PET/CT imaging in lung cancer: indications and findings

Predicting lung cancer's metastats' locations using bioclinical model

Background

Lung cancer is a global leading cause of cancer-related deaths, and metastasis profoundly influences treatment outcomes. The limitations of conventional imaging in detecting small metastases highlight the crucial need for advanced diagnostic approaches.

Methods

This study developed a bioclinical model using three-dimensional CT scans to predict the spatial spread of lung cancer metastasis. Utilizing a three-layer biological model, we identified regions with a high probability of metastasis colonization and validated the model on real-world data from 10 patients.

Findings

The validated bioclinical model demonstrated a promising 74% accuracy in predicting metastasis locations, showcasing the potential of integrating biophysical and machine learning models. These findings underscore the significance of a more comprehensive approach to lung cancer diagnosis and treatment.

Interpretation

This study's integration of biophysical and machine learning models contributes to advancing lung cancer diagnosis and treatment, providing nuanced insights for informed decision-making.

Machine Learning Techniques to Predict Timeliness of Care among Lung Cancer Patients

Delays in the assessment, management, and treatment of lung cancer patients may adversely impact prognosis and survival. This study is the first to use machine learning techniques to predict the quality and timeliness of care among lung cancer patients, utilising data from the Victorian Lung Cancer Registry (VLCR) between 2011 and 2022, in Victoria, Australia. Predictor variables included demographic, clinical, hospital, and geographical socio-economic indices. Machine learning methods such as random forests, k-nearest neighbour, neural networks, and support vector machines were implemented and evaluated using 20% out-of-sample cross validations via the area under the curve (AUC). Optimal model parameters were selected based on 10-fold cross validation. There were 11,602 patients included in the analysis. Evaluated quality indicators included, primarily, overall proportion achieving "time from referral date to diagnosis date ≤ 28 days" and proportion achieving "time from diagnosis date to first treatment date (any intent) ≤ 14 days". Results showed that the support vector machine learning methods performed well, followed by nearest neighbour, based on out-of-sample AUCs of 0.89 (in-sample = 0.99) and 0.85 (in-sample = 0.99) for the first indicator, respectively. These models can be implemented in the registry databases to help healthcare workers identify patients who may not meet these indicators prospectively and enable timely interventions.

Transformed Giant Congenital Melanocytic Nevus on 18 F-FDG PET/CT

ABSTRACT

A 50-year-old man experienced cough and bloody sputum for 1 month. CT detected a mass in the right lung. Staging 18 F-FDG PET/CT revealed multiple hypermetabolic lesions in the lung, mediastinum, liver, and bones. Further physical examination revealed black patches in the skin covering most parts of the body, which presented at his birth and were growing very slowly, consistent with giant congenital melanocytic nevus. Pathology examination after biopsy of the lung demonstrated metastatic melanoma.

Deep Learning to Predict the Cell Proliferation and Prognosis of Non-Small Cell Lung Cancer Based on FDG-PET/CT Images

(1) Background: Cell proliferation (Ki-67) has important clinical value in the treatment and prognosis of non-small cell lung cancer (NSCLC). However, current detection methods for Ki-67 are invasive and can lead to incorrect results. This study aimed to explore a deep learning classification model for the prediction of Ki-67 and the prognosis of NSCLC based on FDG-PET/CT images. (2) Methods: The FDG-PET/CT scan results of 159 patients with NSCLC confirmed via pathology were analyzed retrospectively, and the prediction models for the Ki-67 expression level based on PET images, CT images and PET/CT combined images were constructed using Densenet201. Based on a Ki-67 high expression score (HES) obtained from the prediction model, the survival rate of patients with NSCLC was analyzed using Kaplan-Meier and univariate Cox regression. (3) Results: The statistical analysis showed that Ki-67 expression was significantly correlated with clinical features of NSCLC, including age, gender, differentiation state and histopathological type. After a comparison of the three models (i.e., the PET model, the CT model, and the FDG-PET/CT combined model), the combined model was found to have the greatest advantage in Ki-67 prediction in terms of AUC (0.891), accuracy (0.822), precision (0.776) and specificity (0.902). Meanwhile, our results indicated that HES was a risk factor for prognosis and could be used for the survival prediction of NSCLC patients. (4) Conclusions: The deep-learning-based FDG-PET/CT radiomics classifier provided a novel non-invasive strategy with which to evaluate the malignancy and prognosis of NSCLC.

Ensemble Federated Learning Approach for Diagnostics of Multi-Order Lung Cancer

The early detection and classification of lung cancer is crucial for improving a patient's outcome. However, the traditional classification methods are based on single machine learning models. Hence, this is limited by the availability and quality of data at the centralized computing server. In this paper, we propose an ensemble Federated Learning-based approach for multi-order lung cancer classification. This approach combines multiple machine learning models trained on different datasets allowing for improvising accuracy and generalization. Moreover, the Federated Learning approach enables the use of distributed data while ensuring data privacy and security. We evaluate the approach on a Kaggle cancer dataset and compare the results with traditional machine learning models. The results demonstrate an accuracy of 89.63% with lung cancer classification.

Deep learning-based dynamic PET parametric Ki image generation from lung static PET

OBJECTIVES

PET/CT is a first-line tool for the diagnosis of lung cancer. The accuracy of quantification may suffer from various factors throughout the acquisition process. The dynamic PET parametric K_i provides better quantification and improve specificity for cancer detection. However, parametric imaging is difficult to implement clinically due to the long acquisition time (~ 1 h). We propose a dynamic parametric imaging method based on conventional static PET using deep learning.

METHODS

Based on the imaging data of 203 participants, an improved cycle generative adversarial network incorporated with squeeze-and-excitation attention block was introduced to learn the potential mapping relationship between static PET and K_i parametric images. The image quality of the synthesized images was qualitatively and quantitatively evaluated by using several physical and clinical metrics. Statistical analysis of correlation and consistency was also performed on the synthetic images.

RESULTS

Compared with those of other networks, the images synthesized by our proposed network exhibited superior performance in both qualitative and quantitative evaluation, statistical analysis, and clinical scoring. Our synthesized K_i images had significant correlation (Pearson correlation coefficient, 0.93), consistency, and excellent quantitative evaluation results with the K_i images obtained in standard dynamic PET practice.

CONCLUSIONS

Our proposed deep learning method can be used to synthesize highly correlated and consistent dynamic parametric images obtained from static lung PET.

KEY POINTS

• Compared with conventional static PET, dynamic PET parametric K_i imaging has been shown to provide better quantification and improved specificity for cancer detection. • The purpose of this work was to develop a dynamic parametric imaging method based on static PET images using deep learning. • Our proposed network can synthesize highly correlated and consistent dynamic parametric images, providing an additional quantitative diagnostic reference for clinicians.

