Patient-Specific Finetuning of Deep Learning Models for Adaptive Radiotherapy in Prostate CT
This work addresses the need for precise and adaptive contouring in radiotherapy planning for prostate cancer patients, offering a personalized approach that could enhance clinical outcomes.
The paper tackled the problem of improving segmentation accuracy for adaptive radiotherapy in prostate CT by fine-tuning a pre-trained CNN model to individual patients using their earlier treatment scans, achieving significantly better Mean Surface Distance metrics for prostate, seminal vesicles, bladder, and rectum compared to the baseline model.
Contouring of the target volume and Organs-At-Risk (OARs) is a crucial step in radiotherapy treatment planning. In an adaptive radiotherapy setting, updated contours need to be generated based on daily imaging. In this work, we leverage personalized anatomical knowledge accumulated over the treatment sessions, to improve the segmentation accuracy of a pre-trained Convolution Neural Network (CNN), for a specific patient. We investigate a transfer learning approach, fine-tuning the baseline CNN model to a specific patient, based on imaging acquired in earlier treatment fractions. The baseline CNN model is trained on a prostate CT dataset from one hospital of 379 patients. This model is then fine-tuned and tested on an independent dataset of another hospital of 18 patients, each having 7 to 10 daily CT scans. For the prostate, seminal vesicles, bladder and rectum, the model fine-tuned on each specific patient achieved a Mean Surface Distance (MSD) of $1.64 \pm 0.43$ mm, $2.38 \pm 2.76$ mm, $2.30 \pm 0.96$ mm, and $1.24 \pm 0.89$ mm, respectively, which was significantly better than the baseline model. The proposed personalized model adaptation is therefore very promising for clinical implementation in the context of adaptive radiotherapy of prostate cancer.