Morphological computed tomography features of surgically resectable pulmonary squamous cell carcinomas: Impact on prognosis and comparison with adenocarcinomas

Non-invasively Discriminating the Pathological Subtypes of Non-small Cell Lung Cancer with Pretreatment 18F-FDG PET/CT Using Deep Learning

RATIONALE AND OBJECTIVES

To develop an end-to-end deep learning (DL) model for non-invasively predicting non-small cell lung cancer (NSCLC) pathological subtypes based on 18F-fluorodeoxyglucose (FDG) positron emission tomography/computed tomography (PET/CT) images, and to explore the potential value of DL technology.

MATERIALS AND METHODS

Preoperative 18F-FDG PET/CT images of 189 patients with NSCLC were retrospectively collected. The whole cohort was randomly divided into a training cohort, a validation cohort, and an internal/extended test cohort at the ratio of 6:2:2 after preprocessing the images. In the training and validation cohorts, seven DL models-Shufflenet, VGG16, Googlenet, Inception v3, Resnet50, Densenet201, and Mobilenet v2-were trained and optimized. The generalization ability and clinical utility of the optimal model were evaluated in the internal and extended test cohorts. Moreover, Spearman's correlation analysis was used to evaluate the correlation between DL features and traditional radiological features such as tumor size and maximum standardized uptake values (SUVmax).

RESULTS

Some DL features were significantly correlated with SUVmax and tumor size (P < 0.05). The Mobilenet v2 model achieved the best performance during the model development and validation phases. In the internal test group (area under the receiver operating characteristic curve [AUC]: 0.744, area under the precision-recall curve [AP]: 0.759) and extended test group (AUC: 0.767, AP: 0.768), the Mobilenet v2 model showed good generalization ability and reproducibility. Meanwhile, the decision curve analysis revealed that patients can benefit from the decisions made based on the Mobilenet v2 model.

CONCLUSION

DL models offer great potential for classifying NSCLC pathological subtypes. Specifically, the Mobilenet v2 model performs well at end-to-end non-invasive pathological subtype stratification of NSCLC.

Diagnostic value of dual-layer spectral detector CT in differentiating lung adenocarcinoma from squamous cell carcinoma

Background and objective

The pathological type of non–small cell lung cancer is considered to be an important factor affecting the treatment and prognosis. The purpose of this study was to investigate the diagnostic value of spectral parameters of dual-layer spectral detector computed tomography (DLCT) in determining efficacy to distinguish adenocarcinoma (AC) and squamous cell carcinoma (SC), and their combined diagnostic efficacy was also analyzed.

Methods

This is a single-center prospective study, and we collected 70 patients with lung SC and 127 patients with lung AC confirmed by histopathological examination. Morphological parameters, plain scan CT value, biphasic enhanced CT value, and spectral parameters were calculated. The diagnostic efficiency of morphological parameters, spectral parameters, and spectral parameters combined with morphological parameters was obtained by statistical analysis.

Results

In univariate analysis, seven morphological CT features differed significantly between SC and AC: tumor location (distribution), lobulation, spicule, air bronchogram, vacuole sign, lung atelectasis and/or obstructive pneumonia, and vascular involvement (all p &lt; 0.05). In the arterial phase and the venous phase, the spectral parameters of AC were higher than those of SC (AP-Zeff: 8.07 ± 0.23 vs. 7.85 ± 0.16; AP-ID: 1.41 ± 0.47 vs. 0.94 ± 0.28; AP-NID: 0.13 ± 0.04 vs. 0.09 ± 0.03; AP-λ: 3.42 ± 1.10 vs. 2.33 ± 0.96; VP-Zeff: 8.26 ± 0.23 vs. 7.96 ± 0.16; VP-ID: 1.18 ± 0.51 vs. 1.16 ± 0.30; VP-NID: 0.39 ± 0.13 vs. 0.29 ± 0.08; VP-λ: 4.42 ± 1.28 vs. 2.85 ± 0.72; p &lt; 0.001). When conducting multivariate analysis combining CT features and DLCT parameters with the best diagnostic efficacy, the independent predictors of AC were distribution on peripheral (OR, 4.370; 95% CI, 1.485–12.859; p = 0.007), presence of air bronchogram (OR, 5.339; 95% CI, 1.729–16.484; p = 0.004), and presence of vacuole sign ( OR, 7.330; 95% CI, 1.030–52.184; p = 0.047). Receiver operating characteristic curves of the SC and AC showed that VP-λ had the best diagnostic performance, with an area under the curve (AUC) of 0.864 and sensitivity and specificity rates of 85.8% and 74.3%, respectively; the AUC was increased to 0.946 when morphological parameters were combined, and sensitivity and specificity rates were 89.8% and 87.1%, respectively.

