Malignant Solitary Pulmonary Nodules Research Articles

Highly Sensitive and Specific Panels of Plasma Exosomal microRNAs for Identification of Malignant Pulmonary Nodules.

With wide application of computed tomography (CT) in early lung cancer screening, solitary pulmonary nodules (SPNs) are frequently detected. Due to their high etiological diversity and potential for malignancy, rapid and accurate identification and malignant SPNs are crucial in the clinical management. In the present study, plasma exosomal microRNAs were identified and evaluated as sensitive and specific indicators for malignant SPNs. Exosomal miRNAs isolated from the plasmas of pathologically confirmed patients with SPN (four malignant and four benign, designated as the screening set) were subjected for high throughput sequencing and eight candidate miRNAs were selected. The pre-operation plasma levels of the candidate miRNAs in 77 patients with SPN (48 malignant and 29 benign, designated as the identification set) were detected by quantitative PCR, five miRNAs were identified as potential biomarkers for malignant SPNs, and the diagnostic values of the five miRNAs each alone or combined were then analyzed by AUROC analysis. The prediction values of the identified miRNAs were further evaluated in 95 patients with SPN (double blind, 74 malignant and 21 benign, designated as the validation set). High-throughput sequencing identified 45 miRNAs with statistical differences between benign and malignant SPNs. Among the eight candidate miRNAs in the identification set, miR-1-3p alone had the best diagnostic value, with the sensitivities and specificities of 89.6% and 100% for malignant SPNs. Unexpectedly, when miR-1-3p was combined with miR-99a-5p, both the sensitivity and specificity reached 100% for malignant SPNs. miR-1-3p+miR-125b-5p and miR-1-3p+miR-218-5p were also good indicators of malignant SPNs with sensitivities of 95.8% and 97.9%, specificities of 100% and 96.6%. Further analysis of these microRNA combinations in the validation set indicated that the PPV were 91.4%, 97.4%, and 93.5% and the NPV were 100%, 100%, and 88.9% for miR-1-3p+miR-99a-5p, miR-1-3p+miR-218-5p, and miR-1-3p+miR-125b-5p, with the sensitivities were 100%, 100%, and 97.3% and the specificities were 66.7%, 90.5%, and 76.2% for miR-1-3p+miR-99a-5p, miR-1-3p+miR-218-5p, and miR-1-3p+miR-125b-5p, respectively. Through high throughput sequencing, qPCR determination of plasma microRNAs and AUROC analysis, miR-1-3p combined with miR-99a-5p, miR-125b-5p, or miR-218-5p have been found to be sensitive and specific indicators of malignant SPNs in both the identification and validation sets. Our results indicate that the panels of plasma miRNAs can be used as diagnostic biomarkers for malignant SPNs.

Predicting malignant potential of solitary pulmonary nodules in patients with COVID-19 infection: a comprehensive analysis of CT imaging and tumor markers

ObjectiveTo analyze the value of combining computed tomography (CT) with serum tumor markers in the differential diagnosis of benign and malignant solitary pulmonary nodules (SPNs).MethodsThe case data of 267 patients diagnosed with SPNs in the First Affiliated Hospital of Zhengzhou University from March 2020 to January 2023 were retrospectively analyzed. All individuals diagnosed with coronavirus disease 2019 (COVID-19) were confirmed via respiratory specimen viral nucleic acid testing. The included cases underwent CT, serum tumor marker testing and pathological examination. The diagnostic efficacy and clinical significance of CT, serum tumor marker testing and a combined test in identifying benign and malignant SPNs were analyzed using pathological histological findings as the gold standard. Finally, a nomogram mathematical model was established to predict the malignant probability of SPNs.ResultsOf the 267 patients with SPNs, 91 patients were not afflicted with COVID-19, 36 exhibited malignant characteristics, whereas 55 demonstrated benign features. Conversely, within the cohort of 176 COVID-19 patients presenting with SPNs, 62 were identified as having malignant SPNs, and the remaining 114 were diagnosed with benign SPNs. CT scans revealed statistically significant differences between the benign and malignant SPNs groups in terms of CT values (P<0.001), maximum nodule diameter (P<0.001), vascular convergence sign (P<0.001), vacuole sign (P = 0.0007), air bronchogram sign (P = 0.0005), and lobulation sign (P = 0.0005). Malignant SPNs were associated with significantly higher levels of carcinoembryonic antigen (CEA) and neuron-specific enolase (NSE) compared to benign SPNs (P < 0.05), while no significant difference was found in carbohydrate antigen 125 (CA125) levels (P = 0.054 for non-COVID-19; P = 0.072 for COVID-19). The sensitivity (95.83%), specificity (95.32%), and accuracy (95.51%) of the comprehensive diagnosis combining serum tumor markers and CT were significantly higher than those of CT alone (70.45%, 79.89%, 76.78%) or serum tumor marker testing alone (56.52%, 73.71%, 67.79%) (P < 0.05). A visual nomogram predictive model for malignant pulmonary nodules was constructed.ConclusionCombining CT with testing for CEA, CA125, and NSE levels offers high diagnostic accuracy and sensitivity, enables precise differentiation between benign and malignant nodules, particularly in the context of COVID-19, thereby reducing the risk of unnecessary surgical interventions.

