Spiculation Sign Research Articles

Clinical and CT characteristics for predicting lymph node metastasis in patients with synchronous multiple primary lung adenocarcinoma

PurposeThis study aims to investigate the risk factors for lymph node metastasis (LNM) in synchronous multiple primary lung cancer (sMPLC) using clinical and CT features, and to offer guidance for preoperative LNM prediction and lymph node (LN) resection strategy.Materials and methodsA retrospective analysis was conducted on the clinical data and CT features of patients diagnosed with sMPLC at the Third Affiliated Hospital of Kunming Medical University from January 1, 2018 to December 31, 2022. Patients were classified into two groups: the LNM group and the non-LNM (n-LNM) group. The study utilized univariate analysis to examine the disparities in clinical data and CT features between the two groups. Additionally, multivariate analysis was employed to discover the independent risk variables for LNM. The diagnostic efficacy of various parameters was evaluated using the receiver operating characteristic (ROC) curve.ResultsAmong the 688 patients included in this study, 59 exhibited LNM. Univariate analysis revealed significant differences between the LNM and n-LNM groups in terms of gender, smoking history, CYFRA21-1 level, CEA level, NSE level, lesion type, total lesion diameter, main lesion diameter, spiculation sign, lobulation sign, cavity sign, and pleural traction sign. Logistic regression identified CEA level (OR = 1.042, 95%CI: 1.009-1.075), lesion type (OR = 9.683, 95%CI: 3.485-26.902), and main lesion diameter (OR = 1.677, 95%CI: 1.347-2.089) as independent predictors of LNM. The regression equation for the joint prediction was as follows: logit(p)= -7.569+0.041*CEA level +2.270* lesion type +0.517* main lesion diameter.ROC curve analysis showed that the AUC for CEA level was 0.765 (95% CI, 0.694–0.836), for lesion type was 0.794 (95% CI, 0.751–0.838), for main lesion diameter was 0.830 (95% CI, 0.784–0.875), and for the combine predict model was 0.895 (95% CI, 0.863–0.928).ConclusionThe combination of clinical and imaging features can better predict the status of LNM of sMPLC, and the prediction efficiency is significantly higher than that of each factor alone, and can provide a basis for lymph node management decision.

A clinically applicable model more suitable for predicting malignancy or benignity of pulmonary ground glass nodules in women patients

BackgroundIn recent years, clinicians often encounter patients with multiple pulmonary nodules in their clinical practices. As most of these ground glass nodules (GGNs) are small in volume and show no spicule sign, it is difficult to use Mayo Clinic Model to make early diagnosis of lung cancer accurately, especially in large numbers of nonsmoking women who have no tumor history. Other clinical models are disadvantaged by a relatively high false-positive or false-negative rate. Therefore, there is an urgent need to establish a new model of predicting malignancy or benignity of pulmonary GGNs for the sake of making accurate and early diagnosis of lung cancer.MethodsIncluded in this study were GGNs surgically resected from patients who were admitted to Yiwu Central Hospital from January 2018 to March 2024, including both male and female patients, there is no gender specific issue. The nature of all these GGN tissues was confirmed pathologically. The case data were statistically analyzed to establish a mathematical prediction model, the prediction performance of which was verified by the pathological results.ResultsAltogether 261 GGN patients met the inclusion criteria. Using the results of logistic regression analysis, a mathematical prediction equation was established as follows: Malignant probability (mP) = ex/ (1 + ex); when mP was > 0.5, the GGN was considered as malignant, and when mP was ≤ 0.5, it was considered as benign. x= -2.46 + 1.032*gender + 1.85*mGGN + 1.40*VCS-0.0027*mean CT value of the nodule + 0.078*maximum diameter of the nodule, where e represents the natural logarithm; if the patient was a female, gender = 1 (otherwise = 0); if the pulmonary nodule was a mixed GGN, mGGN = 1 (otherwise = 0); if the pulmonary nodule had vascular convergence sign, VCS = 1 (otherwise = 0). The prediction performance of the mathematical prediction model was verified as follows: the negative prediction value was 0.97156 and the positive prediction value was 0.3800 in the model group versus 1 and 0.25 in the verification group.ConclusionIn this study, we identified female gender, mGGN, VCS, mean CT value and maximum nodule diameter as five key factors for predicting malignancy or benignity of pulmonary nodules, based on which we established a mathematical prediction model. This novel innovation may provide a useful auxiliary tool for predicting malignancy and benignity of pulmonary nodules, especially in women patients.

Value of spectral parameters in the differential diagnosis of benign and malignant breast nodules in non-enhanced chest CT.

