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Background
Breast cancer is a complex disease involving several histological types (ductal and lobular) and subtypes. Each histological subtype is characterized by the presence or the absence of estrogen receptors (ER), progesterone receptors (PgR) and human epidermal growth factor receptor 2 (HER2). The classification of a breast cancer into the correct histological subtype is essential since the presence or the absence of receptors determines the choice of treatment. In particular, the presence of ER allows the use of hormone therapy [Roy2023]. Over time, 30 % of breast cancers that are not metastatic at diagnosis will be so, and in almost 38 % of cases, receptors expressed or not, will be different from the initial disease, requiring a biopsy to adapt therapies. But, over time, different tumor clones will emerge, causing metastasis expressing various receptors to coincide, making the biopsy flawed [Amir2012].
18F-Fluoroestradiol (18F-FES) is a radiolabeled estrogen analog that binds specifically to ER, allowing the visualization of ER-positive lesions. 18F-FES PET/CT imaging has emerged as a valuable tool for early detection and selection of appropriate therapies in complex cases, and in particular in lobular types, classically not avid in 18F-Fluorodeoxyglucose (FDG). However, the interpretation of 18F-FES PET/CT images could be complicated by the enterohepatic metabolic cycle. The liver rapidly metabolizes 18F-FES, leading to high uptake in the liver, high excretion through the biliary system and significant reabsorption in the small intestine. This enterohepatic metabolic cycle can obscure potential hepatic, digestive and peritoneal lesions commonly found in lobular types [Ulaner2021].
Objectives
The goal of this PhD is to develop and evaluate dynamic FES-PET imaging protocols to address the challenges posed by the enterohepatic metabolic cycle. First, by capturing early images, we aim to mitigate the difficulties associated with hepatic uptake and biliary excretion, which can obscure lesion detection. But, given the variability in the onset of the enterohepatic cycle among patients, dynamic imaging may offer a more comprehensive approach [Pedersen2024]. It could allow us to track the temporal distribution of FES, thereby enhancing the differentiation between true lesions and artifacts caused by metabolic processes.
This PhD aims to address these challenges through three primary tasks:
1. Dynamic FES-PET Imaging. The objective is to develop and optimize dynamic PET imaging protocols for FES to enhance lesion detection by mitigating the impact of hepatic, biliary and digestive uptake. By implementing dynamic acquisition techniques, we aim to capture the temporal distribution of FES, allowing for the differentiation between true lesions and artifacts from the enterohepatic metabolic cycle. This approach is expected to improve detection sensitivity and specificity, leading to more accurate diagnostic information and better identification of ER-positive lesions.
2. Predictive Analysis and Quantitative Biomarkers. The goal is to integrate FES-PET with FDG-PET and CT iodine imaging to develop predictive models and quantitative biomarkers for breast cancer prognosis. Machine learning and artificial intelligence (AI) techniques may be utilized to analyze multi-modal imaging data, aiming to identify patterns and biomarkers that predict treatment response and disease progression. Collaboration with other hospitals will leverage larger patient databases and clinical expertise, enhancing the robustness and applicability of the predictive models. The expected outcome is improved predictive models that guide personalized treatment decisions and enhance patient outcomes.
3. Impact of Respiratory Motion on Image Quality. The objective is Impact of Respiratory Motion on Image Quality. Studies will be conducted to quantify respiratory motion effects, and motion correction algorithms will be implemented in the image reconstruction process. This is expected to improve image clarity and accuracy, ensuring reliable diagnostic interpretation.
Environment
The recruited person will work in a multidisciplinary team composed of medical physicists, physicians, researchers and computer scientists of CREATIS laboratory and the LUMEN, the nuclear medicine department of the Léon Bérard Cancer Center (Lyon). The recruited person will be employed from United Imaging with a CIFRE contract.
Expected skills and other information
- Expected skills: medical physics, data analysis, image processing, AI
- Technical skills: Python (required)
- English (required) and French (optional)
Location: Lyon, CREATIS laboratory, Léon Bérard Cancer Center, France
Bibliography
[Roy2023] Roy M, Fowler AM, Ulaner GA, Mahajan A. Molecular Classification of Breast Cancer. PET Clin. 2023 Oct;18(4):441-458. doi: 10.1016/j.cpet.2023.04.002. Epub 2023 May 31. PMID: 37268505.
[Amir2012] Horgan AM, Amir E, Walter T, Knox JJ. Adjuvant therapy in the treatment of biliary tract cancer: a systematic review and meta-analysis. J Clin Oncol. 2012 Jun 1;30(16):1934-40. doi: 10.1200/JCO.2011.40.5381. Epub 2012 Apr 23. PMID: 22529261.
[Ulaner2021] Ulaner GA, Jhaveri K, Chandarlapaty S, Hatzoglou V, Riedl CC, Lewis JS, Mauguen A. Head-to-Head Evaluation of 18F-FES and 18F-FDG PET/CT in Metastatic Invasive Lobular Breast Cancer. J Nucl Med. 2021 Mar;62(3):326-331. doi: 10.2967/jnumed.120.247882. Epub 2020 Jul 17. PMID: 32680923; PMCID: PMC8049349.
[Pedersen2024] Pedersen, M.A., Munk, O.L., Dias, A.H. et al. Dynamic whole-body [18F]FES PET/CT increases lesion visibility in patients with metastatic breast cancer. EJNMMI Res 14, 24 (2024). https://doi.org/10.1186/s13550-024-01080-y