Thin-section CT of the mediastinum in preoperative N-staging of non-small cell lung cancer: comparison with FDG PET

Preoperative 18F-FDG SUVmax >6.3 or Size >2.3 cm of primary lesions predict lymph nodes metastasis with higher negative predictive value in peripheral cT1 non-small-cell lung cancer

BACKGROUND

Sublobar resection is suitable for peripheral cT1N0M0 non-small-cell lung cancer (NSCLC). The traditional PET-CT criterion (lymph node size ≥1.0 cm or SUVmax ≥2.5) for predicting lymph nodes metastasis (LNM) has unsatisfactory performance.

OBJECTIVE

We explore the clinical role of preoperative SUVmax and the size of the primary lesions for predicting peripheral cT1 NSCLC LNM.

METHODS

We retrospectively analyzed 174 peripheral cT1 NSCLC patients underwent preoperative 18F-FDG PET-CT and divided into the LNM and non-LNM group by pathology. We compared the differences of primary lesions' baseline characteristics between the two groups. The risk factors of LNM were determined by univariate and multivariate analysis, and we assessed the diagnostic efficacy with the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, positive predictive value and negative predictive value (NPV).

RESULTS

Of the enrolled cases, the incidence of LNM was 24.7%. The preoperative SUVmax >6.3 or size >2.3 cm of the primary lesions were independent risk factors of peripheral cT1 NSCLC LNM (ORs, 95% CIs were 6.18 (2.40-15.92) and 3.03 (1.35-6.81). The sensitivity, NPV of SUVmax >6.3 or size >2.3 cm of the primary lesions were higher than the traditional PET-CT criterion for predicting LNM (100.0 vs. 86.0%, 100.0 vs. 89.7%). A Hosmer-Lemeshow test showed a goodness-of-fit (P = 0.479).

CONCLUSIONS

The excellent sensitivity and NPV of preoperative of the SUVmax >6.3 or size >2.3 cm of the primary lesions based on 18F-FDG PET-CT might identify the patients at low-risk LNM in peripheral cT1 NSCLC.

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

Purpose

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

Patients and Methods

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

Results

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

Conclusion

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

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.

Salvage surgery for advanced non-small cell lung cancer after targeted therapy: A case series

Background

Tumor recurrence or residual tumor after targeted therapy is common in patients with advanced non‐small cell lung cancer (NSCLC). There is a lack of high‐level evidence on which type of treatment should be employed for these patients and the role of salvage surgery has not been well reported in the literature.

Methods

A retrospective analysis of patients who underwent salvage surgery in our center between January 2016 and June 2019 for advanced NSCLC after targeted therapy was performed.

Results

A total number of nine patients were identified, including five males and four females, with a median age of 56 years (range, 40–65 years), all diagnosed with lung adenocarcinoma stage IIIa–IVb. All patients had received targeted therapy according to individual positive mutation of driver gene(s). Salvage surgery was performed for tumor recurrence or residual tumor after a duration of 2–46 months of targeted therapy. A negative surgical margin was achieved in all cases. Postoperative complication rate was 11.1% (1/9). All patients were alive at the time of this analysis and two patients had disease progression. After a median follow‐up of 17 months (range: 5–44 months), the median event‐free survival and postoperative survival was 14 months (range: 2–44 months) and 17 months (range: 5–44 months) respectively.

Conclusions

Salvage surgery may be a feasible and promising therapeutic option for tumor recurrence or residual tumor in advanced NSCLC in selective patients after targeted therapy.

Key points

Salvage surgery is feasible in selected patients with advanced NSCLC and provides promising survival outcomes after targeted therapy failure.

Salvage surgery provides precise molecular and pathological information which is most important for subsequent therapy.

Development of a predictive radiomics model for lymph node metastases in pre-surgical CT-based stage IA non-small cell lung cancer

OBJECTIVES

To develop and validate predictive models using clinical parameters, radiomic features and a combination of both for lymph node metastasis (LNM) in pre-surgical CT-based stage IA non-small cell lung cancer (NSCLC) patients.

