Association between parenchymal enhancement of the contralateral breast in dynamic contrast-enhanced MR imaging and outcome of patients with unilateral invasive breast cancer

Purpose: To retrospectively investigate whether parenchymal enhancement in dynamic contrast material-enhanced magnetic resonance (MR) imaging of the contralateral breast in patients with unilateral invasive breast cancer is associated with therapy outcome. Materials and Methods: After obtaining approval of the institutional review board and patients' written informed consent, 531 women with unilateral invasive breast cancer underwent dynamic contrastenhanced MR imaging between 2000 and 2008. The contralateral parenchyma was segmented automatically, in which the mean of the top 10% late enhancement was calculated. Cox regression was used to test associations between parenchymal enhancement, patient and tumor characteristics, and overall survival and invasive disease-free survival. Subset analyses were performed and stratified according to immunohistochemical subtypes and type of adjuvant treatment received. Results: Median follow-up was 86 months. Age (P < .001) and immunohistochemical subtype (P = .042) retained significance in multivariate analysis for overall survival. In patients with estrogen receptor-positive and human epidermal growth factor receptor 2 (HER2)-negative breast cancer (n = 398), age (P < .001), largest diameter on MR images (P = .049), and parenchymal enhancement (P = .011) were significant. In patients who underwent endocrine therapy (n = 174), parenchymal enhancement was the only significant covariate for overall survival and invasive disease-free survival (P < .001). Conclusion: Results suggest that parenchymal enhancement in the contralateral breast of patients with invasive unilateral breast cancer is significantly associated with long-term outcome, particularly in patients with estrogen receptor-positive, human epidermal growth factor receptor 2-negative breast cancer. Lower value of the mean top 10% enhancement of the parenchyma shows potential as a predictive biomarker for relatively poor outcome in patients who undergo endocrine therapy. These results should, however, be validated in a larger study

Prediction of Poor Outcome after Tisagenlecleucel in Patients with Relapsed or Refractory Diffuse Large B Cell Lymphoma (DLBCL) Using Artificial Intelligence Analysis of Pre-Infusion PET/CT

Introduction In the pivotal JULIET trial patients with relapsed or refractory Diffuse Large B cell Lymphoma (DLBC) received a single intravenous infusion of Tisagenlecleucel. In long-term follow-up analysis, an overall response rate (ORR) of 53% and a complete response (CR) of 39% were reported. In 115 evaluable patients, 62% had disease progression or died. At 40.3 months follow up, the median progression-free survival (PFS) was 2.9 months and the median overall survival (OS) was 11.1 months. The median PFS and OS of 35% of the patients with complete response at 3 months, 6 months, or both, were not reached suggesting durable response [1].Although high metabolic tumor volume (MTV) measured by [18F] FDG PET/CT during CART cell therapy was found to be predictive of early relapse [2], pretreatment available factors - such as high IPI, elevated LDH, low platelets, and MTV - do not fully elucidate which individuals have poor outcome after therapy [1-3]. Accurate prediction of poor outcome in individuals at the treatment-decision stage may lead to more effective patient selection, preventing unnecessary cost and adverse treatment effects.The aim of this study is to demonstrate feasibility of identifying a subgroup of patients at very high risk of poor outcome (death or disease progression) prior to infusion of Tisagenlecleucel, using Artificial Intelligence (AI) to characterize pre-infusion FDG PET/CT in combination with clinicopathological parameters.Material and Methods In this secondary analysis of data from the prospective JULIET trial [1,4], 115 FDG PET/CT data sets were included from 115 patients with R-R DLBCL from 27 treatment sites between 2015 and 2018. All patients received a single intravenous infusion of Tisagenlecleucel . The pre-infusion FDG PET/CT images were processed automatically using deep learning. Clinicopathological parameters were added: patient age, IPI, LDH, platelet count, MTV, and LDH.A novel automated test ("AI signature") was developed to identify a subgroup of patients at very high risk of poor outcome (death or disease progression). In short, an attention-gated convolutional neural network (AG-CNN) was trained to delineate the PET/CT disease foci automatically and consistently across the different treatment centers. MTV was automatically derived, and disease foci were further characterized by their activations in the most densely compressed layer in latent space of the AG-CNN. After additional dimensionality reduction, the AI signature was derived using multivariate Cox regression, random survival forests, Receiver Operating Characteristics (ROC) analysis, and Kaplan Meier modelling. The AI signature was validated using nested 5-fold cross validation: data were partitioned five times into training and testing folds. Models derived from the training folds were tested on the testing folds. Median testing performance and interquartile range (IQR) were reported.Results The median patient age was 56 years (IQR 46-64). The median follow-up-time was 80 days (IQR 29-554). After 5-fold cross validation, 52.4% of the patients (IQR 39.1-56.5) had a negative AI-signature of whom 100% (IQR 92.9-100) had poor outcome. 47.6% Of the patients (IQR 39.1-56.5) had a positive AI-signature of whom 55.6% (range 53.3-61.5) had poor outcome. Median PFS in long-term follow up was 13.8% (range 11.5-14.6) and 29.8% (range 29.6-33.2) in patients with negative and positive AI-signature, respectively. The median cross-validated area under the ROC curve after multivariate Cox regression was 0.74 (IQR 0.70 - 0.81). The HR was 0.36 (95% CI 0.13 - 0.95; P = . 0.0432) after multivariable correction for age, IPI, LDH, platelet count, MTV, and LDH.Conclusions Results from the JULIET trial data indicate that an automated test based on AI analysis of pre-infusion FDG PET/CT and clinicopathological parameters is feasible to identify a subgroup of patients at very high risk of poor outcome after Tisagenlecleucel. The test retained significance after multivariable correction for other parameters known to be associated with CART response, IPI, LDH, age, platelet count, MTV, and LDH. Follow-up studies will focus on validating these findings in independent patient cohorts

