亚洲免费av电影一区二区三区,日韩爱爱视频,51精品视频一区二区三区,91视频爱爱,日韩欧美在线播放视频,中文字幕少妇AV,亚洲电影中文字幕,久久久久亚洲av成人网址,久久综合视频网站,国产在线不卡免费播放

        ?

        膀胱癌淋巴結(jié)轉(zhuǎn)移術(shù)前評(píng)估研究進(jìn)展

        2023-12-29 00:00:00王麗鵑劉自曉胡偉劉洋秦衛(wèi)軍徐肖攀盧虹冰

        摘要:膀胱癌是泌尿系統(tǒng)常見(jiàn)的惡性腫瘤,相對(duì)于淋巴結(jié)陰性的患者,淋巴結(jié)陽(yáng)性患者預(yù)后極差。術(shù)前淋巴結(jié)狀態(tài)的準(zhǔn)確評(píng)估可為治療決策提供有用信息,如盆腔淋巴結(jié)清掃范圍和新輔助化療的應(yīng)用等。然而當(dāng)前臨床主要依靠影像學(xué)檢查與病理檢查對(duì)膀胱癌患者的淋巴結(jié)狀態(tài)進(jìn)行評(píng)估,但敏感性較低,易導(dǎo)致部分患者分期過(guò)高或分期不足。針對(duì)膀胱癌淋巴結(jié)轉(zhuǎn)移的術(shù)前精準(zhǔn)評(píng)估問(wèn)題,本文著重從臨床診斷、影像學(xué)方法、影像組學(xué)以及基因組學(xué)等方法進(jìn)行回顧分析,并對(duì)術(shù)前從不同層面預(yù)測(cè)膀胱癌淋巴結(jié)轉(zhuǎn)移方法的應(yīng)用前景進(jìn)行展望。

        關(guān)鍵詞:膀胱癌;淋巴結(jié)轉(zhuǎn)移;盆腔淋巴結(jié)清掃;新輔助化療;影像組學(xué)

        中圖分類號(hào): R737.14;R445" 文獻(xiàn)標(biāo)志碼: A" 文章編號(hào):1000-503X(2023)03-0464-07

        DOI:10.3881/j.issn.1000-503X.15333

        Research Progress in Preoperative Evaluation of Lymph Node Metastasis of Bladder Cancer

        WANG Lijuan1,2,LIU Zixiao1,2,HU Wei3,LIU Yang1,2,QIN Weijun3,XU Xiaopan1,2,LU Hongbing1,2

        1Department of Military Medical Information Technology,Air Force Medical University,Xi’an 710032,China

        2Shaanxi Provincial Key Laboratory of Biological Electromagnetic Detection and Intelligent Perception,Xi’an 710032,China

        3Department of Radiology,Xijing Hospital,Air Force Medical University,Xi’an 710032,China

        Corresponding author:LU Hongbing Tel:029-84711411,E-mail:luhb@fmmu.edu.cn;

        XU Xiaopan Tel:029-84774840,E-mail:alexander-001@163.com

        ABSTRACT:Bladder cancer is a common malignant tumor of the urinary system.The prognosis of patients with positive lymph nodes is worse than that of patients with negative lymph nodes.An accurate assessment of preoperative lymph node statushelps to make treatmentdecisions,such as the extent of pelvic lymphadenectomy and the use of neoadjuvant chemotherapy.Imaging examination and pathological examination are the primary methods used to assess the lymph node status of bladder cancer patients before surgery.However,these methods have low sensitivity and may lead to inaccuate staging of patients.We reviewed the research progress and made an outlook on the application of clinical diagnosis,imaging techniques,radiomics,and genomics in the preoperative evaluation of lymph node metastasis in bladder cancer patients at different stages.

        Key words:bladder cancer;lymph node metastasis;pelvic lymphadenectomy;neoadjuvant chemotherapy;radiomics

        Acta Acad Med Sin,2023,45(3):464-470

        膀胱癌是泌尿系統(tǒng)常見(jiàn)的惡性腫瘤,位居全球癌癥死亡率第9位,居男性發(fā)病率第6位,其中,淋巴結(jié)轉(zhuǎn)移是膀胱癌發(fā)展及復(fù)發(fā)的重要原因[1-2]。膀胱癌患者發(fā)生淋巴結(jié)轉(zhuǎn)移時(shí),患者的5年生存率將會(huì)明顯下降(60%比15%~31%),淋巴結(jié)轉(zhuǎn)移陰性患者復(fù)發(fā)的概率遠(yuǎn)低于陽(yáng)性患者的概率(30%比80%)[3-4]。同時(shí),術(shù)前淋巴結(jié)轉(zhuǎn)移評(píng)估對(duì)治療策略的選擇具有指導(dǎo)作用,尤其對(duì)術(shù)前新輔助化療的應(yīng)用及術(shù)中盆腔淋巴清掃范圍的選擇等具有重要意義[2,4]。

