Histopathology Of Lung Cancer Research Articles

Data set for the reporting of lung cancer: recommendations from the International Collaboration on Cancer Reporting (ICCR).

Lung cancer is the leading cause of cancer related deaths worldwide, although some patients with early-stage disease can be cured with surgical resection. Standardised reporting of all clinically relevant pathological parameters is essential for best patient care and is also important for ongoing data collection and refinement of important pathological features that impact patient prognosis, staging and clinical care. Using the established International Collaboration on Cancer Reporting (ICCR) procedure, a representative international expert panel of nine lung pathologists as well as an oncologist was convened. Essential core elements and suggested non-core elements were identified for inclusion in the resected lung cancer pathology data set based on predetermined levels of evidence as well as consensus expert opinion. A lung cancer histopathology reporting guide was developed that includes relevant clinical, macroscopic, microscopic and ancillary testing. Critical review and discussion of current evidence was incorporated into the new data set including changes from the 2021 World Health Organisation (WHO) Classification of Thoracic Tumours, fifth edition, new requirements for grading invasive non-mucinous adenocarcinomas, assessment of response to neoadjuvant therapy and requirements for molecular testing in early-stage resected lung carcinomas. This ICCR data set represents incorporation of all relevant parameters for histology reporting of lung cancer resection specimens. Routine use of this data set is recommended for all pathology reporting of resected lung cancer and it is freely available worldwide on the ICCR website (https://www.iccr-cancer.org/datasets/published-datasets/). Widespread implementation will help to ensure consistent and comprehensive pathology reporting and data collection essential for lung cancer patient care, clinical trials and other research.

Lymph node metastasis prediction with non-small cell lung cancer histopathology imaging.

Lymph node metastasis prediction with non-small cell lung cancer histopathology imaging.

Inception-v3 with reduce learning rate for optimization of lung cancer histopathology classification

Inception-v3 with reduce learning rate for optimization of lung cancer histopathology classification

Lung cancer histopathology image classification using transfer learning with convolution neural network model.

Lung cancer (LC) is a harmful malignant tumor and potentially lethal illness. Therefore, early detection of LC is an urgent need, and dependent on the type of histology and the type of disease. The use of deep learning algorithms (DL) is required to analyse the histopathology images of LC and make treatment decisions accordingly. This study aimed to apply pretrained EfficientNetB7 model to facilitate the process of classifying LC histopathology images as primary malignancy categories (adenocarcinoma, squamous cell carcinoma and large cell carcinoma) for early treatment of LC patients. Also, aims to analyse the performance of the proposed model using the accuracy measure. The dataset of 15000 histopathology images of lung cancer were examined. EfficientNetB7, a special type of convolution neural network (CNN), pretrained with ImageNet for transfer learning were trained on this dataset. Accuracy metric was used for the evaluation of the proposed model The feature extraction was performed by applying transfer learning using EfficientNetB7 as pretrained model. The proposed model achieved 99.77% accuracy, while previous studies model achieved over 90 to 99% accuracy. The employment of CNN based EfficientNetB7 model for the classification of LC based on histopathology images can speed up the diagnosis of LC and reduce the burden on pathologists for the early treatment of patients.

Segmentation and Classification of Lung Cancer using Deep Learning Techniques

To increase the survival rates for lung cancer, early identification and diagnosis are essential. The detection becomes crucial for a number of reasons, including visual similarity, heterogeneity, and low contrast variation. The several varieties of lung cancer that can be categorised histologically are determined by the kind of cells a pathologist can detect under a microscope. 15% of lung cancer cases are caused by small cell lung cancer (SCLC), while 85% of cases are caused by non-small cell lung cancer (NSCLC). The three main types of NSCLC are lung adenocarcinoma, big cell carcinoma, and squamous cell carcinoma (SQCC). Deep learning models provides high level services to the healthcare domain. The proposed research focuses on applying deep learning models to segment and classify images of lung cancer histopathology. Images are segmented using the U-Net model. In order to categorise the different forms of lung cancer histopathology images, classification models like ResNet-50, VGG-16, and EfficientNet-B5 and Ensemble models of these three models were utilised. For the purpose of evaluating all the classifiers, performance metrics like accuracy, precision, recall, and F1 Score were computed. The U-Net model achieves 81% accuracy. ResNet-50 achieved an accuracy of 91%, VGG-16 94%, EfficientNet-B5 97%, and Ensemble model 99%, respectively. Among all models, an ensemble model is the most accurate.

