Radiotherapy (RT) is a cornerstone of cancer treatment, utilizing high doses of radiation to kill cancer cells and shrink tumors. Following RT, patient-reported outcomes (PROs) collected via standardized questionnaires are crucial for monitoring quality of life and side effects. However, traditional statistical and machine learning methods, which rely on structured numerical data, often fail to capture the nuanced emotions and semantic meaning within patients' health status. To address this, we developed a novel framework using zero- and few-shot Large Language Models (LLMs) to identify patients experiencing mild to severe depression. Furthermore, we enhanced classification performance through parameter-efficient fine-tuning with Low-Rank Adaptation (LoRA). The key is to freeze the pre-trained model's weights and add trainable low-rank matrices to each Transformer layer, drastically reducing the number of parameters in few-shot LLMs that need to be trained. Experiments on a prostate cancer PRO dataset for depression demonstrated that our fine-tuned LLMs consistently outperformed other baseline methods across key metrics, including AUC, AUCPR, Precision, and F1-score.