Selective versus systematic lymph node dissection (other than sampling) for clinical N2-negative non-small cell lung cancer: a meta-analysis of observational studies

Impact of the number of dissected lymph nodes on machine learning-based prediction of postoperative lung cancer recurrence: a single-hospital retrospective cohort study

BACKGROUND

The optimal number of lymph nodes to be dissected during lung cancer surgery to minimise the postoperative recurrence risk remains undetermined. This study aimed to elucidate the impact of the number of dissected lymph nodes on the risk of postoperative recurrence of non-small cell lung cancer (NSCLC) using machine learning algorithms and statistical analyses.

METHODS

We retrospectively analysed 650 patients with NSCLC who underwent complete resection. Five machine learning models were trained using clinicopathological variables to predict postoperative recurrence. The relationship between the number of dissected lymph nodes and postoperative recurrence was investigated in the best-performing model using Shapley additive explanations values and partial dependence plots. Multivariable Cox proportional hazard analysis was performed to estimate the HR for postoperative recurrence based on the number of dissected nodes.

RESULTS

The random forest model demonstrated superior predictive performance (area under the receiver operating characteristic curve: 0.92, accuracy: 0.83, F1 score: 0.64). The partial dependence plot of this model revealed a non-linear dependence of the number of dissected lymph nodes on recurrence prediction within the range of 0-20 nodes, with the weakest dependence at 10 nodes. A linear increase in the dependence was observed for ≥20 dissected nodes. A multivariable analysis revealed a significantly elevated risk of recurrence in the group with ≥20 dissected nodes in comparison to those with <20 nodes (adjusted HR, 1.45; 95% CI 1.003 to 2.087).

CONCLUSIONS

The number of dissected lymph nodes was significantly associated with the risk of postoperative recurrence of NSCLC. The risk of recurrence is minimised when approximately 10 nodes are dissected but may increase when >20 nodes are removed. Limiting lymph node dissection to approximately 20 nodes may help to preserve a favourable antitumour immune environment. These findings provide novel insights into the optimisation of lymph node dissection during lung cancer surgery.

Lymph node sampling-what are the numbers?

Lung cancer is a deadly disease. Lymph node staging is the most important prognostic factor, and lymphatic drainage of the lung is complex. Major advances have been made in this field over the last several decades, but there is much left to understand and improve upon. Herein, we review the history of the lymphatic system and the creation of lymph node maps, the evolution of tumor, node, and metastasis lung cancer classification, the importance of lung cancer staging, techniques for lymph node dissection, and our recommendations regarding a minimum number of nodes to sample during pulmonary resection.

Development and validation of nomograms based on clinical characteristics and CT reports for the preoperative prediction of precise lymph node dissection in lung cancer

OBJECTIVE

To develop and validate nomograms for preoperative prediction of precision lymph node (LN) dissection in lung cancer.

PATIENTS AND METHODS

The prediction models of each group LNs (LNx) were developed in a primary cohort that consisted of 1380 patients with clinicopathologically confirmed lung cancer. Clinical characteristics and CT reports were extracted. Patients with LNx dissection were divided into training cohort and testing cohort. Nomograms were built through univariate and multivariate regression analysis in the training cohort and internally verified in the testing cohort. The accuracy of the models was verified by constructing survival analysis in patients without LNx dissection.

RESULTS

Due to the lack of sufficient patients for LN1, 8, 13, a total of 10 nomograms were constructed in this study, including LN-2 ∼ 7, 9 ∼ 12. According to the nomogram of each group LN, the most common independent risk factors predicting LN status were CT-reported lymphadenectasis, tumor diameter and location, and the others include age, gender, and whether there were multiple nodules, etc. All models showed good discrimination, with the average C-index of 0.738 in the training cohort and 0.707 in the testing cohort. Survival analysis in patients without LNx dissection all showed the high accuracy of each nomogram to predict LN metastasis status and TNM staging.

CONCLUSION

We constructed nomograms to predict the metastasis status of each group of lymph nodes based on clinical characteristics and CT reports. Surgeons can accurately determine the extent of lymph node dissection in patients with lung cancer based on our nomogram models before surgery.

Clinicopathological models for predicting lymph node metastasis in patients with early-stage lung adenocarcinoma: the application of machine learning algorithms

Background

Lymph node metastasis (LNM) status can be a critical decisive factor for clinical management of lung cancer. Accurately evaluating the risk of LNM during or after the surgery can be helpful for making clinical decisions. This study aims to incorporate clinicopathological characteristics to develop reliable machine learning (ML)-based models for predicting LNM in patients with early-stage lung adenocarcinoma.

