Development and Validation of a Clinical Prediction Model for N2 Lymph Node Metastasis in Non-Small Cell Lung Cancer

What is new in mediastinal staging?

PURPOSE OF REVIEW

Appropriate staging is of utmost importance in nonsmall cell lung cancer (NSCLC), as the pathologic stage dictates both overall prognosis and appropriate therapeutic pathways. This article seeks to review the current recommendations for mediastinal staging of NSCLC and available modalities to achieve this. Landmark publications pertaining to recent advancements in NSCLC treatments are also highlighted and the role of specific bronchoscopic modalities for tissue acquisition are reviewed.

RECENT FINDINGS

Recent advancements in the treatment of NSCLC have made accurate mediastinal staging more important than ever. Guidelines and recommendations outlining patients that warrant invasive mediastinal staging are available and a systematic approach should be utilized when sampling is performed. Ensuring the adequacy of tissue for the growing number of molecular biomarkers that must be tested has been the focus of many recent studies.

SUMMARY

Appropriate mediastinal staging is crucial for the management of patients with NSCLC as is obtaining adequate tissue for diagnostic and therapeutic purposes. EBUS-TBNA is sufficient for the diagnosis of nonsmall cell and small cell lung carcinomas, but EBUS-guided intranodal forceps and cryobiopsy may provide more optimal specimen for patients with benign disease, such as sarcoidosis, or in cases of lymphoma. Further studies are necessary to better delineate the role of these techniques in the diagnosis and staging of mediastinal diseases before they become the primary diagnostic modalities.

PET/CT based cross-modal deep learning signature to predict occult nodal metastasis in lung cancer

Occult nodal metastasis (ONM) plays a significant role in comprehensive treatments of non-small cell lung cancer (NSCLC). This study aims to develop a deep learning signature based on positron emission tomography/computed tomography to predict ONM of clinical stage N0 NSCLC. An internal cohort (n = 1911) is included to construct the deep learning nodal metastasis signature (DLNMS). Subsequently, an external cohort (n = 355) and a prospective cohort (n = 999) are utilized to fully validate the predictive performances of the DLNMS. Here, we show areas under the receiver operating characteristic curve of the DLNMS for occult N1 prediction are 0.958, 0.879 and 0.914 in the validation set, external cohort and prospective cohort, respectively, and for occult N2 prediction are 0.942, 0.875 and 0.919, respectively, which are significantly better than the single-modal deep learning models, clinical model and physicians. This study demonstrates that the DLNMS harbors the potential to predict ONM of clinical stage N0 NSCLC.

Prediction Models for Mediastinal Metastasis and Its Detection by Endobronchial Ultrasound-Guided Transbronchial Needle Aspiration in Potentially Operable Non-Small Cell Lung Cancer: A Prospective Study

BACKGROUND

Prediction models for mediastinal metastasis and its detection by endobronchial ultrasound-guided transbronchial needle aspiration (EBUS-TBNA) have not been developed using a prospective cohort of potentially operable patients with non-small cell lung cancer (NSCLC).

RESEARCH QUESTION

Can mediastinal metastasis and its detection by EBUS-TBNA be predicted with prediction models in NSCLC?

STUDY DESIGN AND METHODS

For the prospective development cohort, 589 potentially operable patients with NSCLC were evaluated (July 2016-June 2019) from five Korean teaching hospitals. Mediastinal staging was performed using EBUS-TBNA (with or without the transesophageal approach). Surgery was performed for patients without clinical N (cN) 2-3 disease by endoscopic staging. The prediction model for lung cancer staging-mediastinal metastasis (PLUS-M) and a model for mediastinal metastasis detection by EBUS-TBNA (PLUS-E) were developed using multivariable logistic regression analyses. Validation was performed using a retrospective cohort (n = 309) from a different period (June 2019-August 2021).

RESULTS

The prevalence of mediastinal metastasis diagnosed by EBUS-TBNA or surgery and the sensitivity of EBUS-TBNA in the development cohort were 35.3% and 87.0%, respectively. In PLUS-M, younger age (< 60 years and 60-70 years compared with ≥ 70 years), nonsquamous histology (adenocarcinoma and others), central tumor location, tumor size (> 3-5 cm), cN1 or cN2-3 stage by CT, and cN1 or cN2-3 stage by PET-CT were significant risk factors for N2-3 disease. Areas under the receiver operating characteristic curve (AUCs) for PLUS-M and PLUS-E were 0.876 (95% CI, 0.845-0.906) and 0.889 (95% CI, 0.859-0.918), respectively. Model fit was good (PLUS-M: Hosmer-Lemeshow P = .658, Brier score = 0.129; PLUS-E: Hosmer-Lemeshow P = .569, Brier score = 0.118). In the validation cohort, PLUS-M (AUC, 0.859 [95% CI, 0.817-0.902], Hosmer-Lemeshow P = .609, Brier score = 0.144) and PLUS-E (AUC, 0.900 [95% CI, 0.865-0.936], Hosmer-Lemeshow P = .361, Brier score = 0.112) showed good discrimination ability and calibration.

INTERPRETATION

PLUS-M and PLUS-E can be used effectively for decision-making for invasive mediastinal staging in NSCLC.

TRIAL REGISTRY

ClinicalTrials.gov; No.: NCT02991924; URL: www.

CLINICALTRIALS

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Predicting N2 lymph node metastasis in presurgical stage I-II non-small cell lung cancer using multiview radiomics and deep learning method

BACKGROUND

Accurate diagnosis of N2 lymph node status of the resectable stage I-II non-small cell lung cancer (NSCLC) before surgery is crucial, while there is lack of corresponding method clinically.

PURPOSE

To develop and validate a model to quantitively predict the N2 lymph node metastasis in presurgical clinical stage I-II NSCLC using multiview radiomics and deep learning method.

METHODS

In this study, 140 NSCLC patients were enrolled and randomly divided into training and test sets. Univariate and multiple analysis method were used step by step to establish the clinical model; Then a multiview radiomics modeling scheme was designed, in which the optimal input feature set was determined by subcategorizing radiomics features (C1: original; C2: LoG and C3: wavelet) and comparison of corresponding radiomics model. The minimum-redundancy maximum-relevance (mRMR) selection and the least absolute shrinkage and selection operator (LASSO) algorithm were used for the feature selection and construction of each radiomics model (Rad). Next, an end-to-end ResNet18 architecture and transfer learning techniques were designed to construct a deep learning model (DL). Subsequently, the screened clinical risk factors and constructed Rad and DL models were combined and compared and a nomogram was constructed. Finally, the diagnostic performance of all constructed models were evaluated and compared using receiver operating characteristic curve (ROC) analysis, Delong test, Calibration analysis, Hosmer-Lemeshow test, and decision curves, respectively.

RESULTS

Carcinoma embryonic antigen (CEA) level and spiculation were screened to make up the Clinical model, while seven radiomics features in the optimal input feature set C2 + C3 were selected to construct the Rad. DL was constructed by training on 1.8 million natural images and small sample data of our N2 lymph node volume of interest (VOI) images. Except for the Clinical model, all other models showed good predictive accuracy and consistency in both training set and test set. DL (area under curve (AUC): 0.83) was better than Rad (AUC: 0.76) in predictive accuracy, but their difference was not significant (p = 0.45). The combined models showed better diagnostic performance than the model only clinical or image risk factors were used (AUC for Clinical, Rad + DL, Rad + Clinical, DL + Clinical, and Rad + DL + Clinical were respectively 0.66, 0.86, 0.82, 0.86, and 0.88). Finally, the Rad + DL + Clinical model with the best diagnostic performance was selected to draw the final nomogram for clinical use.

CONCLUSION

This study proposes a nomogram based on multiview radiomics, deep learning, and clinical features that can be efficiently used to quantitively predict presurgical N2 diseases in patients with clinical stage I-II NSCLC.

