Clinical implication of minimal presence of solid or micropapillary subtype in early-stage lung adenocarcinoma

Prognostic and clinicopathological significance of the new grading system for invasive pulmonary adenocarcinoma: A systematic review and meta-analysis

In 2020, the International Association for the Study of Lung Cancer (IASLC) introduced a new grading system for invasive pulmonary adenocarcinoma (IPA). This meta-analysis aimed to validate the prognostic utility of this grading system and identify relevant clinicopathological features. The PubMed, Embase, Web of Science, and Cochrane Library databases were searched for relevant studies published between January 1, 2020 and March 5, 2024. Hazard ratios (HRs) with corresponding 95 % confidence intervals (CIs) were pooled to evaluate the effect of IASLC grading on prognosis. Odds ratios with corresponding 95 % CIs were pooled to assess relevant clinicopathological features. Twenty-two studies comprising 12,515 patients with IPA were included. Regarding overall survival, grade 3 adenocarcinomas had a worse prognosis compared with grades 1-2 (HR: 2.26, 95 % CI: 1.79-2.85, P<0.001), grade 1 (HR: 4.75, 95 % CI: 2.61-8.66, P<0.001), or grade 2 (HR: 1.71, 95 % CI: 1.28-2.29, P<0.001). Considering recurrence-free survival, grade 3 tumors had a higher recurrence risk than grades 1-2 (HR: 1.92, 95 % CI: 1.53-2.41, P<0.001), grade 1 (HR: 4.43, 95 % CI: 2.91-6.73, P<0.001), or grade 2 (HR: 1.67, 95 % CI: 1.33-2.10, P<0.001). In the subgroup analysis of stage I patients, grade 3 tumors exhibited a similarly poor prognosis. In addition, grade 3 adenocarcinomas were associated with aggressive clinicopathological features. This study demonstrated that the IASLC grading system is a robust predictor of prognostic stratification in patients with IPA, and warrants further promotion and worldwide implementation.

European multicentre study evaluating the prognosis of peripheral early-stage lung adenocarcinoma patients operated on by segmentectomy or lobectomy

OBJECTIVES

To analyse impact of segmentectomy on oncological outcomes of different peripheral early-stage lung adenocarcinoma patterns.

METHODS

Retrospective multicentre study including patients who underwent either lobectomy or segmentectomy in 6 European centres from 2015 to 2021, for ≤2 cm pathological peripheral lung adenocarcinoma. Overall and disease-free survivals were assessed by cox-regression and lung cancer-specific survival by competing regression analyses to adjust for patient- and tumour-related factors both in the entire dataset and the in aggressive adenocarcinoma patterns dataset.

RESULTS

Lobectomy and segmentectomy were performed in 481 (71%) and 193 (29%) patients, respectively. Propensity score matching was performed (n = 191). One hundred and 8 patients had a least an aggressive pattern. Five-year disease-free, overall and lung cancer-specific survivals were similar between patients who underwent lobectomy or segmentectomy in both entire and aggressive pattern datasets. In patients with aggressive pattern, 5-year disease-free (lobectomy 87.3%; segmentectomy 86.6%, P = 0.62), overall (lobectomy 86.4%; segmentectomy 95.6%, P = 0.61) and lung cancer-specific (lobectomy 100%; segmentectomy 95.6%, P = 0.13) survivals did not differ. Segmentectomy was not an independent risk factor for disease-free survival, neither for overall survival nor for lung cancer-specific survival in any of the 2 datasets. In patients with aggressive pattern, loco-regional recurrence (linearized risks: lobectomy 8.21; segmentectomy 11.3) was higher in patients who underwent segmentectomy.

CONCLUSIONS

Resection should not be extended (to lobectomy) on patients who underwent segmentectomy for pathologically proven early-stage adenocarcinoma with aggressive patterns.

Predicting prognosis in patients with stage IA lung adenocarcinoma with a micropapillary component using a nomogram based on computed tomography radiomics and clinicopathologic factors: a retrospective analysis

Background

Patients with stage IA lung adenocarcinoma (ADC) with an micropapillary (MIP) component are at a higher risk of recurrence after radical surgical resection; however, adding adjuvant chemotherapy (ACT) to their postoperative course remains controversial. This study determined the predictive factors that influence the prognosis of these patients and identified those at high risk of recurrence.

Methods

Between January 2012 and December 2018, 254 eligible patients with stage IA lung ADC with an MIP component were categorized into training (n=169) and validation (n=85) cohorts. Clinicopathological and radiomics features were included in univariate and multivariate analyses, and statistically significant predictors were used to develop the nomogram. Area under the curve (AUC) of receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA) were used to validate the model. The calculated risk scores for each patient were risk-stratified using the X-tile procedure, and survival analyses were performed among the different risk subgroups.

Results

T1c stage, MIP ≥1%, spread through air space (STAS), carcinoembryonic antigen (CEA) >5 µg/L, and radiomics features were independent predictors of overall survival (OS) and disease-free survival (DFS) in patients with lung ADC with an MIP component at stage IA. Incorporating this into the nomogram, the AUCs of the nomogram predicting 3-, 5-, and 7-year OS and DFS were 0.910, 0.914, and 0.904 and 0.868, 0.838, and 0.848, respectively, in the training cohort and 0.879, 0.895, and 0.899 and 0.817, 0.805, and 0.811, respectively, in the validation cohort, showing good differentiation. The OS and DFS survival analyses among different risk subgroups showed that the nomogram could well distinguish between low- and high-risk groups.

Conclusions

We developed and validated a nomogram based on clinicopathological factors and radiomics features, which can be used as a powerful tool for predicting postoperative recurrence and survival in patients with stage IA lung ADC containing an MIP component.

Deep learning-based characterization of pathological subtypes in lung invasive adenocarcinoma utilizing 18F-deoxyglucose positron emission tomography imaging

OBJECTIVE

To evaluate the diagnostic efficacy of a deep learning (DL) model based on PET/CT images for distinguishing and predicting various pathological subtypes of invasive lung adenocarcinoma.

METHODS

A total of 250 patients diagnosed with invasive lung cancer were included in this retrospective study. The pathological subtypes of the cancer were recorded. PET/CT images were analyzed, including measurements and recordings of the short and long diameters on the maximum cross-sectional plane of the CT image, the density of the lesion, and the associated imaging signs. The SUVmax, SUVmean, and the lesion's long and short diameters on the PET image were also measured. A manual diagnostic model was constructed to analyze its diagnostic performance across different pathological subtypes. The acquired images were first denoised, followed by data augmentation to expand the dataset. The U-Net network architecture was then employed for feature extraction and network segmentation. The classification network was based on the ResNet residual network to address the issue of gradient vanishing in deep networks. Batch normalization was applied to ensure the feature matrix followed a distribution with a mean of 0 and a variance of 1. The images were divided into training, validation, and test sets in a ratio of 6:2:2 to train the model. The deep learning model was then constructed to analyze its diagnostic performance across different pathological subtypes.

