The role of immune checkpoint inhibitors in the first-line treatment for patients with advanced biliary tract cancer: a systematic review and meta-analysis of randomized trials.

Background: We performed a systematic review and meta-analysis to further explore the impact of the addition of immunotherapy to gemcitabine-cisplatin as first-line treatment for advanced biliary tract cancer (BTC) patients. Methods: Literature research was performed, and hazard ratio values and 95% confidence intervals were calculated. Heterogeneity among studies was assessed using the tau-squared estimator ( τ 2 ) . The total Cochrane Q test (Q) was also assessed. The overall survival rate, objective response rate, and progression-free survival in the selected studies were assessed. Results: A total of 1,754 participants were included. Heterogeneity among the studies selected was found to be non-significant (p = 0.78; tau2 = 0, I2 = 0%). The model estimation results and the forest plot suggested that the test for the overall effect was significant (Z = -3.51; p< 0.01). Conclusion: The results of the current meta-analysis further confirm the role of immune checkpoint inhibitors plus gemcitabine-cisplatin as the new standard first-line treatment for advanced BTC patients. Systematic review registration: https://www.crd.york.ac.uk/prospero, identifier CRD42023488095.

Explainable 3D CNN based on baseline breast DCE-MRI to give an early prediction of pathological complete response to neoadjuvant chemotherapy.

BACKGROUND: So far, baseline Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) has played a key role for the application of sophisticated artificial intelligence-based models using Convolutional Neural Networks (CNNs) to extract quantitative imaging information as earlier indicators of pathological Complete Response (pCR) achievement in breast cancer patients treated with neoadjuvant chemotherapy (NAC). However, these models did not exploit the DCE-MRI exams in their full geometry as 3D volume but analysed only few individual slices independently, thus neglecting the depth information. METHOD: This study aimed to develop an explainable 3D CNN, which fulfilled the task of pCR prediction before the beginning of NAC, by leveraging the 3D information of post-contrast baseline breast DCE-MRI exams. Specifically, for each patient, the network took in input a 3D sequence containing the tumor region, which was previously automatically identified along the DCE-MRI exam. A visual explanation of the decision-making process of the network was also provided. RESULTS: To the best of our knowledge, our proposal is competitive than other models in the field, which made use of imaging data alone, reaching a median AUC value of 81.8%, 95%CI [75.3%; 88.3%], a median accuracy value of 78.7%, 95%CI [74.8%; 82.5%], a median sensitivity value of 69.8%, 95%CI [59.6%; 79.9%] and a median specificity value of 83.3%, 95%CI [82.6%; 84.0%], respectively. The median and CIs were computed according to a 10-fold cross-validation scheme for 5 rounds. CONCLUSION: Finally, this proposal holds high potential to support clinicians on non-invasively early pursuing or changing patient-centric NAC pathways.