Mathematical models for intraoperative prediction of metastasis to regional lymph nodes in patients with clinical stage I non-small cell lung cancer

It remains challenging to determine the regions of metastasis to lymph nodes during operation for clinical stage I non-small cell lung cancer (NSCLC). This study aimed to establish intraoperative mathematical models with nomograms for predicting the hilar-intrapulmonary node metastasis (HNM) and the mediastinal node metastasis (MNM) in patients with clinical stage I NSCLC. The clinicopathological variables of 585 patients in a derivation cohort who underwent thoracoscopic lobectomy with complete lymph node dissection were retrospectively analyzed for their association with the HNM or the MNM. After analyzing the variables, we developed multivariable logistic models with nomograms to estimate the risk of lymph node metastasis in different regions. The predictive efficacy was then validated in a validation cohort of 418 patients. It was confirmed that carcinoembryonic antigen (>5.75 ng/mL), CYFRA211 (>2.85 ng/mL), the maximum diameter of tumor (>2.75 cm), tumor differentiation (grade III), bronchial mucosa and cartilage invasion, and vascular invasion were predictors of HNM, and carcinoembryonic antigen (>8.25 ng/mL), CYFRA211 (>2.95 ng/mL), the maximum diameter of tumor (>2.75 cm), tumor differentiation (grade III), bronchial mucosa and cartilage invasion, vascular invasion, and visceral pleural invasion were predictors of MNM. The validation of the prediction models based on the above results demonstrated good discriminatory power. Our predictive models are helpful in the decision-making process of specific therapeutic strategies for the regional lymph node metastasis in patients with clinical stage I NSCLC.

Correlation analysis between unenhanced and enhanced CT radiomic features of lung cancers presenting as solid nodules and their efficacy for predicting hilar and mediastinal lymph node metastases

Objectives

If hilar and mediastinal lymph node metastases occur in solid nodule lung cancer is critical for tumor staging, which determines the treatment strategy and prognosis of patients. We aimed to develop an effective model to predict hilar and mediastinal lymph node metastases by using texture features of solid nodule lung cancer.

Methods

Two hundred eighteen patients with solid nodules on CT images were analyzed retrospectively. The 3D tumors were delineated using ITK-SNAP software. Radiomics features were extracted from unenhanced and enhanced CT images based on AK software. Correlations between radiomics features of unenhanced and enhanced CT images were analyzed with Spearman rank correlation analysis. According to pathological findings, the patients were divided into no lymph node metastasis group and lymph node metastasis group. All patients were randomly divided into training group and test group at a ratio of 7:3. Valuable features were selected. Multivariate logistic regression was used to build predictive models. Two predictive models were established with unenhanced and enhanced CT images. ROC analysis was used to estimate the predictive efficiency of the models.

Results

A total of 7 categories of features, including 107 features, were extracted. There was a high correlation between the 7 categories of features from unenhanced CT images and enhanced CT images (all r > 0.7, p < 0.05). Among them, the shape features had the strongest correlation (mean r = 0.98). There were 5 features in the enhanced model and the unenhanced model, which had important predicting significance. The AUCs were 0.811 and 0.803, respectively. There was no significant difference in the predictive performance of the two models (DeLong's test, p = 0.05).

Conclusion

Our study models achieved higher accuracy for predicting hilar and mediastinal lymph node metastasis of solid nodule lung cancer and have some value in promoting the staging accuracy of lung cancer. Our results show that CT radiomics features have potential to predict hilar and mediastinal lymph node metastases in solid nodular lung cancer. In addition, enhanced and unenhanced CT radiomics models had comparable predictive power in predicting hilar and mediastinal lymph node metastases.

Definitions of Central Tumors in Radiologically Node-Negative, Early-Stage Lung Cancer for Preoperative Mediastinal Lymph Node Staging: A Dual-Institution, Multireader Study

BACKGROUND

Definitions for central lung cancer (CLC) have been ambiguous in guidelines, causing difficulty in selecting candidates for invasive mediastinal staging among patients with radiologically node-negative, early-stage lung cancer.

RESEARCH QUESTION

What is the optimal definition for CLC that is robust to interreader and institutional variation to select candidates for invasive mediastinal staging among those with clinical T1N0M0 lung cancer?

STUDY DESIGN AND METHODS

Two retrospective cohorts were evaluated for the associations of central lung cancer according to 13 definitions based on chest CT scan with occult nodal metastasis. Univariate and multivariate ordinal logistic regression analyses were performed with the pathologic N category as an ordinal outcome. Robust definitions, which retained statistical significance across multireader, dual-institutional datasets, were identified. For these definitions, binary diagnostic performance and interreader agreement were investigated.

RESULTS

In the two cohorts, 807 patients (median age, 63 years; interquartile range [IQR], 56-71 years; 410 women; 33 pN1, 48 pN2, and 1 pN3) and 510 patients (median age, 65 years; IQR, 58-71 years; 267 women; 33 pN1, 20 pN2, and no pN3) were included, respectively. Three definitions robust to interreader variation and dataset heterogeneity were identified: definition 7 (concentric lines arising from the midline, inner one-third, medial margin; adjusted OR, 2.01; 95% CI, 1.13-3.51; P = .02), definition 10 (location index-based inner one-third, center; adjusted OR, 3.60; 95% CI, 1.49-8.25; P = .003), and definition 12 (location index-based inner one-third, medial margin; adjusted OR, 3.57; 95% CI, 1.91-6.52; P < .001). Definition 12 showed higher interreader agreement than definition 7 (Cohen κ, 0.80 vs 0.66; P = .005). Nevertheless, the sensitivity and positive predictive value of the three definitions were < 50%.

INTERPRETATION

Three definitions exhibited robust associations with occult nodal metastasis. However, selecting candidates for invasive mediastinal staging solely based on a central tumor location would be suboptimal.

Using Natural Language Processing and Machine Learning to Preoperatively Predict Lymph Node Metastasis for Non-Small Cell Lung Cancer With Electronic Medical Records: Development and Validation Study

Background

Lymph node metastasis (LNM) is critical for treatment decision making of patients with resectable non–small cell lung cancer, but it is difficult to precisely diagnose preoperatively. Electronic medical records (EMRs) contain a large volume of valuable information about LNM, but some key information is recorded in free text, which hinders its secondary use.

Objective

This study aims to develop LNM prediction models based on EMRs using natural language processing (NLP) and machine learning algorithms.

Methods

We developed a multiturn question answering NLP model to extract features about the primary tumor and lymph nodes from computed tomography (CT) reports. We then combined these features with other structured clinical characteristics to develop LNM prediction models using machine learning algorithms. We conducted extensive experiments to explore the effectiveness of the predictive models and compared them with size criteria based on CT image findings (the maximum short axis diameter of lymph node >10 mm was regarded as a metastatic node) and clinician’s evaluation. Since the NLP model may extract features with mistakes, we also calculated the concordance correlation between the predicted probabilities of models using NLP-extracted features and gold standard features to explore the influence of NLP-driven automatic extraction.

Results

Experimental results show that the random forest models achieved the best performances with 0.792 area under the receiver operating characteristic curve (AUC) value and 0.456 average precision (AP) value for pN2 LNM prediction and 0.768 AUC value and 0.524 AP value for pN1&N2 LNM prediction. And all machine learning models outperformed the size criteria and clinician’s evaluation. The concordance correlation between the random forest models using NLP-extracted features and gold standard features is 0.950 and improved to 0.984 when the top 5 important NLP-extracted features were replaced with gold standard features.

Conclusions

The LNM models developed can achieve competitive performance using only limited EMR data such as CT reports and tumor markers in comparison with the clinician’s evaluation. The multiturn question answering NLP model can extract features effectively to support the development of LNM prediction models, which may facilitate the clinical application of predictive models.

A Radiomics Nomogram for Preoperative Prediction of Clinical Occult Lymph Node Metastasis in cT1-2N0M0 Solid Lung Adenocarcinoma

Background

Clinical occult lymph node metastasis (cOLNM) means that the lymph node is negatively diagnosed by preoperative computed tomography (CT), but has been proven to be positive by postoperative pathology. The aim of this study was to establish and validate a nomogram based on radiomics features for the preoperative prediction of cOLNM in early-stage solid lung adenocarcinoma patients.

