Assessing Quantitative Parenchymal Features at Baseline Dynamic Contrast-enhanced MRI and Cancer Occurrence in Women with Extremely Dense Breasts.

Background Automated identification of quantitative breast parenchymal enhancement features on dynamic contrast-enhanced (DCE) MRI scans could provide added value in assessment of breast cancer risk in women with extremely dense breasts. Purpose To automatically identify quantitative properties of the breast parenchyma on baseline DCE MRI scans and assess their association with breast cancer occurrence in women with extremely dense breasts. Materials and Methods This study represents a secondary analysis of the Dense Tissue and Early Breast Neoplasm Screening trial. MRI was performed in eight hospitals between December 2011 and January 2016. After segmentation of fibroglandular tissue, quantitative features (including volumetric density, volumetric morphology, and enhancement characteristics) of the parenchyma were extracted from baseline MRI scans. Principal component analysis was used to identify parenchymal measures with the greatest variance. Multivariable Cox proportional hazards regression was applied to assess the association between breast cancer occurrence and quantitative parenchymal features, followed by stratification of significant features into tertiles. Results A total of 4553 women (mean age, 55.7 years ± 6 [SD]) with extremely dense breasts were included; of these women, 122 (3%) were diagnosed with breast cancer. Five principal components representing 96% of the variance were identified, and the component explaining the greatest independent variance (42%) consisted of MRI features relating to volume of enhancing parenchyma. Multivariable analysis showed that volume of enhancing parenchyma was associated with breast cancer occurrence (hazard ratio [HR], 1.09; 95% CI: 1.01, 1.18; P = .02). Additionally, women in the high tertile of volume of enhancing parenchyma showed a breast cancer occurrence twice that of women in the low tertile (HR, 2.09; 95% CI: 1.25, 3.61; P = .005). Conclusion In women with extremely dense breasts, a high volume of enhancing parenchyma on baseline DCE MRI scans was associated with increased occurrence of breast cancer as compared with a low volume of enhancing parenchyma. © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Grimm in this issue.

Deep Learning for Automated Triaging of 4581 Breast MRI Examinations from the DENSE Trial.

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 [BI-RADS] 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 without lesions. The DL model had an average area under the receiver operating characteristic curve of 0.83 (95% CI: 0.80, 0.85) in the differentiation between normal breast MRI examinations and MRI examinations with lesions. Conclusion Automated analysis of breast MRI examinations in women with dense breasts dismissed nearly 40% of MRI scans without lesions while not missing any cancers. ClinicalTrials.gov: NCT01315015 © RSNA, 2021 Online supplemental material is available for this article. See also the editorial by Joe in this issue.

Eligibility of patients for minimally invasive breast cancer therapy based on MRI analysis of tumor proximity to skin and pectoral muscle.

There is growing interest in minimally invasive breast cancer therapy. Eligibility of patients is, however, dependent on several factors related to the tumor and treatment technology. The aim of this study is to assess the proportion of patients eligible for minimally invasive breast cancer therapy for different safety and treatment margins based on breast tumor location. Patients with invasive ductal cancer were selected from the MARGINS cohort. Semiautomatic segmentation of tumor, skin, and pectoral muscle was performed in Magnetic Resonance images. Shortest distances of tumors to critical organs (ie, skin and pectoral muscle) were calculated. Proportions of eligible patients were determined for different safety and treatment margins. Three-hundred-forty-eight patients with 351 tumors were included. If a 10 mm safety margin to skin and pectoral muscle is required without treatment margin, 72.3% of patients would be eligible for minimally invasive treatment. This proportion decreases to 45.9% for an additional treatment margin of 5 mm. Shortest distances between tumors and critical organs are larger in older patients and in patients with less aggressive tumor subtypes. If a 10 mm safety margin to skin and pectoral muscle is required, more than two-thirds of patients would be eligible for minimally invasive breast cancer therapy.

Automated rating of background parenchymal enhancement in MRI of extremely dense breasts without compromising the association with breast cancer in the DENSE trial.

