Lung Cancer Nodules Research Articles

Multi-scale characterization of tumor-draining lymph nodes in resectable lung cancer treated with neoadjuvant immune checkpoint inhibitors

SummaryBackgroundRegional lymph node (LN) acts as a pivotal organ for antitumor immunity. Paradoxically, tumor-draining LNs (TDLNs) are usually the first site of tumor metastasis in lung cancer. It is largely unknown about the association between the status of TDLNs and the response of primary tumor beds to immune checkpoint inhibitors (ICIs) in lung cancer patients. Also, studies characterizing the TDLNs in response to ICIs are scarce.MethodsWe characterized and compared the radiological, metabolic (18F-FDG) and pathologic responses between primary tumor beds and paired TDLNs (invaded/non-invaded) from 68 lung cancer patients who underwent neoadjuvant ICIs plus surgery. Additionally, we performed the spatial profiling of immune and non-immune cells within TDLNs using multiplexed immunofluorescence. Therapy responses (e.g., pathologic complete (pCR) or major response (MPR)) of primary lung tumor beds and paired TDLNs were investigated separately.FindingsWe observed that responses of TDLNs to ICIs markedly differ from their paired primary lung tumors regarding the radiological, metabolic (18F-FDG uptake), and pathologic alterations. Neoadjuvant ICIs therapy specifically decreased 18F-FDG-reflected metabolic activity in the primary tumor beds with pCR/MPR but not their TDLNs counterparts. Furthermore, the presence of invaded TDLNs was associated with poor pathologic responses in the matched primary tumor beds and predictive of rapid post-treatment tumor relapse. Spatial profiling demonstrated exclusion of T cell infiltrates within the metastatic lesions of invaded TDLNs, and diminished multiple immune and non-immune compositions in non-involved regions surrounding the metastatic lesions.InterpretationThese results provide the first clinically-relevant evidence demonstrating unique response patterns of TDLNs under ICIs treatment and revealing the underappreciated association of TDLNs status with the response of their paired primary tumors to ICIs in lung cancer.FundingThis work was supported by the National Natural Science Foundation of China (82072570 to F. Yao; 82002941 to B. Sun), the excellent talent program of Shanghai Chest Hospital (to F.Y), the Basic Foundation Program for Youth of Shanghai Chest Hospital (2021YNJCQ2 to H.Yang), and the Innovative Research Team of High-level Local Universities in Shanghai (SHSMU-ZLCX20212302 to F. Yao).

Clinical target volume automatic segmentation based on lymph node stations for lung cancer with bulky lump lymph nodes.

BackgroundThe lack of standardized delineation of lymph node station in lung cancer radiotherapy leads to nonstandard clinical target volume (CTV) contouring, especially in patients with bulky lump gross target volume lymph nodes (GTVnd). This study defines lymph node region boundaries in radiotherapy for lung cancer and automatically contours lymph node stations based on the International Association for the Study of Lung Cancer (IASLC) lymph node map.MethodsComputed tomography (CT) scans of 200 patients with small cell lung cancer were collected. The lymph node zone boundaries were defined based on the IASLC lymph node map, with adjustments to meet radiotherapy requirements. Contours of lymph node stations were confirmed by two experienced oncologists. A model (DiUNet) was constructed by incorporating the contours of GTVnd to precisely contour the boundaries. Quantitative evaluation metrics and clinical evaluations were conducted.ResultsThe mean 3D Dice similarity coefficient (Dice similarity coefficient) values of DiUNet in most lymph node stations was greater than 0.7, 98.87% of the lymph node station slices are accepted. The mean DiUNet score was not significantly different from that of the man contoured in the evaluation of lymph node stations and CTV.ConclusionThis is the first study to propose a method that automatically contours lymph node regions station by station based on the IASLC lymph node map with bulky lump GTVnd. Delineation of lymph node stations based on the DiUNet model is a promising strategy to obtain accuracy and efficiency for CTV delineation in lung cancer patients, especially for bulky lump GTVnd.

Efficient procedure planning for comprehensive lymph node staging bronchoscopy.

