注意力引导多任务学习的前列腺癌盆腔淋巴结转移预测

doi:10.16183/j.cnki.jsjtu.2023.491

上海交通大学学报 ›› 2025, Vol. 59 ›› Issue (8): 1216-1224.doi: 10.16183/j.cnki.jsjtu.2023.491

• 电子信息与电气工程 • 上一篇

注意力引导多任务学习的前列腺癌盆腔淋巴结转移预测

张志远¹, 胡冀苏², 张跃跃³, 钱旭升², 周志勇², 戴亚康¹^,²()

1.徐州医科大学医学影像学院, 江苏徐州 221000
2.中国科学院苏州生物医学工程技术研究所, 江苏苏州 215163
3.苏州大学附属第二医院, 江苏苏州 215000

收稿日期:2023-09-25 修回日期:2023-11-08 接受日期:2023-11-24 出版日期:2025-08-28 发布日期:2025-08-26
通讯作者: 戴亚康 E-mail:daiyk@sibet.ac.cn
作者简介:张志远(1999—),硕士生,从事医学影像分析研究.
基金资助:
中国科学院青年创新促进会项目(2021324);苏州市卫健委临床重点病种诊疗专项(LCZX202001);苏州市科技计划项目(SKY2022003)

Attention-Guided Multi-Task Learning for Prostate Cancer Pelvic Lymph Node Metastasis Prediction

ZHANG Zhiyuan¹, HU Jisu², ZHANG Yueyue³, QIAN Xusheng², ZHOU Zhiyong², DAI Yakang¹^,²()

1. School of Medical Imaging, Xuzhou Medical University, Xuzhou 221000, Jiangsu, China
2. Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Science, Suzhou 215163, Jiangsu, China
3. The Second Affiliated Hospital of Suzhou University, Suzhou 215000, Jiangsu, China

Received:2023-09-25 Revised:2023-11-08 Accepted:2023-11-24 Online:2025-08-28 Published:2025-08-26
Contact: DAI Yakang E-mail:daiyk@sibet.ac.cn

摘要/Abstract

摘要：

基于前列腺癌原发灶的术前磁共振影像定量特征预测盆腔淋巴结转移(PLNM)是治疗方案制定的重要参考依据.然而,现有预测方法对肿瘤原发灶内部的异质性信息提取不足,导致提取的图像定量特征与PLNM关联性较弱.针对这一问题,提出一种以肿瘤分割任务为辅助任务的注意力引导多任务学习网络用于PLNM预测.首先,在肿瘤分割网络中,提出多分支各向异性大核注意力模块,通过不同分支和各向异性大卷积核的融合扩大的感受野以有效捕获肿瘤的局部和全局信息.其次,在PLNM预测网络中,设计多尺度特征交互融合注意力模块,对多尺度特征进行层次化融合筛选.在320例数据集的实验中,所提方法的精度召回曲线下面积值和受试者操作特征曲线下面积值分别为(85.44±2.04)%和 (91.86±2.18)%,优于经典的单任务分类方法和多任务方法.

关键词: 前列腺癌盆腔淋巴结转移, 多任务学习, 多分支各向异性大核注意力模块, 多尺度特征交互融合注意力模块, 多参数磁共振

Abstract:

The prediction of pelvic lymph node metastasis (PLNM) based on quantitative preoperative magnetic resonance imaging features of prostate cancer primary tumor is an important reference for treatment planning. However, current prediction methods inadequately capture the heterogeneity within the primary tumor, resulting in a weak correlation between extracted quantitative image features and PLNM prediction. To address the aforementioned issues, an attention-guided multi-task learning network with tumor segmentation as an auxiliary task is proposed for PLNM prediction. First, within the tumor segmentation network, a multi-branch anisotropic large kernel attention module is introduced, where a larger receptive field is obtained through different branches and anisotropic large convolutional kernels, effectively capturing both local and global tumor information. Then, within the PLNM prediction network, a multi-scale feature interaction fusion attention module is introduced to hierarchically fuse and select features from multiple scales. The experimental results on a dataset of 320 cases demonstrate that the area under the precision-recall curve and the area under the receiver operating characteristic curve of the method proposed are (85.44±2.04)% and (91.86±2.18)%, which are superior to state-of-the-art methods and multi-task approaches.

