[摘要]"隨著醫(yī)學(xué)技術(shù)的不斷進步,高級可視化技術(shù)在結(jié)直腸手術(shù)中的應(yīng)用日益受到關(guān)注,包括熒光成像、激光散斑襯比成像、三維重建技術(shù)、增強現(xiàn)實和混合現(xiàn)實等。這些技術(shù)不僅可提供精確的解剖結(jié)構(gòu),還可在術(shù)中實時反映生理信息,顯著提高外科醫(yī)生在手術(shù)過程中的決策能力和操作精度。本文就目前國內(nèi)外開展的高級可視化技術(shù)在結(jié)直腸手術(shù)中的應(yīng)用進行綜述,并討論這些技術(shù)在改進手術(shù)規(guī)劃和實時導(dǎo)航、提高手術(shù)安全性和術(shù)后恢復(fù)效果方面的潛力。
[關(guān)鍵詞]"高級可視化技術(shù);結(jié)直腸手術(shù);混合現(xiàn)實;熒光成像;激光散斑襯比成像
[中圖分類號]"R656.9;R657.1""""""[文獻標識碼]"A""""""[DOI]"10.3969/j.issn.1673-9701.2025.15.022
隨著現(xiàn)代醫(yī)學(xué)向精準化、個性化發(fā)展,高級可視化技術(shù)正逐步重塑外科手術(shù)方式。高級可視化技術(shù)是指通過整合計算機科學(xué)、生物醫(yī)學(xué)工程等多學(xué)科技術(shù),實時生成高精度醫(yī)學(xué)影像的技術(shù)體系。相較于傳統(tǒng)CT、MRI等二維靜態(tài)顯影技術(shù),高級可視化技術(shù)不僅可實現(xiàn)解剖結(jié)構(gòu)的三維動態(tài)追蹤,更具備多種形式的影像融合,顯著提高可視化的精準度。結(jié)直腸手術(shù)因涉及復(fù)雜解剖結(jié)構(gòu)和重要功能保護,對可視化技術(shù)有較高的要求。近年來,近紅外熒光成像、激光散斑襯比成像、血管三維重建、增強現(xiàn)實和混合現(xiàn)實等先進技術(shù)逐漸被引入結(jié)直腸手術(shù)中。這些高級可視化技術(shù)不僅支持術(shù)前的細致規(guī)劃,還提供術(shù)中實時導(dǎo)航和監(jiān)控,幫助外科醫(yī)生更好地識別關(guān)鍵解剖結(jié)構(gòu),制定精準個體化手術(shù)策略,減少并發(fā)癥的發(fā)生。本文綜述高級可視化技術(shù)在結(jié)直腸外科領(lǐng)域的應(yīng)用進展,并展望未來發(fā)展方向。
1""近紅外熒光成像技術(shù)
近紅外熒光成像技術(shù)(near-infrared"fluorescence"imaging,NIRF)通過近紅外光激發(fā)熒光染料并捕獲其發(fā)射信號實現(xiàn)組織顯影,同時結(jié)合光學(xué)成像的高敏感度與近紅外光的組織穿透能力,廣泛應(yīng)用于外科手術(shù)中[1]。吲哚菁綠(indocyanine"green,ICG)是一種水溶性熒光染料,可在近紅外波段發(fā)出熒光,具有良好的生物相容性和低毒性[2]。近年來,吲哚菁綠近紅外熒光成像技術(shù)(ICG"near-infrared"fluorescence"imaging,ICG-NIRF)在結(jié)直腸手術(shù)中的臨床應(yīng)用主要集中在以下關(guān)鍵領(lǐng)域。
1.1""實時腸管血流灌注監(jiān)測
吻合口瘺(anastomotic"leakage,AL)是結(jié)直腸外科醫(yī)生最關(guān)注的并發(fā)癥之一,在低位吻合術(shù)中的發(fā)生率高達17.5%[3]。腸管血流灌注不良是AL發(fā)生的重要危險因素[4]。傳統(tǒng)評估主要依賴外科醫(yī)生對腸壁色澤及動脈搏動的主觀判斷,但研究證實此類方法易低估實際風(fēng)險[5-6]。ICG-NIRF技術(shù)通過靜脈注射ICG后實時捕捉熒光信號,量化腸管血流灌注以彌補主觀偏差,精準指導(dǎo)安全吻合口位置選擇,從而降低AL風(fēng)險。兩項獨立臨床研究顯示在結(jié)直腸術(shù)中應(yīng)用ICG-NIRF評估腸管灌注后,5%~12%的結(jié)直腸手術(shù)術(shù)中更改原定的吻合口位置,術(shù)后AL發(fā)生率從12.4%降至2.8%,顯著提高手術(shù)安全性[7-8]。
