The diagnosis and management of multiple ground-glass nodules in the lung.

The prevalence of low-dose CT (LDCT) in lung cancer screening has gradually increased, and more and more lung ground glass nodules (GGNs) have been detected. So far, a consensus has been reached on the treatment of single pulmonary ground glass nodules, and there have been many guidelines that can be widely accepted. However, at present, more than half of the patients have more than one nodule when pulmonary ground glass nodules are found, which means that different treatment methods for nodules may have different effects on the prognosis or quality of life of patients. This article reviews the research progress in the diagnosis and treatment strategies of pulmonary multiple lesions manifested as GGNs.

Diagnostic yield using electromagnetic navigation bronchoscopy for peripheral pulmonary nodules <2 cm.

BACKGROUND: Although electromagnetic navigation bronchoscopy (ENB) is highly sensitive in the diagnosis of peripheral pulmonary nodules (PPNs), its diagnostic yield for subgroups of smaller PPNs is under evaluation. OBJECTIVES: Diagnostic yield evaluation of biopsy using ENB for PPNs <2 cm. DESIGN: The diagnostic yield, sensitivity, specificity, positive predictive value, and negative predictive value of the ENB-mediated biopsy for PPNs were evaluated. METHODS: Patients who had PPNs with diameters <2 cm and underwent ENB-mediated biopsy between May 2015 and February 2020 were consecutively enrolled. The final diagnosis was made via pathological examination after surgery. RESULTS: A total of 82 lesions from 65 patients were analyzed. The median tumor size was 11 mm. All lesions were subjected to ENB-mediated biopsy, of which 29 and 53 were classified as malignant and benign, respectively. Subsequent segmentectomy, lobectomy, or wedge resection, following pathological examinations were performed on 64 nodules from 57 patients. The overall sensitivity, specificity, positive predictive value, and negative predictive value for nodules <2 cm were 53.3%, 91.7%, 92.3%, and 51.2%, respectively. The receiver operating curve showed an area under the curve of 0.721 (p < 0.001). Additionally, the sensitivity, specificity, positive predictive value, and negative predictive value were 62.5%, 100%, 100%, and 42.9%, respectively, for nodules with diameters equal to or larger than 1 cm; and 30.8%, 86.7%, 66.7%, and 59.1%, respectively, for nodules less than 1 cm. In the subgroup analysis, neither the lobar location nor the distance of the PPNs to the pleura affected the accuracy of the ENB diagnosis. However, the spiculated sign had a negative impact on the accuracy of the ENB biopsy (p = 0.010). CONCLUSION: ENB has good specificity and positive predictive value for diagnosing PPNs <2 cm; however, the spiculated sign may negatively affect ENB diagnostic accuracy. In addition, the diagnostic reliability may only be limited to PPNs equal to or larger than 1 cm.

Impact of deep learning image reconstruction on volumetric accuracy and image quality of pulmonary nodules with different morphologies in low-dose CT.

BACKGROUND: This study systematically compares the impact of innovative deep learning image reconstruction (DLIR, TrueFidelity) to conventionally used iterative reconstruction (IR) on nodule volumetry and subjective image quality (IQ) at highly reduced radiation doses. This is essential in the context of low-dose CT lung cancer screening where accurate volumetry and characterization of pulmonary nodules in repeated CT scanning are indispensable. MATERIALS AND METHODS: A standardized CT dataset was established using an anthropomorphic chest phantom (Lungman, Kyoto Kaguku Inc., Kyoto, Japan) containing a set of 3D-printed lung nodules including six diameters (4 to 9 mm) and three morphology classes (lobular, spiculated, smooth), with an established ground truth. Images were acquired at varying radiation doses (6.04, 3.03, 1.54, 0.77, 0.41 and 0.20 mGy) and reconstructed with combinations of reconstruction kernels (soft and hard kernel) and reconstruction algorithms (ASIR-V and DLIR at low, medium and high strength). Semi-automatic volumetry measurements and subjective image quality scores recorded by five radiologists were analyzed with multiple linear regression and mixed-effect ordinal logistic regression models. RESULTS: Volumetric errors of nodules imaged with DLIR are up to 50% lower compared to ASIR-V, especially at radiation doses below 1 mGy and when reconstructed with a hard kernel. Also, across all nodule diameters and morphologies, volumetric errors are commonly lower with DLIR. Furthermore, DLIR renders higher subjective IQ, especially at the sub-mGy doses. Radiologists were up to nine times more likely to score the highest IQ-score to these images compared to those reconstructed with ASIR-V. Lung nodules with irregular margins and small diameters also had an increased likelihood (up to five times more likely) to be ascribed the best IQ scores when reconstructed with DLIR. CONCLUSION: We observed that DLIR performs as good as or even outperforms conventionally used reconstruction algorithms in terms of volumetric accuracy and subjective IQ of nodules in an anthropomorphic chest phantom. As such, DLIR potentially allows to lower the radiation dose to participants of lung cancer screening without compromising accurate measurement and characterization of lung nodules.