Dual-layer spectral computed tomography (DSCT) is capable of acquiring both conventional and spectral images during one routine scan, and is widely used for the quantitative and qualitative analyses of substances, differential diagnosis, and disease staging. However, limited research has been conducted on its performance in the differential diagnosis of benign and malignant breast nodules using non-enhanced scans. This study aimed to assess the diagnostic performance of multiple quantitative parameters derived from non-enhanced DSCT in differentiating benign from malignant breast nodules. This retrospective cross-sectional study examined a total of 121 breast nodules from 114 patients (malignant group: n=68; benign group: n=53) identified during chest physical examination or routine admission for the treatment of breast diseases at The First Affiliated Hospital of Soochow University from March 2023 to December 2023. All the patients underwent DSCT scanning and pathological diagnosis. The DSCT quantitative parameters, including the effective atomic number (Zeff), computed tomography (CT) attenuation values at 40-70 keV, and the slope of the spectral Hounsfield unit curve (λHU), in non-enhanced images were measured. The λHU was calculated as follows: λHU = CT70 keV - CT40 keV/30 HU. Additionally, typical radiological features were analyzed. A DSCT parameter diagnostic model and a conventional CT diagnostic model were assessed using receiver operating characteristic (ROC) curves. The Delong test was used to assess and compare the diagnostic performance of each model. The DSCT parameters, including the Zeff (P<0.001), λHU (P<0.001), and CT attenuation values at 40 keV (P<0.001) and 50 keV (P=0.001), as well as the presence of the lobular sign (P<0.001) and spicule sign (P<0.001), exhibited statistically significant differences between the benign and malignant groups. The logistic regression analysis revealed that the Zeff [odds ratio (OR): 9.22; 95% confidence interval (CI): 2.11-40.35; P=0.003], λHU (OR: 0.64; 95% CI: 0.52-0.79; P<0.001), 40 keV CT attenuation value (OR: 8.69; 95% CI: 3.28-23.06; P<0.001), 50 keV CT attenuation value (OR: 0.01; 95% CI: 0.001-0.07; P<0.001), and lobular sign (OR: 3.95; 95% CI: 1.52-10.31; P=0.005) were independent predictors of malignancy. Compared to the benign group, the malignant group had a higher likelihood of presenting with the lobular sign and higher Zeff values but lower λHU values. The ROC curve indicated that the Zeff had the highest diagnostic efficacy [area under the curve (AUC) of the ROC =0.792, 95% CI: 0.71-0.87]. Further, the DSCT parameter diagnostic model had improved diagnostic efficacy with an AUC of 0.899 (95% CI: 0.84-0.96), which was higher than the AUC of the conventional CT diagnostic model (AUC =0.796, 95% CI: 0.72-0.87). The Delong test revealed a statistically significant difference between these two models (P=0.04). DSCT parameters derived from non-enhanced DSCT images, such as the Zeff value and λHU, can be used to differentiate benign and malignant breast nodules, and the differential diagnosis efficacy of the DSCT parameters is higher than that of conventional CT parameters.

Discriminating bronchiolar adenoma from peripheral lung cancer by thin-section computed tomography (CT): a 2-center study.

Bronchiolar adenoma (BA) is frequently misdiagnosed as peripheral lung cancer (PLC) because it resembles PLC. Computed tomography (CT) examination is an effective tool for detecting and diagnosing lung diseases. To date, there has been no comprehensive study on the differential diagnosis of BAs and PLCs using thin-section computed tomography (TSCT) based on a large sample, and the efficiency of CT in diagnosing BAs has not been verified. The goal of this study was to distinguish BA from PLC by summarizing their clinical and TSCT characteristics. A retrospective cross-sectional study on 71 cases with BAs and 218 matched controls with PLCs (from March 2020 to May 2023) within 2 centers (The First Affiliated Hospital of Chongqing Medical University and the Second Affiliated Hospital of Army Medical University) was conducted to investigate their clinical and radiological differences. The clinical characteristics and TSCT features of BAs and PLCs were summarized and compared. A multivariate logistic regression analysis was performed to reveal the key predictors of BAs. The BAs and PLCs exhibited significant differences in TSCT features. Multivariate analysis revealed that the lesion being located in basal segments [odds ratio (OR), 17.835; 95% confidence interval (CI): 6.977-45.588; P<0.001], irregular shape (OR, 4.765; 95% CI: 1.877-12.099; P=0.001), negative of spiculation sign (OR, 7.436; 95% CI: 2.063-26.809; P=0.002), central vessel sign with pulmonary artery (OR, 3.576; 95% CI: 1.557-8.211; P=0.003), peripheral vessel sign with pulmonary vein (OR, 12.444; 95% CI: 4.934-31.383; P<0.001), and distance from lesion edge to pleura (D-ETP) ≤5 mm (OR, 5.535; 95% CI: 2.346-13.057; P<0.001) were independent predictors of BAs, and the area under the curve (AUC) of this model was 0.935; 95% CI: 0.901-0.960 (sensitivity: 88.0%, specificity: 86.03%, P<0.001). Peripheral pulmonary nodules locating in the basal segment of lower lobe with irregular shape, central vessel sign with pulmonary artery, peripheral vessel sign with pulmonary vein and D-ETP ≤5 mm, but without spiculation sign, should be highly suspected of BAs.