1.2"轉(zhuǎn)移淋巴結(jié)識別
結(jié)直腸癌預(yù)后與淋巴結(jié)轉(zhuǎn)移狀態(tài)密切相關(guān),淋巴結(jié)定位精度直接決定腫瘤分期準確性,區(qū)域淋巴結(jié)清掃完整性直接影響復(fù)發(fā)風(fēng)險?;贗CG-NIRF的淋巴結(jié)定位技術(shù)已被廣泛研究,通過前哨淋巴結(jié)識別、轉(zhuǎn)移淋巴結(jié)鑒別及清掃范圍實時引導(dǎo),顯著提升結(jié)直腸癌分期準確性和患者預(yù)后。研究表明ICG-NIRF在早期結(jié)直腸癌前哨淋巴結(jié)成像中具有良好效果,可提供更準確的預(yù)后分層并指導(dǎo)術(shù)后管理[9]。Lin等[10]采用術(shù)前瘤周注射法發(fā)現(xiàn)轉(zhuǎn)移性淋巴結(jié)熒光強度顯著低于非轉(zhuǎn)移性,這可能與淋巴管引流受阻有關(guān)。與之形成鮮明對比的是,Liberale等[11]靜脈注射15min后僅有轉(zhuǎn)移性淋巴結(jié)顯影。這可能源于腫瘤新生血管通透性較高,導(dǎo)致ICG外滲滯留,正常組織因ICG快速代謝特性完成熒光清除。這兩種不同結(jié)果提示臨床使用ICG-NIRF時需注意注射途徑。Wu等[12]對直腸癌伴有側(cè)方淋巴結(jié)轉(zhuǎn)移的患者進行ICG-NIRF引導(dǎo)下腹腔鏡側(cè)方淋巴結(jié)清掃術(shù),所有轉(zhuǎn)移淋巴結(jié)均在術(shù)中被識別,實現(xiàn)根治性手術(shù)。
1.3""術(shù)中輸尿管損傷預(yù)防
醫(yī)源性輸尿管損傷在結(jié)直腸手術(shù)中的發(fā)生率為0.31%~1.00%[13-14];主要類型包括斷裂、結(jié)扎、壓迫及熱損傷等。盡管總體發(fā)生率較低,但顯著增加術(shù)后死亡率和二次手術(shù)率[15]。ICG經(jīng)靜脈注射后主要由肝臟排泄,經(jīng)泌尿系統(tǒng)的排泄率不足1%,因此在輸尿管顯像中,ICG并不是一個良好的造影劑。亞甲藍作為傳統(tǒng)顯影劑通過腎臟代謝顯像,但其致敏性及血氧干擾缺陷限制臨床應(yīng)用[16]。新型顯影劑的研發(fā)為術(shù)中輸尿管識別提供更優(yōu)選擇:IRDye"800CW近紅外熒光劑在動物實驗中證實其肝腎代謝特性及安全性,改良型IRDye"800BK可提升組織對比度[17-18]。UreterGlow-11染料憑借聚乙二醇化結(jié)構(gòu)延長顯影時間至12h,其特異性光譜偏移有效避免術(shù)中多染料干擾[19]。ASP5354染料具有與ICG類似的結(jié)構(gòu),在急性腎損傷模型中維持60min顯影時間,為腎功能不全患者提供新的選擇[20]。
目前,NIRF技術(shù)已被集成進腹腔鏡系統(tǒng)中,在多種外科手術(shù)中展現(xiàn)出重要價值。然而,該技術(shù)仍存在以下不足:①依賴染料,存在過敏風(fēng)險;②染料殘留,干擾二次評估,存在假陽性情況;③熒光信號衰減,深層組織顯影不佳;④定性分析為主,缺乏定量評估。未來的發(fā)展方向應(yīng)主要集中在尋找改良型熒光染料和提高成像深度上。
2""激光散斑襯比成像
