Dual-Centre Harmonised Multimodal Positron Emission Tomography/Computed Tomography Image Radiomic Features and Machine Learning Algorithms for Non-small Cell Lung Cancer Histopathological Subtype Phenotype Decoding

Article Article scientifique Article Dual-Centre Harmonised Multimodal Positron Emission Tomography/Computed Tomography Image Radiomic Features and Machine Learning Algorithms for Non-small Cell Lung Cancer Histopathological Subtype Phenotype Decoding eng 2023-08-08 2023-11 author Khodabakhshi Zahra author Amini Shahram 873784 author Hajianfar Ghasem author Oveisi Mehrdad author Shiri Lord Isaac 0000-0002-5735-0736 930888 author Zaidi Habib 0000-0001-7559-5297 98447 Département de radiologie et informatique médicale (RADIO) 713-725 10.1016/j.clon.2023.08.003 37599160 0936-6555 https://linkinghub.elsevier.com/retrieve/pii/S0936655523002777 Aims: We aimed to build radiomic models for classifying non-small cell lung cancer (NSCLC) histopathological subtypes through a dual-centre dataset and comprehensively evaluate the effect of ComBat harmonisation on the performance of single- and multimodality radiomic models. Materials and methods: A public dataset of NSCLC patients from two independent centres was used. Two image fusion methods, namely guided filtering-based fusion and image fusion based on visual saliency map and weighted least square optimisation, were used. Radiomic features were extracted from each scan, including first-order, texture and moment-invariant features. Subsequently, ComBat harmonisation was applied to the extracted features from computed tomography (CT), positron emission tomography (PET) and fused images to correct the centre effect. For feature selection, least absolute shrinkage and selection operator (Lasso) and recursive feature elimination (RFE) were investigated. For machine learning, logistic regression (LR), support vector machine (SVM) and AdaBoost were evaluated for classifying NSCLC subtypes. Training and evaluation of the models were carried out in a robust framework to offset plausible errors and performance was reported using area under the curve, balanced accuracy, sensitivity and specificity before and after harmonisation. N-way ANOVA was used to assess the effect of different factors on the performance of the models. Results: Support vector machine fed with selected features by recursive feature elimination from a harmonised PET feature set achieved the highest performance (area under the curve = 0.82) in classifying NSCLC histopathological subtypes. Although the performance of the models did not significantly improve for CT images after harmonisation, the performance of PET and guided filtering-based fusion feature signatures significantly improved for almost all models. Although the selection of the image modality and feature selection methods was effective on the performance of the model (ANOVA P-values <0.001), machine learning and harmonisation did not change the performance significantly (ANOVA P-values = 0.839 and 0.292, respectively). Conclusion: This study confirmed the potential of radiomic analysis on PET, CT and hybrid images for histopathological classification of NSCLC subtypes. Histopathology NSCLC PET/CT Machine learning Radiomics Algorithms Carcinoma, Non-Small-Cell Lung / diagnostic imaging Carcinoma, Non-Small-Cell Lung / pathology Humans Lung Neoplasms / diagnostic imaging Lung Neoplasms / pathology Machine Learning Positron Emission Tomography Computed Tomography Dewey 616.8 Dewey 616.0757 Swiss National Science Foundation Towards patient-specific hybrid whole-body PET parametric imaging 176052 CH Projects 62 Département de radiologie et informatique médicale 227 16 Centre interfacultaire de neurosciences 0D9 false 287 Imagerie Médicale (TEP et TEMP) 542 Clinical oncology 35 11 dc800834-efdf-45bf-96eb-40f80865cc34 Article (Published version) Restricted clinoncol2023.pdf 1650527 application/pdf 8b97a0a3215875552d70091cc96391c5032523e441a97ffef772532b516056a1 46197524-2223-4dee-afbc-4a84b2776345 Supplemental data Restricted 1-s2.0-S0936655523002777-mmc1.pdf 655862 application/pdf Supplementary Tables 1-3. 737dc53415fba36ee66cfdcbae2bd344b48be6b00f5a1475a4450ff219d9c82f restrictedAccess Faculté de médecine / Section de médecine clinique / Département de radiologie et informatique médicale Centres et instituts / Centre interfacultaire de neurosciences

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Dual-Centre Harmonised Multimodal Positron Emission Tomography/Computed Tomography Image Radiomic Features and Machine Learning Algorithms for Non-small Cell Lung Cancer Histopathological Subtype Phenotype Decoding	Clinical oncology			2023	96	0

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Amini, Shahram