Purpose: For a patient at risk of having lung cancer, accurate disease staging is vital as it dictates disease prognosis and treatment. Accurate staging requires a comprehensive sampling of lymph nodes within the chest via bronchoscopy. Unfortunately, physicians are generally unable to plan and perform sufficiently comprehensive procedures to ensure accurate disease staging. We propose a method for planning comprehensive lymph node staging procedures. Approach: Drawing on a patient's chest CT scan, the method derives a multi-destination tour for efficient navigation to a set of lymph nodes. We formulate the planning task as a traveling salesman problem. To solve the problem, we apply the concept of ant colony optimization (ACO) to derive an efficient airway tour connecting the target nodes. The method has three main steps: (1)CT preprocessing, to define important chest anatomy; (2)graph and staging zone construction, to set up the necessary data structures and clinical constraints; and (3)tour computation, to derive the staging plan. The plan conforms to the world standard International Association for the Study of Lung Cancer (IASLC) lymph node map and recommended clinical staging guidelines. Results: Tests with a patient database indicate that the method derives optimal or near-optimal tours in under a few seconds, regardless of the number of target lymph nodes (mean tour length = 1.4% longer than the optimum). A brute force optimal search, on the other hand, generally cannot reach a solution in under 10min. for patients exhibiting nodes, and other methods provide poor solutions. We also demonstrate the method's utility in an image-guided bronchoscopy system. Conclusions: The method provides an efficient computational approach for planning a comprehensive lymph node staging bronchoscopy. In addition, the method shows promise for driving an image-guided bronchoscopy system or robotics-assisted bronchoscopy system tailored to lymph node staging.

946P Non-examination of lymph nodes (LN) in early-stage non-small cell lung cancer (eNSCLC) is associated with wedge resections and underutilization of adjuvant (adv) chemotherapy

LN examination among eNSCLC patients (pts) is critical in fulfilling the IASLC complete resection criteria and in perioperative chemotherapy decisions. Previous studies have shown that LN non-examination is associated with overall survival rates similar to pts with LN metastasis, presumably due to inadequate adv treatment (tx). This study evaluated the prevalence of LN exam and adv tx in US Medicare eNSCLC pts.

EP01.05-007 Radiomics Based Machine Learning Model for Sub-cm Lung Nodule Malignancy Diagnosis in the PanCan Screening Study

Lung cancers detected by Low Dose Computed Tomography (LDCT) screening programs have been shown to improve patient mortality rates. As lung cancer is the number one cancer killer in Canada, there is a need for developing tools to assist physicians in identifying potential cancerous lung nodules during screening studies. These tools are especially desirable for notoriously difficult-to-classify small, sub-cm nodules (<8mm solid component) as these cases tend to have very little pixel-based information.

The pattern of lymph node metastasis in peripheral pulmonary nodules patients and risk prediction models.

BackgroundFor peripheral pulmonary nodules, the regularity of lymph node (LN) metastasis has not been studied. This study aimed to evaluate the metastasis pattern of intrapulmonary and relevant mediastinal lymph nodes in early-stage lung cancer, and further selected patients who were of low risk of LN metastasis as potential population to receive sub-lobectomy.MethodsThis study prospectively included consecutive patients with peripheral clinical T1N0M0 disease who underwent complete resection with LN dissection or sampling from August 2014 to July 2015. The patients were followed up to 15, May 2021. Univariable or multivariable Logistic analysis was used to identify the risk factors. Models predicting LN metastasis risk were conducted. The area under the curve for the receiver operating characteristic curves was used to evaluate the diagnostic value. Disease-free survival and overall survival were compared between groups.ResultsFinally, 201 patients were included in this study. For patients with negative tumor-bearing (TB) 13 and 14 station LNs, the positive rate of other lymph node stations was extremely low. Maximum CT value, pleural indentation and CEA level were risk factors for N1 station LNs metastasis. Besides, the factors above and lobulation sign were risk factors for skip metastasis beyond TB 13 and 14 station LNs. We constructed two scoring tables to predict N1 station metastasis and skip metastasis beyond TB 13 and 14 station. The AUC were 0·837 and 0·823, respectively. Based on the first table, 40·9% of patients suffered N1 station LNs metastasis and 27·3% had N2 disease in “high risk group” while the proportion was only 5·7% and 4·5% in “low risk group”. For patients with negative TB13 and TB14 station LNs, based on the latter table, 11·1% of patients had N1 stations LNs metastasis and 16·7% had pN2 disease in “high risk group” while only 2·3% patients in “low risk group” suffered this kind of metastasis.ConclusionFor peripheral pulmonary nodules patients, stations 13 and 14 LNs may be the sentinel nodes. For patients with low risk of N1 metastasis and skip metastasis, sub-lobar resection might be sufficient for those who were of negative TB 13 and 14 station LNs.

