Phase II Clinical Study of Three-Dimensional Printed Coplanar Template Combined with CT-Guided Percutaneous Core Needle Biopsy of Pulmonary Nodules in Elderly Patients

Background: As the population age structure gradually ages, more and more elderly people were found to have pulmonary nodules during physical examinations. Most elderly people had underlying diseases such as heart, lung, brain and blood vessels and cannot tolerate surgery. Computed tomography (CT)-guided percutaneous core needle biopsy (CNB) was the first choice for pathological diagnosis and subsequent targeted drugs, immune drugs or ablation treatment. CT-guided percutaneous CNB requires clinicians with rich CNB experience to ensure high CNB accuracy, but it was easy to cause complications such as pneumothorax and hemorrhage. Three-dimensional (3D) printing coplanar template (PCT) combined with CT-guided percutaneous pulmonary CNB biopsy has been used in clinical practice, but there was no prospective, randomized controlled study. Methods: Elderly patients with lung nodules admitted to the Department of Oncology of our hospital from January 2019 to January 2023 were selected. A total of 225 elderly patients were screened, and 30 patients were included after screening. They were randomly divided into experimental group (Group A: 30 cases) and control group (Group B: 30 cases). Group A was given 3D-PCT combined with CT-guided percutaneous pulmonary CNB biopsy, Group B underwent CT-guided percutaneous pulmonary CNB. The primary outcome measure of this study was the accuracy of diagnostic CNB, and the secondary outcome measures were CNB time, number of CNB needles, number of pathological tissues and complications. Results: The diagnostic accuracy of group A and group B was 96.67% and 76.67%, respectively (P = 0.026). There were statistical differences between group A and group B in average CNB time (P = 0.001), number of CNB (1 vs more than 1, P = 0.029), and pathological tissue obtained by CNB (3 vs 1, P = 0.040). There was no statistical difference in the incidence of pneumothorax and hemorrhage between the two groups (P > 0.05). Conclusions: 3D-PCT combined with CT-guided percutaneous CNB can improve the puncture accuracy of elderly patients, shorten the puncture time, reduce the number of punctures, and increase the amount of puncture pathological tissue, without increasing pneumothorax and hemorrhage complications. We look forward to verifying this in a phase III randomized controlled clinical study. .

Advancements in Medication Rule for Pulmonary Nodules: A Review of Current Research Progress

This paper reviewed the literature on medication rule of pulmonary nodules in recent years. It is found that contemporary doctors pay more attention to regulating Qi, clearing heat and detoxifying, eliminating phlegm, dissolving phlegm and dissipating masses. They use mild drugs, cold and warm treatments in parallel, combining the tastes of pungent, bitterness, and sweetness at the same time. The treatment focuses on the five viscera with emphasis on the lung meridian while also considering the spleen and stomach functions as well as soothing liver stagnation. This information aims to provide some reference for clinical treatment of pulmonary nodules.

Genetic susceptibility loci of lung cancer are associated with malignant risk of pulmonary nodules and improve malignancy diagnosis based on CEA levels

Objective:The heightened prevalence of pulmonary nodules(PN)has escalated its significance as a public health concern.While the precise identification of high-risk PN carriers for malignancy remains an ongoing challenge,genetic variants hold potentials as determinants of disease susceptibility that can aid in diagnosis.Yet,current understanding of the genetic loci associated with malignant PN(MPN)risk is limited.Methods:A frequency-matched case-control study was performed,comprising 247 MPN cases and 412 benign NP(BNP)controls.We genotyped 11 established susceptibility loci for lung cancer in a Chinese cohort.Loci associated with MPN risk were utilized to compute a polygenic risk score(PRS).This PRS was subsequently incorporated into the diagnostic evaluation of MPNs,with emphasis on serum tumor biomarkers.Results:Loci rs10429489G>A,rs17038564A>G,and rs12265047A>G were identified as being associated with an increased risk of MPNs.The PRS,formulated from the cumulative risk effects of these loci,correlated with the malignant risk of PNs in a dose-dependent fashion.A high PRS was found to amplify the MPN risk by 156%in comparison to a low PRS[odds ratio(OR)=2.56,95%confidence interval(95%CI),1.40−4.67].Notably,the PRS was observed to enhance the diagnostic accuracy of serum carcinoembryonic antigen(CEA)in distinguishing MPNs from BPNs,with diagnostic values rising from 0.716 to 0.861 across low-to high-PRS categories.Further bioinformatics investigations pinpointed rs10429489G>A as an expression quantitative trait locus.Conclusions:Loci rs10429489G>A,rs17038564A>G,and rs12265047A>G contribute to MPN risk and augment the diagnostic precision for MPNs based on serum CEA concentrations.

