Advanced PET imaging in oncology: status and developments with current and future relevance to lung cancer care

Uncovering the Links Between Dietary Sugar and Cancer: A Narrative Review Exploring the Impact of Dietary Sugar and Fasting on Cancer Risk and Prevention

Over the last several years, the scientific community has grown concerned about the relationship between dietary sugar intake and cancer development. The main causes of concern are the increasing intake of processed foods rich in sugar and the rising incidence of cancer cases. This study aims to uncover the complex relationship between sugar consumption and cancer development and its progression, with a particular focus on investigating whether fasting can protect against this condition. Our review provides a detailed discussion of the molecular aspects of the sugar-cancer relationship and an analysis of the existing literature. It explains how sugar affects cell signaling, inflammation, and hormonal pathways associated with the development of cancer. We also explored the new role of fasting in the prevention of cancer and its impact on cancer patients. This encompasses fasting-triggered autophagy, metabolic alterations, and possible health benefits, which form the major concern of this paper. Thus, by deepening the knowledge of these relations and providing the results of the analysis accompanied by concise and meaningful illustrations to facilitate the understanding of the data, we open the door to the further development of ideas to minimize the rates of cancer and improve overall well-being.

Development and Validation of the Random Forest Model via Combining CT-PET Image Features and Demographic Data for Distant Metastases among Lung Cancer Patients

The work aimed at developing and validating a random forest model of CT-PET image features combined with demographic data to diagnose distant metastases among lung cancer patients. This study involved lung cancer patients from The Cancer Genome Atlas lung adenocarcinoma (TCGA-LUAD) dataset, the lung PET-CT dataset, the lung squamous cell carcinoma (LSCC) dataset, and the National Cancer Institute's Clinical Proteomic Tumor Analysis Consortium lung adenocarcinoma (CPTAC-LUAD) dataset and collected the information on 178 CT, 178 PET, and the patients' age, history of smoking, and gender. We conducted image processing and feature extraction. Finally, 4 computed tomography (CT) image features and 2 positron emission tomography (PET) image features were extracted. Four prediction models based on CT image features, PET image features, and demographic data were developed, and the area under the receiver operating characteristic (ROC) curve was used to evaluate the performance of prediction models. A total of 178 eligible samples were randomly divided into a training set (n = 134) and a testing set (n = 44) at a ratio of 3 : 1, with 2021 as a random number. ROC analyses illustrated that the predictive performance for distant metastases of combining CT-PET image features and demographic data for training and testing were 0.923 (95% confidence interval (CI): 0.873-0.973) and 0.873 (95% CI: 0.757-0.990). In addition, the predictive performance of the combined model in the testing set was significantly better than that of the CT-demographic data model (0.716, 95% CI: 0.531-0.902), PET-demographic data model (0.802, 95% CI: 0.633-0.970), and CT-PET model (0.797, 95% CI: 0.666-0.928). The random forest model via combining CT-PET image features and demographic data could have great performance in predicting distant metastases among lung cancer patients.

New PET Tracers: Current Knowledge and Perspectives in Lung Cancer

PET/CT with the tracer 2-[¹⁸F]fluoro-2-deoxy-D-glucose ([¹⁸F]FDG) has improved diagnostic imaging in cancer and is routinely used for diagnosing, staging and treatment planning in lung cancer patients. However, pitfalls of [¹⁸F]FDG-PET/CT limit the use in specific settings. Additionally, lung cancer is still the leading cause of cancer associated death and has high risk of recurrence after curative treatment. These circumstances have led to the continuous search for more sensitive and specific PET tracers to optimize lung cancer diagnosis, staging, treatment planning and evaluation. The objective of this review is to present and discuss current knowledge and perspectives of new PET tracers for use in lung cancer. A literature search was performed on PubMed and clinicaltrials.gov, limited to the past decade, excluding case reports, preclinical studies and studies on established tracers such as [¹⁸F]FDG and DOTATE. The most relevant papers from the search were evaluated. Several tracers have been developed targeting specific tumor characteristics and hallmarks of cancer. A small number of tracers have been studied extensively and evaluated head-to-head with [¹⁸F]FDG-PET/CT, whereas others need further investigation and validation in larger clinical trials. At this moment, none of the tracers can replace [¹⁸F]FDG-PET/CT. However, they might serve as supplementary imaging methods to provide more knowledge about biological tumor characteristics and visualize intra- and inter-tumoral heterogeneity.

A review on advances in 18F-FDG PET/CT radiomics standardisation and application in lung disease management

Radiomics analysis quantifies the interpolation of multiple and invisible molecular features present in diagnostic and therapeutic images. Implementation of 18-fluorine-fluorodeoxyglucose positron emission tomography/computed tomography (¹⁸F-FDG PET/CT) radiomics captures various disorders in non-invasive and high-throughput manner. ¹⁸F-FDG PET/CT accurately identifies the metabolic and anatomical changes during cancer progression. Therefore, the application of ¹⁸F-FDG PET/CT in the field of oncology is well established. Clinical application of ¹⁸F-FDG PET/CT radiomics in lung infection and inflammation is also an emerging field. Combination of bioinformatics approaches or textual analysis allows radiomics to extract additional information to predict cell biology at the micro-level. However, radiomics texture analysis is affected by several factors associated with image acquisition and processing. At present, researchers are working on mitigating these interrupters and developing standardised workflow for texture biomarker establishment. This review article focuses on the application of ¹⁸F-FDG PET/CT in detecting lung diseases specifically on cancer, infection and inflammation. An overview of different approaches and challenges encountered on standardisation of ¹⁸F-FDG PET/CT technique has also been highlighted. The review article provides insights about radiomics standardisation and application of ¹⁸F-FDG PET/CT in lung disease management.