RESULTS

Statistically significant differences (P < 0.05) were observed among the four different subtypes in PET/CT imaging performance. The AUC and diagnostic accuracy of the manual diagnostic model for different pathological subtypes were as follows: APA: 0.647, 0.664; SPA: 0.737, 0.772; PPA: 0.698, 0.780; LPA: 0.849, 0.904. Chi-square tests indicated significant statistical differences among these subtypes (P < 0.05). The AUC and diagnostic accuracy of the deep learning model for the different pathological subtypes were as follows: APA: 0.854, 0.864; SPA: 0.930, 0.936; PPA: 0.878, 0.888; LPA: 0.900, 0.920. Chi-square tests also indicated significant statistical differences among these subtypes (P < 0.05). The Delong test showed that the diagnostic performance of the deep learning model was superior to that of the manual diagnostic model (P < 0.05).

CONCLUSIONS

The deep learning model based on PET/CT images exhibits high diagnostic efficacy in distinguishing and diagnosing various pathological subtypes of invasive lung adenocarcinoma, demonstrating the significant potential of deep learning techniques in accurately identifying and predicting disease subgroups.

The impact of postoperative adjuvant therapy on EGFR-mutated stage IA lung adenocarcinoma with micropapillary pathological subtypes

BACKGROUND

Micropapillary (MPP) adenocarcinoma is considered one of the most aggressive pathological types of lung adenocarcinoma (LADC). This retrospective study aimed to evaluate the prognostic significance and benefit of postoperative adjuvant therapy (PAT) in stage IA LADC patients with different proportions of MPP components.

MATERIALS AND METHODS

We retrospectively examined clinical stage IA LADC patients who underwent surgical resection between August 2012 and December 2019. In terms of the proportion of MPP components (TPM), the tumors were reclassified into three categories: MPP patterns absent (TPMN); low proportions of MPP components (TPML); and high proportions of MPP components (TPMH). The dates of recurrence and metastasis were identified based on physical examinations and were confirmed by histopathological examination.

RESULTS

Overall, 505 (TPMN, n = 375; TPML, n = 92; TPMH, n = 38) patients harboring EGFR mutations were enrolled in the study. Male sex (P = 0.044), high pathological stage (P < 0.001), and MPP pathological subtype (P < 0.001) were more frequent in the TPM-positive (TPMP) group than in the TPM-negative (TPMN) group. Five-year disease-free survival (DFS) rates were significantly lower in the TPMP group than in the TPMN group (84.5% vs. 93.4%, P = 0.006). In addition, patients with high proportions (greater than 10%) of MPP components had worse overall survival (OS) (91.0% vs. 98.9%, P = 0.025) than those with low proportions (5%≤ TPM ≤ 10%). However, postoperative EGFR tyrosine kinase inhibitors (TKIs) or adjuvant chemotherapy (ACT) cannot improve DFS and OS between EGFR-mutated patients with different proportions of MPP components.

CONCLUSION

MPP was related to earlier recurrence and shortened survival time, even in stage IA. Further research needs a larger sample size to clarify that EGFR-mutated stage IA patients with MPP components obtain survival benefits from adjuvant therapy.

Sublobar resection for lung adenocarcinoma less than 2 cm containing solid or micropapillary components radiologically presented as consolidation-to-tumor ratio (CTR) ≤0.25 [ground-glass opacity (GGO)]

Background

The suitability of sublobar resection as a surgical approach for early-stage non-small cell lung cancer (NSCLC) remains unclear. This study investigated the feasibility of sublobar resection in patients with pathological-stage IA adenocarcinoma less than 2 cm characterized by a high-risk pathological subtype but exhibiting radiologically noninvasive features.

Methods

We conducted a retrospective review of patients diagnosed with pathological stage IA lung adenocarcinoma who underwent surgical intervention between 2013 and 2017. The inclusion criteria included a maximum tumor diameter of 2.0 cm or less, a consolidation-to-tumor ratio (CTR) of 0.25 or less, and a histopathological confirmation of a solid or micropapillary component. Patients were categorized into sublobar resection and lobectomy groups, and propensity score matching was employed to mitigate potential confounders. The primary endpoints were lung cancer-specific survival (LCSS) and overall survival (OS).

Results

The study comprised 149 patients, with 84 in the lobectomy group and 65 in the limited resection group. In the overall cohort, the 5-year LCSS was 100% for both groups, while the 5-year OS was 97.6% (95% CI: 94.41-100.00%) in the lobectomy group and 100% in the sublobar resection group (P=0.21). After propensity score matching, the LCSS remained at 100% for both groups, and the 5-year OS was 97.14% in the lobectomy group and 100% in the sublobar resection group (P=0.32).

Conclusions

Based on our experience, for lung adenocarcinoma containing solid/micropapillary subtype, a size less than 2 cm, and a CTR ≤0.25, the oncological outcomes appeared to be comparable between sublobar resection and lobectomy, suggesting that sublobar resection might serve as an equivalent alternative to lobectomy for such lesions.

CT morphological features and histogram parameters to predict micropapillary or solid components in stage IA lung adenocarcinoma

Objectives

This study aimed to construct prediction models based on computerized tomography (CT) signs, histogram and morphology features for the diagnosis of micropapillary or solid (MIP/SOL) components of stage IA lung adenocarcinoma (LUAC) and to evaluate the models' performance.

Methods

This clinical retrospective study included image data of 376 patients with stage IA LUAC based on postoperative pathology, admitted to Putian First Hospital from January 2019 to June 2023. According to the presence of MIP/SOL components in postoperative pathology, patients were divided into MIP/SOL+ and MIP/SOL- groups. Cases with tumors ≤ 3 cm and ≤ 2 cm were separately analyzed. Each subgroup of patients was then randomly divided into a training set and a test set in a ratio of 7:3. The training set was used to build the prediction model, and the test set was used for internal validation.

Results

For tumors ≤ 3 cm, ground-glass opacity (GGO) [odds ratio (OR) = 0.244; 95% confidence interval (CI): 0.103-0.569; p = 0.001], entropy (OR = 1.748; 95% CI: 1.213-2.577; p = 0.004), average CT value (OR = 1.002; 95% CI: 1.000-1.004; p = 0.002), and kurtosis (OR = 1.240; 95% CI: 1.023-1.513; p = 0.030) were independent predictors of MIP/SOL components of stage IA LUAC. The area under the ROC curve (AUC) of the nomogram prediction model for predicting MIP/SOL components was 0.816 (95% CI: 0.756-0.877) in the training set and 0.789 (95% CI: 0.689-0.889) in the test set. In contrast, for tumors ≤ 2 cm, kurtosis was no longer an independent predictor. The nomogram prediction model had an AUC of 0.811 (95% CI: 0.731-0.891) in the training set and 0.833 (95% CI: 0.733-0.932) in the test set.

Conclusion

For tumors ≤ 3 cm and ≤ 2 cm, GGO, average CT value, and entropy were the same independent influencing factors in predicting MIP/SOL components of stage IA LUAC. The nomogram prediction models have potential diagnostic value for identifying MIP/SOL components of early-stage LUAC.

The International Association for the Study of Lung Cancer (IASLC) Staging Project for Lung Cancer: Recommendation to Introduce Spread Through Air Spaces as a Histologic Descriptor in the Ninth Edition of the TNM Classification of Lung Cancer. Analysis of 4061 Pathologic Stage I NSCLC

INTRODUCTION

Spread through air spaces (STAS) consists of lung cancer tumor cells that are identified beyond the edge of the main tumor in the surrounding alveolar parenchyma. It has been reported by meta-analyses to be an independent prognostic factor in the major histologic types of lung cancer, but its role in lung cancer staging is not established.

