New IASLC/ATS/ERS classification and invasive tumor size are predictive of disease recurrence in stage I lung adenocarcinoma

Diameter thresholds for pure ground-glass pulmonary nodules at low-dose CT screening: Chinese experience

BACKGROUND

Limited research exists on screening thresholds for low-dose CT in detecting malignant pure ground-glass lung nodules (pGGNs) in the Chinese population.

MATERIALS AND METHODS

A retrospective analysis of the Guangzhou Lung-Care programme was conducted, retrieving average transverse diameter, location, histopathology, frequency and follow-up intervals. Diagnostic performances for 'lung cancers' were evaluated using areas under the curve (AUCs), decision curve analysis (DCA), sensitivities and specificities, with thresholds ranging from 5 mm to 10 mm. We divide malignant pGGNs into three groups: (1) minimally invasive adenocarcinoma (MIA) and invasive adenocarcinoma (IA), (2) atypical adenomatous hyperplasia (AAH) and adenocarcinoma in situ (AIS) and MIA and IA and (3) IA-only.

RESULTS

In 'MIA+IA', increasing the threshold from 5 mm to 8 mm improved specificity (60.97% to 88.85%, p<0.001) and positive predictive values (PPVs; 5.87% to 14.88%, p<0.001), but decreased sensitivity (94.44% to 75.56%, p<0.001). Further raising threshold from 8 mm reduced sensitivity (75.56% to 60.00%, p<0.001), while slightly increasing specificity (88.85% to 93.47%, p<0.001) and PPVs (14.88% to 19.15%, p<0.001). Increasing threshold from 5 mm to 7 mm enhanced the AUC for 'MIA+IA' (from 0.711 to 0.829), 'AAH+AIS+MIA+IA' (from 0.748 to 0.804) and 'IA-only' (from 0.783 to 0.833). At 8 mm, the AUCs for these categories were similar. However, increasing the threshold from 7 mm to 10 mm resulted in reduced AUCs for 'MIA+IA' (0.829 to 0.767), 'AAH+AIS+MIA+IA' (0.804 to 0.744) and 'IA-only' (0.833 to 0.800). DCA reveals that the 8 mm predictive model demonstrates greater clinical utility compared with models with other thresholds.

CONCLUSIONS

Increasing the diameter threshold for positive results for pGGNs, up to 8 mm could enhance diagnostic performance.

TRIAL REGISTRATION NUMBER

NCT04938804.

Multimodal integration to identify the invasion status of lung adenocarcinoma intraoperatively

Evaluating the invasiveness of lung adenocarcinoma is crucial for determining the appropriate surgical strategy, impacting postoperative outcomes. This study developed a multimodality model combining radiomics, intraoperative frozen section (FS) pathology, and clinical indicators to predict invasion status. The study enrolled 1,424 patients from two hospitals, divided into multimodal training, radiology testing, and pathology testing cohorts. A prospective validation cohort of 114 patients was selected between March and May 2023. The radiomics + pathology + clinical indicators multimodality model (multi-RPC model) achieved an area under the curve (AUC) of 0.921 (95% confidence interval [CI] 0.899-0.939) in the multimodal training cohort and 0.939 (95% CI 0.878-0.975) in the validation cohort, outperforming single- and dual-modality models. The multi-RPC model's predictive accuracy of 0.860 (95% CI 0.782-0.918) suggests that it could significantly reduce inappropriate surgical procedures, enhancing precision oncology by integrating multimodal information to guide surgical decisions.

CT-based radiomics combined with clinical features for invasiveness prediction and pathological subtypes classification of subsolid pulmonary nodules

Purpose

To construct optimal models for predicting the invasiveness and pathological subtypes of subsolid nodules (SSNs) based on CT radiomics and clinical features.

Materials and Methods

This study was a retrospective study involving two centers. A total of 316 patients with 353 SSNs confirmed as atypical adenomatous hyperplasia (AAH), adenocarcinoma in situ (AIS), minimally invasive adenocarcinoma (MIA) and invasive adenocarcinoma (IAC) were included from January 2019 to February 2023. Models based on CT radiomics and clinical features were constructed for classification of AAH/AIS and MIA, MIA and IAC, as well as lepidic-predominant adenocarcinoma (LPA) and acinar-predominant adenocarcinoma (APA). Receiver operating characteristic (ROC) curve was used to evaluate the model performance. Finally, the nomograms based on the optimal models were established.

Results

The nomogram based on the combined model (AAH/AIS versus MIA) consisting of lobulation, the GGN-vessel relationship, diameter, CT value, consolidation tumor ratio (CTR) and rad-score performed the best (AUC=0.841), while age, CT value, CTR and rad-score were the significant features for distinguishing MIA from IAC, the nomogram based on these features performed the best (AUC=0.878). There were no significant differences in clinical features between LPA and APA, while the radiomics model based on rad-score showed good performance for distinguishing LPA from APA (AUC=0.926).

Conclusions

The nomograms based on radiomics and clinical features could predict the invasiveness of SSNs accurately. Moreover, radiomics models showed good performance in distinguishing LPA from APA.

Habitat Imaging With Tumoral and Peritumoral Radiomics for Prediction of Lung Adenocarcinoma Invasiveness on Preoperative Chest CT: A Multicenter Study

BACKGROUND. Tumor growth processes result in spatial heterogeneity, with the development of tumor subregions (i.e., habitats) having unique biologic characteristics. OBJECTIVE. The purpose of our study was to develop and validate a habitat model combining tumor and peritumoral radiomic features on chest CT for predicting invasiveness of lung adenocarcinoma. METHODS. This retrospective study included 1156 patients (mean age, 57.5 years; 464 men, 692 women), from three centers and a public dataset, who underwent chest CT before lung adenocarcinoma resection (variable date ranges across datasets). Patients from one center formed training (n = 500) and validation (n = 215) sets; patients from the other sources formed three external test sets (n = 249, 113, 79). For each patient, a single nodule was manually segmented on chest CT. The nodule segmentation was combined with an automatically generated 4-mm peritumoral region into a whole-volume volume of interest (VOI). A gaussian mixture model (GMM) identified voxel clusters with similar first-order energy across patients. GMM results were used to divide each patient's whole-volume VOI into multiple habitats, which were defined consistently across patients. Radiomic features were extracted from each habitat. After feature selection, a habitat model was developed for predicting invasiveness, with the use of pathologic assessment as a reference. An integrated model was constructed, combining features extracted from habitats and whole-volume VOIs. Model performance was evaluated, including in subgroups based on nodule density (pure ground-glass, part-solid, and solid). The code for habitat imaging and model construction is publicly available (https://github.com/Shangyoulan/Habitat/). RESULTS. Invasive cancer was diagnosed in 626 of 1156 patients. GMM identified four as the optimal number of voxel clusters and thus of per-patient tumor habitats. The habitat model had an AUC of 0.932 in the validation set and 0.881, 0.880, and 0.764 in the three external test sets. The integrated model had an AUC of 0.947 in the validation set and 0.936, 0.908, and 0.800 in the three external test sets. In the three external test sets combined, across nodule densities, AUCs for the habitat model were 0.836-0.869 and for the integrated model were 0.846-0.917. CONCLUSION. Habitat imaging combining tumoral and peritumoral radiomic features could help predict lung adenocarcinoma invasiveness. Prediction is improved when combining information on tumor subregions and the tumor overall. CLINICAL IMPACT. The findings may aid personalized preoperative assessments to guide clinical decision-making in lung adenocarcinoma.

Predicting invasion in early-stage ground-glass opacity pulmonary adenocarcinoma: a radiomics-based machine learning approach

BACKGROUND

To design a pulmonary ground-glass nodules (GGN) classification method based on computed tomography (CT) radiomics and machine learning for prediction of invasion in early-stage ground-glass opacity (GGO) pulmonary adenocarcinoma.

METHODS

This retrospective study included pulmonary GGN patients who were histologically confirmed to have adenocarcinoma in situ (AIS), minimally invasive adenocarcinoma (MIA), or invasive adenocarcinoma cancer (IAC) from 2020 to 2023. CT images of all patients were automatically segmented and 107 radiomic features were obtained for each patient. Classification models were developed using random forest (RF) and cross-validation, including three one-versus-others models and one three-class model. For each model, features were ranked by normalized Gini importance, and a minimal subset was selected with a cumulative importance exceeding 0.9. These selected features were then used to train the final models. The models' performance metrics, including area under the curve (AUC), accuracy, sensitivity, and specificity, were computed. AUC and accuracy were compared to determine the final optimal method.

RESULTS

The study comprised 193 patients (mean age 54 ± 11 years, 65 men), including 65 AIS, 54 MIA, and 74 IAC, divided into one training cohort (N = 154) and one test cohort (N = 39). The final three-class RF model outperformed three individual one-versus-others models in distinguishing each class from the other two. For the multiclass classification model, the AUC, accuracy, sensitivity, and specificity were 0.87, 0.79, 0.62, and 0.88 for AIS; 0.90, 0.79, 0.54, and 0.89 for MIA; and 0.87, 0.69, 0.73, and 0.67 for IAC, respectively.

CONCLUSIONS

A radiomics-based multiclass RF model could effectively differentiate three types of pulmonary GGN, which enabled early diagnosis of GGO pulmonary adenocarcinoma.

Establishment of artificial intelligence model for precise histological subtyping of lung adenocarcinoma and its application to quantitative and spatial analysis

BACKGROUND

The histological subtype of lung adenocarcinoma is a major prognostic factor. We developed a new artificial intelligence model to classify lung adenocarcinoma images into seven histological subtypes and adopted the model for whole-slide images to investigate the relationship between the distribution of histological subtypes and clinicopathological factors.

METHODS

Using histological subtype images, which are typical for pathologists, we trained and validated an artificial intelligence model. Then, the model was applied to whole-slide images of resected lung adenocarcinoma specimens from 147 cases.

