Intraoperative Conversion to ALPPS in a Case of Intrahepatic Cholangiocarcinoma

Background. Surgical resection remains the best treatment option for intrahepatic cholangiocarcinoma (ICC). Two-stage liver resection combining in situ liver transection with portal vein ligation (ALPPS) has been described as a promising method to increase the resectability of liver tumors also in the case of ICC. Presentation of Case. A 46-year-old male patient presented with an ICC-typical lesion in the right liver. The indication for primary liver resection was set and planed as a right hepatectomy. In contrast to the preoperative CT-scan, the known lesion showed further progression in a macroscopically steatotic liver. Therefore, the decision was made to perform an ALPPS-procedure to avoid an insufficient future liver remnant (FLR). The patient showed an uneventful postoperative course after the first and second step of the ALPPS-procedure, with sufficient increase of the FLR. Unfortunately, already 2.5 months after resection the patient had developed new tumor lesions found by the follow-up CT-scan. Discussion. The presented case demonstrates that an intraoperative conversion to an ALPPS-procedure is safely applicable when the FLR surprisingly seems to be insufficient. Conclusion. ALPPS should also be considered a treatment option in well-selected patients with ICC. However, the experience concerning the outcome of ALPPS in case of ICC remains fairly small

Increasing trust and fairness in machine learning applications within the mortgage industry

The integration of machine learning in applications provides opportunities for increased efficiency in many organisations. However, the deployment of such systems is often hampered by the lack of insight into how their decisions are reached, resulting in concerns about trust and fairness. In this article, we investigate to what extent the addition of explainable AI components to ML applications can contribute to alleviating these issues. As part of this research, explainable AI functionality was developed for an existing ML model used for mortgage fraud detection at a large international financial institution based in The Netherlands A system implementing local explanation techniques was deployed to support the day-to-day work of fraud detection experts working with the model. In addition, a second system implementing global explanation techniques was developed to support the model management processes involving data-scientists, legal experts and compliance officers. A controlled experiment using actual mortgage applications was carried out to measure the effectiveness of these two systems, using both quantitative and qualitative assessment methods. Our results show that the addition of explainable AI functionality results in a statistically significant improvement in the levels of trust and usability by its daily users. The explainable AI system implementing global interpretability was found to considerably increase confidence in the ability to perform the processes focused on compliance and fairness. In particular, bias detection towards demographic groups successfully aided in the identification and removal of bias towards applicants with a migration background