Development and validation of an automated Tomotherapy planning method for cervical cancer

Development and validation of an automated Tomotherapy planning method for cervical cancer | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Development and validation of an automated Tomotherapy planning method for cervical cancer Feiru Han, Yi Xue, Sheng Huang, Tong Lu, Yining Yang, Yuanjie Cao, and 7 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4328154/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract Purpose This study aimed to develop an automated Tomotherapy (TOMO) planning method for cervical cancer treatment, and to validate its feasibility and effectiveness. Materials and Methods The study enrolled 30 cervical cancer patients treated with TOMO at our center. Utilizing scripting and Python environment within the RayStation (RaySearch Labs, Sweden) treatment planning system (TPS), we developed automated planning methods for TOMO and volumetric modulated arc therapy (VMAT) techniques. The clinical manual TOMO (M-TOMO) plans for the 30 patients were re-optimized using automated planning scripts for both TOMO and VMAT, creating automated TOMO (A-TOMO) and automated VMAT (A-VMAT) plans. we compared it with M-TOMO and A-VMAT plans. The primary evaluated relevant dosimetric parameters and treatment plan efficiency were assessed using the two-sided Wilcoxon signed-rank test for statistical analysis,with a P-value < 0.05 indicating statistical significance. Results A-TOMO plans maintained similar target dose uniformity compared to M-TOMO plans, with improvements in target conformity and faster dose drop-off outside the target, and demonstrated significant statistical differences (P + <0.01). A-TOMO plans also significantly outperformed M-TOMO plans in reducing V 50Gy , V 40Gy , and D mean for the bladder and rectum, as well as D mean for the bowel bag, femoral heads, and kidneys (all P + <0.05). Additionally, A-TOMO plans demonstrated better consistency in plan quality. Furthermore, the quality of A-TOMO plans was comparable to or superior than A-VMAT plans. In terms of efficiency, A-TOMO significantly reduced the time required for treatment planning to approximately 20 minutes. Conclusion We have successfully developed an A-TOMO planning method for cervical cancer. Compared to M-TOMO plans, A-TOMO plans improved target conformity and reduced radiation dose to OARs. Additionally, the quality of A-TOMO plans was on par with or surpasses that of A-VMAT plans. The A-TOMO planning method significantly improved the efficiency of treatment planning. Automated Tomotherpay Volumetric Modulated Arc Therapy radiotherapy Treatment planning Cervial cancer Figures Figure 1 Figure 2 Figure 3 Figure 4 I. Introduction Cervical cancer ranks as the fourth most prevalent malignant tumor among women globally [ 1 ] . Radiotherapy has served as a critical treatment modality for patients with locally advanced, lymph node-positive, and/or high-risk cervical cancer [ 2 – 5 ] , as well as the standard regimen for postoperative adjuvant therapy [ 6 , 7 ] . Radiotherapy for cervical cancer could lead to severe toxic reactions in the gastrointestinal, urinary, and hematological systems [ 8 , 9 ] . Optimizing dose distribution with advanced radiotherapy techniques could help to minimize the side effects [ 10 ] . Tomotherapy (TOMO) and volumetric modulated arc therapy (VMAT) are two widely used and effective external beam radiotherapy techniques for treating cervical cancer [ 11 , 12 ] . In clinical practice, radiotherapy dosimetrists manually created treatment plans using treatment planning systems (TPS), which often require optimization with multiple iterations and trial-and-error to meet dose constraints. However, due to the large treatment area required for cervical cancer, a delicate balance between achieving sufficient tumor coverage and protecting organs at risk (OARs) is necessary. Furthermore, the variations in patients anatomy make the process of manual planning complex, cumbersome, and time-consuming [ 13 , 14 ] . The variations of planner's expertise, execution standards, and effort expended, potentially lead to inconsistent plan quality, which not only increase the risk of toxicity to OARs but also complicate the outcomes and interpretations of clinical trials [ 15 ] . Therefore, it is essential to enhance the plan quality and consistency. The application of automated radiotherapy planning allows for procedures to be executed with as little human intervention as possible, effectively improving the quality, efficiency, and consistency of radiotherapy planning [ 16 ] . There are primarily two types of strategies for automated planning: strategies based on atlas prediction of optimization objectives and script-based strategies that emulate manual optimization. The atlas-based methods use data from previous radiotherapy plans to train models that predict dose distribution for new patients [ 13 ] . However, this method is highly dependent on the training datasets [ 17 ] , and tends to struggle when adapting to new cases that differ significantly from the training data. In contrast, the script-based methods don’t require a prior training or learning step [ 18 , 19 ] . In the radiotherapy of cervical cancer, particularly with the VMAT technique, mature automated planning methods have already been developed and have been proven to achieve better plan quality and greater efficiency than manual planning [ 20 – 23 ] . However, research into the automated planning for TOMO plans in cervical cancer remains unreported, suggesting room for improvement and development. It is well known that the Precision (Accuray Inc., Sunnyvale, CA, USA) TPS for TOMO lacks the function for implementing scripts, whereas RayStation (RaySearch Labs, Sweden) TPS possesses functions for TOMO planning and scripting. In this study, we developed a script-based automated TOMO (A-TOMO) planning method for external beam radiotherapy in cervical cancer using RayStation TPS. And the A-TOMO plans were compared with those from manual TOMO (M-TOMO) planning and automated VMAT (A-VMAT) planning to validate its feasibility and effectiveness. II. Materials and Methods A. Patients In this study, we consecutively selected 30 cervical cancer patients who underwent TOMO treatment at our center in 2023. This study strictly adhered to the ethical principles outlined in the Declaration of Helsinki and the protocol was approved by the institutional review board and ethics committee at ####Cancer Institute & Hospital. All participants provided written informed consent. Among these 30 patients, 26 patients underwent definitive radiotherapy, and 4 received postoperative radiotherapy. All the TOMO plans were manually designed, with all patients receiving a dose of 50.4 Gy in 28 fractions. The patients were treated in two positions: 12 in the prone position and 18 in the supine position. The average volume of the planning target volume (PTV) was 1347.45 ± 179.28 cc, ranging from 1122.82 to 1862.99 cc. The average overlapping rate of the bladder in the PTV was 37 ± 11%, with a range from 13–64%. For the rectum, the average overlap rate with the PTV was 52 ± 13%, ranging from 24–72%. The average overlapping volume of the bowel bag in the PTV was 115.79 ± 52.09 cc, ranging from 8.05 to 205.67 cc. B. CT simulation and Contouring CT scans were performed to obtain images with a thickness of 5mm. To ensure bladder filling, patients were instructed to drink 500 ml of water 30 minutes before the scans. Following the radiotherapy oncology group (RTOG) guidelines, radiotherapy oncologists delineated the targets and OARs on the CT images, including the clinical target volume (CTV), PTV, bladder, rectum, bowel bag, femoral heads, and kidneys. For patients undergoing definitive radiotherapy, the CTV encompassed the uterus and its lymphatic drainage area. For those receiving postoperative radiotherapy, the CTV included the lymphatic drainage area of the radical uterus. The PTV was defined by expanding the CTV by 7 mm in all three dimensions. C. Radiotherapy planning C.1. Radiotherapy planning protocol The radiotherapy planning protocol in our center adhered to the dose limits for the PTV and OARs as recommended by the National Comprehensive Cancer Network (NCCN) guidelines and the recommendations of Quantitative Analysis of Normal Tissue Effects in the Clinic (QUANTEC), as detailed in Table 1 . Here, D max denoted the maximum dose, V xGy represented the volume receiving x Gy in the dose volume histogram (DVH), and D mean indicated the mean dose. This protocol of dose volume limits ensured the priority fulfillment of hard constraints for indexes, which meant that in case of conflicts between OARs protection and PTV coverage, partial prescription coverage of PTV could be compromised. Table 1 Target and OAR dose constraints ROI Index Constraint Type PTV D max ≤ 115% Hard D max ≤ 110% Soft V 50.4Gy ≥ 95% Soft bladder V 50Gy < 50% Hard V 40Gy < 60% Soft rectum V 50Gy < 50% Hard V 40Gy < 60% Soft bowel bag V 45Gy < 195cc Hard Femoral_Head (L,R) V 50Gy < 5% Hard kidney(L,R) D mean < 15Gy Hard kidney(L,R) D mean < 10Gy Soft L, Left ; R, Right C.2. M-TOMO planning The clinical M-TOMO plans for the 30 patients were created using the Precision TPS with the TOMO Radixact Linac (Accuray Inc., Sunnyvale, CA, USA). These plans utilized the dynamic jaw mode with a jaw width was set to 2.51 cm. Three plans used a pitch of 0.287, while the remaining 27 plans had a pitch of 0.43. The average modulation factor was 1.99 ± 0.41. Helical mode was employed, and dose calculation was performed using the collapsed cone algorithm, with the final dose calculation was performed in high-resolution mode. The radiotherapy plans for these 30 patients were randomly assigned to 14 dosimetrists, each employing potentially different manual optimization strategies. All plans were approved by oncologists for clinical treatment. C.3. A-TOMO planning C.3.1. Creation of auxiliary structures The method for creating auxiliary structures is detailed in Table 2 . To facilitate meeting clinical dose requirements and to concentrate PTV dose deficits in areas overlapping with OARs as much as possible, we processed these overlapping