Use of Magnetic Resonance Imaging for N-Staging in Patients with Non-Small Cell Lung Cancer. A Systematic Review

Computed DWI MRI Results in Superior Capability for N-Stage Assessment of Non-Small Cell Lung Cancer Than That of Actual DWI, STIR Imaging, and FDG-PET/CT

BACKGROUND

Computed diffusion-weighted imaging (cDWI) is a mathematical computation technique that generates DWIs for any b-value by using actual DWI (aDWI) data with at least two different b-values and may improve differentiation of metastatic from nonmetastatic lymph nodes.

PURPOSE

To determine the appropriate b-value for cDWI to achieve a better diagnostic capability for lymph node staging (N-staging) in non-small cell lung cancer (NSCLC) patients compared to aDWI, short inversion time (TI) inversion recovery (STIR) imaging, or positron emission tomography with 2-[fluorine-18] fluoro-2-deoxy-d-glucose combined with computed tomography (FDG-PET/CT).

STUDY TYPE

Prospective.

SUBJECTS

A total of 245 (127 males and 118 females; mean age 72 years) consecutive histopathologically confirmed NSCLC patients.

FIELD STRENGTH/SEQUENCE

A 3 T, half-Fourier single-shot turbo spin-echo sequence, electrocardiogram (ECG)-triggered STIR fast advanced spin-echo (FASE) sequence with black blood and STIR acquisition and DWI obtained by FASE with b-values of 0 and 1000 sec/mm² .

ASSESSMENT

From aDWIs with b-values of 0 and 1000 (aDWI₁₀₀₀ ) sec/mm² , cDWI using 400 (cDWI₄₀₀ ), 600 (cDWI₆₀₀ ), 800 (cDWI₈₀₀ ), and 2000 (cDWI₂₀₀₀ ) sec/mm² were generated. Then, 114 metastatic and 114 nonmetastatic nodes (mediastinal and hilar lymph nodes) were selected and evaluated with a contrast ratio (CR) for each cDWI and aDWI, apparent diffusion coefficient (ADC), lymph node-to-muscle ratio (LMR) on STIR, and maximum standard uptake value (SUV_max ).

STATISTICAL TESTS

Receiver operating characteristic curve (ROC) analysis, Youden index, and McNemar's test.

RESULTS

Area under the curve (AUC) of CR₆₀₀ was significantly larger than the CR₄₀₀ , CR₈₀₀ , CR₂₀₀₀ , aCR₁₀₀₀ , and SUV_max . Comparison of N-staging accuracy showed that CR₆₀₀ was significantly higher than CR₄₀₀ , CR₂₀₀₀ , ADC, aCR₁₀₀₀ , and SUV_max , although there were no significant differences with CR₈₀₀ (P = 0.99) and LMR (P = 0.99).

DATA CONCLUSION

cDWI with b-value at 600 sec/mm² may have potential to improve N-staging accuracy as compared with aDWI, STIR, and PET/CT.

EVIDENCE LEVEL

2 TECHNICAL EFFICACY: Stage 2.

Value of radiomics model based on multi-parametric magnetic resonance imaging in predicting epidermal growth factor receptor mutation status in patients with lung adenocarcinoma

Background

The epidermal growth factor receptor (EGFR) is an important therapeutic target for patients with non-small-cell lung cancer (NSCLC). Radiomics and radiogenomics have emerged as attractive research topics aiming to extract mineable high-dimensional features from medical images and show potential to correlate with the gene mutation. Herein, we aim to develop a magnetic resonance imaging (MRI)-based radiomics model for pretreatment prediction of the EGFR status in patients with lung adenocarcinoma.