激光散斑襯比成像(laser"speckle"contrast"imaging,LSCI)是一種基于后向散射光與紅細胞相互作用所產(chǎn)生動態(tài)變化的成像技術(shù)。該技術(shù)無須依賴外源性造影劑即可進行術(shù)中實時血流成像,同時提供精確的血流灌注定量數(shù)據(jù)[21]。近年來,已有多項研究嘗試將LSCI應(yīng)用于結(jié)直腸手術(shù)中,以評估腸管血流灌注情況及指導(dǎo)近端腸管吻合口位置的選擇。Liu等[22-24]通過動物實驗證實LSCI可精確區(qū)分缺血區(qū)、分水嶺區(qū)與灌注良好區(qū)梯度變化,并可通過分水嶺區(qū)灌注值異常升高鑒別動靜脈淤血。Hoffman等[25]進一步研究確定缺血區(qū)激光散斑相對灌注單位(relative"perfusionu"unit,RPU)臨界值為69。在兩項多中心臨床研究中Heeman等[26]和Skinner等[27]在104例結(jié)直腸切除術(shù)中觀察到約17%的手術(shù)醫(yī)生根據(jù)LSCI灌注評估情況調(diào)整吻合口位置,平均調(diào)整距離為1.2~1.3cm;該現(xiàn)象在左側(cè)結(jié)直腸手術(shù)中尤為明顯,平均調(diào)整距離增至3.7cm,同時AL發(fā)生率降至2.5%。LSCI與ICG-NIRF的效能比較研究未證實LSCI對缺血界限的測定與ICG-NIRF有顯著差異;但兩種技術(shù)聯(lián)合應(yīng)用后監(jiān)測精度顯著高于ICG-NIRF[24,27]。然而,LSCI仍存在局限性,其對組織運動高度敏感,呼吸或腸蠕動易導(dǎo)致偽影產(chǎn)生和RPU劇烈波動。研究發(fā)現(xiàn)通過優(yōu)化探頭距離、同步呼吸周期和采用垂直偏振光等方式可將RPU波動從20%降低至5%以下[22,24,28]。未來,結(jié)合高性能計算平臺和更精密的傳感器,LSCI有望突破這些瓶頸,為結(jié)直腸手術(shù)提供更精細和實時的血流評估。
3""血管三維重建技術(shù)
在結(jié)直腸手術(shù)中,系膜血管變異繁多且分型復(fù)雜,合理保留功能血管是外科醫(yī)師制定手術(shù)策略時的重要考量[29]。因此,系統(tǒng)的術(shù)前血管評估是保障術(shù)中精細解剖的重要基礎(chǔ)。CT血管造影(computed"tomography"angiography,CTA)作為常規(guī)術(shù)前評估手段存在一定局限性:主要呈現(xiàn)動脈主干及一級分支結(jié)構(gòu),對靜脈系統(tǒng)、二級分支及邊緣弓動脈顯影效果較差。近年來,血管三維重建技術(shù)取得顯著進展,利用CT影像數(shù)據(jù)和計算機深度學(xué)習(xí)生成個體化虛擬模型,直觀展示腸系膜血管解剖結(jié)構(gòu)及變異情況,在精度上超越傳統(tǒng)CTA[30];通過精準重建血管結(jié)構(gòu),為腹腔鏡完整結(jié)腸系膜切除術(shù)和中央血管結(jié)扎提供重要的可視化支持,提升手術(shù)安全性[31]。在右半結(jié)腸手術(shù)中,血管三維重建可精準定位回結(jié)腸動脈、右結(jié)腸動脈及中結(jié)腸動脈分支,輔助制定血管離斷策略。Kearns等[32]在右半結(jié)腸手術(shù)中聯(lián)合應(yīng)用三維重建與ICG-NIRF,使血管識別準確率提升至95%,吻合口灌注評估符合率100%,術(shù)中出血量降低51%。在左半結(jié)腸及直腸手術(shù)中,血管三維重建技術(shù)通過重建腸系膜下動靜脈分支模式指導(dǎo)左結(jié)腸動脈保留策略,顯著降低術(shù)后結(jié)腸缺血風(fēng)險[33-34]。Guerriero等[35]進行技術(shù)革新,通過引入虛擬現(xiàn)實技術(shù)動態(tài)模擬手術(shù)路徑,使術(shù)者對左結(jié)腸動脈變異起源的解剖定位效率較傳統(tǒng)二維CT提升60%。盡管血管三維重建技術(shù)優(yōu)勢顯著,其推廣仍面臨挑戰(zhàn)。模型構(gòu)建耗時及專業(yè)軟件依賴限制其臨床普及,同時現(xiàn)有技術(shù)基于術(shù)前靜態(tài)圖像數(shù)據(jù),難以應(yīng)對術(shù)中動態(tài)變化,需更強大的算法模型予以突破。
4""增強現(xiàn)實和混合現(xiàn)實技術(shù)