Conclusion

The quantitative parameters of the DLCT spectrum are of great value in the diagnosis of SC and AC, and the combination of morphological parameters and spectral parameters is helpful to distinguish SC from AC.

Relapse predictability of topological signature on pretreatment planning CT images of stage I non-small cell lung cancer patients before treatment with stereotactic ablative radiotherapy

Background

This study aimed to explore the predictability of topological signatures linked to the locoregional relapse (LRR) and distant metastasis (DM) on pretreatment planning computed tomography images of stage I non‐small cell lung cancer (NSCLC) patients before treatment with stereotactic ablative radiotherapy (SABR).

Methods

We divided 125 primary stage I NSCLC patients (LRR: 34, DM: 22) into training (n = 60) and test datasets (n = 65), and the training dataset was augmented to 260 cases using a synthetic minority oversampling technique. The relapse predictabilities of the conventional wavelet‐based features (WF), topology‐based features [BF, Betti number (BN) map features; iBF, inverted BN map features], and their combined features (BWF, iBWF) were compared. The patients were stratified into high‐risk and low‐risk groups using the medians of the radiomics scores in the training dataset.

Results

For the LRR in the test, the iBF, iBWF, and WF showed statistically significant differences (p < 0.05), and the highest nLPC was obtained for the iBF. For the DM in the test, the iBWF showed a significant difference and the highest nLPC.

Conclusion

The iBF indicated the potential of improving the LRR and DM prediction of stage I NSCLC patients prior to undergoing SABR.

CT findings of basaloid squamous cell carcinoma of the lung in 12 patients: A distinct category of squamous cell carcinoma in 2015 WHO classification of lung tumors

Basaloid squamous cell carcinoma (SCC) is very rare subtype of SCC of the lung and it is important to distinguish basaloid to other subtypes of SCCs, since the prognosis of basaloid subtype is considered poorer than that of other non-basaloid subtypes of SCCs. Aim of this study was to assess computed tomography (CT) findings of basaloid SCC of the lung in 12 patients.From January 2016 to April 2021, 12 patients with surgically proven basaloid SCC of the lung were identified. CT findings were analyzed, and the imaging features were compared with histopathologic reports. Clinical and demographic features were also analyzed.Axial location of the tumor was central in 5 patients, while 7 was in peripheral. Of the 7 patients whose tumors were located in the peripheral, margin of the tumor were smooth (n  = 2), lobulated (n  = 2), or spiculated (n  = 3). After contrast injection, net enhancement value ranged from 15.8 to 71.8 HU (median, 36.4 HU). Endobronchial growth were seen in 5 patients and these patients accompanied obstructive pneumonia or atelectasis. Internal profuse necrosis, cavitation, or calcifications were not seen.On CT, basaloid squamous cell presents as solitary nodule or mass with moderate enhancement. Tumor was located either peripheral or central compartment of the lung and cavitation was absent.

Tumor size in patients with severe pulmonary emphysema might be underestimated on preoperative CT

OBJECTIVES

To evaluate the effect of emphysema on tumor diameter measured on preoperative computed tomography (CT) images versus pathological specimens.

MATERIALS AND METHODS

We investigated patients who underwent primary lung cancer surgery: 55 patients (57 tumors) with severe emphysema and 57 patients (57 tumors) without emphysema. The tumor diameters measured in the postoperative pathological specimens were compared with those measured on the axial CT images and on multiplanar reconstruction (MPR) CT images by two independent radiologists; a subgroup analysis according to tumor size was also performed. A paired or unpaired t test was performed, depending on the tested subjects.

RESULTS

In the emphysema group, the mean axial CT diameter was significantly smaller than the mean pathological diameter (p = 0.025/0.001 for reader 1/2), whereas in the non-emphysema group, the mean axial CT diameter was not significantly different from the pathological one for both readers. The difference between CT axial diameter and pathological diameter (= CT diameter - pathological diameter) was significantly smaller (i.e., had a stronger tendency toward underestimation on radiological measurements) in the emphysema group compared with the non-emphysema group (p = 0.014/0.008 for reader 1/2), and the difference was significantly smaller in tumors sized > 30 mm than tumors sized ≤ 20 mm in both groups.