        目前,術(shù)前膀胱癌患者的淋巴結(jié)狀態(tài)主要依靠影像進(jìn)行診斷,如CT和MRI,對(duì)淋巴結(jié)位置、大小、形狀和內(nèi)部結(jié)構(gòu)進(jìn)行觀察,短徑大于10mm是目前臨床普遍認(rèn)可的淋巴結(jié)轉(zhuǎn)移的判別標(biāo)準(zhǔn)[2],但臨床上很多正常大小的淋巴結(jié)已經(jīng)發(fā)生轉(zhuǎn)移。術(shù)前病理也常用于判斷膀胱癌患者的淋巴結(jié)狀態(tài),但由于存在病理異質(zhì)性,診斷結(jié)果存在較高的假陰性,術(shù)前診斷為陰性的膀胱癌患者中大約25%~30%已經(jīng)發(fā)生淋巴結(jié)轉(zhuǎn)移[3]。因此,多個(gè)研究團(tuán)隊(duì)正在探索新的方法對(duì)術(shù)前淋巴結(jié)轉(zhuǎn)移進(jìn)行評(píng)估,如影像組學(xué)、基因組學(xué)等。改善膀胱癌患者淋巴結(jié)轉(zhuǎn)移的術(shù)前評(píng)估手段對(duì)于準(zhǔn)確診斷、治療選擇和預(yù)后評(píng)估均具有重要意義。

        膀胱癌淋巴結(jié)轉(zhuǎn)移對(duì)治療策略影響

        淋巴結(jié)轉(zhuǎn)移對(duì)盆腔清掃范圍的影響 準(zhǔn)確的術(shù)前淋巴結(jié)分期對(duì)于確定盆腔淋巴結(jié)清掃范圍、選擇適合新輔助化療的候選者具有重要意義。目前,美國(guó)國(guó)家癌癥綜合網(wǎng)膀胱癌指南建議[5],盆腔淋巴結(jié)清掃應(yīng)被視為根治性膀胱切除術(shù)的重要組成部分,但在行根治性膀胱切除術(shù)時(shí)盆腔淋巴結(jié)清掃的范圍一直是一個(gè)有爭(zhēng)議的問(wèn)題[3,5]。一些研究表明,與標(biāo)準(zhǔn)盆腔淋巴結(jié)清掃的范圍相比,部分膀胱癌患者將從擴(kuò)大盆腔淋巴結(jié)清掃中獲益[6]。一項(xiàng)隨機(jī)臨床試驗(yàn)顯示[7],在接受根治性膀胱切除術(shù)治療的膀胱癌患者中,與局限的盆腔淋巴結(jié)清掃術(shù)相比,擴(kuò)大的盆腔淋巴結(jié)清掃術(shù)可以減少死亡事件的發(fā)生。顯然,患有淋巴結(jié)轉(zhuǎn)移的患者可能會(huì)受益于擴(kuò)大盆腔淋巴結(jié)清掃。因此,準(zhǔn)確預(yù)測(cè)術(shù)前淋巴結(jié)狀態(tài)可能有助于在膀胱切除術(shù)中確定盆腔淋巴結(jié)清掃范圍,并為預(yù)后提供有用信息,從而減輕患者疾病治療負(fù)擔(dān)[4,8]。

        淋巴結(jié)轉(zhuǎn)移對(duì)新輔助化療選擇影響 多項(xiàng)研究表明[9-11],新輔助化療更適用于局部晚期膀胱癌或微轉(zhuǎn)移的患者。Cha等[11]收集了從2000~2010年的1484名患者,發(fā)現(xiàn)膀胱癌淋巴結(jié)陽(yáng)性患者接受輔助化療后的3年無(wú)復(fù)發(fā)生存率高達(dá)60%,而接受新輔助化療后的3年無(wú)復(fù)發(fā)生存率僅為26%。在Zamboni等[12]的一項(xiàng)多中心研究中發(fā)現(xiàn)新輔助化療對(duì)膀胱癌分期為cT3-T4且淋巴結(jié)為陰性的患者獲益更多。Del Bene等[13]對(duì)新輔助化療和輔助化療的效果進(jìn)行了對(duì)比發(fā)現(xiàn)接受新輔助化療后的淋巴結(jié)陰性患者5年無(wú)疾病生存期和總生存率更長(zhǎng)(5年無(wú)疾病生存期率47%比9%;5年總生存率51%比25%)。這些研究表明新輔助化療更適用于淋巴結(jié)陰性的膀胱癌患者,當(dāng)發(fā)生淋巴結(jié)轉(zhuǎn)移時(shí),輔助化療或成為治療首選策略。

