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

DCE-MRI based deep learning analysis of intratumoral subregion for predicting Ki-67 expression level in breast cancer

OBJECTIVE

To evaluate whether deep learning (DL) analysis of intratumor subregion based on dynamic contrast-enhanced MRI (DCE-MRI) can help predict Ki-67 expression level in breast cancer.

MATERIALS AND METHODS

A total of 290 breast cancer patients from two hospitals were retrospectively collected. A k-means clustering algorithm confirmed subregions of tumor. DL features of whole tumor and subregions were extracted from DCE-MRI images based on 3D ResNet18 pre-trained model. The logistic regression model was constructed after dimension reduction. Model performance was assessed using the area under the curve (AUC), and clinical value was demonstrated through decision curve analysis (DCA).

RESULTS

The k-means clustering method clustered the tumor into two subregions (habitat 1 and habitat 2) based on voxel values. Both the habitat 1 model (validation set: AUC = 0.771, 95 %CI: 0.642-0.900 and external test set: AUC = 0.794, 95 %CI: 0.696-0.891) and the habitat 2 model (AUC = 0.734, 95 %CI: 0.605-0.862 and AUC = 0.756, 95 %CI: 0.646-0.866) showed better predictive capabilities for Ki-67 expression level than the whole tumor model (AUC = 0.686, 95 %CI: 0.550-0.823 and AUC = 0.680, 95 %CI: 0.555-0.804). The combined model based on the two subregions further enhanced the predictive capability (AUC = 0.808, 95 %CI: 0.696-0.921 and AUC = 0.842, 95 %CI: 0.758-0.926), and it demonstrated higher clinical value than other models in DCA.

CONCLUSIONS

The deep learning model derived from subregion of tumor showed better performance for predicting Ki-67 expression level in breast cancer patients. Additionally, the model that integrated two subregions further enhanced the predictive performance.

Biomechanical parameters quantified by MR elastography for predicting response to neoadjuvant chemotherapy and disease-free survival in breast cancer: a prospective longitudinal study

BACKGROUND

Little is known regarding biomechanical properties derived from multifrequency MR elastography temporal changes during neoadjuvant chemotherapy (NAC) and associated with pathologic complete response (pCR) and disease-free survival (DFS) in breast cancer. We aimed to investigate temporal changes in NAC-associated biomechanical parameters and assess biomechanical parameters as a predictor of pCR and DFS in breast cancer.

METHODS

In this prospective longitudinal study, participants with breast cancer who received NAC were enrolled from February 2021 to May 2023. All participants underwent multifrequency MR-elastography at four timepoints: before NAC (T1) and after 2 (T2), 4 (T3), and 6 (T4) cycles. Tomoelastography postprocessing provided biomechanical maps of shear-wave-speed (c) and loss-angle (φ) as proxies of stiffness and viscosity. The biomechanical parameters were validated by means of correlation with histopathologic measurements. Generalized estimating equations were used to compare temporal changes in biomechanical parameters at four time points. Logistic regression was used for pCR analysis and Cox proportional hazards regression was used for survival analysis. Predictive performance was assessed with area under the receiver operating characteristic curve (AUC) analysis.

RESULTS

A total of 235 women (50.6 ± 7.9 years) with 964 scans were enrolled. Biomechanical parameters were supported by positive correlations with pathologic examination-based stroma fraction (c: r =.76, P <.001; φ: r =.49, P =.008) and cellularity (c: r =.58, P =.001; φ: r =.40, P =.035). Progesterone receptor, human epidermal growth factor receptor-2 (HER2), T2-c, and T2-φ were independently associated with pCR (all P <.05). Estrogen receptor, HER2, clinical stage, and change in φ at the early stage of NAC were associated with PFS (all P <.05). The predictive model, which incorporated biomechanical parameters and clinicopathologic characteristics significantly outperformed the clinicopathologic model in predicting pCR (AUC: 0.95, 95% confidence interval [CI]: 0.92, 0.98 vs. 0.79, 95%CI: 0.73, 0.84; P <.001). The predictive model also showed good discrimination ability for DFS (C-index = 0.82, 95%CI: 0.72, 0.90) and stratified prognosis into low-risk and high-risk groups (log-rank, P <.001).

