Preoperative Texture Analysis Using 11C-Methionine Positron Emission Tomography Predicts Survival after Surgery for Glioma

Clinicopathologic and metabolic variables from 18F-FDG PET/CT in the prediction of recurrence pattern in stage I-III non-small cell lung cancer after curative surgery

AIM

This study aimed to analyze the clinicopathologic and metabolic parameters derived from staging 18F-FDG PET/CT that can predict recurrence patterns in non-small-cell lung cancer (NSCLC) after curative surgery.

MATERIAL AND METHODS

Retrospective study included stage I-III NSCLC patients with a baseline 18F-FDG PET/CT scan. Relapse patterns were analyzed based on location, lesion, and organ-specific recurrence. Clinicopathologic variables were recorded. Three distinct categories of variables were obtained: standardized uptake value (SUV)-based metrics, heterogeneity parameters, and morphological features. The relation of relapse patterns with clinicopathologic and metabolic parameters was analyzed using the uni-multivariate logistic regression.

RESULTS

Out of 173 patients, 104 experienced recurrences, with 66% presenting distant involvement and 56.7% exhibiting polymetastatic disease at initial recurrence. Patient age, pathologic lymphovascular invasion, and normalized SUVmax perimeter distance (nSPD) were considered as risk factors for early recurrence. Adenocarcinoma histology was identified as an independent variable for distant recurrence. Patient age, number of metastatic mediastinal lymph nodes at staging (nN), sphericity, normalized SUVpeak to centroid distance (nSCD), entropy, low gray-level run emphasis, and high gray-level run emphasis were independent variables for polymetastatic disease. Certain variables were correlated with organ-specific recurrence. Bone recurrence was related to nN and SUVmean. Brain recurrence was related to adenocarcinoma histology. Lung recurrence was associated with coefficient of variation and nSPD.

CONCLUSION

The metabolic profile of lung primary tumors obtained from 18F-FDG PET/CT seems to be predictive of recurrence patterns that are closely linked to the overall survival of NSCLC patients. These findings could help in the development of personalized follow-up strategies based on an individual's recurrence pattern.

Radiomics for residual tumour detection and prognosis in newly diagnosed glioblastoma based on postoperative [11C] methionine PET and T1c-w MRI

Personalized treatment strategies based on non-invasive biomarkers have potential to improve patient management in patients with newly diagnosed glioblastoma (GBM). The residual tumour burden after surgery in GBM patients is a prognostic imaging biomarker. However, in clinical patient management, its assessment is a manual and time-consuming process that is at risk of inter-rater variability. Furthermore, the prediction of patient outcome prior to radiotherapy may identify patient subgroups that could benefit from escalated radiotherapy doses. Therefore, in this study, we investigate the capabilities of traditional radiomics and 3D convolutional neural networks for automatic detection of the residual tumour status and to prognosticate time-to-recurrence (TTR) and overall survival (OS) in GBM using postoperative [11C] methionine positron emission tomography (MET-PET) and gadolinium-enhanced T1-w magnetic resonance imaging (MRI). On the independent test data, the 3D-DenseNet model based on MET-PET achieved the best performance for residual tumour detection, while the logistic regression model with conventional radiomics features performed best for T1c-w MRI (AUC: MET-PET 0.95, T1c-w MRI 0.78). For the prognosis of TTR and OS, the 3D-DenseNet model based on MET-PET integrated with age and MGMT status achieved the best performance (Concordance-Index: TTR 0.68, OS 0.65). In conclusion, we showed that both deep-learning and conventional radiomics have potential value for supporting image-based assessment and prognosis in GBM. After prospective validation, these models may be considered for treatment personalization.

Imaging biomarkers for clinical applications in neuro-oncology: current status and future perspectives

Biomarker discovery and development are popular for detecting the subtle diseases. However, biomarkers are needed to be validated and approved, and even fewer are ever used clinically. Imaging biomarkers have a crucial role in the treatment of cancer patients because they provide objective information on tumor biology, the tumor's habitat, and the tumor's signature in the environment. Tumor changes in response to an intervention complement molecular and genomic translational diagnosis as well as quantitative information. Neuro-oncology has become more prominent in diagnostics and targeted therapies. The classification of tumors has been actively updated, and drug discovery, and delivery in nanoimmunotherapies are advancing in the field of target therapy research. It is important that biomarkers and diagnostic implements be developed and used to assess the prognosis or late effects of long-term survivors. An improved realization of cancer biology has transformed its management with an increasing emphasis on a personalized approach in precision medicine. In the first part, we discuss the biomarker categories in relation to the courses of a disease and specific clinical contexts, including that patients and specimens should both directly reflect the target population and intended use. In the second part, we present the CT perfusion approach that provides quantitative and qualitative data that has been successfully applied to the clinical diagnosis, treatment and application. Furthermore, the novel and promising multiparametric MR imageing approach will provide deeper insights regarding the tumor microenvironment in the immune response. Additionally, we briefly remark new tactics based on MRI and PET for converging on imaging biomarkers combined with applications of bioinformatics in artificial intelligence. In the third part, we briefly address new approaches based on theranostics in precision medicine. These sophisticated techniques merge achievable standardizations into an applicatory apparatus for primarily a diagnostic implementation and tracking radioactive drugs to identify and to deliver therapies in an individualized medicine paradigm. In this article, we describe the critical principles for imaging biomarker characterization and discuss the current status of CT, MRI and PET in finiding imaging biomarkers of early disease.

Radiomics in Oncological PET Imaging: A Systematic Review-Part 1, Supradiaphragmatic Cancers

Radiomics is an upcoming field in nuclear oncology, both promising and technically challenging. To summarize the already undertaken work on supradiaphragmatic neoplasia and assess its quality, we performed a literature search in the PubMed database up to 18 February 2022. Inclusion criteria were: studies based on human data; at least one specified tumor type; supradiaphragmatic malignancy; performing radiomics on PET imaging. Exclusion criteria were: studies only based on phantom or animal data; technical articles without a clinically oriented question; fewer than 30 patients in the training cohort. A review database containing PMID, year of publication, cancer type, and quality criteria (number of patients, retrospective or prospective nature, independent validation cohort) was constructed. A total of 220 studies met the inclusion criteria. Among them, 119 (54.1%) studies included more than 100 patients, 21 studies (9.5%) were based on prospectively acquired data, and 91 (41.4%) used an independent validation set. Most studies focused on prognostic and treatment response objectives. Because the textural parameters and methods employed are very different from one article to another, it is complicated to aggregate and compare articles. New contributions and radiomics guidelines tend to help improving quality of the reported studies over the years.