METHODS

To assess the clinical importance of STAS in lung cancer staging, we evaluated 4061 surgically resected pathologic stage I R0 NSCLC collected from around the world in the International Association for the Study of Lung Cancer database. We focused on whether STAS could be a useful additional histologic descriptor to supplement the existing ones of visceral pleural invasion (VPI) and lymphovascular invasion (LVI).

RESULTS

STAS was found in 930 of 4061 of the pathologic stage I NSCLC (22.9%). Patients with tumors exhibiting STAS had a significantly worse recurrence-free and overall survival in both univariate and multivariable analyses involving cohorts consisting of all NSCLC, specific histologic types (adenocarcinoma and other NSCLC), and extent of resection (lobar and sublobar). Interestingly, STAS was independent of VPI in all of these analyses.

CONCLUSIONS

These data support our recommendation to include STAS as a histologic descriptor for the Ninth Edition of the TNM Classification of Lung Cancer. Hopefully, gathering these data in the coming years will facilitate a thorough analysis to better understand the relative impact of STAS, LVI, and VPI on lung cancer staging for the Tenth Edition TNM Stage Classification.

Association of the SHOX2 and RASSF1A methylation levels with the pathological evolution of early-stage lung adenocarcinoma

Background The methylation of SHOX2 and RASSF1A shows promise as a potential biomarker for the early screening of lung cancer, offering a solution to remedy the limitations of morphological diagnosis. The aim of this study is to diagnose lung adenocarcinoma by measuring the methylation levels of SHOX2 and RASSF1A, and provide an accurate pathological diagnosis to predict the invasiveness of lung cancer prior to surgery.Material and methods The methylation levels of SHOX2 and RASSF1A were quantified using a LungMe® test kit through methylation-specific PCR (MS-PCR). The diagnostic efficacy of SHOX2 and RASSF1A and the cutoff values were validated using ROC curve analysis. The hazardous factors influencing the invasiveness of lung adenocarcinoma were calculated using multiple regression.Results: The cutoff values of SHOX2 and RASSF1A were 8.3 and 12.0, respectively. The sensitivities of LungMe® in IA, MIA and AIS patients were 71.3% (122/171), 41.7% (15/36), and 16.1% (5/31) under the specificity of 94.1% (32/34) for benign lesions. Additionally, the methylation level of SHOX2, RASSF1A and LungMe® correlated with the high invasiveness of clinicopathological features, such as age, gender, tumor size, TNM stage, pathological type, pleural invasion and STAS. The tumor size, age, CTR values and LungMe® methylation levels were identified as independent hazardous factors influencing the invasiveness of lung adenocarcinoma.Conclusion: SHOX2 and RASSF1A combined methylation can be used as an early detection indicator of lung adenocarcinoma. SHOX2 and RASSF1A combined (LungMe®) methylation is significantly correlated to age, gender, tumor size, TNM stage, pathological type, pleural invasion and STAS. The SHOX2 and RASSF1A methylation levels, tumor size and CTR values could predict the invasiveness of the tumor prior to surgery, thereby providing guidance for the surgical procedure.

Establishment and validation of a clinicopathological prediction model for postoperative recurrence of stage IA lung adenocarcinoma

Background

With the popularization of low-dose spiral computed tomography (CT), an increasing number of stage IA lung cancers have been discovered. Patients with stage IA lung adenocarcinoma who undergo radical surgical resection tend to have a favourable prognosis. However, A significant proportion of patients undergo postoperative recurrence and metastasis. The purpose of this study was to screen out the risk factors in patients with stage IA lung adenocarcinoma and establish a nomogram model to help clinicians identify high-risk patient groups.

Methods

A nomogram was conducted based on a retrospective study of 731 patients with stage IA lung adenocarcinoma. Concordance index (C-index), clinical decision analysis, receiver operating characteristic (ROC) curve and calibration curve were used to evaluate the discrimination and calibration of the nomogram. Survival curves were drawn by Kaplan-Meier method, and significance was determined by log-rank test. According to nomogram scores, the patients were divided into low- and high-risk subgroups.

Results

The internal and external cohorts included 731 and 235 eligible patients. In univariate and multivariate analyses, the independent factors for recurrence-free survival (RFS) were all selected in the nomogram. C-indexes of the nomogram were 0.812 (95% confidence interval: 0.756-0.868) and 0.817 in the internal and external validation, respectively, showing that the prominent prediction performance was great. Nomogram scores showed that patients in the low-risk group (5-RFS rate, 0.797 to 0.99) had better RFS than patients in the high-risk group (5-RFS rate, 0.10 to 0.797) (P<0.001).

Conclusions

A nomogram model was established that can be beneficial to evaluate RFS in patients with stage IA lung adenocarcinoma after curative resection. It can be of value in helping clinicians develop treatment strategies to improve patient survival.

Computed tomography-based 3D convolutional neural network deep learning model for predicting micropapillary or solid growth pattern of invasive lung adenocarcinoma

PURPOSE

To investigate the value of a computed tomography (CT)-based deep learning (DL) model to predict the presence of micropapillary or solid (M/S) growth pattern in invasive lung adenocarcinoma (ILADC).

MATERIALS AND METHODS

From June 2019 to October 2022, 617 patients with ILADC who underwent preoperative chest CT scans in our institution were randomly placed into training and internal validation sets in a 4:1 ratio, and 353 patients with ILADC from another institution were included as an external validation set. Then, a self-paced learning (SPL) 3D Net was used to establish two DL models: model 1 was used to predict the M/S growth pattern in ILADC, and model 2 was used to predict that pattern in ≤ 2-cm-diameter ILADC.

RESULTS

For model 1, the training cohort's area under the curve (AUC), accuracy, recall, precision, and F1-score were 0.924, 0.845, 0.851, 0.842, and 0.843; the internal validation cohort's were 0.807, 0.744, 0.756, 0.750, and 0.743; and the external validation cohort's were 0.857, 0.805, 0.804, 0.806, and 0.804, respectively. For model 2, the training cohort's AUC, accuracy, recall, precision, and F1-score were 0.946, 0.858, 0.881,0.844, and 0.851; the internal validation cohort's were 0.869, 0.809, 0.786, 0.794, and 0.790; and the external validation cohort's were 0.831, 0.792, 0.789, 0.790, and 0.790, respectively. The SPL 3D Net model performed better than the ResNet34, ResNet50, ResNeXt50, and DenseNet121 models.

CONCLUSION

The CT-based DL model performed well as a noninvasive screening tool capable of reliably detecting and distinguishing the subtypes of ILADC, even in small-sized tumors.

The presence of micropapillary and/or solid subtypes is an independent prognostic factor for patients undergoing curative resection for stage I lung adenocarcinoma with ground-glass opacity

Background

Non-predominant or even minimal micropapillary and/or solid (MP/S) subtypes have been reported to exert an unfavorable prognostic influence on surgically resected lung adenocarcinoma (ADC). Currently, there is a lack of evidence to demonstrate that high-grade pathological subtypes, including MP/S components, impact the prognosis of patients with surgically resected lung ADCs with ground-glass opacity (GGO). In this investigation, we explored the prognostic implications of minimal MP/S components in lung ADCs with GGO.

