Tarai Sambit, Lundström Elin, Ahmad Nouman, Strand Robin, Ahlström Håkan, Kullberg Joel
Radiology, Department of Surgical Sciences, Uppsala University, Uppsala, SE-75185, Sweden.
Antaros Medical AB, Mölndal, SE-43153, Sweden.
Heliyon. 2024 Dec 10;11(1):e41038. doi: 10.1016/j.heliyon.2024.e41038. eCollection 2025 Jan 15.
: Accurate tumor detection and quantification are important for optimized therapy planning and evaluation. Total tumor burden is also an appealing biomarker for clinical trials. Manual examination and annotation of oncologic PET/CT is labor-intensive and demands a high level of expertise. One significant challenge is the risk for human error, leading to potential omission of especially small tumors and tumors with low FDG uptake. : In this study, we introduced an automated framework with segmentation prior, from a tissue-wise multi-channel multi-angled based approach, to enhance tumor segmentation in whole-body FDG-PET/CT. : The proposed framework utilized a segmentation prior generated from tumor segmentations in tissue-wise multi-channel projections of the standardized uptake value (SUV) from PET. Projections were created from various angles and the tissues were identified based on their CT Hounsfield values. The resulting segmentation masks were subsequently backprojected into a unified 3D volume for creation of the segmentation prior. Finally, the segmentation prior was provided as an additional input channel along with the CT and SUV images to three variants of 3D segmentation networks (3D UNet, dynUNet, nnUNet) to enhance the overall tumor segmentation performance. All the methods were independently evaluated using 5-fold cross-validation on the autoPET dataset and subsequently tested on the U-CAN dataset. : Combining the segmentation prior with the original SUV and CT images improved overall tumor segmentation performance significantly compared to a baseline network. The increase in Dice coefficient for lymphoma, lung cancer, and melanoma across different segmentation networks were: 3D UNet ( , , ), dynUNet ( , , ), and nnUNet ( , , ), respectively; *, p-value < 0.05; ns, non-significance. : The increased segmentation accuracy could be attributed to the segmentation prior generated from tissue-wise SUV projections, revealing information from various tissues that was useful for segmentation of tumors. The results from this study highlight the potential of the proposed method as a valuable future tool for time-efficient quantification of tumor burden in oncologic FDG-PET/CT.
准确的肿瘤检测和定量对于优化治疗方案规划和评估至关重要。总肿瘤负荷也是临床试验中一个有吸引力的生物标志物。对肿瘤PET/CT进行人工检查和标注工作量大，且需要高水平的专业知识。一个重大挑战是存在人为误差的风险，这可能导致特别小的肿瘤以及FDG摄取低的肿瘤被遗漏。
在本研究中，我们引入了一个基于组织层面多通道多角度方法的带有分割先验的自动化框架，以增强全身FDG-PET/CT中的肿瘤分割。
所提出的框架利用了从PET标准化摄取值（SUV）的组织层面多通道投影中的肿瘤分割生成的分割先验。从不同角度创建投影，并根据其CT亨氏值识别组织。随后将得到的分割掩码反投影到统一的3D体积中以创建分割先验。最后，将分割先验作为一个额外的输入通道与CT和SUV图像一起提供给三种3D分割网络变体（3D UNet、dynUNet、nnUNet），以提高整体肿瘤分割性能。所有方法在autoPET数据集上使用5折交叉验证进行独立评估，随后在U-CAN数据集上进行测试。
与基线网络相比，将分割先验与原始SUV和CT图像相结合显著提高了整体肿瘤分割性能。不同分割网络中淋巴瘤、肺癌和黑色素瘤的Dice系数增加分别为：3D UNet（ ， ， ）、dynUNet（ ， ， ）和nnUNet（ ， ， ）；*，p值<0.05；ns，无显著性。
分割准确性的提高可归因于从组织层面SUV投影生成的分割先验，它揭示了来自各种组织的对肿瘤分割有用的信息。本研究结果突出了所提出方法作为一种有价值的未来工具用于肿瘤FDG-PET/CT中肿瘤负荷高效定量的潜力。