增強現(xiàn)實(augmented"reality,AR)和混合現(xiàn)實(mixed"reality,MR)作為新興高級可視化技術(shù),在外科培訓(xùn)與手術(shù)實施中的應(yīng)用日益受到關(guān)注。AR技術(shù)可將虛擬影像投射到真實視野,實現(xiàn)初級平面融合;其借助頭戴顯示裝置將建模好的器官、血管等解剖結(jié)構(gòu)投影至術(shù)者視野,實現(xiàn)術(shù)中解剖結(jié)構(gòu)的動態(tài)可視化導(dǎo)航。Leblanc等[36-37]初步探索AR技術(shù)的應(yīng)用,利用AR模擬器(ProMIS"2.5)進行腹腔鏡乙狀結(jié)腸切除術(shù)訓(xùn)練對比,結(jié)果顯示手輔助術(shù)式在結(jié)腸游離及吻合階段效率顯著優(yōu)于傳統(tǒng)腹腔鏡。在結(jié)直腸癌肝轉(zhuǎn)移灶切除領(lǐng)域,AR技術(shù)同樣發(fā)揮重要作用。Ntourakis等[38]成功應(yīng)用AR技術(shù)輔助切除結(jié)直腸癌化療后隱匿性肝轉(zhuǎn)移灶,4處12~24mm的病灶均實現(xiàn)R0切除且無局部復(fù)發(fā)。Zeng等[39]創(chuàng)新性地將AR技術(shù)聯(lián)合ICG-NIRF用于實時導(dǎo)航,精準定位深處腫瘤,在腹腔鏡肝實質(zhì)保留切除術(shù)中實現(xiàn)術(shù)中出血量減少與手術(shù)效率提升。AR技術(shù)雖在靜態(tài)可視化中表現(xiàn)良好,但在術(shù)中動態(tài)操作的實時互動及反饋精度仍是未來發(fā)展的關(guān)鍵。
MR技術(shù)是在AR技術(shù)的基礎(chǔ)上的進一步發(fā)展。MR通過深度傳感器和空間映射技術(shù),將真實環(huán)境與虛擬影像進行雙向深度融合,提供更豐富的多維信息和更多元的交互方式。外科醫(yī)生通過手勢和語音操作MR頭顯設(shè)備(HoloLens),實時查看并操作患者解剖結(jié)構(gòu)的動態(tài)全息圖,為手術(shù)解剖提供新的信息層次[40]。Luzon等[41]在右側(cè)結(jié)腸切除術(shù)中使用MR導(dǎo)航系統(tǒng),發(fā)現(xiàn)當外科醫(yī)生的視線保持與手術(shù)臺垂直的角度時,目標誤差距離較短,證實視覺定位對手術(shù)精度的影響。Ryu等[42]使用HoloLens"2代對比研究顯示MR輔助導(dǎo)航組在解剖變異顯著的右半結(jié)腸切除術(shù)中可視化評分提升42%,且未增加術(shù)中并發(fā)癥。在手術(shù)機器人輔助手術(shù)領(lǐng)域,Huber等[43]研究表明在手術(shù)機器人輔助經(jīng)肛門全直腸系膜切除術(shù)中,MR技術(shù)通過增強術(shù)野三維感知,顯著提升外科醫(yī)生的空間意識和術(shù)中導(dǎo)航能力。MR技術(shù)應(yīng)通過改進圖像融合算法和提升硬件設(shè)備,深化虛擬現(xiàn)實融合,增強術(shù)中模擬與預(yù)測功能,輔助外科醫(yī)生在復(fù)雜解剖結(jié)構(gòu)中進行更精準的導(dǎo)航。
5""小結(jié)與展望
綜上所述,高級可視化技術(shù)已成為結(jié)直腸手術(shù)領(lǐng)域的重要助力。ICG-NIRF在血流監(jiān)測、淋巴結(jié)顯影及輸尿管保護方面成效顯著;LSCI實現(xiàn)無染料的血流灌注定量評估;血管三維重建技術(shù)為手術(shù)規(guī)劃提供精準的血管信息;AR和MR通過虛擬現(xiàn)實深度融合,在術(shù)中發(fā)揮導(dǎo)航作用,極大地提升手術(shù)操作的精準度。然而,這些技術(shù)在應(yīng)用中仍面臨挑戰(zhàn)。ICG-NIRF依賴染料,信號易衰減,定量分析困難;LSCI操作要求高,易受干擾;血管三維重建技術(shù)對原始影像要求高;AR和MR數(shù)據(jù)處理復(fù)雜,設(shè)備昂貴。此外,部分技術(shù)缺乏臨床試驗,實際應(yīng)用安全性及有效性難以得到充分驗證。