The increasing volume of CT imaging data and the limited availability of radiologists pose challenges for timely and accurate cancer assessment. While lesion diameters are routinely measured in clinical workflows, volumetric analysis remains uncommon due to the time-intensive nature of manual segmentation. Automated segmentation methods enable precise, reproducible, and efficient quantification of tumor burden over time. By leveraging information from prior examinations, longitudinal analysis can further enhance the accuracy and consistency of follow-up segmentations. We propose an approach for longitudinal lesion segmentation through neural instance optimization (NIO).Apre-trained segmentation network is fine-tuned on a patient’s prior scan to capture individual lesion-specific characteristics, which are subsequently leveraged during inference on the follow-up examination. The proposed method is applied on two public datasets. While promising results are achieved on synthetic longitudinal data (median Dice of 0.74/0.83 without/with NIO), no quantitative improvement was achieved on the second real-world longitudinal dataset. Our experiments reveal the potential of the proposed method but also its limited ability to deal with domain shifts between prior and follow-up present in real-world scenarios.