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REVIEW 4 major objections 4 minor 2 cited by

Automating RT Planning at Scale: High Quality Data For AI Training

T0 review · 4 major / 4 minor · reviewed 2026-08-10 · deepseek-v4-flash

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Pith's one-line read The paper claims that a fully automated, iterative treatment-planning pipeline can generate radiotherapy plans of quality comparable to manual plans in 0.1 to 1 hour instead of 3 to 6 hours, and that the released HMM-RT dataset is more…

desk verdict A valuable large public RT dataset and a credible engineering pipeline, but the 'comparable to manual plans' claim is circular and needs independent evaluation before you trust it. read the letter →

arxiv 2501.11803 v6 pith:4VAQM3TH submitted 2025-01-21 cs.HC cs.LGcs.RO

Riqiang Gao , Mamadou Diallo , Han Liu , Anthony Magliari , Jonathan Sackett , Wilko Verbakel , Sandra Meyers , Rafe Mcbeth
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Masoud Zarepisheh Simon Arberet Martin Kraus Florin C. Ghesu Ali Kamen
This is my paper · ORCID
classification cs.HCcs.LGcs.RO
keywords RadiotherapytreatmentplanningAutomatedpipelineDosepredictionplandatasetIMRT/VMATHead-and-neckcancerLungOrgan-at-riskcontouring
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper tries to establish that the subjective, time-consuming work of manual radiotherapy treatment planning can be replaced by an automated iterative pipeline that produces plans of comparable quality far more quickly. The authors show that by scripting contouring, beam setup, optimization, and dose calculation, then repeatedly using dose-volume histogram points and scorecard metrics to tighten the planning objectives, they can generate plans that match manual plans in quality. At this scale, they release 3,730 treatment plans for head-and-neck and lung cancer, more than ten times the size of the largest existing curated public dataset. If true, AI researchers gain a scalable source of standardized high-quality plans for training dose-prediction and planning models.

What carries the argument

The load-bearing mechanism is Algorithm 1, the iterative objective-refinement loop. For each organ at risk, the pipeline samples the dose-volume histogram at multiple volume points, computes a target dose and priority from hand-tuned rules, adds scorecard-derived objectives with margins, and re-optimizes until successive plans are close. This loop converts the qualitative notion of plan quality into explicit dose objectives, making convergence toward a high scorecard score an automated process.

What would settle it

Run the pipeline on a held-out set of patients from an institution with different contouring conventions and count the plans that fail the scorecard after iteration; if the failure rate is high, or if a blinded dosimetrist review judges the automated plans clinically unacceptable despite high scores, the comparability claim would fall.

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Extended reading notes

Core claim

The central claim is that dosimetrist-style iterative planning can be encoded as a closed loop: auto-contour organs, script beam setup, seed objectives from knowledge-based planning, then repeatedly read dose-volume histogram points and scorecard metrics from the current plan and convert each point into a tightened optimization objective with a safety margin, re-optimizing until the plan converges. Running this loop at scale produced 3,730 deliverable IMRT and VMAT plans across head-and-neck and lung cohorts, with DVHs comparable to clinical plans. The same loop, with objectives derived only from a predicted 3D dose, reproduces a reference plan closely enough to turn dose predictions into deliverable plans.

Load-bearing premise

The central claim rests on the assumption that the hand-tuned margin rules and the dosimetric scorecard are a valid proxy for clinical quality, so that iterative convergence to high scorecard scores means convergence to clinically good plans; the paper does not report how many plans failed the scorecard and were discarded or re-planned.

Editorial extensions

If this is right

  • AI dose-prediction models can be trained on a standardized corpus an order of magnitude larger than existing public benchmarks, with plans produced by one consistent pipeline.
  • The pipeline can re-plan legacy clinical cohorts using current treatment-planning technology, removing the bias of older, lower-quality plans from AI training data.
  • Dose-prediction models can be evaluated by downstream deliverability: predicted dose is converted into a deliverable plan and compared by DVH, not only by voxel error.
  • Researchers can generate arbitrarily many plans by varying beam configurations, prescriptions, and structures, enabling controlled studies of model generalization across treatment sites and protocols.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • Beyond the paper: if the dosimetric scorecard is a valid proxy for clinical quality, the same iterative loop could be reused as an automated plan-refinement tool inside planning software, not just as a data-generation pipeline.
  • Beyond the paper: the hand-tuned margin and priority rules are the most manual part of the loop, so a natural extension is to learn those mapping functions from paired manual-plan data, which would remove the remaining human step.
  • Beyond the paper: the released dataset could test whether larger, more standardized corpora actually improve out-of-distribution dose prediction, a hypothesis the paper motivates but does not itself evaluate.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 4 minor