此外,目前研究多聚焦單一技術(shù),缺乏多種技術(shù)融合的深入探索,如高級可視化技術(shù)的整合將有利于實現(xiàn)優(yōu)勢互補,構(gòu)建個性化手術(shù)方案:術(shù)前,血管三維重建技術(shù)構(gòu)建精細血管模型;術(shù)中,ICG-NIRF和LSCI技術(shù)監(jiān)測血供,ICG-NIRF定位缺血區(qū),LSCI提供定量數(shù)據(jù);同時,AR或MR技術(shù)投射患者解剖生理數(shù)據(jù),提供實時導(dǎo)航。這些技術(shù)的整合為外科醫(yī)生提供更全面、更直觀的患者信息,有利于優(yōu)化手術(shù)規(guī)劃和實施過程,有效改善患者的治療效果和預(yù)后。
高級可視化技術(shù)隨著其他技術(shù)的發(fā)展而升級。近期,DeepSeek等性能卓越的人工智能(artificial"intelligence,AI)崛起,其強大的深度學(xué)習(xí)、圖像識別、數(shù)據(jù)分析能力將為高級可視化技術(shù)提供不可忽視的助力。ICG-NIRF與LSCI借助AI深度學(xué)習(xí)能力,或可消除染料殘留、運動偽影等影響,幫助精準識別組織缺血區(qū)。結(jié)合AI圖像識別算法,血管三維重建技術(shù)也許能全自動生成重建模型,降低成本?;贏I強大的數(shù)據(jù)分析能力,AR和MR技術(shù)有望將術(shù)中器械定位與患者生理解剖信息進行動態(tài)融合,建立全自動手術(shù)導(dǎo)航系統(tǒng)。未來術(shù)者有機會通過AR或MR頭顯,實時調(diào)取疊加于真實術(shù)野的路徑規(guī)劃,完成高度自動化的手術(shù),實現(xiàn)高水平的精準治療。除軟件層面的突破外,還有硬件技術(shù)的創(chuàng)新。當前,手術(shù)機器人系統(tǒng)被國內(nèi)數(shù)十家醫(yī)療中心引進,其高自由度機械臂、震顫過濾系統(tǒng)、三維高清視野顯著提升狹窄術(shù)野操作精度。若能與高級可視化技術(shù)結(jié)合,將為精準化手術(shù)提供新的助力:將ICG-NIRF、LSCI等實時組織灌注數(shù)據(jù)集成至系統(tǒng)生成復(fù)合視圖;利用機器人臂端追蹤器與AR和MR空間坐標系統(tǒng)實現(xiàn)動態(tài)校準;構(gòu)建力覺傳感-血流灌注參數(shù)聯(lián)動的腸管牽拉預(yù)警模型。隨著個性化醫(yī)療的不斷發(fā)展,基于患者特征的定制化可視化方案正逐步常態(tài)化,為精準治療提供強力支撐。這些技術(shù)的廣泛應(yīng)用不僅可提升手術(shù)療效與降低圍術(shù)期不良事件風(fēng)險,還將推動醫(yī)學(xué)向更精準、高效的方向邁進。
利益沖突:所有作者均聲明不存在利益沖突。
[參考文獻]
[1] LANDSMAN"M"L,"KWANT"G,"MOOK"G"A,"et"al."Light-absorbing"properties,"stability,"and"spectral"stabilization"of"indocyanine"green[J]."J"Appl"Physiol,"1976,"40(4):"575–583.
[2] REINHART"M"B,"HUNTINGTON"C"R,"BLAIR"L"J,""et"al."Indocyanine"green:"Historical"context,"current"applications,"and"future"considerations[J]."Surg"Innov,"2016,"23(2):"166–175.
[3] WEI"F"Z,"MEI"S"W,"WANG"Z"J,"et"al."HAMP"as"a"prognostic"biomarker"for"colorectal"cancer"based"on"tumor"microenvironment"analysis[J]."Front"Oncol,"2022,"12:"884474.
