Clinical Study Of ¹⁸F-FDG PET/CT In Predicting Pathological Subtypes And Growth Patterns Of Early Lung Adenocarcinoma

The First Part:A Simple Prediction Model Using Fluorodeoxyglucose-PET and High-resolution Computed Tomography for Discrimination of Invasive Adenocarcinomas among Lung Adenocarcinoma Manifesting as Solitary Ground-glass Opacity NodulesBackground:Pulmonary ground-glass opacity nodules(GGNs)are hazy enhanced opacity in the lung with clear or unclear margin on computed tomography(CT),within which the vascular and bronchial margins are preserved.With the popularization of low-dose CT lung cancer screening,the detection rate of lung adenocarcinoma has been gradually increasing.Previous reports demonstrated that most of the persistent GGNs are preinvasive or minimally invasive adenocarcinoma(MIA),and 20%-30%of the nodules grow to invasive adenocarcinoma(IAC)due to multistep progression.When lung adenocarcinoma presents as a solitary pulmonary nodule(SPN),it is often still in stage I.However,even in stage IA,the prognosis of IAC is still significantly worse than that of adenocarcinoma in situ(AIS)and MIA.Therefore,it is crucial to determine the invasiveness of solitary GGNObjective:To analyze the FDG-PET and high-resolution computed tomography(HRCT)features of early lung adenocarcinoma manifesting as solitary ground-glass opacity nodules(GGNs),and to establish a new risk model for predicting the invasiveness of early lung adenocarcinoma.Methods:We retrospectively analyzed the data of clinical stage IA lung adenocarcinoma patients who received preoperative PET/CT and HRCT examination.Patients were divided into IAC group and preinvasive-MIA group.The correlations between FDG-PET parameters,HRCT parameters and pathological invasiveness,and their predictive efficacy were analyzed.A mathematical model for predicting pathological invasiveness of early lung adenocarcinoma was established and assessed.Results:This study enrolled 56 patients,48 were in IAC group and 8 were in preinvasive-MIA group.Compared with those in preinvasive-MIA group,GGNs in IAC group showed larger diameter,higher ground-glass opacity(GGO)density and more pleural indentation signs(70.8%)on HRCT;they also showed higher maximum standardized uptake value(SUV)and SUV index on FDG-PET(P=0.001-0.037).Logistic regression analysis found a risk model for predicting IAC of solitary GGNs that were established by CTGGO and SUV index.Receiver operating characteristic curves showed that this model had the highest area under the curve(AUC),sensitivity,specificity and accuracy(AUC,0.948;sensitivity,95.8%;specificity,87.5%;accuracy,94.6%).Conclusion:Using HRCT combined with FDG-PET to establish the corresponding mathematical prediction model has the potential to identify IAC in early lung adenocarcinoma preoperatively.The second part:Value of High-resolution CT and PET/CT Combined Invasive Evaluation Modality in Early Lung Cancer Treatment DecisionBackground:Lung cancer is by far the leading cause of cancer-related deaths,accounting for approximately 23%of all cancer deaths.Adenocarcinoma is the most common type of lung cancer tissue in most countries,accounting for about half of all lung cancers.The early diagnosis of IAC and the difference from preinvasive-MIA are important for the clinical management of patients with isolated and multifocal GGN,and have become one of the main areas of pulmonary surgeryObjective:The purpose of this study is to evaluate HRCT combined with PET/CT for preoperative differentiation of IAC from preinvasive-MIA in lung adenocarcinoma manifesting as GGNs 3 cm or smaller.Methods:We retrospectively analyzed the data of patients with lung adenocarcinoma with GGNs that were 3 cm or smaller between November 2011 and November 2018.The HRCT and PET/CT parameters for GGNs were compared to differentiate between IAC and preinvasive-MIA.Qualitative and quantitative parameters were analyzed using univariate and multivariate logistic regression models.The diagnostic performance of different parameters was compared using ROC curves and the McNemar testResults:The study enrolled 89 patients(24 men and 65 women)with lung adenocarcinoma who had a mean(±SD)age of 60.1 ± 8.1 years(range,36-78 years).The proportions of mixed GGN type,polygonal or irregular shape,lobulated or spiculated edge,and dilated,distorted,or cutoff bronchial sign were higher for IAC GGNs than for preinvasive-MIA GGNs,and the attenuation value of the ground-glass opacity component on CT(CTGGO),maximum standardized uptake value,and the standardized uptake value(SUV)index(i.e.,the ratio of the tumor maximum SUV to the liver mean SUV)for IAC GGNs were also higher(P=0.001-0.022).Logistic regression analyses showed that the CTGGO and SUV index were independent predictors for IAC GGNs.The accuracy of CTGGO in combination with the SUV index for predicting IAC was 81.4%on a per-GGN basis and 85.4%on a per-patient basis.The combined HRCT and PET/CT modality had higher sensitivity and accuracy than did morphologic features,HRCT,and PET/CT measurement parameters alone(P<0.001).Conclusion:The combined HRCT and PET/CT modality is an effective method to preoperatively identify IAC in lung adenocarcinoma manifesting as GGNs 3 cm