CONCLUSIONS

During NAC, patients with higher tumor stiffness and viscosity are less likely to achieve DFS and pCR. The biomechanical parameters exhibit excellent biological interpretability and serve as valuable biomarkers for predicting pCR and DFS in patients with breast cancer.

Nomogram for Predicting Neoadjuvant Chemotherapy Response in Breast Cancer Using MRI-based Intratumoral Heterogeneity Quantification

Background Intratumoral heterogeneity (ITH) in breast cancer contributes to treatment failure and relapse. Noninvasive methods to quantify ITH are currently limited. Purpose To quantify ITH in breast cancer using pretreatment MRI, develop a nomogram to predict pathologic complete response (pCR) to neoadjuvant chemotherapy (NAC) and recurrence-free survival (RFS), and investigate biologic pathways associated with nomogram scores. Materials and Methods This retrospective study included patients with breast cancer who underwent NAC at nine centers between April 1988 and December 2023. Tumor regions on MRI scans were clustered and integrated with global pixel distribution patterns to calculate ITH scores. A nomogram for predicting pCR was developed using multivariable logistic regression. A survival dataset was used to evaluate the association between nomogram score and RFS, and a genomics dataset was used to explore the relationship between nomogram score and biologic pathways. Results The study included 1448 women (median age, 49 years [IQR, 43-54 years]). To predict pCR to NAC, the 505 patients from center A served as the training set, and the patients from center B, centers C-F, and center G served as three external validation sets (n = 331, 107, and 384, respectively). The survival set included patients from centers A and H (n = 179), and the genomics set included patients from center I (n = 74). The ITH score was an independent predictor of pCR (odds ratio, 0.12 [95% CI: 0.03, 0.43]; P < .001). The nomogram model achieved area under the receiver operating characteristic curve values of 0.82, 0.81, and 0.79, respectively, in the three external validation sets. A lower nomogram score was correlated with poorer RFS (hazard ratio, 4.04 [95% CI: 1.90, 8.60]; P < .001) and was associated with upregulation of biologic pathways related to tumor proliferation. Conclusion A nomogram model combining ITH score and clinicopathologic variables showed good performance in predicting pCR to NAC and RFS. Published under a CC BY 4.0 license. Supplemental material is available for this article.

Image quality and diagnostic performance of deep learning reconstruction for diffusion- weighted imaging in 3 T breast MRI

PURPOSE

This study aimed to assess the image quality and the diagnostic value of deep learning reconstruction (DLR) for diffusion-weighted imaging (DWI) compared with conventional single-shot echo-planar imaging (ss-EPI) in 3 T breast MRI.

METHODS

Between January and July 2023, this single-center prospective study involved patients who underwent both clinical breast MRI and additional DWIs including accelerated (fast DLR) and high-resolution (HR DLR) for the research purpose. Two radiologists independently evaluated image quality, including fat suppression homogeneity, image blurring, artifacts, and lesion conspicuity. The optimal cutoff value of the ADC value was determined based on a separate dataset comprising 98 breast lesions in 81 patients from a previous retrospective study. ADC values from 62 breast lesions (55 malignant, 7 benign) in 50 patients were analyzed to compare diagnostic performance across three DWI datasets.

RESULTS

The study cohort included 50 patients (median age, 55.3 years). Fast DLR and HR DLR showed significantly better image quality compared to ss-EPI (P < 0.05), with no significant difference between two DLR methods (P > 0.05). DLR protocols consistently outperform ss-EPI for reducing artifacts across all lesion types and lesion size (P < 0.05). Mean ADC values measured in the phantom and clinical images were not significantly different across DWI protocols (P > 0.05). No significant difference in the diagnostic performance with the AUC of 0.846 in ss-EPI, 0.828 in fast DLR and 0.855 in HR DLR (P > 0.05). Fast DLR showed a significantly lower standard deviation of ADC values compared to ss-EPI in malignant, mass-type lesions and those smaller than 2 cm (P < 0.05).