Integrating Machine Learning in Clinical Practice for Characterizing the Malignancy of Solitary Pulmonary Nodules in PET/CT Screening.

The study investigates the efficiency of integrating Machine Learning (ML) in clinical practice for diagnosing solitary pulmonary nodules' (SPN) malignancy. Patient data had been recorded in the Department of Nuclear Medicine, University Hospital of Patras, in Greece. A dataset comprising 456 SPN characteristics extracted from CT scans, the SUVmax score from the PET examination, and the ultimate outcome (benign/malignant), determined by patient follow-up or biopsy, was used to build the ML classifier. Two medical experts provided their malignancy likelihood scores, taking into account the patient's clinical condition and without prior knowledge of the true label of the SPN. Incorporating human assessments into ML model training improved diagnostic efficiency by approximately 3%, highlighting the synergistic role of human judgment alongside ML. Under the latter setup, the ML model had an accuracy score of 95.39% (CI 95%: 95.29-95.49%). While ML exhibited swings in probability scores, human readers excelled in discerning ambiguous cases. ML outperformed the best human reader in challenging instances, particularly in SPNs with ambiguous probability grades, showcasing its utility in diagnostic grey zones. The best human reader reached an accuracy of 80% in the grey zone, whilst ML exhibited 89%. The findings underline the collaborative potential of ML and human expertise in enhancing SPN characterization accuracy and confidence, especially in cases where diagnostic certainty is elusive. This study contributes to understanding how integrating ML and human judgement can optimize SPN diagnostic outcomes, ultimately advancing clinical decision-making in PET/CT screenings.

WITHDRAWN: Diffusion kurtosis imaging (DKI) in characterization in indetermined solitary pulmonary nodules (SPNs): comparison with conventional DWI and quantitative

We contrasted traditional DWI with quantitative imaging to better understand the function of diffusion kurtosis imaging (DKI) in characterising solitary pulmonary nodules (SPNs).Forty-seven consecutive patients with SPNs (30 males, 17 females, median age: 61 years; range: 29 to 86 years) were included in this paper between March 2018 and July 2020. Quantitative DCE-MRI and multi-b factor DWI (b values ranging from 0 to 2000 sec/mm2) were obtained. We compared the ADC, Kapp, Dapp, Ktrans, Kep, Ve, and iAUC values between the malignant and benign groups, as well as between the various LC subtypes. The purpose of the ROC curves was to evaluate the diagnostic value.Kapp, Ktrans, Ve and iAUC values were obviously higher for malignant SPNs compared to that of benign SPNs (P < 0.035). ADC was obviously higher in benignity compared to malignant SPNs (P = 0.001). There exist no obvious difference in Dapp and Kep between the two groups (P = 0.06). Kapp value has highest sensitivity (81.8%) and accuracy (75.7%), and ADC value has highest specificity (80.0%). Combination of ADC and iAUC enhanced the sensitivity to 81.8%, specificity to 86.7% and precision to 83.8%.