        基于影像的膀胱癌患者淋巴結(jié)轉(zhuǎn)移診斷研究

        淋巴結(jié)轉(zhuǎn)移的影像學(xué)評(píng)估 目前,對(duì)比增強(qiáng)CT是術(shù)前評(píng)估淋巴結(jié)分期的標(biāo)準(zhǔn)臨床程序?;诔R?guī)CT、MRI影像學(xué)檢查[14-16],在預(yù)測(cè)膀胱癌淋巴結(jié)轉(zhuǎn)移方面主要關(guān)注淋巴結(jié)的大小、形狀和內(nèi)部結(jié)構(gòu),目前臨床上將淋巴結(jié)短徑≥10mm認(rèn)定為淋巴結(jié)轉(zhuǎn)移[2,16],但由于常規(guī)影像檢查的敏感性較低(31%~45%),導(dǎo)致假陰性較高(25%)[3]。Li等[15]回顧性地分析CT和MRI在檢測(cè)盆腔淋巴結(jié)轉(zhuǎn)移中的診斷性能,發(fā)現(xiàn)在184個(gè)轉(zhuǎn)移性淋巴結(jié)中,82個(gè)影像學(xué)檢測(cè)僅51個(gè)被確認(rèn)為陽(yáng)性。影像學(xué)檢查檢測(cè)到的轉(zhuǎn)移性淋巴結(jié)數(shù)量占經(jīng)病理學(xué)驗(yàn)證的27.7%,遠(yuǎn)少于病理學(xué)檢測(cè)到的轉(zhuǎn)移性淋巴結(jié),這表明常規(guī)的影像學(xué)檢查容易漏診轉(zhuǎn)移性淋巴結(jié)。Horn等[17]對(duì)231名未接受根治性膀胱切除術(shù)和盆腔淋巴結(jié)切除術(shù)患者的CT影像進(jìn)行統(tǒng)計(jì)分析,發(fā)現(xiàn)CT的敏感性僅為52.6%,這可能是因?yàn)镃T成像的軟組織對(duì)比度低,對(duì)于大小變化不大的淋巴結(jié)轉(zhuǎn)移檢測(cè)能力不足導(dǎo)致的。Woo等[18]最近的一項(xiàng)薈萃分析發(fā)現(xiàn),MRI識(shí)別膀胱癌轉(zhuǎn)移淋巴結(jié)的特異性盡管高達(dá)94%,但綜合敏感性僅為56%。Salminen等[19]報(bào)告MRI敏感性范圍為40.7%~86%。由于無(wú)法檢測(cè)與微轉(zhuǎn)移有關(guān)的淋巴結(jié),僅以尺寸大小作為檢測(cè)標(biāo)準(zhǔn)缺乏準(zhǔn)確性,在膀胱癌患者中超過(guò)90%的正常大小的轉(zhuǎn)移性淋巴結(jié)短軸徑≤5mm,表明MRI在淋巴結(jié)評(píng)估中的能力有限[14]。綜上所述,常規(guī)的影像學(xué)檢查在判斷淋巴結(jié)轉(zhuǎn)移方面的性能有待提高。

        最近多項(xiàng)研究表明PET/CT可用來(lái)區(qū)分淋巴結(jié)的良惡性[20-22]。Girard等[20]采用18F-FDG PET/CT,結(jié)合最大標(biāo)準(zhǔn)化攝取值和軸位淋巴結(jié)大小評(píng)估的方法,對(duì)61例膀胱癌患者術(shù)前的1012個(gè)淋巴結(jié)進(jìn)行綜合評(píng)估,發(fā)現(xiàn)PET/CT診斷準(zhǔn)確率達(dá)84%,表明PET/CT可提高膀胱癌患者術(shù)前淋巴結(jié)分期的診斷準(zhǔn)確性。Jeong等[22]進(jìn)行的前瞻性研究和薈萃分析表明,PET/CT合并敏感性為47%~70%,特異性為87%~100%。Thomas等[23]在一項(xiàng)多中心前瞻性研究中發(fā)現(xiàn)68Ga-PASM-11PET/CT在術(shù)前檢測(cè)盆腔淋巴結(jié)轉(zhuǎn)移敏感性僅為40%,Dekalo等[24]在一項(xiàng)小數(shù)據(jù)的回顧性研究中發(fā)現(xiàn)68Ga-PASM PET/CT在術(shù)前檢測(cè)盆腔淋巴結(jié)轉(zhuǎn)移敏感性為68%。上述研究表明目前PET/CT對(duì)淋巴結(jié)轉(zhuǎn)移的檢測(cè)敏感性仍不夠高,容易漏診陽(yáng)性淋巴結(jié),并且由于PET/CT空間分辨率有限,在檢測(cè)非腫大陽(yáng)性淋巴結(jié)時(shí)(<5 mm)的準(zhǔn)確性較低,易造成假陰性判斷。

        淋巴結(jié)轉(zhuǎn)移的影像組學(xué)和深度學(xué)習(xí)分析 鑒于傳統(tǒng)影像技術(shù)在術(shù)前識(shí)別淋巴結(jié)轉(zhuǎn)移方面的局限性,通過(guò)高通量提取影像中的高維數(shù)據(jù)構(gòu)建分類預(yù)測(cè)模型的方法在預(yù)測(cè)淋巴結(jié)轉(zhuǎn)移方面已顯示出極大潛力[25-26]。如采用紋理分析和傳統(tǒng)機(jī)器學(xué)習(xí)方法,包括支持向量機(jī)、邏輯回歸、樸素貝葉斯和線性判別分析,對(duì)惡性和良性淋巴結(jié)進(jìn)行檢測(cè),最佳受試者工作特征曲線下的面積(area under the curve,AUC)可達(dá)89%[27-29]。Song等[30]則采用基于XGBoost的分類器,對(duì)PET/CT圖像中的惡性淋巴結(jié)進(jìn)行檢測(cè),其特異性高達(dá)93%,但敏感性僅為55.8%。

