A Prediction Model for Pathologic N2 Disease in Lung Cancer Patients with a Negative Mediastinum by Positron Emission Tomography

Lymph node metastasis in early invasive lung adenocarcinoma: Prediction model establishment and validation based on genomic profiling and clinicopathologic characteristics

BACKGROUND

The presence of lymph node (LN) metastasis directly affects the treatment strategy for lung adenocarcinoma (LUAD). Next-generation sequencing (NGS) has been widely used in patients with advanced LUAD to identify targeted genes, while early detection of pathologic LN metastasis using NGS has not been assessed.

METHODS

Clinicopathologic features and molecular characteristics of 224 patients from Ruijin Hospital were analyzed to detect factors associated with LN metastases. Another 140 patients from Huashan Hospital were set as a test cohort.

RESULTS

Twenty-four out of 224 patients were found to have lymph node metastases (10.7%). Pathologic LN-positive tumors showed higher mutant allele tumor heterogeneity (p < 0.05), higher tumor mutation burden (p < 0.001), as well as more frequent KEAP1 (p = 0.001), STK11 (p = 0.004), KRAS (p = 0.007), CTNNB1 (p = 0.017), TP53, and ARID2 mutations (both p = 0.02); whereas low frequency of EGFR mutation (p = 0.005). A predictive nomogram involving male sex, solid tumor morphology, higher T stage, EGFR wild-type, and TP53, STK11, CDKN2A, KEAP1, ARID2, KRAS, SDHA, SPEN, CTNNB1, DICER1 mutations showed outstanding efficiency in both the training cohort (AUC = 0.819) and the test cohort (AUC = 0.780).

CONCLUSION

This study suggests that the integration of genomic profiling and clinical features identifies early-invasive LUAD patients at higher risk of LN metastasis. Improved identification of LN metastasis is beneficial for the optimization of the patient's therapy decisions.

Computed tomography characteristics of cN0 primary non-small cell lung cancer predict occult lymph node metastasis

RATIONALE

Occult lymph node metastasis (OLNM) is frequently found in patients with resectable non-small cell lung cancer (NSCLC), despite using diagnostic methods recommended by guidelines.

OBJECTIVES

To evaluate the risk of OLNM in NSCLC patients using the radiologic characteristics of the primary tumor on computed tomography (CT).

METHODS

We retrospectively reviewed clinicopathologic features of 2042 clinical T1-4N0 NSCLC patients undergoing curative intent pulmonary resection. Unique radiological features (i.e., air-bronchogram throughout the whole tumor, heterogeneous ground-glass opacity (GGO), mainly cystic appearance, endobronchial location), percentage of solid portion, and shape of tumor margin were analyzed via a stepwise approach. We used multivariable logistic regression to assess the relationship between OLNM and tumor characteristics.

RESULTS

Compared with the other unique features, endobronchial tumors were associated with the highest risk of OLNM (OR = 3.9, 95% confidence interval (CI) = 2.29-6.62), and heterogeneous GGO and mainly cystic tumors were associated with a low risk of OLNM. For tumors without unique features, the percentage of the solid portion was measured, and solid tumors were associated with OLNM (OR = 2.49, 95% CI = 1.86-3.35). Among part-solid tumors with solid proportion > 50%, spiculated margin, and peri-tumoral GGO were associated with OLNM.

CONCLUSIONS

The risk of OLNM could be assessed using radiologic characteristics on CT. This could allow us to adequately select optimal candidates for invasive nodal staging procedures (INSPs) and complete systematic lymph node dissection.

CLINICAL RELEVANCE STATEMENT

These data may be helpful for clinicians to select appropriate candidates for INSPs and complete surgical systematic lymph node dissection in NSCLC patients.

KEY POINTS

Lymph node metastasis status plays a key role in both prognostication and treatment planning. Solid tumors, particularly endobronchial tumors, were associated with occult lymph node metastasis (OLNM). The risk of OLNM can be assessed using radiologic characteristics acquired from CT images.

[18F]FAPI adds value to [18F]FDG PET/CT for diagnosing lymph node metastases in stage I-IIIA non-small cell lung cancer: a prospective study

BACKGROUND

This study investigates the value of fluorine 18 ([18F])-labeled fibroblast activation protein inhibitor (FAPI) for lymph node (LN) metastases in patients with stage I-IIIA non-small cell lung cancer (NSCLC).

METHODS

From November 2021 to October 2022, 53 patients with stage I-IIIA NSCLC who underwent radical resection were prospectively included. [18F]-fluorodeoxyglucose (FDG) and [18F]FAPI examinations were performed within one week. LN staging was validated using surgical and pathological findings. [18F]FDG and [18F]FAPI uptake was compared using the Wilcoxon signed-ranks test. Furthermore, the diagnostic value of nodal groups was investigated.

RESULTS

In 53 patients (median age, 64 years, range: 31-76 years), the specificity of [18F]FAPI for detecting LN metastasis was significantly higher than that of [18F]FDG (P < 0.001). High LN risk category, greater LN short-axis dimension(≥ 1.0 cm), absence of LN calcification or high-attenuation, and higher LN FDG SUVmax (≥ 10.1) were risk factors for LN metastasis(P < 0.05). The concurrence of these four risk factors accurately predicted LN metastases (Positive Predictive Value [PPV] 100%), whereas the presence of one to three risk factors was unable to accurately discriminate the nature of LNs (PPV 21.7%). Adding [18F]FAPI in this circumstance improved the diagnostic value. LNs with an [18F]FAPI SUVmax<6.2 were diagnosed as benign (Negative Predictive Value 93.8%), and LNs with an [18F]FAPI SUVmax≥6.2 without calcification or high-attenuation were diagnosed as LN metastasis (PPV 87.5%). Ultimately, the integration of [18F]FDG and [18F]FAPI PET/CT resulted in the highest accuracy for N stage (83.0%) and clinical decision revisions for 29 patients.

CONCLUSION

In patients with stage I-IIIA NSCLC, [18F]FAPI contributed additional valuable information to reduce LN diagnostic uncertainties after [18F]FDG PET/CT. Integrating [18F]FDG and [18F]FAPI PET/CT resulted in more precise clinical decisions.

TRIAL REGISTRATION

The Chinese Clinical Trial Registry: ChiCTR2100044944 (Registered: 1 April 2021, https://www.chictr.org.cn/showprojEN.html?proj=123995 ).

The role of adenocarcinoma subtypes and immunohistochemistry in predicting lymph node metastasis in early invasive lung adenocarcinoma

BACKGROUND

Identifying lymph node metastasis areas during surgery for early invasive lung adenocarcinoma remains challenging. The aim of this study was to develop a nomogram mathematical model before the end of surgery for predicting lymph node metastasis in patients with early invasive lung adenocarcinoma.