[Research Progress in Imaging-based Diagnosis of Benign and Malignant Enlarged Lymph Nodes in Non-small Cell Lung Cancer]

Non-small cell lung cancer (NSCLC) can be detected with enlarged lymph nodes on imaging, but their benignity and malignancy are difficult to determine directly, making it difficult to stage the tumor and design radiotherapy target volumes. The clinical diagnosis of malignant lymph nodes is often based on the short diameter of lymph nodes ≥1 cm or the maximum standard uptake value ≥2.5, but the sensitivity and specificity of these criteria are too low to meet the clinical needs. In recent years, many advances have been made in diagnosing benign and malignant lymph nodes using other imaging parameters, and with the development of radiomics, deep learning and other technologies, models of mining the image information of enlarged lymph node regions further improve the diagnostic accuracy. The purpose of this paper is to review recent advances in imaging-based diagnosis of benign and malignant enlarged lymph nodes in NSCLC for more accurate and noninvasive assessment of lymph node status in clinical practice.
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Predictive value of baseline metabolic tumor volume for non-small-cell lung cancer patients treated with immune checkpoint inhibitors: A meta-analysis

Background

Immune checkpoint inhibitors (ICIs) have emerged as a promising treatment option for advanced non-small-cell lung cancer (NSCLC) patients, highlighting the need for biomarkers to identify responders and predict the outcome of ICIs. The purpose of this study was to evaluate the predictive value of baseline standardized uptake value (SUV), metabolic tumor volume (MTV) and total lesion glycolysis (TLG) derived from 18F-FDG-PET/CT in advanced NSCLC patients receiving ICIs.

Methods

PubMed and Web of Science databases were searched from January 1st, 2011 to July 18th, 2022, utilizing the search terms “non-small-cell lung cancer”, “PET/CT”, “standardized uptake value”, “metabolic tumor volume”, “ total lesion glycolysis”, and “immune checkpoint inhibitors”. Studies that analyzed the association between PET/CT parameters and objective response, immune-related adverse events (irAEs) and prognosis of NSCLC patients treated with ICIs were included. We extracted the hazard ratio (HR) with a 95% confidence interval (CI) for progression-free survival (PFS) and overall survival (OS). We performed a meta-analysis of HR using Review Manager v.5.4.1.

Results

Sixteen studies were included for review and thirteen for meta-analysis covering 770 patients. As for objective response and irAEs after ICIs, more studies with consistent assessment methods are needed to determine their relationship with MTV. In the meta-analysis, low SUVmax corresponded to poor PFS with a pooled HR of 0.74 (95% CI, 0.57-0.96, P=0.02). And a high level of baseline MTV level was related to shorter PFS (HR=1.45, 95% CI, 1.11-1.89, P<0.01) and OS (HR, 2.72; 95% CI, 1.97-3.73, P<0.01) especially when the cut-off value was set between 50-100 cm³. SUVmean and TLG were not associated with the prognosis of NSCLC patients receiving ICIs.

Conclusions

High level of baseline MTV corresponded to shorter PFS and OS, especially when the cut-off value was set between 50-100 cm³. MTV is a potential predictive value for the outcome of ICIs in NSCLC patients.

Automated extraction of information of lung cancer staging from unstructured reports of PET-CT interpretation: natural language processing with deep-learning

BACKGROUND

Extracting metastatic information from previous radiologic-text reports is important, however, laborious annotations have limited the usability of these texts. We developed a deep-learning model for extracting primary lung cancer sites and metastatic lymph nodes and distant metastasis information from PET-CT reports for determining lung cancer stages.

METHODS

PET-CT reports, fully written in English, were acquired from two cohorts of patients with lung cancer who were diagnosed at a tertiary hospital between January 2004 and March 2020. One cohort of 20,466 PET-CT reports was used for training and the validation set, and the other cohort of 4190 PET-CT reports was used for an additional-test set. A pre-processing model (Lung Cancer Spell Checker) was applied to correct the typographical errors, and pseudo-labelling was used for training the model. The deep-learning model was constructed using the Convolutional-Recurrent Neural Network. The performance metrics for the prediction model were accuracy, precision, sensitivity, micro-AUROC, and AUPRC.

RESULTS

For the extraction of primary lung cancer location, the model showed a micro-AUROC of 0.913 and 0.946 in the validation set and the additional-test set, respectively. For metastatic lymph nodes, the model showed a sensitivity of 0.827 and a specificity of 0.960. In predicting distant metastasis, the model showed a micro-AUROC of 0.944 and 0.950 in the validation and the additional-test set, respectively.

CONCLUSION

Our deep-learning method could be used for extracting lung cancer stage information from PET-CT reports and may facilitate lung cancer studies by alleviating laborious annotation by clinicians.

The prognosis of non-small cell lung cancer patients according to endobronchial metastatic lesion

To evaluate the prognosis of non-small cell lung cancer (NSCLC) patients according to endobronchial metastatic lesion (EML), especially those not identified on positron emission tomography or computed tomography. We evaluated progression-free survival (PFS) and overall survival (OS) according to the presence of EML in patients with NSCLC who were diagnosed at a tertiary hospital between January 2010 and December 2019. A total of 364 patients were enrolled in this study. EML was found in 69 (19.0%) patients with NSCLC. In the patients with EML versus the patients without EML, median PFS was 7.0 (3.5–13.5) and 9.5 (5.5–17.5) months (P = 0.011), and median OS was 12.0 (6.0–30.0) versus 20.0 (10.0–39.0) months (P = 0.016), respectively. Median PFS and OS rates were highest in epidermal growth factor receptor (EGFR) (+) and EML (−) patients and lowest in EGFR (−) and EML (+) patients (P < 0.001). By multivariate cox regression analysis, PFS in overall patients with NSCLC was significantly associated with EML, EGFR mutation, performance status, and pleural effusion. NSCLC patients with EML had worse prognoses of PFS and OS than patients without EML.

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.

Assessment of Correlation between Dual-Energy Ct (De-Ct)-Derived Iodine Concentration and Local Flourodeoxyglucose (Fdg) Uptake in Patients with Primary Non-Small-Cell Lung Cancer

(1) The current literature contains several studies investigating the correlation between dual-energy-derived iodine concentration (IC) and positron emission tomography (PET)-derived Flourodeoxyglucose (18F-FDG) uptake in patients with non-small-cell lung cancer (NSCLC). In previously published studies, either the entire tumor volume or a region of interest containing the maximum IC or 18F-FDG was assessed. However, the results have been inconsistent. The objective of this study was to correlate IC with FDG both within the entire volume and regional sub-volumes of primary tumors in patients with NSCLC. (2) In this retrospective study, a total of 22 patients with NSCLC who underwent both dual-energy CT (DE-CT) and 18F-FDG PET/CT were included. A region of interest (ROI) encircling the entire primary tumor was delineated, and a rigid registration of the DE-CT, iodine maps and FDG images was performed for the ROI. The correlation between tumor measurements and area-specific measurements of ICpeak and the peak standardized uptake value (SUVpeak) was found. Finally, a correlation between tumor volume and the distance between SUVpeak and ICpeak centroids was found. (3) For the entire tumor, moderate-to-strong correlations were found between SUVmax and ICmax (R = 0.62, p = 0.002), and metabolic tumor volume vs. total iodine content (R = 0.91, p < 0.001), respectively. For local tumor sub-volumes, a negative correlation was found between ICpeak and SUVpeak (R = −0.58, p = 0.0046). Furthermore, a strong correlation was found between the tumor volume and the distance in millimeters between SUVpeak and ICpeak centroids (R = 0.81, p < 0.0001). (4) In patients with NSCLC, high FDG uptakes and high DE-CT-derived iodine concentrations correlated on a whole-tumor level, but the peak areas were positioned at different locations within the tumor. 18F-FDG PET/CT and DE-CT provide complementary information and might represent different underlying patho-physiologies.

