Biological correlates of tumor perfusion and its heterogeneity in newly diagnosed breast cancer using dynamic first-pass 18F-FDG PET/CT.

PURPOSE: The aim of this prospective study is to analyze the global tumor blood flow (BF) and its heterogeneity in newly diagnosed breast cancer (BC) according to tumor biological characteristics and molecular subtypes. These perfusion parameters were compared to those classically derived from metabolic studies to investigate links between perfusion and metabolism. METHODS: Two hundred seventeen newly diagnosed BC patients underwent a 18F-FDG PET/CT exam before any treatment. A 2-min dynamic acquisition, centered on the chest, was performed immediately after intravenous injection of 3 MBq/kg of 18F-FDG, followed by a two-step static acquisition 90 min later. Tumor BF was calculated (in ml/min/g) using a single compartment kinetic model. In addition to standard PET parameters, texture features (TF) describing the heterogeneity of tumor perfusion and metabolism were extracted. Patients were divided into three groups: Luminal (HR+/HER2-), HER2 (HER2+), and TN (HR-/HER2-). Global and TF parameters of BF and metabolism were compared in different groups of patients according to tumor biological characteristics. RESULTS: Tumors with lymph node involvement showed a higher perfusion, whereas no significant differences in SUV_max or SUV_mean were reported. TN tumors had a higher metabolic activity than HER2 and luminal tumors but no significant differences in global BF values were noted. HER2 tumors exhibited a larger tumor heterogeneity of both perfusion and metabolism compared to luminal and TN tumors. Heterogeneity of perfusion appeared well correlated to that of metabolism. CONCLUSIONS: The study of breast cancer perfusion shows a higher BF in large tumors and in tumors with lymph node involvement, not paralleled by similar modifications in tumor global metabolism. In addition, the observed correlation between the perfusion heterogeneity and the metabolism heterogeneity suggests that tumor perfusion and consequently the process of tumor angiogenesis might be involved in the metabolism heterogeneity previously shown in BC.

Respective contribution of baseline clinical data, tumour metabolism and tumour blood-flow in predicting pCR after neoadjuvant chemotherapy in HER2 and Triple Negative breast cancer.

BACKGROUND: The aim of this study is to investigate the added value of combining tumour blood flow (BF) and metabolism parameters, including texture features, with clinical parameters to predict, at baseline, the pathological complete response (pCR) to neoadjuvant chemotherapy (NAC) in patients with newly diagnosed breast cancer (BC). METHODS: One hundred and twenty-eight BC patients underwent a 18F-FDG PET/CT before any treatment. Tumour BF and metabolism parameters were extracted from first-pass dynamic and delayed PET images, respectively. Standard and texture features were extracted from BF and metabolic images. Prediction of pCR was performed using logistic regression, random forest and support vector classification algorithms. Models were built using clinical (C), clinical and metabolic (C+M) and clinical, metabolic and tumour BF (C+M+BF) information combined. Algorithms were trained on 80% of the dataset and tested on the remaining 20%. Univariate and multivariate features selections were carried out on the training dataset. A total of 50 shuffle splits were performed. The analysis was carried out on the whole dataset (HER2 and Triple Negative (TN)), and separately in HER2 (N=76) and TN (N=52) tumours. RESULTS: In the whole dataset, the highest classification performances were observed for C+M models, significantly (p-value<0.01) higher than C models and better than C+M+BF models (mean balanced accuracy of 0.66, 0.61, and 0.64 respectively). For HER2 tumours, equal performances were noted for C and C+M models, with performances higher than C+M+BF models (mean balanced accuracy of 0.64, and 0.61 respectively). Regarding TN tumours, the best classification results were reported for C+M models, with better performances than C and C+M+BF models but not significantly (mean balanced accuracy of 0.65, 0.63, and 0.62 respectively). CONCLUSION: Baseline clinical data combined with global and texture tumour metabolism parameters assessed by 18F-FDG PET/CT provide a better prediction of pCR after NAC in patients with BC compared to clinical parameters alone for TN, and HER2 and TN tumours together. In contrast, adding BF parameters to the models did not improve prediction, regardless of the tumour subgroup analysed.

Development and optimisation of a preclinical cone beam computed tomography-based radiomics workflow for radiation oncology research.

