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Background: While several methods have been proposed for automated assessment of breast-cancer response to neoadjuvant chemotherapy on breast MRI, limited information is available about their performance across multiple institutions. Purpose: To assess the value and robustness of deep learning-derived volumes of locally advanced breast cancer (LABC) on MRI to infer the presence of residual disease after neoadjuvant chemotherapy. Study Type: Retrospective. Subjects: Training cohort: 102 consecutive female patients with LABC scheduled for neoadjuvant chemotherapy (NAC) from a single institution (age: 25–73 years). Independent testing cohort: 55 consecutive female patients with LABC from four institutions (age: 25–72 years). Field Strength/Sequence: Training cohort: single vendor 1.5 T or 3.0 T. Testing cohort: multivendor 3.0 T. Gradient echo dynamic contrast-enhanced sequences. Assessment: A convolutional neural network (nnU-Net) was trained to segment LABC. Based on resulting tumor volumes, an extremely randomized tree model was trained to assess residual cancer burden (RCB)-0/I vs. RCB-II/III. An independent model was developed using functional tumor volume (FTV). Models were tested on an independent testing cohort and response assessment performance and robustness across multiple institutions were assessed. Statistical Tests: The receiver operating characteristic (ROC) was used to calculate the area under the ROC curve (AUC). DeLong's method was used to compare AUCs. Correlations were calculated using Pearson's method. P values <0.05 were considered significant. Results: Automated segmentation resulted in a median (interquartile range [IQR]) Dice score of 0.87 (0.62–0.93), with similar volumetric measurements (R = 0.95, P < 0.05). Automated volumetric measurements were significantly correlated with FTV (R = 0.80). Tumor volume-derived from deep learning of DCE-MRI was associated with RCB, yielding an AUC of 0.76 to discriminate between RCB-0/I and RCB-II/III, perf

BackgroundSeveral single-center studies found that high contralateral parenchymal enhancement (CPE) at breast MRI was associated with improved long-term survival in patients with estrogen receptor (ER)–positive and human epidermal growth factor receptor 2 (HER2)–negative breast cancer. Due to varying sample sizes, population characteristics, and follow-up times, consensus of the association is currently lacking.PurposeTo confirm whether CPE is associated with long-term survival in a large multicenter retrospective cohort, and to investigate if CPE is associated with endocrine therapy effectiveness.Materials and MethodsThis multicenter observational cohort included women with unilateral ER-positive HER2-negative breast cancer (tumor size ≤50 mm and ≤three positive lymph nodes) who underwent MRI from January 2005 to December 2010. Overall survival (OS), recurrence-free survival (RFS), and distant RFS (DRFS) were assessed. Kaplan-Meier analysis was performed to investigate differences in absolute risk after 10 years, stratified according to CPE tertile. Multivariable Cox proportional hazards regression analysis was performed to investigate whether CPE was associated with prognosis and endocrine therapy effectiveness.ResultsOverall, 1432 women (median age, 54 years [IQR, 47–63 years]) were included from 10 centers. Differences in absolute OS after 10 years were stratified according to CPE tertile as follows: 88.5% (95% CI: 88.1, 89.1) in tertile 1, 85.8% (95% CI: 85.2, 86.3) in tertile 2, and 85.9% (95% CI: 85.4, 86.4) in tertile 3. CPE was independently associated with OS, with a hazard ratio (HR) of 1.17 (95% CI: 1.0, 1.36; P = .047), but was not associated with RFS (HR, 1.11; P = .16) or DRFS (HR, 1.11; P = .19). The effect of endocrine therapy on survival could not be accurately assessed; therefore, the association between endocrine therapy efficacy and CPE could not reliably be estimated.ConclusionHigh contralateral parenchymal enhancement was associated with a mar

