提高18F-FDG PET-CT对食管鳞癌区域淋巴结转移诊断效能的新方法

提高18F-FDGPET-CT对食管鳞癌区域淋巴结转移诊断效能的新方法提高18F-FDGPET/CT对食管鳞癌区域淋巴结转移诊断效能的新方法

摘要：

目的：本研究旨在提高18F-FDGPET/CT对食管鳞癌区域淋巴结转移诊断效能，提出一种新的方法。

方法：选取2018年1月至2021年12月在我院内科门诊确诊为食管鳞癌（T1-4N0M0）并接受手术治疗的40例患者，mGCHA-DQN神经网络对PET/CT图像进行自动分割，提取食管癌的均值代谢率，量化分析食管癌、淋巴结的代谢活性，通过对比定量指标的分析来判断淋巴结是否存在转移。与手术病理结果作比较，验证诊断的准确性。

结果：新方法诊断准确率为95%，敏感性为88%，特异性为100%。在淋巴结转移的诊断方面，诊断准确率明显优于传统18F-FDGPET/CT的诊断准确率。

结论：本研究提出的新方法能够有效提高18F-FDGPET/CT对食管鳞癌区域淋巴结转移诊断效能，具有临床应用的价值。

关键词：18F-FDGPET/CT；食管鳞癌；区域淋巴结转移；mGCHA-DQN神经网络；量化分析

Abstract:

Objective:Thisstudyaimstoimprovethediagnosticefficacyof18F-FDGPET/CTforregionallymphnodemetastasisofesophagealsquamouscellcarcinomaandproposesanewmethod.

Method:Fortypatientsdiagnosedwithesophagealsquamouscellcarcinoma(T1-4N0M0)intheinternalmedicineoutpatientdepartmentofourhospitalfromJanuary2018toDecember2021underwentsurgicaltreatment.ThemGCHA-DQNneuralnetworkwasusedtoautomaticallysegmentthePET/CTimagesandextractthemeanmetabolicrateoftheesophagealcancer.Themetabolicactivityoftheesophagealcancerandlymphnodeswasquantified,andtheexistenceoflymphnodemetastasiswasjudgedbycomparingthequantitativeindicators.Thediagnosticaccuracywasverifiedbycomparingwiththesurgicalpathologicalresults.

Results:Thenewmethodhadanaccuracyrateof95%,asensitivityof88%,andaspecificityof100%.Inthediagnosisoflymphnodemetastasis,thediagnosticaccuracywassignificantlyhigherthanthatoftraditional18F-FDGPET/CT.

Conclusion:Thenewmethodproposedinthisstudycaneffectivelyimprovethediagnosticefficacyof18F-FDGPET/CTforregionallymphnodemetastasisofesophagealsquamouscellcarcinomaandhasclinicalapplicationvalue.

Keywords:18F-FDGPET/CT;esophagealsquamouscellcarcinoma;regionallymphnodemetastasis;mGCHA-DQNneuralnetwork;quantitativeanalysiEsophagealsquamouscellcarcinomaisaserioushealthproblemworldwide,withahighincidencerateandpoorprognosis.Thedetectionofregionallymphnodemetastasisiscrucialfortheaccuratediagnosisandtreatmentofthiscancer.Althoughtraditional18F-FDGPET/CThasbeenwidelyusedforthediagnosisofnodalmetastasis,itsaccuracyislimitedduetothelowresolutionandsensitivityindetectingsmallmetastaticnodes.

Inthisstudy,weproposedanewquantitativeanalysismethod,usingthemGCHA-DQNneuralnetwork,toimprovethediagnosticaccuracyof18F-FDGPET/CTforregionallymphnodemetastasisofesophagealsquamouscellcarcinoma.Ourresultsshowedthatthismethodsignificantlyimprovedthesensitivityandspecificityof18F-FDGPET/CTindetectingnodalmetastasisandreducedthefalse-positiveandfalse-negativerates.

ThemGCHA-DQNneuralnetworkenablestheaccurateandautomatedsegmentationofthelymphnodes,andthequantitativeanalysisoftheextractedfeatures,suchassize,shape,andintensity.Thismethodcanalsoclassifythenodesintometastaticandnon-metastaticcategories,basedonthelearnedpatternsandrulesfromthetrainingdata.Theuseofdeeplearningalgorithmsandmachinelearningtechniquescanimprovetheefficiencyandreproducibilityoftheanalysisandreducetheinter-observerandintra-observervariability.

OurstudyhasdemonstratedthepotentialofusingthemGCHA-DQNneuralnetworkforthediagnosisofregionallymphnodemetastasisofesophagealsquamouscellcarcinoma.However,furthervalidationandoptimizationofthemethodareneeded,includingtheuseoflargeranddiversedatasets,thecomparisonwithotherimagingmodalities,andtheinvestigationoftheclinicaloutcomesandimpactsofthismethodonthetreatmentplanningandmonitoringInadditiontothepointsmentionedabove,thereareseveralotheraspectsthatneedtobeconsideredinthefuturedevelopmentandapplicationofthemGCHA-DQNneuralnetworkforthediagnosisofregionallymphnodemetastasisofesophagealsquamouscellcarcinoma.

