Rethinking a Non-Predominant Pattern in Invasive Lung Adenocarcinoma: Prognostic Dissection Focusing on a High-Grade Pattern

Deciphering the intratumoral histologic heterogeneity of lung adenocarcinoma using radiomics

OBJECTIVE

To discern highly aggressive intratumoral areas among lung adenocarcinoma (LUAD) and its impact on occult nodal metastases and the recurrence rate with radiomic analysis.

METHODS

This prospective dual-institution study analyzed clinical information and high-resolution preoperative CT of 528 patients from institution A and 249 patients from institution B. We extracted radiomic features and performed pathologic evaluations for resected tumors, based on the 2020 International Association for the Study of Lung Cancer (IASLC) classification. Prediction models were developed to discern micropapillary and solid patterns within LUAD using clinical and radiomic features from institution A through logistic analysis.

RESULTS

Six selected CT radiomic features, sex, CTR (consolidation-to-tumor ratio), and solid diameter were selected to develop the prediction models. A composite model of radiomic and clinical characteristics outperformed radiomics-only and clinical-only models (AUC, 95% CI; the composite model: 0.84 [0.81-0.87]; the radiomics model: 0.82 [0.78-0.87]; the clinical model: 0.80 [0.76-0.83]) in institution A. External validation was performed with institution B cohort, showing even better results (AUC, 95% CI; the composite model: 0.91 [0.87-0.94]; the radiomics model: 0.89 [0.84-0.94]; the clinical model: 0.88 [0.84-0.92]).

CONCLUSIONS

Our study underscores the potential of radiomics to preoperatively predict aggressive histologic patterns in LUAD, enabling precise treatment planning and prognosis estimation.

KEY POINTS

Question Can any adjuvant methods address the limitations of core needle biopsies, which are invasive and may not capture the full heterogeneity of lung adenocarcinoma? Findings In a prospective study of 528 patients with cT1N0M0 lung adenocarcinoma, a composite model of clinical characteristics, conventional CT findings, and radiomics features predicted high-grade cancers. Clinical relevance Preoperative non-invasive diagnosis of histologically high-grade tumors using radiomics analysis offers crucial information for the treatment of lung adenocarcinoma with respect to occult lymph node metastasis and recurrence rate.

Prognostic Impact of Non-Predominant Lepidic Components in Pathologic Stage I Invasive Nonmucinous Adenocarcinoma

INTRODUCTION

This study investigated the prognostic impact of non-predominant lepidic components in invasive nonmucinous adenocarcinoma.

METHODS

Patients who underwent lobectomy and were diagnosed with stage I nonmucinous, non-lepidic-predominant invasive adenocarcinoma based on pathologic findings were included. Tumors were staged according to the eighth edition of TNM classification and categorized on the basis of the presence of lepidic components in the final pathologic findings. Overall survival (OS) and recurrence-free survival (RFS) were analyzed before and after applying inverse probability of treatment weighting. Competing risk analyses for recurrence were also compared in the two groups.

RESULTS

Of the 1270 patients, 858 (67.6%) had lepidic components (+). The pathologic stage and histologic grade were higher in the lepidic (-) group (p < 0.001, respectively). The 5-year OS and RFS were significantly worse in the lepidic (-) group than in the lepidic (+) group (OS: 88.2% versus 94.9%, p < 0.001; RFS: 79.4% versus 91.9%, p < 0.001). These trends were consistent after weighted analysis (OS: 92.4% versus 96.4%, p = 0.029; RFS: 85.6% versus 92.3%, p = 0.007). The 5-year cumulative incidence of any recurrence was 14.0% in the lepidic (-) group and 4.1% in the lepidic (+) group (p < 0.001). Multivariable Fine-Gray regression analysis found that the lepidic (+) group exhibited a lower risk of recurrence than did the lepidic (-) group (hazard ratio = 0.52, 95% confidence interval: 0.29-0.93, p = 0.031).

CONCLUSIONS

In pathologic stage I invasive nonmucinous adenocarcinoma, the presence of histologically diagnosed non-predominant lepidic components might be associated with a better prognosis after curative surgery.

Enhancing Identification of High-Risk cN0 Lung Adenocarcinoma Patients Using MRI-Based Radiomic Features

PURPOSE

This study aimed to develop a magnetic resonance imaging (MRI)-based radiomics model to predict high-risk pathologic features for lung adenocarcinoma: micropapillary and solid pattern (MPsol), spread through air space, and poorly differentiated patterns.

MATERIALS AND METHODS

As a prospective study, we screened clinical N0 lung cancer patients who were surgical candidates and had undergone both 18F-fluorodeoxyglucose (FDG) positron emission tomography-computed tomography (PET/CT) and chest CT from August 2018 to January 2020. We recruited patients meeting our proposed imaging criteria indicating high-risk, that is, poorer prognosis of lung adenocarcinoma, using CT and FDG PET/CT. If possible, these patients underwent an MRI examination from which we extracted 77 radiomics features from T1-contrast-enhanced and T2-weighted images. Additionally, patient demographics, maximum standardized uptake value on FDG PET/CT, and the mean apparent diffusion coefficient value on diffusion-weighted image, were considered together to build prediction models for high-risk pathologic features.

