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REVIEW 4 major objections 4 minor 31 references

Unsupervised deep learning model for fast energy layer pre-selection of delivery-efficient proton arc therapy plan optimization of nasopharyngeal carcinoma

T0 review · 4 major / 4 minor · reviewed 2026-08-06 · deepseek-v4-flash

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Pith's one-line read SPArc_dl, an unsupervised deep-learning model, pre-selects proton arc energy layers in under a second and cuts energy-switch time by 37.2% while improving plan quality on 35 nasopharyngeal carcinoma cases.

desk verdict The spot-count representation is genuinely new, but plan-quality claims depend on a dose proxy the paper never validates and a baseline it itself calls suboptimal. read the letter →

arxiv 2506.15803 v2 pith:NY4UR7EX submitted 2025-06-18 physics.med-ph cs.AI

Bohan Yang , Gang Liu , Yang Zhong , Rirao Dao , Yujia Qian , Ke Shi , Anke Tang , Yong Luo
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Qi Kong Jingnan Liu
This is my paper · ORCID
classification physics.med-phcs.AI
keywords protonarctherapyenergylayerselectionunsuperviseddeeplearningspot-countrepresentationnasopharyngealcarcinomaplanoptimizationdeliveryefficiencyrobustness
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 claims that energy-layer pre-selection for proton arc therapy can be reduced to an unsupervised image-like segmentation problem, with no labeled plans and no dose calculation during the selection step. The model, SPArc_dl, takes spot-count matrices that record how many proton pencil beams hit the target (and how many hit the target while avoiding the brainstem) at each gantry angle and energy layer, and it outputs one energy layer per angle. On 35 nasopharyngeal-carcinoma cases, that recipe delivered improved dose conformity and homogeneity, lower brainstem dose, and 37.2% less energy-switch time than the established SPArc_ps method, with inference in about 0.05 seconds. The paper's own robustness analysis shows the advantage comes with a cost: CTV dose-volume histograms under setup and range uncertainties are broader for SPArc_dl, so the method improves nominal quality and speed more than it improves worst-case stability.

What carries the argument

The load-bearing object is the spot-count representation: for each structure $S$ a matrix $M \in \mathbb{R}^{G \times E}$ where $G$ is the number of gantry angles, $E$ is the number of energy layers, and entry $(g,e)$ counts the proton spots from angle $g$ at layer $e$ that intersect $S$. Element-wise subtraction gives $M_{CTV-BRS}=M_{CTV}-M_{BRS}$, the count of spots covering the target while missing the brainstem; these two matrices, concatenated with a per-angle WET vector, become a 2-channel image-like tensor for a U-Net. The network outputs a probability $P$ over energy layers for every angle, and the training loss combines two cross-entropy terms that push the selected layer toward the binarized maximum of $M_{CTV}$ and of $M_{CTV-BRS}$ with a third term that extracts the switch time $T(P_{g-1},P_g)$ from an energy-layer time table. This machinery makes clinical priorities directly differentiable, so the model can be trained end-to-end without any dose engine or ground-truth plan.

What would settle it

Run SPArc_dl and SPArc_ps on new nasopharyngeal cases with 2-degree angular sampling and full dose optimization; the central claim fails if SPArc_dl does not reduce energy-switch time while keeping CTV D98, CI, HI, and brainstem dose at least as good as SPArc_ps. A more direct test of the proxy: find one case where increasing the spot count $M_{CTV}$ at an angle lowers the actually optimized D98—that would show that the count representation is not measuring the dose effect the model is trained to maximize.

