Beyond black-box models: explainable AI for embryo ploidy prediction and patient-centric consultation.

PURPOSE: To determine if an explainable artificial intelligence (XAI) model enhances the accuracy and transparency of predicting embryo ploidy status based on embryonic characteristics and clinical data. METHODS: This retrospective study utilized a dataset of 1908 blastocyst embryos. The dataset includes ploidy status, morphokinetic features, morphology grades, and 11 clinical variables. Six machine learning (ML) models including Random Forest (RF), Linear Discriminant Analysis (LDA), Logistic Regression (LR), Support Vector Machine (SVM), AdaBoost (ADA), and Light Gradient-Boosting Machine (LGBM) were trained to predict ploidy status probabilities across three distinct datasets: high-grade embryos (HGE, n = 1107), low-grade embryos (LGE, n = 364), and all-grade embryos (AGE, n = 1471). The model's performance was interpreted using XAI, including SHapley Additive exPlanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME) techniques. RESULTS: The mean maternal age was 38.5 ± 3.85 years. The Random Forest (RF) model exhibited superior performance compared to the other five ML models, achieving an accuracy of 0.749 and an AUC of 0.808 for AGE. In the external test set, the RF model achieved an accuracy of 0.714 and an AUC of 0.750 (95% CI, 0.702-0.796). SHAP's feature impact analysis highlighted that maternal age, paternal age, time to blastocyst (tB), and day 5 morphology grade significantly impacted the predictive model. In addition, LIME offered specific case-ploidy prediction probabilities, revealing the model's assigned values for each variable within a finite range. CONCLUSION: The model highlights the potential of using XAI algorithms to enhance ploidy prediction, optimize embryo selection as patient-centric consultation, and provides reliability and transparent insights into the decision-making process.

STK31 is a cell-cycle regulated protein that contributes to the tumorigenicity of epithelial cancer cells.

Serine/threonine kinase 31 (STK31) is one of the novel cancer/testis antigens for which its biological functions remain largely unclear. Here, we demonstrate that STK31 is overexpressed in many human colorectal cancer cell lines and tissues. STK31 co-localizes with pericentrin in the centrosomal region throughout all phases of the cell cycle. Interestingly, when cells undergo mitosis, STK31 also localizes to the centromeres, central spindle, and midbody. This localization behavior is similar to that of chromosomal passenger proteins, which are known to be the important players of the spindle assembly checkpoint. The expression of STK31 is cell cycle-dependent through the regulation of a putative D-box near its C-terminal region. Ectopically-expressed STK31-GFP increases cell migration and invasive ability without altering the proliferation rate of cancer cells, whereas the knockdown expression of endogenous STK31 by lentivirus-derived shRNA results in microtubule assembly defects that prolong the duration of mitosis and lead to apoptosis. Taken together, our results suggest that the aberrant expression of STK31 contributes to tumorigenicity in somatic cancer cells. STK31 might therefore act as a potential therapeutic target in human somatic cancers.