The Lancet Regional Health: Western Pacific最新文献

{"title":"Trends in mortality for gastric cancer from 2011 to 2020 with prediction to 2030: a Bayesian age-period-cohort analysis","authors":"Zhe Liu,&nbsp;Peng Yin","doi":"10.1016/j.lanwpc.2024.101298","DOIUrl":"10.1016/j.lanwpc.2024.101298","url":null,"abstract":"<div><h3>Background</h3><div>Gastric cancer was the 3rd most common cause of cancer deaths, accounting for 11.3% of all cancer deaths in China in 2018. The study aims to analyze trends in gastric cancer mortality in China from 2011 to 2020, and predict the future burden of gastric cancer from 2021 to 2030.</div></div><div><h3>Methods</h3><div>Relevant data on gastric cancer were obtained from the National Mortality Surveillance System, which is available from the Chinese Center for Disease Control and Prevention. All deaths with underlying cause of death as gastric cancer (International Classification of Diseases-10 code: C16) were included. We analyzed the numbers and age-standardized mortality rates (ASMR) for gastric cancer by sex and urbanicity in China during 2011-2020. A Bayesian age-period-cohort (BAPC) prediction model was used to predict gastric cancer mortality by sex and urbanicity in China from 2021 to 2030. The gastric cancer death data from 2011 to 2020 were categorized into five-year age groups, from 0-4 to 80+. Population data for 2011-2020 were obtained from the National Bureau of Statistics. Projected population data for 2021-2030 were based on estimates derived from 2011-2020 population. The standard population for calculating ASMR was derived from the China Census in 2020.</div></div><div><h3>Findings</h3><div>In 2020, the ASMR for gastric cancer was 22.24/100,000, accounting for 291.20 thousand deaths in China, including 199.66 thousand males and 91.53 thousand females. The ASMR in males (32.57/100,000) was higher than that in females (13.09/100,000). From 2011 to 2020, the number of gastric cancer deaths in China showed a gradual downward trend, with a notable decline in the ASMR. From 2021 to 2030, the ASMR for gastric cancer in China is expected to continue declining. In 2030, It is anticipated that the ASMR of gastric cancer in China will be 11.68/100,000, resulting in an estimated 218.93 thousand deaths. The ASMR for males is projected to decrease to 17.78/100,000, representing a 45.4% reduction from 2020. For females, the ASMR will decrease to 6.81/100,000 corresponding to a significant reduction of 48.0% from the 2020. In 2030, the projected ASMR for gastric cancer is 11.67/100,000 in urban areas and 12.04/100,000 in rural areas, accounting for 111.90 thousand and 125.92 thousand deaths, respectively. Compared to 2020, the ASMR for gastric cancer in 2030 shows a 38.7% and 50.9% reduction in urban and rural areas, respectively.</div></div><div><h3>Interpretation</h3><div>From 2011 to 2020, both the number of deaths and the ASMR of gastric cancer in China gradually declined. The number of gastric cancer deaths in China is projected to continue declining through 2030. The findings indicate that the preventive and control measures for gastric cancer are effective and may provide useful reference for development of preventive and control strategies for other major cancers in China.</div></div>","PeriodicalId":22792,"journal":{"name":"The Lancet Regional Health: Western Pacific","volume":"55 ","pages":"Article 101298"},"PeriodicalIF":7.6,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143427887","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Using machine learning algorithms to predict colorectal cancer","authors":"Xingjian Xiao ,&nbsp;Bo Hong ,&nbsp;Kubra Maqsood ,&nbsp;Xiaohan Yi ,&nbsp;Guoqun Xie ,&nbsp;Hailei Zhao ,&nbsp;Bo Sun ,&nbsp;Jianying Mao ,&nbsp;Shiyou Liu ,&nbsp;Xianglong Xu","doi":"10.1016/j.lanwpc.2024.101355","DOIUrl":"10.1016/j.lanwpc.2024.101355","url":null,"abstract":"&lt;div&gt;&lt;h3&gt;Background&lt;/h3&gt;&lt;div&gt;Colorectal cancer (CRC) is the second most common type of cancer in China, with middle-aged and elderly adults being at high risk. However, the colonoscopy examination rate among middle-aged and elderly adults is very low. As of 2020, the colonoscopy examination rate in China was 914.8 per 100,000 people, and the distribution across regions was extremely uneven. Given the high incidence and mortality rates of colorectal cancer and the low screening rate of colonoscopies in the initial screening positive population for colorectal cancer, further interventions will be needed. The objective of this study was to use machine learning and 0.2 million consultation data to predict colorectal cancer and identify important predictors.