Enhanced CT-Based Intratumoral and Peritumoral Radiomics Nomograms Predict High-Grade Patterns of Invasive Lung Adenocarcinoma

Rationale and ObjectivesExtraction of intratumoral and peritumoral radiomics features combined with clinical factors to establish nomograms to predict high-grade patterns (micropapillary and solid) of invasive adenocarcinoma of the lung (IAC). Materials and MethodsA retrospective study was conducted on 463 patients with pathologically confirmed IAC. Patients were randomized in a 7:3 ratio into a training cohort (n = 324) and a testing cohort (n = 139). A total of 2,154 CT-based radiomic features were extracted from each of the four regions: gross tumor volume (GTV) and gross peritumoral tumor volume (GPTV3, GPTV6, GPTV9) containing peri-tumor regions of 3 mm, 6 mm, and 9 mm. A radiomics nomogram was constructed based on the optimal radiomics model and clinically independent predictors. ResultsThe GPTV3 radiomics model showed better predictive performance in the testing group compared to the GTV (0.840), GPTV6 (0.843) and GPTV9 (0.734) models, with an AUC value of 0.889 in the testing group. In the clinical model, tumor density and the presence of a spiculation sign were identified as independent predictors. The nomogram, which combined these independent predictors with the GPTV3-Radscore, proved to be clinically useful.窗体顶端 ConclusionThe GPTV3 radiomics model was superior to the GTV, GPTV6 and GPTV9 radiomics models in predicting high-grade patterns (HGP) of IAC. In addition, nomograms based on GPTV3 radiomics features and clinically independent predictors can further improve the prediction efficiency.

Characteristics of atypical Pulmonary Tuberculosis without typical clinical features diagnosed by pathology

PurposeSome patients with pulmonary tuberculosis (PTB) do not display typical clinical features, leading to delays in diagnosis and treatment. MethodsWe retrospectively analyzed PTB patients admitted to the Second Affiliated Hospital of Chongqing Medical University between 2017 and 2020. They are divided into pathological group (diagnosed through pathological biopsy) and control group (diagnosed via sputum or lavage fluid). Clinical data of both groups were compared. Based on radiographic features, the pathological group was further divided into the inflammation group, peripheral nodule group, and central occupancy group. We then statistically analyzed the computed tomography (CT) signs, bronchoscopic manifestations and results of pathological biopsy for each subgroup. ResultsThe pathological group consisted of 75 patients, while the control group had 338 patients. Multivariate logistic regression analysis showed that the pathological group had more diabetes (OR = 3.266, 95% CI = 1.609–6.630, P = 0.001), lower ESR (OR = 0.984, 95% CI = 0.971–0.998, P = 0.022), and lower CRP (OR = 0.990, 95% CI = 0.980–0.999, P = 0.036). In the three subgroups, the exudative lesions in the inflammation group were mostly located in atypical areas of PTB. The lobulation sign and spiculation sign were frequently observed in the peripheral nodule group. All presented with significant hilar mediastinal lymphadenopathy in the central occupancy group. In the pathological group, bronchoscopic manifestations typically included mucosal edema and bronchial stenosis. ConclusionDiabetes is an independent risk factor for atypical PTB. Expression of erythrocyte sedimentation rate (ESR) and C-reactive protein (CRP) in atypical PTB is low. Radiologically, it is most easily misdiagnosed when presented as peripheral solid nodules or masses, so a biopsy is recommended.

Application of 18F-FDG PET/CT imaging radiomics in the differential diagnosis of single-nodule pulmonary metastases and second primary lung cancer in patients with colorectal cancer.

It is crucially essential to differentially diagnose single-nodule pulmonary metastases (SNPMs) and second primary lung cancer (SPLC) in patients with colorectal cancer (CRC), which has important clinical implications for treatment strategies. In this study, we aimed to establish a feasible differential diagnosis model by combining 18F-fluorodeoxyglucose positron-emission tomography (18F-FDG PET) radiomics, computed tomography (CT) radiomics, and clinical features. CRC patients with SNPM or SPLC who underwent 18F-FDG PET/CT from January 2013 to July 2022 were enrolled in this retrospective study. The radiomic features were extracted by manually outlining the lesions on PET/CT images, and the radiomic modeling was realized by various screening methods and classifiers. In addition, clinical features were analyzed by univariate analysis and logistic regression (LR) analysis to be included in the combined model. Finally, the diagnostic performances of these models were illustrated by the receiver operating characteristic (ROC) curves and the area under the curve (AUC). We studied data from 61 patients, including 36 SNPMs and 25 SPLCs, with an average age of 65.56 ± 10.355 years. Spicule sign and ground-glass opacity (GGO) were significant independent predictors of clinical features (P = 0.012 and P < 0.001, respectively) to build the clinical model. We achieved a PET radiomic model (AUC = 0.789), a CT radiomic model (AUC = 0.818), and a PET/CT radiomic model (AUC = 0.900). The PET/CT radiomic models were combined with the clinical model, and a well-performing model was established by LR analysis (AUC = 0.940). For CRC patients, the radiomic models we developed had good performance for the differential diagnosis of SNPM and SPLC. The combination of radiomic and clinical features had better diagnostic value than a single model.

