Purpose
Lymphoma lesion detection and segmentation on whole-body FDG-PET/CT are a challenging task because of the diversity of involved nodes, organs or physiological uptakes. We sought to ...investigate the performances of a three-dimensional (3D) convolutional neural network (CNN) to automatically segment total metabolic tumour volume (TMTV) in large datasets of patients with diffuse large B cell lymphoma (DLBCL).
Methods
The dataset contained pre-therapy FDG-PET/CT from 733 DLBCL patients of 2 prospective LYmphoma Study Association (LYSA
)
trials. The first cohort (
n
= 639) was used for training using a 5-fold cross validation scheme. The second cohort (
n
= 94) was used for external validation of TMTV predictions. Ground truth masks were manually obtained after a 41% SUVmax adaptive thresholding of lymphoma lesions. A 3D U-net architecture with 2 input channels for PET and CT was trained on patches randomly sampled within PET/CTs with a summed cross entropy and Dice similarity coefficient (DSC) loss. Segmentation performance was assessed by the DSC and Jaccard coefficients. Finally, TMTV predictions were validated on the second independent cohort.
Results
Mean DSC and Jaccard coefficients (± standard deviation) in the validations set were 0.73 ± 0.20 and 0.68 ± 0.21, respectively. An underestimation of mean TMTV by − 12 mL (2.8%) ± 263 was found in the validation sets of the first cohort (
P
= 0.27). In the second cohort, an underestimation of mean TMTV by − 116 mL (20.8%) ± 425 was statistically significant (
P
= 0.01).
Conclusion
Our CNN is a promising tool for automatic detection and segmentation of lymphoma lesions, despite slight underestimation of TMTV. The fully automatic and open-source features of this CNN will allow to increase both dissemination in routine practice and reproducibility of TMTV assessment in lymphoma patients.
Personalized medicine represents a major goal in oncology. It has its underpinning in the identification of biomarkers with diagnostic, prognostic, or predictive values. Nowadays, the concept of ...biomarker no longer necessarily corresponds to biological characteristics measured ex vivo but includes complex physiological characteristics acquired by different technologies. Positron-emission-tomography (PET) imaging is an integral part of this approach by enabling the fine characterization of tumor heterogeneity in vivo in a non-invasive way. It can effectively be assessed by exploring the heterogeneous distribution and uptake of a tracer such as 18F-fluoro-deoxyglucose (FDG) or by using multiple radiopharmaceuticals, each providing different information. These two approaches represent two avenues of development for the research of new biomarkers in oncology. In this article, we review the existing evidence that the measurement of tumor heterogeneity with PET imaging provide essential information in clinical practice for treatment decision-making strategy, to better select patients with poor prognosis for more intensive therapy or those eligible for targeted therapy.
Purpose
Fluorodeoxyglucose-positron emission tomography/computed tomography (FDG-PET/CT) is included in the International Myeloma Working Group (IMWG) imaging guidelines for the work-up at diagnosis ...and the follow-up of multiple myeloma (MM) notably because it is a reliable tool as a predictor of prognosis. Nevertheless, none of the published studies focusing on the prognostic value of PET-derived features at baseline consider tumor heterogeneity, which could be of high importance in MM. The aim of this study was to evaluate the prognostic value of baseline PET-derived features in transplant-eligible newly diagnosed (TEND) MM patients enrolled in two prospective independent European randomized phase III trials using an innovative statistical random survival forest (RSF) approach.
Methods
Imaging ancillary studies of IFM/DFCI2009 and EMN02/HO95 trials formed part of the present analysis (IMAJEM and EMN02/HO95, respectively). Among all patients initially enrolled in these studies, those with a positive baseline FDG-PET/CT imaging and focal bone lesions (FLs) and/or extramedullary disease (EMD) were included in the present analysis. A total of 17 image features (visual and quantitative, reflecting whole imaging characteristics) and 5 clinical/histopathological parameters were collected. The statistical analysis was conducted using two RSF approaches (train/validation + test and additional nested cross-validation) to predict progression-free survival (PFS).
Results
One hundred thirty-nine patients were considered for this study. The final model based on the first RSF (train/validation + test) approach selected 3 features (treatment arm, hemoglobin, and SUV
max
Bone Marrow (BM)) among the 22 involved initially, and two risk groups of patients (good and poor prognosis) could be defined with a mean hazard ratio of 4.3 ± 1.5 and a mean log-rank
p
value of 0.01 ± 0.01. The additional RSF (nested cross-validation) analysis highlighted the robustness of the proposed model across different splits of the dataset. Indeed, the first features selected using the train/validation + test approach remained the first ones over the folds with the nested approach.
Conclusion
We proposed a new prognosis model for TEND MM patients at diagnosis based on two RSF approaches.