CONCLUSIONS

DLR DWI in 3T breast MRI improves image quality in both accelerated and high-resolution acquisition settings without compromising diagnostic performance. The use of DLR in DWI of breast MRI could enhance the efficiency and versatility of imaging protocols, offering significant clinical value.

Breast cancer assessment under neoadjuvant systemic therapy using thoracic photon-counting detector computed tomography in prone position: a pilot study

BACKGROUND

Accurate assessment of treatment response to neoadjuvant systemic therapy (NAST) in breast cancer is important prior to surgery. We aimed at evaluating the feasibility of thoracic photon-counting detector computed tomography (PCCT) in assessing treatment response in breast cancers following NAST.

METHODS

We retrospectively included patients with newly diagnosed breast cancer who received contrast-enhanced thoracic PCCT in prone position before and after NAST. Three experienced radiologists measured tumor size, tumor area, iodine uptake within tumors, number of suspicious breast lesions and of suspicious axillary lymph nodes before and after NAST. We compared the initial tumor size to contrast-enhanced magnetic resonance imaging (MRI), the residual tumor size after NAST to histopathology.

RESULTS

Eighteen PCCT exams in nine patients aged 58 ± 14 years (mean ± standard deviation) were analyzed. After NAST, PCCT correctly identified a reduction in tumor burden in 9 of 9 cases and a complete response in 2 of 2 cases, with a significant reduction in tumor size, area, T-stage, number of suspicious breast lesions and of suspicious lymph nodes (p < 0.001 for all) as well as reduction in cutaneous infiltration (p = 0.010). Mean and maximum iodine uptake showed a nonsignificant reduction in cases with residual tumor after NAST (p = 0.092 and 0.363).

CONCLUSION

These preliminary findings suggest that thoracic PCCT can accurately detect local changes in breast cancer after NAST.

RELEVANCE STATEMENT

Thoracic PCCT offers promising potential for accurately assessing breast cancer response to NAST.

TRIAL REGISTRATION

German Clinical Trials Register DRKS00028997.

KEY POINTS

Prone thoracic contrast-enhanced photon-counting detector computed tomography (PCCT) can accurately detect reductions in tumor size, area, and T-stage. Prone PCCT can identify a decrease in the number of suspicious axillary lymph nodes. This technique shows promising results in identifying breast cancer response to neoadjuvant systemic therapy (NAST).

Elastography-based AI model can predict axillary status after neoadjuvant chemotherapy in breast cancer with nodal involvement: a prospective, multicenter, diagnostic study

OBJECTIVE

To develop a model for accurate prediction of axillary lymph node (LN) status after neoadjuvant chemotherapy (NAC) in breast cancer patients with nodal involvement.

METHODS

Between October 2018 and February 2024, 671 breast cancer patients with biopsy-proven LN metastasis who received NAC followed by axillary LN dissection were enrolled in this prospective, multicenter study. Preoperative ultrasound (US) images, including B-mode ultrasound (BUS) and shear wave elastography (SWE), were obtained. The included patients were randomly divided at a ratio of 8:2 into a training set and an independent test set, with five-fold cross-validation applied to the training set. The authors first identified clinicopathological characteristics and conventional US features significantly associated with the axillary LN response and developed corresponding prediction models. The authors then constructed deep learning radiomics (DLR) models based on BUS and SWE data. Models performances were compared, and a combination model was developed using significant clinicopathological data and interpreted US features with the SWE-based DLR model. Discrimination, calibration and clinical utility of this model were analyzed using the receiver operating characteristic curve, calibration curve, and decision curve, respectively.

RESULTS

Axillary pathologic complete response (pCR) was achieved in 52.41% of patients. In the test cohort, the clinicopathologic model had an accuracy of 71.30%, while radiologists' diagnoses ranged from 64.26 to 71.11%, indicating limited to moderate predictive ability for the axillary response to NAC. The SWE-based DLR model, with an accuracy of 80.81%, significantly outperformed the BUS-based DLR model, which scored 59.57%. The combination DLR model boasted an accuracy of 88.70% and a false-negative rate of 8.82%. It demonstrated strong discriminatory ability (AUC, 0.95), precise calibration ( P -value obtained by Hosmer-Lemeshow goodness-of-fit test, 0.68), and practical clinical utility (probability threshold, 2.5-97.5%).