The effect of spatial resolution on deep learning classification of lung cancer histopathology

Objective: The microscopic analysis of biopsied lung nodules represents the gold-standard for definitive diagnosis of lung cancer. Deep learning has achieved pathologist-level classification of non-small cell lung cancer histopathology images at high resolutions (0.5–2 µm/px), and recent studies have revealed tomography–histology relationships at lower spatial resolutions. Thus, we tested whether patterns for histological classification of lung cancer could be detected at spatial resolutions such as those offered by ultra-high-resolution CT. Methods: We investigated the performance of a deep convolutional neural network (inception-v3) to classify lung histopathology images at lower spatial resolutions than that of typical pathology. Models were trained on 2167 histopathology slides from The Cancer Genome Atlas to differentiate between lung cancer tissues (adenocarcinoma (LUAD) and squamous-cell carcinoma (LUSC)), and normal dense tissue. Slides were accessed at 2.5 × magnification (4 µm/px) and reduced resolutions of 8, 16, 32, 64, and 128 µm/px were simulated by applying digital low-pass filters. Results: The classifier achieved area under the curve ≥0.95 for all classes at spatial resolutions of 4–16 µm/px, and area under the curve ≥0.95 for differentiating normal tissue from the two cancer types at 128 µm/px. Conclusions: Features for tissue classification by deep learning exist at spatial resolutions below what is typically viewed by pathologists. Advances in knowledge: We demonstrated that a deep convolutional network could differentiate normal and cancerous lung tissue at spatial resolutions as low as 128 µm/px and LUAD, LUSC, and normal tissue as low as 16 µm/px. Our data, and results of tomography–histology studies, indicate that these patterns should also be detectable within tomographic data at these resolutions.

A multilayer perceptron-based model applied to histopathology image classification of lung adenocarcinoma subtypes.

Lung cancer is one of the most common malignant tumors in humans. Adenocarcinoma of the lung is another of the most common types of lung cancer. In clinical medicine, physicians rely on the information provided by pathology tests as an important reference for the fifinal diagnosis of many diseases. Thus, pathological diagnosis is known as the gold standard for disease diagnosis. However, the complexity of the information contained in pathology images and the increase in the number of patients far exceeds the number of pathologists, especially in the treatment of lung cancer in less-developed countries. This paper proposes a multilayer perceptron model for lung cancer histopathology image detection, which enables the automatic detection of the degree of lung adenocarcinoma infifiltration. For the large amount of local information present in lung cancer histopathology images, MLP IN MLP (MIM) uses a dual data stream input method to achieve a modeling approach that combines global and local information to improve the classifification performance of the model. In our experiments, we collected 780 lung cancer histopathological images and prepared a lung histopathology image dataset to verify the effectiveness of MIM. The MIM achieves a diagnostic accuracy of 95.31% and has a precision, sensitivity, specificity and F1-score of 95.31%, 93.09%, 93.10%, 96.43% and 93.10% respectively, outperforming the diagnostic results of the common network model. In addition, a number of series of extension experiments demonstrated the scalability and stability of the MIM. In summary, MIM has high classifification performance and substantial potential in lung cancer detection tasks.

A Comparative Study of Transfer Learning based Models for Lung Cancer Histopathology Classification

Lung cancer is the deadliest form of cancer, which attracted a lot of attention in the past. Transfer learning is a very popular approach in deep learning as it can apply the knowledge obtained from a previous task to improve the performance in another. In this research, four transfer learning models with different complexity are utilized to detect lung cancer, which are AlexNet, VGG16, ResNet50 and Inception-v3. Since the early detection and histopathological diagnosis can considerably decrease the likelihood of mortality, the lung cancer histopathological images dataset is considered. This dataset contains histopathological images of 3 classes, all of them are considered in this study. Firstly, the four models are trained on the histopathological database from random initialization for 10 epochs. Next, the four models are first pre-trained on ImageNet and then trained on the histopathological dataset for 10 epochs. For each epoch, the testing accuracy is recorded so as to find the optimal number of epochs and determine whether transfer learning models are capable in lung cancer detection. Then, various evaluation metrics e.g., accuracy and precision are used to measure and compare the four models’ performance. The study’s finding shows that AlexNet, VGG16, ResNet50 and Inception-v3 pre-trained on ImageNet are adequate in lung cancer detection. There corresponding accuracy rates are 99.367%, 99.800%, 100% and 100% respectively, which are much higher than that trained from random initialization. Among the four transfer learning models, ResNet50 and Inception-v3 perform the best on lung cancer classification.

