Prognostic significance and adjuvant chemotherapy survival benefits of a solid or micropapillary pattern in patients with resected stage IB lung adenocarcinoma

Dual-energy CT-based radiomics for predicting pathological grading of invasive lung adenocarcinoma

AIMS

The purpose of the study was to build a radiomics model using Dual-energy CT (DECT) to predict pathological grading of invasive lung adenocarcinoma.

MATERIALS AND METHODS

The retrospective study enrolled 107 patients (80 low-grade and 27 high-grade) with invasive lung adenocarcinoma before surgery. Clinical features, radiographic characteristics, and quantitative parameters were measured. Virtual monoenergetic images at 50kev and 150kev were reconstructed for extracting DECT radiomics features. To select features for constructing models, Pearson's correlation analysis, intraclass correlation coefficients, and least absolute shrinkage and selection operator penalized logistic regression were performed. Four models, including the DECT radiomics model, the clinical-DECT model, the conventional CT radiomics model, and the mixed model, were established. Area under the curve (AUC) and decision curve analysis were used to measure the performance and the clinical value of the models.

RESULTS

The radiomics model based on DECT exhibited outstanding performance in predicting tumor differentiation, with an AUC of 0.997 and 0.743 in the training and testing sets, respectively. Incorporating tumor density, lobulation, and effective atomic number at AP, the clinical-DECT model showed a comparable performance with an AUC of 0.836 in both the training and testing sets. In comparison to the conventional CT radiomics model (AUC of 0.998 in the training and 0.529 in the testing set) and the mixed model (AUC of 0.988 in the training and 0.707 in the testing set), the DECT radiomics model demonstrated a greater AUC value and provided patients with a more significant net benefit in the testing set.

CONCLUSIONS

In contrast to the conventional CT radiomics model, the DECT radiomics model produced greater predictive performance in pathological grading of invasive lung adenocarcinoma.

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.

Spatial whole exome sequencing reveals the genetic features of highly-aggressive components in lung adenocarcinoma

In invasive lung adenocarcinoma (LUAD), patients with micropapillary (MIP) or solid (SOL) components had a significantly poorer prognosis than those with only lepidic (LEP), acinar (ACI) or papillary (PAP) components. It is interesting to explore the genetic features of different histologic subtypes, especially the highly aggressive components. Based on a cohort of 5,933 patients, this study observed that in different tumor size groups, LUAD with MIP/SOL components showed a different prevalence, and patients with ALK alteration or TP53 mutations had a higher probability of developing MIP/SOL components. To control individual differences, this research used spatial whole-exome sequencing (WES) via laser-capture microdissection of five patients harboring these five coexistent components and identified genetic features among different histologic components of the same tumor. In tracing the evolution of components, we found that titin (TTN) mutation might serve as a crucial intratumor potential driver for MIP/SOL components, which was validated by a cohort of 146 LUAD patients undergoing bulk WES. Functional analysis revealed that TTN mutations enriched the complement and coagulation cascades, which correlated with the pathway of cell adhesion, migration, and proliferation. Collectively, the histologic subtypes of invasive LUAD were genetically different, and certain trunk genotypes might synergize with branching TTN mutation to develop highly aggressive components.

Deep-learning-based 3D super-resolution CT radiomics model: Predict the possibility of the micropapillary/solid component of lung adenocarcinoma

Objective

Invasive lung adenocarcinoma（ILA） with micropapillary (MPP)/solid (SOL) components has a poor prognosis. Preoperative identification is essential for decision-making for subsequent treatment. This study aims to construct and evaluate a super-resolution（SR） enhanced radiomics model designed to predict the presence of MPP/SOL components preoperatively to provide more accurate and individualized treatment planning.

Methods

Between March 2018 and November 2023, patients who underwent curative intent ILA resection were included in the study. We implemented a deep transfer learning network on CT images to improve their resolution, resulting in the acquisition of preoperative super-resolution CT (SR-CT) images. Models were developed using radiomic features extracted from CT and SR-CT images. These models employed a range of classifiers, including Logistic Regression (LR), Support Vector Machines (SVM), k-Nearest Neighbors (KNN), Random Forest, Extra Trees, Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), and Multilayer Perceptron (MLP). The diagnostic performance of the models was assessed by measuring the area under the curve (AUC).

Result

A total of 245 patients were recruited, of which 109 (44.5 %) were diagnosed with ILA with MPP/SOL components. In the analysis of CT images, the SVM model exhibited outstanding effectiveness, recording AUC scores of 0.864 in the training group and 0.761 in the testing group. When this SVM approach was used to develop a radiomics model with SR-CT images, it recorded AUCs of 0.904 in the training and 0.819 in the test cohorts. The calibration curves indicated a high goodness of fit, while decision curve analysis (DCA) highlighted the model's clinical utility.

Conclusion

The study successfully constructed and evaluated a deep learning（DL）-enhanced SR-CT radiomics model. This model outperformed conventional CT radiomics models in predicting MPP/SOL patterns in ILA. Continued research and broader validation are necessary to fully harness and refine the clinical potential of radiomics when combined with SR reconstruction technology.

The clinicopathological features and possible physiological mechanisms of only the EGFR-T790M primary mutation in patients with lung adenocarcinoma

BACKGROUND

The treatment of non-small cell lung cancer (NSCLC) patients can be complicated by the presence of the EGFR-T790M mutation. Although primary or secondary EGFR-T790M mutations have been extensively studied worldwide, there are few reports on the clinicopathological characteristics and physiological mechanisms of lung adenocarcinoma (LUAD) with only the EGFR-T790M primary mutation.

METHODS

The clinical data of all LUAD patients with only the EGFR-T790M primary mutation were collected. Immunohistochemical staining was performed on cell cycle-related proteins, targeted therapy indicators, and prognosis-related proteins in the specimens obtained from puncture biopsies or surgeries.

OBJECTIVES

The aim of this study is to analyze the clinicopathological features and possible physiological mechanisms of only the EGFR-T790M primary mutation in LUAD, and to offer recommendations for clinical management.

RESULTS

Two patients who have only the T790M de novo mutation were both female (2/12,928, 0.02%). β-catenin and Cyclin D1 were both highly expressed. In case 1, IHC results showed a positive Ki67 and mutant P53 and there was a significant increase in serum CYFRA 21-1. Third-generation of EGFR TKIs resulted in a partial response (PR) time of less than 8 months in case 1. In case 2, the patient underwent surgical resection and adjuvant chemotherapy, resulting in a progression-free survival (PFS) time of 25 months.

CONCLUSION

The results suggest that abnormal activation of the Wnt signaling pathway may be specifically associated with the EGFR-T790M primary mutation in LUAD. Furthermore, it has been observed that patients with significant Ki67, mutant P53, and CYFRA 21-1 expression tend to have a poor prognosis.

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.

Who benefit from adjuvant chemotherapy in stage I lung adenocarcinoma? A multi-dimensional model for candidate selection

BACKGROUND

Despite promising overall survival of stage I lung adenocarcinoma (LUAD) patients, 10-25 % of them still went through recurrence after surgery. [1] While it is still disputable whether adjuvant chemotherapy is necessary for stage I patients. [2] IASLC grading system for non-mucinous LUAD shows that minor high-grade patterns are significant indicator of poor prognosis. [3] Other risk factors, such as, pleura invasion, lympho-vascular invasion, STAS, etc. are also related to poor prognosis. [4-6] There still lack evidence whether IASLC grade itself or together with other risk factors can guide the use of adjuvant therapy in stage I patients. In this article, we tried to establish a multi-variable recurrence prediction model for stage I LUAD patients that is able to identify candidates of adjuvant chemotherapy.

METHODS

We retrospectively collected patients who underwent lung surgery from 2018.8.1 to 2018.12.31 at our institution and diagnosed with lung adenocarcinoma pT1-2aN0M0 (stage I). Clinical data, manifestation on CT scan, pathologic features, driver gene mutations and follow-up information were collected. Cox proportional hazards regression analyses were performed utilizing the non-adjuvant cohort to predict disease free survival (DFS) and a nomogram was constructed and applied to the total cohort. Kaplan-Meier method was used to compare DFS between groups. Statistical analysis was conducted by R version 3.6.3.