METHODS

In this study, we included patients with invasive lung adenocarcinoma measuring ≤ 2 cm who underwent pulmonary resection with definite pathology at Qilu Hospital of Shandong University from January 2020 to January 2022. Preoperative biomarker results, clinical features, and computed tomography characteristics were collected. The enrolled patients were randomized into a training cohort and a validation cohort in a 7:3 ratio. The training cohort was used to construct the predictive model, while the validation cohort was used to test the model independently. Univariate and multivariate logistic regression analyses were performed to identify independent risk factors. The prediction model and nomogram were established based on the independent risk factors. Recipient operating characteristic (ROC) curves were used to assess the discrimination ability of the model. Calibration capability was assessed using the Hosmer-Lemeshow test and calibration curves. The clinical utility of the nomogram was assessed using decision curve analysis (DCA).

RESULTS

The overall incidence of lymph node metastasis was 13.23% (61/461). Six indicators were finally determined to be independently associated with lymph node metastasis. These six indicators were: age (P < 0.001), serum amyloid (SA) (P = 0.008); carcinoma antigen 125 (CA125) (P = 0. 042); mucus composition (P = 0.003); novel aspartic proteinase of the pepsin family A (Napsin A) (P = 0.007); and cytokeratin 5/6 (CK5/6) (P = 0.042). The area under the ROC curve (AUC) was 0.843 (95% CI: 0.779-0.908) in the training cohort and 0.838 (95% CI: 0.748-0.927) in the validation cohort. the P-value of the Hosmer-Lemeshow test was 0.0613 in the training cohort and 0.8628 in the validation cohort. the bias of the training cohort corrected C-index was 0.8444 and the bias-corrected C-index for the validation cohort was 0.8375. demonstrating that the prediction model has good discriminative power and good calibration.

CONCLUSIONS

The column line graphs created showed excellent discrimination and calibration to predict lymph node status in patients with ≤ 2 cm invasive lung adenocarcinoma. In addition, the predictive model has predictive potential before the end of surgery and can inform clinical decision making.

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

gov.

Preoperative prediction of mediastinal lymph node metastasis in non-small cell lung cancer based on 18F-FDG PET/CT radiomics

AIM

To establish and verify a 2-[¹⁸F]-fluoro-2-deoxy-d-glucose (FDG) positron-emission tomography (PET)/computed tomography (CT)-based radiomics nomogram to predict mediastinal lymph node metastasis (LNM) in non-small cell lung cancer (NSCLC) patients preoperatively.

MATERIALS AND METHODS

This retrospective study enrolled 155 NSCLC patients (primary cohort, n=93; validation cohort, n=62). For each patient, 2,704 radiomic features were extracted from the primary lung cancer regions. Four procedures including the Mann-Whitney U-test, Spearman's correlation analysis, minimum redundancy-maximum relevance (mRMR), and least absolute shrinkage and selection operator (LASSO) binary logistic regression were utilised for determining essential features and establishing a radiomics signature. After that, a nomogram was established. The nomogram's potential was assessed based on its discrimination, calibration, and clinical usefulness. The radiomics signature and nomogram predictive performances were evaluated with respect to the area under the receiver operating characteristic curve (AUC), specificity, accuracy, and sensitivity.

RESULTS

The radiomics signature composed of eight selected features had good discriminatory performance of LNM versus non-LNM groups an AUC of 0.851 and 0.826 in primary and validation cohorts, respectively. The nomogram also indicated good discrimination with an AUC of 0.869 and 0.847 in the primary and validation cohorts, respectively. Furthermore, good calibration was demonstrated utilising the nomogram.

CONCLUSIONS

An ¹⁸F-FDG PET/CT-based radiomics nomogram that integrates the radiomics signature and age was promoted to predict mediastinal LNM within NSCLC patients, which could potentially facilitate individualised therapy for mediastinal LNM before treatment. The nomogram was beneficial in clinical practice, as illustrated by decision curve analysis.

A nomogram model of postoperative prognosis for metastatic lung adenocarcinoma: A study based on the SEER database

We have observed that patients with metastatic lung adenocarcinoma can obtain survival benefits from surgical resection of the primary tumor. A model was developed to evaluate the prognosis of patients. The patients with metastatic lung adenocarcinoma were identified in the Surveillance, Epidemiology, and End Results database and divided into surgery group and non-surgical group. Through Kaplan-Meier analysis, the survival rate of the non-surgical group was found to be significantly lower no matter before or after propensity score matching. One thousand one hundred and seventy surgical patients were divided into a training group and a verification group. In the training group, univariate and multivariate Cox models were used to explore the prognostic factors, and logistic regression was used to establish a nomogram based on significant predictors. In total, 12,228 patients with metastatic lung adenocarcinoma were recognized; primary tumor surgery accounted for 9.5%. After propensity score matching, the median survival time of 2 groups was significantly different. For the training group, univariate and multivariate COX analysis was conducted, and a nomogram was constructed. Acceptable agreement has been achieved between the predicted and observed survival rates, and the nomogram can divide patients with metastatic lung adenocarcinoma into different risk groups and predict their prognostic survival rate.

Re-evaluating the need for mediastinal lymph node dissection and exploring lncRNAs as biomarkers of N2 metastasis in T1 lung adenocarcinoma

Background

Although a well-acknowledged component of curative surgery for lung cancer, investigators have recently questioned the need for mediastinal lymph node dissection (MLND) in early-stage lung cancer cases. As such, the accurate prediction of N2 stage prior to surgery has become increasingly critical. But diagnostic biomarkers predicting N2 metastases are deficient, which are urgently needed.

Methods

We extracted the data of non-small cell lung cancer (NSCLC) patients whose clinical information and follow-up data are complete and without preoperative induction therapy from the Surveillance, Epidemiology, and End Results (SEER) database. The SEER program registries routinely collect demographic and clinic data on patients. And the prognostic differences were analyzed according to the presence or absence of MLND in their lung resection using the R package. Subsequently, the correlations between pN2 metastasis and clinical characteristics were analyzed. In parallel, the long non-coding RNAs (lncRNAs) associated with pN2 status were screened in The Cancer Genome Atlas (TCGA) database by expression difference analysis between pN0-N1 and pN2 patients using limma. Their diagnostic efficiency for detecting N2 metastases was evaluated using receiver operating characteristic (ROC) curves, and a combined diagnostic model was constructed using logistic regression and ROC curve analyses in lung adenocarcinoma (LUAD).

Results

There were 16,772 patients in MLND group, and 2,699 cases in no-MLND group. The clinical data from SEER showed that the incidence of N2 metastasis was low in pT1 NSCLC (1,023/16,772, 6.10%), but the prognosis of no-MLND patients was poorer than those who underwent MLND (P<0.001, HR =1.605). Pathological N2 metastasis was correlated with age, histologic type, and tumor size. On the other hand, five lncRNAs (LINC00892, AC099522.2, LINC01481, SCAMP1-AS1, and AC004812.2) were screened and confirmed as potential diagnostic biomarkers for detecting N2 metastasis in pT1 LUAD. The AUC of the combined indicators was 0.857.

