Demo: Generative AI helps Radiotherapy Planning with User Preference

Riqiang Gao; Simon Arberet; Martin Kraus; Han Liu; Wilko F. A. R. Verbakel; Dorin Comaniciu; Florin-Cristian Ghesu; Ali Kamen

cs.CVDec 8, 2025

Demo: Generative AI helps Radiotherapy Planning with User Preference

Authors: Riqiang Gao, Simon Arberet, Martin Kraus, Han Liu, Wilko F. A. R. Verbakel, Dorin Comaniciu, Florin-Cristian Ghesu, Ali Kamen

Organizations: Digital Technology and Innovation, Siemens Healthineers · Varian Medical Systems, Siemens Healthineers

Abstract

Radiotherapy planning is a highly complex process that often varies significantly across institutions and individual planners. Most existing deep learning approaches for 3D dose prediction rely on reference plans as ground truth during training, which can inadvertently bias models toward specific planning styles or institutional preferences. In this study, we introduce a novel generative model that predicts 3D dose distributions based solely on user-defined preference flavors. These customizable preferences enable planners to prioritize specific trade-offs between organs-at-risk (OARs) and planning target volumes (PTVs), offering greater flexibility and personalization. Designed for seamless integration with clinical treatment planning systems, our approach assists users in generating high-quality plans efficiently. Comparative evaluations demonstrate that our method can surpasses the Varian RapidPlan model in both adaptability and plan quality in some scenarios.

Figures & tables

Figure 1: Our flexible dose proposer pipeline. Stage I pre-trains a foundational dose decoder, regularizing the dose prediction to be realistic. The second stage trains a flexible dose prediction model with heterogeneous conditions.

Cohort Index	Cohort 0	Cohort 1	Cohort 2	Cohort 3	Cohort 4	Cohort 5
Train/Valid/Test	370/48/54	147/17/19	128/15/17	103/14/12	52/8/7	20/1/4

Table 1: Data distribution used in Stage II training. The Stage I is pre-trained with 31K doses.

OAR	SpinalCord05	Larynx-PTV	Lips	Mandible-PTV	OCavity-PTV	ParotidCon-PTV	ParotidIps-PTV	Esophagus
RP std	3.02	3.49	2.54	2.93	4.04	3.38	2.96	3.21
ours std	1.18	1.78	1.09	1.27	1.82	1.22	1.78	1.27
RP mean	3.25	6.29	4.71	6.41	7.37	4.51	5.54	2.43
ours mean	1.69	2.94	2.06	2.01	4.13	1.92	2.92	1.18
OAR	SubmandL-PTV	Shoulders	SubmandR-PTV	PosteriorNeck	PharConst-PTV	BrainStem 03	Trachea	better count
RP std	3.25	1.38	2.70	4.30	3.62	0.92	3.40	0

Table 2: Statistics of intra-patient differences of expected and achieved DVHs (

\downarrow

). RP: RapidPlan Magliari et al. (2024) . ours: the predicted dose from FDP and objectives extracted from AI dose for Eclipse planning.

OAR	SpinalCord 05	Larynx-PTV	Lips	Mandible-PTV	OCavity-PTV	ParotidCon-PTV	ParotidIps-PTV	Esophagus
RP std	0.98	2.49	2.69	3.03	6.34	1.29	1.61	0.31
ours std	0.73	2.76	1.92	1.05	3.61	0.56	0.90	0.32
RP mean	0.43	4.24	2.02	2.22	6.00	1.66	2.25	0.23
ours mean	0.45	1.55	1.14	0.83	3.19	0.64	0.84	0.23
OAR	SubmandL-PTV	Shoulders	SubmandR-PTV	Posterior Neck	PharConst-PTV	BrainStem 03	Trachea	better count
RP std	7.12	0.14	5.70	3.76	3.73	1.76	6.51	3

Table 3: Statistics of inter-patient differences of expected and achieved DVHs (

\downarrow

). RP: RapidPlan Magliari et al. (2024) . ours: the predicted dose from FDP and objectives extracted from AI dose for Eclipse planning.

Panel of Figure 2 — Figure 2: Estimated vs. achieved DVHs of RapidPlan and our FDP model. RapidPlan estimations are not well aligned with achieved plans, may due to its limited generalization to unseen patients.

OAR	SpinalCor 05	Larynx-PTV	Lips	Mandible-PTV	OCavity-PTV	ParotidCon-PTV	ParotidIps-PTV	Esophagus
better	47.50	30.00	47.50	31.25	64.56	32.50	48.68	30.23
worse	5.00	21.43	0.00	1.25	1.27	6.25	5.26	4.65
similar	47.50	48.57	52.50	67.50	34.18	61.25	46.05	65.12
OAR	SubmandL-PTV	Shoulders	SubmandR-PTV	Posterior Neck	PharConst-PTV	BrainStem 03	Trachea	OAR count
better	59.57	0.00	71.11	12.50	56.16	7.50	51.16	14
worse	2.13	0.00	6.67	6.25	2.74	3.75	0.00	0

Table 4: Structure-wise plan quality comparison between RapidPlan and our FDP. Each structure is categorized as “better”, “worse”, or “similar”, indicating whether the plan generated by FDP shows improved, reduced, or comparable quality relative to RapidPlan. The “similar” thresholds are 1 Gray for OARs; 0.015 for PTV indices (HI and CI as defined in Sec. 2.1 ). Percentages (%) are reported for each category per structure, while the bold column presents the column counts.

