Evaluation of the Tumor Response After Neoadjuvant Chemotherapy in Breast Cancer Patients: Correlation Between Dynamic Contrast-enhanced Magnetic Resonance Imaging and Pathologic Tumor Cellularity

Non-contrast-enhanced breast MRI for evaluation of tumor volume change after neoadjuvant chemotherapy

PURPOSE

Three-dimensional contrast-enhanced magnetic resonance imaging (3D-Ce-MRI) is a most powerful tool for evaluation of neoadjuvant chemotherapy (NAC). However, the use of contrast agent is invasive, expensive, and time consuming, Thus, contrast agent-free imaging is preferable. We aimed to investigate the tumor volume change after NAC using maximum intensity projection diffusion-weighted image (MIP-DWI) and 3D-Ce-MRI.

METHOD

We finally enrolled 55 breast cancer patients who underwent NAC in 2018. All MRI analyses were performed using SYNAPSE VINCENT® medical imaging system (Fujifilm Medical, Tokyo, Japan). We evaluated the tumor volumes before, during, and after NAC. Tumor volume before NAC on 3D-Ce-MRI was termed Pre-CE and those during and after NAC were termed Post-CE. The observer raised the lower end of the window width until the tumor was clearly visible and then manually deleted the non-tumor tissues. A month thereafter, the same observer who was blinded to the 3D-Ce-MRI results randomly evaluated the tumor volumes (Pre-DWI and Post-DWI) using MIP-DWI with the same method. Tumor volume change between ΔCE (Pre-CE - Post-CE/Pre-CE) and ΔDWI (Pre-DWI - Post-DWI/Pre-DWI) and the processing time for both methods (Time-DWI and Time-CE) were compared.

RESULTS

We enrolled 55 patients. Spearman's rho between ΔDWI and ΔCE for pure mass lesions, and non-mass enhancement (NME) was 0.89 (p < 0.01), 0.63(p < 0.01) respectively. Time-DWI was significantly shorter than Time-CE (41.3 ± 21.2 and 199.5 ± 98.3 respectively, p < 0.01).

CONCLUSIONS

Non-contrast-enhanced Breast MRI enables appropriate and faster evaluation of tumor volume change after NAC than 3D-Ce-MRI especially for mass lesions.

Neoadjuvant chemotherapy for breast cancer: an evaluation of its efficacy and research progress

Neoadjuvant chemotherapy (NAC) for breast cancer is widely used in the clinical setting to improve the chance of surgery, breast conservation and quality of life for patients with advanced breast cancer. A more accurate efficacy evaluation system is important for the decision of surgery timing and chemotherapy regimen implementation. However, current methods, encompassing imaging techniques such as ultrasound and MRI, along with non-imaging approaches like pathological evaluations, often fall short in accurately depicting the therapeutic effects of NAC. Imaging techniques are subjective and only reflect macroscopic morphological changes, while pathological evaluation is the gold standard for efficacy assessment but has the disadvantage of delayed results. In an effort to identify assessment methods that align more closely with real-world clinical demands, this paper provides an in-depth exploration of the principles and clinical applications of various assessment approaches in the neoadjuvant chemotherapy process.

Contrast-Enhanced Mammography (CEM) Capability to Distinguish Molecular Breast Cancer Subtypes

With breast cancer ranking first among the most common malignant neoplasms in the world, new techniques of early detection are in even more demand than before. Our awareness of tumors' biology is expanding and may be used to treat patients more efficiently. A link between radiology and pathology was searched for in our study, as well as the answer to the question of whether a tumor type can be seen on contrast-enhanced mammography and if such knowledge may serve as part of precision medicine.

Robustness Evaluation of a Deep Learning Model on Sagittal and Axial Breast DCE-MRIs to Predict Pathological Complete Response to Neoadjuvant Chemotherapy

To date, some artificial intelligence (AI) methods have exploited Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) to identify finer tumor properties as potential earlier indicators of pathological Complete Response (pCR) in breast cancer patients undergoing neoadjuvant chemotherapy (NAC). However, they work either for sagittal or axial MRI protocols. More flexible AI tools, to be used easily in clinical practice across various institutions in accordance with its own imaging acquisition protocol, are required. Here, we addressed this topic by developing an AI method based on deep learning in giving an early prediction of pCR at various DCE-MRI protocols (axial and sagittal). Sagittal DCE-MRIs refer to 151 patients (42 pCR; 109 non-pCR) from the public I-SPY1 TRIAL database (DB); axial DCE-MRIs are related to 74 patients (22 pCR; 52 non-pCR) from a private DB provided by Istituto Tumori “Giovanni Paolo II” in Bari (Italy). By merging the features extracted from baseline MRIs with some pre-treatment clinical variables, accuracies of 84.4% and 77.3% and AUC values of 80.3% and 78.0% were achieved on the independent tests related to the public DB and the private DB, respectively. Overall, the presented method has shown to be robust regardless of the specific MRI protocol.