Efficacy of thoracoscopic anatomical segmentectomy for small pulmonary nodules

BACKGROUND Small pulmonary nodules are tissue shadows and thoracoscopic segmentectomy in China is still at the exploratory stage with limited application.AIM To evaluate the efficacy of thoracoscopic anatomical segmentectomy for small pulmonary nodules.METHODS Medical records of 86 patients with small pulmonary nodules treated at our hospital between August 2016 and October 2019 were retrospectively analyzed;40 cases who underwent thoracoscopic lobectomy were set as a reference group,and 46 cases who underwent thoracoscopic anatomical segmentectomy were set as an observation group.Preoperative and postoperative parameters were measured in both groups,including the percentage of forced expiratory volume in the first second(FEV1%),the percentage of forced vital capacity(FVC%),and the FEV1/FVC ratio(FEV1/FVC).Patients with positive pathological diagnosis received tests for neuron-specific enolase,carbohydrate antigen 125(CA125),CA19-9,and squamous cell carcinoma antigen.Intraoperative bleeding volume,drainage volume,the number of dissected lymph nodes,drainage time,hospital stay,treatment cost,postoperative complications,and postoperative pain condition were compared between the two groups.RESULTS No significant difference was observed in the results of four serum tumor marker(CA125,CA19-9,squamous cell carcinoma antigen,and neuron-specific enolase),the number of dissected lymph nodes,treatment cost,or preoperative pulmonary ventilation index between the two groups.Intraoperative bleeding volume,drainage volume,drainage time,hospital stay,and visual analogue scale score were significantly lower in the observation group(P<0.05).The results of FEV1%,FVC%,and FEV1/FVC were significantly higher in the observation group(P<0.05).CONCLUSION The efficacy of thoracoscopic anatomical segmentectomy and lobectomy for small pulmonary nodules shows no significant difference in terms of lesion removal,but anatomical segmentectomy is less invasive with fewer postoperative complications and less influence on lung function.

Role of the texture features of images in the diagnosis of solitary pulmonary nodules in different sizes

Objective： To explore the role of the texture features of images in the diagnosis of solitary pulmonary nodules （SPNs） in different sizes. Materials and methods： A total of 379 patients with pathologically confirmed SPNs were enrolled in this study. They were divided into three groups based on the SPN sizes： ≤10, 11-20, and 〉20 mm. Their texture features were segmented and extracted. The differences in the image features between benign and malignant SPNs were compared. The SPNs in these three groups were determined and analyzed with the texture features of images. Results： These 379 SPNs were successfully segmented using the 2D Otsu threshold method and the self-adaptive threshold segmentation method. The texture features of these SPNs were obtained using the method of grey level co-occurrence matrix （GLCM）. Of these 379 patients, 120 had benign SPNs and 259 had malignant SPNs. The entropy, contrast, energy, homogeneity, and correlation were 3.5597±0.6470, 0.5384±0.2561, 0.1921±0.1256, 0.8281±0.0604, and 0.8748±0.0740 in the benign SPNs and 3.8007±0.6235, 0.6088±0.2961, 0.1673±0.1070, 0.7980±0.0555, and 0.8550±0.0869 in the malignant SPNs （all P〈0.05）. The sensitivity, specificity, and accuracy of the texture features of images were 83.3%, 90.0%, and 86.8%, respectively, for SPNs sized 〈10 mm, and were 86.6%, 88.2%, and 87.1%, respectively, for SPNs sized 11-20 mm and 94.7%, 91.8%, and 93.9%, respectively, for SPNs sized 〉20 mm. Conclusions： The entropy and contrast of malignant pulmonary nodules have been demonstrated to be higher in comparison to those of benign pulmonary nodules, while the energy, homogeneity correlation of malignant pulmonary nodules are lower than those of benign pulmonary nodules. The texture features of images can reflect the tissue features and have high sensitivity, specificity, and accuracy in differentiating SPNs. The sensitivity and accuracy increase for larger SPNs.