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Extended reading notes

Core claim

The central claim is that the spot-count representation—counting proton spots that intersect a structure, arranged in a matrix over sorted gantry angles and energy layers—captures enough of the dose-coverage information to choose energy layers without supervised ground truth. A U-Net maps the concatenated matrices $M_{CTV}$ and $M_{CTV-BRS}$, together with water-equivalent-thickness values per angle, to a probability tensor over energy layers; a softmax per gantry angle selects the most probable layer. The tri-objective loss maximizes spot counts on CTV, maximizes spot counts on CTV while avoiding the brainstem, and penalizes energy-layer switch time read from a machine-specific time table. Across five-fold cross-validation on 35 patients, the resulting plans beat SPArc_ps on conformity index (0.70 vs 0.60), homogeneity index (0.26 vs 0.97), brainstem mean dose (0.54 vs 0.79 Gy) and energy-switch time (51.0 vs 81.1 s, a 37.2% reduction), all with $p<0.01$. The paper also reports that the selected sequences use many unchanged energy layers between consecutive gantry angles, and that unchanged transitions are more time-efficient than descended ones, contradicting the usual preference for descending energy sequences. The paper acknowledges that robustness is weaker: SPArc_dl shows broader CTV DVH bands and larger CTV RVH AUC than SPArc_ps under uncertainty.

Load-bearing premise

The load-bearing premise is that the number of proton spots intersecting a structure is a faithful proxy for radiation dose coverage, so the layer with the most intersecting spots will also be the layer that best covers the target after weight optimization; if that link breaks, the model's dosimetric gains could disappear.

Editorial extensions

If this is right

  • Energy-layer selection becomes a sub-second forward pass: SPArc_dl infers a full arc's energy sequence in about 0.05 seconds on a CPU, compared with 13.5 seconds for SPArc_ps, enabling interactive replanning or rapid what-if exploration.
  • Treatment delivery time drops 37.2% on average (from 81.1 to 51.0 seconds of energy-switch time per arc) because the model chooses many unchanged energy layers between consecutive gantry angles, and unchanged transitions cost zero switch time.
  • Nominal plan quality improves across all reported metrics on the 35 nasopharyngeal cases: higher conformity index, lower homogeneity index, higher D98, lower D2, and lower brainstem mean and max dose, all with $p<0.01$.
  • The unsupervised formulation leaves the objective open-ended: additional OARs, beam-delivery constraints, or robustness scenarios can be added as extra loss terms without generating labels or running a full optimizer to create training targets.
  • The robustness results are a direct corollary of the paper's claim: SPArc_dl's CTV DVH bands and RVH AUC are worse than SPArc_ps, so the speed and nominal-quality gains do not extend to uncertainty robustness.

Reading between the lines

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

  • A testable extension: replace the raw spot-count matrices with a differentiable approximate dose objective; the difference in final plan quality would quantify how much information the spot-count proxy discards.
  • Adding a robustness term to the same unsupervised loss—for example, computing spot counts under several setup shifts and range uncertainties and penalizing their variance—would directly target the paper's reported robustness limitation.
  • The unchanged-energy-layer efficiency result depends on the shape of the machine's ELS time table; on systems where descending energy switches are cheap, the optimal balance of unchanged, descended, and ascended transitions could shift.
  • The 2 × 72 × 103 input is architecture-agnostic, so the same spot-count representation could be used with sequence models or multi-energy-layer-per-angle selection to gain dosimetric flexibility beyond the single-layer-per-angle choice.
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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 paper proposes SPArc_dl, an unsupervised deep learning model for fast energy-layer (EL) pre-selection in proton arc therapy for nasopharyngeal carcinoma. The method introduces a spot-count representation (matrices of proton spots intersecting CTV and CTV-minus-brainstem, structured by gantry angle and energy layer), feeds this representation to a U-Net, and trains with a tri-objective loss: maximizing CTV spot counts, minimizing brainstem spot counts, and penalizing energy-layer-switch (ELS) time. On 35 NPC cases, the method is compared with SPArc_ps, reporting improved conformity index, homogeneity index, D98, and brainstem mean/max dose, a 37.2% reduction in ELS time, and near-instant inference, while also acknowledging inferior plan robustness measured by DVH bands and RVH AUC.

