Malignant Solitary Pulmonary Nodules Research Articles

Evaluation of dual‐energy and perfusion CT parameters for diagnosing solitary pulmonary nodules

BackgroundTo evaluate the correlation and accuracy of dual‐energy CT (DECT) (70/Sn150) and low‐dose volume perfusion CT (VPCT) parameters for the diagnosis of solitary pulmonary nodules (SPN).MethodsA total of 15 patients with benign SPN (mean age 56 ± 7 years) and 34 patients with malignant SPN and clinical indication for surgery (mean age 58 ± 6 years) were enrolled from July 2017 to September 2019 at a single institution. All the patients underwent low‐dose VPCT with a scan volume of 114 mm on the z‐axis and a venous phase enhancement DECT (70/150 Sn) scan just before surgery on the same day. All CT findings were studied in comparison with the pathological results after surgery. Perfusion and dual‐energy CT parameters such as blood flow (BF), blood volume (BV), mean transit time (MTT), flow extraction product (FED), pulmonary nodule enhancement peak (PPnod) and iodine concentration (IC) were evaluated as well as t‐test, chi‐square test, Pearson correlation analysis, and ROC curve analysis to determine the significance of study parameters.ResultsThe effective radiation dosage of the VPCT and DECT scans were 4.67 ± 0.26 mSv and 0.32 ± 0.10 mSv, respectively. Significant correlations were found between iodine concentration from DECT and VPCT parameters (r = 0.376–0.533, p < 0.05). The sensitivity and specificity of IC to differentiate the SPN were 86.67% and 72.73%, which was slightly lower than that of BV (94.44%, 73.33%), FED (88.89%, 80.00%) and PPnod (94.44%, 80.00%).ConclusionsVPCT scans have low radiation dosage achieved by shortening the z‐axis scan range for assessment of SPN. IC from DECT is significantly correlated with VPCT parameters, and VPCT parameters have better diagnostic performance for SPN than DECT parameters.

MRI Image Segmentation Model with Support Vector Machine Algorithm in Diagnosis of Solitary Pulmonary Nodule.

This study focused on the application value of MRI images processed by a Support Vector Machine (SVM) algorithm-based model in diagnosis of benign and malignant solitary pulmonary nodule (SPN). The SVM algorithm was constrained by a self-paced regularization item and gradient value to establish the MRI image segmentation model (SVM-L) for lung. Its performance was compared factoring into the Dice index (DI), sensitivity (SE), specificity (SP), and Mean Square Error (MSE). 28 SPN patients who underwent the parallel MRI examination were selected as research subjects and were divided into the benign group (11 patients) and malignant group (17 patients) according to different plans for diagnosis and treatment. The apparent diffusion coefficient (ADC) at different b values was analyzed, and the steepest slope (SS) and washout ratio (WR) values in the two groups were calculated. The result showed that the MSE, DI, SE, SP values, and operation time of the SVM-L model were (0.41 ± 0.02), (0.84 ± 0.13), (0.89 ± 0.04), (0.993 ± 0.004), and (30.69 ± 2.60)s, respectively, apparently superior to those of the other algorithms, but there were no statistic differences (P > 0.05) in the WR value between the two groups of patients. The SS values of the time-signal curve in the benign and malignant groups were (2.52 ± 0.69) %/s and (3.34 ± 00.41) %/s, respectively. Obviously, the SS value of the benign group was significantly lower than that of the malignant group (P < 0.01). The ADC value with different b values in the benign group was significantly lower than that of the malignant group (P < 0.01). It suggested that the SVM-L model significantly improved the quality of lung MRI images and increased the accuracy to differentiate benign and malignant SPN, providing reference for the diagnosis and treatment of SPN patients.

