首页
Prediction of Response to Neoadjuvant Chemotherapy for Breast Cancer Based on Histomorphology Analysis of Needle Biopsy Images
-
摘要:目的 利用深度学习的方法对乳腺癌患者接受新辅助化疗（NAC）前的穿刺切片进行肿瘤区域和细胞核的自动分割，提取肿瘤区域细胞群特征，从而对乳腺癌NAC病理缓解程度进行预测。方法 收集在江苏省人民医院接受NAC治疗前的68位乳腺癌患者的术前穿刺HE染色切片，两位病理医生对其中12张穿刺切片进行了肿瘤区域的标记，其中8张作为训练集，4张作为测试集，剩余的56张由训练好的肿瘤区分割模型进行肿瘤分割。运用UNet++建立分割模型，分别对乳腺癌病理穿刺切片肿瘤区域和细胞核进行自动分割；然后，根据自动分割的肿瘤区域内细胞核，构建肿瘤内细胞层次的特征；最后运用特征选择方法选择有效的特征，通过五折交叉验证训练分类器模型预测NAC的病理缓解程度的高低。结果 基于68位患者的病理穿刺切片进行预测，最大相关最小冗余（mRMR）的特征选择方法筛选出的10个维度特征和随机森林（RF）分类器结合训练的模型预测结果的准确率最高，准确率达到82.35%，曲线下面积（AUC）值达到0.9082。结论 本模型能够在切片病理图像上自动分割肿瘤区域和细胞核，构建的肿瘤区域细胞核群的特征能够预测患者对NAC的病理缓解程度，方法可靠且可重复性较高，同时发现肿瘤区域细胞核纹理特征在预测中效果较好，进一步证实了肿瘤区域细胞核群对疗效预测具有重要意义。Abstract:Objective The deep learning method was used to automatically segment the tumor area and the cell nucleus based on needle biopsy images of breast cancer patients prior to receiving neoadjuvant chemotherapy (NAC), and then, the features of the cell clusters in the tumor area were identified to predict the level of pathological remission of breast cancer after NAC.Methods 68 breast cancer patients who were to receive NAC at Jiangsu Province Hospital were recruited and the hematoxylin-eosin (HE) stained preoperative biopsy sections of these patients were collected. Unet++ was used to establish a segmentation model and the tumor area and nucleus of the needle biopsy images were automatically segmented accordingly. Then, according to the nuclei in the automatically segmented tumor area, the features of the cells in the tumor were constructed. After that, effective features were selected through the feature selection method and the classifier model was constructed and trained with five-fold cross validation to predict the degree of post-NAC pathological remission.Results Predictions were made based on the needle biopsy images of the 68 patients. The model that combined the 10-dimensional features selected with the minimal redundancy-maximum-relevancy approach (mRMR) and training with the random forest (RF) classifier had the highest prediction accuracy, reaching 82.35%, and an area under curve (AUC) value of 0.9082.Conclusion This model automatically segments tumor areas and cell nucleus on the biopsy images. The features of the cell clusters which are analyzed and identified in the tumor area can be used to predict the pathological response of the patient to NAC. The method is reliable and replicable. In addition, we found that the textural features of cells in the tumor area was a useful predictor of patient response to NAC, which further confirmed that cell cluster in the tumor area is of great significance to the prediction of treatment outcome.
