Lung cancer leads to the highest cancer-related morbidity and mortality worldwide. With the development of multi-slice spiral computed tomography technology and the implement of lung cancer screening, more and more lung nodules have been discovered, many of which are multiple pulmonary nodules. These pulmonary nodules are usually diagnosed as multiple primary lung adenocarcinomas from a pathological perspective. For multiple nodules with different imaging features, the preferred treatment methods are different, and the treatment of each lung nodule is still controversial. In recent years, the interactions between multiple lesions and the evolution of the lesions as well as the inter-tumoral and intratumoral homogeneity and heterogeneity of the genomics also arouse attention. Our review gathered the research progress in multiple pulmonary nodules from the points of histopathology, genomics and surgical management.
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Diagnostic Imaging ; Genotype ; Humans ; Multiple Pulmonary Nodules ; diagnostic imaging ; genetics ; therapy

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

BACKGROUND: At present, more and more studies predict invasive adenocarcinoma (IAC) through three-dimensional features of pulmonary nodules, but few studies have confirmed that three-dimensional features have more advantages in diagnosing IAC than traditional two-dimensional features of pulmonary nodules. This study analyzed the differences of chest computed tomography (CT) features between IAC and minimally invasive adenocarcinoma (MIA) from three-dimensional and two-dimensional levels, and compared the ability of diagnosing IAC. The non-invasive adenocarcinoma group includes precursor glandular lesions (PGL) and minimally invasive adenocarcinoma (MIA). METHODS: The clinical data of 1,045 patients with ground glass opacity (GGO) from January to December 2019 were collected. Then the correlation between preoperative CT image characteristics and pathological results were analyzed retrospectively. The independent influencing factors for the identification of IAC were screened out according to two-dimensional and three-dimensional classification by multivariate Logistic regression and the cut-off point for the identification of IAC was found out through the receiver operating characteristic (ROC) curve. At last, the ability of diagnosing IAC was evaluated by Yoden index. RESULTS: The diameter of nodule, the diameter of solid component, the diameter of mediastinal window nodule in two-dimensional factors, and the volume of nodule, the volume of solid part and the average CT value in three-dimensional factors were independent risk factors for the diagnosis of IAC. These factors were arranged by Yoden index: solid partial volume (0.601)>nodule volume (0.536)>solid component diameter (0.525)>nodule diameter (0.518)>mediastinal window nodule diameter (0.488)>proportion of solid component volume (0.471)>1-tumor disappearance ratio (TDR) (0.468)>consolidation tumor ratio (CTR) (0.394)>average CT value (0.380). CONCLUSIONS The CT features of three-dimensional are better than two-dimensional in the diagnosis of IAC, and the size of solid components is better than the overall size of nodules.
Humans ; Lung Neoplasms/pathology* ; Retrospective Studies ; Neoplasm Invasiveness ; Multiple Pulmonary Nodules/diagnostic imaging* ; Adenocarcinoma/pathology*

BACKGROUND: The detection of pulmonary nodules is a key step to achieving the early diagnosis and therapy of lung cancer. Deep learning based Artificial intelligence (AI) presents as the state of the art in the area of nodule detection, however, a validation with clinical data is necessary for further application. Therefore, the aim of this study is to evaluate the performance of AI in the detection of malignant and non-calcified nodules in chest CT. METHODS: Two hundred chest computed tomography (CT) data were randomly selected from a self-built nodule database from Tianjin Medical University General Hospital. Both the pathology confirmed lung cancers and the nodules in the process of follow-up were included. All CTs were processed by AI and the results were compared with that of radiologists retrieved from the original medical reports. The ground truths were further determined by two experienced radiologists. The size and characteristics of the nodules were evaluated as well. The sensitivity and false positive rate were used to evaluate the effectiveness of AI and radiologists in detecting nodules. The McNemar test was used to determine whether there was a significant difference. RESULTS: A total of 889 non-calcified nodules were determined by experts on chest CT, including 133 lung cancers. Of them, 442 nodules were less than 5 mm. The cancer detection rates of AI and radiologists are 100%. The sensitivity of AI on nodule detection was significantly higher than that of radiologists (99.1% vs 43%, P<0.001). The false-positive rate of AI was 4.9 per CT and decreased to 1.5 when nodules less than 5 mm were excluded. CONCLUSIONS AI achieves the detection of all malignancies and improve the sensitivity of pulmonary nodules detection beyond radiologists, with a low false positive rate after excluding small nodules.
Artificial Intelligence ; Deep Learning ; Humans ; Lung Neoplasms ; diagnosis ; diagnostic imaging ; Multiple Pulmonary Nodules ; diagnosis ; diagnostic imaging ; Tomography, X-Ray Computed

