Subsolid Nodules Research Articles

Deep learning-based growth prediction for sub-solid pulmonary nodules on CT images.

BackgroundEstimating the growth of pulmonary sub-solid nodules (SSNs) is crucial to the successful management of them during follow-up periods. The purpose of this study is to (1) investigate the measurement sensitivity of diameter, volume, and mass of SSNs for identifying growth and (2) seek to establish a deep learning-based model to predict the growth of SSNs.MethodsA total of 2,523 patients underwent at least 2-year examination records retrospectively collected with sub-solid nodules. A total of 2,358 patients with 3,120 SSNs from the NLST dataset were randomly divided into training and validation sets. Patients from the Yibicom Health Management Center and Guangdong Provincial People’s Hospital were collected as an external test set (165 patients with 213 SSN). Trained models based on LUNA16 and Lndb19 datasets were employed to automatically obtain the diameter, volume, and mass of SSNs. Then, the increase rate in measurements between cancer and non-cancer groups was studied to evaluate the most appropriate way to identify growth-associated lung cancer. Further, according to the selected measurement, all SSNs were classified into two groups: growth and non-growth. Based on the data, the deep learning-based model (SiamModel) and radiomics model were developed and verified.ResultsThe double time of diameter, volume, and mass were 711 vs. 963 days (P = 0.20), 552 vs. 621 days (P = 0.04) and 488 vs. 623 days (P< 0.001) in the cancer and non-cancer groups, respectively. Our proposed SiamModel performed better than the radiomics model in both the NLST validation set and external test set, with an AUC of 0.858 (95% CI 0.786–0.921) and 0.760 (95% CI 0.646–0.857) in the validation set and 0.862 (95% CI 0.789–0.927) and 0.681 (95% CI 0.506–0.841) in the external test set, respectively. Furthermore, our SiamModel could use the data from first-time CT to predict the growth of SSNs, with an AUC of 0.855 (95% CI 0.793–0.908) in the NLST validation set and 0.821 (95% CI 0.725–0.904) in the external test set.ConclusionMass increase rate can reflect more sensitively the growth of SSNs associated with lung cancer than diameter and volume increase rates. A deep learning-based model has a great potential to predict the growth of SSNs.

The Different Evaluative Significance of Enlarged Lymph Nodes on Preoperative CT in the N Stage for Patients with Suspected Subsolid and Solid Lung Cancers

To investigate the clinical value of enlarged mediastinal and/or hilar lymph nodes on CT for patients with lung subsolid and solid nodules. The study was performed on patients who underwent surgical treatment for suspected lung cancer. One hundred seventeen subsolid nodules and 101 solid nodules with enlarged lymph nodes (LNs) were included in the study group. The same number of cases with normal LNs with a balanced distribution of the clinical T stage were randomly selected as the control group for each study group. The pathological statuses of the lymph nodes of these patients proven by histopathology after surgery were collected. Lung solid lesions with enlarged LNs were more prone to lymph node metastasis (37.6% vs. 13.9%, p <0.001). However, there were only two and one metastatic lesions in the subsolid group with and without enlarged LNs, respectively. (2/117 vs. 1/117, p=1.000). No pathological lymph node metastasis was observed in subsolid lesions under clinical stage T1b (cT1b), even though some of them (117/234) with enlarged LNs. The evaluative significance of enlarged LNs on CT is different for subsolid and solid lesions. CT is useful for lymph node evaluation and N staging of solid lesions but may has little value for subsolid lesions. There is a very low possibility of lymph node metastasis occurring in patients with subsolid lesions under cT1b. Enlarged LNs on CT may not be critical in subsequent management and treatment for subsolid nodules.