Quantitative Changes in Intratumoral Habitats on MRI Correlate With Pathologic Response in Early-stage ER/PR+ HER2- Breast Cancer Treated With Preoperative Stereotactic Ablative Body Radiotherapy

Objective

To quantitatively evaluate intratumoral habitats on dynamic contrast-enhanced (DCE) breast MRI to predict pathologic breast cancer response to stereotactic ablative body radiotherapy (SABR).

Methods

Participants underwent SABR treatment (28.5 Gy x3), baseline and post-SABR MRI, and breast-conserving surgery for ER/PR+ HER2- breast cancer. MRI analysis was performed on DCE T1-weighted images. MRI voxels were assigned eight habitats based on high (H) or low (L) maximum enhancement and the sequentially numbered dynamic sequence of maximum enhancement (H1-4, L1-4). MRI response was analyzed by percent tumor volume remaining (%VR = volume post-SABR/volume pre-SABR), and percent habitat makeup (%HM of habitat X = habitat X voxels/total voxels in the segmented volume). These were correlated with percent tumor bed cellularity (%TC) for pathologic response.

Results

Sixteen patients completed the trial. The %TC ranged 20%-80%. MRI %VR demonstrated strong correlations with %TC (Pearson R = 0.7-0.89). Pre-SABR tumor %HMs differed significantly from whole breasts (P = 0.005 to <0.00001). Post-SABR %HM of tumor habitat H4 demonstrated the largest change, increasing 13% (P = 0.039). Conversely, combined %HM for H1-3 decreased 17% (P = 0.006). This change correlated with %TC (P < 0.00001) and distinguished pathologic partial responders (≤70 %TC) from nonresponders with 94% accuracy, 93% sensitivity, 100% specificity, 100% positive predictive value, and 67% negative predictive value.

Conclusion

In patients undergoing preoperative SABR treatment for ER/PR+ HER2- breast cancer, quantitative MRI habitat analysis of %VR and %HM change correlates with pathologic response.

Radiomics Analysis of Multi-Phase DCE-MRI in Predicting Tumor Response to Neoadjuvant Therapy in Breast Cancer

Objective: To explore whether the pretreatment dynamic contrast enhancement magnetic resonance imaging (DCE-MRI) and radiomics signatures were associated with pathologic complete response (pCR) to neoadjuvant therapy (NAT) in breast cancer. Method: A retrospective review of 70 patients with breast invasive carcinomas proved by biopsy between June 2017 and October 2020 (26 patients were pathological complete response, and 44 patients were non-pathological complete response). Within the pre-contrast and five post-contrast dynamic series, a total of 1037 quantitative imaging features were extracted from in each phase. Additionally, the Δfeatures (the difference between the features before and after the comparison) were used for subsequent analysis. The least absolute shrinkage and selection operator (LASSO) regression method was used to select features related to pCR, and then use these features to train multiple machine learning classifiers to predict the probability of pCR for a given patient. The area under the curve (AUC), accuracy, sensitivity, and specificity were calculated to assess the predictive performances of the radiomics model for each of the five phases of time points. Result: Among the five phases, each individual phase performed with AUCs ranging from 0.845 to 0.919 in predicting pCR. The best single phases performance was given by the 3rd phase (AUC = 0.919, sensitivity 0.885, specificity 0.864). 5 of the features have significant differences between pCR and non-pCR groups in each phase, most features reach their maximum or minimum in the 2nd or 3rd phase. Conclusion: The radiomic features extracted from each phase of pre-treatment DCE-MRI possess discriminatory power to predict tumor response.

MRI Response to Pre-operative Stereotactic Ablative Body Radiotherapy (SABR) in Early Stage ER/PR+ HER2- Breast Cancer correlates with Surgical Pathology Tumor Bed Cellularity

OBJECTIVE

This study evaluates breast MRI response of ER/PR+ HER2- breast tumors to pre-operative SABR with pathologic response correlation.

