Browsing by Author "Lan, Qing"

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    Improving Assessment in Kidney Transplantation by Multitask General Path Model
    Lan, Qing; Chen, Xiaoyu; Li, Murong; Robertson, John; Lei, Yong; Jin, Ran (2023)
    Kidney transplantation helps end-stage patients regain health and quality-of-life. The decisions for matching donor kidneys and recipients affect success of transplantation. However, current kidney matching decision procedures do not consider viability loss during preservation. The objective here is to forecast heterogeneous kidney viability, based on historical datasets to support kidney matching decision-making. Six recently procured porcine kidneys were used to conduct viability assessment experiments to validate the proposed multitask general path model. The model forecasts kidney viability by transferring knowledge from learning the commonality of all kidneys and the heterogeneity of each kidney. The proposed model provides exactly accurate kidney viability forecasting results compared to the state-of-the-art models including a multitask learning model, a general path model, and a general linear model. The proposed model provides satisfactory kidney viability forecasting accuracy because it quantifies the degradation information from trajectory of a viability loss path. It transfers knowledge of common effects from all kidneys and identifies individual effects of each kidney. This method can be readily extended to other decision-making scenarios in kidney transplantation to improve overall assessment performance. For example, analytical generalizations gained by modeling have been validated based on needle biopsy data targeting the improvement of tissue extraction accuracy. The proposed model applied in multiple kidney assessment processes in transplantation can potentially reduce the kidney discard rate by providing effective kidney matching decisions. Thus, the increased kidney utilization rate will benefit more patients and prolong their lives.
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    Organ Viability Assessment in Transplantation based on Data-driven Modeling
    Lan, Qing (Virginia Tech, 2020-03-03)
    Organ transplantation is one of the most important and effective solutions to save end-stage patients, who have one or more critical organ failures. However, the inadequate organs for transplantation to meet the demands has been the major issue. Even worse, the lack of accurate non-invasive assessment methods wastes 20% of donor organs every year. Currently, the most frequently used organ assessment methods are visual inspections and biopsy. Yet both methods are subjective: the assessment accuracy depends on the evaluator's experience. Moreover, repeating biopsies will potentially damage the organs. To reduce the waste of donor organs, online non-invasive and quantitative organ assessment methods are in great needs. Organ viability assessment is a challenging issue due to four reasons: 1) there are no universally accepted guidelines or procedures for surgeons to quantitatively assess the organ viability; 2) there is no easy-deployed and non-invasive biological in situ data to correlate with organ viability; 3) the organs viability is difficult to model because of heterogeneity among organs; 4) both visual inspection and biopsy can be applied only at present time, and how to forecast the viability of similar-but-non-identical organs at a future time is still in shadow. Motivated by the challenges, the overall objective of this dissertation is to develop online non-invasive and quantitative assessment methods to predict and forecast the organ viability. As a result, four data-driven modeling research tasks are investigated to achieve the overall objective: 1) Quantitative and qualitative models are used to jointly predict the number of dead cells and the liver viability based on features extracted from biopsy images. This method can quantitatively assess the organ viability, which could be used to validate the biopsy results from pathologists to increase the evaluation accuracy. 2) A multitask learning logistic regression model is applied to assess liver viability by using principal component analysis to extract infrared image features to quantify the correlation between liver viability and spatial infrared imaging data. This non-invasive online assessment method can evaluate the organ viability without physical contact to reduce the risk of damaging the organs. 3) A spatial-temporal smooth variable selection method is conducted to improve the liver viability prediction accuracy by considering both spatial and temporal effects from the infrared images without feature engineering. In addition, it provides medical interpretation based on variable selection to highlight the most significant regions on the liver resulting in viability loss. 4) A multitask general path model is implemented to forecast the heterogeneous kidney viability based on limited historical data by learning the viability loss paths of each kidney during preservation. The generality of this method is validated by tissue deformation forecasting in needle biopsy process to potentially improve the biopsy accuracy. In summary, the proposed data-driven methods can predict and forecast the organ viability without damaging the organ. As a result, the increased utilization rate of donor organs will benefit more end-stage patients by dramatically extending their life spans.