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Methods&lt;/h3&gt;&lt;div&gt;Our study was based on a population-based cross-sectional survey. We used data from 5,664 cases with colonoscopy results out of 49,701 initial positive consultations in the colorectal cancer screening project in Baoshan District, Shanghai, from 2013 to 2021. Multiple machine learning models including adaptive boosting classifier and gradient boosting machine were established to predict colorectal cancer. In the setting of outcome indicators, patients diagnosed with colorectal cancer through clinical colonoscopy results are considered to have colorectal cancer. An area under the curve (AUC) of each established model exceeding 0.7 was considered acceptable for predicting colorectal cancer. The optimal model was used to identify predictors of colorectal cancer.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Findings&lt;/h3&gt;&lt;div&gt;The incidence of colorectal cancer and the colonoscopy rate is 3.58% (203/5664) and 11.4% (5664/49,701). Non-invasive predictors such as sociodemographic information, behavioural history, and medical history were used to predict the current occurrence of colorectal cancer. In our study, the accuracy of Gradient Boosting Machine, Support Vector Machine, and Light Gradient Boosting Machine reached 0.86, while the accuracy of eXtreme Gradient Boosting reached 0.84 in predicting the occurrence of colorectal cancer. Among the variables predicting colorectal cancer, age, occupation, education, history of bowel cancer in first-degree relatives, history of cholecystitis are important predictors.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Interpretation&lt;/h3&gt;&lt;div&gt;Using machine learning methods and non-invasive predictors can accurately predict colorectal cancer in individuals with positive initial screening results for colorectal cancer. Our machine learning predictive models can provide further risk for colorectal cancer, which may help increase the colonoscopy examination rate among individuals with positive initial screening results. In individuals with positive colorectal cancer screenings, colonoscopy rates are low. Our machine learning models can enhance screening rates, aiding in disease prevention.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Funding&lt;/h3&gt;&lt;div&gt;This study was supported by Health Promotion and Education o","PeriodicalId":22792,"journal":{"name":"The Lancet Regional Health: Western Pacific","volume":"55 ","pages":"Article 101355"},"PeriodicalIF":7.6,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143427544","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Improvement of early gastric cancer detection via a serum-based sequential screening strategy (4S): a prospective large-scale nationwide study in China","authors":"Xianzhu Zhou,&nbsp;Yiqi Du","doi":"10.1016/j.lanwpc.2024.101286","DOIUrl":"10.1016/j.lanwpc.2024.101286","url":null,"abstract":"&lt;div&gt;&lt;h3&gt;Background&lt;/h3&gt;&lt;div&gt;Gastric cancer (GC) is one of the most important cancers that warrant screening. A sequential strategy incorporating risk stratification may identify the minority for further endoscopic examination, and biomarkers of gastric atrophy could serve as effective prescreening tools at a low cost. However, its feasibility and acceptability has yet to be fully validated in real-world settings, and in which scenarios a higher early cancer detection rate can be achieved remains unclear.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Methods&lt;/h3&gt;&lt;div&gt;This multicenter population-based prospective study was conducted in 266 participating institutions throughout China, spanning sites of communities, hospital (outpatient clinics), and physical exam centers, from 2022 to 2024. Adults aged 40–80 years, with or without mild symptoms, meeting the criteria for being at risk of GC, were invited for serological risk evaluation by pepsinogen and gastrin-17 risk panel. Those identified as intermediate or high risk were subsequently recommended for gastroscopy, establishing the serum-based sequential screening strategy (4S) group. Meanwhile, consecutive endoscopic diagnoses were collected in a real-world clinical setting, set as the control group. The rate of gross screening positivity, endoscopic positivity, early cancer detection, and endoscopic compliance, were compared between the two groups, and among the three screening sites within the ‘4S’ group.