Clinical Stage IA Lung Adenocarcinoma Research Articles

Predicting Recurrence after Sublobar Resection in Patients with Lung Adenocarcinoma Using Preoperative Chest CT Scans.

Background Sublobar resection for lung cancer is usually guided by cutoff values for consolidation size (maximal diameter of the solid tumor component) and consolidation-to-tumor ratio (CTR). The effects of these factors as continuous variables and the reason for established cutoffs are, to the knowledge of the authors, unexplored. Purpose To quantitatively assess the predictive value of CTR and consolidation size for cancer recurrence risk after sublobar resection in clinical stage IA lung adenocarcinoma. Materials and Methods This retrospective study reviewed sublobar resection for clinical stage IA lung adenocarcinoma performed between January 2010 and December 2019. A restricted cubic spline function verified linearity by estimating recurrence probabilities using CTR and consolidation size obtained on preoperative CT scans. Statistical analyses included a Cox proportional hazards model to identify risk factors for cancer recurrence and the Cochran-Armitage trend test for the association between CTR and consolidation size. Results Of 1032 enrolled patients (age, 63.9 years ± 9.9 [SD]; 464 male patients), 523 (50.7%) and 509 (49.3%) underwent wedge resection and segmentectomy, respectively. Among patients with a CTR between 1% and 50% (n = 201), 187 (93.0%) had a consolidation size of less than or equal to 10 mm (P < .001). There was a positive association between the risk of recurrence with CTR and consolidation size (r2 = 0.727; P < .001). The recurrence rate showed the greatest increase when CTR was greater than 50% or consolidation size was greater than 10 mm. Specifically, the recurrence rate increased from 2.1% (three of 146) at 26%-50% CTR to 8.3% (nine of 108) at 51%-75% CTR, and from 4.4% (eight of 183) for 6-10-mm consolidation size to 11.9% (23 of 194) for 11-15-mm consolidation size. The probability of recurrence exhibited linearity and increased with CTR and consolidation size. Conclusion Cancer recurrence risk after sublobar resection for stage IA adenocarcinoma consistently rises with CTR and consolidation size. Current guideline cutoffs for sublobar resection remain clinically relevant given observed recurrence rates. © RSNA, 2024 Supplemental material is available for this article.

CT-based radiomic consensus clustering association with tumor biological behavior in clinical stage IA adenocarcinoma: a retrospective study.

Research has demonstrated that radiomics models are capable of forecasting the characteristics of lung cancer. Nevertheless, due to radiomics' poor interpretability, its applicability in clinical settings remains restricted. This investigation sought to verify the correlation between radiomics features (RFs) and the biological behavior of clinical stage IA adenocarcinomas. A retrospective analysis was conducted on patients diagnosed with clinical stage IA lung adenocarcinoma who underwent resection between May 2005 and December 2018. Detailed radiomics examination of the primary tumor was carried out utilizing preoperative computed tomography (CT) images. Subsequently, patients were grouped based on their RFs using consensus clustering, enabling comparison of tumor biological characteristics among the clusters. Survival disparities among the clusters were evaluated through Kaplan-Meier and Cox analyses. A consensus cluster analysis was performed on 669 patients [median age, 58 years; interquartile range (IQR), 50-64 years, 257 males, 412 females], and three distinct clusters were identified. Cluster 2 was associated with radiological solid adenocarcinoma [119 of 324 (36.7%), P<0.001], larger tumors with median tumor size of 2.1 cm with IQR of 1.7 to 2.5 cm (P<0.001), central tumor [91 of 324 (28.1%), P=0.002], pleural invasion [87 of 324 (26.9%), P<0.001], occult lymph node metastasis (ONM) [106 of 324 (32.7%), P<0.001], and a higher frequency of metastasis or recurrence [62 of 324 (19.1%), P<0.001]. The frequency of histological grade 3 was the highest in Cluster 3 [8 of 34 (23.5%), P<0.001]. Cluster 1 was associated with pure ground glass nodules (pGGNs) [184 of 310 (59.4%), P<0.001], smaller tumors with median tumor size of 1.1 cm with IQR of 0.8 to 1.4 cm (P<0.001), no pleural invasion [276 of 310 (89.0%), P<0.001], histological grade 1 [114 of 248 (46.0%), P<0.001], ONM negative [292 of 310 (94.2%), P<0.001], and a lower rate of metastasis or recurrence [298 of 310 (96.1%), P<0.001]. Differences in tumor biological behavior were detected among consensus clusters based on the RFs of clinical stage IA adenocarcinoma.