CONCLUSIONS

Tumor size is significantly smaller on preoperative CT in patients with severe emphysema compared to patients without emphysema, especially in the case of large tumors. MPR measurement using the widest of three dimensions should be used to select T-stage for patients with severe emphysema.

KEY POINTS

• The presence of emphysema affects the accuracy of tumor size measurements on CT. • Compared to patients without emphysema, the tumor size in severe emphysema patients tends to be measured smaller in preoperative CT than the pathological specimen. • This trend is more evident when large tumors are measured on axial CT images alone.

Radiomic analysis of lung cancer for the assessment of patient prognosis and intratumor heterogeneity

Objective

To determine whether the radiomic features of lung lesions on computed tomography correlate with overall survival in lung cancer patients.

Materials and Methods

This was a retrospective study involving 101 consecutive patients with malignant neoplasms confirmed by biopsy or surgery. On computed tomography images, the lesions were submitted to semi-automated segmentation and were characterized on the basis of 2,465 radiomic variables. The prognostic assessment was based on Kaplan-Meier analysis and log-rank tests, according to the median value of the radiomic variables.

Results

Of the 101 patients evaluated, 28 died (16 dying from lung cancer), and 73 were censored, with a mean overall survival time of 1,819.4 days (95% confidence interval [95% CI]: 1,481.2-2,157.5). One radiomic feature (the mean of the Fourier transform) presented a difference on Kaplan-Meier curves (p < 0.05). A high-risk group of patients was identified on the basis of high values for the mean of the Fourier transform. In that group, the mean survival time was 1,465.4 days (95% CI: 985.2-1,945.6), with a hazard ratio of 2.12 (95% CI: 1.01-4.48). We also identified a low-risk group, in which the mean of the Fourier transform was low (mean survival time of 2,164.8 days; 95% CI: 1,745.4-2,584.1).

Conclusion

A radiomic signature based on the Fourier transform correlates with overall survival, representing a prognostic biomarker for risk stratification in patients with lung cancer.

Magnetic particle targeting for diagnosis and therapy of lung cancers

Over the past decade, the growing interest in targeted lung cancer therapy has guided researchers toward the cutting edge of controlled drug delivery, particularly magnetic particle targeting. Targeting of tissues by magnetic particles has tackled several limitations of traditional drug delivery methods for both cancer detection (e.g., using magnetic resonance imaging) and therapy. Delivery of magnetic particles offers the key advantage of high efficiency in the local deposition of drugs in the target tissue with the least harmful effect on other healthy tissues. This review first overviews clinical aspects of lung morphology and pathogenesis as well as clinical features of lung cancer. It is followed by reviewing the advances in using magnetic particles for diagnosis and therapy of lung cancers: (i) a combination of magnetic particle targeting with MRI imaging for diagnosis and screening of lung cancers, (ii) magnetic drug targeting (MDT) through either intravenous injection and pulmonary delivery for lung cancer therapy, and (iii) computational simulations that models new and effective approaches for magnetic particle drug delivery to the lung, all supporting improved lung cancer treatment. The review further discusses future opportunities to improve the clinical performance of MDT for diagnosis and treatment of lung cancer and highlights clinical therapy application of the MDT as a new horizon to cure with minimal side effects a wide variety of lung diseases and possibly other acute respiratory syndromes (COVID-19, MERS, and SARS).

Identifying Cross-Scale Associations between Radiomic and Pathomic Signatures of Non-Small Cell Lung Cancer Subtypes: Preliminary Results

