Evaluation of segmentation accuracy and the improvement of time effectiveness using deep learning-based segmentation in <sup>177</sup>Lu-DOTATATE dosimetry : The type of article: original research article.
Authors
Affiliations (13)
Affiliations (13)
- Section of Radiation Safety and Quality Assurance, National Cancer Center Hospital, 5-1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045, Japan.
- Division of Research and Development for Boron neutron capture therapy, National Cancer Center Exploratory Oncology Research & Clinical Trial Center, 5-1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045, Japan.
- Department of Radiological Technology, National Cancer Center Hospital, 5-1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045, Japan.
- Section of Radiation Safety and Quality Assurance, National Cancer Center Hospital, 5-1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045, Japan. [email protected].
- Division of Research and Development for Boron neutron capture therapy, National Cancer Center Exploratory Oncology Research & Clinical Trial Center, 5-1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045, Japan. [email protected].
- Medical Physics Laboratory, Division of Health Science, Graduate School of Medicine, Osaka University, Yamadaoka 1-7, Suita city, 565- 0871, Osaka, Japan. [email protected].
- Department of Comprehensive Oncology, Nagasaki University Graduate School of Biomedical Sciences, 5-1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045, Japan.
- Medical Physics Laboratory, Division of Health Science, Graduate School of Medicine, Osaka University, Yamadaoka 1-7, Suita city, 565- 0871, Osaka, Japan.
- Euro MediTech Co., Ltd, 2-20-4, Higashigotanda, Shinagawa-ku, Tokyo, 141-0022, Japan.
- Department of Radiology, Hyogo Cancer Center, 13-70 Kitaouji-cho, Akashi City, Hyogo, 673-8588, Japan.
- Department of Radiation Oncology, National Cancer Center Hospital, 5- 1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045, Japan.
- Department of Radiological Science, Graduate School of Human Health Science, Tokyo Metropolitan University, 7-2-10 Higashi-ogu, Arakawa-ku, Tokyo, 116-8551, Japan.
- Department of Diagnostic Radiology, National Cancer Center Hospital, 5- 1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045, Japan.
Abstract
The efficacy of deep learning-based artificial intelligence segmentation (AI-seg) in <sup>177</sup>Lu-DOTATATE dosimetry remains underexplored. This study evaluates AI-seg's contouring accuracy, dosimetric reliability, and time efficiency. We analyzed 23 patients treated with <sup>177</sup>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. 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 text]0.8). 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). AI-seg with minor adjustment enables faster yet accurate absorbed dose estimation in <sup>177</sup>Lu-DOTATATE. Despite segmentation challenges in hepatomegaly cases, MPs and RTs demonstrated competent contouring performance, supporting collaborative dosimetry workflows with physician oversight.