Conclusions

MLND may be oncologically necessary for selected T1 NSCLC patients based on the metastasis incidence and prognosis. A diagnostic model combining LINC00892, AC099522.2, LINC01481, SCAMP1-AS1, and AC004812.2 expression levels may have the potential to be a diagnostic biomarker for detecting N2 metastasis in pT1 LUAD. This study suggests that MLND might be omitted in patients with lower expression level of this diagnostic model.

Partitioning the lung field based on the depth ratio in three-dimensional space

Background

To explore the feasibility of the depth ratio method partitioning the lung parenchyma and the depth distribution of lung nodules in pulmonary segmentectomy.

Methods

Based on the measurement units, patients were allocated to the chest group, the lobar group, and the symmetrical 3 sectors group. In each unit, the center of the respective bronchial cross-section was set as the starting point (O). Connecting the O point with the center of the lesion (A) and extending to the endpoint (B) on the pleural, the radial line (OB) was trisected to divide the outer, middle, and inner regions. The depth ratio and relevant regional distribution were simultaneously verified using 2-dimensional (2D) coronal, sagittal, and axial computed tomography images and 3-dimensional (3D) reconstruction images.

Results

Two hundred and nine patients were included in this study. The median age was 53 (IQR, 44.5–62) years and 64 were males. The intra-group consistency of the depth ratio region partition was 100%. The consistency of the inter-group region partition differed among the three groups (Kappa values 0.511, 0.517, and 0.923). The chest group, lobar group, and symmetrical 3 sectors group had 69.4%, 26.3%, and 4.8% mediastinum disturbance, respectively (P<0.001).

Conclusions

The depth ratio method in the symmetrical 3 sectors of the lung maximally eliminated the disturbance of the mediastinal structures and more accurately trisected the lung parenchymal in 3D space. Sublobar resection based on subsegments strategy is feasible for outer 2/3 pulmonary nodules when depth ratio is used as the measurement method.

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.

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.

Preoperative clinical and tumor genomic features associated with pathologic lymph node metastasis in clinical stage I and II lung adenocarcinoma

While next-generation sequencing (NGS) is used to guide therapy in patients with metastatic lung adenocarcinoma (LUAD), use of NGS to determine pathologic LN metastasis prior to surgery has not been assessed. To bridge this knowledge gap, we performed NGS using MSK-IMPACT in 426 treatment-naive patients with clinical N2-negative LUAD. A multivariable logistic regression model that considered preoperative clinical and genomic variables was constructed. Most patients had cN0 disease (85%) with pN0, pN1, and pN2 rates of 80%, 11%, and 9%, respectively. Genes altered at higher rates in pN-positive than in pN-negative tumors were STK11 (p = 0.024), SMARCA4 (p = 0.006), and SMAD4 (p = 0.011). Fraction of genome altered (p = 0.037), copy number amplifications (p = 0.001), and whole-genome doubling (p = 0.028) were higher in pN-positive tumors. Multivariable analysis revealed solid tumor morphology, tumor SUVmax, clinical stage, SMARCA4 and SMAD4 alterations were independently associated with pathologic LN metastasis. Incorporation of clinical and tumor genomic features can identify patients at risk of pathologic LN metastasis; this may guide therapy decisions before surgical resection.

Development and validation of tumor-to-blood based nomograms for preoperative prediction of lymph node metastasis in lung cancer

Background

To develop and validate tumor‐to‐blood based nomograms for preoperative prediction of lymph node (LN) metastasis in patients with lung cancer (LC).

Methods

A prediction model was developed in a primary cohort comprising 330 LN stations from patients with pathologically confirmed LC, these data having been gathered from January 2016 to June 2019. Tumor‐to‐blood variables of LNs were calculated from positron emission tomography‐computed tomography (PET‐CT) images of LC and the short axis diameters of LNs were measured on CT images. Tumor‐to‐blood variables, number of stations suspected of harboring LN metastasis according to PET, and independent clinicopathological risk factors were included in the final nomograms. After being internally validated, the nomograms were used to assess an independent validation cohort containing 101 consecutive LN stations accumulated from July 2019 to March 2020.

Results

Four tumor‐to‐blood variables (left atrium, inferior vena cava, liver, and aortic arch) and the maximum standardized uptake value (SUVmax) for LNs were found to be significantly associated with LN status (p < 0.001 for both primary and validation cohorts). Five predictive nomograms were built. Of these, one with LN SUVmax/left atrium SUVmax was found to be optimal for predicting LN status with AUC 0.830 (95% confidence interval [CI]: 0.774–0.886) in the primary cohort and AUC 0.865 (95% CI: 0.782–0.948) in the validation cohort. All models showed good discrimination, with a modest C‐index, and good calibration in both primary and validation cohorts.

Conclusions

We have developed tumor‐to‐blood based nomograms that incorporate identified clinicopathological risk factors and facilitate preoperative prediction of LN metastasis in LC patients.

Pre-Operative Prediction of Mediastinal Node Metastasis Using Radiomics Model Based on 18F-FDG PET/CT of the Primary Tumor in Non-Small Cell Lung Cancer Patients

Purpose: We investigated whether a fluorine-18-fluorodeoxy glucose positron emission tomography/computed tomography (¹⁸F-FDG PET/CT)-based radiomics model (RM) could predict the pathological mediastinal lymph node staging (pN staging) in patients with non-small cell lung cancer (NSCLC) undergoing surgery.

Methods: A total of 716 patients with a clinicopathological diagnosis of NSCLC were included in this retrospective study. The prediction model was developed in a training cohort that consisted of 501 patients. Radiomics features were extracted from the ¹⁸F-FDG PET/CT of the primary tumor. Support vector machine and extremely randomized trees were used to build the RM. Internal validation was assessed. An independent testing cohort contained the remaining 215 patients. The performances of the RM and clinical node staging (cN staging) in predicting pN staging (pN0 vs. pN1 and N2) were compared for each cohort. The area under the curve (AUC) of the receiver operating characteristic curve was applied to assess the model's performance.

Results: The AUC of the RM [0.81 (95% CI, 0.771–0.848); sensitivity: 0.794; specificity: 0.704] for the predictive performance of pN1 and N2 was significantly better than that of cN in the training cohort [0.685 (95% CI, 0.644–0.728); sensitivity: 0.804; specificity: 0.568], (P-value = 8.29e-07, as assessed by the Delong test). In the testing cohort, the AUC of the RM [0.766 (95% CI, 0.702–0.830); sensitivity: 0.688; specificity: 0.704] was also significantly higher than that of cN [0.685 (95% CI, 0.619–0.747); sensitivity: 0.799; specificity: 0.568], (P = 0.0371, Delong test).