Gene mutation characteristics of clinical stage ⅠA lung adenocarcinoma and their relations with patients' long-term prognosis

Objective: To explore the gene mutation characteristics and the relationship between gene mutations and long-term prognosis in clinical stage ⅠA lung adenocarcinoma patients. Methods: A retrospective analysis was conducted on 63 clinical stage ⅠA lung adenocarcinoma patients who underwent surgical resection at the Cancer Hospital of the Chinese Academy of Medical Sciences from January 2007 to October 2012, with documented postoperative recurrence or metastasis, as well as those who had a follow-up duration of 10 years or more without recurrence or metastasis. Whole exome sequencing (WES) technology was used to analyze the gene mutation profiles in tumor tissues and univariate and multivariate Cox regression analysis were used to clarify the influencing factors for patient prognosis. Results: After long term follow-up, 13 out of the 63 patients (21%) experienced recurrence or metastasis. WES technology analysis revealed that the most common tumor related gene mutations occurred in epidermal growth factor receptor (EGFR), with a mutation rate of 65.1% (41/63), followed by tumor protein p53 (TP53), fatatypical cadherin 1 (FAT1), low density lipoprotein receptor-related protein 1B (LRP1B), mechanistic target of rapamycin (MTOR), phosphatidylinositol-4,5-bisphosphate 3-kinase catalytic subunit gamma (PIK3CG), and SWI/SNF related, matrix associated, actin dependent regulator of chromatin, subfamily A, member 4 (SMARCA4), with mutation rates of 30.2% (19/63), 20.6% (13/63), 15.9% (10/63), 15.9% (10/63), 15.9% (10/63), and 15.9% (10/63), respectively. Multivariate Cox regression analysis showed that PIK3CG mutations (HR=21.52, 95% CI: 3.19-145.01),smoothened (SMO) mutations (HR=35.28, 95% CI: 3.12-398.39), catenin beta 1 (CTNNB1) mutations (HR=332.86, 95% CI: 15.76-7 029.05), colony stimulating factor 1 receptor (CSF1R) mutations (HR=8 109.60, 95% CI: 114.19-575 955.17), and v-Raf murine sarcoma viral oncogene homolog B (BRAF) mutations (HR=23.65, 95% CI: 1.86-300.43) were independent risk factors affecting the prognosis of clinical stage ⅠA lung adenocarcinoma patients. Conclusions: PIK3CG, SMO, CTNNB1, CSF1R, BRAF gene mutations are closely related to long-term recurrence or metastasis in clinical stage ⅠA lung adenocarcinoma. Patients with these gene mutations should be given closer clinical attention.

Utility of PET for Nodal Staging in Subsolid Clinical Stage IA (T1 N0) Lung Adenocarcinoma

BackgroundPositron emission tomography (PET) is the standard of care for non-small cell lung cancer (NSCLC) clinical staging, but it may have limited utility in evaluating subsolid lung adenocarcinomas that can have relatively indolent behavior without hypermetabolic activity. MethodsThe sensitivity and specificity of PET for determining pathologic lymph node status and disease-free survival were assessed in patients operated on for cT1 N0 subsolid lung adenocarcinoma from January 2006 to June 2022 (at Stanford University School of Medicine, Stanford, CA). Patients with clinical or pathologic tumor size >30 mm, hilar or mediastinal lymph node size >1cm, and purely solid tumors were excluded. ResultsPET was available in 498 of 534 (93.2%) patients and more often was used in older patients with larger and more solid tumors. The overall pathologic lymph node–positive rate was 8.4% (45 of 534). PET specificity was 95.1%, but sensitivity was only 20.0%. A tumor diameter of 18.5 mm and a solid component percentage of 62.5% had the maximum predictive accuracy for pathologic lymph node positivity, with a 0% and 1.5% rate of pathologic and PET lymph node positivity, respectively, for tumors with values lower than those thresholds. There was no significant difference in 5-year disease-free survival between individuals who did and did not undergo PET scanning (76.6% vs 96.8%; P = .07). Conversely, 134 (26.9%) patients who underwent PET scanning had 171 incidentally detected hypermetabolic lesions unrelated to lung cancer, with only 13 of 134 (9.7%) patients identified as having non-NSCLC premalignant or malignant conditions requiring further therapy. ConclusionsPET scan use for subsolid lung adenocarcinoma has high specificity but limited sensitivity for predicting pathologic lymph node positivity. PET also has no association with disease-free survival and often detects clinically unimportant findings rather than changing lung cancer management, particularly for patients with smaller and less solid tumors.