Mediastinal lymphnode positivity clinical scoring system for lung adenocarsinoma-mediastinal lymph node evaluation and staging

Aim: The study-cohort aims to assess PET-CT's correlation with adenocarcinomas' subtypes and propose a scoring system for mediastinal lymph nodes staging. Material and Method: The patient cohort is a multicenter, retrospective analysis of 268 patient that underwent surgery for NSCLC adenocarcinoma. Preoperative PET-CT results for mediastinal lymph node staging was pathologically confirmed on tissue specimens obtained at anatomical resection. Statistical evaluation of PET CT, radiological and pathological outcomes were performed on all subgroups. Results: The low FDG affinity in the lepidic pattern was statistically significant in the study (p

Patterns of Pretreatment Diagnostic Assessment in Patients Treated with Stereotactic Body Radiation Therapy (SBRT) for Non-Small Cell Lung Cancer (NSCLC): Special Characteristics in the COVID Pandemic and Influence on Outcomes

The pandemic raised a discussion about the postponement of medical interventions for non-small cell lung cancer (NSCLC). We analyzed the characteristics of pretreatment diagnostic assessment in the pandemic and the influence of diagnostic assessment on outcomes. A total of 96 patients with stereotactic body radiation therapy (SBRT) for NSCLC were included. The number of patients increased from mean 0.9 (2012–2019) to 1.45 per month in the COVID era (p < 0.05). Pandemic-related factors (contact reduction, limited intensive care unit resources) might have influenced clinical decision making towards SBRT. The time from pretreatment assessment (multidisciplinary tumor board decision, bronchoscopy, planning CT) to SBRT was longer during the COVID period (p < 0.05). Reduced services, staff shortage, or appointment management to mitigate infection risks might explain this finding. Overall survival, progression-free survival, locoregional progression-free survival, and distant progression-free survival were superior in patients who received a PET/CT scan prior to SBRT (p < 0.05). This supports that SBRT guidelines advocate the acquisition of a PET/CT scan. A longer time from PET/CT scan/conventional staging to SBRT (<10 vs. ≥10 weeks) was associated with worse locoregional control (p < 0.05). The postponement of diagnostic or therapeutic measures in the pandemic should be discussed cautiously. Patient- and tumor-related features should be evaluated in detail.

Role of PET/CT in post-therapeutic assessment of bronchogenic carcinoma

Background

Lung cancer is the most common among all kinds of cancers. It still constitutes the leading cause of cancer-related deaths worldwide, even with major advancements in prevention and treatments available. More than 85% of the cases are of non-small cell lung cancer (NSCLC), while less than 15% are of small cell lung cancers (SCLCs).

Patients and methods

This is a prospective study of 20 patients confirmed histopathologically to have bronchogenic carcinoma, who came for assessment of therapeutic response. All patients underwent positron emission tomography/computed tomography (PET/CT) before and after therapy. Semiquantitative assessment was used to determine maximum standardized uptake value (SUVmax). Treatment response evaluation was assessed according to the Response Evaluation Criteria in Solid Tumors (RECIST) criteria.

Results

Comparison of the pre- and post-treatment SUVmax in the responder and non-responder groups revealed that the post-treatment SUV was significantly lower than the baseline SUV in the responder group (P = 0.008). The responder post-treatment SUV and ∆ SUV were significantly lower than the non-responder values (P = 0.014 and 0.0004 respectively). The optimum threshold values of post-treatment SUV and ∆ SUV threshold defined by the receiver operating characteristic (ROC) curve analysis were ≤ 8 and ≤ −48.3 respectively. The sensitivity, specificity, PPV, NPV, and AUC of post-treatment SUV for predicting tumor response were 100%, 66.67%, 66.7%, 100%, and 0.833 respectively. The sensitivity, specificity, PPV, NPV, and AUC of ∆ SUV for predicting tumor response were 100%, 91.67%, 88.9%, 100%, and 0.979% respectively.

Conclusion

PET/CT proved itself as useful, efficient, and reliable tool in follow-up of lung cancer patients as it gives an early and accurate metabolic response assessment before any CT changes, leading to early modification of therapy or confirmation of its efficiency.

Is It Possible to Establish a Reliable Correlation between Maximum Standardized Uptake Value of 18-Fluorine Fluorodeoxyglucose Positron Emission Tomography/Computed Tomography and Histological Types of Non-Small Cell Lung Cancer? Analysis of the Italian VATS Group Database

BACKGROUND

Although positron emission tomography/computed tomography, often integrated with 2-deoxy-2-[fluorine-18] fluorine-D-glucose (18F-FDG-PET/CT), is fundamental in the assessment of lung cancer, the relationship between metabolic avidity of different histotypes and maximum standardized uptake value (SUVmax) has not yet been thoroughly investigated. The aim of the study is to establish a reliable correlation between Suvmax and histology in non-small cell lung cancer (NSCLC), in order to facilitate patient management.

METHODS

We retrospectively assessed the data about lung cancer patients entered in the Italian Registry of VATS Group from January 2014 to October 2019, after establishing the eligibility criteria of the study. In total, 8139 patients undergoing VATS lobectomy were enrolled: 3260 females and 4879 males. The relationship between SUVmax and tumor size was also analyzed.

RESULTS

The mean values of SUVmax in the most frequent types of lung cancer were as follows: (a) 4.88 ± 3.82 for preinvasive adenocarcinoma; (b) 5.49 ± 4.10 for minimally invasive adenocarcinoma; (c) 5.87 ± 4.18 for invasive adenocarcinoma; and (d) 8.85 ± 6.70 for squamous cell carcinoma. Processing these data, we displayed a statistically difference (p < 0.000001) of FDG avidity between adenocarcinoma and squamous cell carcinoma. Moreover, by classifying patients into five groups based on tumor diameter and after evaluating the SUVmax value for each group, we noted a statistical correlation (p < 0.000001) between size and FDG uptake, also confirmed by the post hoc analysis.

CONCLUSIONS

There is a correlation between SUVmax, histopathology outcomes and tumor size in NSCLC. Further clinical trials should be performed in order to confirm our data.

Complications of Lung Transplantation: Update on Imaging Manifestations and Management

As lung transplantation has become the most effective definitive treatment option for end-stage chronic respiratory diseases, yearly rates of this surgery have been steadily increasing. Despite improvement in surgical techniques and medical management of transplant recipients, complications from lung transplantation are a major cause of morbidity and mortality. Some of these complications can be classified on the basis of the time they typically occur after lung transplantation, while others may occur at any time. Imaging studies, in conjunction with clinical and laboratory evaluation, are key components in diagnosing and monitoring these conditions. Therefore, radiologists play a critical role in recognizing and communicating findings suggestive of lung transplantation complications. A description of imaging features of the most common lung transplantation complications, including surgical, medical, immunologic, and infectious complications, as well as an update on their management, will be reviewed here. Keywords: Pulmonary, Thorax, Surgery, Transplantation Supplemental material is available for this article. © RSNA, 2021.

