Radiological tumor classification across imaging modality and histology.
Radiological tumor classification across imaging modality and histology.
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DOI：
10.1038/s42256-021-00377-0
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发表时间：
2021-09
影响因子：
23.8
通讯作者：
Li R
中科院分区：
文献类型：
--
作者：
Wu J;Li C;Gensheimer M;Padda S;Kato F;Shirato H;Wei Y;Schönlieb CB;Price SJ;Jaffray D;Heymach J;Neal JW;Loo BW Jr;Wakelee H;Diehn M;Li R
放射组学是指从放射学扫描中高通量提取定量特征，并广泛用于搜索用于预测临床结果的成像生物标志物。目前的放射组学特征具有有限的再现性和普遍性，因为大多数特征取决于成像模式和肿瘤组织学，使得它们对扫描协议的变化敏感。在这里，我们提出了新的放射学功能，专门设计，以确保不同组织和成像对比度的兼容性。这些特征提供了肿瘤形态学和空间异质性的系统表征。在一项1,682例患者的国际多机构研究中，我们发现并验证了三种恶性肿瘤和两种主要成像方式的四种统一成像亚型。这些肿瘤亚型表现出不同的分子特征，并在常规治疗后复发。在用免疫疗法治疗的晚期肺癌中，与其他亚型相比，一种亚型与生存率提高和肿瘤浸润淋巴细胞增加相关。深度学习可以实现自动肿瘤分割和可重复的亚型识别，这可以促进实际实施。统一的放射学肿瘤分类可以为精准医学的预后和治疗反应提供信息。
Radiomics refers to the high-throughput extraction of quantitative features from radiological scans and is widely used to search for imaging biomarkers for prediction of clinical outcomes. Current radiomic signatures suffer from limited reproducibility and generalizability, because most features are dependent on imaging modality and tumor histology, making them sensitive to variations in scan protocol. Here, we propose novel radiological features that are specially designed to ensure compatibility across diverse tissues and imaging contrast. These features provide systematic characterization of tumor morphology and spatial heterogeneity. In an international multi-institution study of 1,682 patients, we discover and validate four unifying imaging subtypes across three malignancies and two major imaging modalities. These tumor subtypes demonstrate distinct molecular characteristics and prognoses after conventional therapies. In advanced lung cancer treated with immunotherapy, one subtype is associated with improved survival and increased tumor-infiltrating lymphocytes compared with the others. Deep learning enables automatic tumor segmentation and reproducible subtype identification, which can facilitate practical implementation. The unifying radiological tumor classification may inform prognosis and treatment response for precision medicine.
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影响因子：
4.6
作者：
Li A;Barati Farimani A;Zhang YJ
通讯作者：
Zhang YJ
DOI：
10.1186/s13058-017-0846-1
发表时间：
2017-05-18
期刊：
Breast cancer research : BCR
影响因子：
--
作者：
Braman NM;Etesami M;Prasanna P;Dubchuk C;Gilmore H;Tiwari P;Plecha D;Madabhushi A
通讯作者：
Madabhushi A
影响因子：
14.9
作者：
Ritchie ME;Phipson B;Wu D;Hu Y;Law CW;Shi W;Smyth GK
通讯作者：
Smyth GK
影响因子：
19.7
作者：
Berenguer, Roberto;del Rosario Pastor-Juan, Maria;Sabater, Sebastia
通讯作者：
Sabater, Sebastia
影响因子：
4.6
作者：
Li, Angran;Chen, Ruijia;Zhang, Yongjie Jessica
通讯作者：
Zhang, Yongjie Jessica