Significance. If the central claims hold, the work would be a useful step toward fast, delivery-efficient proton arc planning: the spot-count representation is an original way to make EL pre-selection amenable to deep learning, the unsupervised formulation avoids supervised ground-truth generation, and the sub-second inference time is practically attractive. The authors also report cross-validation, quantitative metrics, and an explicit robustness limitation, which are strengths. However, the significance is currently limited because the dosimetric superiority claim depends on an unvalidated proxy (spot counts as a surrogate for dose), on a potentially weak baseline, and on a one-energy-layer-per-angle constraint that changes the optimization problem relative to SPArc_ps. The robustness results the authors themselves report suggest the proxy does not fully capture clinically relevant dose behavior.

major comments (4)
  1. [Sections 2.2–2.3, Eq. (4)] The network is trained to maximize spot counts intersecting CTV and CTV-minus-brainstem, but the final dosimetric claims (CI, HI, D98, brainstem dose) are measured on plans after spot-weight optimization. No evidence is provided that higher spot counts at the selected energy layers correlate with better final dose distributions. This is load-bearing: if the proxy is not predictive, the reported dosimetric improvements cannot be attributed to the learned pre-selection. The paper's own robustness evaluation (Fig. 3c) shows larger CTV RVH AUC for SPArc_dl, which is consistent with the proxy missing important dose behavior. Please add a direct validation of the spot-count proxy against final dose metrics, or substantially temper the dosimetric claims.
  2. [Section 2.2, inference stage] SPArc_dl selects exactly one energy layer per gantry angle via the highest softmax probability. SPArc_ps, by contrast, appears to use multiple energy layers per angle (e.g., the diagonal bands in Fig. 3a). This is not a like-for-like comparison: the one-layer-per-angle constraint drastically reduces the downstream optimization space, and the reported improvements in CI, HI, and brainstem dose could be artifacts of this constraint rather than of the learned pre-selection quality. The authors should report the number of energy layers per gantry angle for both methods and analyze the sensitivity of plan quality to relaxing the one-layer constraint.
  3. [Sections 1 and 2.4] The baseline comparability is not established. The introduction states that SPArc_ps 'may need more objective functions on organs at risk' for head and neck cancer, which suggests the comparator may not be well tuned for NPC. The evaluation section does not state whether the same downstream spot-weight optimizer, objective function, and optimization parameters were used for both SPArc_dl and SPArc_ps. Without this information, the dosimetric comparison may reflect a weak baseline or an inconsistent optimization setup rather than the benefit of SPArc_dl. Please specify the common downstream optimization protocol and, ideally, add a baseline that is optimized with the same OAR objectives.
  4. [Section 3.2, Table 2] The 37.2% ELS time reduction is a direct consequence of the Ltime term in Eq. (4) and of the one-layer-per-angle selection; it is an expected outcome of the loss design, not an independent 'unintentional' finding. Moreover, the statement that unchanged ELS is more time-efficient than descended ELS is definitional, since Table 2 assigns zero time to unchanged ELS. The paper should present this result as a designed efficiency feature and focus the novelty claims on plan quality, which is the part that needs independent support.
minor comments (4)
  1. [Section 2.3, Eq. (1)] The loss uses binarized maximum spot counts as target labels for a cross-entropy term. Calling the method 'unsupervised' is somewhat misleading; 'self-supervised' or 'pseudo-label-based' would be more precise, since the network is trained toward data-derived targets rather than with no labels at all.
  2. [Section 3.1, Table 1] The p-values are reported without specifying the statistical test, whether the comparisons are paired, or whether any multiple-comparison correction was applied. Please add a methods sentence describing the statistical analysis.
  3. [Section 2.4] The CT voxel size is reported as '1.36 × 1.36 × 3 cm^3,' which is likely a typo for mm^3. Also, the units for the ELS time table in Fig. 1(b) are not defined in the caption or text.
  4. [Data availability statement] The statement that no suitable repository exists is unusual; if data cannot be shared, at least provide the code or a detailed algorithmic description to support reproducibility of the spot-count representation and loss function.

