Diagnosis Of Solitary Pulmonary Nodules Research Articles

Differentiation of granulomatous nodules with lobulation and spiculation signs from solid lung adenocarcinomas using a CT deep learning model

BackgroundThe diagnosis of solitary pulmonary nodules has always been a difficult and important point in clinical research, especially granulomatous nodules (GNs) with lobulation and spiculation signs, which are easily misdiagnosed as malignant tumors. Therefore, in this study, we utilised a CT deep learning (DL) model to distinguish GNs with lobulation and spiculation signs from solid lung adenocarcinomas (LADCs), to improve the diagnostic accuracy of preoperative diagnosis.Methods420 patients with pathologically confirmed GNs and LADCs from three medical institutions were retrospectively enrolled. The regions of interest in non-enhanced CT (NECT) and venous contrast-enhanced CT (VECT) were identified and labeled, and self-supervised labels were constructed. Cases from institution 1 were randomly divided into a training set (TS) and an internal validation set (IVS), and cases from institutions 2 and 3 were treated as an external validation set (EVS). Training and validation were performed using self-supervised transfer learning, and the results were compared with the radiologists’ diagnoses.ResultsThe DL model achieved good performance in distinguishing GNs and LADCs, with area under curve (AUC) values of 0.917, 0.876, and 0.896 in the IVS and 0.889, 0.879, and 0.881 in the EVS for NECT, VECT, and non-enhanced with venous contrast-enhanced CT (NEVECT) images, respectively. The AUCs of radiologists 1, 2, 3, and 4 were, respectively, 0.739, 0.783, 0.883, and 0.901 in the (IVS) and 0.760, 0.760, 0.841, and 0.844 in the EVS.ConclusionsA CT DL model showed great value for preoperative differentiation of GNs with lobulation and spiculation signs from solid LADCs, and its predictive performance was higher than that of radiologists.

Dual-layer detector spectral CT-based machine learning models in the differential diagnosis of solitary pulmonary nodules.

The benign and malignant status of solitary pulmonary nodules (SPNs) is a key determinant of treatment decisions. The main objective of this study was to validate the efficacy of machine learning (ML) models featured with dual-layer detector spectral computed tomography (DLCT) parameters in identifying the benign and malignant status of SPNs. 250 patients with pathologically confirmed SPN were included in this study. 8 quantitative and 16 derived parameters were obtained based on the regions of interest of the lesions on the patients' DLCT chest enhancement images. 6 ML models were constructed from 10 parameters selected after combining the patients' clinical parameters, including gender, age, and smoking history. The logistic regression model showed the best diagnostic performance with an area under the receiver operating characteristic curve (AUC) of 0.812, accuracy of 0.813, sensitivity of 0.750 and specificity of 0.791 on the test set. The results suggest that the ML models based on DLCT parameters are superior to the traditional CT parameter models in identifying the benign and malignant nature of SPNs, and have greater potential for application.

Alveolar Adenoma: A 20-Year Experience at a Western Balkan University Hospital and a Literature Review.

Alveolar adenoma is a rare benign lung tumor. We report 5 patients within a 20-year period diagnosed with this rare tumor and its clinical and histopathological features, in order to emphasize its significance in differential diagnosis of other lung tumors. Alveolar adenoma was more frequently diagnosed in female patients (80%). The patients ranged in age from 52 to 68. Eighty percent of the patients had symptoms like dyspnea, and shoulder or back pain. Three patients underwent a video-assisted thoracoscopic surgery, while 2 patients underwent a thoracotomy in order to remove the tumor. The tumor was localized within the right upper lobe, right lower lobe, and left lower lobe in 40%, 40%, and 20% of patients, respectively. The tumor size ranged from 14 to 30 mm. A frozen section finding of all 5 tumors reported that the lesion was benign. In all analyzed tumors, immunohistochemical analysis showed positive expression of TTF-1 and pankeratin in surface epithelial cells, and Vimentin positivity and TTF-1 and pankeratin negativity in round stromal cells. Alveolar adenoma should be carefully considered in the differential diagnosis of solitary pulmonary nodules. Complete surgical removal of the lesion is widely used in its primary management.