A girl, aged 15 years, was admitted due to sudden convulsion once and multiple pulmonary nodules on lung CT. Acrocyanosis or acropachy/toe deformity was not observed. Laboratory examinations showed an increase in hemoglobin (162 g/L) and a reduction in arterial partial pressure of oxygen (61.5 mm Hg). Lung CT showed irregular slightly high-density nodules in the middle lobe of the right lung, and contrast-enhanced CT scan showed obvious enhancement with thick vascular shadow locally. An investigation of medical history revealed that the girl's mother had a history of epistaxis and resection of pulmonary mass and the girl presented with tongue telangiectasia. The girl was diagnosed with hereditary hemorrhagic telangiectasia and pulmonary arteriovenous malformation. she was given interventional embolization therapy. Transcutaneous oxygen saturation reached 98% without oxygen inhalation on the day after surgery. Pulmonary angiography at 3 months after surgery showed the recurrence of pulmonary vascular malformation, and embolization of pulmonary arterial fistula was not performed since the guide wire could not enter the branch artery. There was still a need for long-term follow-up.
Adolescent ; Arteriovenous Fistula ; Arteriovenous Malformations ; Female ; Humans ; Multiple Pulmonary Nodules ; Neoplasm Recurrence, Local ; Pulmonary Artery/diagnostic imaging* ; Seizures

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.
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Artificial Intelligence ; China ; Early Detection of Cancer ; Humans ; Lung Neoplasms/diagnostic imaging* ; Multiple Pulmonary Nodules ; Sensitivity and Specificity ; Solitary Pulmonary Nodule

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

Objective: To investigate the influence of CT reconstruction algorithm, radiation dose, and contrast agent on the stability of radiomic features of pure ground-glass density pulmonary nodules. Methods: A total of 50 pure ground-glass density pulmonary nodules in 35 patients were prospectively selected from Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College in 2018. After reconstructing the original image of the same patient's pulmonary nodules, six sequences of different parameters were obtained. ITK-SNAP software was used to segment different sequences of pure ground-glass density pulmonary nodules. All scanning data were extracted by A. K. software. The radiomic features with good retest reliability were selected by the intraclass correlation coefficient. The statistical software of R language was used to analyze the characteristic parameters. All the radiomic feature values of different sequences were paired and compared. The number of radiomic features changed by acquisition and reconstruction parameters was counted. The influence of different parameters on the reproducibility of pure ground-glass density pulmonary nodules was compared. Results: A total of 391 radiomic features were extracted from 50 cases of pure ground-glass density pulmonary nodules. 320 features with an intraclass correlation coefficient greater than 0.75 were selected for further analysis. By changing the three parameters of CT reconstruction algorithm, radiation dose, and contrast agent simultaneously, the changed radiomic features of the pure ground-glass density pulmonary nodules reach 60.9% (195/320), including 6.7% (1/15) morphological feature, 100.0% (40/40) histogram features, and 58.1% (154/265) texture features. When only one parameter was changed (keeping the other two parameters unchanged), changing the CT reconstruction algorithm, radiation dose, and contrast agent respectively, the changed radiomic features of pure ground-glass density pulmonary nodules were 10.6% (34/320), 30.9% (99/320) and 50.6% (162/320), the difference was statistically significant (P<0.05). When the radiation dose and contrast agent were changed, the radiomic features obtained by the FBP reconstruction algorithm had smaller changes than the features obtained by the 50% ASiR-V algorithm (P=0.001). Conclusions: CT reconstruction algorithm, radiation dose, and contrast agent will affect the radiomic features of pure ground-glass density pulmonary nodules. The contrast agent has the most significant influence on the radiomic features, followed by radiation dose and CT reconstruction algorithm minimum. Compared with morphological features, histogram features and texture features are more likely to be affected by CT reconstruction algorithms, radiation doses, and contrast agents. Compared with the 50% ASiR-V algorithm, the radiomic features obtained by the FBP reconstruction algorithm are less affected by the radiation dose and contrast agent. The influence of these parameters should be fully considered in the radiomic analysis.
Algorithms ; Contrast Media ; Humans ; Lung Neoplasms/diagnostic imaging* ; Multiple Pulmonary Nodules ; Reproducibility of Results ; Tomography, X-Ray Computed/methods*