Circularity Check

2 steps flagged · score 6.0 of 10

The ELS-time reduction and the 'unchanged ELS is efficient' observation are built into the training loss and the ELS time table; the dosimetric plan-quality results are evaluated independently and are not circular.

  1. fitted input called prediction [Abstract/Sec. 3.2 (Table 2) vs. Sec. 2.3 (Eqs. 2 and 4)]
    "SPArc_dl produces EL pre-selection that significantly improves ... delivery efficiency. ... Compared to SPArc_particle_swarm, ... shortens the ELS time by 37.2% (p < 0.01). The third part of Eq.(4) is introduced to penalize the ELS time, which can be generated using the ELS time table ( T Bels) and formularized as Eq.(2)."

    The headline ELS-time reduction is the same quantity as the Ltime term in Eq. (4), evaluated with the same ELS time table used to define the loss. Because the network is trained to minimize Ltime, reporting the 37.2% reduction as a main result is reporting an optimized objective, not an independent prediction derived from the model. This is the fitted-input-called-prediction pattern: the measured improvement is forced by the training objective. The dosimetric comparisons are separate, so the circularity is only partial.

  2. self definitional [Sec. 2.3 (ELS time table) and Sec. 3.2 (unintentional reveal)]
    "Note the ELS time is zero when EL is unchanged. ... the results of SPArc dl unintentionally reveals using unchanged ELS may be more time-wise efficient than descended ELS."

    The claimed 'unintentional' discovery is a direct restatement of the ELS time table: an unchanged transition is assigned zero time, while a descended transition has positive time. The model's preference for unchanged transitions is therefore a consequence of minimizing Ltime, not an empirical finding. Presenting this definitional property as a revealed result renames the input time table as an output of the study.

full rationale

The dosimetric plan-quality claims (CI, HI, D98, D2, brainstem dose) are not circular: they are measured on final plans after spot-weight optimization, which is not the spot-count proxy used in training. The absence of proof that the proxy predicts final DVH quality is a validity/robustness concern, not a reduction to the input. The self-citation to Qian et al. for the WET feature is minor and not load-bearing; the SPArc_ps comparisons are recomputed here. However, the delivery-efficiency result is the training objective itself, and the 'unchanged ELS is more efficient' observation is definitional. These two steps make the paper partially circular, but the central dosimetric content gives it independent substance, hence score 6.

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

The model introduces no new physical entities. The free parameters are the three loss weights tuned on the validation data. The core assumptions are the spot-count/dose proxy and the fidelity of the ELS time table.

free parameters (3)
  • gamma (loss weight for CTV spot-count CE) = 0.5
    Tuned to balance target coverage objective against OAR and time objectives.
  • zeta (loss weight for CTV-BRS spot-count CE) = 0.4
    Tuned to weight OAR-sparing objective.
  • upsilon (loss weight for ELS time penalty) = 0.1
    Tuned to weight delivery efficiency objective.
assumptions (3)
  • domain assumption Spot count intersecting a structure approximates dose coverage.
    Section 2.2 states 'We approximate the dose coverage of a volume as the number of spots intersection with the volume.' This is the basis for the input representation and loss functions.
  • domain assumption matRad provides the complete initial set of energy layers that span the target at all gantry angles.
    Section 2.1 says the method 'depends on the open-source software matRad' to generate the initial complete range of EL. The quality of the final plan depends on this initial set.
  • domain assumption The ELS time table accurately represents the delivery time costs for energy switches.
    Section 2.3 uses the ELS time table T_Bels to compute Ltime and report ELS time savings. The table itself is not provided numerically.

