Histologic features are important prognostic indicators in early stages lung adenocarcinomas

SPOP expression is associated with tumor-infiltrating lymphocytes in pancreatic cancer

BACKGROUND

Speckle Type POZ Protein (SPOP), despite its tumor type-dependent role in tumorigenesis, primarily as a tumor suppressor gene is associated with a variety of different cancers. However, its function in pancreatic cancer remains uncertain.

METHODS

SPOP expression and the association between its expression and patient prognosis and immune function were evaluated using The Cancer Genome Atlas (TCGA), Genotype-Tissue Expression (GTEx), The Tumor Immune Estimation Resource 2.0 (TIMER2.0) database, cBioportal, and various bioinformatic databases. Enrichment analysis of SPOP and the association between SPOP expression with clinical stage and grade were analyzed using the R software package. Then immunohistochemistry (IHC) was used to estimate the correlation between SPOP and tumor-infiltrating lymphocytes (TILs) in patients with pancreatic cancer.

RESULTS

As part of our study, we assessed that SPOP was anomalously expressed in kinds of cancers, associated with clinical stage and outcomes. Meanwhile, SPOP also played a crucial role in the tumor microenvironment (TME). The expression level of SPOP was significantly correlated to tumor-infiltrating immune cells (TICs) in pancreatic cancer.

CONCLUSIONS

Our study uncovered the potential corrections in SPOP with TICs, suggesting that SPOP may act as a biomarker for immunotherapy in pancreatic cancer.

Invasion and Grading of Pulmonary Non-Mucinous Adenocarcinoma

Lung adenocarcinoma staging and grading were recently updated to reflect the link between histologic growth patterns and outcomes. The lepidic growth pattern is regarded as "in-situ," whereas all other patterns are regarded as invasive, though with stratification. Solid, micropapillary, and complex glandular patterns are associated with worse prognosis than papillary and acinar patterns. These recent changes have improved prognostic stratification. However, multiple pitfalls exist in measuring invasive size and in classifying lung adenocarcinoma growth patterns. Awareness of these limitations and recommended practices will help the pathology community achieve consistent prognostic performance and potentially contribute to improved patient management.

Prognostication of lung adenocarcinomas using CT-based deep learning of morphological and histopathological features: a retrospective dual-institutional study

OBJECTIVES

To develop and validate CT-based deep learning (DL) models that learn morphological and histopathological features for lung adenocarcinoma prognostication, and to compare them with a previously developed DL discrete-time survival model.

METHODS

DL models were trained to simultaneously predict five morphological and histopathological features using preoperative chest CT scans from patients with resected lung adenocarcinomas. The DL score was validated in temporal and external test sets, with freedom from recurrence (FFR) and overall survival (OS) as outcomes. Discrimination was evaluated using the time-dependent area under the receiver operating characteristic curve (TD-AUC) and compared with the DL discrete-time survival model. Additionally, we performed multivariable Cox regression analysis.

RESULTS

In the temporal test set (640 patients; median age, 64 years), the TD-AUC was 0.79 for 5-year FFR and 0.73 for 5-year OS. In the external test set (846 patients; median age, 65 years), the TD-AUC was 0.71 for 5-year OS, equivalent to the pathologic stage (0.71 vs. 0.71 [p = 0.74]). The prognostic value of the DL score was independent of clinical factors (adjusted per-percentage hazard ratio for FFR (temporal test), 1.02 [95% CI: 1.01-1.03; p < 0.001]; OS (temporal test), 1.01 [95% CI: 1.002-1.02; p = 0.01]; OS (external test), 1.01 [95% CI: 1.005-1.02; p < 0.001]). Our model showed a higher TD-AUC than the DL discrete-time survival model, but without statistical significance (2.5-year OS: 0.73 vs. 0.68; p = 0.13).

CONCLUSION

The CT-based prognostic score from collective deep learning of morphological and histopathological features showed potential in predicting survival in lung adenocarcinomas.

CLINICAL RELEVANCE STATEMENT

Collective CT-based deep learning of morphological and histopathological features presents potential for enhancing lung adenocarcinoma prognostication and optimizing pre-/postoperative management.

KEY POINTS

• A CT-based prognostic model was developed using collective deep learning of morphological and histopathological features from preoperative CT scans of 3181 patients with resected lung adenocarcinoma. • The prognostic performance of the model was comparable-to-higher performance than the pathologic T category or stage. • Our approach yielded a higher discrimination performance than the direct survival prediction model, but without statistical significance (0.73 vs. 0.68; p=0.13).

Ex-vivo 1.5T MR Imaging versus CT in Estimating the Size of the Pathologically Invasive Component of Lung Adenocarcinoma Spectrum Lesions

PURPOSE

The purpose of this study was to investigate whether ex-vivo MRI enables accurate estimation of the invasive component of lung adenocarcinoma.

METHODS

We retrospectively reviewed 32 patients with lung adenocarcinoma who underwent lung lobectomy. The specimens underwent MRI at 1.5T. The boundary between the lesion and the normal lung was evaluated on a 5-point scale in each three MRI sequences, and a one-way analysis of variance and post-hoc tests were performed. The invasive component size was measured histopathologically. The maximum diameter of each solid component measured on CT and MR T1-weighted (T1W) images and the maximum size obtained from histopathologic images were compared using the Wilcoxon signed-rank test. Inter-reader agreement was evaluated using intraclass correlation coefficients (ICC).

RESULTS

T1W images were determined to be optimal for the delineation of the lesions (P < 0.001). The histopathologic invasive area corresponded to the area where the T1W ex-vivo MR image showed a high signal intensity that was almost equal to the intravascular blood signal. The maximum diameter of the solid component on CT was overestimated compared with the maximum invasive size on histopathology (mean, 153%; P < 0.05), while that on MRI was evaluated mostly accurately without overestimation (mean, 108%; P = 0.48). The interobserver reliability of the measurements using CT and MRI was good (ICC = 0.71 on CT, 0.74 on MRI).

CONCLUSION

Ex-vivo MRI was more accurate than conventional CT in delineating the invasive component of lung adenocarcinoma.

Defining Morphologic Features of Invasion in Pulmonary Nonmucinous Adenocarcinoma With Lepidic Growth: A Proposal by the International Association for the Study of Lung Cancer Pathology Committee

INTRODUCTION

Since the eight edition of the Union for International Cancer Control and American Joint Committee on Cancer TNM classification system, the primary tumor pT stage is determined on the basis of presence and size of the invasive components. The aim of this study was to identify histologic features in tumors with lepidic growth pattern which may be used to establish criteria for distinguishing invasive from noninvasive areas.

METHODS

A Delphi approach was used with two rounds of blinded anonymized analysis of resected nonmucinous lung adenocarcinoma cases with presumed invasive and noninvasive components, followed by one round of reviewer de-anonymized and unblinded review of cases with known outcomes. A digital pathology platform was used for measuring total tumor size and invasive tumor size.

RESULTS

The mean coefficient of variation for measuring total tumor size and tumor invasive size was 6.9% (range: 1.7%-22.3%) and 54% (range: 14.7%-155%), respectively, with substantial variations in interpretation of the size and location of invasion among pathologists. Following the presentation of the results and further discussion among members at large of the International Association for the Study of Lung Cancer Pathology Committee, extensive epithelial proliferation (EEP) in areas of collapsed lepidic growth pattern is recognized as a feature likely to be associated with invasive growth. The EEP is characterized by multilayered luminal epithelial cell growth, usually with high-grade cytologic features in several alveolar spaces.

CONCLUSIONS

Collapsed alveoli and transition zones with EEP were identified by the Delphi process as morphologic features that were a source of interobserver variability. Definition criteria for collapse and EEP are proposed to improve reproducibility of invasion measurement.

Epithelial cell size dysregulation in human lung adenocarcinoma

Human cells tightly control their dimensions, but in some cancers, normal cell size control is lost. In this study we measure cell volumes of epithelial cells from human lung adenocarcinoma progression in situ. By leveraging artificial intelligence (AI), we reconstruct tumor cell shapes in three dimensions (3D) and find airway type 2 cells display up to 10-fold increases in volume. Surprisingly, cell size increase is not caused by altered ploidy, and up to 80% of near-euploid tumor cells show abnormal sizes. Size dysregulation is not explained by cell swelling or senescence because cells maintain cytoplasmic density and proper organelle size scaling, but is correlated with changes in tissue organization and loss of a novel network of processes that appear to connect alveolar type 2 cells. To validate size dysregulation in near-euploid cells, we sorted cells from tumor single-cell suspensions on the basis of size. Our study provides data of unprecedented detail for cell volume dysregulation in a human cancer. Broadly, loss of size control may be a common feature of lung adenocarcinomas in humans and mice that is relevant to disease and identification of these cells provides a useful model for investigating cell size control and consequences of cell size dysregulation.