Establishment and validation of multiclassification prediction models for pulmonary nodules based on machine learning.

BACKGROUND: Lung cancer is the leading cause of cancer-related death worldwide. This study aimed to establish novel multiclassification prediction models based on machine learning (ML) to predict the probability of malignancy in pulmonary nodules (PNs) and to compare with three published models. METHODS: Nine hundred fourteen patients with PNs were collected from four medical institutions (A, B, C and D), which were organized into tables containing clinical features, radiologic features and laboratory test features. Patients were divided into benign lesion (BL), precursor lesion (PL) and malignant lesion (ML) groups according to pathological diagnosis. Approximately 80% of patients in A (total/male: 632/269, age: 57.73 ± 11.06) were randomly selected as a training set; the remaining 20% were used as an internal test set; and the patients in B (total/male: 94/53, age: 60.04 ± 11.22), C (total/male: 94/47, age: 59.30 ± 9.86) and D (total/male: 94/61, age: 62.0 ± 11.09) were used as an external validation set. Logical regression (LR), decision tree (DT), random forest (RF) and support vector machine (SVM) were used to establish prediction models. Finally, the Mayo model, Peking University People's Hospital (PKUPH) model and Brock model were externally validated in our patients. RESULTS: The AUC values of RF model for MLs, PLs and BLs were 0.80 (95% CI: 0.73-0.88), 0.90 (95% CI: 0.82-0.99) and 0.75 (95% CI: 0.67-0.88), respectively. The weighted average AUC value of the RF model for the external validation set was 0.71 (95% CI: 0.67-0.73), and its AUC values for MLs, PLs and BLs were 0.71 (95% CI: 0.68-0.79), 0.98 (95% CI: 0.88-1.07) and 0.68 (95% CI: 0.61-0.74), respectively. The AUC values of the Mayo model, PKUPH model and Brock model were 0.68 (95% CI: 0.62-0.74), 0.64 (95% CI: 0.58-0.70) and 0.57 (95% CI: 0.49-0.65), respectively. CONCLUSIONS: The RF model performed best, and its predictive performance was better than that of the three published models, which may provide a new noninvasive method for the risk assessment of PNs.

Subsolid Nodules: Significance and Current Understanding.

Subsolid nodules are heterogeneously appearing and behaving entities, commonly encountered incidentally and in high-risk populations. Accurate characterization of subsolid nodules, and application of evolving surveillance guidelines, facilitates evidence-based and multidisciplinary patient-centered management.

Management of incidental pulmonary nodules in CT examination.

Lung cancer is the leading cause of cancer-related death in Denmark and the world. The increase in CT examinations has led to an increase in detection of pulmonary nodules divided into solid and subsolid (including ground glass and part solid). Risk factors for malignancy include age, smoking, female gender, and specific ethnicities. Nodule traits like size, spiculation, upper-lobe location, and emphysema correlate with higher malignancy risk. Managing these potentially malignant nodules relies on evidence-based guidelines and risk stratification. These risk stratification models can standardize the approach for the management of incidental pulmonary findings, as argued in this review.

Development and external validation of a multimodal integrated feature neural network (MIFNN) for the diagnosis of malignancy in small pulmonary nodules (≤10 mm).

