scholarly journals Adaptive Aggregated Attention Network for Pulmonary Nodule Classification

2021 ◽  
Vol 11 (2) ◽  
pp. 610
Author(s):  
Kai Xia ◽  
Jianning Chi ◽  
Yuan Gao ◽  
Yang Jiang ◽  
Chengdong Wu
Keyword(s):  
Lung Cancer ◽  
False Positive ◽  
False Positive Rate ◽  
Pulmonary Nodules ◽  
Attention Mechanism ◽  
Accurate Diagnosis ◽  
Gradient Boosting ◽  
Attention Network ◽  
Multi Scale ◽  
Positive Rate

Lung cancer has one of the highest cancer mortality rates in the world and threatens people’s health. Timely and accurate diagnosis can greatly reduce the number of deaths. Therefore, an accurate diagnosis system is extremely important. The existing methods have achieved significant performances on lung cancer diagnosis, but they are insufficient in fine-grained representations. In this paper, we propose a novel attentive method to differentiate malignant and benign pulmonary nodules. Firstly, the residual attention network (RAN) and squeeze-and-excitation network (SEN) were utilized to extract spatial and contextual features. Secondly, a novel multi-scale attention network (MSAN) was proposed to capture multi-scale attention features automatically, and the MSAN integrated the advantages of the spatial attention mechanism and contextual attention mechanism, which are very important for capturing the salient features of nodules. Finally, the gradient boosting machine (GBM) algorithm was used to differentiate malignant and benign nodules. We conducted a series of experiments on the Lung Image Database Consortium image collection (LIDC-IDRI) database, achieving an accuracy of 91.9%, a sensitivity of 91.3%, a false positive rate of 8.0%, and an F1-score of 91.0%. The experimental results demonstrate that our proposed method outperforms the state-of-the-art methods with respect to accuracy, false positive rate, and F1-Score.

scholarly journals Artificial Intelligence Tools for Refining Lung Cancer Screening

2020 ◽  
Vol 9 (12) ◽  
pp. 3860
Author(s):  
J. Luis Espinoza ◽  
Le Thanh Dong
Keyword(s):  
Artificial Intelligence ◽  
Lung Cancer ◽  
Cancer Screening ◽  
Computer Tomography ◽  
False Positive ◽  
False Positive Rate ◽  
Lung Cancer Screening ◽  
Disease Stage ◽  
Screening Programs ◽  
Positive Rate

Nearly one-quarter of all cancer deaths worldwide are due to lung cancer, making this disease the leading cause of cancer death among both men and women. The most important determinant of survival in lung cancer is the disease stage at diagnosis, thus developing an effective screening method for early diagnosis has been a long-term goal in lung cancer care. In the last decade, and based on the results of large clinical trials, lung cancer screening programs using low-dose computer tomography (LDCT) in high-risk individuals have been implemented in some clinical settings, however, this method has various limitations, especially a high false-positive rate which eventually results in a number of unnecessary diagnostic and therapeutic interventions among the screened subjects. By using complex algorithms and software, artificial intelligence (AI) is capable to emulate human cognition in the analysis, interpretation, and comprehension of complicated data and currently, it is being successfully applied in various healthcare settings. Taking advantage of the ability of AI to quantify information from images, and its superior capability in recognizing complex patterns in images compared to humans, AI has the potential to aid clinicians in the interpretation of LDCT images obtained in the setting of lung cancer screening. In the last decade, several AI models aimed to improve lung cancer detection have been reported. Some algorithms performed equal or even outperformed experienced radiologists in distinguishing benign from malign lung nodules and some of those models improved diagnostic accuracy and decreased the false-positive rate. Here, we discuss recent publications in which AI algorithms are utilized to assess chest computer tomography (CT) scans imaging obtaining in the setting of lung cancer screening.

scholarly journals Incidence Lung Cancer after a Negative CT Screening in the National Lung Screening Trial: Deep Learning-Based Detection of Missed Lung Cancers

