Correlation between sperm oxidative stress and sperm DNA damage in subfertile men

ABSTRACT Background: Approximately 15 % of all couples in the Western countries experience infertility. Up to half of these cases are thought to solely or partly depend upon the male factor. In most cases male infertility is unexplained. During the last decade there has been a growing focus on sperm DNA damage as a cause of male infertility. Sperm chromatin structure assay (SCSA) is increasingly used to evaluate male fertility status as it determines sperm DNA damage. This DNA damage can originate from several sources, the major being oxidative stress, i.e excess of reactive oxygen species (ROS). Increased ROS may be a result of different factors, for instance chronic diseases, infections or lifestyle factors such as smoking and high body mass index (BMI). Despite this, sperm ROS is not tested for in routine infertility assessment. A new promising analysis for assessing sperm ROS production is the nitro blue tetrazolium assay (NBT). Objectives: The primary objective was to study the correlation between sperm oxidative stress assessed with NBT assay and sperm DNA fragmentation index (DFI) assessed with SCSA in subfertile men. Secondly, the objective was to correlate these parameters to BMI as well as to the level of serum testosterone. Method: The prospective study was conducted on a cohort of forty-seven subfertile men. To assess sperm quality the NBT assay, SCSA and World Health Organization (WHO) sperm analysis were performed. For all men testosterone, lutenizing hormone (LH) and BMI were measured. Result: There was a tendency to a correlation between NBT outcome and DFI (n = 39, r = 0.281, P = 0.084), however not statistically significant. Men with a BMI > 30 kg/m2 had an increased risk for having a DFI > 30% (OR = 8.6, P = 0.070) compared to the normal weighted men, although not statistically significant. Conclusion: There was a weak association between sperm oxidative stress and sperm DNA damage. A high BMI was correlated to an increased risk of DNA damage. Although non of these findings were statistically significant they show the same tendencies as previous reports. Sperm oxidative stress seems likely as a cause of male infertility and large scale clinical trials are warranted to further evaluate the NBT assay. The hope for the future is to be able to sort out the men with sperm oxidative stress caused infertility, who potentially would benefit from antioxidant strategies

Generalisation effects of predictive uncertainty estimation in deep learning for digital pathology

Deep learning (DL) has shown great potential in digital pathology applications. The robustness of a diagnostic DL-based solution is essential for safe clinical deployment. In this work we evaluate if adding uncertainty estimates for DL predictions in digital pathology could result in increased value for the clinical applications, by boosting the general predictive performance or by detecting mispredictions. We compare the effectiveness of model-integrated methods (MC dropout and Deep ensembles) with a model-agnostic approach (Test time augmentation, TTA). Moreover, four uncertainty metrics are compared. Our experiments focus on two domain shift scenarios: a shift to a different medical center and to an underrepresented subtype of cancer. Our results show that uncertainty estimates increase reliability by reducing a models sensitivity to classification threshold selection as well as by detecting between 70 and 90% of the mispredictions done by the model. Overall, the deep ensembles method achieved the best performance closely followed by TTA.Funding Agencies|Swedish e-Science Research Center; VINNOVA [2017-02447]</p

Gigapixel end-to-end training using streaming and attention

Current hardware limitations make it impossible to train convolutional neural networks on gigapixel image inputs directly. Recent developments in weakly supervised learning, such as attention-gated multiple instance learning, have shown promising results, but often use multi-stage or patch-wise training strategies risking suboptimal feature extraction, which can negatively impact performance. In this paper, we propose to train a ResNet-34 encoder with an attention-gated classification head in an end-to-end fashion, which we call StreamingCLAM, using a streaming implementation of convolutional layers. This allows us to train end-to-end on 4-gigapixel microscopic images using only slide-level labels.We achieve a mean area under the receiver operating characteristic curve of 0.9757 for metastatic breast cancer detection (CAMELYON16), close to fully supervised approaches using pixel-level annotations. Our model can also detect MYC-gene translocation in histologic slides of diffuse large B-cell lymphoma, achieving a mean area under the ROC curve of 0.8259. Furthermore, we show that our model offers a degree of interpretability through the attention mechanism.Funding Agencies|Innovative Medicines Initiative 2 Joint Undertaking [945358]; European Union; EFPIA, Belgium</p

Generalization of Deep Learning in Digital Pathology : Experience in Breast Cancer Metastasis Detection

Simple Summary Pathology is a cornerstone in cancer diagnostics, and digital pathology and artificial intelligence-driven image analysis could potentially save time and enhance diagnostic accuracy. For clinical implementation of artificial intelligence, a major question is whether the computer models maintain high performance when applied to new settings. We tested the generalizability of a highly accurate deep learning model for breast cancer metastasis detection in sentinel lymph nodes from, firstly, unseen sentinel node data and, secondly, data with a small change in surgical indication, in this case lymph nodes from axillary dissections. Model performance dropped in both settings, particularly on axillary dissection nodes. Retraining of the model was needed to mitigate the performance drop. The study highlights the generalization challenge of clinical implementation of AI models, and the possibility that retraining might be necessary. Poor generalizability is a major barrier to clinical implementation of artificial intelligence in digital pathology. The aim of this study was to test the generalizability of a pretrained deep learning model to a new diagnostic setting and to a small change in surgical indication. A deep learning model for breast cancer metastases detection in sentinel lymph nodes, trained on CAMELYON multicenter data, was used as a base model, and achieved an AUC of 0.969 (95% CI 0.926-0.998) and FROC of 0.838 (95% CI 0.757-0.913) on CAMELYON16 test data. On local sentinel node data, the base model performance dropped to AUC 0.929 (95% CI 0.800-0.998) and FROC 0.744 (95% CI 0.566-0.912). On data with a change in surgical indication (axillary dissections) the base model performance indicated an even larger drop with a FROC of 0.503 (95%CI 0.201-0.911). The model was retrained with addition of local data, resulting in about a 4% increase for both AUC and FROC for sentinel nodes, and an increase of 11% in AUC and 49% in FROC for axillary nodes. Pathologist qualitative evaluation of the retrained model s output showed no missed positive slides. False positives, false negatives and one previously undetected micro-metastasis were observed. The study highlights the generalization challenge even when using a multicenter trained model, and that a small change in indication can considerably impact the model s performance.Funding Agencies|Vinnova [2017-02447]</p