組織切片AI識別可預測結直腸癌預後
作者：
小柯機器人發布時間：2020/2/10 9:11:05
近日，挪威奧斯陸大學醫院Håvard E Danielsen研究團隊研究了預測結直腸癌結局的深度學習。該研究於2020年2月1日發表於國際一流學術期刊《柳葉刀》雜誌上。
為了完善輔助治療的選擇，早期結直腸癌患者分層的預後標記物急需改善。該研究的目的是通過使用深度學習直接分析掃描的常規蘇木精和伊紅染色切片，從而開發一種生物標記物，來預測大腸癌切除術後患者的預後。
研究組從四個隊列中疾病預後明顯較好或較差的患者中提取超過1200萬個圖像塊，用於訓練10個卷積神經網絡，以構建分類超大型異構圖像。
結合10個網絡的預後生物標記物通過非明顯預後的患者來確定。該標記物在920名患者身上進行了測試，載玻片在英國製備，然後根據預先確定的方案在1122名患者身上進行獨立驗證，這些患者使用單藥卡培他濱進行治療，載玻片在挪威製備。所有隊列只包括可切除腫瘤的患者。
來自四個隊列的828名患者有明確的腫瘤特異性生存率，將其作為訓練隊列。1645名患者生存率不明顯，用於校正。在驗證隊列的初步分析中，生物標記物不良預後與良好預後的風險比為3.84，在校正了相同隊列單變量分析中已建立的預後標記物，例如pN期、pT期、淋巴侵犯、靜脈血管侵犯之後，該風險比為3.04。
總之，結合蘇木精和伊紅染色腫瘤組織切片的數字掃描，開發了一種臨床可用的預後標記物。在大量獨立的患者群體中，該檢測已廣泛評估，與已建立的分子和形態學預後標記物相互依賴，並優於後者，且在腫瘤期和淋巴結期結果一致。
生物標記物將II期和III期患者進行足夠明顯的預後分層，這可用於指導輔助治療的選擇，避免對極低風險患者進行治療，並確定患者受益於更密集的治療方案。
附：英文原文
Title: Deep learning for prediction of colorectal cancer outcome: a discovery and validation study
Author: Ole-Johan Skrede, Sepp De Raedt, Andreas Kleppe, Tarjei S Hveem, Knut Liestl, 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, Hvard E Danielsen
Issue&Volume: 2020/02/01
Abstract:
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.
DOI: 10.1016/S0140-6736(19)32998-8
Source: https://www.thelancet.com/journals/lancet/article/PIIS0140-6736(19)32998-8/fulltext