FINDINGS

A total of 913 stage I LUAD patients were included in this study. Median follow-up time is 48.1 months.4-year and 5-year DFS are 92.9 % and 89.6 % for the total cohort. 65 patient experienced recurrence or death. 4-year DFS are 97.0 %,94.6 % and 76.2 %, and 5-year DFS are 95.5 %, 90.0 % and 74.1 % in IASLC Grade1, 2 and 3, respectively(p < 0.0001). High-risk patients defined by single risk factors, such as, IASLC grade 3, pleura invasion, STAS, less LN resected could not benefit from adjuvant therapy. A LASSO-COX regression model was built and patients are divided into high-risk and low-risk groups. In the high-risk group, patients underwent adjuvant chemotherapy have longer DFS than those who did not (p = 0.024), while in the low-risk group, patients underwent adjuvant chemotherapy have inferior DFS than those who did not (p < 0.001).

INTERPRETATION

IASLC grading is a significant indicator of DFS, however it could not guide adjuvant therapy in our stage I LUAD cohort. Growth patterns and T indicators together with other risk factors could identify high-risk patients that are potential candidate of adjuvant therapy, including some stage IA LUAD patients.

Lung adenocarcinoma: selection of surgical approaches in solid adenocarcinoma from the viewpoint of clinicopathologic features and tumor microenvironmental heterogeneity

Introduction

Solid adenocarcinoma represents a notably aggressive subtype of lung adenocarcinoma. Amidst the prevailing inclination towards conservative surgical interventions for diminutive lung cancer lesions, the critical evaluation of this subtype's malignancy and heterogeneity stands as imperative for the formulation of surgical approaches and the prognostication of long-term patient survival.

Methods

A retrospective dataset, encompassing 2406 instances of non-solid adenocarcinoma (comprising lepidic, acinar, and papillary adenocarcinoma) and 326 instances of solid adenocarcinoma, was analyzed to ascertain the risk factors concomitant with diverse histological variants of lung adenocarcinoma. Concurrently, RNA-sequencing data delineating explicit pathological subtypes were extracted from 261 cases in the TCGA database and 188 cases in the OncoSG database. This data served to illuminate the heterogeneity across lung adenocarcinoma (LUAD) specimens characterized by differential histological features.

Results

Solid adenocarcinoma is associated with an elevated incidence of pleural invasion, microscopic vessel invasion, and lymph node metastasis, relative to other subtypes of lung adenocarcinoma. Furthermore, the tumor microenvironment (TME) in solid pattern adenocarcinoma displayed suboptimal oxygenation and acidic conditions, concomitant with augmented tumor cell proliferation and invasion capacities. Energy and metabolic activities were significantly upregulated in tumor cells of the solid pattern subtype. This subtype manifested robust immune tolerance and capabilities for immune evasion.

Conclusion

This present investigation identifies multiple potential metrics for evaluating the invasive propensity, metastatic likelihood, and immune resistance of solid pattern adenocarcinoma. These insights may prove instrumental in devising surgical interventions that are tailored to patients diagnosed with disparate histological subtypes of LUAD, thereby offering valuable directional guidance.

Prognostic significance and survival benefits of postoperative adjuvant chemotherapy in patients with stage IA lung adenocarcinoma with non-predominant micropapillary components

BACKGROUND

The prognostic significance of adjuvant chemotherapy (ACT) for patients with stage IA micropapillary non-predominant (MPNP) lung adenocarcinoma (LUAD) remains unknown. This study aimed to investigate the effects of postoperative ACT in patients with stage IA MPNP-LUAD.

METHODS

A total of 149 patients with pathological stage IA MPNP-LUAD who underwent surgery at our center were retrospectively analyzed. Propensity score matching (PSM) analysis was conducted to reduce potential selection bias. Kaplan-Meier analyses were used to assess the impact of ACT on recurrence-free survival (RFS), overall survival (OS), and disease-specific survival (DSS). Subgroup analyses were performed for the survival outcomes based on the percentage of micropapillary components. Cox proportional hazards regression analyses were applied to identify risk factors associated with survival.

RESULTS

The receipt or non-receipt of postoperative ACT had no significant effect on RFS, OS, and DSS among all enrolled patients with stage IA MPNP-LUAD (P > 0.05). For patients with a micropapillary component > 5%, the 5-year rates of RFS, OS, and DSS were significantly higher in the ACT group compared to the observation group, both before and after PSM (P < 0.05). However, the differences between the two groups were not significant for patients with a micropapillary component ≤ 5% (P > 0.05). The resection range (HR = 0.071; 95% CI: 0.020-0.251; P < 0.001), tumor size (HR = 2.929; 95% CI: 1.171-7.330; P = 0.022), and ACT (HR = 0.122; 95% CI: 0.037-0.403; P = 0.001) were identified as independent prognostic factors for RFS through Cox regression analysis.

CONCLUSION

Patients with stage IA MPNP-LUAD who have a micropapillary component greater than 5% might benefit from postoperative ACT, while those with a micropapillary component ≤ 5% did not appear to derive the same benefit from postoperative ACT.

A nomogram based on CT intratumoral and peritumoral radiomics features preoperatively predicts poorly differentiated invasive pulmonary adenocarcinoma manifesting as subsolid or solid lesions: a double-center study

Background

The novel International Association for the Study of Lung Cancer (IASLC) grading system suggests that poorly differentiated invasive pulmonary adenocarcinoma (IPA) has a worse prognosis. Therefore, prediction of poorly differentiated IPA before treatment can provide an essential reference for therapeutic modality and personalized follow-up strategy. This study intended to train a nomogram based on CT intratumoral and peritumoral radiomics features combined with clinical semantic features, which predicted poorly differentiated IPA and was tested in independent data cohorts regarding models' generalization ability.

Methods

We retrospectively recruited 480 patients with IPA appearing as subsolid or solid lesions, confirmed by surgical pathology from two medical centers and collected their CT images and clinical information. Patients from the first center (n =363) were randomly assigned to the development cohort (n = 254) and internal testing cohort (n = 109) in a 7:3 ratio; patients (n = 117) from the second center served as the external testing cohort. Feature selection was performed by univariate analysis, multivariate analysis, Spearman correlation analysis, minimum redundancy maximum relevance, and least absolute shrinkage and selection operator. The area under the receiver operating characteristic curve (AUC) was calculated to evaluate the model performance.

Results

The AUCs of the combined model based on intratumoral and peritumoral radiomics signatures in internal testing cohort and external testing cohort were 0.906 and 0.886, respectively. The AUCs of the nomogram that integrated clinical semantic features and combined radiomics signatures in internal testing cohort and external testing cohort were 0.921 and 0.887, respectively. The Delong test showed that the AUCs of the nomogram were significantly higher than that of the clinical semantic model in both the internal testing cohort(0.921 vs 0.789, p< 0.05) and external testing cohort(0.887 vs 0.829, p< 0.05).

Conclusion

The nomogram based on CT intratumoral and peritumoral radiomics signatures with clinical semantic features has the potential to predict poorly differentiated IPA manifesting as subsolid or solid lesions preoperatively.

Value of adjuvant chemotherapy for patients with pT2N0M0 non-small cell lung cancer receiving radical resection

BACKGROUND

Associations between adjuvant chemotherapy (ACT) and the improvement in survival for patients with pT2N0M0 non-small cell lung cancer (NSCLC) who received R0 resection remain controversial. This study aimed to evaluate the value of ACT for patients with pT2N0M0 NSCLCs, and to identify the subgroups who could benefit from ACT.

METHODS

Multivariable Cox proportional hazards regression models were used to estimate independent prognostic factors. High-risk factor (HRF) included visceral pleural invasion (VPI), lymphovascular invasion (LVI) and poor differentiation/undifferentiated tumors.