A combined microfluidic deep learning approach for lung cancer cell high throughput screening toward automatic cancer screening applications

Lung cancer is a leading cause of cancer death in both men and women worldwide. The high mortality rate in lung cancer is in part due to late-stage diagnostics as well as spread of cancer-cells to organs and tissues by metastasis. Automated lung cancer detection and its sub-types classification from cell’s images play a crucial role toward an early-stage cancer prognosis and more individualized therapy. The rapid development of machine learning techniques, especially deep learning algorithms, has attracted much interest in its application to medical image problems. In this study, to develop a reliable Computer-Aided Diagnosis (CAD) system for accurately distinguishing between cancer and healthy cells, we grew popular Non-Small Lung Cancer lines in a microfluidic chip followed by staining with Phalloidin and images were obtained by using an IX-81 inverted Olympus fluorescence microscope. We designed and tested a deep learning image analysis workflow for classification of lung cancer cell-line images into six classes, including five different cancer cell-lines (P-C9, SK-LU-1, H-1975, A-427, and A-549) and normal cell-line (16-HBE). Our results demonstrate that ResNet18, a residual learning convolutional neural network, is an efficient and promising method for lung cancer cell-lines categorization with a classification accuracy of 98.37% and F1-score of 97.29%. Our proposed workflow is also able to successfully distinguish normal versus cancerous cell-lines with a remarkable average accuracy of 99.77% and F1-score of 99.87%. The proposed CAD system completely eliminates the need for extensive user intervention, enabling the processing of large amounts of image data with robust and highly accurate results.

[Expert Consensus for Thermal Ablation of Pulmonary Subsolid Nodules (2021 Edition)]

局部热消融技术在肺部结节治疗领域正处在起步与发展阶段，为了肺结节热消融治疗的临床实践和规范发展，由“中国医师协会肿瘤消融治疗技术专家组”“中国医师协会介入医师分会肿瘤消融专业委员会”“中国抗癌协会肿瘤消融治疗专业委员会”“中国临床肿瘤学会消融专家委员会”组织多学科国内有关专家，讨论制定了“热消融治疗肺部亚实性结节专家共识（2021年版）”。主要内容包括：①肺部亚实性结节的临床评估；②热消融治疗肺部亚实性结节技术操作规程、适应证、禁忌证、疗效评价和相关并发症；③存在的问题和未来发展方向。

Current role of nanoparticles in the treatment of lung cancer

BACKGROUND

Worldwide, lung cancer is one of the leading causes of cancer death. Nevertheless, new therapeutic agents have been developed to treat lung cancer that could change this mortality-rate. Interestingly, incredible advances have occurred in recent years in the development and application of nanotechnology in the detection, diagnosis, and treatment of lung cancer.

AIM

Nanoparticles (NPs) have the ability to incorporate multiple drugs and targeting agents and therefore lead to an improved bioavailability, sustained delivery, solubility, and intestinal absorption.

RELEVANCE FOR PATIENTS

This review briefly summarizes the latest innovations in therapeutic nanomedicine in lung cancer with examples on magnetic, lipid, and polymer NP. Emphasis will be placed on future studies and ongoing clinical trials in this field.

Dual energy imaging in cardiothoracic pathologies: A primer for radiologists and clinicians

Recent advances in dual-energy imaging techniques, dual-energy subtraction radiography (DESR) and dual-energy CT (DECT), offer new and useful additional information to conventional imaging, thus improving assessment of cardiothoracic abnormalities. DESR facilitates detection and characterization of pulmonary nodules. Other advantages of DESR include better depiction of pleural, lung parenchymal, airway and chest wall abnormalities, detection of foreign bodies and indwelling devices, improved visualization of cardiac and coronary artery calcifications helping in risk stratification of coronary artery disease, and diagnosing conditions like constrictive pericarditis and valvular stenosis. Commercially available DECT approaches are classified into emission based (dual rotation/spin, dual source, rapid kilovoltage switching and split beam) and detector-based (dual layer) systems. DECT provide several specialized image reconstructions. Virtual non-contrast images (VNC) allow for radiation dose reduction by obviating need for true non contrast images, low energy virtual mono-energetic images (VMI) boost contrast enhancement and help in salvaging otherwise non-diagnostic vascular studies, high energy VMI reduce beam hardening artifacts from metallic hardware or dense contrast material, and iodine density images allow quantitative and qualitative assessment of enhancement/iodine distribution. The large amount of data generated by DECT can affect interpreting physician efficiency but also limit clinical adoption of the technology. Optimization of the existing workflow and streamlining the integration between post-processing software and picture archiving and communication system (PACS) is therefore warranted.

PET/CT-Guided Biopsy of Suspected Lung Lesions Requires Less Rebiopsy Than CT-Guided Biopsy Due to Inconclusive Results

The purpose of this study was to compare ¹⁸F-FDG PET/CT and CT performance in guiding percutaneous biopsies with histologic confirmation of lung lesions. Methods: We prospectively evaluated 341 patients, of whom 216 underwent ¹⁸F-FDG PET/CT-guided biopsy and 125 underwent CT-guided biopsy. The pathology results, lesion size, complications, and rebiopsy rate in the 2 groups were evaluated. Results: Of the 216 biopsies with PET/CT guidance, histology demonstrated 170 lesions (78.7%) to be malignant and 46 (21.3%) to be benign. In the CT-guided group, of 125 lesions, 77 (61.6%) were malignant and 48 (38.4%) were benign (P = 0.001). Inconclusive results prompted the need for a second biopsy in 18 patients: 13 of 125 (10.4%) in the CT group and 5 of 216 (2.3%) in PET group (P = 0.001). Complications were pneumothorax (13.2%), hemothorax (0.8%), and hemoptysis (0.6%). No life-threatening adverse events or fatalities were reported. The difference in complication rates between the 2 groups was not significant (P = 0.6). Malignant lesions showed a greater mean size than benign lesions regardless of the group (P = 0.015). Conclusion: PET/CT-guided biopsy of lung lesions led to fewer inconclusive biopsies than CT-guided biopsy, with similar complication rates.

PET/CT implication on bronchogenic carcinoma TNM staging and follow-up using RECIST/PERCIST criteria: a comparative study with CT

Background

To evaluate the role of PET/CT on bronchogenic carcinoma staging as well as treatment response evaluation on follow-up compared to CT study alone.

Methods

A prospective study of 60 patients confirmed histopathologically to have non-small cell bronchogenic carcinoma, 30 of them came for staging (group T) while the rest 30 came for follow-up (group F) to assess therapy response. All patients underwent PET/CT with data analysis done using the eighth edition tumor, nodal, metastatic staging (TNM) staging for group T and RECIST/PERCIST criteria for group F. The CT data alone transferred to a blind radiologist for analysis using the same parameters. The results were collected and compared.

Results

Regarding group T, 12 patients showed different TNM staging between PET/CT and CT alone, 5 cases with different T stagings, 4 cases with different N stagings, and 5 cases with different M stagings. Also, 8 cases showed different surgical stagings. Regarding group F, 9 cases showed a difference between RECIST obtained by CT and PERCIST obtained by PET/CT with most of the cases (6 cases) showed change from partial or stable response to progressive response.