Summary. The manuscript introduces the Automated Iterative RT Planning (AIRTP) pipeline, which combines AI-based OAR contouring, scripted Eclipse ESAPI-based beam setup and optimization, RapidPlan-based objectives, and an iterative scorecard-guided refinement loop to generate deliverable radiotherapy plans at scale. The authors release the HMM-RT dataset (3,730 plans from 1,622 patients across nine head-and-neck and lung cohorts) to support an AAPM 2025 challenge, and report a five-fold reduction in planning time (0.1-1 hour vs. 3-6 hours manual). The central claims are that AIRTP produces treatment plans of quality comparable to manually generated plans and that HMM-RT contains more than ten times the number of plans of OpenKBP, the largest previous well-curated public dataset.

Significance. If the quality and scale claims hold, this work addresses a real bottleneck in radiotherapy AI research: the scarcity of large, standardized, deliverable plan data. The public release of DICOM and processed numpy data, the challenge infrastructure, the baseline model, and the pretrained weights are concrete community resources. The scale advantage (3,730 vs. 340 plans) is well supported by Table 1, and the multi-cohort, multi-institution design with variable prescription doses is a substantive step beyond OpenKBP. However, the load-bearing claim of comparability to manual plans currently rests on circular and anecdotal evidence; the paper itself concedes in Sec. 4.2 and Sec. 5 that definitive comparison is not established. Additional independent validation would be needed to convert this from a useful dataset contribution into a demonstrated clinically comparable plan-generation method.

major comments (4)
  1. [Sec. 4.2, Table 3; Algorithm 1 (lines 8-19)] The evidence for 'quality comparable to manual plans' is circular: the Varian scorecard is both the optimization objective in the iterative refinement (Algorithm 1, lines 8-19, and Step 3 in Sec. 3) and the quality metric reported in Table 3. High scores under scorecard-guided optimization are expected and do not by themselves establish clinical acceptability or comparability to manual plans. The paper should compare the scorecard scores of the clinical/manual plans in the same cohorts, or add an independent evaluation (e.g., blinded dosimetrist scoring, or external plan-quality metrics that were not used during optimization).
  2. [Sec. 4.2, Fig. 5] The direct comparison to clinical plans is limited to two randomly selected head-and-neck cases with qualitative DVH overlays; no lung cases, no quantitative paired DVH statistics, and no blinded review are reported. The manuscript's own statements—'we cannot definitively claim that the AIRTP plan is superior... based solely on DVHs or scorecards' (Sec. 4.2) and the comparison 'may not be entirely fair, as the AIRTP and clinical plans follow different guidelines' (Sec. 5)—are more cautious than the abstract's claim of quality 'comparable to those generated manually.' Please provide a quantitative comparison across a larger matched set (e.g., paired DVH metrics or blinded clinical review) or soften the abstract claim to match the evidence.
  3. [Table 1] Table 1 states that 'corner cases' are 'remove or re-plan' to make plans 'more aligned to scorecard,' but the number and characteristics of excluded or re-planned cases are not disclosed. Without this information, the released 3,730 plans may represent a favorable selection rather than the pipeline's steady-state output. Please report the pass rate at each pipeline stage, the number of plans re-planned or discarded per cohort, the criteria used, and a sensitivity analysis of the released dataset to these filtering decisions.
  4. [Sec. 3, Step 3; Algorithm 1] The convergence of the iterative refinement is supported only by the statement that 'when the parameters are properly tuned, empirical evidence shows that plan quality tends to converge toward a high score' (Sec. 3, Step 3). No convergence curves, no formal stopping criterion (Algorithm 1 leaves 'MAX iteration' and the 'close enough' condition undefined), and no sensitivity analysis for the hand-tuned margins and priorities in Figs. 10 and 11 are provided. Since the scalable-quality claim depends on this convergence, please show score-versus-iteration curves for a representative set of cases and quantify sensitivity to the Dose2Obj and Scorecard2Dose constants.
minor comments (4)
  1. [Throughout] There are several typographical errors, including 'an novel' (Sec. 1), 'Miscorsoft' (Sec. 4.1), 'V olumetric' (Sec. 2), 'featuresmore than 10 timesnumber' (Abstract), 'coustomized' (Appendix C), and 'T otal Lung-GTV' (Fig. 6b).
  2. [Sec. 3, Step 2; Fig. 4] The text states that head-and-neck VMAT plans use 'four arcs,' while the Fig. 4 caption and Fig. 6b label show 'two arcs'; please clarify whether this is two full arcs or four partial arcs, and ensure consistency across the text and figures.
  3. [Algorithm 1] The stopping criterion is described qualitatively ('Plan i & i+1 close enough?'); please state the exact quantitative condition used in the actual runs (e.g., score difference threshold, dose difference threshold, or fixed iteration count).
  4. [Table 3] The footnote says the validation/testing plan quality scores are based on subsets of 41 and 50 plans, respectively; please report the mean and standard deviation of the score distribution over these subsets, and clarify whether the same subset is used for all phases.