(1) Background: Despite the complementarity between radiology and histopathology, both from a diagnostic and a prognostic perspective, quantitative analyses of these modalities are usually performed in disconnected silos. This work presents initial results for differentiating two major non-small cell lung cancer (NSCLC) subtypes by exploring cross-scale associations between Computed Tomography (CT) images and corresponding digitized pathology images. (2) Methods: The analysis comprised three phases, (i) a multi-resolution cell density quantification to identify discriminant pathomic patterns for differentiating adenocarcinoma (ADC) and squamous cell carcinoma (SCC), (ii) radiomic characterization of CT images by using Haralick descriptors to quantify tumor textural heterogeneity as represented by gray-level co-occurrences to discriminate the two pathological subtypes, and (iii) quantitative correlation analysis between the multi-modal features to identify potential associations between them. This analysis was carried out using two publicly available digitized pathology databases (117 cases from TCGA and 54 cases from CPTAC) and a public radiological collection of CT images (101 cases from NSCLC-R). (3) Results: The top-ranked cell density pathomic features from the histopathology analysis were correlation, contrast, homogeneity, sum of entropy and difference of variance; which yielded a cross-validated AUC of 0.72 ± 0.02 on the training set (CPTAC) and hold-out validation AUC of 0.77 on the testing set (TCGA). Top-ranked co-occurrence radiomic features within NSCLC-R were contrast, correlation and sum of entropy which yielded a cross-validated AUC of 0.72 ± 0.01. Preliminary but significant cross-scale associations were identified between cell density statistics and CT intensity values using matched specimens available in the TCGA cohort, which were used to significantly improve the overall discriminatory performance of radiomic features in differentiating NSCLC subtypes (AUC = 0.78 ± 0.01). (4) Conclusions: Initial results suggest that cross-scale associations may exist between digital pathology and CT imaging which can be used to identify relevant radiomic and histopathology features to accurately distinguish lung adenocarcinomas from squamous cell carcinomas.

Clinicopathological characteristics of solitary cavitary lung cancer: a case-control study

Background

With the emerging radiological techniques and the increasing incidence of adenocarcinoma, the composition and structure of cavitary lung cancer have been significantly changed. The aim of the study was to demonstrate clinicopathological characteristics of solitary cavitary lung cancer which was ≤3 cm.

Methods

A case-control study was designed through retrospective data analysis of clinicopathological data of 946 cases with solitary lung cancer smaller than 3 cm. Univariable and multivariable analysis were used to identify the risk factors of cavitation.

Results

Cavitary lung cancer occurred more frequently in patients who were elderly (P=0.044), male (P=0.004), who had a smoking history (P=0.018), higher carcinoembryonic antigen (CEA) level (P<0.001), peripheral lesions (P=0.017), solid nodules (P<0.001), spiculation (P=0.003), vascular convergence (P<0.001), air bronchogram (P=0.004), larger tumor size (P<0.001), advanced T stage (P<0.001), lymph node metastasis (P=0.028) and advanced pTNM stage (P=0.004). In addition, cavitary lung cancer was more common in papillary predominant tumors (P=0.017), while noncavitary lung cancer occurred more frequently in AIS/MIA (P=0.002) and lepidic predominant tumors (P<0.001). It was confirmed that cavitation was significantly associated with elderly (P=0.013), male (P=0.003), larger maximum tumor diameter (P<0.001), solid nodules (P<0.001), larger pT size (P=0.016) and advanced pN stage (P=0.036) in multivariable analysis. ROC curves showed that the AUV was greater in maximum tumor diameter than in pT size predicting cavitation (0.71 vs. 0.66). A cut off value of 20.9 mm showed a discriminatory power of cavitation with a sensitivity of 68.7% and a specificity of 71.2%.

Conclusions

Comparing with noncavitary lung cancer, cavitary lung cancer smaller than 3 cm may have worse prognostic clinical, radiological and pathological characteristics. Especially, cavitary lung cancer present as more solid nodules on CT images and present with more invasive on pathological findings.

Homological radiomics analysis for prognostic prediction in lung cancer patients

PURPOSE

This study explored a novel homological analysis method for prognostic prediction in lung cancer patients.

MATERIALS AND METHODS

The potential of homology-based radiomic features (HFs) was investigated by comparing HFs to conventional wavelet-based radiomic features (WFs) and combined radiomic features consisting of HFs and WFs (HWFs), using training (n = 135) and validation (n = 70) datasets, and Kaplan-Meier analysis. A total of 13,824 HFs were derived through homology-based texture analysis using Betti numbers, which represent the topologically invariant morphological characteristics of lung cancer. The prognostic potential of HFs was evaluated using statistically significant differences (p-values, log-rank test) to compare the survival curves of high- and low-risk patients. Those patients were stratified into high- and low-risk groups using the medians of the radiomic scores of signatures constructed with an elastic-net-regularized Cox proportional hazard model. Furthermore, deep learning (DL) based on AlexNet was utilized to compare HFs by stratifying patients into the two groups using a network that was pre-trained with over one million natural images from an ImageNet database.