Objectives. Current lung cancer screening protocols primarily evaluate pulmonary nodules, yet often neglect the malignancy risk associated with small nodules (≤10 mm). This study endeavors to optimize the management of pulmonary nodules in this population by devising and externally validating a Multimodal Integrated Feature Neural Network (MIFNN). We hypothesize that the fusion of deep learning algorithms with morphological nodule features will significantly enhance diagnostic accuracy.Materials and Methods. Data were retrospectively collected from the Lung Nodule Analysis 2016 (LUNA16) dataset and four local centers in Beijing, China. The study includes patients with small pulmonary nodules (≤10 mm). We developed a neural network, termed MIFNN, that synergistically combines computed tomography (CT) images and morphological characteristics of pulmonary nodules. The network is designed to acquire clinically relevant deep learning features, thereby elevating the diagnostic accuracy of existing models. Importantly, the network's simple architecture and use of standard screening variables enable seamless integration into standard lung cancer screening protocols.Results. In summary, the study analyzed a total of 382 small pulmonary nodules (85 malignant) from the LUNA16 dataset and 101 small pulmonary nodules (33 malignant) obtained from four specialized centers in Beijing, China, for model training and external validation. Both internal and external validation metrics indicate that the MIFNN significantly surpasses extant state-of-the-art models, achieving an internal area under the curve (AUC) of 0.890 (95% CI: 0.848-0.932) and an external AUC of 0.843 (95% CI: 0.784-0.891).Conclusion. The MIFNN model significantly enhances the diagnostic accuracy of small pulmonary nodules, outperforming existing benchmarks by Zhanget alwith a 6.34% improvement for nodules less than 10 mm. Leveraging advanced integration techniques for imaging and clinical data, MIFNN increases the efficiency of lung cancer screenings and optimizes nodule management, potentially reducing false positives and unnecessary biopsies.Clinical relevance statement. The MIFNN enhances lung cancer screening efficiency and patient management for small pulmonary nodules, while seamlessly integrating into existing workflows due to its reliance on standard screening variables.

[A Growth Prediction Model of Pulmonary Ground-Glass Nodules Based on Clinical Visualization Parameters].

Objective To establish a model for predicting the growth of pulmonary ground-glass nodules (GGN) based on the clinical visualization parameters extracted by the 3D reconstruction technique and to verify the prediction performance of the model. Methods A retrospective analysis was carried out for 354 cases of pulmonary GGN followed up regularly in the outpatient of pulmonary nodules in Zhoushan Hospital of Zhejiang Province from March 2015 to December 2022.The semi-automatic segmentation method of 3D Slicer was employed to extract the quantitative imaging features of nodules.According to the follow-up results,the nodules were classified into a resting group and a growing group.Furthermore,the nodules were classified into a training set and a test set by the simple random method at a ratio of 7∶3.Clinical and imaging parameters were used to establish a prediction model,and the prediction performance of the model was tested on the validation set. Results A total of 119 males and 235 females were included,with a median age of 55.0 (47.0,63.0) years and the mean follow-up of (48.4±16.3) months.There were 247 cases in the training set and 107 cases in the test set.The binary Logistic regression analysis showed that age (95%CI=1.010-1.092,P=0.015) and mass (95%CI=1.002-1.067,P=0.035) were independent predictors of nodular growth.The mass (M) of nodules was calculated according to the formula M=V×(CTmean+1000)×0.001 (where V is the volume,V=3/4πR3,R:radius).Therefore,the logit prediction model was established as ln[P/(1-P)]=-1.300+0.043×age+0.257×two-dimensional diameter+0.007×CTmean.The Hosmer-Lemeshow goodness of fit test was performed to test the fitting degree of the model for the measured data in the validation set (χ2=4.515,P=0.808).The check plot was established for the prediction model,which showed the area under receiver-operating characteristic curve being 0.702. Conclusions The results of this study indicate that patient age and nodule mass are independent risk factors for promoting the growth of pulmonary GGN.A model for predicting the growth possibility of GGN is established and evaluated,which provides a basis for the formulation of GGN management strategies.

Bubble-like lucency in pulmonary ground glass nodules on computed tomography: a specific pattern of air-containing space for diagnosing neoplastic lesions.