RESULT

The model achieved an accuracy of 99.7% in training sets and 90.4% in validation sets consisting of typical tiles of histological subtyping for pathologists. When the model was applied to whole-slide images, the predominant subtype according to the artificial intelligence model classification matched that determined by pathologists in 75.5% of cases. The predominant subtype and tumor grade (using the WHO fourth and fifth classifications) determined by the artificial intelligence model resulted in similar recurrence-free survival curves to those determined by pathologists. Furthermore, we stratified the recurrence-free survival curves for patients with different proportions of high-grade components (solid, micropapillary and cribriform) according to the physical distribution of the high-grade component. The results suggested that tumors with centrally located high-grade components had a higher malignant potential (P < 0.001 for 5-20% high-grade component).

CONCLUSION

The new artificial intelligence model for histological subtyping of lung adenocarcinoma achieved high accuracy, and subtype quantification and subtype distribution analyses could be achieved. Artificial intelligence model therefore has potential for clinical application for both quantification and spatial analysis.

Predicting histopathological features of aggressiveness in lung cancer using CT radiomics: a systematic review

PURPOSE

To examine the accuracy of CT radiomics to predict histopathological features of aggressiveness in lung cancer using a systematic review of test accuracy studies.

METHODS

Data sources searched included Medline, Embase, Web of Science, and Cochrane Library from up to 3 November 2023. Included studies reported test accuracy of CT radiomics models to detect the presence of: spread through air spaces (STAS), predominant adenocarcinoma pattern, adenocarcinoma grade, lymphovascular invasion (LVI), tumour infiltrating lymphocytes (TIL) and tumour necrosis, in patients with lung cancer. The primary outcome was test accuracy. Two reviewers independently assessed articles for inclusion and assessed methodological quality using the QUality Assessment of Diagnostic Accuracy Studies-2 tool. A single reviewer extracted data, which was checked by a second reviewer. Narrative data synthesis was performed.

RESULTS

Eleven studies were included in the final analysis. 10/11 studies were in East Asian populations. 4/11 studies investigated STAS, 6/11 investigated adenocarcinoma invasiveness or growth pattern, and 1/11 investigated LVI. No studies investigating TIL or tumour necrosis met inclusion criteria. Studies were of generally mixed to poor methodological quality. Reported accuracies for radiomic models ranged from 0.67 to 0.94.

CONCLUSION

Due to the high risk of bias and concerns regarding applicability, the evidence is inconclusive as to whether radiomic features can accurately predict prognostically important histopathological features of cancer aggressiveness. Many studies were excluded due to lack of external validation. Rigorously conducted prospective studies with sufficient external validity will be required for radiomic models to play a role in improving lung cancer outcomes.

Deep learning-based detection and semi-quantitative model for spread through air spaces (STAS) in lung adenocarcinoma

Tumor spread through air spaces (STAS) is a distinctive metastatic pattern affecting prognosis in lung adenocarcinoma (LUAD) patients. Several challenges are associated with STAS detection, including misdetection, low interobserver agreement, and lack of quantitative analysis. In this research, a total of 489 digital whole slide images (WSIs) were collected. The deep learning-based STAS detection model, named STASNet, was constructed to calculate semi-quantitative parameters associated with STAS density and distance. STASNet demonstrated an accuracy of 0.93 for STAS detection at the tiles level and had an AUC of 0.72-0.78 for determining the STAS status at the WSI level. Among the semi-quantitative parameters, T10S, combined with the spatial location information, significantly stratified stage I LUAD patients on disease-free survival. Additionally, STASNet was deployed into a real-time pathological diagnostic environment, which boosted the STAS detection rate and led to the identification of three easily misidentified types of occult STAS.

Micropapillary Pattern in Invasive Mucinous Adenocarcinoma of the Lung: Comparison With Invasive Non-Mucinous Adenocarcinoma

Background. The presence of a micropapillary pattern is associated with poor outcomes in lung adenocarcinoma. This study aimed to assess the clinicopathological features of micropapillary pattern in mucinous adenocarcinoma of the lung. Methods. The patients were stratified into three groups: the invasive mucinous adenocarcinoma group (60 patients), the mixed invasive mucinous adenocarcinoma group (33 patients), and the invasive non-mucinous adenocarcinoma group (237 patients). The presence of micropapillary pattern and its clinicopathological features were analyzed and compared between the three groups. Results. Compared to mixed invasive mucinous adenocarcinoma, invasive mucinous adenocarcinoma had lower frequencies of micropapillary pattern (28.3% vs 87.9%, respectively; P < .001) and lymph node metastasis (00.0% vs 15.1%, respectively; P = .005). The frequency of tumor spread through air space (STAS) in invasive mucinous adenocarcinoma (23.3%) was higher than that in invasive non-mucinous adenocarcinoma (6.3%; P < .001), while lower than that in mixed invasive mucinous adenocarcinoma (60.6%; P < .001). When the three groups were all accompanied by micropapillary pattern, there was no obvious difference in STAS between invasive mucinous adenocarcinoma with micropapillary pattern and mixed mucinous adenocarcinoma with micropapillary pattern (P > .05). No filigree pattern occurred in invasive mucinous adenocarcinoma with micropapillary pattern. Conclusions. The micropapillary pattern is frequently observed in invasive mucinous adenocarcinoma and has a better prognosis compared to mixed invasive mucinous adenocarcinoma and invasive non-mucinous adenocarcinoma. However, the malignancy of the micropapillary pattern in mixed mucinous adenocarcinoma was similar to that in invasive non-mucinous adenocarcinoma, even with the presence of mucus. These findings suggest that the development mechanisms of the micropapillary pattern in invasive mucinous adenocarcinoma and mixed mucinous adenocarcinoma may differ.

A combination of radiomic features, clinic characteristics, and serum tumor biomarkers to predict the possibility of the micropapillary/solid component of lung adenocarcinoma

BACKGROUND

Invasive lung adenocarcinoma with MPP/SOL components has a poor prognosis and often shows a tendency to recurrence and metastasis. This poor prognosis may require adjustment of treatment strategies. Preoperative identification is essential for decision-making for subsequent treatment.

OBJECTIVE

This study aimed to preoperatively predict the probability of MPP/SOL components in lung adenocarcinomas by a comprehensive model that includes radiomics features, clinical characteristics, and serum tumor biomarkers.

DESIGN

A retrospective case control, diagnostic accuracy study.

METHODS

This study retrospectively recruited 273 patients (males: females, 130: 143; mean age ± standard deviation, 63.29 ± 10.03 years; range 21-83 years) who underwent resection of invasive lung adenocarcinoma. Sixty-one patients (22.3%) were diagnosed with lung adenocarcinoma with MPP/SOL components. Radiomic features were extracted from CT before surgery. Clinical, radiomic, and combined models were developed using the logistic regression algorithm. The clinical and radiomic signatures were integrated into a nomogram. The diagnostic performance of the models was evaluated using the area under the curve (AUC). Studies were scored according to the Radiomics Quality Score and Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis guidelines.

RESULTS

The radiomics model achieved the best AUC values of 0.858 and 0.822 in the training and test cohort, respectively. Tumor size (T_size), solid tumor size (ST_size), consolidation-to-tumor ratio (CTR), years of smoking, CYFRA 21-1, and squamous cell carcinoma antigen were used to construct the clinical model. The clinical model achieved AUC values of 0.741 and 0.705 in the training and test cohort, respectively. The nomogram showed higher AUCs of 0.894 and 0.843 in the training and test cohort, respectively.

CONCLUSION

This study has developed and validated a combined nomogram, a visual tool that integrates CT radiomics features with clinical indicators and serum tumor biomarkers. This innovative model facilitates the differentiation of micropapillary or solid components within lung adenocarcinoma and achieves a higher AUC, indicating superior predictive accuracy.

Computed tomography imaging and clinical characteristics of pulmonary ground-glass nodules ≤2 cm with micropapillary pattern

BACKGROUND

The aim of this study was to investigate the imaging features, lymph node metastasis, and genetic mutations in micropapillary lung adenocarcinoma (imaging with mixed ground-glass nodules) ≤2 cm, to provide a more precise and refined basis for the selection of lung segment resection.

METHODS

A retrospective analysis of 162 patients with surgically resected pathologically confirmed cancers ≤2.0 cm in diameter (50 cases of micropapillary mixed ground-glass nodules [mGGNs], 50 cases of nonmicropapillary mGGNs, and 62 cases of micropapillary SNs [solid nodules]) was performed. mGGNs were classified into five categories according to imaging features. The distribution of these five morphologies in micropapillary with mGGN and nonmicropapillary with mGGN was analyzed. The postoperative pathology and prognosis of lymph node metastasis were also compared between micropapillary mGGNs and micropapillary with SNs. After searching the TCGA database, we demonstrated heterogeneity, high malignancy and high risk of microcapillary lung cancer cancers.

RESULTS

Different pathological subtypes of mGGN differed in morphological features (p < 0.05). The rate of lymph node metastasis was significantly higher in micropapillary mGGNs than in nonmicropapillary mGGNs. In the TCGA database samples, lactate transmembrane protein activity, collagen transcription score, and fibroblast EMT score were remarkably higher in micropapillary adenocarcinoma. Other pathological subtypes had a better survival prognosis and longer disease-free survival compared with micropapillary adenocarcinoma.

CONCLUSION

mGGNs ≤2 cm with a micropapillary pattern have a higher risk of lymph node metastasis compared with SNs, and computed tomography (CT) imaging features can assist in their diagnosis.

Development of the semi-dry dot-blot method for intraoperative detecting micropapillary component in lung adenocarcinoma based on proteomics analysis

BACKGROUND

Micropapillary (MIP) component was a major concern in determining surgical strategy in lung adenocarcinoma (LUAD). We sought to develop a novel method for detecting MIP component during surgery.

METHODS

Differentially expressed proteins between MIP-positive and MIP-negative LUAD were identified through proteomics analysis. The semi-dry dot-blot (SDB) method which visualises the targeted protein was developed to detect MIP component.