sections as described in Fig. 1 (A). We ensured that the overlapping volumes of the rectum and bladder in the PTV were less than or equal to 45% of their total volume, and the overlapping volume of the bowel bag in the PTV was less than or equal to 110 cc, based on clinical requirements and experience. For example, the auxiliary structures PTV new and rectum-ptv, created for preprocessing the overlapping section between the rectum and PTV, are shown in Fig. 2 . To address potential dose control challenges caused by larger bowel volumes, we specifically created an auxiliary structure named "bag" to aid optimization. We set optimization goals based on each patient's bowel volume, calculated as (195/V bag )%, and took the integer part of the value. Table 2 The creation method of auxiliary structures Auxiliary structures Creation method ring0.5 Create ring of PTV with a 5 mm margin ring 1 Create ring of PTV between the 5 mm and 1cm margin ring2 Create ring of PTV between the 1cm to 2 cm margin ring3 Create ring of PTV between the 2 cm to 3 cm margin nt Create subtraction of PTV and PTV rings from the body PTV new Create subtraction of the partial volume of the OAR from the PTV PTV new -3mm Create contraction of PTV new with a 3 mm margin bladder-ptv Create subtraction of PTV new from bladder rectum-ptv Create subtraction of PTV new from rectum bowel bag-ptv Create subtraction of PTV new from bowel bag bag Create the overlap between bowel bag and PTV expanded outward by 2 cm bowel bag_PTV Create the overlap between the bowel bag and the PTV C.3.1. Formulation of A-TOMO planning The A-TOMO planning script was developed using the scripting function in RayStation 11B (RaySearch Labs, Sweden) TPS, within a Python (3.7) environment. This script utilized the standard model of the Radixact Linac for dose optimization and calculation. After verifying contours and region of interest (ROI) names, the CT images were imported into the RayStation system, initiating the automated planning script. The workflow of the A-TOMO planning script is shown in Fig. 1 (B). Firstly, the script automatically selected the CT electron density conversion table and set the outline (body) type to “External”. A dose grid size of 0.3 × 0.3 × 0.3 cm was established. A virtual couch was inserted, and auxiliary structures were created. The script then automatically created a new plan and added beams, adjusting machine parameters, including setting the delivery time factor (DTF) to 1.7, the optimization stopping tolerance to 0, and the maximum number of iterations to 100. The system automatically loaded the optimization objective template, as listed in Table 3 . The entire optimization process consisted of two rounds, each with 100 iterations of optimization. Ultimately, the plan was normalized so that the prescription dose covered 95% of the PTV volume, completing the optimization process. If the final normalized doses to OARs exceeded dose constraints, manual normalization was made by the dosimetrists as needed. Moreover, the A-TOMO planning script defaulted to a pitch of 0.43 and a jaw width of 2.51 cm, employing dynamic jaw mode and the collapsed cone dose calculation algorithm. The average modulation factor for these plans was 1.83 ± 0.17. Table 3 The optimization objective template ROI Type Target (Gy) Volume Weight EUD PTV new Max dose 52.4 1x10 9 PTV new Min DVH 51 97% 5x10 9 PTV new Min DVH 50.4 98% 5x10 9 PTV Max EUD 52 1.5x10 9 A = 150 PTV new -3mm Min dose 51 8x10 9 ring0.5 Max dose 50.4 3x10 8 A = 1 ring1 Max dose 45 3x10 8 A = 1 ring2 Max dose 38 3x10 8 A = 1 ring3 Max dose 28 3x10 8 A = 1 nt Max EUD 20 3x10 8 A = 1 ring1 Max EUD 0 0.01 A = 1 ring2 Max EUD 0 0.01 A = 1 ring3 Max EUD 0 0.01 A = 1 nt Max EUD 0 0.3 A = 1 bag Max DVH 42 (195/V bag )% 3x10 9 bladder Max DVH 38 60% 2x10 9 rectum Max DVH 38 60% 2x10 9 bowel bag_PTV Min dose 40 2x10 9 bladder-ptv Max EUD 0 1 A = 1 rectum-ptv Max EUD 0 0.8 A = 1 bowel bag-ptv Max EUD 0 5 A = 1 FemoralHead_L Max EUD 0 0.5 A = 1 FemoralHead_R Max EUD 0 0.5 A = 1 kidney_L Max EUD 0 1 A = 1 kidney_R Max EUD 0 1 A = 1 C.4. A-VMAT planning The A-VMAT planning script was developed in the RayStation 11B TPS and Python (3.7) environment, employing an optimization strategy similar to that used for the A-TOMO plan. The automated plans were optimized based on a standard model for the Truebeam Linac (Varian Medical Systems, Palo Alto, CA, USA) with 6 MV and a 120-leaf millennium multi-leaf collimator (MLC). In the A-VMAT planning script, two beams with gantry angles from 181° to 179° were automatically created, irradiating in both clockwise and counterclockwise directions. Moreover, the collimator angles were set to 355°. The maximum values for the jaws X1, X2, Y1, Y2 were set to 1, 14, 20, 20, and 14, 1, 20, 20 (cm), respectively, with jaw tracking mode, and the gantry spacing set at 2°. During optimization, the stopping tolerance was set to 0, employing the collapsed cone dose calculation algorithm with a calculation grid size of 0.3 × 0.3 × 0.3 cm. The maximum number of iterations was 100, with the iterations before conversion set at 40. The optimization process included two rounds, each consisting of 100 iterations. The system defaulted to optimizing segment shapes and segment MU. D. Evaluation To evaluate the differences between radiotherapy plans, we conducted comparisons from two perspectives. Firstly, we compared A-TOMO plans with M-TOMO plans; secondly, we compared A-TOMO plans with A-VMAT plans. These comparisons primarily focused on the dosimetric indexes for PTV and OARs. For the assessment of OARs, our analysis concentrated on the dosimetric indexes for the bladder, rectum, and bowel bag (D 0.03cc , V 50Gy %, V 40Gy %, D mean ), FemoralHead_L, FemoralHead_R (V 50Gy %, D mean ) and kidney_L, kidney_R (D mean ). For the PTV, we used D max , conformity index (CI), and homogeneity index (HI) for evaluation. Additionally, we assessed the dose drop-off outside the target volume using gradient index (GI x ) for thresholds of 40Gy, 30Gy, 20Gy, and 10Gy. The CI, HI, GI x were defined in the formulas below: The D 2% , D 98% , and D 98% refered to the doses received by 2%, 98%, and 50% of the PTV volume in the DVH, respectively. A lower HI indicated better uniformity of the dose distribution within the PTV. CI = \(\frac{\text{T}\text{V}\text{P}\text{T}\text{V}\times \text{T}\text{V}\text{P}\text{T}\text{V}}{\text{V}\text{P}\text{T}\text{V} \times \text{V}\text{T}\text{V}\text{P}}\) The TV PTV represented the volume within the PTV encompassed by the prescription dose line, and the V PTV was the volume of the PTV, and the V TVP was the volume encompassed by the prescription dose line. The closer the CI value was to 1, the better the conformity. GI x = \(\frac{\text{V}\text{x}}{\text{V}\text{T}\text{V}\text{P}}\) The V x refered to the volume encompassed by the X Gy dose line, while the V TVP represented the volume encompassed by the prescription dose line. A smaller GI x value indicated a faster dose drop-off outside the target volume. Furthermore, we conducted a statistical analysis on the treatment delivery time as directly displayed by the TPS. To comprehensively evaluate work efficiency, we also assessed the time required to execute the automated treatment planning scripts. E. Statistical analysis Before conducting the statistical analysis, we renormalized the A-TOMO and A-VMAT radiotherapy plans to ensure they matched the prescription dose coverage of PTV as the M-TOMO plans. The data were found not following a normal distribution; hence, we employed the two-sided Wilcoxon signed-rank test as our statistical method, using the wilcoxon function from the scipy.stats library in Python. In this analysis, a P-value less than 0.05 was considered statistically significant. To clearly present and compare the outcomes of different treatment plans, we recorded the mean values and standard deviations of each dataset. III. Results A. A-TOMO Plan versus M-TOMO Plan Comparison In the comparing the A-TOMO plans and M-TOMO plans for the 30 patients, it was found that for 5 patients, neither the manual nor the automated plans could achieve prescription dose coverage of 95% PTV, while meeting the dose constraints for OARs. The dosimetric comparison results are shown in Table 4 . Although both plans demonstrated similar performance in terms of PTV D max and HI (P + =0.92, 0.11), A-TOMO plans significantly surpassed M-TOMO plans in PTV CI (P + <0.01) and achieved greater reductions in OAR doses. Specifically, for the D 0.03cc for the bladder, rectum, and bowel bag, and the V 45Gy for the bowel bag, the two plans were comparable (P + =0.12, 0.57, 0.52 > 0.05; P + =0.11). However, for the V 50Gy %, V 40Gy %, and D mean for the bladder and rectum, as well as the D mean for the bowel bag, kidney_L, and kidney_R, A-TOMO plans were significantly lower than those in M-TOMO plans (P + <0.05). The V 50Gy % for FemoralHead_L and FemoralHead_R in both plans were nearly identical, close to 0. Additionally, the GI 40Gy , GI 30Gy , and GI 20Gy values were significantly lower in A-TOMO plans than in M-TOMO plans (P + <0.01), indicating a faster dose drop-off outside the target volume in A-TOMO plans, thereby exposing normal tissues to lower radiation doses. The mean values ± standard deviations of the dose metrics also indicated better consistency in A-TOMO plans. Figure 3 presents a comparison of representative dose distribution and DVH for key ROIs for a case. B. A-TOMO Plan versus A-VMAT Plan Comparison The dosimetric comparison results between A-TOMO and A-VMAT plans for the 30 patients are shown in Table 4 . The A-VMAT plan showed a slight advantage over the A-TOMO plan in terms of the GI, but it presented slightly higher values for the PTV (D max , D 2% ), bladder (D 0.03cc , V 50Gy %, D mean ), rectum (V 50Gy %, V 40Gy %, D mean ), bowel bag (D 0.03cc , D mean ), FH_L (D mean ), and FH_R (V 50Gy %, D mean ). Although these differences were statistically significant (P − <0.05), the actual numerical differences were within 1Gy and 1%. This suggests that the A-TOMO plan was comparable in overall plan quality to the A-VMAT plan, and may even be superior. Representative dose distributions and DVH for key ROIs of the case are