Abstract 2614: A deep learning model-based lung cancer risk assessment for incidental pulmonary nodules

Abstract Lung cancer is the leading cause of cancer death among both men and women. The solitary pulmonary nodule (SPN) is frequently seen on radiographs and computed tomography (CT) and often provokes additional clinical and imaging activities as an SPN alerts possible early stage lung cancer. However, reliable diagnosis of malignant lung nodules in the workup of SPN is challenging, making it difficult for early stage cancer management to avoid morbidity due to malignant cancer, patient distress, and increased costs caused by more invasive and unwarranted procedures for benign cases. We developed a deep learning model based on convolutional neural networks (CNNs) to predict lung cancer risk for incidental SPN. The model can help better management of incidental lung cancer by providing a reliable characterization of SPNs. Deep learning models currently are applied to nodule detection in low-dose CT scans of lung cancer screening for at-risk population. Our work aims to investigate whether deep learning can differentiate benign from malignant incidental SPN during normal dose CT exams, where the number of patients with SPN is an order more than that of lung screening population. We collected an incidental SPN dataset containing 139 CT scans to generate the nodule malignancy prediction model using 3D CNN. This dataset was collected from either contrast or non-contrast CT scans with regular doses. All 139 patients went through surgeries to remove the tumors. CT images were collected within one year before the surgery. 90 cases are malignant and the rest are benign. Ground truth labels were obtained from biopsies. Data were preprocessed by resampling, normalization, and cropping to unique volume around the SPN. The radiologists annotated the SPN location. Since the two nodule classes are imbalanced, we did online data augmentation to increase the variety of lung nodule images and balance the two classes. We used 80% of the data for training and 20% for testing. The deep learning structure adapted is a 3D Resnet with 68 layers and 33M parameters. The area under the receiving operating characteristics curve (AUC) of the prediction result was 0.81 while the accuracy was 79.6%. Applying GradCam to visualize important regions in CT scans contributing to the final predictions, we found that our trained ResNet model focuses on meaningful areas in lung nodules and detects malignant SPNs in most cases. Our preliminary results shows the feasibility of using deep learning to predict incidental lung nodules and improve diagnostic speed and accuracy. Citation Format: Pengyu Yuan, Tiancheng He, Hien Nguyen, Stephen T. Wong. A deep learning model-based lung cancer risk assessment for incidental pulmonary nodules [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 2614.

Value of Combined Detection of Cytokines and Tumor Markers in the Differential Diagnosis of Benign and Malignant Solitary Pulmonary Nodules

背景与目的近年来，孤立性肺结节（solitary pulmonary nodule, SPN）受到越来越多的关注，部分肺结节被认为是早期肺癌，但如何鉴别肺结节良恶性却是亟待解决的临床难题。本研究旨在探讨细胞因子与肿瘤标志物联合检测对SPN良恶性的鉴别诊断价值，从而提高SPN诊断的准确性。方法纳入81例诊断明确的SPN患者作为研究对象，收集病例的一般临床资料、结节影像学特征、病理学诊断资料、细胞因子系列和肿瘤标志物表达水平。利用单因素和多因素分析筛选可预测肺结节性质的影响指标，并用二元Logistic回归分析构造联合指标；绘制受试者工作特征曲线（receiver operating characteristic curve, ROC），计算曲线下面积及相应的灵敏度、特异度、阳性预测值、阴性预测值和准确率。结果一般临床资料分析示恶性结节出现在右肺上叶的比例最高（40.4%）。恶性结节组中的癌胚抗原（carcinoembryonic antigen, CEA）、细胞角蛋白19片段（cytokeratin 19 fragment 21-1, CYFRA21-1）、白介素6（interleukin-6, IL-6）和白介素8（interleukin-8, IL-8）血清水平高于良性结节组。Logistic回归分析提示，CEA、IL-6、IL-8为预测恶性结节的独立危险因子。ROC曲线分析表明，单项指标CEA、IL-6和IL-8的曲线下面积分别为0.642、0.684和0.749，CEA+IL-6+IL-8联合检测曲线下面积更大，检测效能更高。结论CEA、IL-6和IL-8为恶性结节的独立危险因素。细胞因子和肿瘤标志物联合检测在SPN良恶性鉴别诊断中具有一定的价值。其中CEA+IL-6+IL-8联合检测的诊断价值最高。

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.

