Performing High-Quality Sublobar Resections: Key Differences Between Wedge Resection and Segmentectomy

Lung cancer remains the leading cause of cancer-related deaths worldwide. The introduction of targeted treatments and immunotherapy in lung cancer has transformed patient care by offering "precision medicine" focused on the characteristics of the disease. The same concept has emerged in lung cancer surgery. This "precision surgery" aims to determine the best surgical approach based on the characteristics of the cancer, the patient's cardiorespiratory status, and technological advances in thoracic surgery. While lobectomy continues to be the gold standard for managing early-stage lung cancer, the implementation of screening programs has enabled the earlier detection of smaller tumors. In this context, sublobar resections, particularly segmentectomy, have emerged as valuable options in the treatment of early-stage lung cancer. Recent studies suggest that sublobar resections, including segmentectomy and wedge resection, provide a viable alternative to lobectomy. This review explores the various resection strategies available, tailored to patient and tumor characteristics, and highlights modern preoperative techniques aimed at advancing precision surgery.

Impact of preoperative [18F]FDG PET/CT vs. contrast-enhanced CT in the staging and survival of patients with clinical stage I and II non-small cell lung cancer: a 10-year follow-up study

OBJECTIVES

To elucidate the impact of [18F]FDG positron emission tomography/computed tomography (PET/CT) vs. CT workup on staging and prognostic evaluation of clinical stage (c) I-II NSCLC.

METHODS

We retrospectively identified 659 cI-II NSCLC who underwent CT (267 patients) or preoperative CT followed by PET/CT (392 patients), followed by curative-intended complete resection in our hospital from January 2008 to December 2013. Differences were assessed between preoperative and postoperative stage. Five-year disease-free survival (DFS) and overall survival (OS) rates were calculated using the Kaplan-Meier approach and compared with log-rank test. Impact of preoperative PET/CT on survival was assessed by Cox regression analysis.

RESULTS

The study included 659 patients [mean age, 59.5 years ± 10.8 (standard deviation); 379 men]. The PET/CT group was superior over CT group in DFS [12.6 vs. 6.9 years, HR 0.67 (95% CI 0.53-0.84), p < 0.001] and OS [13.9 vs. 10.5 years, HR 0.64 (95% CI 0.50-0.81), p < 0.001]. In CT group, more patients thought to have cN0 migrated to pN1/2 disease as compared with PET/CT group [26.4% (66/250) vs. 19.2% (67/349), p < 0.001], resulting in more stage cI cases being upstaged to pII-IV [24.7% (49/198) vs. 16.1% (47/292), p = 0.02], yet this was not found in cII NSCLC [27.5% (19/69) vs. 27.0% (27/100), p = 0.94]. Cox regression analysis identified preoperative PET/CT as an independent prognostic factor of OS and DFS (p = 0.002, HR = 0.69, 95% CI 0.54-0.88; p = 0.004, HR = 0.72, 95% CI 0.58-0.90).

CONCLUSION

Addition of preoperative [18F]FDG PET/CT was associated with superior DFS and OS in resectable cI-II NSCLC, which may result from accurate staging and stage-appropriate therapy.

An Investigation of the Relationship Between 18F-FDG PET/CT Parameters of Primary Tumors and Lymph Node Metastasis in Resectable Non-small Cell Lung Cancer

BACKGROUND

Mediastinal lymph node metastasis is an important prognostic factor in non-small cell lung cancer (NSCLC) patients without distant metastases. 18F-2-fluoro-2-deoxy-Dglucose Positron Emission Tomography/Computed Tomography (18F-FDG PET/CT) is recommended for detecting and staging lymph nodes and distant metastases in NSCLC patients.

OBJECTIVE

This study aims to investigate whether maximum standardized uptake (SUVmax), mean standardized uptake (SUVmean), metabolic tumor volume (MTV), and tumor lesion glycolysis (TLG) values of the primary tumor measured by 18F-FDG PET/CT in resectable NSCLC can predict preoperative lymph node metastasis.