displayed in Fig. 3 . Table 4 Comparison of Dosimetric Indexes for Three Types of Plans ROI Parameter A - TOMO M - TOMO A - VMAT P + P − PTV D max (Gy) 55.53 ± 0.63 55.55 ± 1.79 55.93 ± 0.55 0.92 < 0.01 D 98% (Gy) 48.08 ± 1.90 48.4 ± 2.12 48.24 ± 1.90 < 0.01 0.45 D 2% (Gy) 54.33 ± 0.50 54.19 ± 1.61 54.59 ± 0.62 0.49 < 0.01 CI 0.91 ± 0.03 0.88 ± 0.06 0.90 ± 0.03 < 0.01 0.01 HI 0.12 ± 0.04 0.11 ± 0.06 0.12 ± 0.04 0.11 0.06 bladder D 0.03cc (Gy) 55.19 ± 0.60 54.73 ± 1.66 55.51 ± 0.74 0.12 < 0.01 V 50Gy (%) 33.36 ± 7.32 37.54 ± 7.94 33.9 ± 7.67 < 0.01 < 0.01 V 40Gy (%) 48.23 ± 8.59 63.61 ± 10.34 48.34 ± 8.41 < 0.01 0.16 D mean (Gy) 35.02 ± 4.22 42.69 ± 4.31 35.35 ± 4.10 < 0.01 0.01 rectum D 0.03cc (Gy) 55.28 ± 0.57 55.11 ± 1.70 55.33 ± 0.75 0.57 0.20 V 50Gy (%) 35.50 ± 5.08 40.55 ± 9.53 36.43 ± 5.52 0.03 < 0.01 V 40Gy (%) 53.28 ± 6.38 68.54 ± 11.35 54.76 ± 5.75 < 0.01 < 0.01 D mean (Gy) 36.82 ± 2.40 46.50 ± 10.69 37.86 ± 2.10 < 0.01 < 0.01 bowel bag D 0.03cc (Gy) 55.06 ± 0.54 54.90 ± 1.99 55.42 ± 0.73 0.52 < 0.01 V 45Gy (cc) 141.83 ± 47.03 146.39 ± 42.2 141.76 ± 48.22 0.11 0.38 D mean (Gy) 10.94 ± 3.79 12.88 ± 4.72 11.22 ± 4.00 < 0.01 < 0.01 FH_L V 50Gy (%) 0.01 ± 0.02 0.67 ± 2.37 0.00 ± 0.00 0.89 0.29 D mean (Gy) 15.98 ± 1.73 25.21 ± 6.16 17.81 ± 1.73 < 0.01 < 0.01 FH_R V 50Gy (%) 0.00 ± 0.01 0.08 ± 0.23 0.00 ± 0.01 0.04 0.04 D mean (Gy) 15.51 ± 1.82 25.35 ± 5.73 16.92 ± 2.47 < 0.01 < 0.01 kidney_L D mean (Gy) 0.58 ± 2.83 1.04 ± 3.92 0.6 ± 3.55 < 0.01 0.14 kidney_R D mean (Gy) 0.65 ± 3.48 1.13 ± 4.24 0.63 ± 3.70 < 0.01 0.39 GI GI 40Gy 1.55 ± 0.08 1.72 ± 0.13 1.53 ± 0.10 < 0.01 < 0.01 GI 30Gy 2.30 ± 0.13 2.90 ± 0.26 2.28 ± 0.15 < 0.01 < 0.01 GI 20Gy 4.64 ± 0.29 5.42 ± 0.43 4.71 ± 0.33 < 0.01 < 0.01 GI 10Gy 9.54 ± 1.33 9.55 ± 1.47 9.30 ± 1.23 0.61 < 0.01 P + indicated a comparison between A-TOMO and M-TOMO; P − indicated a comparison between A-TOMO and A-VMAT. FH_L standed for FemoralHead_L, and FH_R standed for FemoralHead_R Workflow Efficiency and Treatment Delivery Time Regarding the workflow efficiency, the execution time for the A-TOMO planning script was approximately only 20 minutes, whereas that for the A-VMAT planning script took slightly longer, about 30 minutes. In terms of treatment delivery efficiency, as reported by the TPS and shown in Table 5 , the dose delivery time for A-TOMO plans was slightly increased compared to M-TOMO plans. In contrast, A-VMAT plans demonstrated superior dose delivery efficiency, requiring less than 3 minutes, which was significantly less than the time required for TOMO plans. Table 5 Treatment delivery parameters Beam delivery parameters A - TOMO M - TOMO A - VMAT P + P − Delivery time(min) 7.76 ± 0.95 5.99 ± 1.42 2.63 ± 0.04 < 0.01 < 0.01 P + indicated a comparison between A-TOMO and M-TOMO; P − indicated a comparison between A-TOMO and A-VMAT. IV. Discussion In this study, we successfully developed and validated the first automated Tomotherapy planning method for cervical cancer. This automated planning strategy not only significantly improved the quality of the plans but also enhanced work efficiency. The automated planning method we proposed could automatically generate personalized dose distributions based on the unique anatomical characteristics of different patients, offering greater generalization. The entire process required almost no manual intervention and was compatible with commercial TPS, which was easily applicable in clinical routine. A fundamental principle of radiotherapy is ensuring adequate dose coverage to the target while minimizing the dose to OARs and normal tissue, thereby reducing unnecessary radiation-induced harm to the patient. Our study results demonstrated that A-TOMO plans significantly improved target dose conformity without sacrificing homogeneity, and reduced the dose to OARs and normal tissues compared to clinical M-TOMO plans. This was particularly true for critical indexes such as the V 50Gy %, V 40Gy % and D mean for the rectum and bladder, as well as the D mean for the bowel bag. Although no statistical significance was observed in the V 45Gy (cc) for the bowel bag between the two types of plans, the mean value for A-TOMO plans was 141.83cc, lower than the 146.39cc for the M-TOMO plans. Studies indicated that further reducing the dose to the small intestine and rectum could help decrease gastrointestinal toxicity [ 24 – 26 ] . The improvements in plan quality are influenced by the experience and effort of different dosimetrists and could also be affected by differences between TPS. Notably, differences exist between the Precison TPS and RayStation TPS, with Precison TPS lacking the equivalent uniform dose (EUD) function and allowing a maximum of only three optimization objectives for each ROI. Moreover, in the TOMO optimization process within Precison TPS, each voxel is assigned to only one ROI. Considering that the quality of M-TOMO plans could inevitably be influenced by subjective human factors, we compared A-TOMO plans with A-VMAT plans. The study results showed that the quality of these two types of plans was comparable, further affirming the plan quality of A-TOMO planning. A study by Panda et al. [ 27 ] suggested that VMAT and TOMO were equivalent in treating cervical cancer. A finding corroborated by the experimental data from Simone et al. [ 28 ] , also aligned with our study results. These indicated that the A-TOMO planning method we developed further standardized TOMO planning, effectively showcasing the capabilities of TOMO technique. Dose-volume parameters are simplified substitutes for potential biological effects and didn’t necessarily reflect the entire treatment region's dose distribution [ 29 ] . Combining dose-volume constraints with EUD could yield better dose distributions [ 30 ] . We set the EUD objective value to 0, as a previous study [ 31 ] . Additionally, studies showed that automated plans based on predicted EUD values were superior in quality to manual plans [ 32 , 33 ] . We analyzed the relationship between the overlap of bladder, rectum, bowel bag and PTV with EUD (A = 1) in A-TOMO plans, as shown in Fig. 4 . The Pearson correlation coefficients for bladder and rectum were 0.88 and 0.84, respectively, with R² values of 0.78 and 0.70, indicating a strong linear relationship. It suggested that automated planning based on the predicted EUD method had research potential and was worth further exploration. One of the significant features of the automated planning was its high efficiency, and our study results were consistent with this view [ 34 – 36 ] . In our study, A-TOMO and A-VMAT planning only required about 20 minutes and 30 minutes to complete, respectively. However, as described by dosimetrists, the M-TOMO plans required repeated adjustments and could take several hours from start to submission. Our script set two consecutive rounds of iteration, each with 100 iterations, to seek the final solution. Although we had not yet delved into the possibility of achieving an optimal solution with fewer iterations, this exploration might further shorten the script execution time. In the future, incorporating contouring into the automated planning process could realize a more complete automated planning workflow, further saving time and improving efficiency. Although the A-TOMO plan improved work efficiency, the treatment delivery time was longer compared to M-TOMO plans. This could be due to several reasons, including more stringent dose constraints were set and the DTF parameter in A-TOMO plans was set to 1.7, which made the execution time of each rotation approximately 20 seconds. A previous study indicated that increasing the DTF, while improving plan quality, also led to longer dose delivery times [ 37 ] . Additionally, compared to TOMO plans, VMAT plans had shorter dose delivery times, consistent with previous studies [ 38 ] . Currently, the quality of our developed A-TOMO plan had been preliminarily validated in our treatment center. To further demonstrate its effectiveness and applicability, we plan to collaborate with multiple treatment centers for broader validation. This cross-center collaboration will provide a detailed assessment of our A-TOMO planning method, ensuring its stability and reliability in different settings. Through future multi-center collaboration, we aim to provide a comprehensive and precise validation platform for the automation of cervical cancer radiation therapy planning. Moreover, given the A-TOMO planning method's ability to generate high-quality and consistent plans, it has great potential to serve as a superior data source for training automated planning systems based on the atlas model. Compared to methods that use traditional manual planning as the training set, we anticipate not only improving the model's performance but also creating higher-quality plans. V. Conclusion We have successfully developed an automated Tomotherapy planning method in RayStation TPS for external beam radiotherapy of cervical cancer. This method not only effectively improved the quality of the plans but also significantly enhanced work efficiency. Compared to M-TOMO plans, the A-TOMO plans achieved a higher level of plan quality while significantly reducing the dose to OARs. Moreover, A-TOMO plans demonstrated dose distributions similar to those of A-VMAT plans, further validating their quality and feasibility. Abbreviations TOMO: Tomotherapy VMAT: volumetric modulated arc therapy M-TOMO: Manual TOMO A-TOMO: Automated TOMO A-VMAT: Automated VMAT TPS: Treatment Planning Systems OARs: Organs At Risk PTV: Planning Target Volume RTOG: Radiotherapy Oncology Group CTV: Clinical Target Volume NCCN: National Comprehensive Cancer Network QUANTEC: Quantitative Analysis of Normal Tissue Effects in the Clinic DVH: Dose Volume Histogram ROI: Region Of Interest DTF: Delivery Time Factor MLC: Multi-Leaf Collimator CI: Conformity Index HI: Homogeneity Index GI: Gradient Index EUD: Equivalent Uniform Dose Declarations Authors' contributions ： FH, ZT and SJ conceived the project. FH, YX , SH , TL, YY collected and analyzed the data. YC, HH, YS, WW and ZY provided clinical expertise and definitive supervision of the paper. 