Value of peripheral blood rare cell EGFR gene amplification detection in the evaluation of benign and malignant pulmonary nodules

Objective: To explore the value of detection of epidermal growth factor receptor (EGFR) gene amplification in peripheral blood rare cells in the assessment of benign and malignant pulmonary nodules. Methods: A total of 262 patients with pulmonary nodules were selected as the retrospectively study subjects from the Second Affiliated Hospital of Army Military Medical University and Peking Union Medical College Hospital from July 2022 to August 2023. There were 98 males and 164 females, with the age range from 16 to 79 (52.1±12.1) years. The EGFR gene amplification testing was performed on the rare cells enriched from patients' peripheral blood, and the clinical manifestations, CT imaging features, histopathological and/or pathological cytological confirmed results of patients were collected. The receiver operating characteristic (ROC) curve was used to determine the optimal cut-off value of the method of detection of EGFR gene amplification in peripheral blood rare cells, and its diagnostic efficacy was evaluated. Results: Among the 262 patients, 143 were malignant pulmonary nodules and 119 were benign pulmonary nodules. The differences between malignant pulmonary nodules and benign pulmonary nodules in nodule diameter and nodule density were statistically significant (both P<0.001), while the differences in age, gender and nodule number were not statistically significant (all P>0.05). The number [M (Q1, Q3)] of EGFR gene amplification positive rare cells in patients with malignant pulmonary nodule was 8 (6, 11), which was higher than that in patients with benign pulmonary nodule [2 (1, 4), P<0.001]. The ROC curve results showed that when the optimal cut-off value was 5 (that was, the number of EGFR gene amplification positive rare cells was>5), the area under the curve (AUC) of the detection of EGFR gene amplification in peripheral blood rare cells for discrimination of benign and malignant pulmonary lesions was 0.816 (95%CI: 0.761-0.870), with a sensitivity of 83.2%, a specificity of 80.7%, and an accuracy of 82.1%. Based on the analysis of the diameter of the nodules, the AUC for distinguishing between benign and malignant pulmonary nodules with diameter 5-9 mm and 10-30 mm was 0.797 (95%CI: 0.707-0.887) and 0.809 (95%CI: 0.669-0.949), respectively, with sensitivity, specificity and accuracy reached 75% or above. Based on the analysis of nodule density, the AUC for distinguishing between benign and malignant solid nodule and subsolid nodule was 0.845 (95%CI: 0.751-0.939) and 0.790 (95%CI: 0.701-0.880), respectively, with sensitivity, specificity and accuracy reached 75% or above. Based on the analysis of nodule number, the AUC for distinguishing between benign and malignant solitary pulmonary nodule and multiple pulmonary nodule was 0.830 (95%CI: 0.696-0.965) and 0.817 (95%CI: 0.758-0.877), respectively, with sensitivity, specificity and accuracy reached 80% or above. Conclusion: The detection of EGFR gene amplification in peripheral blood rare cells contributes to the evaluation of benign and malignant pulmonary nodules, and can be used in the auxiliary diagnosis of benign and malignant pulmonary nodules.

Decreased levels of sex hormones in females with solitary pulmonary nodules were risk factors for malignancy

ObjectiveThe purpose of this research was to detect the relationship between the levels of sex hormones in females with solitary pulmonary nodules (SPNs) and their potential malignancies.MethodsA total of 187 consecutive patients with pathologically confirmed SPNs by chest CT were enrolled in our study. They were divided into two groups based on the pathologic findings of SPNs after surgery: benign and malignant SPNs. Progesterone (P), estradiol (E2), and testosterone (T) levels in the two groups were measured. Meanwhile, we used binary logistic regression analysis to analyze the risk factors for SPNs.ResultsOf these 187 patients, 73 had benign SPNs, while 114 had malignant SPNs. We found that the levels of progesterone (P), estradiol (E2), and testosterone (T) were decreased significantly in patients with malignant SPNs compared to patients with benign SPNs (all P < 0.05). Multivariate logistic regression analysis revealed that second-hand smoke, burr sign, lobulation sign, pleural traction sign, vascular convergence sign, vacuole sign, and ≥ 1 cm nodules were independent risk factors for malignant pulmonary nodules in females.ConclusionsDecreased levels of sex hormones in females were associated with malignant pulmonary nodules, suggesting that they can contribute to the diagnosis of lung cancer.

The differential computed tomography features between small benign and malignant solid solitary pulmonary nodules with different sizes.