Comparative proteomic analysis of exhaled breath condensate between lung adenocarcinoma and CT-detected benign pulmonary nodule patients.

Lung cancer is the leading cause of cancer mortality worldwide. The collection of exhaled breath condensate (EBC) is a non-invasive method that may have enormous potential as a biomarker for the early detection of lung cancer. To investigate the proteomic differences of EBC between lung cancer and CT-detected benign nodule patients, and determine whether these proteins could be potential biomarkers. Proteomic analysis was performed on individual samples from 10 lung cancer patients and 10 CT-detected benign nodule patients using data-independent acquisition (DIA) mass spectrometry. A total of 1,254 proteins were identified, and 21 proteins were differentially expressed in the lung adenocarcinoma group compared to the benign nodule group (p< 0.05). The GO analysis showed that most of these proteins were involved in neutrophil-related biological processes, and the KEGG analysis showed these proteins were mostly annotated to pyruvate and propanoate metabolism. Through protein-protein interactions (PPIs) analysis, ME1 and LDHB contributed most to the interaction-network of these proteins. Significantly differentially expressed proteins were detected between lung cancer and the CT-detected benign nodule group from EBC samples, and these proteins might serve as potential novel biomarkers of EBC for early lung cancer detection.

Comparison of Feature Extraction Filters For Lung Cancer Prediction

Lung cancer is a deadly form of cancer that is difficult to detect. It is more important for care to inspect nodules soon and correctly since it generally causes death in both men and women. The number of people diagnosed with lung cancer is directly proportionate to the number of chain smokers. As a result, a variety of procedures have been developed to identify lung cancer in its early stages. A comparative analysis of multiple machine learning-based approaches for lung cancer diagnosis has been reported in this progress. In addition, to use image recognition to identify lung cancer nodules, many classifier methods are linked with numerous segmentation algorithms. [1] CT scan images have been discovered to be more acceptable for having reliable results in this investigation. The experiment is divided into two parts: Identify the most important feature used in lung cancer analysis by CT scan and map it to a computer-related format in the first step. In the second phase, machine learning techniques are used to pick and extract features. As a result, CT scan images are commonly utilized to diagnose cancer. The classification method, Random Forest Classifier was used to investigate lung cancer prediction. The results reveal that classification accuracy improves in the vast majority of situations, indirectly proving reliability. For feature extraction, we used three filters – Gabor, Local Binary Pattern, and Histogram of Oriented Gradients. Hence, their comparisons are shown in this paper. Keywords- Gabor, Histogram of Oriented Gradient, Local Binary Patterns, Lung Cancer Prediction, Random Forest.

Role of Positron Emission Tomography in Staging Lymph Nodes in Non-small Cell Lung Cancer

Role of Positron Emission Tomography in Staging Lymph Nodes in Non-small Cell Lung Cancer

Ground glass opacity detection and segmentation using CT images: an image statistics framework

Lung cancer is one of the most profound causes of cancer-related deaths in the world. Early detection is known to significantly improve the chances of survival. Several detection and diagnostic methods are used for this purpose. CT is one of the most widely used non-invasive medical imaging modalities in this domain. The biggest challenge faced by radiologists in this case is detection and diagnosis of cancerous lung nodules. The growth of ground glass opacity (GGO) lesions is an indication of malignancy. However, GGO is difficult to capture for physicians as it manifests in the form of tiny, faint shadows. This research paper proposes an approach for aiding GGO identification in CT lung images for improved lung cancer prognosis. In the proposed approach, morphological reconstruction is used for segmentation. Once the region of interest (ROI) is extracted, statistical analysis using mean, standard deviation, variance, entropy, skewness, kurtosis, minimum grey scale value, maximum grey scale value and range is performed. The same statistical measures are determined for normal lung and distribution plot is drawn for comparison. It is observed that maximum grey-scale value demonstrates minimum overlap of approximately 7.4%. To reduce this, a joint feature by summing values of feature mean, skewness, and maximum grey-scale value was used. This approach reduced the overlap to approximately 1.32%. Lastly, ANN was used for classification of GGO and non-GGO lung tissue with an achieved accuracy of 99.5%.