OBJECTIVES: Background parenchymal enhancement (BPE) on dynamic contrast-enhanced MRI (DCE-MRI) as rated by radiologists is subject to inter- and intrareader variability. We aim to automate BPE category from DCE-MRI. METHODS: This study represents a secondary analysis of the Dense Tissue and Early Breast Neoplasm Screening trial. 4553 women with extremely dense breasts who received supplemental breast MRI screening in eight hospitals were included. Minimal, mild, moderate and marked BPE rated by radiologists were used as reference. Fifteen quantitative MRI features of the fibroglandular tissue were extracted to predict BPE using Random Forest, Naïve Bayes, and KNN classifiers. Majority voting was used to combine the predictions. Internal-external validation was used for training and validation. The inverse-variance weighted mean accuracy was used to express mean performance across the eight hospitals. Cox regression was used to verify non inferiority of the association between automated rating and breast cancer occurrence compared to the association for manual rating. RESULTS: The accuracy of majority voting ranged between 0.56 and 0.84 across the eight hospitals. The weighted mean prediction accuracy for the four BPE categories was 0.76. The hazard ratio (HR) of BPE for breast cancer occurrence was comparable between automated rating and manual rating (HR = 2.12 versus HR = 1.97, P = 0.65 for mild/moderate/marked BPE relative to minimal BPE). CONCLUSION: It is feasible to rate BPE automatically in DCE-MRI of women with extremely dense breasts without compromising the underlying association between BPE and breast cancer occurrence. The accuracy for minimal BPE is superior to that for other BPE categories.

Validation of Combined Deep Learning Triaging and Computer-Aided Diagnosis in 2901 Breast MRI Examinations From the Second Screening Round of the Dense Tissue and Early Breast Neoplasm Screening Trial.

OBJECTIVES: Computer-aided triaging (CAT) and computer-aided diagnosis (CAD) of screening breast magnetic resonance imaging have shown potential to reduce the workload of radiologists in the context of dismissing normal breast scans and dismissing benign disease in women with extremely dense breasts. The aim of this study was to validate the potential of integrating CAT and CAD to reduce workload and workup on benign lesions in the second screening round of the DENSE trial, without missing cancer. METHODS: We included 2901 breast magnetic resonance imaging scans, obtained from 8 hospitals in the Netherlands. Computer-aided triaging and CAD were previously developed on data from the first screening round. Computer-aided triaging dismissed examinations without lesions. Magnetic resonance imaging examinations triaged to radiological reading were counted and subsequently processed by CAD. The number of benign lesions correctly classified by CAD was recorded. The false-positive fraction of the CAD was compared with that of unassisted radiological reading in the second screening round. Receiver operating characteristics (ROC) analysis was performed and the generalizability of CAT and CAD was assessed by comparing results from first and second screening rounds. RESULTS: Computer-aided triaging dismissed 950 of 2901 (32.7%) examinations with 49 lesions in total; none were malignant. Subsequent CAD classified 132 of 285 (46.3%) lesions as benign without misclassifying any malignant lesion. Together, CAT and CAD yielded significantly fewer false-positive lesions, 53 of 109 (48.6%) and 89 of 109 (78.9%), respectively ( P = 0.001), than radiological reading alone. Computer-aided triaging had a smaller area under the ROC curve in the second screening round compared with the first, 0.83 versus 0.76 ( P = 0.001), but this did not affect the negative predictive value at the 100% sensitivity operating threshold. Computer-aided diagnosis was not associated with significant differences in area under the ROC curve (0.857 vs 0.753, P = 0.08). At the operating thresholds, the specificities of CAT (39.7% vs 41.0%, P = 0.70) and CAD (41.0% vs 38.2%, P = 0.62) were successfully reproduced in the second round. CONCLUSION: The combined application of CAT and CAD showed potential to reduce workload of radiologists and to reduce number of biopsies on benign lesions. Computer-aided triaging (CAT) correctly dismissed 950 of 2901 (32.7%) examinations with 49 lesions in total; none were malignant. Subsequent computer-aided diagnosis (CAD) classified 132 of 285 (46.3%) lesions as benign without misclassifying any malignant lesion. Together, CAT and CAD yielded significantly fewer false-positive lesions, 53 of 109 (48.6%) and 89 of 109 (78.9%), respectively ( P = 0.001), than radiological reading alone.

Toward Computer-Assisted Triaging of Magnetic Resonance Imaging-Guided Biopsy in Preoperative Breast Cancer Patients.