METHODS

This retrospective study included eighty NSCLC patients who underwent preoperative Positron Emission Tomography/Computed Tomography (PET/CT) for diagnosis and staging. The patients were stage I-III and had no distant metastases. Tumor metabolic parameters such as SUVmax, SUVmean, MTV, and TLG at PET/CT imaging were measured for preoperative diagnosis and staging, and the postoperative pathology results of the patients were examined. The pathology results divided patients with and without lymph node metastasis into two groups. The groups were compared with the student's t-test and chi-square test regarding 18F-FDG PET/CT tumor metabolic parameters and other parameters.

RESULTS

Fifteen (18.8%) patients were female, and 65 (81.3%) were male. According to the postoperative pathology results, while 30 (37.5%) patients had lymph node metastasis, 50 (62.5%) did not. There was a significant difference between the groups regarding tumor SUVmax and SUVmean values (p = 0.036, p = 0.045). Overall survival in the N0 group was significantly higher than in the N1 + N2 group (p = 0.034); median survival was 30.2 months in N0 cases and 27.3 months in N1 and N2 groups.

CONCLUSION

SUVmax and SUVmean values are significantly higher in patients with lymph node metastases than in patients without lymph node metastases, and this finding may provide useful information for predicting lymph node metastasis in patients with resectable NSCLC.

PET/CT for Predicting Occult Lymph Node Metastasis in Gastric Cancer

A portion of gastric cancer patients with negative lymph node metastasis at an early stage eventually die from tumor recurrence or advanced metastasis. Occult lymph node metastasis (OLNM] is a potential risk factor for the recurrence and metastasis in these patients, and it is highly important for clinical prognosis. Positron emission tomography (PET)/computed tomography (CT) is used to assess lymph node metastasis in gastric cancer due to its advantages in anatomical and functional imaging and non-invasive nature. Among the major metabolic parameters of PET, the maximum standardized uptake value (SUVmax) is commonly used for examining lymph node status. However, SUVmax is susceptible to interference by a variety of factors. In recent years, the exploration of new PET metabolic parameters, new PET imaging agents and radiomics, has become an active research topic. This paper aims to explore the feasibility and predict the effectiveness of using PET/CT to detect OLNM. The current landscape and future trends of primary metabolic parameters and new imaging agents of PET are reviewed. For gastric cancer patients, the possibility to detect OLNM non-invasively will help guide surgeons to choose the appropriate lymph node dissection area, thereby reducing unnecessary dissections and providing more reasonable, personalized and comprehensive treatments.

Mediastinal Staging with Endobronchial Ultrasound in Early-Stage Non-Small Cell Lung Cancer: Is It Necessary?

Herein we examine the need for minimally invasive mediastinal staging for patients with early-stage non-small cell lung cancer (NSCLC) using endobronchial ultrasound-guided transbronchial needle aspiration (EBUS-TBNA). Early NSCLC, stages 1 and 2, has a 5-year survival rate between 53 and 92%, whereas stages 3 and 4 have a 5-year survival of 36% and below. With more favorable outcomes in earlier stages, greater emphasis has been placed on identifying lung cancer earlier in its disease process. Accurate staging is crucial as it dictates both prognosis and therapy. Inaccurate staging can adversely impact surgical candidacy (if falsely "over-staged") or lead to inadequate treatment (if "under-staged"). Clinical staging utilizes noninvasive methods to evaluate the anatomic extent of disease; however, it remains controversial whether mediastinal staging of early NSCLC with radiological exams alone is sufficient. EBUS-TBNA has altered the landscape of invasive mediastinal staging and is a crucial component to improving confidence in lung cancer staging, specifically in early NSCLC. Radiographic occult lymph node metastasis identified upon review of surgical resection specimens of early NSCLC may support the argument to perform EBUS-TBNA in all cases of early-stage disease. Other data suggest that EBUS-TBNA could be spared in cases of peripheral cT1aN0 and cT1bN0 for which surgical resection with lymph node dissection is planned. By reviewing reported EBUS-TBNA outcomes in patients with early NSCLC, we aim to emphasize the necessity of staging with EBUS in this population.

A narrative review of deep learning applications in lung cancer research: from screening to prognostication

Background and Objective

Deep learning (DL) algorithms have been developed for various tasks, including lung nodule detection on chest radiographs or lung cancer computed tomography screening, potential candidate selection in lung cancer screening, malignancy prediction for indeterminate pulmonary nodules, lung cancer staging, treatment response prediction, prognostication, and prediction of genetic mutations in lung cancer. Furthermore, these DL algorithms have been applied in various clinical settings in order for them to be generalized in real-world clinical practice. Multiple DL algorithms have been corroborated to be on par with experts or current clinical prediction models for several specific tasks. However, no article has yet comprehensively reviewed DL algorithms dedicated to lung cancer research. This narrative review presents an overview of the literature dealing with DL techniques applied in lung cancer research and briefly summarizes the results according to the DL algorithms’ clinical use cases.

