AI-enhanced diagnosis of challenging lesions in breast MRI: a methodology and application primer

Computer-aided diagnosis (CAD) systems have become an important tool in the assessment of breast tumors with magnetic resonance imaging (MRI). CAD systems can be used for the detection and diagnosis of breast tumors as a “second opinion” review complementing the radiologist’s review. CAD systems have many common parts such as image pre-processing, tumor feature extraction and data classification that are mostly based on machine learning (ML) techniques. In this review paper, we describe the application of ML-based CAD systems in MRI of the breast covering the detection of diagnostically challenging lesions such as non-mass enhancing (NME) lesions, multiparametric MRI, neo-adjuvant chemotherapy (NAC) and radiomics all applied to NME. Since ML has been widely used in the medical imaging community, we provide an overview about the state-ofthe-art and novel techniques applied as classifiers to CAD systems. The differences in the CAD systems in MRI of the breast for several standard and novel applications for NME are explained in detail to provide important examples illustrating: (i) CAD for the detection and diagnosis, (ii) CAD in multi-parametric imaging (iii) CAD in NAC and (iv) breast cancer radiomics. We aim to provide a comparison between these CAD applications and to illustrate a global view on intelligent CAD systems based on ANN in MRI of the breast

Objective Perfusion Assessment in Gracilis Muscle Interposition—A Novel Software-Based Approach to Indocyanine Green Derived Near-Infrared Fluorescence in Reconstructive Surgery

Background: Gracilis muscle interposition (GMI) is an established treatment option for complex perineal fistulas and reconstruction. The outcome is limited by complications such as necrosis, impaired wound healing and fistula persistence or recurrence. Quantifiable methods of assessing muscle flap perfusion intraoperatively are lacking. This study evaluates a novel and objective software-based assessment of indocyanine green near-infrared fluorescence (ICG-NIRF) in GMI. Methods: Intraoperative ICG-NIRF visualization data of five patients with inflammatory bowel disease (IBD) undergoing GMI for perineal fistula and reconstruction were analyzed retrospectively. A new software was utilized to generate perfusion curves for the specific regions of interest (ROIs) of each GMI by depicting the fluorescence intensity over time. Additionally, a pixel-to-pixel and perfusion zone analysis were performed. The findings were correlated with the clinical outcome. Results: Four patients underwent GMI without postoperative complications within 3 months. The novel perfusion indicators identified here (shape of the perfusion curve, maximum slope value, distribution and range) indicated adequate perfusion. In one patient, GMI failed. In this case, the perfusion indicators suggested impaired perfusion. Conclusions: We present a novel, software-based approach for ICG-NIRF perfusion assessment, identifying previously unknown objective indicators of muscle flap perfusion. Ready for intraoperative real-time use, this method has considerable potential to optimize GMI surgery in the future

Synthetic data of simulated microcalcification clusters to train and explain deep learning detection models in contrast-enhanced mammography

Deep learning (DL) models can be trained on contrast-enhanced mammography (CEM) images to detect and classify lesions in the breast. As they often put more emphasis on the masses enhanced in the recombined image, they can fail in recognizing microcalcification clusters since these are hardly enhanced and are mainly visible in the (processed) lowenergy image. Therefore, we developed a method to create synthetic data with simulated microcalcification clusters to be used for data augmentation and explainability studies when training DL models. At first 3-dimensional voxel models of simulated microcalcification clusters based on descriptors of the shape and structure were constructed. In a set of 500 simulated microcalcification clusters the range of the size and of the number of microcalcifications per cluster followed the distribution of real clusters. The insertion of these clusters in real images of non-delineated CEM cases was evaluated by radiologists. The realism score was acceptable for single view applications. Radiologists could more easily categorize synthetic clusters into benign versus malignant than real clusters. In a second phase of the work, the role of synthetic data for training and/or explaining DL models was explored. A Mask R-CNN model was trained with synthetic CEM images containing microcalcification clusters. After a training run of 100 epochs the model was found to overfit on a training set of 192 images. In an evaluation with multiple test sets, it was found that this high level of sensitivity was due to the model being capable of recognizing the image rather than the cluster. Synthetic data could be applied for more tests, such as the impact of particular features in both background and lesion models

Perfusion Visualization during Ileal J-Pouch Formation—A Proposal for the Standardization of Intraoperative Imaging with Indocyanine Green Near-Infrared Fluorescence and a Postoperative Follow-Up in IBD Surgery

Background: An anastomotic leak (AL) after a restorative proctocolectomy and an ileal J-pouch increases morbidity and the risk of pouch failure. Thus, a perfusion assessment during J-pouch formation is crucial. While indocyanine green near-infrared fluorescence (ICG-NIRF) has shown potential to reduce ALs, its suitability in a restorative proctocolectomy remains unclear. We aimed to develop a standardized approach for investigating ICG-NIRF and ALs in pouch surgery. Methods: Patients undergoing a restorative proctocolectomy with an ileal J-pouch for ulcerative colitis at an IBD-referral-center were included in a prospective study in which an AL within 30 postoperative days was the primary outcome. Intraoperatively, standardized perfusion visualization with ICG-NIRF was performed and video recorded for postoperative analysis at three time points. Quantitative clinical and technical variables (secondary outcome) were correlated with the primary outcome by descriptive analysis and logistic regression. A novel definition and grading of AL of the J-pouch was applied. A postoperative pouchoscopy was routinely performed to screen for AL. Results: Intraoperative ICG-NIRF-visualization and its postoperative visual analysis in 25 patients did not indicate an AL. The anastomotic site after pouch formation appeared completely fluorescent with a strong fluorescence signal (category 2) in all cases of ALs (4 of 25). Anastomotic site was not changed. ICG-NIRF visualization was reproducible and standardized. Logistic regression identified a two-stage approach vs. a three-stage approach (Odds ratio (OR) = 20.00, 95% confidence interval [CI] = 1.37-580.18, p = 0.029) as a risk factor for ALs. Conclusion: We present a standardized, comparable approach of ICG-NIRF visualization in pouch surgery. Our data indicate that the visual interpretation of ICG-NIRF alone may not detect ALs of the pouch in all cases-quantifiable, objective methods of interpretation may be required in the future