Analysis of Lung Cancer Cases Presenting in Outpatient Department of University Hospital of Nepal

Background Lung cancer is one of the leading cause of cancer related death. Most common histopathology of lung cancer is non-small cell carcinoma of which adenocarcinoma is the most common. There are limited number of studies done in Nepal to know different aspects of lung cancer.
 Objective To know demographic parameters of patients diagnosed as lung cancer in a university hospital. The study also aims to know the different histopathological diagnosis of lung cancer.
 Method All the patients presenting to outpatient department (Cardio Thoracic and Vascular unit) of Dhulikhel Hospital, if are diagnosed as cancer of lung/bronchus will be included in the study. The duration of the study was January 2017 to December 2021. The details on age, gender, presenting symptoms, histopathology of lung cancer, operability will be included in database and will be analyzed.
 Result There were total of 127 patients diagnosed as lung cancer. Male:female ratio was 1.7:1. Overall mean age was 63.23 years (SD 13.5 years, Range 19-89 years). Non small cell carcinoma was the most common type of lung cancer with 83.7%. In non small cell carcinoma, most common type was Squamous cell carcinoma followed by undifferentiated and Adenocarcinoma. Only five (3.93%) cases were in operable stage.
 Conclusion Despite the fact that lung cancer is one of the most common cancer, patients usually present late and mostly are not in operable stage. This study shows that squamous cell carcinoma is the most common histopathology in lung cancer cases.

Chemotherapy versus best supportive care in advanced lung cancer and idiopathic interstitial pneumonias: A retrospective multi-centre cohort study

BackgroundThe clinical questions of whether chemotherapy as initial treatment, compared with best supportive care (BSC), improves overall survival (OS) and whether it increases the occurrence risk of acute exacerbation of idiopathic interstitial pneumonia (IIP) in patients with advanced-stage lung cancer and IIP remain inconclusive. This study addresses these issues, given that chemotherapy-related acute exacerbation of IIP may be a direct cause of mortality in these patients. MethodsWe enrolled 1003 patients from 110 Japanese institutions and collected clinical profiles from 707 and 296 patients in the chemotherapy (men: women, 645:62; mean age, 70.4 ± 6.9 years) and BSC (men: women, 261:35; mean age, 75.2 ± 7.8) groups, respectively. We used propensity score matching to create 222 matched pairs from both groups using patient demographic data (age, sex, smoking status, performance status, history of acute exacerbation of IIP, desaturation on exertion, clinical diagnosis of IIP, high-resolution computed tomography findings, serum fibrotic markers, pulmonary function status, and lung cancer histopathology). Logistic or Cox regression analyses were performed using matched data to assess the effects of chemotherapy on the risk of acute exacerbation of IIP or OS, respectively. ResultsIn the well-matched cohort, chemotherapy improved OS (hazard ratio: 0.629, 95% confidence interval [CI]: 0.506–0.783, p < 0.0001); however, it involved significant acute exacerbation of IIP (odds ratio: 1.787, 95% CI: 1.026–3.113) compared to BSC. ConclusionsCompared with BSC, chemotherapy can improve OS in patients with advanced-stage lung cancer and IIP; however, it increases the risk of acute exacerbation of IIP.

Liquid Biopsy for Guiding Treatment Decisions in Advanced Non-Small Cell Lung Cancer.

Lung cancer is the leading cause of cancer-related deaths in the United States. The 5-year survival rates are poor with traditional therapy alone. New scientific advances in technology involving the human genome, including diagnostic tools to inform on tumor-derived acquired (somatic) mutations that drive cancer formation, are essential to utilize. Targeting cancer cells paired with actionable drugs to shut off growth pathways has significantly improved patient survival. Obtaining mutational analysis can be performed via traditional methods such as tissue; new advances allow comparable information obtained through liquid biopsy to inform targeted treatment decision-making. Getting tissue for additional molecular analysis can pose several challenges for patients. Liquid biopsy is a minimally invasive test (typically blood) analyzed by next-generation sequencing for tumor shed to obtain actionable information for treatment decisions. Analyses between blood and tissue consistently yield high concordance, with liquid biopsy providing faster turnaround time for results than tissue. The utility of liquid biopsy is well proven but not standardized and cannot diagnose lung cancer histopathology, which requires a tissue diagnosis.

