Using deep learning to model the biological dose prediction on bulky lung cancer patients of partial stereotactic ablation radiotherapy.

PURPOSE

To develop a biological dose prediction model considering tissue bio-reactions in addition to patient anatomy for achieving a more comprehensive evaluation of tumor control and promoting the automatic planning of bulky lung cancer.

METHODS

A database containing images and partial stereotactic ablation boost radiotherapy (P-SABR) plans of 94 bulky lung cancer patients was studied. Patient-specific parameters of gross tumor boost volume (GTVb), planning gross target volume (PGTV), and identified organs at risk (OARs) were extracted via Numpy and simple ITK. The original dose and structure maps for P-SABR patients were resampled to have a voxel resolution of 3.9 × 3.9 × 3 mm . Biological equivalent dose (BED) distributions were reprogrammed based on physical dose volumes. A developed deep learning architecture, Nestnet, was adopted as the training framework. We utilized two approaches for data organization to correlate the structures and BED: (a) BED programming before training model (B-Nestnet); (b) BED programming after the training process (D-B Nestnet). The early-stop mechanism was adopted on the validation set to avoid overfitting. The evaluation criteria of predictive accuracy contain the minimum BED of GTVb and PGTV, the maximum and the mean BED of all targets, BED-volume metrics. For comparison, we also used the original Unet for BED prediction. The absolute differences were statistically analyzed with the paired-samples t test.

RESULTS

The statistical outcomes demonstrate that D-B Nestnet model predicts biological dose distributions accurately. The average absolute biases of [max, mean] BED for GTVb, PGTV are [2.1%, 3.3%] and [2.1%, 4.7%], respectively. Averaging across most of OARs, the D-B Nestnet model is capable of predicting the errors of the max and mean BED within 6.3% and 6.1%, respectively. While the compared models performed worse with averaged max and mean BED prediction errors surpassing 10% on some specific OARs.

CONCLUSIONS

The study developed a D-B Nestnet model capable of predicting BED distribution accurately for bulky lung cancer patients in P-SABR. The predicted BED map enables a quick intuitive evaluation of tumor ablation, modification of the ablation range to improve BED of tumor targets, and quality assessment. It represents a major step forward toward automated P-SABR planning on bulky lung cancer in real clinical practice.