RESULTS

Of the 899 patients, 277 (30.8%) patients received ACT. More younger patients (p < 0.001) and male patients (p = 0.007) received ACT. With the increase of pathological tumor size (p < 0.001) and the number of HRFs (p < 0.001), there was a significant rise in the proportion of patients receiving ACT. For all patients, ACT could not improve recurrence-free survival (RFS) (p = 0.672) and overall survival (OS) (p = 0.306). For patients with pathological stage IIA or radiological pure-solid tumors, ACT could significantly improve the OS (p = 0.011 and p = 0.037, respectively), and multivariate analysis revealed that ACT was an independent prognostic factor for patients with pathological stage IIA (p = 0.005). ACT could improve the OS significantly in patients with pathological stage IB pure-solid lung adenocarcinoma (LUAD) (p = 0.043).

CONCLUSION

ACT was valuable for patients with pathological stage IIA (pT2bN0M0) and patients with radiological pure-solid LUAD of pathological stage IB. A combination of radiological features and pathological subtypes could be helpful when selecting patients with pT2N0M0 NSCLCs for ACT.

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

BACKGROUND

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

OBJECTIVE

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

DESIGN

A retrospective case control, diagnostic accuracy study.

METHODS

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

RESULTS

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

CONCLUSION

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

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

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSION

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

Prognostic Factors for Survival of Stage IB Non-small Cell Lung Cancer Patients: A 10-Year Follow-Up Retrospective Study

BACKGROUND

This study aimed to determine the prognostic factors for the long-term outcome of stage IB non-small cell lung cancer (NSCLC).

METHODS

Surgically resected patients with stage IB NSCLC diagnosed (based on TNM 8th edition) between April 2008 and December 2013 were retrospectively reviewed. The prognosis and possible risk factors among the stage IB NSCLC patients were evaluated.

RESULTS

Of the 349 patients identified for the study, 80 (22.9%) received post-surgery adjuvant chemotherapy (ACT). The median follow-up time after surgery was 123.3 months. The 10-year overall survival (OS) rate was 69.6%, and the 10-year recurrence-free survival (RFS) rate was 62.8%. The patients in this cohort were divided into three groups (T1 with visceral pleural invasion [VPI], T2a without VPI, and T2a with VPI), and no significant differences in OS or RFS were found among the groups. Furthermore, survival analysis indicated that the absence of ground-glass opacity (GGO) components portends an adverse long-term OS and RFS. In a subgroup of patients with solid nodules, age older than 65 years (hazard ratio [HR] 1.987; 95% confidence interval [CI] 1.312-3.010; p = 0.001) and ACT (HR 0.392; 95% CI 0.225-0.684; p < 0.001) were independent prognostic factors for OS, whereas lymphovascular invasion (HR 1.792; 95% CI 0.995-3.227; p = 0.052) should be considered as an independent unfavorable prognostic factor for RFS.

CONCLUSIONS

As an upstaging factor, VPI did not further stratify prognosis for the stage IB patients in our cohort. The presence of GGO components had a notable impact on a favorable prognosis in stage IB NSCLCs.

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.

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.

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.

Surgically Treated pT2aN0M0 (Stage IB) Non-Small Cell Lung Cancer: A 20-Year Single-Center Retrospective Study

Introduction The suitability of adjuvant therapy (AT) in patients with stage IB non-small cell lung cancer (NSCLC) is still under debate considering the cost-benefit ratio between improvement in survival and side effects. We retrospectively evaluated survival and incidence of recurrence in radically resected stage IB NSCLC, to determine whether AT could significantly improve prognosis. Methods Between 1998 and 2020, 4692 consecutive patients underwent lobectomy and systematic lymphadenectomy for NSCLC. Two hundred nineteen patients were pathological T2aN0M0 (>3 and ≤4 cm) NSCLC 8th TNM. None received preoperative or AT. Overall survival (OS), cancer specific survival (CSS) and the cumulative incidence of relapse were plotted and log-rank or Gray's tests were used to assess the difference in outcome between groups. Results The most frequent histology was adenocarcinoma (66.7%). Median OS was 146 months. The 5-, 10-, and 15-year OS rates were 79%, 60%, and 47%, whereas the 5-, 10-, and 15-year CSS were 88%, 85%, and 83%, respectively. OS was significantly related to age (p < 0.001) and cardiovascular comorbidities (p = 0.04), whereas number of LNs removed was an independent prognostic factor of CSS (p = 0.02). Cumulative incidence of relapse at 5-, 10-, and 15-year were 23%, 31%, and 32%, respectively, and significantly related to the number of LNs removed (p = 0.01). Patients with more than 20 LNs removed and clinical stage I had a significantly lower relapse (p = 0.02). Conclusions Excellent CSS, up to 83% at 15-year, and relatively low risk of recurrence for stage IB NSCLC (8th TNM) patients suggested that AT for those patients could be reserved only for very selected high-risk cases.

Impact of surgery and adjuvant chemotherapy on the survival of stage I lung adenocarcinoma patients with tumor spread through air spaces

OBJECTIVES

Tumor spread through air spaces (STAS) is a unique mechanism of lung cancer metastasis; however, its clinical value for stage I lung adenocarcinoma (ADC) remains unclear at present. We investigated the (1) prognosis of patients after sublobar resection compared with lobectomy for stage I lung adenocarcinoma with STAS; and (2) potential benefits of adjuvant chemotherapy (ACT) for patients with stage I ADC and STAS.

METHODS

A total of 3328 consecutive patients with stage I ADC were retrospectively identified between 2014 and 2018 at our institution; among them, 600 were diagnosed with STAS. Kaplan-Meier analysis and Cox proportional hazard regression models were used to evaluate the impact of STAS on overall survival (OS) and recurrence-free survival (RFS).

RESULTS

Among stage IA patients with STAS, there was no significant difference between those who underwent sublobar resection and lobectomy in OS (P = 0.919) and RFS (P = 0.066). Multivariate analysis confirmed this result (sublobar resection versus lobectomy, OS: HR = 0.523, 95 % CI, 0.056-18.458, P = 0.714; RFS, HR = 0.360, 95 % CI, 0.115-1.565, P = 0.897). ACT did not improve the prognosis of stage IA patients but did improve the RFS of stage IB patients with high-risk recurrence factors, including poorly differentiated tumors, lymphovascular invasion and visceral pleural invasion (P = 0.046).

CONCLUSIONS

Sublobar and lobectomy resection provided a comparable prognosis for stage IA ADC patients with STAS. When STAS was confirmed postoperatively, ACT should be considered for patients with stage IB with high-risk recurrence factors but not for those with stage IA disease.

The IASLC grading system for invasive pulmonary adenocarcinoma: a potential prognosticator for patients receiving neoadjuvant therapy

Background

Grading system for resected invasive pulmonary adenocarcinoma proposed by the International Association for the Study of Lung Cancer (IASLC) was validated as a strong prognostic indicator. Nonetheless, the efficacy of utilizing such grading system in prognostic assessment of patients receiving neoadjuvant therapy still needs elucidating.

Methods

A retrospective study was conducted including patients with resected adenocarcinoma following neoadjuvant chemotherapy or targeted therapy from August 2012 to December 2020 in Shanghai Pulmonary Hospital. All the surgical specimens were re-evaluated and graded. The prognostic value of the grading system was further validated.

Results

Ultimately, a total of 198 patients were enrolled in this study, and subdivided into three cohorts according to the grading system. There were 13 (6.6%), 37 (18.7%), and 148 (74.7%) patients belonging to Grades 1, 2, and 3, respectively. IASLC grading system demonstrated significant power in prognosis differentiation of the entire cohort [recurrence-free survival (RFS), p < 0.001; overall survival (OS), p < 0.001] and the neoadjuvant chemotherapy and targeted therapy cohorts separately, and was further verified as a significant prognostic indicator for RFS and OS in multivariable Cox analysis. Since the majority of the patients (84.8%) did not achieve major pathologic response (MPR), representing a wide spectrum of survival, the prognostic value of grading system in non-MPR cohort was further evaluated. Similar results were also obtained that IASLC grading system was assessed significant in univariable analysis of RFS (p < 0.001) and univariable analysis of OS (p = 0.001).