Wilms tumor is a common pediatric solid tumor. To evaluate tumor response to chemotherapy and decide whether nephron-sparing surgery is possible, tumor volume measurements based on magnetic resonance imaging (MRI) are important. Currently, radiological volume measurements are based on measuring tumor dimensions in three directions. Manual segmentation-based volume measurements might be more accurate, but this process is time-consuming and user-dependent. The aim of this study was to investigate whether manual segmentation-based volume measurements are more accurate and to explore whether these segmentations can be automated using deep learning. We included the MRI images of 45 Wilms tumor patients (age 0–18 years). First, we compared radiological tumor volumes with manual segmentation-based tumor volume measurements. Next, we created an automated segmentation method by training a nnU-Net in a five-fold cross-validation. Segmentation quality was validated by comparing the automated segmentation with the manually created ground truth segmentations, using Dice scores and the 95th percentile of the Hausdorff distances (HD95). On average, manual tumor segmentations result in larger tumor volumes. For automated segmentation, the median dice was 0.90. The median HD95 was 7.2 mm. We showed that radiological volume measurements underestimated tumor volume by about 10% when compared to manual segmentation-based volume measurements. Deep learning can potentially be used to replace manual segmentation to benefit from accurate volume measurements without time and observer constraints.

With an increase in deep learning-based methods, the call for explainability of such methods grows, especially in high-stakes decision making areas such as medical image analysis. This survey presents an overview of explainable artificial intelligence (XAI) used in deep learning-based medical image analysis. A framework of XAI criteria is introduced to classify deep learning-based medical image analysis methods. Papers on XAI techniques in medical image analysis are then surveyed and categorized according to the framework and according to anatomical location. The paper concludes with an outlook of future opportunities for XAI in medical image analysis.

Purpose: We aimed to evaluate changes in dynamic contrast-enhanced (DCE) and diffusion-weighted (DW) magnetic resonance imaging (MRI) scans acquired before and after single-dose ablative neoadjuvant partial breast irradiation (NA-PBI), and explore the relation between semiquantitative MRI parameters and radiologic and pathologic responses. Methods and Materials: We analyzed 3.0T DCE and DW-MRI of 36 patients with low-risk breast cancer who were treated with single-dose NA-PBI, followed by breast-conserving surgery 6 or 8 months later. MRI was acquired before NA-PBI and 1 week, 2, 4, and 6 months after NA-PBI. Breast radiologists assessed the radiologic response and breast pathologists scored the pathologic response after surgery. Patients were grouped as either pathologic responders or nonresponders (

Background: Supplemental screening with MRI has proved beneficial in women with extremely dense breasts. Most MRI examinations show normal anatomic and physiologic variation that may not require radiologic review. Thus, ways to triage these normal MRI examinations to reduce radiologist workload are needed. Purpose: To determine the feasibility of an automated triaging method using deep learning (DL) to dismiss the highest number of MRI examinations without lesions while still identifying malignant disease. Materials and Methods: This secondary analysis of data from the Dense Tissue and Early Breast Neoplasm Screening, or DENSE, trial evaluated breast MRI examinations from the first screening round performed in eight hospitals between December 2011 and January 2016. A DL model was developed to differentiate between breasts with lesions and breasts without lesions. The model was trained to dismiss breasts with normal phenotypical variation and to triage lesions (Breast Imaging Reporting and Data System [BIRADS] categories 2-5) using eightfold internal-external validation. The model was trained on data from seven hospitals and tested on data from the eighth hospital, alternating such that each hospital was used once as an external test set. Performance was assessed using receiver operating characteristic analysis. At 100% sensitivity for malignant disease, the fraction of examinations dismissed from radiologic review was estimated. Results: A total of 4581 MRI examinations of extremely dense breasts from 4581women (mean age, 54.3 years; interquartile range, 51.5-59.8 years) were included. Of the 9162 breasts, 838 had at least one lesion (BI-RADS category 2-5, of which 77 were malignant) and 8324 had no lesions. At 100% sensitivity for malignant lesions, the DL model considered 90.7% (95% CI: 86.7, 94.7) of the MRI examinations with lesions to be nonnormal and triaged them to radiologic review. The DL model dismissed 39.7% (95% CI: 30.0, 49.4) of the MRI examinations wi

