Keith Wan-Hang Chiu

Purpose: Patients with refractory pancreatic cancer have limited therapeutic option and dismal prognoses. This study aimed to evaluate the efficacy and safety of trifluridine/tipiracil (FTD/TPI) in patients with fluoropyrimidine-refractory pancreatic cancer and their predictive biomarkers. Methods: In this single-arm phase II clinical trial, patients of refractory pancreatic cancer were enrolled and administered with FTD/TPI on days 1-5 and 8-12 of a 28-day cycle. The primary end point was the 16-week progression-free survival (PFS) rate. Secondary end points included median PFS, median overall survival (OS), disease control rate (DCR), and toxicities. Clinical and proteomic biomarkers were assessed before treatment and correlated with PFS and OS. Results: Between March 2021 and June 2023, a total of 28 patients were enrolled. The median age was 64 years (range, 49-80), and 16 (57.1%) patients were male. All patients (100%) received prior fluoropyrimidine. Twenty-three patients (82.1%) received two or more lines of chemotherapy. After a median follow-up of 4.8 months, the 16-week PFS rate was 35.7% (95% CI, 17.8 to 53.2). The median PFS was 3.3 months (95% CI, 1.8 to 4.8) and the median OS was 4.6 months (95% CI, 3.8 to 5.4). The DCR was 50% (14/28; 95% CI, 30.7 to 69.4). The most common grade 3 or worse adverse events were neutropenia (n = 9, 32.1%) and anemia (n = 4, 14.3%) without new safety signals. Neutrophil-to-lymphocyte ratio (NLR) > 5 and high IL1RL1 level were poor prognostic factors, while high APOA4, MSTN, and LUM levels were associated with better prognoses. Conclusion: To our knowledge, this is the first prospective study to suggest the potential efficacy and acceptable safety of FTD/TPI in patients with pancreatic cancer refractory to fluoropyrimidine. Further randomized trials are needed to validate these findings. NLR, IL1RL1, APOA4, MSTN, and LUM levels are promising biomarkers that warrant further investigation.

High-resolution thin-slice computed tomography (CT) images are often compressed into lower-quality thick-slice images for long-term storage, necessitating synthesis for medical diagnosis. In this paper, we propose a novel 3D adversarial diffusion model (3DAD) for high-fidelity synthesis of thin-slice CT from compressed thick-slice CT. 3DAD is composed of a generator and a discriminator for synthesizing denoised thin-slice images from random noise and source images and distinguishing between noised samples from real and denoised synthetic thin-slice images. Specific models were trained on two-slice to six-slice scenarios for abdominal data, using thick-slice CT compressed from real thin-slice CT as the source. 3DAD was evaluated at the time of HCC diagnosis, at the observation and patient levels, using real thin-slice and synthetic thin-slice CT, with DeLong’s test to compare the similarity of receiver operating characteristic (ROC) curves. We further evaluated 3DAD on real-world data with both thin and thick images, with the synthetic image quality assessed by radiologists and in radiomics feature analysis. Based on the external dataset with 548 samples, the achieved mean squared error (MSE), peak signal-to-noise ratio (PSNR), and structural similarity index measure (SSIM) values were 81.374, 29.478, and 0.916, respectively, for the five-slice scenarios at the portal venous phase. The Areas Under Curves (AUCs) achieved were 0.896 on synthetic thin-slice images compared with 0.889 on real thin-slice images at the observation level (p = 0.028) and 0.854 versus 0.846, correspondingly, at the patient level (p = 0.055). For evaluation on the real-world testing dataset after fine-tuning at the portal venous phase, the MSE, PSNR, and SSIM were 70.435, 30.243, and 0.94, respectively. Radiologist evaluation confirmed the high quality of the synthetic image, with no significant difference in the majority of cases across all five parameters, except for radiologist 2, in realistic and consistent situations, under which at least 41 of 43 synthetic images were assessed as equal to or above grade 3. Our 3DAD enabled the synthesis of thick-slice CT images into high-resolution thin-slice images, facilitating high-fidelity volume image application in HCC diagnosis.

Leung KD, et al. Cardiovasc Imaging Asia. 2025 Jan;9(1):2-8. https://doi.org/10.22468/cvia.2024.00129

Large language models showed interpretative reasoning in solving diagnostically challenging medical cases.