Conclusions: The RM based on ¹⁸F-FDG PET/CT has a potential for the pN staging in patients with NSCLC, suggesting that therapeutic planning could be tailored according to the predictions.

The predictive power of artificial intelligence on mediastinal lymphnode metastasis

OBJECTIVE

The aim of this study was to create the preoperative predictive model on mediastinal lymph-node metastasis based on artificial intelligence in surgically resected lung adenocarcinoma.

METHODS

We enrolled 301 surgical resections of patients with clinical stage N0-1 lung adenocarcinoma, who received positron emission tomography preoperatively between 2015 and 2019. We randomly assigned the patients into two groups: the training (n = 201) and validation groups (n = 100). The training group was used to obtain basic data for learning by artificial intelligence, whereas the validation group was used to verify the constructed algorithm. We used an automatic machine learning platform, to create artificial intelligence model. For comparison, multivariate analysis was performed in the training group, whereas for calculating and verifying the prediction accuracy rate, significant predicting factors were applied to the validation group.

RESULTS

Of the 301 patients, 41 patients were diagnosed as mediastinal lymph node metastasis. In multivariate analysis, the maximum standardized uptake value was an individual predictive factor. The accuracy rate of artificial intelligence model was 84%, and the specificity was 98% which were higher than those of the maximum standardized uptake value (61% and 57%). However, in terms of sensitivity, artificial intelligence model remarked low at 12%.

CONCLUSIONS

An artificial intelligence-based diagnostic algorithm showed remarkable specificity compared with the maximum standardized uptake value. Although this model is not ready to practical use and the result was preliminary because of poor sensitivity, artificial intelligence could be able to complement the shortcomings of existing diagnostic modalities.

Utility of PET-CT in non-small cell lung cancer clinical stage IB-IIA according to AJCC 8th edition staging system: an alternative to invasive mediastinal staging?

Objective

Mediastinal nodal staging in lung cancer is essential to determine treatment strategy and prognosis. There are controversies as to whether a mediastinal negative result in PET-CT may spare the invasive staging of the mediastinum. The main endpoint is to evaluate the negative predictive value (NPV) of PET-CT in non-small cell lung cancer (NSCLC) clinical stage IB-IIA without clinical nodal involvement. The secondary endpoint is to evaluate the prevalence of mediastinal and hilar nodal affection in this population.

Methods

We performed an observational descriptive study from January 2010 to January 2020, including 76 patients with clinical stage IB-IIA, who underwent pulmonary resection with systematic nodal sampling (pre-determined lymph node stations based on tumour location) for primary NSCLC. Clinically, nodal involvement was defined as any lymph node greater than 1 cm in the short axis on a CT or with metabolic uptake greater than 2.5 SUV on PET-CT. The prevalence of nodal metastases was recorded.

Results

Fifty six patients had clinical stage IB and 20 had clinical stage IIA. Mean tumour size was 3.74 ± 0.5 cm. Lobectomy was the resection procedure most frequently performed. Of the 76 patients with clinical N0 by PET-CT who underwent surgical resection, 10 (13.1%) were upstaged to pN1 and none were upstaged to pN2. NPV of PET-CT for overall nodal metastasis was 87% (95% CI: 0.79-0.94). NPV of PET-CT for N2 metastasis was 100%.

Conclusion

PET-CT might be an alternative to invasive mediastinal staging in patients with NSCLC clinical stage IB-IIA who are surgical candidates. Further prospective multi-institutional studies are necessary to verify the external validity of our study.

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.

How do we Classify a Central Tumor? Results of a Multidisciplinary Survey from the SEPAR Thoracic Oncology Area

INTRODUCTION

In patients with non-small cell lung cancer (NSCLC) and normal mediastinal imaging tests, centrally located tumors have greater occult mediastinal involvement. Clinical guidelines, therefore, recommend invasive mediastinal staging in this situation. However, definitions of centrality in the different guidelines are inconsistent. The SEPAR Thoracic Oncology area aimed to evaluate the degree of familiarity with various concepts related to tumor site among professionals who see patients with NSCLC in Spain.

METHODS

A questionnaire was distributed to members of Spanish medical societies involved in the management of NSCLC, structured according to the 3 aspects to be evaluated: 1) uniformity in the definition of central tumor location; 2) uniformity in the classification of lesions that extend beyond dividing lines; and 3) ability to delineate lesions in the absence of dividing lines.

RESULTS

A total of 430 participants responded. The most voted definition of centrality was «lesions in contact with hilar structures» (49.7%). The lines most often chosen to delimit the hemitorax were concentric hilar lines (89%). Most participants (92.8%) classified tumors according to the side of the dividing line that contained most of their volume. Overall, 78.6% were able to correctly classify a central lesion in the absence of dividing lines.

CONCLUSIONS

In our survey, the most widely accepted definition of centrality is not one of the proposals specified in the clinical guidelines. The results reflect wide variability in the classification of tumor lesions.

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.

Occult lymph node metastasis in radiologic stage I non-small cell lung cancer: The role of endobronchial ultrasound

RATIONALE

The use of endobronchial ultrasound-guided transbronchial needle aspiration (EBUS-TBNA) is currently recommended for staging non-small cell lung cancer (NSCLC) in centrally located tumors, tumors >3 cm, or with radiologic evidence of lymph node (LN) metastasis. Current guidelines do not recommend staging EBUS-TBNA in patients with stage I NSCLC who do not have any of the aforementioned conditions.

OBJECTIVE

We hypothesize that using EBUS-TBNA is useful for detecting occult metastasis in radiologic stage I NSCLC.

METHODS

In this single-center, retrospective study, charts of patients ≥18 years old who underwent staging EBUS-TBNA from January 2005 to May 2019 were reviewed. Only patients with combined positron-emission tomography and computed tomography (PET/CT) scans consistent with radiologic stage I NSCLC were included. Identified variables included: age, gender, personal history of any cancer, smoking history, tumor location, tumor centrality, tumor size, tumor PET activity, histopathologic type of NSCLC, and LN biopsy results. Patients whose LN samples showed a diagnosis other than NSCLC were excluded. The association between LN positivity, and each of the variables was assessed using Pearson's correlation for categorical variables, and logistic regression analysis for continuous variables.