Key words: prostate cancer pelvic lymph node metastasis (PLNM), multi-task learning, multi-branch anisotropic large kernel attention, multi-scale feature interaction fusion attention, multiparametric magnetic resonance imaging (mpMRI)

中图分类号:

张志远, 胡冀苏, 张跃跃, 钱旭升, 周志勇, 戴亚康. 注意力引导多任务学习的前列腺癌盆腔淋巴结转移预测[J]. 上海交通大学学报, 2025, 59(8): 1216-1224.

ZHANG Zhiyuan, HU Jisu, ZHANG Yueyue, QIAN Xusheng, ZHOU Zhiyong, DAI Yakang. Attention-Guided Multi-Task Learning for Prostate Cancer Pelvic Lymph Node Metastasis Prediction[J]. Journal of Shanghai Jiao Tong University, 2025, 59(8): 1216-1224.

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参考文献 27

[1]	SIEGEL R L, MILLER K D, FUCHS H E, et al. Cancer statistics, 2022[J]. CA: A Cancer Journal for Clinicians, 2022, 72(1): 7-33.
[2]	WILCZAK W, WITTMER C, CLAUDITZ T, et al. Marked prognostic impact of minimal lymphatic tumor spread in prostate cancer[J]. European Urology, 2018, 74(3): 376-386. doi: S0302-2838(18)30423-8 pmid: 29908878
[3]	蒋璋栋, 吴开杰. 前列腺癌淋巴结转移分子机制的研究进展[J]. 现代泌尿外科杂志, 2022, 27(1): 79-83.
	JIANG Zhangdong, WU Kaijie. Progress of molecular mechanism leading to lymph node metastasis in prostate cancer[J]. Journal of Modern Urology, 2022, 27(1): 79-83.
[4]	YAKAR D, DEBATS O A, BOMERS J G R, et al. Predictive value of MRI in the localization, staging, volume estimation, assessment of aggressiveness, and guidance of radiotherapy and biopsies in prostate cancer[J]. Journal of Magnetic Resonance Imaging: JMRI, 2012, 35(1): 20-31.
[5]	扶昭, 陈燕清, 赵春丽, 等. 3.0 T MR扩散加权成像ADC值及ROC曲线分析对前列腺癌淋巴结转移的诊断价值[J]. 磁共振成像, 2021, 12(2): 49-51.
	FU Zhao, CHEN Yanqing, ZHAO Chunli, et al. Value of 3.0 T MR diffusion-weighted imaging and ROC curve in diagnosis of lymph node metastasis in patients with prostate cancer[J]. Chinese Journal of Magnetic Resonance Imaging, 2021, 12(2): 49-51.
[6]	BREMBILLA G, DELL’OGLIO P, STABILE A, et al. Preoperative multiparametric MRI of the prostate for the prediction of lymph node metastases in prostate cancer patients treated with extended pelvic lymph node dissection[J]. European Radiology, 2018, 28(5): 1969-1976. doi: 10.1007/s00330-017-5229-6 pmid: 29270644
[7]	PORPIGLIA F, MANFREDI M, MELE F, et al. Indication to pelvic lymph nodes dissection for prostate cancer: The role of multiparametric magnetic resonance imaging when the risk of lymph nodes invasion according to Briganti updated nomogram is 5[J]. Prostate Cancer and Prostatic Diseases, 2018, 21(1): 85-91.
[8]	FURUBAYASHI N, NEGISHI T, UOZUMI T, et al. Eliminating microscopic lymph node metastasis by performing pelvic lymph node dissection during radical prostatectomy for prostate cancer[J]. Molecular and Clinical Oncology, 2020, 12(2): 104-110. doi: 10.3892/mco.2019.1965 pmid: 31929879
[9]	LIU X, TIAN J Y, WU J Y, et al. Utility of diffusion weighted imaging-based radiomics nomogram to predict pelvic lymph nodes metastasis in prostate cancer[J]. BMC Medical Imaging, 2022, 22(1): 190. doi: 10.1186/s12880-022-00905-3 pmid: 36333664
[10]	ZHENG H X, MIAO Q, LIU Y K, et al. Multiparametric MRI-based radiomics model to predict pelvic lymph node invasion for patients with prostate cancer[J]. European Radiology, 2022, 32(8): 5688-5699. doi: 10.1007/s00330-022-08625-6 pmid: 35238971
