Lung cancer risk and cancer-specific mortality in subjects undergoing routine imaging test when stratified with and without identified lung nodule on imaging study

Gómez-Sáez, Noemí; Hernández-Aguado, Ildefonso; Vilar, J.; González‐Álvarez, Isabel; Lorente, María Fermina; Domingo, M.L.; Pastor-Valero, María; Parker, Lucy Anne; Lumbreras, Blanca

doi:10.1007/s00330-015-3775-3

Cited by 15 publications

(19 citation statements)

References 12 publications

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“…The best semantic model was based on size, concavity, contour and spiculation. The non-size based predictor was based location, fissure attachment, lobulation and spiculation, which are known to be related to malignancy (31–36). …”

Section: Resultsmentioning

confidence: 99%

Radiological Image Traits Predictive of Cancer Status in Pulmonary Nodules

Liu

Balagurunathan

Atwater

et al. 2017

Clinical Cancer Research

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Purpose We propose a systematic methodology to quantify incidentally identified pulmonary nodules based on observed radiological traits (semantics) quantified on a point scale and a machine learning method using these data to predict cancer status. Materials and Methods We investigated 172 patients who had low-dose computed tomography (LDCT) images, with 102 and 70 patients grouped into training and validation cohorts, respectively. On the images, 24 radiological traits were systematically scored and a linear classifier was built to relate the traits to malignant status. The model was formed both with and without size descriptors to remove bias due to nodule size. The multivariate pairs formed on the training set was tested on an independent validation data set to evaluate its performance. Results The best four feature set that included a size measurement (Set 1), was short axis, contour, concavity, and texture, which had an area under the receiver operator characteristic curve (AUROC) of 0.88 (Accuracy= 81%, Sensitivity= 76.2%, Specificity= 91.7%). If size measures were excluded, the four best features (Set 2) were: location, fissure attachment, lobulation, and spiculation which had an AUROC of 0.83 (Accuracy= 73.2%, Sensitivity= 73.8%, Specificity= 81.7%) in predicting malignancy in primary nodules. The validation test AUROC was 0.8 (Accuracy=74.3%, Sensitivity =66.7%, Specificity= 75.6%) and 0.74 (Accuracy=71.4%, Sensitivity = 61.9%, Specificity = 75.5%) for Sets 1 and 2, respectively. Conclusions Radiological image traits are useful in predicting malignancy in lung nodules. These semantic traits can be used in combination with size-based measures to enhance prediction accuracy and reducing false positives.

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Section: Resultsmentioning

confidence: 99%

Radiological Image Traits Predictive of Cancer Status in Pulmonary Nodules

Liu

Balagurunathan

Atwater

et al. 2017

Clinical Cancer Research

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“…During the years 2010 and 2011, all patients ≥ 35 years free of lung cancer referred for thoracic imaging evaluation in two general public hospitals in the Valencian Community (Spain) were prospectively collected from the radiological electronic record (a total of 25,529 patients). Eight expert radiologists working in the radiologist departments at the time in both hospitals determined the presence of SPN in the thoracic study of these patients (a total of 893 (3.5%) out of 25,529 examined) [ 20 ]. Information on diagnostic procedures from SPN discovery and lung cancer diagnosis was collected prospectively for 18 months.…”

Section: Methodsmentioning

confidence: 99%

“…The radiologists described nodule characteristics in a predesigned form consisting of: a) size, in mms, and also expressed as mean (sd) in diameter; b) nodule shape, smooth or irregular (lobular or spiculated); c) location, and d) for those patients who underwent a CT, nodule consistency (solid, partly solid, ground glass, calcification or not specified). As we previously reported [ 20 ], for the evaluation of inter-observer agreement in particular aspects such as nodule size, shape or consistency, the first 300 tests included were evaluated independently by the 8 radiologists. For the intra-observer agreement of these characteristics, the radiologists re-evaluated 200 studies at least 6 months after the first report.…”

Section: Methodsmentioning

confidence: 99%

The Fate of Patients with Solitary Pulmonary Nodules: Clinical Management and Radiation Exposure Associated

et al. 2016

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BackgroundThe appropriate management of the large number of lung nodules detected during the course of routine medical care presents a challenge. We aimed to evaluate the usual clinical practice in solitary pulmonary nodule (SPN) management and associated radiation exposure.MethodsWe examined 893 radiology reports of consecutive patients undergoing chest computed tomography (CT) and radiography at two public hospitals in Spain. Information on diagnostic procedures from SPN detection and lung cancer diagnosis was collected prospectively for 18 months.ResultsMore than 20% of patients with SPN detected on either chest radiograph (19.8%) or CT (26.1%) underwent no additional interventions and none developed lung cancer (100% negative predictive value). 346 (72.0%) patients with SPN detected on chest radiograph and 254 (61.5%) patients with SPN detected on CT had additional diagnostic tests and were not diagnosed with lung cancer. In patients undergoing follow-up imaging for SPNs detected on CT median number of additional imaging tests was 3.5 and the mean cumulative effective dose was 24.4 mSv; for those detected on chest radiograph the median number of additional imaging tests was 2.8 and the mean cumulative effective dose was 10.3 mSv.ConclusionsPatients who did not have additional interventions were not diagnosed of lung cancer. There was an excessive amount of interventions in a high percentage of patients presenting SPN, which was associated with an excess of radiation exposure.

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“…For example, Zhao et al used means of generalized estimating equation analysis to compare CT features of resolving nodules with nonresolving nodules (stable and malignant) [ 11 ]. Gomez Saez et al [ 48 ] used the Poisson regression method to calculate the risk and mortality of lung cancer in patients with solitary pulmonary nodules and found that nodule size, spiculation, and other imaging features were associated with lung cancer. Han et al [ 49 ] quantitatively compared the characteristics of pulmonary nodules in CT images to reflect the powerful characteristics of malignant tumors.…”

Section: System Evaluationmentioning

confidence: 99%

Advances in intelligent diagnosis methods for pulmonary ground-glass opacity nodules

et al. 2018

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Pulmonary nodule is one of the important lesions of lung cancer, mainly divided into two categories of solid nodules and ground glass nodules. The improvement of diagnosis of lung cancer has significant clinical significance, which could be realized by machine learning techniques. At present, there have been a lot of researches focusing on solid nodules. But the research on ground glass nodules started late, and lacked research results. This paper summarizes the research progress of the method of intelligent diagnosis for pulmonary nodules since 2014. It is described in details from four aspects: nodular signs, data analysis methods, prediction models and system evaluation. This paper aims to provide the research material for researchers of the clinical diagnosis and intelligent analysis of lung cancer, and further improve the precision of pulmonary ground glass nodule diagnosis.

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Lung cancer risk and cancer-specific mortality in subjects undergoing routine imaging test when stratified with and without identified lung nodule on imaging study

Abstract: • Lung cancer risk is 8 % for SPN detected on routine radiographs. • Lung cancer risk is 12.4 % for SPN detected in routine chest CT. • Smoking, COPD, SPN diameter and edge were predictors of malignancy. • Lung cancer risk of SPN in routine practice seems higher than in screening.

Cited by 15 publications

References 12 publications

Radiological Image Traits Predictive of Cancer Status in Pulmonary Nodules

Radiological Image Traits Predictive of Cancer Status in Pulmonary Nodules

The Fate of Patients with Solitary Pulmonary Nodules: Clinical Management and Radiation Exposure Associated

Advances in intelligent diagnosis methods for pulmonary ground-glass opacity nodules

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