METHODS

Women enrolled in a phase 2 single institution trial of SABR for ER/PR+ HER2- breast cancer were retrospectively evaluated for radiologic-pathologic correlation of tumor response. These patients underwent baseline breast MRI, SABR (28.5 Gy in 3 fractions), follow-up MRI 5 to 6 weeks post-SABR, and lumpectomy. Tumor size and BI-RADS descriptors on pre and post-SABR breast MRIs were compared to determine correlation with surgical specimen % tumor cellularity (%TC). Reported MRI tumor dimensions were used to calculate percent cubic volume remaining (%VR). Partial MRI response was defined as a BI-RADs descriptor change or %VR ≤ 70%, while partial pathologic response (pPR) was defined as %TC ≤ 70%.

RESULTS

Nineteen patients completed the trial, and %TC ranged 10% to 80%. For BI-RADS descriptor analysis, 12 of 19 (63%) showed change in lesion or kinetic enhancement descriptors post-SABR. This was associated with lower %TC (29% vs. 47%, P = .042). BI-RADS descriptor change analysis also demonstrated high PPV (100%) and specificity (100%) for predicting pPR to treatment (sensitivity 71%, accuracy 74%), but low NPV (29%). MRI %VR demonstrated strong linear correlation with %TC (R = 0.70, P < .001, Pearson's Correlation) and high accuracy (89%) for predicting pPR (sensitivity 88%, specificity 100%, PPV 100%, and NPV 50%).

CONCLUSION

Evaluating breast cancer response on MRI using %VR after pre-operative SABR treatment can help identify patients benefiting the most from neoadjuvant radiation treatment of their ER/PR+ HER2- tumors, a group in which pCR to neoadjuvant therapy is rare.

Correlation between MRI morphological response patterns and histopathological tumor regression after neoadjuvant endocrine therapy in locally advanced breast cancer: a randomized phase II trial

PURPOSE

To correlate MRI morphological response patterns with histopathological tumor regression grading system based on tumor cellularity in locally advanced breast cancer (LABC)-treated neoadjuvant with third-generation aromatase inhibitors.

METHODS

Fifty postmenopausal patients with ER-positive/HER-2-negative LABC treated with neoadjuvant letrozole and exemestane given sequentially in an intra-patient cross-over regimen for at least 4 months with MRI response monitoring at baseline as well as after at least 2 and 4 months on treatment. The MRI morphological response pattern was classified into 6 categories: 0/complete imaging response; I/concentric shrinkage; II/fragmentation; III/diffuse; IV/stable; and V/progressive. Histopathological tumor regression was assessed based on the recommendations from The Royal College of Pathologists regarding tumor cellularity.

RESULTS

Following 2 and 4 months with therapy, the most common MRI pattern was pattern II (24/50 and 21/50, respectively). After 4 months on therapy, the most common histopathological tumor regression grade was grade 3 (21/50). After 4 months an increasing correlation is observed between MRI patterns and histopathology. The overall correlation, between the largest tumor diameter obtained from MRI and histopathology, was moderate and positive (r = 0.50, P-value = 2e-04). Among them, the correlation was highest in type IV (r = 0.53).

CONCLUSION

The type II MRI pattern "fragmentation" was more frequent in the histopathological responder group; and types I and IV in the non-responder group. Type II pattern showed the best endocrine responsiveness and a relatively moderate correlation between sizes obtained from MRI and histology, whereas type IV pattern indicated endocrine resistance but the strongest correlation between MRI and histology.

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

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSION

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

Comparison of Pathologic Response Evaluation Systems After Neoadjuvant Chemotherapy in Breast Cancers: Correlation With Computer-Aided Diagnosis of MRI Features

OBJECTIVE. The objective of our study was to compare pathologic response assessment systems with dynamic contrast-enhanced MRI (DCE-MRI) features and evaluate the predictive performance of DCE-MRI features relative to different pathologic response assessment systems after neoadjuvant chemotherapy (NAC). MATERIALS AND METHODS. This retrospective study included 223 women (mean age, 47.2 years; age range, 31-71 years) who underwent DCE-MRI before and after completing NAC before definitive surgery between January and December 2015. Six MRI features (i.e., tumor size; angio volume; peak enhancement; and persistent, plateau, and washout enhancing components) were measured and correlated with the Miller-Payne grading system, residual cancer burden (RCB) class, and modified in-breast RCB index. Percentage changes in MRI features were correlated with pathologic assessment systems using the Spearman rank correlation test; ROC curves were analyzed to predict pathologic outcomes. RESULTS. All six MRI features correlated most strongly with the in-breast RCB index (r = -0.75 to -0.51, p < 0.001) followed by the Miller-Payne system (r = 0.47-0.72, p < 0.001) and RCB class (r = -0.58 to -0.41, p < 0.001). The in-breast RCB index correlated most strongly with the angio volume reduction rate (r = -0.75, p < 0.001) followed by maximum diameter (r = -0.69, p < 0.001), peak enhancement (r = -0.67, p < 0.001), washout component (r = -0.60, p < 0.001), plateau component (r = -0.59, p < 0.001), and persistent component (r = -0.51, p < 0.001). CONCLUSION. The in-breast RCB index correlated best with changes in DCE-MRI features, and the MRI-measured angio volume reduction rate correlated best with pathologic tumor responses.