Conclusion

PET/CT has a significant role in TNM staging of bronchogenic carcinoma more at T2 staging due to its ability to differentiate the tumoral mass from the nearby pulmonary reaction. Also, PET/CT makes a difference in tumoral follow-up by its ability to detect the functional changes even before structural changes. Finally, PET/CT is a very important tool in management strategy.

Stereotactic body radiation therapy for empirically treated hypermetabolic lung lesions: a single-institutional experience identifying the Charlson score as a key prognostic factor

Background

Though pathologic evidence for non-small cell lung cancer (NSCLC) is preferred, many patients do not receive a biopsy prior to treatment with stereotactic body radiation therapy (SBRT). This study seeks to analyze the overall survival (OS), local control, and toxicity rates for such patients.

Methods

This retrospective review included patients empirically treated with SBRT for presumed non-metastatic NSCLC at a single institution. Inclusion criteria included a hypermetabolic pulmonary lesion noted on positron emission tomography (PET) imaging but no pathological evidence of NSCLC. Patients with another known metastatic tumor were excluded. Statistical analysis was conducted with Cox proportional hazards analysis, univariate analysis, and the Kaplan-Meier method.

Results

Ninety-one treatments in 90 unique patients met inclusion criteria. Patients were a median 77.9 years at the start of treatment and had a median Charlson score of 7. Pre-treatment standardized uptake value (SUV) was a median 4.5 and 1.5 after treatment. At a median follow-up of 12.9 months, 36-month local control of 91.3% was achieved. Twenty-four-month OS and progression-free survival were 65.4% and 44.8%, respectively. On univariate analysis, biologically effective dose (BED) ≥120 Gy was predictive of improved OS (P=0.001), with 36-month OS of 50.5% for patients with BED ≥120 Gy and only 31.6% for patients with BED <120 Gy. On Kaplan-Meier analysis, Charlson score ≥9 was predictive of decreased OS (P=0.04), and BED ≥120 Gy trended towards improved OS (P=0.08). Thirty-two cases of grade <3 toxicity were reported, and only two cases of grade 3 morbidity (fatigue) were noted.

Conclusions

Local control rates for empiric SBRT treatment for hypermetabolic, non-metastatic NSCLC are similar to those for biopsied NSCLC. OS is primarily dependent on a patient’s overall health status, which can be accurately assessed with the Charlson score. BED ≥120 Gy may also contribute to improved OS.

Thoracic positron emission tomography: 18F-fluorodeoxyglucose and beyond

Ongoing technologic and therapeutic advancements in medicine are now testing the limits of conventional anatomic imaging techniques. The ability to image physiology, rather than simply anatomy, is critical in the management of multiple disease processes, especially in oncology. Nuclear medicine has assumed a leading role in detecting, diagnosing, staging and assessing treatment response of various pathologic entities, and appears well positioned to do so into the future. When combined with computed tomography (CT) or magnetic resonance imaging (MRI), positron emission tomography (PET) has become the sine quo non technique of evaluating most solid tumors especially in the thorax. PET/CT serves as a key imaging modality in the initial evaluation of pulmonary nodules, often obviating the need for more invasive testing. PET/CT is essential to staging and restaging in bronchogenic carcinoma and offers key physiologic information with regard to treatment response. A more recent development, PET/MRI, shows promise in several specific lung cancer applications as well. Additional recent advancements in the field have allowed PET to expand beyond imaging with ¹⁸F-flurodeoxyglucose (FDG) alone, now with the ability to specifically image certain types of cell surface receptors. In the thorax this predominantly includes ⁶⁸Ga-DOTATATE which targets the somatostatin receptors abundantly expressed in neuroendocrine tumors, including bronchial carcinoid. This receptor targeted imaging technique permits targeting these tumors with therapeutic analogues such as ¹⁷⁷Lu labeled DOTATATE. Overall, the proper utilization of PET in the thorax has the ability to directly impact and improve patient care.

Correlation between Skip N2 Metastases and SUVmax, Long Diameter of Tumor, and Ki67 Expression in Patients with Non-Small-Cell Lung Cancer

Background

We aim at investigating the correlation between skip N2 metastases (SN2) and SUV_max, long diameter of tumor mass after ¹⁸F-FDG PET/CT, and pathological Ki67 expression in patients with non-small-cell lung cancer (NSCLC).

Methods and Results

We retrospectively analyzed the factors that might affect the pathogenesis of SN2 in these patients. The clinical SN2 symptoms in patients with squamous carcinoma or adenocarcinoma were investigated. The work curve was utilized to analyze the optimal cutoff value for the SUV_max and long diameter of tumor. Multivariate analysis revealed that high expression of Ki67 was a risk factor for mediastinal SN2 (OR = 1.042, 95% CI: 1.009-1.076). Subgroup analysis indicated that the SUV_max of the non-SN2 group was significantly higher than that of the SN2 group in patients with squamous carcinoma (16.3 ± 6.0 vs. 10.7 ± 5.6, P = 0.026). In the patients with adenocarcinoma, the long diameter of tumor in the SN2 group was significantly longer than that of the non-SN2 group (43.8 ± 16.3 mm vs. 30.1 ± 13.8 mm, P = 0.032). The Ki67 expression in the SN2 group was significantly higher than that of the non-SN2 group (51.7 ± 24.0 vs. 30.0 ± 19.2, P = 0.028).

Conclusions

The differences of clinical features of the patients in the SN2 group and non-SN2 group in the NSCLC patients were associated with the pathological subtypes, which were featured by lower SUV_max in the SN2 of the squamous carcinoma, and longer diameter of SN2 in the adenocarcinoma patients.

Use of PET/CT to aid clinical decision-making in cases of solitary pulmonary nodule: a probabilistic approach

Objective

To determine the frequency with which ¹⁸F-FDG-PET/CT findings change the probability of malignancy classification of solitary pulmonary nodules.

Materials and Methods

This was a retrospective analysis of all ¹⁸F-FDG-PET/CT examinations performed for the investigation of a solitary pulmonary nodule between May 2016 and May 2017. We reviewed medical records and PET/CT images to collect the data necessary to calculate the pre-test probability of malignancy using the Swensen model and the Herder model. The probability of malignancy was classified as low if < 5%, intermediate if 5-65%, and high if > 65%. Cases classified as intermediate in the Swensen model were reclassified by the Herder model.

Results

We reviewed the records for 33 patients, of whom 17 (51.5%) were male. The mean age was 68.63 ± 12.20 years. According to the Swensen model, the probability of malignancy was intermediate in 23 cases (69.7%). Among those, the application of the Herder model resulted in the probability of malignancy being reclassified as low in 6 (26.1%) and as high in 8 (34.8%).

Conclusion

¹⁸F-FDG-PET/CT was able to modify the probability of malignancy classification of a solitary pulmonary nodule in more than 50% of the cases evaluated.

Prognostic value of anthropometric measures extracted from whole-body CT using deep learning in patients with non-small-cell lung cancer

INTRODUCTION

The aim of the study was to extract anthropometric measures from CT by deep learning and to evaluate their prognostic value in patients with non-small-cell lung cancer (NSCLC).