PURPOSE: To investigate the computed tomography (CT) characteristics of air-containing space and its specific patterns in neoplastic and non-neoplastic ground glass nodules (GGNs) for clarifying their significance in differential diagnosis. MATERIALS AND METHODS: From January 2015 to October 2022, 1328 patients with 1,350 neoplastic GGNs and 462 patients with 465 non-neoplastic GGNs were retrospectively enrolled. Their clinical and CT data were analyzed and compared with emphasis on revealing the differences of air-containing space and its specific patterns (air bronchogram and bubble-like lucency [BLL]) between neoplastic and non-neoplastic GGNs and their significance in differentiating them. RESULTS: Compared with patients with non-neoplastic GGNs, female was more common (P < 0.001) and lesions were larger (P < 0.001) in those with neoplastic ones. Air bronchogram (30.1% vs. 17.2%), and BLL (13.0% vs. 2.6%) were all more frequent in neoplastic GGNs than in non-neoplastic ones (each P < 0.001), and the BLL had the highest specificity (93.6%) in differentiation. Among neoplastic GGNs, the BLL was more frequently detected in the larger (14.9 ± 6.0 mm vs. 11.4 ± 4.9 mm, P < 0.001) and part-solid (15.3% vs. 10.7%, P = 0.011) ones, and its incidence significantly increased along with the invasiveness (9.5-18.0%, P = 0.001), whereas no significant correlation was observed between the occurrence of BLL and lesion size, attenuation, or invasiveness. CONCLUSION: The air containing space and its specific patterns are of great value in differentiating GGNs, while BLL is a more specific and independent sign of neoplasms.

Approach to Pulmonary Nodules in Connective Tissue Disease.

The assessment of pulmonary nodules is a common and often challenging clinical scenario. This evaluation becomes even more complex in patients with connective tissue diseases (CTDs), as a range of disease-related factors must also be taken into account. These diseases are characterized by immune-mediated chronic inflammation, leading to tissue damage, collagen deposition, and subsequent organ dysfunction. A thorough examination of nodule features in these patients is required, incorporating anatomic and functional information, along with patient demographics, clinical factors, and disease-specific knowledge. This integrated approach is vital for effective risk stratification and precise diagnosis. This review article addresses specific CTD-related factors that should be taken into account when evaluating pulmonary nodules in this patient group.

Shape-Sensing Robotic-Assisted Bronchoscopy versus Computed Tomography-Guided Transthoracic Biopsy for the Evaluation of Subsolid Pulmonary Nodules.

INTRODUCTION: Lung cancer remains the leading cause of cancer death worldwide. Subsolid nodules (SSN), including ground-glass nodules (GGNs) and part-solid nodules (PSNs), are slow-growing but have a higher risk for malignancy. Therefore, timely diagnosis is imperative. Shape-sensing robotic-assisted bronchoscopy (ssRAB) has emerged as reliable diagnostic procedure, but data on SSN and how ssRAB compares to other diagnostic interventions such as CT-guided transthoracic biopsy (CTTB) are scarce. In this study, we compared diagnostic yield of ssRAB versus CTTB for evaluating SSN. METHODS: A retrospective study of consecutive patients who underwent either ssRAB or CTTB for evaluating GGN and PSN with a solid component less than 6 mm from February 2020 to April 2023 at Mayo Clinic Florida and Rochester. Clinicodemographic information, nodule characteristics, diagnostic yield, and complications were compared between ssRAB and CTTB. RESULTS: A total of 66 nodules from 65 patients were evaluated: 37 PSN and 29 GGN. Median size of PSN solid component was 5 mm (IQR: 4.5, 6). Patients were divided into two groups: 27 in the ssRAB group and 38 in the CTTB group. Diagnostic yield was 85.7% for ssRAB and 89.5% for CTTB (p = 0.646). Sensitivity for malignancy was similar between ssRAB and CTTB (86.4% vs. 88.5%; p = 0.828), with no statistical difference. Complications were more frequent in CTTB with no significant difference (8 vs. 2; p = 0.135). CONCLUSION: Diagnostic yield for SSN was similarly high for ssRAB and CTTB, with ssRAB presenting less complications and allowing mediastinal staging within the same procedure.

Introduction of the Danish Lung Nodule Registry: A part of the Danish Lung Cancer Registry.