Survival of Lung Cancer Patients by Histopathology in Taiwan from 2010 to 2016: A Nationwide Study.

Objective: Lung cancer poses a tremendous threat to the modern world. According to Taiwan’s Ministry of Health and Welfare, lung cancer took first place in total cancer deaths in 2021. This study investigated the overall lung cancer survival based on histopathology between 2010 and 2016 in Taiwan. Method: Data from 2010 to 2016 was collected from the Taiwan Cancer Registry (TCR). The characteristics and overall survival of 71,334 lung cancer patients were analyzed according to the tumor, node, metastasis (TNM) 7th staging system. Univariate and multivariate analyses were performed to identify differences in 1-year, 3-year, and 5-year survival between different histopathologies of lung cancer. Results: The 1-year overall survival rate increased from 54.07% in 2010 to 66.14% in 2016. The 3-year overall survival rate increased from 26.57% in 2010 to 41.12% in 2016 in all patients. Among the histopathologies of lung cancer, 3-year overall survival of adenocarcinoma patients increased the most and largely contributed to the increased 3-year overall survival of all lung cancer patients. Conclusions: The introduction of target therapy has led to a tremendous increase in overall survival for lung adenocarcinoma patients. However, target therapy differs by histopathology. Choosing the right target therapy and determining the correct histopathology of lung cancer is a pivotal key in increasing the overall survival of patients. Together with immune therapy, the landscape of lung cancer treatments is changing.

Dysbiosis of the Gut Microbiome Is Associated With Histopathology of Lung Cancer.

Lung cancer is a malignancy with high incidence and mortality worldwide. Previous studies have shown that the gut microbiome plays an important role in the development and progression of metabolic cancers. However, data on the characteristics of the gut microbiome with different histopathology types of lung cancer remain scant. We collected stool samples from 28 healthy people (HP) and 61 lung cancer patients. The lung cancer patients were classified into three types according to their histopathology: Atypical Adenomatous Hyperplasia/Adenocarcinoma in situ (AAH/AIS), Minimally Invasive Adenocarcinoma (MIA), and Invasive Adenocarcinoma (IA). In addition, we employed 16S rRNA gene amplicon sequencing to analyze the characteristics of the gut microbiome in these patients. Our analysis revealed that the categorized cancer patients had unique intestinal flora characteristics, and had lower density and flora diversity compared to healthy people. Besides, the structure of the flora families and genera was more complex, and each group presented specific pathogenic microbiota. The patients in the AAH/AIS group and HP group had relatively similar flora structure compared with the IA and MIA groups. In addition, we identified several flora markers that showed significant changes with the development of lung cancer. Lung cancer gut microbiota showed a decrease in short-chain fatty acids (SCFAs) producing and anti-inflammatory bacteria compared to healthy people, while some pathogenic bacteria such as proinflammatory or tumor-promoting bacteria were more abundant in lung cancer patients. On the other hand, the Kyoto Encyclopedia of Genes and Genomes (KEGG) and Clusters of Orthologous Group (COG) annotation demonstrated suppression of some dominant metabolism-related pathways in lung cancer. These findings provide new biomarkers for the diagnosis and prognostic assessment of lung cancer and lay the basis for novel targeted therapeutic strategies for the prevention and treatment of lung cancer.Clinical Trial Registration[www.ClinicalTrials.gov], identifier [NCT03244605].

