Predicting EGFR mutation status in lung adenocarcinoma presenting as ground-glass opacity: utilizing radiomics model in clinical translation.

OBJECTIVES: This study aimed to establish a non-invasive radiomics model based on computed tomography (CT), with favorable sensitivity and specificity to predict EGFR mutation status in GGO-featured lung adenocarcinoma subsequently guiding the administration of targeted therapy. METHODS: Clinical-pathological information and preoperative CT images of 636 lung adenocarcinoma patients (464, 100, and 72 in the training, internal, and external validation sets, respectively) that underwent GGO lesions resection were included. A total of 1476 radiomics features were extracted with gradient boosting decision tree (GBDT). RESULTS: The established radiomics model containing 102 selected features showed an encouraging discrimination performance of EGFR mutation status (mutant or wild type), and the predictive ability was superior to that of the clinical model (AUC: 0.838 vs. 0.674, 0.822 vs. 0.730, and 0.803 vs. 0.746 for the training, internal validation, and external validation sets, respectively). The combined radiomics plus clinical model showed no additional benefit over the radiomics model in predicting EGFR status (AUC: 0.846 vs. 0.838, 0.816 vs. 0.822, and 0.811 vs. 0.803, respectively, in three cohorts). Uniquely, this model was validated in a cohort of lung adenocarcinoma patients who have undertaken adjuvant EGFR-TKI treatment and harbored unresected GGOs during the medication, leading to a significantly improved potency of EGFR-TKIs (response rate: 25.9% vs. 53.8%, p = 0.006; before and after prediction, respectively). CONCLUSION: This presented radiomics model can be served as a non-invasive and time-saving approach for predicting the EGFR mutation status in lung adenocarcinoma presenting as GGO. KEY POINTS: • We developed a GGO-specific radiomics model containing 102 radiomics features for EGFR mutation status differentiation. • An AUC of 0.822 and 0.803 in the internal and external validation cohorts, respectively, were achieved. • The radiomics model was utilized in clinical translation in an adjuvant EGFR-TKI treatment cohort with unresected GGOs. A significant improvement in the potency of EGFR-TKIs was achieved (response rate: 25.9% vs. 53.8%, p = 0.006; before and after prediction).

Research capacity of global health institutions in China: a gap analysis focusing on their collaboration with other low-income and middle-income countries.

INTRODUCTION: This paper presented qualitative and quantitative data collected on the research capacity of global health institutions in China and aimed to provide a landscaping review of the development of global health as a new discipline in the largest emerging economy of the world. METHODS: Mixed methods were used and they included a bibliometric analysis, a standardised survey and indepth interviews with top officials of 11 selected global health research and educational institutions in mainland China. RESULTS: The bibliometric analysis revealed that each institution had its own focus areas, some with a balanced focus among chronic illness, infectious disease and health systems, while others only focused on one of these areas. Interviews of key staff from each institution showed common themes: recognition that the current research capacity in global health is relatively weak, optimism towards the future, as well as an emphasis on mutual beneficial networking with other countries. Specific obstacles raised and the solutions applied by each institution were listed and discussed. CONCLUSION: Global health institutions in China are going through a transition from learning and following established protocols to taking a more leading role in setting up China's own footprint in this area. Gaps still remain, both in comparison with international institutions, as well as between the leading Chinese institutions and those that have just started. More investment needs to be made, from both public and private domains, to improve the overall capacity as well as the mutual learning and communication within the academic community in China.

Forecasting the long-term trend of COVID-19 epidemic using a dynamic model.

The current outbreak of coronavirus disease 2019 (COVID-19) has recently been declared as a pandemic and spread over 200 countries and territories. Forecasting the long-term trend of the COVID-19 epidemic can help health authorities determine the transmission characteristics of the virus and take appropriate prevention and control strategies beforehand. Previous studies that solely applied traditional epidemic models or machine learning models were subject to underfitting or overfitting problems. We propose a new model named Dynamic-Susceptible-Exposed-Infective-Quarantined (D-SEIQ), by making appropriate modifications of the Susceptible-Exposed-Infective-Recovered (SEIR) model and integrating machine learning based parameter optimization under epidemiological rational constraints. We used the model to predict the long-term reported cumulative numbers of COVID-19 cases in China from January 27, 2020. We evaluated our model on officially reported confirmed cases from three different regions in China, and the results proved the effectiveness of our model in terms of simulating and predicting the trend of the COVID-19 outbreak. In China-Excluding-Hubei area within 7 days after the first public report, our model successfully and accurately predicted the long trend up to 40 days and the exact date of the outbreak peak. The predicted cumulative number (12,506) by March 10, 2020, was only 3·8% different from the actual number (13,005). The parameters obtained by our model proved the effectiveness of prevention and intervention strategies on epidemic control in China. The prediction results for five other countries suggested the external validity of our model. The integrated approach of epidemic and machine learning models could accurately forecast the long-term trend of the COVID-19 outbreak. The model parameters also provided insights into the analysis of COVID-19 transmission and the effectiveness of interventions in China.

Development and validation of an artificial intelligence system for grading colposcopic impressions and guiding biopsies.