RESULTS

Cellular retinoic acid-binding protein 2 (CRABP2) was significantly upregulated in MIP-positive LUAD (P < 0.001), and the high CRABP2 expression zone showed spatial consistency with MIP component. CRABP2 expression was also associated with decreased recurrence-free survival (P < 0.001). In the prospective cohort, the accuracy and sensitivity of detecting MIP component using SDB method by visualising CRABP2 were 82.2% and 72.7%, which were comparable to these of pathologist. Pathologist with the aid of SDB method would improve greatly in diagnostic accuracy (86.4%) and sensitivity (78.2%). In patients with minor MIP component (≤5%), the sensitivity of SDB method (63.6%) was significantly higher than pathologist (45.4%).

CONCLUSIONS

Intraoperative examination of CRABP2 using SDB method to detect MIP component reached comparable performance to pathologist, and SDB method had notable superiority than pathologist in detecting minor MIP component.

Thin-slice computed tomography enables to classify pulmonary subsolid nodules into pre-invasive lesion/minimally invasive adenocarcinoma and invasive adenocarcinoma: a retrospective study

The aim was to investigate the ability of thin-slice computed tomography (TSCT) to differentiate invasive pulmonary adenocarcinomas (IACs) from pre-invasive/minimally invasive adenocarcinoma (AAH-MIAs), manifesting as subsolid nodules (SSNs) of diameter less than 30 mm. The CT findings of 810 patients with single subsolid nodules diagnosed by pathology of resection specimens were analyzed (atypical adenomatous hyperplasia, n = 13; adenocarcinoma in situ, n = 175; minimally invasive adenocarcinoma, n = 285; and invasive adenocarcinoma, n = 337). According to the classification of lung adenocarcinoma published by WHO classification of thoracic tumors in 2015, TSCT features of 368 pure ground-glass nodules (pGGN) and 442 part-solid nodules (PSNs) were compared AAH-MIAs with IACs. Logistic regression and receiver operating characteristic (ROC) curve analyses were performed. In pGGNs, multivariate analysis of factors found to be significant by univariate analysis revealed that higher mean-CT values (p = 0.006, OR 1.006, 95% CI 1.002-1.010), larger tumor size (p < 0.001, OR 1.483, 95% CI 1.304-1.688) with air bronchogram and non-smooth margins were significantly associated with IACs. The optimal cut-off tumor diameter for AAH-MIAs lesions was less than 10.75 mm (sensitivity, 82.8%; specificity, 80.6%) and optimal cut-off mean-CT value - 629HU (sensitivity, 78.1%; specificity, 50.7%). In PSNs, multivariate analysis of factors found to be significant by univariate analysis revealed that smaller tumor diameter (p < 0.001, OR 0.647, 95% CI 0.481-0.871), smaller size of solid component (p = 0.001, OR 83.175, 95% CI 16.748-413.079),and lower mean-CT value of solid component (p < 0.001, OR 1.009, 95% CI 1.004-1.014) were significantly associated with AAH-MIAs (p < 0.05). The optimal cut-off tumor diameter, size of solid component, and mean-CT value of solid component for AAH-MIAs lesions were less than 14.595 mm (sensitivity, 71.1%; specificity, 83.4%), 4.995 mm (sensitivity, 97.8%; specificity, 92.3%) and - 227HU (sensitivity, 65.6%; specificity, 76.3%), respectively. In subsolid nodules, whether pGGN or PSNs, the characteristics of TSCT can help in distinguishing IACs from AAH-MIAs.

Frozen sections accurately predict the IASLC proposed grading system and prognosis in patients with invasive lung adenocarcinomas

INTRODUCTION

The International Association for the Study of Lung Cancer (IASLC) newly proposed grading system for lung adenocarcinomas (ADC) has been shown to be of prognostic significance. Hence, intraoperative consultation for the grading system was important regarding the surgical decision-making. Here, we evaluated the accuracy and interobserver agreement for IASLC grading system on frozen section (FS), and further investigated the prognostic performance.

METHODS

FS and final pathology (FP) slides were reviewed by three pathologists for tumor grading in 373 stage I lung ADC following surgical resection from January to June 2013 (retrospective cohort). A prospective multicenter cohort (January to June 2021, n = 212) were included to confirm the results.

RESULTS

The overall concordance rates between FS and FP were 79.1% (κ = 0.650) and 89.6% (κ = 0.729) with substantial agreement in retrospective and prospective cohorts, respectively. Presence of complex gland was the only independent predictor of discrepancy between FS and FP (presence versus. absence: odds ratio, 2.193; P = 0.015). The interobserver agreement for IASLC grading system on FS among three pathologists were satisfactory (κ = 0.672 for retrospective cohort; κ = 0.752 for prospective cohort). Moreover, the IASLC grading system by FS diagnosis could well predict recurrence-free survival and overall survival for patients with stage I invasive lung ADC.

CONCLUSIONS

Our results suggest that FS had high diagnostic accuracy and satisfactory interobserver agreement for IASLC grading system. Future prospective studies are merited to validate the feasibility of using FS to match patients into appropriate surgical type.

Challenges of the eighth edition of the American Joint Committee on Cancer staging system for pathologists focusing on early stage lung adenocarcinoma

BACKGROUND

The eighth edition of the American Joint Committee on Cancer (AJCC) staging system for lung cancer adopts new criteria for tumor size, and for determining pTis, pT1a(mi), and pT1a. The latter is based on the size of stromal invasion. It is quite challenging for lung pathologists.

METHODS

All patients who had undergone surgical resection for pulmonary adenocarcinoma (ADC) at Chung Shan Medical University Hospital between January 2014 and April 2018 were reviewed, and restaged according to the eighth AJCC staging system. The clinical characteristics and survival of patients with tumor stage 0 (pTis), I or II were analyzed.

RESULTS

In total, 376 patients were analyzed. None of the pTis, pT1a(mi), or pT1a tumors recurred during the follow-up period up to 5 years, but pT1b, pT1c, pT2a, and pT2b tumors all had a few tumor recurrences (p < 0.0001). In addition, 95.2%, 100%, and 77.5% of pTis, pT1a(mi), and pT1a tumors, respectively, had tumor sizes ≤1.0 cm by gross examination. All pTis, pT1a(mi), and pT1a tumors exhibited only lepidic, acinar, or papillary patterns histologically.

CONCLUSIONS

This study demonstrated excellent survival for lung ADC patients with pTis, pT1a(mi), and pT1a tumors when completely excised. To reduce the inconsistencies between pathologists, staging lung ADC with tumors of ≤1 cm in size grossly as pTis, pT1a(mi), or pT1a may not be necessary when the tumors exhibit only lepidic, acinar, or papillary histological patterns. A larger cohort study with sufficient follow-up data is necessary to support this proposal.

A Combination of Radiomic Features, Imaging Characteristics, and Serum Tumor Biomarkers to Predict the Possibility of the High-Grade Subtypes of Lung Adenocarcinoma

RATIONALE AND OBJECTIVES

Lung adenocarcinomas (LADC) containing high-grade subtypes have a poorer prognosis. And some studies have shown that high-grade subtypes have been identified as an independent predictor of local recurrence in patients treated with limited resection. The aim of this study was to construct a combined model based on radiomic features, imaging characteristics and serum tumor biomarkers to predict the possibility of preoperative high-grade subtypes.

MATERIALS AND METHODS

156 patients with LADC were retrospectively recruited in this study. These patients were randomly divided into training and validation cohorts. Radiomics features and imaging characteristics were extracted from plain CT images. A nomogram was developed in a training cohort by univariate and multivariate logistic analysis, and its performance was evaluated by receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA) in the training and validation cohorts.

RESULTS

A total of 1316 radiomic features were extracted from the lesions in plain chest CT images. After applying the mRMR algorithm and the LASSO regression, 4 features were retained. Based on these radiomic features, Radiomic score (Radscore) was calculated for each patient. Spiculation, air bronchogram sign, CYFRA 21-1 and Radscore had been used in the construction of the combined model. The AUC of the combined model was respectively 0.88 (95% CI, 0.82-0.95) and 0.94 (95% CI, 0.86-1.00) in the training and validation cohorts.

CONCLUSION

The combined model based on CT images and serum tumor biomarkers, can predict the high-grade subtypes of LADC in a non-invasive manner, which may influence individual treatment planning, such as the choice of surgical approach and postoperative adjuvant therapy.

Development and Validation of a Nomogram for Predicting the 1-, 3-, and 5-year Survival in Patients with Acinar-predominant Lung Adenocarcinoma

OBJECTIVE

This study aimed to develop a nomogram to predict the overall survival (OS) of patients with acinar-predominant adenocarcinoma (APA).

METHODS

Data from patients with APA obtained from the Surveillance, Epidemiology, and End Results (SEER) database between 2008 and 2016 were used. Significant prognostic factors were incorporated to construct a nomogram for predicting the 1-, 3-, and 5-year OS in these patients. The discrimination and calibration abilities of the nomogram were assessed using a C-index and calibration curves, respectively.

RESULTS

A total of 2242 patients with APA were randomly divided into a training cohort (n=1576) and validation cohort (n=666). The independent prognostic factors for OS incorporated into the nomogram included marital status, age, gender, differentiation grade, T stage, N stage, and M stage. The nomogram showed good prediction capability, as indicated by the C-index [0.713, 95% confidence interval (CI): 0.705-0.721 in the training cohort, and 0.662, 95% CI: 0.649-0.775 in the validation cohort]. The calibration curves demonstrated that the 1-, 3-, and 5-year OS probabilities were consistent between the observed and predicted outcome frequencies. Patients were divided into the high-risk and low-risk groups with the former showing significantly worse survival than the latter (P<0.001).

CONCLUSION

Using the SEER database, a nomogram was established to predict the 1-, 3-, and 5-year OS of patients with APA and was superior to the tumor size, lymph node, and metastasis staging system in terms of evaluating long-term prognosis.

The diagnostic value of CT-guided percutaneous puncture biopsy of pulmonary ground-glass nodules: a meta-analysis

Background

More and more pulmonary ground-glass nodules (GGNs) are screened with the extensive usage of low-dose computed tomography (CT). The need of CT-guided percutaneous puncture biopsy of GGN remains controversial.

Purpose

To explore the diagnostic accuracy of CT-guided percutaneous puncture biopsy of GGNs.