or smallerThe third part:Diagnostic Value of FDG-PET Combined with HRCT in Pathological Subtypes and Growth Patterns of Stage IA Lung Adenocarcinoma Manifesting as Ground-glass Opacity NodulesBackground:IAC can be divided into five subtypes according to growth pattern:lepidic,acinar,papillary,micropapillary,and solid predominant tumors.Different pathologic subtypes and tumor growth patterns had different prognoses.The 5-year disease-free survival rates of AIS and MIA were nearly 100%,but they were 90%,83%,and 84%for lepidic,papillary,and acinar predominant nonmucinous lung adenocarcinoma,respectively.The growth pattern of lung adenocarcinoma was a stage-independent prognostic indicator.Therefore,the ability to preoperatively predict pathologic subtypes and growth patterns of lung adenocarcinoma according to the noninvasive imaging characteristics of GGNs will help in development of a reasonable treatment plan and effective prognosis of lung adenocarcinomaObjective:The purpose of this study was to explore the value of FDG-PET combined with HRCT in predicting the pathologic subtypes and growth patterns of early lung adenocarcinomaMethods:A retrospective analysis was conducted on the PET/CT data on GGNs resected from patients with stage IA lung adenocarcinoma.The efficacy of PET maximum standardized uptake value(SUVmax)combined with HRCT signs in prediction of pathologic subtype and growth pattern of lung adenocarcinoma was evaluatedResults:SUVmax was significantly higher in GGNs with invasive HRCT signs.The diameter of GGN(odds ratio,1.660;P=0.000)and the difference in attenuation value(odds ratio,1.012;P=0.011)between ground-glass components and adjacent lung tissues were independent predictors of FDG uptake by GGNs.SUVmax was higher in invasive adenocarcinoma than in adenocarcinoma in AIS-MIA(median SUV max,2.0 vs 1.1;P=0.008).An SUVmax of 2.0 was the optimal cutoff value for differentiating invasive adenocarcinoma from AIS-MIA.Acinar-papillary adenocarcinoma had a higher SUV max than lepidic adenocarcinoma(median SUVmax,2.1 vs 1.3;P=0.037).An SUVmax of 1.4 was the optimal cutoff value for differentiating the growth pattern of adenocarcinoma.Use of PET/CT with HRCT significantly improved efficacy for differentiating invasive adenocarcinoma from AIS-MIA.However,use of HRCT cannot significantly improve the diagnostic efficacy of FDG-PET in the evaluation of tumors with different growth patternsConclusion:FDG-PET can be used to predict pathologic subtypes and growth patterns of early lung adenocarcinoma.Combined with HRCT,it has value for predicting invasive pathologic subtypes but no significance for predicting invasive growth patternsThe fourth part:Radiomics Based on 18F-FDG PET/CT could Distinguish the Growth Pattern of Early Invasive Lung Adenocarcinoma Manifesting as Ground-glass Opacity NodulesBackground:The definition of preoperative IAC growth pattern is essential for the risk stratification of GGNs and individualized treatment.Although SUVmax is the only independent factor that can distinguish the growth pattern of IAC,its discrimination power is still limited.Radiomics is an emerging field in which a large number of objective and quantitative imaging features are explored to select the features most relevant to clinical,pathological,molecular and genetic features,thereby improving the accuracy of diagnosis and prognosis and the evaluation of treatment effects.The potential of this approach is to quantify the characteristics of tissues and organs beyond the power of visual interpretation or simple metrics.Texture analysis performed on 18F-FDGPET/CT images has shown great diagnostic utility in NSCLCObjective:Establish and validate PET/CT-based radiomics models to predict moderate to high-risk growth patterns in early IACMethods:Eighty-two GGNs from 80 patients with stage IA were included in the preoperative 18F-FDG PET/CT diagnostic scan and histopathological examination.The LIFEx software package was used to extract 52 PET and 49 CT imaging omics features reflecting tumor heterogeneity and phenotype.The minimum absolute contraction and selection operator(LASSO)algorithm is used to select radiographic features and develop radiographic signatures.We used the receiver operating characteristic curve(ROC)to compare the predictive performance of models built from conventional CT indicators,radiomic signatures,and combinations thereof.In addition,a nomogram based on conventional CT indicators and Rad-scores has been developedResults:Eighty-two GGNs were divided into adherent group(17)and acinar-nipple group(65)Seven omics features were selected to calculate the Rad-score.The AUC was 0.819 and the CTGGO was 0.686.The difference between the two was not significant(P=0.177).When Rad-score and CTGGO were combined,the AUC of the joint model increased to 0.846(95%CI[0.741-0.951]after internal verification),which was significantly higher than CTGGO(P=0.007).Further drawing the decision curve of the three,the combined model has higher clinical value.Conclusion:PET/CT-based radiomics models have shown good performance in predicting high-risk growth patterns in early IAC,providing useful methods for risk stratification,clinical management,and personalized treatment.