2020 ◽  
Vol 9 (12) ◽  
pp. 3908
Author(s):  
Jungheum Cho ◽  
Jihang Kim ◽  
Kyong Joon Lee ◽  
Chang Mo Nam ◽  
Sung Hyun Yoon ◽  
...  
Keyword(s):  
Lung Cancer ◽  
False Positive ◽  
False Positive Rate ◽  
High Sensitivity ◽  
Lung Cancers ◽  
Ct Screening ◽  
National Lung Screening Trial ◽  
Positive Rate ◽  
Lung Screening ◽  
Lung Ct

We aimed to analyse the CT examinations of the previous screening round (CTprev) in NLST participants with incidence lung cancer and evaluate the value of DL-CAD in detection of missed lung cancers. Thoracic radiologists reviewed CTprev in participants with incidence lung cancer, and a DL-CAD analysed CTprev according to NLST criteria and the lung CT screening reporting & data system (Lung-RADS) classification. We calculated patient-wise and lesion-wise sensitivities of the DL-CAD in detection of missed lung cancers. As per the NLST criteria, 88% (100/113) of CTprev were positive and 74 of them had missed lung cancers. The DL-CAD reported 98% (98/100) of the positive screens as positive and detected 95% (70/74) of the missed lung cancers. As per the Lung-RADS classification, 82% (93/113) of CTprev were positive and 60 of them had missed lung cancers. The DL-CAD reported 97% (90/93) of the positive screens as positive and detected 98% (59/60) of the missed lung cancers. The DL-CAD made false positive calls in 10.3% (27/263) of controls, with 0.16 false positive nodules per scan (41/263). In conclusion, the majority of CTprev in participants with incidence lung cancers had missed lung cancers, and the DL-CAD detected them with high sensitivity and a limited false positive rate.

scholarly journals Discovery of Novel Cyclophilin A Ligands Using an H/D Exchange– and Mass Spectrometry–Based Strategy

2010 ◽  
Vol 15 (9) ◽  
pp. 1051-1062 ◽  
Author(s):  
Patrick D. Dearmond ◽  
Graham M. West ◽  
Victor Anbalagan ◽  
Michael J. Campa ◽  
Edward F. Patz ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Lung Cancer ◽  
False Positive ◽  
Single Point ◽  
False Positive Rate ◽  
False Negative ◽  
Cyclophilin A ◽  
Maldi Mass Spectrometry ◽  
Molecular Scaffolds ◽  
Positive Rate

Cyclophilin A (CypA) is an overexpressed protein in lung cancer tumors and as a result is a potential therapeutic and diagnostic target. Described here is use of an H/D exchange– and a matrix assisted laser desorption/ionization (MALDI) mass spectrometry–based assay, termed single-point SUPREX (Stability of Unpurified Proteins from Rates of H/D Exchange), to screen 2 chemical libraries, including the 1280-compound LOPAC library and the 9600-compound DIVERSet library, for binding to CypA. This work represents the first application of single-point SUPREX using a pooled ligand approach, which is demonstrated here to yield screening rates as fast as 6 s/ligand. The false-positive and false-negative rates determined in the current work using a set of control samples were 0% and 9%, respectively. A false-positive rate of 20% was found in screening the actual libraries. Eight novel ligands to CypA were discovered, including 2-(α-naphthoyl)ethyltrimethyl-ammonium iodide, (E)-3-(4-t-Butylphenylsulfonyl)-2-propenenitrile, 3-(N-benzyl-N-isopropyl)amino-1-(naphthalen-2-yl)propan-1-one, cis-diammineplatinum (II) chloride, 1-(3,5-dichlorophenyl)-1H-pyrrole-2,5-dione, N-(3-chloro-1, 4-dioxo-1,4-dihydro-2-naphthalenyl)-N-cyclohexylacetamide, 1-[2-(3,4-dimethoxyphenyl)ethyl]-1H-pyrrole-2,5-dione, and 4-(2-methoxy-4-nitrophenyl)-1-methyl-10-oxa-4-azatricyclo[5.2.1.0~2,6~]dec-8-ene-3,5-dione. These compounds, which had moderate binding affinities to CypA (i.e., Kd values in the low micromolar range), provide new molecular scaffolds that might be useful in the development of CypA-targeted diagnostic imaging or therapeutic agents for lung cancer.