Conclusions

The prognostic efficacy of pathological evaluation of the residual proportion of pulmonary adenocarcinoma post-neoadjuvant therapy using IASLC grading system was preliminarily verified. Such grading system might assist prognostic evaluation of neoadjuvant cohort other than traditional pathological parameters.

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.

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

RATIONALE AND OBJECTIVES

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

MATERIALS AND METHODS

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

RESULTS

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

CONCLUSION

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

Validation Study of New IASLC Histology Grading System in Stage I Non-Mucinous Adenocarcinoma Comparing With Minimally Invasive Adenocarcinoma

BACKGROUND

A new histologic grading system for pulmonary non-mucinous invasive adenocarcinoma was proposed by the International Association for the Study of Lung Cancer (IASLC). We evaluated its clinical impact on prognosis in stage I patients, including minimally invasive adenocarcinoma (MIA).

PATIENTS AND METHODS

919 patients underwent surgery for lung adenocarcinoma between 2012 and 2019. Stage I patients (n = 500) were retrospectively reviewed. They were divided into 4 categories: MIA and 3 new IASLC grades (grades 1-3). Cox proportional hazards analysis was performed to identify risk factors associated with recurrence and mortality. Furthermore, we compared the predictability of the IASLC grading system with different models that are based on the clinicopathologic characteristics (baseline model), TNM staging, and predominant histologic pattern. The area under the receiver operating characteristic curve (AUC) was calculated for comparison.

RESULTS

Recurrence-free survival (RFS) and overall survival (OS) were significantly stratified by the IASLC grading system in patients with stage I adenocarcinoma (P < .001 and P = .003, respectively). In multivariate analyses, IASLC grade 3 was a significant factor for RFS (hazard ratio [HR] 3.18, P < .001) and OS (HR 2.31, P = .013). The AUCs of the new IASLC model were 0.781 for recurrence and 0.770 for mortality, compared with those of the predominant pattern (0.769 for recurrence, 0.747 for death) and TNM staging (0.762 for recurrence, 0.747 for death).

CONCLUSION

The IASLC grading system effectively predicted the prognosis of early-stage adenocarcinoma compared with previous models. The IASLC classification appears to improve the current system; therefore, precise pathologic examination for early-stage adenocarcinoma is warranted.

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 long waiting time from diagnosis to treatment decreases the survival of non-small cell lung cancer patients with stage IA1: A retrospective study

Objective

The prognostic effect of delayed treatment on stage IA1 non-small cell lung cancer (NSCLC) patients is still unclear. This study aimed to explore the association between the waiting time before treatment and the prognosis in stage IA1 NSCLC patients.

Methods

Eligible patients diagnosed with pathological stage IA1 NSCLC were included in this study. The clinical endpoints were overall survival (OS) and cancer-specific survival (CSS). The Kaplan-Meier method, the Log-rank test, univariable, and multivariable Cox regression analyses were used in this study. Propensity score matching was used to reduce the bias of data distribution.

Results

There were eligible 957 patients in the study. The length of waiting time before treatment stratified the survival in patients [&lt;3 months vs. ≥3-months, unadjusted hazard ratio (HR) = 0.481, P = 0.007; &lt;2 months vs. ≥2-months, unadjusted HR = 0.564, P = 0.006; &lt;1 month vs. ≥1-month, unadjusted HR = 0.537, P = 0.001]. The 5-year CSS rates were 95.0% and 77.0% in patients of waiting time within 3 months and over 3 months, respectively. After adjusting for other confounders, the waiting time was identified as an independent prognostic factor.

Conclusions

A long waiting time before treatment may decrease the survival of stage IA1 NSCLC patients. We propose that the waiting time for those patients preferably is less than one month and should not exceed two months.

Adjuvant chemotherapy can benefit the survival of stage I lung adenocarcinoma patients with tumour spread through air spaces after resection: Propensity-score matched analysis

Background

It is still unclear whether stage I lung adenocarcinoma patients with tumour spread through air spaces (STAS) can benefit from postoperative adjuvant chemotherapy (ACT) after lobectomy. This study investigated the effect of ACT on the postoperative survival of patients with stage I (STAS⁺) lung adenocarcinoma.

Methods

We retrospectively analysed the clinical data of stage I (STAS⁺) invasive lung adenocarcinoma patients who underwent lobectomy in the Department of Thoracic Surgery of our hospital from January 1, 2013 to January 1, 2016. Propensity score matching (PSM) was performed to group patients to investigate whether ACT could lead to better prognosis of patients.

Results

A total of 593 patients with stage I (STAS⁺) lung adenocarcinoma were enrolled. The study after PSM included 406 patients. Kaplan–Meier survival analysis showed the experimental group had a better 3-year recurrence-free survival (RFS) rate (p = 0.037) and the 5-year RFS rate (p = 0.022) than the control group. It also had higher 5-year overall survival (p = 0.017). The multivariate analysis by Cox proportional hazard regression model showed that stage I STAS⁺ lung adenocarcinoma patients with lymphatic vessel invasion (HR: 1.711, 95% CI: 1.052-2.784; p = 0.045), vascular invasion (HR: 5.014, 95% CI: 3.154-7.969; p < 0.001), and visceral pleural invasion (HR: 2.086, 95% CI: 1.162-3.743; p = 0.014), and without ACT (HR: 1.675, 95% CI: 1.043-2.689; p = 0.033) had a significant survival disadvantage.

Conclusion

ACT can boost the postoperative survival of patients with stage I (STAS⁺) lung adenocarcinoma.

Prognostic and predictive value of the newly proposed grading system of invasive pulmonary adenocarcinoma in Chinese patients: a retrospective multicohort study

Our aim was to validate and analyze the prognostic impact of the novel International Association for the Study of Lung Cancer (IASLC) Pathology Committee grading system for invasive pulmonary adenocarcinomas (IPAs) in Chinese patients and to evaluate its utility in predicting a survival benefit from adjuvant chemotherapy (ACT). In this multicenter, retrospective, cohort study, we included 926 Chinese patients with completely resected stage I IPAs and classified them into three groups (Grade 1, n = 119; Grade 2, n = 431; Grade 3, n = 376) according to the new grading system proposed by the IASLC. Recurrence-free survival (RFS) and overall survival (OS) were estimated by the Kaplan-Meier method, and prognostic factors were assessed using univariable and multivariable Cox proportional hazards models. All included cohorts were well stratified in terms of RFS and OS by the novel grading system. Furthermore, the proposed grading system was found to be independently associated with recurrence and death in the multivariable analysis. Among patients with stage IB IPA (N = 490), the proposed grading system identified patients who could benefit from ACT but who were undergraded by the adenocarcinoma (ADC) classification. The novel grading system not only demonstrated prognostic significance in stage I IPA in a multicenter Chinese cohort but also offered clinical value for directing therapeutic decisions regarding adjuvant chemotherapy.

Ground Glass Opacity and Adjuvant Chemotherapy in Pathological Stage IB-IIA Lung Adenocarcinoma

Background

The prognostic value of ground glass opacity (GGO) in stage IA non-small cell lung cancer (NSCLC) has been widely recognized. However, studies investigating its value in the related stage IB-IIA lung adenocarcinoma (LUAD) remains lacking. The impact of adjuvant chemotherapy (ACT) on pathological stage IB-IIA LUAD is also controversial.

Materials and Methods

We retrospectively reviewed the clinical records of 501 patients with pathological stage IB-IIA LUAD at the Sun Yat-sen University Cancer Center from January 2008 to June 2018. We calculated and compared survival curves using the Kaplan-Meier test and log-rank test. Cox regression models were performed to determine independent prognostic factors of disease-free survival (DFS) and overall survival (OS). We established nomograms to predict the OS and DFS of LUAD patients. Calibration and receiver operator characteristic curves were conducted to assess the predictive performance of two nomograms. Based on the nomogram, we identified candidate patients that may most benefit from ACT after surgery.