Figure 3: Demonstration of user preference with sliding bars. The left/right of sliding bars represents better/worse quality for the specific structure respectively. The Preference 1 (P1) has the preferences of OAR sparing over PTV homogenity, and Preference 2 (P2) has the opposite. The preferences have been captured in the dose prediction, as shown in the DVHs in the right panel.

OAR	SpinalCord05	Larynx-PTV	Lips	Mandible-PTV	OCavity-PTV	ParotidCon-PTV	ParotidIps-PTV	Esophagus
P1 MeanDose	14.59	14.41	13.83	26.25	28.29	4.84	22.03	4.89
P2 MeanDose	16.33	15.47	13.44	27.25	28.04	5.24	23.4	5.55
P1 MaxDose	34.2	62.2	35.3	71.3	69.2	12.5	68.4	16.0
P2 MaxDose	38.3	63.3	36.6	71.5	69.7	15.5	68.8	18.7
OAR	CochleaL	CochleaR	Shoulders	SubmandR-PTV	PosteriorNeck	PharConst-PTV	BrainStem03	Trachea
P1 MeanDose	6.28	1.92	4.13	10.01	26.04	19.82	4.64	9.8

Table 5: The achieved mean dose from Eclipse with different flavors based on Figure 3 .

Figure 5: Screenshot of the Gradio demo interface. A demo video recording can be found in an HuggingFace repo .

Figure 6: Screenshot of deliverable plan in Eclipse derived from our flexible dose proposer (FDP). The upper right shows the BEV of the patients with RT structures and Multi-Leaf Collimator overlaid. Other pannels show the achieved dose overlaid with CT.

Parameter	Value
spatial_dims	3
in_channels	1
out_channels	1
channels	[32, 256, 512, 512]
downsample_parameters	[[2, 4, 1, 1], [2, 4, 1, 1], [1, 3, 1, 1], [1, 3, 1, 1]]
upsample_parameters	[[1, 3, 1, 1, 0], [1, 3, 1, 1, 0], [2, 4, 1, 1, 0], [2, 4, 1, 1, 0]]

Table 7: Stage I network structure parameters.

Parameter	Value
in_channels	7
n_channels	24
exp_r	[2,3,4,4,4,4,4,3,2]
kenerl_size	3
deep_supervision	False
do_res	True

Table 8: Stage II network structure parameters.

Figure 7: The cartoon illustration about intra- and inter- patient differences.

OAR	SpinalCord 05	Larynx-PTV	Lips	Mandible-PTV	OCavity-PTV	ParotidCon-PTV	ParotidIps-PTV	Esophagus
RP std	3.92	2.90	3.15	2.91	4.21	3.25	3.09	3.85
ours std	1.63	1.71	1.26	1.49	2.15	1.06	1.21	1.63
RP mean	5.62	10.75	5.39	8.08	8.68	6.33	7.85	3.89
ours mean	2.57	4.33	1.78	1.99	3.31	1.92	1.90	1.69
OAR	SubmandL-PTV	Shoulder	SubmandR-PTV	Posterior Neck	PharConst-PTV	Brain Stem 03	Trachea	better count
RP std	3.65	1.59	2.30	4.64	3.16	1.49	3.66	0

Table 9: Statistics of intra-patient differences of expected and achieved DVHs (

\downarrow

OAR	SpinalCord 05	Larynx-PTV	Lips	Mandible-PTV	OCavity-PTV	ParotidCon-PTV	ParotidIps-PTV	Esophagus
RP std	2.41	4.44	2.76	2.72	5.28	2.20	2.61	0.57
ours std	1.92	2.52	2.25	0.93	3.62	1.08	1.05	0.54
RP mean	0.88	8.80	1.98	3.05	6.07	2.93	3.66	0.40
ours mean	0.79	4.69	1.41	0.86	2.91	0.92	1.10	0.42
OAR	SubmandL-PTV	Shoulder	SubmandR-PTV	Posterior Neck	PharConst-PTV	Brain Stem 03	Trachea	better count
RP std	7.72	0.23	4.99	4.18	6.14	1.41	7.70	0

Table 10: Statistics of inter-patient differences of expected and achieved DVHs (

\downarrow

OAR	SpinalCord 05	Larynx-PTV	Lips	Mandible-PTV	OCavity-PTV	ParotidCon-PTV	ParotidIps-PTV	Esophagus
better	36.36	17.65	37.66	10.39	50.00	10.39	13.70	21.95
worse	6.49	41.18	3.90	6.49	6.58	29.87	26.03	2.44
similar	57.14	41.18	58.44	83.12	43.42	59.74	60.27	75.61
OAR	SubmandL-PTV	Shoulders	SubmandR-PTV	Posterior Neck	PharConst-PTV	BrainStem 03	Trachea	OAR count
better	38.30	0.00	47.73	1.30	34.29	7.79	36.59	10
worse	23.40	0.00	20.45	10.39	11.43	3.90	0.00	4

Table 11: Quality per structure of achieved plans of RapidPlan and our FDP. “better”, “worse”, “similar” represent the plan derived from FDP has better, worse, or similar quality respectively. Percentages (%) are reported for each category per structure, while the bold column presents the column counts. If the percent of “better” is larger than “worse” of one structure, the “better” count will add 1.

