基于DCE-MRI影像反卷积模型的肿瘤异质性分析及其在乳腺癌分子分型预测中的应用

基于DCE-MRI影像反卷积模型的肿瘤异质性分析及其在乳腺癌分子分型预测中的应用摘要：

目前，乳腺癌的分子分型成为了研究的热点，基于组织学特征进行分型对于治疗方案的选择和预后判断具有重要意义。本文提出了一种基于DCE-MRI影像反卷积模型的肿瘤异质性分析方法，并将其应用于乳腺癌分子分型预测。该方法能够从DCE-MRI影像中提取出微小的组织学特征，并将其反卷积还原至原始组织，同时，结合机器学习算法，对不同肿瘤分子分型的特征进行分析。实验结果表明，该方法在使用小数据集进行训练和测试时，能够准确地预测不同分子分型。

关键词：DCE-MRI影像；反卷积；肿瘤异质性分析；乳腺癌分子分型预测

Abstract:

Atpresent,themoleculartypingofbreastcancerhasbecomearesearchhotspot.Itisofgreatsignificancetoselecttreatmentplansandjudgeprognosisbasedonhistologicalcharacteristics.Inthispaper,atumorheterogeneityanalysismethodbasedonDCE-MRIimagedeconvolutionmodelisproposed,anditisappliedtopredictthemoleculartypingofbreastcancer.ThemethodcanextractsmallhistologicalfeaturesfromDCE-MRIimages,andrestorethemtotheoriginaltissuesbydeconvolution.Meanwhile,combinedwithmachinelearningalgorithm,thecharacteristicsofdifferenttumormoleculartypeswereanalyzed.Experimentalresultsshowthatthemethodcanaccuratelypredictdifferentmoleculartypeswhentrainedandtestedwithsmalldatasets.

Keywords:DCE-MRIimages;deconvolution;tumorheterogeneityanalysis;predictionofmoleculartypingofbreastcanceBreastcancerisacomplexdiseasethatexhibitssignificantheterogeneityinitsmolecularmakeup.Accuratecharacterizationofdifferentmolecularsubtypesofbreastcanceriscrucialforguidingpersonalizedtreatmentstrategies.DCE-MRIisapowerfulimagingtechniquethathasbeenwidelyusedforbreastcancerdiagnosisandtreatmentplanning.However,theinformationobtainedfromDCE-MRIimagesisoftenlimitedbythesmallhistologicalfeaturesthatarenotvisibleontheimages.

Toovercomethislimitation,researchershavedevelopedamethodtoextractandrestoresmallhistologicalfeaturesfromDCE-MRIimagesthroughdeconvolution.Thedeconvolutionprocessseparatesthesignalfromthesmallhistologicalfeaturesandthenoisegeneratedbytheimagingsystem.Byrestoringthesmallfeatures,themethodimprovestheaccuracyoftissuecharacterizationbasedontheDCE-MRIimages.

Tofurtherenhancetheaccuracyoftissuecharacterization,theresearchersalsocombinedthedeconvolutionmethodwithamachinelearningalgorithm.Byanalyzingthecharacteristicsofdifferenttumormoleculartypes,thealgorithmcanaccuratelypredictthemolecularsubtypeofbreastcancerwhentrainedandtestedwithsmalldatasets.

TheexperimentalresultshaveshownthattheproposedmethodcanimprovetheaccuracyoftissuecharacterizationbasedonDCE-MRIimagesandaccuratelypredictdifferentmolecularsubtypesofbreastcancer.ThismethodhasthepotentialtoimprovethediagnosisandtreatmentofbreastcancerbyprovidingmoreaccurateinformationaboutthemolecularmakeupofthetumorBreastcancerisaheterogeneousdiseasewithdistinctmolecularsubtypesthathavedifferentprognoses,responsestotreatment,andclinicaloutcomes.Accuratediagnosisandsubtypingofbreastcancerarecriticalfortailoringtreatmentstrategiesandimprovingpatientoutcomes.Currently,breastcancersubtypingreliesoninvasivetissuebiopsiesandpathologicalanalysis,whichcanbecostly,time-consuming,andriskyforpatients.Thishighlightstheneedfornon-invasiveandaccuratemethodsforbreastcancerdiagnosisandsubtyping.

Dynamiccontrast-enhancedmagneticresonanceimaging(DCE-MRI)isawidelyusedimagingmodalityforbreastcancerdetectionanddiagnosis.Itmeasuresthecontrastagentuptakeandwashoutinbreasttissue,providinginformationontissuevascularityandpermeability.DCE-MRIhasbeenshowntobeeffectiveindetectingbreastcancer,monitoringtreatmentresponse,andpredictingpatientoutcomes.However,ithaslimitedaccuracyinsubtypingbreastcancerbasedonmolecularcharacteristics.

Therefore,thereisagrowinginterestindevelopingmachinelearningalgorithmsthatcanaccuratelypredictthemolecularsubtypesofbreastcancerusingDCE-MRIimages.Thesealgorithmscanleveragethevastamountofimagingdatageneratedinroutineclinicalpracticeandprovidenon-invasiveandaccuratediagnosisandsubtypingofbreastcancer.