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Findings&lt;/h3&gt;&lt;div&gt;In the ‘4S' group, 106,088 participants underwent serological risk assessments and 33.0% (34,979/106,068) fell into the medium to high-risk cohort, of which 33.3% (11,660/34,979) invitees underwent gastroscopy as recommended. Meanwhile, 27,764 subjects were included in the control group. The gross screening positive rate in the ‘4S’ group achieved 3.0‰, and gastroscopy uptake increase with risk prescreening scores (OR = 3.96, P &lt; 0.001). When compared to the control group, the implementation of '4S' screening significantly increased the endoscopic positivity rate (2.2% vs. 0.8%, P &lt; 0.001), and doubled the rate of early cancer detection (62.3% vs. 26.9%, P &lt; 0.001). Compared with screening in hospital setting, community-based screening and physical examinations demonstrated superior capacity to detect tumors at an early stage (77.8% and 77.1% vs. 55.0%, P = 0.008 and 0.002), even though more cases of GC were found in the hospital setting (2.6% vs. 0.9% in community and 1.6% in physical exam). Also, the physical exam showed a poor adherence to gastroscopy (20.5% vs. 41.0% in hospital and 32.2% in community). Community or hospital-based screening showed acceptable cost-effective results by health economic analysis.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Interpretation&lt;/h3&gt;&lt;div&gt;‘4S’ strategy stands out as a practical and economical option in China, as well as in countries encountering similar high-risk GC population. Community screening is highly recommended to improve early GC detection. More ","PeriodicalId":22792,"journal":{"name":"The Lancet Regional Health: Western Pacific","volume":"55 ","pages":"Article 101286"},"PeriodicalIF":7.6,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143427726","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Baseline T2-based all-in-one automated deep learning management system for neoadjuvant therapy efficacy and prognosis in locally advanced rectal cancer","authors":"Kui Sun,&nbsp;Siyi Lu,&nbsp;Hao Wang,&nbsp;Wei Fu","doi":"10.1016/j.lanwpc.2024.101398","DOIUrl":"10.1016/j.lanwpc.2024.101398","url":null,"abstract":"<div><h3>Background</h3><div>Current methods for assessing the efficacy of neoadjuvant therapy and predicting patient survival and recurrence risk in locally advanced rectal cancer prior to treatment are limited. This study aimed to develop a multi-module automated deep learning system to evaluate the pathological complete response (pCR) and prognosis of neoadjuvant therapy in patients at baseline.</div></div><div><h3>Methods</h3><div>This multicenter study retrospectively included T2-weighted images from a total of 354 patients with pathologically confirmed locally advanced rectal cancer who received neoadjuvant therapy from 2018 to 2022. The long-term prognosis of patients was also recorded, including overall survival (OS) and disease-free survival (DFS). Center I contained 227 patients as the development cohort, and centers II and III contained 72 and 55 patients as the external test cohorts, respectively. Lesion delineation was performed manually by a radiologist with ten years of experience. Image preprocessing, including N4 bias field correction, resampling, and image normalization, was performed prior to analysis. The study consisted of four main modules; first, an advanced 3D-SwinUNETR segmentation module was constructed and trained using a development cohort. After 15000 iterations, the best model is saved and the corresponding prediction mask is generated. Second, based on the generated prediction masks, three different analysis modules are used. First, a 3D-ResNet-152 model is constructed and trained with the development cohort to predict pCR for patients. Second, based on the 3D-ResNet-152 model framework, quantitative deep features (QDLs) were extracted, and a prediction model was constructed to evaluate pCR through a feature screening method. Third, radiomics features (RFs) are extracted, and a predictive model is constructed to evaluate pCR through feature screening methods. Finally, a fusion model was constructed based on the three modules to assess neoadjuvant therapy efficacy, OS, and DFS. Dice similarity coefficients (DSC) was used to evaluate the segmentation model, Area under the receiver operating characteristic curve (AUC) was used to assess the predictive performance of neoadjuvant efficacy, Kaplan Meier was used for DFS and OS analysis, and Log-rank was used to test for statistical differences.