Circularity Check

1 steps flagged · score 4.0 of 10

The scorecard is used both as the optimization objective and as the quality metric, so the plan-quality claim is partly self-confirmatory; the dataset-scale and challenge results remain independent.

  1. self definitional [Algorithm 1 (Step 3, lines 8–18); Table 1; Sec. 4.2]
    ""Calculate the ScoreCard for current plan" ... "for items in ScoreCard: Calculate the margin and objective dose and priority based on appended Fig. 11. Add the objective to objective buffer." ... "We use the quality score card to evaluate the plan quality and remove or re-plan corner cases, make plans more aligned to scorecard.""

    The Varian scorecard is not an independent arbiter of quality: Algorithm 1 iteratively converts scorecard DVH points and metrics into optimization objectives with margins, so the optimizer is explicitly driven toward high scorecard scores. Table 1 then uses the same scorecard as an acceptance filter, removing or re-planning corner cases to make plans more aligned to it. When Sec. 4.2 reports that plan quality is high, the support includes scorecard convergence and scorecard-based assessment, which are consequences of optimizing toward and selecting on the very same metric rather than independent evidence of clinical comparability.

full rationale

The paper is largely self-contained: the AIRTP pipeline, dataset construction, and GDP-HMM challenge baseline are described in algorithmic detail, and the 10x scale claim is a direct plan-count comparison against OpenKBP. No load-bearing uniqueness theorem or self-citation chain is used; the RapidPlan model is sourced from Varian's public repository, and co-authored citations such as [35] point to that external model rather than to an unverified result of this paper. The only material circularity is the dual role of the scorecard. Algorithm 1 builds optimization objectives from scorecard DVH points, Table 1 states that corner cases are removed or re-planned to make plans more aligned to the scorecard, and Sec. 4.2 then treats high plan quality as established, with the paper itself conceding that scorecards and DVHs cannot definitively show superiority and that the comparison with clinical plans may be unfair. Thus the scorecard-based portion of the quality claim is partly self-confirmatory, but the dataset scale and challenge results stand independently, so the overall circularity is moderate rather than total.

Assumptions & free parameters 5 free parameters · 5 assumptions · 0 invented entities

The plan quality claim rests on hand-tuned optimization constants and on commercial tools (Eclipse, AI Rad Companion, RapidPlan) treated as valid black boxes. The central heuristic that iterative scorecard-guided refinement converges to high-quality plans is stated as an empirical observation without analysis, and it is listed as an ad hoc axiom.