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Cite this review

Pith. "Pith review of Unsupervised deep learning model for fast energy layer pre-selection of delivery-efficient proton arc therapy plan optimization of nasopharyngeal carcinoma." pith.science (2026). https://pith.science/paper/NY4UR7EX

@misc{pith2026250615803,
  author       = {Pith},
  title        = {Pith review of: Unsupervised deep learning model for fast energy layer pre-selection of delivery-efficient proton arc therapy plan optimization of nasopharyngeal carcinoma},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/NY4UR7EX}},
  note         = {Machine review of arXiv:2506.15803}
}
read the original abstract

Proton arc therapy (PAT) is an emerging and promising modality in radiotherapy, offering improved dose distribution and treatment robustness over intensity-modulated proton therapy. Yet, identifying the optimal energy layer (EL) sequence remains challenging due to the intensive computational demand and prolonged treatment delivery time. This study proposes an unsupervised deep learning model for fast EL pre-selection that minimizes EL switch (ELS) time while maintaining high plan quality. We introduce a novel data representation method, spot-count representation, which encodes the number of proton spots intersecting the target and organs at risk (OAR) in a matrix structured by sorted gantry angles and energy layers. This representation serves as the input of an U-Net style architecture, SPArc_dl, which is trained using a tri-objective function: maximizing spot-counts on target, minimizing spot-counts on OAR, and reducing ELS time. The model is evaluated on 35 nasopharyngeal cancer cases, and its performance is compared to SPArc_particle_swarm (SPArc_ps). SPArc_dl produces EL pre-selection that significantly improves both plan quality and delivery efficiency. Compared to SPArc_ps, it enhances the conformity index by 0.1 (p<0.01), reduces the homogeneity index by 0.71 (p<0.01), lowers the brainstem mean dose by 0.25 (p<0.01), and shortens the ELS time by 37.2% (p < 0.01). The results unintentionally reveal employing unchanged ELS is more time-wise efficient than descended ELS. SPArc_dl's inference time is within 1 second. However, SPArc_dl plan demonstrates limitation in robustness. The proposed spot-count representation lays a foundation for incorporating unsupervised deep learning approaches into EL pre-selection task. SPArc_dl is a fast tool for generating high-quality PAT plans by strategically pre-selecting EL to reduce delivery time while maintaining excellent dosimetric performance.

Figures

Figures reproduced from arXiv: 2506.15803 by the authors.

Figure 1
Figure 1. (a) SPArcdl model architecture. (b) ELS time table displays the time cost when the gantry angle transitions from g − 1 to g. the network, h. In our experiments, the input is of size 2 × 72 × 103, but the network is not limited to this specific size. This input dimension happens to be similar to the dimensionality of an image, which is channel × height × width. In the past decade, many deep learning architecture have… view at source ↗
Figure 2
Figure 2. Comparison of various metrics performance of 35 cases between SPArc [PITH_FULL_IMAGE:figures/full_fig_p009_2.png] view at source ↗
Figure 3
Figure 3. Comparison of SPArcdl and SPArcps plans. (a) The predicted EL delivery sequences of SPArcps and SPArcdl. (b) The dose map overlaid on CT slice with six metrics for each case in nominal distribution. (c) The DVH band and RVH curve of the corresponding cases for robustness analysis. BRS stands for brainstem [PITH_FULL_IMAGE:figures/full_fig_p011_3.png] view at source ↗

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Works this paper leans on

31 extracted references · 29 canonical work pages

  1. [1]

    Physics in Medicine & Biology 65(16), 165006 (2020)

    Bertolet, A., Carabe, A.: Proton monoenergetic arc therapy (pmat) to enhance letd within the target. Physics in Medicine & Biology 65(16), 165006 (2020)

    work page 2020

  2. [2]

    Physics in Medicine & Biology 59(21), 6341 (2014)