Research Progress on Spread Through Air Spaces of Lung Cancer

气腔扩散（spread through air spaces, STAS）是2015版世界卫生组织（World Health Organization, WHO）肺癌病理分类中首次提出的概念，是指在主瘤体边界外的气腔内出现微乳头状细胞簇、实性细胞巢或单个肿瘤细胞。除传统观念的肺腺癌浸润方式（间质、脉管及胸膜侵犯）外，STAS被确定为第四种肺浸润性腺癌的侵袭模式。近年来，关于STAS的研究成为热点，STAS的存在不仅与肺癌病理组织学、基因突变等因素相关，诸多研究也证实其可作为肿瘤复发及预后的独立相关因素。但是，也有研究认为人为因素可以造成STAS的形态学假象，需在临床工作中注意甄别。本文就STAS的分类、相关病理学特征、基因状态改变以及可能造成STAS假象的人为因素等方面的研究进展予以综述。

Towards computationally efficient prediction of molecular signatures from routine histology images

Towards computationally efficient prediction of molecular signatures from routine histology images

P68.03 An AI Workflow to Detect and Report Tumor Cell Proportion of H&E-Stained Tissue Samples

Before performing NGS-based genetic testing for lung cancer tissues, H&E-stained histopathology image reading by a specialized pathologist is usually required to ensure that sample quality can meet sequencing requirements, that is, the minimum threshold for the number of cells in the suspected tumor region. With the great progress of AI algorithms in processing digital images, it has become possible to automate the quality assessment of lung cancer histopathology slides using AI algorithms. We curated a dataset containing 357 H&E-stained histopathology images of lung cancer, in which the tumor regions were labeled by a professional pathologist. The dataset was randomly divided into a training set containing 307 images and a test set of 50 images. The deep learning algorithm was trained on the training set and its performance was evaluated on the test set. The predicted regions output by the algorithm were further quantified and compared with the pathologist's quality assessment report of the slides. The tumor region segmentation performed by the deep learning algorithm achieved an iou of 0.7 on the test set. The correlation between the quantified results of the algorithm's predicted tumor region and the pathologist's slide quality report reached 0.52. The deep learning algorithm has good performance for the lung cancer tumor region segmentation after training on a certain amount of data. The strong correlation between the segmented regions output by the algorithm and the pathologist's slide quality report indicates that automated lung cancer tissue quality assessment using AI algorithms has great potential in the future. The model still has much room for improvement due to the morphological diversity of lung cancer tissue sections and the relatively low quality of the dataset.

Changing trends in histopathology of lung cancer in a tobacco prevalent area of South India

Background: Lung cancer is the most devastating disease of mankind among all cancers in the world with 1.8 million cases. In the west, adenocarcinoma of lung is the most common type, accounts for 38.5% followed by squamous 23% and large cell carcinoma. In India, squamous cell carcinoma was common until few recent studies which described the changing trends. Present study is done at a tertiary care hospital where tobacco cultivation and consumption is rampant in its surrounding places. An attempt to assess the histological trends in this area is made. Methodology: Total of 450 lung cancer patients, were studied in a span of 7 years. Results: Out of 450360 cases were histopathologically proven, 255 (70.83%) were males and 95 (26.38) were females with a male to female ratio of 2.7:1. Adeno carcinoma was found in 215 (59.72%) being most common, followed by squamous cell carcinoma 81(22.50%), small cell 23 (6.38%), large cell 12 (3.3%), and others 31(7.5%). Adenocarcinoma is the most common histopathological type seen in both males and females with 147 (57.64) cases, and 63 (66.31%) cases respectively. Adenocarcinoma is the most common histopathological type seen in both smokers and non-smokers with 246 (68.33%) and 114 (31.66%) respectively. Conclusions: Adenocarcinoma is evolving to be the most common histological type in both smokers and non-smokers in India. Tobacco cessation and change to filtered cigarettes has globally reduced tobacco related histological types of lung cancers. The aetiology of adenocarcinoma should be further evaluated in order to achieve an overall prevention. Keywords: Adenocarcinoma; Lung cancer; Squamous cell carcinoma.

HookNet: Multi-resolution convolutional neural networks for semantic segmentation in histopathology whole-slide images.

We propose HookNet, a semantic segmentation model for histopathology whole-slide images, which combines context and details via multiple branches of encoder-decoder convolutional neural networks. Concentric patches at multiple resolutions with different fields of view, feed different branches of HookNet, and intermediate representations are combined via a hooking mechanism. We describe a framework to design and train HookNet for achieving high-resolution semantic segmentation and introduce constraints to guarantee pixel-wise alignment in feature maps during hooking. We show the advantages of using HookNet in two histopathology image segmentation tasks where tissue type prediction accuracy strongly depends on contextual information, namely (1) multi-class tissue segmentation in breast cancer and, (2) segmentation of tertiary lymphoid structures and germinal centers in lung cancer. We show the superiority of HookNet when compared with single-resolution U-Net models working at different resolutions as well as with a recently published multi-resolution model for histopathology image segmentation. We have made HookNet publicly available by releasing the source code1 as well as in the form of web-based applications2,3 based on the grand-challenge.org platform.

