Liver international : official journal of the International Association for the Study of the Liver · 2025
The Global and Regional Burden of Gallbladder and Biliary Tract Cancer and Attributable Risk Factors: Trends and Predictions.
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Qiu Shuping, Zeng Wanting, Zhang Jilin, Xie Jianfeng, Chen Xiaoping
Аннотация
BACKGROUND: Gallbladder and biliary tract cancer (GBTC) is a serious disease burden. A comprehensive assessment of the disease burden is essential for improving prevention and treatment strategies.
METHODS: The estimated annual percentage change, Joinpoint regression analysis and age-period-cohort model (APCM) were used to comprehensively evaluate the current status and trend of GBTC burden from 1990 to 2021 from the Global Burden of Disease Study. From the perspective of deep learning, a hierarchical weighted long short-term memory network model (SW-LSTM) is proposed for trend prediction to overcome the shortcomings of traditional models.
RESULTS: The global GBTC burden increased non-linearly with age, which was higher in women than in men. With the increase of SDI, the gender difference showed a decreasing trend. Significant period and cohort effects were observed for the indicators in the remaining regions except for some indicators in the low-and low-middle-SDI regions. Age-standardised indicators in the high, high-middle and middle SDI regions showed a downward trend, while the remaining regions showed an upward trend. The proportion of age-standardised mortality rate attributable to high BMI increased with the increase of SDI. The prediction results showed that the SW-LSTM model outperformed the APCM and ARIMA models in prediction accuracy.
CONCLUSIONS: The SW-LSTM model proposed in this paper can provide more accurate prediction information to assist in the development of more targeted prevention strategies. In view of the impact of GBTC on global health, especially among women and the elderly, effective measures should be taken to reverse the increasing trend of GBTC.
METHODS: The estimated annual percentage change, Joinpoint regression analysis and age-period-cohort model (APCM) were used to comprehensively evaluate the current status and trend of GBTC burden from 1990 to 2021 from the Global Burden of Disease Study. From the perspective of deep learning, a hierarchical weighted long short-term memory network model (SW-LSTM) is proposed for trend prediction to overcome the shortcomings of traditional models.
RESULTS: The global GBTC burden increased non-linearly with age, which was higher in women than in men. With the increase of SDI, the gender difference showed a decreasing trend. Significant period and cohort effects were observed for the indicators in the remaining regions except for some indicators in the low-and low-middle-SDI regions. Age-standardised indicators in the high, high-middle and middle SDI regions showed a downward trend, while the remaining regions showed an upward trend. The proportion of age-standardised mortality rate attributable to high BMI increased with the increase of SDI. The prediction results showed that the SW-LSTM model outperformed the APCM and ARIMA models in prediction accuracy.
CONCLUSIONS: The SW-LSTM model proposed in this paper can provide more accurate prediction information to assist in the development of more targeted prevention strategies. In view of the impact of GBTC on global health, especially among women and the elderly, effective measures should be taken to reverse the increasing trend of GBTC.