A case report of primary pulmonary meningioma masquerading as lung metastasis in a patient with rectal carcinoma: role of 18F-FDG PET/CT

BackgroundPrimary pulmonary meningioma (PPM) is an extremely rare disease, which is often misdiagnosed as lung metastasis. Previous studies indicated that PPM usually showed homogeneous enhancement on enhanced CT and high uptake of 18F-fluorodeoxyglucose (18F-FDG) on positron emission tomography/CT (PET/CT). In this study, we report a case of PPM with atypical enhanced CT and 18F-FDG PET/CT features in a patient with rectal carcinoma.Case presentationA 70-year-old male was demonstrated to have rectal carcinoma by biopsy while a solitary pulmonary nodule (SPN) with well-defined edges measuring 13 × 13 × 15 mm was almost simultaneously found in the right lower robe on chest CT scan. Contrast-enhanced CT and PET/CT revealed mild centripetal enhancement of the nodule without accumulation of 18F-FDG. A thoracoscopic wedge resection of the right lower lobe was finally performed and histopathologic examinations and PET/CT imaging showed that the nodule was a PPM.ConclusionPPM is a rare disease with heterogeneity not only in blood supply but also in glucose metabolism. 18F-FDG PET/CT may be an effective method for differentiating benign and malignant SPNs. The diagnosis of PPM depends on pathological and radiological examinations.

AUTOMATED CLASSIFICATION OF SOLITARY PULMONARY NODULES USING CONVOLUTIONAL NEURAL NETWORK BASED ON TRANSFER LEARNING STRATEGY

This study aimed to propose an effective malignant solitary pulmonary nodule classification method based on improved Faster R-CNN and transfer learning strategy. In practice, the existing solitary pulmonary nodule classification methods divide the lung cancer images into two categories only: normal and cancerous. This study proposed the deep convolution neural network to classify the computed tomography (CT) images of lung cancer into four categories: lung adenocarcinoma, lung squamous cell carcinoma, metastatic lung cancer, and normal types of lung cancer. Some high-resolution lung CT images have unnecessary characters such as a large number of high-density continuity features, small-size lung nodule targets, CT image background complexity, and so forth. In this study, the CT image sub-block preprocessing strategy was used to extract nodule features for enhancement and alleviate the aforementioned problems. The experimental results showed that the proposed system was effective in resolving issues such as high false-positive rate and long classification time cost based on the original Faster R-CNN detection method. Meanwhile, the transfer learning strategy was used to improve the classification efficiency so as to avoid the overfitting problem caused by a few labeled samples of lung cancer datasets. The classification results were integrated using the majority vote algorithm. The classification results of the lung CT imaging showed that the proposed method had an average detection accuracy of 89.7% and reduced the rate of misdiagnosis to meet the clinical needs.

Is the Yedikule-solitary pulmonary nodule malignancy risk score sufficient to predict malignancy? An internal validation study.

We aimed to develop a malignancy risk score model for solitary pulmonary nodules (SPNs) using the demographic, radiological and clinical characteristics of patients in our centre. The model was then internally validated for malignancy risk estimation. A total of 270 consecutive patients who underwent surgery for SPN between June 2017 and May 2019 were retrospectively analysed. Using the receiver operating characteristic curve analysis, cut-off values were determined for radiological tumour diameter, maximum standardized uptake value and the Brock University probability of malignancy (BU-PM) model. The Yedikule-SPN malignancy risk model was developed using these cut-off values and demographic, radiological and clinical criteria in the first 180 patients (study cohort) and internally validated with the next 90 patients (validation cohort). The Yedikule-SPN model was then compared with the BU-PM model in terms of malignancy prediction. Malignancy was reported in 171 patients (63.3%). Maximum standardized uptake value and BU-PM scores were sufficient to predict malignancy (P < 0.001 for both), while the effectiveness of nodule size determined on thoracic computed tomography did not reach statistical significance (P = 0.09). When the Yedikule-SPN model developed with the study cohort was applied to the validation cohort, it significantly predicted malignancy (area under the receiver operating characteristic curve: 0.883, 95% confidence interval: 0.827-0.957, P < 0.001). Comparison of patients in the validation group with Yedikule-SPN scores above (n = 53) and below (n = 37) the cut-off value of 65.75 showed that the malignancy rate was significantly higher among patients with Yedikule-SPN score over 65.75 (86.8% vs 21.6%, P < 0.001, odds ratio = 23.821, 95% confidence interval: 7.805-72.701). When compared with the BU-PM model in all patients, the Yedikule-SPN model tended to be a better predictor of malignancy (P = 0.06). The internally validated Yedikule-SPN model is also a good predictor of the malignancy of SPN(s). Prospective and multicentre external validation studies with large patients' cohorts are needed.