Comparison of three mathematical prediction models in patients with a solitary pulmonary nodule

Background： Effective methods for managing patients with solitary pulmonary nodules（SPNs） depend critically on the predictive probability of malignancy.Methods： Between July 2009 and June 2011, data on gender, age, cancer history, tumor familial history, smoking status, tumor location, nodule size, spiculation, calcification, the tumor border, and the final pathological diagnosis were collected retrospectively from 154 surgical patients with an SPN measuring 3-30 mm. Each final diagnosis was compared with the probability calculated by three predicted models—the Mayo, VA, and Peking University（PU） models. The accuracy of each model was assessed using area under the receiver operating characteristics（ROC） and calibration curves.Results： The area under the ROC curve of the PU model [0.800; 95% confidence interval（CI）： 0.708-0.891] was higher than that of the Mayo model（0.753; 95% CI： 0.650-0.857） or VA model（0.728; 95% CI： 0.623-0.833）; however, this finding was not statistically significant. To varying degrees, calibration curves showed that all three models overestimated malignancy.Conclusions： The three predicted models have similar accuracy for prediction of SPN malignancy, although the accuracy is not sufficient. For Chinese patients, the PU model may has greater predictive power.Background： Here, we introduced our short experience on the application of a new CUSA Excel ultrasonic aspiration system, which was provided by Integra Lifesciences corporation, in skull base meningiomas resection.Methods： Ten patients with anterior, middle skull base and sphenoid ridge meningioma were operated using the CUSA Excel ultrasonic aspiration system at the Neurosurgery Department of Shanghai Huashan Hospital from August 2014 to October 2014. There were six male and four female patients, aged from 38 to 61 years old（the mean age was 48.5 years old）. Five cases with tumor located at anterior skull base, three cases with tumor on middle skull base, and two cases with tumor on sphenoid ridge.Results： All the patents received total resection of meningiomas with the help of this new tool, and the critical brain vessels and nerves were preserved during operations. All the patients recovered well after operation.Conclusions： This new CUSA Excel ultrasonic aspiration system has the advantage of preserving vital brain arteries and cranial nerves during skull base meningioma resection, which is very important for skull base tumor operations. This key step would ensure a well prognosis for patients. We hope the neurosurgeons would benefit from this kind of technique.Background： The purposes of this study were to explore the effects of high mobility group protein box 1（HMGB1） gene on the growth, proliferation, apoptosis, invasion, and metastasis of glioma cells, with an attempt to provide potential therapeutic targets for the treatment of glioma. Methods： The expressions of HMGB1 in glioma cells（U251, U-87 MG and LN-18） and one control cell line（SVG p12） were detected by real time PCR and Western blotting, respectively. Then, the effects of HMGB1 on the biological behaviors of glioma cells were detected： the expression of HMGB1 in human glioma cell lines U251 and U-87 MG were suppressed using RNAi technique, then the influences of HMGB1 on the viability, cycle, apoptosis, and invasion abilities of U251 and U-87 MG cells were analyzed using in a Transwell invasion chamber. Also, the effects of HMGB1 on the expressions of cyclin D1, Bax, Bcl-2, and MMP 9 were detected. Results： As shown by real-time PCR and Western blotting, the expression of HMGB1 significantly increased in glioma cells（U251, U-87 MG, and LN-18） in comparison with the control cell line（SVG p12）; the vitality, proliferation and invasive capabilities of U251 and U-87 MG cells in the HMGB1 siR NA-transfected group were significantly lower than those in the blank control group and negative control（NC） siR NA group（P〈0.05） but showed no significant difference between the blank control group and NC siR NA group. The percentage of apoptotic U251 and U-87 MG cells was significantly higher in the HMGB1 siR NA-transfected group than in the blank control group and NC siR NA group（P〈0.05） but was similar between the latter two groups. The HMGB1 siR NA-transfected group had significantly lower expression levels of Cyclin D1, Bcl-2, and MMP-9 protein in U251 and U-87 MG cells and significantly higher expression of Bax protein than in the blank control group and NC siR NA group（P〈0.05）; the expression profiles of cyclin D1, Bax, Bcl-2, and MMP 9 showed no significant change in both blank control group and NC siR NA group. Conclusions： HMGB1 gene may promote the proliferation and migration of glioma cells and suppress its effects of apoptosis. Inhibition of the expression of HMGB1 gene can suppress the proliferation and migration of glioma cells and promote their apoptosis. Our observations provided a new target for intervention and treatment of glioma.

