High-grade tumor classified by new system is a prognostic predictor in resected 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.

Establishing a threshold for maximum standardized uptake value on 18 F-fluorodeoxyglucose PET/CT to predict high-grade lung adenocarcinoma and its prognostic significance

OBJECTIVE

The objective of this study is to determine an optimal threshold for the maximum standardized uptake value (SUVmax) of 18 F-fluorodeoxyglucose PET/CT to predict the newly proposed high-grade tumor classification and assess its prognostic significance in invasive lung adenocarcinoma (LUAD).

METHODS

Surgical specimens from 185 patients with pathological stage I invasive LUAD in the training group, along with 90 patients in the validation group, were analyzed using the novel IASLC grading system. The receiver operating characteristic curve was used to determine the optimal SUVmax threshold and assess its predictive accuracy. Disease-free survival (DFS) and overall survival (OS) were analyzed using Kaplan-Meier survival curves and Cox regression analysis.

RESULTS

Linear correlation analysis demonstrated a significant positive association between SUVmax and the proportion of high-grade histological patterns ( R ² = 0.346, P  < 0.001). The optimal SUVmax cutoff for predicting grade 3 tumors was 3.8, with an area under the curve of 0.866 in the training dataset and 0.899 in the validation dataset. Multivariate logistic regression analysis identified an SUVmax >3.8 as an independent predictor of grade 3 tumors ( P  < 0.001). In Cox regression analysis, SUVmax >3.8 was independently associated with reduced DFS (HR = 4.009, 95% CI: 1.568-10.250, P  = 0.004) and OS (HR = 5.536, 95% CI: 1.175-26.075, P  = 0.030).

CONCLUSION

As a noninvasive preoperative parameter, SUVmax >3.8 is a significant indicator of high-grade tumors as classified by the IASLC grading system and is strongly associated with worse DFS and OS.

Predicting malignant risk of ground-glass nodules using convolutional neural networks based on dual-time-point 18F-FDG PET/CT

BACKGROUND

Accurately predicting the malignant risk of ground-glass nodules (GGOs) is crucial for precise treatment planning. This study aims to utilize convolutional neural networks based on dual-time-point 18F-FDG PET/CT to predict the malignant risk of GGOs.

METHODS

Retrospectively analyzing 311 patients with 397 GGOs, this study identified 118 low-risk GGOs and 279 high-risk GGOs through pathology and follow-up according to the new WHO classification. The dataset was randomly divided into a training set comprising 239 patients (318 lesions) and a testing set comprising 72 patients (79 lesions), we employed a self-configuring 3D nnU-net convolutional neural network with majority voting method to segment GGOs and predict malignant risk of GGOs. Three independent segmentation prediction models were developed based on thin-section lung CT, early-phase 18F-FDG PET/CT, and dual-time-point 18F-FDG PET/CT, respectively. Simultaneously, the results of the dual-time-point 18F-FDG PET/CT model on the testing set were compared with the diagnostic of nuclear medicine physicians.

RESULTS

The dual-time-point 18F-FDG PET/CT model achieving a Dice coefficient of 0.84 ± 0.02 for GGOs segmentation and demonstrating high accuracy (84.81%), specificity (84.62%), sensitivity (84.91%), and AUC (0.85) in predicting malignant risk. The accuracy of the thin-section CT model is 73.42%, and the accuracy of the early-phase 18F-FDG PET/CT model is 78.48%, both of which are lower than the accuracy of the dual-time-point 18F-FDG PET/CT model. The diagnostic accuracy for resident, junior and expert physicians were 67.09%, 74.68%, and 78.48%, respectively. The accuracy (84.81%) of the dual-time-point 18F-FDG PET/CT model was significantly higher than that of nuclear medicine physicians.

CONCLUSIONS

Based on dual-time-point 18F-FDG PET/CT images, the 3D nnU-net with a majority voting method, demonstrates excellent performance in predicting the malignant risk of GGOs. This methodology serves as a valuable adjunct for physicians in the risk prediction and assessment of GGOs.