-
病毒性疾病是人类面临的重大威胁，我们前期研究[1]筛选出一种功能尚未确知的AY358935基因(简称AY基因，GenBank编号：AY358935.1)，表达相对分子质量为10.1×103的分泌蛋白，经生物信息学分析发现其功能可能与抗病毒相关。在本研究中，我们将进一步证实AY358935基因具体的抗病毒作用，并通过全基因组芯片分析了解其作用机制。现报告如下。
1. 材料与方法
1.1 材料
HEK293细胞：源自美国ATCC；水疱口炎病毒(VSV)：由四川大学华西医院生物治疗国家重点实验室文艳君老师提供；G418抗生素:美国Gibco公司；结晶紫：上海超研生物科技有限公司；台盼蓝：北京博奥森生物技术有限公司；DMEM培养基：原粉购自美国Gibco BRL公司，胎牛血清(FBS)和胰酶(Tyrisin)购自美国Gibco公司，按说明调配成液化培养基；羧甲基纤维素(钠)培养基即调制的DMEM(含4%羧甲基纤维素，2%FBS)；羊抗兔二抗(生物素标记)/(辣根过氧化物酶标记)、兔抗人β-actin抗体、SP试剂盒、DAB：北京中杉生物制剂公司。pcDNA3.1质粒购自美国Invitrogen公司，pcDNA3.1-AY质粒及AY蛋白多克隆抗体由本课题组前期制备并鉴定[1]。
1.2 实验方法
1.2.1 细胞转染及筛选鉴定
参照文献[2-4]方法以pcDNA3.1-AY及pcDNA3.1转染HEK293细胞。并通过抗生素G418进行稳定转染株筛选。将稳定转染pcDNA3.1-AY的HEK293细胞称为AY细胞，稳定转染pcDNA3.1的HEK293细胞称为pcDNA3.1细胞。
按照文献方法[2-5]分别收集AY细胞、pcDNA3.1细胞及空白HEK293细胞各5×106个，按标准程序裂解细胞，提取蛋白，通过Western blot鉴定细胞中是否表达AY蛋白。其中一抗对照采用兔抗人β-actin单克隆抗体。
1.2.2 病毒滴度测定
将AY细胞、pcDNA3.1细胞及空白HEK293细胞用24孔板培养，再用DMEM孵育，每孔1×105个，每组3个复孔。待细胞80%~90%生长汇合时，每孔加VSV感染混匀，感染复数(multiplicity of infection, MOI)(表示每个细胞受到感染的病毒颗粒数)为0.001。2 h后吸弃培养液，另加DMEM培养基1 mL。从感染后6 h开始，每间隔6 h吸取20 μL上清，保存于-20℃，共吸取6次。
参照文献[6]采用蚀斑分析(plaque assay)方法检测病毒滴度。分离培养以上3组细胞于24孔板，至95%~100%生长汇合。将病毒液以10倍梯度依次稀释，并加到3组细胞中孵育，隔15 min轻摇一次。孵育1 h后，吸弃培养基，用灭菌PBS轻洗一次，加入羧甲基纤维素培养基500 μL继续培养24~48 h。去除培养基后，加入10 g/L结晶紫染色液500 μL，染色20 min。自来水冲洗，观察计数每孔蚀斑，以空斑数目乘以稀释倍数即为病毒滴度。
1.2.3 细胞死亡率检测
上述3组细胞感染VSV 24 h后，用台盼蓝排斥试验检测每组细胞的死亡率。具体方法如下：收集全部细胞上清，用胰酶消化细胞并与相应的上清混合，1 200 r/min离心3 min，沉淀用PBS稀释成106 mL-1的单细胞悬液。按9:1的比例加入0.4%台盼蓝染液混匀，3 min内，用血细胞计数板显微镜下分别计数活细胞和死细胞，计算细胞死亡率。各组均以未感染VSV的细胞作对照。
1.2.4 全基因组cDNA分析
收集指数期生长的AY细胞5×106个，胰酶消化，2 000 r/min离心3 min弃上清，并用PBS洗净胰酶。加1 mL Trizol到细胞沉淀，反复吹打裂解细胞。把该Trizol裂解液保存于生物冰(低于15 ℃)中，送北京博奥生物芯片公司进一步抽提总RNA，并进行全基因组cDNA芯片检测。以pcDNA3.1细胞作平行对照。
1.3 统计学方法
计数资料以x±s表示。3组间比较用单因素方法分析和t检验，两组样本比较用独立样本t检验，生存曲线比较用log-rank检验，P < 0.05为差异有统计学意义。
2. 结果
2.1 基因转染与鉴定
分别提取AY细胞、pcDNA3.1细胞和空白HEK293细胞的总蛋白，Western blot鉴定结果(图 1)显示，在AY细胞组，目标蛋白表达明显较强，表明pcDNA3.1-AY质粒转染成功，稳定表达。
2.2 抗VSV病毒作用
各组细胞感染MOI为0.001的VSV病毒后，在各时间点采用蚀斑法分析并计算病毒滴度，结果(图 2)显示，AY细胞组上清病毒滴度较pcDNA3.1细胞组和空白HEK293细胞组的上清病毒滴度低，18 h时，上述3组细胞上清病毒滴度分别为(7.16±2.33)×105 PFU/mL、(6.25±2.05)×106 PFU/mL、(7.75±2.54)×106 PFU/mL，AY细胞组上清病毒滴度与其余两组的差异接近10倍(P < 0.01)，之后3组病毒滴度差异逐渐缩小。
![]()
图 2 各组细胞感染VSV病毒不同时间后的病毒滴度Figure 2. Virus titers in different time point post VSV infection* P < 0.01, vs. control group at the same time point病毒感染24 h后，镜下观察(图 3)表明，AY细胞组、pcDNA3.1细胞组和空白HEK293细胞组的细胞死亡率分别为(35.00±6.68)%、(78.33±15.03)%和(83.34±14.98)%，AY细胞组的细胞死亡率较其余两组降低(P < 0.01)。
