Prognostic Evaluation for Hepatocellular Carcinoma with Portal Vein Tumor Thrombus Patients Treated with Transarterial Chemoembolization Plus Molecular Targeted Therapies-Development and Validation of the ABPS Score.

RATIONALE AND OBJECTIVES: Transarterial chemoembolization (TACE) plus molecular targeted therapies has emerged as the main approach for treating hepatocellular carcinoma (HCC) with portal vein tumor thrombus (PVTT). A robust model for outcome prediction and risk stratification of recommended TACE plus molecular targeted therapies candidates is lacking. We aimed to develop an easy-to-use tool specifically for these patients. METHODS: A retrospective analysis was conducted on 384 patients with HCC and PVTT who underwent TACE plus molecular targeted therapies at 16 different institutions. We developed and validated a new prognostic score which called ABPS score. Additionally, an external validation was performed on data from 200 patients enrolled in a prospective cohort study. RESULTS: The ABPS score (ranging from 0 to 3 scores), which involves only Albumin-bilirubin (ALBI, grade 1: 0 score; grade 2: 1 score), PVTT(I-II type: 0 score; III-IV type: 1 score), and systemic-immune inflammation index (SII,<550 × 1012: 0 score; ≥550 × 1012: 1 score). Patients were categorized into three risk groups based on their ABPS score: ABPS-A, B, and C (scored 0, 1-2, and 3, respectively). The concordance index (C-index) of the ABPS scoring system was calculated to be 0.802, significantly outperforming the HAP score (0.758), 6-12 (0.712), Up to 7 (0.683), and ALBI (0.595) scoring systems (all P < 0.05). These research findings were further validated in the external validation cohorts. CONCLUSION: The ABPS score demonstrated a strong association with survival outcomes and radiological response in patients undergoing TACE plus molecular targeted therapy for HCC with PVTT. The ABPS scoring system could serve as a valuable tool to guide treatment selection for these patients.

CFD modeling of ethylene degradation in gas-phase photocatalytic reactors.

Photocatalytic oxidation is a promising technology to degrade volatile organic compounds. The performance of photocatalytic reactors is affected by the hydrodynamics, radiation transfer, mass transfer and reaction kinetics. Baffles may improve the hydrodynamics. The effect of baffles on heterogeneous photocatalytic oxidation of gas-phase ethylene in three annular reactors is simulated using computational fluid dynamics. ANSYS Fluent is used to solve all governing equations. Baffles can improve the uniformity of flow and prolong the residence time. The residence time of the C-type reactor and B-type reactor is 0.5% greater than the unbaffled reactor. Baffles have little effect on the radiation distribution. The concentric arrangement of lamp and the reactor leads to a radial dominance of radiation. The effect of baffles on the diffusion of ethylene is complex. The effective diffusion coefficient at the catalyst surface in the C-type reactor decreases 9.5% and that in the B-type reactor increases 3% with respect to the unbaffled reactor. The outlet ethylene concentration is 4.19 ppmv for the U-type reactor, 3.93 ppmv for the C-type reactor and 3.62 ppmv for the B-type reactor. The optimal performance in the B-type reactor is due to the large diffusion coefficient of ethylene. The arrangement of baffles should enlarge the effective diffusion coefficient at the catalyst surface as far as possible.

Semi-automated pulmonary nodule interval segmentation using the NLST data.

PURPOSE: To study the variability in volume change estimates of pulmonary nodules due to segmentation approaches used across several algorithms and to evaluate these effects on the ability to predict nodule malignancy. METHODS: We obtained 100 patient image datasets from the National Lung Screening Trial (NLST) that had a nodule detected on each of two consecutive low dose computed tomography (LDCT) scans, with an equal proportion of malignant and benign cases (50 malignant, 50 benign). Information about the nodule location for the cases was provided by a screen capture with a bounding box and its axial location was indicated. Five participating quantitative imaging network (QIN) institutions performed nodule segmentation using their preferred semi-automated algorithms with no manual correction; teams were allowed to provide additional manually corrected segmentations (analyzed separately). The teams were asked to provide segmentation masks for each nodule at both time points. From these masks, the volume was estimated for the nodule at each time point; the change in volume (absolute and percent change) across time points was estimated as well. We used the concordance correlation coefficient (CCC) to compare the similarity of computed nodule volumes (absolute and percent change) across algorithms. We used Logistic regression model on the change in volume (absolute change and percent change) of the nodules to predict the malignancy status, the area under the receiver operating characteristic curve (AUROC) and confidence intervals were reported. Because the size of nodules was expected to have a substantial effect on segmentation variability, analysis of change in volumes was stratified by lesion size, where lesions were grouped into those with a longest diameter of <8 mm and those with longest diameter ≥ 8 mm. RESULTS: We find that segmentation of the nodules shows substantial variability across algorithms, with the CCC ranging from 0.56 to 0.95 for change in volume (percent change in volume range was [0.15 to 0.86]) across the nodules. When examining nodules based on their longest diameter, we find the CCC had higher values for large nodules with a range of [0.54 to 0.93] among the algorithms, while percent change in volume was [0.3 to 0.95]. Compared to that of smaller nodules which had a range of [-0.0038 to 0.69] and percent change in volume was [-0.039 to 0.92]. The malignancy prediction results showed fairly consistent results across the institutions, the AUC using change in volume ranged from 0.65 to 0.89 (Percent change in volume was 0.64 to 0.86) for entire nodule range. Prediction improves for large nodule range (≥ 8 mm) with AUC range 0.75 to 0.90 (percent change in volume was 0.74 to 0.92). Compared to smaller nodule range (<8 mm) with AUC range 0.57 to 0.78 (percent change in volume was 0.59 to 0.77). CONCLUSIONS: We find there is a fairly high concordance in the size measurements for larger nodules (≥8 mm) than the lower sizes (<8 mm) across algorithms. We find the change in nodule volume (absolute and percent change) were consistent predictors of malignancy across institutions, despite using different segmentation algorithms. Using volume change estimates without corrections shows slightly lower predictability (for two teams).