Combination of grade and spread through air spaces (STAS) predicts recurrence in early stage lung adenocarcinoma: a retrospective cohort study

Adenocarcinomas, a common subtype of lung cancer, exhibit diverse histological patterns. In 2020, The International Association for the Study of Lung Cancer (IASLC) introduced a grading system emphasizing high-grade components, which has shown prognostic value. Spread through air spaces (STAS) is recognized as a prognostic feature increasing the risk of recurrence in lung cancer. This study evaluates the combination of STAS status and the IASLC-grading system in surgically resected Stage I lung adenocarcinomas. This study is a retrospective analysis of 123 patients with Stage I lung adenocarcinoma who underwent lobectomy between 2011 and 2019. Histological patterns were assessed according to the IASLC criteria, and STAS status was documented. Patients were categorized based on their IASLC Grade and STAS status. Statistical analyses included Kaplan-Meier survival estimates, Cox proportional hazards models, and comparisons using Chi-square and t-tests. The cohort comprised 43 females and 80 males with a mean age of 61.8 ± 7.6 years. STAS positivity was noted in 52.8% of patients. STAS positivity correlated significantly with Grade 3 tumors (p < 0.001). The 5-year recurrence-free survival was significantly lower in STAS-positive patients (70.7% vs. 88.7%, p = 0.026). Patients with Grade 3 and STAS positivity had significantly lower recurrence-free survival compared to other groups (p = 0.002). Grade 3 and STAS positivity were independent predictors of poor recurrence-free survival in multivariate analysis. IASLC Grade 3 tumors and STAS positivity are independent prognostic factors for poor recurrence-free survival in Stage I lung adenocarcinomas. Adjuvant treatment strategies should be considered for patients with these characteristics to improve outcomes.

Prognostic impact of the newly revised IASLC proposed grading system for invasive lung adenocarcinoma: a systematic review and meta-analysis

BACKGROUND

This study aimed to evaluate the prognostic value of the newly revised International Association for the Study of Lung Cancer (IASLC) grading system (2020) on the 5-year overall survival (OS) and recurrence-free survival (RFS) in patients with lung adenocarcinoma (LADC).

METHODS

Clinical studies that investigated the prognostic value of revised IASLC staging system in patients with LADC were retrieved from the PubMed, Web of Science, ScienceDirect, and Cochrane Library databases. This study was conducted in accordance to the 2020 Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines and checklists.

RESULTS

Based on inclusion and exclusion criteria, we included 12 studies for analysis. The grade of LADC was assessed by revised IASLC system, which included three grades. Compared to Grade 3 LADC, grade 1 (total [95% CI]: 1.38 [1.19, 1.60]) and grade 2 (total [95% CI]: 1.29 [1.15, 1.44]) LADC had higher 5-year OS rates. Similarly, Grade 1 (total [95% CI]: 1.76 [1.42, 2.18]) and Grade 2 (total [95% CI]: 1.51 [1.28, 1.77]) had higher 5-year RFS rates Grade 3 LADC. However, 5-year OS and RFS had no significant difference between Grade 1 and Grade 2 patients.

CONCLUSION

This systematic review and meta-analysis provides evidence that the newly revised IASLC grading system is significantly associated with the prognosis of patients with LADC, where Grade 3 indicated unfavorable prognosis.

Predicting pathological grade of stage I pulmonary adenocarcinoma: a CT radiomics approach

Objectives

To investigate the value of CT radiomics combined with radiological features in predicting pathological grade of stage I invasive pulmonary adenocarcinoma (IPA) based on the International Association for the Study of Lung Cancer (IASLC) new grading system.

Methods

The preoperative CT images and clinical information of 294 patients with stage I IPA were retrospectively analyzed (159 training set; 69 validation set; 66 test set). Referring to the IASLC new grading system, patients were divided into a low/intermediate-grade group and a high-grade group. Radiomic features were selected by using the least absolute shrinkage and selection operator (LASSO), the logistic regression (LR) classifier was used to establish radiomics model (RM), clinical-radiological features model (CRM) and combined rad-score with radiological features model (CRRM), and visualized CRRM by nomogram. The area under the curve (AUC) of the receiver operating characteristic (ROC) curve and calibration curve were used to evaluate the performance and fitness of models.