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Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 27 May, 2024 Reviews received at journal 24 May, 2024 Reviews received at journal 24 May, 2024 Reviewers agreed at journal 14 May, 2024 Reviewers agreed at journal 13 May, 2024 Reviewers invited by journal 13 May, 2024 Editor assigned by journal 09 May, 2024 Submission checks completed at journal 01 May, 2024 First submitted to journal 26 Apr, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4328154","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":297535559,"identity":"aa6fda88-08c1-4b27-8e7f-9722b6e87e1f","order_by":0,"name":"Feiru Han","email":"","orcid":"","institution":"Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin’s Clinical Research Center for 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08:08:16","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4328154/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4328154/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":56101711,"identity":"813145e1-9587-4cc6-b4dd-b939e8ca0c0a","added_by":"auto","created_at":"2024-05-08 14:44:27","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":325643,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe workflow of the script-based automated planning method\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A): Flowchart for creating auxiliary structures\u003c/p\u003e\n\u003cp\u003e(B): Flowchart for the execution of the automated planning script\u003c/p\u003e\n\u003cp\u003eNote: V\u003csub\u003ebladder_PTV\u003c/sub\u003e, V\u003csub\u003erectum_PTV\u003c/sub\u003e, and V\u003csub\u003ebowel bag_PTV\u003c/sub\u003e represented the volumes of intersection between the bladder, rectum, and bowel bag with the PTV, respectively. The 'abstract 0.5mm' indicated that with each cycle, the PTV was contracted inward by 0.5mm.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-4328154/v1/6c8d6969263e8dd3a13e8259.png"},{"id":56102272,"identity":"11a27545-a660-4df2-b2f0-a9a21684b6bb","added_by":"auto","created_at":"2024-05-08 14:52:27","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":539530,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eExamples of the auxiliary structure creation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLeft : The green area represented the rectum, and the blue area represented the PTV\u003c/p\u003e\n\u003cp\u003eRight: The red area represented rectum-ptv, the brown area represented PTV\u003csub\u003enew\u003c/sub\u003e, and the remaining green part represented rectum\u003csub\u003enew\u003c/sub\u003e.\u003c/p\u003e\n\u003cp\u003eEquations: rectum - rectum\u003csub\u003enew\u003c/sub\u003e = rectum-ptv ; PTV - (rectum-ptv) = PTV\u003csub\u003enew \u003c/sub\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNote：rectum\u003csub\u003enew\u003c/sub\u003e represented the volume of intersection between the rectum and the PTV after it has been contracted inward\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-4328154/v1/7295d9062a672939e745d7d5.png"},{"id":56101712,"identity":"def3b56f-de77-4c44-9788-8df5a06b6778","added_by":"auto","created_at":"2024-05-08 14:44:27","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":984235,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDose Distributions and DVH of Three Plans of a case\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-4328154/v1/e895d20be81ed6c5e1cf5ca4.png"},{"id":56101714,"identity":"c46b9db5-1529-4471-86e5-967f204adfdb","added_by":"auto","created_at":"2024-05-08 14:44:27","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":187006,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRegression Model of EUD (A=1)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA. Logistic regression of the overlap rate between the bladder and PTV with the bladder's EUD; y=33.05x + 22.61 was the fitted linear equation.\u003c/p\u003e\n\u003cp\u003eB. B. Logistic regression of the overlap rate between the rectum and PTV with the rectum's EUD; y=15.80x + 28.70 was the fitted linear equation.\u003c/p\u003e\n\u003cp\u003eC. Logistic regression of the overlap volume between the bowel bag and PTV with the bowel bag's EUD; the linear correlation was not strong.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-4328154/v1/d85337497a8a2a22ada0e387.png"},{"id":56102773,"identity":"52d2be9e-8fca-42ab-95cc-080feb8501d8","added_by":"auto","created_at":"2024-05-08 15:00:29","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2399869,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4328154/v1/f6544b3a-67f6-4f80-a055-af2d1d1fc415.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Development and validation of an automated Tomotherapy planning method for cervical cancer","fulltext":[{"header":"I. Introduction","content":"\u003cp\u003eCervical cancer ranks as the fourth most prevalent malignant tumor among women globally\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e. Radiotherapy has served as a critical treatment modality for patients with locally advanced, lymph node-positive, and/or high-risk cervical cancer\u003csup\u003e[\u003cspan additionalcitationids=\"CR3 CR4\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e–\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e, as well as the standard regimen for postoperative adjuvant therapy\u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e. Radiotherapy for cervical cancer could lead to severe toxic reactions in the gastrointestinal, urinary, and hematological systems\u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e. Optimizing dose distribution with advanced radiotherapy techniques could help to minimize the side effects\u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e. Tomotherapy (TOMO) and volumetric modulated arc therapy (VMAT) are two widely used and effective external beam radiotherapy techniques for treating cervical cancer\u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e. In clinical practice, radiotherapy dosimetrists manually created treatment plans using treatment planning systems (TPS), which often require optimization with multiple iterations and trial-and-error to meet dose constraints. However, due to the large treatment area required for cervical cancer, a delicate balance between achieving sufficient tumor coverage and protecting organs at risk (OARs) is necessary. Furthermore, the variations in patients anatomy make the process of manual planning complex, cumbersome, and time-consuming\u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e. The variations of planner's expertise, execution standards, and effort expended, potentially lead to inconsistent plan quality, which not only increase the risk of toxicity to OARs but also complicate the outcomes and interpretations of clinical trials\u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e. Therefore, it is essential to enhance the plan quality and consistency.\u003c/p\u003e \u003cp\u003eThe application of automated radiotherapy planning allows for procedures to be executed with as little human intervention as possible, effectively improving the quality, efficiency, and consistency of radiotherapy planning\u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e. There are primarily two types of strategies for automated planning: strategies based on atlas prediction of optimization objectives and script-based strategies that emulate manual optimization. The atlas-based methods use data from previous radiotherapy plans to train models that predict dose distribution for new patients\u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e. However, this method is highly dependent on the training datasets\u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e, and tends to struggle when adapting to new cases that differ significantly from the training data. In contrast, the script-based methods don’t require a prior training or learning step\u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e. In the radiotherapy of cervical cancer, particularly with the VMAT technique, mature automated planning methods have already been developed and have been proven to achieve better plan quality and greater efficiency than manual planning\u003csup\u003e[\u003cspan additionalcitationids=\"CR21 CR22\" citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e–\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/sup\u003e. However, research into the automated planning for TOMO plans in cervical cancer remains unreported, suggesting room for improvement and development.\u003c/p\u003e \u003cp\u003eIt is well known that the Precision (Accuray Inc., Sunnyvale, CA, USA) TPS for TOMO lacks the function for implementing scripts, whereas RayStation (RaySearch Labs, Sweden) TPS possesses functions for TOMO planning and scripting. In this study, we developed a script-based automated TOMO (A-TOMO) planning method for external beam radiotherapy in cervical cancer using RayStation TPS. And the A-TOMO plans were compared with those from manual TOMO (M-TOMO) planning and automated VMAT (A-VMAT) planning to validate its feasibility and effectiveness.\u003c/p\u003e "},{"header":"II. Materials and Methods","content":"\u003cp\u003e\u003cstrong\u003eA. Patients\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn this study, we consecutively selected 30 cervical cancer patients who underwent TOMO treatment at our center in 2023. This study strictly adhered to the ethical principles outlined in the Declaration of Helsinki and the protocol was approved by the institutional review board and ethics committee at ####Cancer Institute \u0026amp; Hospital. All participants provided written informed consent. Among these 30 patients, 26 patients underwent definitive radiotherapy, and 4 received postoperative radiotherapy. All the TOMO plans were manually designed, with all patients receiving a dose of 50.4 Gy in 28 fractions. The patients were treated in two positions: 12 in the prone position and 18 in the supine position. The average volume of the planning target volume (PTV) was 1347.45\u0026thinsp;\u0026plusmn;\u0026thinsp;179.28 cc, ranging from 1122.82 to 1862.99 cc. The average overlapping rate of the bladder in the PTV was 37\u0026thinsp;\u0026plusmn;\u0026thinsp;11%, with a range from 13\u0026ndash;64%. For the rectum, the average overlap rate with the PTV was 52\u0026thinsp;\u0026plusmn;\u0026thinsp;13%, ranging from 24\u0026ndash;72%. The average overlapping volume of the bowel bag in the PTV was 115.79\u0026thinsp;\u0026plusmn;\u0026thinsp;52.09 cc, ranging from 8.05 to 205.67 cc.