Material and Methods

We searched PubMed, EMBASE, the Cochrane Library, and CNKI. Included studies reported the puncture biopsy results of pulmonary GGNs, including the number of true positive (TP), false positive (FP), true negative (TN), and false negative (FN) cases. After evaluating the studies, statistical analysis, and quality assessment, the pooled diagnostic sensitivity (SEN), specificity (SPE), and diagnostic odds ratio (DOR) were calculated. The summary receiver operating characteristic (SROC) curve was constructed and the area under the curve (AUC) was calculated. Subgroup analysis was performed according to whether spiral CT or fluoroscopy-guided CT was used in the study.

Results

This meta-analysis included 14 studies with a total of 759 patients (702 samples). The pooled SEN, SPE, and DOR of CT-guided puncture biopsy of pulmonary GGNs were 0.91 (95% confidence interval [CI] = 0.89–0.94), 0.99 (95% CI = 0.95–1.00), and 138.72 (95% CI = 57.98–331.89), respectively. The AUC was 0.97.

Conclusion

Our results indicated that CT-guided puncture biopsy of GGNs has high SEN, SPE, and DOR, which proved that CT-guided puncture biopsy was a good way to determine the pathological nature of GGN.

Non-contrast and contrast enhanced computed tomography radiomics in preoperative discrimination of lung invasive and non-invasive adenocarcinoma

OBJECTIVE

This study aimed to assess the value of radiomics based on non-contrast computed tomography (NCCT) and contrast-enhanced computed tomography (CECT) images in the preoperative discrimination between lung invasive adenocarcinomas (IAC) and non-invasive adenocarcinomas (non-IAC).

METHODS

We enrolled 1,185 pulmonary nodules (478 non-IACs and 707 IACs) to build and validate radiomics models. An external testing set comprising 63 pulmonary nodules was collected to verify the generalization of the models. Radiomic features were extracted from both NCCT and CECT images. The predictive performance of radiomics models in the validation and external testing sets were evaluated and compared with radiologists' evaluations. The predictive performances of the radiomics models were also compared between three subgroups in the validation set (Group 1: solid nodules, Group 2: part-solid nodules, and Group 3: pure ground-glass nodules).

RESULTS

The NCCT, CECT, and combined models showed good ability to discriminate between IAC and non-IAC [respective areas under the curve (AUCs): validation set = 0.91, 0.90, and 0.91; Group 1 = 0.82, 0.79, and 0.81; Group 2 = 0.93, 0.92, and 0.93; and Group 3 = 0.90, 0.90, and 0.89]. In the external testing set, the AUC of the three models were 0.89, 0.91, and 0.89, respectively. The accuracies of these three models were comparable to those of the senior radiologist and better those that of the junior radiologist.

CONCLUSION

Radiomic models based on CT images showed good predictive performance in discriminating between lung IAC and non-IAC, especially in part solid nodule group. However, radiomics based on CECT images provided no additional value compared to NCCT images.

3D deep learning versus the current methods for predicting tumor invasiveness of lung adenocarcinoma based on high-resolution computed tomography images

Background

Different pathological subtypes of lung adenocarcinoma lead to different treatment decisions and prognoses, and it is clinically important to distinguish invasive lung adenocarcinoma from preinvasive adenocarcinoma (adenocarcinoma in situ and minimally invasive adenocarcinoma). This study aims to investigate the performance of the deep learning approach based on high-resolution computed tomography (HRCT) images in the classification of tumor invasiveness and compare it with the performances of currently available approaches.

Methods

In this study, we used a deep learning approach based on 3D conventional networks to automatically predict the invasiveness of pulmonary nodules. A total of 901 early-stage non-small cell lung cancer patients who underwent surgical treatment at Shanghai Chest Hospital between November 2015 and March 2017 were retrospectively included and randomly assigned to a training set (n=814) or testing set 1 (n=87). We subsequently included 116 patients who underwent surgical treatment and intraoperative frozen section between April 2019 and January 2020 to form testing set 2. We compared the performance of our deep learning approach in predicting tumor invasiveness with that of intraoperative frozen section analysis and human experts (radiologists and surgeons).

Results

The deep learning approach yielded an area under the receiver operating characteristic curve (AUC) of 0.946 for distinguishing preinvasive adenocarcinoma from invasive lung adenocarcinoma in the testing set 1, which is significantly higher than the AUCs of human experts (P<0.05). In testing set 2, the deep learning approach distinguished invasive adenocarcinoma from preinvasive adenocarcinoma with an AUC of 0.862, which is higher than that of frozen section analysis (0.755, P=0.043), senior thoracic surgeons (0.720, P=0.006), radiologists (0.766, P>0.05) and junior thoracic surgeons (0.768, P>0.05).

Conclusions

We developed a deep learning model that achieved comparable performance to intraoperative frozen section analysis in determining tumor invasiveness. The proposed method may contribute to clinical decisions related to the extent of surgical resection.

The prognostic influence of histological subtypes of micropapillary tumors on patients with lung adenocarcinoma ≤ 2 cm

Objective

This study aimed to explore the value of micropapillary histological subtypes in predicting the specific surgical specificity and lymph node metastasis prognosis of early lung adenocarcinoma.

Methods

A total of 390 patients with lung adenocarcinoma were included who underwent surgery in the Department of Thoracic Surgery of the Affiliated Provincial Hospital of Anhui Medical University from January 2016 to December 2017. The data were analysed with SPSS 26.0 statistical software, and the clinicopathological data of the two groups were compared with the chi-square test. The survival rate was calculated by the Kaplan-Meier method, and the difference in survival rate between groups was analysed by the log-rank test. Multivariate survival analysis was performed using the Cox model.

Results

Univariate analysis of the clinicopathological data of the patients showed that the micropapillary histological subtype was significantly associated with the survival rate of patients (p=0.007). The clinicopathological data of the patients were substituted into the Cox model for multivariate analysis, and the results showed that the micropapillary histological subtype was an independent prognostic factor affecting the survival rate of the patients (p=0.009).The average survival time of Group A (micronipple composition > 5%) was 66.7 months; the 1-year, 3-year, and 5-year survival rates were 98.8%, 93.0%, and 80.9%, respectively.The survival of the lobectomy group was better than that of the sublobectomy group and the survival of patients with systematic dissection was better than that of patients with limited lymph node dissection. The average survival time of Group B (micronipple composition ≤ 5%) was 70.5 months; the 1-year, 3-year, and 5-year survival rates were 99.3%, 95.4%, and 90.6%, respectively. There was no difference in the survival rate between the lobectomy group and sublobectomy group, and there was also no difference in survival between systematic lymph node dissection and limited lymph node dissection, The survival rate of Group B was significantly better than that of Group A.

Conclusion

The micropapillary histological component is an independent risk factor after surgery in patients with ≤2 cm lung adenocarcinoma. When the proportion of micropapillary components is different, the prognosis of patients is different when different surgical methods and lymph node dissections are performed. Lobectomy and systematic lymph node dissection are recommended for patients with a micropapillary histological composition >5%; sublobar resection and limited lymph node dissection are recommended for patients with a micropapillary histological composition ≤5%.

A triple-classification for the evaluation of lung nodules manifesting as pure ground-glass sign: a CT-based radiomic analysis

OBJECTIVES

To construct a noninvasive radiomics model for evaluating the pathological degree and an individualized treatment strategy for patients with the manifestation of ground glass nodules (GGNs) on CT images.

METHODS

The retrospective primary cohort investigation included patients with GGNs on CT images who underwent resection between June 2015 and June 2020. The intratumoral regions of interest were segmented semiautomatically, and radiomics features were extracted from the intratumoral and peritumoral regions. After feature selection by ANOVA, Max-Relevance and Min-Redundancy (mRMR) and Least Absolute Shrinkage and Selection Operator (Lasso) regression, a random forest (RF) model was generated. Receiver operating characteristic (ROC) analysis was calculated to evaluate each classification. Shapley additive explanations (SHAP) was applied to interpret the radiomics features.

RESULTS

In this study, 241 patients including atypical adenomatous hyperplasia (AAH) or adenocarcinoma in situ (AIS) (n = 72), minimally invasive adenocarcinoma (MIA) (n = 83) and invasive adenocarcinoma (IAC) (n = 86) were selected for radiomics analysis. Three intratumoral radiomics features and one peritumoral feature were finally identified by the triple RF classifier with an average area under the curve (AUC) of 0.960 (0.963 for AAH/AIS, 0.940 for MIA, 0.978 for IAC) in the training set and 0.944 (0.955 for AAH/AIS, 0.952 for MIA, 0.926 for IAC) in the testing set for evaluation of the GGNs.

CONCLUSION

The triple classification based on intra- and peritumoral radiomics features derived from the noncontrast CT images had satisfactory performance and may be used as a noninvasive tool for preoperative evaluation of the pure ground-glass nodules and developing of individualized treatment strategies.

Solid Attenuation Components Attention Deep Learning Model to Predict Micropapillary and Solid Patterns in Lung Adenocarcinomas on Computed Tomography

BACKGROUND

High-grade adenocarcinoma subtypes (micropapillary and solid) treated with sublobar resection have an unfavorable prognosis compared with those treated with lobectomy. We investigated the potential of incorporating solid attenuation component masks with deep learning in the prediction of high-grade components to optimize surgical strategy preoperatively.

METHODS

A total of 502 patients with pathologically confirmed high-grade adenocarcinomas were retrospectively enrolled between 2016 and 2020. The SACs attention DL model was developed to apply solid-attenuation-component-like subregion masks (tumor area ≥ - 190 HU) to guide the DL model for predicting high-grade subtypes. The SACA-DL was assessed using 5-fold cross-validation and external validation in the training and testing sets, respectively. The performance, which was evaluated using the area under the curve (AUC), was compared between SACA-DL and the DL model without SACs attention (DL_woSACs), the prior radiomics model, or the model based on the consolidation/tumor (C/T) diameter ratio.

