What question did this study set out to answer?

This study assesses the accuracy of AI-based segmentation for dosimetry in 177Lu-DOTATATE and its time effectiveness compared to manual methods.

May 26, 2026Open Access

Evaluation of segmentation accuracy and the improvement of time effectiveness using deep learning-based segmentation in 177Lu-DOTATATE dosimetry

Puntos clave

This study assesses the accuracy of AI-based segmentation for dosimetry in 177Lu-DOTATATE and its time effectiveness compared to manual methods.
Eight medical professionals delineated organs on CT images, comparing manual and AI-based segmentation against a reference contour.
Metrics including Dice similarity coefficient and Hausdorff distance were used for accuracy assessment.
Time for dosimetry processes was compared between manual and AI-seg methods.
Median DSC for AI-seg contours was above 0.988 for all organs, while manual contours averaged 0.955 for liver and 0.929 for spleen and kidneys.
The AI-seg method resulted in a 1.4% increase in absorbed dose for kidneys compared to manual results.
Dosimetry time was significantly shorter with AI-seg (47.0 min) versus manual methods (54.3 min, p = 0.014).

Resumen

BACKGROUND: Lu-DOTATATE dosimetry remains underexplored. This study evaluates AI-seg's contouring accuracy, dosimetric reliability, and time efficiency. METHODS: Lu-DOTATATE. Four medical physicists (MPs) and four radiological technologists (RTs) manually delineated liver (including lesions), spleen, and kidneys on CT images. The most experienced MP modified AI-seg outputs to establish a reference contour, confirmed by a board-certified physician. Both the manual and AI-seg contours were checked against this reference using the Dice similarity coefficient (DSC), Hausdorff distance (HD), and mean distance to agreement (MDA). Maximum, mean, and minimum absorbed doses were calculated from a single time-point image using SurePlan™ MRT (MIM Software Inc.) to assess dosimetric implications. The total time required for dosimetry-including image reconstruction, segmentation, and absorbed dose calculation-was compared between the manual and reference methods. RESULTS: Only one liver delineation by an MP required correction. Median (IQR) DSCs for liver, spleen, and kidneys were 0.955 (0.950-0.961), 0.929 (0.898-0.944), and 0.929 (0.918-0.942) for manual, 0.988 (0.946-0.998), 0.965 (0.858-0.995), and 0.994 (0.985-0.999) for AI-seg, respectively. All organs met the acceptable DSC threshold (Formula: see text0.8). Median HD in liver exceeded 10.0 mm for both methods, with AI-seg exceeding 30.0 mm in a few cases (liver: 3/23; spleen: 2/23) due to hepatomegaly. The median MDA in manual and AI-seg was below 2.0 mm. Median mean absorbed doses in the reference was 4.03 (1.69-5.81) Gy for liver, 1.55 (1.30-2.50) Gy for spleen, and 2.03 (1.45-2.44) Gy for kidneys. Only the kidneys absorbed dose from AI-seg differed significantly, showing a 1.4% increase. Dosimetry using the reference method took significantly less time than the manual approach (47.0 30.0-58.0 min vs. 54.3 49.5-67.0 min, p = 0.014). CONCLUSIONS: Lu-DOTATATE. Despite segmentation challenges in hepatomegaly cases, MPs and RTs demonstrated competent contouring performance, supporting collaborative dosimetry workflows with physician oversight.

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Cite This Study

Nakaichi et al. (Sun,) studied this question.

synapsesocial.com/papers/6a153950b5d9c58d83e8cbff https://doi.org/https://doi.org/10.1186/s40658-026-00897-x

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