Dynamic whole-body [18F]FES PET/CT increases lesion visibility in patients with metastatic breast cancer.

BACKGROUND: Correct classification of estrogen receptor (ER) status is essential for prognosis and treatment planning in patients with breast cancer (BC). Therefore, it is recommended to sample tumor tissue from an accessible metastasis. However, ER expression can show intra- and intertumoral heterogeneity. 16α-[18F]fluoroestradiol ([18F]FES) Positron Emission Tomography/Computed Tomography (PET/CT) allows noninvasive whole-body (WB) identification of ER distribution and is usually performed as a single static image 60 min after radiotracer injection. Using dynamic whole-body (D-WB) PET imaging, we examine [18F]FES kinetics and explore whether Patlak parametric images ( K i ) are quantitative and improve lesion visibility. RESULTS: This prospective study included eight patients with metastatic ER-positive BC scanned using a D-WB PET acquisition protocol. The kinetics of [18F]FES were best characterized by the irreversible two-tissue compartment model in tumor lesions and in the majority of organ tissues. K i values from Patlak parametric images correlated with K i values from the full kinetic analysis, r2 = 0.77, and with the semiquantitative mean standardized uptake value (SUVmean), r2 = 0.91. Furthermore, parametric K i images had the highest target-to-background ratio (TBR) in 162/164 metastatic lesions and the highest contrast-to-noise ratio (CNR) in 99/164 lesions compared to conventional SUV images. TBR was 2.45 (95% confidence interval (CI): 2.25-2.68) and CNR 1.17 (95% CI: 1.08-1.26) times higher in K i images compared to SUV images. These quantitative differences were seen as reduced background activity in the K i images. CONCLUSION: [18F]FES uptake is best described by an irreversible two-tissue compartment model. D-WB [18F]FES PET/CT scans can be used for direct reconstruction of parametric K i images, with superior lesion visibility and K i values comparable to K i values found from full kinetic analyses. This may aid correct ER classification and treatment decisions. Trial registration ClinicalTrials.gov: NCT04150731, https://clinicaltrials.gov/study/NCT04150731.

Improved automated tumor segmentation in whole-body 3D scans using multi-directional 2D projection-based priors.

Early cancer detection, guided by whole-body imaging, is important for the overall survival and well-being of the patients. While various computer-assisted systems have been developed to expedite and enhance cancer diagnostics and longitudinal monitoring, the detection and segmentation of tumors, especially from whole-body scans, remain challenging. To address this, we propose a novel end-to-end automated framework that first generates a tumor probability distribution map (TPDM), incorporating prior information about the tumor characteristics (e.g. size, shape, location). Subsequently, the TPDM is integrated with a state-of-the-art 3D segmentation network along with the original PET/CT or PET/MR images. This aims to produce more meaningful tumor segmentation masks compared to using the baseline 3D segmentation network alone. The proposed method was evaluated on three independent cohorts (autoPET, CAR-T, cHL) of images containing different cancer forms, obtained with different imaging modalities, and acquisition parameters and lesions annotated by different experts. The evaluation demonstrated the superiority of our proposed method over the baseline model by significant margins in terms of Dice coefficient, and lesion-wise sensitivity and precision. Many of the extremely small tumor lesions (i.e. the most difficult to segment) were missed by the baseline model but detected by the proposed model without additional false positives, resulting in clinically more relevant assessments. On average, an improvement of 0.0251 (autoPET), 0.144 (CAR-T), and 0.0528 (cHL) in overall Dice was observed. In conclusion, the proposed TPDM-based approach can be integrated with any state-of-the-art 3D UNET with potentially more accurate and robust segmentation results.

An image registration method for voxel-wise analysis of whole-body oncological PET-CT.