Dynamic contrast-enhanced MRI versus ^(18)F-FDG PET/CT: Which is better in differentiation between malignant and benign solitary pulmonary nodules?

Objective： To prospectively compare the discriminative capacity of dynamic contrast enhanced-magnetic resonance imaging（DCE-MRI） with that of^18F-fluorodeoxyglucose（^18F-FDG） positron emission tomography/computed tomography（PET/CT） in the differentiation of malignant and benign solitary pulmonary nodules（SPNs）.Methods： Forty-nine patients with SPNs were included in this prospective study. Thirty-two of the patients had malignant SPNs, while the other 17 had benign SPNs. All these patients underwent DCE-MRI and ^18F-FDG PET/CT examinations. The quantitative MRI pharmacokinetic parameters, including the trans-endothelial transfer constant（K^trans）, redistribution rate constant（Kep）, and fractional volume（Ve）, were calculated using the Extended-Tofts Linear two-compartment model. The ^18F-FDG PET/CT parameter, maximum standardized uptake value（SUV（max））, was also measured. Spearman＇s correlations were calculated between the MRI pharmacokinetic parameters and the SUV（max） of each SPN. These parameters were statistically compared between the malignant and benign nodules. Receiver operating characteristic（ROC） analyses were used to compare the diagnostic capability between the DCE-MRI and ^18F-FDG PET/CT indexes.Results： Positive correlations were found between K^trans and SUV（max）, and between K（ep） and SUV（max）（P〈0.05）.There were significant differences between the malignant and benign nodules in terms of the K^trans, K（ep） and SUV（max） values（P〈0.05）. The areas under the ROC curve（AUC） of K^trans） K（ep） and SUV（max） between the malignant and benign nodules were 0.909, 0.838 and 0.759, respectively. The sensitivity and specificity in differentiating malignant from benign SPNs were 90.6% and 82.4% for K^trans; 87.5% and 76.5% for K（ep）; and 75.0% and 70.6%for SUV（max）, respectively. The sensitivity and specificity of K^trans and K（ep） were higher than those of SUV（max）, but there was no significant difference between them（P〉0.05）.Conclusions： DCE-MRI can be used to differentiate between benign and malignant SPNs and has the advantage of being radiation free.

Complete Thoracoscopic Segmentectomy and Pulmonary Lobectomy in the Treatment of Small Pulmonary Nodules

Objective:To compare the clinical effect of complete thoracoscopic segmentectomy and pulmonary lobectomy for pulmonary nodules.Methods:In this study,176 patients with pulmonary nodules were treated in Changshu Hospital Affiliated to Nanjing University of Chinese Medicine from January 2019 to June 2021;according to the type of surgery,the patients were divided into group A(complete thoracoscopic segmentectomy)and group B(complete thoracoscopic pulmonary lobectomy),and the clinical effects were analyzed.Results:The intraoperative blood loss,postoperative drainage volume,postoperative hospitalization days,and lung function of patients in group A were significantly better than those in group B(P<0.05),while there was no significant difference in the number of dissected lymph nodes.Conclusion:The clinical effect of complete thoracoscopic segmentectomy for patients with small pulmonary nodules is more significant;it does not only ensure lymph node dissection,but also improve surgical-related indicators and treatment safety as well as speed up the recovery of pulmonary function.Its clinical application value is worthy of key analysis by medical institutions.