Adding predictive and diagnostic values of pulmonary ground-glass nodules on lung cancer via novel non-invasive tests

Pulmonary ground-glass nodules (GGNs) are highly associated with lung cancer. Extensive studies using thin-section high-resolution CT images have been conducted to analyze characteristics of different types of GGNs in order to evaluate and determine the predictive and diagnostic values of GGNs on lung cancer. Accurate prediction of their malignancy and invasiveness is critical for developing individualized therapies and follow-up strategies for a better clinical outcome. Through reviewing the recent 5-year research on the association between pulmonary GGNs and lung cancer, we focused on the radiologic and pathological characteristics of different types of GGNs, pointed out the risk factors associated with malignancy, discussed recent genetic analysis and biomarker studies (including autoantibodies, cell-free miRNAs, cell-free DNA, and DNA methylation) for developing novel diagnostic tools. Based on current progress in this research area, we summarized a process from screening, diagnosis to follow-up of GGNs.

Fusion of CT images and clinical variables based on deep learning for predicting invasiveness risk of stage I lung adenocarcinoma

PURPOSE

To develop a novel multimodal data fusion model by incorporating computed tomography (CT) images and clinical variables based on deep learning for predicting the invasiveness risk of stage I lung adenocarcinoma that manifests as ground-glass nodules (GGNs), and compare the diagnostic performance of it with that of radiologists.

METHODS

A total of 1946 patients with solitary and histopathologically confirmed GGNs with maximum diameter less than 3 cm were retrospectively enrolled. The training dataset containing 1704 GGNs was augmented by resampling, scaling, random cropping, etc., to generate new training data. A multimodal data fusion model based on residual learning architecture and two multilayer perceptron with attention mechanism combining CT images with patient general data and serum tumor markers was built. The distance-based confidence scores (DCS) were calculated and compared among multimodal data models with different combinations. An observer study was conducted and the prediction performance of the fusion algorithms was compared with that of the two radiologists by an independent testing dataset with 242 GGNs.

RESULTS

Among the whole GGNs, 606 GGNs are confirmed as invasive adenocarcinoma (IA) and 1340 are non-IA. The proposed novel multimodal data fusion model combining CT images, patient general data and serum tumor markers achieved the highest accuracy (88.5%), Area under a ROC curve (AUC) (0.957), F1 (81.5%), F1_weighted (81.9%) and Matthews correlation coefficient (MCC) (73.2%) for classifying between IA and non-IA GGNs, which was even better than the senior radiologist's performance (accuracy, 86.1%). In addition, the DCSs for multimodal data suggested that CT image had a stronger influence (0.9540) quantitatively than general data (0.6726) or tumor marker (0.6971).

CONCLUSION

This study demonstrated that the feasibility of integrating different types of data including CT images and clinical variables, and the multimodal data fusion model yielded higher performance for distinguishing IA from non-IA GGNs. This article is protected by copyright. All rights reserved.

Histopathologic Basis for a Chest CT Deep Learning Survival Prediction Model in Patients with Lung Adenocarcinoma

Background A preoperative CT-based deep learning (DL) prediction model was proposed to estimate disease-free survival in patients with resected lung adenocarcinoma. However, the black-box nature of DL hinders interpretation of its results. Purpose To provide histopathologic evidence underpinning the DL survival prediction model and to demonstrate the feasibility of the model in identifying patients with histopathologic risk factors through unsupervised clustering and a series of regression analyses. Materials and Methods For this retrospective study, data from patients who underwent curative resection for lung adenocarcinoma without neoadjuvant therapy from January 2016 to September 2020 were collected from a tertiary care center. Seven histopathologic risk factors for the resected adenocarcinoma were documented: the aggressive adenocarcinoma subtype (cribriform, morular, solid, or micropapillary-predominant subtype); mediastinal nodal metastasis (pN2); presence of lymphatic, venous, and perineural invasion; visceral pleural invasion (VPI); and EGFR mutation status. Unsupervised clustering using 80 DL model-driven CT features was performed, and associations between the patient clusters and the histopathologic features were analyzed. Multivariable regression analyses were performed to investigate the added value of the DL model output to the semantic CT features (clinical T category and radiologic nodule type [ie, solid or subsolid]) for histopathologic associations. Results A total of 1667 patients (median age, 64 years [IQR, 57-71 years]; 975 women) were evaluated. Unsupervised patient clusters 3 and 4 were associated with all histopathologic risk factors (P < .01) except for EGFR mutation status (P = .30 for cluster 3). After multivariable adjustment, model output was associated with the aggressive adenocarcinoma subtype (odds ratio [OR], 1.03; 95% CI: 1.002, 1.05; P = .03), venous invasion (OR, 1.03; 95% CI: 1.004, 1.06; P = .02), and VPI (OR, 1.08; 95% CI: 1.06, 1.10; P < .001), independently of the semantic CT features. Conclusion The deep learning model extracted CT imaging surrogates for the histopathologic profiles of lung adenocarcinoma. © RSNA, 2022 Online supplemental material is available for this article. See also the editorial by Yanagawa in this issue.

Development and validation of a preoperative CT-based radiomic nomogram to predict pathology invasiveness in patients with a solitary pulmonary nodule: a machine learning approach, multicenter, diagnostic study

OBJECTIVES

To develop and validate a preoperative CT-based nomogram combined with radiomic and clinical-radiological signatures to distinguish preinvasive lesions from pulmonary invasive lesions.

METHODS

This was a retrospective, diagnostic study conducted from August 1, 2018, to May 1, 2020, at three centers. Patients with a solitary pulmonary nodule were enrolled in the GDPH center and were divided into two groups (7:3) randomly: development (n = 149) and internal validation (n = 54). The SYSMH center and the ZSLC Center formed an external validation cohort of 170 patients. The least absolute shrinkage and selection operator (LASSO) algorithm and logistic regression analysis were used to feature signatures and transform them into models.

RESULTS

The study comprised 373 individuals from three independent centers (female: 225/373, 60.3%; median [IQR] age, 57.0 [48.0-65.0] years). The AUCs for the combined radiomic signature selected from the nodular area and the perinodular area were 0.93, 0.91, and 0.90 in the three cohorts. The nomogram combining the clinical and combined radiomic signatures could accurately predict interstitial invasion in patients with a solitary pulmonary nodule (AUC, 0.94, 0.90, 0.92) in the three cohorts, respectively. The radiomic nomogram outperformed any clinical or radiomic signature in terms of clinical predictive abilities, according to a decision curve analysis and the Akaike information criteria.

CONCLUSIONS

This study demonstrated that a nomogram constructed by identified clinical-radiological signatures and combined radiomic signatures has the potential to precisely predict pathology invasiveness.

KEY POINTS

• The radiomic signature from the perinodular area has the potential to predict pathology invasiveness of the solitary pulmonary nodule. • The new radiomic nomogram was useful in clinical decision-making associated with personalized surgical intervention and therapeutic regimen selection in patients with early-stage non-small-cell lung cancer.

Updates in grading and invasion assessment in lung adenocarcinoma

The pathologic evaluation of lung adenocarcinoma, because of greater understanding of disease progression and prognosis, has become more complex. It is clear that histologic growth patterns reflect indolent and aggressive disease, resulting in clearer morphologic groups that can be the underpinning of a grading system. In addition, the progression of adenocarcinoma from a tumor that preserves alveolar architecture to one that remodels and effaces lung structure has led to criteria that reflect invasive rather than in-situ growth. While some of these are based on tumor cell growth pattern, aspects of this remodeling from desmoplasia to artifacts of lung collapse and sectioning, can lead to difficult to interpret patterns with lower reproducibility between observers. Such scenarios are examined to provide updates on new histologic concepts and to highlight ongoing problem areas.

Prediction of the Growth Rate of Early-Stage Lung Adenocarcinoma by Radiomics

Objectives

To investigate the value of imaging in predicting the growth rate of early lung adenocarcinoma.

Methods

From January 2012 to June 2018, 402 patients with pathology-confirmed lung adenocarcinoma who had two or more thin-layer CT follow-up images were retrospectively analyzed, involving 407 nodules. Two complete preoperative CT images and complete clinical data were evaluated. Training and validation sets were randomly assigned according to an 8:2 ratio. All cases were divided into fast-growing and slow-growing groups. Researchers extracted 1218 radiomics features from each volumetric region of interest (VOI). Then, radiomics features were selected by repeatability analysis and Analysis of Variance (ANOVA); Based on the Univariate and multivariate analyses, the significant radiographic features is selected in training set. A decision tree algorithm was conducted to establish the radiographic model, radiomics model and the combined radiographic-radiomics model. Model performance was assessed by the area under the curve (AUC) obtained by receiver operating characteristic (ROC) analysis.