RESULTS

For the training dataset, the p-values between the two survival curves were 6.7 × 10^-6 (HF), 5.9 × 10^-3 (WF), 7.4 × 10^-6 (HWF), and 1.1 × 10^-3 (DL). The p-values for the validation dataset were 3.4 × 10^-5 (HF), 6.7 × 10^-1 (WF), 1.7 × 10^-7 (HWF), and 1.2 × 10^-1 (DL).

CONCLUSION

This study demonstrates the excellent potential of HFs for prognostic prediction in lung cancer patients.

Artificial intelligence, machine learning, computer-aided diagnosis, and radiomics: advances in imaging towards to precision medicine

The discipline of radiology and diagnostic imaging has evolved greatly in recent years. We have observed an exponential increase in the number of exams performed, subspecialization of medical fields, and increases in accuracy of the various imaging methods, making it a challenge for the radiologist to "know everything about all exams and regions". In addition, imaging exams are no longer only qualitative and diagnostic, providing now quantitative information on disease severity, as well as identifying biomarkers of prognosis and treatment response. In view of this, computer-aided diagnosis systems have been developed with the objective of complementing diagnostic imaging and helping the therapeutic decision-making process. With the advent of artificial intelligence, "big data", and machine learning, we are moving toward the rapid expansion of the use of these tools in daily life of physicians, making each patient unique, as well as leading radiology toward the concept of multidisciplinary approach and precision medicine. In this article, we will present the main aspects of the computational tools currently available for analysis of images and the principles of such analysis, together with the main terms and concepts involved, as well as examining the impact that the development of artificial intelligence has had on radiology and diagnostic imaging.

CT-based radiomics for prediction of histologic subtype and metastatic disease in primary malignant lung neoplasms

PURPOSE

As some of the most important factors for treatment decision of lung cancer (which is the deadliest neoplasm) are staging and histology, this work aimed to associate quantitative contrast-enhanced computed tomography (CT) features from malignant lung tumors with distant and nodal metastases (according to clinical TNM staging) and histopathology (according to biopsy and surgical resection) using radiomics assessment.

METHODS

A local cohort of 85 patients were retrospectively (2010-2017) analyzed after approval by the institutional research review board. CT images acquired with the same protocol were semiautomatically segmented by a volumetric segmentation method. Tumors were characterized by quantitative CT features of shape, first-order, second-order, and higher-order textures. Statistical and machine learning analyses assessed the features individually and combined with clinical data.

RESULTS

Univariate and multivariate analyses identified 40, 2003, and 45 quantitative features associated with distant metastasis, nodal metastasis, and histopathology (adenocarcinoma and squamous cell carcinoma), respectively. A machine learning model yielded the highest areas under the receiver operating characteristic curves of 0.92, 0.84, and 0.88 to predict the same previous patterns.

CONCLUSION

Several radiomic features (including wavelet energies, information measures of correlation and maximum probability from co-occurrence matrix, busyness from neighborhood intensity-difference matrix, directionalities from Tamura's texture, and fractal dimension estimation) significantly associated with distant metastasis, nodal metastasis, and histology were discovered in this work, presenting great potential as imaging biomarkers for pathological diagnosis and target therapy decision.

Non-invasive classification of non-small cell lung cancer: a comparison between random forest models utilising radiomic and semantic features

OBJECTIVE

Non-invasive distinction between squamous cell carcinoma and adenocarcinoma subtypes of non-small-cell lung cancer (NSCLC) may be beneficial to patients unfit for invasive diagnostic procedures or when tissue is insufficient for diagnosis. The purpose of our study was to compare the performance of random forest algorithms utilizing CT radiomics and/or semantic features in classifying NSCLC.

METHODS

Two thoracic radiologists scored 11 semantic features on CT scans of 106 patients with NSCLC. A set of 115 radiomics features was extracted from the CT scans. Random forest models were developed from semantic (RM-sem), radiomics (RM-rad), and all features combined (RM-all). External validation of models was performed using an independent test data set (n = 100) of CT scans. Model performance was measured with out-of-bag error and area under curve (AUC), and compared using receiver-operating characteristics curve analysis on the test data set.

RESULTS

The median (interquartile-range) error rates of the models were: RF-sem 24.5 % (22.6 - 37.5 %), RF-rad 35.8 % (34.9 - 38.7 %), and RM-all 37.7 % (37.7 - 37.7). On training data, both RF-rad and RF-all gave perfect discrimination (AUC = 1), which was significantly higher than that achieved by RF-sem (AUC = 0.78; p < 0.0001). On test data, however, RM-sem model (AUC = 0.82) out-performed RM-rad and RM-all (AUC = 0.5 and AUC = 0.56; p < 0.0001), neither of which was significantly different from random guess ( p = 0.9 and 0.6 respectively).