RESULTS

From the 2,892 initially screened patients, 188 were included. Of those, 13 (6.9%; 95% CI, 4%-11%) had a malignancy-positive LN biopsy. The number needed to test (NNT) in order to detect one case of any occult metastasis was 15. Among the included variables, a significant association was found between LN positivity and tumor centrality, with central tumors found in 61.5% of patients with positive LN (n = 8) (p < 0.01). This association stayed significant after adjusting for age, gender, smoking history, tumor size, tumor location, and PET activity (p = 0.015). Among patients with malignancy-positive LN biopsies, five (38.5%; 95% CI, 17.6%-64.6%) were upstaged to N1, and eight (61.5%; 95% CI, 35.4%-82.4%) were upstaged to N2, with NNT of 23 to detect one case of occult N2 metastasis. Subgroup analysis comparing LN-positive patients based on their N stage did not show statistically significant association with any of the variables.

CONCLUSION

Based on our results and along with the existing evidence, EBUS-TBNA should be recommended as part of the routine staging in all patients with radiologic stage I NSCLC.

A nomogram for predicting the risk of lymph node metastasis in T1-2 non-small-cell lung cancer based on PET/CT and clinical characteristics

Background

Accurately predicting the risk level for a lymph node metastasis is critical in the treatment of non-small cell lung cancer (NSCLC). This study aimed to construct a novel nomogram to identify patients with a risk of lymph node metastasis in T1–2 NSCLC based on positron emission tomography/computed tomography (PET/CT) and clinical characteristics.

Methods

From January 2011 to November 2017, the records of 318 consecutive patients who had undergone PET/CT examination within 30 days before surgical resection for clinical T1–2 NSCLC were retrospectively reviewed. A nomogram to predict the risk of lymph node metastasis was constructed. The model was confirmed using bootstrap resampling, and an independent validation cohort contained 156 patients from June 2017 to February 2020 at another institution.

Results

Six factors [age, tumor location, histology, the lymph node maximum standardized uptake value (SUVmax), the tumor SUVmax and the carcinoembryonic antigen (CEA) value] were identified and entered into the nomogram. The nomogram developed based on the analysis showed robust discrimination, with an area under the receiver operating characteristic curve of 0.858 in the primary cohort and 0.749 in the validation cohort. The calibration curve for the probability of lymph node metastasis showed excellent concordance between the predicted and actual results. Decision curve analysis suggested that the nomogram was clinically useful.

Conclusions

We set up and validated a novel and effective nomogram that can predict the risk of lymph node metastasis for individual patients with T1–2 NSCLC. This model may help clinicians to make treatment recommendations for individuals.

Prediction of mediastinal lymph node metastasis based on 18F-FDG PET/CT imaging using support vector machine in non-small cell lung cancer

OBJECTIVE

The purpose of this study was to develop a classification method based on support vector machine (SVM) to improve the diagnostic performance of ¹⁸F-fluorodeoxyglucose (FDG) positron emission tomography/computed tomography (PET/CT) to detect the lymph node (LN) metastasis in non-small cell lung cancer (NSCLC).

METHOD

Two hundred nineteen lymph nodes (37 metastatic) from 71 patients were evaluated in this study. SVM models were developed with 7 LN features. The area under the curve (AUC) and accuracy of 9 models were compared to select the best model. The best SVM model was simplified on the basis of the feature weights and value distribution to further suit the clinical application.

RESULTS

The maximum, minimum, and mean accuracy of the best model was 91.89% (68/74, 95% CI 83.11~96.54%), 66.22% (49/74, 95% CI 54.85~75.98%), and 80.09% (59,266/74,000, 95% CI 70.27~89.19%), respectively, with an AUC of 0.94, 0.66, and 0.81, respectively. The best SVM model was finally simplified into a score rule: LNs with scores more than 3.0 were considered as malignant ones, whereas LNs with scores less than 1.5 tended to be benign ones. For the LNs with scores within a range of 1.5-3.0, metastasis was suspected.

CONCLUSION

An SVM model based on ¹⁸F-FDG PET/CT images was able to predict the metastatic LNs for patients with NSCLC. The ratio of the maximum of standard uptake value of LNs to aortic arch played a major role in the model. After simplification, the model could be transferred into a scoring method which may partly help clinicians determine the clinical staging of patients with NSCLC relatively easier.

KEY POINTS

• The SVM model based on ¹⁸F-FDG PET/CT features may help clinicians to make a decision for metastatic mediastinal lymph nodes in patients with NSCLC. • The SUR_blood plays a major role in the SVM model. • The score rule based on the SVM model simplified the complexity of the model and may partly help clinicians determine the clinical staging of patients with NSCLC relatively easier.

Geometrical Measurement of Central Tumor Location in cT1N0M0 NSCLC Predicts N1 but Not N2 Upstaging

BACKGROUND

In patients with non-small cell lung cancer (NSCLC) and normal mediastinum, the central tumor location predicts occult nodal disease (both N1 and N2). We evaluated a novel definition of central location based on a geometrical measurement of the tumor location within the lung that could predict N2, N1, or both.

METHODS

This retrospective study included patients with confirmed NSCLC, radiologically and metabolically staged T1 N0 M0, who underwent invasive mediastinal staging and/or lung resection. The central tumor location was measured considering 2 ratios. The inner margin ratio (IMR) and outer margin ratio (OMR) were both calculated as the distance from the inner margin of the lung to both margins of the tumor (inner [IMR], outer [OMR]) divided by the lung width. Optimal cutoffs for IMR and OMR were calculated. Tumors with values lower than the cutoffs were considered central. Prevalences of N1 and N2 upstaging were estimated and bivariate logistic regression analysis was performed to predict the odds of N1 and N2 upstaging using IMR and OMR cutoffs.

RESULTS

A total of 209 patients were included. The prevalence of N1 and N2 upstaging was 11% and 5.3%, respectively. Cutoffs of 0.5 for IMR and 0.64 for OMR were estimated. Both ratios predicted N1 upstaging (adjusted odds ratio [95% confidence interval]: 4.2 [1.5-12]; P < .007; area under the curve, 0.65) but did not predict N2 upstaging.

CONCLUSIONS

Central tumor location can be assessed by means of IMR and OMR and predicts N1 upstaging in patients with radiologically and metabolically T1 N0 M0 tumors. This is important for the selection of patients for therapies that require N0 tumors.

How Effective Is Neoadjuvant Therapy Followed by Surgery for Pathologic Single-Station N2 Non-Small Cell Lung Cancer?