    Cao, W., Lim, G., Liao, L., Li, Y., Jiang, S., Li, X., Li, H., Suzuki, K., Zhu, X.R., Gomez, D., et al.: Proton energy optimization and reduction for intensity-modulated proton therapy. Physics in Medicine & Biology 59(21), 6341 (2014)

    work page 2014

  3. [3]

    Physics in Medicine & Biology 65(16), 165002 (2020)

    Carabe, A., Karagounis, I.V., Huynh, K., Bertolet, A., Fran¸ cois, N., Kim, M.M., Maity, A., Abel, E., Dale, R.: Radiobiological effectiveness difference of proton arc beams versus conventional proton and photon beams. Physics in Medicine & Biology 65(16), 165002 (2020)

    work page 2020

  4. [4]

    Radiation Oncology 15, 1–11 (2020)

    Chang, S., Liu, G., Zhao, L., Dilworth, J.T., Zheng, W., Jawad, S., Yan, D., Chen, P., Stevens, C., Kabolizadeh, P., et al.: Feasibility study: spot-scanning proton arc therapy (sparc) for left-sided whole breast radiotherapy. Radiation Oncology 15, 1–11 (2020)

    work page 2020

  5. [5]

    Acta Oncologica 57(3), 435–437 (2018)

    Ding, X., Li, X., Qin, A., Zhou, J., Yan, D., Stevens, C., Krauss, D., Kabolizadeh, P.: Have we reached proton beam therapy dosimetric limitations?–a novel robust, delivery-efficient and continuous spot-scanning proton arc (sparc) therapy is to improve the dosimetric outcome in treating prostate cancer. Acta Oncologica 57(3), 435–437 (2018)

    work page 2018

  6. [6]

    International Journal of Radiation Oncology* Biology* Physics 96(5), 1107–1116 (2016)

    Ding, X., Li, X., Zhang, J.M., Kabolizadeh, P., Stevens, C., Yan, D.: Spot-scanning proton arc (sparc) therapy: the first robust and delivery-efficient spot-scanning proton arc therapy. International Journal of Radiation Oncology* Biology* Physics 96(5), 1107–1116 (2016)

    work page 2016

  7. [7]

    Acta Oncologica 58(4), 483–490 (2019)

    Ding, X., Zhou, J., Li, X., Blas, K., Liu, G., Wang, Y., Qin, A., Chinnaiyan, P., Yan, D., Stevens, C., et al.: Improving dosimetric outcome for hippocampus and cochlea sparing whole brain radiotherapy using spot-scanning proton arc therapy. Acta Oncologica 58(4), 483–490 (2019)

    work page 2019

  8. [8]

    Medical physics 37(1), 154–163 (2010)

    Gillin, M.T., Sahoo, N., Bues, M., Ciangaru, G., Sawakuchi, G., Poenisch, F., Arjomandy, B., Martin, C., Titt, U., Suzuki, K., et al.: Commissioning of the discrete spot scanning proton beam delivery system at the university of texas md anderson cancer center, proton therapy center, houston. Medical physics 37(1), 154–163 (2010)

    work page 2010

Show all 31 references
  1. [9]

    Nature Reviews Clinical Oncology 17(12), 771–781 (2020)

    Huynh, E., Hosny, A., Guthier, C., Bitterman, D.S., Petit, S.F., Haas-Kogan, D.A., Kann, B., Aerts, H.J., Mak, R.H.: Artificial intelligence in radiation oncology. Nature Reviews Clinical Oncology 17(12), 771–781 (2020)

    2020

  2. [10]

    Radiation Oncology 13, 1–9 (2018)

    Li, X., Kabolizadeh, P., Yan, D., Qin, A., Zhou, J., Hong, Y., Guerrero, T., Grills, I., Stevens, C., Ding, X.: Improve dosimetric outcome in stage iii non-small-cell lung cancer treatment using spot-scanning proton arc (sparc) therapy. Radiation Oncology 13, 1–9 (2018)

    2018

  3. [11]

    Radiation Oncology 15, 1–11 (2020)