Nomogram using intratumoral and peritumoral radiomics for the preoperative prediction of visceral pleural invasion in clinical stage IA lung adenocarcinoma

BackgroundAccurate prediction of visceral pleural invasion (VPI) in lung adenocarcinoma before operation can provide guidance and help for surgical operation and postoperative treatment. We investigate the value of intratumoral and peritumoral radiomics nomograms for preoperatively predicting the status of VPI in patients diagnosed with clinical stage IA lung adenocarcinoma.MethodsA total of 404 patients from our hospital were randomly assigned to a training set (n = 283) and an internal validation set (n = 121) using a 7:3 ratio, while 81 patients from two other hospitals constituted the external validation set. We extracted 1218 CT-based radiomics features from the gross tumor volume (GTV) as well as the gross peritumoral tumor volume (GPTV5, 10, 15), respectively, and constructed radiomic models. Additionally, we developed a nomogram based on relevant CT features and the radscore derived from the optimal radiomics model.ResultsThe GPTV10 radiomics model exhibited superior predictive performance compared to GTV, GPTV5, and GPTV15, with area under the curve (AUC) values of 0.855, 0.842, and 0.842 in the three respective sets. In the clinical model, the solid component size, pleural indentation, solid attachment, and vascular convergence sign were identified as independent risk factors among the CT features. The predictive performance of the nomogram, which incorporated relevant CT features and the GPTV10-radscore, outperformed both the radiomics model and clinical model alone, with AUC values of 0.894, 0.828, and 0.876 in the three respective sets.ConclusionsThe nomogram, integrating radiomics features and CT morphological features, exhibits good performance in predicting VPI status in lung adenocarcinoma.

An advanced nomogram model using deep learning radiomics and clinical data for predicting occult lymph node metastasis in lung adenocarcinoma

PurposeTo evaluate the effectiveness of deep learning radiomics nomogram in distinguishing the occult lymph node metastasis (OLNM) status in clinical stage IA lung adenocarcinoma. MethodsA cohort of 473 cases of lung adenocarcinomas from two hospitals was included, with 404 cases allocated to the training cohort and 69 cases to the testing cohort. Clinical characteristics and semantic features were collected, and radiomics features were extracted from the computed tomography (CT) images. Additionally, deep transfer learning (DTL) features were generated using RseNet50. Predictive models were developed using the logistic regression (LR) machine learning algorithm. Moreover, gene analysis was conducted on RNA sequencing data from 14 patients to explore the underlying biological basis of deep learning radiomics scores. ResultThe training and testing cohorts achieved AUC values of 0.826 and 0.775 for the clinical model, 0.865 and 0.801 for the radiomics model, 0.927 and 0.885 for the DTL-radiomics model, and 0.928 and 0.898 for the nomogram model. The nomogram model demonstrated superiority over the clinical model. The decision curve analysis (DCA) revealed a net benefit in predicting OLNM for all models. The investigation into the biological basis of deep learning radiomics scores identified an association between high scores and pathways related to tumor proliferation and immune cell infiltration in the microenvironment. ConclusionsThe nomogram model, incorporating clinical-semantic features, radiomics, and DTL features, exhibited promising performance in predicting OLNM. It has the potential to provide valuable information for non-invasive lymph node staging and individualized therapeutic approaches.