![]()
图 3 各组细胞感染VSV病毒后24 h细胞状态(A, ×100)和细胞活性(B)Figure 3. Observe the cells states (A, ×100) and the activity of cells (B) at 24 h post VSV infection under a microscope* P < 0.01, vs. VSV in the same group2.3 全基因组cDNA分析结果
全基因组cDNA分析结果(附表)显示，与pcDNA3.1细胞对照比较，pcDNA3.1-AY细胞有30个基因表达上调在3倍以上。其中CHRM1上调最高(59倍)，其次为MTAP44(55倍)，上调在15倍以上的基因有FCER2(37倍)、IFIT4(26倍)、c-fos(23倍)、IP-10(22倍)、IL-8(18倍)、CCL20(16倍)、GIP2(15倍)、ISG15(15倍)。除了FCER和c-fos外，其余受调基因都是已知的干扰素激活基因(IFN-stimulated genes, ISGs)27%、干扰素效应基因(抗病毒基因, 17%)、细胞致炎因子(20%)。
附表 AY稳定转染后表达变化上调3倍以上的基因附表. The genes expressing that up-regulated by more than 3 times after stably transfected with AYDescription GenBank accession No. Gene Fold change IFN-activated genes IFN-induced, hepatitis C virus- associated microtubularaggregate protein (44×103) NM_006417 MTAP44 55.27 Interferon-inducedrotein with tetratricopeptide repeats 4 NM_001549 IFIT4/RIG-G 25.91 IFN-γ-inducible protein 10 NM_001565 IP-10/CXCL10 21.98 Interferon alpha-inducible protein AY888621 GIP2 15.40 Interferon alpha-inducible protein 27 NM_005532 IFI27 9.91 Interferon-induced protein with tetratricopeptide repeats 1 NM_001548 IFIT1/ISG56 5.44 Interferon-stimulated transcription factor 3, gamma 48×103 NM_006084 ISGF3G/IRF9 3.60 Interferon regulatory factor 7 U73036 IRF7 3.05 IFN effector gene Interferon-induced protein with tetratricopeptide repeats 1 NM_001548 IFIT1/ISG56 5.44 2′-5′-oligoadenylate synthetase 3 NM_006187 OAS3 4.44 2′-5′-oligoadenylate synthetase 1 NM_016816 OAS1 3.60 2′-5′-oligoadenylate synthetase-like protein NM_003733 OASL 5.66 IFN-stimulated protein, 15×103 NM_005101 ISG15 14.64 Chemokines and cytokines IFN-γ-inducible protein 10 NM_001565 IP-10/CXCL10 21.98 Interleukin 8 NM_000584 IL-8 18.40 Chemokine (C-C motif) ligand 20 NM_004591 CCL20 16.47 Chemokine (C-C motif) ligand 2 NM_002982 CCL2 4.43 Tumor necrosis factor receptor superfamily, member 9 NM_001561 TNFRSF9 6.75 Tumor necrosis factor receptor superfamily, member 12A NM_016639 TNFRSF12A 4.51 Allergic response Fc fragment of IgE, low affinity Ⅱ, receptor for (CD23) NM_002002 FCER2 36.87 Cell growth and maintenance Cholinergic receptor, muscarinic 1 NM_000738 CHRM1 59.17 v-fos FBJ murine osteosarcoma viral oncogene homolog NM_005252 c-fos 22.92 