BACKGROUND: Computed tomography images are easy to misjudge because of their complexity, especially images of solitary pulmonary nodules, of which diagnosis as benign or malignant is extremely important in lung cancer treatment. Therefore, there is an urgent need for a more effective strategy in lung cancer diagnosis. In our study, we aimed to externally validate and revise the Mayo model, and a new model was established. METHODS: A total of 1450 patients from three centers with solitary pulmonary nodules who underwent surgery were included in the study and were divided into training, internal validation, and external validation sets (n = 849, 365, and 236, respectively). External verification and recalibration of the Mayo model and establishment of new logistic regression model were performed on the training set. Overall performance of each model was evaluated using area under receiver operating characteristic curve (AUC). Finally, the model validation was completed on the validation data set. RESULTS: The AUC of the Mayo model on the training set was 0.653 (95% confidence interval [CI]: 0.613-0.694). After re-estimation of the coefficients of all covariates included in the original Mayo model, the revised Mayo model achieved an AUC of 0.671 (95% CI: 0.635-0.706). We then developed a new model that achieved a higher AUC of 0.891 (95% CI: 0.865-0.917). It had an AUC of 0.888 (95% CI: 0.842-0.934) on the internal validation set, which was significantly higher than that of the revised Mayo model (AUC: 0.577, 95% CI: 0.509-0.646) and the Mayo model (AUC: 0.609, 95% CI, 0.544-0.675) (P < 0.001). The AUC of the new model was 0.876 (95% CI: 0.831-0.920) on the external verification set, which was higher than the corresponding value of the Mayo model (AUC: 0.705, 95% CI: 0.639-0.772) and revised Mayo model (AUC: 0.706, 95% CI: 0.640-0.772) (P < 0.001). Then the prediction model was presented as a nomogram, which is easier to generalize. CONCLUSIONS After external verification and recalibration of the Mayo model, the results show that they are not suitable for the prediction of malignant pulmonary nodules in the Chinese population. Therefore, a new model was established by a backward stepwise process. The new model was constructed to rapidly discriminate benign from malignant pulmonary nodules, which could achieve accurate diagnosis of potential patients with lung cancer.
Humans ; Lung ; Lung Neoplasms/diagnostic imaging* ; Multiple Pulmonary Nodules ; Risk Assessment ; Solitary Pulmonary Nodule/diagnostic imaging* ; Tomography, X-Ray Computed

Pulmonary ground glass nodule (GGN) is a term of radiological manifestation, which may be malignant or benign. The management for pulmonary GGN remains controversial. Both Fleischner society and National Comprehensive Cancer Network (NCCN) panel updated the guideline for the management of GGN in 2017. Compared with previous versions, the indication for surgery or biopsy is stricter, and the recommended follow-up interval is prolonged. In clinical practice, the size of GGN component, the size of consolidation component, dynamic change during follow-up and computed tomography (CT) value are the four factors that help surgeons to decide the timing of surgery. There are some misunderstandings for the management of GGN, such as the administration of antibiotics, the use of positron emission tomography-computed tomography (PET-CT), pure GGN adjacent to visceral pleura, and GGN with penetrating vessel. In conclusion, GGN is a kind of slowly growing lesion, which can be followed up safely.
.
Humans ; Positron Emission Tomography Computed Tomography ; Retrospective Studies ; Solitary Pulmonary Nodule ; diagnosis ; diagnostic imaging ; surgery

OBJECTIVE: In order to improve the accuracy of the current pulmonary nodule location detection method based on CT images, reduce the problem of missed detection or false detection, and effectively assist imaging doctors in the diagnosis of pulmonary nodules. METHODS: Propose a novel method for detecting the location of pulmonary nodules based on multiscale convolution. First, image preprocessing methods are used to eliminate the noise and artifacts in lung CT images. Second, multiple adjacent single-frame CT images are selected to be concatenate into multi-frame images, and the feature extraction is carried out through the artificial neural network model U-Net improved by multi-scale convolution to enhanced feature extraction capability for pulmonary nodules of different sizes and shapes, so as to improve the accuracy of feature extraction of pulmonary nodules. Finally, using point detection to improve the loss function of U-Net training process, the accuracy of pulmonary nodule location detection is improved. RESULTS: The accuracy of detecting pulmonary nodules equal or larger than 3 mm and smaller than 3 mm are 98.02% and 96.94% respectively. CONCLUSIONS This method can effectively improve the detection accuracy of pulmonary nodules on CT image sequence, and can better meet the diagnostic needs of pulmonary nodules.
Humans ; Lung Neoplasms/diagnostic imaging* ; Solitary Pulmonary Nodule/diagnostic imaging* ; Tomography, X-Ray Computed ; Neural Networks, Computer

OBJECTIVETo assess inter-observer variations of pulmonary nodule marking in routine clinical chest digital radiograph (DR) softcopy reading by using a lung nodule computer toolkit.