OAR	SpinalCord05	Larynx-PTV	Lips	Mandible-PTV	OCavity-PTV	ParotidCon-PTV	ParotidIps-PTV	Esophagus
RP std	1.48	1.70	1.47	2.91	2.05	2.33	1.44	2.95
ours std	1.57	1.42	0.99	1.76	1.83	1.31	1.90	1.81
RP mean	1.83	4.32	4.53	5.28	4.61	4.95	3.54	2.58
ours mean	1.82	2.96	4.59	2.62	3.93	2.00	2.83	1.62
OAR	SubmandL-PTV	Shoulders	SubmandR-PTV	Posterior Neck	PharConst-PTV	BrainStem03	Trachea	better count
RP std	3.25	1.25	2.18	1.41	2.48	1.06	4.43	6

Table 12: Statistics of intra-patient differences of expected and achieved DVHs (

\downarrow

OAR	SpinalCord05	Larynx-PTV	Lips	Mandible-PTV	OCavity-PTV	ParotidCon-PTV	ParotidIps-PTV	Esophagus
RP std	0.12	0.29	1.37	1.16	4.49	1.11	0.66	0.39
ours std	0.12	0.41	0.67	0.85	1.87	0.63	0.68	0.39
RP mean	0.38	4.23	4.93	2.80	9.90	2.61	2.59	0.38
ours mean	0.37	0.72	4.09	1.11	2.69	0.98	1.00	0.36
OAR	SubmandL-PTV	Shoulders	SubmandR-PTV	PosteriorNeck	PharConst-PTV	BrainStem03	Trachea	better count
RP std	0.14	0.08	3.14	0.13	2.14	0.00	0.53	5

Table 13: Statistics of inter-patient differences of expected and achieved DVHs (

\downarrow

OAR	SpinalCord 05	Larynx-PTV	Lips	Mandible-PTV	OCavity-PTV	ParotidCon-PTV	ParotidIps-PTV	Esophagus
better	25.00	0.00	50.00	75.00	75.00	25.00	50.00	50.00
worse	0.00	50.00	0.00	25.00	0.00	0.00	0.00	0.00
similar	75.00	50.00	50.00	0.00	25.00	75.00	50.00	50.00
OAR	SubmandL-PTV	Shoulders	SubmandR-PTV	Posterior Neck	PharConst-PTV	BrainStem03	Trachea	OAR count
better	50.00	0.00	66.67	0.00	100.00	0.00	75.00	10
worse	50.00	0.00	0.00	25.00	0.00	0.00	0.00	2

Table 14: Quality per structure of achieved plans of RapidPlan and our FDP. “better”, “worse”, “similar” represent the plan derived from FDP has better, worse, or similar quality respectively. Percentages (%) are reported for each category per structure, while the bold column presents the column counts. If the percent of “better” is larger than “worse” of one structure, the “better” count will add 1.

Figure 8: Illustration of translating 3D dose prediction to optimization objectives in Eclipse TPS.

Radiotherapy Dose Prediction

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Any2Any 3D Diffusion Models with Knowledge Transfer: A Radiotherapy Planning Study

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Open paper AskPDF arXiv

Voxel-wise dose prediction is a critical yet challenging task in practical radiotherapy (RT) planning, as bespoke models trained from scratch often struggle to generalize across diverse clinical settings. Meanwhile, generative models trained on billion-scale datasets from vision domains have achieved impressive performance. Herein, we propose DiffKT3D, a unified Any2Any 3D diffusion framework that leverages prior knowledge from pretrained video diffusion models for efficient and clinically meaningful dose prediction. To enable flexible conditioning across multiple clinical modalities (CT, anatomical structures, body, beam settings, etc.), we introduce an Any2Any conditional paradigm utilizing modality-specific embeddings without cross-attention overhead. Further, we design a novel reinforcement learning (RL) post-training mechanism guided by a clinically-informed Scorecard explicitly tailored to institutional treatment preferences. Compared with winner of GDP-HMM challenge, DiffKT3D sets a new state-of-the-art in dose prediction by reducing voxel-level MAE from 2.07 to 1.93. In addition, DiffKT3D achieves superior image quality and preference match. These results demonstrate that transferring diffusion priors via modality-aware conditioning and clinically aligned RL post-training can provide a robust and generalizable solution for RT planning across various clinical scenarios.

Abstract

Radiotherapy Dose Prediction

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