The optimal treatment strategy for pathologic single-station N2 (pN2a1) non-small cell lung cancer (NSCLC)-surgery first followed by adjuvant treatment (SF) or neoadjuvant therapy followed by surgery (NS)-remains unclear. We compared disease-free survival (DFS) and overall survival (OS) after NS versus SF for pN2a1 NSCLC. We retrospectively identified patients with pN2a1 NSCLC resected between 2000 and 2018. Patients in the SF group had cN0 disease and were treated with surgery before adjuvant chemotherapy; patients in the NS group had known preoperative nodal disease, cN2 disease, and were treated with neoadjuvant therapy before surgery. The matching-weights procedure was applied to generate a cohort with similar characteristics between groups. DFS and OS were calculated using the Kaplan-Meier approach and compared between groups using weighted log-rank test and Cox proportional hazards models. We identified 227 patients with pN2a1 disease: 121 treated with SF and 106 with NS. After the matching-weights procedure, 5- and 10-year DFS were 45% and 27% for SF versus 26% and 21% for NS (log-rank P = 0.056; hazard ratio [HR], 1.61; 95% confidence interval [CI], 0.98-2.65); 5- and 10-year OS were 49% and 30% for SF versus 43% and 20% for NS (log-rank P = 0.428; HR, 1.24; 95% CI, 0.67-2.28). SF and NS for pN2a1 NSCLC resulted in similar survival. A study comparing SF for known preresectional pN2a1 with occult pN2a1 disease could be a next step. Further investigation of SF for known N2a1 versus occult pN2a1 disease could power a clinical trial focused on N2a NSCLC.

The Incidence of Node-Positive Non-small-Cell Lung Cancer Undergoing Sublobar Resection and the Role of Radiation in Its Management

Purpose: To identify the incidence, preoperative risk factors, and prognosis associated with pathologically positive lymph node (pN+) in patients undergoing a sub-lobar resection (SLR). Methods: This is a retrospective study using the National Cancer Database (NCDB) from 2004 to 2014 analyzing SLR excluding those with any preoperative chemotherapy and/or radiation, follow-up <3 months, stage IV disease, or >1 tumor nodule. Multivariable modeling (MVA) was used to determine factors associated with overall survival (OS). Propensity score matching (PSM) was used to determine preoperative risk factors for pN+ in patients having at least one node examined to assess radiation's effect on OS in those patients with pN+ and to determine whether SLR was associated with inferior OS as compared to lobectomy for each nodal stage. Results: A total of 40,202 patients underwent SLR, but only 58.3% had one lymph node examined. Then, 2,615 individuals had pN+ which decreased progressively from 15.1% in 2004 to 8.9% in 2014 (N1, from 6.3 to 3.0%, and N2, from 8.4 to 5.9%). A lower risk of pN+ was noted for squamous cell carcinomas, bronchioloalveolar adenocarcinoma (BAC), adenocarcinomas, and right upper lobe locations. In the pN+ group, OS was worse without chemotherapy or radiation. Radiation was associated with a strong trend for OS in the entire pN+ group (p = 0.0647) which was largely due to the effects on those having N2 disease (p = 0.009) or R1 resections (p = 0.03), but not N1 involvement (p = 0.87). PSM noted that SLR was associated with an inferior OS as compared to lobectomy by nodal stage in the overall patient population and even for those with tumors <2 cm. Conclusion: pN+ incidence in SLRs has decreased over time. SLR was associated with inferior OS as compared to lobectomy by nodal stage. Radiation appears to improve the OS in patients undergoing SLR with pN+, especially in those with N2 nodal involvement and/or positive margins.

Clinicopathologic Significance of False-Positive Lymph Node Status on FDG-PET in Lung Cancer

INTRODUCTION

2-[¹⁸F] Fluoro-d-deoxyglucose (FDG) positron emission tomography (PET) is a relevant diagnostic procedure for staging lung cancer. However, accurate evaluation of lymph node metastases by PET is controversial because of false-positive FDG uptake.

PATIENTS AND METHODS

A total of 245 patients with lung cancer were retrospectively analyzed. Standardized maximum uptake values (SUV_max) of the primary tumor and lymph nodes were compared to pathologic lymph node metastases to correlate PET findings with clinicopathologic variables and patient outcomes.

RESULTS

The SUV_max values of metastatic lymph nodes were significantly higher than those of lymph nodes without metastases (P = .0036). When SUV_max ≥ 4 was defined as PET positive for metastasis, the sensitivity, specificity, and accuracy were 48.1%, 79.8%, and 73.1%, respectively. Multivariate logistic regression analysis showed that age > 75 years, bilateral hilar FDG uptake, and no lymph node swelling were significant factors related to false-positive lymph node metastases. Smoking status, FDG uptake in the primary tumor, and concurrent lung diseases were not significant factors.

CONCLUSION

Metastatic lymph nodes show higher FDG uptake than false-positive lymph nodes, and older patient age, bilateral hilar FDG uptake, and no swollen nodes are associated with no metastases. Patients with lymph node metastases have worse survival than those with false-positive FDG-PET findings. However, abnormal FDG uptake in the lymph node is an important prognostic factor.

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.

Lung Adenocarcinoma has a Higher Risk of Lymph Node Metastasis than Squamous Cell Carcinoma: A Propensity Score-Matched Analysis

BACKGROUND

Controversy still exists in which subtype of non-small-cell lung cancer [squamous cell carcinoma (SCC) or adenocarcinoma] is more likely to have lymph node (LN) metastasis. The aim of this study is to compare the pattern of LN metastasis in two cohorts of matched patients surgically treated for SCC or adenocarcinoma.

METHODS

A retrospective analysis of patients undergoing lobectomy or segmentectomy with systematic lymphadenectomy without preoperative treatment for lung SCC or adenocarcinoma was conducted in this study. Data for analysis consisted of age, gender, tumor size, lobe-specific tumor location, tumor location (peripheral or central), and pathologic findings. We conducted the propensity score-matched (PSM) analysis to eliminate potential bias effects of possible confounding factors.

RESULTS

From January 2015 to December 2016 in our department, we finally included a total of 387 patients (including 63 patients with SCC and 324 patients with adenocarcinoma) for analysis. For the unmatched cohort, there was no sufficient evidence of significantly different number of positive LNs (P = 0.90) and rate of LN metastasis (P = 0.23) between SCC patients and adenocarcinoma patients. However, potential confounding factors, for example gender, tumor size, tumor location, tumor differentiation, and total number of dissected LNs, were significantly different between patients with SCC and those with adenocarcinoma. In the analysis of matched cohort after PSM analysis, those above confounding factors were comparable between the two groups. However, patients with adenocarcinoma had significantly more mean positive LNs (2.2 and 0.7; P = 0.008) and a higher rate of LN metastasis (53% and 29%; P = 0.016) than those with SCC.

CONCLUSIONS

Lung adenocarcinoma had a higher risk of LN metastasis than SCC, suggesting that different therapeutic modalities may be indicated for the two different subtypes of lung cancer.

Occult mediastinal lymph node metastasis in FDG-PET/CT node-negative lung adenocarcinoma patients: Risk factors and histopathological study

BACKGROUND

The aim of this study was to investigate predictive factors of occult mediastinal lymph node metastasis (MLNM) in preoperative 18 F-fluorodeoxy-glucose PET/CT node-negative lung adenocarcinoma patients.