scholarly journals Clinical Utility of a Plasma-Based miRNA Signature Classifier Within Computed Tomography Lung Cancer Screening: A Correlative MILD Trial Study

2014 ◽  
Vol 32 (8) ◽  
pp. 768-773 ◽  
Author(s):  
Gabriella Sozzi ◽  
Mattia Boeri ◽  
Marta Rossi ◽  
Carla Verri ◽  
Paola Suatoni ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Lung Cancer ◽  
Cancer Screening ◽  
Validation Study ◽  
False Positive ◽  
Diagnostic Performance ◽  
False Positive Rate ◽  
Lung Cancer Screening ◽  
Risk Groups ◽  
Positive Rate

Purpose Recent screening trial results indicate that low-dose computed tomography (LDCT) reduces lung cancer mortality in high-risk patients. However, high false-positive rates, costs, and potential harms highlight the need for complementary biomarkers. The diagnostic performance of a noninvasive plasma microRNA signature classifier (MSC) was retrospectively evaluated in samples prospectively collected from smokers within the randomized Multicenter Italian Lung Detection (MILD) trial. Patients and Methods Plasma samples from 939 participants, including 69 patients with lung cancer and 870 disease-free individuals (n = 652, LDCT arm; n = 287, observation arm) were analyzed by using a quantitative reverse transcriptase polymerase chain reaction–based assay for MSC. Diagnostic performance of MSC was evaluated in a blinded validation study that used prespecified risk groups. Results The diagnostic performance of MSC for lung cancer detection was 87% for sensitivity and 81% for specificity across both arms, and 88% and 80%, respectively, in the LDCT arm. For all patients, MSC had a negative predictive value of 99% and 99.86% for detection and death as a result of disease, respectively. LDCT had sensitivity of 79% and specificity of 81% with a false-positive rate of 19.4%. Diagnostic performance of MSC was confirmed by time dependency analysis. Combination of both MSC and LDCT resulted in a five-fold reduction of LDCT false-positive rate to 3.7%. MSC risk groups were significantly associated with survival (χ12 = 49.53; P < .001). Conclusion This large validation study indicates that MSC has predictive, diagnostic, and prognostic value and could reduce the false-positive rate of LDCT, thus improving the efficacy of lung cancer screening.

scholarly journals The detection of lung cancer using massive artificial neural network based on soft tissue technique

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Kishore Rajagopalan ◽  
Suresh Babu
Keyword(s):  
Neural Network ◽  
Lung Cancer ◽  
Artificial Neural Network ◽  
Soft Tissue ◽  
False Positive ◽  
False Positive Rate ◽  
X Ray ◽  
Positive Rate ◽  
Image Set ◽  
Artificial Neural

Abstract Background A proposed computer aided detection (CAD) scheme faces major issues during subtle nodule recognition. However, radiologists have not noticed subtle nodules in beginning stage of lung cancer while a proposed CAD scheme recognizes non subtle nodules using x-ray images. Method Such an issue has been resolved by creating MANN (Massive Artificial Neural Network) based soft tissue technique from the lung segmented x-ray image. A soft tissue image recognizes nodule candidate for feature extortion and classification. X-ray images are downloaded using Japanese society of radiological technology (JSRT) image set. This image set includes 233 images (140 nodule x-ray images and 93 normal x-ray images). A mean size for a nodule is 17.8 mm and it is validated with computed tomography (CT) image. Thirty percent (42/140) abnormal represents subtle nodules and it is split into five stages (tremendously subtle, very subtle, subtle, observable, relatively observable) by radiologists. Result A proposed CAD scheme without soft tissue technique attained 66.42% (93/140) sensitivity and 66.76% accuracy having 2.5 false positives per image. Utilizing soft tissue technique, many nodules superimposed by ribs as well as clavicles have identified (sensitivity is 72.85% (102/140) and accuracy is 72.96% at one false positive rate). Conclusion In particular, a proposed CAD system determine sensitivity and accuracy in support of subtle nodules (sensitivity is 14/42 = 33.33% and accuracy is 33.66%) is statistically higher than CAD (sensitivity is 13/42 = 30.95% and accuracy is 30.97%) scheme without soft tissue technique. A proposed CAD scheme attained tremendously minimum false positive rate and it is a promising technique in support of cancerous recognition due to improved sensitivity and specificity.