Results

The number of patients with pure solid, part GGO, and pure GGO nodules was 240, 242, and 19, respectively, and 125 patients who received ACT. Patients with consolidation-to-tumor ratio (CTR) <0.75 had longer OS (P = 0.026) and DFS (P = 0.003). Pathological tumor size and at least 10 lymph nodes (LNs) resection were independent prognostic factors of both OS and DFS. CTR <0.75 was positively associated with DFS. The C-index of nomograms predicting individual OS and DFS was 0.660 and 0.634, respectively. Based on the nomogram for OS, ACT was found to be a positive prognostic indicator of OS (P = 0.031, HR = 0.5141, 95% CI 0.281-0.942) in patients with nomogram total points ≥5.

Conclusion

CTR <0.75 is associated with a better DFS in patients with stage IB-IIA LUAD. Nomograms developed by integrating pathological tumor size, at least 10 LNs resection, and CTR ≥0.75 for predicting individual OS and DFS displayed a good predictive capacity and clinical value, which were also proved to be a useful tool for selecting patients most benefiting from ACT.

The clinical prognostic factors of patients with stage IB lung adenocarcinoma

Background

Lung adenocarcinoma (ADC) at stage IB has its own prognostic characteristics. This study aimed to investigate the clinical factors that may affect the prognosis of patients with stage IB ADC.

Methods

The data of ADC cases were selected from the Surveillance, Epidemiology, and End Results (SEER) database (2010–2016) and patients in Zhongshan Hospital, Fudan University (Department of Thoracic Surgery, 2015–2016). Kaplan-Meier method was used to obtain the overall survival (OS). Factors that significantly related to the prognosis were evaluated by univariate and multivariate analysis (UVA, MVA) using the Cox model. A nomogram was developed and validated to predict the 3-year OSs of those patients.

Results

7,605 patients with stage IB ADC were included ultimately and were divided into two groups, a training cohort (n=5,324) and a test cohort (n=2,281). Besides, there was a validation cohort (n=272) for the verification of the nomogram model. Those with significantly older age, male, the white race, lower grades of tumor differentiation, larger tumor size (31–40 mm) without pleural layer (PL) invasion as well as receiving sublobectomy suffered from poorer survival (P<0.001), which were identified as independent factors for stage IB ADC (P<0.001), and according to which, a nomogram model was created.

Conclusions

Age, sex, race, histological grade, surgery to the primary site, and tumor size combined with PL invasion were independent risk factors for stage IB ADC, based on which a nomogram was constructed to predict the prognosis.

Consolidation Tumor Ratio Combined With Pathological Features Could Predict Status of Lymph Nodes of Early-Stage Lung Adenocarcinoma

INTRODUCTION

Stage IA lung adenocarcinoma manifested as part-solid nodules (PSNs), has attracted immense attention owing to its unique characteristics and the definition of its invasiveness remains unclear. We sought to develop a nomogram for predicting the status of lymph nodes of this kind of nodules.

METHODS

A total of 2,504 patients between September 2018 to October 2020 with part-solid nodules in our center were reviewed. Their histopathological features were extracted from paraffin sections, whereas frozen sections were reviewed to confirm the consistency of frozen sections and paraffin sections. Univariate and multivariate logistic regression analyses and Akaike information criterion (AIC) variable selection were performed to assess the risk factors of lymph node metastasis and construct the nomogram. The nomogram was subjected to bootstrap internal validation and external validation. The concordance index (C-index) was applied to evaluate the predictive accuracy and discriminative ability.

RESULTS

We enrolled 215 and 161 eligible patients in the training cohort and validation cohort, respectively. The sensitivity between frozen and paraffin sections on the presence of micropapillary/solid subtype was 78.4%. Multivariable analysis demonstrated that MVI, the presence of micropapillary/solid subtype, and CTR >0.61 were independently associated with lymph node metastasis (p < 0.01). Five risk factors were integrated into the nomogram. The nomogram demonstrated good accuracy in estimating the risk of lymph node metastasis, with a C-index of 0.945 (95% CI: 0.916-0.974) in the training cohort and a C-index of 0.975 (95% CI: 0.954-0.995) in the validation cohort. The model's calibration was excellent in both cohorts.

CONCLUSION

The nomogram established showed excellent discrimination and calibration and could predict the status of lymph nodes for patients with ≤3 cm PSNs. Also, this prediction model has the prediction potential before the end of surgery.

Efficacy and Benefit of Postoperative Chemotherapy in Micropapillray or Solid Predominant Pattern in Stage IB Lung Adenocarcinoma: A Systematic Review and Meta-Analysis

Background: The benefit of postoperative chemotherapy remains controversial for patients with either a micropapillary or solid pattern in stage IB non-small cell lung cancer. This study is designed to explore the significance of postoperative chemotherapy in patients with either a micropapillary or solid pattern in stage IB lung adenocarcinoma.

Method: To conduct the meta-analysis, PubMed, Cochrane Library, Embase and Medline were used to collect literature on long-term follow-up studies published before March, 2021, involving postoperative chemotherapy for patients with both a micropapillary or solid pattern in stage IB lung adenocarcinoma as compared to non-postoperative chemotherapy. Survival data was extracted from the literature, including the overall survival and disease-free survival. Based on overall survival and disease-free survival, hazard ratios and their 95% of confidence intervals were applied to assess the prognostic effect of postoperative chemotherapy. Review Manager software was used to merge the effect size for the meta-analysis.

Result: In total, 6 papers with 956 patients were included. In terms of the prognosis of patients suffering from lung cancer when receiving postoperative chemotherapy, this study comprehensively reviews and evaluates the available evidence of micropapillary or solid patterns. After excluding the heterogeneity between the studies, we found that the pooled results from 6 studies report that postoperative chemotherapy was associated with a better overall survival rate when compared with non-postoperative chemotherapy (hazard ratio = 0.58, 95% confidence interval, 0.44–0.77; P = 0.0002). Postoperative chemotherapy also significantly improved the disease-free survival in patients with either a micropapillary or a solid pattern in stage IB lung adenocarcinoma (postoperative chemotherapy vs. non-postoperative chemotherapy, hazard ratio = 0.51, 95% confidence interval, 0.40–0.64; P < 0.001). However, a subgroup analysis showed that compared with non-postoperative chemotherapy, tumor size was unrelated to the prognosis of patients in stage IB undergoing postoperative chemotherapy (hazard ratio = 0.98, 95% confidence interval, 0.94–1.02; P = 0.27).

Conclusion: Postoperative chemotherapy results in a better long-term survival rate for patients with either a solid or a micropapillary pattern in stage IB lung adenocarcinoma. Multi-center, prospective, clinical trials are needed to validate our findings.

Clinical efficacy of adjuvant chemotherapy in stage IB (< 4 cm) non-small cell lung cancer patients with high-risk factors

BACKGROUND/AIMS

Adjuvant chemotherapy is the standard of care for resected stage II-IIIA non-small cell lung cancer (NCSLC). The efficacy of adjuvant chemotherapy in stage IB (< 4 cm) NSCLC with high-risk factors is controversial.

METHODS

This retrospective multicenter study included 285 stage IB NSCLC patients with high-risk factors according to the 8th edition tumor, node, metastasis (TNM) classification from four academic hospitals. High-risk factors included visceral pleural invasion, vascular invasion, lymphatic invasion, lung neuroendocrine tumors, and micropapillary histology patterns.

RESULTS

Of the 285 patients, 127 (44.6%) were included in the adjuvant chemotherapy group and 158 (55.4%) were included in the non-adjuvant chemotherapy group. The median follow-up was 41.5 months. Patients in the adjuvant chemotherapy group had a significantly reduced recurrence rate and risk of mortality than those in the non-adjuvant chemotherapy group (hazards ratio, 0.408; 95% confidence interval, 0.221 to 0.754; p = 0.004 and hazards ratio, 0.176; 95% confidence interval, 0.057 to 0.546; p = 0.003, respectively). Adjuvant chemotherapy should be particularly considered for the high-risk factors such as visceral pleural involvement or vascular invasion. Based on the subgroup analysis, adjuvant chemotherapy should be considered when visceral pleural involvement is present, even if the tumor size is < 3 cm.

