Selective identification and localization of indolent and aggressive prostate cancers via CorrSigNIA: an MRI-pathology correlation and deep learning framework.
Selective identification and localization of indolent and aggressive prostate cancers via CorrSigNIA: an MRI-pathology correlation and deep learning framework.
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DOI：
10.1016/j.media.2021.102288
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发表时间：
2022-01
影响因子：
10.9
通讯作者：
Rusu M
中科院分区：
文献类型：
--
作者：
Bhattacharya I;Seetharaman A;Kunder C;Shao W;Chen LC;Soerensen SJC;Wang JB;Teslovich NC;Fan RE;Ghanouni P;Brooks JD;Sonn GA;Rusu M
在磁共振成像(MRI)上检测前列腺癌并区分惰性和侵袭性疾病的自动化方法可以帮助早期诊断和治疗计划。现有的前列腺癌自动检测方法大多依赖于精度有限的地面真实标记，忽略了在切除组织上观察到的疾病病理特征，并且当它们共存于混合病变中时，不能选择性地识别侵袭性(格里森型≥4)和惰性(格里森型=3)癌。在本文中，我们提出了一种放射-病理融合方法，CorsSigNIA，用于在MRI上选择性地识别和定位惰性和侵袭性前列腺癌。CorSigNIA使用注册的MRI和来自前列腺癌根治术患者的整体组织病理学图像来获得准确的地面真实标签，并了解放射图像和病理图像之间的相关特征。然后将这些相关特征用于卷积神经网络体系结构，以在前列腺癌MRI上检测和定位正常组织、惰性癌和侵袭性癌。CorSigNIA在98名男性的数据集上进行了培训和验证，其中包括74名接受根治性前列腺切除术的男性和24名前列腺癌MRI正常的男性。CorSigNIA在三个独立的测试组上进行了测试，其中包括55名接受根治性前列腺切除术的男性，275名接受靶向活检的男性，以及15名前列腺癌MRI正常的男性。CorsigNIA在区分男性癌症和非癌症方面的准确率达到80%，在根治性前列腺切除术和活检队列患者中发现癌症的病变水平ROC-AUC为0.81±0.31，在根治性前列腺切除术和活检队列患者中检测临床显著癌症的病变水平ROC-AUC分别为0.82±0.31和0.86±0.26。在不同的评估指标和队列中，CorSigNIA的表现一直优于其他方法。在临床环境中，Corr SigNIA可用于前列腺癌检测以及前列腺癌惰性和侵袭性成分的选择性识别，从而通过帮助指导定向活检、减少不必要的活检以及选择和计划治疗来改善前列腺癌的护理。
Automated methods for detecting prostate cancer and distinguishing indolent from aggressive disease on Magnetic Resonance Imaging (MRI) could assist in early diagnosis and treatment planning. Existing automated methods of prostate cancer detection mostly rely on ground truth labels with limited accuracy, ignore disease pathology characteristics observed on resected tissue, and cannot selectively identify aggressive (Gleason Pattern≥4) and indolent (Gleason Pattern=3) cancers when they co-exist in mixed lesions. In this paper, we present a radiology-pathology fusion approach, CorrSigNIA, for the selective identification and localization of indolent and aggressive prostate cancer on MRI. CorrSigNIA uses registered MRI and whole-mount histopathology images from radical prostatectomy patients to derive accurate ground truth labels and learn correlated features between radiology and pathology images. These correlated features are then used in a convolutional neural network architecture to detect and localize normal tissue, indolent cancer, and aggressive cancer on prostate MRI. CorrSigNIA was trained and validated on a dataset of 98 men, including 74 men that underwent radical prostatectomy and 24 men with normal prostate MRI. CorrSigNIA was tested on three independent test sets including 55 men that underwent radical prostatectomy, 275 men that underwent targeted biopsies, and 15 men with normal prostate MRI. CorrSigNIA achieved an accuracy of 80% in distinguishing between men with and without cancer, a lesion-level ROC-AUC of 0.81±0.31 in detecting cancers in both radical prostatectomy and biopsy cohort patients, and lesion-levels ROC-AUCs of 0.82±0.31 and 0.86±0.26 in detecting clinically significant cancers in radical prostatectomy and biopsy cohort patients respectively. CorrSigNIA consistently outperformed other methods across different evaluation metrics and cohorts. In clinical settings, CorrSigNIA may be used in prostate cancer detection as well as in selective identification of indolent and aggressive components of prostate cancer, thereby improving prostate cancer care by helping guide targeted biopsies, reducing unnecessary biopsies, and selecting and planning treatment.
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DOI：
10.1016/j.compmedimag.2017.12.002
发表时间：
2018-01-01
影响因子：
5.7
作者：
Losnegard, Are;Reisaeter, Lars;Lundervold, Arvid
通讯作者：
Lundervold, Arvid
影响因子：
10.6
作者：
Litjens, Geert;Debats, Oscar;Huisman, Henkjan
通讯作者：
Huisman, Henkjan
DOI：
10.3390/e21040356
发表时间：
2019-04-02
期刊：
Entropy (Basel, Switzerland)
影响因子：
--
作者：
García G;Colomer A;Naranjo V
通讯作者：
Naranjo V
影响因子：
10.6
作者：
Cao, Ruiming;Bajgiran, Amirhossein Mohammadian;Sung, Kyunghyun
通讯作者：
Sung, Kyunghyun
DOI：
10.1109/tpami.2017.2699184
发表时间：
2018-04-01
影响因子：
23.6
作者：
Chen, Liang-Chieh;Papandreou, George;Yuille, Alan L.
通讯作者：
Yuille, Alan L.