Role of Segmentation in Lung Nodules CT Scan Images for High Performance: Case of Recent Findings

Early stage of lung cancer detection is being very vital in today’s scenario. Several promising technological diagnostic automated tools developed and are used to predict the patient's survival using intelligent lung nodules analysis in Computed Tomography (CT) images. Recently, the advancement of human health diagnostic technologies, Computer-Aided Detection Systems (CADe) are developed diurnal rhythms to provide higher accuracy and better performance rate. In this research work, we focuses on a documenting the automatic segmentation of lung cancer nodules by various methods developed in recent years of the timeline with respect to the realization. The proposed work is criterion based objectives to establish the performance measures and to propose new solution for Lung nodules segmentation and detection on CT Scanned digital images, which is the fundamental and epochal measure to attain the high level of performance. In analyzing each of these technical execution on detection on lung nodule, and experimented to 4 different kinds of lung nodules CT scanned digital images. The phenomenon show segmentation methods involving convolution neural networks were better when compared to other existing methods. U-Net method gave improved accuracy. Dice score 0.981±0.009 and sensitivity 0.994±0.002 are maximum by using the method Dense R2UNet. F1-score 97% is best achieved by Cascade Convolutional Network. Recall rate is achieved to the maximum 99.1% through Improved 3D-UNet Neural Network. Precision 0.982±0.009 and accuracy 0.988±0.018 are best attained by Dense R2UNet. In the case, semi-transparent nodules segmentation, Watershed method found to be highly appropriate select. Watershed arse nodules segment correspond well to vessels and semi-transparent nodules, and exhibit low sensitivity in solitary or lone nodules. The higher rate of efficient segmentation is directly proportional to higher rate of performance. Keywords: CNN, Lung Cancer, CT Image Segmentation, Watershed, R2Unet.

Analysis of lung cancer screening through low-dose spiral computed tomography among petroleum company staffs in Sichuan-Chongqing Area in 2020

Objective: To provide scientific evidence for early lung cancer screening, to analyze the incidence of pulmonary nodules among petroleum company staffs in Sichuan-Chongqing Area. Methods: In January 2021 , 6002 petroleum company staffs in Sichuan-Chongqing Area which scanned by low-dose spiral computed tomography (LDCT) of chest in medical examination center in 2020 were retrospectively collected as objects. Their imaging and clinical data were collected. χ(2) test was used to analyze the differences in the detection rates of lung nodules and suspected lung cancer nodules among workers in petroleum company staffs of different genders, ages and types of work. Results: Among the 6002 objects, 3853 (64.2%) were male and 2149 (35.8%) were female, with an average age of (47.25±12.13) years old. A total of 431 cases (7.2%) of pulmonary nodules and 57 cases (0.9%) of suspected lung cancer nodules were detected. 45 cases were followed up with surgical treatment, and 41 cases (91.1%) of lung cancer were diagnosed by postoperative pathology. There were significant differences in the detection rates of pulmonary nodules and suspected lung cancer nodules between different age groups (χ(2)=51.23, 18.81 , P<0.001). The detection rates of pulmonary nodules in the age groups 51-60 years old and ≥61 years old were higher than those in the age groups≤40 years old and 41-50 years old (P<0.05). The detection rate of suspected lung cancer nodules in the age group≥ 61 years old was higher than those in the age groups≤40 years old, 41-50 years old and 51-60 years old (P< 0.05) . And the detection rate of suspected lung cancer pulmonary nodules in oil workers was higher than that of ordinary workers (P<0.05) . Among female objects, the detection rate of pulmonary nodules in oil workers was higher than that in ordinary workers (χ(2)=8.09, P=0.004) . The detection rate of pulmonary nodules in oil workers aged ≥61 years old was higher than ordinary workers (χ(2)=37.94, P<0.001) . Among male objects, the detection rate of suspected lung cancer pulmonary nodules in oil workers was higher than that in ordinary workers (χ(2)=8.42, P=0.004) . The detection rates of suspected lung cancer pulmonary nodules in oil workers aged 51-60 years old and ≥61 years old groups were higher than those of ordinary workers (χ(2)=4.70, 8.74; P=0.030, 0.003) . Conclusion: LDCT is suitable for early lung cancer screening for petroleum company staffs. During the clinical screening process, LDCT should be used as a routine physical examination item for petroleum workers older than 51 years old.