CONCLUSIONS

The combination SWE-based DLR model can predict the axillary status after NAC in patients with node-positive breast cancer, and thus, may inform clinical decision-making to help avoid unnecessary axillary LN dissection.

Enhancing detection of high-level axillary lymph node metastasis after neoadjuvant therapy in breast cancer patients with nodal involvement: a combined approach of axilla ultrasound and breast elastography

PURPOSE

To develop a combined approach using shear wave elastography (SWE) and conventional ultrasound (US) to determine the extent of positive axillary lymph nodes (LNs) following neoadjuvant therapy (NAT) in breast cancer patients with nodal involvement.

METHODS

This prospective, multicenter study was registered on the Chinese Clinical Trial Registry (ChiCTR2400085035). From October 2018 to February 2024, a total of 303 breast cancer patients with biopsy-proven positive LN were enrolled. The conventional US features of axillary LNs and SWE characteristics of breast lesions after NAT were analyzed. The diagnostic performances of axilla US, breast SWE, and their combination in detecting residual metastasis in axillary level III after NAT were assessed.

RESULTS

Pathologically positive LN(s) in axilla level III were detected in 13.75% of cases following NAT. The kappa value for the axilla level with positive LN confirmed by surgical pathology and detected by US is 0.39 (p < 0.001). The AUC of conventional axilla US to determine the status of axilla level III LNs after NAT was 0.67, with a sensitivity of 51.52%, a specificity of 74.36%. The breast SWE displayed moderate performance for detecting residual metastasis in axilla level III following NAT, with an AUC of 0.79, sensitivity of 84.85%, and specificity of 74.36%. Compared to axilla US and breast SWE alone, the combination of axilla US with breast SWE achieved a stronger discriminatory ability (AUC, 0.86 vs 0.67 vs 0.79, p < 0.05, Delong's test) and precise calibration (X2 = 13.90, p = 0.085, HL test), with an improved sensitivity of 93.94% and a comparable specificity of 75.64%%.

CONCLUSIONS

SWE outperformed conventional US in identifying the axilla levels with nodal metastasis following NAT in patients with initially diagnosed positive axilla. Furthermore, combining breast SWE with axilla US showed good diagnostic performance for detecting residual metastasis in axilla level III after NAT.

Development of an Ultrasound-based Nomogram for Predicting Pathologic Complete Response and Axillary Response in Node-Positive Patients with Triple- Negative Breast Cancer

BACKGROUND

The accurate prediction of pathological complete response (pCR) in the breast and axillary lymph nodes (ALN) before neoadjuvant chemotherapy (NAC) is of utmost importance for the development of treatment strategies. We aim to construct a nomogram on ultrasound (US) and clinical-pathologic factors to predict breast and ALN pCR in node-positive triple-negative breast cancers (TNBCs).

METHODS

Patients identified with TNBCs from institution 1 (n = 328) were used for training cohort and those from institution 2 (n = 192) were for validation cohort. US was conducted before and after NAC, and characteristics were obtained from medical records. Univariate and multivariate regression analysis were performed to identify US and clinical-pathologic factors associated with breast and ALN pCR in the training cohort. The assessment of predictive performance was conducted using the receiving operating characteristic curve (ROC), discrimination, and calibration.

RESULTS

Overall, 34.6% of patients achieved breast pCR and 48.1% of patients achieved ALN pCR. The nomogram 1 used for predicting pCR in the breast (AUC, 0.84; 95% CI: 0.79, 0.88) outperformed the clinical (AUC, 0.73; 95% CI: 0.68, 0.78) and US models (AUC, 0.79; 95% CI: 0.74, 0.83). The nomogram 2 used for predicting pCR in the axllia (AUC, 0.83; 95% CI: 0.78, 0.87) also outperformed the clinical (AUC, 0.64; 95% CI: 0.58, 0.69) and US models (AUC, 0.80; 95% CI: 0.75, 0.84). The calibration curve and discrimination curve indicate that the nomogram has good calibration performance and clinical applicability.