Prediction of Lymphovascular invasion status in breast cancer based on magnetic resonance imaging radiomics features

ObjectiveThis study intended to investigate the feasibility and effectiveness of using clinical magnetic resonance imaging (MRI) radiomics features to predict lymphovascular invasion (LVI) status in breast cancer (BC) patients. MethodsA total of 182 BC patients were retrospectively collected and randomly divided into a training set (n = 127) and a validation set (n = 55) in a 7:3 ratio. Based on pathological examination results, the training set was further divided into LVI group (n = 60) and non-LVI group (n = 67), and the validation set was divided into LVI group (n = 24) and non-LVI group (n = 31). General data and MRI examination indicators were compared. Multivariate logistic regression was utilized to analyze MRI radiomics features and clinically relevant indicators that were significant in the baseline information of patients in training set, independent risk factors were identified, and a logistic regression model was built. The accuracy of logistic model was validated using ROC curves in training and validation sets. ResultsAge, pathohistological classification, tumor length, tumor width, presence or absence of Magnetic Resonance Spectroscopy (MRS) cho peak, presence or absence of spicule sign, peritumoral enhancement, and peritumoral edema were statistically significant (P < 0.05) between the two groups. Multivariate logistic regression analysis presented that spicule and peritumoral edema were independent risk factors for LVI in BC patients (P < 0.05). The ROC curve illustrated that AUC of the logistic regression model in the training set was 0.807 (95%CI: 0.730–0.885) and that in the validation set was 0.837 (95%CI: 0.731–0.944). ConclusionRadiomics features of spicule sign and peritumoral edema were independent risk factors for LVI in BC patients. A logistic regression model based on these factors, along with age, could accurately predict LVI occurrence in BC patients, providing data support for diagnosis and modeling of LVI in BC patients.

Development and validation of a clinic-radiological model to predict tumor spread through air spaces in stage I lung adenocarcinoma

ObjectivesTumor spread through air spaces (STAS) is associated with poor prognosis and impacts surgical options. We aimed to develop a user-friendly model based on 2-[18F] FDG PET/CT to predict STAS in stage I lung adenocarcinoma (LAC).Materials and methodsA total of 466 stage I LAC patients who underwent 2-[18F] FDG PET/CT examination and resection surgery were retrospectively enrolled. They were split into a training cohort (n = 232, 20.3% STAS-positive), a validation cohort (n = 122, 27.0% STAS-positive), and a test cohort (n = 112, 29.5% STAS-positive) according to chronological order. Some commonly used clinical data, visualized CT features, and SUVmax were analyzed to identify independent predictors of STAS. A prediction model was built using the independent predictors and validated using the three chronologically separated cohorts. Model performance was assessed using ROC curves and calculations of AUC.ResultsThe differences in age (P = 0.009), lesion density subtype (P < 0.001), spiculation sign (P < 0.001), bronchus truncation sign (P = 0.001), and SUVmax (P < 0.001) between the positive and negative groups were statistically significant. Age ≥ 56 years [OR(95%CI):3.310(1.150–9.530), P = 0.027], lesion density subtype (P = 0.004) and SUVmax ≥ 2.5 g/ml [OR(95%CI):3.268(1.021–1.356), P = 0.005] were the independent factors predicting STAS. Logistic regression was used to build the A-D-S (Age-Density-SUVmax) prediction model, and the AUCs were 0.808, 0.786 and 0.806 in the training, validation, and test cohorts, respectively.ConclusionsSTAS was more likely to occur in older patients, in solid lesions and higher SUVmax in stage I LAC. The PET/CT-based A-D-S prediction model is easy to use and has a high level of reliability in diagnosing.

Preoperative prediction of vasculogenic mimicry in lung adenocarcinoma using a CT radiomics model

To develop and validate a non-invasive computed tomography (CT)-based radiomics model for predicting vasculogenic mimicry (VM) status in lung adenocarcinoma (LA). Two hundred and three patients with LA were enrolled retrospectively and grouped into training and test groups with a ratio of 7:3. Uni- and multivariate logistic regression analyses were performed in the training cohort to screen the independent clinical and radiological factors for VM, and the clinical model was then established. A radiomics model was established based on the rad-scores through support vector machine (SVM). A radiomics nomogram model was subsequently constructed by combining the rad-score with clinical-radiological factors. The receiver operating characteristic curve (ROC), calibration curves, and decision curve analysis (DCA) were conducted to evaluate the performance of the three models. Nine selected radiomics features were selected for the radiomics model and the maximum length and spiculation sign were constructed for the clinical model. The radiomics nomogram model integrating the maximum length, spiculation sign, and rad-score yielded the best AUC in both the training (AUC=0.925) and test cohorts (AUC=0.978), in comparison with the radiomics model (AUC=0.907 and 0.964, in both the training and test cohorts) and the clinical model (AUC=0.834 and 0.836 in both training and test cohorts). The CT-based radiomics nomogram model showed satisfying discriminating performance for preoperatively and non-invasively predicting VM expression status in LA patients.

