Clinical Stage IA Lung Adenocarcinoma Research Articles

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.

Clinical significance of the combination of preoperative SUVmax and CEA in patients with clinical stage IA lung adenocarcinoma.

BackgroundPreoperative maximum standardized uptake value (SUVmax) of 2‐[18F]‐fluoro‐2‐deoxy‐D‐glucose positron emission tomography and serum carcinoembryonic antigen (CEA) have been reported as prognostic factors for lung adenocarcinoma. However, the significance of combined SUVmax and CEA in early‐stage lung adenocarcinoma is not well known.MethodsWe retrospectively evaluated the relationship between the combination of SUVmax and CEA and the prognosis of 410 patients with clinical stage IA lung adenocarcinoma who underwent resection. The cutoff values for SUVmax and CEA were determined by receiver operating characteristic curve analysis, and patients were categorized into high SC (SUVmax and CEA) group (SUVmax ≥2.96 and CEA ≥5.3), moderate SC group (either SUVmax <2.96 and CEA ≥5.3 or SUVmax ≥2.96 and CEA <5.3) and low SC group (SUVmax <2.96 and CEA <5.3).ResultsKaplan–Meier curve analysis showed that patients with clinical stage IA lung adenocarcinoma in the high SC group had significantly shorter overall survival (OS) and recurrence‐free survival (RFS) than the other groups (p = 0.011 and p < 0.0001, respectively). Multivariate analysis showed that high SC was an independent prognostic factor of OS (p = 0.029) and RFS (p < 0.0001).ConclusionsHigh values of SUVmax and CEA were associated with poor OS and RFS in patients with stage IA lung adenocarcinoma. Simultaneous evaluation of SUVmax and CEA may be an effective prognostic marker to determine the optimal treatment strategy of early‐stage lung adenocarcinoma.

Prognostic Stratification According to Size and Dominance of Radiologic Solid Component in Clinical Stage IA Lung Adenocarcinoma

IntroductionAlthough several studies have investigated the prognostic significance of the radiographic appearance of stage IA lung adenocarcinoma, the prognostic impact of solid component size or consolidation-to-tumor ratio (CTR) of part-solid nodules (PSNs) still remains controversial. This study aimed to clarify the combined prognostic impact of the mentioned radiographic features of PSNs and compare it with that of pure solid nodules in the current TNM classification. MethodsWe retrospectively investigated 1014 patients with clinical stage IA (TNM eighth edition) adenocarcinoma who underwent curative resection. Overall survival (OS) and pathologic characteristics of pure solid nodules, solid-dominant PSNs (CTR > 0.5), and ground-glass opacity (GGO)-dominant PSNs (CTR ≤ 0.5) were compared according to T category. ResultsPatients with pure solid nodules (297 cases) had significantly shorter OS compared with those with PSNs (717 cases) (p < 0.001) but a marginal difference compared with those with solid-dominant PSNs (286 cases) (p = 0.051). No significant difference in OS was found according to T category in those with GGO-dominant PSNs (431 cases). Patients with cT1b and T1c solid-dominant PSNs had significantly worse prognosis compared with those with other PSNs and had comparable prognosis with those with cT1b pure solid nodules (p = 0.892). Higher frequency of nodal and lymphovascular involvement and pathologic upstaging was observed with T category progression in solid-dominant PSNs. ConclusionsAn hierarchy of prognosis and pathologic malignant characteristics was observed according to T category in patients with solid-dominant PSNs but not in those with GGO-dominant PSNs, suggesting the importance of classifying PSNs on the basis of solid component size and CTR for accurate prognostic comparison with pure solid nodules.

Which will carry more weight when CTR > 0.5, solid component size, CTR, tumor size or SUVmax?