Background and purpose: Radiomics features derived from medical images have the potential to act as imaging biomarkers to improve diagnosis and predict treatment response in oncology. However, the complex relationships between radiomics features and the biological characteristics of tumours are yet to be fully determined. In this study, we developed a preclinical cone beam computed tomography (CBCT) radiomics workflow with the aim to use in vivo models to further develop radiomics signatures. Materials and methods: CBCT scans of a mouse phantom were acquired using onboard imaging from a small animal radiotherapy research platform (SARRP, Xstrahl). The repeatability and reproducibility of radiomics outputs were compared across different imaging protocols, segmentation sizes, pre-processing parameters and materials. Robust features were identified and used to compare scans of two xenograft mouse tumour models (A549 and H460). Results: Changes to the radiomics workflow significantly impact feature robustness. Preclinical CBCT radiomics analysis is feasible with 119 stable features identified from scans imaged at 60 kV, 25 bin width and 0.26 mm slice thickness. Large variation in segmentation volumes reduced the number of reliable radiomics features for analysis. Standardization in imaging and analysis parameters is essential in preclinical radiomics analysis to improve accuracy of outputs, leading to more consistent and reproducible findings. Conclusions: We present the first optimised workflow for preclinical CBCT radiomics to identify imaging biomarkers. Preclinical radiomics has the potential to maximise the quantity of data captured in in vivo experiments and could provide key information supporting the wider application of radiomics.

Critical analysis of the effect of various methodologies to compute breast cancer tumour blood flow-based texture features using first-pass 18F-FDG PET.

PURPOSE: Assessment of tumour blood flow (BF) heterogeneity using first-pass FDG PET/CT and textural feature (TF) analysis is an innovative concept. We aim to explore the relationship between BF heterogeneity measured with different TFs calculation methods and the response to neoadjuvant chemotherapy (NAC) in patients with newly diagnosed breast cancer (BC). METHODS: One hundred and twenty-five patients were enrolled. Dynamic first-pass and delayed FDG PET/CT scans were performed before NAC. Nine TFs were calculated from perfusion and metabolic PET images using relative (RR) or absolute (AR) rescaling strategies with two textural matrix calculation methods. Patients were classified according to presence or absence of a pathologic complete response (pCR) after NAC. The relationship between BF texture features and conventional features were analysed using spearman correlations. The TFs' differences between pCR and non-pCR groups were evaluated using Mann-Whitney tests and descriptive factorial discriminant analysis (FDA). RESULTS: Relation between tumour BF-based TFs and global BF parameters were globally similar to those observed for tumour metabolism. None of the TFs was significantly different between pCR and non-pCR groups in the Mann-Whitney analysis, after Benjamini-Hochberg correction. Using a RR led to better discriminations between responders and non-responders in the FDA analysis. The best results were obtained by combining all the PET features, including BF ones. CONCLUSION: A better differentiation of patients reaching a pCR was observed using a RR. Moreover, BF heterogeneity might bring a useful information when combined with metabolic PET parameters to predict the pCR after neoadjuvant chemotherapy.

Gender-related and geographic trends in interactions between radiotherapy professionals on Twitter.

Background and purpose: Twitter presence in academia has been linked to greater research impact which influences career progression. The purpose of this study was to analyse Twitter activity of the radiotherapy community around ESTRO congresses with a focus on gender-related and geographic trends. Materials and methods: Tweets, re-tweets and replies, here designated as interactions, around the ESTRO congresses held in 2012-2021 were collected. Twitter activity was analysed temporally and, for the period 2016-2021, the geographical span of the ESTRO Twitter network was studied. Tweets and Twitter users collated during the 10 years analysed were ranked based on number of 'likes', 're-tweets' and followers, considered as indicators of leadership/influence. Gender representation was assessed for the top-end percentiles. Results: Twitter activity around ESTRO congresses was multiplied by 60 in 6 years growing from 150 interactions in 2012 to a peak of 9097 in 2018. In 2020, during the SARS-CoV-2 pandemic, activity dropped by 60 % to reach 2945 interactions and recovered to half the pre-pandemic level in 2021. Europe, North America and Oceania were strongly connected and remained the main contributors. While overall, 58 % of accounts were owned by men, this proportion increased towards top liked/re-tweeted tweets and most-followed profiles to reach up to 84 % in the top-percentiles. Conclusion: During the SARS-CoV-2 pandemic, Twitter activity around ESTRO congresses substantially decreased. Men were over-represented on the platform and in most popular tweets and influential accounts. Given the increasing importance of social media presence in academia the gender-based biases observed may help in understanding the gender gap in career progression.