METHODS

We reviewed the clinical data and PET/CT parameters of 360 consecutive pulmonary adenocarcinoma patients who were scheduled to undergo anatomical pulmonary resection and systemic mediastinal node dissection. The nodal metastasis was pathologically defined and all resected tumors were classified according to the 2011 IASLC/ATS/ERS classification. Univariate and multivariate analysis were conducted to evaluate the associations between clinicopathological variables and MLNM.

RESULTS

Of all 360 patients, 54 (15.0%) had pathological N2 diseases. The serum CEA level, nodule type, hilar nodal SUVmax, tumor SUVmax, size, location and histologic subtype were associated with MLNM significantly on univariate analysis. On multivariate analysis, CEA ≥ 5.0 ng/mL (P < 0.001), solid nodule (P = 0.012), tumor SUVmax ≥ 3.7 (P < 0.027), hilar nodal SUVmax ≥ 2.0 (P < 0.001) and centrally located tumor (P = 0.035) were independent risk factors for MLNM. The area under the ROC curve (AUC) for tumor SUVmax and hilar nodal SUVmax in predicting MLNM was 0.764 and 0.730, respectively, and the combined use of five factors yielded a higher AUC of 0.885.

CONCLUSION

Increased primary tumor and hilar lymph node SUVmax, solid nodule, centrally located tumor and increased CEA level predicted the increased risk of mediastinal lymph node metastasis. Combined use of these factors improved the diagnostic capacity for predicting N2 disease preoperatively. Invasive mediastinal staging should be considered for patients with these risk factors, even those with a negative mediastinum on PET/CT.

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

Developing prediction models for clinical use using logistic regression: an overview

Prediction models help healthcare professionals and patients make clinical decisions. The goal of an accurate prediction model is to provide patient risk stratification to support tailored clinical decision-making with the hope of improving patient outcomes and quality of care. Clinical prediction models use variables selected because they are thought to be associated (either negatively or positively) with the outcome of interest. Building a model requires data that are computer-interpretable and reliably recorded within the time frame of interest for the prediction. Such models are generally defined as either diagnostic, likelihood of disease or disease group classification, or prognostic, likelihood of response or risk of recurrence. We describe a set of guidelines and heuristics for clinicians to use to develop a logistic regression-based prediction model for binary outcomes that is intended to augment clinical decision-making.

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.

Variability in invasive mediastinal staging for lung cancer: A multicenter regional study

OBJECTIVE

Prior studies have reported underuse of-but not variability in-invasive mediastinal staging in the pretreatment evaluation of patients with lung cancer. We sought to compare rates of invasive mediastinal staging for lung cancer across hospitals participating in a regional quality improvement and research collaborative.

METHODS

We conducted a retrospective study (2011-2013) of patients undergoing resected lung cancer from the Surgical Clinical Outcomes and Assessment Program in Washington State. Invasive mediastinal staging included mediastinoscopy and/or endobronchial/esophageal ultrasound-guided nodal aspiration. We used a mixed-effects model to mitigate the influence of small sample sizes at any 1 hospital on rates of invasive staging and to adjust for hospital-level differences in the frequency of clinical stage IA disease.

RESULTS

A total of 406 patients (mean age, 68 years; 69% clinical stage IA; and 67% lobectomy) underwent resection at 5 hospitals (4 community and 1 academic). Invasive staging occurred in 66% of patients (95% confidence interval [CI], 61%-71%). CI inspection revealed that 2 hospitals performed invasive staging significantly more often than the overall average (94%, [95% CI, 89%-96%] and 84% [95% CI, 78%-88%]), whereas 2 hospitals performed invasive staging significantly less often than overall average (31% [95% CI, 21%-44%] and 17% [95% CI, 7%-36%]).

CONCLUSIONS

Rates of invasive mediastinal staging varied significantly across hospitals providing surgical care for patients with lung cancer. Future studies that aim to understand the reasons underlying variability in care may inform quality improvement initiatives or lead to the development of novel staging algorithms.

Randomized Phase II Trial Comparing Chemoradiotherapy with Chemotherapy for Completely Resected Unsuspected N2-Positive Non-Small Cell Lung Cancer

INTRODUCTION

We investigated whether concurrent chemoradiotherapy (CCRT) would increase survival in patients with completely resected unsuspected N2-positive NSCLC versus in patients who received adjuvant chemotherapy alone.

METHODS

Eligible patients were randomly assigned (1:1) to either the CCRT arm or the chemotherapy arm. In the CCRT arm, patients received concurrent thoracic radiotherapy (50 Gy in 25 fractions) with five cycles of weekly paclitaxel (50 mg/m²) and cisplatin (25 mg/m²), followed by two additional cycles of paclitaxel (175 mg/m²) plus cisplatin (80 mg/m²) at 3-week intervals. In the chemotherapy arm, patients received four cycles of adjuvant paclitaxel (175 mg/m²) and carboplatin (area under the curve = 5.5) every 3 weeks. The primary end point was disease-free survival.

RESULTS

We enrolled and analyzed 101 patients (51 received CCRT and 50 received chemotherapy). In all, 74 and 27 patients were preoperatively staged as N0 and N1 diseases, respectively. The baseline characteristics were well balanced between the two arms. The median disease-free survival of the CCRT arm was 24.7 months, which was not significantly different from that of the chemotherapy arm (21.9 months) (hazard ratio = 0.94, 95% confident interval: 0.58-1.52, p = 0.40). There was no difference in overall survival (74.3 months in CCRT arm and 83.5 months in the chemotherapy arm) (hazard ratio = 1.33, 95% confident interval: 0.71-2.49).

CONCLUSIONS

There was no survival benefit from adjuvant CCRT compared with from platinum-based chemotherapy alone for completely resected unsuspected N2-positive NSCLC. However, the role of sequential radiotherapy administered after adjuvant chemotherapy is being evaluated, and further study is needed to evaluate the optimal radiotherapy approach for completely resected N2-positive NSCLC.

A Model to Predict the Use of Surgical Resection for Advanced-Stage Non-Small Cell Lung Cancer Patients

BACKGROUND

For advanced-stage non-small cell lung cancer, chemotherapy and chemoradiotherapy are the primary treatments. Although surgical intervention in these patients is associated with improved survival, the effect of selection bias is poorly defined. Our objective was to characterize selection bias and identify potential surgical candidates by constructing a Surgical Selection Score (SSS).

METHODS

Patients with clinical stage IIIA, IIIB, or IV non-small cell lung cancer were identified in the National Cancer Data Base from 1998 to 2012. Logistic regression was used to develop the SSS based on clinical characteristics. Estimated area under the receiver operating characteristic curve was used to assess discrimination performance of the SSS. Kaplan-Meier analysis was used to compare patients with similar SSSs.