Prospective Study of Low-and Standard-dose Chest CT for Pulmonary Nodule Detection:A Comparison of Image Quality,Size Measurements and Radiation Exposure

Objective:To comprehensively and accurately analyze the out-performance of low-dose chest CT(LDCT)vs.standard-dose CT(SDCT).Methods:The image quality,size measurements and radiation exposure for LDCT and SDCT protocols were evaluated.A total of 117 patients with extra-thoracic malignancies were prospectively enrolled for non-enhanced CT scanning using LDCT and SDCT protocols.Three experienced radiologists evaluated subjective image quality independently using a 5-point score system.Nodule detection efficiency was compared between LDCT and SDCT based on nodule characteristics(size and volume).Radiation metrics and organ doses were analyzed using Radimetrics.Results:The images acquired with the LDCT protocol yielded comparable quality to those acquired with the SDCT protocol.The sensitivity of LDCT for the detection of pulmonary nodules(n=650)was lower than that of SDCT(n=660).There was no significant difference in the diameter and volume of pulmonary nodules between LDCT and SDCT(for BMI<22 kg/m^(2),4.37 vs.4.46 mm,and 43.66 vs.46.36 mm^(3);for BMI>22 kg/m^(2),4.3 vs.4.41 mm,and 41.66 vs.44.86 mm^(3))(P>0.05).The individualized volume CT dose index(CTDI_(vol)),the size specific dose estimate and effective dose were significantly reduced in the LDCT group compared with the SDCT group(all P<0.0001).This was especially true for dose-sensitive organs such as the lung(for BMI<22 kg/m^(2),2.62 vs.12.54 mSV,and for BMI>22 kg/m^(2),1.62 vs.9.79 mSV)and the breast(for BMI<22 kg/m^(2),2.52 vs.10.93 mSV,and for BMI>22 kg/m^(2),1.53 vs.9.01 mSV)(P<0.0001).Conclusion:These results suggest that with the increases in image noise,LDCT and SDCT exhibited a comparable image quality and sensitivity.The LDCT protocol for chest scans may reduce radiation exposure by about 80% compared to the SDCT protocol.

Adaptive Order Polynomial Fitting for Pulmonary Nodule Segmentation in Chest Radiograph

Segmentation of pulmonary nodules in chest radiographs is a particularly challenging task due to heavy noise and superposition of ribs,vessels,and other complicated anatomical structures in lung field. In this paper,an adaptive order polynomial fitting based raycasting algorithm is proposed for pulmonary nodule segmentation in chest radiographs. Instead of detecting nodule edge points directly,the nodule intensity profiles are first fitted by using the polynomials with adaptively determined orders. Then,the edge positions are identified through analyzing the local minimum of the fitted curves.The performance of the proposed algorithm was evaluated over an image database with 148 nodule cases in chest radiographs that were collected from a variety of digital radiograph modalities. The preliminary results show the proposed algorithm can obtain a high rate of successful segmentations.

Decision-tree analysis for cost-effective management of solitary pulmonary nodules in China

AIM: To analyze the cost-effectiveness of the diagnosis of solitary pulmonary nodule(SPN) in China. METHODS: Decision analysis models were constructed to assess the cost-effectiveness of four strategies for the management of SPN: computed tomography(CT) alone, CT plus CT-guided automated cutting needle biopsy(ACNB), CT plus positron emission tomography/computed tomography(PET/CT), CT plus diffusionweighted magnetic resonance imaging(DWI) plus PET/CT. RESULTS: The prevalence of lung cancer among SPN discovered in the clinical setting was approximately 50%. The CT plus ACNB strategy had higher diagnostic accuracies(87% vs 81%), with a cost saving of $1945 RMB per patient, and reducing unnecessary thoracotomy by 16.5%; this was associated with a 4.5% missed diagnosis rate. CT plus DWI plus PET/CT strategy also had higher accuracies(95% vs 81%), with a cost saving of $590 RMB per patient, and reducing unneces-sary thoracotomy by 13.5%; this was accompanied by 0.3% missed diagnosis rate. CT plus PET strategy is cost effective at a prevalence rate of 0-34%, but there was a larger prevalence range of lung cancer for CT plus ACNB strategy(from 0 to 0.6) and CT plus DWI plus PET/CT strategy(from 0 to 0.64). CONCLUSION: CT plus DWI plus PET/CT strategy was cost-effective, and had a higher accuracy accompanied by a lower missed diagnosis rate than CT plus ACNB strategy.