Computed tomography (CT) has been widely known to be the first choice for the diagnosis of solid solitary pulmonary nodules (SSPNs). However, the smaller the SSPN is, the less the differential CT signs between benign and malignant SSPNs there are, which brings great challenges to their diagnosis. Therefore, this study aimed to investigate the differential CT features between small (≤15 mm) benign and malignant SSPNs with different sizes. From May 2018 to November 2021, CT data of 794 patients with small SSPNs (≤15 mm) were retrospectively analyzed. SSPNs were divided into benign and malignant groups, and each group was further classified into three cohorts: cohort I (diameter ≤6 mm), cohort II (6 mm < diameter ≤8 mm), and cohort III (8 mm < diameter ≤15 mm). The differential CT features of benign and malignant SSPNs in three cohorts were identified. Multivariable logistic regression analyses were conducted to identify independent factors of benign SSPNs. In cohort I, polygonal shape and upper-lobe distribution differed significantly between groups (all P<0.05) and multiparametric analysis showed polygonal shape [adjusted odds ratio (OR): 12.165; 95% confidence interval (CI): 1.512-97.872; P=0.019] was the most effective variation for predicting benign SSPNs, with an area under the receiver operating characteristic curve (AUC) of 0.747 (95% CI: 0.640-0.855; P=0.001). In cohort II, polygonal shape, lobulation, pleural retraction, and air bronchogram differed significantly between groups (all P<0.05), and polygonal shape (OR: 8.870; 95% CI: 1.096-71.772; P=0.041) and the absence of pleural retraction (OR: 0.306; 95% CI: 0.106-0.883; P=0.028) were independent predictors of benign SSPNs, with an AUC of 0.778 (95% CI: 0.694-0.863; P<0.001). In cohort III, 12 CT features showed significant differences between groups (all P<0.05) and polygonal shape (OR: 3.953; 95% CI: 1.508-10.361; P=0.005); calcification (OR: 3.710; 95% CI: 1.305-10.551; P=0.014); halo sign (OR: 6.237; 95% CI: 2.838-13.710; P<0.001); satellite lesions (OR: 6.554; 95% CI: 3.225-13.318; P<0.001); and the absence of lobulation (OR: 0.066; 95% CI: 0.026-0.167; P<0.001), air space (OR: 0.405; 95% CI: 0.215-0.764; P=0.005), pleural retraction (OR: 0.297; 95% CI: 0.179-0.493; P<0.001), bronchial truncation (OR: 0.165; 95% CI: 0.090-0.303; P<0.001), and air bronchogram (OR: 0.363; 95% CI: 0.208-0.633; P<0.001) were independent predictors of benign SSPNs, with an AUC of 0.869 (95% CI: 0.840-0.897; P<0.001). CT features vary between SSPNs with different sizes. Clarifying the differential CT features based on different diameter ranges may help to minimize ambiguities and discriminate the benign SSPNs from malignant ones.

Risk analysis of visceral pleural invasion in malignant solitary pulmonary nodules that appear touching the pleural surface.

The preoperative determination of visceral pleural invasion (VPI) in patients with malignant solitary pulmonary nodules (SPNs) is essential for determining the surgical range and selecting adjuvant chemotherapy. This study aimed to systematically investigate risk factors of VPI in patients with SPN and construct a preoperative predictive model for such patients. This is a retrospective study. The clinical, radiological, and pathological characteristics of study subjects were reviewed, and the groups with and without VPI were compared. Multivariate logistic analysis was utilized to identify independent risk factors for VPI. Moreover, a predictive nomogram was constructed to assess the likelihood of VPI occurrence. Of the 364 enrolled cases, SPNs adjacent to the pleura with VPI were found in 110 (30.2%) patients. By incorporating four preoperative variables, including tumor diameter (>2 cm), maximum computed tomography value (>200 Hu), air bronchogram sign, and age, a preoperative predictive nomogram was constructed. The nomogram demonstrated good discriminative ability, with a C-index of 0.736 (95% CI (0.662-0.790)). Furthermore, our data indicated that the air bronchogram sign (odd ratio (OR) 1.81, 95% CI (0.99-3.89), p = 0.048), a maximum diameter >2 cm (OR 24.48, 95% CI (8.43-71.07), p < 0.001), pathological type (OR 5.01, 95% CI (2.61-9.64), p < 0.001), and Ki-67 >30% (OR 2.95, 95% CI (1.40-6.21), p = 0.004) were overall independent risk factors for VPI. This study investigated the risk factors for VPI in malignant SPNs touching the pleural surface. Additionally, a nomogram was developed to predict the likelihood of VPI in such patients, facilitating informed decision-making regarding surgical approaches and treatment protocols.