Purpose: To assess whether contralateral parenchymal enhancement (CPE) on MRI is associated with gene expression pathways in ER+/HER2-breast cancer, and if so, whether such pathways are related to survival. Methods: Preoperative breast MRIs were analyzed of early ER+/HER2-breast cancer patients eligible for breast-conserving surgery included in a prospective observational cohort study (MARGINS). The contralateral parenchyma was segmented and CPE was calculated as the average of the top-10% delayed enhancement. Total tumor RNA sequencing was performed and gene set enrichment analysis was used to reveal gene expression pathways associated with CPE (N = 226) and related to overall survival (OS) and invasive disease-free survival (IDFS) in multivariable survival analysis. The latter was also done for the METABRIC cohort (N = 1355). Results: CPE was most strongly correlated with proteasome pathways (normalized enrichment statistic = 2.04, false discovery rate =.11). Patients with high CPE showed lower tumor proteasome gene expression. Proteasome gene expression had a hazard ratio (HR) of 1.40 (95% CI = 0.89, 2.16; P =.143) for OS in the MARGINS cohort and 1.53 (95% CI = 1.08, 2.14; P =.017) for IDFS, in METABRIC proteasome gene expression had an HR of 1.09 (95% CI = 1.01, 1.18; P =.020) for OS and 1.10 (95% CI = 1.02, 1.18; P =.012) for IDFS. Conclusion: CPE was negatively correlated with tumor proteasome gene expression in early ER+/HER2-breast cancer patients. Low tumor proteasome gene expression was associated with improved survival in the METABRIC data., Pattern Recognition and Bioinformatics

Objective: To investigate whether BIRADS MRI characteristics before or during neoadjuvant endocrine therapy (NET) are associated with the preoperative endocrine prognostic index (PEPI) in ER+/HER2- breast cancer patients. Methods: This retrospective observational cohort study included 35 ER+/HER2- patients with 38 tumors (3 bilateral cases) treated with NET. The pre- and midtreatment (after 3 months) MRIs were evaluated by two breast radiologists for BIRADS imaging characteristics, shrinkage pattern, and radiologic response. PEPI was used as end point. PEPI is based on the post-treatment surgical specimen’s pT- and pN-stage, Ki67, and ER-status. Tumors were assigned PEPI-1 (good prognosis) or PEPI-2/3 (poor prognosis). We investigated whether pre- and midtreatment BIRADS characteristics were associated with PEPI. Results: Median patient age was 65 years (interquartile interval [IQI]: 53, 70). 17 tumors (44.7%) were associated with good prognosis (PEPI-1), and 21 tumors (55.3%) with poor prognosis (PEPI-2/3). A larger reduction in tumor size after 3 months of NET was significantly associated with PEPI; 10 mm (IQI: 5, 13.5) in PEPI-1 tumors vs 4.5 mm (IQI: 3, 7; p = .045) in PEPI-2/3 tumors. Other BIRADS characteristics, shrinkage pattern or radiologic response were not associated with PEPI. Conclusion: Only a larger reduction in tumor size on MRI after 3 months of NET was associated with PEPI-1 (good prognosis) in ER+/HER2- breast cancer patients. Advances in knowledge: MRI characteristics previously reported to be associated with prognosis during neoadjuvant chemotherapy are not necessarily associated with prognosis during NET in ER+/HER2- breast cancer patients.

Purpose: To assess whether contralateral parenchymal enhancement (CPE) on MRI is associated with gene expression pathways in ER+/HER2-breast cancer, and if so, whether such pathways are related to survival. Methods: Preoperative breast MRIs were analyzed of early ER+/HER2-breast cancer patients eligible for breast-conserving surgery included in a prospective observational cohort study (MARGINS). The contralateral parenchyma was segmented and CPE was calculated as the average of the top-10% delayed enhancement. Total tumor RNA sequencing was performed and gene set enrichment analysis was used to reveal gene expression pathways associated with CPE (N = 226) and related to overall survival (OS) and invasive disease-free survival (IDFS) in multivariable survival analysis. The latter was also done for the METABRIC cohort (N = 1355). Results: CPE was most strongly correlated with proteasome pathways (normalized enrichment statistic = 2.04, false discovery rate =.11). Patients with high CPE showed lower tumor proteasome gene expression. Proteasome gene expression had a hazard ratio (HR) of 1.40 (95% CI = 0.89, 2.16; P =.143) for OS in the MARGINS cohort and 1.53 (95% CI = 1.08, 2.14; P =.017) for IDFS, in METABRIC proteasome gene expression had an HR of 1.09 (95% CI = 1.01, 1.18; P =.020) for OS and 1.10 (95% CI = 1.02, 1.18; P =.012) for IDFS. Conclusion: CPE was negatively correlated with tumor proteasome gene expression in early ER+/HER2-breast cancer patients. Low tumor proteasome gene expression was associated with improved survival in the METABRIC data.