RESULTS

We identified 300,572 patients with stage IIIA, IIIB, or IV non-small cell lung cancer without missing data; 6% (18,701) underwent surgical intervention. The surgical cohort was 57% stage IIIA (n = 10,650), 19% stage IIIB (n = 3,483), and 24% stage IV (n = 4,568). The areas under the receiver operating characteristic curve from the best-fit logistic regression model in the training and validation sets were not significantly different, at 0.83 (95% confidence interval, 0.82 to 0.83) and 0.83 (95% confidence interval, 0.82 to 0.83). The range of SSS is 43 to 1,141. As expected, SSS was a good predictor of survival. Within each quartile of SSS, patients in the surgical group had significantly longer survival than nonsurgical patients (p < 0.001).

CONCLUSIONS

A prediction model for selection of patients for surgical intervention was created. Once validated and prospectively tested, this model may be used to identify patients who may benefit from surgical intervention.

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.

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.

Vascular endothelial growth factor C complements the ability of positron emission tomography to predict nodal disease in lung cancer

OBJECTIVE

Vascular endothelial growth factors (VEGFs) C and D are biologically rational markers of nodal disease that could improve the accuracy of lung cancer staging. We hypothesized that these biomarkers would improve the ability of positron emission tomography (PET) to predict nodal disease among patients with suspected or confirmed non-small cell lung cancer (NSCLC).

METHODS

A cross-sectional study (2010-2013) was performed of patients prospectively enrolled in a lung nodule biorepository, staged by computed tomography (CT) and PET, and who underwent pathologic nodal evaluation. Enzyme-linked immunosorbent assay was used to measure biomarker levels in plasma from blood drawn before anesthesia. Likelihood ratio testing was used to compare the following logistic regression prediction models: ModelPET, ModelPET/VEGF-C, ModelPET/VEGF-D, and ModelPET/VEGF-C/VEGF-D. To account for 5 planned pairwise comparisons, P values <.01 were considered significant.

RESULTS

Among 62 patients (median age, 67 years; 48% men; 87% white; and 84% NSCLC), 58% had fluorodeoxyglucose uptake in hilar and/or mediastinal lymph nodes. The prevalence of pathologically confirmed lymph node metastases was 40%. Comparisons of prediction models revealed the following: ModelPET/VEGF-C versus ModelPET (P = .0069), ModelPET/VEGF-D versus ModelPET (P = .1886), ModelPET/VEGF-C/VEGF-D versus ModelPET (P = .0146), ModelPET/VEGF-C/VEGF-D versus ModelPET/VEGF-C (P = .2818), and ModelPET/VEGF-C/VEGF-D versus ModelPET/VEGF-D (P = .0095). In ModelPET/VEGF-C, higher VEGF-C levels were associated with an increased risk of nodal disease (odds ratio, 2.96; 95% confidence interval, 1.26-6.90).

CONCLUSIONS

Plasma levels of VEGF-C complemented the ability of PET to predict nodal disease among patients with suspected or confirmed NSCLC. VEGF-D did not improve prediction.

International Association for the Study of Lung Cancer/American Thoracic Society/European Respiratory Society classification predicts occult lymph node metastasis in clinically mediastinal node-negative lung adenocarcinoma

OBJECTIVES

We investigated the role of the 2011 International Association for the Study of Lung Cancer, American Thoracic Society, and European Respiratory Society (IASLC/ATS/ERS) classification in predicting occult lymph node metastasis in clinically mediastinal node-negative lung adenocarcinoma.

METHODS

We reviewed lung adenocarcinoma patients who had clinically N2-negative status, were evaluated by preoperative positron emission tomography combined with computed tomography (PET/CT) and had undergone lobectomy or pneumonectomy at Memorial Sloan Kettering Cancer Center (n = 297). Tumours were classified according to the 2011 IASLC/ATS/ERS classification. The associations between occult lymph node metastasis and clinicopathological variables were analysed using Fisher's exact test and logistic regression analysis.

RESULTS

Thirty-two (11%) cN0-1 patients had occult mediastinal lymph node metastasis (pN2) whereas 25% of cN1 patients had pN2 disease. Increased micropapillary pattern was associated with increased risk of pN2 disease (P = 0.001). On univariate analysis, high maximum standard uptake value of the primary tumour on PET/CT (P = 0.019) and the presence of micropapillary (P = 0.014) and solid pattern (P = 0.014) were associated with occult pN2 disease. On multivariable analysis, micropapillary pattern was positively associated with risk of pN2 disease (odds ratio = 3.41; 95% confidence intervals = 1.42-8.19; P = 0.006).

CONCLUSIONS

The presence of micropapillary pattern is an independent predictor of occult mediastinal lymph node metastasis. Our observations have potential therapeutic implications for management of early-stage lung adenocarcinoma.

What is quality, and can we define it in lung cancer?-the case for quality improvement

Decades worth of advances in diagnostics and therapeutics are associated with only marginal improvements in survival among lung cancer patients. An obvious explanation is late stage at presentation, but gaps in the quality of care may be another reason for stifled improvements in survival rates. A framework for quality put forth by Avedis Donabedian consists of measuring structures-of-care, processes, and outcomes. Using this approach to explore for potential quality gaps, there is evidence of inexplicable variability in outcomes across patients and hospitals; variation in outcomes across differing provider types (structures-of-care); and variation in approaches to staging (processes-of-care). However, this research has limitations and incontrovertible evidence of quality gaps is challenging to obtain. Other challenges to defining quality include scientific and clinical uncertainty among providers and the fact that quality is a multi-dimensional construct that cannot be measured by a single metric. Nonetheless, two facts compel us to pursue quality improvement: (I) both empirically and anecdotally, actual care falls short of expected care; and (II) evidence of potential quality gaps is not ignorable primarily on ethical grounds.

Watch the weathercock: changes in re-staging 18F-FDG PET/CT scan predict the probability of relapse in locally advanced non-small cell lung cancer

INTRODUCTION

Induction treatment is be coming the gold standard for locally advanced non-small cell lung cancers (LA-NSCLC). In contrast to baseline positron emission/computed tomography scan (PET/CT scan), re-staging PET/CT scan has been poorly studied in LA-NSCLC.

MATERIALS AND METHODS

We retrospectively explored the efficacy of re-staging PET/CT scan to diagnose response and to predict disease-free survival (DFS) in 55 induction-treated LA-NSCLC further treated with curative surgery or radiation but not with adjuvant therapy.

RESULTS

Re-staging N status by PET/CT scan significantly correlated with pathological N status. Radiological or metabolic response in the re-staging PET/CT scan was associated with a significantly better DFS, which decreased from 25.8 to 19.3, to 11.2, and to 9.4 months in cN0, cN1, cN2, and cN3 patients, respectively.

CONCLUSION

Re-staging PET/CT scan helps to define response and consolidation treatment in induction-treated LA-NSCLC and predicts DFS. Further extended studies should confirm our results.