Methods

A retrospective cohort study was conducted on 1,004 consecutive patients undergoing curative resection for pathologic stage (p-stage) I lung ADCs featuring GGO on computed tomography (CT) scans between January 2014 and December 2016. Tumors were categorized into MP/S positive (MP/S+) group and MP/S negative (MP/S-) group. MP/S+ tumors were defined when MP/S subtypes constituted ≥1% of the entire tumor. The prognostic impact of MP/S subtypes was evaluated using Kaplan-Meier analysis, Cox proportional hazard model and restricted cubic spine (RCS) model.

Results

A total of 86 (8.6%) cases with MP/S+ tumors and 918 (91.4%) cases with MP/S- tumors were identified. The solid component tumor diameter and pathological invasive tumor size of MP/S+ tumors were both significantly larger than that of MP/S- tumors (13.0 vs. 4.0 mm, P<0.001, and 18.0 vs. 10.0 mm, P<0.001, respectively). After a median follow-up of 7.3 years, the presence of MP/S components was significantly associated with decreased RFS (5-year RFS, MP/S+ 88.3% vs. MP/S- 97.4%; P<0.001; HR =1.02). The presence of a histologic lepidic (Lep) component demonstrated a prognostic advantage in both MP/S- (5-year RFS, MP/S-Lep+ 98.0% vs. MP/S-Lep- 95.3%; P=0.01; HR =0.89) and MP/S+ subgroups (5-year RFS, MP/S+Lep+ 93.4% vs. MP/S+Lep- 83.2%; P=0.10; HR =0.84). MP/S+ components ≥5% were the only tumor-related factor that independently affected RFS [hazard ratio (HR) =1.77; 95% confidence interval (CI): 1.07-2.94] according to multivariate analysis. There was a progressively negative impact of the proportion of MP/S subtypes on RFS as illustrated by RCS model.

Conclusions

The presence of MP/S patterns in stage I GGO-featured lung ADCs exhibit significant prognostic value and may have implications for tailored postoperative treatment and surveillance strategies, especially when the proportion exceeds 5% of the entire tumor.

Predictive value of systemic immune-inflammation index in the high-grade subtypes components of small-sized lung adenocarcinoma

BACKGROUND

Identification of micropapillary and solid subtypes components in small-sized (≤ 2 cm) lung adenocarcinoma plays a crucial role in determining optimal surgical procedures. This study aims to propose a straightforward prediction method utilizing preoperative available indicators.

METHODS

From January 2019 to July 2022, 341 consecutive patients with small-sized lung adenocarcinoma who underwent curative resection in thoracic surgery department of Xuanwu Hospital, Capital Medical University were retrospectively analyzed. The patients were divided into two groups based on whether solid or micropapillary components ≥ 5% or not (S/MP5+ and S/MP5-). Univariate analysis and multivariate logistic regression analysis were utilized to identify independent predictors of S/MP5+. Then a nomogram was constructed to intuitively show the results. Finally, the calibration curve with a 1000 bootstrap resampling and the receiver operating characteristic (ROC) curve were depicted to evaluate its performance.

RESULTS

According to postoperative pathological results, 79 (23.2%) patients were confirmed as S/MP5+ while 262 (76.8%) patients were S/MP5-. Based on multivariate analysis, maximum diameter (p = 0.010), consolidation tumor ratio (CTR) (p < 0.001) and systemic immune-inflammation index (SII) (p < 0.001) were identified as three independent risk factors and incorporated into the nomogram. The calibration curve showed good concordance between the predicted and actual probability of S/MP5+. Besides, the model showed certain discrimination, with an area under ROC curve of 0.893.

CONCLUSIONS

The model constructed based on SII is a practical tool to predict high-grade subtypes components of small-sized lung adenocarcinoma preoperatively and contribute to determine the optimal surgical approach.

Micropapillary or solid component predicts worse prognosis in pathological IA stage lung adenocarcinoma: A meta-analysis

BACKGROUND

Micropapillary and solid patterns indicate worse survival in lung adenocarcinoma patients, even in pathological stage IB patients. However, whether the presence of micropapillary or solid components is related to worse prognosis in pathological IA stage lung adenocarcinoma remains unclear.

METHODS

Several databases were searched up to December 31, 2022 for relevant studies investigating the association between micropapillary and solid components and the survival of IA stage lung adenocarcinoma patients. Primary and secondary outcomes are disease-free survival (DFS) and overall survival (OS), respectively. Hazard ratios (HRs) and 95% confident intervals (CIs) were combined, and subgroup analysis stratified by the pathological subtype and proportion of components was further performed.

RESULTS

A total of 19 studies with 12,562 cases were included. Pooled results indicated that micropapillary or solid components obviously predicted worse DFS (HR = 2.40, 95% CI: 1.96-2.94, P < .001) and OS (HR = 2.30, 95% CI: 1.68-3.15, P < .001). Subgroup analysis based on pathological subtype showed that both micropapillary and solid components were significantly associated with worse DFS (micropapillary: HR = 2.70, 95% CI: 1.70-4.28, P < .001; solid: HR = 3.98, 95% CI: 2.10-7.54, P < .001) and OS (micropapillary: HR = 2.29, 95% CI: 1.17-4.48, P = .015; solid: HR = 4.18, 95% CI: 1.72-10.17, P = .002). In addition, further subgroup analysis stratified by the proportion of micropapillary and solid components (>5%/1% or predominant) showed similar results.

CONCLUSION

Micropapillary and solid patterns predicted a significantly worse prognosis in pathological IA stage lung adenocarcinoma patients.

Standardized Classification of Lung Adenocarcinoma Subtypes and Improvement of Grading Assessment Through Deep Learning

The histopathologic distinction of lung adenocarcinoma (LADC) subtypes is subject to high interobserver variability, which can compromise the optimal assessment of patient prognosis. Therefore, this study developed convolutional neural networks capable of distinguishing LADC subtypes and predicting disease-specific survival, according to the recently established LADC tumor grades. Consensus LADC histopathologic images were obtained from 17 expert pulmonary pathologists and one pathologist in training. Two deep learning models (AI-1 and AI-2) were trained to predict eight different LADC classes. Furthermore, the trained models were tested on an independent cohort of 133 patients. The models achieved high precision, recall, and F1 scores exceeding 0.90 for most of the LADC classes. Clear stratification of the three LADC grades was reached in predicting the disease-specific survival by the two models, with both Kaplan-Meier curves showing significance (P = 0.0017 and 0.0003). Moreover, both trained models showed high stability in the segmentation of each pair of predicted grades with low variation in the hazard ratio across 200 bootstrapped samples. These findings indicate that the trained convolutional neural networks improve the diagnostic accuracy of the pathologist and refine LADC grade assessment. Thus, the trained models are promising tools that may assist in the routine evaluation of LADC subtypes and grades in clinical practice.