Results

In the training set, RM, CRM, and CRRM achieved AUCs of 0.825 [95% CI (0.735-0.916)], 0.849 [95% CI (0.772-0.925)], and 0.888 [95% CI (0.819-0.957)], respectively. For the validation set, the AUCs were 0.879 [95% CI (0.734-1.000)], 0.888 [95% CI (0.794-0.982)], and 0.922 [95% CI (0.835-1.000)], and for the test set, the AUCs were 0.814 [95% CI (0.674-0.954)], 0.849 [95% CI (0.750-0.948)], and 0.860 [95% CI (0.755-0.964)] for RM, CRM, and CRRM, respectively.

Conclusion

All three models performed well in predicting pathological grade, especially the combined model, showing CT radiomics combined with radiological features had the potential to distinguish the pathological grade of early-stage IPA.

The presence of a cribriform pattern is related to poor prognosis in lung adenocarcinoma after surgical resection: A meta-analysis

OBJECTIVE

Previous studies reported that the cribriform pattern (CP) was associated with poor prognosis in lung adenocarcinoma (ADC) patients; therefore, a meta-analysis was performed to thoroughly evaluate the prognostic impact of cribriform pattern in postoperative ADC patients.

METHODS

Eligible studies were retrieved from PubMed, Embase databases, and Web of Science until April 2023. Studies evaluating the effect of the cribriform pattern on the prognosis of postoperative ADC patients were included. Subsequently, subgroup analysis was conducted according to the proportion of the cribriform pattern, with disease-free survival (DFS) and/or overall survival (OS) as outcomes. Hazard ratios (HRs) and 95% confidence intervals (CIs) were used as effect estimates in the meta-analyses, which were performed with a random-effects model despite the heterogeneity.

RESULTS

Nine studies published between 2015 and 2022 were included, with 4,289 ADC patients in total. The pooled results revealed a significantly poorer DFS (HR1.56, 95%CI 1.18-2.06, P = 0.11, I2 = 45%) and OS (HR2.11, 95%CI 1.63-2.72, P = 0.01, I2 = 56%) in patients with the cribriform pattern. Furthermore, the subgroup analysis showed that patients with a cribriform pattern (DFS: HR1.32, 95% CI 1.04-1.68 OS:HR2.30, 95% CI 1.55-3.39) and patients with a predominantly cribriform pattern (DFS:HR2.04, 95% CI 1.32--3.15 OS: HR1.92, 95% CI 1.41-2.61) were associated with poor prognosis.

CONCLUSIONS

The presence of a cribriform pattern is related to poor prognosis in postoperative ADC patients, despite not being a main tumor component. However, the results should be confirmed by large-scale and prospective studies owing to the small sample and potential heterogeneity.

Prognostic impact of the IASLC grading system of lung adenocarcinoma: a systematic review and meta-analysis

AIMS

Tumour grading is an essential part of the pathologic assessment that promotes patient management. The International Association for the Study of Lung Cancer (IASLC) proposed a grading system for non-mucinous lung adenocarcinoma in 2020. We aimed to validate the prognostic impact of this novel grading system on overall survival (OS) and recurrence-free survival (RFS) based on literature data.

METHODS AND RESULTS

The review protocol was registered in PROSPERO (CRD42023396059). We aimed to identify randomized or non-randomized controlled trials published after 2020 comparing different IASLC grade categories in Medline, Embase, and CENTRAL. Hazard ratios (HRs) with 95% confidence intervals (CIs) of OS and RFS were pooled and the Quality In Prognosis Studies (QUIPS) tool was used to assess the risk of bias in the included studies. Ten articles were eligible for this review. Regarding OS estimates, grade 1 lung adenocarcinomas were better than grade 3 both in univariate and multivariate analyses (HROSuni = 0.19, 95% CI: 0.05-0.66, p = 0.009; HROSmulti = 0.21, 95% CI: 0.12-0.38, p < 0.001). Regarding RFS estimates, grade 3 adenocarcinomas had a worse prognosis than grade 1 in multivariate analysis (HRRFSmulti: 0.22, 95% CI: 0.14-0.35, p < 0.001).