The long-term course of subsolid nodules and predictors of interval growth on chest CT: a systematic review and meta-analysis

ObjectivesTo calculate the pooled incidence of interval growth after long-term follow-up and identify predictors of interval growth in subsolid nodules (SSNs) on chest CT.MethodsA search of MEDLINE (PubMed), Cochrane Library, Web of Science Core Collection, and Embase was performed on November 08, 2021, for relevant studies. Patient information, CT scanner, and SSN follow-up information were extracted from each included study. A random-effects model was applied along with subgroup and meta-regression analyses. Study quality was assessed by the Newcastle–Ottawa scale, and publication bias was assessed by Egger’s test.ResultsOf the 6802 retrieved articles, 16 articles were included and analyzed, providing a total of 2898 available SSNs. The pooled incidence of growth in the 2898 SSNs was 22% (95% confidence interval [CI], 15–29%). The pooled incidence of growth in the subgroup analysis of pure ground-glass nodules was 26% (95% CI: 12–39%). The incidence of SSN growth after 2 or more years of stability was only 5% (95% CI: 3–7%). An initially large SSN size was found to be the most frequent risk factor affecting the incidence of SSN growth and the time of growth.ConclusionsThe pooled incidence of SSN growth was as high as 22%, with a 26% incidence reported for pure ground-glass nodules. Although the incidence of growth was only 5% after 2 or more years of stability, long-term follow-up is needed in certain cases. Moreover, the initial size of the SSN was the most frequent risk factor for growth.Key Points• Based on a meta-analysis of 2898 available subsolid nodules in the literature, the pooled incidence of growth was 22% for all subsolid nodules and 26% for pure ground-glass nodules.• After 2 or more years of stability on follow-up CT, the pooled incidence of subsolid nodule growth was only 5%.• Given the incidence of subsolid nodule growth, management of these lesions with long-term follow-up is preferred.

A Nomogram Incorporating Tumor-Related Vessels for Differentiating Adenocarcinoma In Situ from Minimally Invasive and Invasive Adenocarcinoma Appearing as Subsolid Nodules

To develop a nomogram incorporating the quantity of tumor-related vessels (TRVs) and conventional CT features (CCTFs) for the preoperative differentiation of adenocarcinoma in situ (AIS) from minimally invasive adenocarcinoma (MIA) and invasive adenocarcinoma (IAC) appearing as subsolid nodules. High-resolution CT target scans of 274 subsolid nodules from 268 patients were included in this study and randomly assigned to the training and validation groups at a ratio of 7:3. A nomogram incorporating CCTFs with the category of TRVs (CTRVs, using TRVs as categorical variables) and a final nomogram combining the number of TRVs (QTRVs) and CCTFs were constructed using multivariable logistic regression analysis. The performance levels of the two nomograms were evaluated and validated on the training and validation datasets and then compared. The CCTF-QTRV nomogram incorporating abnormal air bronchogram, density, number of dilated and distorted vessels and number of adherent vessels showed more favorable predictive efficacy than the CCTF-CTRV nomogram (training cohort: area under the curve (AUC) = 0.893 vs. 0.844, validation cohort: AUC = 0.871 vs. 0.807). The net reclassification index (training cohort: 0.188, validation cohort: 0.326) and the integrated discrimination improvement values (training cohort: 0.091, validation cohort: 0.125) indicated that the CCTF-QTRV nomogram performed significantly better discriminative ability than the CCTF-CTRV nomogram (all p-value < 0.05). The nomogram incorporating the QTRVs and CCTFs showed favorable predictive efficacy for differentiating AIS from MIA-IAC appearing as subsolid nodules and may serve as a potential tool to provide individual care for these patients.

A CT-based nomogram for predicting the risk of adenocarcinomas in patients with subsolid nodule according to the 2021 WHO classification

PurposeTo establish a nomogram for predicting the risk of adenocarcinomas in patients with subsolid nodules (SSNs) according to the 2021 WHO classification.MethodsA total of 656 patients who underwent SSNs resection were retrospectively enrolled. Among them, 407 patients were assigned to the derivation cohort and 249 patients were assigned to the validation cohort. Univariate and multi-variate logistic regression algorithms were utilized to identity independent risk factors of adenocarcinomas. A nomogram based on the risk factors was generated to predict the risk of adenocarcinomas. The discrimination ability of the nomogram was evaluated using the concordance index (C-index), its performance was calibrated using a calibration curve, and its clinical significance was evaluated using decision curves and clinical impact curves.ResultsLesion size, mean CT value, vascular change and lobulation were identified as independent risk factors for adenocarcinomas. The C-index of the nomogram was 0.867 (95% CI, 0.833-0.901) in derivation cohort and 0.877 (95% CI, 0.836-0.917) in validation cohort. The calibration curve showed good agreement between the predicted and actual risks. Analysis of the decision curves and clinical impact curves revealed that the nomogram had a high standardized net benefit.ConclusionsA nomogram for predicting the risk of adenocarcinomas in patients with SSNs was established in light of the 2021 WHO classification. The developed model can be adopted as a pre-operation tool to improve the surgical management of patients.