[11]	HOU Y, BAO J, SONG Y, et al. Integration of clinicopathologic identification and deep transferrable image feature representation improves predictions of lymph node metastasis in prostate cancer[J]. EBioMedicine, 2021, 68: 103395.
[12]	ZHOU Y, CHEN H J, LI Y F, et al. Multi-task learning for segmentation and classification of tumors in 3D automated breast ultrasound images[J]. Medical Image Analysis, 2021, 70: 101918.
[13]	CHENG J H, LIU J, KUANG H L, et al. A fully automated multimodal MRI-based multi-task learning for glioma segmentation and IDH genotyping[J]. IEEE Transactions on Medical Imaging, 2022, 41(6): 1520-1532.
[14]	LAI H R, FU S R, ZHANG J, et al. Prior knowledge-aware fusion network for prediction of macrovascular invasion in hepatocellular carcinoma[J]. IEEE Transactions on Medical Imaging, 2022, 41(10): 2644-2657.
[15]	FUTREGA M, MILESI A, MARCINKIEWICZ M, et al. Optimized U-net for brain tumor segmentation[C]// International MICCAI Brainlesion Workshop. Cham: Springer, 2022: 15-29.
[16]	GUO M H, LU C Z, LIU Z N, et al. Visual attention network[J]. Computational Visual Media, 2023, 9(4): 733-752.
[17]	VASWANI A, SHAZEER N, PARMAR N, et al. Attention is all you need[C]// Advances in Neural Information Processing Systems, New York, USA: Curran Associates Inc, 2017: 6000-6010.
[18]	CIPOLLA R, GAL Y, KENDALL A. Multi-task learning using uncertainty to weigh losses for scene geometry and semantics[C]// 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake City, USA: IEEE, 2018: 7482-7491.
[19]	KLEIN S, STARING M, MURPHY K, et al. Elastix: A toolbox for intensity-based medical image registration[J]. IEEE Transactions on Medical Imaging, 2010, 29(1): 196-205. doi: 10.1109/TMI.2009.2035616 pmid: 19923044
[20]	ISENSEE F, JAEGER P F, KOHL S A A, et al. nnU-Net: A self-configuring method for deep learning-based biomedical image segmentation[J]. Nature Methods, 2021, 18(2): 203-211. doi: 10.1038/s41592-020-01008-z pmid: 33288961
[21]	SAITO T, REHMSMEIER M. The precision-recall plot is more informative than the ROC plot when evaluating binary classifiers on imbalanced datasets[J]. PLoS One, 2015, 10(3): e0118432.
[22]	LUQUE A, CARRASCO A, MARTÍN A, et al. The impact of class imbalance in classification performance metrics based on the binary confusion matrix[J]. Pattern Recognition, 2019, 91: 216-231.
[23]	LUO Y N, WANG Z S. An improved ResNet algorithm based on CBAM[C]// 2021 International Conference on Computer Network, Electronic and Automation. Xi’an, China: IEEE, 2021: 121-125.
[24]	HUANG G, LIU Z, VAN DER MAATEN L, et al. Densely connected convolutional networks[C]// 2017 IEEE Conference on Computer Vision and Pattern Recognition. Honolulu, USA: IEEE, 2017: 2261-2269.
[25]	TAN M X, LE Q V. EfficientNet: Rethinking model scaling for convolutional neural networks[C]// International Conference on Machine Learning, Long Beach, USA: PMLR, 2019: 6105-6114.
[26]	SZEGEDY C, IOFFE S, VANHOUCKE V, et al. Inception-v4, inception-ResNet and the impact of residual connections on learning[C]// Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence. San Francisco, USA: ACM, 2017: 4278-4284.
[27]	HU J, SHEN L, SUN G. Squeeze-and-excitation networks[C]// 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake City, USA: IEEE, 2018: 7132-7141.

