Journal of the National Cancer Center最新文献

Background

Methods

Results

Conclusions

Background

Methods

Results

Conclusion

Objective

To develop a deep learning model to predict lymph node (LN) status in clinical stage IA lung adenocarcinoma patients.

Methods

This diagnostic study included 1,009 patients with pathologically confirmed clinical stage T1N0M0 lung adenocarcinoma from two independent datasets (699 from Cancer Hospital of Chinese Academy of Medical Sciences and 310 from PLA General Hospital) between January 2005 and December 2019. The Cancer Hospital dataset was randomly split into a training cohort (559 patients) and a validation cohort (140 patients) to train and tune a deep learning model based on a deep residual network (ResNet). The PLA Hospital dataset was used as a testing cohort to evaluate the generalization ability of the model. Thoracic radiologists manually segmented tumors and interpreted high-resolution computed tomography (HRCT) features for the model. The predictive performance was assessed by area under the curves (AUCs), accuracy, precision, recall, and F1 score. Subgroup analysis was performed to evaluate the potential bias of the study population.

Results

A total of 1,009 patients were included in this study; 409 (40.5%) were male and 600 (59.5%) were female. The median age was 57.0 years (inter-quartile range, IQR: 50.0–64.0). The deep learning model achieved AUCs of 0.906 (95% CI: 0.873–0.938) and 0.893 (95% CI: 0.857–0.930) for predicting pN0 disease in the testing cohort and a non-pure ground glass nodule (non-pGGN) testing cohort, respectively. No significant difference was detected between the testing cohort and the non-pGGN testing cohort (P = 0.622). The precisions of this model for predicting pN0 disease were 0.979 (95% CI: 0.963–0.995) and 0.983 (95% CI: 0.967–0.998) in the testing cohort and the non-pGGN testing cohort, respectively. The deep learning model achieved AUCs of 0.848 (95% CI: 0.798–0.898) and 0.831 (95% CI: 0.776–0.887) for predicting pN2 disease in the testing cohort and the non-pGGN testing cohort, respectively. No significant difference was detected between the testing cohort and the non-pGGN testing cohort (P = 0.657). The recalls of this model for predicting pN2 disease were 0.903 (95% CI: 0.870–0.936) and 0.931 (95% CI: 0.901–0.961) in the testing cohort and the non-pGGN testing cohort, respectively.

Conclusions

The superior performance of the deep learning model will help to target the extension of lymph node dissection and reduce the ineffective lymph node dissection in early-stage lung adenocarcinoma patients.

Background

A milestone goal of the Healthy China Program (2019–2030) is to achieve 5-year cancer survival at 43.3% for all cancers combined by 2022. To assess the progress towards this target, we analyzed the updated survival for all cancers combined and 25 specific cancer types in China from 2019 to 2021.

Methods

We conducted standardized data collection and quality control for cancer registries across 32 provincial-level regions in China, and included 6,410,940 newly diagnosed cancer patients from 281 cancer registries during 2008–2019, with follow-up data on vital status available until December 2021. We estimated the age-standardized 5-year relative survival overall and by site, age group, and period of diagnosis using the International Cancer Survival Standard Weights, and quantified the survival changes to assess the progress in cancer control.

Results

In 2019–2021, the age-standardized 5-year relative survival for all cancers combined was 43.7% (95% confidence interval [CI], 43.6–43.7). The 5-year relative survival varied by cancer type, ranging from 8.5% (95% CI, 8.2–8.7) for pancreatic cancer to 92.9% (95% CI, 92.4–93.3) for thyroid cancer. Eight cancers had 5-year survival of over 60%, including cancers of the thyroid, breast, testis, bladder, prostate, kidney, uterus, and cervix. The 5-year relative survival was generally lower in males than in females. From 2008 to 2021, we observed significant survival improvements for cancers of the lung, prostate, bone, uterus, breast, cervix, nasopharynx, larynx, and bladder. The most significant improvement was in lung cancer.

Conclusions

Progress in cancer control was evident in China. This highlights the importance of a comprehensive approach to control and prevent cancer.

Objective

To analyze the impact of global population aging on cancer epidemiology, with a focus on the incidence and mortality rates among individuals aged 60 years and above.

