PSMA-Hornet: Fully-automated, multi-target segmentation of healthy organs in PSMA PET/CT images

Deep Learning-Powered CT-Less Multitracer Organ Segmentation From PET Images: A Solution for Unreliable CT Segmentation in PET/CT Imaging

PURPOSE

The common approach for organ segmentation in hybrid imaging relies on coregistered CT (CTAC) images. This method, however, presents several limitations in real clinical workflows where mismatch between PET and CT images are very common. Moreover, low-dose CTAC images have poor quality, thus challenging the segmentation task. Recent advances in CT-less PET imaging further highlight the necessity for an effective PET organ segmentation pipeline that does not rely on CT images. Therefore, the goal of this study was to develop a CT-less multitracer PET segmentation framework.

PATIENTS AND METHODS

We collected 2062 PET/CT images from multiple scanners. The patients were injected with either 18 F-FDG (1487) or 68 Ga-PSMA (575). PET/CT images with any kind of mismatch between PET and CT images were detected through visual assessment and excluded from our study. Multiple organs were delineated on CT components using previously trained in-house developed nnU-Net models. The segmentation masks were resampled to coregistered PET images and used to train 4 different deep learning models using different images as input, including noncorrected PET (PET-NC) and attenuation and scatter-corrected PET (PET-ASC) for 18 F-FDG (tasks 1 and 2, respectively using 22 organs) and PET-NC and PET-ASC for 68 Ga tracers (tasks 3 and 4, respectively, using 15 organs). The models' performance was evaluated in terms of Dice coefficient, Jaccard index, and segment volume difference.

RESULTS

The average Dice coefficient over all organs was 0.81 ± 0.15, 0.82 ± 0.14, 0.77 ± 0.17, and 0.79 ± 0.16 for tasks 1, 2, 3, and 4, respectively. PET-ASC models outperformed PET-NC models ( P < 0.05) for most of organs. The highest Dice values were achieved for the brain (0.93 to 0.96 in all 4 tasks), whereas the lowest values were achieved for small organs, such as the adrenal glands. The trained models showed robust performance on dynamic noisy images as well.

CONCLUSIONS

Deep learning models allow high-performance multiorgan segmentation for 2 popular PET tracers without the use of CT information. These models may tackle the limitations of using CT segmentation in PET/CT image quantification, kinetic modeling, radiomics analysis, dosimetry, or any other tasks that require organ segmentation masks.

From code sharing to sharing of implementations: Advancing reproducible AI development for medical imaging through federated testing

BACKGROUND

The reproducibility crisis in AI research remains a significant concern. While code sharing has been acknowledged as a step toward addressing this issue, our focus extends beyond this paradigm. In this work, we explore "federated testing" as an avenue for advancing reproducible AI research and development especially in medical imaging. Unlike federated learning, where a model is developed and refined on data from different centers, federated testing involves models developed by one team being deployed and evaluated by others, addressing reproducibility across various implementations.

METHODS

Our study follows an exploratory design aimed at systematically evaluating the sources of discrepancies in shared model execution for medical imaging and outputs on the same input data, independent of generalizability analysis. We distributed the same model code to multiple independent centers, monitoring execution in different runtime environments while considering various real-world scenarios for pre- and post-processing steps. We analyzed deployment infrastructure by comparing the impact of different computational resources (GPU vs. CPU) on model performance. To assess federated testing in AI models for medical imaging, we performed a comparative evaluation across different centers, each with distinct pre- and post-processing steps and deployment environments, specifically targeting AI-driven positron emission tomography (PET) imaging segmentation. More specifically, we studied federated testing for an AI-based model for surrogate total metabolic tumor volume (sTMTV) segmentation in PET imaging: the AI algorithm, trained on maximum intensity projection (MIP) data, segments lymphoma regions and estimates sTMTV.

RESULTS

Our study reveals that relying solely on open-source code sharing does not guarantee reproducible results due to variations in code execution, runtime environments, and incomplete input specifications. Deploying the segmentation model on local and virtual GPUs compared to using Docker containers showed no effect on reproducibility. However, significant sources of variability were found in data preparation and pre-/post- processing techniques for PET imaging. These findings underscore the limitations of code sharing alone in achieving consistent and accurate results in federated testing.

CONCLUSION

Achieving consistently precise results in federated testing requires more than just sharing models through open-source code. Comprehensive pipeline sharing, including pre- and post-processing steps, is essential. Cloud-based platforms that automate these processes can streamline AI model testing across diverse locations. Standardizing protocols and sharing complete pipelines can significantly enhance the robustness and reproducibility of AI models.

Prostate-specific Membrane Antigen: Interpretation Criteria, Standardized Reporting, and the Use of Machine Learning

Prostate-specific membrane antigen targeting positron emission tomography (PSMA-PET) is routinely used for the staging and restaging of patients with various stages of prostate cancer. For clear communication with referring physicians and to improve inter-reader agreement, the use of standardized reporting templates is mandatory. Increasingly, tumor volume is used by reporting and response assessment frameworks to prognosticate patient outcome or measure response to therapy. However, the quantification of tumor volume is often too time-consuming in routine clinical practice. Machine learning-based tools can facilitate the quantification of tumor volume for improved outcome prognostication.

Comparison of Multiparametric MRI, [68Ga]Ga-PSMA-11 PET-CT, and Clinical Nomograms for Primary T and N Staging of Intermediate-to-High-Risk Prostate Cancer

PURPOSE OF THE REPORT

Although multiparametric magnetic resonance imaging (mpMRI) is commonly used for the primary staging of prostate cancer, it may miss non-enlarged metastatic lymph nodes. Positron emission tomography-computed tomography targeting the prostate-specific membrane antigen (PSMA PET-CT) is a promising method to detect non-enlarged metastatic lymph nodes, but more data are needed.

MATERIALS AND METHODS

In this single-center, prospective study, we enrolled patients with intermediate-to-high-risk prostate cancer scheduled for radical prostatectomy with pelvic node dissection. Before surgery, prostate imaging with mpMRI and PSMA PET-CT was used to assess lymph node involvement (LNI), extra-prostatic extension (EPE), and seminal vesicle involvement (SVI). Additionally, we used clinical nomograms to estimate the risk of these three outcomes.

RESULTS

Of the 74 patients included, 61 (82%) had high-risk prostate cancer, and the rest had intermediate-risk cancer. Histopathology revealed LNI in 20 (27%) patients, SVI in 26 (35%), and EPE in 52 (70%). PSMA PET-CT performed better than mpMRI at detecting LNI (area under the curve (AUC, 95% confidence interval): 0.779 (0.665-0.893) vs. 0.655 (0.529-0.780)), but mpMRI was better at detecting SVI (AUC: 0.775 (0.672-0.878) vs. 0.585 (0.473-0.698)). The MSKCC nomogram performed well at detecting both LNI (AUC: 0.799 (0.680-0.918)) and SVI (0.772 (0.659-0.885)). However, when the nomogram was used to derive binary diagnoses, decision curve analyses showed that the MSKCC nomogram provided less net benefit than mpMRI and PSMA PET-CT for detecting SVI and LNI, respectively.

CONCLUSIONS

mpMRI and [68Ga]Ga-PSMA-11 PET-CT are complementary techniques to be used in conjunction for the primary T and N staging of prostate cancer.