Fibroblast growth factor 18 NM_003862 FGF18 4.50 Fibroblast growth factor 21 NM_019113 FGF21 4.46 Dual specificity phosphatase 1 NM_004417 DUSP1 3.62 Dual specificity phosphatase 2 NM_004418 DUSP2 3.14 CD44 molecule (Indian blood group) NM_000610 CD44 4.77 CD24 molecule NM_013230 CD24 3.26 Syndecan 4 NM_002999 SDC4 3.70 Collagen, type Ⅱ, alpha 1 NM_033150 COL2A1 3.62 3. 讨论
近年来，一些介导天然免疫的宿主抗病毒小分子不断被发现，干扰素、防御素、抗菌肽、细胞黏着分子(cell adhesion molecule，CAF)、APOBEC家族，以及化学趋化因子如巨噬细胞炎症蛋白1(macrophage inflammatory protein 1，MIP-1)、调节活化正常T细胞表达分泌因子(regulated upon activation normal T-cell expressed and secreted gene，RANTES)、人干扰素诱导蛋白10(C-X-C motif chemokine 10，CXCL10)等，在启动机体早期免疫应答，及时清除感染病原体，调节适应性免疫反应等方面起着重要的作用[7-14]。
在前期研究[1]中，我们筛选出一个功能尚未确知的基因AY358935，生物学信息分析及鉴定结果显示，AY358935基因所表达的蛋白可能是一个VSV病毒感染后的早期反应蛋白，参与了宿主早期的抗病毒应答。在本研究中进一步分析了AY358935对VSV的抗病毒作用，并进行了全基因组cDNA分析。
本研究发现，AY358935基因转染HEK293细胞并感染VSV后，其细胞上清病毒滴度及细胞死亡率，与对照组比较明显降低，镜下观察发现该组细胞收缩，间隙增大，只有少量细胞变圆死亡，而对照的两组细胞形态大部分变圆，脱壁，稀疏，以上结果提示AY358935基因具有抗VSV病毒的作用。为排除AY358935基因对HEK293细胞本身生长的影响，本实验还同时检测到，在不受病毒感染时，各组细胞生长状态一致，死亡率无明显差别，表明AY358935基因表达对HEK293细胞生长无明显影响。
目前对AY358935基因的功能分析未见确切报道，本实验全基因组cDNA分析结果显示，AY358935高表达的细胞，大量基因表达上调，如干扰素激活基因56(IFN-stimulated gene 56，ISG56)、干扰素激活基因15(IFN-stimulated gene 15，ISG15)、寡腺苷酸合成酶1(oligoadenylate synthetase 1, OAS1)、寡腺苷酸合成酶3(oligoadenylate synthetase 3, OAS3)等，也涉及一些重要的趋化因子如CXCL10、巨噬细胞炎性蛋白20抗体基因(C-C motif ligand 20，CCL20)的表达调节。其中涉及天然免疫应答的基因占64%，分别是干扰素激活基因(27%)、干扰素效应基因(17%)、细胞因子和趋化因子(20%)。天然免疫应答是机体防御病毒感染的重要途径。分析认为，AY358935基因抗病毒机制可能是通过调控天然免疫分子应答。
ISGs是受干扰素表达活化，并参与完成干扰素生物学功能的一大类基因。IFN-α/β与细胞膜表面受体结合，激活贾纳斯激酶(Janus kinase，JAK)家族，激活JAK磷酸化信号传导及转录激活因子(signal transducers and activators of transcription，STATs)，STATs蛋白同源或异源二聚体化，并与其它因子形成转录复合物，从而激活ISGs的转录[15-17]。虽然大部分ISGs的具体功能还未确知，但很多ISGs在宿主抗病毒防御反应中的重要性已逐渐阐明。
在本研究中，OAS1、OAS3和寡腺苷酸合成酶L(oligoadenylate synthetase L，OASL)作为重要的抗病毒ISGs，在AY358935表达细胞中也明显上调表达，可能贡献于AY358935的抗病毒反应。OAS蛋白酶为dsRNA所激活，激活的OAS通过2′-5′磷酸二酯连接寡聚化ATP，进一步结合和激活核酸分解酶Rnase L，从而降解病毒RNA和细胞RNA，抑制病毒的复制[18]。基因芯片分析结果还显示，随着AY358935的表达增加，ISG15和ISG56的表达水平也分别上升14.64倍及5.44倍。有文献报道，体外细胞在干扰素处理或病毒感染时，ISG15和ISG56基因表达也显著上调[19]。ISG15可能增强干扰素的抗病毒效应，其作用底物分子如JAK、细胞外调节蛋白激酶(extracellular regulated protein kinases，ERK)、双链RNA依赖的蛋白质激酶(double-stranded RNA-dependent protein kinase，PKR)等，大多参与天然免疫反应[20]。