Methods

we performed a narrative review by searching the Embase and OVID-MEDLINE databases for articles published in English from October, 2016 until September, 2021 and reviewing the bibliographies of key references to identify important literature related to DL in lung cancer research. The background, development, results, and clinical implications of each DL algorithm are briefly discussed. Lastly, we end this review article by highlighting future directions in lung cancer research using DL techniques.

Key Content and Findings

DL algorithms have been introduced to show comparable or higher performance than human experts in various clinical settings. Specifically, they have been actively applied to detect lung nodules in chest radiographs or computed tomography (CT) examinations, optimize candidate selection for lung cancer screening (LCS), predict the malignancy of lung nodules, stage lung cancer, and predict treatment response, patients’ prognoses, and genetic mutations in lung cancers.

Conclusions

DL algorithms have corroborated their potential value for various tasks, ranging from lung cancer screening to prognostication of lung cancer patients. Future research is warranted for the clinical application of these algorithms in daily clinical practice and verification of their real-world clinical usefulness.

Lobe-specific nodal dissection with intraoperative frozen section analysis for clinical stage-I non-small cell lung cancer: a validation study by propensity score matching

OBJECTIVE

Lobe-specific nodal dissection (LND) is increasingly used for non-small cell lung cancer (NSCLC) in Japan; however, its treatment validity remains unclarified. Since 2013, LND has been used as a standard procedure for clinical stage-I (c-stage-I) NSCLC at our institution. We aimed to evaluate its validity using intraoperative frozen section analysis (FSA) for c-stage-I NSCLC.

METHODS

The participants comprised patients with NSCLC who underwent LND between 2013 and 2016 (n = 307) or systematic nodal dissection (SND) between 2002 and 2013 (n = 367) for c-stage-I disease. FSA was routinely performed in LND to examine at least three stations. Outcomes were compared between the LND and SND groups. Patients in whom LND was converted to SND due to metastasis on FSA of the sampled lymph node were still categorized into the LND group, i.e., intention-to-treat analysis. The prognostic impact was compared using propensity score matching.

RESULTS

The rate of conversion from LND to SND was 10.4%. Of the patients converted to SND, 12.5% had metastases outside the LND area. False-negative N2 results were detected in only 0.7% of the LND group patients after FSA. After matching, each group had 220 patients. There were no significant between-group differences in the lymph-node recurrence rate (7% vs. 6%), 5-year recurrence-free survival (80.1% vs. 79.0%), and overall survival (90.4% vs. 90.3%).

CONCLUSIONS

LND with intraoperative FSA is a valid modality that could serve as a standard surgical procedure for c-stage-I NSCLC. Intraoperative FSA may lower the residual lymph-node metastasis risk in LND.

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.

What Should Thoracic Surgeons Consider during Surgery for Ground-Glass Nodules?: Lymph Node Dissection

Thoracic surgeons need to be aware of several important points regarding intraoperative lymph node dissection during surgery for non-small cell lung cancer with ground-glass opacities. The first point relates to the need for lymph node dissection during sublobar resection. Since even patients undergoing sublobar resection may benefit from lymph node dissection, it should be selectively performed according to adequate indications, which require further study. Second, there seems to be no difference in postoperative morbidity between systematic sampling and systematic dissection, but the survival benefit from systematic dissection remains unclear. The results of randomized controlled trials on this topic are conflicting, and their evidence is jeopardized by a high risk of bias in terms of the study design. Therefore, further randomized controlled trials with a sound design should investigate this issue. Third, more favorable survival outcomes tend to be positively associated with the number of examined lymph nodes. Minimum requirements for the number of examined lymph nodes in non-small cell lung cancer should be defined in the future. Finally, lobe-specific lymph node dissection does not have a negative prognostic impact. It should not be routinely performed, but it can be recommended in selected patients with smaller, less invasive tumors. Results from an ongoing randomized controlled trial on this topic should be awaited.

