基于DCE-MRI影像反卷積模型的腫瘤異質(zhì)性分析及其在乳腺癌分子分型預(yù)測(cè)中的應(yīng)用

基于DCE-MRI影像反卷積模型的腫瘤異質(zhì)性分析及其在乳腺癌分子分型預(yù)測(cè)中的應(yīng)用摘要：

目前，乳腺癌的分子分型成為了研究的熱點(diǎn)，基于組織學(xué)特征進(jìn)行分型對(duì)于治療方案的選擇和預(yù)后判斷具有重要意義。本文提出了一種基于DCE-MRI影像反卷積模型的腫瘤異質(zhì)性分析方法，并將其應(yīng)用于乳腺癌分子分型預(yù)測(cè)。該方法能夠從DCE-MRI影像中提取出微小的組織學(xué)特征，并將其反卷積還原至原始組織，同時(shí)，結(jié)合機(jī)器學(xué)習(xí)算法，對(duì)不同腫瘤分子分型的特征進(jìn)行分析。實(shí)驗(yàn)結(jié)果表明，該方法在使用小數(shù)據(jù)集進(jìn)行訓(xùn)練和測(cè)試時(shí)，能夠準(zhǔn)確地預(yù)測(cè)不同分子分型。

關(guān)鍵詞：DCE-MRI影像；反卷積；腫瘤異質(zhì)性分析；乳腺癌分子分型預(yù)測(cè)

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