Results

Sixty-two radiomics features and one radiographic features were selected for predicting the growth rate of pulmonary nodules. The combined radiographic-radiomics model (AUC 0.78) performed better than the radiographic model (0.727) and the radiomics model (0.710) in the validation set.

Conclusions

The model has good clinical application value and development prospects to predict the growth rate of early lung adenocarcinoma through the combined radiographic-radiomics model.

3D deep learning based classification of pulmonary ground glass opacity nodules with automatic segmentation

Classifying ground-glass lung nodules (GGNs) into atypical adenomatous hyperplasia (AAH), adenocarcinoma in situ (AIS), minimally invasive adenocarcinoma (MIA), and invasive adenocarcinoma (IAC) on diagnostic CT images is important to evaluate the therapy options for lung cancer patients. In this paper, we propose a joint deep learning model where the segmentation can better facilitate the classification of pulmonary GGNs. Based on our observation that masking the nodule to train the model results in better lesion classification, we propose to build a cascade architecture with both segmentation and classification networks. The segmentation model works as a trainable preprocessing module to provide the classification-guided 'attention' weight map to the raw CT data to achieve better diagnosis performance. We evaluate our proposed model and compare with other baseline models for 4 clinically significant nodule classification tasks, defined by a combination of pathology types, using 4 classification metrics: Accuracy, Average F1 Score, Matthews Correlation Coefficient (MCC), and Area Under the Receiver Operating Characteristic Curve (AUC). Experimental results show that the proposed method outperforms other baseline models on all the diagnostic classification tasks.

Consolidation volume and integration of computed tomography values on three-dimensional computed tomography may predict pathological invasiveness in early lung adenocarcinoma

PURPOSE

To investigate the relationship between three-dimensional computed tomography (3D-CT) findings and pathological invasiveness in lung adenocarcinoma.

METHODS

We retrospectively evaluated 95 patients who underwent surgical resection of lung adenocarcinoma of ≤ 20 mm. The diameters, volumes, and CT values of tumor consolidation were analyzed. We defined the modified CT value by setting air as 0 and water as 1000 and assumed a correlation with pathological invasiveness. Pre-invasive lesions and minimally invasive adenocarcinomas were classified as non-invasive adenocarcinoma. We compared the clinico-radiological features with pathological invasiveness. Receiver operator characteristic (ROC) curves and recurrence-free survival curves were constructed.

RESULTS

Twenty-six non-invasive adenocarcinomas and 69 invasive adenocarcinomas were evaluated. The multivariate analysis revealed that the consolidation volume and the integration of modified CT values were the most important predictors of pathological invasion. The area under the ROC curve and the cut-off values of the consolidation volume were 0.868 and 75 mm³, respectively. The area under the ROC curve and the cut-off values of the integration of modified CT values were 0.871 and 80,000, respectively. There was no recurrence in cases with values below the cut-off across all parameters.

CONCLUSION

The consolidation volume and integration of modified CT values were shown to be highly predictive of pathological invasiveness.

Factors distinguishing invasive from pre-invasive adenocarcinoma presenting as pure ground glass pulmonary nodules

Background

To investigate predictors of pathological invasiveness and prognosis of lung adenocarcinoma in patients with pure ground-glass nodules (pGGNs).

Methods

Clinical and computed tomography (CT) features of invasive adenocarcinomas (IACs) and pre-IACs were retrospectively compared in 641 consecutive patients with pGGNs and confirmed lung adenocarcinomas who had undergone postoperative CT follow-up. Potential predictors of prognosis were investigated in these patients.

Results

Of 659 pGGNs in 641 patients, 258 (39.1%) were adenocarcinomas in situ, 265 (40.2%) were minimally invasive adenocarcinomas, and 136 (20.6%) were IACs. Respective optimal cutoffs for age, serum carcinoembryonic antigen concentration, maximal diameter, mean diameter, and CT density for distinguishing pre-IACs from IACs were 53 years, 2.19 ng/mL, 10.78 mm, 10.09 mm, and − 582.28 Hounsfield units (HU). Univariable analysis indicated that sex, age, maximal diameter, mean diameter, CT density, and spiculation were significant predictors of lung IAC. In multivariable analysis age, maximal diameter, and CT density were significant predictors of lung IAC. During a median follow-up of 41 months no pGGN IACs recurred.

Conclusions

pGGNs may be lung IACs, especially in patients aged > 55 years with lesions that are > 1 cm in diameter and exhibit CT density > − 600 HU. pGGN IACs of < 3 cm in diameter have good post-resection prognoses.

Surgery versus stereotactic body radiotherapy for clinical stage I non-small-cell lung cancer: propensity score-matching analysis including the ratio of ground glass nodules

PURPOSE

To investigate whether surgery and stereotactic body radiotherapy (SBRT) yield comparable outcomes for clinical stage (c-stage) I non-small-cell lung cancer (NSCLC), propensity score-matching (PSM) analysis was conducted.

METHODS

This single-institutional retrospective study included patients who underwent surgery (n = 574) or SBRT (n = 182) between 2004 and 2014. PSM was performed based on tumor diameter, age, sex, performance status, forced expiratory volume, Charlson comorbidity index, and ground glass nodules (GGN) defined as cTis or cT1mi according to the 8th TNM classification.

RESULTS

The median follow-up durations for the surgery and SBRT groups were 66 and 69 months, respectively. The multivariate analysis revealed that non-GGN was a significant factor for poorer overall survival (OS) and disease-free survival (DFS): hazard ratio (HR) 19.95% confidence interval (CI) 4.7-79, P < 0.001; and HR 28, 95% CI 6.9-110, P < 0.001, respectively. PSM identified 120 patients from each group. The 5-year OS and DFS rates of the surgery vs SBRT groups were 71% (95% CI 61-79) vs 64% (95% CI 54-72) (P = 0.41) and 63% (95% CI 53-72) vs 55% (95% CI 45-63) (P = 0.23) after PSM, respectively.

CONCLUSION

The PSM analyses including the ratio of GGN demonstrated that the OS and DFS for patients with c-stage I NSCLC in the surgery group were slightly superior to those for those in the SBRT group, although both survivals were not significantly different between the two therapeutic approaches.

Radiological-pathological correlation of subsolid pulmonary nodules: A single centre retrospective evaluation of the 2011 IASLC adenocarcinoma classification system

INTRODUCTION

The 2011 IASLC classification system proposes guidelines for radiologists and pathologists to classify adenocarcinomas spectrum lesions as preinvasive, minimally invasive adenocarcinoma (MIA), or invasive adenocarcinoma (IA). IA portends the worst clinical prognosis, and the imaging distinction between MIA and IA is controversial.

MATERIALS AND METHODS

Subsolid pulmonary nodules resected by microcoil localization over a three-year period were retrospectively reviewed by three chest radiologists and a pulmonary pathologist. Nodules were classified radiologically based on preoperative computed tomography (CT), with the solid nodule component measured on mediastinal windows applied to high-frequency lung kernel reconstructions, and pathologically according to 2011 IASLC criteria. Radiology interobserver and radiological-pathological variability of nodule classification, and potential reasons for nodule classification discordance were assessed.

RESULTS

Seventy-one subsolid nodules in 67 patients were included. The average size of invasive disease focus at histopathology was 5 mm (standard deviation 5 mm). Radiology interobserver agreement of nodule classification was good (Cohen's Kappa = 0.604, 95 % CI: 0.447 to 0.761). Agreement between consensus radiological interpretation and pathological category was fair (Cohen's Kappa = 0.236, 95 % CI: 0.054-0.421). Radiological and pathological nodule classification were concordant in 52 % (37 of 71) of nodules. The IASLC proposed CT solid component cut-off of 5 mm to distinguish MIA and IA yielded a sensitivity of 59 % and specificity of 80 %. Common reasons for nodule classification discordance included multiple solid components within a nodule on CT, scar and stromal collapse at pathology, and measurement variability.

CONCLUSION

Solid component(s) within persistent part-solid pulmonary nodules raise suspicion for invasive adenocarcinoma. Preoperative imaging classification is frequently discordant from final pathology, reflecting interpretive and technical challenges in radiological and pathological analysis.