Methods

A total of 244 patients with clinical T1-2N0M0 solid lung adenocarcinoma who underwent preoperative contrast-enhanced chest CT were divided into a primary group (n = 160) and an independent validation group from another hospital (n = 84). The records of 851 radiomics features of each primary tumor were extracted. LASSO analysis was used to reduce the data dimensionality and select features. Multivariable logistic regression was utilized to identify independent predictors of cOLNM and develop a predictive nomogram. The performance of the predictive model was assessed by its calibration and discrimination. Decision curve analysis (DCA) was performed to estimate the clinical usefulness of the nomogram.

Results

The predictive model consisted of a clinical factor (CT-reported tumor size) and a radiomics feature (Rad-score). The nomogram presented good discrimination, with a C-index of 0.782 (95% CI, 0.768–0.796) in the primary cohort and 0.813 (95% CI, 0.787–0.839) in the validation cohort, and good calibration. DCA showed that the radiomics nomogram was clinically useful.

Conclusion

This study develops and validates a nomogram that incorporates clinical and radiomics factors. It can be tailored for the individualized preoperative prediction of cOLNM in early-stage solid lung adenocarcinoma patients.

Deep Learning for Prediction of N2 Metastasis and Survival for Clinical Stage I Non-Small Cell Lung Cancer

Background Preoperative mediastinal staging is crucial for the optimal management of clinical stage I non-small cell lung cancer (NSCLC). Purpose To develop a deep learning signature for N2 metastasis prediction and prognosis stratification in clinical stage I NSCLC. Materials and Methods In this retrospective study conducted from May 2020 to October 2020 in a population with clinical stage I NSCLC, an internal cohort was adopted to establish a deep learning signature. Subsequently, the predictive efficacy and biologic basis of the proposed signature were investigated in an external cohort. A multicenter diagnostic trial (registration number: ChiCTR2000041310) was also performed to evaluate its clinical utility. Finally, on the basis of the N2 risk scores, the instructive significance of the signature in prognostic stratification was explored. The diagnostic efficiency was quantified with the area under the receiver operating characteristic curve (AUC), and the survival outcomes were assessed using the Cox proportional hazards model. Results A total of 3096 patients (mean age ± standard deviation, 60 years ± 9; 1703 men) were included in the study. The proposed signature achieved AUCs of 0.82, 0.81, and 0.81 in an internal test set (n = 266), external test cohort (n = 133), and prospective test cohort (n = 300), respectively. In addition, higher deep learning scores were associated with a lower frequency of EGFR mutation (P = .04), higher rate of ALK fusion (P = .02), and more activation of pathways of tumor proliferation (P < .001). Furthermore, in the internal test set and external cohort, higher deep learning scores were predictive of poorer overall survival (adjusted hazard ratio, 2.9; 95% CI: 1.2, 6.9; P = .02) and recurrence-free survival (adjusted hazard ratio, 3.2; 95% CI: 1.4, 7.4; P = .007). Conclusion The deep learning signature could accurately predict N2 disease and stratify prognosis in clinical stage I non-small cell lung cancer. © RSNA, 2021 Online supplemental material is available for this article. See also the editorial by Park and Lee in this issue.

Segmentectomy for inner location small-sized non-small-cell lung cancer: Is it feasible?

BACKGROUND

The efficacy of segmentectomy for inner small-sized non-small-cell lung cancer (NSCLC) remains unknown. We aimed to elucidate whether segmentectomy for inner small-sized NSCLC, defined using novel three-dimensional measuring method, yields feasible oncological outcomes compared to segmentectomy for outer lesions.

METHODS

We retrospectively analyzed patients with small-sized (<2cm) cN0 NSCLC who underwent segmentectomy between January 2007 and December 2020. Tumor centrality ratio, which was measured by using three dimensional reconstruction software, was evaluated, with the location of tumor origin confirmed pathologically. Cases with a ratio below and above 2/3 were allocated to the 'Inner group' and 'Outer group', respectively. Oncological outcomes were compared between the two groups.

RESULTS

Our cohort was divided into the 'Inner group' (n=75) and 'Outer group' (n=127). The proximal distance from a tumor exceeded 20 mm in all cases. Tumor centrality ratio was associated with the pathological origin of a tumor. The rate of unforeseen positive lymph node metastasis was significantly higher in the 'Inner group' (p=0.04). There were no significant differences in the 5-year recurrence free survival (RFS; 91% versus 87%, p=0.67). Univariate analysis identified age, consolidation/tumor ratio, the presence of ground-glass-opacity (GGO) and lymphovascular invasion, but not tumor centrality, as significant prognostic factors for RFS. In the multivariate analysis, the presence of GGO and lymphovascular invasion remained significant.

CONCLUSIONS

Regarding oncological outcomes, segmentectomy with a safety proximal distance could be feasible, even for inner small-sized NSCLC. Tumor invasiveness, not tumor centrality, may influence tumor recurrence. (242 words).

Flubendazole Elicits Antitumor Effects by Inhibiting STAT3 and Activating Autophagy in Non-small Cell Lung Cancer

Non-small cell lung carcinoma (NSCLC) is a major neoplastic disease with a high mortality worldwide; however, effective treatment of this disease remains a challenge. Flubendazole, a traditional anthelmintic drug, possesses potent antitumor properties; however, the detailed molecular mechanism of flubendazole activity in NSCLC needs to be further explored. In the present study, flubendazole was found to exhibit valid antitumor activity in vitro as well as in vivo. Flubendazole blocked phosphorylation of STAT3 in a dose- and time-dependent manner and regulated the transcription of STAT3 target genes encoding apoptotic proteins. Further, flubendazole inhibited STAT3 activation by inhibiting its phosphorylation and nuclear localization induced by interleukin-6 (IL-6). Notably, the autophagic flux of NSCLC cell lines was increased after flubendazole treatment. Furthermore, flubendazole downregulated the expression of BCL2, P62, and phosphorylated-mTOR, but it upregulated LC3-I/II and Beclin-1 expression, which are the main genes associated with autophagy. Collectively, these data contribute to elucidating the efficacy of flubendazole as an anticancer drug, demonstrating its potential as a therapeutic agent via its suppression of STAT3 activity and the activation of autophagy in NSCLC.

Induction therapy is not associated with improved survival in large cT4N0 non-small cell lung cancers

BACKGROUND

The 8^th edition staging for non-small cell lung cancer reclassified tumors >7 cm as stage IIIA (T4N0); previously, such tumors without nodal disease were considered stage IIB (T3N0). This study tested the hypothesis that induction chemotherapy for these stage IIIA patients does not improve survival compared to primary surgery.

METHODS

The National Cancer Database was queried for non-small cell lung cancer patients with tumor size >7 cm who underwent surgical resection from 2010 - 2015. Patients with clinically node-positive disease or tumor invasion of major structures were excluded. Patients undergoing induction chemotherapy followed by surgery (IC) were compared to patients undergoing primary surgery (PS). Propensity-score matching was performed.

RESULTS

In total, 1,610 patients with cT4N0 disease based on tumor size >7 cm and no tumor invasion underwent surgical resection: 1,346 (83.6%) comprised the PS group and 264 (16.4%) the IC group. After propensity-score matching, IC had a higher rate of pN0 (78.4% vs 66.0%, p<0.001) and less lymphovascular invasion (13.9% vs 26.3%, p<0.001), but longer postoperative stay (6 vs 5 days, p<0.001) and higher 30-day mortality (3.5% vs 0%, p=0.002). Median 5-year survival was similar between IC and PS (53.5% vs 62.2%, p=0.075), and IC was not independently associated with survival (HR 1.45, p=0.146).

CONCLUSIONS

Patients with cT4N0 non-small cell lung cancer based on tumor size >7 cm and no tumor invasion of major structures have similar overall survival with either IC or PS. IC should not be routinely given for this subset of stage IIIA patients.