free parameters (5)
  • Dose2Obj margin and weight constants = value*0.95, value-3, value-2, value-1; weights 80/120
    Hand-chosen constants in Fig 10 convert current DVH doses into next-iteration optimization objectives. They are not fitted to an external benchmark and directly determine plan quality and convergence.
  • Scorecard2Dose conversion constants = 0.85, -7, priority base 60, slope terms 20/7/3
    Hand-chosen constants in Fig 11 map scorecard metric targets to optimization objectives and priorities. These affect how aggressively the scorecard is satisfied.
  • Lung laterality beam-angle threshold = 5 cm
    Section 3, Step 2: if the tumor is more than 5 cm lateral from mid-sagittal plane, choose left/right lung template; otherwise full-angle coverage. This planning choice influences achievable lung plan quality.
  • Iteration convergence stopping rule = not stated; loop until plans are 'close enough'
    Algorithm 1 loops until plan changes are close enough; the threshold is not specified, so the stopping rule is an uncontrolled free choice.
  • Beam configuration templates = HaN VMAT 4 arcs, collimators 30/330; HaN IMRT 9 or 15 fields; lung VMAT 2 arcs, IMRT 7 or 9 fields
    Chosen in Section 3 Step 2; not derived from any objective criterion, directly affects dose distributions and plan quality.
assumptions (5)
  • domain assumption Eclipse/TrueBeam with AXB dose calculation produces clinically accurate doses.
    Section 4.1 states the planning environment uses TrueBeam PO.18.0.1 and AXB 18.0.1. The paper assumes these dose computations are correct and deliverable.
  • domain assumption AI Rad Companion auto-contouring produces OAR contours accurate enough for planning.
    Section 3 Step 1 relies on internal AI Rad Companion segmentations to define OARs; no contour accuracy evaluation is reported.
  • domain assumption The Varian RapidPlan model and scorecard are valid, standardized planning guidelines.
    Section 3 uses a RapidPlan model from Varian Medical Affairs and scorecard metrics as the ground truth for plan quality. The paper assumes these external tools encode clinically appropriate objectives.
  • domain assumption Clinical PTVs and prescription doses from TCIA cohorts preserve original clinical intent.
    Section 4.1 says CT, PTVs, and prescribed doses are extracted from TCIA datasets; the paper keeps original clinical intent instead of re-contouring PTVs.
  • ad hoc to paper Iterative scorecard-guided optimization converges to high-quality plans when parameters are tuned.
    Section 3 Step 3 states 'When the parameters are properly tuned, empirical evidence shows that plan quality tends to converge toward a high score.' No proof or convergence analysis is provided; this is the central heuristic of the pipeline.

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Pith. "Pith review of Automating RT Planning at Scale: High Quality Data For AI Training." pith.science (2026). https://pith.science/paper/4VAQM3TH

@misc{pith2026250111803,
  author       = {Pith},
  title        = {Pith review of: Automating RT Planning at Scale: High Quality Data For AI Training},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/4VAQM3TH}},
  note         = {Machine review of arXiv:2501.11803}
}
read the original abstract

Radiotherapy (RT) planning is complex, subjective, and time-intensive. Advances with artificial intelligence (AI) promise to improve its precision and efficiency, but progress is often limited by the scarcity of large, standardized datasets. To address this, we introduce the Automated Iterative RT Planning (AIRTP) system, a scalable solution for generating high-quality treatment plans. This scalable solution is designed to generate substantial volumes of consistently high-quality treatment plans, overcoming a key obstacle in the advancement of AI-driven RT planning. Our AIRTP pipeline adheres to clinical guidelines and automates essential steps, including organ-at-risk (OAR) contouring, helper structure creation, beam setup, optimization, and plan quality improvement, using AI integrated with RT planning software like Varian Eclipse. Furthermore, a novel approach for determining optimization parameters to reproduce 3D dose distributions, i.e. a method to convert dose predictions to deliverable treatment plans constrained by machine limitations is proposed. A comparative analysis of plan quality reveals that our automated pipeline produces treatment plans of quality comparable to those generated manually, which traditionally require several hours of labor per plan. Committed to public research, the first data release of our AIRTP pipeline includes nine cohorts covering head-and-neck and lung cancer sites to support an AAPM 2025 challenge. To our best knowledge, this dataset features more than 10 times number of plans compared to the largest existing well-curated public dataset. Repo: https://github.com/RiqiangGao/GDP-HMM_AAPMChallenge.