Radial Endobronchial Ultrasound-guided Transbronchial Cryobiopsy versus Forceps Biopsy for the Diagnosis of Solitary Pulmonary Nodules: A Prospective Randomised Trial.

Improvements in pulmonary diagnostic imaging and the development of lung cancer screening are increasing the prevalence of Solitary pulmonary nodules (SPNs). Fluoroscopically guided radial endobronchial ultrasound (EBUS) with transbronchial forceps biopsy (TB-FB) has been the conventional diagnostic method. Transbronchial cryobiopsy (TB-CB) is an alternative biopsy method. We sought to compare transbronchial cryobiopsy to transbronchial forceps biopsy for the diagnosis of SPNs. A prospective, single-centre, randomised controlled trial was conducted at the Royal Adelaide Hospital (RAH). Patients with SPNs were randomised to either 5 transbronchial forceps biopsies or one transbronchial cryobiopsy. Complete blinding of investigators and participants was not possible, as transbronchial cryobiopsy required general anaesthesia. The primary outcome was diagnostic yield with secondary outcomes of specimen size, diagnostic yield for subsets challenging to access with forceps and safety. The overall diagnostic yield for the 28 enrolled subjects was 76.8%(22/28). The diagnostic yield was 91.7% (11/12 patients) for transbronchial cryobiopsy and 68.8% (11/16 patients) for forceps biopsy (p=0.14). Median biopsy sizes were consistently larger for the cryobiopsy arm at 7.0mm compared to 2.5mm(p<0.0001). An eccentric EBUS image signalling the probe was adjacent to the nodule occurred in 4/28 cases, and TB-CB confirmed a diagnosis in 3/3 randomised to this arm. There were no major complications with either technique. Transbronchial cryobiopsy under the guidance of fluoroscopy and radial EBUS facilitates larger biopsy specimens without a significant increase in major complications. Further research is required to confirm the effect on diagnostic yield; however, our study supports a role for TB-CB in the diagnosis of SPNs and small, nodule-adjacent biopsies. Reference number of R20160213(HREC/16/RAH/37).

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.

Deep Learning-Based Digitally Reconstructed Tomography of the Chest in the Evaluation of Solitary Pulmonary Nodules: A Feasibility Study.

Computed tomography (CT) is preferred for evaluating solitary pulmonary nodules (SPNs) but access or availability may be lacking, in addition, overlapping anatomy can hinder detection of SPNs on chest radiographs. We developed and evaluated the clinical feasibility of a deep learning algorithm to generate digitally reconstructed tomography (DRT) images of the chest from digitally reconstructed frontal and lateral radiographs (DRRs) and use them to detect SPNs. This single-institution retrospective study included 637 patients with noncontrast helical CT of the chest (mean age 68 years, median age 69 years, standard deviation 11.7 years; 355 women) between 11/2012 and 12/2020, with SPNs measuring 10-30 mm. A deep learning model was trained on 562 patients, validated on 60 patients, and tested on the remaining 15 patients. Diagnostic performance (SPN detection) from planar radiography (DRRs and CT scanograms, PR) alone or with DRT was evaluated by two radiologists in an independent blinded fashion. The quality of the DRT SPN image in terms of nodule size and location, morphology, and opacity was also evaluated, and compared to the ground-truth CT images RESULTS: Diagnostic performance was higher from DRT plus PR than from PR alone (area under the receiver operating characteristic curve 0.95-0.98 versus 0.80-0.85; p < 0.05). DRT plus PR enabled diagnosis of SPNs in 11 more patients than PR alone. Interobserver agreement was 0.82 for DRT plus PR and 0.89 for PR alone; and interobserver agreement for size and location, morphology, and opacity of the DRT SPN was 0.94, 0.68, and 0.38, respectively. For SPN detection, DRT plus PR showed better diagnostic performance than PR alone. Deep learning can be used to generate DRT images and improve detection of SPNs.

Retracted: Value of ddPCR in the Preoperative Diagnosis of Solitary Pulmonary Nodules Based on the Observation of Virtual Reality Images of Smart Medical Treatment.

[This retracts the article DOI: 10.1155/2021/9920617.].

Qualitative (and Quantitative) Values of the Lung-RADS and Computed Tomography in Diagnosing Solitary Pulmonary Nodules.