A clinical spectrum of resectable lung adenocarcinoma with micropapillary component (MPC) concurrently presenting as mixed ground-glass opacity nodules

BACKGROUND

In clinical practice, preoperative identification of mixed ground-glass opacity (mGGO) nodules with micropapillary component (MPC) to facilitate the implementation of individualized therapeutic strategies and avoid unnecessary surgery is increasingly importantOBJECTIVE: This study aimed to build a predictive model based on clinical and radiological variables for the early identification of MPC in lung adenocarcinoma presenting as mGGO nodules.

METHODS

The enrolled 741 lung adenocarcinoma patients were randomly divided into a training cohort and a validation cohort (3:1 ratio). The pathological specimens and preoperative images of malignant mGGO nodules from the study subjects were retrospectively reviewed. Furthermore, in the training cohort, selected clinical and radiological variables were utilized to construct a predictive model for MPC prediction.

RESULTS

The MPC was found in 228 (43.3%) patients in the training cohort and 72 (41.1%) patients in the validation cohort. Based on the predictive nomogram, the air bronchogram was defined as the most dominant independent risk factor for MPC of mGGO nodules, followed by the maximum computed tomography (CT) value (> 200), adjacent to pleura, gender (male), and vacuolar sign. The nomogram demonstrated good discriminative ability with a C-index of 0.783 (95%[CI] 0.744-0.822) in the training cohort and a C-index of 0.799 (95%[CI] 0.732-0.866) in the validation cohort Additionally, by using the bootstrapping method, this predictive model calculated a corrected AUC of 0.774 (95% CI: 0.770-0.779) in the training cohort.

CONCLUSIONS

This study proposed a predictive model for preoperative identification of MPC in known lung adenocarcinomas presenting as mGGO nodules to facilitate individualized therapy. This nomogram model needs to be further externally validated by subsequent multicenter studies.

Establishment and visualization of a model based on high-resolution CT qualitative and quantitative features for prediction of micropapillary or solid components in invasive lung adenocarcinoma

OBJECTIVE

To predict the existence of micropapillary or solid components in invasive adenocarcinoma, a model was constructed using qualitative and quantitative features in high-resolution computed tomography (HRCT).

METHODS

Through pathological examinations, 176 lesions were divided into two groups depending on the presence or absence of micropapillary and/or solid components (MP/S): MP/S- group (n = 128) and MP/S + group (n = 48). Multivariate logistic regression analyses were used to identify independent predictors of the MP/S. Artificial intelligence (AI)-assisted diagnostic software was used to automatically identify the lesions and extract corresponding quantitative parameters on CT images. The qualitative, quantitative, and combined models were constructed according to the results of multivariate logistic regression analysis. The receiver operating characteristic (ROC) analysis was conducted to evaluate the discrimination capacity of the models with the area under the curve (AUC), sensitivity, and specificity calculated. The calibration and clinical utility of the three models were determined using the calibration curve and decision curve analysis (DCA), respectively. The combined model was visualized in a nomogram.

RESULTS

The multivariate logistic regression analysis using both qualitative and quantitative features indicated that tumor shape (P =  0.029 OR =  4.89; 95% CI 1.175-20.379), pleural indentation (P = 0.039 OR =  1.91; 95% CI 0.791-4.631), and consolidation tumor ratios (CTR) (P < 0.001; OR = 1.05; 95% CI 1.036-1.070) were independent predictors for MP/S + . The areas under the curve (AUC) of the qualitative, quantitative, and combined models in predicting MP/S + were 0.844 (95% CI 0.778-0.909), 0.863 (95% CI 0.803-0.923), and 0.880 (95% CI 0.824-0.937). The combined model of AUC was the most superior and statistically better than qualitative model.

CONCLUSION

The combined model could assist doctors to evaluate patient's prognoses and devise personalized diagnostic and treatment protocols for patients.

Association of Molecular Profiles and Mutational Status With Distinct Histological Lung Adenocarcinoma Subtypes. An Analysis of the LACE-Bio Data

BACKGROUND

Adjuvant chemotherapy (AC) is indicated for stage II and stage III lung adenocarcinomas (ADC). Using the LACE Bio II database, we analyzed the distribution of various mutations across the subtypes of ADCs and studied the prognostic and predictive roles of PD-L1, TMB, and Tumor Infiltrating Lymphocytes (TILs).

MATERIALS AND METHODS

Clinical and genomic data from the LACE Bio II data were extracted. Patients were divided into ADC subtypes, in which the grouping was done based on their known clinical behavior (Lepidic [LEP], Acinar/Papillary [ACI or PAP], Micropapillary/Solid [MIP or SOL], Mucinous [MUC] and Others). Kaplan-Meier (KM) and log-rank test were used to compare survival based on PD-L1, TMB, TILs and combinations of TMB with PD-L1 and TILs. Adjusted Hazard Ratios (HR) were analyzed with Overall Survival (OS), Disease-Free Survival (DFS) and Lung Cancer-Specific Survival (LCSS) as endpoints.

RESULTS

A total of 375 ADC patients were identified. MIP/SOL was the subtype most commonly positive for various biomarkers. PD-L1 Negative/high TMB was associated with better outcomes in terms of OS (HR = 0.46 [0.23-0.89], P = .021) and DFS (HR = 0.52 [0.30-0.90], P = .02), relative to PD-L1 Negative/low TMB. High TMB predicted worse outcome with AC use in terms of OS (ratio of hazard ratio rHR = 2.75 [1.07-7.04], P = .035). Marked TILs had better outcome with AC for DFS (rHR = 0.22 [0.06-0.87], P = .031 and LCSS (rHR = 0.08 [0.01-0.66], P = .019) respectively. There was also a beneficial effect of AC among patients with Marked TILs/low TMB in terms of DFS (rHR = 0.06 [0.01-0.53], P = .011).

CONCLUSION

High TMB has a prognostic role in resectable lung ADC. The high TMB group had a poor outcome with AC, suggesting that this group may be better served with immune checkpoint therapy.

A Nomogram Based on Consolidation Tumor Ratio Combined with Solid or Micropapillary Patterns for Postoperative Recurrence in Pathological Stage IA Lung Adenocarcinoma

BACKGROUND

Patients with pathological stage IA lung adenocarcinoma (LUAD) are at risk of relapse. The value of the TNM staging system is limited in predicting recurrence. Our study aimed to develop a precise recurrence prediction model for stage IA LUAD.

MATERIALS AND METHODS

Patients with pathological stage IA LUAD who received surgical treatment at Zhongshan Hospital Fudan University were retrospectively analyzed. Multivariate Cox proportional hazards regression models were used to create nomograms for recurrence-free survival (RFS). The predictive performance of the model was assessed using calibration plots and the concordance index (C-index).

RESULTS

The multivariate Cox regression analysis revealed that CTR (0.75 < CTR ≤ 1; HR = 9.882, 95% CI: 2.036-47.959, p = 0.004) and solid/micropapillary-predominance (SMPP; >5% and the most dominant) (HR = 4.743, 95% CI: 1.506-14.933, p = 0.008) were independent prognostic factors of RFS. These risk factors were used to construct a nomogram to predict postoperative recurrence in these patients. The C-index of the nomogram for predicting RFS was higher than that of the eighth T-stage system (0.873 for the nomogram and 0.643 for the eighth T stage). The nomogram also achieved good predictive performance for RFS with a well-fitted calibration curve.