EP02.01-015 Computer-Aided Volumetry by Multidetector Computed Tomography is Efficient for Prognostic Prediction of Early-Stage Solid Lung Cancers

On the basis of computed tomography (CT) findings, published recommendations for the clinical care of pulmonary nodules differentiate between solid and subsolid nodules, and solid nodules have a significantly worse prognosis than subsolid nodules, even if the nodules are subcentimeter in size. The prognosis of patients with subsolid nodules is predicted by solid size of the tumor. However, there are almost no established criteria for assessing the prognostic relevance of solid tumors on CT. In this study, we investigated the association between three-dimensional (3D) volumetry value on CT scan and the postoperative prognosis in patients with early-stage solid lung adenocarcinoma.

EP01.06-009 Frequency of Lung Adenocarcinoma in Patients with Growing Nonsolid and Part-Solid Nodules with Solid Component ≤5 mm

The 2011 Classification of adenocarcinoma of the lung added terms “preinvasive” terminology for pathological diagnosis of resection specimens; Atypical adenomatous hyperplasia (AAH), Adenocarcinoma in situ (AIS) which on CT usually present as nonsolid nodules and minimally invasive adenocarcinoma, which at CT usually present as nonsolid nodules but may have a solid component of up to 5 mm. Previous studies has reported that subsolid nodule have a high likelihood of pathological diagnosis of malignancy varying between 83.6% -100%.

Is it really necessary to perform mediastinal lymphadenectomy in surgery for ground glass opacity-featured lung adenocarcinoma?

: An increasing number of lung ground-glass opacity (GGO) are detected along with the popularity of low-dose computed tomography (LDCT) screening. Despite the excellent prognosis of GGO-featured lung cancers, systematic lymphadenectomy is still recommended for all resectable lesions. Much effort has been made to determine the conditions surrounding a limited resection for subsolid nodules. However, the necessity to perform systematic lymphadenectomy in radiologic GGO-featured lung adenocarcinomas (LUADs) remains controversial. Pure GGOs were reported had a 100% 5-year survival rate with no lymphatic metastasis, and the lymphatic metastasis rate in part-solid nodules (PSNs), especially in GGO-predominant PSNs, was significantly lower than that in pure solid nodules. The impact of lymphadenectomy on the prognosis and the specific predictors for lymphatic metastasis in PSNs are also discussed. Systematic lymphadenectomy was not yet proved to confer a survival benefit in patients with GGO-featured lung cancers. Integration of pre-/intra-operative information such as tumor size, consolidation tumor ratio (CTR) and pathological information is warrant for solid-predominant PSNs. Pre-invasive lesions rarely occurred lymph node metastasis and intraoperative frozen section (FS) results might provide pathological information before lymphadenectomy strategy determination. Finally, although randomized controlled trials are difficult to conduct in LUAD manifesting as GGO, more prospective studies are required to justify the indication of systematic lymphadenectomy.

Preoperative prediction of the degree of differentiation of lung adenocarcinoma presenting as sub-solid or solid nodules with a radiomics nomogram

To develop and validate a radiomics nomogram for prediction of degree of differentiation in lung adenocarcinoma presenting as sub-solid or solid nodules. A total of 438 patients with histopathologically confirmed adenocarcinoma (248 non-poorly differentiated and 190 poorly differentiated) were divided into training cohort (n=235) and internal validation cohort (n=203) according to surgery sequence. Sixty patients form public TCIA dataset were selected for external validation. One thousand, two hundred and eighteen radiomics features were extracted from each volumetric region of interest and a least absolute shrinkage and selection operator logistic regression was applied to select meaningful radiomic features for building a radiomics score (Rad-score) model. A nomogram model incorporating the Rad-score and type was established after multivariable logistic regression. The discrimination efficiency, calibration efficacy, and clinical utility value of the nomogram were evaluated. The Rad-score model could predict the differentiation degree of lung adenocarcinoma with an area under the curve (AUC) of 0.83 (95% confidence interval [CI]: 0.78-0.89) in the internal validation cohort. The AUC of the nomogram and radiographic model was 0.86 (95% CI: 0.80-0.91), 0.78 (95% CI: 0.72-0.84) in the internal validation cohort respectively. The AUC of the nomogram in the external validation cohort was 0.73 (95% CI: 0.58-0.88). Delong's test showed that the nomogram performed better than radiographic features alone (p=0.001). The proposed radiomics nomogram has the potential to predict the differentiation degree of lung adenocarcinoma preoperatively.