Whole-body positron emission tomography-computed tomography (PET-CT) imaging in oncology provides comprehensive information of each patient's disease status. However, image interpretation of volumetric data is a complex and time-consuming task. In this work, an image registration method targeted towards computer-aided voxel-wise analysis of whole-body PET-CT data was developed. The method used both CT images and tissue segmentation masks in parallel to spatially align images step-by-step. To evaluate its performance, a set of baseline PET-CT images of 131 classical Hodgkin lymphoma (cHL) patients and longitudinal image series of 135 head and neck cancer (HNC) patients were registered between and within subjects according to the proposed method. Results showed that major organs and anatomical structures generally were registered correctly. Whole-body inverse consistency vector and intensity magnitude errors were on average less than 5 mm and 45 Hounsfield units respectively in both registration tasks. Image registration was feasible in time and the nearly automatic pipeline enabled efficient image processing. Metabolic tumor volumes of the cHL patients and registration-derived therapy-related tissue volume change of the HNC patients mapped to template spaces confirmed proof-of-concept. In conclusion, the method established a robust point-correspondence and enabled quantitative visualization of group-wise image features on voxel level.

Comparative Analysis of Stk11/Lkb1 versus Pten Deficiency in Lung Adenocarcinoma Induced by CRISPR/Cas9.

This study focused on STK11, PTEN, KRAS, and TP53, which are often found to be mutated in lung cancer. We compared Stk11 and Pten implication in lung cancer in combination with loss of Trp53 and gain of function of Kras in a CRISPR/Cas9 mouse model. Mice with loss of Stk11, Trp53, and KrasG12D mutation (SKT) reached human endpoint at around four months post-initiation. In comparison, mice with loss of Pten, Trp53, and KrasG12D mutation (PKT) survived six months or longer post-initiation. Pathological examination revealed an increase in proliferation in SKT deficient lung epithelia compared to PKT. This difference was independent of Pten loss, indicating that loss of Pten is dispensable for cell proliferation in lung adenocarcinoma. Furthermore, tumors with loss of Stk11, Trp53, and KrasG12D mutation had a significantly higher progression rate, monitored by PET/MRI scanning, compared to mice with loss of Pten, Trp53, and KrasG12D mutation, revealing that mutations in Stk11 are essential for adenocarcinoma progression. Overall, by using the CRISPR/Cas9 mouse model of lung adenocarcinoma, we showed that mutations in Stk11 are a key driver, whereas loss of Pten is dispensable for adenocarcinoma progression.

Focal skeletal FDG uptake indicates poor prognosis in cHL regardless of extent and first-line chemotherapy.

18 F-fluoro-2-deoxy-D-glucose positron emission tomography/computed tomography (FDG-PET/CT) is used for staging classical Hodgkin lymphoma (cHL) with high sensitivity for skeletal involvement. However, it is unclear whether a single bone lesion carries the same adverse prognosis as multifocal lesions and if this is affected by type of chemotherapy [ABVD (adriamycin, bleomycin, vincristine, dacarbazine) versus BEACOPP (bleomycin, etoposide, adriamycin, cyclophosphamide, vincristine, procarbazine, prednisone)]. We reviewed the clinico-pathological and outcome data from 209 patients with newly diagnosed cHL staged by FDG-PET/CT. Patterns of skeletal/bone marrow uptake (BMU) were divided into 'low' and 'high' diffuse BMU (i.e. without focal lesions), and unifocal or multifocal lesions. Additional separate survival analysis was performed, taking type of chemotherapy into account. Forty patients (19·2%) had skeletal lesions (20 unifocal, 20 multifocal). The 3-year progression-free-survival (PFS) was 80% for patients with 'low BMU', 87% for 'high BMU', 69% for 'unifocal' and 51% for 'multifocal' lesions; median follow-up was 38 months. The presence of bone lesions, both uni- and multifocal, was associated with significantly inferior PFS (log rank P = 0·0001), independent of chemotherapy type. Thus, increased diffuse BMU should not be considered as a risk factor in cHL, whereas unifocal or multifocal bone lesions should be regarded as important predictors of adverse outcome, irrespective of the chemotherapy regimen used.