Value of circulating tumor cells in the diagnosis and treatment of solitary pulmonary nodules.

BackgroundLung cancer has become the most common malignant tumor worldwide, with the highest rates of morbidity and mortality. The detection of circulating tumor cells (CTCs) can be simple, rapid, and minimally invasive, thus endowing them with a high value in the diagnosis of malignant tumors. We aimed to explore the correlation between CTCs in peripheral blood and benign or malignant solitary pulmonary nodules (SPNs).MethodsA total of 223 patients with SPNs from January 2018 to May 2020 were recruited. During the same period, 20 healthy volunteers were recruited as controls. Venous blood samples were collected from participants for detecting CTCs using a folate receptor (FR)-positive cell detection kit, as well as tumor biomarkers.ResultsA significant difference in the level of CTCs were observed between the malignant SPNs group, the benign SPNs group, and the control group, which was markedly higher in the malignant SPNs group (10.48±3.49 FU/3 mL) than both the benign SPNs and control groups (6.38±0.53 and 4.45±1.21 FU/3 mL, respectively) (P<0.001). In addition, the level of CTCs was significantly higher in the benign SPNs group than in the control group (P=0.023). In particular, in the malignant SPNs group, patients older than 60 years (11.45±3.92 FU/3 mL) presented a notably higher level of CTCs than other patients (9.55±2.74 FU/3 mL). The patients were then classified according to the pathological subtypes of lung cancer. There was a significant difference in level of CTCs among patients with squamous cell carcinoma (9.10±1.94 FU/3 mL), adenocarcinoma (10.77±3.71 FU/3 mL), and adenosquamous cell carcinoma (11.78±2.61 FU/3 mL). Binary logistic regression analysis suggested that CTCs were an independent risk factor of malignant SPN (OR =3.698, 95% CI: 1.136–11.035, P=0.030). The sensitivity and specificity of CTCs in diagnosing malignant SPNs was significantly higher than tumor biomarkers (single or combined) [sensitivity =89.1%; specificity =92.3%; area under curve (AUC) (95% CI) =0.907 (0.861–0.942)].ConclusionsPeripheral blood CTCs can be used in the diagnosis of malignant SPNs and are recommended for clinical application.

A novel clinical model for predicting malignancy of solitary pulmonary nodules: a multicenter study in chinese population

BackgroundThis study aimed to establish and validate a novel clinical model to differentiate between benign and malignant solitary pulmonary nodules (SPNs).Methods Records from 295 patients with SPNs in Sun Yat-sen University Cancer Center were retrospectively reviewed. The novel prediction model was established using LASSO logistic regression analysis by integrating clinical features, radiologic characteristics and laboratory test data, the calibration of model was analyzed using the Hosmer-Lemeshow test (HL test). Subsequently, the model was compared with PKUPH, Shanghai and Mayo models using receiver-operating characteristics curve (ROC), decision curve analysis (DCA), net reclassification improvement index (NRI), and integrated discrimination improvement index (IDI) with the same data. Other 101 SPNs patients in Henan Tumor Hospital were used for external validation cohort.ResultsA total of 11 variables were screened out and then aggregated to generate new prediction model. The model showed good calibration with the HL test (P = 0.964). The AUC for our model was 0.768, which was higher than other three reported models. DCA also showed our model was superior to the other three reported models. In our model, sensitivity = 78.84%, specificity = 61.32%. Compared with the PKUPH, Shanghai and Mayo models, the NRI of our model increased by 0.177, 0.127, and 0.396 respectively, and the IDI changed − 0.019, -0.076, and 0.112, respectively. Furthermore, the model was significant positive correlation with PKUPH, Shanghai and Mayo models.ConclusionsThe novel model in our study had a high clinical value in diagnose of MSPNs.

The application of dual-layer spectral detector computed tomography in solitary pulmonary nodule identification.