Methods

A total of 709 lung adenocarcinoma patients with tumor size ≤2 cm were enrolled for analysis and modeling by multiple ML algorithms. The receiver operating characteristic (ROC) curve and decision curve were used for evaluating model’s predictive performance and clinical usefulness. Feature selection based on potential models was performed to identify most-contributed predictive factors.

Results

LNM occurred in 11.3% (80/709) of patients with lung adenocarcinoma. Most models reached high areas under the ROC curve (AUCs) >0.9. In the decision curve, all models performed better than the treat-all and treat-none lines. The random forest classifier (RFC) model, with a minimal number of five variables introduced (including carcinoembryonic antigen, solid component, micropapillary component, lymphovascular invasion and pleural invasion), was identified as the optimal model for predicting LNM, because of its excellent performance in both ROC and decision curves.

Conclusions

The cost-efficient application of RFC model could precisely predict LNM during or after the operation of early-stage adenocarcinomas (sensitivity: 87.5%; specificity: 82.2%). Incorporating clinicopathological characteristics, it is feasible to predict LNM intraoperatively or postoperatively by ML algorithms.

Managing screening-detected subsolid nodules-the Asian perspective

The broad application of low-dose computed tomography (CT) screening has resulted in the detection of many small pulmonary nodules. In Asia, a large number of these detected nodules with a radiological ground glass pattern are reported as lung adenocarcinomas or premalignant lesions, especially among female non-smokers. In this review article, we discuss controversial issues and conditions involving these subsolid pulmonary nodules that we often face in Asia, including a lack or insufficiency of current guidelines; the roles of preoperative biopsy and imaging; the location of lesions; appropriate selection of localization techniques; the roles of dissection and sampling of frozen sections and lymph nodes; multifocal lesions; and the roles of non-surgical treatment modalities. For these complex issues, we have tried to present up-to-date evidence and our own opinions regarding the management of subsolid nodules. It is our hope that this article helps surgeons and physicians to manage the complex issues involving ground glass nodules (GGNs) in a balanced manner in their daily practice and provokes further discussion towards better guidelines and/or algorithms.

Surgical Consideration Based on Lymph Nodes Spread Patterns in Patients with Peripheral Right Middle Non-small Cell Lung Cancer 3 cm or Less

BACKGROUND

The optimal extent of lung resection and lymph nodes dissection for peripheral early-stage right middle non-small cell lung cancer (NSCLC) still remains controversial. In this study, we analyzed the patterns of lymph nodes metastasis (LNM) of patients with peripheral right middle NSCLC ≤ 3 cm, aiming to provide evidences for surgical choice for early-stage peripheral right middle lobe NSCLC.

METHODS

We retrospectively investigated the clinical and pathological data of patients diagnosed with peripheral right middle lobe NSCLC ≤ 3 cm between January 2015 and December 2019. The LNM patterns were analyzed by tumor size.

RESULTS

A total of 60 patients were included for analysis. The tumor size was preoperatively divided as follows: ≤ 1 cm (13 patients); > 1 cm but ≤ 2 cm (36 patients); > 2 cm but ≤ 3 cm (11 patients). Fifty-four patients were categorized as N0 group, 1 patient as N1 group, and 5 patients as N2 group. In the upper zone, 3 patients were found to have LNM. In the subcarinal zone, another 3 patients had LNM. But the lymph nodes of all these patients were negative in the lower zone. In station 10, 1 patient (1.67%) was found to have LNM, while in station 11-13, 2 patients (3.33%) were found to have LNM.

CONCLUSION

For the right middle lobe peripheral NSCLC ≤ 1 cm, sublobar resection with lymph node sampling may be a feasible treatment. For cancers > 1 cm but ≤ 2 cm, lobectomy with lobe-specific lymph node dissection (especially station 2R and 4R) may be a preferred choice. For tumors > 2 cm but ≤ 3 cm, lobectomy with systematic lymph node dissection may still be the standard of care.

Metastatic Patterns of Mediastinal Lymph Nodes in Small-Size Non-small Cell Lung Cancer (T1b)

Background: Lymph node metastasis (LNM) status is critical to the treatment. Fewer studies has focused on LNM in patients with small-size non-small cell lung cancer (NSCLC). This study aims to investigate clinicopathological characteristics associated with skip N2 (SN2) and non-skip N2 (NSN2) metastasis, and their metastatic patterns in NSCLC with tumor size of 1–2 cm.

Methods: We reviewed the records of NSCLC patients with tumor size of 1–2 cm who underwent lobectomy with systematic lymph node dissection (LND) between January 2013 and June 2019. Clinical, radiographical, and pathological characteristics were compared among N1, SN2, and NSN2 groups. Metastatic patterns of mediastinal lymph node were analyzed based on final pathology.