Background: Lung-RADS classification and CT signs can both help in the differential diagnosis of SPNs. The purpose of this study was to investigate the diagnostic value of these two methods and the combination of the two methods for solitary pulmonary nodules (SPNs). Methods: A total of 296 cases of SPNs were retrospectively analyzed. All the SPNs were classified according to the Lung-RADS grading version 1.1. The scores of each lesion were calculated according to their CT signs. Imaging features, such as the size and margin of the lesions, pleural traction, spiculation, lobulation, bronchial cutoff, air bronchogram, vacuoles, tumor vasculature, and cavity signs, were analyzed. The imaging results were compared with the pathology examination findings. Receiver operating characteristic (ROC) curves were applied to compare the values of the different methods in differentially diagnosing benign and malignant SPNs. Results: The sensitivity, specificity, and accuracy of Lung-RADS grading for diagnosing SPNs were 34.0%, 94.4%, and 47.6%, respectively. The area under the ROC curve (AUC) was 0.600 (p < 0.001). The sensitivity, specificity, and accuracy of the CT sign scores were 56.3%, 70.0%, and 60.5%, respectively, and the AUC was 0.657 (p < 0.001). The sensitivity, specificity, and accuracy of the combination of the two methods for diagnosing SPNs were 93.2%, 61.1%, and 83.5%, and the AUC was 0.777 (p < 0.001). Conclusion: The combination of Lung-RADS classification and CT signs significantly improved the differential diagnosis of SPNs.

Combined measurement of circulating tumor cell counts and serum tumor marker levels enhances the screening efficiency for malignant versus benign pulmonary nodules.

The high false-positive rate for pulmonary nodules (PNs) from using low-dose computed tomography (LDCT) screening can lead to overuse of invasive procedures, overtreatment, and patient anxiety. Therefore, it is very important to develop new diagnostic methods. A negative enrichment-fluorescence in situ hybridization (NE-FISH) approach was used to detect circulating tumor cells (CTCs) in patients with PNs. We evaluated whether or not the combination of CTC counts with serum tumor marker levels (CEA, CA 125, CYFRA 21-1, SCC) could improve the diagnostic ability for distinguishing patients with malignant pulmonary nodules (MPNs) from those with benign pulmonary nodules (BPNs). Moreover, the potential clinical application of this combination for the diagnosis of solitary pulmonary nodules (SPNs) with a diameter ≤2 cm was also investigated. The combination of CTC counts and tumor marker levels had a sensitivity of 80.12% and the area under the receiver operating characteristics curve (AUCROC ) of 0.853 (95% confidence interval [CI]: 0.800-0.897, p < 0.001) for the differential diagnosis of PNs. For early cancer stages, the sensitivity was 75.38% (AUCROC =0.780, 95% CI: 0.713-0.838, p < 0.001). In addition, for SPNs within 2cm the combination of CTC counts and tumor marker levels was still the most valuable diagnostic tool with a sensitivity of 78.95% and AUCROC of 0.888. The combination of CTC counts and serum tumor marker levels is helpful for improving the diagnosis of PNs, especially in the early stages of cancer and for SPNs within 2cm.

Diagnostic Accuracy of a Convolutional Neural Network Assessment of Solitary Pulmonary Nodules Compared With PET With CT Imaging and Dynamic Contrast-Enhanced CT Imaging Using Unenhanced and Contrast-Enhanced CT Imaging

