the selection of different temporal models than conventional low-rank reconstruction and the greater error was observed with PCB. CS-TV showed overall underestimation in both K^trans and V_e. For the Monte-Carlo simulations, MOCCO was found to provide the most accurate reconstruction results for curves with intermediate lesion kinetics in the presence of noise. Initial in vivo experiences are reported in one patient volunteer. Overall, MOCCO was able to provide reconstructed time-series data that resulted in a more accurate measurement of PK parameters than PCB and CS-TV.

Contrast-enhanced digital breast tomosythesis and breast MRI to monitor response to neoadjuvant chemotherapy: patient tolerance and preference

OBJECTIVE

Contrast-enhanced digital breast tomosynthesis (CE-DBT) is a novel imaging technique, combining contrast-enhanced spectral mammography and tomosynthesis. This may offer an alternative imaging technique to breast MRI for monitoring of response to neoadjuvant chemotherapy. This paper addresses patient experience and preference regarding the two techniques.

METHODS

Conducted as part of a prospective pilot study; patients were asked to complete questionnaires pertaining to their experience of CE-DBT and MRI following pre-treatment and end-of-treatment imaging. Questionnaires consisted of eight questions answered on a categorical scale, two using a visual analogue scale (VAS), and a question to indicate preference of imaging technique. Statistical analysis was performed with Wilcoxon signed rank test and McNemar test for related samples using SPSS v. 25.

RESULTS

18 patients were enrolled in the pilot study. Matched CE-DBT and MRI questionnaires were completed after 22 patient episodes. Patient preference was indicated after 31 patient episodes. Overall, on 77% of occasions patients preferred CE-DBT with no difference between pre-treatment and end-of-treatment imaging. Overall experience (p = 0.008), non-breast pain (p = 0.046), anxiety measured using VAS (p = 0.003), and feeling of being put at ease by staff (p = 0.023) was better for CE-DBT. However, more breast pain was experienced during CE-DBT when measured on both VAS (p = 0.011) and categorical scale (p = 0.021).

CONCLUSION

Our paper suggests that patients prefer CE-DBT to MRI, adding further evidence in favour of contrast-enhanced mammographic techniques.

ADVANCES IN KNOWLEDGE

Contrast mammographic techniques offer an alternative, more accessible imaging technique to breast MRI. Whilst other studies have addressed patient experience of contrast-enhanced spectral mammography, this is the first study to directly explore patient preference for CE-DBT over MRI in the setting of neoadjuvant chemotherapy, finding that overall, patients preferred CE-DBT despite the relatively long breast compression.

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.

MRI Changes in Breast Skin Following Preoperative Therapy for Patients with Inflammatory Breast Cancer

RATIONALE AND OBJECTIVES

Preoperative systemic therapy (PST) followed by mastectomy and radiation improves survival for patients with inflammatory breast cancer (IBC). Residual disease within the skin post-PST adversely impacts surgical outcome and risk of local-regional recurrence (LRR). We aimed to assess magnetic resonance imaging (MRI) breast skin changes post-PST with pathologic response and its impact on surgical resectability.

MATERIALS AND METHODS

We retrospectively reviewed 152 baseline and post-PST breast MRIs of 76 patients with IBC. Using the ACR-BIRADS MRI lexicon, we correlated skin thickness, qualitative enhancement, and kinetic analysis with pathologic response in the skin at mastectomy.

RESULTS

Baseline MRI showed skin thickening in all 76 patients, 75/76 (99%) showed skin enhancement, 54/75 (72%) had medium/fast initial kinetics, usually with persistent delayed kinetics in 49/54 (91%). Following PST, 66/76 (87%) had residual skin thickening with 64/76 (84%) showing a decrease; 33/76 (43%) had persistent enhancement. The median thickness post-PST was 4.7 mm with residual tumor in the skin, and 3.0 mm without residual tumor (p = 0.008). Regardless of pathologic response, the majority of patients had persistent skin thickening on MRI following PST (100% [14/14] with residual tumor and 84% [52/62] without residual tumor). There was no association between post-PST skin thickness on breast MRI and rate of LRR.

CONCLUSION

Patients with IBC have skin thickening and enhancement on baseline breast MRI, with a statistically significant reduction in skin thickness following successful PST. Despite persistent skin changes on MRI, patients achieving a partial or complete parenchymal response to PST may proceed to mastectomy with low LRR rates.

