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The prediction of lung tumour growth is the key to early treatment of lung cancer. However, the lack of intuitive and clear judgments about the future development of the tumour often leads patients to miss the best treatment opportunities. Combining the characteristics of the variational autoencoder and recurrent neural networks, this study proposes a tumour growth prediction via a conditional recurrent variational autoencoder. The proposed model uses a variational autoencoder to reconstruct tumour images at different times. Meanwhile, the recurrent units are proposed to infer the relationship between tumour images according to the chronological order. The different tumour development varies in different patients, patients' condition is adopted to achieve personalised prediction. To solve the problem of blurred results, the authors add the total variation regularisation term into the object function. The proposed method was tested on longitudinal studies, National Lung Screening Trial and cooperative hospital dataset, with three points on lung tumours. The precision, recall, and dice similarity coefficient reach 82.22, 79.89 and 82.49%, respectively. Both quantitative and qualitative experimental results show that the proposed method can produce realistic tumour images. [ABSTRACT FROM AUTHOR]

The prediction of lung tumour growth is the key to early treatment of lung cancer. However, the lack of intuitive and clear judgments about the future development of the tumour often leads patients to miss the best treatment opportunities. Combining the characteristics of the variational autoencoder and recurrent neural networks, this study proposes a tumour growth prediction via a conditional recurrent variational autoencoder. The proposed model uses a variational autoencoder to reconstruct tumour images at different times. Meanwhile, the recurrent units are proposed to infer the relationship between tumour images according to the chronological order. The different tumour development varies in different patients, patients' condition is adopted to achieve personalised prediction. To solve the problem of blurred results, the authors add the total variation regularisation term into the object function. The proposed method was tested on longitudinal studies, National Lung Screening Trial and cooperative hospital dataset, with three points on lung tumours. The precision, recall, and dice similarity coefficient reach 82.22, 79.89 and 82.49%, respectively. Both quantitative and qualitative experimental results show that the proposed method can produce realistic tumour images. [ABSTRACT FROM AUTHOR]

Early identification and classification of pulmonary nodules are essential for improving the survival rates of individuals with lung cancer and are considered to be key requirements for computer-assisted diagnosis. To address this topic, the present study proposed a method for predicting the malignant phenotype of pulmonary nodules based on weighted voting rules. This method used the pulmonary nodule regions of interest as the input data and extracted the features of the pulmonary nodules using the Denoising Auto Encoder, ResNet-18. Moreover, the software also modifies texture and shape features to assess the malignant phenotype of the pulmonary nodules. Based on their classification accuracy (Acc), the different classifiers were assigned to different weights. Finally, an integrated classifier was obtained to score the malignant phenotype of the pulmonary nodules. The present study included training and testing experiments conducted by extracting the corresponding lung nodule image data from the Lung Image Database Consortium-Image Database Resource Initiative. The results of the present study indicated a final classification Acc of 93.10±2.4%, demonstrating the feasibility and effectiveness of the proposed method. This method includes the powerful feature extraction ability of deep learning combined with the ability to use traditional features in image representation. [ABSTRACT FROM AUTHOR]

Background: The objective of this study was to compare three kinds of lymphadenectomy methods along the recurrent laryngeal nerve (RLN) and assess the safety and effectiveness of the new method. Methods: A total of 194 patients with esophageal cancer who underwent minimally invasive esophagectomy (MIE) at our institution from May 2013 to May 2017 were analyzed retrospectively. According to the method of lymphadenectomy along the left RLN, the patients were divided into three groups: 75 cases underwent the conventional method (A group), 80 cases the skeletonized method (B group) and 39 cases the modified Bascule method (C group). The number of dissected lymph nodes and surgical outcomes were recorded and compared to identify differences among the three groups. Results: The frequency of metastasis to the LRLN lymph node was 18.6% among all patients, and 12%, 20% and 28% in groups A, B and C, respectively. The number of harvested lymph nodes (total/chest/LRLN/LRLN+) in group B and group C were significantly greater than that of group A, but not significant between group B and group C. The hoarseness rate in group C was 15.4%, which was lower than the rate in group B (21.3%) and higher than the rate in group A (13.3%), but there was no statistical significance. Conclusions: The new method for lymphadenectomy along the left RLN during MIE in the semi‐prone position is safe and reliable. It provides sufficient lymph node dissection along the left RLN. [ABSTRACT FROM AUTHOR]

Background Several indices have been developed to predict survival of brain metastases ( BM) based on prognostic factors. However, such models were designed for general brain metastases from different kinds of cancers, and prognostic factors vary between cancers and histological subtypes. Recently, studies have indicated that epidermal growth factor receptor ( EGFR) mutation status may be a potential prognostic biological factor in BM from lung adenocarcinoma. Thus, we sought to define the role of EGFR mutation in prognoses and introduce a prognostic model specific for BM from lung adenocarcinoma. Methods Data of 256 patients with BM from lung adenocarcinoma identified with EGFR mutations were collected. Independent prognostic factors were confirmed using a Cox regression model. The new prognostic model was developed based on the results of multivariable analyses. The score of each factor was calculated by six-month survival. Prognostic groups were divided into low, medium, and high risk based on the total scores. The prediction ability of the new model was compared to the three existing models. Results EGFR mutation and Karnofsky performance status were independent prognostic factors and were thus integrated into the new prognostic model. The new model was superior to the three other scoring systems regarding the prediction of three, six, and 12-month survival by pairwise comparison of the area under the curve. Conclusion Our proposed prognostic model specific for BM from lung adenocarcinoma incorporating EGFR mutation status was valid in predicting patient survival. Further verification is warranted, with prospective testing using large sample sizes. [ABSTRACT FROM AUTHOR]

Background: We aimed to identify the factors determining long-term survival after surgical management for bronchial typical carcinoid (TC) and atypical carcinoids (AC) and to compare the clinical outcome of the different surgical strategies used in the two periods of 1980-1994 and 1995-2005. Methods: Records of 82 patients with an initial pathological diagnosis of bronchial carcinoid tumor who underwent surgical management from January 1980 to December 2009 were reviewed. Tumors were classified as TC or AC using the 2004 World Health Organization criteria. Results: There were 60 TC and 22 AC. Surgical procedures included lobectomies, sleeve or bronchoplastic resections, pneumonectomies, wedge resections, and segmental resections. Significantly fewer pneumonectomies and more sleeve and bronchoplastic resections were performed after 1994. The prognosis was more favorable for TC than AC. Comparing lymph node status N0 with N1 + N2, 5- and 10-year survival was 92% and 85% vs. 61% and 41%. No patient with lymph node involvement survived more than 15 years. Conclusions: Tumor subtype and lymph node status have the greatest impact on long-term survival following surgery. AC and/or regional lymph node metastases have the worst prognosis. Formal anatomic and tissue-saving lung resection plus systematic radical mediastinal lymphadenectomy for TC and AC should be standard. [ABSTRACT FROM AUTHOR]