Dataset Information

Differential diagnosis of solitary lung nodules with gene expression ratios.

ABSTRACT: We have developed a new technique that uses the ratios of select gene expression levels to translate complex genomic data into simple clinically relevant tests for the diagnosis and prognosis of cancer. We determined whether select gene pair ratio combinations can be used to detect and diagnose lung cancer with high accuracy and sensitivity.We used gene expression profiling data to train a ratio-based predictor model to discriminate among a set of samples (n = 145 total) composed of normal lung, small cell lung cancer, adenocarcinoma, squamous cell carcinoma, and pulmonary carcinoid (the training set). We then examined the optimal test in an independent set of samples (the test set, n = 122). Finally, we used one aspect of the test to determine whether the gene ratio technique was capable of detecting cancer in specimens from fine-needle aspirations performed ex vivo with normal lung (n = 14) and suspected tumor nodules (n = 15) acquired at our institution.We found that a ratio-based test with 23 genes could be used to classify training set samples with 90% accuracy. This same test was similarly accurate (88%) when applied to the test set of samples. We also found that this test was 87% and 100% accurate at detecting cancer in normal and tumorous fine-needle aspiration specimens, respectively.The gene expression ratio diagnostic technique is likely to aid in the differential diagnosis of solitary lung nodules in patients with suspected cancer and may also prove useful in developing lung cancer screening strategies that incorporate analysis of fine-needle aspiration specimens.

SUBMITTER: Gordon GJ

PROVIDER: S-EPMC2194803 | biostudies-literature | 2006 Sep

REPOSITORIES: biostudies-literature

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Differential diagnosis of solitary lung nodules with gene expression ratios.

Gordon Gavin J GJ Deters Levi A LA Nitz Matthew D MD Lieberman Barry C BC Yeap Beow Y BY Bueno Raphael R

The Journal of thoracic and cardiovascular surgery 20060901 3

<h4>Objective</h4>We have developed a new technique that uses the ratios of select gene expression levels to translate complex genomic data into simple clinically relevant tests for the diagnosis and prognosis of cancer. We determined whether select gene pair ratio combinations can be used to detect and diagnose lung cancer with high accuracy and sensitivity.<h4>Methods</h4>We used gene expression profiling data to train a ratio-based predictor model to discriminate among a set of samples (n = 1 ...[more]

PMID: 16935118

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Project description:ObjectiveTo determine the value of contourlet textural features obtained from solitary pulmonary nodules in two dimensional CT images used in diagnoses of lung cancer.Materials and methodsA total of 6,299 CT images were acquired from 336 patients, with 1,454 benign pulmonary nodule images from 84 patients (50 male, 34 female) and 4,845 malignant from 252 patients (150 male, 102 female). Further to this, nineteen patient information categories, which included seven demographic parameters and twelve morphological features, were also collected. A contourlet was used to extract fourteen types of textural features. These were then used to establish three support vector machine models. One comprised a database constructed of nineteen collected patient information categories, another included contourlet textural features and the third one contained both sets of information. Ten-fold cross-validation was used to evaluate the diagnosis results for the three databases, with sensitivity, specificity, accuracy, the area under the curve (AUC), precision, Youden index, and F-measure were used as the assessment criteria. In addition, the synthetic minority over-sampling technique (SMOTE) was used to preprocess the unbalanced data.ResultsUsing a database containing textural features and patient information, sensitivity, specificity, accuracy, AUC, precision, Youden index, and F-measure were: 0.95, 0.71, 0.89, 0.89, 0.92, 0.66, and 0.93 respectively. These results were higher than results derived using the database without textural features (0.82, 0.47, 0.74, 0.67, 0.84, 0.29, and 0.83 respectively) as well as the database comprising only textural features (0.81, 0.64, 0.67, 0.72, 0.88, 0.44, and 0.85 respectively). Using the SMOTE as a pre-processing procedure, new balanced database generated, including observations of 5,816 benign ROIs and 5,815 malignant ROIs, and accuracy was 0.93.ConclusionOur results indicate that the combined contourlet textural features of solitary pulmonary nodules in CT images with patient profile information could potentially improve the diagnosis of lung cancer.