OBJECTIVES: Incidental MR-detected breast lesions (ie, additional lesions to the index cancer) pose challenges in the preoperative workup of patients with early breast cancer. We pursue computer-assisted triaging of magnetic resonance imaging (MRI)-guided breast biopsy of additional lesions at high specificity. MATERIALS AND METHODS: We investigated 316 consecutive female patients (aged 26 to 76 years; mean, 54 years) with early breast cancer who received preoperative multiparametric breast MRI between 2013 and 2016. In total, 82 (26%) of 316 patients had additional breast lesions on MRI. These 82 patients had 101 additional lesions in total, 51 were benign and 50 were malignant. We collected 4 clinical features and 46 MRI radiomic features from T1-weighted dynamic contrast-enhanced imaging, high-temporal-resolution dynamic contrast-enhanced imaging, T2-weighted imaging, and diffusion-weighted imaging. A multiparametric computer-aided diagnosis (CAD) model using 10-fold cross-validated ridge regression was constructed. The sensitivities were calculated at operating points corresponding to 98%, 95%, and 90% specificity. The model calibration performance was evaluated by calibration plot analysis and goodness-of-fit tests. The model was tested in an independent testing cohort of 187 consecutive patients from 2017 and 2018 (aged 35 to 76 years; mean, 59 years). In this testing cohort, 45 (24%) of 187 patients had 55 additional breast lesions in total, 23 were benign and 32 were malignant. RESULTS: The multiparametric CAD model correctly identified 48% of the malignant additional lesions with a specificity of 98%. At specificity 95% and 90%, the sensitivity was 62% and 72%, respectively. Calibration plot analysis and goodness-of-fit tests indicated that the model was well fitted.In the independent testing cohort, the specificity was 96% and the sensitivity 44% at the 98% specificity operating point of the training set. At operating points 95% and 90%, the specificity was 83% at 69% sensitivity and the specificity was 78% at 81% sensitivity, respectively. CONCLUSIONS: The multiparametric CAD model showed potential to identify malignant disease extension with near-perfect specificity in approximately half the population of preoperative patients originally indicated for a breast biopsy. In the other half, patients would still proceed to MRI-guided biopsy to confirm absence of malignant disease. These findings demonstrate the potential to triage MRI-guided breast biopsy.

Computer-Aided Diagnosis in Multiparametric Magnetic Resonance Imaging Screening of Women With Extremely Dense Breasts to Reduce False-Positive Diagnoses.

OBJECTIVES: To reduce the number of false-positive diagnoses in the screening of women with extremely dense breasts using magnetic resonance imaging (MRI), we aimed to predict which BI-RADS 3 and BI-RADS 4 lesions are benign. For this purpose, we use computer-aided diagnosis (CAD) based on multiparametric assessment. MATERIALS AND METHODS: Consecutive data were used from the first screening round of the DENSE (Dense Tissue and Early Breast Neoplasm Screening) trial. In this trial, asymptomatic women with a negative screening mammography and extremely dense breasts were screened using multiparametric MRI. In total, 4783 women, aged 50 to 75 years, enrolled and were screened in 8 participating hospitals between December 2011 and January 2016. In total, 525 lesions in 454 women were given a BI-RADS 3 (n = 202), 4 (n = 304), or 5 score (n = 19). Of these lesions, 444 were benign and 81 were malignant on histologic examination.The MRI protocol consisted of 5 different MRI sequences: T1-weighted imaging without fat suppression, diffusion-weighted imaging, T1-weighted contrast-enhanced images at high spatial resolution, T1-weighted contrast-enhanced images at high temporal resolution, and T2-weighted imaging. A machine-learning method was developed to predict, without deterioration of sensitivity, which of the BI-RADS 3- and BI-RADS 4-scored lesions are actually benign and could be prevented from being recalled. BI-RADS 5 lesions were only used for training, because the gain in preventing false-positive diagnoses is expected to be low in this group. The CAD consists of 2 stages: feature extraction and lesion classification. Two groups of features were extracted: the first based on all multiparametric sequences, the second based only on sequences that are typically used in abbreviated MRI protocols. In the first group, 49 features were used as candidate predictors: 46 were automatically calculated from the MRI scans, supplemented with 3 clinical features (age, body mass index, and BI-RADS score). In the second group, 36 image features and the same 3 clinical features were used. Each group was considered separately in a machine-learning model to differentiate between benign and malignant lesions. We developed a Ridge regression model using 10-fold cross validation. Performance of the models was analyzed using an accuracy measure curve and receiver-operating characteristic analysis. RESULTS: Of the total number of BI-RADS 3 and BI-RADS 4 lesions referred to additional MRI or biopsy, 425/487 (87.3%) were false-positive. The full multiparametric model classified 176 (41.5%) and the abbreviated-protocol model classified 111 (26.2%) of the 425 false-positive BI-RADS 3- and BI-RADS 4-scored lesions as benign without missing a malignant lesion.If the full multiparametric CAD had been used to aid in referral, recall for biopsy or repeat MRI could have been reduced from 425/487 (87.3%) to 311/487 (63.9%) lesions. For the abbreviated protocol, it could have been 376/487 (77.2%). CONCLUSIONS: Dedicated multiparametric CAD of breast MRI for BI-RADS 3 and 4 lesions in screening of women with extremely dense breasts has the potential to reduce false-positive diagnoses and consequently to reduce the number of biopsies without missing cancers.