Deep learning for multi-task medical image segmentation in multiple modalities

Automatic segmentation of medical images is an important task for many clinical applications. In practice,a wide range of anatomical structures are visualised using different imaging modalities. In this paper,we investigate whether a single convolutional neural network (CNN) can be trained to perform different segmentation tasks. A single CNN is trained to segment six tissues in MR brain images,the pectoral muscle in MR breast images,and the coronary arteries in cardiac CTA. The CNN therefore learns to identify the imaging modality,the visualised anatomical structures,and the tissue classes. For each of the three tasks (brain MRI,breast MRI and cardiac CTA),this combined training procedure resulted in a segmentation performance equivalent to that of a CNN trained specifically for that task,demonstrating the high capacity of CNN architectures. Hence,a single system could be used in clinical practice to automatically perform diverse segmentation tasks without task-specific training

Semi-automated primary tumor volume measurements by dynamic contrast-enhanced MRI in patients with head and neck cancer

Background: Tumor volume is a significant prognostic factor in the treatment of malignant head and neck tumors. Unfortunately, it is not routinely measured because of the workload involved. Methods: Twenty-one patients, between 2009 and 2010, were studied. Dynamic contrast-enhanced MRI (DCE-MRI) at 3.0T was performed. A workstation previously developed for semi-automated segmentation of breast cancers on DCE-MRI was used to segment the head and neck cancers. The Pearson correlation analysis was used to assess the agreement between volumetric measurements and the manually derived gross tumor volume (GTV). Results: In 90.5% of the patients (19 of 21) correlation could be made between DCE-MRI and the manually derived GTV. The Pearson correlation coefficient between the automatically derived tumor volume at DCE-MRI and the manually derived GTVs was R2 = 0.95 (p <.001). Conclusion: Semi-automated tumor volumes on DCE-MRI were representative of those derived from the manually derived GTV (R2 = 0.95; p <.001)

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 mm 3 × 0.64 mm 3 × 1.00 mm 3 . 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

Reducing distortions in echo-planar breast imaging at ultrahigh field with high-resolution off-resonance maps

