Margin and robustness settings for a library-of-plans IMPT strategy for locally advanced cervical cancer.

Objective.This study aims to determine a margin and robustness setting for treating locally advanced cervical cancer (LACC) with a library-of-plans (LoP) based online-adaptive intensity-modulated proton therapy (IMPT).Approach.We analyzed 13 LACC patients with delineated planning and weekly repeat CT scans (reCTs). For each patient, 120 IMPT treatments of 25 fractions were simulated with a LoPs approach. Six different robustness settings (2-7 mm set-up robustness (SR) plus 3% range robustness (RR)) were used to create those 120 IMPT plans. Each fraction was simulated with a weekly reCT, combined with the sampling of inter- and intrafraction treatment uncertainties. The fraction doses were accumulated to obtain a treatment dose to the target volumes, distinguishing between the low-risk clinical target volume (CTV-T-LR) and the elective CTV (CTV-E). If one of the two targets obtained an adequate coverage for more than 90% of the treatments, different anisotropic margins were sampled on top of the robustness setting to the other target to obtain the Pareto-optimal margin in terms of adequate coverage versus increase in target volume.Main results.The percentage of treatments that reach the dose criterionV42.75Gy> 95% for the CTV-T-LR was 22.3%, 28.5%, 51.2%, 73.1%, 85.3%, and 90.0% for 2, 3, 4, 5, 6, and 7 mm SR plus 3% RR and for the CTV-E, this percentage was 60.4%, 73.8%, 86.5%, 92.3%, 96.9%, and 98.5%. The Pareto-optimal margin combined with a 5 mm/3% robustness setting for the CTV-T-LR with an adequate coverage for >90% of the treatments was given by {0, 1, 0, 3, 3, 0} mm in the left, right, anterior, posterior, cranial, caudal direction.Significance.Our study evaluated combinations of robustness and anisotropic margin settings for IMPT for LACC. With 5 mm SR and 3% RR for CTV-E and CTV-T-LR plus a margin to the CTV-T-LR of {0, 1, 0, 3, 3, 0} mm in left, right, anterior, posterior, cranial, and caudal ensured an adequate coverage for >90% of the simulated IMPT treatments.

Kuipers SC ，Godart J ，Negenman EM ，Corbeau A ，Zolnay AG ，Deuzeman HH ，de Boer SM ，Nout RA ，Hoogeman MS ... -  《-》

Assessment of robustness against setup uncertainties using probabilistic scenarios in lung cancer: a comparison of proton with photon therapy.

Teoh S ，George B ，Fiorini F ，Vallis KA ，Van den Heuvel F ... -  《-》

Exploring the dosimetric impact of systematic and random setup uncertainties in robust optimization of head and neck IMPT plans.

This study aims to compare the dosimetric impact of incorporating systematic and random setup uncertainties in the robust optimization of head and neck cancer (HNC) Intensity Modulated Proton Therapy (IMPT) plans. Bilateral HNC patients (n = 10) previously treated with conventional photon therapy at our institution were included. Both systematic and random setup uncertainties were incorporated into the robust optimization process of IMPT planning. Dosimetric comparisons were made between plans optimized with systematic (IMPT-S) versus random (IMPT-R) setup uncertainties, assessing both the clinical target volume (CTVs) and organs at risk (OARs) across various dosimetric metrics. Both plans applied a fixed range uncertainty of ± 3 % and a maximum setup uncertainty of ± 3 mm. Both IMPT-S and IMPT-R plans achieved similar target coverage, meeting robustness criteria for CTVs. On average, the D95% voxel-wise min to the high-risk CTV (CTV_HR) was slightly higher in IMPT-S plans by 1.78 ± 0.72 % compared to IMPT-R plans. However, IMPT-R plans provided better OAR sparing, which was evident in both nominal and voxel-wise maximum values. While random setup errors in robust optimization improved OAR sparing, the clinical impact may be minimal where OAR doses are already well below tolerance levels. Both IMPT-S and IMPT-R techniques met the robustness criteria for CTVs in HNC IMPT planning. Incorporating random setup uncertainties in robust optimization improves OAR sparing compared to systematic setup uncertainties. Further research is needed to explore the broader applicability of random setup errors and to integrate random uncertainties in robustness evaluations for a more comprehensive assessment of treatment plans.