Bronchoscopic Diagnosis of Solitary Pulmonary Nodules with the Use of NIR Spectroscopy

Background: Recently, SPN has become a much more frequently encountered issue in bronchology. Efficient and reliable guidance method for SPN morphological proof is highly needed. Objectives: The aim of study was to compare the diagnostic values of NIR (near infrared) spectroscopy with EBUS for SPN diagnostic. Fluoroscopic guidance with TBB and needle biopsy were done in all patients. Methods: In our study, we used two types of monitoring systems. Fluoroscopic guidance was combined with either a radial EBUS or a NIR spectroscopy probe for tissue confirmation. 139 male and 71 female patients, having a medial age of 68 years with CT/PET findings of metabolically active SPN were examined between 2/2010 and 2/2013. We designed an instrument for measurement of the penetration of the NIR through lung tissue. Indicating and source fibers were navigated towards the SPN. An EBUS radial probe was used, during fluoroscopic navigation. Results: The statistical analysis of the results obtained showed a comparative specificity and sensitivity of the NIR spectroscopy, with radial EBUS. Conclusions: NIR spectroscopy produced similar efficacies as the radial EBUS. However, the number of positive biopsies was more dependent upon the ability to direct the confirmatory device to the SPN during fluoroscopic guidance than on the type of the device.

Primary pulmonary meningioma and minute pulmonary meningothelial-like nodules: Rare pulmonary nodular lesions requiring more awareness in clinical practice

In this editorial,we comment on an article by Ruan et al published in a recent issue of the World Journal of Clinical Case.Pulmonary meningothelial proliferative lesions,including primary pulmonary meningiomas,minute pulmonary meningothelial-like nodules,and metastatic pulmonary meningiomas are rare pulmonary lesions.These lesions are difficult to differentiate from lung cancers based on clinical and imaging manifestations.Herein,we briefly introduce the clinical,imaging,and pathological characteristics of these lesions and discuss their pathogenesis to strengthen the current understanding of pulmonary meningothelial proliferative lesions in clinical diagnosis and therapy.

Pathological diagnosis and immunohistochemical analysis of minute pulmonary meningothelial-like nodules:A case report

BACKGROUND Minute Pulmonary Meningothelial-like Nodules(MPMNs)are rare benign pulmonary nodules,which are more common in elderly women and have a higher detection rate in lung tissues of patients with lung malignant diseases.Its origin is not yet clear.At present,there are few reports on the diagnostic methods such as imaging and pathological manifestations of MPMNs.This article reports a 70-year-old female patient with pulmonary adenocarcinoma combined with MPMNs and reviews of the relevant literature.CASE SUMMARY A 70-year-old women was admitted to our institution with feeling sour in her back and occasional cough for more than 2 mo.Computerized electronic scanning scan and 3D reconstruction images in our institution showed there were multiple ground-glass nodules in both of her two lungs.The biggest one was in the apicoposterior segment of left upper lobe,about 2.5 mm×9 mm in size.We performed thoracoscopic resection of the left upper lung apicoposterior segment of the patient,and the final pathological report was minimally invasive adenocarcinoma.Re-examination of high resolution computed tomography 21 mo after surgery showed multiple ground-glass nodules in both lungs,and a new groundglass nodule was found in the superior segment of the right lower lobe.We took pathological biopsy of the right upper lung and right lower lung nodules for the patient under thoracoscopy.The histomorphology of the right lower lobe nodule showed multiple lesions in the lung tissue,and the small foci in the alveolar septum were distributed in mild form of the aggregation of short spindle cells.The immunohistochemistry showed that the lesion was epithelial membrane antigen(EMA)(+),somatostatin receptor 2a(SSTR2a)(+),S-100(-),chromogranin A(-),Syn(-),cytokeratin(-)and HMB-45(-).The final diagnosis was minimally invasive adenocarcinoma,accompanied by MPMNs.We recommend that patients continue to receive treatment after surgery and to do regular follow-up observations.CONCLUSION The imaging manifestations of MPMNs are atypical,histomorphology and immunohistochemistry can assist in its diagnosis.This article reviews the relevant literature of MPMNs immunohistochemistry and shows that MPMNs are positive for EMA,SSTR2a,and progesterone receptor.

