Subsolid pulmonary nodules: Controversy and perspective

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.

Retrospective Analysis of Subsolid Nodules' Frequency Using Chest Computed Tomography Detection in an Outpatient Population

INTRODUCTION

The study was designed to evaluate the frequency of detection and the characteristics of subsolid nodules (SSNs) in outpatients' chest computed tomography (CT) scans from a private hospital in Southern Brazil.

METHODS

A retrospective analysis of all chest CT scans was performed in adult patients from ambulatory care (non-lung cancer screening population) over a thirty-day period. Inclusion criteria were age > 18 years and lung-scanning protocols, including standard-dose high-resolution chest CT (HRCT), enhanced CT, CT angiography, and low-dose chest CT (LDCT). SSNs main features collected were mean diameter, number, density (pure or heterogenous ground glass nodules and part-solid), and localization. TheLungRADS system and the updated Fleischner Society's pulmonary nodules recommendations were used for categorization only for study purposes, although not specifically fitting the population. The presence of emphysema, as well as calcified and solid nodules were also addressed. Statistical analysis was performed using R software, categorial variables are shown as absolute or relative frequencies, and continuous variables as mean and interquartile ranges.

RESULTS

Chest computed tomography were performed in 756 patients during the study period (September 2019), and 650 met the inclusion criteria. The IQR for age was 53/73 years; most participants were female (58.3%) and 10.6% had subsolid nodules detected.

CONCLUSIONS

The frequency of SSNs detection in patients in daily clinical practice, not related to screening populations, is not negligible. Regardless of the final etiology, follow-up is often indicated, given the likelihood of malignancy for persistent lesions.

Multi-Omic Biomarkers Improve Indeterminate Pulmonary Nodule Malignancy Risk Assessment

The clinical management of patients with indeterminate pulmonary nodules is associated with unintended harm to patients and better methods are required to more precisely quantify lung cancer risk in this group. Here, we combine multiple noninvasive approaches to more accurately identify lung cancer in indeterminate pulmonary nodules. We analyzed 94 quantitative radiomic imaging features and 41 qualitative semantic imaging variables with molecular biomarkers from blood derived from an antibody-based microarray platform that determines protein, cancer-specific glycan, and autoantibody-antigen complex content with high sensitivity. From these datasets, we created a PSR (plasma, semantic, radiomic) risk prediction model comprising nine blood-based and imaging biomarkers with an area under the receiver operating curve (AUROC) of 0.964 that when tested in a second, independent cohort yielded an AUROC of 0.846. Incorporating known clinical risk factors (age, gender, and smoking pack years) for lung cancer into the PSR model improved the AUROC to 0.897 in the second cohort and was more accurate than a well-characterized clinical risk prediction model (AUROC = 0.802). Our findings support the use of a multi-omics approach to guide the clinical management of indeterminate pulmonary nodules.

Management of ground-glass opacities and sub-solid pulmonary nodules: a surgeon's perspective

The regular use of high-resolution computed tomogram scans has led to an increase in the detection of asymptomatic ground-glass opacities and sub-solid nodules at an early stage. Different growth patterns of these lesions are making decision-making a real challenge. With growing experience and improving radiology interventions, management of these lesions is changing constantly. However, with variations in growth patterns and outcomes, immediate treatment options as well as follow-up surveillance and subsequent interventions can be confounding for the clinicians. This mini review describes algorithms for managing these ground-glass opacities (GGOs) and sub-solid nodules (SSNs) with a focus on the surgical options.

Características clinicopatológicas de nódulos pulmonares: Experiencia en Clínica Reina Sofía, Bogotá, Colombia