METHODS

This retrospective study included 649 pre-surgical CT-based stage IA NSCLC patients from our hospital. One hundred and thirty-eight (21 %) of the 649 patients had LNM after surgery. A total of 396 radiomic features were extracted from the venous phase contrast enhanced computed tomography (CECT). The training group included 455 patients (97 with and 358 without LNM) and the testing group included 194 patients (41 with and 153 without LNM). The least absolute shrinkage and selection operator (LASSO) algorithm was used for radiomic feature selection. The random forest (RF) was used for model development. Three models (a clinical model, a radiomics model, and a combined model) were developed to predict LNM in early stage NSCLC patients. The area under the receiver operating characteristic (ROC) curve (AUC) value and decision curve analysis were used to evaluate the performance in LNM status (with or without LNM) using the three models.

RESULTS

The ROC analysis (also decision curve analysis) showed predictive performance for LNM of the radiomics model (AUC values for training and testing, respectively 0.898 and 0.851) and of the combined model (0.911 and 0.860, respectively). Both performed better than the clinical model (0.739 and 0.614, respectively; delong test p-values both<0.001).

CONCLUSION

A radiomics model using the venous phase of CE-CT has potential for predicting LNM in pre-surgical CT-based stage IA NSCLC patients.

A new approach to predict lymph node metastasis in solid lung adenocarcinoma: a radiomics nomogram

Background

Lymph node metastasis (LNM) of lung cancer is an important factor related to survival and recurrence. The association between radiomics features of lung cancer and LNM remains unclear. We developed and validated a radiomics nomogram to predict LNM in solid lung adenocarcinoma.

Methods

A total of 159 eligible patients with solid lung adenocarcinoma were divided into training (n=106) and validation cohorts (n=53). Radiomics features were extracted from venous-phase CT images. We built a radiomics nomogram using a multivariate logistic regression model combined with CT-reported lymph node (LN) status. The performance of the radiomics nomogram was evaluated using the area under curve (AUC) of receiver operating characteristic curve. We performed decision curve analysis (DCA) within training and validation cohorts to assess the clinical usefulness of the nomogram.

Results

Fourteen radiomics features were chosen from 94 candidate features to build a radiomics signature that significantly correlated with LNM. The model showed good calibration and discrimination in the training cohort, with an AUC of 0.871 (95% CI: 0.804-0.937), sensitivity of 85.71% and specificity of 77.19%. In the validation cohort, AUC was 0.856 (95% CI: 0.745-0.966), sensitivity was 91.66%, and specificity was 82.14%. DCA demonstrated that the nomogram was clinically useful. The nomogram also showed good predictive ability in patients at high risk for LNM in the CT-reported LN negative (cN0) subgroup.

Conclusions

The radiomics nomogram, based on preoperative CT images, can be used as a noninvasive method to predict LNM in patients with solid lung adenocarcinoma.

The diagnostic ability of 18F-FDG PET/CT for mediastinal lymph node staging using 18F-FDG uptake and volumetric CT histogram analysis in non-small cell lung cancer

OBJECTIVES

To evaluate the clinical implications of lymph node (LN) density on ¹⁸F-FDG PET/CT for mediastinal LN characterization in non-small cell lung cancer (NSCLC).

METHODS

One hundred and fifty-two patients with 271 mediastinal LNs who underwent PET/CT and endobronchial ultrasound-guided transbronchial needle aspiration for staging were enrolled. Maximum standardized uptake value (SUVmax), short axis diameter, LN-to-primary cancer ratio of SUVmax, and median Hounsfield unit (HU) based on CT histogram were correlated to histopathology.

RESULTS

Of 271 nodes, 162 (59.8 %) were malignant. SUVmax, short axis diameter, and LPR of malignant LNs were higher than those of benign nodes. Among malignant LNs, 71.0 % had median HU between 25 and 45, while 78.9 % of benign LNs had values <25 HU or >45 HU. Using a cutoff value of 4.0, SUVmax showed the highest diagnostic ability for detecting malignant LNs with a specificity of 94.5 %, but showing a sensitivity of 70.4 %. Using additional density criteria (median HU 25-45) in LNs with 2.0< SUVmax ≤4.0, the sensitivity increased to 88.3 % with the specificity of 82.6 %.

CONCLUSIONS

LN density is useful for the characterization of LNs with mild ¹⁸F-FDG uptake. The risk of mediastinal LN metastasis in NSCLC patients could be further stratified using both ¹⁸F-FDG uptake and LN density.

KEY POINTS

• SUVmax showed the highest diagnostic ability for detecting malignant LNs. • LN density was useful in characterization of LNs with mild FDG uptake. • SUVmax and LN density together could stratify the risk of LN metastasis.