Correlations between iodine uptake, invasive CT features and pleural invasion in adenocarcinomas with pleural contact

Pleural contact in lung cancers does not always imply pleural invasion (PI). This study was designed to determine whether specific invasive CT characteristics or iodine uptake can aid in the prediction of PI. The sample population comprised patients with resected solid lung adenocarcinomas between April 2019 and May 2022. All participants underwent a contrast enhanced spectral CT scan. Two proficient radiologists independently evaluated the CT features and iodine uptake. Logistic regression analyses were employed to identify predictors for PI, via CT features and iodine uptake. To validate the improved diagnostic efficiency, accuracy analysis and ROC curves were subsequently used. A two-tailed P value of less than 0.05 was considered statistically significant. We enrolled 97 consecutive patients (mean age, 61.8 years ± 10; 48 females) in our study. The binomial logistic regression model revealed that a contact length > 10 mm (OR 4.80, 95% CI 1.92, 11.99, p = 0.001), and spiculation sign (OR 2.71, 95% CI 1.08, 6.79, p = 0.033) were independent predictors of PI, while iodine uptake was not. Enhanced sensitivity (90%) and a greater area under the curve (0.73) were achieved by integrating the two aforementioned CT features in predicting PI. We concluded that the combination of contact length > 10 mm and spiculation sign can enhance the diagnostic performance of PI.

Value of dual-layer spectral detector computed tomography in the diagnosis of benign/malignant solid solitary pulmonary nodules and establishment of a prediction model.

This study aimed to investigate the role of spectral detector computed tomography (SDCT) quantitative parameters and their derived quantitative parameters combined with lesion morphological information in the differential diagnosis of solid SPNs. This retrospective study included basic clinical data and SDCT images of 132 patients with pathologically confirmed SPNs (102 and 30 patients in the malignant and benign groups, respectively). The morphological signs of SPNs were evaluated and the region of interest (ROI) was delineated from the lesion to extract and calculate the relevant SDCT quantitative parameters, and standardise the process. Differences in qualitative and quantitative parameters between the groups were statistically analysed. A receiver operating characteristic (ROC) curve was constructed to evaluate the efficacy of the corresponding parameters in the diagnosis of benign and malignant SPNs. Statistically significant clinical data, CT signs and SDCT quantitative parameters were analysed using multivariate logistic regression to determine the independent risk factors for predicting benign and malignant SPNs, and the best multi-parameter regression model was established. Inter-observer repeatability was assessed using the intraclass correlation coefficient (ICC) and Bland-Altman plots. Malignant SPNs differed from benign SPNs in terms of size, lesion morphology, short spicule sign, and vascular enrichment sign (P< 0.05). The SDCT quantitative parameters and their derived quantitative parameters of malignant SPNs (SAR40keV, SAR70keV, Δ40keV, Δ70keV, CER40keV, CER70keV, NEF40keV, NEF70keV, λ, NIC, NZeff) were significantly higher than those of benign SPNs (P< 0.05). In the subgroup analysis, most parameters could distinguish between benign and adenocarcinoma groups (SAR40keV, SAR70keV, Δ40keV, Δ70keV, CER40keV, CER70keV, NEF40keV, NEF70keV, λ, NIC, and NZeff), and between benign and squamous cell carcinoma groups (SAR40keV, SAR70keV, Δ40keV, Δ70keV, NEF40keV, NEF70keV, λ, and NIC). However, there were no significant differences between the parameters in the adenocarcinoma and squamous cell carcinoma groups. ROC curve analysis indicated that NIC, NEF70keV, and NEF40keV had higher diagnostic efficacy for differentiating benign and malignant SPNs (area under the curve [AUC]:0.869, 0.854, and 0.853, respectively), and NIC was the highest. Multivariate logistic regression analysis showed that size (OR=1.138, 95% CI 1.022-1.267, P=0.019), Δ70keV (OR=1.060, 95% CI 1.002-1.122, P=0.043), and NIC (OR=7.758, 95% CI 1.966-30.612, P=0.003) were independent risk factors for the prediction of benign and malignant SPNs. ROC curve analysis showed that the AUC of size, Δ70keV, NIC, and a combination of the three for differential diagnosis of benign and malignant SPNs were 0.636, 0.846, 0.869, and 0.903, respectively. The AUC for the combined parameters was the largest, and the sensitivity, specificity, and accuracy were 88.2%, 83.3% and 86.4%, respectively. The SDCT quantitative parameters and their derived quantitative parameters in this study exhibited satisfactory inter-observer repeatability (ICC: 0.811-0.997). SDCT quantitative parameters and their derivatives can be helpful in the differential diagnosis of benign and malignant solid SPNs. The quantitative parameter, NIC, is superior to the other relevant quantitative parameters and when NIC is combined with lesion size and Δ70keV value for comprehensive diagnosis, the efficacy could be further improved.

The predictive value of chest computed tomography images, tumor markers, and metabolomics in the identification of benign and malignant pulmonary nodules.