RESULTS

We classified 313 and 189 patients into training and testing cohorts, respectively. The SACA-DL achieved an AUC of 0.91 for the cross-validation, which was significantly superior to those of the DL_woSACs (AUC = 0.88; P = 0.02), prior radiomics model (AUC = 0.85; P = 0.004), and C/T ratio (AUC = 0.84; P = 0.002). An AUC of 0.93 was achieved for external validation in the SACA-DL and was significantly better than those of the DL_woSACs (AUC = 0.89; P = 0.04), prior radiomics model (AUC = 0.85; P < 0.001), and C/T ratio (AUC = 0.85; P < 0.001).

CONCLUSIONS

The combination of solid-attenuation-component-like subregion masks with the DL model is a promising approach for the preoperative prediction of high-grade adenocarcinoma subtypes.

Development and validation of qualitative and quantitative models to predict invasiveness of lung adenocarcinomas manifesting as pure ground-glass nodules based on low-dose computed tomography during lung cancer screening

BACKGROUND

Due to different management strategy and prognosis of different subtypes of lung adenocarcinomas appearing as pure ground-glass nodules (pGGNs), it is important to differentiate invasive adenocarcinoma (IA) from adenocarcinoma in situ/minimally invasive adenocarcinoma (AIS/MIA) during lung cancer screening. The aim of this study was to develop and validate the qualitative and quantitative models to predict the invasiveness of lung adenocarcinoma appearing as pGGNs based on low-dose computed tomography (LDCT) and compare their diagnostic performance with that of intraoperative frozen section (FS).

METHODS

A total of 223 consecutive pathologically confirmed pGGNs from March 2018 to December 2020 were divided into a primary cohort (96 IAs and 64 AIS/MIAs) and validation cohort (39 IAs and 24 AIS/MIAs) according to scans (Brilliance iCT and Somatom Definition Flash) performed at Sichuan Cancer Hospital and Institute. The following LDCT features of pGGNs were analyzed: the qualitative features included nodule location, shape, margin, nodule-lung interface, lobulation, spiculation, pleural indentation, air bronchogram, vacuole, and vessel type, and the quantitative features included the diameter, volume, and mean attenuation. Multivariate logistic regression analysis was used to build a qualitative model, quantitative model, and combined qualitative and quantitative model. The diagnostic performance was assessed according to the following factors: the area under curve (AUC) of the receiver operating characteristic (ROC) curve, sensitivity, specificity, and accuracy.

RESULTS

The AUCs of the qualitative model, quantitative model, combined qualitative and quantitative model, and the FS diagnosis were 0.854, 0.803, 0.873, and 0.870, respectively, in the primary cohort and 0.884, 0.855, 0.875, and 0.946, respectively, in the validation cohort. No significant difference of the AUCs was found among the radiological models and the FS diagnosis in the primary or validation cohort (all corrected P>0.05). Among the radiological models, the combined qualitative and quantitative model consisting of vessel type and volume showed the highest accuracy in both the primary and validation cohorts (0.831 and 0.889, respectively).

CONCLUSIONS

The diagnostic performances of the qualitative and quantitative models based on LDCT to differentiate IA from AIS/MIA in pGGNs are equivalent to that of intraoperative FS diagnosis. The vessel type and volume can be preoperative and non-invasive biomarkers to assess the invasive risk of pGGNs in lung cancer screening.

A lepidic gene signature predicts patient prognosis and sensitivity to immunotherapy in lung adenocarcinoma

BACKGROUND

Lung adenocarcinoma, the most common type of lung cancer, has a high level of morphologic heterogeneity and is composed of tumor cells of multiple histological subtypes. It has been reported that immune cell infiltration significantly impacts clinical outcomes of patients with lung adenocarcinoma. However, it is unclear whether histologic subtyping can reflect the tumor immune microenvironment, and whether histologic subtyping can be applied for therapeutic stratification of the current standard of care.

METHODS

We inferred immune cell infiltration levels using a histological subtype-specific gene expression dataset. From differential gene expression analysis between different histological subtypes, we developed two gene signatures to computationally determine the relative abundance of lepidic and solid components (denoted as the L-score and S-score, respectively) in lung adenocarcinoma samples. These signatures enabled us to investigate the relationship between histological composition and clinical outcomes in lung adenocarcinoma using previously published datasets.

RESULTS

We found dramatic immunological differences among histological subtypes. Differential gene expression analysis showed that the lepidic and solid subtypes could be differentiated based on their gene expression patterns while the other subtypes shared similar gene expression patterns. Our results indicated that higher L-scores were associated with prolonged survival, and higher S-scores were associated with shortened survival. L-scores and S-scores were also correlated with global genomic features such as tumor mutation burdens and driver genomic events. Interestingly, we observed significantly decreased L-scores and increased S-scores in lung adenocarcinoma samples with EGFR gene amplification but not in samples with EGFR gene mutations. In lung cancer cell lines, we observed significant correlations between L-scores and cell sensitivity to a number of targeted drugs including EGFR inhibitors. Moreover, lung cancer patients with higher L-scores were more likely to benefit from immune checkpoint blockade therapy.

CONCLUSIONS

Our findings provided further insights into evaluating histology composition in lung adenocarcinoma. The established signatures reflected that lepidic and solid subtypes in lung adenocarcinoma would be associated with prognosis, genomic features, and responses to targeted therapy and immunotherapy. The signatures therefore suggested potential clinical translation in predicting patient survival and treatment responses. In addition, our framework can be applied to other types of cancer with heterogeneous histological subtypes.

Heterogeneity of PD-L1 expression between invasive and lepidic components of lung adenocarcinomas

The dynamics of PD-L1 expression are poorly understood over the development of lung adenocarcinomas from pre-invasive lesions to fully invasive carcinomas. Given the importance of PD-L1 expression for the selection of patients to receive immunotherapy in the metastatic setting and possibly in the neoadjuvant setting, we sought to evaluate the agreement of PD-L1 expression in invasive and lepidic components of resected tumor specimens. We stained 86 adenocarcinomas for PD-L1 using the SP263 clone. We assessed the agreement of PD-L1 expression by tumor cells and immune cells between lepidic and invasive components. When both lepidic and invasive components were considered, PD-L1 positive immune cells and tumor cells were observed in 50 (58.1%) and 18 (20.9%) samples, respectively, using a ≥ 1% PD-L1 expression cutoff. Using a ≥ 1% cutoff for PD-L1 expression, positively stained tumor cells were observed in 11 (13%) lepidic and 15 (17%) invasive patterns, with agreement in 76 (88%) specimens and disagreement in 10 (12%) specimens (ĸ = 0.549). At ≥ 1% PD-L1 expression cutoff, PD-L1 positive immune cells were observed in 31 (35%) lepidic and 32 (37%) invasive patterns with an agreement of PD-L1 expression in 49 (57%) specimens and disagreement in 37 (43%) specimens (ĸ = 0.073). In our study of early stage adenocarcinomas of the lung, there was poor agreement in PD-L1 expression between paired invasive and lepidic components of tumors. Our data suggest that the non-invasive tumor components may not be as immunostimulatory as the invasive components, resulting in less adaptive expression of PD-L1.

A clinicopathological study of lung adenocarcinomas with pure ground-glass opacity > 3 cm on high-resolution computed tomography

OBJECTIVES

This study aimed to discuss whether a diameter of 3 cm is a threshold for diagnosing lung adenocarcinomas presenting with radiological pure ground-glass mass (PGGM, pure ground-glass opacity > 3 cm) as adenocarcinomas in situ or minimally invasive adenocarcinomas (AIS-MIAs). Another aim was to identify CT features and patient prognosis that differentiate AIS-MIAs from invasive adenocarcinomas (IACs) in patients with PGGMs.

METHODS

From June 2007 to October 2015, 69 resected PGGMs with HRCT and followed up for ≥ 5 years were included in this study and divided into AIS-MIA (n = 13) and IAC (n = 56) groups. Firth's logistic regression model was performed to determine CT characteristics that helped distinguish IACs from AIS-MIAs. The discriminatory power of the significant predictors was tested with the area under the receiver operating characteristics curve (AUC). Disease recurrence was also evaluated.

RESULTS

Univariable and multivariable analyses identified that the mean CT attenuation (odds ratio: 1.054, p = 0.0087) was the sole significant predictor for preoperatively discriminating IACs from AIS-MIAs in patients with PGGMs. The CT attenuation had an excellent differentiating accuracy (AUC: 0.981), with the optimal cut-off value at -600 HU (sensitivity: 87.5%; specificity: 100%). Additionally, no recurrence was observed in patients manifesting with PGGMs > 3 cm, and the 5-year recurrence-free survival and overall survival rates were both 100%, even in cases of IAC.

CONCLUSIONS

This study demonstrated that PGGMs > 3 cm could still be AIS-MIAs. When PGGMs are encountered in clinical practice, the CT value may be the only valuable parameter to preoperatively distinguish IACs from AIS-MIAs.

KEY POINTS

• Patients with pure ground-glass opacity > 3 cm in diameter are rare but can be diagnosed as adenocarcinomas in situ or minimally invasive adenocarcinomas. • The mean CT attenuation is the sole significant CT parameter that differentiates invasive adenocarcinoma from adenocarcinoma in situ or minimally invasive adenocarcinoma in patients with pure ground-glass opacity > 3 cm. • Lung adenocarcinoma with pure ground-glass opacity > 3 cm has an excellent prognosis, even in cases of invasive adenocarcinoma.

Prognostic impact according to the proportion of the lepidic subtype in stage IA acinar-predominant lung adenocarcinoma

Background

Adenocarcinoma is the most common type of lung cancer and most adenocarcinomas have heterogeneous subtypes. Acinar‐predominant adenocarcinoma is the most common. This study aimed to identify the prognostic impact of other mixed histological subtypes in acinar‐predominant lung adenocarcinoma.

Methods

The medical records of patients with pathological stage IA acinar‐predominant lung adenocarcinoma between January 2010 and April 2016 were reviewed. The patients were divided into two groups according to the proportion of the lepidic subtype, with a cutoff value of 20%, and prognostic factors were analyzed.

