Diagnosis And Prognostic Value Of ¹⁸F-FDG PET/CT Imaging In Newly Diagnosed Breast Cancer Patients

PART I Staging and prognostic value of ¹⁸F-FDG PET/CT imaging in newly diagnosed breast cancer patientsObjective The purpose of this study was to investigate the value of¹⁸F-fluorodeoxyglucose positron emission tomography/computed tomography(¹⁸F-FDG PET/CT)in the staging and prognosis of preoperative breast cancer patients,and to evaluate the relationship between maximum standardized uptake value（SUVmax）of primary lesion in breast cancer and the clinicopathological parameters.Methods We retrospectively analyzed patients with breast cancer who visited the PET center of Wuhan Union Hospital from January 2012 to December 2018.All patients underwent ¹⁸F-FDG PET/CT scans before treatment.Relevant clinical data were collected,including age,tumor size,clinical stage,pathological type,tumor subtypes,histological grade,Ki-67 index,metastasis state and treatment methods.Maximum standard uptake value（SUVmax）was extracted from PET images.The value of staging in preoperative ¹⁸F-FDG PET/CT,correlation of SUVmax,clinicopathological parameters and survival prognosis[overall survival（OS）and progression-free survival（PFS）]were evaluated.Results Eighty-one female patients（average age,53.0±11.7y;range of the age,25-81y）were included in this study.There were 65 cases of invasive ductal carcinoma,6 cases of invasive lobular carcinoma,3 of carcinoma in situ,3 cases of mixed invasive carcinoma,2 cases of mucinous carcinoma,1 case of medullary carcinoma,and 1 case of Paget’s disease of breast.For the molecular subtyping of this group of patients,there were 20 cases of Luminal A,30 cases of Luminal B,16 cases of Her-2Positive,and 15 cases of triple negative breast cancer.Compared with pathological staging,the accuracy rates of preoperative PET/CT for breast cancer staging were:T stage 92.6%;N stage 87.7%;M stage 97.5%.All patients were ¹⁸F-FDG-avid,and the average SUVmax of the primary tumor was 6.8±4.0（range 1.3-20.7）.The higher SUVmax of primary tumor in ¹⁸F-FDG PET/CT before treatment was significantly correlated with larger tumors（>2 cm,p=0.000）,higher histological stage（II,III and IV stages,p=0.024）,higher histological grade（grade 3,p=0.000）,high Ki-67 index（≥14%,p=0.037）.SUVmax was significantly correlated with different molecular types（p=0.022）.SUVmax of HER-2 positive and triple negative breast cancers was significantly higher than that of Luminal A type.There were no significant correlation with age（≤50y group vs.>50y group,p=0.207）,pathological types[invasive ductal carcinoma（IDC）vs.invasive lobular carcinoma（ILC）,p=0.117),and lymph node metastasis（positive vs.negative,p=0.350）.During follow-up,10 patients were excluded because of lost to follow-up,11 patients（18.6%）of the remaining 71patients had recurrence or progression,and 12 patients（16.9%）died.The OS was5.3-84.8 months,with a median OS of 32.0 months;the PFS was 1.1-64.4 months,with a median PFS of 28.7 months;the 3-year OS rate was 42.3%,and the 3-year PFS rate was 38.0%.The patients were divided according to the SUVmax cutoff on receiver operating characteristic（ROC）analysis for OS and PFS[<7.65 group vs.≥7.65 group,（p=0.000,AUC=0.803）].Kaplan-Meier univariate survival analysis showed that histological stage,distant metastasis,and SUVmax were prognostic risk factors for OS in patients;histological stage,lesion size,SUVmax,immunohistochemical classification,and distant metastases were prognostic risk factors for PFS in breast cancer patients.Cox proportional hazard model multivariate survival analysis:SUVmax（HR=4.1,95%CI=1.19-14.37,p=0.025）and distant metastasis（HR=5.8,95%CI=1.5-22.2,p=0.018）were independent prognostic factor for OS in breast cancer patients;SUVmax（HR=3.9,95%CI=1.41-10.77,p=0.009）,tumor size（HR=2.1,95%CI=1.04-4.04,p=0.038）and distant metastases（HR=4.6,95%CI=1.49-13.93,p=0.008）were independent prognostic factors for PFS in breast cancer patients.Conclusion Pretreatment ¹⁸F-FDG PET/CT could help newly diagnosed breast cancer patients to be accurately staged,especially in lymph nodes and distant metastases,which had higher clinical value.SUVmax was correlated with known clinicopathological parameters affecting prognosis.Patients with higher SUVmax in