Invasive puncture biopsy is currently the main method of identifying benign and malignant pulmonary nodules (PNs). This study aimed to investigate the application effect of chest computed tomography (CT) images, tumor markers (TMs), and metabolomics in the identification of benign and malignant PNs (MPNs). A total of 110 patients with PNs who were hospitalized in Dongtai Hospital of Traditional Chinese Medicine from March 2021 to March 2022 were selected as the study cohort. A retrospective analysis study of chest CT imaging, serum TMs testing, and plasma fatty acid (FA) metabolomics was performed on all participants. According to the pathological results, participants were divided into a MPN group (n=72) and a benign PN (BPN) group (n=38). The morphological signs of CT images, the levels and positive rate of serum TMs, and the plasma FA indicator were compared between groups. There were significant differences between the MPN group and the BPN group in the CT morphological signs, including location of PN and the number of patients with or without lobulation sign, spicule sign, and vessel convergence sign (P<0.05). Serum carcinoembryonic antigen (CEA), cytokeratin-19 fragment (CYFRA 21-1), neuron-specific enolase (NSE), and squamous cell carcinoma antigen (SCC-Ag) were not significantly different between the 2 groups. The serum contents of CEA and CYFRA 21-1 in the MPN group were remarkably higher than those in the BPN group (P<0.05). The plasma levels of palmitic acid, total omega-3 polyunsaturated FA (W3), nervonic acid, stearic acid, docosatetraenoic acid, linolenic acid, eicosapentaenoic acid, total saturated FA, and total FA were much higher in the MPN group than the BPN group (P<0.05). In conclusion, chest CT images and TMs, combined with metabolomics, has a good application effect in the diagnosis of BPNs and MPNs, and is worthy of further promotion.

Establishment of a malignancy and benignancy prediction model of sub-centimeter pulmonary ground-glass nodules based on the inflammation-cancer transformation theory.

BackgroundIn recent years, Chinese clinicians are frequently encountered by patients with multiple lung nodules and these intensity ground-glass nodules (GGNs) are usually small in size and some of them have no spicule sign. In addition, early lung cancer is diagnosed in large numbers of non-heavy smokers and individuals with no caner history. Obviously, the Mayo model is not applicable to these patients. The aim of the present study is to develop a new and more applicable model that can predict malignancy or benignancy of pulmonary GGNs based on the inflammation-cancer transformation theory.Materials and methodsIncluded in this study were patients who underwent surgical resection or lung puncture biopsy of GGNs in Shanghai 10th People’s Hospital between January 1, 2018 and May 31, 2021 with the inclusion criterion of the maximum diameter of GGN < 1.0 cm. All the included patients had their pulmonary GGNs diagnosed by postoperative pathology. The patient data were analyzed to establish a prediction model and the predictive value of the model was verified.ResultsAltogether 100 GGN patients who met the inclusion criteria were included for analysis. Based on the results of logistic stepwise regression analysis, a mathematical predication equation was established to calculate the malignancy probability as follows: Malignancy probability rate (p) = ex/(1 + ex); p > 0.5 was considered as malignant and p ≤ 0.5 as benign, where x = 0.9650 + [0.1791 × T helper (Th) cell] + [0.2921 × mixed GGN (mGGN)] + (0.4909 × vascular convergence sign) + (0.1058 × chronic inflammation). According to this prediction model, the positive prediction rate was 73.3% and the negative prediction rate was 100% versus the positive prediction rate of 0% for the Mayo model.ConclusionBy focusing on four major factors (chronic inflammation history, human Th cell, imaging vascular convergence sign and mGGNs), the present prediction model greatly improves the accuracy of malignancy or benignancy prediction of sub-centimeter pulmonary GGNs. This is a breakthrough innovation in this field.

Detection of CEA and ProGRP Levels in BALF of Patients with Peripheral Lung Cancer and Their Relationship with CT Signs.

Background Lung cancer is a common clinical thoracic malignant tumor, which had a serious impact on the safety of patients, currently ranking first in all malignant tumors in morbidity and mortality, with generally less than 5% survival rate in 5 years. Objective To investigate the relationship and significance between carcinoembryonic antigen (CEA) and precursor gastrin-releasing peptide (ProGRP) changing levels in bronchoalveolar lavage fluid (BALF) and CT imaging features in patients with peripheral lung cancer. Methods A total of 90 patients diagnosed with peripheral lung cancer as the lung cancer group and 60 patients with benign lung diseases as the control group in our hospital from May 2019 to October 2021 were selected to compare the differences of CEA and ProGRP in BALF by the classification of CT features. Results The levels of CEA and ProGRP in the BALF of the lung cancer group were significantly higher than those of the control group; the proportion of patients with lobulation sign, burr sign, ground glass sign, pleural effusion, and lesion diameter ≥ 3.0 cm in the lung cancer group was higher than that in the control group; the CEA level in BALF of lung cancer patients with spicule sign, pleural effusion, and lesion diameter ≥ 3.0 cm was significantly higher than that without these symptoms, while ProGRP level in the BALF of lung cancer patients with lobulation sign, burr sign, ground glass sign, pleural effusion, and lesion diameter ≥ 3.0 cm was significantly higher than that of lung cancer patients without these symptoms. Conclusion The check of CEA and ProGRP in BALF in combination with CT features has a certain clinical value for the diagnosis of lung cancer. At the same time, the increased levels of CEA and ProGRP in BALF have a certain correlation with the changes of malignant signs of lung cancer in CT examination.