Indeterminate pulmonary subsolid nodules in patients with no history of cancer: growing prediction, CT pattern, and pathological diagnosis.

PURPOSE We aimed to evaluate and compare the growth patterns among pathological types of inde- terminate subsolid nodules in patients without a history of cancer as observed on computed tomography (CT). METHODS This retrospective study included 77 consecutive patients with 80 indeterminate subsolid nod- ules on unenhanced thin-section CT. Subsolid nodules were classified into 2 growth pattern groups based on volume: growth (n = 35) and non-growth (n = 42). According to the pathologi- cal diagnosis, subsolid nodules were further subdivided into 3 groups: adenocarcinoma in situ (growth, n = 8 vs. non-growth, n = 22), minimally invasive adenocarcinoma (n = 14 vs. n = 15), and invasive adenocarcinoma (n=13 vs. n=5). Kaplan-Meier and Cox proportional hazards regres- sion analyses were performed to identify the risk factors for subsolid nodules growth. The CT findings of the 35 subsolid nodules in the growth group were compared among the 3 pathologi- cal groups. RESULTS In the growth group, the overall mean volume doubling time and mass doubling time (MDT) were 811.5 days and 616.5 days, respectively. Patient's age (odds ratio=1.041, P=.045) and CT subtype of non-solid nodule and part-solid nodule (odds ratio=3.430, P=.002) could predict subsolid nodule growth. The baseline volume, mass, and mean CT value were larger in the inva- sive adenocarcinoma group than in the adenocarcinoma in situ group (all P < .01). The shortest volume doubling time was observed in the invasive adenocarcinoma group, followed by the minimally invasive adenocarcinoma group and the adenocarcinoma in situ group. A shorter mass doubling time was observed in the minimally invasive adenocarcinoma group than in the adenocarcinoma in situ group (all P < .02). CONCLUSION As age increases, the risk of pulmonary subsolid nodule growth increases by 4% each year, and part-solid nodules have a 3 times higher risk of growth compared to non-solid nodules in patients with no history of cancer. Subsolid nodules with more aggressive pathological charac- teristics grow at a faster rate.

Malignancy risk stratification for solitary pulmonary nodule: A clinical practice guideline.

The detection rate of the solitary pulmonary nodule (SPN) is increasing with the popularization of CT scanning. Malignancy risk stratification for SPN is a major clinical difficulty. There have been several guidelines for SPN assessment. Inconsistency of these guidelines makes the clinical application difficult and confusing. In this Rapid Recommendation, solid and subsolid SPNs are recommended to be evaluated respectively. Six factors, namely the combination of age with sex, smoking history, history of malignancy, family history of malignancy, and nodule size, are recommended for malignancy risk stratification for both kinds of SPNs; the border of nodules (spiculation and lobulation) is recommended for evaluating solid SPNs and the density of nodules (pure or mixed ground-glass nodule) is recommended for subsolid nodules. Among them, smoking history and radiologic features (nodule diameter, border, and density) are of relatively higher importance. A scoring system was proposed to assist malignancy risk stratification of SPNs, with a total score ranging from six points to 15 points (if solid) or 17 points (if subsolid). For each SPN, regardless of solid or subsolid in nature, a total score of ≤ 7 points suggested a low risk of being malignant, while 7 to 9 points suggested medium risk, and ≥ 9 points suggested high risk. This rapid recommendation was developed using the MAGIC (Making GRADE the Irresistible Choice) methodological framework. First, a clinical subcommittee identified the topic of recommendation and requested evidence. Then, an independent evidence synthesis subcommittee performed a comprehensive literature review and evaluated the evidence. Finally, based on findings from the systematic review and use of real-world data, the clinical subcommittee formulated recommendations, including the scoring system, through a consensus procedure. A total of 13857 patients with SPNs were included in the meta-analysis and the association between 12 candidate factors and the risk of SPNs being malignant was studied. Eventually, seven factors were recommended for SPNs evaluation, and a scoring system was proposed. The parameters included are objective. Therefore, this recommendation is feasible in clinical practice. However, there are several uncertainties, such as a lack of further verification. It might be misclassified by the scoring system. Clinicians could choose the most suitable scheme according to the recommendation, along with their own experience in specific situations.