Methods

A total of 92 patients with pathologically confirmed lung adenocarcinoma were retrospectively enrolled in this study. EGFR genotype was analyzed by sequence testing. All patients were randomized into training and test group in a 7:3 ratio using the R software. Radiomics features were extracted from T2 weighted imaging (T2WI), diffusion-weighted imaging (DWI), and apparent diffusion coefficient (ADC); radiomics signatures were built using the least absolute shrinkage and selection operator (LASSO) and logistic regression. Preoperative clinical factors and image features associated with EGFR were also evaluated. A nomogram including sex, smoking status, and radiomics signatures was constructed. A total of five radiomics models were built, and the area under the curve (AUC) was used to evaluate their performance of EGFR mutation prediction.

Results

Among the three single-sequence models, the ADC model showed the best prediction performance. The AUCs of the ADC, DWI, T2WI prediction model in the test cohort were 0.805 (95% CI: 0.610 to 1.000), 0.722 (95% CI: 0.519 to 0.924), and 0.655 (95% CI: 0.438 to 0.872), respectively. Compared with the single-sequence model, the multi-sequence prediction model showed better performed [AUC_test =0.838 (95% CI: 0.685 to 0.992)]. The AUC of the nomogram in the training group was 0.925 (95% CI: 0.855 to 0.994) and 0.727 (95% CI: 0.531 to 0.924) in the test group, respectively.

Conclusions

The radiomics model based on MRI might have the potential to predict EGFR mutation in patients with lung adenocarcinoma. The multi-sequence model had better performance than other models.

Magnetic particle targeting for diagnosis and therapy of lung cancers

Over the past decade, the growing interest in targeted lung cancer therapy has guided researchers toward the cutting edge of controlled drug delivery, particularly magnetic particle targeting. Targeting of tissues by magnetic particles has tackled several limitations of traditional drug delivery methods for both cancer detection (e.g., using magnetic resonance imaging) and therapy. Delivery of magnetic particles offers the key advantage of high efficiency in the local deposition of drugs in the target tissue with the least harmful effect on other healthy tissues. This review first overviews clinical aspects of lung morphology and pathogenesis as well as clinical features of lung cancer. It is followed by reviewing the advances in using magnetic particles for diagnosis and therapy of lung cancers: (i) a combination of magnetic particle targeting with MRI imaging for diagnosis and screening of lung cancers, (ii) magnetic drug targeting (MDT) through either intravenous injection and pulmonary delivery for lung cancer therapy, and (iii) computational simulations that models new and effective approaches for magnetic particle drug delivery to the lung, all supporting improved lung cancer treatment. The review further discusses future opportunities to improve the clinical performance of MDT for diagnosis and treatment of lung cancer and highlights clinical therapy application of the MDT as a new horizon to cure with minimal side effects a wide variety of lung diseases and possibly other acute respiratory syndromes (COVID-19, MERS, and SARS).

Preoperative staging of the mediastinum is an essential and multidisciplinary task

Mediastinal staging is a crucial step in the management of patients with NSCLC. With the recent development of novel techniques, mediastinal staging has evolved from an activity of interest mainly for thoracic surgeons to a joint effort carried out by many specialists. In this regard, the debate of cases in MDT sessions is crucial for optimal management of patients. Current evidence-based clinical guidelines for preoperative NSCLC staging recommend that mediastinal staging should be performed with increasing invasiveness. Image-based techniques are the first approach, although they have limited accuracy and findings must be confirmed by pathology in almost all cases. In this setting, the advent of radiomics is promising. Invasive staging depends on procedural factors rather than diagnostic performance. The choice between endoscopy-based or surgical procedures should depend on the local expertise of each centre. As the extension of mediastinal disease in terms of number of involved lymph nodes and nodal stations affects prognosis and the choice of treatment, systematic samplings are preferred over random targeted samplings. Following this approach, a diagnosis of single mediastinal nodal involvement can be unreliable if all reachable mediastinal nodal stations have not been assessed. The performance of confirmatory mediastinoscopy after a negative endoscopy-based procedure is controversial but currently recommended. Current indications of invasive staging in patients with radiologically normal mediastinum have to be re-evaluated, especially for central tumour location.