METHODS

A convolutional neural network was trained to perform automatic segmentation of subcutaneous adipose tissue (SAT), visceral adipose tissue (VAT), and muscular body mass (MBM) from low-dose CT images in 189 patients with NSCLC who underwent pretherapy PET/CT. After a fivefold cross-validation in a subset of 35 patients, anthropometric measures extracted by deep learning were normalized to the body surface area (BSA) to control the various patient morphologies. VAT/SAT ratio and clinical parameters were included in a Cox proportional-hazards model for progression-free survival (PFS) and overall survival (OS).

RESULTS

Inference time for a whole volume was about 3 s. Mean Dice similarity coefficients in the validation set were 0.95, 0.93, and 0.91 for SAT, VAT, and MBM, respectively. For PFS prediction, T-stage, N-stage, chemotherapy, radiation therapy, and VAT/SAT ratio were associated with disease progression on univariate analysis. On multivariate analysis, only N-stage (HR = 1.7 [1.2-2.4]; p = 0.006), radiation therapy (HR = 2.4 [1.0-5.4]; p = 0.04), and VAT/SAT ratio (HR = 10.0 [2.7-37.9]; p < 0.001) remained significant prognosticators. For OS, male gender, smoking status, N-stage, a lower SAT/BSA ratio, and a higher VAT/SAT ratio were associated with mortality on univariate analysis. On multivariate analysis, male gender (HR = 2.8 [1.2-6.7]; p = 0.02), N-stage (HR = 2.1 [1.5-2.9]; p < 0.001), and the VAT/SAT ratio (HR = 7.9 [1.7-37.1]; p < 0.001) remained significant prognosticators.

CONCLUSION

The BSA-normalized VAT/SAT ratio is an independent predictor of both PFS and OS in NSCLC patients.

KEY POINTS

• Deep learning will make CT-derived anthropometric measures clinically usable as they are currently too time-consuming to calculate in routine practice. • Whole-body CT-derived anthropometrics in non-small-cell lung cancer are associated with progression-free survival and overall survival. • A priori medical knowledge can be implemented in the neural network loss function calculation.

Tumor-node-metastasis staging and treatment patterns of 73,167 patients with lung cancer in Brazil

OBJECTIVE

To characterize the clinical and histological profile, as well as treatment patterns, of patients with early-stage, locally advanced (LA), or advanced/metastatic (AM) lung cancer, diagnosed between 2000 and 2014, in Brazil.

METHODS

This was an analytical cross-sectional epidemiological study employing data obtained for the 2000-2014 period from the hospital cancer registries of two institutions in Brazil: the José Alencar Gomes da Silva National Cancer Institute, in the city of Rio de Janeiro; and the São Paulo Cancer Center Foundation, in the city of São Paulo.

RESULTS

We reviewed the data related to 73,167 patients with lung cancer. The proportions of patients with early-stage, LA, and AM lung cancer were 13.3%, 33.2%, and 53.4%, respectively. The patients with early-stage lung cancer were older and were most likely to receive a histological diagnosis of adenocarcinoma; the proportion of patients with early-stage lung cancer remained stable throughout the study period. In those with LA lung cancer, squamous cell carcinoma predominated, and the proportion of patients with LA lung cancer decreased significantly over the period analyzed. Those with AM lung cancer were younger and were most likely to have adenocarcinoma; the proportion of patients with AM lung cancer increased significantly during the study period. Small cell carcinoma accounted for 9.2% of all cases. In our patient sample, the main treatment modality was chemotherapy.

CONCLUSIONS

It is noteworthy that the frequency of AM lung cancer increased significantly during the study period, whereas that of LA lung cancer decreased significantly and that of early-stage lung cancer remained stable. Cancer treatment patterns, by stage, were in accordance with international guidelines.

Neo-antigen specific T cell responses indicate the presence of metastases before imaging

Non-small cell lung cancer (NSCLC) causes 19% of all Australian cancer deaths, with a 5-year survival post-resection of around 60%. Post-operative recurrence is due to metastases that were undetectable pre-operatively, or growth of microscopic locoregional residual disease. However, post-operative imaging modalities typically only detect more advanced tumours; where PET-CT has a detection limit of 6–7 mm. Detection of small deposits of lung metastatic disease is of importance in order to facilitate early and potentially more effective treatment. In this study, in a murine model of lung metastatic disease, we explore whether neo-antigen specific T cells are a sensitive marker for the detection of lung cancer after primary tumour resection. We determine lung metastatic disease by histology, and then compare detection by PET-CT and neo-antigen specific T cell frequency. Detection of lung metastatic disease within the histology positive group by PET-CT and neo-antigen specific T cell frequency were 22.9% and 92.2%, respectively. Notably, neo-antigen specific T cells in the lung draining lymph node were indicative of metastatic disease (82.8 ± 12.9 spots/10⁵ cells; mean ± SE), compared to healthy lung control (28.5 ± 8.6 spots/10⁵ cells; mean ± SE). Potentially, monitoring tumour neo-antigen specific T cell profiles is a highly sensitive method for determining disease recurrence.

Machine learning for radiomics-based multimodality and multiparametric modeling

Due to the recent developments of both hardware and software technologies, multimodality medical imaging techniques have been increasingly applied in clinical practice and research studies. Previously, the application of multimodality imaging in oncology has been mainly related to combining anatomical and functional imaging to improve diagnostic specificity and/or target definition, such as positron emission tomography/computed tomography (PET/CT) and single-photon emission CT (SPECT)/CT. More recently, the fusion of various images, such as multiparametric magnetic resonance imaging (MRI) sequences, different PET tracer images, PET/MRI, has become more prevalent, which has enabled more comprehensive characterization of the tumor phenotype. In order to take advantage of these valuable multimodal data for clinical decision making using radiomics, we present two ways to implement the multimodal image analysis, namely radiomic (handcrafted feature) based and deep learning (machine learned feature) based methods. Applying advanced machine (deep) learning algorithms across multimodality images have shown better results compared with single modality modeling for prognostic and/or prediction of clinical outcomes. This holds great potentials for providing more personalized treatment for patients and achieve better outcomes.

Radical consolidative treatment provides a clinical benefit and long-term survival in patients with synchronous oligometastatic non-small cell lung cancer: A phase II study

OBJECTIVES

Evidence is rapidly accumulating for the use of radical consolidative treatment (RCT) for patients with oligometastatic non-small cell lung cancer (NSCLC). Nonetheless, published studies have several limitations, including a selection of patients whose favorable characteristics might dictate therapeutic success, as well as scarce prospective data regarding overall survival (OS). The objective of this study was to determine whether RCT increases OS in patients with oligometastatic NSCLC.

MATERIALS AND METHODS

In this prospective, single-arm phase II study, we sought to evaluate the efficacy of RCT in patients with oligometastatic NSCLC in terms of OS. Patients with pathologically confirmed stage IV NSCLC who presented ≤5 synchronous, any-site metastases (including central nervous system [CNS] metastases), as assessed by PET-CT, were included. All patients received four initial cycles of systemic treatment. Following, those with stable disease/partial response received RCT to the primary site and metastases. The response to RCT was evaluated with PET-CT. The primary end-point was OS. Secondary end-points included progression-free survival (PFS) and best response by PET-CT. The study is registered in clinicaltrials.gov (NCT02805530).