CONCLUSIONS

We developed and validated a nomogram based on CTR and SMP patterns for predicting postoperative recurrence in pathological stage IA LUAD. This model is simple to operate and has better predictive performance than the eighth T stage system, making it suitable for selecting further adjuvant treatment and follow-up.

Multiscale reconstruction of bronchus and cancer cells in human lung adenocarcinoma

BACKGROUND

While previous studies primarily focused on the structure of the normal whole mouse lung, the whole bronchus and cytoarchitectural details of the mouse intact lung lobe have been discovered at single-cell resolution. Revealing the sophisticated lung adenocarcinoma structure at three-dimensional (3D) and single-cell level remains a fundamental and critical challenge for the pathological mechanism research of lung adenocarcinoma (LA).

METHODS

Fluorescence micro-optical Sectioning Tomography (fMOST) combined with PI staining were used to obtain the 3D imaging of the human LA tissue at single-cell resolution.

RESULTS

With a spatial resolution of 0.32 × 0.32 × 1.0 μm³, the dataset of human LA with single-cell precision consists of two channels, each of which contains information about the bronchi and the cytoarchitecture. The bronchial wall is thicker and the lumen is smaller in the cancer tissue, in which its original normal structure is vanished. More solid components, more clustered cancer cells with larger nucleoli, and more significant atypia are found in cancer tissue. In paracancerous tissue, the bronchial wall cells have a monolayer or bilayer structure, cluster along the wall, and are relatively dispersed. Few fibrous structures and occasional dissemination of spread through air spaces (STAS) are observed.

CONCLUSIONS

Based on the human LA tissue dataset obtained by fMOST and PI staining, the bronchi and cells were reconstructed and visualized. This work provides a technical roadmap for studying the bronchus and cytoarchitectural structure and their spatial relationship in LA tissue, which may help with the understanding of the main histological structure of LA among pathologists.

[Advances in the Study of Invasive Non-mucinous Adenocarcinoma with Different Pathological Subtypes]

Lung cancer is the leading cause of cancer death in the world today, and adenocarcinoma is the most common histopathological type of lung cancer. In May 2021, World Health Organization (WHO) released the 5th edition of the WHO classification of thoracic tumors, which classifies invasive non-mucinous adenocarcinoma (INMA) into lepidic adenocarcinoma, acinar adenocarcinoma, papillary adenocarcinoma, solid adenocarcinoma, and micropapillary adenocarcinoma based on its histological characteristics. These five pathological subtypes differ in clinical features, treatment and prognosis. A complete understanding of the characteristics of these subtypes is essential for the clinical diagnosis, treatment options, and prognosis predictions of patients with lung adenocarcinoma, including recurrence and progression. This article will review the grading system, morphology, imaging prediction, lymph node metastasis, surgery, chemotherapy, targeted therapy and immunotherapy of different pathological subtypes of INMA.
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Prediction of high-grade patterns of stage IA lung invasive adenocarcinoma based on high-resolution CT features: a bicentric study

OBJECTIVES

This study aims to predict the high-grade pattern (HGP) of stage IA lung invasive adenocarcinoma (IAC) based on the high-resolution CT (HRCT) features.

METHODS

The clinical, pathological, and HRCT imaging data of 457 patients (from bicentric) with pathologically confirmed stage IA IAC (459 lesions in total) were retrospectively analyzed. The 459 lesions were classified into high-grade pattern (HGP) (n = 101) and non-high-grade pattern (n-HGP) (n = 358) groups depending on the presence of HGP (micropapillary and solid) in pathological results. The clinical and pathological data contained age, gender, smoking history, tumor stage, pathological type, and presence or absence of tumor spread through air spaces (STAS). CT features consisted of lesion location, size, density, shape, spiculation, lobulation, vacuole, air bronchogram, and pleural indentation. The independent predictors for HGP were screened by univariable and multivariable logistic regression analyses. The clinical, CT, and clinical-CT models were constructed according to the multivariable analysis results.

RESULTS

The multivariate analysis suggested the independent predictors of HGP, encompassing tumor size (p = 0.001; OR = 1.090, 95% CI 1.035-1.148), density (p < 0.001; OR = 9.454, 95% CI 4.911-18.199), and lobulation (p = 0.002; OR = 2.722, 95% CI 1.438-5.154). The AUC values of clinical, CT, and clinical-CT models for predicting HGP were 0.641 (95% CI 0.583-0.699) (sensitivity = 69.3%, specificity = 79.2%), 0.851 (95% CI 0.806-0.896) (sensitivity = 79.2%, specificity = 79.6%), and 0.852 (95% CI 0.808-0.896) (sensitivity = 74.3%, specificity = 85.8%).

CONCLUSION

The logistic regression model based on HRCT features has a good diagnostic performance for the high-grade pattern of stage IA IAC.

KEY POINTS

• The AUC values of clinical, CT, and clinical-CT models for predicting high-grade patterns were 0.641 (95% CI 0.583-0.699), 0.851 (95% CI 0.806-0.896), and 0.852 (95% CI 0.808-0.896). • Tumor size, density, and lobulation were independent predictive markers for high-grade patterns. • The logistic regression model based on HRCT features has a good diagnostic performance for the high-grade patterns of invasive adenocarcinoma.

Validation of the novel International Association for the Study of Lung Cancer grading system and prognostic value of filigree micropapillary and discohesive growth pattern in invasive pulmonary adenocarcinoma

INTRODUCTION

The Pathology Committee of the International Association for the Study of Lung Cancer (IASLC) proposed a new histological grading system based on the combination of predominant and high-grade patterns in 2020.

MATERIALS AND METHODS

Pathological sections from 631 patients with stage I-III invasive lung adenocarcinoma were reviewed. We then determined the histological grade according to the new grading system and confirmed the pathological features that included the filigree micropapillary and discohesive growth pattern. Applying of the novel IASLC grading system in prognosis stratification was verified and the clinical significance of the pathological characteristics was explored.

RESULTS

Cox multivariable analysis revealed that in the stage I-III invasive lung adenocarcinoma, the IASLC grading system was significantly associated with disease-free survival (DFS) [hazard ratio (HR) = 1.419; 95 % confidence interval (CI): 1.040-1.937; P = 0.027] and overall survival (OS) (HR = 1.899; 95 % CI: 1.168-3.087; P = 0.010). In patients with IASLC Grades 1 and 2, the simultaneous presence of filigree micropapillary and discohesive growth pattern was significantly correlated with DFS (HR = 1.899; 95 % CI:1.168-3.087; P = 0.010). However, the filigree micropapillary and discohesive growth pattern did not affect the OS (HR = 2.786; P = 0.317). The competitive risk model revealed that in the stage I cohort, the simultaneous presence of filigree micropapillary and discohesive growth pattern was a risk factor for recurrence and metastasis [sub- distribution HR (SHR) = 1.987; 95 %CI: 1.122-3.518; P = 0.019].

CONCLUSION

Our study verified that the new prognostic stratification system was an effective stratification tool. Filigree micropapillary and discohesive growth pattern may also be risk factors for DFS, postoperative recurrence and metastasis.