Solitary pulmonary nodules (SPNs) measuring 8 to 30mm in diameter require further workup to determine the likelihood of malignancy. What is the diagnostic performance of a lung cancer prediction convolutional neural network (LCP-CNN) in SPNs using unenhanced and contrast-enhanced CT imaging compared with the current clinical workup? This was a post hoc analysis of the Single Pulmonary Nodule Investigation: Accuracy and Cost-Effectiveness of Dynamic Contrast Enhanced Computed Tomography in the Characterisation of Solitary Pulmonary Nodules trial, a prospective multicenter study comparing the diagnostic accuracy of dynamic contrast-enhanced (DCE) CT imaging with PET imaging in SPNs. The LCP-CNN was designed and validated in an external cohort. LCP-CNN-generated risk scores were created from the noncontrast and contrast-enhanced CT scan images from the DCE CT imaging. The gold standard was histologic analysis or 2 years of follow-up. The area under the receiver operating characteristic curves (AUC) were calculated using LCP-CNN score, maximum standardized uptake value, and DCE CT scan maximum enhancement and were compared using the DeLong test. Two hundred seventy participants (mean ± SD age, 68.3 ± 8.8 years; 49%women) underwent PET with CT scan imaging and DCE CT imaging with CT scan data available centrally for LCP-CNN analysis. The accuracy of the LCP-CNN on the noncontrast images (AUC, 0.83; 95%CI, 0.79-0.88) was superior to that of DCE CT imaging (AUC, 0.76; 95%CI, 0.69-0.82; P= .03) and equal to that of PET with CT scan imaging (AUC, 0.86; 95%CI, 0.81-0.90; P= .35). The presence of contrast resulted in a small reduction in diagnostic accuracy, with the AUC falling from 0.83 (95%CI, 0.79-0.88) on the noncontrast images to 0.80 to 0.83 after contrast (P< .05 for 240s after contrast only). An LCP-CNN algorithm provides an AUC equivalent to PET with CT scan imaging in the diagnosis of solitary pulmonary nodules. ClinicalTrials.gov Identifier; No.: NCT02013063.

Role of computed tomography in the diagnosis of solitary pulmonary nodule with solid component: a narrative review

Role of computed tomography in the diagnosis of solitary pulmonary nodule with solid component: a narrative review

Diagnostic Performance of Machine Learning Models Based on 18F-FDG PET/CT Radiomic Features in the Classification of Solitary Pulmonary Nodules.

Objectives:This study aimed to evaluate the ability of 18fluorine-fluorodeoxyglucose (18F-FDG) positron emission tomography/computed tomography (PET/CT) radiomic features combined with machine learning methods to distinguish between benign and malignant solitary pulmonary nodules (SPN).Methods:Data of 48 patients with SPN detected on 18F-FDG PET/CT scan were evaluated retrospectively. The texture feature extraction from PET/CT images was performed using an open-source application (LIFEx). Deep learning and classical machine learning algorithms were used to build the models. Final diagnosis was confirmed by pathology and follow-up was accepted as the reference. The performances of the models were assessed by the following metrics: Sensitivity, specificity, accuracy, and area under the receiver operator characteristic curve (AUC).Results:The predictive models provided reasonable performance for the differential diagnosis of SPNs (AUCs ~0.81). The accuracy and AUC of the radiomic models were similar to the visual interpretation. However, when compared to the conventional evaluation, the sensitivity of the deep learning model (88% vs. 83%) and specificity of the classic learning model were higher (86% vs. 79%).Conclusion:Machine learning based on 18F-FDG PET/CT texture features can contribute to the conventional evaluation to distinguish between benign and malignant lung nodules.

Predictors of malignancy in patients with solitary pulmonary nodules undergoing pulmonary resection.

BackgroundThe management of a solitary pulmonary nodule is a challenging issue in pulmonary disease. Although many factors have been defined as predictors for malignancy in solitary pulmonary nodules, the accurate diagnosis can only be established with the permanent histological diagnosis.ObjectiveWe tried to clarify the possible predictors of malignancy in solitary pulmonary nodules in patients who had definitive histological diagnosis.MethodsWe made a retrospective study to collect the data of patients with solitary pulmonary nodules who had histological diagnosis either before or after surgery. We made a statistical analysis of both the clinic and radiological features of these nodules with respect to malignancy both in contingency tables and with logistic regression analysis.ResultsWe had a total of 223 patients with a radiological diagnosis of solitary pulmonary nodule. Age, smoking status and pack years of smoking, maximum standardized uptake value (SUVmax), and radiological features such as solid component, spiculation, pleural tag, lobulation, calcification, and higher density were significant predictors of malignancy in contingency tables. Age, smoking status and smoking (pack/year), SUVmax, and radiological features including spiculation, pleural tag, lobulation, calcification, and higher density were the significant predictors in univariate analysis. However, multivariate analysis revealed only SUVmax greater than 2.5 (p < 0.0001), spiculation (p = 0.009), and age older than 61 years (p = 0.015) as the significant predictors for malignancy.ConclusionAge, SUVmax, and spiculation are the independent predictors of malignancy in patients with solitary pulmonary nodules.