RESULTS

Thirty-seven patients were included in the analysis. The mean age was 55.8 years (range: 33-75 years). At diagnosis, 43.2% of patients presented with CNS metastases. Following RCT, 19 (51.4%) patients achieved a complete-response (CR) by PET-CT, while 18 (48.6%) had a non-complete response (NON-CR). The median OS was nonreached (NR) and was positively affected by CR on PET-CT (NR vs. 27.4 [95% CI: 16.4-38.3]; p = 0.011). The median PFS was 23.5 months (95% CI: 13.6-33.3) and was positively affected by CR on PET-CT (NR vs. 14.3 [95% CI: 11.7-16.9]; p < 0.001; HR: 0.19 [0.07-0.52]; p=0.001).

CONCLUSION

Patients with oligometastatic NSCLC who undergo RCT have a high response rate and favorable OS. Patients with a CR by PET-CT have significantly longer OS, rendering this an important potential prognostic marker.

PET/CT and brain MRI role in staging NSCLC: prospective assessment of the accuracy, reliability and cost-effectiveness

Aim:

To determine whether PET/CT and brain MRI used in staging NSCLC can be accurate, reliable and cost-effective tools. NSCLC represents 80–85% of lung cancer and adequate information on the initial tumor staging is critical for planning an optimal therapeutic strategy.

Patients & methods:

Data from 30 newly diagnosed NSCLC patients in Greece were collected and prospectively recorded. Patients with potential resectable disease were evaluated to ensure that there are no detectable metastases that would rule out the possibility of a curative surgery.

Results:

Divergence occurred in 50% of cases of staging with CT or PET/CT alone, while metastases undetectable by the CT were revealed using PET/CT. Unnecessary thoracotomies were avoided by 10% of patients and another 10% was operated on after chemotherapy with a better prognosis.

Conclusion:

PET/CT and brain MRI combined are reliable for correct staging, reducing avoidable thoracotomies, morbidity rates and costs.

CT-guided Transthoracic Core-Needle Biopsies of Mediastinal and Lung Lesions in 235 Consecutive Patients: Factors Affecting the Risks of Complications and Occurrence of a Final Diagnosis of Malignancy

OBJECTIVE

To assess the impact of patient-, lesion- and procedure-related factors on the risks of complications and final diagnosis of malignancy in PCNB of mediastinal and lung lesions.

MATERIAL AND METHODS

We studied a large single-center cohort of 235 consecutive patients (66.8% men; 58.5±18.0 years) with a range of thoracic benign and malignant lesions, who underwent PCNB performed along 24 months by a single experienced radiologist. Diagnostic accuracy analyses of PCNB for malignancy were performed, as well as estimations of relative risk and logistic regression models in order to assess possible associations between such factors and malignancy/complications.

RESULTS

155 lesions (65.9%) were diagnosed as malignant. Overall accuracy was 91.1%, with sensitivity of 87.1%, specificity of 98.8%, positive predictive value of 99.3%, and negative predictive value of 79.8%. Pneumothorax (49/235; 20.8%) and hemorrhage (37/235; 15.7%) were the most common complications. Emphysema, smoking, older age, intrapulmonary location, deeper location, smaller size, presence of cavitations and irregular contours of the lesions, and smaller needle-pleural angles were the most consistent factors related to the occurrence of complications. Emphysema, older age, smoking, solid and deeper lesions were also significantly associated with a final diagnosis of malignancy after PCNB.

CONCLUSION

CT-guided PCNB of mediastinal and lung lesions is a safe procedure with high diagnostic accuracy for malignancy.

Novel Quantitative Imaging for Predicting Response to Therapy: Techniques and Clinical Applications

The current standard of Response Evaluation Criteria in Solid Tumors (RECIST)-based tumor response evaluation is limited in its ability to accurately monitor treatment response. Radiomics, an approach involving computerized extraction of several quantitative imaging features, has shown promise in predicting as well as monitoring response to therapy. In this article, we provide a brief overview of radiomic approaches and the various analytical methods and techniques, specifically in the context of predicting and monitoring treatment response for non-small cell lung cancer (NSCLC). We briefly summarize some of the various types of radiomic features, including tumor shape and textural patterns, both within the tumor and within the adjacent tumor microenvironment. Additionally, we also discuss work in delta-radiomics or change in radiomic features (e.g., texture within the nodule) across longitudinally interspersed images in time for monitoring changes in therapy. We discuss the utility of these approaches for NSCLC, specifically the role of radiomics as a prognostic marker for treatment effectiveness and early therapy response, including chemoradiation, immunotherapy, and trimodality therapy.

The role of positron emission tomography in the diagnosis, staging and response assessment of non-small cell lung cancer

Lung cancer is a common disease and the leading cause of cancer-related mortality, with non-small cell lung cancer (NSCLC) accounting for the majority of cases. Following diagnosis of lung cancer, accurate staging is essential to guide clinical management and inform prognosis. Positron emission tomography (PET) in conjunction with computed tomography (CT)-as PET-CT has developed as an important tool in the multi-disciplinary management of lung cancer. This article will review the current evidence for the role of ¹⁸F-fluorodeoxyglucose (FDG) PET-CT in NSCLC diagnosis, staging, response assessment and follow up.

The flip-flop fungus sign: an FDG PET/CT sign of benignity

Benign granulomatous processes such as fungal infection may mimic metastatic lung cancer on FDG PET/CT. We found that these processes often have draining lymph node(s) with equal or greater FDG activity than associated lung nodule(s), a "flip-flop" of what is commonly seen in lung cancer. The aim of this study was to examine the utility of this "flip-flop fungus" (FFF) sign for diagnosing benign pulmonary disease. FDG PET/CT scans performed between 9/09-3/13 for the indications of pulmonary nodule or mass were reviewed. Scans with at least one hilar or mediastinal FDG avid draining node were included. Patients with a history of cancer, lack of pathologic confirmation, or without at least two years of imaging follow-up were excluded. A total of 209 FDG PET/CT exams were included and reviewed in a blinded fashion. A positive FFF sign had a sensitivity of 60.0% (95% CI: 47.6-71.5%) and specificity of 84.9% (95% CI: 77.8-90.4%) (P<0.0001) for benign disease. With additional strict imaging criteria applied, the FFF sign had a specificity of 98.6% (95% CI: 94.9-99.8%) (P<0.0001) and a positive predictive value of 90.0% (95% CI: 68.3-98.5%). A positive FFF sign was predominately due to granulomatous disease (91%), mostly histoplasmosis (73%). A positive FFF sign combined with positive fungal serology (n=16) had a specificity of 100% for benign disease. The FFF sign predicts benign disease in patients with a lung nodule(s) and an FDG avid draining lymph node(s) that would otherwise be considered worrisome for cancer.