        針對(duì)膀胱癌,目前僅有中山大學(xué)林天歆團(tuán)隊(duì)基于118名患者數(shù)據(jù),結(jié)合增強(qiáng)CT影像組學(xué)特征和CT報(bào)告的淋巴結(jié)狀態(tài),通過(guò)Lasso-Cox模型篩選出9個(gè)與淋巴結(jié)轉(zhuǎn)移相關(guān)的特征,并構(gòu)成影像組學(xué)列線圖,該模型在訓(xùn)練集上獲得的AUC為92.62%,驗(yàn)證集上AUC為89.86%[26],在淋巴結(jié)轉(zhuǎn)移預(yù)測(cè)上顯示出良好的準(zhǔn)確性。該團(tuán)隊(duì)另一項(xiàng)基于膀胱MRI的淋巴結(jié)轉(zhuǎn)移影像組學(xué)研究中,也采用類似方法構(gòu)建了影像組學(xué)列線圖,最終在驗(yàn)證集中的AUC達(dá)89.02%[25],這兩項(xiàng)研究結(jié)果表明影像組學(xué)方法在術(shù)前評(píng)估膀胱癌患者的淋巴結(jié)狀態(tài)方面同樣具有巨大潛力。但目前兩種列線圖主要基于人工提取的腫瘤影像特征并結(jié)合人工判斷,考慮其病例數(shù)較少且來(lái)自單一中心,影像組學(xué)模型的泛化性需進(jìn)一步提升[31]。

        深度學(xué)習(xí)技術(shù)在淋巴結(jié)檢測(cè)及分類中也得到了初步應(yīng)用[32-35]。Tekchandani[35]在與Pham[29]使用同一共同數(shù)據(jù)集的情況下基于深度卷積神經(jīng)網(wǎng)絡(luò)模型對(duì)淋巴結(jié)的良惡性進(jìn)行了分類,在沒(méi)有任何手工特征提取和數(shù)據(jù)擴(kuò)充方法的情況下,準(zhǔn)確率達(dá)到63.14%。Jeong等[34]采用8個(gè)深度卷積神經(jīng)網(wǎng)絡(luò)模型對(duì)影像中的淋巴結(jié)良惡性進(jìn)行分類,其最高準(zhǔn)確率可達(dá)90.4%。因此,深度學(xué)習(xí)模型在臨床環(huán)境中或可用于膀胱癌患者術(shù)前淋巴結(jié)狀態(tài)的評(píng)估。

        基于基因組學(xué)的膀胱癌淋巴結(jié)轉(zhuǎn)移診斷研究

        從分子層面揭示與淋巴結(jié)轉(zhuǎn)移相關(guān)的基因,在實(shí)現(xiàn)精準(zhǔn)診療的同時(shí),有助于推進(jìn)靶向藥物研發(fā),改善膀胱癌患者的生存結(jié)局。隨著高通量測(cè)序技術(shù)的不斷發(fā)展,基因表達(dá)特征已成功應(yīng)用于預(yù)測(cè)乳腺癌、前列腺癌、頭頸部癌、肝癌和膀胱癌等患者的淋巴結(jié)轉(zhuǎn)移[36-38]。Smith等[39]采用根治性膀胱切除術(shù)和經(jīng)尿道膀胱腫瘤切除手術(shù)的標(biāo)本,構(gòu)建的基于20個(gè)基因表達(dá)的模型,可用于預(yù)測(cè)膀胱癌患者的淋巴結(jié)狀態(tài)。另外,Seiler等[40]采用全轉(zhuǎn)錄組基因構(gòu)建了預(yù)測(cè)膀胱癌患者淋巴結(jié)轉(zhuǎn)移的KNN51分類器,發(fā)現(xiàn)該分類器在驗(yàn)證集上的AUC可達(dá)82%。上述研究表明,通過(guò)構(gòu)建基因組學(xué)模型,尋找可靠的分子標(biāo)志物,有助于提高膀胱癌患者淋巴結(jié)狀態(tài)的預(yù)測(cè)性能,但其性能仍需在多中心、大規(guī)模研究中進(jìn)一步驗(yàn)證。

        基因組和臨床病理特征的結(jié)合,有望進(jìn)一步提升膀胱癌患者淋巴結(jié)轉(zhuǎn)移的預(yù)測(cè)能力。Wu等[3]綜合考慮轉(zhuǎn)錄基因的表達(dá)、膀胱腫瘤的狀態(tài)、影像報(bào)告的淋巴結(jié)狀態(tài)以及經(jīng)尿道膀胱腫瘤切除術(shù)標(biāo)本的病理檢測(cè)結(jié)果等多種數(shù)據(jù),構(gòu)建了基于5個(gè)轉(zhuǎn)錄基因(ADRA1D,COL10A1,DKK2,HIST2K3D和MMP11)表達(dá)水平的分類器,用于預(yù)測(cè)膀胱癌患者的淋巴結(jié)狀態(tài)。在測(cè)試集中AUC高達(dá)88.58%,表明充分利用患者的各項(xiàng)檢測(cè)數(shù)據(jù)能有效提升術(shù)前膀胱癌患者淋巴結(jié)狀態(tài)評(píng)估的準(zhǔn)確性。