Prediction of Unexpected N2 Disease Associated With Clinical T1-2N0-1M0 Non-Small-Cell Lung Cancer

BACKGROUND

Despite the recent development of radiologic mediastinal staging modality, unexpected mediastinal lymph node metastasis still occurs. Preoperative accurate nodal staging is important to determine the optimal treatment. Therefore, this study aimed to identify predictors of unexpected N2 disease in non-small-cell lung cancer (NSCLC).

PATIENTS AND METHODS

Data from a multicenter database of 2802 patients with clinical T1-2N0-1M0 NSCLC who underwent anatomical segmentectomy or lobectomy were retrospectively analyzed. Unexpected N2 disease was defined as pathologic N2 disease with clinical N0 or N1. The predictive criteria of unexpected N2 disease were established on the basis of the multivariable analysis results of a derivation cohort of 2019 patients, and the criteria were further tested in a validation cohort of 783 patients.

RESULTS

In multivariable analyses, maximum standardized uptake value (SUV_max) of the primary tumor on 18-fluoro-2-deoxyglucose positron emission tomography/computed tomography (odds ratio, 1.072; 95% confidence interval, 1.018-1.129; P = .008) and clinical N1 (vs. clinical N0) disease (odds ratio, 5.40; 95% confidence interval, 1.829-15.94; P = .002) were independent predictors of unexpected N2 disease. The predictive criteria of unexpected N2 disease was defined as tumors with SUV_max of ≥ 3.1, determined by receiver operating characteristic curves, and clinical N1 disease. This criterion showed diagnostic accuracy of 90.6% (sensitivity 32.0%, specificity 94.5%) in the derivation cohort and 91.3% (sensitivity 32.6%, specificity 94.7%) in the validation cohort.

CONCLUSION

The predictive criteria of unexpected N2 disease (tumors with SUV_max of ≥ 3.1 and clinical N1) can be used to select candidates for preoperative invasive mediastinal staging in patients with clinical T1-2N0-1M0 NSCLC.

Predictive Value of 18F-Fluorodeoxyglucose Positron Emission Tomography or Positron Emission Tomography/Computed Tomography for Assessment of Occult Lymph Node Metastasis in Non-Small Cell Lung Cancer

OBJECTIVE

The purpose of the current study was to investigate the diagnostic performance of 18F fluorodeoxyglucose (FDG) positron emission tomography (PET) or positron emission tomography/computed tomography (PET/CT) for the prediction of occult lymph node metastasis (OLNM) in non-small cell lung cancer (NSCLC) patients through a systematic review and meta-analysis.

METHODS

The PubMed, Cochrane, and EMBASE database, from the earliest available date of indexing through March 31, 2020, were searched for studies evaluating the diagnostic performance of preoperative 18F FDG PET or PET/CT for the prediction of OLNM in NSCLC patients.

RESULTS

Across 14 studies (3,535 patients), the pooled sensitivity for 18F FDG PET or PET/CT was 0.79 (95% CI; 0.70-0.86) with heterogeneity (I2 = 81.5, p < 0.001) and a pooled specificity of 0.65 (95% CI; 0.57-0.72) with heterogeneity (I2 = 93.7, p < 0.001). Likelihood ratio (LR) syntheses gave an overall positive likelihood ratio (LR+) of 2.3 (95% CI; 1.9-2.6) and a negative likelihood ratio (LR-) of 0.32 (95% CI; 0.23-0.44). The pooled diagnostic odds ratio (DOR) was 7 (95% CI; 5-10). The hierarchical summary receiver operating characteristic curve indicates that the area under the curve was 0.77 (95% CI; 0.74-0.81).

CONCLUSION

The current meta-analysis showed a moderate sensitivity and specificity of 18F FDG PET or PET/CT for the prediction of OLNM in NSCLC patients. The DOR was low and the likelihood ratio scatter-gram indicated that 18F FDG PET or PET/CT might not be useful for the prediction of OLNM in NSCLC patients and not for its exclusion.

