Background and purpose
Tumor recurrence after liver transplantation (LT) impedes the curative chance for hepatocellular carcinoma (HCC) patients. This study aimed to develop a deep pathomics score (DPS) for predicting tumor recurrence after liver transplantation using deep learning.
Patients and methods
Two datasets of 380 HCC patients who underwent LT were enrolled. Residual convolutional neural networks were used to identify six histological structures of HCC. The individual risk score of each structure and DPS were derived by a modified DeepSurv network. Cox regression analysis and Concordance index were used to evaluate the prognostic significance. The cellular exploration of prognostic immune biomarkers was performed by quantitative and spatial proximity analysis according to three panels of 7-color immunofluorescence.
Results
The overall classification accuracy of HCC tissue was 97%. At the structural level, immune cells were the most significant tissue category for predicting post-LT recurrence (HR 1.907, 95% CI 1.490–2.440). The C-indices of DPS achieved 0.827 and 0.794 in the training and validation cohorts, respectively. Multivariate analysis for recurrence-free survival (RFS) showed that DPS (HR 4.795, 95% CI 3.017–7.619) was an independent risk factor. Patients in the high-risk subgroup had a shorter RFS, larger tumor diameter and a lower proportion of clear tumor borders. At the cellular level, a higher infiltration of intratumoral NK cells was negatively correlated with recurrence risk.
Conclusions
This study established an effective DPS. Immune cells were the most significant histological structure related to HCC recurrence. DPS performed well in post-LT recurrence prediction and the identification of clinicopathological features.
We present an in-process measurement of surface roughness by combining an optical probe of laser-scattering phenomena and adaptive optics for aberration correction. Measurement results of five steel samples with a roughness ranging from 0.2 to 3.125 μm demonstrate excellent correlation between the peak power and average roughness with a correlation coefficient (R(2)) of 0.9967. The proposed adaptive-optics-assisted system is in good agreement with the stylus method, and error values of less than 8.7% are obtained for average sample roughness in the range of 0.265 to 1.119 μm. The proposed system can be used as a rapid in-process roughness monitor/estimator to further increase the precision and stability of manufacturing processes in situ.
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