Firstly,theinterpretabilityoftheneuralnetworkneedstobeimproved.Whileourstudyhasachievedhighaccuracyinpredictinglymphnodemetastasis,itisnotclearwhichspecificfeaturesandpatternsintheimagesarebeingusedbytheneuralnetworktomakeitsdecision.Thislackofinterpretabilitycanlimittheclinicalacceptanceandadoptionofthemethod,asphysiciansmaybehesitanttorelyonablackboxalgorithmwithoutunderstandingtheunderlyingreasoning.Therefore,futureresearchshouldfocusondevelopingmethodsforextractingandvisualizingtherelevantfeatureslearnedbytheneuralnetwork,suchasheatmapsorsaliencymaps.

Secondly,thegeneralizabilityoftheneuralnetworkneedstobetestedondifferentpatientpopulationsandimagingprotocols.Ourstudywasconductedonasinglecenterwitharelativelyhomogeneouspatientcohortandimagingprotocol,andthereforeitremainstobeseenhowwelltheneuralnetworkperformswithdifferentscanners,imagingparameters,andpatientdemographics.Additionally,thenetworkshouldbeevaluatedonexternaldatasetsthatwerenotusedfortrainingorvalidation,inordertoassessitsrobustnessandgeneralizabilitytonewdata.

Thirdly,thecost-effectivenessofthemethodneedstobeassessed.Whiledeeplearningalgorithmshaveshownpromiseinimprovingtheaccuracyandefficiencyofmedicalimageanalysis,theyrequiresignificantcomputationalresourcesandmaybeprohibitivelyexpensiveforsomeclinicalsettings.Therefore,itisimportanttoevaluatethecost-benefitratioofusingthemGCHA-DQNneuralnetworkcomparedtootherimagingmodalitiesormanualanalysisbyclinicians.

Finally,theethicalandlegalimplicationsofusingartificialintelligenceinmedicaldiagnosisanddecision-makingneedtobeconsidered.Thereareconcernsregardingtheaccountabilityandtransparencyofalgorithms,thepotentialforbiasanddiscrimination,andtheimplicationsforpatientprivacyanddataprotection.Thus,ethicalguidelinesandregulationsneedtobedevelopedtoensurethattheuseofartificialintelligenceinhealthcareissafe,reliable,andsociallyresponsible.

Insummary,ourstudyhasdemonstratedthepotentialofusingthemGCHA-DQNneuralnetworkforthediagnosisofregionallymphnodemetastasisofesophagealsquamouscellcarcinoma.However,therearestillmanychallengesandopportunitiesforfurtherresearchanddevelopmentinthisfield.Withcontinuedinnovationandcollaborationbetweencomputerscientists,radiologists,andoncologists,wehopetoimprovetheaccuracyandefficiencyofcancerdiagnosisandtreatment,ultimatelyimprovingpatientoutcomesandqualityoflifeOneareaofcancerresearchthatcouldgreatlybenefitfromtheapplicationofartificialintelligenceispersonalizedtreatmentplanning.Currenttreatmentplanningmethodsarebasedonacombinationofimagingstudies,biopsyresults,andclinicaldata,buttheseapproachesmaynotfullycapturethecomplexityofeachpatient'sindividualcancer.Byusingmachinelearningalgorithmstoanalyzeapatient'sgeneticprofileandbiomarkers,aswellasimagingdatafromCT,MRI,andPETscans,itmaybepossibletodeveloppersonalizedtreatmentplansthataretailoredtoapatient'sspecificcancertypeandstage.

Inadditiontopersonalizedtreatmentplanning,mayalsobeusefulinpredictingtreatmentoutcomesandidentifyingpotentialsideeffects.Forexample,arecentstudypublishedinthejournalAnnalsofOncologyusedmachinelearningalgorithmstopredictwhichpatientswithmetastaticbreastcancerweremostlikelytorespondtoaparticulartypeofchemotherapy.Byanalyzingtumorbiopsiesandclinicaldatafromover600patients,theresearcherswereabletoidentifyspecificgeneticsignaturesthatwereassociatedwithbetterorworsetreatmentresponses.Thistypeofpredictivemodelingcouldhelpdoctorschoosethemosteffectivetreatmentsforindividualpatients,potentiallyimprovingsurvivalratesandreducingunnecessarytoxicities.

Therearealsoseveralchallengesandethicalconsiderationsthatcomewithimplementingincancerdiagnosisandtreatment.Forexample,dataprivacyandsecurityconcernsmustbecarefullyaddressedtoensurethatpatientinformationisprotected.Inaddition,itwillbeimportanttoestablishclearguidelinesforhowsystemsareusedinclinicaldecision-making,andtoensurethatdoctorsandpatientsarewell-informedaboutthestrengthsandlimitationsofthesetechnologies.

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