Clinical implications of changes in the diversity of c-MYC copy number variation after neoadjuvant chemotherapy in breast cancer

Chemotherapy can alter the makeup of a tumor cell population by exerting selection pressure. We examined the change in Shannon index, a mathematical diversity measure used in ecology, for c-MYC copy number variation (CNV) after neoadjuvant chemotherapy and evaluated its clinical significance in breast cancer. Associations between Shannon indices for c-MYC CNV in pre- and post-neoadjuvant chemotherapy breast cancer samples and clinicopathologic features of tumors as well as patient survival were analyzed in 144 patients. A change in c-MYC amplification and copy number gain status was found in 14.3% and 33.6% with most cases showing positive to negative conversion. The chemo-sensitive group showed a significant decrease in Shannon index after neoadjuvant chemotherapy. However, there was no difference in diversity indices between pre- and post-neoadjuvant chemotherapy specimens in the chemo-resistant group. In survival analyses, high Shannon indices for c-MYC CNV in post-neoadjuvant chemotherapy samples as well as those in pre-neoadjuvant chemotherapy samples were revealed as independent prognostic factors for poor disease-free survival not only in the whole group but also in the chemo-resistant subgroup. These findings suggest that a change in Shannon index for c-MYC CNV after neoadjuvant chemotherapy reflects chemo-responsiveness and that Shannon indices after neoadjuvant chemotherapy have a prognostic value in breast cancer patients who receive neoadjuvant chemotherapy.

Three-dimensional ultrasonography of the breast; An adequate replacement for MRI in neoadjuvant chemotherapy tumour response evaluation? - RESPONDER trial

BACKGROUND

Accurate measurement of tumour response during and after neoadjuvant chemotherapy (NAC) is important and may influence treatment decisions in invasive breast cancer patients. Breast MRI forms the gold standard but is more burdensome, time consuming and costly. In this study response measurement was done with 3-D ultrasound by Automated Breast Volume Scanner (ABVS) and compared to breast MRI. Moreover, patient satisfaction with both techniques was compared.

METHODS AND MATERIALS

A single-institution, prospective observational pilot study evaluating tumour response by ABVS in addition to breast MRI (standard care) was performed in 25 invasive breast cancer patients receiving NAC. Tumour response was evaluated comparing longest tumour diameters as well as tumour volumes at predefined time points using Bland-Altman analysis. Volume measurements for breast MRI were obtained using a fully immersive virtual reality system (a Barco I-Space) and V-Scope software. Same software was used to obtain ABVS volume measurements using an in-house developed desktop VR system. Inter- and intra-observer agreement was evaluated by Intraclass Correlation Coefficient (ICC).

RESULTS

Twenty-five patients were eligible for baseline measurement, 20 for a mid-NAC response evaluation, and five for a post-NAC response evaluation. MRI and ABVS showed absolute concordance in 73% of patients for the mid-NAC evaluation, with a 'good' correlation for the difference in longest diameter measurement (ICC 0.73, p < 0.01) as compared to baseline assessment. Concerning difference in volume measurement in the mid-NAC response evaluation showed a 'fair' correlation (ICC 0.52, p < 0.01) and in the post-NAC response evaluation an 'excellent' correlation (ICC 0.98, p < 0.01). 'Excellent' inter- and intra-observer agreement was found (ICC 0.88, p < 0.01) with comparable limits of agreement (LOA) for observer 1 and 2 in both diameter and volume measurement. Patient satisfaction was higher for ABVS compared to breast MRI, 93% versus 12% respectively.

CONCLUSION

ABVS showed 'good' correlation with MRI tumour response evaluation in breast cancer patients during NAC with 'excellent' inter- and intra-observer agreement. ABVS has patients' preference over breast MRI and could be considered as alternative to breast MRI, in case results on an on-going prospective trial confirm these results (NTR6799).