Evidence map›Paper›PMID 34993103›Full record

ArticleQuantitative imaging in medicine and surgery2022

Semi-automatic quantitative analysis of the pelvic bony structures on apparent diffusion coefficient maps based on deep learning: establishment of reference ranges.

Xiang Liu, Chao Han, Ziying Lin, Zhaonan Sun, Yaofeng Zhang, Xiangpeng Wang, Xiaodong Zhang, Xiaoying Wang

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Article in Quantitative imaging in medicine and surgery, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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8 authors.

Xiang LiuDepartment of Radiology, Peking University First Hospital, Beijing, China.
Chao HanDepartment of Radiology, Peking University First Hospital, Beijing, China.
Ziying LinDepartment of Radiology, Peking University First Hospital, Beijing, China.
Zhaonan SunDepartment of Radiology, Peking University First Hospital, Beijing, China.
Yaofeng ZhangBeijing Smart Tree Medical Technology Co. Ltd., Beijing, China.
Xiangpeng WangBeijing Smart Tree Medical Technology Co. Ltd., Beijing, China.
Xiaodong ZhangDepartment of Radiology, Peking University First Hospital, Beijing, China.
Xiaoying WangDepartment of Radiology, Peking University First Hospital, Beijing, China.

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6 · The paper itself

Abstract

backgroundApparent diffusion coefficient (ADC) maps provide quantitative information on both normal and abnormal tissues. However, it is difficult to distinguish between these tissues unless consistent and precise ADC values can be obtained from normal tissues. For this study we developed a deep learning-based convolutional neural network (CNN) for pelvic bony structure segmentation and established the reference ranges of ADC parameters for normal pelvic bony structures.

methodsWe retrospectively enrolled 767 prostate cancer (PCa) patients for quantitative ADC analyses of normal pelvic bony structures. A subset of 288 patients who did not receive treatment for PCa (S1) were used to develop a CNN model for the segmentation of 8 pelvic bony structures (lumbar vertebra, sacrococcyx, ilium, acetabulum, femoral head, femoral neck, ischium, and pubis). The proposed CNN was used for the automated segmentation of these pelvic bony structures from a subset of 405 patients who did not receive treatment (S2) and 74 patients who received treatment [radiotherapy (S3) or endocrine therapy (S4)]. The 95% confidence interval (CI) was used to establish reference ranges for the ADC values from the normal pelvic bony structures of S1 and S2.

resultsThe Dice scores (Sørensen-Dice coefficient) for the CNN segmentation of the 8 pelvic bones on the ADC maps ranged from 0.90±0.02 (ilium) to 0.95±0.03 (femoral head) in the S1 testing set. In the S2 data set, the Dice scores showed no significant difference among the different scanners (P>0.05), and no significant differences were found among the S2, S3, and S4 data sets. The correlation analysis revealed that the b value and field strength were significantly correlated with ADC values (all P<0.001), while age and treatment were not significant variables (all P>0.05). The ADC reference ranges (95% CI) were as follows: lumbar vertebra, 1.11 (0.90-1.54); sacrococcyx, 0.82 (0.61-1.15); ilium, 0.57 (0.45-0.62); acetabulum, 0.59 (0.40-0.69); femoral head, 0.46 (0.25-0.58); femoral neck, 0.43 (0.25-0.48); ischium, 0.45 (0.26-0.55); and pubis, 0.57 (0.45-0.65).

conclusionsThis study preliminarily established reference ranges for the ADC values of normal pelvic bony structures. The image acquisition parameters had an influence on the ADC values.

Indexed as

apparent diffusion coefficient maps (ADC maps)convolutional neural network (CNN)pelvic bonesQuantitative evaluationreference range

Identifiers

PMID34993103
PMCPMC8666747

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