CONCLUSION

Non-invasive classification of NSCLC can be done accurately using random forest classification models based on well-known CT-derived descriptive features. However, radiomics-based classification models performed poorly in this scenario when tested on independent data and should be used with caution, due to their possible lack of generalizability to new data.

ADVANCES IN KNOWLEDGE

Our study describes novel CT-derived random forest models based on radiologist-interpretation of CT scans (semantic features) that can assist NSCLC classification when histopathology is equivocal or when histopathological sampling is not possible. It also shows that random forest models based on semantic features may be more useful than those built from computational radiomic features.

Radiomics-based features for pattern recognition of lung cancer histopathology and metastases

BACKGROUND AND OBJECTIVES

lung cancer is the leading cause of cancer-related deaths in the world, and its poor prognosis varies markedly according to tumor staging. Computed tomography (CT) is the imaging modality of choice for lung cancer evaluation, being used for diagnosis and clinical staging. Besides tumor stage, other features, like histopathological subtype, can also add prognostic information. In this work, radiomics-based CT features were used to predict lung cancer histopathology and metastases using machine learning models.

METHODS

local image datasets of confirmed primary malignant pulmonary tumors were retrospectively evaluated for testing and validation. CT images acquired with same protocol were semiautomatically segmented. Tumors were characterized by clinical features and computer attributes of intensity, histogram, texture, shape, and volume. Three machine learning classifiers used up to 100 selected features to perform the analysis.

RESULTS

radiomics-based features yielded areas under the receiver operating characteristic curve of 0.89, 0.97, and 0.92 at testing and 0.75, 0.71, and 0.81 at validation for lymph nodal metastasis, distant metastasis, and histopathology pattern recognition, respectively.

CONCLUSIONS

the radiomics characterization approach presented great potential to be used in a computational model to aid lung cancer histopathological subtype diagnosis as a "virtual biopsy" and metastatic prediction for therapy decision support without the necessity of a whole-body imaging scanning.

Clinical and radiological characteristics of central pulmonary adenocarcinoma: a comparison with central squamous cell carcinoma and small cell lung cancer and the impact on treatment response

Purpose

The proportion of central pulmonary adenocarcinoma (ADC) in central-type lung cancer has been gradually increasing due to the overall increasing incidence of pulmonary ADC. But the clinical and radiological characteristics of central ADCs remain unclear. In this study, we compared the clinical and radiological characteristics of central ADCs with those of small cell lung cancers (SCLCs) and squamous cell carcinomas (SQCCs) and investigated the impact of these characteristics on patients’ treatment response.

Patients and methods

The medical records of 302 consecutive patients with central lung cancer from July 2014 to September 2016 were retrospectively reviewed. There were 99 patients with ADC, 95 with SQCC and 108 with SCLC. Computed tomography images were interpreted by two radiologists. Treatment response was determined by Response Evaluation Criteria In Solid Tumors 1.1.

Results

Univariate analyses found that younger age, female sex, no history of smoking, higher levels of carcinoembryonic antigen (CEA), contralateral hilum lymphadenopathy, contralateral lung metastasis, pleural nodules and pleural metastasis to the interlobular fissure were significantly correlated with central ADC. Multivariate logistic regression analyses revealed that compared with central SQCC, female sex, younger age, no history of smoking, higher levels of CEA and contralateral hilum lymphadenopathy were the significantly independent indicators of central pulmonary ADC. Furthermore, compared with central SCLC, younger age, higher levels of CEA and cytokeratin 19 fragment (Cyfra21-1), lower levels of neuron-specific enolase, pleural nodules and lack of vascular involvement were significantly associated with central ADC. In 85 central ADC patients who received first-line platinum-based chemotherapy, both univariate and multivariate logistic regression analyses revealed that pulmonary emphysema had a negative correlation with treatment response (odds ratio=8.04, p=0.02).

Conclusion

Our study revealed that central pulmonary ADCs exhibited more aggressive clinical and radiological characteristics. Pulmonary emphysema was an independent and negative indicator for treatment response of central ADC.