Differentiating between malignant solitary pulmonary nodules (SPNs) and other lung diseases remains a substantial challenge. The latest generation of dual-energy computed tomography (CT), which realizes dual-energy technology at the detector level, has clinical potential for distinguishing lung cancer from other benign SPNs. This study aimed to evaluate the performance of dual-layer spectral detector CT (SDCT) for the differentiation of SPNs. Spectral images of 135 SPNs confirmed by pathology were retrospectively analyzed in both the arterial phase (AP) and the venous phase (VP). Patients were classified into two groups [the malignant group (n=93) and the benign group (n=42)], with the malignant group further divided into small cell lung cancer (SCLC, n=30) and non-small cell lung cancer (NSCLC, n=63) subtypes. The slope of the spectral Hounsfield Unit (HU) curve (λHU), normalized iodine concentration (NIC), CT values of 40 keV monochromatic images (CT40keV), and normalized arterial enhancement fraction (NAEF) in contrast-enhanced images were calculated and compared between the benign and malignant groups, as well as between the SCLC and NSCLC subgroups. ROC curve analysis was performed to assess the diagnostic performance of the above parameters. Seventy cases were randomly selected and independently measured by two radiologists, and intraclass correlation coefficient (ICC) and Bland-Altman analyses were performed to calculate the reliability of the measurements. Except for NAEF (P=0.23), the values of the parameters were higher in the malignant group than in the benign group (all P<0.05). NIC, λHU, and CT40keV performed better in the VP (NICVP, λVPHU, and CTVP40keV) (P<0.001), with an area under the ROC curve (AUC) of 0.93, 0.89, and 0.89 respectively. With respective cutoffs of 0.31, 1.83, and 141.00 HU, the accuracy of NICVP, λVPHU, and CTVP40keV was 91.11%, 85.19%, and 88.15%, respectively. In the subgroup differentiating NSCLC and SCLC, the diagnostic performances of NICAP (AUC =0.89) were greater than other parameters. NICAP had an accuracy of 86.02% when the cutoff was 0.14. ICC and Bland-Altman analyses indicated that the measurement of SDCT has great reproducibility. Quantitative measures from SDCT can help to differentiate benign from malignant SPNs and may help with the further subclassification of malignant cancer into SCLC and NSCLC.

Establishment and validation of a prediction model for the probability of malignancy in solid solitary pulmonary nodules in northwest China.

To construct a prediction model of solitary pulmonary nodules (SPNs), to predict the possibility of malignant SPNs in patients aged 15-85 years in northwest China for clinical diagnostic and therapeutic decision-making. The features of SPNs were assessed by multivariate logistic regression, followed by visualization using a nomogram. Hosmer lemeshow was applied to evaluate the fitting degree of the model. The area under the receiver operating characteristic (ROC) curve was identified to determine the discriminative ability of the model. Lobulation, spiculation, pleural-tag, carcinoembryonic antigen, neuron-specific enolase, and total serum proteinwere independent predictors of malignant pulmonary nodules (p < .05). Lobulation (100 points) scored the highest in the nomogram, and the Hosmer-Lemeshow goodness-of-fit statistic was 0.805 (p > .05). The area under curve (AUC) of the modeling and validation groups using logistic regression were 0.859 (95% CI, 0.805-0.903) and 0.823 (95% CI, 0.738-0.890), respectively. Moreover, the AUC of our model was higher than that of the Mayo model, VA model, and Peking University (AUC 0.823 vs. 0.655 vs. 0.603 vs. 0.521). Our prediction model is more suitable for predicting the possibility of malignant SPNs in northwest China, andcan be calculated using a nomogram to determine further treatments.

Prediction of malignancy for solitary pulmonary nodules based on imaging, clinical characteristics and tumor marker levels.