Usefulness of Positron Emission Tomography in the Examination of Hilar and Mediastinal Lymphadenopathies in Patients with Suspicion of Lung Cancer

Introduction

Lung cancer is a major health problem. Mediastinal staging performed with the aid of imaging techniques is essential for appropriate disease treatment and prognosis. Accordingly, this study aimed to ascertain the usefulness of positron emission tomography (PET) in mediastinal staging, establish the best maximum standardized uptake value (SUV_max) cutoff point, compare its usefulness to that of computed tomography (CT), and determine the influence of histological tumour subtype.

Methods

We conducted a retrospective study across a period of 3 years on 128 patients with suspicion of lung cancer and analyzed their demographic and radiological characteristics using CT and PET to perform the mediastinal examination. Histology was regarded as the gold standard.

Results

PET displayed a high sensitivity (95%) and negative predictive value (NPV) (92%), outperforming CT (89% and 85%, respectively). Percentage agreement with histology was also higher (0.207 and 0.241 for CT and PET, respectively; p < 0.001). Taking an SUV_max value of 0.5 as that which would ensure greatest diagnostic accuracy, S and NPV were 100%, though percentage agreement did not increase (0.189; p < 0.001). PET discriminatory power was not affected by histological tumour subtype.

Conclusions

The results of our study indicate that PET might be a useful test for examination of the mediastinum in lung cancer patients. Its high NPV suggests that the absence of mediastinal uptake could be used to proceed to surgical treatment without the need for further tests or examinations. Nevertheless, studies directly aimed to answer this specific question are needed.

Classification of pulmonary lesion based on multiparametric MRI: utility of radiomics and comparison of machine learning methods

OBJECTIVES

We develop and validate a radiomics model based on multiparametric magnetic resonance imaging (MRI) in the classification of the pulmonary lesion and identify optimal machine learning methods.

MATERIALS AND METHODS

This retrospective analysis included 201 patients (143 malignancies, 58 benign lesions). Radiomics features were extracted from multiparametric MRI, including T2-weighted imaging (T2WI), T1-weighted imaging (TIWI), and apparent diffusion coefficient (ADC) map. Three feature selection methods, including recursive feature elimination (RFE), t test, and least absolute shrinkage and selection operator (LASSO), and three classification methods, including linear discriminate analysis (LDA), support vector machine (SVM), and random forest (RF) were used to distinguish benign and malignant pulmonary lesions. Performance was compared by AUC, sensitivity, accuracy, precision, and specificity. Analysis of performance differences in three randomly drawn cross-validation sets verified the stability of the results.

RESULTS

For most single MR sequences or combinations of multiple MR sequences, RFE feature selection method with SVM classifier had the best performance, followed by RFE with RF. The radiomics model based on multiple sequences showed a higher diagnostic accuracy than single sequence for every machine learning method. Using RFE with SVM, the joint model of T1WI, T2WI, and ADC showed the highest performance with AUC = 0.88 ± 0.02 (sensitivity 83%; accuracy 82%; precision 91%; specificity 79%) in test set.

CONCLUSION

Quantitative radiomics features based on multiparametric MRI have good performance in differentiating lung malignancies and benign lesions. The machine learning method of RFE with SVM is superior to the combination of other feature selection and classifier methods.

KEY POINTS

• Radiomics approach has the potential to distinguish between benign and malignant pulmonary lesions. • Radiomics model based on multiparametric MRI has better performance than single-sequence models. • The machine learning methods RFE with SVM perform best in the current cohort.

Carinal resection

Tracheobronchial tumors with involvement of the carina represent a challenging problem in the pulmonary surgery. Carinal resection is referred to removal and reconstruction of the airway itself, whereas concomitant removal of the lung parenchyma (usually a whole lung) is termed as carinal pneumonectomy. Thorough preoperative workup of these patients is mandatory. Meticulous surgical technique and aggressive postoperative management is required for the best outcomes in these difficult cases. In the paper authors review surgical technique, evaluation and management of this challenging patient population.