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REVIEW 3 major objections 5 minor 27 references

Development and experimental validation of an in-house treatment planning system with greedy energy layer optimization for fast IMPT

T0 review · 3 major / 5 minor · reviewed 2026-08-12 · deepseek-v4-flash

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Pith's one-line read An in-house proton treatment planning system with greedy energy-layer selection can produce IMPT plans that are faster to deliver and still pass clinical QA.

desk verdict A useful, honestly-reported engineering validation of greedy energy-layer optimization on a clinical proton machine, but the dose-engine validation is benchmarked against its own calibration source. read the letter →

arxiv 2411.18074 v1 pith:YA7RKP5C submitted 2024-11-27 physics.med-ph

Aoxiang Wang , Ya-Nan Zhu , Jufri Setianegara , Yuting Lin , Peng Xiao , Qingguo Xie , Hao Gao This is my paper · ORCID
classification physics.med-ph
keywords protontherapyintensity-modulatedpencilbeamscanningtreatmentplanningsystemenergylayeroptimizationorthogonalmatchingpursuitblocksparsitypatient-specificqualityassurance
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

The paper argues that an open, in-house treatment planning system can do what closed commercial platforms make difficult: optimize intensity-modulated proton therapy (IMPT) plans over the number of energy layers, not just spot weights, and then deliver those plans on a real proton machine. The authors built a pencil-beam dose engine matched to an IBA Proteus ONE machine, added a greedy block-selection algorithm that picks a preset number of energy layers, and tested it on abdomen, brain, and lung cases. In their hands the optimized plans used substantially fewer energy layers (for example 40 instead of 78 for the brain), cut measured delivery time from 105 to 54 seconds, and still passed patient-specific quality assurance with gamma pass rates above 95%. If correct, this removes a practical barrier: energy-layer optimization can be experimentally validated and clinically used without waiting for commercial TPS vendors to add it.

What carries the argument

The load-bearing object is the energy-layer optimization formulated as block-sparse inverse planning and solved by a modified orthogonal matching pursuit. The full dose-influence matrix is partitioned into blocks, one block per proton energy, and limiting the number of energies is expressed as the constraint that the beam-intensity vector have at most N nonzero blocks. At each iteration the method picks the energy block whose dose most reduces the residual of the quadratic dose-matching problem, then re-optimizes the full plan (with DVH and minimum-monitor-unit constraints) on the enlarged support, keeping the new energy only if the plan objective actually decreases. This turns a nonconvex combinatorial selection into a sequence of tractable projections.

What would settle it

Measure the integral depth dose and lateral profiles of single spots for a spread of energies on the Proteus ONE with an ionization chamber or film, then compare them to IH-TPS predictions: if gamma pass rates fall below 95% (2 mm, 2%) on those beam-data measurements, the dose engine's machine model is the weak link. Alternatively, run the ELO plans through an independent Monte Carlo engine and a heterogeneous phantom measurement; disagreement there would separate beam-model error from the optimization's benefit.

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Extended reading notes

Core claim

The central claim is that a self-contained in-house TPS can combine a clinically matched pencil-beam dose engine with a greedy energy-layer optimization and produce IMPT plans that are both faster to deliver and clinically acceptable. The paper reports that the dose engine's single-spot depth-dose and lateral profiles agree with RayStation's Monte Carlo to gamma pass rates of 96.5% to 100% (2 mm, 2%), and that full plans agree with RayStation at 95.6% to 97.1% (2 mm, 2%). With the greedy selection, the number of energy layers fell from 50 to 30 for the abdomen, 78 to 40 for the brain, and 100 to 70 for the lung, with measured delivery times dropping from 110 to 90 s, 105 to 54 s, and 178 to 127 s respectively. Recalculated in RayStation, the reduced-energy plans retained comparable target coverage and organ-at-risk sparing, and all delivered beams passed the clinical 95% gamma threshold.

Load-bearing premise

The load-bearing premise is that IH-TPS's beam model, built from RayStation Monte Carlo single-spot dose simulations in water, faithfully represents the actual IBA Proteus ONE machine when applied to patient anatomy by the pencil-beam algorithm.

Editorial extensions

If this is right

  • Energy-layer reduction is a practical, experimentally verified lever: in the reported cases it cut measured delivery time by roughly 18% to 49%.
  • Because the TPS is in-house, new optimization constraints such as dose-rate limits or delivery-sequence penalties can be added and validated on a clinical machine without waiting for a commercial vendor.
  • The greedy selection extends beyond the simplified quadratic problem to full clinical plans with DVH objectives and minimum-monitor-unit constraints.
  • Plans with fewer energy layers remain deliverable when recalculated in the clinical TPS, meaning the efficiency gain is not an artifact of the in-house dose engine alone.