Purpose: DWI is a promising modality in breast MRI, but its clinical acceptance is slow. Analysis of DWI is hampered by geometric distortion artifacts, which are caused by off-resonant spins in combination with the low phase-encoding bandwidth of the EPI sequence used. Existing correction methods assume smooth off-resonance fields, which we show to be invalid in the human breast, where high discontinuities arise at tissue interfaces. Methods: We developed a distortion correction method that incorporates high-resolution off-resonance maps to better solve for severe distortions at tissue interfaces. The method was evaluated quantitatively both ex vivo in a porcine tissue phantom and in vivo in 5 healthy volunteers. The added value of high-resolution off-resonance maps was tested using a Wilcoxon signed rank test comparing the quantitative results obtained with a low-resolution off-resonance map with those obtained with a high-resolution map. Results: Distortion correction using low-resolution off-resonance maps corrected most of the distortions, as expected. Still, all quantitative comparison metrics showed increased conformity between the corrected EPI images and a high-bandwidth reference scan for both the ex vivo and in vivo experiments. All metrics showed a significant improvement when a high-resolution off-resonance map was used (P < 0.05), in particular at tissue boundaries. Conclusion: The use of off-resonance maps of a resolution higher than EPI scans significantly improves upon existing distortion correction techniques, specifically by superior correction at glandular tissue boundaries

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.Contrast-enhanced magnetic resonance imaging (MRI) may be used in combination with x-ray mammography to screen asymptomatic women for breast cancer. Supplemental MRI screening in women with extremely dense breasts improved the detection of cancer.1 Similar observations were reported for women at increased lifetime risk. Nonetheless, breast MRI screening has lower specificity compared with mammography1–3 and it invokes additional workload.To reduce the workload of breast magnetic resonance (MR) radiologists, researchers have focused on automated lesion detection.4,5 One focused on identifying normal scans using computer-aided triaging (CAT).6 Computer-aided diagnosis (CAD) of dynamic contrast-enhanced MRI7,8 and multiparametric MRI1,9 was found to further increase specificity.10–14A recently reported CAT—developed on data from 4783 MRI examinations from the first screening round of the DENSE trial—dismissed approximately 40% of normal breast examinations without dismissing malignant disease.6 In addition to CAT, CAD was developed on the same data to distinguish between 444 benign and 81 malignant lesions. It is yet unknown whether CAD is complementary to CAT to increase the positive predictive value (PPV) of MRI screening in women with extremely dense breasts while maintaining high negative predictive value (NPV) and minimizing the number of normal scans to be read by radiologists.The aim of this study was to validate the potential of combining CAT with CAD in the second screening round of DENSE to minimize work load as well as minimizing the number of biopsies on benign lesions without dismissing malignant breast disease

Prediction model for extensive ductal carcinoma in situ around early-stage invasive breast cancer

Objectives: Ductal carcinoma in situ (DCIS) is a risk factor for incomplete resection of breast cancer. Especially, extensive DCIS (E-DCIS) or extensive intraductal component often results in positive resection margins. Detecting DCIS around breast cancer before treatment may therefore alter surgery. The purpose of this study was to develop a prediction model for E-DCIS around early-stage invasive breast cancer, using clinicohistopathological and dynamic contrast-enhanced magnetic resonance imaging (MRI) features. Materials and Methods: Dynamic contrast-enhanced MRI and local excision were performed in 322 patients with 326 ductal carcinomas. Tumors were segmented from dynamic contrast-enhanced MRI, followed by 3-dimensional extension of the margins with 10 mm. Amount of fibroglandular tissue (FGT) and enhancement features in these extended margins were automatically extracted from the MRI scans. Clinicohistopathological features were also obtained. Principal component analysis and multivariable logistic regression were used to develop a prediction model for E-DCIS. Discrimination and calibration were assessed, and bootstrapping was applied for internal validation. Results: Extensive DCIS occurred in 48 (14.7%) of 326 tumors. Incomplete resection occurred in 56.3% of these E-DCIS-positive versus 9.0% of E-DCIS-negative tumors (P < 0.001). Five components with eigenvalue exceeding 1 were identified; 2 were significantly associated with E-DCIS. The first, positively associated, component expressed early and overall enhancement in the 10-mm tissue margin surrounding the MRI-visible tumor. The second, positively associated, component expressed human epidermal growth factor receptor 2 and amount of FGT around the MRI-visible tumor. The area under the curve value was 0.79 (0.76 after bootstrapping). Conclusions: Human epidermal growth factor receptor 2 status, early and overall enhancement in the 10-mm margin around the MRI-visible tumor, and amount of FGT in the 10 mm around the MRI-visible tumor were associated with E-DCIS