Neoadjuvant approach in patients with early breast cancer: patient assessment, staging, and planning

Neoadjuvant treatment (NAT) has become an option in early stage (stage I-II) breast cancer (EBC). New advances in systemic and targeted therapies have increased rates of pathologic complete response increasing the number of patients undergoing NAT. Clear benefits of NAT are downstaging the tumor and the axillary nodes to de-escalate surgery and to evaluate response to treatment. Selection of patients for NAT in EBC rely in several factors that are related to patient characteristics (i.e, age and comorbidities), to tumor histology, to stage at diagnosis and to the potential changes in surgical or adjuvant treatments when NAT is administered.

Imaging and histologic confirmation is performed to assess extent of disease y to confirm diagnosis. Besides mammogram and ultrasound, functional breast imaging MRI has been incorporated to better predict treatment response and residual disease. Contrast enhanced mammogram (CEM), shear wave elastography (SWE), or Dynamic Optical Breast Imaging (DOBI) are emerging techniques under investigation for assessment of response to neoadjuvant therapy as well as for predicting response. Surgical plan should be delineated after NAT taking into account baseline characteristics, tumor response and patient desire.

In the COVID era, we have witnessed also the increasing use of NAT in patients who may be directed to surgery, unable to have it performed as surgery has been reserved for emergency cases only.

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.

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.

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

OBJECTIVES

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

KEY POINTS

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

Dynamic contrast-enhanced (DCE) imaging: state of the art and applications in whole-body imaging

Dynamic contrast-enhanced (DCE) imaging is a non-invasive technique used for the evaluation of tissue vascularity features through imaging series acquisition after contrast medium administration. Over the years, the study technique and protocols have evolved, seeing a growing application of this method across different imaging modalities for the study of almost all body districts. The main and most consolidated current applications concern MRI imaging for the study of tumors, but an increasing number of studies are evaluating the use of this technique also for inflammatory pathologies and functional studies. Furthermore, the recent advent of artificial intelligence techniques is opening up a vast scenario for the analysis of quantitative information deriving from DCE. The purpose of this article is to provide a comprehensive update on the techniques, protocols, and clinical applications - both established and emerging - of DCE in whole-body imaging.

Prediction of post transarterial chemoembolization MR images of hepatocellular carcinoma using spatio-temporal graph convolutional networks

Magnetic resonance imaging (MRI) plays a critical role in the planning and monitoring of hepatocellular carcinomas (HCC) treated with locoregional therapies, in order to assess disease progression or recurrence. Dynamic contrast-enhanced (DCE)-MRI sequences offer temporal data on tumor enhancement characteristics which has strong prognostic value. Yet, predicting follow-up DCE-MR images from which tumor enhancement and viability can be measured, before treatment of HCC actually begins, remains an unsolved problem given the complexity of spatial and temporal information. We propose an approach to predict future DCE-MRI examinations following transarterial chemoembolization (TACE) by learning the spatio-temporal features related to HCC response from pre-TACE images. A novel Spatial-Temporal Discriminant Graph Neural Network (STDGNN) based on graph convolutional networks is presented. First, embeddings of viable, equivocal and non-viable HCCs are separated within a joint low-dimensional latent space, which is created using a discriminant neural network representing tumor-specific features. Spatial tumoral features from independent MRI volumes are then extracted with a structural branch, while dynamic features are extracted from the multi-phase sequence with a separate temporal branch. The model extracts spatio-temporal features by a joint minimization of the network branches. At testing, a pre-TACE diagnostic DCE-MRI is embedded on the discriminant spatio-temporal latent space, which is then translated to the follow-up domain space, thus allowing to predict the post-TACE DCE-MRI describing HCC treatment response. A dataset of 366 HCC's from liver cancer patients was used to train and test the model using DCE-MRI examinations with associated pathological outcomes, with the spatio-temporal framework yielding 93.5% classification accuracy in response identification, and generating follow-up images yielding insignificant differences in perfusion parameters compared to ground-truth post-TACE examinations.

Contrast-enhanced mammography: what the radiologist needs to know

Contrast-enhanced mammography (CEM) is a combination of standard mammography and iodinated contrast material administration. During the last decade, CEM has found its place in breast imaging protocols: after i.v. administration of iodinated contrast material, low-energy and high-energy images are retrieved in one acquisition using a dual-energy technique, and a recombined image is constructed enabling visualisation of areas of contrast uptake. The increased incorporation of CEM into everyday clinical practice is reflected in the installation of dedicated equipment worldwide, the (commercial) availability of systems from different vendors, the number of CEM examinations performed, and the number of scientific articles published on the subject. It follows that ever more radiologists will be confronted with this technique, and thus be required to keep up to date with the latest developments in the field. Most importantly, radiologists must have sufficient knowledge on how to interpret CEM images and be acquainted with common artefacts and pitfalls. This comprehensive review provides a practical overview of CEM technique, including CEM-guided biopsy; reading, interpretation and structured reporting of CEM images, including the accompanying learning curve, CEM artefacts and interpretation pitfalls; indications for CEM; disadvantages of CEM; and future developments.