Evaluation of the prognostic relevance of the recommended minimum number of lymph nodes in colorectal cancer—a propensity score analysis

Purpose Nodal status in colorectal cancer (CRC) is an important prognostic factor, and adequate lymph node (LN) staging is crucial. Whether the number of resected and analysed LN has a direct impact on overall survival (OS), cancer-specific survival (CSS) and disease-free survival (DFS) is much discussed. Guidelines request a minimum number of 12 LN to be analysed. Whether that threshold marks a prognostic relevant cut-off remains unknown. Methods Patients operated for stage I–III CRC were identified from a prospectively maintained database. The impact of the number of analysed LN on OS, CSS and DFS was assessed using Cox regression and propensity score analysis. Results Of the 687 patients, 81.8% had ≥ 12 LN resected and analysed. Median LN yield was 17.0 (IQR 13.0–23.0). Resection and analysis of ≥ 12 LN was associated with improved OS (HR = 0.73, 95% CI: 0.56–0.95, p = 0.033), CSS (HR 0.52, 95% CI: 0.31–0.85, p = 0.030) and DFS (HR = 0.73, 95% CI: 0.57–0.95, p = 0.030) in multivariate Cox analysis. After adjusting for biasing factors with propensity score matching, resection of ≥ 12 LN was significantly associated with improved OS (HR = 0.59; 95% CI: 0.43–0.81; p = 0.002), CSS (HR = 0.34; 95% CI: 0.20–0.60; p < 0.001) and DFS (HR = 0.55; 95% CI: 0.41–0.74; p < 0.001) compared to patients with < 12 LN. Conclusion Eliminating biasing factors by a propensity score matching analysis underlines the prognostic importance of the number of analysed LN. The set threshold marks the minimum number of required LN but nevertheless represents a cut-off regarding outcome in stage I–III CRC. This analysis therefore highlights the significance and importance of adherence to surgical oncological standards

MSCDA: Multi-level Semantic-guided Contrast Improves Unsupervised Domain Adaptation for Breast MRI Segmentation in Small Datasets

Deep learning (DL) applied to breast tissue segmentation in magnetic resonance imaging (MRI) has received increased attention in the last decade, however, the domain shift which arises from different vendors, acquisition protocols, and biological heterogeneity, remains an important but challenging obstacle on the path towards clinical implementation. In this paper, we propose a novel Multi-level Semantic-guided Contrastive Domain Adaptation (MSCDA) framework to address this issue in an unsupervised manner. Our approach incorporates self-training with contrastive learning to align feature representations between domains. In particular, we extend the contrastive loss by incorporating pixel-to-pixel, pixel-to-centroid, and centroid-to-centroid contrasts to better exploit the underlying semantic information of the image at different levels. To resolve the data imbalance problem, we utilize a category-wise cross-domain sampling strategy to sample anchors from target images and build a hybrid memory bank to store samples from source images. We have validated MSCDA with a challenging task of cross-domain breast MRI segmentation between datasets of healthy volunteers and invasive breast cancer patients. Extensive experiments show that MSCDA effectively improves the model's feature alignment capabilities between domains, outperforming state-of-the-art methods. Furthermore, the framework is shown to be label-efficient, achieving good performance with a smaller source dataset. The code is publicly available at \url{https://github.com/ShengKuangCN/MSCDA}.Comment: 17 pages, 8 figure

Risk of regional recurrence in triple-negative breast cancer patients: a Dutch cohort study

Triple-negative breast cancer is associated with early recurrence and low survival rates. Several trials investigate the safety of a more conservative approach of axillary treatment in clinically T1-2N0 breast cancer. Triple-negative breast cancer comprises only 15 % of newly diagnosed breast cancers, which might result in insufficient power for representative results for this subgroup. We aimed to provide a nationwide overview on the occurrence of (regional) recurrences in triple-negative breast cancer patients with a clinically T1-2N0 status. For this cohort study, 2548 women diagnosed between 2005 and 2008 with clinically T1-2N0 triple-negative breast cancer were selected from the Netherlands Cancer Registry. Follow-up data until 2014 were analyzed using Kaplan–Meier. Sentinel lymph node biopsy was performed in 2486 patients, and (completion) axillary lymph node dissection in 562 patients. Final pathologic nodal status was pN0 in 78.5 %, pN1mi in 4.5 %, pN1 in 12.3 %, pN2–3 in 3.6 %, and pNx in 1.1 %. During a follow-up of 5 years, regional recurrence occurred in 2.9 %, local recurrence in 4.2 % and distant recurrence in 12.2 %. Five-year disease-free survival was 78.7 %, distant disease-free survival 80.5 %, and 5-year overall survival 82.3 %. Triple-negative clinically T1-2N0 breast cancer patients rarely develop a regional recurrence. Their disease-free survival is more threatened by distant recurrence, affecting their overall survival. Consequently, it seems justified to include triple-negative breast cancer patients in randomized controlled trials investigating the safety of minimizing axillary staging and treatment