BackgroundThis study was conducted to explore the clinical significance of the maximum standard uptake value (SUVmax) in the clinical stage IA lung adenocarcinoma with tumor size ≤ 2 cm and consolidation to tumor ratio (CTR) > 0.5. MethodsWe retrospectively reviewed non-small cell lung cancer patients who underwent surgeries between January 2014 and March 2017. Clinical stage IA lung adenocarcinoma patients with tumor of size ≤ 2 cm and CTR > 0.5 were enrolled. The patients were divided into two groups: part-solid and pure-solid based on whether CTR = 1.0 or not. Nodules with any amount of solid or micropapillary components were regarded as the high-risk subtype. Time-dependent ROC curve was used to determine the best cut-off value. Finally, we analyzed the relationship between SUVmax, high-risk subtypes, node metastasis and 5-year relapse-free survival and overall survival. ResultsTotally, 270 patients were included. The distribution of pathological subtypes (p < 0.001), SUVmax (p < 0.001), and pathological N stage (p < 0.001) were different between the two groups. Multivariable analysis indicated that SUVmax could predict high-risk subtypes in cases of part-solid nodules (p < 0.001) and both high-risk subtypes (p = 0.022) and node metastasis (p < 0.001) in cases of pure-solid ones. SUVmax ≥ 2.6 and SUVmax ≥ 5.1 were strongly associated with 5-year relapse-free survival (p < 0.001) and 5-year overall survival (p < 0.001) among all the patients, respectively. ConclusionPart-solid nodules with 0.5 < CTR < 1 and pure-solid nodules in lung adenocarcinoma show different clinicopathological characteristics, especially in SUVmax. SUVmax is significantly associated with high-risk subtypes, node metastasis, 5-year relapse-free survival and overall survival.

MA10.05 Potential of CT and PET-Based Radiomics for the Diagnosis of Lung Adenocarcinomas Indicated for Limited Resection

We previously developed radiomics technology using computed tomography (CT) to identify cases of preinvasive lung adenocarcinoma that may be indicated for limited resection. This study aimed to investigate whether the maximum standardized uptake value (SUVmax) on positron emission tomography (PET) has an add-on effect for this technology. This retrospective observational study was conducted at two medical centers. The resected pulmonary lesions were reviewed between April 2014 and March 2020. Patients with clinical stage IA lung adenocarcinomas were included. Thin-section CT images (less than 2.5 mm) were used to assess the clinical stage. For the image analysis algorithm of plain CT-based radiomics, we previously selected seven parameters: tumor volume (cm3), volume rate of the solid component (%solid; %), mean CT value (Hounsfield units), kurtosis, skewness, variance, and entropy. In this study, a new prediction model based on multiple logistic regression comprising eight parameters (CT＋PET-based radiomics) was generated, including SUVmax, after standardization to eliminate inter-facility bias. Next, stepwise variable selection was applied to the prediction model to evaluate whether SUVmax was a more important factor for identifying pathological preinvasive adenocarcinomas. A bootstrap method for randomized selection was used to assess the accuracy of the results. A total of 262 lung adenocarcinomas were categorized into the invasive group (n = 166 [63%]) or a preinvasive group (n = 96 [37%]), the latter of which comprised adenocarcinomas in situ (n = 32 [33%]) and minimally invasive adenocarcinomas (n = 64 [67%]). Before the surgery, the mean total tumor size of the invasive group (17.0 ± 6.5 mm) was significantly greater than that of the preinvasive group (13.9 ± 5.6 mm; p < 0.001). The pathological T factors were Tis (n = 32), T1mi (n = 64), T1a (n = 83), T1b (n = 66), and T1c (n = 17). Although spread through air spaces (STAS) was observed in 24 specimens (9.2%), five STAS-positive cases (5.2%) were noted in the preinvasive group. Upstaging was confirmed in one patient with lymph node-positive metastasis (N1) after pathological diagnosis. However, there were no cases of upstaging among the preinvasive tumors. Before standardization, the median SUVmax was 0.6 (interquartile range [IQR], 0.00–1.40) in the preinvasive group and 1.7 [IQR, 0.99–2.85]) in the invasive group (p < 0.001). After a stepwise method involving the eight factors, %solid (odds ratio [OR], 0.30; 95% confidence interval [CI], 0.20–0.46; p < 0.001) and entropy (OR, 0.46; 95% CI, 0.32–0.67; p < 0.001) were selected, but not SUVmax. The area under the curve was 0.83 (95% CI, 0.78–0.88). In the bootstrapping analysis, SUVmax was never selected as a key factor compared with the original parameters of CT-based radiomics. Even when the five STAS-positive cases in the preinvasive group were reclassified into the invasive group, SUVmax did not remain a key factor in the discrimination. In the identification of cases of preinvasive adenocarcinomas for which limited resection may be indicated, SUVmax did not increase the diagnostic efficacy of the original CT-based radiomics.

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

ObjectivesOur 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.MethodsWe 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.ResultsA 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).ConclusionsCTR 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.