Mammography is considered the gold standard for breast cancer screening. Multiple risk factors that affect breast cancer development have been identified; however, there is an ongoing debate regarding the significance of these factors. Machine learning (ML) models and Shapley Additive Explanation (SHAP) methodology can rank risk factors and provide explanatory model results. This study used ML algorithms with SHAP to analyze the risk factors between two different age groups and evaluate the impact of each factor in predicting positive mammography. The ML model was built using data from the risk factor questionnaires of women participating in a breast cancer screening program from 2017 to 2021. Three ML models, least absolute shrinkage and selection operator (lasso) logistic regression, extreme gradient boosting (XGBoost), and random forest (RF), were applied. RF generated the best performance. The SHAP values were then applied to the RF model for further analysis. The model identified age at menarche, education level, parity, breast self-examination, and BMI as the top five significant risk factors affecting mammography outcomes. The differences between age groups ranked by reproductive lifespan and BMI were higher in the younger and older age groups, respectively. The use of SHAP frameworks allows us to understand the relationships between risk factors and generate individualized risk factor rankings. This study provides avenues for further research and individualized medicine.

This study aimed to investigate the important predictors related to predicting positive mammographic findings based on questionnaire-based demographic and obstetric/gynecological parameters using the proposed integrated machine learning (ML) scheme. The scheme combines the benefits of two well-known ML algorithms, namely, least absolute shrinkage and selection operator (Lasso) logistic regression and extreme gradient boosting (XGB), to provide adequate prediction for mammographic anomalies in high-risk individuals and the identification of significant risk factors. We collected questionnaire data on 18 breast-cancer-related risk factors from women who participated in a national mammographic screening program between January 2017 and December 2020 at a single tertiary referral hospital to correlate with their mammographic findings. The acquired data were retrospectively analyzed using the proposed integrated ML scheme. Based on the data from 21,107 valid questionnaires, the results showed that the Lasso logistic regression models with variable combinations generated by XGB could provide more effective prediction results. The top five significant predictors for positive mammography results were younger age, breast self-examination, older age at first childbirth, nulliparity, and history of mammography within 2 years, suggesting a need for timely mammographic screening for women with these risk factors.

Intraoperative navigation systems have been widely applied in spinal fusion surgery to improve the implantation accuracy of spinal screws using orthogonal tomographic and surface-rendering imaging. However, these images contain limited anatomical information and no information on bone volume contact by the implanted screw, which has been proven to affect the stability of implanted screws. This study proposed a novel drilled surface imaging technique that displays anatomical integration properties to calculate the contact bone volume (CBV) of the screws implanted along an implantation trajectory. A cylinder was used to represent the area traversed by the screws, which was manually rotated and translated to a predetermined implantation trajectory according to a vertebra model obtained using computed tomography (CT) image volumes. The drilled surface image was reconstructed by interpolating the CT numbers at the predefined sampling points on the cylinder surface. The anatomical integration property and CBV of the screw implanted along the transpedicular trajectory (TT) and cortical bone trajectory (CBT) were evaluated and compared. The drilled surface image fully revealed the contact anatomical structure of the screw under the trajectories, improving the understanding of the anatomical integration of the screw and surrounding tissues. On average, the CBV of the CBT was 30% greater than that of the TT. The proposed drilled surface image may be applied in preoperative planning and integrated into intraoperative navigation systems to evaluate the anatomical integration and degree of bone contact of the screw implanted along a trajectory.

Although widely used, CT-guided lung nodule localization is associated with a significant risk of complications, including pneumothorax and pulmonary hemorrhage. This study identified potential risk factors affecting the complications associated with CT-guided lung nodule localization. Data from patients with lung nodules who underwent preoperative CT-guided localization with patent blue vital (PBV) dye at Shin Kong Wu Ho-Su Memorial Hospital, Taiwan, were retrospectively collected. Logistic regression analysis, the chi-square test, and the Mann–Whitney test were used to analyze the potential risk factors for procedure-related complications. We included 101 patients with a single nodule (49 with pneumothorax and 28 with pulmonary hemorrhage). The results revealed that men were more susceptible to pneumothorax during CT-guided localization (odds ratio: 2.48, p = 0.04). Both deeper needle insertion depth (odds ratio: 1.84, p = 0.02) and nodules localized in the left lung lobe (odds ratio: 4.19, p = 0.03) were associated with an increased risk of pulmonary hemorrhage during CT-guided localization. In conclusion, for patients with a single nodule, considering the needle insertion depth and patient characteristics during CT-guided localization procedures is probably important for reducing the risk of complications.