CONCLUSION

Adjuvant chemotherapy may be useful for patients with stage IB NSCLC with high-risk factors and is more relevant for patients with visceral pleural involvement or vascular invasion.

Preoperative CT-Based Radiomics Combined With Nodule Type to Predict the Micropapillary Pattern in Lung Adenocarcinoma of Size 2 cm or Less: A Multicenter Study

Purpose

To construct an optimal radiomics model for preoperative prediction micropapillary pattern (MPP) in adenocarcinoma (ADC) of size ≤ 2 cm, nodule type was used for stratification to construct two radiomics models based on high-resolution computed tomography (HRCT) images.

Materials and Methods

We retrospectively analyzed patients with pathologically confirmed ADC of size ≤ 2 cm who presented to three hospitals. Patients presenting to the hospital with the greater number of patients were included in the training set (n = 2386) and those presenting to the other two hospitals were included in the external validation set (n = 119). HRCT images were used for delineation of region of interest of tumor and extraction of radiomics features; dimensionality reduction was performed for the features. Nodule type was used to stratify the data and the random forest method was used to construct two models for preoperative prediction MPP in ADC of size ≤ 2 cm. Model 1 included all nodule types and model 2 included only solid nodules. The receiver operating characteristic curve was used to assess the prediction performance of the two models and independent validation was used to assess its generalizability.

Results

Both models predicted ADC with MPP preoperatively. The area under the curve (AUC) of prediction performance of models 1 and 2 were 0.91 and 0.78, respectively. The prediction performance of model 2 was lower than that of model 1. The AUCs in the external validation set were 0.81 and 0.72, respectively. The DeLong test showed statistically significant differences between the training and validation sets in model 1 (p = 0.0296) with weak generalizability. There was no statistically significant difference between the training and validation sets in model 2 (p = 0.2865) with some generalizability.

Conclusion

Nodule type is an important factor that affects the performance of radiomics predictor model for MPP with ADC of size ≤ 2 cm. The radiomics prediction model constructed based on solid nodules alone, can be used to evaluate MPP and may contribute to proper surgical planning in patients with ADC of size ≤ 2 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.

Benefit of adjuvant chemotherapy for patients with stage IB non-small cell lung cancer: a systematic review and meta-analysis

Background

Adjuvant chemotherapy (ACT) is routinely the recommended treatment for patients with advanced non-small cell lung cancer (NSCLC) but remains a controversial option in stage IB patients. We therefore pooled the current evidence to determine the prognostic impact of ACT in stage IB NSCLC patients in the context of the eighth tumor, node, metastasis (TNM) staging system.

Methods

Five electronic databases were searched for eligible studies up to December 2020 without language restrictions. The primary and secondary outcomes were overall survival (OS) and disease-free survival (DFS). Search results were filtered by a set of eligibility criteria and analyzed in line with PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. The risk of bias was assessed independently using a modified set. Stata 16.0 was used for general data analysis and meta-analysis, and subgroup analyses were performed to investigate the source of interstudy heterogeneity.

Results

In all, 12 eligible studies were identified and 15,678 patients included. Our results demonstrated that ACT was associated with improved OS [n=11; hazard ratio (HR) =0.65; 95% confidence interval (CI): 0.60–0.70; P<0.001; I²=33.4%, P=0.131] and DFS (n=9; HR =0.73; 95% CI: 0.63–0.83; P<0.001; I²=66.7%, P=0.002) in stage IB NSCLC patients. Subgroup analysis by histology indicated that administration of ACT conferred more favorable survival to both stage IB squamous cell carcinoma (n=1; HR =0.56; 95% CI: 0.28–0.84; P<0.001) and adenocarcinoma (n=6; HR =0.59; 95% CI: 0.47–0.71; P<0.001; I²=31.0%, P=0.203). Meanwhile, both platinum-based ACT (n=7; HR =0.62; 95% CI: 0.51–0.74; P<0.001; I²=44.8%, P=0.093) and other regimens (n=2; HR =0.66; 95% CI: 0.61–0.72; P<0.001; I²=0.7%, P=0.316) could benefit patients with stage IB disease.

Discussion

ACT might provide survival benefits to patients with stage IB NSCLC irrespective of histology or regimens. Patient selection and time trend biases were inevitable due to the limitation of retrospective studies. More prospective studies should be initiated to investigate the optimal ACT regimens in different histologic types in stage IB NSCLC patients.

Enhanced CT-Based Radiomics to Predict Micropapillary Pattern Within Lung Invasive Adenocarcinoma

Objective

We aimed to investigate whether enhanced CT-based radiomics can predict micropapillary pattern (MPP) of lung invasive adenocarcinoma (IAC) in the pre-op phase and to develop an individual diagnostic predictive model for MPP in IAC.

Methods

170 patients who underwent complete resection for pathologically confirmed lung IAC were included in our study. Of these 121 were used as a training cohort and the other 49 as a test cohort. Clinical features and enhanced CT images were collected and assessed. Quantitative CT analysis was performed based on feature types including first order, shape, gray-level co-occurrence matrix-based, gray-level size zone matrix-based, gray-level run length matrix-based, gray-level dependence matrix-based, neighboring gray tone difference matrix-based features and transform types including Log, wavelet and local binary pattern. Receiver operating characteristic (ROC) and area under the curve (AUC) were used to value the ability to identify the lung IAC with MPP using these characteristics.

Results

Using quantitative CT analysis, one thousand three hundred and seventeen radiomics features were deciphered from R (https://www.r-project.org/). Then these radiomic features were decreased to 14 features after dimension reduction using the least absolute shrinkage and selection operator (LASSO) method in R. After correlation analysis, 5 key features were obtained and used as signatures for predicting MPP within IAC. The individualized prediction model which included age, smoking, family tumor history and radiomics signature had better identification (AUC=0.739) in comparison with the model consisting only of radiomics features (AUC=0.722). DeLong test showed that the difference in AUC between the two models was statistically significant (P<0.01). Compared with the simple radiomics model, the more comprehensive individual prediction model has better prediction performance.

Conclusion

The use of radiomics approach is of great value in the diagnosis of tumors by non-invasive means. The individualized prediction model in the study, when incorporated with age, smoking and radiomics signature, had effective predictive performance of lung IAC with MPP lesions. The combination of imaging features and clinical features can provide additional diagnostic value to identify the micropapillary pattern in IAC and can affect clinical diagnosis and treatment.

[Pathological and Molecular Features of Lung Micropapillary Adenocarcinoma]

肺微乳头腺癌作为高级别肺腺癌，具频发转移、淋巴结浸润、复发率高和总体生存率低的临床特征。该亚型肿瘤中存在特征致癌因子通路的激活和肿瘤免疫微环境的建立。本文拟对近年来微乳头腺癌的病理学表现及分子学特征研究进展作一综述，旨在加深对微乳头型病变的认识，进而为制定特异性治疗策略奠定基础。

Impact of the micropapillary component on the timing of recurrence in patients with resected lung adenocarcinoma

OBJECTIVES

A micropapillary (MIP) component is reported to be associated with a poor prognosis in patients with completely resected lung adenocarcinoma. The purpose of this study was to investigate the impact of an MIP component on the timing of postoperative recurrence using hazard curves.

METHODS

A total of 1289 patients with lung adenocarcinoma who underwent complete pulmonary resection from 2008 to 2015 were studied. Hazard curves representing the changes in hazard over time were evaluated.

RESULTS

The hazard curve displayed an initial wide, high peak within 1 year after surgery in patients with an MIP component, whereas some gentle peaks around the second year were noted in patients without an MIP component. The presence of an MIP component was associated with a worse recurrence-free survival and an early recurrence in stage I patients but not in advanced-stage patients. In multivariable Cox regression, the presence of an MIP component and lymph node metastasis, pleural invasion and gender were associated with a poor prognosis.

CONCLUSIONS

Patients with an MIP component retained a high risk of early recurrence after surgery, and the risk for recurrence persisted over the long term. Even after complete resection in stage I lung adenocarcinoma patients, an MIP component remains correlated with a poor prognosis.