Methods

We utilized open-source data, retrieving population age estimates from the United Nations Population Division website. The GLOBOCAN 2020 database provided estimates for cancer cases and deaths in 2020 and 2040, while the Global Burden of Disease 2019 database supplied estimates of new cancer cases worldwide from 2000 to 2019. Inclusion criteria considered individuals aged 60 years and over, focusing on the top five deadliest cancers. The cohort-component method was employed for population prediction, with age-specific incidence and mortality rates estimated for 2020 used to forecast the cancer burden.

Results

In 2021, the global population aged over 60 years accounted for 13.7%, with Europe/North America and Australia/New Zealand having the highest proportions. The older population is predicted to reach 19.2% by 2040. In 2020, of the 19.3 million new cancer cases worldwide, 64% occurred in individuals aged 60 and above, contributing to 71.3% of cancer-related deaths. The five most common cancer sites were the lung, colorectum, prostate, breast, and stomach. Cancer incidence and deaths are projected to rise significantly among older individuals, reaching 20.7 million new cases and 12.7 million deaths by 2040. Older age, tobacco use, dietary factors, alcohol consumption, and high body mass index (BMI) were identified as major risk factors for various cancers in this demographic.

Conclusions

This study reveals a significant rise in cancer incidence and mortality among the elderly due to global population aging. The urgency for targeted interventions in cancer prevention, screening, and treatment for older individuals is emphasized. Despite acknowledged limitations, these findings contribute valuable insights to inform strategies for managing cancer in the elderly amidst evolving demographic trends.

Background

Emerging evidence suggests that cell deaths are involved in tumorigenesis and progression, which may be treated as a novel direction of cancers. Recently, a novel type of programmed cell death, disulfidptosis, was discovered. However, the detailed biological and clinical impact of disulfidptosis and related regulators remains largely unknown.

Methods

In this work, we first enrolled pancancer datasets and performed multi-omics analysis, including gene expression, DNA methylation, copy number variation and single nucleic variation profiles. Then we deciphered the biological implication of disulfidptosis in clear cell renal cell carcinoma (ccRCC) by machine learning. Finally, a novel agent targeting at disulfidptosis in ccRCC was identified and verified.

Results

We found that disulfidptosis regulators were dysregulated among cancers, which could be explained by aberrant DNA methylation and genomic mutation events. Disulfidptosis scores were depressed among cancers and negatively correlated with epithelial mesenchymal transition. Disulfidptosis regulators could satisfactorily stratify risk subgroups in ccRCC, and a novel subtype, DCS3, owning with disulfidptosis depression, insensitivity to immune therapy and aberrant genome instability were identified and verified. Moreover, treating DCS3 with NU1025 could significantly inhibit ccRCC malignancy.

Conclusion

This work provided a better understanding of disulfidptosis in cancers and new insights into individual management based on disulfidptosis.

Background

The incidence of early-onset colorectal cancer (EOCRC), which exhibits differential clinical, pathological, and molecular features compared to late-onset CRC (LOCRC), is rising globally. The potential differential effects of blood glucose on EOCRC compared to LOCRC have not been investigated.

Methods

This study analyzed 374,568 participants from the UK Biobank cohort and 172,809 participants from the Kailuan cohort. The linear associations between blood glucose and EOCRC/LOCRC were estimated using Cox regression models. Restricted cubic spline (RCS) analysis and non-linear Mendelian randomization (MR) analysis using a 70-SNPs genetic instrument for fasting glucose were used to explore the potential non-linear associations.

Results

Participants in the highest quintile of blood glucose had higher overall CRC risk compared to the lowest quintile (HR = 1.10 in the UK Biobank cohort, 95% CI: 1.01–1.21, P-trend = 0.012; HR = 1.23 in the Kailuan cohort, 95% CI: 1.01–1.51, P-trend = 0.036). Elevated glucose (>7.0 mmol/L) was more strongly associated with increased risk of EOCRC (HR = 1.61, 95% CI: 1.07–2.44) than with LOCRC (HR = 1.14, 95% CI: 1.02–1.27) in the UK Biobank cohort (P-heterogeneity = 0.014). Elevated glucose (>7.0 mmol/L) was associated with increased risk of LOCRC (HR = 1.25, 95% CI: 1.04–1.65) in the Kailuan cohort as well. There was no evidence for non-linear associations between blood glucose and risks of EOCRC/LOCRC.

Conclusions

This study showed a positive association between blood glucose and CRC risk in a dose-response manner, particularly for EOCRC, suggesting that tighter glucose control should be a priority for younger age groups.