Objective: To investigate the detection and diagnostic efficacy of chest radiographs for ≤30 mm pulmonary nodules and the factors affecting them, and to compare the level of consistency among readers. Methods: A total of 43 patients with asymptomatic pulmonary nodules who consulted in Cancer Hospital, Chinese Academy of Medical Sciences from 2012 to 2014 and had chest CT and X-ray chest radiographs during the same period were retrospectively selected, and one nodule ≤30 mm was visible on chest CT images in the whole group (total 43 nodules in the whole group). One senior radiologist with more than 20 years of experience in imaging diagnosis reviewed CT images and recording the size, morphology, location, and density of nodules was selected retrospectively. Six radiologists with different levels of experience (2 residents, 2 attending physicians and 2 associate chief physicians independently reviewed the chest images and recorded the time of review, nodule detection, and diagnostic opinion. The CT imaging characteristics of detected and undetected nodules on X images were compared, and the factors affecting the detection of nodules on X-ray images were analyzed. Detection sensitivity and diagnosis accuracy rate of 6 radiologists were calculated, and the level of consistency among them was compared to analyze the influence of radiologists' seniority and reading time on the diagnosis results. Results: The number of nodules detected by all 6 radiologists was 17, with a sensitivity of detection of 39.5%(17/43). The number of nodules detected by ≥5, ≥4, ≥3, ≥2, and ≥1 physicians was 20, 21, 23, 25, and 28 nodules, respectively, with detection sensitivities of 46.5%, 48.8%, 53.5%, 58.1%, and 65.1%, respectively. Reasons for false-negative result of detection on X-ray images included the size, location, density, and morphology of the nodule. The sensitivity of detecting ≤30 mm, ≤20 mm, ≤15 mm, and ≤10 mm nodules was 46.5%-58.1%, 45.9%-54.1%, 36.0%-44.0%, and 36.4% for the 6 radiologists, respectively; the diagnosis accuracy rate was 19.0%-85.0%, 16.7%-6.5%, 18.2%-80.0%, and 0%-75.0%, respectively. The consistency of nodule detection among 6 doctors was good (Kappa value: 0.629-0.907) and the consistency of diagnostic results among them was moderate or poor (Kappa value: 0.350-0.653). The higher the radiologist's seniority, the shorter the time required to read the images. The reading time and the seniority of the radiologists had no significant influence on the detection and diagnosis results (P>0.05). Conclusions: The ability of radiographs to detect lung nodules ≤30 mm is limited, and the ability to determine the nature of the nodules is not sufficient, and the increase in reading time and seniority of the radiologists will not improve the diagnostic accuracy. X-ray film exam alone is not suitable for lung cancer diagnosis.
Humans ; Retrospective Studies ; Solitary Pulmonary Nodule/diagnostic imaging* ; Radiography ; Multiple Pulmonary Nodules/diagnostic imaging* ; Tomography, X-Ray Computed/methods* ; Lung Neoplasms/diagnostic imaging* ; Sensitivity and Specificity ; Radiographic Image Interpretation, Computer-Assisted/methods*

In this paper, we review and summarize some progresses achieved in last few years in computer-aided diagnosis of multi-dimensional medical images for breast tumors, micro-calcification, lung nodules, colonic polyps and coronary arterial diseases.
Breast Neoplasms ; diagnosis ; Calcinosis ; diagnosis ; Colonic Polyps ; diagnosis ; Coronary Artery Disease ; diagnosis ; Diagnosis, Computer-Assisted ; methods ; Diagnostic Imaging ; trends ; Female ; Humans ; Multiple Pulmonary Nodules ; diagnosis