Lung cancer assessment in CT scans: results from a Chinese dedicated cancer hospital

Lung cancer is the leading cause of cancer death in the world. It accounts for nearly 20% of all cancer deaths. In China, lung cancer incidence increases rapidly both in men and women. This causes a series of public problems. It is estimated that from 2015 to 2030, lung cancer mortality in China is likely to increase by nearly 40%. Smoking and air pollution are two significant risk factors for lung cancer. Since lung cancer usually presented at a relatively late stage, the long-term survival rate is still low. The combination of early detection of lung nodules and early treatment of lung cancer can improve the prognosis of lung cancer. The aims of this thesis are first to explore the status of lung cancer in China and to evaluate the characteristics and work-up of pulmonary nodules in a Chinese dedicated cancer hospital. The second aim is to develop lung nodule classification models using CT characteristics suited for the Chinese clinical population. The third aim is to evaluate the feasibility of an automatic nodule detection system in a Chinese lung cancer screening program

Automatic Pulmonary Nodule Detection in CT Scans Using Convolutional Neural Networks Based on Maximum Intensity Projection

Accurate pulmonary nodule detection is a crucial step in lung cancer screening. Computer-aided detection (CAD) systems are not routinely used by radiologists for pulmonary nodule detection in clinical practice despite their potential benefits. Maximum intensity projection (MIP) images improve the detection of pulmonary nodules in radiological evaluation with computed tomography (CT) scans. Inspired by the clinical methodology of radiologists, we aim to explore the feasibility of applying MIP images to improve the effectiveness of automatic lung nodule detection using convolutional neural networks (CNNs). We propose a CNN-based approach that takes MIP images of different slab thicknesses (5 mm, 10 mm, 15 mm) and 1 mm axial section slices as input. Such an approach augments the two-dimensional (2-D) CT slice images with more representative spatial information that helps discriminate nodules from vessels through their morphologies. Our proposed method achieves sensitivity of 92.67% with 1 false positive per scan and sensitivity of 94.19% with 2 false positives per scan for lung nodule detection on 888 scans in the LIDC-IDRI dataset. The use of thick MIP images helps the detection of small pulmonary nodules (3 mm-10 mm) and results in fewer false positives. Experimental results show that utilizing MIP images can increase the sensitivity and lower the number of false positives, which demonstrates the effectiveness and significance of the proposed MIP-based CNNs framework for automatic pulmonary nodule detection in CT scans. The proposed method also shows the potential that CNNs could gain benefits for nodule detection by combining the clinical procedure.Comment: Submitted to IEEE TM

Galvatron: Efficient Transformer Training over Multiple GPUs Using Automatic Parallelism

Transformer models have achieved state-of-the-art performance on various domains of applications and gradually becomes the foundations of the advanced large deep learning (DL) models. However, how to train these models over multiple GPUs efficiently is still challenging due to a large number of parallelism choices. Existing DL systems either rely on manual efforts to make distributed training plans or apply parallelism combinations within a very limited search space. In this approach, we propose Galvatron, a new system framework that incorporates multiple popular parallelism dimensions and automatically finds the most efficient hybrid parallelism strategy. To better explore such a rarely huge search space, we 1) involve a decision tree to make decomposition and pruning based on some reasonable intuitions, and then 2) design a dynamic programming search algorithm to generate the optimal plan. Evaluations on four representative Transformer workloads show that Galvatron could perform automatically distributed training with different GPU memory budgets. Among all evluated scenarios, Galvatron always achieves superior system throughput compared to previous work with limited parallelism

Case Report: Replacement of PD-1 inhibitors with PD-L1 inhibitors in the treatment of squamous non-small-cell lung carcinoma

BackgroundImmune checkpoint inhibitor (ICI)-associated cardiotoxicity is a relatively uncommon immune-related adverse effects (irAEs) with a high mortality rate. There are few recommendations for the replacement of different immune checkpoint inhibitors in domestic and international reports.Case presentationWe report a case of a patient with squamous non-small cell lung carcinoma (squamous NSCLC) who developed cardiotoxicity after being treated with a programmed death-1 (PD-1) inhibitor and then changed to a PD-L1 inhibitor to continue the treatment. A significant benefit was observed after four cycles of immunotherapy, and no further cardiotoxicity occurred after the treatment was started.ConclusionThis case demonstrates that myocardial damage induced by tislelizumab (PD-1 inhibitor) can be improved after switching to sugemalimab (PD-L1 inhibitor) and that antitumor immunotherapy is effective. This result may have important implications for optimizing immunotherapy management regimens in cancer patients