Combination of generative adversarial network and convolutional neural network for automatic subcentimeter pulmonary adenocarcinoma classification

Background

The efficient and accurate diagnosis of pulmonary adenocarcinoma before surgery is of considerable significance to clinicians. Although computed tomography (CT) examinations are widely used in practice, it is still challenging and time-consuming for radiologists to distinguish between different types of subcentimeter pulmonary nodules. Although there have been many deep learning algorithms proposed, their performance largely depends on vast amounts of data, which is difficult to collect in the medical imaging area. Therefore, we propose an automatic classification system for subcentimeter pulmonary adenocarcinoma, combining a convolutional neural network (CNN) and a generative adversarial network (GAN) to optimize clinical decision-making and to provide small dataset algorithm design ideas.

Methods

A total of 206 nodules with postoperative pathological labels were analyzed. Among them were 30 adenocarcinomas in situ (AISs), 119 minimally invasive adenocarcinomas (MIAs), and 57 invasive adenocarcinomas (IACs). Our system consisted of two parts, a GAN-based image synthesis, and a CNN classification. First, several popular existing GAN techniques were employed to augment the datasets, and comprehensive experiments were conducted to evaluate the quality of the GAN synthesis. Additionally, our classification system processes were based on two-dimensional (2D) nodule-centered CT patches without the need of manual labeling information.

Results

For GAN-based image synthesis, the visual Turing test showed that even radiologists could not tell the GAN-synthesized from the raw images (accuracy: primary radiologist 56%, senior radiologist 65%). For CNN classification, our progressive growing wGAN improved the performance of CNN most effectively (area under the curve =0.83). The experiments indicated that the proposed GAN augmentation method improved the classification accuracy by 23.5% (from 37.0% to 60.5%) and 7.3% (from 53.2% to 60.5%) in comparison with training methods using raw and common augmented images respectively. The performance of this combined GAN and CNN method (accuracy: 60.5%±2.6%) was comparable to the state-of-the-art methods, and our CNN was also more lightweight.

Conclusions

The experiments revealed that GAN synthesis techniques could effectively alleviate the problem of insufficient data in medical imaging. The proposed GAN plus CNN framework can be generalized for use in building other computer-aided detection (CADx) algorithms and thus assist in diagnosis.

Prognostic impact of cancer-associated active fibroblasts and invasive architectural patterns on early-stage lung adenocarcinoma

BACKGROUND

Invasion is a crucial indicator of the prognosis in lung adenocarcinoma. The 2015 WHO classification of lung tumors defined invasion of adenocarcinoma mainly by the presence of non-lepidic histological subtypes including papillary, acinar, micropapillary and solid patterns, and the presence of cancer-associated active fibroblasts (CAF). In this study, we focused specifically on early-stage lepidic adenocarcinoma with CAF to evaluate its prognostic significance.

METHODS

We included 1032 resected cases of lung adenocarcinoma, which consisted of pathological stage IA invasive cancer and adenocarcinoma in situ (AIS). Invasive adenocarcinoma was classified into two subgroups according to the type of invasion, INV-1 and INV-2. We defined INV-1 as adenocarcinoma of a non-lepidic histological subtype with or without CAF, and INV-2 as lepidic adenocarcinoma with CAF. The clinicopathological characteristics and prognosis were retrospectively analyzed.

RESULTS

Included cases were classified into 696 (67.4 %) INV-1, 170 (16.5 %) INV-2, and 166 (16.1 %) AIS. The estimated 5-year recurrence-free probabilities of INV-1, INV-2, and AIS were 92.9 %, 100 %, and 100 %, respectively (p <  0.001). Although there were significant differences between INV-1 and INV-2 in terms of gender (more males in INV-1, p =  0.039), smoking habit (more smokers in INV-1, p =  0.046), and lymphovascular invasion (more invasion in INV-1, p <  0.001), there was no difference between AIS and INV-2.

CONCLUSION

The presence of CAF is not always associated with a worse prognosis, and therefore it does not seem appropriate to include the presence of CAF alone in diagnostic criteria for invasion in early-stage lung adenocarcinoma.

Genomic and immune profiling of pre-invasive lung adenocarcinoma

Adenocarcinoma in situ and minimally invasive adenocarcinoma are the pre-invasive forms of lung adenocarcinoma. The genomic and immune profiles of these lesions are poorly understood. Here we report exome and transcriptome sequencing of 98 lung adenocarcinoma precursor lesions and 99 invasive adenocarcinomas. We have identified EGFR, RBM10, BRAF, ERBB2, TP53, KRAS, MAP2K1 and MET as significantly mutated genes in the pre/minimally invasive group. Classes of genome alterations that increase in frequency during the progression to malignancy are revealed. These include mutations in TP53, arm-level copy number alterations, and HLA loss of heterozygosity. Immune infiltration is correlated with copy number alterations of chromosome arm 6p, suggesting a link between arm-level events and the tumor immune environment.

[Computed tomography findings, clinicopathological features, genetic characteristics and prognosis of in situ and minimally invasive lung adenocarcinomas]

OBJECTIVE

To investigate the computed tomography findings, clinicopathological features, genetic characteristics and prognosis of in situ adenocarcinoma (AIS) and minimally invasive adenocarcinoma (MIA) of the lung.

METHODS

We retrospectively analyzed the data including computed tomography (CT) images, histopathological findings, Ki-67 immunostaining, and genetic mutations in patients with lung adenocarcinoma undergoing surgery at our hospital between 2014 and 2019.

RESULTS

Of the total of 480 patients with lung adenocarcinoma we reviewed, 73 (15.2%) had AIS (n=28) or MIA (n=45) tumors. The age of the patients with MIA was significantly younger than that of patients with AIS (P &lt; 0.02). CT scans identified pure ground-glass nodules in 46.4% of AIS cases and in 44.4% of MIA cases. Multiple GGOs were more common in MIA than in AIS cases (P &lt; 0.05), and bluured tumor margins was less frequent in AIS cases (P &lt; 0.05). No significant difference was found in EGFR mutations between MIA and AIS cases. A Ki-67 labeling index (LI) value ≥2.8% did not differentiate MIA from AIS. The follow-up time in MIA group was significantly shorter than that in AIS group, but no recurrence or death occurred.

CONCLUSIONS

Despite similar surgical outcomes and favorable survival outcomes, the patients with AIS and MIA show differences in terms of age, CT findings, EGFR mutations and Ki-67 LI.

Cytological differences between invasive and noninvasive or minimally invasive lung adenocarcinomas diagnosed in Japanese patients using needle biopsy specimens of pulmonary lesions ≤3 cm in diameter

BACKGROUND

According to the WHO classification for lung cancer, adenocarcinoma in situ (AIS) and minimally invasive adenocarcinoma (MIA) have a better prognosis than invasive adenocarcinoma (IAD). However, detecting the foci of invasion in lung adenocarcinomas radiologically remains difficult. The present study examined whether or not differences in the cytological characteristics between IAD and AIS or MIA (noninvasive or minimally invasive adenocarcinomas [NMIAD]) plays a role in the differential diagnosis.

METHODS

Seventy surgical resection specimens of primary lung adenocarcinoma with preoperative cytology, in which several parameters were evaluated and assessed.

RESULTS

The histopathological diagnoses of surgical resection specimens were AIS in 8, MIA in 31, IAD in 31 including lepidic adenocarcinoma in 9, and papillary adenocarcinoma in 22. NMIAD had a 100% 5-year recurrence-free survival (RFS), while IAD had an 82.8% 5-year RFS. The numbers of tumor cells (at ×10 magnification in 10 fields) were 60.3 ± 40.5 in IAD and 39.8 ± 28.7 in NMIAD (P = 0.0017). A univariate analysis of cytological parameters revealed significant differences in large tumor cell clusters, three-dimensional (3D) tumor cell clusters, and irregular nuclear contours between the two groups. The frequency of irregular nuclear contours continued to be significantly different according to a multivariate analysis.

CONCLUSION

Large or 3D tumor cell clusters and irregular nuclear contours may be important cytological factors for distinguishing IAD from NMIAD, with the latter being potentially more important for distinguishing between the two groups.

Computed Tomography-Based Score Indicative of Lung Cancer Aggression (SILA) Predicts the Degree of Histologic Tissue Invasion and Patient Survival in Lung Adenocarcinoma Spectrum

OBJECTIVE

Most computed tomography (CT)-detected lung cancers are adenocarcinomas (ACs), representing lesions with variable tissue invasion, aggressiveness, and clinical outcome. Visual radiologic characterization of AC pulmonary nodules is both inconsistent and inadequate to confidently predict histopathologic classification or prognosis. Comprehensive pathologic interpretation requires full nodule resection. We have described a computerized scoring system for AC detected on CT scans that can noninvasively estimate the degree of histologic invasion and simultaneously predict patient survival.