Nodule Detection Using Local Binary Pattern Features to Enhance Diagnostic Decisions

Pulmonary nodules are small, round, or oval-shaped growths on the lungs. They can be benign (noncancerous) or malignant (cancerous). The size of a nodule can range from a few millimeters to a few centimeters in diameter. Nodules may be found during a chest X-ray or other imaging test for an unrelated health problem. In the proposed methodology pulmonary nodules can be classified into three stages. Firstly, a 2D histogram thresholding technique is used to identify volume segmentation. An ant colony optimization algorithm is used to determine the optimal threshold value. Secondly, geometrical features such as lines, arcs, extended arcs, and ellipses are used to detect oval shapes. Thirdly, Histogram Oriented Surface Normal Vector (HOSNV) feature descriptors can be used to identify nodules of different sizes and shapes by using a scaled and rotation-invariant texture description. Smart nodule classification was performed with the XGBoost classifier. The results are tested and validated using the Lung Image Consortium Database (LICD). The proposed method has a sensitivity of 98.49% for nodules sized 3–30 mm.

Analysis of the Prevalence Characteristics of Lung Nodules and Their Influencing Factors in The Health Check-Up Population

Objective: To study the prevalence characteristics of pulmonary nodules and their influencing factors in the health check-up population. Methods: A total of 500 cases of health checkups were selected for the data study to analyze the detection and prevalence characteristics of pulmonary nodules. The influencing factors between the pulmonary nodules group and the no pulmonary nodules group were analyzed. Results: A total of 209 cases were detected, of which there were more males than females, and for female patients, the detection rate increased with age (P < 0.05). The distribution of gender, age, smoking, respiratory symptoms, and exposure to kitchen fumes between the two groups was compared (P < 0.05). The multifactorial analysis concluded that risk factors include, respiratory symptoms, older age, exposure to kitchen fumes, and smoking. In addition, patients who exercised more had a lower risk of developing lung nodules. Conclusion: The detection rate of pulmonary nodules was high in the health check-up population and the influencing factors analyzed involved the presence of respiratory symptoms, older age, kitchen fume exposure, and smoking, while the positive influencing factor was increased exercise.

Neural network-based computer-aided diagnosis in distinguishing malignant from benign solitary pulmonary nodules by computed tomography

Background Computer-aided diagnosis （CAD） of lung cancer is the subject of many current researches. Statistical methods and artificial neural networks have been applied to more quantitatively characterize solitary pulmonary nodules （SPNs）. In this study, we developed a CAD scheme based on an artificial neural network to distinguish malignant from benign SPNs on thin-section computed tomography （CT） images, and investigated how the CAD scheme can help radiologists with different levels of experience make diagnostic decisions. Methods Two hundred thin-section CT images of SPNs with proven diagnoses （135 small peripheral lung cancers and 65 benign nodules） were analyzed. Three clinical features and nine CT signs of each case were studied by radiologists, and the indices of qualitative diagnosis were quantified. One hundred and forty nodules were selected randomly to form training samples, on which the neural network model was built. The remaining 60 nodules, forming test samples, were presented to 9 radiologists with 3-20 years of clinical experience, accompanied by standard reference images. The radiologists were asked to determine whether a nodule was malignant or benign first without and then with CAD output. Diagnostic performance was evaluated by receiver operating characteristic （ROC） analysis. Results CAD outputs on test samples had higher agreement with pathological diagnoses （Kappa=0.841, P〈0.001）. Compared with diagnostic results without CAD output, the average area under the ROC curve with CAD output was 0.96 （P〈0.001） for junior radiologists, 0.94 （P=0.014） for secondary radiologists and 0.96 （P=0.221） for senior radiologists, respectively. The differences in diagnostic performance with CAD output among the three levels of radiologists were not statistically significant （P=0.584, 0.920 and 0.707, respectively）. Conclusions This CAD scheme based on an artificial neural network could improve diagnostic performance and assist radiologists in distinguishing malignant from benign SPNs on thin-section CT images.

