Towards automatic pulmonary nodule management in lung cancer screening with deep learning

The introduction of lung cancer screening programs will produce an unprecedented amount of chest CT scans in the near future, which radiologists will have to read in order to decide on a patient follow-up strategy. According to the current guidelines, the workup of screen-detected nodules strongly relies on nodule size and nodule type. In this paper, we present a deep learning system based on multi-stream multi-scale convolutional networks, which automatically classifies all nodule types relevant for nodule workup. The system processes raw CT data containing a nodule without the need for any additional information such as nodule segmentation or nodule size and learns a representation of 3D data by analyzing an arbitrary number of 2D views of a given nodule. The deep learning system was trained with data from the Italian MILD screening trial and validated on an independent set of data from the Danish DLCST screening trial. We analyze the advantage of processing nodules at multiple scales with a multi-stream convolutional network architecture, and we show that the proposed deep learning system achieves performance at classifying nodule type that surpasses the one of classical machine learning approaches and is within the inter-observer variability among four experienced human observers.Comment: Published on Scientific Report

Protein pathway activation mapping of colorectal metastatic progression reveals metastasis-specific network alterations

The mechanism by which tissue microecology influences invasion and metastasis is largely unknown. Recent studies have indicated differences in the molecular architecture of the metastatic lesion compared to the primary tumor, however, systemic analysis of the alterations within the activated protein signaling network has not been described. Using laser capture microdissection, protein microarray technology, and a unique specimen collection of 34 matched primary colorectal cancers (CRC) and synchronous hepatic metastasis, the quantitative measurement of the total and activated/phosphorylated levels of 86 key signaling proteins was performed. Activation of the EGFR-PDGFR-cKIT network, in addition to PI3K/AKT pathway, was found uniquely activated in the hepatic metastatic lesions compared to the matched primary tumors. If validated in larger study sets, these findings may have potential clinical relevance since many of these activated signaling proteins are current targets for molecularly targeted therapeutics. Thus, these findings could lead to liver metastasis specific molecular therapies for CRC. \uc2\ua9 2012 Springer Science+Business Media Dordrecht

Pleural abnormalities in lung cancer screening trial: prevalence, features, and relation with cancer

Purpose: To describe pleural findings in lung cancer screening participants, to compare asbestos-related pleural findings with self-reported asbestos exposure, and to evaluate relation with lung cancer. Methods and Materials: Pleural abnormalities were reviewed in 2303 baseline low-dose computed tomography (LDCT) and divided into two categories: "specific" associated to pleural plaques or diffuse pleural thickening, and "unspecific" if otherwise. Pleural abnormalities and concomitant parenchymal findings were visually scored according to LDCT features. Self-disclosure of asbestos exposure was collected from each participant. Frequency of lung cancer was detailed according to presence of pleural findings. Statistical analyses included comparison of mean or median, contingency tables, and odds ratio (OR) of lung cancer. Results: 193/2303 (8.4%) participants showed pleural abnormalities, with 27/2303 (1.2%) subjects with specific and 166/2303 (7.2%) with unspecific pleural findings. 42/193(21.2%) showed parenchymal abnormalities, with positive association to specific pleural findings (p=0.02). 150/2303(6.5%) subjects disclosed asbestos exposure,with the highest frequency in subjects with specific pleural findings (6/27; 22.2%). Frequency of lung cancer was similar between subjects with or without pleural abnormalities (p=0.39). Lung cancers were 2/27 (7.4%) in subjects with specific and 5/166 (3.0%) with unspecific pleural findings (p=0.31). Parenchymal abnormalities were significantly associated with risk of lung cancer (OR 12.42). Conclusion: Risk of lung cancer was not related to pleural abnormalities, either specific or unspecific. Parenchymal abnormalities were a risk factor for lung cancer among subjects with pleural abnormalities. The majority of subjects with specific pleural findings were not aware of asbestos exposure. LDCT findings should be integrated in models of lung cancer risk

Prevalence and features of pleural abnormalities in lung cancer screening trial: relation with asbestos exposure and risk of lung cancer

Objectives: To describe pleural findings in lung cancer screening participants, to compare asbestos–related pleural findings with self-reported asbestos exposure, and to evaluate relation with lung cancer. Methods and Materials: Pleural abnormalities were reviewed in 2301 baseline low-dose computed tomography (LDCT) and divided into two categories: “specific” associated to pleural plaques or diffuse pleural thickening, and “unspecific” if otherwise. Pleural abnormalities (Figure 1) and concomitant parenchymal findings (Figure 2) were visually scored. Self- disclosure of asbestos exposure was collected. Frequency of lung cancer was detailed according to presence of pleural findings. Statistical analyses included comparison of mean or median, contingency tables, and odds ratio (OR) of lung cancer. Results: 193/2301 (8.4%) participants showed pleural abnormalities, with 27/2301(1.2%) subjects with specific and 166/2301(7.2%) with unspecific pleural findings. 42/193(21.2%) showed parenchymal abnormalities, with positive association to specific pleural findings (p=0.02). 150/2301(6.5%) subjects disclosed asbestos exposure, with the highest frequency in subjects with specific pleural findings (6/27; 22.2%). Frequency of lung cancer was similar between subjects with or without pleural abnormalities (p=0.39). Lung cancers were 2/27(7.4%) in subjects with specific and 5/166(3.0%) with unspecific pleural findings (p=0.31). Parenchymal abnormalities were significantly associated with risk of lung cancer (OR 12.42). Conclusion: Risk of lung cancer was not related to pleural abnormalities, either specific or unspecific. Parenchymal abnormalities were a risk factor for lung cancer among subjects with pleural abnormalities. The majority of subjects with specific pleural findings were not aware of asbestos exposure. LDCT findings should be integrated in models of lung cancer risk

Correction: Circulating microRNA signature as liquid-biopsy to monitor lung cancer in low-dose computed tomography screening

[This corrects the article DOI: 10.18632/oncotarget.5210.]