BACKGROUND: The majority of lung cancer cases are diagnosed late, resulting in poor prognosis and high mortality rates. Early detection and management of lung cancer can improve patient outcomes and reduce mortality rates. Pulmonary nodules are key factors in the early detection of lung cancer, they are common in high-risk populations and require correct classification to determine whether they are benign or malignant. Over the last decade a steep increase in the number of thoracic CT scans has been seen in Denmark, resulting in substantial resources allocated to CT follow-up of incidentally detected pulmonary nodules. The implementation of a nationwide Danish prospective pulmonary nodule registry is to methodically record pulmonary nodules and thereby evaluate the scope of pulmonary nodule follow-up, the nature of the nodules, and the clinical progression of patients with pulmonary nodules. METHODS: A prospective pulmonary nodule registry (Danish Lung Nodule Registry) will be a natural appendix to the Danish Lung Cancer Registry. Three new ICD-10 classification codes will be introduced, defining the type of nodule: /DR91.1/ Solid nodule /DR91.2/ Part-solid nodule; /DR91.3/ Non-solid nodule. Furthermore, an additional letter will describe whether the imaging exam is performed on suspicion of lung cancer (A), or the finding is incidental (B). Registration of the nodules will be performed by the departments of respiratory medicine who manage follow-up of pulmonary nodules. It is estimated that around 7000 nodules will be registered annually. DISCUSSION: The registration of patients in the lung nodule registry complies with current Danish legislation. The registry will be seamlessly integrated with other nationwide Danish registries, including the Danish Lung Cancer Registry, to collect additional patient data and improve the quality and scope of the data acquired. The results from these comprehensive epidemiological studies will be of significant interest and offer valuable research opportunities.

The clinical significance of indeterminate pulmonary nodules in patients with primary bone sarcoma: a systematic review.

OBJECTIVE: To report the incidence of indeterminate pulmonary nodules (IPN) and the rate of progression of IPNs to metastasis in patients with primary bone cancers. We also aimed to evaluate clinical or radiological parameters that may identify IPNs more likely to progress to metastatic disease and their effect on overall or event-free survival in patients with primary bone sarcoma. METHODS: A systematic search of the electronic databases Medline, Embase, and Cochrane Library was undertaken for eligible articles on IPNs in patients with primary bone sarcomas, published in the English language from inception of the databases to 2023. The Newcastle-Ottawa Quality Assessment Form for Cohort Studies was utilized to evaluate risk of bias in included studies. RESULTS: Six studies, involving 1667 patients, were included in this systematic review. Pooled quantitative analysis found the rate of incidence of IPN to be 18.1% (302 out of 1667) and the rate of progression to metastasis to be 45.0% (136 out of 302). Nodule size (more than 5 mm diameter), number (more than or equal to 4), distribution (bilaterally distributed), incomplete calcification, and lobulated margins were associated with an increased likelihood of IPNs progressing to metastasis, however, their impact on overall or event-free survival remains unclear. CONCLUSION: The risk of IPNs progressing to metastasis in patients with primary bone sarcoma is non-negligible. Large IPNs have a high risk to be an actual metastasis. We suggest that IPNs in these patients be followed up for a minimum of 2 years with CT imaging at 3, 6, and 12 month intervals, particularly for nodules measuring >5 mm in average diameter. ADVANCES IN KNOWLEDGE: This is the first systematic review on IPNs in patients with primary bone sarcomas only and proposes viable management strategies for such patients.

Image-Guided VATS in the Hybrid Operation Room Facilitates Early Diagnosis and Concurrent Treatment of Subcentimeter Nonpalpable Lung Nodules.

OBJECTIVE: As lung cancer screening increases, the detection of small, nonpalpable lung lesions is on the rise. The hybrid operation room (OR), which combines percutaneous or endobronchial fiducial placement with on-table computed tomography (CT) and fluoroscopic guidance, improves localization and facilitates the diagnosis and treatment of smaller, nonpalpable lung nodules with greater accuracy. METHODS: In 35 consecutive months, 55 veterans underwent 60 image-guided video-assisted thoracic surgery procedures for lesion resection. Of the cases, 36% were found during lung cancer screening. All patients received their care in the hybrid OR, where cone-beam CT scan technology was used to place an average of 1.6 fiducials percutaneously (n = 55) or via augmented navigational bronchoscopy (n = 5). RESULTS: A total of 66 lesions were resected. The median lesion size was 8 mm with an interquartile range of 6 to 14. The patients underwent nonanatomical resection with lymph node dissection using radiologic guidance. When indicated, an anatomical resection was subsequently performed. Of 47 total non-small cell lung cancer lesions, 83% were diagnosed at stage IA1 or IA2. The median surgical margin was 15 mm; the margin was usually 1.5 times as wide as the lesion. CONCLUSIONS: The hybrid OR technology gives a 3-dimensional assessment of the small lung lesions, allowing for a tissue-saving resection while achieving good surgical margins. During lung cancer screening, smaller, nonpalpable lung nodules are frequently found. This technology allows resection of subcentimeter lesions, which would otherwise be unresectable at this early stage, possibly improving survival.