BACKGROUND: Colposcopy diagnosis and directed biopsy are the key components in cervical cancer screening programs. However, their performance is limited by the requirement for experienced colposcopists. This study aimed to develop and validate a Colposcopic Artificial Intelligence Auxiliary Diagnostic System (CAIADS) for grading colposcopic impressions and guiding biopsies. METHODS: Anonymized digital records of 19,435 patients were obtained from six hospitals across China. These records included colposcopic images, clinical information, and pathological results (gold standard). The data were randomly assigned (7:1:2) to a training and a tuning set for developing CAIADS and to a validation set for evaluating performance. RESULTS: The agreement between CAIADS-graded colposcopic impressions and pathology findings was higher than that of colposcopies interpreted by colposcopists (82.2% versus 65.9%, kappa 0.750 versus 0.516, p < 0.001). For detecting pathological high-grade squamous intraepithelial lesion or worse (HSIL+), CAIADS showed higher sensitivity than the use of colposcopies interpreted by colposcopists at either biopsy threshold (low-grade or worse 90.5%, 95% CI 88.9-91.4% versus 83.5%, 81.5-85.3%; high-grade or worse 71.9%, 69.5-74.2% versus 60.4%, 57.9-62.9%; all p < 0.001), whereas the specificities were similar (low-grade or worse 51.8%, 49.8-53.8% versus 52.0%, 50.0-54.1%; high-grade or worse 93.9%, 92.9-94.9% versus 94.9%, 93.9-95.7%; all p > 0.05). The CAIADS also demonstrated a superior ability in predicting biopsy sites, with a median mean-intersection-over-union (mIoU) of 0.758. CONCLUSIONS: The CAIADS has potential in assisting beginners and for improving the diagnostic quality of colposcopy and biopsy in the detection of cervical precancer/cancer.

The challenges of colposcopy for cervical cancer screening in LMICs and solutions by artificial intelligence.

BACKGROUND: The World Health Organization (WHO) called for global action towards the elimination of cervical cancer. One of the main strategies is to screen 70% of women at the age between 35 and 45 years and 90% of women managed appropriately by 2030. So far, approximately 85% of cervical cancers occur in low- and middle-income countries (LMICs). The colposcopy-guided biopsy is crucial for detecting cervical intraepithelial neoplasia (CIN) and becomes the main bottleneck limiting screening performance. Unprecedented advances in artificial intelligence (AI) enable the synergy of deep learning and digital colposcopy, which offers opportunities for automatic image-based diagnosis. To this end, we discuss the main challenges of traditional colposcopy and the solutions applying AI-guided digital colposcopy as an auxiliary diagnostic tool in low- and middle- income countries (LMICs). MAIN BODY: Existing challenges for the application of colposcopy in LMICs include strong dependence on the subjective experience of operators, substantial inter- and intra-operator variabilities, shortage of experienced colposcopists, consummate colposcopy training courses, and uniform diagnostic standard and strict quality control that are hard to be followed by colposcopists with limited diagnostic ability, resulting in discrepant reporting and documentation of colposcopy impressions. Organized colposcopy training courses should be viewed as an effective way to enhance the diagnostic ability of colposcopists, but implementing these courses in practice may not always be feasible to improve the overall diagnostic performance in a short period of time. Fortunately, AI has the potential to address colposcopic bottleneck, which could assist colposcopists in colposcopy imaging judgment, detection of underlying CINs, and guidance of biopsy sites. The automated workflow of colposcopy examination could create a novel cervical cancer screening model, reduce potentially false negatives and false positives, and improve the accuracy of colposcopy diagnosis and cervical biopsy. CONCLUSION: We believe that a practical and accurate AI-guided digital colposcopy has the potential to strengthen the diagnostic ability in guiding cervical biopsy, thereby improves cervical cancer screening performance in LMICs and accelerates the process of global cervical cancer elimination eventually.

Tracking development assistance for health from China, 2007-2017.

INTRODUCTION: In recent years, China has increased its international engagement in health. Nonetheless, the lack of data on contributions has limited efforts to examine contributions from China. Existing estimates that track development assistance for health (DAH) from China have relied primarily on one dataset. Furthermore, little is known about the disbursing agencies especially the multilaterals through which contributions are disbursed and how these are changing across time. In this study, we generated estimates of DAH from China from 2007 through 2017 and disaggregated those estimates by disbursing agency and health focus area. METHODS: We identified the major government agencies providing DAH. To estimate DAH provided by each agency, we leveraged publicly available development assistance data in government agencies' budgets and financial accounts, as well as revenue statements from key international development agencies such as the WHO. We reported trends in DAH from China, disaggregated contributions by disbursing bilateral and multilateral agencies, and compared DAH from China with other traditional donors. We also compared these estimates with existing estimates. RESULTS: DAH provided by China grew dramatically, from US$323.1 million in 2007 to $652.3 million in 2017. During this period, 91.8% of DAH from China was disbursed through its bilateral agencies, including the Ministry of Commerce ($3.7 billion, 64.1%) and the National Health Commission ($917.1 million, 16.1%); the other 8.2% was disbursed through multilateral agencies including the WHO ($236.5 million, 4.1%) and the World Bank ($123.1 million, 2.2%). Relative to its level of economic development, China provided substantially more DAH than would be expected. However, relative to population size and government spending, China's contributions are modest. CONCLUSION: In the current context of plateauing in the growth rate of DAH contributions, China has the potential to contribute to future global health financing, especially financing for health system strengthening.