Reading between the lines

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

  • If the beam model were calibrated directly from measured machine data rather than from RayStation Monte Carlo simulations in water, the greedy selection could plausibly push energy counts even lower than the 30, 40, and 70 values reported, since the paper's own comparison shows the RayStation-recalculated plans degraded slightly relative to the IH-TPS-computed ones.
  • The same block-greedy idea transfers naturally to other combinatorial choices in radiotherapy, such as beam-angle selection or minimum-monitor-unit constraints; a testable extension is applying it to FLASH dose-rate optimization, where delivery time constraints are tighter.
  • A stronger validation would compare IH-TPS dose against independent Monte Carlo or measured data in heterogeneous phantoms rather than against RayStation, which supplied the base beam data; such a test would separately quantify dose-engine accuracy and the benefit of the ELO method.
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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

3 major / 5 minor

Summary. The paper reports an in-house treatment planning system (IH-TPS) for pencil-beam scanning proton therapy, combining a pencil-beam dose engine with a greedy energy-layer optimization (ELO) algorithm inspired by block orthogonal matching pursuit. The system is tested on three clinical cases (abdomen, brain, lung) by comparing IH-TPS dose distributions with RayStation Monte Carlo, by measuring patient-specific QA on an IBA Proteus ONE machine, and by recording delivery times with and without ELO. The authors report that ELO reduces energy layers and delivery times (e.g., brain: 78 to 40 layers, 105 to 54 s) while preserving plan quality, with QA pass rates above 95% in all cases.

Significance. The work is practically significant if the claims hold: it demonstrates a route to integrate energy-layer optimization into a self-developed TPS, with measured delivery-time reductions on a clinical machine. The patient-specific QA and the direct measurement of delivery times are genuine strengths, as is the explicit use of clinical cases with standard beam angles and DVH objectives. However, the dosimetric validation of the IH-TPS dose engine is not fully independent: the beam model is parameterized from RayStation Monte Carlo data and then compared back against RayStation, leaving the absolute accuracy of the pencil-beam engine in patient geometries without a direct measurement-based check. The plan-quality preservation claim is also weaker than stated when the ELO plans are recalculated in RayStation, as the reported conformity and Dmax metrics show systematic degradation.

major comments (3)
  1. [§2.1 and §3.1]
  2. [§3.3 and Table 3]
  3. [§2.3 and §3.3]
minor comments (5)
  1. [References]
  2. [Equation (2)]
  3. [§2.2.2 algorithm]
  4. [§3.1.1]
  5. [Figure 1 caption]

Circularity Check

2 steps flagged · score 6.0 of 10

Dose-engine validation is partially circular: IH-TPS beam parameters are derived from RayStation MC data and then 'verified' against RayStation, while the experimental QA chain also routes through RayStation recalculations.

  1. fitted input called prediction [Section 2.1 (beam model construction) and Section 3.1.1 (single-spot validation)]
    "To create the beam data for IH-TPS, RayStation's Monte Carlo (MC) dose engine is used to simulate the 3D dose of different energy layers in a 40×40×40 cm³ water phantom... These base data are used to calculate the beam parameters for all energy layers, including integrated depth dose (IDD), range, peak, and spot sizes... The results demonstrated a good agreement between IH-TPS and RayStation."

    The IH-TPS beam model is constructed by extracting IDD, range, peak, and spot sizes from RayStation MC single-spot simulations. The single-spot validation in Section 3.1.1 then uses RayStation as the reference for the very same quantities. This is a goodness-of-fit check against the data that generated the beam parameters, not an independent prediction of the dose engine. The reported gamma agreement is therefore partly enforced by construction.

  2. other [Section 2.3 (experimental validation protocol) and Section 3.3 (RayStation recalculation and QA)]
    "proton spot weights and locations were scripted as a csv file to be imported to RayStation TPS for delivery on our proton machine and recalculating 3D dose in RayStation for comparison... all newly generated plans using IH-TPS were recalculated in RayStation... we conducted QA and all cases exceeded the 95% pass rate."

    The patient-specific QA validates deliverability of the spot lists, but the reference dose for comparison is computed by RayStation, the same system whose MC engine supplied the IH-TPS beam model. The experimental chain therefore tests consistency between RayStation's dose model and delivered dose, not the independent accuracy of IH-TPS's pencil-beam prediction in patient geometry. A biased IH-TPS dose engine could still pass this chain because every comparison is routed through RayStation.

full rationale

The central ELO contribution is genuinely independent: the greedy block-selection algorithm is specified in the paper, the energy-layer reductions and delivery-time measurements are machine data, and the reported QA pass rates are experimental. The partial circularity lies in the dose-engine validation. Section 2.1 derives IH-TPS beam parameters from RayStation MC simulations, and Section 3.1 then uses RayStation as the benchmark for both single-spot and plan-dose agreement. That is a consistency check with the model's training source rather than an independent validation. Section 3.3 likewise recalculates IH-TPS plans in RayStation and reports QA against that RayStation-dependent chain, so the experimental validation does not isolate IH-TPS's dose accuracy. The ELO efficiency gains remain independent evidence for the paper's practical claim, but one of the central validation claims—dose agreement with RayStation—reduces partly to a fit against its own input data. Score 6 reflects this partial but concrete circularity.