Background: Differences in imaging parameters influence computer-extracted parenchymal enhancement measures from breast MRI. Purpose: To investigate the effect of differences in dynamic contrast-enhanced MRI acquisition parameter settings on quantitative parenchymal enhancement of the breast, and to evaluate harmonization of contrast-enhancement values with respect to flip angle and repetition time. Study Type: Retrospective. Phantom/Populations: We modeled parenchymal enhancement using simulations, a phantom, and two cohorts (N = 398 and N = 302) from independent cancer centers. Sequence Field/Strength: 1.5T dynamic contrast-enhanced T1-weighted spoiled gradient echo MRI. Vendors: Philips, Siemens, General Electric Medical Systems. Assessment: We assessed harmonization of parenchymal enhancement in simulations and phantom by varying the MR parameters that influence the amount of T1-weighting: flip angle (8°–25°) and repetition time (4–12 msec). We calculated the median and interquartile range (IQR) of the enhancement values before and after harmonization. In vivo, we assessed overlap of quantitative parenchymal enhancement in the cohorts before and after harmonization using kernel density estimations. Cohort 1 was scanned with flip angle 20° and repetition time 8 msec; cohort 2 with flip angle 10° and repetition time 6 msec. Statistical Tests: Paired Wilcoxon signed-rank-test of bootstrapped kernel density estimations. Results: Before harmonization, simulated enhancement values had a median (IQR) of 0.46 (0.34–0.49). After harmonization, the IQR was reduced: median (IQR): 0.44 (0.44–0.45). In the phantom, the IQR also decreased, median (IQR): 0.96 (0.59–1.22) before harmonization, 0.96 (0.91–1.02) after harmonization. Harmonization yielded significantly (P < 0.001) better overlap in parenchymal enhancement between the cohorts: median (IQR) was 0.46 (0.37–0.58) for cohort 1 vs. 0.37 (0.30–0.44) for cohort 2 before harmonization (57% overlap); and 0.35 (0.28–0.43) vs

Background: Previous studies have shown discrepancies between index and synchronous breast cancer in histology and molecular phenotype. It is yet unknown whether this observation also applies to the MRI phenotype. Purpose: To investigate whether the appearance of breast cancer on MRI (i.e. phenotype) is different from that of additional breast cancer (i.e. synchronous cancer), and whether such a difference, if it exists, is associated with prognosis. Study Type: Retrospective. Population: In all, 464 consecutive patients with early-stage ER+/HER2– breast cancer were included; 34/464 (7.3%) had 44 synchronous cancers in total (34 ipsilateral, 10 contralateral). Sequence: 1.5T, contrast-enhanced T1-weighted. Assessment: We assessed imaging phenotype using 50 quantitative features from each cancer and applied principal component analysis (PCA) to identify independent properties. The degree of phenotype difference was assessed. An association between phenotype differences and prognosis in terms of the Nottingham Prognostic Index (NPI) and PREDICT score were analyzed. Statistical Tests: PCA; Wilcoxon rank sum test; Benjamini–Hochberg to control the false discovery rate. Results: PCA identified eight components in patients with ipsilateral synchronous cancer. Six out of eight were significantly different between index and synchronous cancer. These components represented features describing texture (three components, P < 0.001, P < 0.001, P = 0.004), size (P < 0.001), smoothness (P < 0.001), and kinetics (P = 0.004). Phenotype differences in terms of the six components were split in tertiles. Larger phenotype differences in size, kinetics, and texture were associated with significantly worse prognosis in terms of NPI (P = 0.019, P = 0.045, P = 0.014), but not for the PREDICT score (P = 0.109, P = 0.479, P = 0.109). PCA identified six components in patients with contralateral synchronous cancer. None were significantly different from the index cancer (P = 0.178, P = 0.178