Clinical Outcomes of Resected Pure Ground-Glass, Heterogeneous Ground-Glass, and Part-Solid Pulmonary Nodules.

BACKGROUND. Increased (but not definitively solid) attenuation within pure ground-glass nodules (pGGNs) may indicate invasive adenocarcinoma and the need for resection rather than surveillance. OBJECTIVE. The purpose of this study was to compare the clinical outcomes among resected pGGNs, heterogeneous ground-glass nodules (GGNs), and part-solid nodules (PSNs). METHODS. This retrospective study included 469 patients (335 female patients and 134 male patients; median age, 68 years [IQR, 62.5-73.5 years]) who, between January 2012 and December 2020, underwent resection of lung adenocarcinoma that appeared as a subsolid nodule on CT. Two radiologists, using lung windows, independently classified each nodule as a pGGN, a heterogeneous GGN, or a PSN, resolving discrepancies through discussion. A heterogeneous GGN was defined as a GGN with internal increased attenuation not quite as dense as that of pulmonary vessels, and a PSN was defined as having an internal solid component with the same attenuation as that of the pulmonary vessels. Outcomes included pathologic diagnosis of invasive adenocarcinoma, 5-year recurrence rates (locoregional or distant), and recurrence-free survival (RFS) and overall survival (OS) over 7 years, as analyzed by Kaplan-Meier and Cox proportional hazards regression analyses, with censoring of patients with incomplete follow-up. RESULTS. Interobserver agreement for nodule type, expressed as a kappa coefficient, was 0.69. Using consensus assessments, 59 nodules were pGGNs, 109 were heterogeneous GGNs, and 301 were PSNs. The frequency of invasive adenocarcinoma was 39.0% in pGGNs, 67.9% in heterogeneous GGNs, and 75.7% in PSNs (for pGGNs vs heterogeneous GGNs, p < .001; for pGGNs vs PSNs, p < .001; and for heterogeneous GGNs vs PSNs, p = .28). The 5-year recurrence rate was 0.0% in patients with pGGNs, 6.3% in those with heterogeneous GGNs, and 10.8% in those with PSNs (for pGGNs vs heterogeneous GGNs, p = .06; for pGGNs vs PSNs, p = .02; and for heterogeneous GGNs vs PSNs, p = .18). At 7 years, RFS was 97.7% in patients with pGGNs, 82.0% in those with heterogeneous GGNs, and 79.4% in those with PSNs (for pGGNs vs heterogeneous GGNs, p = .02; for pGGNs vs PSNs, p = .006; and for heterogeneous GGNs vs PSNs, p = .40); OS was 98.0% in patients with pGGNs, 84.6% in those with heterogeneous GGNs, and 82.9% in those with PSNs (for pGGNs vs heterogeneous GGNs, p = .04; for pGGNs vs PSNs, p = .01; and for heterogeneous GGNs vs PSNs, p = .50). CONCLUSION. Resected pGGNs had excellent clinical outcomes. Heterogeneous GGNs had relatively worse outcomes, more closely resembling outcomes for PSNs. CLINICAL IMPACT. The findings support surveillance for truly homogeneous pGGNs versus resection for GGNs showing internal increased attenuation even if not having a true solid component.

Comparison of lung cancer occurring in fibrotic versus non-fibrotic lung on chest CT.

PURPOSE: Evaluate the behavior of lung nodules occurring in areas of pulmonary fibrosis and compare them to pulmonary nodules occurring in the non-fibrotic lung parenchyma. METHODS: This retrospective review of chest CT scans and electronic medical records received expedited IRB approval and a waiver of informed consent. 4500 consecutive patients with a chest CT scan report containing the word fibrosis or a specific type of fibrosis were identified using the system M*Model Catalyst (Maplewood, Minnesota, U.S.). The largest nodule was measured in the longest dimension and re-evaluated, in the same way, on the follow-up exam if multiple time points were available. The nodule doubling time was calculated. If the patient developed cancer, the histologic diagnosis was documented. RESULTS: Six hundred and nine patients were found to have at least one pulmonary nodule on either the first or the second CT scan. 274 of the largest pulmonary nodules were in the fibrotic tissue and 335 were in the non-fibrotic lung parenchyma. Pathology proven cancer was more common in nodules occurring in areas of pulmonary fibrosis compared to nodules occurring in areas of non-fibrotic lung (34% vs 15%, p < 0.01). Adenocarcinoma was the most common cell type in both groups but more frequent in cancers occurring in non-fibrotic tissue. In the non-fibrotic lung, 1 of 126 (0.8%) of nodules measuring 1 to 6 mm were cancer. In contrast, 5 of 49 (10.2%) of nodules in fibrosis measuring 1 to 6 mm represented biopsy-proven cancer (p < 0.01). The doubling time for squamous cell cancer was shorter in the fibrotic lung compared to non-fibrotic lung, however, the difference was not statistically significant (p = 0.24). 15 incident lung nodules on second CT obtained ≤ 18 months after first CT scan was found in fibrotic lung and eight (53%) were diagnosed as cancer. CONCLUSIONS: Nodules occurring in fibrotic lung tissue are more likely to be cancer than nodules in the nonfibrotic lung. Incident pulmonary nodules in pulmonary fibrosis have a high likelihood of being cancer.