Introducción. El cáncer de pulmón es la primera causa de mortalidad por cáncer a nivel mundial, lo que hace que sea considerado un problema de salud pública. Existen diferentes hallazgos imagenológicos que hacen sospechar la presencia de cáncer de pulmón, uno de los cuales son los nódulos pulmonares; sin embargo, estos también pueden verse en entidades benignas. Métodos. Se incluyeron 66 pacientes con biopsia de nódulo pulmonar en la Clínica Reina Sofía, en la ciudad de Bogotá, D.C., Colombia, entre el 1° de marzo del 2017 y el 28 de febrero del 2020. Se analizaron las características demográficas de los pacientes, las características morfológicas e histopatológicas de los nódulos pulmonares y la correlación entre sus características imagenológicas e histopatológicas. Resultados. El 69,2 % de los nódulos estudiados tenían etiología maligna, de estos el 55,5 % era de origen metástasico y el 44,5 % eran neoplasias primarias de pulmón, con patrón sólido en el 70,6 % de los casos. El patrón histológico más frecuente fue adenocarcinoma. Respecto a las características radiológicas, en su mayoría los nódulos malignos medían de 1 a 2 cm, de morfología lisa y distribución múltiple, localizados en lóbulos superiores. Conclusiones. La caracterización de los nódulos pulmonares brinda información relevante que orienta sobre los diagnósticos más frecuentes en nuestro medio, cuando se estudian nódulos sospechosos encontrados incidentalmente o en el seguimiento de otro tumor. Como el nódulo es la manifestación del cáncer temprano del pulmón, establecer programas de tamización que permitan el diagnóstico oportuno, es hoy día una imperiosa necesidad, para reducir la mortalidad.

Signature identification of relapse-related overall survival of early lung adenocarcinoma after radical surgery

Background

The widespread use of low-dose chest CT screening has improved the detection of early lung adenocarcinoma. Radical surgery is the best treatment strategy for patients with early lung adenocarcinoma; however, some patients present with postoperative recurrence and poor prognosis. Through this study, we hope to establish a model that can identify patients that are prone to recurrence and have poor prognosis after surgery for early lung adenocarcinoma.

Materials and Methods

We screened prognostic and relapse-related genes using The Cancer Genome Atlas (TCGA) database and the GSE50081 dataset from the Gene Expression Omnibus (GEO) database. The GSE30219 dataset was used to further screen target genes and construct a risk prognosis signature. Time-dependent ROC analysis, calibration degree analysis, and DCA were used to evaluate the reliability of the model. We validated the TCGA dataset, GSE50081, and GSE30219 internally. External validation was conducted in the GSE31210 dataset.

Results

A novel four-gene signature (INPP5B, FOSL2, CDCA3, RASAL2) was established to predict relapse-related survival outcomes in patients with early lung adenocarcinoma after surgery. The discovery of these genes may reveal the molecular mechanism of recurrence and poor prognosis of early lung adenocarcinoma. In addition, ROC analysis, calibration analysis and DCA were used to verify the genetic signature internally and externally. Our results showed that our gene signature had a good predictive ability for recurrence and prognosis.

Conclusions

We established a four-gene signature and predictive model to predict the recurrence and corresponding survival rates in patients with early lung adenocarcinoma after surgery. These may be helpful for reforumulating post-operative consolidation treatment strategies.

Predicting Malignancy and Invasiveness of Pulmonary Subsolid Nodules on CT Images Using Deep Learning

Background

To develop and validate a deep learning-based model on CT images for the malignancy and invasiveness prediction of pulmonary subsolid nodules (SSNs).

Materials and Methods

This study retrospectively collected patients with pulmonary SSNs treated by surgery in our hospital from 2012 to 2018. Postoperative pathology was used as the diagnostic reference standard. Three-dimensional convolutional neural network (3D CNN) models were constructed using preoperative CT images to predict the malignancy and invasiveness of SSNs. Then, an observer reader study conducted by two thoracic radiologists was used to compare with the CNN model. The diagnostic power of the models was evaluated with receiver operating characteristic curve (ROC) analysis.

Results

A total of 2,614 patients were finally included and randomly divided for training (60.9%), validation (19.1%), and testing (20%). For the benign and malignant classification, the best 3D CNN model achieved a satisfactory AUC of 0.913 (95% CI: 0.885-0.940), sensitivity of 86.1%, and specificity of 83.8% at the optimal decision point, which outperformed all observer readers' performance (AUC: 0.846±0.031). For pre-invasive and invasive classification of malignant SSNs, the 3D CNN also achieved satisfactory AUC of 0.908 (95% CI: 0.877-0.939), sensitivity of 87.4%, and specificity of 80.8%.

Conclusion

The deep-learning model showed its potential to accurately identify the malignancy and invasiveness of SSNs and thus can help surgeons make treatment decisions.