Circular RNA ubiquitin-associated protein 2 enhances autophagy and promotes colorectal cancer progression and metastasis via miR-582-5p/FOXO1 signaling

Numerous circular RNAs (circRNAs) have been identified as vital regulators in various cancers. The newly reported circular RNA ubiquitin-associated protein 2 (circUBAP2) is a critical player in cell growth and metastasis in various types of cancers, although its role in colorectal cancer (CRC) has yet to be fully elucidated. We find that circUBAP2 is upregulated in CRC tissues and cell lines to induce autophagy both in vitro and in vivo. The effects of circUBAP2 on migration, invasion, and proliferation may be partially related to autophagy. Mechanistically, we uncover that circUBAP2 can directly interact with miR-582-5p and subsequently act as a microRNA sponge to regulate the expression of the miR-582-5p target gene forkhead box protein O1 (FOXO1) and downstream signaling molecules, which collectively advance the progression and metastasis of CRC. These results suggest that circUBAP2 acts as an oncogene via a novel circUBAP2/miR-582-5p/FOXO1 axis, providing a potential biomarker and therapeutic target for CRC management.

Machine Learning for the Prediction of Lymph Nodes Micrometastasis in Patients with Non-Small Cell Lung Cancer: A Comparative Analysis of Two Practical Prediction Models for Gross Target Volume Delineation

Purpose

The lymph node gross target volume (GTV) delineation in patients with non-small cell lung cancer (NSCLC) is crucial for prognosis. This study aimed to develop a predictive model that can be used to differentiate between lymph nodes micrometastasis (LNM) and non-lymph nodes micrometastasis (non-LNM).

Patients and Methods

A retrospective study involving 1524 patients diagnosed with NSCLC was collected in the First Hospital of Wuhan between January 1, 2017, and April 1, 2020. Duplicated and useless variables were excluded, and 16 candidate variables were selected for further analysis. The random forest (RF) algorithm and generalized linear (GL) algorithm were used to screen out the variables that greatly affected the LNM prediction, respectively. The area under the curve (AUC) was compared between the RF model and GL model.

Results

The RF model revealed that the variables, including pathology, degree of differentiation, maximum short diameter of lymph node, tumor diameter, pulmonary membrane invasion, clustered lymph nodes, and T stage, were more significant for LNM prediction. Multifactorial logistic regression analysis for the GL model indicated that vascular invasion, tumor diameter, degree of differentiation, pulmonary membrane invasion, and maximum standard uptake value (SUVmax) were positively associated with LNM. The AUC for the RF model and GL model was 0.83 (95% CI: 0.75 to 0.90) and 0.64 (95% CI: 0.60 to 0.70), respectively.

Conclusion

We successfully established an accurate and optimized RF model that could be used to predict LNM in patients with NSCLC. This model can be used to evaluate the risk of an individual patient experiencing LNM and therefore facilitate the choice of treatment.

A Prediction Model to Optimize Invasive Mediastinal Staging Procedures for Non-Small Cell Lung Cancer in Patients With a Radiologically Normal Mediastinum: The Quebec Prediction Model

BACKGROUND

Current guideline-recommended criteria for invasive mediastinal staging in patients with a radiologically normal mediastinum fail to identify a significant proportion of patients with occult mediastinal disease (OMD), despite it leading to a large number of invasive staging procedures.

RESEARCH QUESTION

Which variables available before surgery predict the probability of OMD in patients with a radiologically normal mediastinum?

STUDY DESIGN AND METHODS

We identified all cTxN0/N1M0 non-small cell lung cancer tumors staged by CT imaging and PET with CT imaging in our institution between 2014 and 2018 who underwent gold standard surgical lymph node dissection or were demonstrated to have OMD before surgery by invasive mediastinal staging techniques and divided them into a derivation and an independent validation cohort to create the Quebec Prediction Model (QPM), which allows calculation of the probability of OMD.

RESULTS

Eight hundred three patients were identified (development set, n = 502; validation set, n = 301) with a prevalence of OMD of 9.1%. The developed prediction model included largest mediastinal lymph node size (P < .001), tumor centrality (P = .23), presence of cN1 disease (P = .29), and lesion standardized uptake value (P = .09). Using a calculated probability of more than 10% as a threshold to identify OMD, this model had a sensitivity, specificity, positive predictive value, and negative predictive value in the derivation cohort of 73.9% (95% CI, 58.9%-85.7%), 81.1% (95% CI, 77.2%-84.6%), 28.3% (95% CI, 23.4%-33.8%), and 96.8% (95% CI, 95.0%-98.1%), respectively. It performed similarly in the validation cohort (P = .77, Hosmer-Lemeshow test; P = .5163, Pearson χ² and unweighted sum-of-squares statistics; and P = .0750, Stukel score test) and outperformed current guideline-recommended criteria in identifying patients with OMD (area under the receiver operating characteristic curve [AUC] for American College of Chest Physicians guidelines criteria, 0.65 [95% CI, 0.59-0.71]; AUC for European Society of Thoracic Surgeons guidelines criteria, 0.60 [95% CI, 0.54-0.67]; and AUC for the QPM, 0.85 [95% CI, 0.80-0.90]).

INTERPRETATION

The QPM allows the clinician to integrate available information from CT and PET imaging to minimize invasive staging procedures that will not modify management, while also minimizing the risk of unforeseen mediastinal disease found at surgery.

Predicting Lymph Node Metastasis in Non-small Cell Lung Cancer: Prospective External and Temporal Validation of the HAL and HOMER Models

BACKGROUND

Two models, the Help with the Assessment of Adenopathy in Lung cancer (HAL) and Help with Oncologic Mediastinal Evaluation for Radiation (HOMER), were recently developed to estimate the probability of nodal disease in patients with non-small cell lung cancer (NSCLC) as determined by endobronchial ultrasound-transbronchial needle aspiration (EBUS-TBNA). The objective of this study was to prospectively externally validate both models at multiple centers.

RESEARCH QUESTION

Are the HAL and HOMER models valid across multiple centers?

STUDY DESIGN AND METHODS

This multicenter prospective observational cohort study enrolled consecutive patients with PET-CT clinical-radiographic stages T1-3, N0-3, M0 NSCLC undergoing EBUS-TBNA staging. HOMER was used to predict the probability of N0 vs N1 vs N2 or N3 (N2|3) disease, and HAL was used to predict the probability of N2|3 (vs N0 or N1) disease. Model discrimination was assessed using the area under the receiver operating characteristics curve (ROC-AUC), and calibration was assessed using the Brier score, calibration plots, and the Hosmer-Lemeshow test.

RESULTS

Thirteen centers enrolled 1,799 patients. HAL and HOMER demonstrated good discrimination: HAL ROC-AUC = 0.873 (95%CI, 0.856-0.891) and HOMER ROC-AUC = 0.837 (95%CI, 0.814-0.859) for predicting N1 disease or higher (N1|2|3) and 0.876 (95%CI, 0.855-0.897) for predicting N2|3 disease. Brier scores were 0.117 and 0.349, respectively. Calibration plots demonstrated good calibration for both models. For HAL, the difference between forecast and observed probability of N2|3 disease was +0.012; for HOMER, the difference for N1|2|3 was -0.018 and for N2|3 was +0.002. The Hosmer-Lemeshow test was significant for both models (P = .034 and .002), indicating a small but statistically significant calibration error.

INTERPRETATION

HAL and HOMER demonstrated good discrimination and calibration in multiple centers. Although calibration error was present, the magnitude of the error is small, such that the models are informative.

Development and Validation of a Nomogram for Preoperative Prediction of Lymph Node Metastasis in Lung Adenocarcinoma Based on Radiomics Signature and Deep Learning Signature

Background and Purpose

The preoperative LN (lymph node) status of patients with LUAD (lung adenocarcinoma) is a key factor for determining if systemic nodal dissection is required, which is usually confirmed after surgery. This study aimed to develop and validate a nomogram for preoperative prediction of LN metastasis in LUAD based on a radiomics signature and deep learning signature.

Materials and Methods

This retrospective study included a training cohort of 200 patients, an internal validation cohort of 40 patients, and an external validation cohort of 60 patients. Radiomics features were extracted from conventional CT (computed tomography) images. T-test and Extra-trees were performed for feature selection, and the selected features were combined using logistic regression to build the radiomics signature. The features and weights of the last fully connected layer of a CNN (convolutional neural network) were combined to obtain a deep learning signature. By incorporating clinical risk factors, the prediction model was developed using a multivariable logistic regression analysis, based on which the nomogram was developed. The calibration, discrimination and clinical values of the nomogram were evaluated.