ImmunoPET Imaging of CD146 in Murine Models of Intrapulmonary Metastasis of Non-Small Cell Lung Cancer

CD146 has been identified as an excellent biomarker for lung cancer as its overexpression in solid tumors has been linked to disease progression, invasion, and metastasis. Previously, our group described a positive correlation between ⁶⁴Cu-labeled YY146 uptake and increased expression of CD146 in six human lung cancer cell lines using subcutaneous tumor models. In this study, we investigate a monoclonal antibody called YY146 for immunoPET imaging of CD146 in two intrapulmonary metastasis models of non-small cell lung cancer (NSCLC). The binding and immunoreactivity of the tracer were assessed by in vitro assays. Radiolabeling of YY146 with positron emitting Cu-64 (⁶⁴Cu-NOTA-YY146) enabled PET imaging of intrapulmonary metastasis. Mice were intravenously injected with two million tumor cells, and CT imaging was used to verify the presence of lung metastases. ⁶⁴Cu-NOTA-YY146 was injected into tumor-bearing mice, and animals were subjected to PET/CT imaging at 4, 24, and 48 h postinjection. Both the average and maximum lung PET signal intensities were quantified and compared between high and low CD146-expressing metastases. Further validation was accomplished through immunofluorescence imaging of resected tissues with CD31 and CD146. In flow cytometry, YY146 revealed strong binding to CD146 in H460 cells due to its high expression with minimal binding to CD146-low expressing H358 cells. Both YY146 and NOTA-YY146 showed similar binding, suggesting that NOTA conjugation did not elicit any negative effects on its binding affinity. Imaging of ⁶⁴Cu-NOTA-YY146 in H460 tumor-bearing mice revealed rapid, persistent, and highly specific tracer accumulation. Uptake of ⁶⁴Cu-NOTA-YY146 in the whole lung was calculated for H460 and H358 as 7.43 ± 0.38 and 3.95 ± 0.47% ID/g at 48 h postinjection (n = 4, p < 0.05), and the maximum lung signals were determined to be 13.85 ± 1.07 (H460) and 6.08 ± 0.73% ID/g (H358) at equivalent time points (n = 4, p < 0.05). To ensure the specificity of the tracer, a nonspecific antibody was injected into H460 tumor-bearing mice. Ex vivo biodistribution and immunofluorescence imaging validated the PET findings. In summary, ⁶⁴Cu-NOTA-YY146 allowed for successful imaging of CD146-expressing intrapulmonary metastases of NSCLC in mice. This preliminary study provides evidence supporting the future clinical utilization of ⁶⁴Cu-NOTA-YY146 for possible treatment monitoring of CD146-targeted therapy or improving patient stratification.

The liquid biopsy in lung cancer

The incidence of lung cancer has significantly increased over the last century, largely due to smoking, and remains the most common cause of cancer deaths worldwide. This is often due to lung cancer first presenting at late stages and a lack of curative therapeutic options at these later stages. Delayed diagnoses, inadequate tumor sampling, and lung cancer misdiagnoses are also not uncommon due to the limitations of the tissue biopsy. Our better understanding of the tumor microenvironment and the systemic actions of tumors, combined with the recent advent of the liquid biopsy, may allow molecular diagnostics to be done on circulating tumor markers, particularly circulating tumor DNA. Multiple liquid biopsy molecular methods are presently being examined to determine their efficacy as surrogates to the tumor tissue biopsy. This review will focus on new liquid biopsy technologies and how they may assist in lung cancer detection, diagnosis, and treatment.

Clinical Indications and Impact on Management: Fourth and Subsequent Posttherapy Follow-up 18F-FDG PET/CT Scans in Oncology Patients

The Centers for Medicare and Medicaid Services coverage includes 3 posttherapy ¹⁸F-FDG PET/CT scans per patient and per tumor type. Any additional follow-up ¹⁸F-FDG PET/CT scans will be reimbursed at the discretion of a local Medicare administrator, if deemed medically necessary. This study aimed to investigate common clinical indications for performing a fourth or additional follow-up ¹⁸F-FDG PET/CT scans that could affect the management of patients. Methods: This was a retrospective institutional review of 433 oncology patients (203 men; mean age, 55 y), including a total of 1,659 fourth or subsequent follow-up PET/CT scans after completion of primary treatment. Twelve indications for performing a fourth or subsequent follow-up PET/CT scan were determined, and the impact of each of the 12 indications on patients' management was evaluated. Results: The primary tumors were breast cancer (92 patients, 426 scans), non-Hodgkin lymphoma (77 patients, 208 scans), Hodgkin disease (41 patients, 182 scans), colorectal cancer (70 patients, 286 scans), melanoma (69 patients, 271 scans), and lung cancer (84 patients, 286 scans). The indications were categorized in 4 groups: PET/CT for diagnosis of tumor recurrence (303/1,659, 18.3%), PET/CT before starting therapy for tumor recurrence (64/1,659, 3.9%), PET/CT to assess therapy response for tumor recurrence (507/1,659, 30.6%), and follow-up PET/CT after completion of treatment for tumor recurrence (785/1,659, 47.3%). Overall, fourth and subsequent follow-up ¹⁸F-FDG PET/CT scans resulted in change in management in 31.6% of the scans (356 of 1,128) when the scans were obtained for medical necessities (indications 1-11), and in 5.6% of the scans (30/531) when the scans were obtained without any medical necessity (indication 12). Conclusion: The fourth and subsequent PET/CT scans obtained after completion of primary treatment led to a change in management in 31.6% of the scans when acquired for appropriate clinical reasons. Performing follow-up PET/CT without appropriate medical reason had a low impact on patients' management and should be avoided.

CT scan prior to radiotherapy in unresectable, locally advanced, non-small cell carcinoma of the lung: is it always necessary?

PURPOSE

There is broad consensus regarding evaluating response to chemotherapy (CHT) by means of computerized tomography (CT) in patients with localized or locally advanced non-small cell lung carcinoma (NSCLC). We present a study comparing the usefulness of CT versus chest X-ray (XR) and clinical findings when indicating radiotherapy (RT) following CHT.

METHODS

Ninety-eight of 150 subjects with unresectable locally advanced NSCLC were blindly and independently evaluated by XR and CT, with pairs of chest XR and CT (before and after CHT). A null hypothesis (H0) was established of the conditioned probability of detecting progression by CT and not by XR of 10 % or more, with a statistical power of 80 %.

RESULTS

Sensitivity, specificity, positive and negative predictive value of XR versus CT were 98, 89, 99, and 80 % respectively. A 4 % (p = 0.0451) probability of improvement of CT versus XR was calculated, enabling the H0 to be ruled out.

CONCLUSION

The CT failed to prove to be significantly superior to the chest XR + clinical picture in indicating a change in treatment approach in patients with unresectable locally advanced NSCLC after CHT.

Role of positron emission tomography-computed tomography in non-small cell lung cancer

Lung cancer is the leading cause of cancer-related mortality worldwide. Non-small cell carcinoma and small cell carcinoma are the main histological subtypes and constitutes around 85% and 15% of all lung cancer respectively. Multimodality treatment plays a key role in the successful management of lung cancer depending upon the histological subtype, stage of disease, and performance status. Imaging modalities play an important role in the diagnosis and accurate staging of the disease, in assessing the response to neoadjuvant therapy, and in the follow-up of the patients. Last decade has witnessed voluminous upsurge in the use of positron emission tomography-computed tomography (PET-CT); role of PET-CT has widened exponentially in the management of lung cancer. The present article reviews the role of 18-fluoro-deoxyglucose PET-CT in the management of non small cell lung cancer with emphasis on staging of the disease and the assessment of response to neoadjuvant therapy based on available literature.