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• W4295036110 abstract "Accurate identification of histological subtypes of non-small cell lung cancer (NSCLC) is a critical step prior to treatment decisions. It has been confirmed that radiomic approaches have the potential to non-invasively decode the tumor phenotype. Most of the recent radiomic studies focused on single-center dataset, which prevents generalizability of the radiomic models. To assess the effect of ComBat harmonization method on the performance of radiomics prognostic models, we harmonized multi-centric radiomics feature-sets extracted from CT, PET and fused images of NSCLC patients and utilized them to classify patients into two main subtypes of NSCLC, namely Adenocarcinoma (ADC) and Squamous Cell Carcinoma (SCC). The dataset included 178 patients with histologically proven NSCLC, from two independent institutions (94 and 84 patients). Two-hundred and twenty-one radiomic features were extracted from each single modality (PET, CT) and fused images (fused with guided filtering fusion, GFF). ComBat harmonization was applied on the extracted feature to harmonize the distribution of features. The top 10 radiomic features were selected from each feature set using Recursive Feature Elimination (RFE). The performance of three different machine learning algorithms, including Support Vector Machine (SVM), AdaBoost, And Logistic Regression (LR) were evaluated by randomly splitting the data into training set (70% of data) and test set (30%) for 100 times. Lastly, the mean and 95% Confidence Interval of AUC, accuracy, sensitivity, and specificity on the test sets were reported. Regarding PET models, harmonized feature-set trained with SVM and LR classifiers, achieved highest score with AUC of 0.81±0.05 0.81±0.06, respectively. Regarding CT models, SVM classifier achieved best results regardless of being harmonized (AUC: 0.81±0.05) or not (AUC: 0.81±0.06). For fused scans AdaBoost classifier applied to harmonized feature-set had highest performance (AUC: 0.79±0.06). ComBat harmonization significantly improved the performance of models based on PET and fused images, while having no significant results on CT feature set." @default.
• W4295036110 created "2022-09-09" @default.
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• W4295036110 date "2021-10-16" @default.
• W4295036110 modified "2023-10-16" @default.
• W4295036110 title "Histopathological Subtype Phenotype Decoding Using Harmonized PET/CT Image Radiomics Features and Machine Learning" @default.
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• W4295036110 doi "https://doi.org/10.1109/nss/mic44867.2021.9875734" @default.
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