Comparison of the safety and effectiveness of the four-hook needle and hook wire for the preoperative positioning of localization ground glass nodules.

BACKGROUND: With the implementation of lung cancer screening programs, an increasing number of pulmonary nodules have been detected.Video-assisted thoracoscopic surgery (VATS) could provide adequate tissue specimens for pathological analysis, and has few postoperative complications.However, locating the nodules intraoperatively by palpation can be difficult for thoracic surgeons. The preoperative pulmonary nodule localization technique is a very effective method.We compared the safety and effectiveness of two methods for the preoperative localization of pulmonary ground glass nodules. METHODS: From October 2020 to April 2021, 133 patients who underwent CT-guided single pulmonary nodule localization were retrospectively reviewed. All patients underwent video-assisted thoracoscopic surgery (VATS) after successful localization. Statistical analysis was used to evaluate the localization accuracy, safety, information related to surgery and postoperative pathology information. The aim of this study was to evaluate the clinical effects of the two localization needles. RESULTS: The mean maximal transverse nodule diameters in the four-hook needle and hook wire groups were 8.97 ± 3.85 mm and 9.00 ± 3.19 mm, respectively (P = 0.967). The localization times in the four-hook needle and hook wire groups were 20.58 ± 2.65 min and 21.43 ± 3.06 min, respectively (P = 0.09). The dislodgement rate was significantly higher in the hook wire group than in the four-hook needle group (7.46% vs. 0, P = 0.024). The mean patient pain scores based on the visual analog scale in the four-hook needle and hook wire groups were 2.87 ± 0.67 and 6.10 ± 2.39, respectively (P = 0.000). All ground glass nodules (GGNs) were successfully resected by VATS. CONCLUSIONS: Preoperative pulmonary nodule localization with both a four-hook needle and hook wire is safe, convenient and effective.

Machine learning-based radiomics to distinguish pulmonary nodules between lung adenocarcinoma and tuberculosis.

BACKGROUND: Radiomics is increasingly utilized to distinguish pulmonary nodules between lung adenocarcinoma (LUAD) and tuberculosis (TB). However, it remains unclear whether different segmentation criteria, such as the inclusion or exclusion of the cavity region within nodules, affect the results. METHODS: A total of 525 patients from two medical centers were retrospectively enrolled. The radiomics features were extracted according to two regions of interest (ROI) segmentation criteria. Multiple logistic regression models were trained to predict the pathology: (1) The clinical model relied on clinical-radiological semantic features; (2) The radiomics models (radiomics+ and radiomics-) utilized radiomics features from different ROIs (including or excluding cavities); (3) the composite models (composite+ and composite-) incorporated both above. RESULTS: In the testing set, the radiomics+/- models and the composite+/- models still possessed efficient prediction performance (AUC ≥ 0.94), while the AUC of the clinical model was 0.881. In the validation set, the AUC of the clinical model was only 0.717, while that of the radiomics+/- models and the composite+/- models ranged from 0.801 to 0.825. The prediction performance of all the radiomics+/- and composite+/- models were significantly superior to that of the clinical model (p < 0.05). Whether the ROI segmentation included or excluded the cavity had no significant effect on these models (radiomics+ vs. radiomics-, composite+ model vs. composite-) (p > 0.05). CONCLUSIONS: The present study established a machine learning-based radiomics strategy for differentiating LUAD from TB lesions. The ROI segmentation including or excluding the cavity region may exert no significant effect on the predictive ability.