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eB. CT simulation and Contouring\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCT scans were performed to obtain images with a thickness of 5mm. To ensure bladder filling, patients were instructed to drink 500 ml of water 30 minutes before the scans. Following the radiotherapy oncology group (RTOG) guidelines, radiotherapy oncologists delineated the targets and OARs on the CT images, including the clinical target volume (CTV), PTV, bladder, rectum, bowel bag, femoral heads, and kidneys. For patients undergoing definitive radiotherapy, the CTV encompassed the uterus and its lymphatic drainage area. For those receiving postoperative radiotherapy, the CTV included the lymphatic drainage area of the radical uterus. The PTV was defined by expanding the CTV by 7 mm in all three dimensions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eC. Radiotherapy planning\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eC.1. Radiotherapy planning protocol\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe radiotherapy planning protocol in our center adhered to the dose limits for the PTV and OARs as recommended by the National Comprehensive Cancer Network (NCCN) guidelines and the recommendations of Quantitative Analysis of Normal Tissue Effects in the Clinic (QUANTEC), as detailed in Table \u003cspan\u003e1\u003c/span\u003e. Here, D\u003csub\u003emax\u003c/sub\u003e denoted the maximum dose, V\u003csub\u003exGy\u003c/sub\u003e represented the volume receiving x Gy in the dose volume histogram (DVH), and D\u003csub\u003emean\u003c/sub\u003e indicated the mean dose. This protocol of dose volume limits ensured the priority fulfillment of hard constraints for indexes, which meant that in case of conflicts between OARs protection and PTV coverage, partial prescription coverage of PTV could be compromised.\u003c/p\u003e\n\u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 1\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eTarget and OAR dose constraints\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003eROI\u003cbr\u003e\u003c/th\u003e\n \u003cth align=\"left\"\u003eIndex\u003cbr\u003e\u003c/th\u003e\n \u003cth align=\"left\"\u003eConstraint\u003cbr\u003e\u003c/th\u003e\n \u003cth align=\"left\"\u003eType\u003cbr\u003e\u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003ePTV\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eD\u003csub\u003emax\u003c/sub\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026le;\u0026thinsp;115%\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eHard\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eD\u003csub\u003emax\u003c/sub\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026le;\u0026thinsp;110%\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eSoft\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eV\u003csub\u003e50.4Gy\u003c/sub\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026ge;\u0026thinsp;95%\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eSoft\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003ebladder\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eV\u003csub\u003e50Gy\u003c/sub\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026lt;\u0026thinsp;50%\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eHard\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eV\u003csub\u003e40Gy\u003c/sub\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026lt;\u0026thinsp;60%\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eSoft\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003erectum\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eV\u003csub\u003e50Gy\u003c/sub\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026lt;\u0026thinsp;50%\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eHard\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eV\u003csub\u003e40Gy\u003c/sub\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026lt;\u0026thinsp;60%\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eSoft\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003ebowel bag\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eV\u003csub\u003e45Gy\u003c/sub\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026lt;\u0026thinsp;195cc\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eHard\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eFemoral_Head (L,R)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eV\u003csub\u003e50Gy\u003c/sub\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026lt;\u0026thinsp;5%\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eHard\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003ekidney(L,R)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eD\u003csub\u003emean\u003c/sub\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026lt;\u0026thinsp;15Gy\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eHard\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003ekidney(L,R)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eD\u003csub\u003emean\u003c/sub\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026lt;\u0026thinsp;10Gy\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eSoft\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eL, Left ; R, Right\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eC.2. M-TOMO planning\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe clinical M-TOMO plans for the 30 patients were created using the Precision TPS with the TOMO Radixact Linac (Accuray Inc., Sunnyvale, CA, USA). These plans utilized the dynamic jaw mode with a jaw width was set to 2.51 cm. Three plans used a pitch of 0.287, while the remaining 27 plans had a pitch of 0.43. The average modulation factor was 1.99\u0026thinsp;\u0026plusmn;\u0026thinsp;0.41. Helical mode was employed, and dose calculation was performed using the collapsed cone algorithm, with the final dose calculation was performed in high-resolution mode. The radiotherapy plans for these 30 patients were randomly assigned to 14 dosimetrists, each employing potentially different manual optimization strategies. All plans were approved by oncologists for clinical treatment.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eC.3. A-TOMO planning\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eC.3.1. Creation of auxiliary structures\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe method for creating auxiliary structures is detailed in Table \u003cspan\u003e2\u003c/span\u003e. To facilitate meeting clinical dose requirements and to concentrate PTV dose deficits in areas overlapping with OARs as much as possible, we processed these overlapping sections as described in Fig. \u003cspan\u003e1\u003c/span\u003e(A). We ensured that the overlapping volumes of the rectum and bladder in the PTV were less than or equal to 45% of their total volume, and the overlapping volume of the bowel bag in the PTV was less than or equal to 110 cc, based on clinical requirements and experience. For example, the auxiliary structures PTV\u003csub\u003enew\u003c/sub\u003e and rectum-ptv, created for preprocessing the overlapping section between the rectum and PTV, are shown in Fig. \u003cspan\u003e2\u003c/span\u003e. To address potential dose control challenges caused by larger bowel volumes, we specifically created an auxiliary structure named \u0026quot;bag\u0026quot; to aid optimization. We set optimization goals based on each patient\u0026apos;s bowel volume, calculated as (195/V\u003csub\u003ebag\u003c/sub\u003e)%, and took the integer part of the value.\u003c/p\u003e\n\u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 2\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eThe creation method of auxiliary structures\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003eAuxiliary structures\u003cbr\u003e\u003c/th\u003e\n \u003cth align=\"left\"\u003eCreation method\u003cbr\u003e\u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003ering0.5\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eCreate ring of PTV with a 5 mm margin\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003ering 1\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eCreate ring of PTV between the 5 mm and 1cm margin\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003ering2\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eCreate ring of PTV between the 1cm to 2 cm margin\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003ering3\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eCreate ring of PTV between the 2 cm to 3 cm margin\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003ent\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eCreate subtraction of PTV and PTV rings from the body\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003ePTV\u003csub\u003enew\u003c/sub\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eCreate subtraction of the partial volume of the OAR from the PTV\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003ePTV\u003csub\u003enew\u003c/sub\u003e-3mm\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eCreate contraction of PTV\u003csub\u003enew\u003c/sub\u003e with a 3 mm margin\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003ebladder-ptv\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eCreate subtraction of PTV\u003csub\u003enew\u003c/sub\u003e from bladder\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003erectum-ptv\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eCreate subtraction of PTV\u003csub\u003enew\u003c/sub\u003e from rectum\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003ebowel bag-ptv\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eCreate subtraction of PTV\u003csub\u003enew\u003c/sub\u003e from bowel bag\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003ebag\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eCreate the overlap between bowel bag and PTV expanded outward by 2 cm\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003ebowel