A CT-Based Radiomic Signature for the Differentiation of Pulmonary Hamartomas from Carcinoid Tumors.

Radiomics is a new image processing technology developed in recent years. In this study, CT radiomic features are evaluated to differentiate pulmonary hamartomas (PHs) from pulmonary carcinoid tumors (PCTs). A total of 138 patients (78 PCTs and 60 PHs) were evaluated. The Radcloud platform (Huiying Medical Technology Co., Ltd., Beijing, China) was used for managing the data, clinical data, and subsequent radiomics analysis. Two hand-crafted radiomics models are prepared in this study: the first model includes the data regarding all of the patients to differentiate between the groups; the second model includes 78 PCTs and 38 PHs without signs of fat tissue. The separation of the training and validation datasets was performed randomly using an (8:2) ratio and 620 random seeds. The results revealed that the MLP method (RF) was best for PH (AUC = 0.999) and PCT (AUC = 0.999) for the first model (AUC = 0.836), and PC (AUC = 0.836) in the test set for the second model. Radiomics tumor features derived from CT images are useful to differentiate the carcinoid tumors from hamartomas with high accuracy. Radiomics features may be used to differentiate PHs from PCTs with high levels of accuracy, even without the presence of fat on the CT. Advances in knowledge: CT-based radiomic holds great promise for a more accurate preoperative diagnosis of solitary pulmonary nodules (SPNs).

Development of a diagnostic model for malignant solitary pulmonary nodules based on radiomics features

BackgroundThis study proposed a precise diagnostic model for malignant solitary pulmonary nodules (SPNs). This model can be used to identify objective and quantifiable image features and guide the clinical treatment strategy adopted for SPNs. This model will help clinicians optimize management strategies for SPN.MethodsIn this retrospective study, the clinical data of 455 patients of SPN with defined pathological diagnosis between September 2016 and August 2019 were collected and analyzed. The data included pathological diagnosis, preoperative computed tomography (CT) diagnosis, gender, age, smoking history, family history of tumor, previous history, and contact history data. The quantitative image features and radiomic information of the SPNs were provided using computer-aided detection (CAD) “digital lung” software. The Chi-squared test was used to assess the accuracy between CAD and conventional CT in the diagnosis of SPNs. The diagnostic model for benign or malignant SPNs was developed using a multivariate logistic regression analysis that comprises 6 radiomic factors (irregularity, average diameter, COPD910, proportion of emphysema, proportion of fat, and average density of related blood vessels). The area under the receiver operating characteristic curve was used to evaluate the performance of the model in determining SPN risk of malignancy.ResultsThere was a statistical difference in the accuracy of CAD and conventional CT in diagnosing SPNs. According to the golden standard pathological diagnosis, the diagnostic accuracy of CAD (81%) was higher than that of conventional CT (63.7%) (P<0.05). Six variables (i.e., irregularity, the mean diameter, COPD910, the proportion of emphysema, the proportion of fat, and the vascular density) were identified using multivariable logistic regression to establish the diagnostic model for distinguish benign or malignant SPNs. The area under the receiver operating characteristic (ROC) curve (AUC) of the diagnostic model was 0.876 (95% CI: 0.8445–0.9076), and its sensitivity and specificity were 81.25% and 82.56% respectively.ConclusionsThe proposed diagnostic model, which comprises 6 radiomic factors, is accurate and effective at diagnosing benign or malignant SPNs.

Solitary pulmonary nodule as the initial manifestation of isolated metastasis from prostate cancer without bone involvement: A case report.

IntroductionIsolated lung metastases from prostate cancer without any other organ involvement are rare. They are commonly in the form of diffuse or multiple lesions and rarely emerge as a solitary pulmonary nodule.Presentation of caseA 61-year-old man who had undergone a laparoscopic-assisted radical prostatectomy for prostate cancer 16 months prior presented with a growing solitary pulmonary nodule. Positron emission tomography/computed tomography showed an abnormal uptake in the nodule without any other organ involvement. A surgical specimen by a thoracoscopic wedge resection proved a diagnosis of a metastasis from prostate cancer. He is currently alive only with worsening pulmonary metastases at 7 years after the lung surgery.DiscussionA rare entity of isolated pulmonary metastases could be a sole finding of metastatic prostate cancer over the years and its initial manifestation could emerge as a solitary pulmonary nodule. It poses a diagnostic challenge because primary lung cancer is the leading differential diagnosis of solitary pulmonary nodules and is also one of the most frequent second primary malignancies in prostate cancer survivors.ConclusionAn aggressive surgical biopsy is essential for definitive histopathological and immunohistochemical analyses of solitary pulmonary nodules to distinguish a rare form of an isolated pulmonary relapse from a second primary lung cancer in prostate cancer survivors.