A comparison of machine learning methods for radiomics modeling in prediction of occult lymph node metastasis in clinical stage IA lung adenocarcinoma patients.

Accurate prediction of occult lymph node metastasis (ONM) is an important basis for determining whether lymph node (LN) dissection is necessary in clinical stage IA lung adenocarcinoma patients. The aim of this study is to determine the best machine learning algorithm for radiomics modeling and to compare the performances of the radiomics model, the clinical-radilogical model and the combined model incorporate both radiomics features and clinical-radilogical features in preoperatively predicting ONM in clinical stage IA lung adenocarcinoma patients. Patients with clinical stage IA lung adenocarcinoma undergoing curative surgery from one institution were retrospectively recruited and assigned to training and test cohorts. Radiomics features were extracted from the preoperative computed tomography (CT) images of the primary tumor. Seven machine learning algorithms were used to construct radiomics models, and the model with the best performance, evaluated using the area under the curve (AUC), was selected. Univariate and multivariate logistic regression analyses were performed on the clinical-radiological features to identify statistically significant features and to develop a clinical model. The optimal radiomics and clinical models were integrated to build a combined model, and its predictive performance was assessed using receiver operating characteristic curves, Brier score, and decision curve analysis (DCA). This study included 258 patients who underwent resection (training cohort, n=182; test cohort, n=76). Six radiomics features were identified. Among the seven machine learning algorithms, extreme gradient boosting (XGB) demonstrated the highest performance for radiomics modeling, with an AUC of 0.917. The combined model improved the AUC to 0.933 and achieved a Brier score of 0.092. DCA revealed that the combined model had optimal clinical efficacy. The superior performance of the combined model, based on XGB algorithm in predicting ONM in patients with clinical stage IA lung adenocarcinoma, might aid surgeons in deciding whether to conduct mediastinal LN dissection and contribute to improve patients' prognosis.

Evaluation of Prognosis in Patients with Lung Adenocarcinoma with Atypical Solid Nodules on Thin-Section CT Images.

Purpose To evaluate the clinicopathologic characteristics and prognosis of patients with clinical stage IA lung adenocarcinoma with atypical solid nodules (ASNs) on thin-section CT images. Materials and Methods Data from patients with clinical stage IA lung adenocarcinoma who underwent resection between January 2005 and December 2012 were retrospectively reviewed. According to their manifestations on thin-section CT images, nodules were classified as ASNs, subsolid nodules (SSNs), and typical solid nodules (TSNs). The clinicopathologic characteristics of the ASNs were investigated, and the differences across the three groups were analyzed. The Kaplan-Meier method and multivariable Cox analysis were used to evaluate survival differences among patients with ASNs, SSNs, and TSNs. Results Of the 254 patients (median age, 58 years [IQR, 53-66]; 152 women) evaluated, 49 had ASNs, 123 had SSNs, and 82 had TSNs. Compared with patients with SSNs, those with ASNs were more likely to have nonsmall adenocarcinoma (P < .001), advanced-stage adenocarcinoma (P = .004), nonlepidic growth adenocarcinoma (P < .001), and middle- or low-grade differentiation tumors (P < .001). Compared with patients with TSNs, those with ASNs were more likely to have no lymph node involvement (P = .009) and epidermal growth factor receptor mutation positivity (P = .018). Average disease-free survival in patients with ASNs was significantly longer than that in patients with TSNs (P < .001) but was not distinguishable from that in patients with SSNs (P = .051). Conclusion ASNs were associated with better clinical outcomes than TSNs in patients with clinical stage IA lung adenocarcinoma. Keywords: Adenocarcinoma, Atypical Solid Nodules, CT, Disease-free Survival, Lung, Prognosis, Pulmonary Supplemental material is available for this article. Published under a CC BY 4.0 license.

