Deep learning techniques have been widely applied to lung nodule malignancy prediction tasks. Recently, the emergence of Vision-Language Models (VLMs) has enabled the use of textual information, further improving diagnostic accuracy. Nevertheless, two key limitations persist: (1) the insufficient utilization of clinical data to enhance comput-er-aided diagnosis, and (2) the limited ability of existing frameworks to leverage similar cases in the diagnostic process. To address these issues, we propose a clinical data-driven, retrieval-augmented VLM framework for lung nodule malignancy prediction. The proposed framework comprises a multimodal encoder, a retrieval-augmented module, and a text encoder. Lesion classification is achieved by evaluating the similarities between the combined visual and clinical data features and the text features of predefined categories, thereby establishing a robust mechanism for malignancy prediction. Moreover, the retrieval-augmented module further refines the prediction process by incorporating similar cases retrieved using clinical data as a query, thus facilitating more informed and accurate decisions. Overall, this framework comprehensively utilizes clinical data by integrating it into CT image features and enabling cross-interaction in the retrieval-augmented module to support diagnosis with similar cases. Experimental results on the publicly available LIDC-IDRI dataset demonstrate that the proposed framework achieves significant improvements in lung nodule malignancy prediction, with an approximate 3% increase in accuracy. Our code is released on Github: https://github.com/chenn-clear/ClinicalRA .