Purpose To explore the feasibility of the deep learning-based segmentation of extra-pelvic region and metastases in advanced prostate cancer based on metastasis reporting and data system for prostate cancer(MET-RADS-P).Materials and Methods Four datasets(68,91,57 and 263 patients with head,neck,chest and abdomen metastases,respectively)from Jan 2017 to Jan 2022 in Peking University First Hospital were retrospectively collected for the development of the classification model of scanning range and segmentation model of different regions and metastases according to the scanning sites(head,neck,chest and abdomen).In addition,90 patients with prostate cancer confirmed by pathology and underwent whole-body MRI were collected for external validation of the developed model.The manual annotation of the regions and metastases were used as the"reference standard"for the model evaluation.The evaluation indexes included dice similarity coefficient(DSC)and volumetric similarity(VS).Results In the external validation set,the classification accuracy of head,neck,chest and abdomen were 100%(90/90),98.89%(89/90),96.67%(87/90)and 94.44%(85/90),respectively.The range of DSC,VS values of the segmentation model for organs in different regions were(0.86±0.10)-(0.99±0.01),(0.89±0.10)-(0.99±0.01),respectively.The range of DSC,VS values of the segmentation model for metastases in different regions were(0.65±0.07)-(0.72±0.13),(0.74±0.04)-(0.82±0.13),respectively.Conclusion The 3D U-Net model based on deep learning may achieve the segmentation of extra-pelvic region and metastasis in advanced prostate cancer.

Paeonia veitchii and P. lactiflora are both original plants of the famous Chinese medicinal drug Paeoniae Radix Rubra in the Chinese Pharmacopoeia. They have important medicinal value and great potential in the flower market. The selection of stable and reliable reference genes is a necessary prerequisite for molecular research on P. veitchii. In this study, two reference genes, Actin and GAPDH, were selected as candidate genes from the transcriptome data of P. veitchii. The expression levels of the two candidate genes in different tissues(phloem, xylem, stem, leaf, petiole, and ovary) and different growth stages(bud stage, flowering stage, and dormant stage) of P. veitchii were detected using real-time fluorescence quantitative technology(qRT-PCR). Then, the stability of the expression of the two reference genes was comprehensively analyzed using geNorm, NormFinder, BestKeeper, ΔCT, and RefFinder. The results showed that the expression patterns of Actin and GAPDH were stable in different tissues and growth stages of P. veitchii. Furthermore, the expression levels of eight genes(Pv-TPS01, Pv-TPS02, Pv-CYP01, Pv-CYP02, Pv-CYP03, Pv-BAHD01, Pv-UGT01, and Pv-UGT02) in different tissues were further detected based on the transcriptome data of P. veitchii. The results showed that when Actin and GAPDH were used as reference genes, the expression trends of the eight genes in different tissues of P. veitchii were consistent, validating the reliability of Actin and GAPDH as reference genes for P. veitchii. In conclusion, this study finds that Actin and GAPDH can be used as reference genes for studying gene expression levels in different tissues and growth stages of P. veitchii.
Real-Time Polymerase Chain Reaction/methods* ; Paeonia/genetics* ; Actins/genetics* ; Reproducibility of Results ; Transcriptome ; Glyceraldehyde-3-Phosphate Dehydrogenases/genetics* ; Reference Standards ; Gene Expression Profiling/methods*