Comparative accuracy and cost-effectiveness of dynamic contrast-enhanced CT and positron emission tomography in the characterisation of solitary pulmonary nodules

IntroductionDynamic contrast-enhanced CT (DCE-CT) and positron emission tomography/CT (PET/CT) have a high reported accuracy for the diagnosis of malignancy in solitary pulmonary nodules (SPNs). The aim of this study was...

Role of 18F-FDG PET/CT Radiomics Features in the Differential Diagnosis of Solitary Pulmonary Nodules: Diagnostic Accuracy and Comparison between Two Different PET/CT Scanners.

The aim of this retrospective study was to investigate the ability of 18 fluorine-fluorodeoxyglucose positron emission tomography/CT (18F-FDG-PET/CT) metrics and radiomics features (RFs) in predicting the final diagnosis of solitary pulmonary nodules (SPN). We retrospectively recruited 202 patients who underwent a 18F-FDG-PET/CT before any treatment in two PET scanners. After volumetric segmentation of each lung nodule, 8 PET metrics and 42 RFs were extracted. All the features were tested for significant differences between the two PET scanners. The performances of all features in predicting the nature of SPN were analyzed by testing three classes of final logistic regression predictive models: two were built/trained through exploiting the separate data from the two scanners, and the other joined the data together. One hundred and twenty-seven patients had a final diagnosis of malignancy, while 64 were of a benign nature. Comparing the two PET scanners, we found that all metabolic features and most of RFs were significantly different, despite the cross correlation being quite similar. For scanner 1, a combination between grey level co-occurrence matrix (GLCM), histogram, and grey-level zone length matrix (GLZLM) related features presented the best performances to predict the diagnosis; for scanner 2, it was GLCM and histogram-related features and metabolic tumour volume (MTV); and for scanner 1 + 2, it was histogram features, standardized uptake value (SUV) metrics, and MTV. RFs had a significant role in predicting the diagnosis of SPN, but their accuracies were directly related to the scanner.

Update on Pulmonary Ossifications in the Differential Diagnosis of Solitary Pulmonary Nodules.

Pulmonary ossifications have often been regarded as rare, post-mortem findings without any clinical significance. We have investigated the occurrence of pulmonary ossifications in patients undergoing thoracic procedures, and how this may affect the differential diagnosis of solitary pulmonary nodules. In addition, we have performed a literature search on the occurrence and possible pathogenesis of these ossifications. From January 2008 until August 2019, we identified pulmonary ossifications in 34 patients who underwent elective pulmonary surgery. Pre-operative imaging was unable to differentiate these ossifications from solid tumors. A definitive diagnosis was made by an experienced pathologist (VS, ML). The PubMed database was researched in December 2019 with the search terms “pulmonary ossifications”; “heterotopic ossifications”; and “solitary pulmonary nodule”. In total, 27 patients were male, with a mean age of 63 ± 12 years (age 41 to 82 on diagnosis). All lesions were identified on thoracic CT and marked for resection by a multidisciplinary team. A total of 17 patients were diagnosed with malignancy concurrent with ossifications. There was a clear predilection for the right lower lobe (12 cases, 35.3%) and most ossifications had a nodular form (70.6%). We could not identify a clear association with any other pathology, either cancerous or non-cancerous in origin. Oncologic or pulmonary comorbidities did not influence patient survival. Pulmonary ossifications are not as seldom as thought and are not just a curiosity finding by pathologists. These formations may be mistaken for a malignant space-occupying lesion, both pre-and perioperatively, as they are indistinguishable in imaging. We propose these ossifications as an underestimated addition to the differential diagnosis of a solitary pulmonary nodule.