A New Prognostic Index Combines the Metabolic Response and RECIST 1.1 to Evaluate the Therapeutic Response in Patients With Non-Small Cell Lung Cancer

Aim: Response Evaluation Criteria in Solid Tumors (RECIST) is occasionally insufficient for evaluation. We proposed a new prognostic index (NPI) that combines the standardized uptake value (SUV), metabolic tumor volume (MTV), and RECIST. Methods: In total, 116 patients with lung cancer who underwent consecutive positron emission tomography-computed tomography prior to and after the initial treatment were included. We formulated the NPI by estimating the hazard ratios of overall survival for ΔMTV, ΔSUVmax, and ΔD (tumor size based on RECIST). Progression-free survival (PFS) and overall survival (OS) were compared between RECIST and the NPI. Results: ROC curve analysis identified two cutoff values based on the NPI (≤ -49.3% and ≥43.4%) to discriminate partial remission (NPR), stable disease (NSD) and progressive disease (NPD). Based on RECIST, survival analysis did not discriminate significantly on either PFS or OS between the PR, SD, and PD groups. However, according to the NPI, PFS and OS differed significantly between the NPR, NSD, and NPD groups (training set: PFS, p = 0.048; OS, p = 0.026; validation set: PFS, p = 0.004; OS, p = 0.023). Moreover, therapeutic response based on NPI was independent prognostic factor for both PFS [NPR as reference, NSD: hazard ratio (HR) 2.04; 95% confidence interval (95% CI) 1.35-3.08; p = 0.001; NPD: HR 6.87; 95% CI 3.03-15.57; p < 0.001] and OS (NPR as reference, NSD: HR 1.64; 95% CI 1.05-2.57; p = 0.031; NPD: HR 3.56; 95% CI 1.59-7.95; p = 0.002). Conclusion: The NPI showed superiority for evaluation of the therapeutic response and survival for patients with non-small cell lung cancer, overcoming the limitations of RECIST.

Minimizing residual occult nodal metastasis in NSCLC: recent advances, current status and controversies

Introduction: Nodal involvement in lung cancer is a significant determinant of prognosis and treatment management. New evidence exists regarding the management of occult lymph node metastasis and residual disease in the fields of imaging, mediastinal staging, and operative management.Areas covered: This review summarizes the latest body of knowledge on the identification and management of occult lymph node metastasis in NSCLC. We focus on tumor-specific characteristics; imaging modalities; invasive mediastinal staging; and operative management including, technique, degree of resection, and lymph node examination.Expert opinion: Newly identified risk-factors associated with nodal metastasis including tumor histology, location, radiologic features, and metabolic activity are not included in professional societal guidelines due to the heterogeneity of their reporting and uncertainty on how to adopt them into practice. Imaging as a sole diagnostic method is limited. We recommend confirmation with invasive mediastinal staging. EBUS-FNA is the best initial method, but adoption has not been uniform. The diagnostic algorithm is less certain for re-staging of mediastinal nodes after neoadjuvant therapy. Mediastinal node sampling during lobectomy remains the gold-standard, but evidence supports the use of minimally invasive techniques. More study is warranted regarding sublobar resection. No consensus exists regarding lymph node examination, but new evidence supports reexamination of current quality metrics.

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.

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.

18F-fluorodeoxyglucose positron emission tomography/computed tomography in the evaluation of clinically node-negative non-small cell lung cancer

One in four non-small cell lung cancer (NSCLC) patients are diagnosed at an early-stage. Following the results of the National Lung Screening Trial that demonstrated a survival benefit for low-dose computed tomography screening in high-risk patients, the incidence of early-stage NSCLC is expected to increase. Use of 18F-fluorodeoxyglucose positron emission tomography/computed tomography during initial diagnosis of these early-stage lesions has been increasing. Traditionally, positron emission tomography/computed tomography scans have been utilized for mediastinal nodal staging and to rule out distant metastases in suspected early-stage NSCLC. In clinically node-negative NSCLC, the use of sublobar resection and selective lymph node dissection has been increasing as a therapeutic option. The higher rate of locoregional recurrences after limited resection and the significant incidence of occult lymph node metastases underscores the need to further stratify clinically node-negative NSCLC in order to select patients for limited resection versus lobectomy with complete mediastinal lymph node dissection. In this report, we review the published data, and discuss the significance and potential role of 18F-fluorodeoxyglucose positron emission tomography/computed tomography evaluation for clinically node-negative NSCLC. Consequently, the literature review demonstrates that maximum standardized uptake value is a predictive factor for occult nodal metastasis with an accuracy of 55-77%. In addition, maximum standardized uptake value is a predictor for worse overall, as well as disease-free, survival.