    Liu, G., Li, X., Qin, A., Zheng, W., Yan, D., Zhang, S., Stevens, C., Kabolizadeh, P., Ding, X.: Improve the dosimetric outcome in bilateral head and neck cancer (hnc) treatment using spot- scanning proton arc (sparc) therapy: a feasibility study. Radiation Oncology 15, 1–11 (2020)

    2020

  4. [12]

    Acta Oncologica 59(10), 1178–1185 (2020)

    Liu, G., Li, X., Zhao, L., Zheng, W., Qin, A., Zhang, S., Stevens, C., Yan, D., Kabolizadeh, P., Ding, X.: A novel energy sequence optimization algorithm for efficient spot-scanning proton arc (sparc) treatment delivery. Acta Oncologica 59(10), 1178–1185 (2020)

    2020

  5. [13]

    Frontiers in oncology 11, 664455 (2021) Preprint 16

    Liu, G., Zhao, L., Qin, A., Grills, I., Deraniyagala, R., Stevens, C., Zhang, S., Yan, D., Li, X., Ding, X.: Lung stereotactic body radiotherapy (sbrt) using spot-scanning proton arc (sparc) therapy: a feasibility study. Frontiers in oncology 11, 664455 (2021) Preprint 16

    2021

  6. [14]

    arXiv preprint arXiv:1711.05101 (2017)

    Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. arXiv preprint arXiv:1711.05101 (2017)

    2017 arXiv

  7. [15]

    Medical Physics 51(7), 4970–4981 (2024)

    Ma, Y., Li, Y., Xu, P., Zhang, H., Zhang, X., Liu, X., Li, Q.: A machine learning-based approach to predict energy layer for each field in spot-scanning proton arc therapy for lung cancer: A feasibility study. Medical Physics 51(7), 4970–4981 (2024)

    2024

  8. [16]

    Advances in neural information processing systems 32 (2019)

    Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: An imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019)

    2019

  9. [17]

    Physics in Medicine & Biology 69(1), 015004 (2023)

    Qian, Y., Fan, Q., Dao, R., Li, X., Yang, Z., Zhang, S., Yang, K., Quan, H., Tu, B., Ding, X., et al.: A novel planning framework for efficient spot-scanning proton arc therapy via particle swarm optimization (sparc-particle swarm). Physics in Medicine & Biology 69(1), 015004 (2023)

    2023

  10. [18]

    In: Medical image computing and computer-assisted intervention–MICCAI 2015: 18th international conference, Munich, Germany, October 5-9, 2015, proceedings, part III 18

    Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: Medical image computing and computer-assisted intervention–MICCAI 2015: 18th international conference, Munich, Germany, October 5-9, 2015, proceedings, part III 18. pp....

    2015

  11. [19]

    Physics in Medicine & Biology 61(21), N565 (2016)

    Sanchez-Parcerisa, D., Kirk, M., Fager, M., Burgdorf, B., Stowe, M., Solberg, T., Carabe, A.: Range optimization for mono-and bi-energetic proton modulated arc therapy with pencil beam scanning. Physics in Medicine & Biology 61(21), N565 (2016)

    2016

  12. [20]

    International journal of radiation oncology, biology, physics 38(4), 891–897 (1997)

    Sandison, G.A., Papiez, E., Bloch, C., Morphis, J.: Phantom assessment of lung dose from proton arc therapy. International journal of radiation oncology, biology, physics 38(4), 891–897 (1997)

    1997

  13. [21]

    International Journal of Radiation Oncology* Biology* Physics 87(1), 188–194 (2013)

    Seco, J., Gu, G., Marcelos, T., Kooy, H., Willers, H.: Proton arc reduces range uncertainty effects and improves conformality compared with photon volumetric modulated arc therapy in stereotactic body radiation therapy for non-small cell lung cancer. International Journal of R...

