# Early Detection of Residual/Recurrent Lung Malignancies on Post-Radiation FDG PET/CT
###### Abstract  
Positron Emission Tomography/Computed Tomography (PET/CT) using Fluorodeoxyglucose (FDG) is an important imaging modality for assessing treatment outcomes in patients with pulmonary malignant neoplasms undergoing radiation therapy. However, distinguishing between benign post-radiation changes and residual or recurrent malignancies on PET/CT images is challenging. Leveraging the potential of artificial intelligence (AI), we aimed to develop a hybrid fusion model integrating radiomics and Convolutional Neural Network (CNN) architectures to improve differentiation between benign post-radiation changes and residual or recurrent malignancies on PET/CT images. We retrospectively collected post-radiation PET/CTs with identified labels for benign changes or residual/recurrent malignant lesions from 95 lung cancer patients who received radiation therapy. Firstly, we developed separate radiomics and CNN models using handcrafted and self-learning features, respectively. Then, to build a more reliable model, we fused the probabilities from the two models through an evidential reasoning approach to derive the final prediction probability. Five-folder cross-validation was performed to evaluate the proposed radiomics, CNN, and fusion models. Overall, the hybrid fusion model outperformed the other two models in terms of sensitivity, specificity, accuracy, and the area under the curve (AUC) with values of 0.67, 0.72, 0.69, and 0.72, respectively. Evaluation results on the three AI models we developed suggest that handcrafted features and learned features may provide complementary information for residual or recurrent malignancy identification in PET/CT. PET/CT; lung malignancies detection; CNN; radiomics; evidential reasoning Article  
Early Detection of Residual/Post-Radiation FDG PET/CT  
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[PERSON], [PERSON], J. Early Detection of Residual/Recurrent Lung Malignancies on Post-Radiation PETwith two each representing benign inflammation and residual/recurrent malignancy as shown on PET/CT scans. The difficulty in interpreting these PET/CT images, particularly following radiation therapy, introduces considerable variability in human interpretation. Studies have shown that the accuracy of these evaluations largely depends on the experience and expertise of nuclear medicine physicians and radiologists. This variability is reflected in a wide range of interobserver agreement scores, with kappa values ranging from moderate agreement (\(\kappa\) = 0.57-0.69) to low agreement (\(\kappa\) as low as 0.22) in more ambiguous cases [8]. Such variability underscores the inherent limitations of human interpretation, especially in complex or borderline cases where distinguishing inflammation from malignancy is crucial for guiding further treatment. To address this critical clinical issue, this study aims to develop an artificial intelligence (AI) model specifically designed to analyze post-radiation PET/CT scans in lung cancer patients. The primary goal is to accurately classify these scans as either positive or negative for residual or recurrent malignancy--a distinction essential for optimizing subsequent treatment decisions. Quantitative image feature analysis, including radiomics, has shown substantial promise in enhancing the differentiation between local recurrence and radiation-induced inflammatory changes. Radiomics-based models [9; 10] extract and analyze a wide range of quantitative imaging features, offering an objective approach that has been demonstrated to outperform traditional visual assessments by physicians. For example, radiomics-based approaches have demonstrated superior performance over evaluation by physicians for the early classification of local recurrence on follow-up CT scans after lung RT [11]. For early local recurrence detection within 6 months following definitive lung SBRT, [PERSON] et al.