Discussion to: Oncological characteristics of EGFR-mutated clinical stage IA lung adenocarcinoma with radiologically pure-solid appearance

Discussion to: Oncological characteristics of EGFR-mutated clinical stage IA lung adenocarcinoma with radiologically pure-solid appearance

A CT-based deep learning model: visceral pleural invasion and survival prediction in clinical stage IA lung adenocarcinoma

Pathologic visceral pleural invasion (VPI) in patients with early-stage lung cancer can result in the upstaging of T1 to T2, in addition to having implications for surgical resection and prognostic outcomes. This study was designed with the goal of establishing and validating a CT-based deep learning (DL) model capable of predicting VPI status and stratifying patients based on their prognostic outcomes. In total, 2077 patients from three centers with pathologically confirmed clinical stage IA lung adenocarcinoma were enrolled. DL signatures were extracted with a 3D residual neural network. DL model was able to effectively predict VPI status. VPI predicted by the DL models, as well as pathologic VPI, was associated with shorter disease-free survival. The established deep learning signature provides a tool capable of aiding the accurate prediction of VPI in patients with clinical stage IA lung adenocarcinoma, thus enabling prognostic stratification.

Oncological characteristics of epidermal growth factor receptor–mutated clinical stage IA lung adenocarcinoma with radiologically pure-solid appearance

ObjectivesWe evaluated the clinicopathological and oncological characteristics of epidermal growth factor receptor–mutated clinical stage IA radiological pure-solid lung adenocarcinoma and compared them with those of a ground-glass opacity component. MethodsBetween 2008 and 2020, data from 1014 surgically resected clinical stage 0-IA epidermal growth factor receptor–mutated lung adenocarcinomas were evaluated. Oncological outcomes were assessed using multivariable analysis. Overall survival was estimated using Kaplan–Meier analysis and the log-rank test. The cumulative incidence of recurrence was estimated using the Gray's test. ResultsOf these, 233 (23%) were radiologically pure-solid tumors, which demonstrated a higher proportion of nodal metastasis, micropapillary component, spread through alveolar space, and Ex19 subtype compared with those of tumors with ground-glass opacity (P < .001). Multivariable analysis revealed that the presence of ground-glass opacity was an independently significant factor for overall survival (P = .037) and cumulative incidence of recurrence (P < .001). In cases where the oncological outcomes were stratified by the presence of ground-glass opacity component, the 5-year overall survival was excellent at more than 90% in tumors with ground-glass opacity despite clinical-T categories (P = .2044); however, tumor size significantly affected survival only in pure-solid tumors (T1a, 100%; T1b, 77.7%; T1c, 68.5%; P = .0056). Furthermore, the cumulative incidence of recurrence was low in tumors with ground-glass opacity despite the clinical-T categories, whereas tumor size significantly affected the cumulative incidence of recurrence only in pure-solid tumors (5-year cumulative incidence of recurrence: T1a-b, 18.9%; T1c, 41.3%; P < .001). ConclusionsOncologic behavior and prognosis of radiologically pure-solid tumors were significantly poorer than those of tumors with ground-glass opacity among patients with epidermal growth factor receptor–mutated early-stage lung adenocarcinoma. These findings imply distinct tumorigenesis based on the presence of ground-glass opacity, even in tumors with epidermal growth factor receptor mutations.

P1.27-05 Two- and Three-dimensional Radiomic Measurements for Clinical Stage IA Lung Adenocarcinoma: Less is More

P1.27-05 Two- and Three-dimensional Radiomic Measurements for Clinical Stage IA Lung Adenocarcinoma: Less is More

Development of a novel combined nomogram integrating deep-learning-assisted CT texture and clinical–radiological features to predict the invasiveness of clinical stage IA part-solid lung adenocarcinoma: a multicentre study

