Utility of Cell-Free DNA from Bronchoalveolar Lavage Fluids Using Next Generation Sequencing in Predicting Malignant Solitary Pulmonary Nodules

Purpose: To explore the utility of cell-free DNA (cfDNA) from bronchoalveolar lavage fluids (BALF) using next generation sequencing (NGS) in differentiating malignant tumors from benign solitary pulmonary nodule (SPN).Methods: Between January 1st 2019 and January 1st 2021, 40 subjects undergoing computed tomography (CT) examination who were diagnosed with SPN, were prospectively enrolled at Zhangzhou Affiliated Hospital of Fujian Medical University (Zhangzhou, Fujian, China). And pathological diagnosis were finally confirmed from tissue specimens obtained by surgical resection. For each patient, the mutations of gene were analysed using NGS in both extraction of cfDNA isolated from BALF and tissue. Results: Of 40 patients, 55% of them were diagnosed with lung adenocarcinoma, 20% with benign nodules, and 10% with small cell carcinoma. And patients with squamous carcinoma, adenosquamous carcinoma or large cell neuroendocrine carcinoma account for 5%, respectively. And 62.5% of malignant SPN (10/16) had at least one alteration. The most common alterations were TP53 (31.25%), followed by EGFR (18.75%), KRAS (12.5%), PIK3CA (6.25%), ERBB2 (6.25%), ALK (6.25%) and ROS1 (6.25%). Besides, there are some differences shown in the heatmap of gene mutation in the histologic sample. And there was a colse correlation between the mutations found in the tissue and BALF. For all 40 patients, the sensitivity, specificity, and concordance of BALF in predicting malignant nodules were 68.8%, 100%, and 75%, respectively. Conclusions: By NGS, tumor-specific mutations of cfDNA from BALF may be benefical to predicting malignant SPN, which may be taken into consideration for personalized cancer diagnosis.

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 to compare the accuracy and cost-effectiveness of these.MethodsIn this prospective multicentre trial, 380 participants with an SPN (8–30 mm) and no recent history of malignancy underwent DCE-CT and PET/CT. All patients underwent either biopsy with histological diagnosis or completed CT follow-up. Primary outcome measures were sensitivity, specificity and overall diagnostic accuracy for PET/CT and DCE-CT. Costs and cost-effectiveness were estimated from a healthcare provider perspective using a decision-model.Results312 participants (47% female, 68.1±9.0 years) completed the study, with 61% rate of malignancy at 2 years. The sensitivity, specificity, positive predictive value and negative predictive values for DCE-CT were 95.3% (95% CI 91.3 to 97.5), 29.8% (95% CI 22.3 to 38.4), 68.2% (95% CI 62.4% to 73.5%) and 80.0% (95% CI 66.2 to 89.1), respectively, and for PET/CT were 79.1% (95% CI 72.7 to 84.2), 81.8% (95% CI 74.0 to 87.7), 87.3% (95% CI 81.5 to 91.5) and 71.2% (95% CI 63.2 to 78.1). The area under the receiver operator characteristic curve (AUROC) for DCE-CT and PET/CT was 0.62 (95% CI 0.58 to 0.67) and 0.80 (95% CI 0.76 to 0.85), respectively (p<0.001). Combined results significantly increased diagnostic accuracy over PET/CT alone (AUROC=0.90 (95% CI 0.86 to 0.93), p<0.001). DCE-CT was preferred when the willingness to pay per incremental cost per correctly treated malignancy was below £9000. Above £15 500 a combined approach was preferred.ConclusionsPET/CT has a superior diagnostic accuracy to DCE-CT for the diagnosis of SPNs. Combining both techniques improves the diagnostic accuracy over either test alone and could be cost-effective.Trial registration numberNCT02013063

A wavelet features derived radiomics nomogram for prediction of malignant and benign early-stage lung nodules

This study was to develop a radiomics nomogram mainly using wavelet features for identifying malignant and benign early-stage lung nodules for high-risk screening. A total of 116 patients with early-stage solitary pulmonary nodules (SPNs) (≤ 3 cm) were divided into a training set (N = 70) and a validation set (N = 46). Radiomics features were extracted from plain LDCT images of each patient. A radiomics signature was then constructed with the LASSO with the training set. Combined with independent risk factors, a radiomics nomogram was built with a multivariate logistic regression model. This radiomics signature, consisting of one original and nine wavelet features, achieved favorable predictive efficacy than Mayo Clinic Model. The radiomics nomogram with radiomics signature and age also showed good calibration and discrimination in the training set (AUC 0.9406; 95% CI 0.8831–0.9982) and the validation set (AUC 0.8454; 95% CI 0.7196–0.9712). The decision curve indicated the clinical usefulness of our nomogram. The presented radiomics nomogram shows favorable predictive accuracy for identifying malignant and benign lung nodules in early-stage patients and is much better than the Mayo Clinic Model.

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.

Clinical-radiological predictive model in differential diagnosis of small (≤ 20 mm) solitary pulmonary nodules

Background There is a lack of clinical-radiological predictive models for the small (≤ 20 mm) solitary pulmonary nodules (SPNs). We aim to establish a clinical-radiological predictive model for differentiating malignant and benign small SPNs. Materials and methods Between January 2013 and December 2018, a retrospective cohort of 250 patients with small SPNs was used to construct the predictive model. A second retrospective cohort of 101 patients treated between January 2019 and December 2020 was used to independently test the model. The model was also compared to two other models that had previously been identified. Results In the training group, 250 patients with small SPNs including 156 (62.4%) malignant SPNs and 94 (37.6%) benign SPNs patients were included. Multivariate logistic regression analysis indicated that older age, pleural retraction sign, CT bronchus sign, and higher CEA level were the risk factors of malignant small SPNs. The predictive model was established as: X = − 10.111 + [0.129 × age (y)] + [1.214 × pleural retraction sign (present = 1; no present = 0)] + [0.985 × CT bronchus sign (present = 1; no present = 0)] + [0.21 × CEA level (ug/L)]. Our model had a significantly higher region under the receiver operating characteristic (ROC) curve (0.870; 50% CI: 0.828–0.913) than the other two models. Conclusions We established and validated a predictive model for estimating the pre-test probability of malignant small SPNs, that can help physicians to choose and interpret the outcomes of subsequent diagnostic tests.