方法	(平均值±方差)/%
方法	AUPRC	AUROC	SEN	SPE	ACC	F₁
基线	78.91±6.07	90.11±3.21	67.02±10.42	91.27±2.66	84.37±3.99	70.73±7.94
基线+MBALKA	83.26±4.13	91.13±1.79	78.07±4.85	86.46±6.01	84.06±4.48	73.15±5.94
基线+MSFIFA	81.16±5.21	90.82±1.70	74.74±15.09	87.74±6.22	84.04±2.83	72.96±6.47
基线+MBALKA+MSFIFA	85.44±2.04	91.86±2.18	73.74±12.20	94.33±3.29	88.45±2.30	78.10±5.38

方法	(平均值±方差)/%
方法	AUPRC	AUROC	SEN	SPE	ACC	F₁
单任务	78.13±5.20	89.54±2.50	56.08±8.44	92.11±4.65	81.86±2.98	63.60±5.77
多任务 (等权重)	82.82±4.54	90.69±2.54	69.30±16.73	90.80±5.72	84.68±3.06	71.25±7.84
多任务 (不确定权重)	85.44±2.04	91.86±2.18	73.74±12.20	94.33±3.29	88.45±2.30	78.10±5.38

方法	(平均值±方差)/%
方法	AUPRC	AUROC	SEN	SPE	ACC	F₁
CBAMResNet50^[23]	78.83±5.01	89.33±3.68	58.25±12.11	90.79±5.12	81.55±4.44	63.88±9.64
DenseNet121^[24]	82.03±3.38	90.24±1.70	67.08±11.58	90.35±6.25	83.73±2.76	69.87±5.14
EfficientNet^[25]	80.10±6.68	88.25±3.84	64.74±5.55	87.36±11.00	80.93±8.67	66.82±10.87
InceptionV4^[26]	83.15±2.13	90.31±1.21	78.01±9.64	81.71±10.11	80.67±6.28	70.02±6.34
SeResNet50^[27]	83.30±4.80	90.30±3.18	73.63±2.41	92.14±4.50	86.88±3.58	76.34±5.13
本文方法	85.44±2.04	91.86±2.18	73.74±12.20	94.33±3.29	88.45±2.30	78.10±5.38

方法	(平均值±方差)/%
方法	AUPRC	AUROC	SEN	SPE	ACC	F₁
CMSVNet^[12]	82.68±4.53	89.81±2.42	81.29±8.49	80.32±6.49	80.62±2.44	70.49±1.49
MTTU-Net^[13]	81.80±6.53	91.65±2.41	84.62±11.37	85.07±11.00	84.96±5.79	76.58±5.30
PKAF-Net^[14]	78.68±5.16	88.52±2.03	69.12±11.76	91.29±5.08	85.00±2.10	72.09±4.67
多任务 (不确定权重)	85.44±2.04	91.86±2.18	73.74±12.20	94.33±3.29	88.45±2.30	78.10±5.38

注意力引导多任务学习的前列腺癌盆腔淋巴结转移预测

Attention-Guided Multi-Task Learning for Prostate Cancer Pelvic Lymph Node Metastasis Prediction

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