METHODS

The Computer-Aided Nodule Assessment and Risk Yield has been validated to characterize CT-detected nodules across the spectrum of AC. With the use of unsupervised clustering, nine natural exemplars were identified as basic radiographic features of AC nodules. We now introduce the Score Indicative of Lung Cancer Aggression (SILA), which is a cumulative aggregate of normalized distributions of ordered Computer-Aided Nodule Assessment and Risk Yield exemplars. The SILA values for each of 237 unique nodules in AC were compared with the histopathologically defined maximum linear extent of tumor invasion. With use of the SILA, Kaplan-Meier survival and Cox proportionality analysis were performed on patients with stage I AC, who comprised a subset of our cohort.

RESULTS

The SILA discriminated between indolent and invasive AC (p < 0.0001). In addition, prediction of linear extent of histopathologic tumor invasion was possible. In stage I AC, three separate SILA prognosis groups were identified: indolent, intermediate, and poor, with 5-year survival rates of 100%, 79%, 58%, respectively. Cox proportionality hazard modeling predicted a 50% increase in mortality, for a 0.1 unit increase in the SILA over a median follow-up time of 3.6 years (p < 0.0002).

CONCLUSIONS

The SILA is a computer-based analytic measure allowing noninvasive approximation of histologic invasion and prediction of patient survival in CT-detected AC nodules.

Best Practices Recommendations for Diagnostic Immunohistochemistry in Lung Cancer

Since the 2015 WHO classification was introduced into clinical practice, immunohistochemistry (IHC) has figured prominently in lung cancer diagnosis. In addition to distinction of small cell versus non-small cell carcinoma, patients' treatment of choice is directly linked to histologic subtypes of non-small cell carcinoma, which pertains to IHC results, particularly for poorly differentiated tumors. The use of IHC has improved diagnostic accuracy in the classification of lung carcinoma, but the interpretation of IHC results remains challenging in some instances. Also, pathologists must be aware of many interpretation pitfalls, and the use of IHC should be efficient to spare the tissue for molecular testing. The International Association for the Study of Lung Cancer Pathology Committee received questions on practical application and interpretation of IHC in lung cancer diagnosis. After discussions in several International Association for the Study of Lung Cancer Pathology Committee meetings, the issues and caveats were summarized in terms of 11 key questions covering common and important diagnostic situations in a daily clinical practice with some relevant challenging queries. The questions cover topics such as the best IHC markers for distinguishing NSCLC subtypes, differences in thyroid transcription factor 1 clones, and the utility of IHC in diagnosing uncommon subtypes of lung cancer and distinguishing primary from metastatic tumors. This article provides answers and explanations for the key questions about the use of IHC in diagnosis of lung carcinoma, representing viewpoints of experts in thoracic pathology that should assist the community in the appropriate use of IHC in diagnostic pathology.

3D Deep Learning from CT Scans Predicts Tumor Invasiveness of Subcentimeter Pulmonary Adenocarcinomas

: Identification of early-stage pulmonary adenocarcinomas before surgery, especially in cases of subcentimeter cancers, would be clinically important and could provide guidance to clinical decision making. In this study, we developed a deep learning system based on 3D convolutional neural networks and multitask learning, which automatically predicts tumor invasiveness, together with 3D nodule segmentation masks. The system processes a 3D nodule-centered patch of preprocessed CT and learns a deep representation of a given nodule without the need for any additional information. A dataset of 651 nodules with manually segmented voxel-wise masks and pathological labels of atypical adenomatous hyperplasia (AAH), adenocarcinomas in situ (AIS), minimally invasive adenocarcinoma (MIA), and invasive pulmonary adenocarcinoma (IA) was used in this study. We trained and validated our deep learning system on 523 nodules and tested its performance on 128 nodules. An observer study with 2 groups of radiologists, 2 senior and 2 junior, was also investigated. We merged AAH and AIS into one single category AAH-AIS, comprising a 3-category classification in our study. The proposed deep learning system achieved better classification performance than the radiologists; in terms of 3-class weighted average F1 score, the model achieved 63.3% while the radiologists achieved 55.6%, 56.6%, 54.3%, and 51.0%, respectively. These results suggest that deep learning methods improve the yield of discriminative results and hold promise in the CADx application domain, which could help doctors work efficiently and facilitate the application of precision medicine. SIGNIFICANCE: Machine learning tools are beginning to be implemented for clinical applications. This study represents an important milestone for this emerging technology, which could improve therapy selection for patients with lung cancer.

3D convolutional neural network for differentiating pre-invasive lesions from invasive adenocarcinomas appearing as ground-glass nodules with diameters ≤3 cm using HRCT

Background

Identification of pre-invasive lesions (PILs) and invasive adenocarcinomas (IACs) can facilitate treatment selection. This study aimed to develop an automatic classification framework based on a 3D convolutional neural network (CNN) to distinguish different types of lung cancer using computed tomography (CT) data.

Methods

The CT data of 1,545 patients suffering from pre-invasive or invasive lung cancer were collected from Fudan University Shanghai Cancer Center. All of the data were preprocessed through lung mask extraction and 3D reconstruction to adapt to different imaging scanners or protocols. The general flow for the classification framework consisted of nodule detection and cancer classification. The performance of our classification algorithm was evaluated using a receiver operating characteristic (ROC) analysis, with diagnostic results from three experienced radiologists.

Results

The sensitivity, specificity, accuracy, and AUC (area under the ROC curve) values of our proposed automatic classification method were 88.5%, 80.1%, 84.0%, and 89.2%, respectively. The results of the CNN classification method were compared to those of three experienced radiologists. The AUC value of our method (AUC =0.892) was higher than those of all radiologists (radiologist 1: 80.5%; radiologist 2: 83.9%; and radiologist 3: 86.7%).

Conclusions

The 3D CNN-based classification algorithm is a promising tool for the diagnosis of pre-invasive and invasive lung cancer and for the treatment choice decision.

Clinicopathological Characteristics and Mutations Driving Development of Early Lung Adenocarcinoma: Tumor Initiation and Progression

Lung cancer is the leading cause of cancer-related deaths worldwide, with lung adenocarcinoma representing the most common lung cancer subtype. Among all lung adenocarcinomas, the most prevalent subset develops via tumorigenesis and progression from atypical adenomatous hyperplasia (AAH) to adenocarcinoma in situ (AIS), to minimally invasive adenocarcinoma (MIA), to overt invasive adenocarcinoma with a lepidic pattern. This stepwise development is supported by the clinicopathological and molecular characteristics of these tumors. In the 2015 World Health Organization classification, AAH and AIS are both defined as preinvasive lesions, whereas MIA is identified as an early invasive adenocarcinoma that is not expected to recur if removed completely. Recent studies have examined the molecular features of lung adenocarcinoma tumorigenesis and progression. EGFR-mutated adenocarcinoma frequently develops via the multistep progression. Oncogene-induced senescence appears to decrease the frequency of the multistep progression in KRAS- or BRAF-mutated adenocarcinoma, whose tumor evolution may be associated with epigenetic alterations and kinase-inactive mutations. This review summarizes the current knowledge of tumorigenesis and tumor progression in early lung adenocarcinoma, with special focus on its clinicopathological characteristics and their associations with driver mutations (EGFR, KRAS, and BRAF) as well as on its molecular pathogenesis and progression.

Radiologic-Pathologic Correlation of Solid Portions on Thin-section CT Images in Lung Adenocarcinoma: A Multicenter Study

BACKGROUND

Measuring the size of invasiveness on computed tomography (CT) for the T descriptor size was deemed important in the 8th edition of the TNM lung cancer classification. We aimed to correlate the maximal dimensions of the solid portions using both lung and mediastinal window settings on CT imaging with the pathologic invasiveness (> 0.5 cm) in lung adenocarcinoma patients.

MATERIALS AND METHODS

The study population consisted of 378 patients with a histologic diagnosis of adenocarcinoma in situ (AIS), minimally invasive adenocarcinoma (MIA), invasive adenocarcinoma (IVA)-lepidic, IVA-acinar and/or IVA-papillary, and IVA-micropapillary and/or solid adenocarcinoma. A panel of 15 radiologists was divided into 2 groups (group A, 9 radiologists; and group B, 6 radiologists). The 2 groups independently measured the maximal and perpendicular dimensions of the solid components and entire tumors on the lung and mediastinal window settings. The solid proportion of nodule was calculated by dividing the solid portion size (lung and mediastinal window settings) by the nodule size (lung window setting). The maximal dimensions of the invasive focus were measured on the corresponding pathologic specimens by 2 pathologists.