Clinical Characteristics and Risk Factors Analysis for Visceral Pleural Invasion in Mixed Ground-glass Nodular Lung Adenocarcinoma

背景与目的目前国内肺癌仍是发病率和致死率最高的恶性肿瘤，肺腺癌是其最常见的亚型，影像学中表现为混合性磨玻璃结节(mixed ground glass nodule, mGGN)的肺癌逐渐增多。脏层胸膜侵犯(visceral pleural invasion, VPI)是影响mGGN型肺腺癌预后的重要因素。本研究旨在探索分析mGGN型肺腺癌发生VPI的危险因素。方法回顾性分析2016年11月-2019年11月南京医科大学第一附属医院收治的接受根治性手术的128例mGGN型肺腺癌患者的临床资料，包括影像、病理和生物学特征，其中男性40例、女性88例，年龄30岁-81(60.3±9.3)岁。采用单因素卡方检验与多因素Logistic回归分析调查mGGN型肺腺癌发生VPI的各项危险因素。结果符合纳入标准的128例mGGN型肺腺癌患者中57例被诊断有VPI发生。性别、实性成分最大径、实性成分比例(consolidation tumor ratio, CTR)、毛刺征、肺部疾病史、高血压家族史、肿瘤-胸膜空间位置分型(relation of lesion to pleura, RLP)、支气管与结节走行关系在VPI(+)组和VPI(-)组间的差异均有统计学意义(P < 0.05)。Logistic多因素回归分析发现RLP(OR=3.529, 95%CI: 1.430-8.713, P=0.006)和支气管与结节走行关系(OR=3.993, 95%CI: 1.517-10.51, P=0.005)是VPI发生的独立危险因素(P < 0.05)。结论临床诊治应综合上述参数评估mGGN型肺腺癌发生VPI的可能性。RLP和支气管与结节走行关系异常作为VPI的独立危险因素，对判断mGGN型肺腺癌发生VPI具有一定的指导意义。

Pretreatment prediction of tumour spread through air spaces in clinical stage I non-small-cell lung cancer.

OBJECTIVESThe aim of this study was to construct a nomogram prediction model for tumour spread through air spaces (STAS) in clinical stage I non-small-cell lung cancer (NSCLC) and discuss its potential application value.METHODSA total of 380 patients with clinical stage I NSCLC in Tianjin Chest Hospital were collected as the training cohort and 285 patients in Fujian Provincial Hospital were collected as the validation cohort. Univariable and multivariable logistic regression analyses were performed to determine independent factors for STAS in the training cohort. Based on the results of the multivariable analysis, the nomogram prediction model of STAS was constructed by R software.RESULTSThe incidence of STAS in the training cohort was 39.2%. STAS was associated with worse overall survival and recurrence-free survival (P < 0.01). Univariable analysis showed that maximum tumour diameter, consolidation-to-tumour ratio, spiculation, vacuole and carcinoembryonic antigen were associated with STAS (P < 0.05). Multivariable analysis showed that maximum tumour diameter, consolidation-to-tumour ratio, spiculation sign and vacuole were independent risk factors for STAS (P < 0.05). Based on this, the nomogram prediction model of STAS in clinical stage I NSCLC was constructed and internally validated by bootstrap. The Hosmer–Lemeshow test showed a χ2 value of 7.218 (P = 0.513). The area under the receiver operating characteristic curve and C-index were 0.724 (95% confidence interval: 0.673–0.775). The external validation conducted on the validation cohort produced an area under the receiver operating characteristic curve of 0.759 (95% confidence interval: 0.703–0.816).CONCLUSIONSThe constructed nomogram prediction model of STAS in clinical stage I NSCLC has good calibration and can potentially be applied to guide treatment selection.

Predicting Ki-67 labeling index level in early-stage lung adenocarcinomas manifesting as ground-glass opacity nodules using intra-nodular and peri-nodular radiomic features.

To explore the diagnostic value of radiomics in differentiating between lung adenocarcinomas appearing as ground-glass opacity nodules (GGO) with high- and low Ki-67 expression levels. From January 2018 to January 2021, patients with pulmonary GGO who received lung resection were evaluated for potential enrollment. The included GGOs were then randomly divided into a training cohort and a validation cohort with a ratio of 7:3. Logistic regression (LR), decision tree (DT), support vector machines (SVM), and adaboost (AB) were applied for radiomic model construction. Area under the curve (AUC) of the receiver operating characteristic (ROC) curve was used to evaluate the diagnostic efficacy of the established models. Seven hundred and sixty-nine patients with 769 GGOs were included in this study. Two hundred and forty-five GGOs were confirmed to be of high Ki-67 labeling index (LI). In the training cohort, gender, age, spiculation sign, pleural indentation sign, bubble sign, and maximum 2D diameter of the nodule were found to be significantly different between high- and low Ki-67 LI groups (p < 0.05), and spiculation sign and maximum 2D diameter of the nodule were further confirmed to be risk factors for Ki-67 LI. The radiomic model established using SVM exhibited an AUC of 0.731 in the validation cohort, which was higher than that of the clinical-radiographic model (AUC=0.675). Moreover, radiomic model combining both intra- and peri-nodular features showed better diagnostic efficacy than using intra-nodular features alone (AUC=0.731 and 0.720, respectively). The established radiomic model exhibited good diagnostic efficacy in differentiating between lung adenocarcinoma GGOs with high and low Ki-67 LI, which was higher than the clinical-radiographic model. Peri-nodular radiomic features showed added benefits to the radiomic model. As a novel noninvasive method, radiomics have the potential to be applied in the preliminary classification of Ki-67 expression level in lung adenocarcinoma GGOs.