Principal Component Analysis Applied to Radiomics Data: Added Value for Separating Benign from Malignant Solitary Pulmonary Nodules.

Here, we report on the added value of principal component analysis applied to a dataset of texture features derived from 39 solitary pulmonary lung nodule (SPN) lesions for the purpose of differentiating benign from malignant lesions, as compared to the use of SUVmax alone. Texture features were derived using the LIFEx software. The eight best-performing first-, second-, and higher-order features for separating benign from malignant nodules, in addition to SUVmax (MaximumGreyLevelSUVbwIBSI184IY), were included for PCA. Two principal components (PCs) were retained, of which the contributions to the total variance were, respectively, 87.6% and 10.8%. When included in a logistic binomial regression analysis, including age and gender as covariates, both PCs proved to be significant predictors for the underlying benign or malignant character of the lesions under study (p = 0.009 for the first PC and 0.020 for the second PC). As opposed to SUVmax alone, which allowed for the accurate classification of 69% of the lesions, the regression model including both PCs allowed for the accurate classification of 77% of the lesions. PCs derived from PCA applied on selected texture features may allow for more accurate characterization of SPN when compared to SUVmax alone.

A clinical–radiological predictive model for solitary pulmonary nodules and the relationship between radiological features and pathological subtype

To develop a clinical-radiological model to predict the malignancy of solitary pulmonary nodules (SPNs) and to evaluate the accuracy of chest computed tomography imaging characteristics of SPN in diagnosing pathological type. The predictive model was developed using a retrospective cohort of 601 SPN patients (Group A) between July 2015 and July 2020. The established model was tested using a second retrospective cohort of 124 patients between August 2020 and August 2021 (Group B). The radiological characteristics of all adenocarcinomas in two groups were analysed to determine the correlation between radiological and pathological characteristics. Malignant nodules were found in 78.87% of cases and benign in 21.13%. Two clinical characteristics (age and gender) and four radiological characteristics (calcification, vascular convergence, pleural retraction sign, and density) were identified as independent predictors of malignancy in patients with SPN using logistic regression analysis. The area under the receiver operating characteristic curve (0.748) of the present model was greater than the other two reported models. Diameter, spiculation, lobulation, vascular convergence, and pleural retraction signs differed significantly among pre-invasive lesions, minimally invasive adenocarcinoma, and invasive adenocarcinoma. Only diameter and density were significantly different among invasive adenocarcinoma subtypes. Older age, male gender, no calcification, vascular convergence, pleural contraction sign, and lower density were independent malignancy predictors of SPNs. Furthermore, the pathological classification can be clarified based on the radiological characteristics of SPN, providing a new option for the prevention and treatment of early lung cancer.

Nomogram combining clinical and radiological characteristics for predicting the malignant probability of solitary pulmonary nodules measuring ≤ 2 cm.

At present, how to identify the benign or malignant nature of small (≤ 2 cm) solitary pulmonary nodules (SPN) are an urgent clinical challenge. This retrospective study aimed to develop a clinical prediction model combining clinical and radiological characteristics for assessing the probability of malignancy in SPNs measuring ≤ 2 cm. In this study, we included patients with SPNs measuring ≤ 2 cm who underwent pulmonary resection with definite pathology at Qilu Hospital of Shandong University from January 2020 to December 2021. Clinical features, preoperative biomarker results, and computed tomography characteristics were collected. The enrolled patients were randomized at a ratio of 7:3 into a training cohort of 775 and a validation cohort of 331. The training cohort was used to construct the predictive model, while the validation cohort was used to test the model independently. Univariate and multivariate logistic regression analyses were performed to identify independent risk factors. The prediction model and nomogram were established based on the independent risk factors. The receiver operating characteristic (ROC) curve was used to evaluate the identification ability of the model. The calibration power was evaluated using the Hosmer-Lemeshow test and calibration curve. The clinical utility of the nomogram was also assessed by decision curve analysis (DCA). A total of 1,106 patients were included in this study. Among them, the malignancy rate of SPNs was 85.08% (941/1,106). We finally identified the following six independent risk factors by logistic regression: age, carcinoembryonic antigen, nodule shape, calcification, maximum diameter, and consolidation-to-tumor ratio. The area under the ROC curve (AUC) for the training cohort was 0.764 (95% confidence interval [CI]: 0.714-0.814), and the AUC for the validation cohort was 0.729 (95% CI: 0.647-0.811), indicating that the prediction accuracy of nomogram was relatively good. The calibration curve of the predictive model also demonstrated a good calibration in both cohorts. DCA proved that the clinical prediction model was useful in clinical practice. We developed and validated a predictive model and nomogram for estimating the probability of malignancy in SPNs measuring ≤ 2 cm. With the application of predictive models, thoracic surgeons can make more rational clinical decisions while avoiding overtreatment and wasting medical resources.