Leveraging advances in immunopathology and artificial intelligence to analyze in vitro tumor models in composition and space

Cancer is the leading cause of death worldwide. Unfortunately, efforts to understand this disease are confounded by the complex, heterogenous tumor microenvironment (TME). Better understanding of the TME could lead to novel diagnostic, prognostic, and therapeutic discoveries. One way to achieve this involves in vitro tumor models that recapitulate the in vivo TME composition and spatial arrangement. Here, we review the potential of harnessing in vitro tumor models and artificial intelligence to delineate the TME. This includes (i) identification of novel features, (ii) investigation of higher-order relationships, and (iii) analysis and interpretation of multiomics data in a (iv) holistic, objective, reproducible, and efficient manner, which surpasses previous methods of TME analysis. We also discuss limitations of this approach, namely inadequate datasets, indeterminate biological correlations, ethical concerns, and logistical constraints; finally, we speculate on future avenues of research that could overcome these limitations, ultimately translating to improved clinical outcomes.

Automated procedure assessing the accuracy of HRCT-PET registration applied in functional virtual bronchoscopy

Background

Bronchoscopy serves as direct visualisation of the airway. Virtual bronchoscopy provides similar visual information using a non-invasive imaging procedure(s). Early and accurate image-guided diagnosis requires the possible highest performance, which might be approximated by combining anatomical and functional imaging. This communication describes an advanced functional virtual bronchoscopic (fVB) method based on the registration of PET images to high-resolution diagnostic CT images instead of low-dose CT images of lower resolution obtained from PET/CT scans. PET/CT and diagnostic CT data were collected from 22 oncological patients to develop a computer-aided high-precision fVB. Registration of segmented images was performed using elastix.

Results

For virtual bronchoscopy, we used an in-house developed segmentation method. The quality of low- and high-dose CT image registrations was characterised by expert’s scoring the spatial distance of manually paired corresponding points and by eight voxel intensity-based (dis)similarity parameters. The distribution of (dis)similarity parameter correlating best with anatomic scoring was bootstrapped, and 95% confidence intervals were calculated separately for acceptable and insufficient registrations. We showed that mutual information (MI) of the eight investigated (dis)similarity parameters displayed the closest correlation with the anatomy-based distance metrics used to characterise the quality of image registrations. The 95% confidence intervals of the bootstrapped MI distribution were [0.15, 0.22] and [0.28, 0.37] for insufficient and acceptable registrations, respectively. In case of any new patient, a calculated MI value of registered low- and high-dose CT image pair within the [0.28, 0.37] or the [0.15, 0.22] interval would suggest acceptance or rejection, respectively, serving as an aid for the radiologist.

Conclusion

A computer-aided solution was proposed in order to reduce reliance on radiologist’s contribution for the approval of acceptable image registrations.

A clinicopathologic analysis of microscopic extension in small cell lung cancer and lung adenocarcinoma: Determination of clinical target volume with precise radiotherapy

PURPOSE

The identification of the clinical target volume (CTV) is particularly important in the precise radiotherapy of lung cancer. The purpose of this study was to determine the extension margin from gross tumor volume (GTV) to CTV in primary small cell lung cancer (SCLC) and lung adenocarcinoma (ADC) by microscopic extension (ME).

MATERIAL AND METHODS

The data of 25 cases of SCLC and 29 cases of ADC from August 2015 to August 2020 were analyzed. The measurement of tumor size between preoperative thoracic computed tomography (CT) and postoperative macroscopic specimens was compared, and the ME range of tumor cells was measured under a microscope to determine its correlation with clinical features and pathological manifestations.

RESULTS

A total of 217 slides were examined, corresponding to 103 slides for SCLC and 114 slides for ADC. The radiologic sizes of the tumors in SCLC and ADC were 12.8 and 7.9 mm, respectively (p = 0.09), and the macroscopic sizes were 12.5 and 8.5 mm, respectively (p = 0.07). There was a significant correlation between the radiologic and macroscopic size of the same tumor sample (r = 0.886). Compared with ADC, more SCLC tumor cells infiltrated through vascular or lymphatic dissemination (16% vs. 9%, p = 0.047). The mean ME value was 2.81 mm for SCLC and 2.02 mm for ADC (p = 0.012). To take into account 95% of the ME, a margin of 8 and 7.7 mm must be expanded for SCLC and ADC, respectively. The ME value of the tumor was related to the presence of atelectasis, the location of the tumor, and the Ki-67 cell proliferation index.

CONCLUSION

The GTV of the tumor was contoured according to CT images, which was basically consistent with the actual tumor size. The GTVs of SCLC and ADC should be expanded by 8 and 7.7 mm, respectively, to fully cover the subclinical lesions in 95% of cases.

Exhaled breath analysis in disease detection

Investigating the use of exhaled breath analysis to diagnose and monitor different diseases has attracted much interest in recent years. This review introduces conventionally used methods and some emerging technologies aimed at breath analysis and their relevance to lung disease, airway inflammation, gastrointestinal disorders, metabolic disorders and kidney diseases. One section correlates breath components and specific diseases, whereas the other discusses some unique ideas, strategies, and devices to analyze exhaled breath for the diagnosis of some common diseases. This review aims to briefly introduce the potential application of exhaled breath analysis for the diagnosis and screening of various diseases, thereby providing a new avenue for the detection of non-invasive diseases.