RESULTS

The solid proportion was larger in the following descending order: IVA-micropapillary and/or solid, IVA-acinar and/or papillary, IVA-lepidic, MIA, and AIS. For both groups A and B, a solid portion > 0.8 cm in the lung window setting or > 0.6 cm in the mediastinal window setting on CT was a significant indicator of pathologic invasiveness > 0.5 cm (P < .001; receiver operating characteristic analysis using Youden's index).

CONCLUSION

A solid portion > 0.8 cm on the lung window setting or solid portion > 0.6 cm on the mediastinal window setting on CT predicts for histopathologic invasiveness to differentiate IVA from MIA and AIS.

Computer-Aided Diagnosis of Ground-Glass Opacity Nodules Using Open-Source Software for Quantifying Tumor Heterogeneity

OBJECTIVE

The purposes of this study are to develop quantitative imaging biomarkers obtained from high-resolution CTs for classifying ground-glass nodules (GGNs) into atypical adenomatous hyperplasia (AAH), adenocarcinoma in situ (AIS), minimally invasive adenocarcinoma (MIA), and invasive adenocarcinoma (IAC); to evaluate the utility of contrast enhancement for differential diagnosis; and to develop and validate a support vector machine (SVM) to predict the GGN type.

MATERIALS AND METHODS

The heterogeneity of 248 GGNs was quantified using custom software. Statistical analysis with a univariate Kruskal-Wallis test was performed to evaluate metrics for significant differences among the four GGN groups. The heterogeneity metrics were used to train a SVM to learn and predict the lesion type.

RESULTS

Fifty of 57 and 51 of 57 heterogeneity metrics showed statistically significant differences among the four GGN groups on unenhanced and contrast-enhanced CT scans, respectively. The SVM predicted lesion type with greater accuracy than did three expert radiologists. The accuracy of classifying the GGNs into the four groups on the basis of the SVM algorithm was 70.9%, whereas the accuracy of the radiologists was 39.6%. The accuracy of SVM in classifying the AIS and MIA nodules was 73.1%, and the accuracy of the radiologists was 35.7%. For indolent versus invasive lesions, the accuracy of the SVM was 88.1%, and the accuracy of the radiologists was 60.8%. We found that contrast enhancement does not significantly improve the differential diagnosis of GGNs.

CONCLUSION

Compared with the GGN classification done by the three radiologists, the SVM trained regarding all the heterogeneity metrics showed significantly higher accuracy in classifying the lesions into the four groups, differentiating between AIS and MIA and between indolent and invasive lesions. Contrast enhancement did not improve the differential diagnosis of GGNs.

Lymph node involvement according to lung adenocarcinoma subtypes: lymph node involvement is influenced by lung adenocarcinoma subtypes

Backgrounds

Invasive adenocarcinoma subtypes are known to be associated with prognosis; however, the underlying reason remains unclear. To find out the reason, we investigated the possible influence of lymph node (LN) involvement by the constituent histologic subtypes in the tumor and clarified the different prognosis according to the predominant histologic subtypes in the tumor and LN.

Methods

A total of 97 consecutive patients who underwent surgical resection for lung invasive adenocarcinoma between February 2009 and December 2015 were included. We analyzed the associations of the histologic subtypes between the tumor and LN and disease-free survival (DFS) according to the histologic subtypes and predicted the histologic subtype in LN involvement using the component ratio of the predominant histologic subtype in the tumor. A P value <0.05 was considered statistically significant.

Results

Acinar and papillary subtypes occupied the majority of the predominant histological subtypes (tumor 73.2%, LN 71.1%). The tumor showed significantly more constituent histologic subtypes than LN (P<0.001). Micropapillary and solid predominant subtype were more common in poorer differentiation (tumor P<0.001, LN P=0.001). The predominant histologic subtype in the tumor was not the same as that in LN and micropapillary and solid predominant subtypes were significantly more prone to LN involvement than other subtypes (P<0.001). Regarding the predominant histologic subtypes in the tumor, there was no significant difference in DFS between micropapillary and solid predominant subtypes and other subtypes. However, regarding the predominant histologic subtypes in LN, micropapillary and solid predominant subtypes had significantly lower DFS than other subtypes (P=0.010). Solid predominant subtype had a significant cutoff value for prediction of the predominant histologic subtype in LN using the component ratio of the predominant histologic subtype in the tumor (cutoff value 12.5%, sensitivity 70.0%, specificity 82.4%, area 0.775, P<0.001).

Conclusions

The present study presented a possible reason of discrepancies in outcomes according to the lung adenocarcinoma constituent subtypes. Micropapillary and solid predominant subtypes had poorer prognosis than other subtypes, which might be explained by being more prone to LN involvement.

Detection of tumor spread through airspaces by airway secretion cytology from resected lung cancer specimens

It currently remains unclear whether tumor spread through airspaces (STAS) actually exist in vivo or are an artifact. The morphologies of STAS and tumor cell clusters in airway secretions collected from the segmental or lobar bronchus of resected lung adenocarcinomas and squamous cell carcinomas were compared among 48 patients. The EGFR status of tumor cell clusters in airway secretions was also compared with that of the main tumor in EGFR mutant adenocarcinomas. Tumor cell clusters were observed in the airway secretion cytology of ten patients (20.8%), and eight patients were adenocarcinoma (20.0% of adenocarcinoma). The morphology of STAS closely resembled that of tumor cell clusters detected in airway secretion cytology. The positive rates of airway secretion cytology were 83.3%, 100%, and 50% in papillary adenocarcinoma, micropapillary adenocarcinoma, and invasive mucinous adenocarcinoma, respectively. Among three EGFR mutant adenocarcinomas, the EGFR mutation subtypes of the main tumors in FFPE sections and tumor cell clusters in airway secretions were identical. These indicate that STAS may be detected in the airway secretion cytology. STAS is common in papillary or micropapillary adenocarcinoma and may spread as far as the segmental or lobar bronchus at the time of surgery.

Evaluation of grading systems in stage I lung adenocarcinomas: a retrospective cohort study

AIMS

There is no internationally accepted grading system for lung adenocarcinoma despite the new WHO classification. The architectural grade, the Kadota grade and the Sica score were evaluated and compared with overall (OS) and disease-free survival (DFS).

METHODS

Comprehensive histological subtyping was used in a series of resected stage I lung adenocarcinoma to identify subtypes of adenocarcinomas, the architectural grade, the Kadota grade, the Sica grade, the mitotic count, nuclear atypia, the presence of lymphovascular, vascular and airway propagation, necrosis, and micropapillary or solid growth pattern in any percentage. Statistical models fitted included Kaplan-Meier estimates and Cox proportional hazard regression models.

RESULTS

261 stage I adenocarcinomas were included. The 5-year survivals of different subtypes were as follows: lepidic (n=40, OS: 92.5%; DFS 91.6%), acinar (n=54, OS: 81.8%; DFS: 68.6%), papillary (n=49, OS: 73.6%; DFS: 61.0%), solid (n=95, OS: 64.7%; DFS: 57.8%) and micropapillary (n=23, OS: 34.8%; DFS: 33.5%). Concerning the architectural grade, there were significant differences between OS and DFS of low and intermediate (pOS=0.005, pDFS<0.001), low and high (pOS<0.001, pDFS<0.001) and intermediate and high grades (pOS=0.002, pDFS<0.001). Low-grade and intermediate grade tumours did not differ in survival according to Kadota grade and Sica grade. In the multivariable model, architectural grade was found to be an independent prognostic marker. In another model, architectural pattern proved to be superior to architectural grade.

CONCLUSIONS

Of the three grading systems compared, the architectural grade makes the best distinction between the outcome of low-grade, intermediate-grade and high-grade stage I adenocarcinomas.

Radiological prediction of tumor invasiveness of lung adenocarcinoma on thin-section CT

To evaluate thin-section computed tomography (CT) (TSCT) features that differentiate adenocarcinoma in situ (AIS), minimally invasive adenocarcinoma (MIA), and invasive adenocarcinoma (IVA), and to determine the size of solid portion on CT that correlates to pathological invasive components. Forty-eight patients were included. Nodules were classified into ground-glass nodule (GGN), part-solid, solid, and heterogeneous. Visual density of GGNs was subjectively evaluated using reference standard images: faint GGN (Ga), <-700 Hounsfield unit (HU); intermediate GGN (Gb), from -700 to -400 HU; dense GGN (Gc), >-400 HU; and mixed (Ga + Gb, Ga + Gc, and Gb + Gc). The evaluated TSCT findings included margin of nodule, distribution of solid portion, distribution of air bronchiologram, and pleural indentation. The longest diameters of the solid portion and the entire tumor were measured. Invasive diameters were measured in pathological specimens. Twenty-two AISs (16 GGNs [7 Ga, 5 Gb, 2 Gc, 1 Ga + Gc, 1 Gb + Gc], 4 part-solids, and 2 heterogeneous), 6 MIAs (1 GGN [Gb + Gc], 3 part-solids, and 2 solids), and 20 IVAs (1 GGN [Gb], 3 part-solids, and 16 solid) were found. The longest diameter (mean ± standard deviation) of the solid portion and total tumor were 9.7 ± 9.7 and 18.9 ± 5.6 mm, respectively. Significant differences in TSCT findings between AIS and IVA were margin of nodule (Pearson chi-squared test, P = 0.004), distribution of air bronchiologram (P = 0.0148), and pleural indentation (P = 0.0067). A solid portion >5.3 mm on TSCT indicated MIA or IVA, and >7.3 mm indicated IVA (receiver operating characteristic analysis, P < 0.0001). Irregular margin, air bronchiologram with disruption and/or irregular dilatation, and pleural indentation may distinguish IVA from AIS. A 5.3 to 7.3 mm solid portion on TSCT indicates MIA/IVA, and a solid portion >7.3 mm on TSCT indicates IVA.