Serum lncRNA THRIL predicts benign and malignant pulmonary nodules and promotes the progression of pulmonary malignancies

BackgroundThis project aimed to research the significance of THRIL in the diagnosis of benign and malignant solitary pulmonary nodules (SPNs) and to investigate the role of THRIL/miR-99a in malignant SPNs.MethodsThe study groups consisted of 169 patients with SPN and 74 healthy subjects. The differences in THRIL levels were compared between the two groups and the healthy group. The receiver operating characteristic curve (ROC) was utilized to analyze the THRIL’s significance in detecting benign and malignant SPN. Pearson correlation and binary regression coefficients represented the association between THRIL and SPN. CCK-8 assay, Transwell assay, and flow cytometry were utilized to detect the regulatory effect of THRIL silencing. The interaction between THRIL, miR-99a, and IGF1R was confirmed by the double luciferase reporter gene.ResultsThere were differences in THRIL expression in the healthy group, benign SPN group, and malignant SPN group. High accuracy of THRIL in the diagnosis of benign SPN and malignant SPN was observed. THRIL was associated with the development of SPN. The expression of THRIL was upregulated and miR-99a was downregulated in lung cancer cells. The double luciferase report experiment confirmed the connections between THRIL/miR-99a/IGF1R. Silencing THRIL could suppress cell proliferation, migration, and invasion and promote cell apoptosis by binding miR-99a.ConclusionThe detection of THRIL in serum is useful for the assessment of malignant SPN. THRIL can regulate the expression of IGF1R through miR-99a, thereby promoting the growth of lung cancer cells and inhibiting apoptosis.

CLSSL-ResNet: Predicting malignancy of solitary pulmonary nodules from CT images by chimeric label with self-supervised learning.

Pulmonary granulomatous nodules (GN) with spiculation or lobulation have a similar morphological appearance to solid lung adenocarcinoma (SADC) under computed tomography (CT). However, these two kinds of solid pulmonary nodules (SPN) have different malignancies and are sometimes misdiagnosed. This study aims to predict malignancies of SPNs by a deep learning model automatically. A chimeric label with self-supervised learning (CLSSL) is proposed to pre-train a ResNet-based network (CLSSL-ResNet) for distinguishing isolated atypical GN from SADC in CT images. The malignancy, rotation, and morphology labels are integrated into a chimeric label and utilized to pre-train a ResNet50. The pre-trained ResNet50 are then transferred and fine-tuned to predict the malignancy of SPN. Two image datasets of 428 subjects (Dataset1, 307; Dataset2, 121) from different hospitals are collected. Dataset1 is divided into training, validation, and test data by a ratio of 7:1:2 to develop the model. Dataset2 is utilized as an external validation dataset. CLSSL-ResNet achieves an area under the ROC curve (AUC) of 0.944 and an accuracy (ACC) of 91.3%, which was much higher than that of the consensus of two experienced chest radiologists (77.3%). CLSSL-ResNet also outperforms other self-supervised learning models and many counterparts of other backbone networks. In Dataset2, AUC and ACC of CLSSL-ResNet are 0.923 and 89.3%, respectively. Additionally, the ablation experiment result indicates higher efficiency of the chimeric label. CLSSL with morphology labels can increase the ability of feature representation by deep networks. As a non-invasive method, CLSSL-ResNet can distinguish GN from SADC via CT images and may support clinical diagnoses after further validation.

Value of spectral computed tomography-derived quantitative parameters based on full volume analysis in the diagnosis of benign/malignant and pathological subtypes of solitary pulmonary nodules.