Minor histological components predict the recurrence of patients with resected stage I acinar- or papillary-predominant lung adenocarcinoma

Objective

Invasive lung adenocarcinoma is composed of five different histological subgroups with diverse biological behavior and heterogeneous morphology, the acinar/papillary-predominant lung adenocarcinomas are the most common subgroups and recognized as an intermediate-grade group. In the real world, clinicians primarily consider predominant patterns and ignore the impact of minor components in the prognosis of lung adenocarcinoma. The study evaluated the clinicopathologic characteristics of the lepidic, solid, and micropapillary patterns as non-predominant components and whether the minimal patterns had prognostic value on acinar/papillary-predominant lung adenocarcinomas.

Methods

A total of 153 acinar/papillary-predominant lung adenocarcinoma patients with tumor size ≤4 cm were classified into four risk subgroups based on the presence of lepidic and micropapillary/solid components: MP/S^-Lep⁺, MP/S⁺Lep⁺, MP/S^-Lep^-, and MP/S⁺Lep^- groups. The Cox-proportional hazard regression model was used to assess disease-free survival (DFS).

Results

The risk subgroups based on the non-predominant patterns were associated with differentiation (P = 0.001), lymphovascular invasion (P = 0.001), and recurrence (P = 0.003). In univariate analysis, DFS was correlated with non-predominant components (P = 0.014), lymphovascular invasion (P = 0.001), carcinoembryonic antigen (CEA) (P = 0.001), and platelet-to-lymphocyte ratio (PLR) (P = 0.012). In the multivariate analysis, non-predominant components (P = 0.043) and PLR (P = 0.032) were independent prognostic factors for DFS. The 5-year survival rates of MP/S^-Lep⁺, MP/S⁺Lep⁺, MP/S^-Lep^- and MP/S⁺Lep^- subgroups were 93.1%,92.9%,73.1%,61.9%, respectively. The MP/S^-Lep⁺ subgroup had the favorable prognosis than MP/S⁺Lep^- subgroup with a statistically significant difference (P = 0.002). As minor components, the lepidic patterns were a protective factor, and the solid and micropapillary components were poor factors. The recurrence was related to the presence of non-predominant patterns rather than their proportion. Adjuvant chemotherapy did not significantly improve the prognosis of the MP/S⁺Lep^- subgroup (P = 0.839).

Conclusions

Regardless of the proportion, the presence of micropapillary/solid components and the absence of lepidic patterns are aggressive factors of DFS in patients with resected stage I acinar- or papillary-predominant lung adenocarcinoma.

Establishment and validation of a radiological-radiomics model for predicting high-grade patterns of lung adenocarcinoma less than or equal to 3 cm

Objective

We aimed to develop a Radiological-Radiomics (R-R) based model for predicting the high-grade pattern (HGP) of lung adenocarcinoma and evaluate its predictive performance.

Methods

The clinical, pathological, and imaging data of 374 patients pathologically confirmed with lung adenocarcinoma (374 lesions in total) were retrospectively analyzed. The 374 lesions were assigned to HGP (n = 81) and non-high-grade pattern (n-HGP, n = 293) groups depending on the presence or absence of high-grade components in pathological findings. The least absolute shrinkage and selection operator (LASSO) method was utilized to screen features on the United Imaging artificial intelligence scientific research platform, and logistic regression models for predicting HGP were constructed, namely, Radiological model, Radiomics model, and R-R model. Also, receiver operating curve (ROC) curves were plotted on the platform, generating corresponding area under the curve (AUC), sensitivity, specificity, and accuracy. Using the platform, nomograms for R-R models were also provided, and calibration curves and decision curves were drawn to evaluate the performance and clinical utility of the model. The statistical differences in the performance of the models were compared by the DeLong test.

Results

The R-R model for HGP prediction achieved an AUC value of 0.923 (95% CI: 0.891-0.948), a sensitivity of 87.0%, a specificity of 83.4%, and an accuracy of 84.2% in the training set. In the validation set, this model exhibited an AUC value of 0.920 (95% CI: 0.887-0.945), a sensitivity of 87.5%, a specificity of 83.3%, and an accuracy of 84.2%. The DeLong test demonstrated optimal performance of the R-R model among the three models, and decision curves validated the clinical utility of the R-R model.

Conclusion

In this study, we developed a fusion model using radiomic features combined with radiological features to predict the high-grade pattern of lung adenocarcinoma, and this model shows excellent diagnostic performance. The R-R model can provide certain guidance for clinical diagnosis and surgical treatment plans, contributing to improving the prognosis of patients.

[A Review on Pathological High-risk Factors and Postoperative Adjuvant Chemotherapy in Stage IA Lung Adenocarcinoma]

The survival rate needs to be improved in early stage non-small cell lung cancer patients. The risk of recurrence is relatively high in invasive adenocarcinoma patients with a solid or micropapillary component, lymphovascular invasion or tumor spread through air spaces. Systemic treatment options including radical surgical resection should be explored for this population. Adjuvant chemotherapy is not recommended for patients in stage IA in current guidelines. This article is a review on the research progress of the above pathological high-risk factors and the role of adjuvant chemotherapy in patients with pathological high-risk factors in stage IA lung adenocarcinoma.
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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%.

Genomics Analysis and Nomogram Risk Prediction of Occult Lymph Node Metastasis in Non-Predominant Micropapillary Component of Lung Adenocarcinoma Measuring ≤ 3 cm

Background

The efficacy of sublobar resection and selective lymph node dissection is gradually being accepted by thoracic surgeons for patients within early-stage non-small cell lung cancer (NSCLC). Nevertheless, there are still some NSCLC patients develop lymphatic metastasis at clinical T1 stage. Lung adenocarcinoma with a micropapillary (MP) component poses a higher risk of lymph node metastasis and recurrence even when the MP component is not predominant. Our study aimed to explore the genetic features and occult lymph node metastasis (OLNM) risk factors in patients with a non-predominant micropapillary component (NP-MPC) in a large of patient’s cohort with surgically resected lung adenocarcinoma.

Methods

Between January 2019 and December 2021, 6418 patients who underwent complete resection for primary lung adenocarcinoma at the Qilu Hospital of Shandong University. In our study, 442 patients diagnosed with lung adenocarcinoma with NP-MPC with a tumor size ≤3 cm were included. Genetic alterations were analyzed using amplification refractory mutation system-polymerase chain reaction (ARMS-PCR). Abnormal protein expression of gene mutations was validated using immunohistochemistry. A nomogram risk model based on clinicopathological parameters was developed to predict OLNM. This model was invalidated using the calibration plot and concordance index.

Results

In our retrospective cohort, the incidence rate of the micropapillary component was 11.17%, and OLNM was observed in 20.13% of the patients in our study. ARMS-PCR suggested that EGFR exon 19 del was the most frequent alteration in NP-MCP patients compared with other gene mutations (frequency: 21.2%, P<0.001). Patients harboring exon 19 del showed significantly higher risk of OLNM (P< 0.001). A nomogram was developed based on five risk parameters, which showed good calibration and reliable discrimination ability (C-index = 0.84) for evaluating OLNM risk.

Conclusions.