scholarly journals The detection of lung cancer using MTANN (Massive Training Artificial Neural Network) based soft tissue technique

2020 ◽  
Author(s):  
Kishore Rajagopalan ◽  
Suresh babu
Keyword(s):  
Lung Cancer ◽  
Soft Tissue ◽  
False Positive ◽  
False Positive Rate ◽  
Computer Aided Detection ◽  
X Ray ◽  
Cad System ◽  
Computer Aided ◽  
Positive Rate ◽  
Image Set

Abstract Background An existing computer aided detection (CAD) scheme faces major issues during subtle nodule recognition. However, radiologists have not noticed subtle nodules in beginning stage of lung cancer. Method In the proposed computer aided detection (CAD) system, this issue has been resolved by creating MTANN based soft tissue technique from the lung segmented x-ray image. X-ray images are downloaded using JSRT(Japanese society of radiological technology) image set. JSRT image set includes 233 images (140 nodule x-ray images and 93 normal x-ray images). A mean size for a nodule is 17.8 mm and it is validated with computed tomography (CT) image. Thirty percent (42/140) abnormal represent subtle nodules and it is split into five stages (tremendously subtle, very subtle, subtle, observable, relatively observable) by radiologists. Result An existing computer aided detection (CAD) scheme attained 66.42% (93/140) sensitivity having 2.5 false positives (FPs) per image. Utilizing MTANN based soft tissue technique, many nodules superimposed by ribs as well as clavicles have identified (sensitivity is 72.85% (102/140) at one false positive rate). Conclusion In particular, proposed computer aided detection (CAD) system using soft tissue technique determine sensitivity in support of subtle nodules (14/42=33.33%) is statistically higher than CAD (13/42=30.95%) scheme without soft tissue technique. A proposed CAD scheme attained tremendously minimum false positive rate and it is a promising technique in support of cancerous recognition.

A problem in diagnosing N3 disease using FDG-PET in patients with lung cancer —High false positive rate with visual assessment—

2004 ◽  
Vol 18 (6) ◽  
pp. 483-488 ◽  
Author(s):  
Masaki Hara ◽  
Norio Shiraki ◽  
Masato Itoh ◽  
Yuta Shibamoto ◽  
Akihiko Iida ◽  
...  
Keyword(s):  
Lung Cancer ◽  
False Positive ◽  
Fdg Pet ◽  
False Positive Rate ◽  
Visual Assessment ◽  
High False Positive Rate ◽  
Positive Rate

scholarly journals Filtering de novo indels in parent-offspring trios

2020 ◽  
Vol 21 (S16) ◽  
Author(s):  
Yongzhuang Liu ◽  
Jian Liu ◽  
Yadong Wang
Keyword(s):  
False Positive ◽  
De Novo ◽  
False Positive Rate ◽  
Gradient Boosting ◽  
Whole Genome ◽  
Sequencing Data ◽  
Clinical Practices ◽  
Computational Approaches ◽  
Exome Sequencing Data ◽  
Positive Rate

Abstract Background Identification of de novo indels from whole genome or exome sequencing data of parent-offspring trios is a challenging task in human disease studies and clinical practices. Existing computational approaches usually yield high false positive rate. Results In this study, we developed a gradient boosting approach for filtering de novo indels obtained by any computational approaches. Through application on the real genome sequencing data, our approach showed it could significantly reduce the false positive rate of de novo indels without a significant compromise on sensitivity. Conclusions The software DNMFilter_Indel was written in a combination of Java and R and freely available from the website at https://github.com/yongzhuang/DNMFilter_Indel.

Sign in / Sign up

Export Citation Format

Share Document