Results

A total of 215 patients with stage IA acinar‐predominant adenocarcinoma were reviewed. The 20% or more lepidic subtype group had a low value of SUVmax (p = 0.001), good differentiation (p < 0.001) and a low incidence of the solid histological subtype (p = 0.016). Recurrence was significantly lower in the 20% or more lepidic subtype group (p = 0.008). The disease‐free survival (p = 0.007) and overall survival (p = 0.046) were significantly different between the two groups. Multivariate analysis showed that lymphovascular invasion (p = 0.006) and no or less than 20% lepidic subtype (p = 0.036) were significant prognostic factors for disease‐free survival.

Conclusions

The lepidic proportion may be useful to predict recurrence in acinar‐predominant stage IA lung adenocarcinoma.

High-risk-pattern lung adenocarcinoma with epidermal growth factor receptor mutation is associated with distant metastasis risk and may benefit from adjuvant targeted therapy

OBJECTIVES

This study aimed to evaluate the value of the high-risk-pattern histology (micropapillary and solid components) for predicting distant metastasis in lung adenocarcinoma and to determine the survival benefit with adjuvant targeted therapy for resected non-small cell lung cancer with high-risk-pattern histology.

METHODS

Patients receiving surgery for non-small cell lung cancer were included in this retrospective study. Histological classification was performed according to 2015 World Health Organization classification. Tumours with micropapillary and solid components were defined as high-risk-pattern tumours. Univariable and multivariable Cox regression analyses were used for survival analysis. Adjuvant targeted therapy was alternative for patients with epidermal growth factor receptor (EGFR)-mutation and refusing adjuvant chemotherapy, and outcome was evaluated between 2 groups.

RESULTS

The 514 patients (78 in high-risk group and 436 in low-risk group) were followed up for a median of 64 months. High-risk-pattern adenocarcinoma was significantly more common in male patients (P < 0.001) and in smokers (P < 0.001). Among patients with EGFR mutation (n = 164), the high-risk pattern was significantly associated with distant metastasis (P = 0.028) including brain metastasis (P = 0.022). In the 42 patients with high-risk pattern plus EGFR mutation, survival was significantly better after treatment with adjuvant targeted therapy than with chemotherapy (5-year overall survival: 56.4 ± 2.6 vs 44.7 ± 3.7 months, P = 0.011; 5-year disease-free survival: 54.0 ± 3.3 vs 41.9 ± 4.5 months, P = 0.006).

CONCLUSIONS

High-risk pattern is associated with distant metastasis in non-small cell lung cancer after surgery. Adjuvant targeted therapy may be superior to chemotherapy for treatment of patients with high-risk pattern and EGFR mutation.

Clinicopathological predictors of survival in resected primary lung adenocarcinoma

Aims

Primary lung adenocarcinoma consists of a spectrum of clinical and pathological subtypes that may impact on overall survival (OS). Our study aims to evaluate the impact of adenocarcinoma subtype and intra-alveolar spread on survival after anatomical lung resection and identify different prognostic factors based on stage and histological subtype.

Methods

Newly diagnosed patients undergoing anatomical lung resections without induction therapy, for pT1-3, N0-2 lung adenocarcinoma from April 2011 to March 2013, were included. The effect of clinical–pathological factors on survival was retrospectively assessed.

Results

Two hundred and sixty-two patients were enrolled. The 1-year, 3-year and 5-year OS were 88.8%, 64.3% and 51.1%, respectively. Univariate analysis showed lymphovascular, parietal pleural and chest wall invasion to confer a worse 1-year and 5-year prognosis (all p<0.0001). Solid predominant adenocarcinomas exhibited a significantly worse OS (p=0.014). Multivariate analysis did not identify solid subtype as an independent prognostic factor; however, identified stage >IIa, lymphovascular invasion (p=0.002) and intra-alveolar spread (p=0.009) as significant independent predictors of worse OS. Co-presence of intra-alveolar spread and solid predominance significantly reduced OS. Disease-free survival (DFS) was reduced with parietal pleural (p=0.0007) and chest wall invasion (p<0.0001), however, adenocarcinoma subtype had no significant impact on DFS.

Conclusions

Our study demonstrates that solid predominant adenocarcinoma, intra-alveolar spread and lymphovascular invasion confer a worse prognosis and should be used as a prognostic tool to determine appropriate adjuvant treatment.

Predicting Pathological Invasiveness of Lung Adenocarcinoma Manifesting as GGO-Predominant Nodules: A Combined Prediction Model Generated From DECT

RATIONALE AND OBJECTIVES

To evaluate qualitative and quantitative indicators generated from Dual-energy computed tomography (DECT) for preoperatively differentiating between invasive adenocarcinoma (IAC) and preinvasive or minimally invasive adenocarcinoma (MIA) lesions manifesting as ground-glass opacity-predominant (GGO-predominant) nodules.

MATERIALS AND METHODS

We retrospectively enrolled 143 cases of completely resected GGO-predominant lung adenocarcinoma with DECT examinations between December 2017 and July 2019. Qualitative and quantitative parameters of GGO-predominant nodules were compared after grouping nodules into IAC and preinvasive-MIA groups. A multivariate logistic regression models were used for analyzing these parameters. The diagnostic performance of different parameters was compared by receiver operating characteristic (ROC) curves and Z tests.

RESULTS

This study included 137 patients (58 years ± 11; male: female = 52:91) with 143 GGO-predominant nodules. The proportion of margins, internal dilated/distorted/cut-off bronchi, internal thickened/stiff/distorted vasculature, pleural indentation, and vascular convergence were higher in the IAC group than in the preinvasive-MIA group, as were the maximum diameter (D_max), the diameter of the solid component (D_solid) and the enhanced monochromatic CT value at 40 keV-190 keV (CT_{40 keV-190 keV}) (p range: 0.001-0.019). Logistic regression analyses revealed that margin, D_max, and CT_{60 keV} values were independent predictors of the IAC group. The area under the curve (AUC) for the combination of margin, D_max, and CT_{60 keV} was 0.896 (90.2% sensitivity, 70.7% specificity, 84.6% accuracy), which was significantly higher than that for each two of them (all p < 0.05).

CONCLUSION

The combined prediction model generated from DECT allows for effective preoperative differentiation between IAC and preinvasive-MIA in GGO-predominant lung adenocarcinomas.

Can peritumoral regions increase the efficiency of machine-learning prediction of pathological invasiveness in lung adenocarcinoma manifesting as ground-glass nodules?

BACKGROUND

The peri-tumor microenvironment plays an important role in the occurrence, growth and metastasis of cancer. The aim of this study is to explore the value and application of a CT image-based deep learning model of tumors and peri-tumors in predicting the invasiveness of ground-glass nodules (GGNs).

METHODS

Preoperative thin-section chest CT images were reviewed retrospectively in 622 patients with a total of 687 pulmonary GGNs. GGNs are classified according to clinical management strategies as invasive lesions (IAC) and non-invasive lesions (AAH, AIS and MIA). The two volumes of interest (VOIs) identified on CT were the gross tumor volume (GTV) and the gross volume of tumor incorporating peritumoral region (GPTV). Three dimensional (3D) DenseNet was used to model and predict GGN invasiveness, and five-fold cross validation was performed. We used GTV and GPTV as inputs for the comparison model. Prediction performance was evaluated by sensitivity, specificity, and area under the receiver operating characteristic curve (AUC).

RESULTS

The GTV-based model was able to successfully predict GGN invasiveness, with an AUC of 0.921 (95% CI, 0.896-0.937). Using GPTV, the AUC of the model increased to 0.955 (95% CI, 0.939-0.971).

CONCLUSIONS

The deep learning method performed well in predicting GGN invasiveness. The predictive ability of the GPTV-based model was more effective than that of the GTV-based model.

Dependence of radiomic features on pixel size affects the diagnostic performance of radiomic signature for the invasiveness of pulmonary ground-glass nodule

OBJECTIVE

To investigate the effect of reducing pixel size on the consistency of radiomic features and the diagnostic performance of the downstream radiomic signatures for the invasiveness for pulmonary ground-glass nodules (GGNs) on CTs.

METHODS

We retrospectively collected the clinical data of 182 patients with GGNs on high resolution CT (HRCT). The CT images of different pixel sizes (0.8mm, 0.4mm, 0.18 mm) were obtained by reconstructing the single HRCT scan using three combinations of field of view and matrix size. For each pixel size setting, radiomic features were extracted for all GGNs and radiomic signatures for the invasiveness of GGNs were built through two modeling pipelines for comparison.

RESULTS

The study finally extracted 788 radiomic features. 87% radiomic features demonstrated inter pixel size variation. By either modeling pipeline, the radiomic signature under small pixel size performed significantly better than those under middle or large pixel sizes in predicting the invasiveness of GGNs (p's value <0.05 by Delong test). With the independent modeling pipeline, the three pixel size bounded radiomic signatures shared almost no common features.

CONCLUSIONS

Reducing pixel size could cause inconsistency in most radiomic features and improve the diagnostic performance of the downstream radiomic signatures. Particularly, super HRCTs with small pixel size resulted in more accurate radiomic signatures for the invasiveness of GGNs.

ADVANCES IN KNOWLEDGE

The dependence of radiomic features on pixel size will affect the performance of the downstream radiomic signatures. The future radiomic studies should consider this effect of pixel size.

Approach to the Subsolid Nodule

Most focal persistent ground glass nodules (GGNs) do not progress over 10 years. Research suggests that GGNs that do not progress, those that do, and solid lung cancers are fundamentally different diseases, although histologically they seem similar. Surveillance of GGNs to identify those that gradually progress is safe and does not risk losing a window. GGNs with 5 mm solid component or less than 10 mm consolidation (mediastinal and lung windows, respectively, on thin slice CT) are highly curable with resection. The optimal type of resection is unclear; sublobar resection is reasonable but an adequate margin is critically important.

Prognosis of early stage pulmonary mucinous adenocarcinoma with different treatments

BACKGROUND

The effects of surgical approach and adjuvant chemotherapy (AC) of early stage pulmonary mucinous adenocarcinoma (MAD) have not been thoroughly studied yet. This study intends to clarify whether AC provides clinical benefit to the early stage MAD patients and the survival difference between surgical approaches.