The Application and Value of 3T Magnetic Resonance Imaging in the Display of Pulmonary Nodules.

ObjectiveThe aim of this study was to evaluate the sensitivity and accuracy of multi-sequence 3T magnetic resonance imaging (MRI) in the detection of different types of pulmonary nodules.MethodsA total of 68 patients with pulmonary nodules identified using computed tomography (CT) subsequently underwent MRI. Using CT images with a slice thickness of 1 mm as the gold standard, the sensitivity of three MRI sequences in detecting different types of pulmonary nodules was calculated, and the image quality was also evaluated. Nodule types included solid nodules, ground glass nodules (GGN), and part-solid nodules (PSN). Statistical analyses of data were conducted using the software SPSS 21.0. The intra-class correlation coefficient was calculated in order to compare the consistency of nodule size in both MRI and CT.ResultsCT detected 188 pulmonary nodules in 68 patients, including 87 solid nodules and 101 sub-solid nodules, the latter comprising 46 PSNs and 55 GGNs. The average nodule diameter was approximately 7.7 mm. The sensitivity of MRI in detecting nodules ≥ 6 mm in diameter and those of > 8 mm in diameter was 92% and 100%, respectively, and the sequence with the highest detection rate was T2-BLADE. In relation to solid nodules, the sequence with the highest detection rate was T1 Star-VIBE, while the T2-BLADE sequence demonstrated the highest detection rate of sub-solid nodules. The image quality of the T1 Star-VIBE sequence was better than that of both the T2-HASTE and the T2-BLADE sequences. The consistency of CT and MRI sequences for nodule size was high with a consistency coefficient of 0.94–0.98.ConclusionThe detection rate of MRI for nodules with a diameter of > 8 mm was 100%. The T2-BLADE sequence had the highest detection sensitivity. The sequence with the best image quality was the T1 Star-VIBE.

Improved interobserver agreement on nodule type and Lung-RADS classification of subsolid nodules using computer-aided solid component measurement

PurposeThe Lung CT Screening Reporting and Data System (Lung-RADS) classification of subsolid nodules (SSNs) can be challenging due to limited interobserver agreement in determining the type and size of the nodule. Our study aimed to assess the effect of a computer-aided method on the interobserver agreement of Lung-RADS classification for SSNs. Materials and methodsThis study consisted of 156 SSNs in 121 patients who underwent initial CT screening for lung cancer. Three independent readers determined the nodule type and measured the size of the entire nodule as well as the solid component, first without and then assisted by a semi-automated computer-aided tool. They assigned to each nodule the corresponding Lung-RADS 1.1 category. Agreement in size measurements was assessed by intraclass correlation coefficient (ICC) and Bland-Altman indexes, while agreement in nodule type and Lung-RADS was determined using Fleiss kappa statistics. The relationship between final diagnosis of the nodules and Lung-RADS classifications was also evaluated. ResultsAmong the 156 nodules, manual size measurement reached an ICC of 0.994, and 48 nodules contained solid component measured by all the three readers both manually and semi-automatically. ICCs for the solid component measurement were 0.952, 0.997 and 0.996 for manual diameter, semi- automated diameter and volume measurement, respectively. Bias and 95% limits of agreement for average diameter of solid component were smaller with semi-automated measurements than with manual measurements. Kappa values of semi-automated assessment for nodule type (0.974) and Lung-RADS classification (0.958 for diameter and 0.952 for volume) were higher than with the manual measurements (0.783 for nodule type and 0.652 for Lung-RADS classification). Compared to manual work, the semi-automated assessment identified more 4B nodules among the 26 pathologically confirmed invasive adenocarcinomas (IACs). ConclusionSemi-automated assessment could improve the interobserver agreement of nodule type and Lung-RADS classification for SSNs, and be inclined to classify SSNs corresponding to pathologically confirmed IACs into higher risk categories.