METHODSA total of 601 chest posterior-anterior DR images were randomly selected from routine outpatient screening in Peking Union Medical College Hospital. Two chest radiologists with experience more than ten years were first asked to read the images and mark all suspicious nodules independently by using computer toolkit IQQA-Chest, and to indicate the likelihood for each nodule detected. They were also asked to draw the boundary of the identified nodule manually on an enlarged region of interest, which was instantly analyzed by IQQA-Chest. Two sets of diagnostic reports, including the marked nodules, likelihood, manually drawn boundaries, quantitative measurements, and radiologists' names, were automatically generated and stored by the computer system. One week later, the two radiologists read the same images together by using the same computer toolkit without referring to their previous reading results. Marking procedure was the same except that consensus was reached for each suspicious region. Statistical analysis tools provided in the IQQA-Chest were used to compare all the three sets of reading results.

RESULTSIn the independent readings, Reader 1 detected 409 nodules with a mean diameter of 12.4 mm in 241 patients, and Reader 2 detected 401 nodules with a mean diameter of 12.6 mm in 253 patients. In the consensus reading, a total of 352 nodules with a mean diameter of 12.4 mm were detected in 220 patients. Totally, 42.3% of Reader 1's and 45.1% of Reader 2's marks were confirmed by the consensus reading. About 40% of each reader's marks agreed with the other. There were only 130 (14.4%) out of the total 904 unique nodules were confirmed by both readers and the consensus reading. Moreover, 5.6% (51/904) of the marked regions were rated identical likelihood in all three readings. Statistical analysis showed significant differences between Readers 1 and 2, and between consensus and Reader 2 in determining the likelihood of the marks (P < 0.01), but not between consensus and Reader 1. No significant difference in terms of size was observed in nodule segmentation between either two of the three readings.

CONCLUSIONLarge variations in nodule marking and nodule-likelihood determination but not in nodule size were observed between experts as well as between single-person reading and consensus reading.

Adult ; Aged ; Computers ; Female ; Humans ; Lung Neoplasms ; diagnostic imaging ; Male ; Middle Aged ; Multiple Pulmonary Nodules ; diagnostic imaging ; Observer Variation ; Radiography ; Solitary Pulmonary Nodule ; diagnostic imaging

Low-dose computed tomography (CT) for lung cancer screening has been proven to reduce lung cancer deaths in the screening group compared with the control group. The increasing number of pulmonary nodules being detected by CT scans significantly increase the workload of the radiologists for scan interpretation. Artificial intelligence (AI) has the potential to increase the efficiency of pulmonary nodule discrimination and has been tested in preliminary studies for nodule management. As more and more artificial AI products are commercialized, the consensus statement has been organized in a collaborative effort by Thoracic Surgery Committee, Department of Simulated Medicine, Wu Jieping Medical Foundation to aid clinicians in the application of AI-assisted management for pulmonary nodules.
.
Artificial Intelligence ; China ; Early Detection of Cancer ; Humans ; Lung Neoplasms/diagnostic imaging* ; Multiple Pulmonary Nodules ; Sensitivity and Specificity ; Solitary Pulmonary Nodule

Due to emphasis on early screening for lung cancer, the detection rate of multiple ground glass opacities (GGOs) on computed tomography (CT) image increases in recent years, and research on multifocal adenocarcinomas presented by GGOs has been thriving. It is more common in women and non-smokers and has excellent prognosis both in patients with natural history and after surgery. These clinical features suggest that it is likely to be a distinct disease entity. From the perspective of molecular genetics, lesions in the same individual are likely to have distinct clonal features. Therefore, genetic heterogeneity is the most prominent feature of multifocal pulmonary adenocarcinomas with GGOs. The genetic heterogeneity is expected to assist the diagnosis of multifocal pulmonary adenocarcinoma and intrapulmonary metastasis, and also suggests that genetic testing of the GGO lesions is of great therapeutic significance. Some GGO lesions may harvest the similar clonal feature, which provide new evidence for the theory of spread through air spaces (STAS).
.
Adenocarcinoma ; diagnostic imaging ; genetics ; pathology ; Adenocarcinoma of Lung ; Humans ; Lung Neoplasms ; diagnostic imaging ; genetics ; pathology ; Retrospective Studies ; Solitary Pulmonary Nodule ; diagnostic imaging ; genetics ; pathology ; Tomography, X-Ray Computed

Combining the speed of two-dimensional detection with the precision of three-dimensional detection, an automatic algorithm based on high resolution CT images is proposed to identify nodules in this paper. Nodule candidates are extracted by a two-dimensional convergence index (CI) filter, then a three-dimensional Hessian matrix detection filter is introduced to reduce false positive lung nodules, Experiments show that the algorithm is effective with a sensitivity of 90% and the false positive lung nodules per slice is 0.33.
Algorithms ; False Positive Reactions ; Humans ; Sensitivity and Specificity ; Solitary Pulmonary Nodule ; diagnostic imaging ; Tomography, X-Ray Computed ; methods

OBJECTIVETo assess the features of fluorine-18 fluorodeoxyglucose (FDG) uptake in patients with benign pulmonary nodules.