        總結(jié)與展望

        淋巴結(jié)轉(zhuǎn)移是膀胱癌患者重要的治療決策及預(yù)后評(píng)估因素,術(shù)前準(zhǔn)確判斷淋巴結(jié)狀態(tài)對(duì)于臨床決策和改善患者預(yù)后具有重要意義。針對(duì)術(shù)前淋巴結(jié)轉(zhuǎn)移預(yù)測(cè)存在的問(wèn)題,影像組學(xué)和基因組學(xué)研究已展示出巨大潛力。近期研究發(fā)現(xiàn)[41-42],影像組學(xué)特征與致癌基因及腫瘤發(fā)生發(fā)展的信號(hào)通路有關(guān),利用與基因組高度相關(guān)的影像特征尋找與膀胱癌患者淋巴結(jié)轉(zhuǎn)移的關(guān)鍵基因及信號(hào)通路,或可在揭示膀胱癌患者淋巴結(jié)轉(zhuǎn)移相關(guān)影像組學(xué)特征的生物學(xué)意義的同時(shí),提高淋巴結(jié)轉(zhuǎn)移術(shù)前無(wú)創(chuàng)評(píng)價(jià)的能力,構(gòu)建影像-基因組模型用于新輔助化療患者的選擇及指導(dǎo)盆腔淋巴結(jié)清掃的范圍,對(duì)于避免不必要的過(guò)度治療及不必要的擴(kuò)大盆腔淋巴結(jié)清掃亦具有重要臨床意義。

        目前對(duì)淋巴結(jié)轉(zhuǎn)移的影像學(xué)研究大部分僅關(guān)注腫瘤區(qū)域的放射組學(xué)特征??紤]到淋巴結(jié)及腫瘤周圍區(qū)域的影像數(shù)據(jù)有助于淋巴結(jié)狀態(tài)的監(jiān)測(cè),表明在構(gòu)建預(yù)測(cè)模型時(shí)從多個(gè)區(qū)域整合影像信息有助于改善模型性能。近期已有大量學(xué)者將腫瘤周圍區(qū)域納入研究[43-45],并取得了較好的結(jié)果。Das等[43],整合了腫瘤、瘤周和淋巴結(jié)區(qū)域的影像組學(xué)特征構(gòu)建了肺癌患者淋巴結(jié)轉(zhuǎn)移預(yù)測(cè)模型,該模型最終在外部驗(yàn)證中的AUC為0.79,表現(xiàn)出較好的鑒別能力。Ding等[46]對(duì)腫瘤周圍區(qū)域的影像組學(xué)特征進(jìn)行分析發(fā)現(xiàn)腫瘤周特征有助于提升術(shù)前乳腺癌前哨淋巴結(jié)狀態(tài)的預(yù)測(cè)性能。因此,我們有理由認(rèn)為結(jié)合淋巴結(jié)區(qū)域和腫瘤區(qū)域的特征,有望進(jìn)一步提升評(píng)估性能。

        在方法技術(shù)方面,基于醫(yī)學(xué)影像的深度學(xué)習(xí)技術(shù)已廣泛用于惡性腫瘤的檢測(cè)與診斷,在淋巴結(jié)轉(zhuǎn)移預(yù)測(cè)方面的應(yīng)用近期得到極大關(guān)注。JAMA、Lancet等雜志的近期研究利用深度學(xué)習(xí)技術(shù)從數(shù)字病理圖像中提取特征,以提升淋巴結(jié)狀態(tài)評(píng)估的準(zhǔn)確性和效率[47-51]。Ehteshami等[47]利用成熟的深度學(xué)習(xí)技術(shù),如VGG-Net,GoogleNet和ResNet,對(duì)乳腺癌淋巴結(jié)轉(zhuǎn)移進(jìn)行評(píng)估,不僅獲得了與病理學(xué)檢查相似的診斷結(jié)果,還大大減少了診斷時(shí)間。Steiner等[48]使用深度學(xué)習(xí)技術(shù)對(duì)數(shù)字病理圖像進(jìn)行評(píng)估,將乳腺癌淋巴結(jié)微轉(zhuǎn)移的檢測(cè)靈敏度從83%提高到91%。Pham等[52]則針對(duì)肺癌淋巴結(jié)的假陽(yáng)性問(wèn)題開(kāi)發(fā)了一種新型兩步深度學(xué)習(xí)算法,使誤差平均減少了36.4%。綜上所述,盡管目前深度學(xué)習(xí)算法尚未應(yīng)用于預(yù)測(cè)膀胱癌患者淋巴結(jié)的狀態(tài)評(píng)估上,但基于在其他癌癥患者淋巴結(jié)狀態(tài)評(píng)估中的應(yīng)用,該技術(shù)在膀胱癌患者術(shù)前淋巴結(jié)檢測(cè)、狀態(tài)評(píng)估及臨床模型構(gòu)建中有巨大應(yīng)用潛力。

        未來(lái)我們有望將分子、病理以及影像數(shù)據(jù)進(jìn)行結(jié)合,在術(shù)前從不同的層面對(duì)膀胱癌患者的淋巴結(jié)狀態(tài)進(jìn)行評(píng)估,以制定最佳的治療管理策略,并有望輔助藥物研發(fā)人員開(kāi)發(fā)靶向治療藥物,最終實(shí)現(xiàn)精準(zhǔn)診療,改善膀胱癌患者的生存結(jié)局,其診療模式如圖1所示。

        參 考 文 獻(xiàn)

        [1]Sung H,F(xiàn)erlay J,Siegel RL,et al.Global cancer statistics 2020:globocan estimates of incidence and mortality worldwide for 36 cancers in 185 countries[J].CA Cancer J Clin,2021,71(3):209-249.DOI:10.3322/caac.21660.

        [2]Shankar PR,Barkmeier D,HadjiiskiL,et al.A pictorial review of bladder cancer nodal metastases[J].Transl Androl Urol,2018,7(5):804-813.DOI:10.21037/tau.2018.08.25.