Maximum Standardized Uptake Value (SUVmax) of Primary Tumor Predicts Occult Neck Metastasis in Oral Cancer

The aim of this study was to investigate the predictability of occult lymph node metastasis using maximum standardized uptake value (SUV_max) in the primary tumor on pre-treatment 18-fluorodeoxyglucose positron emission tomography FDG-PET in oral squamous cell carcinoma (OSCC) patients who were clinically node negative (cN0) before surgery. A retrospective analysis of all patients treated at the University Hospital Zurich from 2007 to 2016 for OSCC with available pre-therapeutic FDG-PET was performed. We assessed the correlation of SUV_max of the primary tumors with the presence of occult nodal disease in the neck dissection specimen (pN+). The study included a total of 71 patients. In the nodal negative group (cN0/pN0), the median SUV_max of primary tumors was 9.0 (interquartile range (IQR) 7.4–13.9), while it was 11.4 (IQR 9.9–15.7) in the occult metastatic group (cN0/pN+). The difference was statistically significant (independent samples median test, P = 0.037). In a multivariable model, the only independent predictor of occult metastatic disease for cN0 patients was a SUV_max ≥ 9.5 (P = 0.028). Further, primary tumors with SUV_max ≥ 9.5 had a significantly higher risk of local recurrence (Log rank test, P = 0.020). In conclusion, we showed that higher SUV_max (≥9.5) of the primary tumor is associated with higher occurrence of occult metastatic nodal disease and worse local survival. High SUV_max of the primary tumor may encourage clinicians towards more aggressive treatment.

Radiomics Approach to Prediction of Occult Mediastinal Lymph Node Metastasis of Lung Adenocarcinoma

OBJECTIVE

The purpose of this study was to evaluate the prognostic impact of radiomic features from CT scans in predicting occult mediastinal lymph node (LN) metastasis of lung adenocarcinoma.

MATERIALS AND METHODS

A total of 492 patients with lung adenocarcinoma who underwent preoperative unenhanced chest CT were enrolled in the study. A total of 300 radiomics features quantifying tumor intensity, texture, and wavelet were extracted from the segmented entire-tumor volume of interest of the primary tumor. A radiomics signature was generated by use of the relief-based feature method and the support vector machine classification method. A ROC regression curve was drawn for the predictive performance of radiomics features. Multivariate logistic regression models based on clinicopathologic and radiomics features were compared for discriminating mediastinal LN metastasis.

RESULTS

Clinical variables (sex, tumor diameter, tumor location) and predominant subtype were risk factors for pathologic mediastinal LN metastasis. The accuracy of radiomics signature for predicting mediastinal LN metastasis was 91.1% in ROC analysis (AUC, 0.972; sensitivity, 94.8%; specificity, 92%). Radiomics signature (Akaike information criterion [AIC] value, 80.9%) showed model fit superior to that of the clinicohistopathologic model (AIC value, 61.1%) for predicting mediastinal LN metastasis.

CONCLUSION

The radiomics signature of a primary tumor based on CT scans can be used for quantitative and noninvasive prediction of occult mediastinal LN metastasis of lung adenocarcinoma.

Standardized uptake value and radiological density attenuation as predictive and prognostic factors in patients with solitary pulmonary nodules: our experience on 1,592 patients

BACKGROUND

Multislice computed tomography (MSCT) increased detection of solitary pulmonary nodules (SPNs), changing the management based on radiological and clinical factors. When 18-fluorine fluorodeoxyglucose positron emission tomography combined with computed tomography (¹⁸F-FDG-PET/CT) was considered for the evaluation of nodules, the maximum standardized uptake value (SUVmax) more than 2.5 is used frequently as a cut off for malignancy. The purpose of this study is to evaluate SUVmax PET/CT and pulmonary attenuation patterns at MSCT in patients with SPN according to morphological and pathological characteristics of the lesion.

METHODS

A retrospective study on 1,592 SPN patients was carried out following approval by the Italian Registry of VATS Lobectomies.