Association of postoperative recurrence with radiological and clinicopathological features in patients with stage IA-IIA lung adenocarcinoma

OBJECTIVES

To retrospectively investigate whether radiological and clinicopathological characteristics were associated with the presence of stage IA-IIA lung adenocarcinoma in patients at high risk for a postoperative recurrence.

MATERIALS AND METHODS

Three hundred twelve patients with biopsy-proven node-negative early-stage (IA-IIA) lung adenocarcinoma met the inclusion criteria for this study. Demographics data and histopathological findings were collected from medical records. Computed tomography (CT) performed approximately 1 month before surgery was manually scored using 23 CT descriptors. Univariate analyses were applied to demonstrate an association between clinicopathological and radiological features and 2-/5-year recurrences. Multivariate logistic regression was performed to assess the ability of radiological and clinicopathological features to discriminate low and high-risk factors for recurrence. A ROC curve was used to evaluate prediction performance.

RESULTS

Univariate analysis revealed that the 2-year recurrence was associated with six radiological features and two clinicopathological features, while 5-year recurrence was associated with five radiological features and two clinicopathological features. A multivariate logistic regression model of combined clinicopathological and radiological features showed that stage IIA (OR = 2.87), solid texture (solid part > 50 %: OR = 4.81; solid part = 100 %: OR = 3.61), pleural attachment (OR = 3.97) and bronchovascular bundle thickening (OR = 2.16) were associated with the independent predictors of 2-year recurrence, and stage IIA (OR = 3.52), solid texture (solid part > 50 %: OR = 3.56; solid part = 100 %: OR = 2.44) and pleural attachment (OR = 4.57) were associated with 5-year recurrence. Combined radiological and clinicopathological features could be significant indicators of 2- and 5-year recurrences (AUC = 0.784 and AUC = 0.815, respectively).

CONCLUSIONS

The combination of radiological and clinicopathological features has the potential to help predict postoperative recurrence in patients with stage IA-IIA lung adenocarcinomas and guide oncologists and patients whether to undergo additional treatment after surgery.

Prognostic Impact of Radiological Consolidation Tumor Ratio in Clinical Stage IA Pulmonary Ground Glass Opacities

Objectives

Our study aimed to validate pathologic findings of ground-glass nodules (GGOs) of different consolidation tumor ratios (CTRs), and to explore whether GGOs could be stratified according to CTR with an increment of 0.25 based on its prognostic role.

Methods

We retrospectively evaluated patients with clinical stage IA GGOs who underwent curative resection between 2011 and 2016. The patients were divided into 4 groups according to CTR step by 0.25. Cumulative survival rates were calculated by the Kaplan-Meier method. Univariate and multivariate Cox regression analyses were conducted to obtain the risk factors on relapse-free survival (RFS). The surv_function of the R package survminer was used to determine the optimal cutoff value. Receiver operating characteristic (ROC) analysis was generated to validate optimal cutoff points of factors.

Results

A total of 862 patients (608 women; median age, 59y) were included, with 442 patients in group A (CTR ≤ 0.25), 210 patients in group B (0.25<CTR ≤ 0.5), 173 patients in group C (0.5<CTR ≤ 0.75), and 37 patients in group D (0.75<CTR<1). The rate of adenocarcinoma in situ (AIS) or minimally invasive adenocarcinoma (MIA) in group A (70.6%) was much higher than other three groups (p<0.001). Multivariable Cox regression revealed that CTR (HR, 1.865; 95%CI, 1.312-2.650; p = 0.001) and lymph node metastasis (HR, 10.407; 95%CI, 1.957-55.343; p = 0.006) were independent prognostic factors for recurrence free survival. In addition, CTR was the only risk factor for the presence of micropapillary or solid pattern (OR=133.9, 95%CI:32.2-556.2, P<0.001) and lymph node metastasis (OR=292498.8, 95%CI:1.2-7.4×10¹⁰, P=0.047). Paired comparison showed that rate of presence of micropapillary or solid pattern was highest in group D, followed by group C and group A/B (p<0.001). Lymph node metastasis occurred in group D only (p=0.002).

Conclusions

CTR is an independent prognostic factor for clinical stage IA lung adenocarcinoma manifesting as GGO in CT scan. Radiologic cutoffs of CTR 0.50 and 0.75 were able to subdivide patients with different prognosis.

Characterization of Tumor Microenvironment in Lung Adenocarcinoma Identifies Immune Signatures to Predict Clinical Outcomes and Therapeutic Responses

Background and Objective

Increasing evidence has elucidated the clinicopathological significance of individual TME component in predicting outcomes and immunotherapeutic efficacy in lung adenocarcinoma (LUAD). Therefore, we aimed to investigate whether comprehensive TME-based signatures could predict patient survival and therapeutic responses in LUAD, and to assess the associations among TME signatures, single nucleotide variations and clinicopathological characteristics.

Methods

In this study, we comprehensively estimated the TME infiltration patterns of 493 LUAD patients and systematically correlated the TME phenotypes with genomic characteristics and clinicopathological features of LUADs using two proposed computational algorithms. A TMEscore was then developed based on the TME signature genes, and its prognostic value was validated in different datasets. Bioinformatics analysis was used to evaluate the efficacy of the TMEscore in predicting responses to immunotherapy and chemotherapy.

Results

Three TME subtypes were identified with no prognostic significance exhibited. Among them, naïve B cells accounted for the majority in TMEcluster1, while M2 TAMs and M0 TAMs took the largest proportion in TMEcluster2 and TMEcluster3, respectively. A total of 3395 DEGs among the three TME clusters were determined, among which 217 TME signature genes were identified. Interestingly, these signature genes were mainly involved in T cell activation, lymphocyte proliferation and mononuclear cell proliferation. With somatic variations and tumor mutation burden (TMB) of the LUAD samples characterized, a genomic landscape of the LUADs was thereby established to visualize the relationships among the TMEscore, mutation spectra and clinicopathological profiles. In addition, the TMEscore was identified as not only a prognosticator for long-term survival in different datasets, but also a predictive biomarker for the responses to immune checkpoint blockade (ICB) and chemotherapeutic agents. Furthermore, the TMEscore exhibited greater accuracy than other conventional biomarkers including TMB and microsatellite instability in predicting immunotherapeutic response (p < 0.001).

Conclusion

In conclusion, our present study depicted a comprehensive landscape of the TME signatures in LUADs. Meanwhile, the TMEscore was proved to be a promising predictor of patient survival and therapeutic responses in LUADs, which might be helpful to the future administration of personalized adjuvant therapy.

Could tumor spread through air spaces benefit from adjuvant chemotherapy in stage I lung adenocarcinoma? A multi-institutional study

Background:

The benefit of adjuvant chemotherapy (ACT) remains unknown for patients with stage I lung adenocarcinoma (ADC) with spread through air spaces (STAS). This study investigated the effect of adjuvant chemotherapy in stage I ADC/STAS-positive patients.

Methods:

A total of 3346 patients with stage I ADC from five institutions in China were identified from 2009 to 2013, of whom 1082 were diagnosed with STAS (32.3%). By using the Kaplan–Meier method and Cox proportional hazard regression model, we explored the impact of STAS on prognosis, and determined if the use of adjuvant chemotherapy was associated with improved outcomes in patients with stage I ADC/STAS-positive. A validation cohort was also included in this study.

Results:

Patients with stage I ADC/STAS-positive in the primary cohort had unfavorable overall survival (OS) and disease-free survival (DFS). A multivariate Cox regression model confirmed the survival disadvantages of STAS in patients with stage I ADC [OS: hazards ratio (HR) = 1.877, 95% confidence interval (CI): 1.579–2.231; p < 0.001; DFS: HR = 1.895, 95% CI: 1.614–2.225; p < 0.001]. Lobectomy was associated with better OS and DFS than sublobar resection (SR) in both stage IA and IB ADC/STAS-positive. Similar results were observed in the validation cohort. For patients with stage IB ADC/STAS-positive, ACT was revealed as an independent factor for favorable survival (OS: HR = 0.604, 95% CI: 0.397–0.919; p = 0.018; DFS: HR = 0.565, 95% CI: 0.372–0.858; p = 0.007). However, among patients with stage IA ADC/STAS-positive, ACT was associated with improved outcomes only for those undergoing SR (OS: HR = 0.787, 95% CI: 0.359–0.949; p = 0.034; DFS: HR = 0.703, 95% CI: 0.330–0.904; p = 0.029).