Trial registration
IMAJEM: NCT01309334 and EMN02/HO95: NCT01134484
Glioblastoma is the most common malignant adult brain tumor and has a very poor patient prognosis. The mean survival for highly proliferative glioblastoma is only 10 to 14 months despite an ...aggressive current therapeutic approach known as Stupp's protocol, which consists of debulking surgery followed by radiotherapy and chemotherapy. Despite several clinical trials using anti-angiogenic targeted therapies, glioblastoma medical care remains without major progress in the last decade. Recent progress in nuclear medicine, has been mainly driven by advances in biotechnologies such as radioimmunotherapy, radiopeptide therapy, and radionanoparticles, and these bring a new promising arsenal for glioblastoma therapy. For therapeutic purposes, nuclear medicine practitioners classically use β
particle emitters like
I,
Y,
Re, or
Lu. In the glioblastoma field, these radioisotopes are coupled with nanoparticles, monoclonal antibodies, or peptides. These radiopharmaceutical compounds have resulted in a stabilization and/or improvement of the neurological status with only transient side effects. In nuclear medicine, the glioblastoma-localized and targeted internal radiotherapy proof-of-concept stage has been successfully demonstrated using β
emitting isotopes. Similarly, α particle emitters like
Bi,
At, or
Ac appear to be an innovative and interesting alternative. Indeed, α particles deliver a high proportion of their energy inside or at close proximity to the targeted cells (within a few micrometers from the emission point versus several millimeters for β
particles). This physical property is based on particle-matter interaction differences and results in α particles being highly efficient in killing tumor cells with minimal irradiation of healthy tissues and permits targeting of isolated tumor cells. The first clinical trials confirmed this idea and showed good therapeutic efficacy and less side effects, thus opening a new and promising era for glioblastoma medical care using α therapy. The objective of this literature review is focused on the developing field of nuclear medicine and aims to describe the various parameters such as targets, vectors, isotopes, or injection route (systemic and local) in relation to the clinical and preclinical results in glioblastoma pathology.
Background
Oncological pretargeting has been implemented and tested in several different ways in preclinical models and clinical trials over more than 30 years. Despite highly promising results, ...pretargeting has not achieved market approval even though it could be considered the ultimate theranostic, combining PET imaging with short-lived positron emitters and therapy with radionuclides emitting beta or alpha particles.
Results
We have reviewed the pretargeting approaches proposed over the years, discussing their suitability for imaging, particularly PET imaging, and therapy, as well as their limitations. The reviewed pretargeting modalities are the avidin-biotin system, bispecific anti-tumour x anti-hapten antibodies and bivalent haptens, antibody-oligonucleotide conjugates and radiolabelled complementary oligonucleotides, and approaches using click chemistry. Finally, we discuss recent developments, such as the use of small binding proteins for pretargeting that may offer new perspectives to cancer pretargeting.
Conclusions
While pretargeting has shown promise and demonstrated preclinical and clinical proof of principle, full-scale clinical development programs are needed to translate pretargeting into a clinical reality that could ideally fit into current theranostic and precision medicine perspectives.
Purpose
Multiple myeloma (MM) is a bone marrow cancer that accounts for 10% of all hematological malignancies. It has been reported that FDG PET imaging provides prognostic information for both ...baseline and therapeutic follow-up of MM patients using visual analysis. In this study, we aim to develop a computer-assisted method based on PET quantitative image features to assist diagnoses and treatment decisions for MM patients.
Methods
Our proposed model relies on a two-stage method with Random Survival Forest (RFS) and variable importance (VIMP) for both feature selection and prediction. The targeted variable for prediction is the progression-free survival (PFS). We consider texture-based (radiomics), conventional (e.g., SUVmax) and clinical biomarkers. We evaluate PFS predictions in terms of C-index and final prognosis separation in two risk groups, from a database of 66 patients who were part of the prospective multi-centric french IMAJEM study.
Results
Our method (VIMP + RSF) provides better results (1-C-index of 0.36) than conventional methods such as Lasso–Cox and gradient-boosting Cox (0.48 and 0.56, respectively). We experimentally proved the interest of using selection (0.61 for RSF without selection) and showed that VIMP selection is more stable and gives better results than minimal depth and variable hunting (0.47 and 0.43). The approach gives better prognosis group separation (a
p
value of 0.05 against 0.11 to 0.4 for others).
Conclusion
Our results confirm the predictive value of radiomics for MM patients, in particular, they demonstrate that quantitative/heterogeneity image-based features reduce the error of the predicted progression. To our knowledge, this is the first work using RFS on PET images for the progression prediction of MM patients. Moreover, we provide an analysis of the feature selection process, which points toward the identification of clinically relevant biomarkers.
Purpose
To assess the rate and pattern of incidental interstitial lung abnormalities suggestive of COVID-19 on 18F-FDG PET/CT in asymptomatic cancer patients during the period of active COVID-19 ...circulation between March and April 2020 in a geographic area of low prevalence of the virus.
Methods
1396 18F-FDG PET/CT performed between January 1, 2020, and February 21, 2020, and between March 16, 2020, and April 17, 2020 for routine oncological indication were retrospectively analyzed. No patients had symptoms suggestive of COVID-19 at the time of the 18F-FDG PET/CT. Incidental interstitial pneumonias suggestive of COVID-19 were identified, and the 18F-FDG PET/CT patterns were described. We compared the incidence of these lesions in the pre-COVID and pandemic phases.
Results
We observed a 1.6% increase in interstitial lung abnormalities during the period of COVID-19 circulation. All had < 50% lung involvement. We describe a case series with typical and atypical interstitial pneumonias suggestive of COVID-19 as unilateral or bilateral with ground-glass opacity, consolidation, or crazy-paving patterns.
Conclusion
The relatively low increase in incidental findings suggestive of COVID-19 infection on 18F-FDG PET/CT in asymptomatic cancer patients was in accordance with the low COVID-19 transmission in our geographic region. Nevertheless, nuclear medicine physicians should familiarize themselves with typical and atypical 18F-FDG PET/CT patterns suggestive of COVID-19 pneumonia and initiate appropriate intervention where necessary.
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