bag_PTV\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eCreate the overlap between the bowel bag and the PTV\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eC.3.1. Formulation of A-TOMO planning\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe A-TOMO planning script was developed using the scripting function in RayStation 11B (RaySearch Labs, Sweden) TPS, within a Python (3.7) environment. This script utilized the standard model of the Radixact Linac for dose optimization and calculation. After verifying contours and region of interest (ROI) names, the CT images were imported into the RayStation system, initiating the automated planning script. The workflow of the A-TOMO planning script is shown in Fig. \u003cspan\u003e1\u003c/span\u003e(B). Firstly, the script automatically selected the CT electron density conversion table and set the outline (body) type to \u0026ldquo;External\u0026rdquo;. A dose grid size of 0.3 \u0026times; 0.3 \u0026times; 0.3 cm was established. A virtual couch was inserted, and auxiliary structures were created. The script then automatically created a new plan and added beams, adjusting machine parameters, including setting the delivery time factor (DTF) to 1.7, the optimization stopping tolerance to 0, and the maximum number of iterations to 100. The system automatically loaded the optimization objective template, as listed in Table \u003cspan\u003e3\u003c/span\u003e. The entire optimization process consisted of two rounds, each with 100 iterations of optimization. Ultimately, the plan was normalized so that the prescription dose covered 95% of the PTV volume, completing the optimization process. If the final normalized doses to OARs exceeded dose constraints, manual normalization was made by the dosimetrists as needed. Moreover, the A-TOMO planning script defaulted to a pitch of 0.43 and a jaw width of 2.51 cm, employing dynamic jaw mode and the collapsed cone dose calculation algorithm. The average modulation factor for these plans was 1.83\u0026thinsp;\u0026plusmn;\u0026thinsp;0.17.\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 3\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eThe optimization objective template\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003eROI\u003cbr\u003e\u003c/th\u003e\n \u003cth align=\"left\"\u003eType\u003cbr\u003e\u003c/th\u003e\n \u003cth align=\"left\"\u003eTarget (Gy)\u003cbr\u003e\u003c/th\u003e\n \u003cth align=\"left\"\u003eVolume\u003cbr\u003e\u003c/th\u003e\n \u003cth align=\"left\"\u003eWeight\u003cbr\u003e\u003c/th\u003e\n \u003cth align=\"left\"\u003eEUD\u003cbr\u003e\u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003ePTV\u003csub\u003enew\u003c/sub\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eMax dose\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e52.4\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e1x10\u003csup\u003e9\u003c/sup\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003ePTV\u003csub\u003enew\u003c/sub\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eMin DVH\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e51\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e97%\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e5x10\u003csup\u003e9\u003c/sup\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003ePTV\u003csub\u003enew\u003c/sub\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eMin DVH\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e50.4\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e98%\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e5x10\u003csup\u003e9\u003c/sup\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003ePTV\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eMax EUD\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e52\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e1.5x10\u003csup\u003e9\u003c/sup\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eA\u0026thinsp;=\u0026thinsp;150\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003ePTV\u003csub\u003enew\u003c/sub\u003e-3mm\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eMin dose\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e51\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e8x10\u003csup\u003e9\u003c/sup\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003ering0.5\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eMax dose\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e50.4\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e3x10\u003csup\u003e8\u003c/sup\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eA\u0026thinsp;=\u0026thinsp;1\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003ering1\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eMax dose\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e45\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e3x10\u003csup\u003e8\u003c/sup\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eA\u0026thinsp;=\u0026thinsp;1\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003ering2\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eMax dose\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e38\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e3x10\u003csup\u003e8\u003c/sup\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eA\u0026thinsp;=\u0026thinsp;1\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003ering3\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eMax dose\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e28\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e3x10\u003csup\u003e8\u003c/sup\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eA\u0026thinsp;=\u0026thinsp;1\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003ent\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eMax EUD\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e20\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e3x10\u003csup\u003e8\u003c/sup\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eA\u0026thinsp;=\u0026thinsp;1\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003ering1\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eMax EUD\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0.01\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eA\u0026thinsp;=\u0026thinsp;1\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003ering2\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eMax EUD\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0.01\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eA\u0026thinsp;=\u0026thinsp;1\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003ering3\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eMax EUD\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0.01\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eA\u0026thinsp;=\u0026thinsp;1\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003ent\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eMax EUD\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0.3\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eA\u0026thinsp;=\u0026thinsp;1\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003ebag\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eMax DVH\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e42\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e(195/V\u003csub\u003ebag\u003c/sub\u003e)%\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e3x10\u003csup\u003e9\u003c/sup\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003ebladder\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eMax DVH\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e38\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e60%\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e2x10\u003csup\u003e9\u003c/sup\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003erectum\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eMax DVH\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e38\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e60%\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e2x10\u003csup\u003e9\u003c/sup\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003ebowel bag_PTV\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eMin dose\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e40\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e2x10\u003csup\u003e9\u003c/sup\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003ebladder-ptv\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eMax EUD\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e1\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eA\u0026thinsp;=\u0026thinsp;1\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003erectum-ptv\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eMax EUD\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0.8\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eA\u0026thinsp;=\u0026thinsp;1\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003ebowel bag-ptv\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eMax EUD\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e5\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eA\u0026thinsp;=\u0026thinsp;1\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eFemoralHead_L\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eMax EUD\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0.5\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eA\u0026thinsp;=\u0026thinsp;1\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eFemoralHead_R\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eMax EUD\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0.5\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eA\u0026thinsp;=\u0026thinsp;1\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003ekidney_L\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eMax EUD\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e1\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eA\u0026thinsp;=\u0026thinsp;1\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003ekidney_R\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eMax EUD\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e1\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eA\u0026thinsp;=\u0026thinsp;1\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eC.4. A-VMAT planning\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe A-VMAT planning script was developed in the RayStation 11B TPS and Python (3.7) environment, employing an optimization strategy similar to that used for the A-TOMO plan. The automated plans were optimized based on a standard model for the Truebeam Linac (Varian Medical Systems, Palo Alto, CA, USA) with 6 MV and a 120-leaf millennium multi-leaf collimator (MLC). In the A-VMAT planning script, two beams with gantry angles from 181\u0026deg; to 179\u0026deg; were automatically created, irradiating in both clockwise and counterclockwise directions. Moreover, the collimator angles were set to 355\u0026deg;. The maximum values for the jaws X1, X2, Y1, Y2 were set to 1, 14, 20, 20, and 14, 1, 20, 20 (cm), respectively, with jaw tracking mode, and the gantry spacing set at 2\u0026deg;. During optimization, the stopping tolerance was set to 0, employing the collapsed cone dose calculation algorithm with a calculation grid size of 0.3 \u0026times; 0.3 \u0026times; 0.3 cm. The maximum number of iterations was 100, with the iterations before conversion set at 40. The optimization process included two rounds, each consisting of 100 iterations. The system defaulted to optimizing segment shapes and segment MU.