研究发现，干扰素家族成员较多，分为Ⅰ型、Ⅱ型和Ⅲ型，分别包括多个亚类，新的干扰素成员不断涌现[21-23]。一项关于ISGs研究的基因芯片综合分析表明，对于人源或鼠源的细胞系，用IFN-α、IFN-β或IFN-γ处理后，至少筛选出超过300个ISGs。这些ISGs涉及不同功能不同类别，包括宿主防御、免疫调节、信号转导、生长代谢等方面[24-26]。而本实验AY358935表达的HEK293细胞中，所有表达上调基因的生物学功能也涉及宿主防御、免疫调节等，由此推测，作为一个相对分子质量与干扰素类似的分泌蛋白，AY358935抗病毒机制可能也与干扰素相关，甚至不排除属于新类型干扰素基因。倘若如此，该基因在抗病毒方面的研发应用将具有重要价值。
综上，AY358935基因参与病毒感染的早期应答，具有明确的抗VSV病毒作用，其机制可能涉及干扰素相关的天然免疫应答，值得进一步研究。
-
![]()
图 1 基于组织形态学的乳腺癌NAC疗效的预测流程图
Figure 1. The flowchart of treatment outcome prediction of neoadjuvant chemotherapy for breast cancer based on histomorphology analysis
A: Needle biopsy image before chemotherapy; B: The marking of needle biopsy image; C: Original patch; D: The marking of patch; E: UNet++; F: Cell cluster segmentation image patch; G: The marking of cell; H: UNet++; I: Needle biopsy image; J: Patch extracted with sliding window; K: Original patch; L: The result of tumor area segmentation; M: The result of cell cluster segmentation; N: The result of segmentation of cell clusters and tumor areas; O: Classifier model; P: The result of prediction of the classifier and grading of response to NAC; Q: Visualization of features of cell clusters in the tumor area.
![]()
图 3 肿瘤区域和细胞核分割结果图。 ×50
Figure 3. Tumor area and cell segmentation results of whole slide image. ×50
A: Original patch; B: The result of tumor segmentation，with red area representing tumor and blue area representing non-tumor; C: The result of cell segmentation, with yellow representing cells in tumor area, and green representing cells out of tumor area.
![]()
图 4 特征可视化结果。 ×400
Figure 4. The results of feature visualization. ×400
A: The display of the Delaunay triangle of cells in tumor area, calculating the distance between the cell centers; B: The area of each tumor cell calculated by the Tyson polygon; C: The visualization of nuclear morphology; D: The visualization of cell texture, showing the texture on the nucleus.
![]()
图 5 ROC曲线
Figure 5. ROC curves
The red line represents the combination result of ten dimensional feature selected by mRMR and its optimal classifier random forest; the green line represents the combination result of Relief's ten dimensional feature and RF, and the purple line represents the combination result of Fisher and SVM，the blue line represents the combination result of ten dimensional feature selected by Wilcoxon rank-sum test and SVM. AUC: Area under curve; ACC: Accuracy.