METHODS

All cases of stage I MAD were identified from the SEER database during the period of 2009-2014. The primary cohort was divided into AC and surgery (S) groups. Meanwhile, the patients with tumor ≤1 cm were divided into lobectomy and sublobar resection group. Clinical characteristics, treatments and survival data including overall survival (OS) and cancer-specific survival (CSS) were analyzed.

RESULTS

A total of 1,816 patients were included in the final cohort. Referring to surgical procedure, 140 patients received lobectomy and 75 patients received sublobar resection. AC showed worse survival outcomes than surgery alone (OS: 71.2 vs. 93.4 months; CSS: 74.9 vs. 101.1 months). No significant difference was observed between lobectomy group and sublobar resection group (OS: 97.3 vs. 93.1 months; CSS: 103.7 vs. 101.3 months). Consistent results were also shown after the propensity score matching analysis (PSM) was applied.

CONCLUSIONS

Early stage of MAD has an ideal prognosis. AC may bring adverse effects which would lower OS and CSS of stage I MAD patients. No significant difference is observed in the comparison of prognosis between lobectomy and sublobar resection in tumor size ≤1 cm MAD patients.

Predicting the invasiveness of lung adenocarcinomas appearing as ground-glass nodule on CT scan using multi-task learning and deep radiomics

Background

Due to different treatment method and prognosis of different subtypes of lung adenocarcinomas appearing as ground-glass nodules (GGNs) on computed tomography (CT) scan, it is important to classify invasive adenocarcinomas from non-invasive adenocarcinomas. The purpose of this paper is to build and evaluate the performance of deep learning networks on the differentiation the invasiveness of lung adenocarcinoma appearing as GGNs.

Methods

This retrospective study included 886 GGNs from 794 pathological confirmed patients with lung adenocarcinoma for training and testing the proposed networks. Three deep learning networks, namely XimaNet (deep learning-based classification model), XimaSharp (classification and nodule segmentation model), and Deep-RadNet (deep learning and radiomics combined classification model, i.e., deep radiomics) were built. Three classification tasks, namely task 1: classification of AAH/AIS and MIA, task 2: classification of MIA and IAC, and task 3: classification of non-invasive adenocarcinomas and invasive adenocarcinomas (AAH/AIS&MIA and IAC) were conducted to evaluate the model performance. The Z-test was used to compare the model performance.

Results

The AUC for classification of AAH/AIS with MIA were 0.891, 0.841 and 0.779 for Deep-RadNet, XimaNet and XimaSharp respectively. The AUC for classification of MIA with IAC were 0.889, 0.785 and 0.778 for three networks and AUC for classification of AAH/AIS&MIA with IAC were 0.941, 0.892 and 0.827 respectively. The performance of deep_RadNet was better than the other two models with the Z-test (P<0.05).

Conclusions

Deep-RadNet with the visual heat map could evaluate the invasiveness of GGNs accurately and intuitively, providing a theoretical basis for individualized and accurate medical treatment of patients with GGNs.

New additional scoring formula on the Pathological Features in Stage I Lung Adenocarcinoma Patients: Impact on Survival

Background: Histological heterogeneity of lung adenocarcinoma may result in different prognosis among patients with the same TNM pathological stage. However, no objective evaluation system of lung adenocarcinoma based on pathological features has been widely accepted for assessing the prognosis. Methods: We retrospectively analyzed 179 patients with stage I lung adenocarcinoma after complete surgical resection. The pathological classification was according to the IASLC/ATS/ERS adenocarcinoma classifications, and the detailed abundance ratio using HE staining of primary tumor specimens was recorded. A new additional scoring formula on the pathological features (ASP) was established. The association of the ASP score with the patients' survival was examined. Results: The ASP scoring was significantly associated with smoking history (p=0.004), lymphatic vessel invasion (p<0.001), vascular invasion, differentiation (p<0.001) and Ki67 (p<0.001). The patients in the high-ASP-score group tended to have vascular invasion (odds ratio [OR]: 1.637, 95% confidence interval [CI]: 1.923-13.745, p=0.001) and high Ki67 expression (OR: 2.625, 95%CI: 1.328-5.190, p=0.006) by logistic regression analyses. The prognosis differed significantly in the Kaplan-Meier survival curves, and the 5-year survival rates in the low and high ASP score groups were 97.8% and 89.6%, respectively (p=0.018). Based on the univariate analysis, female (OR: 0.111, 95%CI: 0.014-0.906, p=0.040), long smoking history (OR: 7.250, 95%CI: 1.452-36.195, p=0.016), poor differentiation characteristics correlation (OR: 12.691, 95%CI: 1.557-103.453, p=0.018), and high ASP score (OR: 5.788, 95%CI: 1.138-29.423, p=0.034) were shown to be independently associated with an unfavorable prognosis. Conclusion: The ASP score can effectively screen high-risk patients for complete surgical resection of stage I lung adenocarcinoma.

Deep learning combined with radiomics may optimize the prediction in differentiating high-grade lung adenocarcinomas in ground glass opacity lesions on CT scans

PURPOSE

Adenocarcinoma (ADC) is the most common histological subtype of lung cancers in non-small cell lung cancer (NSCLC) in which ground glass opacifications (GGOs) found on computed tomography (CT) scans are the most common lesions. However, the presence of a micropapillary or a solid component is identified as an independent predictor of prognosis, suggesting a more extensive resection. The purpose of our study is to explore imaging phenotyping using a method combining radiomics with deep learning (RDL) to predict high-grade patterns within lung ADC.

METHODS

Included in this study were 111 patients differentiated as having GGOs and pathologically confirmed ADC. Four different groups of methods were compared to classify the GGOs for the prediction of the pathological subtypes of high-grade lung ADCs in definitive hematoxylin and eosin stain, including radiomics with gray-level features, radiomics with textural features, deep learning method, and the RDL.

RESULTS

We evaluated the performance of different models on 111 NSCLC patients using 4-fold cross-validation. The proposed RDL has achieved an overall accuracy of 0.913, which significantly outperforms the other methods (p <  0.01, analysis of variation, ANOVA). In addition, we also verified the generality and practical effectiveness of these models on an independent validation dataset of 28 patients. The results showed that our RDL framework with an accuracy of 0.966 significantly surpassed other methods.

CONCLUSION

High-grade lung ADC based on histologic pattern spectrum in GGO lesions might be predicted by the framework combining radiomics with deep learning, which reveals advantage over radiomics alone.

Microarray analysis of the expression profile of immune-related gene in rapid recurrence early-stage lung adenocarcinoma

Background

Although much progress has been made in the diagnosis of early-stage lung adenocarcinoma (ES-LUAD), the prognosis for ES-LUAD patients with rapid recurrence is still poor. Importantly, there is currently no effective and precise method to screen patients who may develop rapid recurrence. Therefore, it is necessary to identify potential differentially expressed genes (DEGs) in ES-LUAD patients with rapid recurrence and non-rapid recurrence.

Methods

Affymetrix GeneChip Human Transcriptome Array was used to identify DEGs between ES-LUAD patients with rapid recurrence and non-rapid recurrence. Rapid recurrence was defined as recurrence-free survival (RFS) ≦ 1 year and non-rapid recurrence was defined as RFS ≧ 3 years. The biological functions of the DEGs were analyzed by GO and KEGG pathway enrichment analyses. The protein–protein interaction (PPI) network of identified DEGs was conducted by STRING and Cytoscape software. The expression level of crucial hub genes and tumor-infiltrating lymphocytes (TILs) was verified by immunohistochemistry (IHC).

Results

A total of 416 DEGs were identified between ES-LUAD patients with and without rapid recurrence. The results of GO analysis revealed that 2 of the top 10 categories in the domain of cellular component, 2 of the top 10 in the domain of molecular function, and 9 of the top 10 in the domain of biological process were functionally related to immunity. The results of KEGG analysis showed that 6 of the top 8 pathways were functionally involved in immune regulation and inflammatory response. The PPI network analysis identified ten crucial nodal protein, including EGFR, MMP9, IL-1β, PTGS2, MMP1, and 5 histone proteins, which constituted 25 key interactions. IL-1β and PTGS2 expression were closely related to immunity and IHC analysis further revealed that low expression of IL-1β and PTGS2 is associated with rapid recurrence. Kaplan–Meier analysis further revealed that LUAD patients with lower IL-1β or PTGS2 expression had a worse RFS. When the TIL density of CD3⁺, CD4⁺, CD8⁺ and CD20⁺ subsets was less than 20%, ES-LUAD patients have a higher probability of rapid recurrence.

Conclusion

There were significant differences in the expression of immune-related genes between patients with rapid recurrence and patient with non-rapid recurrence. Immune-related genes such as IL-1β and PTGS2 and TIL density (20%) play important roles in rapid recurrence of ES-LUAD. This study provided a theoretical basis for distinguishing the two types of patients from an immunological perspective.

Electronic supplementary material

The online version of this article (10.1007/s00432-020-03287-7) contains supplementary material, which is available to authorized users.

Prognostic Impact of Histopathologic Features in Pulmonary Invasive Mucinous Adenocarcinomas

OBJECTIVES

The prognostic significance of pathologic features and invasive size has not been well studied for invasive mucinous adenocarcinoma (IMA). This study evaluates the significance of pathologic features and invasive size in relation to clinical outcome.

METHODS

We reviewed the pathologic features in 84 IMAs, including histologic pattern, nuclear atypia, mitosis, necrosis, and lymphovascular invasion. The invasive size was calculated from the total size using the percentage of invasive components. Cases were subdivided into two pathologic grades based on five pathologic features, and the pathologic grade and adjusted T (aT) stage were correlated with disease-free and overall survival (OS).

RESULTS

Necrosis and N stage were significantly associated with aT stage, and a significant association was noted between OS and aT stage. Nuclear atypia, mitosis, and lymphovascular and pleural invasion also showed a significant association with OS. High-grade tumors showing a significantly worse OS compared with low-grade tumors, as well as pathologic grade (hazard ratio [HR], 2.337; P = .043) and aT stage (HR, 1.875; P = .003), were independent prognostic factors in multivariate analysis.