primary tumors of breast cancer may have a higher risk of recurrence or progression.SUVmax of the primary tumor and distant metastasis of the disease were independent prognostic factors of breast cancer OS and PFS.PART II Radiomics features of ¹⁸F-FDG PET/CT predicting breast cancer molecular subtype: a preliminary study Purpose The molecular subtyping of breast cancer is closely related to the therapeutic strategy and patient prognosis.This study aimed to investigate the feasibility of predicting molecular subtypes of breast cancer by imaging radiomics features extracted from ¹⁸F-FDG PET/CT images before treatment.Methods We retrospectively analyzed breast cancer patients who underwent¹⁸F-FDG PET/CT examinations from January 2012 to December 2018.Clinical parameters,including age,tumor size,initial T,N,and M category,pretreatment CEA,CA125,CA153 were also collected.Regions of interest（ROI）were drawn manually on PET and CT images.We used IF Foundry（Intelligence Foundry 2.1,GE Healthcare）to extract functional imaging parameters（maximum standardized uptake value [SUVmax],mean standardized uptake value [SUVmean],metabolically active tumor volume [MTV],total lesion glycolysis [TLG],CT HUmean and CT Volume）and radiomics features（histogram,shape,textural and contour features,Intra-perinodular features [Ipris],co-occurrence of Local Anisotropic Gradient Orientations features,and filter-based features including Wavelets,Gabor,local binary pattern [LBP] and Wavelets + LBP）.The steps of features screening were as follow:processed missing values,processed constant values,analyzed the statistical difference and correlation de-redundancy.Features with significant differences between different systems,different collection intervals,and different collection duration were eliminated.Modeling was conducted by Soft Max multi-class logistic regression,calculated by the three-fold cross-validation,and the results were evaluated with accuracy（ACC）.Multi-class logistic regression modeling applied two strategies,one-versus-one（OVO）and one-versus-rest（OVR）,and selected different random seeds.The three-fold cross-validation was repeated 500 times to obtain the ACC distribution histogram and average.Multi-classification studies were performed on six combinations of CT,PET,PET/CT,PET/CT+functional imaging features,PET/CT+biomarker features,PET/CT+functional imaging features+biomarker features.Results Eighty female patients（average 52.91±11.75,age range 25-81y）were included in this study,including 65 cases of invasive ductal carcinoma,6 cases of invasive lobular carcinoma,3 cases of ductal carcinoma in situ,3 cases of mixed invasive carcinoma,2 cases of mucinous carcinoma,and 1 case of medullary carcinoma.Patients were divided into 4 subgroups（Luminal A,19 cases;Luminal B,30 cases;Her-2 positive,16 cases;triple negative,15 cases）according to the molecular subtyping.After screening,the radiomics features included 21 CT and 19 PET dimensions.The 21 dimensions of CT features were original 1,textural 1,Co LIAGe2 D 5,wavelets + LBP 4,Gabor 6,PLBP 1,WILBP 3.The 19 dimensions of PET features were Co LIAGe2 D 1,Wavelets + LBP 4,Gabor 12,PLBP 1,WILBP 1.The accuracy of PET and CT radiomics features for single-mode and multi-mode modeling classification were:PET radiomic features OVR 0.515（0.511-0.519）,OVO0.511（0.507-0.515）;CT radiomic features OVR 0.471（0.469-0.473）,OVO 0.473（0.470-0.476）;PET/CT radiomic features OVR 0.606（0.601-0.610）,OVO 0.592（0.589-0.596）;PET/CT+functional imaging features OVR 0.590（0.586-0.594）,OVO0.583（0.580-0.587）;PET/CT+biomarker features OVR 0.605（0.601-0.609）,OVO0.599（0.595-0.603）;PET/CT+functional imaging features+biomarkers features OVR0.593（0.589-0.597）,OVO 0.586（0.582-0.590）.Conclusion The results of PET/CT radiomics features（OVR and OVO）were better than those of PET or CT;The accuracy based on PET radiomics features alone is better than CT radiomics features.Combined biomarkers and functional imaging features with PET/CT radiomics features could not add additional value.Compared with OVO,better results are achieved using the OVR strategy for modeling.PET/CT radiomics features could provide predictive value for breast cancer molecular subtyping.More research with large sample size needs to be performed to confirm this.