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The rapid growth of artificial intelligence, particularly in the field of deep learning, has opened up new advances in analyzing and processing large and complex datasets. Prospects and emerging trends in this area engage the development of methods, techniques, and algorithms to build autonomous systems that perform tasks with minimal human action. In medical practice, radiological imaging technologies systematically boost progress in the clinical monitoring of cancer through the information that can be analyzed in these images. This review gives insight into deep learning-based approaches that strengthen the assessment of the response to the treatment of non-small-cell lung cancer. This systematic survey delves into the various approaches to morphological and metabolic changes observed in computerized tomography (CT) and positron emission tomography (PET) imaging. We highlight the challenges and opportunities for feasible integration of deep learning computer-based tools in evaluating treatments in lung cancer patients, after which CT and PET-based strategies are contrasted. The investigated deep learning methods are organized and described as instruments for classification, clustering, and prediction, which can contribute to the design of automated and objective assessment of lung tumor responses to treatments.