Validation of a deep learning computer aided system for CT based lung nodule detection, classification, and growth rate estimation in a routine clinical population.

In this study, we evaluated a commercially available computer assisted diagnosis system (CAD). The deep learning algorithm of the CAD was trained with a lung cancer screening cohort and developed for detection, classification, quantification, and growth of actionable pulmonary nodules on chest CT scans. Here, we evaluated the CAD in a retrospective cohort of a routine clinical population. In total, a number of 337 scans of 314 different subjects with reported nodules of 3-30 mm in size were included into the evaluation. Two independent thoracic radiologists alternately reviewed scans with or without CAD assistance to detect, classify, segment, and register pulmonary nodules. A third, more experienced, radiologist served as an adjudicator. In addition, the cohort was analyzed by the CAD alone. The study cohort was divided into five different groups: 1) 178 CT studies without reported pulmonary nodules, 2) 95 studies with 1-10 pulmonary nodules, 23 studies from the same patients with 3) baseline and 4) follow-up studies, and 5) 18 CT studies with subsolid nodules. A reference standard for nodules was based on majority consensus with the third thoracic radiologist as required. Sensitivity, false positive (FP) rate and Dice inter-reader coefficient were calculated. After analysis of 470 pulmonary nodules, the sensitivity readings for radiologists without CAD and radiologist with CAD, were 71.9% (95% CI: 66.0%, 77.0%) and 80.3% (95% CI: 75.2%, 85.0%) (p < 0.01), with average FP rate of 0.11 and 0.16 per CT scan, respectively. Accuracy and kappa of CAD for classifying solid vs sub-solid nodules was 94.2% and 0.77, respectively. Average inter-reader Dice coefficient for nodule segmentation was 0.83 (95% CI: 0.39, 0.96) and 0.86 (95% CI: 0.51, 0.95) for CAD versus readers. Mean growth percentage discrepancy of readers and CAD alone was 1.30 (95% CI: 1.02, 2.21) and 1.35 (95% CI: 1.01, 4.99), respectively. The applied CAD significantly increased radiologist's detection of actionable nodules yet also minimally increasing the false positive rate. The CAD can automatically classify and quantify nodules and calculate nodule growth rate in a cohort of a routine clinical population. Results suggest this Deep Learning software has the potential to assist chest radiologists in the tasks of pulmonary nodule detection and management within their routine clinical practice.

Electromagnetic Navigation Transthoracic Nodule Localization for Minimally Invasive Thoracic Surgery.

The increased use of chest computed tomography (CT) has led to an increased detection of pulmonary nodules requiring diagnostic evaluation and/or excision. Many of these nodules are identified and excised via minimally invasive thoracic surgery; however, subcentimeter and subsolid nodules are frequently difficult to identify intra-operatively. This can be mitigated by the use of electromagnetic transthoracic needle localization. This protocol delineates the step-by-step process of electromagnetic localization from the pre-operative period to the postoperative period and is an adaptation of the electromagnetically guided percutaneous biopsy previously described by Arias et al. Pre-operative steps include obtaining a same day CT followed by the generation of a three-dimensional virtual map of the lung. From this map, the target lesion(s) and an entry site are chosen. In the operating room, the virtual reconstruction of the lung is then calibrated with the patient and the electromagnetic navigation platform. The patient is then sedated, intubated, and placed in the lateral decubitus position. Using a sterile technique and visualization from multiple views, the needle is inserted into the chest wall at the prechosen skin entry site and driven down to the target lesion. Dye is then injected into the lesion and, then, continuously during needle withdrawal, creating a tract for visualization intra-operatively. This method has many potential benefits when compared to the CT-guided localization, including a decreased radiation exposure and decreased time between the dye injection and the surgery. Dye diffusion from the pathway occurs over time, thereby limiting intra-operative nodule identification. By decreasing the time to surgery, there is a decrease in wait time for the patient, and less time for dye diffusion to occur, resulting in an improvement in nodule localization. When compared to electromagnetic bronchoscopy, airway architecture is no longer a limitation as the target nodule is accessed via a transparenchymal approach. Details of this procedure are described in a step-by-step fashion.