</div></div><div><h3>Findings</h3><div>In the segmentation module, the DSC for the two external cohorts was 0.703±0.020 and 0.698±0.025, respectively. The fusion model demonstrated the best efficacy for assessing pCR, achieving AUCs of 0.756 and 0.751. Log-rank analysis indicated the fusion model's effectiveness in risk-stratifying OS, with p-values of 0.033 and 0.023, and suggested potential stratification for DFS, with p-values of 0.068 and 0.044.</div></div><div><h3>Interpretation</h3><div>This deep learning-based approach can effectively assess the neoadjuvant therapy efficacy and long-term prognosis at baseline.</div></div>","PeriodicalId":22792,"journal":{"name":"The Lancet Regional Health: Western Pacific","volume":"55 ","pages":"Article 101398"},"PeriodicalIF":7.6,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143427817","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Using machine learning algorithms to predict colorectal polyps","authors":"Xingjian Xiao ,&nbsp;Shiyou Liu ,&nbsp;Kubra Maqsood ,&nbsp;Xiaohan Yi ,&nbsp;Guoqun Xie ,&nbsp;Hailei Zhao ,&nbsp;Bo Sun ,&nbsp;Jianying Mao ,&nbsp;Xianglong Xu","doi":"10.1016/j.lanwpc.2024.101356","DOIUrl":"10.1016/j.lanwpc.2024.101356","url":null,"abstract":"&lt;div&gt;&lt;h3&gt;Background&lt;/h3&gt;&lt;div&gt;Colorectal cancer (CRC) is the third most common cancer worldwide, and colorectal polyps (CRP) represent a necessary pathway to the development of CRC. Surveys indicate that the prevalence of colorectal polyps is 20% at age 45, increasing to over 50% to 60% by age 85 globally. In China, the prevalence of colorectal polyps among residents is approximately 18.1%, and there is a certain correlation with age: the older the age, the higher the prevalence. Until now, no studies have been conducted on utilizing non-invasive factors to predict colorectal polyps.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Methods&lt;/h3&gt;&lt;div&gt;Our study was based on a population-based cross-sectional survey. We included data from 5,461 cases with colonoscopy results among 49,701 initial positive consultations in the colorectal cancer screening project conducted in Baoshan District, Shanghai, from 2013 to 2021. Multiple machine learning models including adaptive boosting classifier and gradient boosting machine were established to predict colorectal polyps. In the setting of outcome indicators, patients diagnosed with colorectal polyps through clinical colonoscopy results, pathological findings, and imaging techniques are considered to have colorectal polyps. An area under the curve (AUC) of each established model exceeding 0.7 was considered acceptable for predicting colorectal polyps. The optimal model was used to identify predictors of colorectal polyps.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Findings&lt;/h3&gt;&lt;div&gt;Non-invasive predictors such as sociodemographic information, behavioural history, and medical history were used to predict the current occurrence of colorectal. In our study, the AUC of Random Forest and eXtreme Gradient Boosting reached 0.71, Adaptive Boosting Machine, Gradient Boosting Machine and Light Gradient Boosting Machine reached 0.7 in predicting the occurrence of colorectal cancer. Among the various variables predicting colorectal polyps, age, smoking, gender, cancer history, FOBT (Fecal Occult Blood Test), occupation, and education level are important predictors of colorectal polyps.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Interpretation&lt;/h3&gt;&lt;div&gt;Using non-invasive factors and machine learning algorithms can accurately predict the occurrence of colorectal polyps in individuals with positive initial screening results. In the context of low colonoscopy examination rates, our machine learning predictive models may help prompt patients to undergo further examinations and interventions, thereby improve the earlier diagnosis and treatment. The rate of colonoscopy examinations is very low, even among individuals with positive initial screening results. We propose a machine learning approach that can identify individuals with colorectal polyps in this group, thereby increasing the screening rate for colorectal cancer and helping to prevent the disease.