Preoperative diagnosis of malignant pulmonary nodules in lung cancer screening with a radiomics nomogram

Background:Lung cancer is the most commonly diagnosed cancer worldwide.Its survival rate can be significantly improved by early screening.Biomarkers based on radiomics features have been found to provide important physiological information on tumors and considered as having the potential to be used in the early screening of lung cancer.In this study,we aim to establish a radiomics model and develop a tool to improve the discrimination between benign and malignant pulmonary nodules.Methods:A retrospective study was conducted on 875 patients with benign or malignant pulmonary nodules who underwent computed tomography(CT)examinations between June 2013 and June 2018.We assigned 612 patients to a training cohort and 263 patients to a validation cohort.Radiomics features were extracted from the CT images of each patient.Least absolute shrinkage and selection operator(LASSO)was used for radiomics feature selection and radiomics score calculation.Multivariate logistic regression analysis was used to develop a classification model and radiomics nomogram.Radiomics score and clinical variables were used to distinguish benign and malignant pulmonary nodules in logistic model.The performance of the radiomics nomogram was evaluated by the area under the curve(AUC),calibration curve and Hosmer-Lemeshow test in both the training and validation cohorts.Results:A radiomics score was built and consisted of 20 features selected by LASSO from 1288 radiomics features in the training cohort.The multivariate logistic model and radiomics nomogram were constructed using the radiomics score and patients’age.Good discrimination of benign and malignant pulmonary nodules was obtained from the training cohort(AUC,0.836;95%confidence interval[CI]:0.793-0.879)and validation cohort(AUC,0.809;95%CI:0.745-0.872).The Hosmer-Lemeshow test also showed good performance for the logistic regression model in the training cohort(P=0.765)and validation cohort(P=0.064).Good alignment with the calibration curve indicated the good performance of the nomogram.Conclusions:The established radiomics nomogram is a noninvasive preoperative prediction tool for malignant pulmonary nodule diagnosis.Validation revealed that this nomogram exhibited excellent discrimination and calibration capacities,suggesting its clinical utility in the early screening of lung cancer.

Risk assessment of malignancy in solitary pulmonary nodules in lung computed tomography:a multivariable predictive model study

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.

An Inception Module CNN Classifiers Fusion Method on Pulmonary Nodule Diagnosis by Signs

A "sign" on a lung CT image refers to a radiologic finding that suggests a pathological progression of some specific disease. Analysis of CT signs is helpful to understand the pathological origin of the lesion. In-depth study of lung nodules classification with different CT signs will help to distinguish benign and malignant nodules more clearly and accurately. To this end, we propose an Inception module-based ensemble classification method for pulmonary nodule diagnosis with different nodule signs. We first construct a Convolutional Neural Network(CNN) classifier adopting Inception modules and pre-train it on ImageNet. We then fine-tune this pre-trained classifier on 10 different lung nodule sign sample sets, and fuse these 10 classifiers with an artificial immune ensemble algorithm. The overall sensitivity, specificity, and accuracy of our proposed Artificial Immune Algorithm-based Inception Networks Fusion(AIA-INF) algorithm are 82.22%, 93.17%, and 88.67%, respectively, which are significantly higher than those of the alternative Bagging and Boosting methods. The experimental results show that our Inception-based ensemble classifier offers promising performance, and compared with other CADx systems, this scheme can offer a more detailed reference for diagnosis, and can be valuable for junior radiologist training.