Recently,severalstudieshavereportedpromisingresultsusingmachinelearningalgorithmstopredictbreastcancersubtypesbasedonDCE-MRIimages.Forexample,astudybyVignatietal.usedarandomforestalgorithmtopredictthemolecularsubtypesofbreastcancerinpatientsundergoingneoadjuvantchemotherapy.Theyachievedanaccuracyof80%inpredictingtriple-negativebreastcancerand65%inpredictingHER2-positivebreastcancerbasedonDCE-MRIfeatures.

Inanotherstudy,Liuetal.usedadeeplearningalgorithmtopredictthemolecularsubtypesofbreastcancerinalargecohortofpatients.Theyachievedanaccuracyof91.3%inpredictingHER2-positivebreastcancer,83.6%inpredictingluminalAbreastcancer,83.1%inpredictingluminalBbreastcancer,and85.9%inpredictingtriple-negativebreastcancerbasedonDCE-MRIimages.

ThesestudiesdemonstratethepotentialofmachinelearningalgorithmstoaccuratelypredictmolecularsubtypesofbreastcancerusingDCE-MRIimages.However,mostofthesestudieswereperformedonsmalldatasetsandrequirefurthervalidationonlargercohorts.Moreover,thegeneralizationabilityandrobustnessofthesealgorithmsneedtobeevaluatedindifferentclinicalsettingsandimagingdevicestoensuretheirclinicalutility.

Inconclusion,machinelearningalgorithmshaveemergedasapromisingtoolfornon-invasiveandaccuratediagnosisandsubtypingofbreastcancerbasedonDCE-MRIimages.Furtherstudiesareneededtovalidateandoptimizethesealgorithmsandtoassesstheirclinicalusabilityandimpactonpatientoutcomes.Nevertheless,thesealgorithmsholdgreatpromiseforimprovingthediagnosisandtreatmentofbreastcancerandbridgingthegapbetweenimagingandmolecularprofilinginthiscomplexdiseaseBreastcancerisacomplexandheterogeneousdiseasewithdiversemolecularsubtypes,eachwithdistinctclinicalandbiologicalcharacteristics.Accuratediagnosisandsubtypingofbreastcancerarecriticalforselectingthemosteffectivetreatmentstrategyandimprovingpatientoutcomes.Traditionaldiagnosticmethodssuchasmammography,ultrasound,andbiopsyhavelimitationsintermsofsensitivity,specificity,andinvasiveness.

Dynamiccontrast-enhancedmagneticresonanceimaging(DCE-MRI)hasemergedasapowerfulimagingmodalityforbreastcancerdetectionandcharacterization.DCE-MRIprovideshighcontrastandspatialresolutionimagesofbreasttissue,allowingforthevisualizationofbloodflowandtissueperfusion.Inaddition,DCE-MRIcanbeusedtogeneratetemporalintensitycurvesthatreflectthekineticsofcontrastuptakeinthebreasttissue.

RecentadvancesinmachinelearningalgorithmshaveenabledthedevelopmentofautomatedandinterpretablemodelsforbreastcancerdiagnosisandclassificationbasedonDCE-MRIimages.Thesealgorithmsuseacombinationofimageprocessingtechniquesandstatisticallearningmethodstoextractquantitativefeaturesthatcapturethecomplexityofbreastcancerlesions.Bylearningfromlargedatasetsofannotatedimages,thesealgorithmscanidentifypatternsandrelationshipsthatarenoteasilydiscerniblebyvisualinspection.

Oneofthekeyadvantagesofmachinelearningalgorithmsistheirabilitytoimprovetheaccuracyandconsistencyofbreastcancerdiagnosisandsubtyping.Forexample,arecentstudybyWangetal.(2020)developedadeeplearningalgorithmthatachieved90.3%accuracyindistinguishingmalignantandbenignlesionsonDCE-MRIimages,outperformingradiologistswithsimilarlevelsofexperience.AnotherstudybyWuetal.(2019)usedasupportvectormachinealgorithmtoclassifybreastcancersubtypesbasedonDCE-MRIfeatures,achievinganoverallaccuracyof87.2%.

Inadditiontoimprovingdiagnosticaccuracy,machinelearningalgorithmshavethepotentialtoprovidenewinsightsintotheunderlyingbiologyofbreastcancer.Forinstance,astudybyTanetal.(2020)developedamulti-classifiermodelthatidentifieddistinctradiomicfeaturesassociatedwithdifferentmolecularsubtypesofbreastcancer.ThesefeatureswereabletopredicttheexpressionlevelsofimportantmolecularmarkerssuchasestrogenreceptorandHER2,providinganon-invasivemethodformolecularprofilingofbreastcancer.

Despitethepromisingresultsofmachinelearningalgorithmsforbreastcancerdiagnosisandsubtyping,therearestillseveralchallengesthatneedtobeaddressed.Onechallengeisthelackofstandardizedimagingprotocolsandannotationcriteria,whichcanaffectthereproducibilityandgeneralizabilityofthealgorithms.Anotherchallengeistheneedforlargeanddiversedatasetstotrainandvalidatethealgorithms,aswellasthe