RESULTS

All patients underwent VATS lobectomy. On histologic examination, 98.1% had primary or second metachronous primary lung cancers. In addition, 10.7% presented occult lymph node metastases (pN1 or pN2) on histological examination. Nodule attenuation on CT was associated with the histology of the lesion (p= 0.030); in particular, pure ground glass opacities (pGGOs) and partially solid nodules were related to adenocarcinomatous histotypes. Conversely, a significant relationship between SUVmax and age, nodule size, pathological node status (pN) was found (P=0.007, P=0.000 and P=0.002 respectively).

CONCLUSIONS

Nodule attenuation can predict the histology of the lesion whereas SUVmax may relate to the propensity to lymph node metastases.

Variations in positron emission tomography-computed tomography findings for patients receiving neoadjuvant and non-neoadjuvant therapy for non-small cell lung cancer

BACKGROUND

The aims of this study were to predict locoregional lymph node (LN) metastases using positron emission tomography-computed tomography (PET-CT) and investigate variations in PET-CT findings for patients receiving neoadjuvant (NT) and non-neoadjuvant (non-NT) for non-small cell lung cancer (NSCLC).

METHODS

Data from 578 consecutive patients from January 2010 to December 2015 who met this study inclusion criteria were retrospectively reviewed. All patients underwent curative and complete resections for NSCLC in a Korean hospital. We analyzed the associations between maximum standard uptake value (SUVmax) and pathological stage, compared disease-free survival (DFS) and overall survival (OS), investigated relationships among SUVmax values, evaluated LN status and compared pathologically negative and positive LNs by SUVmax, and assessed the influence of neoadjuvant therapy on SUVmax. All LNs were analyzed separately for N1 and N2.

RESULTS

(I) For non-NT, we found significantly positive associations between pathological stage and SUVmax (tumor, N1 LN, and N2 LN, all P<0.001). For NT, we found positive correlations between pathological stage and tumor and N2 LN SUVmax, except for N1 LN (tumor P=0.005, N1 LN P=0.981, N2 LN P=0.045); (II) for non-NT, the low SUVmax group had higher DFS and OS than the high SUVmax group (DFS: tumor SUVmax P<0.001, N1 LN SUVmax P=0.002, N2 LN SUVmax P=0.027; OS: tumor SUVmax P<0.001, N1 LN SUVmax P=0.006, N2 LN SUVmax P=0.006). For NT, the low SUVmax group had nonsignificantly higher DFS and OS than the high SUVmax group. When age, sex, and SUVmax were equal, pathological stages were significantly higher for NT than non-NT (P<0.001). Non-NT had significantly higher DFS (P=0.001) and OS (P=0.024) than NT; (III) for non-NT, tumor, N1 LN, and N2 SUVmax were positively associated (all P<0.001). For NT, tumor and N2 SUVmax (P<0.001), and N1 and N2 SUVmax (P=0.025) correlated positively; tumor and N1 LN SUVmax did not (P=0.911); (IV) for non-NT, there was significant cutoff values for prediction of LN metastases using both tumor and LN SUVmax (N1 LN: tumor SUVmax cutoff 5.95, sensitivity 66.3%, specificity 66.0%, area =0.748, P<0.001; N1 LN SUVmax cutoff 2.05, sensitivity 57.83%, specificity 66.43%, area =0.676, P<0.001) (N2 LN: tumor SUVmax cutoff 5.95, sensitivity 63.04%, specificity 63.95%, area =0.726, P<0.001; N2 LN SUVmax cutoff 2.05, sensitivity 65.22%, specificity 69.96%, area =0.678, P<0.001). For NT, no SUVmax values significantly predicted LN metastases; () Pathologically malignant and benign LN SUVmax after neoadjuvant therapy were not different (N1 LN P=0.570, N2 LN P=0.105). For non-NT, pathologically malignant LN SUVmax was significantly higher than pathologically benign LN SUVmax (N1 LN P<0.001, N2 LN P=0.001).

CONCLUSIONS

This study showed variations in PET-CT findings for NT and non-NT, which should be verified for evaluation and management, especially for surgery planning. SUV max is not a reliable predictor of lymphatic involvement after neoadjuvant therapy in patients with NSCLC. Surgery should not be withheld or delayed based on lack of knowledge about variations in PET-CT findings, which must be interpreted in conjunction with other conditions. Further studies on interpretation of PET-CT findings, especially for NT, are needed for better management and prognosis.