Relationship between EGFR, ALK Gene Mutation and Imaging and Pathological Features in Invasive Lung Adenocarcinoma

背景与目的当前针对肺腺癌表皮生长因子受体(epidermal growth factor receptor, EGFR)与间变性淋巴瘤激酶(anaplastic lymphoma kinase, ALK)基因突变位点的靶向治疗研究进展十分迅速，为晚期肺腺癌患者的治疗带来新的希望，然而目前EGFR、ALK基因突变在腺癌患者中所具有的特异性影像学与病理学特征仍存在较多争议，本研究进一步探索浸润性肺腺癌EGFR、ALK基因突变与影像学、病理学特征的相关性。方法纳入2018年1月-2019年12月在我们中心接受手术的525例肺腺癌患者。根据术后基因检测的结果将患者分为EGFR基因、ALK基因突变组与野生组，并将EGFR基因突变组分层为外显子19、外显子21突变亚型，将突变组与野生组的病理学特征如组织学亚型、淋巴结转移、脏层胸膜侵犯(visceral pleural invision, VPI)等，影像学特征如肿瘤最大直径、实性成分占比(consolidation tumor ratio, CTR)、分叶征、毛刺征、胸膜牵拉征、空气支气管征、空泡征等进行单因素分析及多因素逻辑回归分析，探讨基因突变组是否具有特异性表现。结果EGFR基因突变组常见于女性(OR=2.041, P=0.001)，存在更多胸膜牵拉征征象(OR=1.506, P=0.042)，病理学上与淋巴结转移及VPI相关性较小(P > 0.05)。其中外显子21突变型腺癌多见于年龄相对较大者(OR=1.022, P=0.036)及女性(OR=2.010, P=0.007)，常伴有较大肿瘤直径(OR=1.360, P=0.039)及胸膜牵拉征(OR=1.754, P=0.029)。外显子19突变型腺癌常见于女性(OR=2.230, P=0.009)、肿瘤实性成分比例较高(OR=1.589, P=0.047)、存在更多分叶征(OR=2.762, P=0.026)。ALK基因突变易发生于有吸烟史(OR=2.950, P=0.045)及较年轻患者(OR=1.070, P=0.002)，病理学上存在更多微乳头型成分(OR=4.184, P=0.019)及VPI(OR=2.986, P=0.034)。结论EGFR、ALK基因突变型腺癌具有特异的影像学及临床病理学特征，且EGFR外显子19或21突变具有不同的影像学特征，对制定肺结节临床处理策略具有重要意义。

Clinical features of 19 cases of lower respiratory papillomatosis

Objective: To explore the clinical characteristics and prognosis of lower respiratory papilloma(LRP)and therefore to improve clinical diagnosis and treatment. Methods: We performed a retrospective analysis of patients who were diagnosed with LRP in our department from October 2008 to October 2020. Results: Nineteen patients were enrolled and 12 were male and 7 were female. The average age of the 7 adult patients was (41.3±17.5)years and that of the 12 pediatric patients was (5.5±3.5)years. Ten (83.3%)of the pediatric patients showed disease onset at an age of less than 5 years. The main symptoms were cough and sputum production (13/19), dyspnea (15/19), hoarseness (10/19) and signs of stridor or wheezing (7/19). Chest CT examination was performed in 9 patients, which showed nodules or masses (9/9), cystic thin-walled cavity (4/9), obstructive pneumonia (2/9), atelectasis (2/9), and spicule sign (1/9). The upper respiratory tract was affected in all the pediatric patients (12/12) and 3/7 of the adult patients. Eighteen cases (18/19) were diagnosed by bronchoscopy, 1 (1/9) by thoracoscopy. Eighteen cases (18/19) showed mulberry-like and papillary lesions under bronchoscopy. All the cases were histologically confirmed as squamous cell papilloma, with 17 cases(17/19)showing tissue HPV6/11(+), 2 negative (2/19). The positive rate of HPV6 was 36.8%, ant that of HPV11 was 21.1%, while the double positive rate of HPV6/11 was 31.6%, and HPV16/18 were negative in all the 19 cases. Isolated respiratory papillomatosis was found in 4 cases (4/19), and multiple papillomatosis in 15 cases (15/19). Seventeen cases (17/19) underwent endoscopic interventional therapy, and the result showed that 15 cases relapsed, and 2 cases had no recurrence. One patient was treated with thoracoscopic lobectomy, and died 4 months after surgery. One patient gave up treatment. Conclusions: LRP is a rare clinical disease with a chronic course, and isolated LRP is even rarer. Young patients often suffer from upper respiratory tract involvement, and the main symptoms are cough, sputum production, dyspnea and hoarseness. CT scanning showed nodules and masses, cystic thin-walled cavities or signs of airway obstruction. Bronchoscopy often demonstrates papillary lesions. The diagnosis depends on pathology, with squamous cell papilloma being the most common, and most tests are positive for HPV6/11. It is suggested that the incidence is associated with low-risk HPV infection. Endoscopic resection is the main treatment, which is prone to relapse. The treatment should take into account the pathological changes of upper respiratory tract, and the etiological treatment of HPV should be stressed.