CONCLUSIONS

The pathologic grading system stratified IMAs into high- and low-grade tumors with significant differences in OS. Invasive size may provide a better prognostic stratification for OS.

Heterogeneity of Glucose Transport in Lung Cancer

Increased glucose uptake is a known hallmark of cancer. Cancer cells need glucose for energy production via glycolysis and the tricarboxylic acid cycle, and also to fuel the pentose phosphate pathway, the serine biosynthetic pathway, lipogenesis, and the hexosamine pathway. For this reason, glucose transport inhibition is an emerging new treatment for different malignancies, including lung cancer. However, studies both in animal models and in humans have shown high levels of heterogeneity in the utilization of glucose and other metabolites in cancer, unveiling a complexity that is difficult to target therapeutically. Here, we present an overview of different levels of heterogeneity in glucose uptake and utilization in lung cancer, with diagnostic and therapeutic implications.

Computed tomography density is not associated with pathological tumor invasion for pure ground-glass nodules

OBJECTIVE

Pure ground-glass nodules are considered to be radiologically noninvasive in lung adenocarcinoma. However, some pure ground-glass nodules are found to be invasive adenocarcinoma pathologically. This study aims to identify the computed tomography parameters distinguishing invasive adenocarcinoma from adenocarcinoma in situ and minimally invasive adenocarcinoma.

METHODS

From May 2011 to December 2015, patients with completely resected adenocarcinoma appearing as pure ground-glass nodules were reviewed. To evaluate the association between computed tomography features and the invasiveness of pure ground-glass nodules, logistic regression analyses were conducted.

RESULTS

Among 432 enrolled patients, 118 (27.3%) were classified as adenocarcinoma in situ, 213 (49.3%) were classified as minimally invasive adenocarcinoma, 101 (23.4%) were classified as invasive adenocarcinoma. There was no postoperative recurrence for patients with pure ground-glass nodules. Logistic regression analyses demonstrated that computed tomography size was the only independent radiographic factor associated with adenocarcinoma in situ (odds ratio, 47.165; 95% confidence interval, 19.279-115.390; P < .001), whereas computed tomography density was not (odds ratio, 1.002; 95% confidence interval, 0.999-1.005; P = .127). Further analyses revealed that there was no distributional difference in computed tomography density among 3 groups (P = .173). Even after propensity score matching for adenocarcinoma in situ/minimally invasive adenocarcinoma and invasive adenocarcinoma, no significant difference in computed tomography density was observed (P = .741). The subanalyses for pure ground-glass nodules with 1 cm or more in size also indicated similar results.

CONCLUSIONS

In patients with pure ground-glass nodules, computed tomography size was the only radiographic parameter associated with tumor invasion. Measuring computed tomography density provided no advantage in differentiating invasive adenocarcinoma from adenocarcinoma in situ and minimally invasive adenocarcinoma.

Volume Doubling Times of Lung Adenocarcinomas: Correlation with Predominant Histologic Subtypes and Prognosis

Background The volume doubling time (VDT) is a key parameter in the differentiation of aggressive tumors from slow-growing tumors. How different histologic subtypes of primary lung adenocarcinomas vary in their VDT and the prognostic value of this measurement is unknown. Purpose To investigate differences in VDT between the predominant histologic subtypes of primary lung adenocarcinomas and to assess the correlation between VDT and prognosis. Materials and Methods This retrospective study included patients who underwent at least two serial CT examinations before undergoing operation between July 2010 and December 2018. Three-dimensional tumor segmentation was performed on two CT images and VDTs were calculated. VDTs were compared between predominant histologic subtypes and lesion types by using Kruskal-Wallis tests. Disease-free survival (DFS) was obtained in patients undergoing surgical procedures before July 2017. Univariable and multivariable Cox proportional hazards regression analyses were performed to determine predictors of DFS. Results Among 268 patients (mean age, 64 years ± 8 [standard deviation]; 143 men), there were 30 lepidic, 87 acinar, 109 papillary, and 42 solid or micropapillary predominant subtypes. The median VDT was 529 days (interquartile range, 278-872 days) for lung adenocarcinomas. VDTs differed across subtypes (P < .001) and were shortest in solid or micropapillary subtypes (229 days; interquartile range, 77-530 days). Solid lesions (VDT, 248 days) had shorter VDTs than subsolid lesions (part-solid lesions, 665 days; nonsolid lesions, 648 days) (P < .001). In the 148 patients (mean age, 64 years ± 8; 89 men) included in the survival analysis, 35 patients had disease recurrence and 17 patients died. VDT (<400 days) was an independent risk factor for poor DFS (hazard ratio, 2.6; P = .01) and higher TNM stage. Adding VDT to TNM stage improved model performance (C-index, 0.69 for TNM stage vs 0.77 for combined VDT class and TNM stage; P = .002). Conclusion Volume doubling times varied significantly according to the predominant histologic subtypes of lung adenocarcinoma and had additional prognostic value for disease-free survival. © RSNA, 2020 Online supplemental material is available for this article. See also the editorial by Ko in this issue.

Clinicopathological features, survival outcomes, and appropriate surgical approaches for stage I acinar and papillary predominant lung adenocarcinoma

Background

Whether prognosis differs between lung acinar predominant adenocarcinoma (ACN) and papillary predominant adenocarcinoma (PAP) patients remains controversial. Furthermore, the appropriate surgical plan for each subtype is undetermined.

Methods

Data of stage I ACN or PAP patients from 2004 to 2015 were retrospectively reviewed by SEER*Stat 8.3.5. The primary outcome was overall survival (OS) and lung cancer specific survival (LCSS).

Results

1531 patients (PAP, 484; ACN, 1047) were included. ACN patients had better OS (P = .001) and LCSS (P = .003) than PAP patients. Among stage I ACN patients, lobectomy with mediastinal lymph node dissection (Lob) (P = .001) or segmentectomy (Seg) (P = .003) provided a better OS than wedge resection (Wed). And ACN patients who received Lob had a equivalent LCSS, compared to those who received Seg (P = .895). For patients with PAP in stage I, those who received Lob tended to have a better prognosis than that received Seg (HR of OS, 0.605, 95% CI: 0.263‐1.393; HR of LCSS, 0.541, 95% CI: 0.194‐1.504) or Wed (HR of OS, 0.735, 95% CI: 0.481‐1.123; HR of LCSS, 0.688, 95% CI: 0.402‐1.180).

Conclusions

Among patients with lung adenocarcinoma in stage I, those with ACN have a better OS and LCSS than that with PAP. For patients with stage I ACN, Seg and Lob, rather than Wed, seem to be an equivalent treatment choice; however, Seg is the prior option because it could preserve more lung function than Lob. For patients with PAP, Lob tends to be a better choice than Wed and Seg, although the prognostic difference between them is nonsignificant.

Clinicopathologic Features and Genetic Alterations in Adenocarcinoma In Situ and Minimally Invasive Adenocarcinoma of the Lung: Long-Term Follow-Up Study of 121 Asian Patients

BACKGROUND

Adenocarcinoma in situ (AIS) and minimally invasive adenocarcinoma (MIA) are both small tumors with good prognosis after surgical resection, and most of them present as ground glass opacities (GGOs) on computed tomography (CT) screening. However, the differences in clinicopathologic features and genetic alterations between AIS and MIA are poorly elaborated, and few studies have evaluated the prognosis of MIA with different invasive components. Meanwhile, the histological features of lung lesions presenting as unchanged pure GGOs are barely understood.

METHODS

Clinicopathologic features and genetic alterations of AIS (n = 59) and MIA (n = 62) presenting as GGOs were analyzed. Long-term preoperative observation (ranging from 2 to 1967 days) and postoperative follow-up (ranging from 0 to 92 months) was conducted.

RESULTS

The tumor size and consolidation/tumor ratio were significantly larger in the MIA cohort than those in the AIS cohort both on CT and microscopy images. Immunohistochemically, the expression of p53, Ki67, and cyclin D1 was higher in MIA than in AIS. The EGFR mutation rate was significantly higher in MIA, while other genetic alterations showed no differences. Six MIA cases showed recurrence or metachronous adenocarcinoma and all the cases with a predominant micropapillary invasive pattern demonstrated this feature.

CONCLUSIONS

The current CT measurements may be helpful in distinguishing AIS from MIA, but show limited utility in predicting the histology of unchanged pure GGOs. The invasive pattern may have an influence on the postoperative process of MIA; therefore, further studies are needed to evaluate the current diagnostic criteria and treatment strategy for MIA.

Emphysema is associated with the aggressiveness of COPD-related adenocarcinomas

OBJECTIVES

To compare the differences in radiologic and pathologic features of surgically resected chronic obstructive pulmonary disease (COPD)-related adenocarcinomas according to the presence of emphysema.

METHODS

A total of 216 smokers with surgically resected lung adenocarcinoma were included in this retrospective study, and 102 patients were diagnosed with COPD. We classified COPD patients as emphysematous or non-emphysematous group based on the emphysema severity on computed tomography (CT) and evaluated the differences in the CT and pathologic features between the two groups. The relationship between emphysema and disease-free survival was assessed using a Kaplan-Meier curve.

RESULTS

Lung adenocarcinomas in emphysema group presented a more aggressive pathologic grade and higher prevalence of solid lesions (vs subsolid lesions) on CT than those in non-emphysematous group (P = 0.006 and <0.001, respectively). After adjustment for age, sex, smoking pack-years and tumor size, emphysema group had a greater risk for higher histologic grade and higher prevalence of solid lesions than non-emphysema group (odds ratio, 3.445; 95% confidence interval, 1.124-10.564; P = 0.030, odds ratio, 6.192; 95% confidence interval, 1.804-21.254; P = 0.004, respectively). Kaplan-Meier survival curves showed that patients with emphysema had significantly impaired disease-free survival compared with those without emphysema (median disease-free survival = 37.0 vs 57.5 months, P = 0.038).

CONCLUSION

Adenocarcinomas in emphysema-present COPD had more aggressive features of pathology and CT findings, and worse disease-free survival than those without emphysema. These findings might provide an insight into the different pathobiology and prognostic implications of lung adenocarcinomas according to the presence of emphysema in patients with COPD.