To develop a novel combined nomogram based on deep-learning-assisted computed tomography (CT) texture (DL-TA) and clinical-radiological features for the preoperative prediction of invasiveness in patients with clinical stage IA lung adenocarcinoma manifesting as part-solid nodules (PSNs). This study was conducted from January 2015 to October 2021 at three centres: 355 patients with 355 PSN lung adenocarcinomas who underwent surgical resection were included and classified into the training (n=222) and validation (n=133) cohorts. PSN segmentation on CT images was performed automatically with a commercial deep-learning algorithm, and CT texture features were extracted. The least absolute shrinkage and selection operator was used for feature selection and transformed into a DL-TA score. The combined nomogram that incorporated the DL-TA score and identified clinical-radiological features was developed for the prediction of pathological invasiveness of the PSNs and validated in terms of discrimination and calibration. The present study generated a combined nomogram for predicting the invasiveness of PSNs that included age, consolidation-to-tumour ratio, smoking status, and DL-TA score, with a C-index of 0.851 (95% confidence interval: 0.826-0.877) for the training cohort and 0.854 (95% confidence interval: 0.817-0.891) for the validation cohort, indicating good discrimination. Furthermore, the model had a Brier score of 0.153 for the training cohort and 0.135 for the validation cohort, indicating good calibration. The developed combined nomogram consisting of the DL-TA score and clinical-radiological features and has the potential to predict the individual risk for the invasiveness of stage IA PSN lung adenocarcinomas.

Number of involved nodal stations: a better lymph node classification for clinical stage IA lung adenocarcinoma

BackgroundWith the popularization of lung cancer screening, more early-stage lung cancers are being detected. This study aims to compare three types of N classifications, including location-based N classification (pathologic nodal classification [pN]), the number of lymph node stations (nS)-based N classification (nS classification), and the combined approach proposed by the International Association for the Study of Lung Cancer (IASLC) which incorporates both pN and nS classification to determine if the nS classification is more appropriate for early-stage lung cancer. MethodsWe retrospectively reviewed the clinical data of lung cancer patients treated at the Cancer Hospital, Chinese Academy of Medical Sciences between 2005 and 2018. Inclusion criteria was clinical stage IA lung adenocarcinoma patients who underwent resection during this period. Sub-analyses were performed for the three types of N classifications. The optimal cutoff values for nS classification were determined with X-tile software. Kaplan‒Meier and multivariate Cox analyses were performed to assess the prognostic significance of the different N classifications. The prediction performance among the three types of N classifications was compared using the concordance index (C-index) and decision curve analysis (DCA). ResultsOf the 669 patients evaluated, 534 had pathological stage N0 disease (79.8%), 82 had N1 disease (12.3%) and 53 had N2 disease (7.9%). Multivariate Cox analysis indicated that all three types of N classifications were independent prognostic factors for prognosis (all P < 0.001). However, the prognosis overlaps between pN (N1 and N2, P = 0.052) and IASLC-proposed N classification (N1b and N2a1 [P = 0.407], N2a1 and N2a2 [P = 0.364], and N2a2 and N2b [P = 0.779]), except for nS classification subgroups (nS0 and nS1 [P < 0.001] and nS1 and nS >1 [P = 0.006]). There was no significant difference in the C-index values between the three N classifications (P = 0.370). The DCA results demonstrated that the nS classification provided greater clinical utility. ConclusionThe nS classification might be a better choice for nodal classification in clinical stage IA lung adenocarcinoma.

Nomograms for intraoperative prediction of lymph node metastasis in clinical stage IA lung adenocarcinoma.

Accurate prediction of lymph node metastasis (LNM) is critical for selecting optimal surgical procedures in early-stage lung adenocarcinoma (LUAD). This study aimed to develop nomograms for intraoperative prediction of LNM in clinical stage IA LUAD. A total of 1227 patients with clinical stage IA LUADs on computed tomography (CT) were enrolled to construct and validate nomograms for predicting LNM (LNM nomogram) and mediastinal LNM (LNM-N2 nomogram). Recurrence-free survival (RFS) and overall survival (OS) were compared between limited mediastinal lymphadenectomy (LML) and systematic mediastinal lymphadenectomy (SML) in the high- and low-risk groups for LNM-N2, respectively. Three variables were incorporated into the LNM nomogram and the LNM-N2 nomogram, including preoperative serum carcinoembryonic antigen (CEA) level, CT appearance, and tumor size. The LNM nomogram showed good discriminatory performance, with C-indexes of 0.879 (95% CI, 0.847-0.911) and 0.880 (95% CI, 0.834-0.926) in the development and validation cohorts, respectively. The C-indexes of the LNM-N2 nomogram were 0.812 (95% CI, 0.766-0.858) and 0.822 (95% CI, 0.762-0.882) in the development and validation cohorts, respectively. LML and SML had similar survival outcomes among patients with low risk of LNM-N2 (5-year RFS, 88.1% vs. 89.5%, Pp = 0.790; 5-year OS, 96.0% vs. 93.0%, p = 0.370). However, for patients with high risk of LNM-N2, LML was associated with worse survival (5-year RFS, 64.0% vs. 77.4%, p = 0.036; 5-year OS, 66.0% vs. 85.9%, p = 0.038). We developed and validated nomograms to predict LNM and LNM-N2 intraoperatively in patients with clinical stage IA LUAD on CT. These nomograms may help surgeons to select optimal surgical procedures.