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eD. Evaluation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo evaluate the differences between radiotherapy plans, we conducted comparisons from two perspectives. Firstly, we compared A-TOMO plans with M-TOMO plans; secondly, we compared A-TOMO plans with A-VMAT plans. These comparisons primarily focused on the dosimetric indexes for PTV and OARs. For the assessment of OARs, our analysis concentrated on the dosimetric indexes for the bladder, rectum, and bowel bag (D\u003csub\u003e0.03cc\u003c/sub\u003e, V\u003csub\u003e50Gy\u003c/sub\u003e%, V\u003csub\u003e40Gy\u003c/sub\u003e%, D\u003csub\u003emean\u003c/sub\u003e), FemoralHead_L, FemoralHead_R (V\u003csub\u003e50Gy\u003c/sub\u003e%, D\u003csub\u003emean\u003c/sub\u003e) and kidney_L, kidney_R (D\u003csub\u003emean\u003c/sub\u003e). For the PTV, we used D\u003csub\u003emax\u003c/sub\u003e, conformity index (CI), and homogeneity index (HI) for evaluation. Additionally, we assessed the dose drop-off outside the target volume using gradient index (GI\u003csub\u003ex\u003c/sub\u003e) for thresholds of 40Gy, 30Gy, 20Gy, and 10Gy. The CI, HI, GI\u003csub\u003ex\u003c/sub\u003e were defined in the formulas below:\u003c/p\u003e\n\u003cp\u003e\u003cimg src=\"data:image/png;base64,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\"\u003e\u003c/p\u003e\n\n\u003cp\u003eThe D\u003csub\u003e2%\u003c/sub\u003e, D\u003csub\u003e98%\u003c/sub\u003e, and D\u003csub\u003e98%\u003c/sub\u003e refered to the doses received by 2%, 98%, and 50% of the PTV volume in the DVH, respectively. A lower HI indicated better uniformity of the dose distribution within the PTV.\u003c/p\u003e\n\u003cp\u003eCI = \u003cspan\u003e\u003cspan\u003e\\(\\frac{\\text{T}\\text{V}\\text{P}\\text{T}\\text{V}\\times \\text{T}\\text{V}\\text{P}\\text{T}\\text{V}}{\\text{V}\\text{P}\\text{T}\\text{V} \\times \\text{V}\\text{T}\\text{V}\\text{P}}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003eThe TV\u003csub\u003ePTV\u003c/sub\u003e represented the volume within the PTV encompassed by the prescription dose line, and the V\u003csub\u003ePTV\u003c/sub\u003e was the volume of the PTV, and the V\u003csub\u003eTVP\u003c/sub\u003e was the volume encompassed by the prescription dose line. The closer the CI value was to 1, the better the conformity.\u003c/p\u003e\n\u003cp\u003eGI\u003csub\u003ex\u003c/sub\u003e =\u003cspan\u003e\u003cspan\u003e\\(\\frac{\\text{V}\\text{x}}{\\text{V}\\text{T}\\text{V}\\text{P}}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003eThe V\u003csub\u003ex\u003c/sub\u003e refered to the volume encompassed by the X Gy dose line, while the V\u003csub\u003eTVP\u003c/sub\u003e represented the volume encompassed by the prescription dose line. A smaller GI\u003csub\u003ex\u003c/sub\u003e value indicated a faster dose drop-off outside the target volume.\u003c/p\u003e\n\u003cp\u003eFurthermore, we conducted a statistical analysis on the treatment delivery time as directly displayed by the TPS. To comprehensively evaluate work efficiency, we also assessed the time required to execute the automated treatment planning scripts.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eE. Statistical analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBefore conducting the statistical analysis, we renormalized the A-TOMO and A-VMAT radiotherapy plans to ensure they matched the prescription dose coverage of PTV as the M-TOMO plans. The data were found not following a normal distribution; hence, we employed the two-sided Wilcoxon signed-rank test as our statistical method, using the wilcoxon function from the scipy.stats library in Python. In this analysis, a P-value less than 0.05 was considered statistically significant. To clearly present and compare the outcomes of different treatment plans, we recorded the mean values and standard deviations of each dataset.\u003c/p\u003e"},{"header":"III. Results","content":"\u003cp\u003e\u003cstrong\u003eA. A-TOMO Plan versus M-TOMO Plan Comparison\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn the comparing the A-TOMO plans and M-TOMO plans for the 30 patients, it was found that for 5 patients, neither the manual nor the automated plans could achieve prescription dose coverage of 95% PTV, while meeting the dose constraints for OARs. The dosimetric comparison results are shown in Table\u0026nbsp;\u003cspan\u003e4\u003c/span\u003e. Although both plans demonstrated similar performance in terms of PTV D\u003csub\u003emax\u003c/sub\u003e and HI (P\u003csup\u003e+\u003c/sup\u003e=0.92, 0.11), A-TOMO plans significantly surpassed M-TOMO plans in PTV CI (P\u003csup\u003e+\u003c/sup\u003e\u0026lt;0.01) and achieved greater reductions in OAR doses. Specifically, for the D\u003csub\u003e0.03cc\u003c/sub\u003e for the bladder, rectum, and bowel bag, and the V\u003csub\u003e45Gy\u003c/sub\u003e for the bowel bag, the two plans were comparable (P\u003csup\u003e+\u003c/sup\u003e=0.12, 0.57, 0.52\u0026thinsp;\u0026gt;\u0026thinsp;0.05; P\u003csup\u003e+\u003c/sup\u003e=0.11). However, for the V\u003csub\u003e50Gy\u003c/sub\u003e%, V\u003csub\u003e40Gy\u003c/sub\u003e%, and D\u003csub\u003emean\u003c/sub\u003e for the bladder and rectum, as well as the D\u003csub\u003emean\u003c/sub\u003e for the bowel bag, kidney_L, and kidney_R, A-TOMO plans were significantly lower than those in M-TOMO plans (P\u003csup\u003e+\u003c/sup\u003e\u0026lt;0.05). The V\u003csub\u003e50Gy\u003c/sub\u003e% for FemoralHead_L and FemoralHead_R in both plans were nearly identical, close to 0. Additionally, the GI\u003csub\u003e40Gy\u003c/sub\u003e, GI\u003csub\u003e30Gy\u003c/sub\u003e, and GI\u003csub\u003e20Gy\u003c/sub\u003e values were significantly lower in A-TOMO plans than in M-TOMO plans (P\u003csup\u003e+\u003c/sup\u003e\u0026lt;0.01), indicating a faster dose drop-off outside the target volume in A-TOMO plans, thereby exposing normal tissues to lower radiation doses. The mean values\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviations of the dose metrics also indicated better consistency in A-TOMO plans. Figure\u0026nbsp;\u003cspan\u003e3\u003c/span\u003e presents a comparison of representative dose distribution and DVH for key ROIs for a case.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eB. A-TOMO Plan versus A-VMAT Plan Comparison\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe dosimetric comparison results between A-TOMO and A-VMAT plans for the 30 patients are shown in Table\u0026nbsp;\u003cspan\u003e4\u003c/span\u003e. The A-VMAT plan showed a slight advantage over the A-TOMO plan in terms of the GI, but it presented slightly higher values for the PTV (D\u003csub\u003emax\u003c/sub\u003e, D\u003csub\u003e2%\u003c/sub\u003e), bladder (D\u003csub\u003e0.03cc\u003c/sub\u003e, V\u003csub\u003e50Gy\u003c/sub\u003e%, D\u003csub\u003emean\u003c/sub\u003e), rectum (V\u003csub\u003e50Gy\u003c/sub\u003e%, V\u003csub\u003e40Gy\u003c/sub\u003e%, D\u003csub\u003emean\u003c/sub\u003e), bowel bag (D\u003csub\u003e0.03cc\u003c/sub\u003e, D\u003csub\u003emean\u003c/sub\u003e), FH_L (D\u003csub\u003emean\u003c/sub\u003e), and FH_R (V\u003csub\u003e50Gy\u003c/sub\u003e%, D\u003csub\u003emean\u003c/sub\u003e). Although these differences were statistically significant (P\u003csup\u003e\u0026minus;\u003c/sup\u003e\u0026lt;0.05), the actual numerical differences were within 1Gy and 1%. This suggests that the A-TOMO plan was comparable in overall plan quality to the A-VMAT plan, and may even be superior. Representative dose distributions and DVH for key ROIs of the case are displayed in Fig.\u0026nbsp;\u003cspan\u003e3\u003c/span\u003e.