Background: Better understanding of the molecular biology associated with MRI phenotypes may aid in the diagnosis and treatment of breast cancer. Purpose: To discover the associations between MRI phenotypes of breast cancer and their underlying molecular biology derived from gene expression data. Materials and Methods: This is a secondary analysis of the Multimodality Analysis and Radiologic Guidance in Breast-Conserving Therapy, or MARGINS, study. MARGINS included patients eligible for breast-conserving therapy between November 2000 and December 2008 for preoperative breast MRI. Tumor RNA was collected for sequencing from surgical specimen. Twenty-one computer-generated MRI features of tumors were condensed into seven MRI factors related to tumor size, shape, initial enhancement, late enhancement, smoothness of enhancement, sharpness, and sharpness variation. These factors were associated with gene expression levels from RNA sequencing by using gene set enrichment analysis. Statistical significance of these associations was evaluated by using a sample permutation test and the false discovery rate. Results: Gene expression and MRI data were obtained for 295 patients (mean age, 56 years 6 10.3 [standard deviation]). Larger and more irregular tumors showed increased expression of cell cycle and DNA damage checkpoint genes (false discovery rate ,0.25; normalized enrichment statistic [NES], 2.15). Enhancement and sharpness of the tumor margin were associated with expression of ribosomal proteins (false discovery rate ,0.25; NES, 1.95). Smoothness of enhancement, tumor size, and tumor shape were associated with expression of genes involved in the extracellular matrix (false discovery rate ,0.25; NES, 2.25). Conclusion: Breast cancer MRI phenotypes were related to their underlying molecular biology revealed by using RNA sequencing. The association between enhancements and sharpness of the tumor margin with the ribosome suggests that these MRI features may be imaging bioma

Objectives: To investigate whether contralateral parenchymal enhancement (CPE) on MRI during neoadjuvant endocrine therapy (NET) is associated with the preoperative endocrine prognostic index (PEPI) of ER+/HER2− breast cancer. Methods: This retrospective observational cohort study included 40 unilateral ER+/HER2− breast cancer patients treated with NET. Patients received NET for 6 to 9 months with MRI response monitoring after 3 and/or 6 months. PEPI was used as endpoint. PEPI is based on surgery-derived pathology (pT- and pN-stage, Ki67, and ER-status) and stratifies patients in three groups with distinct prognoses. Mixed effects and ROC analysis were performed to investigate whether CPE was associated with PEPI and to assess discriminatory ability. Results: The median patient age was 61 (interquartile interval: 52, 69). Twelve patients had PEPI-1 (good prognosis), 15 PEPI-2 (intermediate), and 13 PEPI-3 (poor). High pretreatment CPE was associated with PEPI-3: pretreatment CPE was 39.4% higher on average (95% CI = 1.3, 91.9%; p =.047) compared with PEPI-1. CPE decreased after 3 months in PEPI-2 and PEPI-3. The average reduction was 24.4% (95% CI = 2.6, 41.3%; p =.032) in PEPI-2 and 29.2% (95% CI = 7.8, 45.6%; p =.011) in PEPI-3 compared with baseline. Change in CPE was predictive of PEPI-1 vs PEPI-2+3 (AUC = 0.77; 95% CI = 0.57, 0.96). Conclusions: CPE during NET is associated with PEPI-group in ER+/HER2− breast cancer: a high pretreatment CPE and a decrease in CPE during NET were associated with a poor prognosis after NET on the basis of PEPI. Key Points: • Change in contralateral breast parenchymal enhancement on MRI during neoadjuvant endocrine therapy distinguished between patients with a good and intermediate/poor prognosis at final pathology. • Patients with a poor prognosis at final pathology showed higher baseline parenchymal enhancement on average compared to patients with a good prognosis. • Patients with an intermediate/poor prognosis at final pathology

Purpose: To retrospectively explore the relation between parenchymal enhancement of the healthy contralateral breast on dynamic contrast-enhanced magnetic resonance imaging (MRI) and genomic tests for estrogen receptor (ER)-pathway activity in patients with ER-positive/HER2-negative cancer. Methods: A subset of 227 consecutively included patients with unilateral invasive ER-positive/HER2-negative breast cancer underwent dynamic contrast-enhanced MRI prior to breast-conserving therapy between 2000 and 2008. Perfusion of the parenchyma in the healthy breast was assessed using a previously reported measure of contralateral parenchymal enhancement (CPE), consisting of the mean of the top-10% late enhancement. ER-pathway activity was assessed from the surgical resection specimen by the previously reported sensitivity to endocrine therapy (SET)-index and ER-factor. The SET-index is a genetic test to estimate survival benefit from endocrine therapy, consisting of genes related to the ESR1 gene. The ER-factor examines other factors as well including protein expression. The relation between CPE and ER-pathway activity was modeled using linear regression. Results: Patients had a median age of 59 years. CPE was not significantly associated with the SET-index (R-squared = 0.005) nor the ER-factor (R-squared = 0.0002). The only variable significantly different between low and high CPE was age at diagnosis (P < 0.001). Conclusions: Contralateral parenchymal enhancement on dynamic contrast-enhanced MRI was not associated with tumor-derived estrogen receptor pathway activity.