Results

Multivariate logistic regression analysis showed that the radiomics signature, deep learning signature, and CT-reported LN status were independent predictors. The prediction model developed by all the independent predictors showed good discrimination (C-index, 0.820; 95% CI, 0.762 to 0.879) and calibration (Hosmer-Lemeshow test, P=0.193) capabilities for the training cohort. Additionally, the model achieved satisfactory discrimination (C-index, 0.861; 95% CI, 0.769 to 0.954) and calibration (Hosmer-Lemeshow test, P=0.775) when applied to the external validation cohort. An analysis of the decision curve showed that the nomogram had potential for clinical application.

Conclusions

This study presents a prediction model based on radiomics signature, deep learning signature, and CT-reported LN status that can be used to predict preoperative LN metastasis in patients with LUAD.

A Prediction Model to Help with Oncologic Mediastinal Evaluation for Radiation: HOMER

Rationale: When stereotactic ablative radiotherapy is an option for patients with non–small cell lung cancer (NSCLC), distinguishing between N0, N1, and N2 or N3 (N2|3) disease is important.

Objectives: To develop a prediction model for estimating the probability of N0, N1, and N2|3 disease.

Methods: Consecutive patients with clinical-radiographic stage T1 to T3, N0 to N3, and M0 NSCLC who underwent endobronchial ultrasound–guided staging from a single center were included. Multivariate ordinal logistic regression analysis was used to predict the presence of N0, N1, or N2|3 disease. Temporal validation used consecutive patients from 3 years later at the same center. External validation used three other hospitals.

Measurements and Main Results: In the model development cohort (n = 633), younger age, central location, adenocarcinoma, and higher positron emission tomography–computed tomography nodal stage were associated with a higher probability of having advanced nodal disease. Areas under the receiver operating characteristic curve (AUCs) were 0.84 and 0.86 for predicting N1 or higher (vs. N0) disease and N2|3 (vs. N0 or N1) disease, respectively. Model fit was acceptable (Hosmer-Lemeshow, P = 0.960; Brier score, 0.36). In the temporal validation cohort (n = 473), AUCs were 0.86 and 0.88. Model fit was acceptable (Hosmer-Lemeshow, P = 0.172; Brier score, 0.30). In the external validation cohort (n = 722), AUCs were 0.86 and 0.88 but required calibration (Hosmer-Lemeshow, P < 0.001; Brier score, 0.38). Calibration using the general calibration method resulted in acceptable model fit (Hosmer-Lemeshow, P = 0.094; Brier score, 0.34).

Conclusions: This prediction model can estimate the probability of N0, N1, and N2|3 disease in patients with NSCLC. The model has the potential to facilitate decision-making in patients with NSCLC when stereotactic ablative radiotherapy is an option.

Circular RNA circHIPK3 modulates autophagy via MIR124-3p-STAT3-PRKAA/AMPKα signaling in STK11 mutant lung cancer

The role of circular RNA in cancer is emerging. A newly reported circular RNA HIPK3 (circHIPK3) is critical in cell proliferation of various cancer types, although its role in non-small cell lung cancer (NSCLC), has yet to be elucidated. Our results provided evidence that silencing of circHIPK3 significantly impaired cell proliferation, migration, invasion and induced macroautophagy/autophagy. Mechanistically, we uncovered that autophagy was induced upon loss of circHIPK3 via the MIR124-3p-STAT3-PRKAA/AMPKa axis in STK11 mutant lung cancer cell lines (A549 and H838). STAT3 abrogation as well as transfection with a MIR124-3p mimic, recapitulated the induction of autophagy. We also demonstrated antagonistic regulation on autophagy between circHIPK3 and linear HIPK3 (linHIPK3). We therefore propose that the ratio between circHIPK3 and linHIPK3 (C:L ratio) may reflect autophagy levels in cancer cells. We observed that a high C:L ratio (>0.49) was an indicator of poor survival, especially in advanced-stage NSCLC patients. These results support that circHIPK3 is a key autophagy regulator in a subset of lung cancer and has potential clinical use as a prognostic factor. The circular RNA HIPK3 (circHIPK3) functions as an oncogene and autophagy regulator may potential use as a prognostic marker and therapeutic target in lung cancer.Abbreviations 3-MA: 3-methyladenine; AMPK: AMP-activated protein kinase; ATG7: autophagy related 7; Baf-A: bafilomycin A1; BECN1: beclin 1; circHIPK3: circular HIPK3; CQ: chloroquine; GAPDH: glyceraldehyde-3-phosphate dehydrogenase; GFP: green fluorescent protein; HIPK3: homeodomain interacting protein kinase 3; IL6R: interleukin 6 receptor; MAP1LC3B/LC3B: microtubule associated protein 1 light chain 3 beta; NSCLC: non-small cell lung cancer; RFP: red fluorescent protein; RPS6KB1/S6K: ribosomal protein S6 kinase B1; SQSTM1/p62: sequestosome 1; STAT3: signal transducer and activator of transcription 3; STK11: serine/threonine kinase 11.

CT-based radiomics signature for the stratification of N2 disease risk in clinical stage I lung adenocarcinoma

Background

Risk stratification of N2 disease is vital for selecting candidates to receive invasive mediastinal staging modalities. In this study, we aimed to stratify the risk of N2 metastasis in clinical stage I lung adenocarcinoma using radiomics analysis.

Methods

Two datasets of patients with clinical stage I lung adenocarcinoma who underwent lung resection were included (training dataset, 880; validation dataset, 322). Using PyRadiomics, 1,078 computed tomography (CT)-based radiomics features were extracted after semi-automated lung nodule segmentation. In order to predict N2 status, a radiomics signature was constructed after selecting the optimal radiomics feature subset by sequentially applying minimum-redundancy-maximum-relevance and least absolute shrinkage and selection operator (LASSO) techniques. Its performance was validated in the validation dataset.

Results

The incidences of N2 metastasis were 8.4% and 7.1% in the training and validation datasets, respectively. Unsupervised cluster analysis revealed that radiomics features significantly correlated with lymph node status and pathological subtypes. For N2 disease prediction, five radiomics features were selected to establish the radiomics signature, which showed a significantly better predictive performance than clinical factors (P<0.001). The area under the receiver operating characteristic curve was 0.81 (0.77-0.86) and 0.69 (0.63-0.75) for radiomics signature and clinical factors, respectively, in the training dataset, and 0.82 (0.71-0.92) and 0.64 (0.52-0.75), respectively, in the validation dataset.

Conclusions

The established CT-based radiomics signature could stratify the risk of N2 metastasis in clinical stage I lung adenocarcinoma, thus assisting clinicians in making patient-specific mediastinal staging strategy.

Preoperative Risk Assessment of Lymph Node Metastasis in cT1 Lung Cancer: A Retrospective Study from Eastern China

Background

Lymph node status of clinical T1 (diameter ≤ 3 cm) lung cancer largely affects the treatment strategies in the clinic. In order to assess lymph node status before operation, we aim to develop a noninvasive predictive model using preoperative clinical information.

Methods

We retrospectively reviewed 924 patients (development group) and 380 patients (validation group) of clinical T1 lung cancer. Univariate analysis followed by polytomous logistic regression was performed to estimate different risk factors of lymph node metastasis between N1 and N2 diseases. A predictive model of N2 metastasis was established with dichotomous logistic regression, externally validated and compared with previous models.

Results

Consolidation size and clinical N stage based on CT were two common independent risk factors for both N1 and N2 metastases, with different odds ratios. For N2 metastasis, we identified five independent predictors by dichotomous logistic regression: peripheral location, larger consolidation size, lymph node enlargement on CT, no smoking history, and higher levels of serum CEA. The model showed good calibration and discrimination ability in the development data, with the reasonable Hosmer-Lemeshow test (p = 0.839) and the area under the ROC being 0.931 (95% CI: 0.906-0.955). When externally validated, the model showed a great negative predictive value of 97.6% and the AUC of our model was better than other models.