Rana S ，Padannayil NM ，Zeidan Y ，Pokharel S ，Richter S ，Kasper M ，Saeed H ... -  《-》

The impact of setup errors on dose distribution in cervical cancer radiotherapy and the margin from CTV to PTV.

This study calculates the needed margin from clinical target volume (CTV) to planning target volume (PTV) in IMRT for cervical cancer. It also assesses the impact of setup errors on target and organ at risk (OAR) dose distribution. We retrospectively analyzed 50 cervical cancer patients who underwent IMRT, with 210 CBCT scans. We calculated the CTV-to-PTV margin and simulated setup errors in the TPS to reassess dose distribution impacts on targets and OAR. Setup errors in X(anterior-posterior,AP), Y(cranial-caudal,CC), and Z(left-right,LR) directions were (1.4 ± 1.0) mm, (2.3 ± 1.5) mm, and (1.9 ± 1.2) mm, respectively, leading to CTV-to-PTV margins of 4.4 mm, 6.4 mm, and 5.8 mm. X-axis errors did not significantly affect target dosimetry (P > 0.05), but Y and Z errors did (P < 0.05). X-axis errors impacted the small intestine and rectum (P < 0.05), Y-axis errors mainly affected the colon (P < 0.05), and Z-axis errors affected the colon, small intestine, and rectum (P < 0.05). Our study underscores the need to account for setup errors in radiotherapy for cervical cancer. Customizing the CTV-to-PTV margin based on institutional error data is key to maintaining target dose coverage and optimizing treatment outcomes.

Li Z ，Cheng Y ，Dong J ，Han L ，Chen L ，Huang S ，Zhang M ，Wu M ，Kong F ，Yan H ... -  《-》

Comparison of Two Modern Survival Prediction Tools, SORG-MLA and METSSS, in Patients With Symptomatic Long-bone Metastases Who Underwent Local Treatment With Surgery Followed by Radiotherapy and With Radiotherapy Alone.