[4] TRENCHEVA"K,"MORRISSEY"K"P,"WELLS"M,"et"al."Identifying"important"predictors"for"anastomotic"leak"after"colon"and"rectal"resection:"Prospective"study"on"616"patients[J]."Ann"Surg,"2013,"257(1):"108–113.
[5] MARKUS"P"M,"MARTELL"J,"LEISTER"I,"et"al."Predicting"postoperative"morbidity"by"clinical"assessment[J]."Br"J"Surg,"2005,"92(1):"101–106.
[6] KARLICZEK"A,"HARLAAR"N"J,"ZEEBREGTS"C"J,""et"al."Surgeons"lack"predictive"accuracy"for"anastomotic"leakage"in"gastrointestinal"surgery[J]."Int"J"Colorectal"Dis,"2009,"24(5):"569–576.
[7] VARDHAN"S,"DESHPANDE"S"G,"SINGH"A,"et"al."Techniques"for"diagnosing"anastomotic"leaks"intraoperatively"in"colorectal"surgeries:"A"review[J]."Cureus,"2023,"15(1):"e34168.
[8] HASEGAWA"H,"TSUKADA"Y,"WAKABAYASHI"M,"et"al."Impact"of"intraoperative"indocyanine"green"fluorescence"angiography"on"anastomotic"leakage"after"laparoscopic"sphincter-sparing"surgery"for"malignant"rectal"tumors[J]."Int"J"Colorectal"Dis,"2020,"35(3):"471–480.
[9] SIKKENK"D"J,"STERKENBURG"A"J,"BURGHGRAEF"T"A,"et"al."Robot-assisted"fluorescent"sentinel"lymph"node"identification"in"early-stage"colon"cancer[J]."Surg"Endosc,"2023,"37(11):"8394–8403.
[10] LIN"W,"LI"Q,"SHENG"J,"et"al."Quantitative"analysis"of"peri-intestinal"lymph"node"metastasis"using"indocyanine"green"fluorescence"imaging"technology[J]."Medicine"(Baltimore),"2024,"103(35):"e39240.
[11] LIBERALE"G,"VANKERCKHOVE"S,"GALDON"M"G,"et"al."Fluorescence"imaging"after"intraoperative"intravenous"injection"of"indocyanine"green"for"detection"of"lymph"node"metastases"in"colorectal"cancer[J]."Eur"J"Surg"Oncol,"2015,"41(9):"1256–1260.
[12] WU"C"Y,"ZHONG"W"J,"YE"K."Preliminary"study"of"indocyanine"green-guided"laparoscopic"lateral"lymph"node"dissection"for"rectal"cancer[J]."PLoS"One,"2024,"19(7):"e0307077.
[13] MARCELISSEN"T"A"T,"DEN"HOLLANDER"P"P,"TUYTTEN"T"R"A"H,"et"al."Incidence"of"iatrogenic"ureteral"injury"during"open"and"laparoscopic"colorectal"surgery:"A"single"center"experience"and"review"of"the"literature[J]."Surg"Laparosc"Endosc"Percutan"Tech,"2016,"26(6):"513–515.
[14] MAYO"J"S,"BRAZER"M"L,"BOGENBERGER"K"J,"et"al."Ureteral"injuries"in"colorectal"surgery"and"the"impact"of"laparoscopic"and"robotic-assisted"approaches[J]."Surg"Endosc,"2021,"35(6):"2805–2816.
[15] MCCLELLAND"P"H,"LIU"T,"JOHNSON"R"P,"et"al."Iatrogenic"urinary"injuries"in"colorectal"surgery:"Outcomes"and"risk"factors"from"a"nationwide"cohort[J]."Tech"Coloproctol,"2024,"28(1):"137.
[16] BARNES"T"G,"HOMPES"R,"BIRKS"J,"et"al."Methylene"blue"fluorescence"of"the"ureter"during"colorectal"surgery[J]."Surg"Endosc,"2018,"32(9):"4036–4043.
[17] SCHOLS"R"M,"LODEWICK"T"M,"BOUVY"N"D,"et"al."Application"of"a"new"dye"for"near-infrared"fluorescence"laparoscopy"of"the"ureters:"Demonstration"in"a"pig"model[J]."Dis"Colon"Rectum,"2014,"57(3):"407–411.
