A clinical decision support system optimising adjuvant chemotherapy for colorectal cancers by integrating deep learning and pathological staging markers: a development and validation study,The Lancet Oncology

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A clinical decision support system optimising adjuvant chemotherapy for colorectal cancers by integrating deep learning and pathological staging markers: a development and validation study
The Lancet Oncology ( IF 51.1 ) Pub Date : 2022-08-11 , DOI: 10.1016/s1470-2045(22)00391-6
Andreas Kleppe ₁ , Ole-Johan Skrede ₁ , Sepp De Raedt ₁ , Tarjei S Hveem ₂ , Hanne A Askautrud ₂ , Jørn E Jacobsen ₃ , David N Church ₄ , Arild Nesbakken ₅ , Neil A Shepherd ₆ , Marco Novelli ₇ , Rachel Kerr ₈ , Knut Liestøl ₁ , David J Kerr ₉ , Håvard E Danielsen ₁₀

Affiliation

Institute for Cancer Genetics and Informatics, Oslo University Hospital, Oslo, Norway; Department of Informatics, University of Oslo, Oslo, Norway.
Institute for Cancer Genetics and Informatics, Oslo University Hospital, Oslo, Norway.
Institute for Cancer Genetics and Informatics, Oslo University Hospital, Oslo, Norway; Department of Research and Development, Vestfold Hospital Trust, Tønsberg, Norway.
National Institute of Health Research Oxford Biomedical Research Centre, Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Oxford, UK; Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK.
Department of Gastrointestinal Surgery, Oslo University Hospital, Oslo, Norway; Institute of Clinical Medicine, University of Oslo, Oslo, Norway; KG Jebsen Colorectal Cancer Research Centre, Oslo, Norway.
Gloucestershire Cellular Pathology Laboratory, Cheltenham General Hospital, Cheltenham, UK.
Institute for Cancer Genetics and Informatics, Oslo University Hospital, Oslo, Norway; Research Department of Pathology, University College London, London, UK.
Department of Oncology, University of Oxford, Oxford, UK.
Nuffield Division of Clinical Laboratory Sciences, University of Oxford, Oxford, UK.
Institute for Cancer Genetics and Informatics, Oslo University Hospital, Oslo, Norway; Department of Informatics, University of Oslo, Oslo, Norway; Nuffield Division of Clinical Laboratory Sciences, University of Oxford, Oxford, UK.

Background

The DoMore-v1-CRC marker was recently developed using deep learning and conventional haematoxylin and eosin-stained tissue sections, and was observed to outperform established molecular and morphological markers of patient outcome after primary colorectal cancer resection. The aim of the present study was to develop a clinical decision support system based on DoMore-v1-CRC and pathological staging markers to facilitate individualised selection of adjuvant treatment.

Methods

We estimated cancer-specific survival in subgroups formed by pathological tumour stage (pT<4 or pT4), pathological nodal stage (pN0, pN1, or pN2), number of lymph nodes sampled (≤12 or >12) if not pN2, and DoMore-v1-CRC classification (good, uncertain, or poor prognosis) in 997 patients with stage II or III colorectal cancer considered to have no residual tumour (R0) from two community-based cohorts in Norway and the UK, and used these data to define three risk groups. An external cohort of 1075 patients with stage II or III R0 colorectal cancer from the QUASAR 2 trial was used for validation; these patients were treated with single-agent capecitabine. The proposed risk stratification system was evaluated using Cox regression analysis. We similarly evaluated a risk stratification system intended to reflect current guidelines and clinical practice. The primary outcome was cancer-specific survival.

Findings

The new risk stratification system provided a hazard ratio of 10·71 (95% CI 6·39–17·93; p<0·0001) for high-risk versus low-risk patients and 3·06 (1·73–5·42; p=0·0001) for intermediate versus low risk in the primary analysis of the validation cohort. Estimated 3-year cancer-specific survival was 97·2% (95% CI 95·1–98·4; n=445 [41%]) for the low-risk group, 94·8% (91·7–96·7; n=339 [32%]) for the intermediate-risk group, and 77·6% (72·1–82·1; n=291 [27%]) for the high-risk group. The guideline-based risk grouping was observed to be less prognostic and informative (the low-risk group comprised only 142 [13%] of the 1075 patients).

Interpretation

Integrating DoMore-v1-CRC and pathological staging markers provided a clinical decision support system that risk stratifies more accurately than its constituent elements, and identifies substantially more patients with stage II and III colorectal cancer with similarly good prognosis as the low-risk group in current guidelines. Avoiding adjuvant chemotherapy in these patients might be safe, and could reduce morbidity, mortality, and treatment costs.

Funding

The Research Council of Norway.

中文翻译：

通过整合深度学习和病理分期标记优化结直肠癌辅助化疗的临床决策支持系统：开发和验证研究

背景

DoMore-v1-CRC 标记物是最近使用深度学习和传统的苏木精和伊红染色组织切片开发的，并且被观察到优于原发性结直肠癌切除术后患者结果的既定分子和形态学标记物。本研究的目的是开发基于 DoMore-v1-CRC 和病理分期标志物的临床决策支持系统，以促进辅助治疗的个体化选择。

方法

我们估计了由病理肿瘤分期（pT<4 或 pT4）、病理淋巴结分期（pN0、pN1 或 pN2）、取样的淋巴结数量（≤12 或 >12）（如果不是 pN2）和来自挪威和英国的两个社区队列的 997 名 II 或 III 期结直肠癌患者的 DoMore-v1-CRC 分类（预后良好、不确定或不良），被认为没有残留肿瘤 (R0)，并使用了这些数据定义三个风险组。来自 QUASAR 2 试验的 1075 名 II 期或 III 期 R0 结直肠癌患者的外部队列用于验证；这些患者接受了单药卡培他滨治疗。使用 Cox 回归分析对建议的风险分层系统进行了评估。我们同样评估了旨在反映当前指南和临床实践的风险分层系统。主要结果是癌症特异性生存率。

发现

新的风险分层系统提供的风险比为 10·71 (95% CI 6·39–17·93；p<0·0001) 对高风险与低风险患者和 3·06 (1·73–5 ·42；p=0·0001) 在验证队列的主要分析中，中风险与低风险。估计的 3 年癌症特异性生存率为 97·2% (95% CI 95·1–98·4；n=445 [41%])，低风险组为 94·8% (91·7–96 ·7；n=339 [32%]) 为中等风险组，77·6% (72·1–82·1；n=291 [27%]) 为高风险组。观察到基于指南的风险分组的预后和信息量较少（低风险组仅包括 1075 名患者中的 142 名 [13%]）。