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Funding&lt;/h3&gt;&lt;div&gt;This study was supported by Health Promotion and Education of the Key medical Specialty of Baoshan District, ","PeriodicalId":22792,"journal":{"name":"The Lancet Regional Health: Western Pacific","volume":"55 ","pages":"Article 101356"},"PeriodicalIF":7.6,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143427545","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Closing the gap in dementia research by community-based cohort studies in the Chinese population","authors":"Xiaowen Zhou ,&nbsp;Zhenxu Xiao ,&nbsp;Wanqing Wu ,&nbsp;Yuntao Chen ,&nbsp;Changzheng Yuan ,&nbsp;Yue Leng ,&nbsp;Yao Yao ,&nbsp;Qianhua Zhao ,&nbsp;Albert Hofman ,&nbsp;Eric Brunner ,&nbsp;Ding Ding","doi":"10.1016/j.lanwpc.2025.101465","DOIUrl":"10.1016/j.lanwpc.2025.101465","url":null,"abstract":"<div><div>China accounts for 1/5 of the global population and China faces a particularly heavy dementia burden due to its rapidly ageing population. Unique historical events, genetic background, sociocultural factors, lifestyle, and the COVID-19 pandemic further influence cognitive outcomes in the Chinese population. We searched PubMed, Web of Science, and Embase for community-based cohort studies related to dementia in the Chinese population, and summarized the characteristics, methodologies, and major findings published over the last 25 years from 39 cohorts. We identified critical research gaps and propose future directions, including enhancing sample representativeness, investigating China-specific risk factors, expanding exposure measurements to the whole life-span, collecting objective data, conducting administer-friendly domain-specific cognitive assessments, adopting pathological diagnostic criteria, standardizing biobank construction, verifying multi-modal biomarkers, examining social and genetic-environmental aspects, and monitoring post-COVID cognitive health, to approach high quality of dementia studies that can provide solid evidence to policy making and promote global brain health research.</div></div>","PeriodicalId":22792,"journal":{"name":"The Lancet Regional Health: Western Pacific","volume":"55 ","pages":"Article 101465"},"PeriodicalIF":7.6,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11788756/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143123676","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Highlights of the ESMO Asia Congress 2024","authors":"Jiefang Huang","doi":"10.1016/j.lanwpc.2025.101482","DOIUrl":"10.1016/j.lanwpc.2025.101482","url":null,"abstract":"","PeriodicalId":22792,"journal":{"name":"The Lancet Regional Health: Western Pacific","volume":"55 ","pages":"Article 101482"},"PeriodicalIF":7.6,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143178030","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Regulatory flexibilities balancing unmet needs, benefits and risks in the approvals of imported cancer drugs in China: a cohort study from 2012 to 2021","authors":"Xiangyun Mao ,&nbsp;Jiachen Xu ,&nbsp;Xiaozhen Liu ,&nbsp;Shu Kong ,&nbsp;Yi Li ,&nbsp;Xiaoyin Bai ,&nbsp;Jiaxuan Yang ,&nbsp;Aaron S. Kesselheim ,&nbsp;Guanqiao Li","doi":"10.1016/j.lanwpc.2025.101483","DOIUrl":"10.1016/j.lanwpc.2025.101483","url":null,"abstract":"&lt;div&gt;&lt;h3&gt;Background&lt;/h3&gt;&lt;div&gt;China has historically relied on importing new drugs to fulfill domestic clinical needs. However, stringent requirements for local clinical trials for these imported drugs has often delayed their market approval, restricting timely access for patients. To address this issue, China has implemented regulatory flexibility in certain contexts, allowing for expedited approval processes when appropriate. This study aimed to evaluate the characteristics of novel cancer drugs qualifying for flexible approval in China from 2012 to 2021, focusing on pivotal trials features, clinical benefits, safety profiles, and unmet medical needs.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Methods&lt;/h3&gt;&lt;div&gt;This cohort study identified all newly imported cancer drugs and their indications approved by the China’s National Medical Products Administration (NMPA) from 2012 to 2021. Indications meeting standard requirements were categorized as regular approvals, while those supported by limited clinical data from Chinese patients were classified as flexible approvals. Development strategies, pivotal trials characteristics, and clinical outcomes were extracted from publicly available review documents and drug labels. Unmet medical needs were assessed based on two dimensions: the availability of standard-of-care treatments and the novelty of medicines. We compared the pivotal trial characteristics, efficacy end points, safety (serious adverse events) and the extent of unmet clinical needs, between flexible and regular approvals using Chi-square tests. A random-effects meta-regression was conducted to examine the association between flexible status and hazard ratios (HRs) for overall survival (OS) and progression-free survival (PFS).