Conclusion

In this study, we analyzed risk factors for both N1 and N2 metastases and built a predictive model to evaluate possibilities of N2 metastasis of clinical T1 lung cancers before the surgery. Our model will help to select patients with low probability of N2 metastasis and assist in clinical decision to further management.

The utility of endosonography for mediastinal staging of non-small cell lung cancer in patients with radiological N0 disease

OBJECTIVES

Recent practice guidelines recommend endosonography for patients with radiological N0 non-small cell lung cancer (NSCLC) when the primary tumors are >3 cm in diameter or centrally located. However, any role for endosonography remains debatable. We evaluated the utility of endosonography in patients with radiological N0 NSCLC based on tumor centrality, diameter and histology.

MATERIALS AND METHODS

Patients who underwent staging endobronchial ultrasound-guided transbronchial needle aspiration (EBUS-TBNA) with or without transesophageal bronchoscopic ultrasound-guided fine needle aspiration (EUS-B-FNA) for radiological N0 NSCLC were retrospectively investigated using prospectively collected endosonography data. The radiological N0 stage was defined by node diameter as evident on computed tomography images and ¹⁸F-FDG uptake using integrated positron emission tomography-computed tomography.

RESULTS

In total of 168 patients, the median size of the primary tumor was 39 mm, and 41 % of tumors were centrally located. The prevalence of occult mediastinal metastases was 11.3 % (19/168). The sensitivity of endosonography in terms of diagnosing occult mediastinal metastases was only 47 % (9/19); 6 of 10 patients with false-negative endosonography data exhibited metastases in accessible nodes. The diagnostic performance of endosonography did not differ by tumor centrality or diameter. Patients with adenocarcinoma histology showed higher prevalence of occult mediastinal metastases and higher false-negative results in endosonography compared with those with non-adenocarcinoma histology.

CONCLUSION

Not all patients with radiological N0 NSCLC benefit from endosonography, given the low prevalence of occult mediastinal metastases and the poor sensitivity of endosonography in this population. The strategy of invasive mediastinal staging needs to be tailored considering the histology of the tumor in this population.

Which definition of a central tumour is more predictive of occult mediastinal metastasis in nonsmall cell lung cancer patients with radiological N0 disease?

Background

Guidelines recommend invasive mediastinal staging for centrally located tumours, even in radiological N0 nonsmall cell lung cancer (NSCLC). However, there is no uniform definition of a central tumour that is more predictive of occult mediastinal metastasis.

Methods

A total of 1337 consecutive patients with radiological N0 disease underwent invasive mediastinal staging. Tumours were categorised into central and peripheral by seven different definitions.

Results

About 7% (93 out of 1337) of patients had occult N2 disease, and they had significantly larger tumour size and more solid tumours on computed tomography. After adjustment for patient- and tumour-related characteristics, only the central tumour definition of the inner one-third of the hemithorax adopted by drawing concentric lines arising from the midline significantly predicted occult N2 disease (adjusted OR 2.13, 95% CI 1.17–3.87; p=0.013). This association was maintained after excluding patients with pure ground-glass nodules (adjusted OR 2.54, 95% CI 1.37–4.71; p=0.003) or only including those with solid tumours (adjusted OR 2.30, 95% CI 1.08–4.88; p=0.030).

Conclusions

We suggest that a central tumour should be defined using the inner one-third of the hemithorax adopted by drawing concentric lines from the midline. This is particularly useful for predicting occult N2 disease in patients with NSCLC.

Central tumours defined as located in the inner one-third of the hemithorax adopted by drawing concentric lines from the midline are associated with occult mediastinal metastasis in patients with NSCLC and radiological N0 diseasehttp://ow.ly/scg630nbRmY

Centrally located lung cancer and risk of occult nodal disease: an objective evaluation of multiple definitions of tumour centrality with dedicated imaging software

Introduction

Current guidelines recommend invasive mediastinal staging in patients with centrally located radiographic stage T1N0M0 nonsmall cell lung cancer (NSCLC). The lack of a specific definition of a central tumour has resulted in discrepancies among guidelines and heterogeneity in practice patterns.

Methods

Our objective was to study specific definitions of tumour centrality and their association with occult nodal disease. Pre-operative chest computed tomography scans from patients with clinical (c) T1N0M0 NSCLC were processed with a dedicated software system that divides the lungs in thirds following vertical and concentric lines. This software accurately assigns tumours to a specific third based both on the location of the centre of the tumour and its most medial aspect, creating eight possible definitions of central tumours.

Results

607 patients were included in our study. Surgery was performed for 596 tumours (98%). The overall pathological (p) N disease was: 504 (83%) N0, 56 (9%) N1, 47 (8%) N2 and no N3. The prevalence of N2 disease remained relatively low regardless of tumour location. Central tumours were associated with upstaging from cN0 to any N (pN1/pN2). Two definitions were associated with upstaging to any N: concentric lines, inner one-third, centre of the tumour (OR 3.91, 95% CI 1.85–8.26; p<0.001) and concentric lines, inner two-thirds, most medial aspect of the tumour (OR 1.91, 95% CI 1.23–2.97; p=0.004).

Conclusions

We objectively identified two specific definitions of central tumours. While the rate of occult mediastinal disease was relatively low regardless of tumour location, central tumours were associated with upstaging from cN0 to any N.

Prediction Model for Nodal Disease Among Patients With Non-Small Cell Lung Cancer

BACKGROUND

We characterized the performance characteristics of guideline-recommended invasive mediastinal staging (IMS) for lung cancer and developed a prediction model for nodal disease as a potential alternative approach to staging.

METHODS

We conducted a prospective cohort study of adults with suspected/confirmed non-small cell lung cancer without evidence of distant metastatic disease (by computed tomography/positron emission tomography) who underwent nodal evaluation by IMS and/or at the time of resection. The true-positive rate was the proportion of patients with true nodal disease selected to undergo IMS based on guideline recommendations, and the false-positive rate was the proportion of patients without true nodal disease selected to undergo IMS. Logistic regression was used to predict nodal disease using radiographic predictors.

RESULTS

Among 123 eligible subjects, 31 (25%) had pathologically confirmed nodal disease. A guideline-recommended invasive staging strategy had a true-positive rate and false-positive rate of 100% and 65%, respectively. The prediction model fit the data well (goodness-of-fit test, p = 0.55) and had excellent discrimination (optimism corrected c-statistic, 0.78; 95% confidence interval, 0.72 to 0.89). Exploratory analysis revealed that use of the prediction model could achieve a false-positive rate of 44% and a true-positive rate of 97%.

CONCLUSIONS

A guideline-recommended strategy for IMS selects all patients with true nodal disease and most patients without nodal disease for IMS. Our prediction model appears to maintain (within a margin of error) the sensitivity of a guideline-recommended invasive staging strategy and has the potential to reduce the use of invasive procedures.

A Texture Analysis-Based Prediction Model for Lymph Node Metastasis in Stage IA Lung Adenocarcinoma

BACKGROUND

Some clinical N0 lung adenocarcinomas have been pathologically diagnosed as N1 or N2. To improve the preoperative diagnostic accuracy of lymph node disease, we developed a prediction model for lymph node metastasis in cT1 N0 M0 lung adenocarcinoma based on computed tomography texture analysis and clinical characteristics to estimate the probability of lymph node metastasis.

METHODS

The records of 501 consecutive patients with cT1 N0 M0 lung adenocarcinoma who underwent computed tomography scan and pulmonary resection with systematic lymph nodes dissection or lymph nodes sampling were reviewed. Each nodule was manually segmented, and its computerized texture features were extracted. Multivariate logistic regression with fivefold validation was used to estimate independent predictors and build the prediction model. The prediction model was then externally validated. A nomogram was developed based on logistic regression results.

RESULTS

Among 501 patients, 41 were diagnosed with positive lymph nodes (8.18%). Four independent predictors were identified: the skewness and 90th percentile of computed tomography number, nodule compactness, and carcinoembryonic antigen level. This model showed good calibration (Hosmer-Lemeshow test, p = 0.337), with an area under the curve of 0.883 (95% confidence interval, 0.842 to 0.924; p < 0.001). The area under the curve was 0.808 (95% confidence interval, 0.735 to 0.880) when validated with independent data.