表 1 组织形态学特征表
Table 1 The list of histomorphological features
Morphological feature n Texture features 80 Grayscale 15 LBP 16 Gabor 24 Laws 25 Morphological characteristics 24 Frequency domain 10 Distance 7 Invariant moment 7 Global characteristics 24 Delaunay triangle 8 Voronoi 12 Minimum spanning tree 4 Clustering characteristics 26 Basic parameters 6 Clustering coefficient 6 Clustering edge 4 Clustering node 4 Parameters at 90% distance 6 
下载: 导出CSV
表 2 MP分级的临床意义
Table 2 The clinical meaning of Miller-Payne (MP) system
MP Morphological characteristics of tumor cells Low grades Level 1 No reduction in tumor cell Level 2 No more than 30% reduction of tumor cell Level 3 30%−90% reduction of tumor cell High grades Level 4 More than 90% reduction of tumor cell Level 5 No tumor cell or ductal carcinoma in situ
下载: 导出CSV
表 3 四种特征排序前10维特征结果
Table 3 The results of top ten features in four feature ranking
Feature ranking method (10) and classifier 5-fold cross-validation (AUC) 5-fold cross-validation (ACC) mRMR-RF 0.9082 0.8235 Relief-RF 0.8725 0.8235 Fisher-SVM 0.8676 0.7794 Wilcoxon-SVM 0.8174 0.7206 AUC: Area under curve; ACC: Accuracy.
下载: 导出CSV
-
[1] 储春强, 华志元, 刘晓, 等. 乳腺癌术后至化疗的间隔时间对乳腺癌患者治疗效果的影响. 癌症进展,2019,17(24): 2956–2958. DOI: 10.11877/j.issn.1672-1535.2019.17.24.23 [2] DESANTIS C E, MA J, GAUDET M M, et al. Breast cancer statistics, 2019. CA Cancer J Clin,2019,69(6): 438–451. DOI: 10.3322/caac.21583
[3] 朱林林, 王海燕, 颜宪书. 超声造影对浸润性乳腺癌患者的新辅助化疗早期应答的预测价值. 中国药物与临床,2020,20(3): 363–365. DOI: 10.11655/zgywylc2020.03.010 [4] SYMMANS W F, WEI C, GOULD R, et al. Long-term prognostic risk after neoadjuvant chemotherapy associated with residual cancer burden and breast cancer subtype. J Clin Oncol,2017,35(10): 1049–1060. DOI: 10.1200/JCO.2015.63.1010
[5] KRIJGSMAN D, VAN LEEUWEN M, VAN DER VEN J, et al. Quantitative whole slide assessment of tumor-infiltrating CD8-positive lymphocytes in ER-positive breast cancer in relation to clinical outcome. IEEE J Biomed Health Inform,2021,25(2): 381–392. DOI: 10.1109/JBHI.2020.3003475
[6] KUMAR S, BADHE B A, KRISHNAN K M, et al. Study of tumour cellularity in locally advanced breast carcinoma on neo-adjuvant chemotherapy. J Clin Diagn Res,2014,8(4): FC09–13. DOI: 10.7860/JCDR/2014/7594.4283
[7] YAMAGUCHI R, TANAKA M, YANO A, et al. Tumor-infiltrating lymphocytes are important pathologic predictors for neoadjuvant chemotherapy in patients with breast cancer. Human Pathol,2012,43(10): 1688–1694. DOI: 10.1016/j.humpath.2011.12.013
[8] ALI H R, DARIUSH A, PROVENZANO E, et al. Computational pathology of pre-treatment biopsies identifies lymphocyte density as a predictor of response to neoadjuvant chemotherapy in breast cancer. Breast Cancer Res,2016,18(1): 21–32. DOI: 10.1186/s13058-016-0682-8
[9] ASANO Y, KASHIWAGI S, GOTO W, et al. Tumour-infiltrating CD8 to FOXP3 lymphocyte ratio in predicting treatment responses to neoadjuvant chemotherapy of aggressive breast cancer. Brit J Surg,2016,103(7): 845–854. DOI: 10.1002/bjs.10127
[10] GARCÍA-MARTÍNEZ E, GIL G L, BENITO A C, et al. Tumor-infiltrating immune cell profiles and their change after neoadjuvant chemotherapy predict response and prognosis of breast cancer. Breast Cancer Res,2014,16(6): 488. DOI: 10.1186/s13058-014-0488-5
[11] NISHIMURA R, OSAKO T, OKUMURA Y, et al. Clinical significance of Ki-67 in neoadjuvant chemotherapy for primary breast cancer as a predictor for chemosensitivity and for prognosis. Breast Cancer,2010,17(4): 269–275. DOI: 10.1007/s12282-009-0161-5
[12] LI H J, WHITNEY J, BERA K, et al. Quantitative nuclear histomorphometric features are predictive of oncotype DX risk categories in ductal carcinoma in situ: preliminary findings. Breast Cancer Res,2019,21(1): 114–130. DOI: 10.1186/s13058-019-1200-6
[13] OGURA M, YAMAMOTO Y, MIYASHITA H, et al. Quantitative analysis of nuclear shape in oral squamous cell carcinoma is useful for predicting the chemotherapeutic response. Med Mol Morphol,2016,49(2): 76–82. DOI: 10.1007/s00795-015-0121-4
[14] DIAO J A, CHUI W F, WANG J K, et al. Dense, high-resolution mapping of cells and tissues from pathology images for the interpretable prediction of molecular phenotypes in cancer. bioRxiv, 2020[2020-10-08]. doi: 10.1101/2020.08.02.233197.