&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Findings&lt;/h3&gt;&lt;div&gt;Among 59 novel cancer drugs approved for importation to China between 2012 and 2021, 56 products with 92 indications were included in this analysis, based on the availability of their review documents. Of these, 48 indications (52%) qualified for flexible approvals, while 44 indications (48%) received regular approvals. The median number of Chinese patients involved in the datasets for flexible approvals was significantly lower than for regular approvals (27 [IQR, 0–62] vs. 165 [IQR, 99–245], p &lt; 0.001). Flexible approvals were more frequently supported by early-phase (18/61 vs. 1/60, p &lt; 0.001) and single-arm (22/61 vs. 1/60, p &lt; 0.001) pivotal trials, with response rates frequently used as the primary endpoint (24/61 vs. 1/60, p &lt; 0.001). Meta-regression analysis revealed that flexible approvals were associated with improved OS (HR 0.61 vs. 0.72, p &lt; 0.01), and a weaker association for PFS (HR 0.39 vs. 0.51, p = 0.03). The rate of serious adverse events was slightly higher, but not significantly, in the flexible approval group than the regular approval group (43% vs. 35%, p = 0.06). Flexible approvals were more likely to be indicated for diseases with no available existing drugs (31/48 vs.","PeriodicalId":22792,"journal":{"name":"The Lancet Regional Health: Western Pacific","volume":"55 ","pages":"Article 101483"},"PeriodicalIF":7.6,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143178056","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A large-scale prospective nested case-control study: developing a comprehensive risk prediction model for early detection of pancreatic cancer in the community-based ESPRIT-AI cohort","authors":"Chaoliang Zhong ,&nbsp;Penghao Li ,&nbsp;Jia Zhao ,&nbsp;Xue Han ,&nbsp;Beilei Wang ,&nbsp;Gang Jin","doi":"10.1016/j.lanwpc.2024.101310","DOIUrl":"10.1016/j.lanwpc.2024.101310","url":null,"abstract":"<div><h3>Background</h3><div>Pancreatic cancer (PC) remains a significant public health concern due to its late diagnosis and limited effective screening methods. This study aimed to develop a robust risk prediction model for early detection, utilizing a large prospective cohort to ensure generalizability.</div></div><div><h3>Method</h3><div>We established a large-scale, continuous, real-world cohort, termed the Artificial Intelligence-based Early Screening of Pancreatic Cancer and High-Risk Tracing (ESPRIT-AI). This cohort encompasses 12 community health centers in Yangpu District, Shanghai, China. Based on this comprehensive dataset, we conducted a prospective, nested case-control study. Nine centers served as the training cohort, while three centers served as the test cohort. A total of 51,490 participants aged 50-75 years underwent annual health examinations from 2021.1 to 2023.12. The risk-related information and informed consent were collected from all the participants. PC diagnosis was obtained from the Center for Disease Control and Prevention's cancer registry. Model training utilized a 1:20 case-control ratio, employing LASSO regression and expert opinion to select features. Multiple machine learning algorithms were compared, with the best performing algorithm selected for the final predictive model, subsequently validated using a real-world external test cohort. The study was registered with <span><span>ClinicalTrials.gov</span><svg><path></path></svg></span> (NCT04743479).</div></div><div><h3>Findings</h3><div>The cohort was divided into training (n=39,929, including 45 cases and 900 nested controls) and test (n=11,561, including 15 cases and 11,546 controls) sets. Following variable selection, four optimal variables were identified: Body Mass Index (BMI), Fasting Blood Glucose (FBG), Symptom, and Age. Multiple machine learning algorithms were evaluated, with the Random Forest demonstrating superior performance and selected as the final model. In a large-scale, independent real-world test cohort, the model demonstrated a specificity of 97.21% and sensitivity of 33.33%. The model effectively stratified the population, identifying 316 high-risk individuals (2.73% of the test set), among whom 5 were diagnosed with PC. This resulted in a PC prevalence of 1.58% within the high-risk group, representing a 1.93-fold increase compared to the 0.82% prevalence in newly diagnosed diabetes.