Knowledge-based and deep learning-based automated chest wall segmentation in magnetic resonance images of extremely dense breasts.

PURPOSE: Segmentation of the chest wall, is an important component of methods for automated analysis of breast magnetic resonance imaging (MRI). Methods reported to date show promising results but have difficulties delineating the muscle border correctly in breasts with a large proportion of fibroglandular tissue (i.e., dense breasts). Knowledge-based methods (KBMs) as well as methods based on deep learning have been proposed, but a systematic comparison of these approaches within one cohort of images is currently lacking. Therefore, we developed a KBM and a deep learning method for segmentation of the chest wall in MRI of dense breasts and compared their performances. METHODS: Two automated methods were developed, an optimized KBM incorporating heuristics aimed at shape, location, and gradient features, and a deep learning-based method (DLM) using a dilated convolution neural network. A data set of 115 T1-weighted MR images was randomly selected from MR images of women with extremely dense breasts (ACR BI-RADS category 4) participating in a screening trial of women (mean age 56.6 yr, range 49.5-75.2 yr) with dense breasts. Manual segmentations of the chest wall, acquired under supervision of an experienced breast radiologist, were available for all data sets. Both methods were optimized using the same randomly selected 36 MRI data sets from a total of 115 data sets. Each MR data set consisted of 179 transversal images with voxel size 0.64 mm3  × 0.64 mm3  × 1.00 mm3 . In the remaining 79 data sets, the results of both segmentation methods were qualitatively evaluated. A radiologist reviewed the segmentation results of both methods in all transversal images (n = 14 141) and determined whether the result would impact the ability to accurately determine the volume of fibroglandular and fatty tissue and whether segmentations masked breast regions that might harbor lesions. When no relevant deviation was detected, the result was considered successful. In addition, all segmentations were quantitatively assessed using the Dice similarity coefficient (DSC) and Hausdorff distance (HD), 95th percentile of the Hausdorff distance (HD95), false positive fraction (FPF), and false negative fraction (FNF) metrics. RESULTS: According to the radiologist's evaluation, the DLM had a significantly higher success rate than the KBM (81.6% vs 78.4%, P < 0.01). The success rate was further improved to 92.1% by combining both methods. Similarly, the DLM had significantly lower values for FNF (0.003 ± 0.003 vs 0.009 ± 0.011, P < 0.01) and HD95 (2.58 ± 1.78 mm vs 3.37 ± 2.11, P < 0.01). However, the KBM resulted in a significantly lower FPF than the DLM (0.018 ± 0.009 vs 0.030 ± 0.009, P < 0.01).There was no significant difference between the KBM and DLM in terms of DSC (0.982 ± 0.006 vs 0.984 ± 0.008, P = 0.08) or HD (24.14 ± 20.69 mm vs 12.81 ± 27.28 mm, P = 0.05). CONCLUSION: Both optimized knowledge-based and DLM showed good results to segment the pectoral muscle in women with dense breasts. Qualitatively assessed, the DLM was the most robust method. A quantitative comparison, however, did not indicate a preference for one method over the other.

Sorafenib-induced hypothyroidism is associated with increased type 3 deiodination.

BACKGROUND: Therapy with tyrosine kinase inhibitors is associated with thyroid dysfunction. Decreased serum thyroid hormone levels during tyrosine kinase inhibitors are also observed in athyreotic patients with thyroid carcinoma. We therefore hypothesized that tyrosine kinase inhibitors may influence thyroid hormone metabolism. AIM: The aim was to study the effects of sorafenib therapy on serum thyroid hormone concentrations and iodothyronine deiodination in athyreotic patients. DESIGN: The design included a prospective open, single-center, single-arm 26-wk study. METHODS: We measured serum thyroxine (T4), free T4, 3,5,3-triiodothyronine (T3), free T3, reverse T3 (rT3), and TSH concentrations at baseline and after 26 wk in 21 patients with progressive nonmedullary thyroid carcinoma treated with sorafenib. Ratios of T3/T4 and T3/rT3, which are independent of substrate availability and reflect iodothyronine deiodination, were calculated. RESULTS: Serum free T4 and T3 levels, adjusted for levothyroxine dose per kilogram body weight, decreased by 11 and 18%, respectively, whereas TSH levels increased. The serum T3/T4 and T3/rT3 ratios decreased by 18 and 22%, respectively, which is compatible with increased type 3 deiodination. CONCLUSIONS: Sorafenib enhances T4 and T3 metabolism, which is probably caused by increased type 3 deiodination.