The Role of Extent of Surgical Resection and Lymph Node Assessment for Clinical Stage I Pulmonary Lepidic Adenocarcinoma: An Analysis of 1991 Patients

BACKGROUND

This study examined the association of extent of lung resection, pathologic nodal evaluation, and survival for patients with clinical stage I (cT1-2N0M0) adenocarcinoma with lepidic histologic features in the National Cancer Data Base.

METHODS

The association between extent of surgical resection and long-term survival for patients in the National Cancer Data Base with clinical stage I lepidic adenocarcinoma who underwent lobectomy or sublobar resection was evaluated using Kaplan-Meier and Cox proportional hazards regression analyses.

RESULTS

Of the 1991 patients with cT1-2N0M0 lepidic adenocarcinoma who met the study criteria, 1544 underwent lobectomy and 447 underwent sublobar resection. Patients treated with sublobar resection were older, more likely to be female, and had higher Charlson/Deyo comorbidity scores, but they had smaller tumors and lower T status. Of the patients treated with lobectomy, 6% (n = 92) were upstaged because of positive nodal disease, with a median of seven lymph nodes sampled (interquartile range 4-10). In an analysis of the entire cohort, lobectomy was associated with a significant survival advantage over sublobar resection in univariate analysis (median survival 9.2 versus 7.5 years, p = 0.022, 5-year survival 70.5% versus 67.8%) and after multivariable adjustment (hazard ratio = 0.81, 95% confidence interval: 0.68-0.95, p = 0.011). However, lobectomy was no longer independently associated with improved survival when compared with sublobar resection (hazard ratio = 0.99, 95% confidence interval: 0.77-1.27, p = 0.905) in a multivariable analysis of a subset of patients in which only those patients who had undergone a sublobar resection including lymph node sampling were compared with patients treated with lobectomy.

CONCLUSIONS

Surgeons treating patients with stage I lung adenocarcinoma with lepidic features should cautiously utilize sublobar resection rather than lobectomy, and they must always perform adequate pathologic lymph node evaluation.

Comprehensive study of mutational and clinicopathologic characteristics of adenocarcinoma with lepidic pattern in surgical resected lung adenocarcinoma

PURPOSE

Although many studies have explored clinicopathologic characteristics and prognosis of lung adenocarcinoma, a few literatures reported the mutational status of lung adenocarcinomas with lepidic pattern and whether there is difference between adenocarcinomas with pure lepidic component and lepidic predominant adenocarcinomas remain unknown.

METHODS

One hundred and thirty-three patients including 92 adenocarcinomas with pure lepidic component and 41 lepidic predominant adenocarcinomas were subjected to the study. All the clinicopathologic data, the follow-up information and the status of gene mutations including EGFR, KRAS, HER2, BRAF, AKT1, ALK, RET and ROS1 were investigated.

RESULTS

Of the 133 lung adenocarcinomas with lepidic pattern, 87.22 % (116/133) were detected harboring mutations in our tested genes, among which 90.52 % (105/116) harbored EGFR mutation. There are three KRAS mutations and two BRAF mutations in our cohort, and we revealed two ALK fusion and one RET fusion. No ROS1 fusion was discovered. There was no significant difference in gene mutations between adenocarcinomas with pure lepidic component and lepidic predominant adenocarcinomas except EGFR mutation (p = 0.039). Lepidic predominant adenocarcinomas seemed to have more EGFR mutation. The post-recurrence survival was significantly prolonged in patients who received TKIs.

CONCLUSIONS

Adenocarcinoma with lepidic pattern is a low-grade lung tumor with favorable prognosis and displays frequent EGFR mutation. Compared with lepidic predominant adenocarcinomas, lung adenocarcinomas with pure lepidic component have a better prognosis. On the basis of these results, we also suggested the application of EGFR-TKIs therapy for EGFR mutation-positive patients after recurrence could achieve prolonged survival.

Lung adenocarcinomas: correlation of computed tomography and pathology findings

Adenocarcinoma is the most common histologic type of lung cancer. Recent lung adenocarcinoma classifications from the International Association for the Study of Lung cancer, the American Thoracic Society and the European Respiratory Society (IASLC/ETS/ERS, 2011) and World Health Organization (WHO, 2015) define a wide range of adenocarcinoma types and subtypes featuring different prognosis and management. This spectrum of lesions translates into various CT presentations and features, which generally show good correlation with histopathology, stressing the key role of the radiologist in the diagnosis and management of those patients. This review aims at helping radiologists to understand the basics of the up-to-date adenocarcinoma pathological classifications, radio-pathological correlations and how to use them in the clinical setting, as well as other imaging-related correlations (radiogenomics, quantitative analysis, PET-CT).

Surgical pathology of early stage non-small cell lung carcinoma

The histologic classification of non-small cell lung carcinoma (NSCLC), particularly adenocarcinoma (ADC), has undergone extensive study in recent decades, ultimately resulting in an extensively updated classification system. The 2015 World Health Organization (WHO) classification of ADC provides greatly improved prognostic information in comparison to the 2004 WHO classification. Several issues still require further investigation: lepidic predominant ADC, prognostic significance of poor prognostic subtypes such as micropapillary carcinoma, the more recently described concept of spread of tumor through airspaces (STAS), and the utility of sublobar resections. While limited resection appears to be suitable for tumors with a ground glass radiographic appearance, which typically correspond to adenocarcinoma in situ (MIS) or minimally invasive adenocarcinoma (MIA) histologically, the role of sublobar resection in radiographic solid tumors is not as clear, and the impact of histologic subtypes with a poor prognosis needs further evaluation. Squamous cell carcinoma (SCC) has not been as extensively studied and the current classification lacks subclassification with significant prognostic information.

Persistent Pure Ground-Glass Nodules Larger Than 5 mm: Differentiation of Invasive Pulmonary Adenocarcinomas From Preinvasive Lesions or Minimally Invasive Adenocarcinomas Using Texture Analysis

OBJECTIVE

To evaluate the differentiating potentials of computed tomography texture analysis for invasive pulmonary adenocarcinomas (IPAs) from their preinvasive lesions or minimally invasive adenocarcinomas (MIAs) manifesting as persistent pure ground-glass nodules (PGGNs) larger than 5 mm.

MATERIALS AND METHODS

This institutional review board-approved retrospective study included 63 patients (23 men and 40 women) with 66 PGGNs larger than 5 mm on unenhanced computed tomography from 2005 to 2013. All PGGNs were pathologically confirmed and categorized into 2 groups [IPAs (n = 11) vs preinvasive lesions (n = 41)/MIAs (n = 14)]. Each PGGN was segmented manually, and their texture features were quantitatively extracted. To identify significant differentiating factors of IPAs from preinvasive lesions/MIAs, multivariate logistic regression and C-statistic analyses were performed.

RESULTS

Between IPAs and preinvasive lesions/MIAs, nodule size, volume, mass, entropy, effective diameter, and surface area were significantly different (P < 0.05), and homogeneity and gray level co-occurrence matrix inverse difference moment showed marginal significance (P < 0.10). Subsequent multivariate analysis revealed larger nodule mass [adjusted odds ratio (OR), 11.92], higher entropy (adjusted OR, 35.12), and lower homogeneity (adjusted OR, 0.278 × 10) as independent differentiating factors of IPAs. Subgroup analysis showed that larger nodule mass, higher entropy, and lower homogeneity were also significant differentiating variables of IPAs in nodules of diameter 10 mm or larger. A multiple logistic regression model using these features showed excellent [area under the curve (AUC), 0.962] and significantly higher differentiating performance compared to nodule size (AUC, 0.712) or mass (AUC, 0.788) alone.