Conclusion:

The presence of STAS was correlated with poor prognosis in patients with stage I ADC. Our study suggested that ACT might be considered for patients with stage IB ADC/STAS-positive and those with stage IA ADC/STAS-positive who underwent SR.

New International Association for the Study of Lung Cancer (IASLC) Pathology Committee Grading System for the Prognostic Outcome of Advanced Lung Adenocarcinoma

Simple Summary

This study investigated the association between survival outcome and the new grading system among advanced stage lung adenocarcinoma (LADC) (stages IIIA, IIIB and IV) patients who were diagnosed as LADC with a pathologic report according to a new grading system by the International Association for the Study of Lung Cancer (IASLC) pathology committee. The results indicate that the poorly differentiated group had a poorer prognosis in PFS, as did patients with wild-type EGFR who were treated with chemotherapy. No survival difference could be found among EGFR mutation patients. Older age and a lower body mass index also led to worse survival. Patients with poorly differentiated adenocarcinoma likewise had worse survival, especially compared to those with moderately differentiated adenocarcinoma. Our findings highlight that the therapeutic regimen should be adjusted for wild-type EGFR patients with poorly differentiated adenocarcinoma treated with chemotherapy to provide better outcomes. No survival difference could be seen among EGFR mutation patients.

Abstract

The impact of the new International Association for the Study of Lung Cancer pathology committee grading system for advanced lung adenocarcinoma (LADC) on survival is unclear, especially in Asian populations. In this study, we reviewed the prognostic outcomes of patients with late-stage disease according to the new grading system. We reviewed 136 LADC cases who underwent a small biopsy from 2007 to 2018. Tumors were classified according to the new grading system for LADC. Baseline characteristics (age, sex, smoking status, body mass index, and driver gene mutations) were analyzed. Kaplan–Meier and Cox regression analyses were used to determine correlations with the new grading system and prognosis. Patients with poorly differentiated adenocarcinoma were significantly correlated with a poor progression-free survival (PFS) (p = 0.013) but not overall survival (OS) (p = 0.154). Subgroup analysis showed that wild-type EGFR patients with poorly differentiated adenocarcinoma treated with chemotherapy had significantly worse PFS (p = 0.011). There was no significant difference in survival among the patients with epidermal growth factor receptor mutations who were treated with tyrosine kinase inhibitors. Patients aged >70 years and those with a BMI ≤ 25 kg/m² and wild-type patients had significantly worse OS in both univariate (HR = 1.822, p = 0.006; HR = 2.250, p = 0.004; HR = 1.537, p = 0.046, respectively) and multivariate analyses (HR = 1.984, p = 0.002; HR = 2.383, p = 0.002; HR = 1.632, p = 0.028, respectively). Despite therapy, patients with poorly differentiated tumors still fared worse than those with better differentiated tumors. No differences were found among the EGFR mutations treated with TKI. Our findings highlight that the therapeutic regimen should be adjusted for EGFR Wild-type patients with poorly differentiated adenocarcinoma treated with chemotherapy to provide better outcomes.

Solid Nodule Appearance as a Predictor of Tumor Spread Through Air Spaces in Patients with Lung Adenocarcinoma: A Propensity Score Matching Study

OBJECTIVE

Spread through air spaces (STAS) has been reported to be an invasive histological pattern with poor prognosis in lung cancer; however, little is known about its intrinsic risk factors. This work analyzed the correlation between pathological and radiological features and STAS in resected lung adenocarcinomas.

PATIENTS AND METHODS

We retrospectively reviewed 1821 consecutive surgically treated patients with histologically diagnosed lung adenocarcinoma (174 positive for STAS and 1647 negative for STAS) from December 2017 to November 2018 at our institution. Propensity score matching identified 170 well-balanced pairs of patients. The correlations between pathological and radiological features and the presence of STAS were analyzed.

RESULTS

Before propensity matching, the incidence rate of STAS was 9.6% in all patients. In matched cohorts, multivariate analysis showed that the presence of STAS was significantly correlated with pure solid nodules (SNs) (p = 0.001) and solid/micropapillary patterns (SMPs) (p = 0.002). The odds ratio for STAS in SN-positive and SMP-positive adenocarcinoma against that in SN-negative and SMP-negative adenocarcinoma was 10.922 (95% confidence interval, 5.826-20.475; p < 0.001). Tumor differentiation, visceral pleural invasion (VPI), lymphovascular invasion (LVI), invasive adenocarcinoma, and non-lepidic subtype were significantly associated with STAS in the univariate analysis (p < 0.05); however, the differences failed to reach a significant level in the multivariate analysis.

CONCLUSION

We found that STAS was significantly correlated with several invasive clinicopathological patterns. The presence of SNs and SMPs were revealed as independent predictors for STAS, which could offer clinicians clues to identify STAS-positive adenocarcinoma.

Micropapillary pattern of stage IIIA-N2 lung adenocarcinoma is a prognostic factor after adjuvant chemoradiotherapy

Aim: This study aims to investigate the significance of a micropapillary pattern in stage IIIA-N₂ lung adenocarcinoma after adjuvant chemoradiotherapy. Patients & methods: A total of 257 patients with stage IIIA-N₂ lung adenocarcinoma were enrolled in this study. Patients were classified into three groups based on the proportion of micropapillary components: micropapillary negative, micropapillary minor component and micropapillary predominant component. Results: The micropapillary predominant group had the shortest median disease-free survival and overall survival times compared with the micropapillary minor component and micropapillary negative groups (median overall survival time: 54 months vs 64 months vs not reached; p = 0.004). Furthermore, the micropapillary pattern was an independent prognostic factor for disease-free survival and overall survival (p < 0.05). Conclusion: The micropapillary pattern of IIIA-N₂ lung adenocarcinoma is related to worse prognosis.

Radiomics nomogram for prediction disease-free survival and adjuvant chemotherapy benefits in patients with resected stage I lung adenocarcinoma

Background

Robust imaging biomarkers are needed for risk stratification in stage I lung adenocarcinoma patients in order to select optimal treatment regimen. We aimed to construct and validate a radiomics nomogram for predicting the disease-free survival (DFS) of patients with resected stage I lung adenocarcinoma, and further identifying candidates benefit from adjuvant chemotherapy (ACT).

Methods

Using radiomics approach, we analyzed 554 patients' computed tomography (CT) images from three multicenter cohorts. Prognostic radiomics features were extracted from computed tomography (CT) images and selected using least absolute shrinkage and selection operator (LASSO) Cox regression model to build a radiomics signature for DFS stratification. The biological basis of radiomics was explored in the Radiogenomics dataset (n=79) by gene set enrichment analysis (GSEA). Then a nomogram that integrated the signature with these significant clinicopathologic factors in the multivariate analysis were constructed in the training cohort (n=238), and its prognostic accuracy was evaluated in the validation cohort (n=237). Finally, the predictive value of nomogram for ACT benefits was assessed.

Results

The radiomics signature with higher score was significantly associated with worse DFS in both the training and validation cohorts (P<0.001). The GSEA presented that the signature was highly correlated to characteristic metabolic process and immune system during cancer progression. Multivariable analysis revealed that age (P=0.031), pathologic TNM stage (P=0.043), histologic subtype (P=0.010) and the signature (P<0.001) were independently associated with patients' DFS. The integrated radiomics nomogram showed good discrimination performance, as well as good calibration and clinical utility, for DFS prediction in the validation cohort. We further found that the patients with high points (point ≥8.788) defined by the radiomics nomogram obtained a significant favorable response to ACT (P=0.04) while patients with low points (point <8.788) showed no survival difference (P=0.7).

Conclusions

The radiomics nomogram could be used for prognostic prediction and ACT benefits identification for patient with resected stage I lung adenocarcinoma.