ObjectiveTo establish a prediction model of malignancy for solitary pulmonary nodules (SPNs) on the basis of imaging, clinical characteristics and tumor marker levels.MethodsTotally, 341 cases of SPNs were enrolled in this retrospective study, in which 70% were selected as the training group (n = 238) and the rest 30% as the verification group (n = 103). The imaging, clinical characteristics and tumor marker levels of patients with benign and malignant SPNs were compared. Influencing factors were identified using multivariate logistic regression analysis. The model was assessed by the area under the curve (AUC) of the receiver operating characteristic curve.ResultsDifferences were evident between patients with benign and malignant SPNs in age, gender, smoking history, carcinoembryonic antigen (CEA), neuron-specific enolase, nodule location, edge smoothing, spiculation, lobulation, vascular convergence sign, air bronchogram, ground-glass opacity, vacuole sign and calcification (all P < 0.05). Influencing factors for malignancy included age, gender, nodule location, spiculation, vacuole sign and CEA (all P < 0.05). The established model was as follows: Y = −5.368 + 0.055 × age + 1.012 × gender (female = 1, male = 0) + 1.302 × nodule location (right upper lobe = 1, others = 0) + 1.208 × spiculation (yes = 1, no = 0) + 2.164 × vacuole sign (yes = 1, no = 0) −0.054 × CEA. The AUC of the model with CEA was 0.818 (95% confidence interval, 0.763–0.865), with a sensitivity of 64.80% and a specificity of 84.96%, and the stability was better through internal verification.ConclusionsThe prediction model established in our study exhibits better accuracy and internal stability in predicting the probability of malignancy for SPNs.

Solitary pulmonary nodule imaging approaches and the role of optical fibre-based technologies.

Solitary pulmonary nodules (SPNs) are a clinical challenge, given there is no single clinical sign or radiological feature that definitively identifies a benign from a malignant SPN. The early detection of lung cancer has a huge impact on survival outcome. Consequently, there is great interest in the prompt diagnosis, and treatment of malignant SPNs. Current diagnostic pathways involve endobronchial/transthoracic tissue biopsies or radiological surveillance, which can be associated with suboptimal diagnostic yield, healthcare costs and patient anxiety. Cutting-edge technologies are needed to disrupt and improve, existing care pathways. Optical fibre-based techniques, which can be delivered via the working channel of a bronchoscope or via transthoracic needle, may deliver advanced diagnostic capabilities in patients with SPNs. Optical endomicroscopy, an autofluorescence-based imaging technique, demonstrates abnormal alveolar structure in SPNs in vivo. Alternative optical fingerprinting approaches, such as time-resolved fluorescence spectroscopy and fluorescence-lifetime imaging microscopy, have shown promise in discriminating lung cancer from surrounding healthy tissue. Whilst fibre-based Raman spectroscopy has enabled real-time characterisation of SPNs in vivo. Fibre-based technologies have the potential to enable in situ characterisation and real-time microscopic imaging of SPNs, which could aid immediate treatment decisions in patients with SPNs. This review discusses advances in current imaging modalities for evaluating SPNs, including computed tomography (CT) and positron emission tomography-CT. It explores the emergence of optical fibre-based technologies, and discusses their potential role in patients with SPNs and suspected lung cancer.

A prediction model to evaluate the pretest risk of malignancy in solitary pulmonary nodules: evidence from a large Chinese southwestern population.

Lung cancer is the leading cause of cancer death and there have been clinical prediction models. This study aimed to evaluate the diagnostic performance of published models and create new models to evaluate the probability of malignant solitary pulmonary nodules (SPNs) in Chinese population. We consecutively enrolled 2061 patients with SPNs from West China Hospital between January 2008 and December 2016, each SPN was pathologically confirmed. First, four published prediction models, Mayo clinic model, Veterans Affairs (VA) model, Brock model and People's Hospital of Peking University (PEH) model were validated in our patients. Then, utilizing logistic regression, decision tree and random forest (RF), we developed three new models and internally validated them. Area under the receiver operating characteristic curve (AUC) values of four published models were as follows: Mayo 0.705 (95% CI 0.658-0.752, n = 726), VA 0.64 6 (95% CI 0.598-0.695, n = 800), Brock 0.575 (95% CI 0.502-0.648, n = 550) and PEH 0.675 (95% CI 0.627-0.723, n = 726). Logistic regression model, decision tree model and RF model were developed, AUC values of these models were 0.842 (95% CI 0.778-0.906), 0.734 (95% CI 0.647-0.821), 0.851 (95% CI 0.789-0.914), respectively. The four published lung cancer prediction models do not apply to our population, and we have established new models that can be used to predict the probability of malignant SPNs.