</div></div><div><h3>Interpretation</h3><div>These findings demonstrated our established model’s capacity to effectively identify a subpopulation with significantly elevated PC risk, potentially facilitating targeted imaging-based early detection strategies, balancing screening benefits and burdens.</div></div><div><h3>Funding</h3><div>This work was funded by the <span>Shanghai Science and Technology Committee</span> Program (grant number 20511101200).</div></div>","PeriodicalId":22792,"journal":{"name":"The Lancet Regional Health: Western Pacific","volume":"55 ","pages":"Article 101310"},"PeriodicalIF":7.6,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143427857","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Trends in burden of liver cancer and underlying etiologies in China, 1990−2021","authors":"Menglong Li ,&nbsp;Huiming He ,&nbsp;Xinyu Zhao ,&nbsp;Mengying Guan ,&nbsp;Nourhan Khattab ,&nbsp;Galal Elshishiney ,&nbsp;Hong You ,&nbsp;Yifei Hu","doi":"10.1016/j.lanwpc.2024.101385","DOIUrl":"10.1016/j.lanwpc.2024.101385","url":null,"abstract":"&lt;div&gt;&lt;h3&gt;Background&lt;/h3&gt;&lt;div&gt;Liver cancer remains a challenging global health issue. In 2020, China accounted for 45.3% of new liver cancer cases worldwide. The high incidence and mortality rates of liver cancer highlight its profound impact, reflected in a mere 12.1% 5-year survival rate in China, posing significant challenges in managing and treating this disease.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Methods&lt;/h3&gt;&lt;div&gt;Data on the number of cases, age-specific rates and age-standardized rates (ASRs) of prevalence, incidence, death, and disability-adjusted life year (DALY) attributed to liver cancer (International Classification of Diseases, 10th revision [ICD-10]: C22.0-22.8 and a proportion of C22.9) and its six etiologies (liver cancer due to hepatitis B, hepatitis C, alcohol use, other causes, NASH and hepatoblastoma) in China between 1990 and 2021 were extracted from the Global Burden of Disease Study 2021. Five-year relative survival rates were estimated using the formula (1–mortality/ incidence) ×100. Temporal trends in liver cancer burden were determined by percent changes and average annual percent change (AAPC). Decomposition analysis was conducted to understand the contributions of population aging, population growth, and epidemiological change to the observed trends.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Findings&lt;/h3&gt;&lt;div&gt;In 2021, the number of liver cancer burden in terms of prevalence, incidence, deaths, and DALYs are 265,539, 196,637, 172,068, and 4,890,023, respectively. The corresponding age-standardized rates were 13.29 (95% UI: 10.75–16.41), 9.52 (95% UI: 7.72–11.78), 8.35 (95% UI: 6.80–10.29), 239.91 (95% UI: 191.98–299.37) per 100,000. The number of prevalence, incidence, deaths and DALYs attributed to liver cancer showed an increasing trend, primarily driven by population aging, then population growth. Meanwhile, decreasing trends were observed in age-standardized death and DALY rates, with AAPCs of –0.32% (95% CI: –0.35% to –0.27%) and –0.79% (95% CI: –0.86% to –0.71%), respectively. Stratified by six etiologies, liver cancer due to hepatitis B, hepatitis C, alcohol use, NASH, and other causes generally showed increasing trends in incidence, prevalence, death and DALYs. Conversely, the burden of hepatoblastoma showed a decreasing trend. The liver cancer burden in 2021 was mainly caused due to hepatitis B, hepatitis C, and alcohol use. Differences in age patterns of liver cancer burden were observed, and the 5-year relative survival rates decreased by age with an overall rate of 12.27%, not far from the goal of 15% by 2030.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Interpretation&lt;/h3&gt;&lt;div&gt;This study provides a comprehensive overview of the liver cancer burden in China across both sexes and underlying etiologies from 1990 to 2021. The incidence, prevalence, death and DALY attributed to liver cancer have shown an increasing trend, primarily driven by population aging, followed by population growth. In 2021, the burden of liver cancer was mainly caused due to hepatitis B, hepatitis C,","PeriodicalId":22792,"journal":{"name":"The Lancet Regional Health: Western Pacific","volume":"55 ","pages":"Article 101385"},"PeriodicalIF":7.6,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143427861","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}