        [3]Wu SX,Huang J,Liu ZW,et al.A genomic-clinicopathologic nomogram for the preoperative prediction of lymph node metastasis in bladder cancer[J].EBioMedicine,2018,31:54-65.DOI:10.1016/j.ebiom.2018.03.034.

        [4]王麗鵑,劉自曉,黃浩霖,等.基于深度網(wǎng)絡(luò)術(shù)前評(píng)估膀胱癌患者淋巴結(jié)狀態(tài)的模型構(gòu)建[J].空軍軍醫(yī)大學(xué)學(xué)報(bào),2022,43(8):847-851.DOI:10.13276/j.issn.2097-1656.2022.07.013.

        [5]Witjes JA,Bruins HM,Cathomas R,et al.European association of urology guidelines on muscle-invasive and metastatic bladder cancer:summary of the 2020 guidelines[J].Eur Urol,2021,79(1):82-104.DOI:10.1016/j.eururo.2020.03.055.

        [6]Moschini M,Afferi L,Gandaglia G,et al.Prediction of the need for an extended lymphadenectomy at the time of radical cystectomy in patients with bladder cancer[J].Eur Urol Focus,2021,7(5):1067-1074.DOI:10.1016/j.euf.2020.09.009.

        [7]Hwang EC,Sathianathen NJ,Imamura M,et al.Extended versus standard lymph node dissection for urothelial carcinoma of the bladder in patients undergoing radical cystectomy[J].Cochrane Database Syst Rev,2019,5(5):CD013336.DOI:10.1002/14651858.CD013336.

        [8]Perera M,McGrath S,Sengupta S,et al.Pelvic lymph node dissection during radical cystectomy for muscle-invasive bladder cancer[J].Nat Rev Urol,2018,15(11):686-692.DOI:10.1038/s41585-018-0066-1.

        [9]Baumann BC,Zaghloul MS,Sargos P,et al.Adjuvant and neoadjuvant radiation therapy for locally advanced bladder cancer[J].Clin Oncol(R Coll Radiol),2021,33(6):391-399.DOI:10.1016/j.clon.2021.03.020.

        [10]Leow JJ,Chong YL,Chang SL,et al.Neoadjuvant and adjuvant chemotherapy for upper tract urothelial carcinoma:a 2020 systematic review and meta-analysis,and future perspectives on systemic therapy[J].Eur Urol,2021,79(5):635-654.DOI:10.1016/j.eururo.2020.07.003.

        [11]Cha EK,Sfakianos JP,Sukhu R,et al.Poor prognosis of bladder cancer patients with occult lymph node metastases treated with neoadjuvant chemotherapy[J].BJU Int,2018,122(4):627-632.DOI:10.1111/bju.14242.

        [12]Zamboni S,Moschini M,Antonelli A,et al.How to improve patient selection for neoadjuvant chemotherapy in bladder cancer patients candidate for radical cystectomy and pelvic lymph node dissection[J].World J Urol,2020,38(5):1229-1233.DOI:10.1007/s00345-019-02916-2.

        [13]Del Bene G,Calabrò F,Giannarelli D,et al.Neoadjuvant vs.adjuvant chemotherapy in muscle invasive bladder cancer(mibc):analysis from the risc database[J].Front Oncol,2018,8:463.DOI:10.3389/fonc.2018.00463.

        [14]Caglic I,Panebianco V,Vargas HA,et al.MRI of bladder cancer:local and nodal staging[J].J Magn Reson Imaging,2020,52(3):649-667.DOI:10.1002/jmri.27090.

        [15]Li Y,Diao F,Shi S,et al.Computed tomography and magnetic resonance imaging evaluation of pelvic lymph node metastasis in bladder cancer[J].Chin J Cancer,2018,37(1):3.DOI:10.1186/s40880-018-0269-0.

        [16]Lam TBL.Optimizing the diagnosis of pelvic lymph node metastasis in bladder cancer using computed tomography and magnetic resonance imaging[J].Cancer Commun(Lond),2018,38(1):2.DOI:10.1186/s40880-018-0271-6.

        [17]Horn T,Zahel T,Adt N,et al.Evaluation of computed tomography for lymph node staging in bladder cancer prior to radical cystectomy[J].Urol Int,2016,96(1):51-56.DOI:10.1159/000440889.

        [18]Woo S,Suh CH,Kim SY,et al.The diagnostic performance of mri for detection of lymph node metastasis in bladder and prostate cancer:an updated systematic review and diagnostic meta-analysis[J].AJR Am J Roentgenol,2018,210(3):W95-W109.DOI:10.2214/AJR.17.18481.

        [19]Salminen AP,Jambor I,Syvanen KT,et al.Update on novel imaging techniques for the detection of lymph node metastases in bladder cancer[J].Minerva Urol Nefrol,2016,68(2):138-149.

        [20]Girard A,Rouanne M,Taconet S,et al.Integrated analysis of 18F-FDG PET/CT improves preoperative lymph node staging for patients with invasive bladder cancer[J].Eur Radiol,2019,29(8):4286-4293.DOI:10.1007/s00330-018-5959-0.