[15] LI J, LI W, SISK A, et al. A multi-resolution model for histopathology image classification and localization with multiple instance learning. Comput Biol Med, 2021, 131: 104253[2020-10-08]. https://doi.org/10.1016/j.compbiomed.2021.104253.
[16] KUMAR N, VERMA R, SHARMA S, et al. A dataset and a technique for generalized nuclear segmentation for computational pathology. IEEE Trans Med Imaging,2017,36(7): 1550–1560. DOI: 10.1109/TMI.2017.2677499
[17] ZHOU Z, SIDDIQUEE M M R, TAJBAKHSH N, et al. Unet++: a nested u-net architecture for medical image segmentation//Deep learning in medical image analysis and multimodal learning for clinical decision support. Cham: Springer, 2018: 3-11.
[18] KINGMA D P, BA J. Adam: a method for stochastic optimization//3rd International Conference for Learning Representations. San Diego.2014: 5-18[2020-10-08]. https://arxiv.org/abs/1412.6980.
[19] ANUAR N, SULTAN A B M. Validate conference paper using dice coefficient. Computer Informat Sci,2010,3(3): 139–145.
[20] MOHANAIAH P, SATHYANARAYANA P, GURUKUMAR L. Image texture feature extraction using GLCM approach. Int J Scientific Res Publication,2013,3(5): 1–5.
[21] DUYCKAERTS C, GODEFROY G. Voronoi tessellation to study the numerical density and the spatial distribution of neurones. J Chem Neuroanatomy,2000,20(1): 83–92. DOI: 10.1016/S0891-0618(00)00064-8
[22] PENG H, LONG F, DING C. Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy. IEEE Trans Pattern Anal Mach Intell,2005,27(8): 1226–1238. DOI: 10.1109/TPAMI.2005.159
[23] CUZICK J. A Wilcoxon‐type test for trend. Statist Med,1985,4(1): 87–90. DOI: 10.1002/sim.4780040112
[24] KONONENKO I. Estimating attributes: analysis and extensions of RELIEF//European conference on machine learning. Berlin, Heidelberg: Springer, 1994: 171-182.
[25] MIKA S, RATSCH G, WESTON J, et al. Fisher discriminant analysis with kernels//Neural networks for signal processing Ⅸ: Proceedings of the 1999 IEEE signal processing society workshop (cat. no. 98th8468). IEEE, 1999: 41-48.
[26] TONG S, KOLLER D. Support vector machine active learning with applications to text classification. J Machine Learn Res,2001,2(11): 45–66. DOI: 10.1162/153244302760185243
[27] 单慧明, 周靖宇, 谢婷婷, 等. MRI影像学特征预测乳腺癌新辅助化疗疗效的可行性. 中国医学影像学杂志,2019,27(12): 905–909. DOI: 10.3969/j.issn.1005-5185.2019.12.006 [28] YU KH, ZHANG C, BERRY G J, et al. Predicting non-small cell lung cancer prognosis by fully automated microscopic pathology image features. Nat Commun, 2016, 7: 12474[2020-10-08]. https://www.nature.com/articles/ncomms12474. doi: 10.1038/ncomms12474.
计量
- 文章访问数: 2326
- HTML全文浏览量: 683
- PDF下载量: 124