Value of Shape and Texture Features from 18F-FDG PET/CT to Discriminate between Benign and Malignant Solitary Pulmonary Nodules: An Experimental Evaluation.

In this paper, we investigate the role of shape and texture features from F-FDG PET/CT to discriminate between benign and malignant solitary pulmonary nodules. To this end, we retrospectively evaluated cross-sectional data from 111 patients (64 males, 47 females, age = 67.5 ± 11.0) all with histologically confirmed benign () or malignant () solitary pulmonary nodules. Eighteen three-dimensional imaging features, including conventional, texture, and shape features from PET and CT were tested for significant differences (Wilcoxon-Mann-Withney) between the benign and malignant groups. Prediction models based on different feature sets and three classification strategies (Classification Tree, k-Nearest Neighbours, and Naïve Bayes) were also evaluated to assess the potential benefit of shape and texture features compared with conventional imaging features alone. Eight features from CT and 15 from PET were significantly different between the benign and malignant groups. Adding shape and texture features increased the performance of both the CT-based and PET-based prediction models with overall accuracy gain being 3.4–11.2 pp and 2.2–10.2 pp, respectively. In conclusion, we found that shape and texture features from F-FDG PET/CT can lead to a better discrimination between benign and malignant lung nodules by increasing the accuracy of the prediction models by an appreciable margin.

GalNAc-T3 and MUC1, a combined predictor of prognosis and recurrence in solitary pulmonary adenocarcinoma initially diagnosed as malignant solitary pulmonary nodule (≤ 3cm).

The significance of the polypeptide N-acetyl-galactosaminyl transferase-3 (GalNAc-T3) and mucin 1 (MUC1) in solitary pulmonary adenocarcinoma (SPA) initially diagnosed as malignant solitary pulmonary nodule (≤ 3cm), especially as a combined predictor of prognosis and recurrence, was explored in this study. A retrospective analysis of 83 patients with SPA (≤ 3cm), which revealed postoperative pathological diagnosis was lung adenocarcinoma after complete resection. Immunohistochemical staining was used to detect the expression of GalNAc-T3 and MUC1 in primary tumor specimens. The relationship between expression and various clinicopathological factors was analyzed, as well as the effects of patients' overall survival (OS) and disease-free survival (DFS). In all patients, GalNAc-T3 was highly expressed in 53 (63.9%) cases; MUC1 was highly expressed in 31 (37.3%) cases. The GalNAc-T3 expression was correlated with differentiation, pathological risk group, N stage, and TNM stage. The group with high GalNAc-T3 expression and low MUC1 expression (GalNAc-T3Hig/MUC1Low) is correlated to pathological differentiation and has a trend related to the TNM stage. The patients with better differentiation, lower pathological risk group, lower N stage, and GalNAc-T3 high expression had better overall survival, especially the GalNAc-T3Hig/MUC1Low group. Moreover, the moderate differentiation, N3 stage, and GalNAc-T3Hig/MUC1Low group were independent predictive factors for OS. Besides, patients with lower N stage, lower TNM stage, higher GalNAc-T3 expression got better disease-free survival (DFS), especially the GalNAc-T3Hig/MUC1Low group. The GalNAc-T3Hig/MUC1Low group was an independent predictive factor for DFS. In conclusion, GalNAc-T3 and MUC1 were combined predictors of prognosis and recurrence in SPA (≤ 3cm).

Assessment of Solitary Pulmonary Nodules Based on Virtual Monochrome Images and Iodine-Dependent Images Using a Single-Source Dual-Energy CT with Fast kVp Switching.