Dynamic Observation of Lung Nodules on Chest CT Before Diagnosis of Early Lung Cancer.

ObjectiveEarly recognition and diagnosis of lung cancer can help improve the prognosis of patients. However, early imaging patterns of malignant lung nodules are not fully clear. To understand the early imaging signs of malignant lung cancer nodules, the changes of the lung nodules before diagnosis were dynamically observed and analyzed.Materials and MethodsThis retrospective study observed dynamic changes of lung nodules before pathological confirmation with consecutive regular chest CT examination from January 2003 to December 2018. At least 3 follow-up CT scans were performed in all cases, and the interval between each follow-up was about 1 year. The size, density, and morphological signs of the nodules were evaluated based on the CT axial image, and a reverse line chart or scatter plot with the diagnosis time as coordinate origin was constructed.ResultsA total of 55 lung nodules in 53 patients (mean age, 58.40 years ±11.43 [standard deviation]; 20 women) were accessed. The follow-up time was 5.96 ± 2.68 years. The average diameters in maximum slice of the lesion at baseline and last scan were 6.83 ± 2.92 mm and 16.65 ± 7.34 mm, respectively. According to the reverse line chart, the nodule growth curve segments within 4 years from the last scan showed an ascending shape, and those beyond 4 years showed a flat shape. There are 90.9% (50/55) GGN and 9.1% (5/55) SN when the lesion first appears, and 21.8% (12/55) GGN, 38.2% (21/55) PSN, and 40% (22/55) SN in the last scan. There are 12.7% (7/55) and 98.2% (54/55) nodules with poor morphological signs at baseline and last scan, respectively.ConclusionAt the time node close to the diagnosis, the growth curve showed an upward pattern; the proportion of PSN and SN rose as the main density types; and the appearance of poor morphological signs of nodules increased. When a persistent lung nodule starts to show a malignant change, a further diagnostic workup is warranted.

The Value of Artificial Intelligence Assisted Diagnosis between Pulmonary Tuberculosis Nodules and Solid Lung Cancer Nodules

The Value of Artificial Intelligence Assisted Diagnosis between Pulmonary Tuberculosis Nodules and Solid Lung Cancer Nodules

Application of Dynamic 18F-FDG PET/CT for Distinguishing Intrapulmonary Metastases from Synchronous Multiple Primary Lung Cancer.

It has been a big challenge to distinguish synchronous multiple primary lung cancer (sMPLC) from primary lung cancer with intrapulmonary metastases (IPM). We aimed to assess the clinical application of dynamic 18F-FDG PET/CT in patients with multiple lung cancer nodules. We enrolled patients with multiple pulmonary nodules who had undergone dynamic 18F-FDG PET/CT and divided them into sMPLC and IPM groups based on comprehensive features. The SUVmax, fitted Ki value based on dynamic scanning, and corresponding maximum diameter (Dmax) from the two largest tumors were determined in each patient. We determined the absolute between-tumor difference of SUVmax/Dmax and Ki/Dmax (ΔSUVmax/Dmax; ΔKi/Dmax) and assessed the between-group differences. Further, the diagnostic accuracy was evaluated by ROC analysis and the correlation between ΔSUVmax/Dmax and ΔKi/Dmax from all groups was determined. There was no significant difference for ΔSUVmax/Dmax between the IPM and sMPLC groups, while the IPM group had a significantly higher ΔKi/Dmax than the sMPLC group. The AUC of ΔKi/Dmax for differentiating sMPLC from IPM was 0.80 (cut-off value of Ki = 0.0059, sensitivity 79%, specificity 75%, p < 0.001). There was a good correlation (Pearson r = 0.91, 95% CI: 0.79-0.96, p < 0.0001) between ΔSUVmax/Dmax and ΔKi/Dmax in the IPM group but not in the sMPLC group (Pearson r = 0.45, p > 0.05). Dynamic 18F-FDG PET/CT could be a useful tool for distinguishing sMPLC from IPM. Ki calculation based on Patlak graphic analysis could be more sensitive than SUVmax in discriminating IPM from sMPLC in patients with multiple lung cancer nodules.