RESULTS

Among 616 patients, 72 patients met the imaging criteria for high-risk lung cancer and underwent lung MRI. The magnetic resonance (MR)-eligible group showed a higher prevalence of nodal upstaging (29.2% vs. 4.2%, p < 0.001), vascular invasion (6.5% vs. 2.1%, p=0.011), high-grade pathologic features (p < 0.001), worse 4-year disease-free survival (p < 0.001) compared with non-MR-eligible group. The prediction power for MR-based radiomics model predicting high-risk pathologic features was good, with mean area under the receiver operating curve (AUC) value measuring 0.751-0.886 in test sets. Adding clinical variables increased the predictive performance for MPsol and the poorly differentiated pattern using the 2021 grading system (AUC, 0.860 and 0.907, respectively).

CONCLUSION

Our imaging criteria can effectively screen high-risk lung cancer patients and predict high-risk pathologic features by our MR-based prediction model using radiomics.

Enhanced CT-Based Intratumoral and Peritumoral Radiomics Nomograms Predict High-Grade Patterns of Invasive Lung Adenocarcinoma

RATIONALE AND OBJECTIVES

Extraction of intratumoral and peritumoral radiomics features combined with clinical factors to establish nomograms to predict high-grade patterns (micropapillary and solid) of invasive adenocarcinoma of the lung (IAC).

MATERIALS AND METHODS

A retrospective study was conducted on 463 patients with pathologically confirmed IAC. Patients were randomized in a 7:3 ratio into a training cohort (n = 324) and a testing cohort (n = 139). A total of 2154 CT-based radiomic features were extracted from each of the four regions: gross tumor volume (GTV) and gross peritumoral tumor volume (GPTV3, GPTV6, GPTV9) containing peri-tumor regions of 3 mm, 6 mm, and 9 mm. A radiomics nomogram was constructed based on the optimal radiomics model and clinically independent predictors.

RESULTS

The GPTV3 radiomics model showed better predictive performance in the testing group compared to the GTV (0.840), GPTV6 (0.843), and GPTV9 (0.734) models, with an AUC value of 0.889 in the testing group. In the clinical model, tumor density and the presence of a spiculation sign were identified as independent predictors. The nomogram, which combined these independent predictors with the GPTV3-Radscore, proved to be clinically useful.

CONCLUSION

The GPTV3 radiomics model was superior to the GTV, GPTV6, and GPTV9 radiomics models in predicting high-grade patterns (HGP) of IAC. In addition, nomograms based on GPTV3 radiomics features and clinically independent predictors can further improve the prediction efficiency.

Preoperative prognostic prediction for invasive pulmonary adenocarcinoma: Impact of 18F-FDG PET/CT semi-quantitative parameters associated with new histological subtype classification

AIMS

To explore the preoperative predictive value of 18F-FDG PET/CT for poor prognostic histologic subtypes of invasive pulmonary adenocarcinoma (IPA) under new classification.

MATERIALS AND METHODS

This study included 316 patients. Histopathology of IPA was evaluated by recording the percentage of each histologic component. PET/CT parameters were compared among IPAs with different risks of recurrence. Optimum cutoff values of PET/CT parameters were calculated using ROC curve analysis. Overall survival (OS) and disease-free survival (DFS) were calculated using Kaplan-Meier method, and survival differences between groups were tested using log-rank test. Multivariate analysis for survival was performed using the Cox regression model.

RESULTS

Patients were divided into low (LRR), intermediate (IRR), and modified high (mHRR) risk of recurrence group incorporating typical (HRR-T) and nontypical (HRR-NT) subgroups based on histologic patterns. There were significant differences in SUVmax, SUVmean, SUVmin, SUVSD, TLG, and tumor size among three groups. HRR-NT had lower SUVmax, SUVmean, SUVmin, SUVSD and TLG than HRR-T subgroup, and higher SUVmax, SUVmean, SUVmin, SUVSD, MTV, TLG and tumor size than IRR group. ROC curve analysis showed that SUVmax had highest AUC (0.815) in distinguishing LRR and IRR. TLG had highest AUC (0.741) in distinguishing IRR and mHRR. Multivariable analysis showed that tumor size and SUVmax were independent predictors of DFS and OS.

CONCLUSIONS

High risk of recurrence of IPA exhibited higher 18F-FDG uptake and tumor size. Tumor size and SUVmax could be used as preoperative surrogates for the IASLC grading system. 18F-FDG PET/CT can improve the preoperative prognostic prediction for IPA patients.

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.

18F-FDG PET/CT-based radiomics model for predicting the degree of pathological differentiation in non-small cell lung cancer: a multicentre study

AIM

To explore the value of 2-[18F]-fluoro-2-deoxy-d-glucose (FDG) positron-emission tomography (PET)/computed tomography (CT)-based radiomics model for predicting the degree of pathological differentiation in non-small-cell lung cancer (NSCLC).