CONCLUSION

The literature data and the result of our meta-analysis demonstrate the prognostic relevance of the IASLC grading system. This supports the inclusion of this prognostic parameter in daily routine worldwide.

Whole-lesion iodine map histogram analysis versus single-slice spectral CT parameters for determining novel International Association for the Study of Lung Cancer grade of invasive non-mucinous pulmonary adenocarcinomas

PURPOSE

The purpose of this study was to evaluate and compare the performances of whole-lesion iodine map histogram analysis to those of single-slice spectral computed tomography (CT) parameters in discriminating between low-to-moderate grade invasive non-mucinous pulmonary adenocarcinoma (INMA) and high-grade INMA according to the novel International Association for the Study of Lung Cancer grading system of INMA.

MATERIALS AND METHODS

Sixty-one patients with INMA (34 with low-to-moderate grade [i.e., grade I and grade II] and 27 with high grade [i.e., grade III]) were evaluated with spectral CT. There were 28 men and 33 women, with a mean age of 56.4 ± 10.5 (standard deviation) years (range: 29-78 years). The whole-lesion iodine map histogram parameters (mean, standard deviation, variance, skewness, kurtosis, entropy, and 1st, 10th, 25th, 50th, 75th, 90th, and 99th percentile) were measured for each INMA. In other sessions, by placing regions of interest at representative levels of the tumor and normalizing them, spectral CT parameters (iodine concentration and normalized iodine concentration) were obtained. Discriminating capabilities of spectral CT and histogram parameters were assessed and compared using area under the ROC curve (AUC) and logistic regression models.

RESULTS

The 1st, 10th, and 25th percentiles of the iodine map histogram analysis, and iodine concentration and normalized iodine concentration of single-slice spectral CT parameters were significantly different between high-grade and low-to-moderate grade INMAs (P < 0.001 to P = 0.002). The 1st percentile of histogram parameters (AUC, 0.84; 95% confidence interval [CI]: 0.73-0.92) and iodine concentration (AUC, 0.78; 95% CI: 0.66-0.88) from single-slice spectral CT parameters had the best performance for discriminating between high-grade and low-to-moderate grade INMAs. At ROC curve analysis no significant differences in AUC were found between histogram parameters (AUC = 0.86; 95% CI: 0.74-0.93) and spectral CT parameters (AUC = 0.81; 95% CI: 0.74-0.93) (P = 0.60).

CONCLUSION

Both whole-lesion iodine map histogram analysis and single-slice spectral CT parameters help discriminate between low-to-moderate grade and high-grade INMAs according to the novel International Association for the Study of Lung Cancer grading system, with no differences in diagnostic performances.

18 F-FDG PET/CT characteristics of IASLC grade 3 invasive adenocarcinoma and the value of 18 F-FDG PET/CT for preoperative prediction: a new prognostication model

OBJECTIVE

This study is performed to investigate the imaging characteristics of the International Association for the Study of Lung Cancer grade 3 invasive adenocarcinoma (IAC) on PET/CT and the value of PET/CT for preoperative predicting this tumor.

MATERIALS AND METHODS

We retrospectively enrolled patients with IAC from August 2015 to September 2022. The clinical characteristics, serum tumor markers, and PET/CT features were analyzed. T test, Mann-Whitney U test, χ 2 test, Logistic regression analysis, and receiver operating characteristic analysis were used to predict grade 3 tumor and evaluate the prediction effectiveness.