Intense expression of EGFR exon 19 del characterizes lung adenocarcinoma in patients with NP-MCP and it’s a potential risk factor for OLNM. We firstly established a nomogram based on age, CYFRA21-1 level, tumor size, micropapillary and solid composition, that was effective in predicting OLNM among NP-MCP of lung adenocarcinoma measuring ≤ 3 cm.

A prognostic nomogram based on a new classification of combined micropapillary and solid components for stage IA invasive lung adenocarcinoma

BACKGROUND

We aimed to develop a prognostic nomogram based on a new classification of combined micropapillary and solid components in pathological stage IA invasive lung adenocarcinoma (LUAD).

METHODS

According to the total proportion of solid and micropapillary components (TPSM), the X-tile software was applied to classify patients into the following three groups: TPSM-low (TPSM-L), TPSM-middle (TPSM-M), and TPSM-high (TPSM-H). The postoperative survival was compared among the three groups. The multivariate Cox regression analysis was performed to identify independent prognostic factors for survival. According to these factors, a nomogram model was developed to provide a personalized prognostic evaluation.

RESULTS

A total of 595 patients with pathological stage IA invasive LUAD were included in our study. The 5-year disease-free survival and overall survival rates in patients with TPSM-H and TPSM-M were significantly lower than those with TPSM-L. The multivariate Cox regression analysis revealed that the TPSM classification was an independent prognostic factor for survival. According to TPSM classification, we developed a nomogram model which had good calibration and reliable discrimination ability to evaluate survival.

CONCLUSIONS

The nomogram based on the combination of micropapillary and solid components has good prognostic value in predicting postoperative recurrence and survival of patients with pathological stage IA invasive LUAD.

Different pulmonary adenocarcinoma growth patterns significantly affect survival

OBJECTIVE

Adenocarcinoma (AC) is the number one pathological entity of lung cancer with approximately 30-40% of cases. It is known to be heterogeneous and has 5 histopathological growth patterns. We evaluated the long-term survival rates of patients with predominant subtypes.

METHODS

290 patients with AC underwent pulmonary resection between 2012 and 2017 at our institution. We excluded all patients with lymph node involvement and distant metastases. Hence, 163 patients were included for further analysis. Predominant growth pattern was defined if more than 10% of cells showed a growth pattern. 1, 3, and 5-year survival rates were evaluated. Survival was assessed by Kaplan-Meier curves and the Cox proportional hazards model was used to identify prognostic factors for overall survival.

RESULTS

Predominant growth patterns >10% were compared to <10% growth patterns of the same subtype. 1-year, 3-year, and 5-year overall survival rates of patients with predominant solid tumor growth >10% differed significantly from patients with <10% (88.4% vs. 97.6%, p = 0.04; 65.8% vs. 87.4% p = 0.001, 36.4% vs. 65.9% p = 0.01). Survival rates did not differ between >10% papillary and acinar growth compared to <10%. Kaplan-Meier curves showed reduced overall survival for patients with solid tumor growth >10% (log-rank 0.002). Solid tumor growth >10% was an independent prognostic factor for worse long-term survival (Hazard ratio: 3.05, p = 0.01).

CONCLUSION

Our study demonstrates that the presence of a predominant solid pattern in pulmonary adenocarcinoma is a factor for an unfavorable prognosis. This should be kept in mind in daily clinical practice.

Prognostic significance of micropapillary and solid patterns in stage IA lung adenocarcinoma

OBJECTIVE

To evaluate the value of the non-predominant micropapillary and solid patterns in prognosis of lung adenocarcinoma.

METHODS

Totally 422 patients diagnosed with stage IA lung adenocarcinomas were included, and all their slides were reviewed. We compared clinicopathological characteristics and survival outcomes between MP- & SD- (both micropapillary and solid component were absent), MP+/SD+ (either micropapillary or solid component was present, but the single or combined percentage of the MP and SD was not greater than 50%) and MPp/SDp (either micropapillary or solid or the combined percentage of these two components was great than 50%).

RESULTS

Patients with MP- & SD- had smaller tumor size (P=0.012) and lower spread through air spaces rates (P<0.001). Patients with MP- & SD- had significantly better 5-year recurrence free survival than MP+/SD+ (91% versus 70%, P<0.001) and MPp/SDp (91% versus 56%, P<0.001). The difference of RFS between MP+/SD+ subgroup and MPp/SDp subgroup was not significant (P=0.177). In the multivariate analysis, patients with MP- & SD- had a better recurrence free survival than the other two groups (versus: MP+/SD+, HR, 3.198; 95% CI, 1.537-6.653; P=0.002; versus MPp/SDp: HR, 4.981; 95% CI, 2.266-10.950; P<0.001).

CONCLUSIONS

The presence of micropapillary or solid patterns, even not predominant, was a risk factor for predicting poor recurrence free survival in very early stage lung adenocarcinoma.

Preoperative Folate Receptor-Positive Circulating Tumor Cell Level Is a Prognostic Factor of Long Term Outcome in Non-Small Cell Lung Cancer Patients

Background

Surgical resection is often the preferred treatment for non-small cell lung cancer (NSCLC) patients. Predictive biomarkers after surgery can help monitoring and treating patients promptly, so as to improve the clinical outcome. In this study, we evaluated one potential candidate biomarker, the folate receptor-positive circulating tumor cell (FR⁺CTC), by investigating its prognostic and predictive significance in NSCLC patients who underwent surgery.

Methods

In this prospective, observational study, we enrolled NSCLC patients who were eligible to receive surgery. Prior to operation, peripheral blood was collected from each patient for an FR⁺CTC analysis. FR⁺CTCs were isolated by negative enrichment using immunomagnetic beads to deplete leukocytes and then quantitatively detected by a ligand-targeted polymerase chain reaction (PCR) method. These patients were then given standard care and were actively followed up for seven years. At the end of the follow-up period, the association between the FR⁺CTC level and the prognosis in these patients was evaluated.

Results

Overall, preoperative FR⁺CTC level was not significantly different among NSCLC patients with adenocarcinoma or non-adenocarcinoma subtypes (P = 0.24). However, between patients with low- and high-risk pathological adenocarcinoma subtypes, the preoperative FR⁺CTC level was significantly different (P = 0.028). Further, patients with lower preoperative FR⁺CTC level had longer relapse-free survival (RFS) and overall survival (OS) than those with higher preoperative FR⁺CTC level (RFS: not reached vs. 33.3 months, P = 0.018; OS: not reached vs. 72.0 months, P = 0.13). In a multivariate COX regression analysis, FR⁺CTC level (HR = 4.10; 95% CI, 1.23–13.64; P=0.022) and pathological stage (HR = 3.16; 95% CI, 1.79–10.14; P = 0.0011) were independent prognostic factors of RFS. Moreover, FR⁺CTC level together with adenocarcinoma subtypes provided additional information on risk for disease recurrence compared with FR⁺CTC or adenocarcinoma subtype alone.

Conclusion

Our study demonstrated that the preoperative FR⁺CTC level was a potential predictor for the prognosis of NSCLC patients underwent surgery. Further, when preoperative FR⁺CTC level is considered together with primary tumor proliferation characteristics, its prognostic value supplements that of these conventional pathological features.