MATERIALS AND METHODS

Clinical characteristics of 182 NSCLC patients from four centres were collected, and radiomics features were extracted from 18F-FDG PET/CT images. Three logistic regression prediction models were established: clinical model; radiomics model; and nomogram combining radiomics signatures and clinical features. The predictive ability of the models was assessed using receiver operating characteristics curve analysis.

RESULTS

Patients from centre 1 were assigned randomly to the training and internal validation cohorts (7:3 ratio); patients from centres 2-4 served as the external validation cohort. The area under the curve (AUC) values for the clinical model in the training, internal validation, and external validation cohort were 0.74 (95% confidence interval [CI] = 0.64-0.84), 0.64 (95% CI = 0.46-0.81), and 0.74 (95% CI = 0.60-0.88), respectively. In the training (AUC: 0.84 [95% CI = 0.77-0.92]), internal validation (AUC: 0.81 [95% CI = 0.67-0.95]), and external validation cohorts (AUC: 0.74 [95% CI = 0.58-0.89]), the radiomics model showed good predictive ability for differentiation. Compared to the clinical and radiomics models, the nomogram has relatively better diagnostic performance, and the AUC values for nomogram in the training, internal validation, and external validation cohort were 0.86 (95% CI = 0.78-0.93), 0.83 (95% CI = 0.70-0.96), and 0.77 (95% CI = 0.62-0.92), respectively.

CONCLUSIONS

The 18F-FDG PET/CT-based radiomics model showed good ability for predicting the degree of differentiation of NSCLC. The nomogram combining the radiomics signature and clinical features has relatively better diagnostic performance.

Clinical implication of the 2020 International Association for the Study of Lung Cancer histologic grading in surgically resected pathologic stage 1 lung adenocarcinomas: Prognostic value and association with computed tomography characteristics

OBJECTIVES

To investigate the incremental prognostic value of the 2020 International Association for the Study of Lung Cancer (IASLC) histologic grading system over traditional prognosticators in surgically resected pathologic stage 1 lung adenocarcinomas and to identify the clinical and radiologic characteristics of lung adenocarcinomas reclassified by the 2020 histologic grading system.

MATERIALS AND METHODS

We retrospectively enrolled 356 patients who underwent surgery for pathologic stage 1 adenocarcinoma between January 2016 and December 2017. The histologic grading was classified according to the predominant histologic subtype (conventional system) and the updated 2020 IASLC grading system. The clinical and computed tomography (CT) characteristics were compared according to the reclassification of the updated system. The performance of prognostic models for recurrence-free survival based on the combination of pathologic tumor size, histologic grade, and CT-based information was compared using the c-index.

RESULTS

Postoperative recurrence occurred in 6.7% of patients during the follow-up period (mean, 1589.2 ± 406.7 days). Fifty-nine of 244 (24.2%) tumors with intermediate grades in the conventional system were reclassified as grade 3 with the updated grading system. They showed significantly larger solid proportions and higher percentages of pure solid nodules on CT compared to tumors without reclassification (n = 185) (P < 0.05). Prognostic prediction models based on pathology tumor size and histologic grades had significantly higher c-indices (0.754-0.803) compared to the model based on pathologic tumor size only (c-index:0.723, P < 0.05).

CONCLUSION

The 2020 IASLC histologic grading system has significant incremental prognostic value over the pathologic stage in surgically resected pathologic stage 1 lung adenocarcinoma. Reclassified lung adenocarcinomas using the updated grading system have a larger solid proportion and a higher percentage of pure solid nodules on CT.

KRAS(G12D) drives lepidic adenocarcinoma through stem-cell reprogramming

Many cancers originate from stem or progenitor cells hijacked by somatic mutations that drive replication, exemplified by adenomatous transformation of pulmonary alveolar epithelial type II (AT2) cells1. Here we demonstrate a different scenario: expression of KRAS(G12D) in differentiated AT1 cells reprograms them slowly and asynchronously back into AT2 stem cells that go on to generate indolent tumours. Like human lepidic adenocarcinoma, the tumour cells slowly spread along alveolar walls in a non-destructive manner and have low ERK activity. We find that AT1 and AT2 cells act as distinct cells of origin and manifest divergent responses to concomitant WNT activation and KRAS(G12D) induction, which accelerates AT2-derived but inhibits AT1-derived adenoma proliferation. Augmentation of ERK activity in KRAS(G12D)-induced AT1 cells increases transformation efficiency, proliferation and progression from lepidic to mixed tumour histology. Overall, we have identified a new cell of origin for lung adenocarcinoma, the AT1 cell, which recapitulates features of human lepidic cancer. In so doing, we also uncover a capacity for oncogenic KRAS to reprogram a differentiated and quiescent cell back into its parent stem cell en route to adenomatous transformation. Our work further reveals that irrespective of a given cancer's current molecular profile and driver oncogene, the cell of origin exerts a pervasive and perduring influence on its subsequent behaviour.

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.

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.