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 cancer resistance to chemotherapy: When should we suspect it and how can we prevent it?

Chemotherapy is an essential treatment for breast cancer, inducing cancer cell death. However, chemoresistance is a problem that limits the effectiveness of chemotherapy. Many factors influence chemoresistance, including drug inactivation, changes in drug targets, overexpression of ABC transporters, epithelial-to-mesenchymal transitions, apoptotic dysregulation, and cancer stem cells. The effectiveness of chemotherapy can be assessed clinically and pathologically. Clinical response evaluation is based on physical examination or imaging (mammography, ultrasonography, computed tomography scan, or magnetic resonance imaging) and includes tumor size changes after chemotherapy. Pathological response evaluation is a method based on tumor residues in histopathological preparations. We should be suspicious of chemoresistance if there are no significant changes clinically according to the Response Evaluation Criteria in Solid Tumors and World Health Organization criteria or pathological changes according to the Miller and Payne criteria, especially after 2–3 cycles of chemotherapy treatments. Chemoresistance is mostly detected after the administration of chemotherapy drugs. No reliable parameters or biomarkers can predict chemotherapy responses appropriately and effectively. Well-known parameters such as cancer type, grade, subtype, estrogen receptor, progesterone receptor, human epidermal growth factor receptor 2, Ki-67, and MDR-1/P-gP have been used for selecting chemotherapy regimens. Some new methods for predicting chemoresistance include chemosensitivity and chemoresistance assays, multigene expressions, and positron emission tomography assays. The latest approaches are based on evaluation of molecular processes and the metabolic activity of cancer cells. Some methods for preventing chemoresistance include using the right regimen, using some combination of chemotherapy methods, conducting adequate monitoring, and using drugs that could prevent the emergence of multidrug resistance.

•

Chemotherapy is an essential treatment in the management of breast cancer.

•

Chemotherapy is carried out based on the selection of regimens for the specific individual and tumor characteristics.

•

Combination therapy, monitoring, and evaluation are used to prevent chemoresistance.

Prediction of Tumor Shrinkage Pattern to Neoadjuvant Chemotherapy Using a Multiparametric MRI-Based Machine Learning Model in Patients With Breast Cancer

Aim: After neoadjuvant chemotherapy (NACT), tumor shrinkage pattern is a more reasonable outcome to decide a possible breast-conserving surgery (BCS) than pathological complete response (pCR). The aim of this article was to establish a machine learning model combining radiomics features from multiparametric MRI (mpMRI) and clinicopathologic characteristics, for early prediction of tumor shrinkage pattern prior to NACT in breast cancer.

Materials and Methods: This study included 199 patients with breast cancer who successfully completed NACT and underwent following breast surgery. For each patient, 4,198 radiomics features were extracted from the segmented 3D regions of interest (ROI) in mpMRI sequences such as T1-weighted dynamic contrast-enhanced imaging (T1-DCE), fat-suppressed T2-weighted imaging (T2WI), and apparent diffusion coefficient (ADC) map. The feature selection and supervised machine learning algorithms were used to identify the predictors correlated with tumor shrinkage pattern as follows: (1) reducing the feature dimension by using ANOVA and the least absolute shrinkage and selection operator (LASSO) with 10-fold cross-validation, (2) splitting the dataset into a training dataset and testing dataset, and constructing prediction models using 12 classification algorithms, and (3) assessing the model performance through an area under the curve (AUC), accuracy, sensitivity, and specificity. We also compared the most discriminative model in different molecular subtypes of breast cancer.

Results: The Multilayer Perception (MLP) neural network achieved higher AUC and accuracy than other classifiers. The radiomics model achieved a mean AUC of 0.975 (accuracy = 0.912) on the training dataset and 0.900 (accuracy = 0.828) on the testing dataset with 30-round 6-fold cross-validation. When incorporating clinicopathologic characteristics, the mean AUC was 0.985 (accuracy = 0.930) on the training dataset and 0.939 (accuracy = 0.870) on the testing dataset. The model further achieved good AUC on the testing dataset with 30-round 5-fold cross-validation in three molecular subtypes of breast cancer as following: (1) HR+/HER2–: 0.901 (accuracy = 0.816), (2) HER2+: 0.940 (accuracy = 0.865), and (3) TN: 0.837 (accuracy = 0.811).

Conclusions: It is feasible that our machine learning model combining radiomics features and clinical characteristics could provide a potential tool to predict tumor shrinkage patterns prior to NACT. Our prediction model will be valuable in guiding NACT and surgical treatment in breast cancer.