Deep convolutional neural networks for multi-planar lung nodule detection: improvement in small nodule identification

Objective: In clinical practice, small lung nodules can be easily overlooked by radiologists. The paper aims to provide an efficient and accurate detection system for small lung nodules while keeping good performance for large nodules. Methods: We propose a multi-planar detection system using convolutional neural networks. The 2-D convolutional neural network model, U-net++, was trained by axial, coronal, and sagittal slices for the candidate detection task. All possible nodule candidates from the three different planes are combined. For false positive reduction, we apply 3-D multi-scale dense convolutional neural networks to efficiently remove false positive candidates. We use the public LIDC-IDRI dataset which includes 888 CT scans with 1186 nodules annotated by four radiologists. Results: After ten-fold cross-validation, our proposed system achieves a sensitivity of 94.2% with 1.0 false positive/scan and a sensitivity of 96.0% with 2.0 false positives/scan. Although it is difficult to detect small nodules (i.e. < 6 mm), our designed CAD system reaches a sensitivity of 93.4% (95.0%) of these small nodules at an overall false positive rate of 1.0 (2.0) false positives/scan. At the nodule candidate detection stage, results show that a multi-planar method is capable to detect more nodules compared to using a single plane. Conclusion: Our approach achieves good performance not only for small nodules, but also for large lesions on this dataset. This demonstrates the effectiveness and efficiency of our developed CAD system for lung nodule detection. Significance: The proposed system could provide support for radiologists on early detection of lung cancer

EvoMoE: An Evolutional Mixture-of-Experts Training Framework via Dense-To-Sparse Gate

Mixture-of-experts (MoE) is becoming popular due to its success in improving the model quality, especially in Transformers. By routing tokens with a sparse gate to a few experts (i.e., a small pieces of the full model), MoE can easily increase the model parameters to a very large scale while keeping the computation cost in a constant level. Most existing works just initialize some random experts, set a fixed gating strategy (e.g., Top-k), and train the model from scratch in an ad-hoc way. We identify that these MoE models are suffering from the immature experts and unstable sparse gate, which are harmful to the convergence performance. In this paper, we propose an efficient end-to-end MoE training framework called EvoMoE. EvoMoE starts from training one single expert and gradually evolves into a large and sparse MoE structure. EvoMoE mainly contains two phases: the expert-diversify phase to train the base expert for a while and spawn multiple diverse experts from it, and the gate-sparsify phase to learn an adaptive sparse gate and activate a dynamic number of experts. EvoMoE naturally decouples the joint learning of both the experts and the sparse gate and focuses on learning the basic knowledge with a single expert at the early training stage. Then it diversifies the experts and continues to train the MoE with a novel Dense-to-Sparse gate (DTS-Gate). Specifically, instead of using a permanent sparse gate, DTS-Gate begins as a dense gate that routes tokens to all experts, then gradually and adaptively becomes sparser while routes to fewer experts. Evaluations are conducted on three popular models and tasks, including RoBERTa for masked language modeling task, GPT for language modeling task and Transformer for machine translation task. The results show that EvoMoE outperforms existing baselines, including Switch, BASE Layer, Hash Layer and StableMoE

Deep learning-based pulmonary nodule detection:Effect of slab thickness in maximum intensity projections at the nodule candidate detection stage