Abstract 4254: Quality assessment platform for molecular pathology from non small cell lung cancer histopathology images using deep learning: A nationwide study of central laboratory in South Korea

Abstract Molecular testing became the most important part of lung cancer diagnostics. Integration of various molecular testing such as real-time PCR, fluorescent in situ hybridization (FISH) and next-generation sequencing into routine clinical practice and their systemic quality control is challenging to a pathologist and even more in the central laboratory. In this study, we trained a variant of DenseNet, a convolutional neural network model with the highest performance in image classification and suitable for quality assessment. Train dataset is composed of the arbitrarily classified dataset made and assessed by a professional pathologist during routine molecular test using whole-slide tissue images and molecular profiles including DNA density and the signal intensity of FISH obtained from 15,000 tests from lung cancer biopsies and resections, which were sent to the central laboratory for molecular studies from 45 hospitals nationwide. The performance of our platform is tested and compared to the quality assessment dataset made from each a general pathologist and a technician. The performance of our method for classifying DNA concentration and the signal intensity of FISH is comparable to that of a general pathologist and we are under improving its average area under the curve (AUC) over 0.95. This result suggests that deep learning model based on a convolutional neural network can assist pathologists for rigorous assessing quality of tissue morphology and signal of FISH for tissue samples with various molecular quality before or during the molecular test and, furthermore, can be applied to quality control of lung cancer molecular testing in a central laboratory. Citation Format: Tae-Jung Kim, Ho Chul Kang, Yonggeun Lee, Seokman Seo, Donghwan Kim. Quality assessment platform for molecular pathology from non small cell lung cancer histopathology images using deep learning: A nationwide study of central laboratory in South Korea [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 4254.

Is there a connection between immunohistochemical markers and grading of lung cancer with apparent diffusion coefficient (ADC) and standardised uptake values (SUV) of hybrid 18F-FDG-PET/MRI?

To correlate tumour grading and prognostic immunohistochemical markers of lung cancer with simultaneously acquired standardised uptake values (SUV) and apparent diffusion coefficient (ADC) derived from hybrid PET/MRI. In this retrospective study, 55 consecutive patients (mean age 62.5±9.2years) with therapy-naïve, histologically proven lung cancer were included. All patients underwent whole-body PET/MRI using 18F-flourdeoxyglucose (18F-FDG) as a radiotracer. Diffusion-weighted imaging of the chest (DWI, b-values: 0, 500, 1000s/mm2 ) was performed simultaneously with PET acquisition. Histopathological tumour grading was available in 43/55 patients. In 15/55 patients, immunohistochemical markers, that is, phospho-AKT Ser473 (pAKTS473), phosphorylated extracellular signal-regulated kinase (pERK), phosphatase and tensin homolog (PTEN), and human epidermal growth factor receptor 2 (erbB2) were available. The average SUVmax, SUVmean, ADCmin and ADCmean in lung cancer primaries were 12.6±5.9, 7.7±4.6, 569.9±96.1s/mm2 and 825.8±93.2s/mm2 , respectively. We found a significant inverse correlation between the ADCmin and SUVmax (r=-0.58, P<0.001) as well as between the ADCmin and SUVmean (r=-0.44, P<0.001). Tumour grading showed a significant positive correlation with SUVmax and SUVmean (R=0.34 and R=0.31, both P<0.05) and a significant inverse correlation with ADCmin and ADCmean (r=-0.30 and r=-0.40, both P<0.05). In addition, erbB2 showed a significant inverse correlation with SUVmax and SUVmean (r=-0.50 and r=-0.49, both P<0.05). The other immunohistochemical markers did not show any significant correlation. 18F-FDG-PET/MRI showed weak to moderate correlations between SUV, ADC, tumour grading and erbB2-expression of lung cancer. Hence, 18F-FDG-PET/MRI may, to some extent, offer complementary information to the histopathology of lung cancer, for the evaluation of tumour aggressiveness and treatment response.