CONCLUSION

The nomogram showed promising predictive performance for predicting breast and ALN pCR in patients with TNBCs.

Comparing shear wave elastography of breast tumors and axillary nodes in the axillary assessment after neoadjuvant chemotherapy in patients with node-positive breast cancer

BACKGROUND

Accurately identifying patients with axillary pathologic complete response (pCR) after neoadjuvant chemotherapy (NAC) in breast cancer patients remains challenging.

PURPOSE

To compare the feasibility of shear wave elastography (SWE) performed on breast tumors and axillary lymph nodes (LNs) in predicting the axillary status after NAC.

MATERIALS AND METHODS

This prospective study included a total of 319 breast cancer patients with biopsy-proven positive node who received NAC followed by axillary lymph node dissection from 2019 to 2022. The correlations between shear wave velocity (SWV) and pathologic characteristics were analyzed separately for both breast tumors and LNs after NAC. We compared the performance of SWV between breast tumors and LNs in predicting the axillary status after NAC. Additionally, we evaluated the performance of the most significantly correlated pathologic characteristic in breast tumors and LNs to investigate the pathologic evidence supporting the use of breast or axilla SWE.

RESULTS

Axillary pCR was achieved in 51.41% of patients with node-positive breast cancer. In breast tumors, there is a stronger correlation between SWV and collagen volume fraction (CVF) (r = 0.52, p < 0.001) compared to tumor cell density (TCD) (r = 0.37, p < 0.001). In axillary LNs, SWV was weakly correlated with CVF (r = 0.31, p = 0.177) and TCD (r = 0.29, p = 0.213). No significant correlation was found between SWV and necrosis proportion in breast tumors or axillary LNs. The predictive performances of both SWV and CVF for axillary pCR were found to be superior in breast tumors (AUC = 0.87 and 0.85, respectively) compared to axillary LNs (AUC = 0.70 and 0.74, respectively).

CONCLUSION

SWE has the ability to characterize the extracellular matrix, and serves as a promising modality for evaluating axillary LNs after NAC. Notably, breast SWE outperform axilla SWE in determining the axillary status in breast cancer patients after NAC.

Delta Radiomics Based on Longitudinal Dual-modal Ultrasound Can Early Predict Response to Neoadjuvant Chemotherapy in Breast Cancer Patients

RATIONALE AND OBJECTIVES

To develop a monitoring model using radiomics analysis based on longitudinal B-mode ultrasound (BUS) and shear wave elastography (SWE) to early predict pathological response to neoadjuvant chemotherapy (NAC) in breast cancer patients.

MATERIALS AND METHODS

In this prospective study, 112 breast cancer patients who received NAC between September 2016 and March 2022 were included. The BUS and SWE data of breast cancer were obtained prior to treatment as well as after two and four cycles of NAC. Radiomics features were extracted followed by measuring the changes in radiomics features compared to baseline after the second and fourth cycles of NAC (△R [C2], △R [C4]), respectively. The delta radiomics signatures were established using a support vector machine classifier.

RESULTS

The area under receiver operating characteristic curve (AUC) values of △RBUS (C2) and △RBUS (C4) for predicting the response to NAC were 0.83 and 0.84, while those of △RSWE (C2) and △RSWE (C4) were 0.88 and 0.90, respectively. △RSWE exhibited significantly superior performance to △RBUS for predicting NAC response (Delong test, p < 0.01). No significant differences were observed in the performances between △R (C2) and △R (C4) based on BUS or SWE data. The longitudinal dual-modal ultrasound radiomics (LDUR) model had an excellent discrimination, good calibration and clinical usefulness, with the AUC, sensitivity and specificity of 0.97, 95.52% and 91.11%, respectively.

CONCLUSION

The LDUR model achieved excellent performance in predicting the pathological response to chemotherapy during the early stages of NAC for breast cancer.