A Novel Hybridized Feature Extraction Approach for Lung Nodule Classification Based on Transfer Learning Technique.

Purpose:In the field of medical diagnosis, deep learning-based computer-aided detection of diseases will reduce the burden of physicians in the diagnosis of diseases especially in the case of lung cancer nodule classification.Materials and Methods:A hybridized model which integrates deep features from Residual Neural Network using transfer learning and handcrafted features from the histogram of oriented gradients feature descriptor is proposed to classify the lung nodules as benign or malignant. The intrinsic convolutional neural network (CNN) features have been incorporated and they can resolve the drawbacks of handcrafted features that do not completely reflect the specific characteristics of a nodule. In the meantime, they also reduce the need for a large-scale annotated dataset for CNNs. For classifying malignant nodules and benign nodules, radial basis function support vector machine is used. The proposed hybridized model is evaluated on the LIDC-IDRI dataset.Results:It has achieved an accuracy of 97.53%, sensitivity of 98.62%, specificity of 96.88%, precision of 95.04%, F1 score of 0.9679, false-positive rate of 3.117%, and false-negative rate of 1.38% and has been compared with other state of the art techniques.Conclusions:The performance of the proposed hybridized feature-based classification technique is better than the deep features-based classification technique in lung nodule classification.

Analysis of screening results and risk factors of high-risk populations of lung cancer in Nanchang city from 2018 to 2019

Objective: To collate and analyze the screening results of high-risk lung cancer populations in communities in Nanchang from 2018 to 2019, and to explore the lung-positive nodules and risk factors for lung cancer. Methods: Data of the screening subjects in 8 administrative districts and 15 street health service centers in Nanchang city, Jiangxi province from November 2018 to October 2019 were collected, people at high risk of lung cancer was assessed, clinical screening of high-risk groups of lung cancer was conducted by low-dose helical computed tomography (LDCT), and risk factors for suspected lung cancer and lung-positive nodules were analyzed. Results: Of the 25 871 people participated in screening, 5 220 were at high risk for lung cancer and 15 374 without other malignant tumors were at high risk. There were 2 417 cases participated in clinical LDCT screening, including 193 cases of lung-positive nodules, 67 cases of suspected lung cancer, 912 cases of other lung diseases, the positive rate of lung cancer or lung-positive nodules was 10.76% (260/2 417). Univariate analysis showed that age, coarse grain intake, oil intake, housing heating, passive smoking, alcohol consumption and mental trauma were associated with positive pulmonary nodules or lung cancer (all P<0.05). Multivariate analysis showed that gender, age, housing heating, smoking and drinking were related to the occurrence of lung nodules or lung cancer (all P<0.05). Conclusions: Men are more likely to develop lung cancer or lung-positive nodules than women. The age is an independent risk factor for lung-positive nodules or lung cancer. In a certain range, age will increase the incidence of lung cancer, housing heating may be the protective factor for lung cancer, while smoking and drinking are risk factors.

One deep learning local-global model based on CT imaging to differentiate between nodular cryptococcosis and lung cancer which are hard to be diagnosed.

