应用MpMRI和18f - psma - pet /CT预测前列腺癌前列腺外展的多模态成像深度学习模型

IF 3.5 2区 医学 Q2 ONCOLOGY
Fei Yao, Heng Lin, Ying-Nan Xue, Yuan-Di Zhuang, Shu-Ying Bian, Ya-Yun Zhang, Yun-Jun Yang, Ke-Hua Pan
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引用次数: 0

摘要

目的:建立结合mpMRI和18F-PSMA-PET/CT的多模态成像深度学习(DL)模型,预测前列腺癌的前列腺外展(EPE),并评估其对提高放射科医生诊断准确性的有效性。方法:回顾性收集病理证实的前列腺癌(PCa)行根治性前列腺切除术(RP)患者的临床和影像学资料。数据收集于2019年1月至2022年6月间的主要机构(中心1,n = 197)和2021年7月至2022年11月间的外部机构(中心2,n = 36)。采用mpMRI和18F-PSMA-PET/CT的多模态DL模型被开发出来,以支持放射科医生使用EPE分级评分系统评估EPE。将DL模型的预测性能与单模态模型进行比较,并与有或没有模型辅助的放射科医生评估进行比较。同时评估了该模型的临床净效益。结果:对于中心1的患者,基于mpMRI的DL模型、PET/CT的DL模型和mpMRI + PET/CT联合多模态DL模型预测EPE的曲线下面积(AUC)分别为0.76(0.72-0.80)、0.77(0.70-0.82)和0.82(0.78-0.87)。在外部测试集(中心2)中,这些模型的auc分别为0.75(0.60-0.88)、0.77(0.72-0.88)和0.81(0.63-0.97)。在内部和外部验证中,与单模态模型相比,多模态深度学习模型显示出更高的预测精度。结论:结合mpMRI和18 F-PSMA PET/CT的多模态成像深度学习模型对前列腺癌EPE的预测效果良好,提高了放射科医生评估EPE的准确性。该模型具有作为一种支持工具的潜力,可以为更加个性化和精确的治疗决策提供支持。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Multimodal imaging deep learning model for predicting extraprostatic extension in prostate cancer using MpMRI and 18 F-PSMA-PET/CT.

Multimodal imaging deep learning model for predicting extraprostatic extension in prostate cancer using MpMRI and 18 F-PSMA-PET/CT.

Multimodal imaging deep learning model for predicting extraprostatic extension in prostate cancer using MpMRI and 18 F-PSMA-PET/CT.

Multimodal imaging deep learning model for predicting extraprostatic extension in prostate cancer using MpMRI and 18 F-PSMA-PET/CT.

Objective: This study aimed to construct a multimodal imaging deep learning (DL) model integrating mpMRI and 18F-PSMA-PET/CT for the prediction of extraprostatic extension (EPE) in prostate cancer, and to assess its effectiveness in enhancing the diagnostic accuracy of radiologists.

Methods: Clinical and imaging data were retrospectively collected from patients with pathologically confirmed prostate cancer (PCa) who underwent radical prostatectomy (RP). Data were collected from a primary institution (Center 1, n = 197) between January 2019 and June 2022 and an external institution (Center 2, n = 36) between July 2021 and November 2022. A multimodal DL model incorporating mpMRI and 18F-PSMA-PET/CT was developed to support radiologists in assessing EPE using the EPE-grade scoring system. The predictive performance of the DL model was compared with that of single-modality models, as well as with radiologist assessments with and without model assistance. Clinical net benefit of the model was also assessed.

Results: For patients in Center 1, the area under the curve (AUC) for predicting EPE was 0.76 (0.72-0.80), 0.77 (0.70-0.82), and 0.82 (0.78-0.87) for the mpMRI-based DL model, PET/CT-based DL model, and the combined mpMRI + PET/CT multimodal DL model, respectively. In the external test set (Center 2), the AUCs for these models were 0.75 (0.60-0.88), 0.77 (0.72-0.88), and 0.81 (0.63-0.97), respectively. The multimodal DL model demonstrated superior predictive accuracy compared to single-modality models in both internal and external validations. The deep learning-assisted EPE-grade scoring model significantly improved AUC and sensitivity compared to radiologist EPE-grade scoring alone (P < 0.05), with a modest reduction in specificity. Additionally, the deep learning-assisted scoring model provided greater clinical net benefit than the radiologist EPE-grade score used by radiologists alone.

Conclusion: The multimodal imaging deep learning model, integrating mpMRI and 18 F-PSMA PET/CT, demonstrates promising predictive performance for EPE in prostate cancer and enhances the accuracy of radiologists in EPE assessment. The model holds potential as a supportive tool for more individualized and precise therapeutic decision-making.

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来源期刊
Cancer Imaging
Cancer Imaging ONCOLOGY-RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING
CiteScore
7.00
自引率
0.00%
发文量
66
审稿时长
>12 weeks
期刊介绍: Cancer Imaging is an open access, peer-reviewed journal publishing original articles, reviews and editorials written by expert international radiologists working in oncology. The journal encompasses CT, MR, PET, ultrasound, radionuclide and multimodal imaging in all kinds of malignant tumours, plus new developments, techniques and innovations. Topics of interest include: Breast Imaging Chest Complications of treatment Ear, Nose & Throat Gastrointestinal Hepatobiliary & Pancreatic Imaging biomarkers Interventional Lymphoma Measurement of tumour response Molecular functional imaging Musculoskeletal Neuro oncology Nuclear Medicine Paediatric.
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