Lancet:用机器学习研发大肠癌预后标志物
创作:Lexi 审核:Lexi 03月01日
  • 用深度学习分析结直肠癌患者肿瘤组织H&E染色切片图像,开发用于预后预测的生物标志物;
  • 训练、调试、测试和检验队列,分别包括828、1645、920和1122例患者;
  • 在验证队列的初步分析中,该生物标志物的不良预后与良好预后的危险比为3.84;
  • 对已建立的、单变量(pN分级、pT分级、淋巴管浸润和静脉血管浸润)分析具有显著性的预后标志物进行调整后,该危险比为3.04;
  • 该标志物可改进对患者预后的风险分层,或能指导辅助疗法选择。
主编推荐语
Lexi
在结直肠癌(CRC)的治疗中,需要进一步改善预后标志物来对早期CRC患者进行分层,以完善辅助治疗的选择。最新发表在Lancet的研究旨在通过使用深度学习直接分析常规扫描的苏木精和伊红染色切片,建立原发性CRC切除术后患者预后生物标志物——DoMore-v1-CRC分类器。该方法已在大量独立的患者群体中进行了广泛评估,与已建立的分子和形态学预后标志物相关并优于它们,并在不同的肿瘤阶段到一致的结果。生物标志物将II期和III期患者划分为足够明确的预后组,能帮助避免在非常低的风险组中进行治疗,并确定哪些患者将受益于更强化的治疗方案,该生物标志物将可能用于指导辅助治疗的选择。
关键字
延伸阅读本研究的原文信息和链接出处,以及相关解读和评论文章。欢迎读者朋友们推荐!
图片
Lancet [IF:60.392]

Deep learning for prediction of colorectal cancer outcome: a discovery and validation study

结直肠癌预后预测的深度学习:一项发现和验证研究

10.1016/S0140-6736(19)32998-8

02-01, Article

Abstract & Authors:展开

Abstract:收起
Background: Improved markers of prognosis are needed to stratify patients with early-stage colorectal cancer to refine selection of adjuvant therapy. The aim of the present study was to develop a biomarker of patient outcome after primary colorectal cancer resection by directly analysing scanned conventional haematoxylin and eosin stained sections using deep learning.
Methods: More than 12 000 000 image tiles from patients with a distinctly good or poor disease outcome from four cohorts were used to train a total of ten convolutional neural networks, purpose-built for classifying supersized heterogeneous images. A prognostic biomarker integrating the ten networks was determined using patients with a non-distinct outcome. The marker was tested on 920 patients with slides prepared in the UK, and then independently validated according to a predefined protocol in 1122 patients treated with single-agent capecitabine using slides prepared in Norway. All cohorts included only patients with resectable tumours, and a formalin-fixed, paraffin-embedded tumour tissue block available for analysis. The primary outcome was cancer-specific survival.
Findings: 828 patients from four cohorts had a distinct outcome and were used as a training cohort to obtain clear ground truth. 1645 patients had a non-distinct outcome and were used for tuning. The biomarker provided a hazard ratio for poor versus good prognosis of 3·84 (95% CI 2·72–5·43; p<0·0001) in the primary analysis of the validation cohort, and 3·04 (2·07–4·47; p<0·0001) after adjusting for established prognostic markers significant in univariable analyses of the same cohort, which were pN stage, pT stage, lymphatic invasion, and venous vascular invasion.
Interpretation: A clinically useful prognostic marker was developed using deep learning allied to digital scanning of conventional haematoxylin and eosin stained tumour tissue sections. The assay has been extensively evaluated in large, independent patient populations, correlates with and outperforms established molecular and morphological prognostic markers, and gives consistent results across tumour and nodal stage. The biomarker stratified stage II and III patients into sufficiently distinct prognostic groups that potentially could be used to guide selection of adjuvant treatment by avoiding therapy in very low risk groups and identifying patients who would benefit from more intensive treatment regimes.
Funding: The Research Council of Norway.

First Authors:
Ole-Johan Skrede,Sepp De Raedt

Correspondence Authors:
Håvard E Danielsen

All Authors:
Ole-Johan Skrede,Sepp De Raedt,Andreas Kleppe,Tarjei S Hveem,Knut Liestøl,John Maddison,Hanne A Askautrud,Manohar Pradhan,John Arne Nesheim,Fritz Albregtsen,Inger Nina Farstad,Enric Domingo,David N Church,Arild Nesbakken,Neil A Shepherd,Ian Tomlinson,Rachel Kerr,Marco Novelli,David J Kerr,Håvard E Danielsen

评论