Lack of incremental value of three-dimensional measurement in assessing invasiveness for lung cancer.

OBJECTIVES: The aim of this study was to evaluate the performance of consolidation-to-tumour ratio (CTR) and the radiomic models in two- and three-dimensional modalities for assessing radiological invasiveness in early-stage lung adenocarcinoma. METHODS: A retrospective analysis was conducted on patients with early-stage lung adenocarcinoma from Guangdong Provincial People's Hospital and Shenzhen People's Hospital. Manual delineation of pulmonary nodules along the boundary was performed on cross-sectional images to extract radiomic features. Clinicopathological characteristics and radiomic signatures were identified in both cohorts. CTR and radiomic score for every patient were calculated. The performance of CTR and radiomic models were tested and validated in the respective cohorts. RESULTS: A total of 818 patients from Guangdong Provincial People's Hospital were included in the primary cohort, while 474 patients from Shenzhen People's Hospital constituted an independent validation cohort. Both CTR and radiomic score were identified as independent factors for predicting pathological invasiveness. CTR in two- and three-dimensional modalities exhibited comparable results with areas under the receiver operating characteristic curves and were demonstrated in the validation cohort (area under the curve: 0.807 vs 0.826, P = 0.059) Furthermore, both CTR in two- and three-dimensional modalities was able to stratify patients with significant relapse-free survival (P < 0.000 vs P < 0.000) and overall survival (P = 0.003 vs P = 0.001). The radiomic models in two- and three-dimensional modalities demonstrated favourable discrimination and calibration in independent cohorts (P = 0.189). CONCLUSIONS: Three-dimensional measurement provides no additional clinical benefit compared to two-dimensional.

Toward an Expert Level of Lung Cancer Detection and Classification Using a Deep Convolutional Neural Network.

BACKGROUND: Computed tomography (CT) is essential for pulmonary nodule detection in diagnosing lung cancer. As deep learning algorithms have recently been regarded as a promising technique in medical fields, we attempt to integrate a well-trained deep learning algorithm to detect and classify pulmonary nodules derived from clinical CT images. MATERIALS AND METHODS: Open-source data sets and multicenter data sets have been used in this study. A three-dimensional convolutional neural network (CNN) was designed to detect pulmonary nodules and classify them into malignant or benign diseases based on pathologically and laboratory proven results. RESULTS: The sensitivity and specificity of this well-trained model were found to be 84.4% (95% confidence interval [CI], 80.5%-88.3%) and 83.0% (95% CI, 79.5%-86.5%), respectively. Subgroup analysis of smaller nodules (<10 mm) have demonstrated remarkable sensitivity and specificity, similar to that of larger nodules (10-30 mm). Additional model validation was implemented by comparing manual assessments done by different ranks of doctors with those performed by three-dimensional CNN. The results show that the performance of the CNN model was superior to manual assessment. CONCLUSION: Under the companion diagnostics, the three-dimensional CNN with a deep learning algorithm may assist radiologists in the future by providing accurate and timely information for diagnosing pulmonary nodules in regular clinical practices. IMPLICATIONS FOR PRACTICE: The three-dimensional convolutional neural network described in this article demonstrated both high sensitivity and high specificity in classifying pulmonary nodules regardless of diameters as well as superiority compared with manual assessment. Although it still warrants further improvement and validation in larger screening cohorts, its clinical application could definitely facilitate and assist doctors in clinical practice.

[Strategy of selecting 16S rRNA hypervariable regions for metagenome-phylogenetic marker genes based analysis].

The advent of next generation sequencing technology enables parallel analysis of the whole microbial community from multiple samples. Particularly, sequencing 16S rRNA hypervariable tags has become the most efficient and cost-effective method for assessing microbial diversity. Due to its short read length of the 2nd-generation sequencing methods that cannot cover the full 16S rRNA genomic region, specific hypervariable regions or V-regions must be selected to act as the proxy. Over the past decade, selection of V-regions has not been consistent in assessing microbial diversity. Here we evaluated the current strategies of selecting 16S rRNA hypervariable regions for surveying microbial diversity. The environmental condition was considered as one of the important factors for selection of 16S rRNA hypervariable regions. We suggested that a pilot study to test different V-regions is required in bacterial diversity studies based on 16S rRNA genes.