The method and efficacy of support vector machine classifiers based on texture features and multi-resolution histogram from (18)F-FDG PET-CT images for the evaluation of mediastinal lymph nodes in patients with lung cancer

OBJECTIVES

In clinical practice, image analysis is dependent on simply visual perception and the diagnostic efficacy of this analysis pattern is limited for mediastinal lymph nodes in patients with lung cancer. In order to improve diagnostic efficacy, we developed a new computer-based algorithm and tested its diagnostic efficacy.

METHODS

132 consecutive patients with lung cancer underwent (18)F-FDG PET/CT examination before treatment. After all data were imported into the database of an on-line medical image analysis platform, the diagnostic efficacy of visual analysis was first evaluated without knowing pathological results, and the maximum short diameter and maximum standardized uptake value (SUVmax) were measured. Then lymph nodes were segmented manually. Three classifiers based on support vector machine (SVM) were constructed from CT, PET, and combined PET-CT images, respectively. The diagnostic efficacy of SVM classifiers was obtained and evaluated.

RESULTS

According to ROC curves, the areas under curves for maximum short diameter and SUVmax were 0.684 and 0.652, respectively. The areas under the ROC curve for SVM1, SVM2, and SVM3 were 0.689, 0.579, and 0.685, respectively.

CONCLUSION

The algorithm based on SVM was potential in the diagnosis of mediastinal lymph nodes.

Patterns of lymph node recurrence after radical surgery impacting on survival of patients with pT1-3N0M0 thoracic esophageal squamous cell carcinoma

The aim of this study was to investigate how patterns of lymph nodes recurrence after radical surgery impact on survival of patients with pT1-3N0M0 thoracic esophageal squamous cell carcinoma. One hundred eighty consecutive patients with thoracic esophageal squamous cell carcinoma underwent radical surgery, and the tumors were staged as pT1-3N0M0 by postoperative pathology. Lymph nodes recurrence was detected with computed tomography 3-120 months after the treatment. The patterns of lymph nodes recurrence including stations, fields and locations of recurrent lymph nodes, and impacts on patterns of survival were statistically analyzed. There was a decreasing trend of overall survival with increasing stations or fields of postoperative lymph nodes involved (all P<0.05). Univariate analysis showed that stations or fields of lymph nodes recurrence, and abdominal or cervical lymph nodes involved were prognostic factors for survival (all P<0.05). Cox analyses revealed that the field was an independent factor (P<0.05, odds ratio=2.73). Lymph nodes involved occurred predominantly in cervix and upper mediastinum (P<0.05). In conclusion, patterns of lymph node recurrence especially the fields of lymph nodes involved are significant prognostic factors for survival of patients with pT1-3N0M0 thoracic esophageal squamous cell carcinoma.

Lymphatic microvessel density combined with CT used in the diagnosis of mediastinal and hilar lymph node metastasis of non-small cell lung cancer

BACKGROUND AND AIMS

Lymphatic microvessel density (LMVD) has been demonstrated to correlate with tumor metastasis. The purpose of this study is to determine whether the criteria combining LMVD with computed tomography (CT) could improve the diagnostic accuracy of lymph node (LN) metastasis in non-small cell lung cancer (NSCLC).

METHODS

Ninety four patients with NSCLC who had chest CT scans preoperatively and LMVD tested by immunohistochemistry postoperatively were randomized into two groups: the training set (n = 66) and the test set (n = 28). Cut-off point of LMVD was selected to separate the LN metastasis-predictive positive and negative groups. On the basis of LMVD levels, chest CTs of the training set were re-analyzed and hypothetical criteria for LN metastasis diagnosis were established. Diagnostic characteristics for LN metastasis were tested by using the combined criteria in the test set as compared to those of CT alone.

RESULTS

There was a significantly positive correlation between LMVD and LN metastasis (p <0.01). For sensitivity, specificity, positive predictive value (PPV) and negative predictive values (NPV), accuracy was 67, 81, 75, 81 and 79% for the combined criteria, respectively. Diagnostic efficacy of the combined criteria was significantly higher than that of CT only (p <0.05).

CONCLUSIONS

Diagnosis of LN metastasis using a combination of LMVD and CT is superior to the CT-only diagnosis. In future clinical trials, it is necessary to evaluate the efficacy of adjuvant therapy for the selection of patients according to the combined criteria.