Conventional dynamic computed tomography (CT) has a low specificity for the distinction between benign and malignant solitary pulmonary nodules (SPNs), and spectral CT has been proposed as a potential alternative. We aimed to investigate the role of quantitative parameters based on full-volume spectral CT in the differential diagnosis of SPNs. This retrospective study included spectral CT images of 100 patients with pathologically confirmed SPNs (78 and 22 in the malignant and benign groups, respectively). All cases were confirmed by postoperative pathology, percutaneous biopsy, and bronchoscopic biopsy. Multiple quantitative parameters derived from spectral CT were extracted from whole-tumor volume and standardized. Differences in quantitative parameters between groups were statistically analyzed. Diagnostic efficiency was evaluated by generating a receiver operating characteristic (ROC) curve. Between-group differences were evaluated using an independent sample t-test or Mann-Whitney U test. Interobserver repeatability was assessed using intraclass correlation coefficients (ICCs) and Bland-Altman plots. Spectral CT-derived quantitative parameters, except attenuation difference between the SPN in the 70 keV and arterial enhancement [ΔS-A(70 keV)], were significantly higher for malignant SPNs than for benign nodules (P<0.05). In the subgroup analysis, most parameters could distinguish between the benign and adenocarcinoma groups, and between the benign and squamous cell carcinoma groups (P<0.05). Only 1 parameter could differentiate the adenocarcinoma and squamous cell carcinoma groups (P=0.020). ROC curve analysis indicated that normalized arterial enhancement fraction in the 70 keV (NEF70 keV), normalized iodine concentration (NIC), and Δ70 keV had high diagnostic efficacy for differentiating SPNs between the benign and malignant SPNs [area under the curve (AUC): 0.867, 0.866, and 0.848, respectively] and between the benign and adenocarcinoma groups (AUC: 0.873, 0.872, and 0.874, respectively). The multiparameters derived from spectral CT exhibited satisfactory interobserver repeatability (ICC: 0.856-0.996). Our study suggests that quantitative parameters derived from whole-volume spectral CT may be useful to improve discrimination of SPNs.

Preoperative diagnosis of solitary pulmonary nodules with a novel hematological index model based on circulating tumor cells.

Preoperative noninvasive diagnosis of the benign or malignant solitary pulmonary nodule (SPN) is still important and difficult for clinical decisions and treatment. This study aimed to assist in the preoperative diagnosis of benign or malignant SPN using blood biomarkers. A total of 286 patients were recruited for this study. The serum FR+CTC, TK1, TP, TPS, ALB, Pre-ALB, ProGRP, CYFRA21-1, NSE, CA50, CA199, and CA242 were detected and analyzed. In the univariate analysis, age, FR+CTC, TK1, CA50, CA19.9, CA242, ProGRP, NSE, CYFRA21-1, and TPS showed the statistical significance of a correlation with malignant SPNs (P <0.05). The highest performing biomarker is FR+CTC (odd ratio [OR], 4.47; 95% CI: 2.57-7.89; P <0.001). The multivariate analysis identified that age (OR, 2.69; 95% CI: 1.34-5.59, P = 0.006), FR+CTC (OR, 6.26; 95% CI: 3.09-13.37, P <0.001), TK1 (OR, 4.82; 95% CI: 2.4-10.27, P <0.001), and NSE (OR, 2.06; 95% CI: 1.07-4.06, P = 0.033) are independent predictors. A prediction model based on age, FR+CTC, TK1, CA50, CA242, ProGRP, NSE, and TPS was developed and presented as a nomogram, with a sensitivity of 71.1% and a specificity of 81.3%, and the AUC was 0.826 (95% CI: 0.768-0.884). The novel prediction model based on FR+CTC showed much stronger performance than any single biomarker, and it can assist in predicting benign or malignant SPNs.

Spectral CT-based radiomics signature for distinguishing malignant pulmonary nodules from benign