Project description:BackgroundComputed tomography (CT) has been widely known to be the first choice for the diagnosis of solid solitary pulmonary nodules (SSPNs). However, the smaller the SSPN is, the less the differential CT signs between benign and malignant SSPNs there are, which brings great challenges to their diagnosis. Therefore, this study aimed to investigate the differential CT features between small (≤15 mm) benign and malignant SSPNs with different sizes.MethodsFrom May 2018 to November 2021, CT data of 794 patients with small SSPNs (≤15 mm) were retrospectively analyzed. SSPNs were divided into benign and malignant groups, and each group was further classified into three cohorts: cohort I (diameter ≤6 mm), cohort II (6 mm < diameter ≤8 mm), and cohort III (8 mm < diameter ≤15 mm). The differential CT features of benign and malignant SSPNs in three cohorts were identified. Multivariable logistic regression analyses were conducted to identify independent factors of benign SSPNs.ResultsIn cohort I, polygonal shape and upper-lobe distribution differed significantly between groups (all P<0.05) and multiparametric analysis showed polygonal shape [adjusted odds ratio (OR): 12.165; 95% confidence interval (CI): 1.512-97.872; P=0.019] was the most effective variation for predicting benign SSPNs, with an area under the receiver operating characteristic curve (AUC) of 0.747 (95% CI: 0.640-0.855; P=0.001). In cohort II, polygonal shape, lobulation, pleural retraction, and air bronchogram differed significantly between groups (all P<0.05), and polygonal shape (OR: 8.870; 95% CI: 1.096-71.772; P=0.041) and the absence of pleural retraction (OR: 0.306; 95% CI: 0.106-0.883; P=0.028) were independent predictors of benign SSPNs, with an AUC of 0.778 (95% CI: 0.694-0.863; P<0.001). In cohort III, 12 CT features showed significant differences between groups (all P<0.05) and polygonal shape (OR: 3.953; 95% CI: 1.508-10.361; P=0.005); calcification (OR: 3.710; 95% CI: 1.305-10.551; P=0.014); halo sign (OR: 6.237; 95% CI: 2.838-13.710; P<0.001); satellite lesions (OR: 6.554; 95% CI: 3.225-13.318; P<0.001); and the absence of lobulation (OR: 0.066; 95% CI: 0.026-0.167; P<0.001), air space (OR: 0.405; 95% CI: 0.215-0.764; P=0.005), pleural retraction (OR: 0.297; 95% CI: 0.179-0.493; P<0.001), bronchial truncation (OR: 0.165; 95% CI: 0.090-0.303; P<0.001), and air bronchogram (OR: 0.363; 95% CI: 0.208-0.633; P<0.001) were independent predictors of benign SSPNs, with an AUC of 0.869 (95% CI: 0.840-0.897; P<0.001).ConclusionsCT features vary between SSPNs with different sizes. Clarifying the differential CT features based on different diameter ranges may help to minimize ambiguities and discriminate the benign SSPNs from malignant ones.

Project description:Diffuse cystic lung diseases are characterized by cysts in more than one lung lobe, the cysts originating from various mechanisms, including the expansion of the distal airspaces due to airway obstruction, necrosis of the airway walls, and parenchymal destruction. The progression of these diseases is variable. One essential tool in the evaluation of these diseases is HRCT, because it improves the characterization of pulmonary cysts (including their distribution, size, and length) and the evaluation of the regularity of the cyst wall, as well as the identification of associated pulmonary and extrapulmonary lesions. When combined with clinical and laboratory findings, HRCT is often sufficient for the etiological definition of diffuse lung cysts, avoiding the need for lung biopsy. The differential diagnoses of diffuse cystic lung diseases are myriad, including neoplastic, inflammatory, and infectious etiologies. Pulmonary Langerhans cell histiocytosis, lymphangioleiomyomatosis, lymphocytic interstitial pneumonia, and follicular bronchiolitis are the most common diseases that produce this CT pattern. However, new diseases have been included as potential determinants of this pattern. RESUMO As doenças pulmonares císticas difusas se caracterizam pela presença de cistos envolvendo mais de um lobo pulmonar, que se originam por diversos mecanismos, incluindo dilatação dos espaços aéreos distais por obstrução, necrose das paredes das vias aéreas e destruição do parênquima. Essas doenças apresentam evolução variável. A TCAR é fundamental na avaliação dessas doenças uma vez que permite uma melhor caracterização dos cistos pulmonares, incluindo sua distribuição, tamanho, extensão e regularidade das paredes, assim como a determinação de outras lesões pulmonares e extrapulmonares associadas. Frequentemente a TCAR é suficiente para a definição etiológica dos cistos pulmonares difusos, associada a achados clínicos e laboratoriais, sem a necessidade de realização de biópsia pulmonar. O diagnóstico diferencial das doenças pulmonares císticas difusas é extenso, incluindo etiologias neoplásicas, inflamatórias e infecciosas, sendo as mais frequentes determinantes desse padrão tomográfico a histiocitose pulmonar de células de Langerhans, a linfangioleiomiomatose, a pneumonia intersticial linfocitária e a bronquiolite folicular. Novas etiologias foram incluídas como potenciais determinantes desse padrão.

Dataset Information

Differential diagnosis of solitary lung nodules with gene expression ratios.

Publications

Differential diagnosis of solitary lung nodules with gene expression ratios.

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