As a result of recent, substantial capacity building, a new landscape for cancer drug trials is emerging in China. However, data on the characteristics of cancer drug trials, and how they have changed over time, are scarce. Based on clinical trials published on the China Food and Drug Administration Registration and Information Disclosure Platform for Drug Clinical Studies, we aimed to systematically review changes over time in clinical trials of cancer drugs in mainland China from 2009 to 2018, to provide insight on the effectiveness of the pharmaceutical industry and identify unmet clinical needs of stakeholders. A total of 1493 trials of 751 newly tested cancer drugs were initiated. Increases over time were observed for the annual number of initiated trials, newly tested drugs, and newly added leading clinical trial units, with a sharp increase after 2016. Of the 1385 trials in which cancer types were identified, solid tumours (325 [23%] trials), non-small-cell lung cancer (232 [17%]), and lymphoma (126 [9%]) were the most common. A markedly uneven distribution was also observed in the geography of leading units with the largest number of leading units located in east China (50 [41%]) and the smallest number located in southwest China (4 [3%]). The growth trends we observed illustrate the progress in and increasing capability of cancer drug research and development achieved in mainland China over the decade from 2009. The low number of clinical trials on tumours with epidemiological characteristics unique to the Chinese population and the unbalanced geographical distribution of leading clinical trial units will provide potential targets for policy makers and other stakeholders. Further research efforts should address cancers uniquely relevant to Chinese populations, globally rare cancers, and the balance between equitable drug access, efficiency, and sustainability of cancer drug research and development in mainland China.
Background Upper gastrointestinal cancers (including oesophageal cancer and gastric cancer) are the most common cancers worldwide. Artificial intelligence platforms using deep learning algorithms have made remarkable progress in medical imaging but their application in upper gastrointestinal cancers has been limited. We aimed to develop and validate the Gastrointestinal Artificial Intelligence Diagnostic System (GRAIDS) for the diagnosis of upper gastrointestinal cancers through analysis of imaging data from clinical endoscopies. Methods This multicentre, case-control, diagnostic study was done in six hospitals of different tiers (ie, municipal, provincial, and national) in China. The images of consecutive participants, aged 18 years or older, who had not had a previous endoscopy were retrieved from all participating hospitals. All patients with upper gastrointestinal cancer lesions (including oesophageal cancer and gastric cancer) that were histologically proven malignancies were eligible for this study. Only images with standard white light were deemed eligible. The images from Sun Yat-sen University Cancer Center were randomly assigned (8:1:1) to the training and intrinsic verification datasets for developing GRAIDS, and the internal validation dataset for evaluating the performance of GRAIDS. Its diagnostic performance was evaluated using an internal and prospective validation set from Sun Yat-sen University Cancer Center (a national hospital) and additional external validation sets from five primary care hospitals. The performance of GRAIDS was also compared with endoscopists with three degrees of expertise: expert, competent, and trainee. The diagnostic accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of GRAIDS and endoscopists for the identification of cancerous lesions were evaluated by calculating the 95% CIs using the Clopper-Pearson method. Findings 1 036 496 endoscopy images from 84 424 individuals were used to develop and test GRAIDS. The diagnostic accuracy in identifying upper gastrointestinal cancers was 0.955 (95% CI 0.952-0.957) in the internal validation set, 0.927 (0.925-0.929) in the prospective set, and ranged from 0.915 (0.913-0.917) to 0.977 (0.977-0.978) in the five external validation sets. GRAIDS achieved diagnostic sensitivity similar to that of the expert endoscopist (0.942 [95% CI 0.924-0.957] vs 0.945 [0.927-0.959]; p=0.692) and superior sensitivity compared with competent (0.858 [0.832-0.880], p<0.0001) and trainee (0.722 [0.691-0.752], p<0.0001) endoscopists. The positive predictive value was 0.814 (95% CI 0.788-0.838) for GRAIDS, 0.932 (0.913-0.948) for the expert endoscopist, 0.974 (0.960-0.984) for the competent endoscopist, and 0.824 (0.795-0.850) for the trainee endoscopist. The negative predictive value was 0.978 (95% CI 0.971-0.984) for GRAIDS, 0.980 (0.974-0.985) for the expert endoscopist, 0.951 (0.942-0.959) for the competent endoscopist, and 0.904 (0.893-0.916) for the trainee endoscopist. Interpretation GRAIDS achieved high diagnostic accuracy in detecting upper gastrointestinal cancers, with sensitivity similar to that of expert endoscopists and was superior to that of non-expert endoscopists. This system could assist community-based hospitals in improving their effectiveness in upper gastrointestinal cancer diagnoses. Copyright (C) 2019 Elsevier Ltd. All rights reserved.