Results: A total of 63 NSCLC patients with tumor size of 1–2 cm were staged as pN2, including 25 (39.7%) SN2 and 38 (60.3%) NSN2. The incidence rates of SN2 and NSN2 were 2.8% (25/884) and 4.3% (38/884), respectively. For all clinicopathological characteristics, no significant difference was observed among the groups of N1, SN2, and NSN2. For the tumor located in each lobe, specific nodal drainage stations were identified: 2R/4R for right upper lobe; 2R/4R and subcarinal node (#7) for right middle lobe and right lower lobe; 4L and subaortic node (#5) for left upper lobe; #7 for left lower lobe. However, there were still a few patients (10.9%, 5/46) had the involvement of lower zone for tumors of upper lobe and the involvement of upper zone for lower lobe.

Conclusions: SN2 occurs frequently in patients with small-size NSCLC. Whether lobe-specific selective LND is suitable for all small-size patients deserves more studies to confirm. Surgeons should be more careful when performing selective LND for tumors located in the lower and upper lobes.

Nodal Involvement Pattern in Clinical Stage IA Non-Small Cell Lung Cancer According to Tumor Location

PURPOSE

The purpose of this study was to investigate lymph node involvement pattern in clinical stage IA non-small cell lung cancer (NSCLC).

PATIENTS AND METHODS

Clinical stage ⅠA NSCLC patients who underwent lobectomy and lymph node resection were included in this retrospective study. Mediastinal lymph node involvement was distinguished by different lobes and tumor size.

RESULTS

From 2000 to 2015, a total of 759 patients were identified: 282 (37.2%) with tumors in the right upper lobe (RUL), 183 (24.1%) in the left upper lobe (LUL), 124 (16.3%) in the right lower lobe (RLL), 103 (13.6%) in the left lower lobe (LLL), and 67 (8.8%) in the right middle lobe (RML). Patients with tumor size ≤1 cm accounted for 19.6%, >1 and ≤2 cm for 47.8%, >2 and ≤3 cm for 32.5%. Patients with pN1 accounted for 8.2%, and pN2 for 12.5%. Among patients with pN2, the inferior mediastinum was involved in 9.7% of RULs and 17.4% of LULs; the superior mediastinum was involved in 52.2% of RLLs and 36.4% of LLLs. Mediastinal lymph node metastasis was found in 13.2% of patients with size >1 and ≤2 cm, and 19.0% of >2 and ≤3 cm. Patients with tumors ≤1 cm had no N2 lymph node involved.

CONCLUSION

Selective lymph node dissection based on tumor location is not recommended in clinical stage ⅠA NSCLC, and systemic lymph node dissection should be performed for NSCLC with size >1 cm.

Development and evaluation of a venous computed tomography radiomics model to predict lymph node metastasis from non-small cell lung cancer

The objective of this study was to develop a venous computed tomography (CT)-based radiomics model to predict the lymph node metastasis (LNM) in patients with non-small cell lung cancer (NSCLC). A total of 411 consecutive patients with NSCLC underwent tumor resection and lymph node (LN) dissection from January 2018 to September 2018 in our hospital. A radiologist with 20 years of diagnostic experience retrospectively reviewed all CT scans and classified all visible LNs into LNM and non-LNM groups without the knowledge of pathological diagnosis. A logistic regression model (radiomics model) in classification of pathology-confirmed NSCLC patients with and without LNM was developed on radiomics features for NSCLC patients. A morphology model was also developed on qualitative morphology features in venous CT scans. A training group included 288 patients (99 with and 189 without LNM) and a validation group included 123 patients (42 and 81, respectively). The receiver operating characteristic curve was performed to discriminate LNM (+) from LNM (-) for CT-reported status, the morphology model and the radiomics model. The area under the curve value in LNM classification on the training group was significantly greater at 0.79 (95% confidence interval [CI]: 0.77-0.81) by use of the radiomics model (build by best 10 features in predicting LNM) compared with 0.51 by CT-reported LN status (P < .001) or 0.66 (95% CI: 0.64-0.68) by morphology model (build by tumor size and spiculation) (P < .001). Similarly, the area under the curve value on the validation group was 0.73 (95% CI: 0.70-0.76) by the radiomics model, compared with 0.52 or 0.63 (95% CI: 0.60-0.66) by the other 2 (both P < .001). A radiomics model shows excellent performance for predicting LNM in NSCLC patients. This predictive radiomics model may benefit patients to get better treatments such as an appropriate surgery.

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.