Trial reportBMC medical imaging2025
Comparative analysis of intestinal tumor segmentation in PET CT scans using organ based and whole body deep learning.
Trial report in BMC medical imaging, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT01287741 (A Phase III, Multicenter, Open-Label Randomized Trial Comparing the Efficacy of GA101), which is not on this map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
A Phase III, Multicenter, Open-Label Randomized Trial Comparing the Efficacy of GA101 (RO5072759) in Combination With CHOP (G-CHOP) Versus Rituximab and CHOP (R-CHOP) in Previously Untreated Patients With CD20-Positive Diffuse Large B-Cell Lymphoma (DLBCL)
Who cites it
1 citing paper in PubMed.
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
background18-Fluoro-deoxyglucose positron emission tomography/computed tomography (FDG-PET/CT) is a valuable imaging tool widely used in the management of cancer patients. Deep learning models excel at segmenting highly metabolic tumors but face challenges in regions with complex anatomy and normal cell uptake, such as the gastro-intestinal tract. Despite these challenges, it remains important to achieve accurate segmentation of gastro-intestinal tumors.
methodsHere, we present an international multicenter comparative study between a novel organ-focused approach and a whole-body training method to evaluate the effectiveness of training data homogeneity in accurately identifying gastro-intestinal tumors. In the organ-focused method, the training data is limited to cases with intestinal tumors which makes the network trained with more homogeneous data and with stronger presence of intestinal tumor signals. The whole body approach extracts the intestinal tumors from the results of a model trained on the whole-body scans. Both approaches were trained using diffuse large B cell (DLBCL) patients from a large multi-center clinical trial (NCT01287741).
resultsWe report an improved mean(±std) Dice score of 0.78(±0.21) for the organ-based approach on the hold-out set, compared to 0.63(±0.30) for the whole-body approach, with the p-value of less than 0.0001. At the lesion level, the proposed organ-based approach also shows increased precision, recall, and F1-score. An independent trial was used to evaluate the generalizability of the proposed method to non-Hodgkin's lymphoma (NHL) patients with follicular lymphoma (FL).
conclusionGiven the variability in structure and metabolism across tissues in the body, our quantitative findings suggest organ-focused training enhances intestinal tumor segmentation by leveraging tissue homogeneity in the training data, contrasting with the whole-body training approach, which, by its very nature, is a more heterogeneous data set.
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