METHODSFrom October 1998 to July 2004, 47 patients with benign pulmonary nodules were imaged with FDG-positron emission tomography (PET). Diagnoses were confirmed by surgery. FDG-PET data was analyzed by visual method and semi-quantitive method. When pulmonary nodules with abnormal FDG intake appeared in PET scans confirmed by visual method, their maximum and mean standard uptake value (SUVmax and SUVmean) and SUV of normal lung (SUVlung) were measured using semiquantitative method.

RESULTSTwenty-one cases showed nothing abnormal in PET scans, including 17 calcification and fibrosis, 2 hamartomas and 2 sclerosing hemangiomas. 26 pulmonary nodules were detected by FDG-PET (17 active tuberculous, 6 inflammatory pseudotumors, 3 cryptococcosis). FDG uptake of these 26 nodules was higher than that of normal lung (SUVmax, SUVmean and SUVlung were 3.04 +/- 1.65, 2.48 +/- 1.35 and 0.40 +/- 0.07, respectively, P < 0.001). Correlations were not found between FDG uptake and nodule size or SUV of normal lung or age or blood glucose level in these 26 patients (P > 0.05). SUV in 9 cases (9/26, 35%) were beyond 2.5.

CONCLUSIONSSome benign pulmonary nodules were FDG avid.

Adult ; Aged ; Diagnosis, Differential ; Female ; Fluorodeoxyglucose F18 ; pharmacokinetics ; Humans ; Lung Neoplasms ; diagnostic imaging ; Male ; Middle Aged ; Radionuclide Imaging ; Radiopharmaceuticals ; pharmacokinetics ; Retrospective Studies ; Sarcoidosis, Pulmonary ; diagnostic imaging ; Solitary Pulmonary Nodule ; diagnostic imaging ; Tuberculosis, Pulmonary ; diagnostic imaging

OBJECTIVETo investigate the morphological features of benign and malignant solitary pulmonary nodules (SPNs) and explore the radiological evidences for the differentiation of SPNs.

METHODSWith SPN Dicom View software, we analyzed and compared images obtained from 23 patients with malignant SPNs and 22 patients with benign SPNs who received CT scanning with or without contrast medium injection.

RESULTSThe enhancement in malignant SPNs group was significantly higher than in the benign SPNs group (P < 0.0001). The irregular enhancement in malignant SPNs group was significantly higher than in the benign SPNs group (P = 0. 0084). The mean range of enhancement was (45.04 +/- 26.76) HU in malignant SPNs group, which was significantly higher than that in the benign SPNs group [(15.70 +/- 17.84) HU, P = 0.033]. The mean peak enhancement value was (136.09 +/- 41.72) HU in malignant SPNs group, which was significantly higher than in benign SPNs group [ (60.60 +/- 60.27) HU, P = 0.007]. The mean enhancement area was (21.69 +/- 21.01)% in malignant SPNs group and (8.61 +/- 10.83)% in benign SPNs group (P = 0.203).

CONCLUSIONThe enhancement range and peak enhancement value as well as the morphologically irregular enhancement of SPNs may provide useful information in the clinical radiological diagnosis of SPNs.

Adult ; Aged ; Aged, 80 and over ; Diagnosis, Differential ; Female ; Humans ; Lung Neoplasms ; diagnostic imaging ; Male ; Middle Aged ; Sensitivity and Specificity ; Solitary Pulmonary Nodule ; diagnostic imaging ; Tomography, Spiral Computed

How to accurately identify mini-nodules in a large amount of high resolution computed tomography (HRCT) images is always a significant and difficult issue in lung nodule computer-aided detection (CAD). This paper describes a new mini-nodules detection method which is based on a multi-feature tracking algorithm. Our detection method began after running the Da-Jing algorithm and morphological operation to extract the lung region of every HRCT image in a sequence. Once the lung had been extracted, a hybrid algorithm, combining gray threshold and improved template matching, was used to obtain the regions of interest (ROD). Next, several characteristics of each ROI were calculated to identify the final results by using multi-feature tracking throughout the whole HRCT image sequence. The results showed that the proposed method would be of high accuracy with a low occurrence of false positives.
Algorithms ; Diagnosis, Computer-Assisted ; methods ; Humans ; Lung Neoplasms ; diagnostic imaging ; Pattern Recognition, Automated ; methods ; Radiographic Image Interpretation, Computer-Assisted ; Solitary Pulmonary Nodule ; diagnostic imaging ; Tomography, X-Ray Computed