Survival estimation for patients with symptomatic skeletal metastases ideally should be made before a type of local treatment has already been determined. Currently available survival prediction tools, however, were generated using data from patients treated either operatively or with local radiation alone, raising concerns about whether they would generalize well to all patients presenting for assessment. The Skeletal Oncology Research Group machine-learning algorithm (SORG-MLA), trained with institution-based data of surgically treated patients, and the Metastases location, Elderly, Tumor primary, Sex, Sickness/comorbidity, and Site of radiotherapy model (METSSS), trained with registry-based data of patients treated with radiotherapy alone, are two of the most recently developed survival prediction models, but they have not been tested on patients whose local treatment strategy is not yet decided. (1) Which of these two survival prediction models performed better in a mixed cohort made up both of patients who received local treatment with surgery followed by radiotherapy and who had radiation alone for symptomatic bone metastases? (2) Which model performed better among patients whose local treatment consisted of only palliative radiotherapy? (3) Are laboratory values used by SORG-MLA, which are not included in METSSS, independently associated with survival after controlling for predictions made by METSSS? Between 2010 and 2018, we provided local treatment for 2113 adult patients with skeletal metastases in the extremities at an urban tertiary referral academic medical center using one of two strategies: (1) surgery followed by postoperative radiotherapy or (2) palliative radiotherapy alone. Every patient's survivorship status was ascertained either by their medical records or the national death registry from the Taiwanese National Health Insurance Administration. After applying a priori designated exclusion criteria, 91% (1920) were analyzed here. Among them, 48% (920) of the patients were female, and the median (IQR) age was 62 years (53 to 70 years). Lung was the most common primary tumor site (41% [782]), and 59% (1128) of patients had other skeletal metastases in addition to the treated lesion(s). In general, the indications for surgery were the presence of a complete pathologic fracture or an impending pathologic fracture, defined as having a Mirels score of ≥ 9, in patients with an American Society of Anesthesiologists (ASA) classification of less than or equal to IV and who were considered fit for surgery. The indications for radiotherapy were relief of pain, local tumor control, prevention of skeletal-related events, and any combination of the above. In all, 84% (1610) of the patients received palliative radiotherapy alone as local treatment for the target lesion(s), and 16% (310) underwent surgery followed by postoperative radiotherapy. Neither METSSS nor SORG-MLA was used at the point of care to aid clinical decision-making during the treatment period. Survival was retrospectively estimated by these two models to test their potential for providing survival probabilities. We first compared SORG to METSSS in the entire population. Then, we repeated the comparison in patients who received local treatment with palliative radiation alone. We assessed model performance by area under the receiver operating characteristic curve (AUROC), calibration analysis, Brier score, and decision curve analysis (DCA). The AUROC measures discrimination, which is the ability to distinguish patients with the event of interest (such as death at a particular time point) from those without. AUROC typically ranges from 0.5 to 1.0, with 0.5 indicating random guessing and 1.0 a perfect prediction, and in general, an AUROC of ≥ 0.7 indicates adequate discrimination for clinical use. Calibration refers to the agreement between the predicted outcomes (in this case, survival probabilities) and the actual outcomes, with a perfect calibration curve having an intercept of 0 and a slope of 1. A positive intercept indicates that the actual survival is generally underestimated by the prediction model, and a negative intercept suggests the opposite (overestimation). When comparing models, an intercept closer to 0 typically indicates better calibration. Calibration can also be summarized as log(O:E), the logarithm scale of the ratio of observed (O) to expected (E) survivors. A log(O:E) > 0 signals an underestimation (the observed survival is greater than the predicted survival); and a log(O:E) < 0 indicates the opposite (the observed survival is lower than the predicted survival). A model with a log(O:E) closer to 0 is generally considered better calibrated. The Brier score is the mean squared difference between the model predictions and the observed outcomes, and it ranges from 0 (best prediction) to 1 (worst prediction). The Brier score captures both discrimination and calibration, and it is considered a measure of overall model performance. In Brier score analysis, the "null model" assigns a predicted probability equal to the prevalence of the outcome and represents a model that adds no new information. A prediction model should achieve a Brier score at least lower than the null-model Brier score to be considered as useful. The DCA was developed as a method to determine whether using a model to inform treatment decisions would do more good than harm. It plots the net benefit of making decisions based on the model's predictions across all possible risk thresholds (or cost-to-benefit ratios) in relation to the two default strategies of treating all or no patients. The care provider can decide on an acceptable risk threshold for the proposed treatment in an individual and assess the corresponding net benefit to determine whether consulting with the model is superior to adopting the default strategies. Finally, we examined whether laboratory data, which were not included in the METSSS model, would have been independently associated with survival after controlling for the METSSS model's predictions by using the multivariable logistic and Cox proportional hazards regression analyses. Between the two models, only SORG-MLA achieved adequate discrimination (an AUROC of > 0.7) in the entire cohort (of patients treated operatively or with radiation alone) and in the subgroup of patients treated with palliative radiotherapy alone. SORG-MLA outperformed METSSS by a wide margin on discrimination, calibration, and Brier score analyses in not only the entire cohort but also the subgroup of patients whose local treatment consisted of radiotherapy alone. In both the entire cohort and the subgroup, DCA demonstrated that SORG-MLA provided more net benefit compared with the two default strategies (of treating all or no patients) and compared with METSSS when risk thresholds ranged from 0.2 to 0.9 at both 90 days and 1 year, indicating that using SORG-MLA as a decision-making aid was beneficial when a patient's individualized risk threshold for opting for treatment was 0.2 to 0.9. Higher albumin, lower alkaline phosphatase, lower calcium, higher hemoglobin, lower international normalized ratio, higher lymphocytes, lower neutrophils, lower neutrophil-to-lymphocyte ratio, lower platelet-to-lymphocyte ratio, higher sodium, and lower white blood cells were independently associated with better 1-year and overall survival after adjusting for the predictions made by METSSS. Based on these discoveries, clinicians might choose to consult SORG-MLA instead of METSSS for survival estimation in patients with long-bone metastases presenting for evaluation of local treatment. Basing a treatment decision on the predictions of SORG-MLA could be beneficial when a patient's individualized risk threshold for opting to undergo a particular treatment strategy ranged from 0.2 to 0.9. Future studies might investigate relevant laboratory items when constructing or refining a survival estimation model because these data demonstrated prognostic value independent of the predictions of the METSSS model, and future studies might also seek to keep these models up to date using data from diverse, contemporary patients undergoing both modern operative and nonoperative treatments. Level III, diagnostic study.

Lee CC ，Chen CW ，Yen HK ，Lin YP ，Lai CY ，Wang JL ，Groot OQ ，Janssen SJ ，Schwab JH ，Hsu FM ，Lin WH ... -  《-》