        [21]Omorphos NP,Ghose EA,Hayes JDB,et al.The increasing indications of FDG-PET/CT in the staging and management of invasive bladder cancer[J].Urol Oncol,2022,40(10):434-441.DOI:10.1016/j.urolonc.2022.05.017.

        [22]Jeong IG,Hong S,You D,et al.FDG PET-CT for lymph node staging of bladder cancer:a prospective study of patients with extended pelvic lymphadenectomy[J].Ann Surg Oncol,2015,22(9):3150-3156.DOI:10.1245/s10434-015-4369-7.

        [23]Hope TA,Eiber M,Armstrong WR,et al.Diagnostic accuracy of 68Ga-PSMA-11 PET for pelvic nodal metastasis detection prior to radical prostatectomy and pelvic lymph node dissection:a multicenter prospective phase 3 imaging trial[J].JAMA Oncol,2021,7(11):1635-1642.DOI:10.1001/jamaoncol.2021.3771.

        [24]Dekalo S,Kuten J,Mintz I,et al.Preoperative 68Ga-PSMA PET/CT defines a subgroup of high-risk prostate cancer patients with favorable outcomes after radical prostatectomy and lymph node dissection[J].Prostate Cancer Prostatic Dis,2021,24(3):910-916.DOI:10.1038/s41391-021-00347-y.

        [25]Wu SX,Zheng JJ,Li Y,et al.Development and validation of an MRI-based radiomics signature for the preoperative prediction of lymph node metastasis in bladder cancer[J].EBioMedicine,2018,34:76-84.DOI:10.1016/j.ebiom.2018.07.029.

        [26]Wu SX,Zheng JJ,Li Y,et al.A radiomics nomogram for the preoperative prediction of lymph node metastasis in bladder cancer[J].Clin Cancer Res,2017,23(22):6904-6911.DOI:10.1158/1078-0432.CCR-17-1510.

        [27]Masuda T,Nakaura T,F(xiàn)unama Y,et al.Machine learning to identify lymph node metastasis from thyroid cancer in patients undergoing contrast-enhanced CT studies[J].Radiography(Lond),2021,27(3):920-926.DOI:10.1016/j.radi.2021.03.001.

        [28]Yu YF,He ZF,Ouyang J,et al.Magnetic resonance imaging radiomics predicts preoperative axillary lymph node metastasis to support surgical decisions and is associated with tumor microenvironment in invasive breast cancer:a machine learning,multicenter study[J].EBioMedicine,2021,69:103460.DOI:10.1016/j.ebiom.2021.103460.

        [29]Pham TD,Watanabe Y,Higuchi M,et al.Texture analysis and synthesis of malignant and benign mediastinal lymph nodes in patients with lung cancer on computed tomography[J].Sci Rep,2017,7:43209.DOI:10.1038/srep43209.

        [30]Song BI.A machine learning-based radiomics model for the prediction of axillary lymph-node metastasis in breast cancer[J].Breast Cancer,2021,28(3):664-671.DOI:10.1007/s12282-020-01202-z.

        [31]Moschini M,Soria F,Klatte T,et al.Validation of preoperative risk grouping of the selection of patients most likely to benefit from neoadjuvant chemotherapy before radical cystectomy[J].Clin Genitourin Cancer,2017,15(2):e267-e273.DOI:10.1016/j.clgc.2016.07.014.

        [32]Li Z,Xia Y.Deep Reinforcement learning for weakly-supervised lymph node segmentation in CT images[J].IEEE J Biomed Health Inform,2021,25(3):774-783.DOI:10.1109/JBHI.2020.3008759.

        [33]Tekchandani H,Verma S,Londhe ND.Mediastinal lymph node malignancy detection in computed tomography images using fully convolutional network[J].Biocybern Biomed Eng,2020,40(1):187-199.DOI:10.1016/j.bbe.2019.05.002.

        [34]Lee JH,Ha EJ,Kim JH.Application of deep learning to the diagnosis of cervical lymph node metastasis from thyroid cancer with CT[J].Eur Radiol,2019,29(10):5452-5457.DOI:10.1007/s00330-019-06098-8.

        [35]Tekchandani H,Verma S,Londhe ND.Severity assessment of lymph nodes in CT images using deep learning paradigm;proceedings of the 2018 Second International Conference on Computing Methodologies and Communication(ICCMC),[C].Erode,India,2018:686-691.DOI:10.1109/ICCMC.2018.8487555.

        [36]Xu K,Wang R,Xie H,et al.Single-cell RNA sequencing reveals cell heterogeneity and transcriptome profile of breast cancer lymph node metastasis[J].Oncogenesis,2021,10(10):66.DOI:10.1038/s41389-021-00355-6.

        [37]Ma J,Chen XQ,Xiang ZL.Identification of a prognostic transcriptome signature for hepatocellular carcinoma with lymph node metastasis[J].Oxid Med Cell Longev,2022,2022:7291406.DOI:10.1155/2022/7291406.

        [38]Peng WT,Lin CJ,Jing SS,et al.A novel seven gene signature-based prognostic model to predict distant metastasis of lymph node-negative triple-negative breast cancer[J].Front Oncol,2021,11:746763.DOI:10.3389/fonc.2021.746763.

        [39]Smith SC,Baras AS,Dancik G,et al.A 20-gene model for molecular nodal staging of bladder cancer:development and prospective assessment[J].Lancet Oncol,2011,12(2):137-143.DOI:10.1016/S1470-2045(10)70296-5.