Multivariate analysis based on the maximum standard unit value of 18F-fluorodeoxyglucose positron emission tomography/computed tomography and computed tomography features for preoperative predicting of visceral pleural invasion in patients with subpleural clinical stage IA peripheral lung adenocarcinoma

Preoperative prediction of visceral pleural invasion (VPI) is important because it enables thoracic surgeons to choose appropriate surgical plans. This study aimed to develop and validate a multivariate logistic regression model incorporating the maximum standardized uptake value (SUVmax) and valuable computed tomography (CT) signs for the non-invasive prediction of VPI status in subpleural clinical stage IA lung adenocarcinoma patients before surgery. A total of 140 patients with subpleural clinical stage IA peripheral lung adenocarcinoma were recruited and divided into a training set (n = 98) and a validation set (n = 42), according to the positron emission tomography/CT examination temporal sequence, with a 7:3 ratio. Next, VPI-positive and VPI-negative groups were formed based on the pathological results. In the training set, the clinical information, the SUVmax, the relationship between the tumor and the pleura, and the CT features were analyzed using univariate analysis. The variables with significant differences were included in the multivariate analysis to construct a prediction model. A nomogram based on multivariate analysis was developed, and its predictive performance was verified in the validation set. The size of the solid component, the consolidation-to-tumor ratio, the solid component pleural contact length, the SUVmax, the density type, the pleural indentation, the spiculation, and the vascular convergence sign demonstrated significant differences between VPI-positive (n = 40) and VPI-negative (n = 58) cases on univariate analysis in the training set. A multivariate logistic regression model incorporated the SUVmax [odds ratio (OR): 1.753, P = 0.002], the solid component pleural contact length (OR: 1.101, P = 0.034), the pleural indentation (OR: 5.075, P = 0.041), and the vascular convergence sign (OR: 13.324, P = 0.025) as the best combination of predictors, which were all independent risk factors for VPI in the training group. The nomogram indicated promising discrimination, with an area under the curve value of 0.892 [95% confidence interval (CI), 0.813-0.946] in the training set and 0.885 (95% CI, 0.748-0.962) in the validation set. The calibration curve demonstrated that its predicted probabilities were in acceptable agreement with the actual probability. The decision curve analysis illustrated that the current nomogram would add more net benefit. The nomogram integrating the SUVmax and the CT features could non-invasively predict VPI status before surgery in subpleural clinical stage IA lung adenocarcinoma patients.

The Utility of SUVmax as an Adaptation Criterion for Limited Resection in Stage IA Non-Small Cell Lung Cancer

Introduction: Although the consolidation diameter of a tumor on computed tomography (CT) is an adaptation criterion for limited resection in early-stage non-small cell lung cancer (NSCLC), whether the maximum standardized uptake value (SUV_max) is also an adaptation criterion for limited resection has not been evaluated. Methods: In total, 478 NSCLC patients with clinical stage IA disease were analyzed, among whom 383 were used to perform a sub-analysis. Results: Multivariate analysis showed that consolidation diameter (odds ratio [OR]: 3.05, p = 0.01), SUV_max (OR: 10.74, p = 0.02), and lymphatic invasion (OR: 10.34, p < 0.01) were risk factors for lymph node metastasis in clinical stage IA NSCLC patients. Furthermore, age (OR: 2.98, p = 0.03), SUV_max (OR: 13.07, p = 0.02), and lymphatic invasion (OR: 5.88, p = 0.02) were risk factors for lymph node metastasis in clinical stage IA lung adenocarcinoma patients according to multivariate analysis. Conclusion: Consolidation diameter of a tumor on CT, SUV_max, and lymphatic invasion are risk factors for lymph node metastasis. However, SUV_max was a risk factor for lymph node metastasis rather than consolidation diameter on CT in lung adenocarcinoma patients. These results suggest that for early-stage lung adenocarcinoma patients, SUV_max is more important for deciding the indication of limited resection than consolidation diameter of the tumor on CT.