CONCLUSIONS

A model based on computerized textures and carcinoembryonic antigen level can assess the lymph node status of patients with cT1 N0 M0 lung adenocarcinoma preoperatively, which could assist surgeons in making subsequent clinical decisions.

A Prediction Model to Help with the Assessment of Adenopathy in Lung Cancer: HAL

RATIONALE

Estimating the probability of finding N2 or N3 (prN2/3) malignant nodal disease on endobronchial ultrasound-guided transbronchial needle aspiration (EBUS-TBNA) in patients with non-small cell lung cancer (NSCLC) can facilitate the selection of subsequent management strategies.

OBJECTIVES

To develop a clinical prediction model for estimating the prN2/3.

METHODS

We used the AQuIRE (American College of Chest Physicians Quality Improvement Registry, Evaluation, and Education) registry to identify patients with NSCLC with clinical radiographic stage T1-3, N0-3, M0 disease that had EBUS-TBNA for staging. The dependent variable was the presence of N2 or N3 disease (vs. N0 or N1) as assessed by EBUS-TBNA. Univariate followed by multivariable logistic regression analysis was used to develop a parsimonious clinical prediction model to estimate prN2/3. External validation was performed using data from three other hospitals.

MEASUREMENTS AND MAIN RESULTS

The model derivation cohort (n = 633) had a 25% prevalence of malignant N2 or N3 disease. Younger age, central location, adenocarcinoma histology, and higher positron emission tomography-computed tomography N stage were associated with a higher prN2/3. Area under the receiver operating characteristic curve was 0.85 (95% confidence interval, 0.82-0.89), model fit was acceptable (Hosmer-Lemeshow, P = 0.62; Brier score, 0.125). We externally validated the model in 722 patients. Area under the receiver operating characteristic curve was 0.88 (95% confidence interval, 0.85-0.90). Calibration using the general calibration model method resulted in acceptable goodness of fit (Hosmer-Lemeshow test, P = 0.54; Brier score, 0.132).

CONCLUSIONS

Our prediction rule can be used to estimate prN2/3 in patients with NSCLC. The model has the potential to facilitate clinical decision making in the staging of NSCLC.

Clinicopathological Characteristics of Lung Adenocarcinoma with Unexpected Lymph Node Metastasis

PURPOSE

The objective is to demonstrate the clinicopathological characteristics of patients with unexpected node-positive lung adenocarcinoma and to analyze predictive factors of unexpected disease.

METHODS

We reviewed 225 patients with lung adenocarcinoma who underwent curative-intent operation between January 2008 and December 2014. Unexpected node-positive diseases were defined as cases with hilar or mediastinal lymph nodes metastasis in spite of both negative significant enlargement of lymph nodes on preoperative chest computed tomography (CT) and negative fluorodeoxyglucose (FDG) uptake in lymph nodes on preoperative positron emission tomography (PET)/CT. We retrospectively analyzed clinical features of these patients and evaluated associated factors for unexpected diseases.

RESULTS

There were 41 patients (18%) with unexpected node-positive disease, consisting of 16 (39%) unexpected pN1 and 25 (61%) unexpected pN2 diseases. The most common predominant subtype was papillary (22 patients; 54%), and 17 patients (41%) had micropapillary component in the tumors. Younger age (p <0.01), left side (p <0.01), larger tumor size (p <0.01), and having a micropapillary component (p <0.01) were significant associated factors of unexpected diseases in multivariate analysis.

CONCLUSION

Histological findings of the primary tumor are often important because they can provide predictive information for lymph nodes status. Having a micropapillary component was one of the significant predictors of unexpected node-positive diseases.

Impact of maximum standardized uptake value of non-small cell lung cancer on detecting lymph node involvement in potential stereotactic body radiotherapy candidates

BACKGROUND

The retrospective study investigated the association between the maximum standardized uptake value (SUV_max) of primary tumor and lymph node involvement in potential stereotactic body radiotherapy (SBRT) candidates.

METHODS

A total of 185 patients with clinical stage I NSCLC were enrolled in the current study. All patients underwent lobectomy with systematic lymph node dissection following preoperative FDG-PET/CT scanning. The association between clinicopathological variables and lymph node involvement was analyzed by univariate and multivariate analysis. Spearman's correlation test was used to evaluate the correlation between them. Receiver operating characteristic (ROC) analysis was performed to calculate the area under the curve.

RESULTS

Among these patients, 22.1% had occult lymph node involvement, 15.1% were N1 and 7.0% were N2. Greater tumor size (P=0.007), elevated CEA (P=0.006), central location (P=0.002), higher SUV_max (P<0.001), solid nodule type (P=0.002), visceral pleural invasion (P=0.001) and presence of micropapillary and solid patterns (P=0.002) were significantly associated with lymph node involvement. In multivariate analysis, lymph node involvement was associated with central location (OR 5.784, 95% CI: 1.584-21.114, P=0.008), SUV_max (increase of 1 unite, OR 1.147, 95% CI: 1.035-1.272, P=0.009) and visceral pleural invasion (OR 3.044, 95% CI: 1.369-6.769, P=0.006). ROC area under the curve of SUV_max for lymph node involvement was 0.770 (95% CI: 0.698-0.841), the sensitivity and specificity were 85.4% and 63.2%, respectively. Spearman's correlation test showed that SUV_max of tumor mostly depended on tumor size and nodule type.

CONCLUSIONS

SUV_max of primary tumor was a predictor of lymph node involvement for potential SBRT candidates. Centrally located tumor and visceral pleural invasion were related to higher rate of nodal metastasis. Lobectomy and systemic lymph node dissection should be performed in these patients, instead of SBRT.

A Model Predicting Lymph Node Status for Patients with Clinical Stage T1aN0-2M0 Nonsmall Cell Lung Cancer

Background:

Lymph node status of patients with early-stage nonsmall cell lung cancer has an influence on the choice of surgery. To assess the lymph node status more correspondingly and accurately, we evaluated the relationship between the preoperative clinical variables and lymph node status and developed one model for predicting lymph node involvement.

Methods:

We collected clinical and dissected lymph node information of 474 patients with clinical stage T1aN0-2M0 nonsmall cell lung cancer (NSCLC). Logistic regression analysis of clinical characteristics was used to estimate independent predictors of lymph node metastasis. The prediction model was validated by another group.

Results:

Eighty-two patients were diagnosed with positive lymph nodes (17.3%), and four independent predictors of lymph node disease were identified: larger consolidation size (odds ratio [OR] = 2.356, 95% confidence interval [CI]: 1.517–3.658, P < 0.001,), central tumor location (OR = 2.810, 95% CI: 1.545–5.109, P = 0.001), abnormal status of tumor marker (OR = 3.190, 95% CI: 1.797–5.661, P < 0.001), and clinical N1–N2 stage (OR = 6.518, 95% CI: 3.242–11.697, P < 0.001). The model showed good calibration (Hosmer–Lemeshow goodness-of-fit, P < 0.766) with an area under the receiver operating characteristics curve (AUC) of 0.842 (95% [CI]: 0.797–0.886). For the validation group, the AUC was 0.810 (95% CI: 0.731–0.889).

Conclusions:

The model can assess the lymph node status of patients with clinical stage T1aN0-2M0 NSCLC, enable surgeons perform an individualized prediction preoperatively, and assist the clinical decision-making procedure.

Undetected lymph node metastases in presumed early stage NSCLC SABR patients

INTRODUCTION

Stereotactic body radiation therapy (SBRT, also called stereotactic ablative body radiation SABR) is the treatment of choice for many patients with early-stage non-small cell lung cancer (NSCLC), including those who are unfit for surgery or refuse surgery.