CONCLUSION

Computed tomography texture features such as higher entropy and lower homogeneity were significant differentiating factors of IPAs presenting as PGGNs larger than 5 mm and have potentials to enhance the differentiating performance.

High expression of Y-box-binding protein 1 correlates with poor prognosis and early recurrence in patients with small invasive lung adenocarcinoma

Background

Prognosis of small (≤2 cm) invasive lung adenocarcinoma remains poor, and identification of high-risk individuals from the patients after complete surgical resection of lung adenocarcinoma has become an urgent problem. YBX1 has been reported to be able to predict prognosis in many cancers (except lung adenocarcinoma) that are independent of TNM (tumor, nodes, metastases) staging, especially small invasive lung adenocarcinoma. Therefore, we examined the significance of YBX1 expression on prognosis and recurrence in patients with small invasive lung adenocarcinoma.

Material and methods

A total of 75 patients with small invasive lung adenocarcinoma after complete resection were enrolled from January 2008 to December 2010. Immunohistochemical staining was used to detect the expression of YBX1, and receiver operating characteristic curve analysis was performed to precisely assess the overall expression of YBX1. Meanwhile, primary lesions were identified based on the International Association for the Study of Lung Cancer, the American Thoracic Society, and the European Respiratory Society’s classification of lung adenocarcinoma. The effect of different clinicopathological factors on patients’ survival was examined. Furthermore, Western blot analysis was used to show the expression of YBX1 in vitro.

Results

Sensitivity and specificity of YBX1 for detecting small invasive lung adenocarcinoma from normal surrounding tissue were 66.7% and 74.7% (area under the receiver operating characteristic curve =0.731; P<0.001), respectively. High YBX1 expression was detected in 31 (41.3%) patients, and in A549, H322, Hcc827, and H1299 lung adenocarcinoma cells but not in HLF cells. In addition to sex, age, tumor size, TNM staging, pleural invasion, and lymph node metastasis, the expression of YBX1 was associated with the International Association for the Study of Lung Cancer, the American Thoracic Society, and the European Respiratory Society pathological grade risk (P=0.026) and differentiation (P=0.009). The patients with low YBX1 expression lived longer than those with high expression (5-year overall survival: 52.3% vs 79.0%; P=0.039) and showed fewer recurrences (P=0.024). In multivariate analyses, high YBX1 expression (odds ratio =2.737; 95% confidence interval: 1.058–7.082; P=0.038) was shown as an independent risk factor of overall survival but not of disease-free survival (odds ratio =1.696; 95% confidence interval: 0.616–4.673; P=0.307).

Conclusion

YBX1 is an important predictor for the prognosis in patients with small invasive lung adenocarcinoma after complete resection.

Comparison of outcomes for patients with lepidic pulmonary adenocarcinoma defined by 2 staging systems: A North American experience

OBJECTIVE

Application of the International Association for the Study of Lung Cancer/American Thoracic Society/European Respiratory Society (IASLC/ATS/ERS) classification of lepidic adenocarcinomas in conjunction with American Joint Committee on Cancer (AJCC) staging has been challenging. We aimed to compare IASLC/ATS/ERS and AJCC classifications, to determine if they could be integrated as a single staging system.

METHODS

We reviewed patients from 2001-2013 who had AJCC stage I lepidic adenocarcinomas, and categorized them according to IASLC/ATS/ERS guidelines: adenocarcinoma in situ (AIS); minimally invasive adenocarcinoma (MIA); or invasive adenocarcinoma (IA). We integrated the 2 classification systems by separating AIS and MIA as being stage 0, and routinely classifying IA as stage I.

RESULTS

Median follow-up was 52 months in 138 patients. The IASLC/ATS/ERS classification demonstrated a higher disease-free survival (DFS) in AIS (100%) and MIA (96%) versus IA (80%) (P = .022), and higher overall survival (OS): 100% for AIS and MIA, versus 90% for IA (P = .049). The AJCC classification identified a DFS of 87% and an OS of 94% for stage I patients. Integration of the 2 systems demonstrated higher DFS in stage 0 (98%) versus I (80%) (P = .006), and higher OS: 100% for stage 0 versus 90% for stage I (P = .014).

CONCLUSIONS

The IASLC/ATS/ERS classification better discriminates AIS and MIA compared with current AJCC staging; however, integration suggests that these categories may be collectively classified in AJCC staging, based on similarly favorable outcomes and distinctive survival rates.

Prognosis and status of lymph node involvement in patients with adenocarcinoma in situ and minimally invasive adenocarcinoma-a systematic literature review and pooled-data analysis

BACKGROUND

Adenocarcinoma in situ (AIS) and minimally invasive adenocarcinoma (MIA) have been brought up that substitute for bronchioloalveolar carcinoma (BAC), according to the new classification of lung adenocarcinoma. There has been increasing opinions that argues for the adjustment of lymph node disposition in patients with such early stage tumors. Therefore, we sought to overview the prognosis and status of lymph node involvement in AIS/MIA patients.

METHODS

PubMed, Springer and Ovid databases were searched for relevant studies. Data was extracted and results summarized to demonstrate the disposition of lymph nodes in AIS/MIA.

RESULTS

Twenty-three studies consisting of 6,137 lung adenocarcinoma were included. AIS/MIA accounted for 821 of the total 6,137. All included patients received curative surgery. After a review of the summarized data we found that only one patient (with MIA) had N1 node metastasis, N2 disease was not found in any of the included patients. In concordance with this, studies that reported 5-year disease free survival (5-year DFS) have almost 100% rate.

CONCLUSIONS

Our findings indicated that patients with AIS/MIA have good survival prognosis after surgical resection, and that recurrence and lymph node metastasis in these patients is rare. Therefore, we strongly encouraged further studies to determine the role of different lymph node disposition strategies.

Expression and prognostic value of GalNAc-T3 in patients with completely resected small (≤2 cm) peripheral lung adenocarcinoma after IASLC/ATS/ERS classification

Background

GalNAc-T3 catalyzes initial glycosylation of mucin-type O-linked protein involved in proliferation, adhesion, and migration of tumor cells. This study was performed to explore the relationships of the expression of GalNAc-T3 in small peripheral lung adenocarcinoma, especially as an indicator of prognosis.

Materials and methods

A retrospective analysis of the patients with small peripheral lung lesions, including 106 adenocarcinoma and two precancerous lesions (atypical adenomatous hyperplasia and adenocarcinoma in situ) after complete surgical resection, was launched. Expression of GalNAc-T3 was examined using immunohistochemistry staining on primary tumor specimens, and the tumors were reclassified in light of the IASLC/ATS/ERS (International Association for the Study of Lung Cancer/American Thoracic Society/European Respiratory Society) adenocarcinoma classifications followed by grading and scoring. Moreover, reverse transcription polymerase chain reaction and Western blot were used to study the expression of GalNAc-T3 in vivo.

Results

The low expression of GalNAc-T3 was found in the cytoplasm of tumor cells in 56 of 108 patients (51.9%) and was associated with IASLC/ATS/ERS classification of high risk groups (P=0.007), high Sica score (P=0.036), poorly differentiated tumor (P=0.023), poor tumor-node-metastasis (TNM) stage (P=0.007), pleural invasion (P=0.007), and vascular invasion (P<0.001) by Pearson’s chi-squared test, but not with sex, age, smoking status, concentration of carcinoembryonic antigen, and lymph node metastasis. In logistic regression analysis, low GalNAc-T3 expression was only correlated with high-ranking TNM stage (odds ratio [OR] =8.975, 95% confidence interval [CI]: 1.797–44.661), vascular invasion (OR =5.668, 95% CI: 1.827–17.578), and the higher risk grade (low risk grade: OR =0.141, 95% CI: 0.027–0.719; moderate risk grade: OR =0.122, 95% CI: 0.017–40.871). The low expression of the GalNAc-T3 usually in adenocarcinoma cell lines was compared with normal bronchial epithelium cell line. Based on the univariate and multivariate analysis, poor TNM stage (P<0.001), pleural invasion (hazard ratio [HR]: 7.958, P=0.021), vascular invasion (HR: 2.403, P=0.040), and low GalNAc-T3 expression (HR: 3.317, P=0.016) were shown to be independently associated with an unfavorable prognosis. However, IASLC/ATS/ERS classification of risk groups and Sica score (P=0.034 and P=0.032, respectively) was correlated with overall survival on Kaplan–Meier method but not Cox regression model.

Conclusion

GalNAc-T3 expression was correlated with the IASLC/ATS/ERS classification and also associated with prognosis of patients with completely resected small (≤2 cm) peripheral lung adenocarcinoma.