Figures

Figures reproduced from arXiv: 2501.11803 by the authors.

Figure 1
Figure 1. Datasets of representative AI models across various domains (see Appendix [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Illustration of automated iterative RT planning (AIRTP) pipeline. The auto contouring, [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. (a) shows the process that increase the quality of planning iteratively. (b) demonstrates the [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figures from the paper (19 more)
Figure 4
Figure 4. Figure 4: Examples of head-and-neck (upper) and lung (lower) cancer treatment plans. In released [PITH_FULL_IMAGE:figures/full_fig_p006_4.png]
Figure 5
Figure 5. Figure 5: The DVH comparisons between our plans and clinical plans are shown, with dashed lines [PITH_FULL_IMAGE:figures/full_fig_p007_5.png]
Figure 6
Figure 6. Figure 6: Re-planning examples following the method shown in Fig. [PITH_FULL_IMAGE:figures/full_fig_p008_6.png]
Figure 7
Figure 7. Figure 7: Examples (Visualization from Varian’s Eclipse) of DICOM data supporting AI training for [PITH_FULL_IMAGE:figures/full_fig_p009_7.png]
Figure 8
Figure 8. Figure 8: Number of clinically contoured OARs varies significantly across patients. As shown in [PITH_FULL_IMAGE:figures/full_fig_p015_8.png]
Figure 9
Figure 9. Figure 9: Overview of the data curation pipeline, from raw data processing to AI model training, [PITH_FULL_IMAGE:figures/full_fig_p018_9.png]
Figure 10
Figure 10. Figure 10: Calculation of optimization objectives based on DVHs during the iterative process. [PITH_FULL_IMAGE:figures/full_fig_p019_10.png]
Figure 11
Figure 11. Figure 11: Calculation of optimization objectives using scorecard items during the iterative process. [PITH_FULL_IMAGE:figures/full_fig_p019_11.png]
Figure 12
Figure 12. Figure 12: Kernel density estimation of the high prescription dose in different data splits. [PITH_FULL_IMAGE:figures/full_fig_p020_12.png]
Figure 13
Figure 13. Figure 13: A baseline for the generalizable dose prediction for GDP-HMM challenge. The MedNeXt [PITH_FULL_IMAGE:figures/full_fig_p021_13.png]
Figure 14
Figure 14. Figure 14: Head-and-neck IMRT example. Page 1 Restricted © Siemens Medical Solutions USA, Inc., 2022 Prediction* Reference [PITH_FULL_IMAGE:figures/full_fig_p022_14.png]
Figure 15
Figure 15. Figure 15: Head-and-neck VMAT example. We present four samples from our validation cohort, illustrating distinct scenarios that a universal AI model should be capable of handling [PITH_FULL_IMAGE:figures/full_fig_p022_15.png]
Figure 16
Figure 16. Figure 16: Lung IMRT example. Page 1 Restricted © Siemens Medical Solutions USA, Inc., 2022 Prediction* Reference [PITH_FULL_IMAGE:figures/full_fig_p023_16.png]
Figure 17
Figure 17. Figure 17: Lung VMAT example. 23 [PITH_FULL_IMAGE:figures/full_fig_p023_17.png]
Figure 18
Figure 18. Figure 18: The example visualization of one plan with angle and beam plates. Except the angle plate, [PITH_FULL_IMAGE:figures/full_fig_p024_18.png]
Figure 19
Figure 19. Figure 19: Six plans from three patients for head-and-neck cancer. [PITH_FULL_IMAGE:figures/full_fig_p025_19.png]
Figure 20
Figure 20. Figure 20: The DVHs of the plans shown in Fig [PITH_FULL_IMAGE:figures/full_fig_p025_20.png]
Figure 21
Figure 21. Figure 21: Six plans from three patients for lung cancer. [PITH_FULL_IMAGE:figures/full_fig_p026_21.png]
Figure 22
Figure 22. Figure 22: The DVHs of the plans shown in Fig [PITH_FULL_IMAGE:figures/full_fig_p026_22.png]

Discussion (0). Continue with ORCID to comment.

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Pith tools

Reviewed August 10, 2026 · model on record in the stance chip above.