96P Transbronchial microwave ablation: Important role in the battle of lung preservation for multifocal lung primaries or metastases

Multifocal small, deep and subsolid lung nodules are increasingly discovered incidentally, often harboring early malignancies. In these cases, lung-preservation rather than major lung resection is the optimal strategy of choice. We retrospectively reviewed all lung nodules treated by electromagnetic navigation bronchoscopy (ENB) transbronchial microwave ablation between March 2019 and January 2022. Among these, we extract the case series of patients with multifocal lung cancers or oligometastases which have been managed by ENB ablation with or without other forms of local treatment. Among 80 patients treated with ENB microwave ablation in the hybrid operating room, 17 had multiple lung nodules which were suspicious of or confirmed malignancies. Reason for lung preserving strategy includes multi-lobar location of nodules (70.6%), inadequate lung function (23.5%), previous major lung resection (17.6%) and patient preference (11.7%). All nodules were relatively deep (mean of 14mm from pleural surface) and small (mean of 11mm in diameter). Eight patients were histologically proven primary lung cancers or metastases, while the rest were highly suspicious of malignancies. Seven patients had ENB ablation for two or more nodules in the same session, while 7 had ENB ablation on separate occasions. Four patients had concurrent wedge resection of other lung nodules in the same operative session. Technically success rate was 100%, and the average length of hospital stay was 1.3 days after same session ENB ablation, and 2 days after concurrent wedge resection. No complications were encountered except mild pain (17.6%) or low grade fever (5.9%). Over a median follow up of 13 months, there was 1 local recurrence which was subsequently treated with stereotactic radiotherapy and 1 local plus distant metastases which was managed by systemic chemotherapy. ENB ablation can be combined with other multimodality treatments including surgical wedge resection, radiotherapy, or percutaneous local ablation, providing personalized therapeutic options. It is an important armamentarium in tackling multifocal lung cancers or oligometastases, as part of lung-preserving strategy.

Disparate genomic characteristics of patients with early-stage lung adenocarcinoma manifesting as radiological subsolid or solid lesions

IntroductionEarly-stage lung adenocarcinoma (LUAD) manifesting as subsolid nodules (SSNs) exhibit more favorable prognosis than solid nodules (SNs). However, the genomic underpinnings behind their indolent tumor behavior remain largely unexplained. MethodsWe identified patients with stage I invasive LUAD who underwent complete surgical resection and broad-panel next-generation sequencing (NGS). Comparative genomic profiling was then performed by radiological subtype (SSNs vs. SNs) regarding the general genomic features, driver genes, oncogenic pathways, therapeutic actionability, and evolutionary trajectory. ResultsIn total, 177 SSN-LUADs and 133 SN-LUADs were included. Compared with SNs, SSN-LUADs possessed lower somatic mutation count (P < 0.001), genomic alteration count (P = 0.002), and intra-tumor heterogeneity (P = 0.006). In terms of driver genes, SSNs harbored more EGFR mutation (77% vs. 62%), but had lower frequencies of genes such as TP53, ARID1A, PIK3CA, CDKN2A, and BRAF (FDR q < 0.1). Besides, RBM10 mutation was independently associated with SSN-LUADs in multivariate analysis (P = 0.033). Three oncogenic pathways (p53, cell cycle, PI3K) were altered with statistical significance in SNs, while only RNA splicing/processing pathway was significantly altered in SSNs (FDR q < 0.1). Also, SSNs had significantly lower number of pathway alterations (P < 0.001). Finally, SSNs and SNs showed distinct evolutionary trajectories regarding somatic mutations during early-stage LUAD progression. ConclusionsThis study performed the first direct comparative genomic profiling in pathologic stage I invasive LUAD by radiological subtype, highlighting a less complex genomic architecture of SSNs, which might be the molecular interpretation of their indolent tumor behavior.

Differential Diagnosis of Nonabsorbable Inflammatory and Malignant Subsolid Nodules with a Solid Component ≤5 mm.