CT characteristics in pulmonary adenocarcinoma with epidermal growth factor receptor mutation

Comprehensively investigate the association of CT morphology and clinical findings of adenocarcinoma with EGFR mutation status. Retrospectively included 282 patients who was pathologically proved as lung adenocarcinoma with known EGFR mutation status (mutations: 138 patients, female: 86, median age: 66 years; wildtype: 144 patients, female: 67, median age: 62 years) and their pre-treatment CT scans were analyzed. CT findings and clinical information were collected. Univariate and multivariable logistic regression analysis were performed. Adjusted for age, gender and smoking history of two groups, significantly more patients with pleural tags, pleural and liver metastases were found in the EGFR mutated group (P = 0.007, 0.004, and 0.043, respectively). Multivariable logistic regression analysis found that the model included age, gender, smoking history, air bronchogram, pleural tags, pleural and liver metastasis had a moderate predictive value for EGFR mutation status (AUC = 0.741, P < .0001). Exon-19 deletion was associated with air bronchogram which adjusted for age, gender and smoking history (P = 0.007, OR: 2.91, 95%CI: 1.25–7.79). The evidence of pleural tags, pleural and liver metastases go along with a higher probability of EGFR mutation in adenocarcinoma patients and air bronchogram is positively associated with Exon-19 deletion mutation.

Ablation of Liver X receptors α and β leads to spontaneous peripheral squamous cell lung cancer in mice

The etiology of peripheral squamous cell lung cancer (PSCCa) remains unknown. Here, we show that this condition spontaneously develops in mice in which the genes for two oxysterol receptors, Liver X Receptor (LXR) α (Nr1h3) and β (Nr1h2), are inactivated. By 1 y of age, most of these mice have to be euthanized because of severe dyspnea. Starting at 3 mo, the lungs of LXRα,β(Dko) mice, but not of LXRα or LXRβ single knockout mice, progressively accumulate foam cells, so that by 1 y, the lungs are covered by a "golden coat." There is infiltration of inflammatory cells and progressive accumulation of lipid in the alveolar wall, type 2 pneumocytes, and macrophages. By 14 mo, there are three histological lesions: one resembling adenomatous hyperplasia, one squamous metaplasia, and one squamous cell carcinoma characterized by expression of transformation-related protein (p63), sex determining region Y-box 2 (Sox2), cytokeratin 14 (CK14), and cytokeratin 13 (CK13) and absence of thyroid transcription factor 1 (TTF1), and prosurfactant protein C (pro-SPC). RNA sequencing analysis at 12 mo confirmed a massive increase in markers of M1 macrophages and lymphocytes. The data suggest a previously unidentified etiology of PSCCa: cholesterol dysregulation and M1 macrophage-predominant lung inflammation combined with damage to, and aberrant repair of, lung tissue, particularly the peripheral parenchyma. The results raise the possibility that components of the LXR signaling may be useful targets in the treatment of PSCCa.

Using air bronchograms on multi-detector CT to predict the invasiveness of small lung adenocarcinoma

PURPOSE

To investigate the prevalence of multidetector CT (MDCT) air bronchograms and their value in predicting the invasiveness of lung adenocarcinomas.

METHODS

MDCT scans of 606 nodules in 582 patients with a lung adenocarcinoma less than 2cm in diameter confirmed by surgery and pathology were reviewed. Air bronchograms were classified into three patterns: type I, bronchus with intact lumen; type II, bronchus with dilated or tortuous lumen; and type III, bronchus with obstructed lumen.

RESULTS

Air bronchograms were demonstrated on MDCT in 210 of 606 (34.7%) lung adenocarcinomas with 16.6% (35/211) preinvasive lesions (PL), 30.5% (50/164) minimally invasive adenocarcinoma (MIA), and 54.1% (125/231) invasive adenocarcinoma (IAC) (P=0.000); 18.3% (44/240) pure ground-glass nodules (GGNs), 44.2% (137/310) mixed GGNs, and 51.8% (29/56) solid nodules (P=0.000). Type I was slightly more common in MIA (36/164, 22.0%) than IAC (40/231, 17.3%) and PL (30/211, 14.2%) but without differences among them (P=0.147). Type II (PL: 5/211, 2.4%; MIA: 13/164, 7.9%; IAC: 53/231, 22.9%) and type III (PL: 0/211; MIA: 1/164, 0.6%; IAC: 32/231, 13.9%) were observed more frequently with increasing lung adenocarcinoma invasiveness (both P=0.000).

CONCLUSIONS

The prevalence and patterns of air bronchograms on MDCT can predict the invasiveness of small lung adenocarcinomas.