Background The incidence of thyroid cancer is rising steadily because of overdiagnosis and overtreatment conferred by widespread use of sensitive imaging techniques for screening. This overall incidence growth is especially driven by increased diagnosis of indolent and well-differentiated papillary subtype and early-stage thyroid cancer, whereas the incidence of advanced-stage thyroid cancer has increased marginally. Thyroid ultrasound is frequently used to diagnose thyroid cancer. The aim of this study was to use deep convolutional neural network (DCNN) models to improve the diagnostic accuracy of thyroid cancer by analysing sonographic imaging data from clinical ultrasounds. Methods We did a retrospective, multicohort, diagnostic study using ultrasound images sets from three hospitals in China. We developed and trained the DCNN model on the training set, 131 731 ultrasound images from 17 627 patients with thyroid cancer and 180 668 images from 25 325 controls from the thyroid imaging database at Tianjin Cancer Hospital. Clinical diagnosis of the training set was made by 16 radiologists from Tianjin Cancer Hospital. Images from anatomical sites that were judged as not having cancer were excluded from the training set and only individuals with suspected thyroid cancer underwent pathological examination to confirm diagnosis. The model's diagnostic performance was validated in an internal validation set from Tianjin Cancer Hospital (8606 images from 1118 patients) and two external datasets in China (the Integrated Traditional Chinese and Western Medicine Hospital, Jilin, 741 images from 154 patients; and the Weihai Municipal Hospital, Shandong, 11 039 images from 1420 patients). All individuals with suspected thyroid cancer after clinical examination in the validation sets had pathological examination. We also compared the specificity and sensitivity of the DCNN model with the performance of six skilled thyroid ultrasound radiologists on the three validation sets. Findings Between Jan 1, 2012, and March 28, 2018, ultrasound images for the four study cohorts were obtained. The model achieved high performance in identifying thyroid cancer patients in the validation sets tested, with area under the curve values of 0.947 (95% CI 0.935-0.959) for the Tianjin internal validation set, 0.912 (95% CI 0.865-0.958) for the Jilin external validation set, and 0.908 (95% CI 0.891-0.925) for the Weihai external validation set. The DCNN model also showed improved performance in identifying thyroid cancer patients versus skilled radiologists. For the Tianjin internal validation set, sensitivity was 93.4% (95% CI 89.6-96.1) versus 96.9% (93.9-98.6; p=0.003) and specificity was 86.1% (81.1-90.2) versus 59.4% (53.0-65.6; p< 0.0001). For the Jilin external validation set, sensitivity was 84.3% (95% CI 73.6-91.9) versus 92.9% (84.1-97.6; p=0.048) and specificity was 86.9% (95% CI 77.8-93.3) versus 57.1% (45.9-67.9; p< 0.0001). For the Weihai external validation set, sensitivity was 84.7% (95% CI 77.0-90.7) versus 89.0% (81.9-94.0; p=0.25) and specificity was 87.8% (95% CI 81.6-92.5) versus 68.6% (60.7-75.8; p< 0.0001). Interpretation The DCNN model showed similar sensitivity and improved specificity in identifying patients with thyroid cancer compared with a group of skilled radiologists. The improved technical performance of the DCNN model warrants further investigation as part of randomised clinical trials. Copyright (C) 2018 Elsevier Ltd. All rights reserved.