PurposeTo investigate the differential clinical and computed tomography (CT) characteristics of pulmonary nonabsorbable inflammatory and malignant subsolid nodules (SSNs) with a solid component ≤5 mm.Patients and MethodsWe retrospectively analyzed 576 consecutive patients who underwent surgical resection and had SSNs with a solid component ≤5 mm on CT images. These patients were divided into inflammatory and malignant groups according to pathology. Their clinical and imaging data were analyzed and compared. Multiple logistic regression analysis was used to identify independent prognostic factors differentiating inflammatory from malignant SSNs. Furthermore, 146 consecutive patients were included as internal validation cohort to test the prediction efficiency of this model.ResultsSignificant differences in 11 clinical characteristics and CT features were found between both groups (P < 0.05). Presence of respiratory symptoms, distribution of middle/lower lobe, irregular shape, part-solid nodule (PSNs), CT value of ground-glass opacity (GGO) areas <−657 Hu, presence of abnormal intra-nodular vessel sign, and interlobular septal thickening were the most effective factors for diagnosing nonabsorbable inflammatory SSNs, with an AUC (95% CI), accuracy, sensitivity, and specificity of 0.843 (95% CI: 0.811–0.872), 89.76%, 72.86%, and 81.23%, respectively. The internal validation cohort obtained an AUC (95% CI), accuracy, sensitivity, and specificity of 0.830 (95% CI: 0.759–0.887), 83.56%, 73.91%, and 76.42%, respectively.ConclusionNonabsorbable inflammatory and malignant SSNs with a solid component ≤5 mm exhibited different clinical and imaging characteristics.

Volumetric analysis of pulmonary nodules: reducing the discrepancy between the diameter-based volume calculation and voxel-counting method.

When assessing the volume of pulmonary nodules on computed tomography (CT) images, there is an inevitable discrepancy between values based on the diameter-based volume calculation and the voxel-counting method, which is derived from the Euclidean distance measurement method on pixel/voxel-based digital image. We aimed to evaluate the ability of a modified diameter measurement method to reduce the discrepancy, and we determined a conversion equation to equate volumes derived from different methods. Two different anthropomorphic phantoms with subsolid and solid nodules were repeatedly scanned under various settings. Nodules in CT images were detected and segmented using a fully automated algorithm and the volume was calculated using three methods: the voxel-counting method (Vvc ), diameter-based volume calculation (Vd ), and a modified diameter-based volume calculation (Vd+ 1), in which one pixel spacing was added to the diameters in the three axes (x-, y-, and z-axis). For each nodule, Vd and Vd +1 were compared to Vvc by computing the absolute percentage error (APE) as follows: APE =100 × (V - Vvc )/Vvc . Comparisons between APEd and APEd+1 according to CT parameter setting were performed using the Wilcoxon signed-rank test. The Jonckheere-Terpstra test was used to evaluate trends across the four different nodule sizes. The deep learning-based computer-aided diagnosis (DL-CAD) successfully detected and segmented all nodules in a fully automatic manner. The APE was significantly less with Vd+1 than with Vd (Wilcoxon signed-rank test, P<0.05) regardless of CT parameters and nodule size. The APE median increased as the size of the nodule decreased. This trend was statistically significant (Jonckheere-Terpstra test, P<0.001) regardless of volume measurement method (diameter-based and modified diameter-based volume calculations). Our modified diameter-based volume calculation significantly reduces the discrepancy between the diameter-based volume calculation and voxel-counting method.

Artificial Intelligence-Aided Diagnosis Software to Identify Highly Suspicious Pulmonary Nodules.

IntroductionTo evaluate the value of artificial intelligence (AI)-assisted software in the diagnosis of lung nodules using a combination of low-dose computed tomography (LDCT) and high-resolution computed tomography (HRCT).MethodA total of 113 patients with pulmonary nodules were screened using LDCT. For nodules with the largest diameters, an HRCT local-target scanning program (combined scanning scheme) and a conventional-dose CT scanning scheme were also performed. Lung nodules were subjectively assessed for image signs and compared by size and malignancy rate measured by AI-assisted software. The nodules were divided into improved visibility and identical visibility groups based on differences in the number of signs identified through the two schemes.ResultsThe nodule volume and malignancy probability for subsolid nodules significantly differed between the improved and identical visibility groups. For the combined scanning protocol, we observed significant between-group differences in subsolid nodule malignancy rates.ConclusionUnder the operation and decision of AI, the combined scanning scheme may be beneficial for screening high-risk populations.