Assumptions & free parameters 3 free parameters · 4 assumptions · 0 invented entities

The central claim rests on calibrated beam model parameters, a user-chosen target layer count, and several domain assumptions about dose calculation and optimization heuristics. The only genuinely independent evidence is the measured QA pass rates and delivery times.

free parameters (3)
  • Beam model parameters for 63 energy layers = Derived from RayStation MC single-spot dose in a 40x40x40 cm3 water phantom (70-225 MeV, 2.5 MeV spacing)
    The IH-TPS dose engine's IDD, range, peak, and spot size parameters are calibrated to match the machine via RayStation MC. All patient and QA calculations depend on this calibration.
  • Target number of energy layers N = Scanned values: abdomen 30/25/20, brain 40/30/20, lung 80/70/60; time measurements use 30/40/70
    N is a user-chosen constraint in Eq. (7), and the reported delivery time reductions are conditional on the chosen N. It is not derived or optimized automatically.
  • Objective weights and MMU threshold = Not reported
    Eq. (2) weights omega_1,i, omega_2,i, omega_3,i and G_min in Eq. (7) are clinically used but their numerical values are not provided, preventing exact reproduction.
assumptions (4)
  • domain assumption The pencil beam dose calculation algorithm [15] accurately models proton doses in patient geometries and heterogeneous media.
    Section 2.1 states the dose engine is based on a modified pencil beam algorithm; the clinical accuracy of the TPS depends on this model.
  • domain assumption RayStation's Monte Carlo dose engine produces sufficiently accurate single-spot base data to calibrate the IH-TPS beam model.
    Section 2.1 uses RayStation MC to simulate base data; if this data is biased, IH-TPS inherits the bias and the RayStation comparison in Section 3.1 is not an independent check.
  • ad hoc to paper Greedy block selection with local objective decrease yields clinically acceptable solutions to the nonconvex block-sparse problem (7).
    Section 2.2.2 states the problem is highly nonconvex and the algorithm accepts a candidate layer only if f decreases; no optimality or approximation guarantee is provided.
  • domain assumption Energy layer switching time dominates delivery time, so reducing energy layers reduces total delivery time.
    Introduction cites [8,9]; Section 4 admits the abdomen case gained less than 20% time for 40% layer reduction because other components matter, so this assumption holds only partially.

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Cite this review

Pith. "Pith review of Development and experimental validation of an in-house treatment planning system with greedy energy layer optimization for fast IMPT." pith.science (2026). https://pith.science/paper/YA7RKP5C

@misc{pith2026241118074,
  author       = {Pith},
  title        = {Pith review of: Development and experimental validation of an in-house treatment planning system with greedy energy layer optimization for fast IMPT},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/YA7RKP5C}},
  note         = {Machine review of arXiv:2411.18074}
}
read the original abstract

Background: Intensity-modulated proton therapy (IMPT) using pencil beam technique scans tumor in a layer by layer, then spot by spot manner. It can provide highly conformal dose to tumor targets and spare nearby organs-at-risk (OAR). Fast delivery of IMPT can improve patient comfort and reduce motion-induced uncertainties. Since energy layer switching time dominants the plan delivery time, reducing the number of energy layers is important for improving delivery efficiency. Although various energy layer optimization (ELO) methods exist, they are rarely experimentally validated or clinically implemented, since it is technically challenging to integrate these methods into commercially available treatment planning system (TPS) that is not open-source. Methods: The dose calculation accuracy of IH-TPS is verified against the measured beam data and the RayStation TPS. For treatment planning, a novel ELO method via greed selection algorithm is proposed to reduce energy layer switching time and total plan delivery time. To validate the planning accuracy of IH-TPS, the 3D gamma index is calculated between IH-TPS plans and RayStation plans for various scenarios. Patient-specific quality-assurance (QA) verifications are conducted to experimentally verify the delivered dose from the IH-TPS plans for several clinical cases. Results: Dose distributions in IH-TPS matched with those from RayStation TPS, with 3D gamma index results exceeding 95% (2mm, 2%). The ELO method significantly reduced the delivery time while maintaining plan quality. For instance, in a brain case, the number of energy layers was reduced from 78 to 40, leading to a 62% reduction in total delivery time. Patient-specific QA validation with the IBA Proteus ONE proton machine confirmed a >95% pass rate for all cases.

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Reviewed August 12, 2026 · model on record in the stance chip above.