[18] VAN"DEN"BOS"J,"AL-TAHER"M,"BOUVY"N"D,nbsp;et"al."Near-infrared"fluorescence"laparoscopy"of"the"ureter"with"three"preclinical"dyes"in"a"pig"model[J]."Surg"Endosc,"2019,"33(3):"986–991.
[19] MAHALINGAM"S"M,"PUTT"K"S,"SRINIVASARAO"M,"et"al."Design"of"a"near"infrared"fluorescent"ureter"imaging"agent"for"prevention"of"ureter"damage"during"abdominal"surgeries[J]."Molecules,"2021,"26(12):"3739.
[20] TERANISHI"K."Evaluation"of"the"utilization"of"near-"infrared"fluorescent"contrast"agent"ASP5354"for"in"vivo"ureteral"identification"in"renal"diseases"using"rat"models"of"gentamicin-induced"acute"kidney"injury[J]."Diagnostics"(Basel),"2023,"13(10):"1823.
[21] KOJIMA"S,"SAKAMOTO"T,"NAGAI"Y,"et"al."Laser"speckle"contrast"imaging"for"intraoperative"quantitative"assessment"of"intestinal"blood"perfusion"during"colorectal"surgery:"A"prospective"pilot"study[J]."Surg"Innov,"2019,"26(3):"293–301.
[22] LIU"Y"Z,"MEHROTRA"S,"NWAIWU"C"A,"et"al."Real-time"quantification"of"intestinal"perfusion"and"arterial"versus"venous"occlusion"using"laser"speckle"contrast"imaging"in"porcine"model[J]."Langenbecks"Arch"Surg,"2023,"408(1):"114.
[23] MEHROTRA"S,"LIU"Y"Z,"NWAIWU"C"A,"et"al."Real-time"quantification"of"bowel"perfusion"using"laparoscopic"laser"speckle"contrast"imaging"(LSCI)"in"a"porcine"model[J]."BMC"Surg,"2023,"23(1):"261.
[24] NWAIWU"C"A,"BUHARIN"V"E,"MACH"A,"et"al."Feasibility"and"comparison"of"laparoscopic"laser"speckle"contrast"imaging"to"near-infrared"display"of"indocyanine"green"in"intraoperative"tissue"blood"flow/tissue"perfusion"in"preclinical"porcine"models[J].nbsp;Surg"Endosc,"2023,"37(2):"1086–1095.
[25] HOFFMAN"J"T,"HEUVELINGS"D"J"I,"VAN"ZUTPHEN"T,"et"al."Real-time"quantification"of"laser"speckle"contrast"imaging"during"intestinal"laparoscopic"surgery:"Successful"demonstration"in"a"porcine"intestinal"ischemia"model[J]."Surg"Endosc,"2024,"38(9):"5292–5303.
[26] HEEMAN"W,"CALON"J,"VAN"DER"BILT"A,"et"al."Dye-free"visualisation"of"intestinal"perfusion"using"laser"speckle"contrast"imaging"in"laparoscopic"surgery:"A"prospective,"observational"multi-centre"study[J]."Surg"Endosc,"2023,"37(12):"9139–9146.
[27] SKINNER"G"C,"LIU"Y"Z,"HARZMAN"A"E,"et"al."Clinical"utility"of"laser"speckle"contrast"imaging"and"real-time"quantification"of"bowel"perfusion"in"minimally"invasive"left-sided"colorectal"resections[J]."Dis"Colon"Rectum,"2024,"67(6):"850–859.
[28] AMINI"N,"ESTEKI"A,"AHMADI"M,"et"al."Impact"of"light"polarization"on"laser"speckle"contrast"imaging"with"a"custom"phantom"for"microvascular"flow[J]."Sci"Rep,"2024,nbsp;14(1):"26652.
[29] LI"J,"ZHAO"X,"YI"B,"et"al."Surgical"anatomy"and"clinical"variation"of"the"left"colonic"artery"in"laparoscopic"anterior"rectal"resection[J]."Front"Surg,"2023,"10:"1190259.
[30] KEARNS"E"C,"MOYNIHAN"A,"DALLI"J,"et"al."Clinical"validation"of"3D"virtual"modelling"for"laparoscopic"complete"mesocolic"excision"with"central"vascular"ligation"for"proximal"colon"cancer[J]."Eur"J"Surg"Oncol,"2024,"50(11):"108597.