RESULTS

Grade 3 tumors had a significantly higher maximum standardized uptake value (SUV max ) and consolidation-tumor-ratio (CTR) ( P  < 0.001), while Grade 1 - 2 tumors were prone to present with air bronchogram sign or vacuole sign ( P  < 0.001). A stepwise logistic regression analysis revealed that smoking history, CEA, SUV max , air bronchogram sign or vacuole sign and CTR were useful predictors for Grade 3 tumors. The established prediction model based on the above 5 parameters generated a high AUC (0.869) and negative predictive value (0.919), respectively.

CONCLUSION

Our study demonstrates that grade 3 IAC has a unique PET/CT imaging feature. The prognostication model established with smoking history, CEA, SUV max , air bronchogram sign or vacuole sign and CTR can effectively predict grade 3 tumors before the operation.

A pretreatment prediction model of grade 3 tumors classed by the IASLC grading system in lung adenocarcinoma

PURPOSE

The new grading system for invasive nonmucinous lung adenocarcinoma (LUAD) in the 2021 World Health Organization Classification of Thoracic Tumors was based on a combination of histologically predominant subtypes and high-grade components. In this study, a model for the pretreatment prediction of grade 3 tumors was established according to new grading standards.

METHODS

We retrospectively collected 399 cases of clinical stage I (cStage-I) LUAD surgically treated in Tianjin Chest Hospital from 2015 to 2018 as the training cohort. Besides, the validation cohort consists of 216 patients who were collected from 2019 to 2020. These patients were also diagnosed with clinical cStage-I LUAD and underwent surgical treatment at Tianjin Chest Hospital. Univariable and multivariable logistic regression analyses were used to select independent risk factors for grade 3 adenocarcinomas in the training cohort. The nomogram prediction model of grade 3 tumors was established by R software.

RESULTS

In the training cohort, there were 155 grade 3 tumors (38.85%), the recurrence-free survival of which in the lobectomy subgroup was better than that in the sublobectomy subgroup (P = 0.034). After univariable and multivariable analysis, four predictors including consolidation-to-tumor ratio, CEA level, lobulation, and smoking history were incorporated into the model. A nomogram was established and internally validated by bootstrapping. The Hosmer-Lemeshow test result was χ2 = 7.052 (P = 0.531). The C-index and area under the receiver operating characteristic curve were 0.708 (95% CI: 0.6563-0.7586) for the training cohort and 0.713 (95% CI: 0.6426-0.7839) for the external validation cohort.

CONCLUSIONS

The nomogram prediction model of grade 3 LUAD was well fitted and can be used to assist in surgical or adjuvant treatment decision-making.

PB-LNet: a model for predicting pathological subtypes of pulmonary nodules on CT images

OBJECTIVE

To investigate the correlation between CT imaging features and pathological subtypes of pulmonary nodules and construct a prediction model using deep learning.

METHODS

We collected information of patients with pulmonary nodules treated by surgery and the reference standard for diagnosis was post-operative pathology. After using elastic distortion for data augmentation, the CT images were divided into a training set, a validation set and a test set in a ratio of 6:2:2. We used PB-LNet to analyze the nodules in pre-operative CT and predict their pathological subtypes. Accuracy was used as the model evaluation index and Class Activation Map was applied to interpreting the results. Comparative experiments with other models were carried out to achieve the best results. Finally, images from the test set without data augmentation were analyzed to judge the clinical utility.

RESULTS

Four hundred seventy-seven patients were included and the nodules were divided into six groups: benign lesions, precursor glandular lesions, minimally invasive adenocarcinoma, invasive adenocarcinoma Grade 1, Grade 2 and Grade 3. The accuracy of the test set was 0.84. Class Activation Map confirmed that PB-LNet classified the nodules mainly based on the lungs in CT images, which is in line with the actual situation in clinical practice. In comparative experiments, PB-LNet obtained the highest accuracy. Finally, 96 images from the test set without data augmentation were analyzed and the accuracy was 0.89.

CONCLUSIONS

In classifying CT images of lung nodules into six categories based on pathological subtypes, PB-LNet demonstrates satisfactory accuracy without the need of delineating nodules, while the results are interpretable. A high level of accuracy was also obtained when validating on real data, therefore demonstrates its usefulness in clinical practice.

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.