        [40]Seiler R,Lam LL,Erho N,et al.Prediction of lymph node metastasis in patients with bladder cancer using whole transcriptome gene expression signatures[J].J Urol,2016,196(4):1036-1041.DOI:10.1016/j.juro.2016.04.061.

        [41]Fan M,Xia PP,Clarke R,et al.Radiogenomic signatures reveal multiscale intratumour heterogeneity associated with biological functions and survival in breast cancer[J].Nat Commun,2020,11(1):4861.DOI:10.1038/s41467-020-18703-2.

        [42]Sun QC,Chen YS,Liang CF,et al.Biologic pathways underlying prognostic radiomics phenotypes from paired MRI and RNA sequencing in glioblastoma[J].Radiology,2021,301(3):654-663.DOI:10.1148/radiol.2021203281.

        [43]Das SK,F(xiàn)ang KW,Xu L,et al.Integrative nomogram of intratumoral,peritumoral,and lymph node radiomic features for prediction of lymph node metastasis in cT1N0M0 lung adenocarcinomas[J].Sci Rep,2021,11(1):10829.DOI:10.1038/s41598-021-90367-4.

        [44]Sun Q,Lin XN,Zhao YS,et al.Deep learning vs.radiomics for predicting axillary lymph node metastasis of breast cancer using ultrasound images:don’t forget the peritumoral region[J].Front Oncol,2020,10:53.DOI:10.3389/fonc.2020.00053.

        [45]Tang X,Huang HL,Du P,et al.Intratumoral and peritumoral CT-based radiomics strategy reveals distinct subtypes of non-small-cell lung cancer[J].J Cancer Res Clin Oncol,2022,148(9):2247-2260.DOI:10.1007/s00432-022-04015-z.

        [46]Ding J,Chen SL,Serrano Sosa M,et al.Optimizing the peritumoral region size in radiomics analysis for sentinel lymph node status prediction in breast cancer[J].Acad Radiol,2022,29 Suppl 1:S223-S228.DOI:10.1016/j.acra.2020.10.015.

        [47]Ehteshami Bejnordi B,Veta M,Johannes van Diest P,et al.Diagnostic assessment of deep learning algorithms for detection of lymph node metastases in women with breast cancer[J].JAMA,2017,318(22):2199-2210.DOI:10.1001/jama.2017.14585.

        [48]Steiner DF,MacDonald R,Liu Y,et al.Impact of deep learning assistance on the histopathologic review of lymph nodes for metastatic breast cancer[J].Am J Surg Pathol,2018,42(12):1636-46.DOI:10.1097/PAS.0000000000001151.

        [49]Bera K,Schalper KA,Rimm DL,et al.Artificial intelligence in digital pathology-new tools for diagnosis and precision oncology[J].Nat Rev Clin Oncol,2019,16(11):703-15.DOI:10.1038/s41571-019-0252-y.

        [50]Zhao JJ,Wang H,Zhang Y,et al.Deep learning radiomics model related with genomics phenotypes for lymph node metastasis prediction in colorectal cancer[J].Radiother Oncol,2022,167:195-202.DOI:10.1016/j.radonc.2021.12.031.

        [51]Skrede OJ,DeRaedt S,KleppeA,et al.Deep learning for prediction of colorectal cancer outcome:a discovery and validation study[J].The Lancet,2020,395(10221):350-360.DOI:10.1016/S0140-6736(19)32998-8.

        [52]Pham HHN,F(xiàn)utakuchi M,Bychkov A,et al.Detection of lung cancer lymph node metastases from whole-slide histopathologic images using a two-step deep learning approach[J].Am J Pathol,2019,189(12):2428-2439.DOI:10.1016/j.ajpath.2019.08.014.

        (收稿日期:2022-10-10)

        亚洲av永久无码天堂网| 精品亚洲人伦一区二区三区| 国产午夜在线观看视频| 女女同恋一区二区在线观看| 玩中年熟妇让你爽视频| 84pao强力打造免费视频34| 91久国产在线观看| 久久国产亚洲精品一区二区三区| 国产欧美日韩精品丝袜高跟鞋| 精品国产av 无码一区二区三区| 无码国产精品一区二区AV| 亚洲av无一区二区三区综合| 色综合久久久无码中文字幕| 亚洲色大成网站www永久一区| 亚洲国产福利成人一区二区 | 久久精品天堂一区二区| 女人18片毛片60分钟| 亚洲av之男人的天堂网站| 9久久精品视香蕉蕉| 白白色日韩免费在线观看| 亚洲夜夜性无码| 中国一 片免费观看| 亚欧视频无码在线观看| 亚洲一区二区三区偷拍视频| 亚洲一区二区三区香蕉| 一本一本久久a久久精品| 日本一区二区三区专区| 青青草免费手机视频在线观看| 亚洲va无码手机在线电影| av一区无码不卡毛片| 久久精品日韩免费视频| 极品粉嫩嫩模大尺度无码视频| 夫妇交换刺激做爰视频| 欧美人与物videos另类| 亚洲中文字幕九色日本| 一夲道无码人妻精品一区二区| 久久久久亚洲AV无码专| 国产精品三级在线不卡| 亚洲午夜久久久久久久久电影网| 中国精学生妹品射精久久| 九色精品国产亚洲av麻豆一|