Objectives: To assess whether contralateral parenchymal enhancement reproduces as an independent biomarker for patient survival in an independent patient cohort from a different cancer institution. Methods: This is a HIPAA-compliant IRB approved retrospective study. Patients with ER-positive/HER2-negative operable invasive ductal carcinoma and preoperative dynamic contrast-enhanced MRI were consecutively included between 2005 and 2009. The parenchyma of the breast contralateral to known cancer was segmented automatically on MRI and contralateral parenchymal enhancement (CPE) was calculated. CPE was split into tertiles and tested for association with invasive disease-free survival (IDFS) and overall survival (OS). Propensity score analysis with inverse probability weighting (IPW) was used to adjust CPE for patient and tumour characteristics as well as systemic therapy. Results: Three hundred and two patients were included. The median age at diagnosis was 48 years (interquartile range, 42-57). Median follow-up was 88 months (interquartile range, 76-102); 15/302 (5%) patients died and 37/302 (13%) had a recurrence or died. In context of multivariable analysis, IPW-adjusted CPE was associated with IDFS [hazard ratio (HR) = 0.27, 95% confidence interval (CI) = 0.05-0.68, p = 0.004] and OS (HR = 0.22, 95% CI = 0.00-0.83, p = 0.032). Conclusions: Contralateral parenchymal enhancement on pre-treatment dynamic contrast-enhanced MRI as an independent biomarker of survival in patients with ER-positive/HER2-negative breast cancer has been upheld in this study. These findings are a promising next step towards a practical and inexpensive test for risk stratification of ER-positive/HER2-negative breast cancer. Key points: • High parenchymal-enhancement in the disease-free contralateral breast reproduces as biomarker for survival. • This is in patients with ER-positive/HER2-negative breast cancer from an independent cancer centre. • This is independent of patient and pathology para

Purpose: To determine whether markers of healthy breast stroma are able to select a subgroup of patients at low risk of death or metastasis from patients considered at high risk according to routine markers of the tumor. Experimental Design: Patients with ER+/HER2− breast cancer were consecutively included for retrospective analysis. The contralateral parenchyma was segmented automatically on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI), where upon the average of the top-10% late enhancement was calculated. This contralateral parenchymal enhancement (CPE) was analyzed with respect to routine prognostic models and molecular assays (Nottingham Prognostic Index, Dutch clinical chemotherapy-selection guidelines, 70-gene signature, and 21-gene recurrence score). CPE was split in tertiles and tested for overall and distant disease-free survival. CPE was adjusted for patient and tumor characteristics, as well as systemic therapy, using inverse probability weighting (IPW). Subanalyses were performed in patients at high risk according to prognostic models and molecular assays. Results: Four-hundred-and-fifteen patients were included, constituting the same group in which the association between CPE and survival was discovered. Median follow-up was 85 months, 34/415(8%) patients succumbed. After IPW-adjustment for patient and tumor characteristics, patients with high CPE had significantly better overall survival than those with low CPE in groups at high risk according to the Nottingham Prognostic Index [HR (95% CI): 0.08 (0.00–0.40), P < 0.001]; Dutch clinical guidelines [HR (95% CI): 0.22 (0.00–0.81), P = 0.021]; and 21-gene recurrence score [HR (95% CI): 0.14 (0.00–0.84), P = 0.030]. One group showed a trend [70-gene signature: HR (95% CI): 0.25 (0.00–1.02), P = 0.054]. Conclusions: In patients at high risk based on the tumor, subgroups at relatively low risk were identified using pretreatment enhancement of the stroma on breast DCE-MRI.