AREAS COVERED

In an effort to develop optimal staging for the evaluation of SBRT candidates, we review the performance of available lymph node staging methods, as well as risk factors for lymph node involvement. Pubmed was searched to identify relevant literature. Current staging methods for NSCLC, including Positron Emission Tomography/Computed Tomography(PET/CT) and endobronchial ultra sound (EBUS), have limited sensitivities. Expert commentary: There are several factors, including primary tumor location, tumor size, and histology that are possibly associated with the sensitivity of PET/CT to detect mediastinal lymph node metastasis. Small lymph node metastases typically remain undetected by PET/CT. Therefore invasive nodal staging procedures are indicated for most presumed early-stage NSCLC patients, but these also have limited sensitivity. Occult lymph node metastasis is associated with adverse outcome in NSCLC. Moreover, there is overwhelming evidence that certain patients who have lymph node metastases detected at the time of surgery derive an overall survival benefit from adjuvant therapies. It remains to be determined if improved detection of lymph node metastases in SABR candidates can indeed improve prognosis.

Survival Implications of Variation in the Thoroughness of Pathologic Lymph Node Examination in American College of Surgeons Oncology Group Z0030 (Alliance)

BACKGROUND

Accurate pathologic nodal staging mandates effective collaboration between surgeons and pathologists. The American College of Surgeons Oncology Group Z0030 trial (ACOSOG Z0030) tightly controlled surgical lymphadenectomy practice but not pathologic examination practice. We tested the survival impact of the thoroughness of pathologic examination (using the number of examined lymph nodes as a surrogate).

METHODS

We re-analyzed the mediastinal lymph node dissection arm of ACOSOG Z0030, using logistic regression and Cox proportional hazards models.

RESULTS

Of 513 patients, 435 were pN0, 60 were pN1, and 17 were pN2. The mean number of mediastinal lymph nodes examined was 13.5, 13.1, and 17.1; station 10 lymph nodes were 2.4, 2.7, and 2.6; station 11 to 14 nodes were 4.6, 6.1, and 6.7; and total lymph nodes were 19.7, 21.3, and 25.4 respectively. The pN category and histologic evaluation were associated with increased number of examined intrapulmonary lymph nodes. Patients with pN1 had more non-hilar N1 nodes than patients with pN0, patients with N2 had more N2 nodes examined than patients with pN0 or pN1. Patients with pN0 had better survival with examination of more N1 nodes; patients with pN1 had better survival with increased mediastinal nodal examination; the likelihood of discovering N2 disease was significantly associated with increased examination of mediastinal and non-hilar N1 lymph nodes.

CONCLUSIONS

Despite rigorously standardized surgical hilar/mediastinal lymphadenectomy, the number of lymph nodes examined was associated with the likelihood of detecting nodal metastasis and survival. This may indicate an effect of incomplete pathologic examination.

[Clinical Study of Surgical Treatment of Non-small Cell Lung Cancer 10 mm or Less in Diameter Under Video-assisted Thoracoscopy]

背景与目的

早期原发性非小细胞肺癌（non-small cell lung cancer, NSCLC）的手术切除及淋巴结切除的合理方式存在较大争议，本研究旨在探讨直径≤10 mm的原发NSCLC的微创切除及淋巴结切除的手术方式。

方法

对2013年7月-2016年3月在我院接受电视胸腔镜手术（video-assisted thoracic surgery, VATS）治疗并有明确病理诊断为NSCLC的共46例患者的临床资料进行回顾性分析。所有患者术前行薄层计算机断层扫描（computed tomography, CT），实性结节5例，混合性磨玻璃结节（mixed ground-glass opacity, mGGO）23例，纯磨玻璃结节（pure ground-glass opacity, pGGO）18例。根据患者具体情况采用不同术式，包括VATS肺叶切除和系统性淋巴结清扫，VATS肺楔形切除和选择性淋巴结切除，VATS肺段切除和选择性淋巴结切除，或仅采用VATS肺楔形切除。其中7例术前行CT引导下Hook-wire定位。

结果

VATS肺叶切除和系统性淋巴结清扫23例（mGGOs 15例，pGGOs 4例，实性结节4例），只有1例实性腺癌结节出现N2淋巴结转移，VATS肺楔形切除和选择性淋巴结切除5例（mGGOs 2例，pGGOs 3例）和VATS肺段切除和选择性淋巴结切除4例（mGGOs 2例，pGGOs 2例）均无淋巴结转移，仅采用VATS肺楔形切除14例（mGGOs 4例，pGGOs 9例，实性结节1例）。7例Hook-wire定位均成功。围手术期无重大并发症，随访1个月-26个月，平均（13.7±8.7）个月，无复发及转移。

结论

直径≤10 mm以mGGO和pGGO为表现的原发性NSCLC淋巴结转移率低，术中可以不进行淋巴结的清扫，实性结节应选择性淋巴结切除或系统性淋巴结清扫。高龄和心肺功能差的患者可以选择楔形切除或肺段切除。术前运用Hook-wire定位安全有效，可为VATS提供便利。

External validation of a prediction model for pathologic N2 among patients with a negative mediastinum by positron emission tomography

BACKGROUND

A prediction model for pathologic N2 (pN2) among lung cancer patients with a negative mediastinum by positron emission tomography (PET) was recently internally validated. Our study sought to determine the external validity of that model.

METHODS

A cohort study [2005-2013] was performed of lung cancer patients with a negative mediastinum by PET. Previously published model coefficients were used to estimate the probability of pN2 based on tumor location and size, nodal enlargement by computed tomography (CT), maximum standardized uptake value (SUVmax) of the primary tumor, N1 disease by PET, and pretreatment histology.

RESULTS

Among 239 patients, 18 had pN2 [7.5%, 95% confidence interval (CI): 4.5-12%]. Model discrimination was excellent (c-statistic 0.80, 95% CI: 0.75-0.85) and the model fit the data well (P=0.191). The accuracy of the model was as follows: sensitivity 100%, 95% CI: 81-100%; specificity 49%, 95% CI: 42-56%; positive predictive value (PPV) 14%, 95% CI: 8-21%, and negative predictive value (NPV) 100%, 95% CI: 97-100%. CI inspection revealed a significantly higher c-statistic in this external validation cohort compared to the internal validation cohort. The model's apparently poor specificity for patient selection is in fact significantly better than usual care (i.e., aggressive but allowable guideline concordant staging) and minimum guideline mandated selection criteria for invasive staging.

CONCLUSIONS

A prediction model for pN2 is externally valid. The high NPV of this model may allow pulmonologists and thoracic surgeons to more comfortably minimize the number of invasive procedures performed among patients with a negative mediastinum by PET.

Patients who do not reach a distance of 500 m during the 6-min walk test have an increased risk of postoperative complications and prolonged hospital stay after lobectomy

OBJECTIVES

Exercise testing is an additional tool to standard pulmonary assessment before radical pulmonary resection in lung cancer patients. Evidence is lacking, supporting the significance of routine implementation of these simple physiological tests in preoperative evaluation.

METHODS

Between April 2009 and October 2011, 253 lung cancer patients, who underwent lobectomy in a single institution, were entered into this study. All of the patients were accepted for resection based on a standard evaluation protocol. Additionally on the day before the surgery, patients performed a 6-min walk test (6MWT). Patients were categorized, depending on the result of 6MWT, in order to stratify their risk of postoperative complications. Threshold values of 6MWT were assessed on the basis of maximum area under ROC curves.

RESULTS

There were 148 men and 105 women with a mean age of 63 years. All patients underwent lobectomies due to primary lung cancer. A distance of 500 m and 100% of the predicted 6MWT were taken as threshold values differentiating risk of postoperative complications. The cut-off value of 500 m separates individuals with an increased risk of postoperative complications [60.6 vs 36.9%, odds ratio (OR): 2631; 95% confidence interval (CI): 1.423-4.880] and prolonged hospitalization (7 vs 6 days). By applying a cut-off value of 500 m, the higher incidence of atrial fibrillation (21.2 vs 11.7%; OR: 2019; 95% CI: 0.904-4.484) and higher requirement for blood transfusion (18.1 vs 9.0%; OR: 2222; 95% CI: 0.928-5.289) fairly reached the level of significance. There were no early postoperative deaths in the analysed groups.

CONCLUSIONS

Patients who walk <500 m during the 6MWT before lobectomy have an increased risk of postoperative complications and prolonged hospital stay.