    2013

  14. [22]

    Medical physics 36(9Part1), 4068–4083 (2009)

    Smith, A., Gillin, M., Bues, M., Zhu, X.R., Suzuki, K., Mohan, R., Woo, S., Lee, A., Komaki, R., Cox, J., et al.: The md anderson proton therapy system. Medical physics 36(9Part1), 4068–4083 (2009)

    2009

  15. [23]

    International Journal of Radiation Oncology* Biology* Physics 92(2), 460–468 (2015)

    Van De Water, S., Kooy, H.M., Heijmen, B.J., Hoogeman, M.S.: Shortening delivery times of intensity modulated proton therapy by reducing proton energy layers during treatment plan optimization. International Journal of Radiation Oncology* Biology* Physics 92(2), 460–468 (2015)

    2015

  16. [24]

    International Journal of Radiation Oncology* Biology* Physics 37(3), 731–736 (1997)

    Van’t Riet, A., Mak, A.C., Moerland, M.A., Elders, L.H., Van Der Zee, W.: A conformation number to quantify the degree of conformality in brachytherapy and external beam irradiation: application to the prostate. International Journal of Radiation Oncology* Biology* Physics 37(...

    1997

  17. [25]

    Physics in Medicine and Biology (2025)

    Wang, S., Fan, Q., Zhao, L., Korevaar, E.W., Ding, X., Zhang, S., Yang, K., Qin, Y., de Jong, B., Both, S., et al.: A novel energy layer optimization strategy based on machine specific delivery sequence for proton arc therapy. Physics in Medicine and Biology (2025)

    2025

  18. [26]

    Medical physics 44(6), 2556–2568 (2017)

    Wieser, H.P., Cisternas, E., Wahl, N., Ulrich, S., Stadler, A., Mescher, H., M¨ uller, L.R., Klinge, T., Gabrys, H., Burigo, L., et al.: Development of the open-source dose calculation and optimization toolkit matrad. Medical physics 44(6), 2556–2568 (2017)

    2017

  19. [27]

    i: dosimetric results

    Wu, Q., Mohan, R., Morris, M., Lauve, A., Schmidt-Ullrich, R.: Simultaneous integrated boost intensity-modulated radiotherapy for locally advanced head-and-neck squamous cell carcinomas. i: dosimetric results. International Journal of Radiation Oncology* Biology* Physics 56(2)...

    2003

  20. [28]

    Physics in Medicine and Biology (2025)

    Wuyckens, S., Janssens, G., Vera, M.C., Sundstr¨ om, J., Di Perri, D., Sterpin, E., Souris, K., Lee, J.A.: Proton arc therapy plan optimization with energy layer pre-selection driven by organ at risk sparing and delivery time. Physics in Medicine and Biology (2025)

    2025

  21. [29]

    Computers in biology and medicine 148, Preprint 17 105609 (2022)

    Wuyckens, S., Saint-Guillain, M., Janssens, G., Zhao, L., Li, X., Ding, X., Sterpin, E., Lee, J.A., Souris, K.: Treatment planning in arc proton therapy: Comparison of several optimization problem statements and their corresponding solvers. Computers in biology and medicine 14...

    2022

  22. [30]

    Medical physics 49(9), 5752–5762 (2022)

    Zhang, G., Shen, H., Lin, Y., Chen, R.C., Long, Y., Gao, H.: Energy layer optimization via energy matrix regularization for proton spot-scanning arc therapy. Medical physics 49(9), 5752–5762 (2022)

    2022

  23. [31]

    Medical physics 37(3), 1210–1219 (2010)

    Zhu, X., Sahoo, N., Zhang, X., Robertson, D., Li, H., Choi, S., Lee, A., Gillin, M.: Intensity modulated proton therapy treatment planning using single-field optimization: the impact of monitor unit constraints on plan quality. Medical physics 37(3), 1210–1219 (2010)

    2010

Pith tools

Reviewed August 6, 2026 · model on record in the stance chip above.