ObjectivesTo evaluate the discriminatory capability of spectral CT-based radiomics to distinguish benign from malignant solitary pulmonary solid nodules (SPSNs).Materials and methodsA retrospective study was performed including 242 patients with SPSNs who underwent contrast-enhanced dual-layer Spectral Detector CT (SDCT) examination within one month before surgery in our hospital, which were randomly divided into training and testing datasets with a ratio of 7:3. Regions of interest (ROIs) based on 40-65 keV images of arterial phase (AP), venous phases (VP), and 120kVp of SDCT were delineated, and radiomics features were extracted. Then the optimal radiomics-based score in identifying SPSNs was calculated and selected for building radiomics-based model. The conventional model was developed based on significant clinical characteristics and spectral quantitative parameters, subsequently, the integrated model combining radiomics-based model and conventional model was established. The performance of three models was evaluated with discrimination, calibration, and clinical application.ResultsThe 65 keV radiomics-based scores of AP and VP had the optimal performance in distinguishing benign from malignant SPSNs (AUC65keV-AP = 0.92, AUC65keV-VP = 0.88). The diagnostic efficiency of radiomics-based model (AUC = 0.96) based on 65 keV images of AP and VP outperformed conventional model (AUC = 0.86) in the identification of SPSNs, and that of integrated model (AUC = 0.97) was slightly further improved. Evaluation of three models showed the potential for generalizability.ConclusionsAmong the 40-65 keV radiomics-based scores based on SDCT, 65 keV radiomics-based score had the optimal performance in distinguishing benign from malignant SPSNs. The integrated model combining radiomics-based model based on 65 keV images of AP and VP with Zeff-AP was significantly superior to conventional model in the discrimination of SPSNs.

Radiomics and Artificial Intelligence Can Predict Malignancy of Solitary Pulmonary Nodules in the Elderly.

Solitary pulmonary nodules (SPNs) are a diagnostic and therapeutic challenge for thoracic surgeons. Although such lesions are usually benign, the risk of malignancy remains significant, particularly in elderly patients, who represent a large segment of the affected population. Surgical treatment in this subset, which usually presents several comorbidities, requires careful evaluation, especially when pre-operative biopsy is not feasible and comorbidities may jeopardize the outcome. Radiomics and artificial intelligence (AI) are progressively being applied in predicting malignancy in suspicious nodules and assisting the decision-making process. In this study, we analyzed features of the radiomic images of 71 patients with SPN aged more than 75 years (median 79, IQR 76-81) who had undergone upfront pulmonary resection based on CT and PET-CT findings. Three different machine learning algorithms were applied-functional tree, Rep Tree and J48. Histology was malignant in 64.8% of nodules and the best predictive value was achieved by the J48 model (AUC 0.9). The use of AI analysis of radiomic features may be applied to the decision-making process in elderly frail patients with suspicious SPNs to minimize the false positive rate and reduce the incidence of unnecessary surgery.

Retracted: Application of CT Postprocessing Reconstruction Technique in Differential Diagnosis of Benign and Malignant Solitary Pulmonary Nodules and Analysis of Risk Factors.

[This retracts the article DOI: 10.1155/2022/9739047.].

Total Lesion Glycolysis Obtained by FDG PET/CT in Diagnosing Solitary Pulmonary Nodules.

To evaluate the diagnostic performance of total lesion glycolysis (TLG) obtained by 18F-FDG PET/ CT to differentiate malignant solitary pulmonary nodules (SPNs) from benign ones. Observational study. Department of Nuclear Medicine, Istanbul Medeniyet University School of Medicine, Istanbul, Turkey, from January 2018 to April 2022. Eighty patients, who were found to have SPNs and underwent PET/CT imaging, were enrolled in this retrospective study. Parameters of PET-CT such as metabolic, volumetric, and metabolovolumetric were assessed concerning diagnostic value. Moreover, maximum standard uptake value (SUVmax) and TLG were combined and compared to improve diagnostic accuracy. The number of the detected benign and malignant SPNs was 38 and 42, respectively. Compared to the benign lesions, the malignant nodules presented significantly higher values in terms of SUVmax, TLG, and the volumes of metabolic tumour (MTV) and CT. Considering all parameters, the highest area under the curve (AUC) was occupied by TLG and SUVmax. The values for the sensitivity, specificity, and accuracy of SUVmax, TLG, and SUVmax combined with TLG were as follows respectively: 97.6%, 63.2%, and 81.2%; 85.7%, 92.1%, and 88.7%; and 85.7%, 94.7%, and 90%. The conventional value of SUVmax does not yield satisfactory results in order to separate the malignant nodules from the benign ones. The SUVmax value could be more valuable if it is used with TLG measurement in diagnosing SPNs. 18F-FDG PET/CT, SUVmax, TLG, Pulmonary nodule.

18F-FDG-PET/CT Value in Discriminating between Benign and Malignant Solitary Pulmonary Nodules

18F-FDG-PET/CT Value in Discriminating between Benign and Malignant Solitary Pulmonary Nodules