Discovering multiple transcripts of human hepatocytes using massively parallel signature sequencing (MPSS).

BACKGROUND: The liver is the largest human internal organ--it is composed of multiple cell types and plays a vital role in fulfilling the body's metabolic needs and maintaining homeostasis. Of these cell types the hepatocytes, which account for three-quarters of the liver's volume, perform its main functions. To discover the molecular basis of hepatocyte function, we employed Massively Parallel Signature Sequencing (MPSS) to determine the transcriptomic profile of adult human hepatocytes obtained by laser capture microdissection (LCM). RESULTS: 10,279 UniGene clusters, representing 7,475 known genes, were detected in human hepatocytes. In addition, 1,819 unique MPSS signatures matching the antisense strand of 1,605 non-redundant UniGene clusters (such as APOC1, APOC2, APOB and APOH) were highly expressed in hepatocytes. CONCLUSION: Apart from a large number of protein-coding genes, some of the antisense transcripts expressed in hepatocytes could play important roles in transcriptional interference via a cis-/trans-regulation mechanism. Our result provided a comprehensively transcriptomic atlas of human hepatocytes using MPSS technique, which could be served as an available resource for an in-depth understanding of human liver biology and diseases.

[Determination of geographical origins of Chinese medical herbs by NIR and pattern recognition].

Geographical origin of medical herbs is an important factor of the quality of many traditional Chinese herbal medicines. The objective of the present study is to investigate whether NIR spectroscopy coupled with pattern recognition techniques could effectively discriminate geographical origins of medical herbs. Nearest neighbor method (NNM) and a SVM-based multiclass classifier were employed to discriminate 269 Angelicae Dahuricae Radix (ADR) samples from 4 provinces and 380 Salviae Miltiorrhizae Radix (SMR) samples from 6 provinces in China. The multiclass classifier achieves leave-one-out cross-validation accuracy of 99% for (ADR) and 95% (SMR). This classification scheme can be a highly accurate approach to the rapid and nondestructive discrimination of medical herbs of different origins.

[Use of FTIR and pattern recognition to determine geographical origins of Chinese medical herbs].

Geographical origin of medical herbs is an important factor of the quality of many traditional Chinese herbal medicines. The objective of this study is to investigate whether FTIR spectroscopy coupled with pattern recognition techniques could effectively discriminate geographical origins of medical herbs. Nearest neighbor method (NNM) and a SVM-based multiclass classifier were employed to discriminate 269 angelicae dahuricae radix (ADR) samples from 4 provinces in China and 380 salviae miltiorrhizae radix (SMR) samples from 6 provinces. A leave-one-out cross-validation accuracy of 99% was achieved by the multiclass classifier. The study shows this classification scheme can be a highly accurate approach for the discrimination of medical herbs of different origins.

Characterizing the microenvironment surrounding phosphorylated protein sites.

Protein phosphorylation plays an important role in various cellular processes. Due to its high complexity, the mechanism needs to be further studied. In the last few years, many methods have been contributed to this field, but almost all of them investigated the mechanism based on protein sequences around protein sites. In this study, we implement an exploration by characterizing the microenvironment surrounding phosphorylated protein sites with a modified shell model, and obtain some significant properties by the rank-sum test, such as the lack of some classes of residues, atoms, and secondary structures. Furthermore, we find that the depletion of some properties affects protein phosphorylation remarkably. Our results suggest that it is a meaningful direction to explore the mechanism of protein phosphorylation from microenvironment and we expect further findings along with the increasing size of phosphorylation and protein structure data.