BACKGROUND AND OBJECTIVE: To investigate the effect of the slab thickness in maximum intensity projections (MIPs) on the candidate detection performance of a deep learning-based computer-aided detection (DL-CAD) system for pulmonary nodule detection in CT scans. METHODS: The public LUNA16 dataset includes 888 CT scans with 1186 nodules annotated by four radiologists. From those scans, MIP images were reconstructed with slab thicknesses of 5 to 50 mm (at 5 mm intervals) and 3 to 13 mm (at 2 mm intervals). The architecture in the nodule candidate detection part of the DL-CAD system was trained separately using MIP images with various slab thicknesses. Based on ten-fold cross-validation, the sensitivity and the F2 score were determined to evaluate the performance of using each slab thickness at the nodule candidate detection stage. The free-response receiver operating characteristic (FROC) curve was used to assess the performance of the whole DL-CAD system that took the results combined from 16 MIP slab thickness settings. RESULTS: At the nodule candidate detection stage, the combination of results from 16 MIP slab thickness settings showed a high sensitivity of 98.0% with 46 false positives (FPs) per scan. Regarding a single MIP slab thickness of 10 mm, the highest sensitivity of 90.0% with 8 FPs/scan was reached before false positive reduction. The sensitivity increased (82.8% to 90.0%) for slab thickness of 1 to 10 mm and decreased (88.7% to 76.6%) for slab thickness of 15-50 mm. The number of FPs was decreasing with increasing slab thickness, but was stable at 5 FPs/scan at a slab thickness of 30 mm or more. After false positive reduction, the DL-CAD system, utilizing 16 MIP slab thickness settings, had the sensitivity of 94.4% with 1 FP/scan. CONCLUSIONS: The utilization of multi-MIP images could improve the performance at the nodule candidate detection stage, even for the whole DL-CAD system. For a single slab thickness of 10 mm, the highest sensitivity for pulmonary nodule detection was reached at the nodule candidate detection stage, similar to the slab thickness usually applied by radiologists

Phytohormones regulate the abiotic stress: An overview of physiological, biochemical, and molecular responses in horticultural crops

Recent changing patterns of global climate have turned out to be a severe hazard to the horticulture crops production. A wide range of biotic and abiotic stresses often affect plants due to their sessile nature. Horticultural crop losses are mainly caused by abiotic factors such as drought, salt, heat, cold, floods, and ultraviolet radiation. For coping up with these adversities, well-developed mechanisms have been evolved in plants, which play a role in perceiving stress signals and enabling optimal growth responses. Interestingly, the use of phytohormones for suppressing the impact of abiotic stress has gained much attention in recent decades. For circumvention of stress at various levels, including physiological, molecular, as well as biochemical, a sophisticated mechanism is reported to be provided by the phytohormones, thus labeling these phytohormones a significant role in plant growth and development. Phytohormones can improves tolerance against abiotic stresses by increasing seed germination, seedling growth, leaf photosynthesis, root growth, and antioxidant enzymes and reducing the accumulation of reactive oxygen species, malonaldehyde, and electrolyte leakage. Recent discoveries highlight the significant role of a variety of phytohormones including melatonin (MEL), Gamma-aminobutyric acid (GABA), jasmonic acid (JA), salicylic acid (SA), brassinosteroids (BRs), and strigolactones (SLs) in abiotic stress tolerance enhancement of horticultural plants. Thus, current review is aimed to summarize the developmental concepts regarding role of phytohormones in abiotic-stress mitigation, mainly in horticultural crops, along with the description of recent studies which identified the role of different phytohormones in stressed environments. Hence, such a review will help in paving the path for sustainable agriculture growth via involvement of phytohormones in enhancement of abiotic stress tolerance of horticultural crops

Lung cancer occurrence attributable to passive smoking among never smokers in China:a systematic review and meta-analysis

Background: Quantifying the occurrence of lung cancer due to passive smoking is a necessary step when forming public health policy. In this study, we estimated the proportion of lung cancer cases attributable to passive smoking among never smokers in China. Methods: Six databases were searched up to July 2019 for original observational studies reporting relative risks (RRs) or odds ratios (ORs) for the occurrence of lung cancer associated with passive smoking in Chinese never smokers. The population attributable fraction (PAF) was then calculated using the combined proportion of lung cancer cases exposed to passive smoking and the pooled ORs from meta-analysis. Data are reported with their 95% confidence intervals. Results: We identified 31 case-control studies of never smokers and no cohort studies. These comprised 9,614 lung cancer cases and 13,093 controls. The overall percentages of lung cancers attributable to passive smoking among never smokers were 15.5% (9.0-21.4%) for 9 population-based studies and 22.7% (16.6-28.3%) for 22 hospital-based studies. The PAFs for women were 17.9% (11.4-24.0%) for the population-based studies and 20.9% (14.7-26.7%) for the hospital-based studies. The PAF for men was only calculable for hospital-based studies, which was 29.0% (95% CI: 8.0-45.2%). Among women, the percentage of lung cancer cases attributable to household exposure (19.5%) was much higher than that due to workplace exposure (7.2%). Conclusions: We conclude that approximately 16% of lung cancer cases among never smokers in China are potentially attributable to passive smoking. This is slightly higher among women (around 18%), with most cases occurring due to household exposure