We aim to evaluate a deep learning (DL) model and radiomic model for preoperative differentiation of nodular cryptococcosis from solitary lung cancer in patients with malignant features on CT images. We retrospectively recruited 319 patients with solitary pulmonary nodules and suspicious signs of malignancy from three hospitals. All lung nodules were resected, and one by one radiologic-pathologic correlation was performed. A three-dimensional DL model was used for tumor segmentation and extraction of three-dimensional radiomic features. We used the Max-Relevance and Min-Redundancy algorithm and the eXtreme Gradient Boosting algorithm to select the nodular radiomics features. We proposed a DL local-global model, a DL local model and radiomic model to preoperatively differentiate nodular cryptococcosis from solitary lung cancer. The DL local-global model includes information of both nodules and the whole lung, while the DL local model only includes information of solitary lung nodules. Five-fold cross-validation was used to select and validate these models. The prediction performance of the model was evaluated using receiver operating characteristic curve (ROC) and calibration curve. A new loss function was applied in our deep learning framework to optimize the area under the ROC curve (AUC) directly. 295 patients were enrolled and they were non-symptomatic, with negative tumor markers and fungus markers in blood tests. These patients have not been diagnosed by the combination of CT imaging, laboratory results and clinical data. The lung volume was slightly larger in patients with lung cancers than that in patients with cryptococcosis (3552.8±1184.6ml vs 3491.9±1017.8ml). The DL local-global model achieved the best performance in differentiating between nodular cryptococcosis and lung cancer (area under the curve [AUC] = 0.88), which was higher than that of the DL local model (AUC = 0.84) and radiomic (AUC = 0.79) model. The DL local-global model is a non-invasive diagnostic tool to differentiate between nodular cryptococcosis and lung cancer nodules which are hard to be diagnosed by the combination of CT imaging, laboratory results and clinical data, and overtreatment may be avoided.

A narrative overview of the prognostic significance of the immune cellular milieu in tumor draining lymph nodes in non-small cell lung cancer

: Lung cancer is both the most common malignancy in the United States and the most fatal. Non-small cell lung cancer accounts for most lung malignancies and involves a complex immune response that has become the recent target of systemic therapy. Late-stage disease is now effectively treated with immunotherapy. As the role of immunotherapy expands, it is becoming increasingly important to understand the biology of the immune response in patients with non-small cell lung cancer and the association of this response with response to therapy and outcomes. In this narrative overview, we review the role of immune cells comprising tumor draining lymph nodes, the structure of tumor draining lymph nodes, and their role in the tumor microenvironment. To identify relevant immune cell interactions within the tumor draining lymph nodes, we reviewed published papers focusing on tumor draining lymph nodes and delineated the significance of the immune cells to the tumor microenvironment in both animal models and humans. This is the first comprehensive review of tumor-draining lymph nodes and their role in the tumor microenvironment and provides a foundation for further investigating the tumor microenvironment and the role of humoral and innate immune response mechanisms in non-small cell lung cancer.

Epigenetic imprinting alterations as effective diagnostic biomarkers for early-stage lung cancer and small pulmonary nodules

BackgroundEarly lung cancer detection remains a clinical challenge for standard diagnostic biopsies due to insufficient tumor morphological evidence. As epigenetic alterations precede morphological changes, expression alterations of certain imprinted genes could serve as actionable diagnostic biomarkers for malignant lung lesions.ResultsUsing the previously established quantitative chromogenic imprinted gene in situ hybridization (QCIGISH) method, elevated aberrant allelic expression of imprinted genes GNAS, GRB10, SNRPN and HM13 was observed in lung cancers over benign lesions and normal controls, which were pathologically confirmed among histologically stained normal, paracancerous and malignant tissue sections. Based on the differential imprinting signatures, a diagnostic grading model was built on 246 formalin-fixed and paraffin-embedded (FFPE) surgically resected lung tissue specimens, tested against 30 lung cytology and small biopsy specimens, and blindly validated in an independent cohort of 155 patients. The QCIGISH diagnostic model demonstrated 99.1% sensitivity (95% CI 97.5–100.0%) and 92.1% specificity (95% CI 83.5–100.0%) in the blinded validation set. Of particular importance, QCIGISH achieved 97.1% sensitivity (95% CI 91.6–100.0%) for carcinoma in situ to stage IB cancers with 100% sensitivity and 91.7% specificity (95% CI 76.0–100.0%) noted for pulmonary nodules with diameters ≤ 2 cm.ConclusionsOur findings demonstrated the diagnostic value of epigenetic imprinting alterations as highly accurate translational biomarkers for a more definitive diagnosis of suspicious lung lesions.