[31] FLETCHER"J,"ILANGOVAN"R,"HANNA"G,"et"al."The"impact"of"three-dimensional"reconstruction"and"standardised"CT"interpretation"(AMIGO)"on"the"anatomical"understanding"of"mesenteric"vascular"anatomy"for"planning"complete"mesocolic"excision"surgery:"A"randomised"crossover"study[J]."Colorectal"Dis,"2022,"24(4):"388–400.
[32] KEARNS"E"C,"MOYNIHAN"A,"KHAN"M"F,"et"al."Comparison"and"impact"of"preoperative"3D"virtual"vascular"modelling"with"intraoperative"indocyanine"green"perfusion"angiography"for"personalized"proximal"colon"cancer"surgery[J]."Eur"J"Surg"Oncol,"2025,"51(3):"109581.
[33] NEPAL"P,"MORI"S,"KITA"Y,"et"al."Anatomical"study"of"the"inferior"mesenteric"vein"using"three-dimensional"computed"tomography"angiography"in"laparoscopy-assisted"surgery"for"left-sided"colorectal"cancer[J]."Surg"Today,"2021,"51(10):"1665–1670.
[34] WANG"Y,"LIU"Z"S,"WANG"Z"B,"et"al."Efficacy"of"laparoscopic"low"anterior"resection"for"colorectal"cancer"patients"with"3D-vascular"reconstruction"for"left"coronary"artery"preservation[J]."World"J"Gastrointest"Surg,"2024,"16(6):"1548–1557.
[35] GUERRIERO"L,"QUERO"G,"DIANA"M,"et"al."Virtual"reality"exploration"and"planning"for"precisionnbsp;colorectal"surgery[J]."Dis"Colon"Rectum,"2018,"61(6):"719–723.
[36] LEBLANC"F,"DELANEY"C"P,"NEARY"P"C,"et"al."Assessment"of"comparative"skills"between"hand-assisted"and"straight"laparoscopic"colorectal"training"on"an"augmented"reality"simulator[J]."Dis"Colon"Rectum,"2010,"53(9):"1323–1327.
[37] LEBLANC"F,"DELANEY"C"P,"ELLIS"C"N,"et"al."Hand-assisted"versus"straight"laparoscopic"sigmoid"colectomy"on"a"training"simulator:"What"is"the"difference?"A"stepwise"comparison"of"hand-assisted"versus"straight"laparoscopic"sigmoid"colectomy"performance"on"an"augmented"reality"simulator[J]."World"J"Surg,"2010,"34(12):"2909–2914.
[38] NTOURAKIS"D,"MEMEO"R,"SOLER"L,"et"al."Augmented"reality"guidance"for"the"resection"of"missing"colorectal"liver"metastases:"An"initial"experience[J]."World"J"Surg,"2016,"40(2):"419–426.
[39] ZENG"X,"LI"X,"LIN"W,"et"al."Efficacy"of"laparoscopic"parenchyma-sparing"hepatectomy"using"augmented"reality"navigation"combined"with"fluorescence"imaging"for"colorectal"liver"metastases:"A"retrospective"cohort"study"using"inverse"probability"treatment"weighting"analysis[J]."Int"J"Surg,"2025,"111(2):"1749–1759.
[40] BUTERA"G,"STURLA"F,"PLUCHINOTTA"F"R,"et"al."Holographic"augmented"reality"and"3D"printing"for"advanced"planning"of"sinus"venosus"ASD/partial"anomalous"pulmonary"venous"return"percutaneous"management[J]."JACC"Cardiovasc"Interv,"2019,"12(14):"1389–1391.
[41] LUZON"J"A,"STIMEC"B"V,"BAKKA"A"O,"et"al."Value"of"the"surgeon’s"sightline"on"hologram"registration"and"targeting"in"mixed"reality[J]."Int"J"Comput"Assist"Radiol"Surg,"2020,"15(12):"2027–2039.
[42] RYU"S,"KITAGAWA"T,"GOTO"K,"et"al.nbsp;Intraoperative"holographic"guidance"using"virtual"reality"and"mixed"reality"technology"during"laparoscopic"colorectal"cancer"surgery[J]."Anticancer"Res,"2022,"42(10):"4849–4856.
[43] HUBER"T,"HADZIJUSUFOVIC"E,"HANSEN"C,"et"al."Head-mounted"mixed-reality"technology"during"robotic-"assisted"transanal"total"mesorectal"excision[J]."Dis"Colon"Rectum,"2019,"62(2):"258–261.
(收稿日期:2025–02–10)
(修回日期:2025–05–16)