With lung cancer being the most common malignancy diagnosed worldwide, lung nodule assessment has proved to be one of big challenges of modern medicine. The aim of this study was to examine the usefulness of Dual Energy Computed Tomography (DECT) in solitary pulmonary nodule (SPN) assessment. Between January 2017 and June 2018; 65 patients (42 males and 23 females) underwent DECT scans in the late arterial phase (AP) and venous phase (VP). We concluded that imaging at an energy level of 65 keV was the most accurate in detecting malignancy in solitary pulmonary nodules (SPNs) measuring ≤30 mm in diameter on virtual monochromatic maps. Both virtual monochromatic images and iodine concentration maps prove to be highly useful in differentiating benign and malignant pulmonary nodules. As for iodine concentration maps, the analysis of venous phase images resulted in the highest clinical usefulness. To summarize, DECT may be a useful tool in the differentiation of benign and malignant SPNs. A single-phase DECT examination with scans acquired 90 s after contrast media injection is recommended.

Predicting lung nodules malignancy

BackgroundIt is critical to developing an accurate method for differentiating between malignant and benign solitary pulmonary nodules. This study aimed was to establish a predicting model of lung nodules malignancy in a real-world setting. MethodsThe authors retrospectively analysed the clinical and computed tomography (CT) data of 121 patients with lung nodules, submitted to percutaneous CT-guided transthoracic biopsy, between 2014 and 2015. Multiple logistic regression was used to screen independent predictors for malignancy and to establish a clinical prediction model to evaluate the probability of malignancy. ResultsFrom a total of 121 patients, 75 (62%) were men and with a mean age of 64.7 years old. Multivariate logistic regression analysis identified six independent predictors of malignancy: age, gender, smoking status, current extra-pulmonary cancer, air bronchogram and nodule size (p<0.05). The area under the curve (AUC) was 0.8573. ConclusionsThe prediction model established in this study can be used to assess the probability of malignancy in the Portuguese population, thereby providing help for the diagnosis of lung nodules and the selection of follow-up interventions.

The value of four imaging modalities to distinguish malignant from benign solitary pulmonary nodules: a study based on 73 cohorts incorporating 7956 individuals.

Solitary pulmonary nodules (SPNs) frequently bother oncologists. The differentiation of malignant from benign nodules with non-invasive approach remains a tough challenge. This study was designed to assess the diagnostic accuracy of dynamic computed tomography (CT), dynamic magnetic resonance imaging (MRI), fluorine 18 fluorodeoxyglucose (18F-FDG) positron emission tomography (PET), and technetium 99m (99mTc) depreotide single photon emission computed tomography (SPECT) for SPNs. Electronic databases of MEDLINE, PubMed, EMBASE, and Cochrane Library were searched to identify relevant trials. The primary evaluation index of diagnostic accuracy was areas under the summary receiver-operating characteristic (SROC) curve. The results were analyzed utilizing Stata 12.0 statistical software. Seventy-three trials incorporating 7956 individuals were recruited. Sensitivities, specificities, positive likelihood ratios, negative likelihood ratios, diagnostic score, diagnostic odds ratios, and areas under the SROC curve with 95% confidence intervals were, respectively, 0.92 (0.89-0.95), 0.64 (0.54-0.74), 2.60 (1.98-3.42), 0.12 (0.08-0.17), 3.10 (2.62-3.59), 22.24 (13.67-36.17), and 0.91 (0.88-0.93) for CT; 0.92 (0.86-0.95), 0.85 (0.77-0.90), 6.01 (3.90-9.24), 0.10 (0.06-0.17), 4.12 (3.41-4.82), 61.39 (30.41-123.93), and 0.94 (0.92-0.96) for MRI; 0.90 (0.86-0.93), 0.73 (0.65-0.79), 3.28 (2.56-4.20), 0.14 (0.10-0.19), 3.16 (2.69-3.64), 23.68 (14.74-38.05), and 0.90 (0.87-0.92) for 18F-FDG PET; and 0.93 (0.88-0.96), 0.70 (0.56-0.81), 3.12 (2.03-4.81), 0.10 (0.06-0.17), 3.43 (2.63-4.22), 30.74 (13.84-68.27), and 0.93 (0.91-0.95) for 99mTc-depreotide SPECT. The dynamic MRI, dynamic CT, 18F-FDG PET, and 99mTc-depreotide SPECT were favorable non-invasive approaches to distinguish malignant SPNs from benign. Moreover, from the viewpoint of cost-effectiveness and avoiding radiation, the dynamic MRI was recommendable for SPNs.