Distinct Prognostic Impact of PET Findings Based on Radiological Appearance in Clinical Stage IA Lung Adenocarcinoma

IntroductionAlthough solid appearance on computed tomography and positive findings on positron emission tomography (PET) have been both associated with poor outcome in lung adenocarcinoma, the extent to which these findings overlap is unknown. This study aimed to determine the differences in prognostic significance of PET findings in part-solid nodules (PSNs) and solid nodules. Materials and MethodsWe retrospectively investigated 417 patients with clinical stage IA adenocarcinoma who underwent curative resection between 2010 and 2017. We compared disease-free survival (DFS), cumulative incidence of disease recurrence (CIR) and clinicopathological characters between PET-positive and negative groups among PSNs and solid nodules, respectively. We used 2.5 as a cut-off value of maximum standardized uptake value (SUV max). ResultsIn PSNs (n = 235), PET-positive group (n = 59) showed more aggressive features in several clinicopathological variables, poorer DFS (P < .001) and higher CIR (P < .001) than PET-negative group (n = 176). In contrast, in solid nodules (n = 182), DFS (P = .521) and CIR (P = .311) were not significantly different between PET-positive (n = 128) and negative groups (n = 54). SUV max was proved to be the independent prognostic factor of DFS by multivariate analysis (HR, 1.155; 95% CI, 1.036-1.287) only in PSNs. ConclusionThese findings showed distinct impact on prognosis of PET findings between PSNs and solid nodules. PET-positive finding was more important prognostic factor in PSNs than in solid nodules among clinical stage IA lung adenocarcinoma.

ASO Visual Abstract: Prognostic Significance of the Maximum Standardized Uptake Value on the Prognosis of Clinical Stage IA Lung Adenocarcinoma Based on the 8th Edition TNM Classification.

ASO Visual Abstract: Prognostic Significance of the Maximum Standardized Uptake Value on the Prognosis of Clinical Stage IA Lung Adenocarcinoma Based on the 8th Edition TNM Classification.

Prognostic Significance of the Maximum Standardized Uptake Value on the Prognosis of Clinical Stage IA Lung Adenocarcinoma Based on the 8th Edition TNM Classification.

There are few reports on the utility of the maximum standardized uptake value (SUVmax) for predicting the prognosis of early-stage lung adenocarcinoma based on the latest tumor-node-metastasis (TNM) classification. This study aimed to determine whether clinicopathologic factors, including the SUVmax, affect prognosis in these patients. We enrolled 527 patients with c-stage IA lung adenocarcinoma who underwent lobectomy or greater resection between 2011 and 2017. Recurrence-free survival (RFS) and overall survival (OS) were analyzed using Kaplan-Meier curves and compared using the log-rank test. Factors associated with RFS and OS were determined using the Cox proportional hazards model. RFS was significantly different based on tumor stage. In contrast, there was no significant difference in OS between patients with stage IA2 and IA3 disease (p = 0.794), although there were significant differences in OS between patients with stage IA1 and IA2 disease (p = 0.024) and between patients with stage IA1 and IA3 disease (p = 0.012). Multivariate analysis demonstrated that SUVmax was independently associated with both RFS and OS among patients with c-stage IA lung adenocarcinoma (RFS, p = 0.017; OS, p = 0.047). Further, even though there was no significant difference in OS between patients with stage IA2 and IA3 disease (n = 410), SUVmax was able to stratify patients with high and low RFS and OS among these patients (RFS, p < 0.001; OS, p < 0.001). SUVmax was an important preoperative factor to evaluate prognosis among patients with c-stage IA lung adenocarcinoma as well as the current TNM classification.