\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 4\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eComparison of Dosimetric Indexes for Three Types of Plans\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"7\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003eROI\u003c/th\u003e\n \u003cth align=\"left\"\u003eParameter\u003c/th\u003e\n \u003cth align=\"left\"\u003eA - TOMO\u003c/th\u003e\n \u003cth align=\"left\"\u003eM - TOMO\u003c/th\u003e\n \u003cth align=\"left\"\u003eA - VMAT\u003c/th\u003e\n \u003cth align=\"left\"\u003eP\u003csup\u003e+\u003c/sup\u003e\u003c/th\u003e\n \u003cth align=\"left\"\u003eP\u003csup\u003e\u0026minus;\u003c/sup\u003e\u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"5\"\u003ePTV\u003c/td\u003e\n \u003ctd align=\"left\"\u003eD\u003csub\u003emax\u003c/sub\u003e (Gy)\u003c/td\u003e\n \u003ctd align=\"char\"\u003e55.53\u0026thinsp;\u0026plusmn;\u0026thinsp;0.63\u003c/td\u003e\n \u003ctd align=\"char\"\u003e55.55\u0026thinsp;\u0026plusmn;\u0026thinsp;1.79\u003c/td\u003e\n \u003ctd align=\"char\"\u003e55.93\u0026thinsp;\u0026plusmn;\u0026thinsp;0.55\u003c/td\u003e\n \u003ctd align=\"char\"\u003e0.92\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\u0026lt;\u0026thinsp;0.01\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eD\u003csub\u003e98%\u003c/sub\u003e (Gy)\u003c/td\u003e\n \u003ctd align=\"char\"\u003e48.08\u0026thinsp;\u0026plusmn;\u0026thinsp;1.90\u003c/td\u003e\n \u003ctd align=\"char\"\u003e48.4\u0026thinsp;\u0026plusmn;\u0026thinsp;2.12\u003c/td\u003e\n \u003ctd align=\"char\"\u003e48.24\u0026thinsp;\u0026plusmn;\u0026thinsp;1.90\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\u0026lt;\u0026thinsp;0.01\u003c/td\u003e\n \u003ctd align=\"char\"\u003e0.45\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eD\u003csub\u003e2%\u003c/sub\u003e (Gy)\u003c/td\u003e\n \u003ctd align=\"char\"\u003e54.33\u0026thinsp;\u0026plusmn;\u0026thinsp;0.50\u003c/td\u003e\n \u003ctd align=\"char\"\u003e54.19\u0026thinsp;\u0026plusmn;\u0026thinsp;1.61\u003c/td\u003e\n \u003ctd align=\"char\"\u003e54.59\u0026thinsp;\u0026plusmn;\u0026thinsp;0.62\u003c/td\u003e\n \u003ctd align=\"char\"\u003e0.49\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\u0026lt;\u0026thinsp;0.01\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eCI\u003c/td\u003e\n \u003ctd align=\"char\"\u003e0.91\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003c/td\u003e\n \u003ctd align=\"char\"\u003e0.88\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06\u003c/td\u003e\n \u003ctd align=\"char\"\u003e0.90\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\u0026lt;\u0026thinsp;0.01\u003c/td\u003e\n \u003ctd align=\"char\"\u003e0.01\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eHI\u003c/td\u003e\n \u003ctd align=\"char\"\u003e0.12\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04\u003c/td\u003e\n \u003ctd align=\"char\"\u003e0.11\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06\u003c/td\u003e\n \u003ctd align=\"char\"\u003e0.12\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04\u003c/td\u003e\n \u003ctd align=\"char\"\u003e0.11\u003c/td\u003e\n \u003ctd align=\"char\"\u003e0.06\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"4\"\u003ebladder\u003c/td\u003e\n \u003ctd align=\"left\"\u003eD\u003csub\u003e0.03cc\u003c/sub\u003e (Gy)\u003c/td\u003e\n \u003ctd align=\"char\"\u003e55.19\u0026thinsp;\u0026plusmn;\u0026thinsp;0.60\u003c/td\u003e\n \u003ctd align=\"char\"\u003e54.73\u0026thinsp;\u0026plusmn;\u0026thinsp;1.66\u003c/td\u003e\n \u003ctd align=\"char\"\u003e55.51\u0026thinsp;\u0026plusmn;\u0026thinsp;0.74\u003c/td\u003e\n \u003ctd align=\"char\"\u003e0.12\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\u0026lt;\u0026thinsp;0.01\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eV\u003csub\u003e50Gy\u003c/sub\u003e (%)\u003c/td\u003e\n \u003ctd align=\"char\"\u003e33.36\u0026thinsp;\u0026plusmn;\u0026thinsp;7.32\u003c/td\u003e\n \u003ctd align=\"char\"\u003e37.54\u0026thinsp;\u0026plusmn;\u0026thinsp;7.94\u003c/td\u003e\n \u003ctd align=\"char\"\u003e33.9\u0026thinsp;\u0026plusmn;\u0026thinsp;7.67\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\u0026lt;\u0026thinsp;0.01\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\u0026lt;\u0026thinsp;0.01\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eV\u003csub\u003e40Gy\u003c/sub\u003e (%)\u003c/td\u003e\n \u003ctd align=\"char\"\u003e48.23\u0026thinsp;\u0026plusmn;\u0026thinsp;8.59\u003c/td\u003e\n \u003ctd align=\"char\"\u003e63.61\u0026thinsp;\u0026plusmn;\u0026thinsp;10.34\u003c/td\u003e\n \u003ctd align=\"char\"\u003e48.34\u0026thinsp;\u0026plusmn;\u0026thinsp;8.41\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\u0026lt;\u0026thinsp;0.01\u003c/td\u003e\n \u003ctd align=\"char\"\u003e0.16\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eD\u003csub\u003emean\u003c/sub\u003e (Gy)\u003c/td\u003e\n \u003ctd align=\"char\"\u003e35.02\u0026thinsp;\u0026plusmn;\u0026thinsp;4.22\u003c/td\u003e\n \u003ctd align=\"char\"\u003e42.69\u0026thinsp;\u0026plusmn;\u0026thinsp;4.31\u003c/td\u003e\n \u003ctd align=\"char\"\u003e35.35\u0026thinsp;\u0026plusmn;\u0026thinsp;4.10\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\u0026lt;\u0026thinsp;0.01\u003c/td\u003e\n \u003ctd align=\"char\"\u003e0.01\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"4\"\u003erectum\u003c/td\u003e\n \u003ctd align=\"left\"\u003eD\u003csub\u003e0.03cc\u003c/sub\u003e (Gy)\u003c/td\u003e\n \u003ctd align=\"char\"\u003e55.28\u0026thinsp;\u0026plusmn;\u0026thinsp;0.57\u003c/td\u003e\n \u003ctd align=\"char\"\u003e55.11\u0026thinsp;\u0026plusmn;\u0026thinsp;1.70\u003c/td\u003e\n \u003ctd align=\"char\"\u003e55.33\u0026thinsp;\u0026plusmn;\u0026thinsp;0.75\u003c/td\u003e\n \u003ctd align=\"char\"\u003e0.57\u003c/td\u003e\n \u003ctd align=\"char\"\u003e0.20\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eV\u003csub\u003e50Gy\u003c/sub\u003e (%)\u003c/td\u003e\n \u003ctd align=\"char\"\u003e35.50\u0026thinsp;\u0026plusmn;\u0026thinsp;5.08\u003c/td\u003e\n \u003ctd align=\"char\"\u003e40.55\u0026thinsp;\u0026plusmn;\u0026thinsp;9.53\u003c/td\u003e\n \u003ctd align=\"char\"\u003e36.43\u0026thinsp;\u0026plusmn;\u0026thinsp;5.52\u003c/td\u003e\n \u003ctd align=\"char\"\u003e0.03\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\u0026lt;\u0026thinsp;0.01\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eV\u003csub\u003e40Gy\u003c/sub\u003e (%)\u003c/td\u003e\n \u003ctd align=\"char\"\u003e53.28\u0026thinsp;\u0026plusmn;\u0026thinsp;6.38\u003c/td\u003e\n \u003ctd align=\"char\"\u003e68.54\u0026thinsp;\u0026plusmn;\u0026thinsp;11.35\u003c/td\u003e\n \u003ctd align=\"char\"\u003e54.76\u0026thinsp;\u0026plusmn;\u0026thinsp;5.75\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\u0026lt;\u0026thinsp;0.01\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\u0026lt;\u0026thinsp;0.01\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eD\u003csub\u003emean\u003c/sub\u003e (Gy)\u003c/td\u003e\n \u003ctd align=\"char\"\u003e36.82\u0026thinsp;\u0026plusmn;\u0026thinsp;2.40\u003c/td\u003e\n \u003ctd align=\"char\"\u003e46.50\u0026thinsp;\u0026plusmn;\u0026thinsp;10.69\u003c/td\u003e\n \u003ctd align=\"char\"\u003e37.86\u0026thinsp;\u0026plusmn;\u0026thinsp;2.10\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\u0026lt;\u0026thinsp;0.01\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\u0026lt;\u0026thinsp;0.01\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003ebowel bag\u003c/td\u003e\n \u003ctd align=\"left\"\u003eD\u003csub\u003e0.03cc\u003c/sub\u003e (Gy)\u003c/td\u003e\n \u003ctd align=\"char\"\u003e55.06\u0026thinsp;\u0026plusmn;\u0026thinsp;0.54\u003c/td\u003e\n \u003ctd align=\"char\"\u003e54.90\u0026thinsp;\u0026plusmn;\u0026thinsp;1.99\u003c/td\u003e\n \u003ctd align=\"char\"\u003e55.42\u0026thinsp;\u0026plusmn;\u0026thinsp;0.73\u003c/td\u003e\n \u003ctd align=\"char\"\u003e0.52\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\u0026lt;\u0026thinsp;0.01\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eV\u003csub\u003e45Gy\u003c/sub\u003e (cc)\u003c/td\u003e\n \u003ctd align=\"char\"\u003e141.83\u0026thinsp;\u0026plusmn;\u0026thinsp;47.03\u003c/td\u003e\n \u003ctd align=\"char\"\u003e146.39\u0026thinsp;\u0026plusmn;\u0026thinsp;42.2\u003c/td\u003e\n \u003ctd align=\"char\"\u003e141.76\u0026thinsp;\u0026plusmn;\u0026thinsp;48.22\u003c/td\u003e\n \u003ctd align=\"char\"\u003e0.11\u003c/td\u003e\n \u003ctd align=\"char\"\u003e0.38\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eD\u003csub\u003emean\u003c/sub\u003e (Gy)\u003c/td\u003e\n \u003ctd align=\"char\"\u003e10.94\u0026thinsp;\u0026plusmn;\u0026thinsp;3.79\u003c/td\u003e\n \u003ctd align=\"char\"\u003e12.88\u0026thinsp;\u0026plusmn;\u0026thinsp;4.72\u003c/td\u003e\n \u003ctd align=\"char\"\u003e11.22\u0026thinsp;\u0026plusmn;\u0026thinsp;4.00\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\u0026lt;\u0026thinsp;0.01\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\u0026lt;\u0026thinsp;0.01\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003eFH_L\u003c/td\u003e\n \u003ctd align=\"left\"\u003eV\u003csub\u003e50Gy\u003c/sub\u003e (%)\u003c/td\u003e\n \u003ctd align=\"char\"\u003e0.01\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02\u003c/td\u003e\n \u003ctd align=\"char\"\u003e0.67\u0026thinsp;\u0026plusmn;\u0026thinsp;2.37\u003c/td\u003e\n \u003ctd align=\"char\"\u003e0.00\u0026thinsp;\u0026plusmn;\u0026thinsp;0.00\u003c/td\u003e\n \u003ctd align=\"char\"\u003e0.89\u003c/td\u003e\n \u003ctd align=\"char\"\u003e0.29\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eD\u003csub\u003emean\u003c/sub\u003e (Gy)\u003c/td\u003e\n \u003ctd align=\"char\"\u003e15.98\u0026thinsp;\u0026plusmn;\u0026thinsp;1.73\u003c/td\u003e\n \u003ctd align=\"char\"\u003e25.21\u0026thinsp;\u0026plusmn;\u0026thinsp;6.16\u003c/td\u003e\n \u003ctd align=\"char\"\u003e17.81\u0026thinsp;\u0026plusmn;\u0026thinsp;1.73\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\u0026lt;\u0026thinsp;0.01\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\u0026lt;\u0026thinsp;0.01\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003eFH_R\u003c/td\u003e\n \u003ctd align=\"left\"\u003eV\u003csub\u003e50Gy\u003c/sub\u003e (%)\u003c/td\u003e\n \u003ctd align=\"char\"\u003e0.00\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/td\u003e\n \u003ctd align=\"char\"\u003e0.08\u0026thinsp;\u0026plusmn;\u0026thinsp;0.23\u003c/td\u003e\n \u003ctd align=\"char\"\u003e0.00\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/td\u003e\n \u003ctd align=\"char\"\u003e0.04\u003c/td\u003e\n \u003ctd align=\"char\"\u003e0.04\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eD\u003csub\u003emean\u003c/sub\u003e (Gy)\u003c/td\u003e\n \u003ctd align=\"char\"\u003e15.51\u0026thinsp;\u0026plusmn;\u0026thinsp;1.82\u003c/td\u003e\n \u003ctd align=\"char\"\u003e25.35\u0026thinsp;\u0026plusmn;\u0026thinsp;5.73\u003c/td\u003e\n \u003ctd align=\"char\"\u003e16.92\u0026thinsp;\u0026plusmn;\u0026thinsp;2.47\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\u0026lt;\u0026thinsp;0.01\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\u0026lt;\u0026thinsp;0.01\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003ekidney_L\u003c/td\u003e\n \u003ctd align=\"left\"\u003eD\u003csub\u003emean\u003c/sub\u003e (Gy)\u003c/td\u003e\n \u003ctd align=\"char\"\u003e0.58\u0026thinsp;\u0026plusmn;\u0026thinsp;2.83\u003c/td\u003e\n \u003ctd align=\"char\"\u003e1.04\u0026thinsp;\u0026plusmn;\u0026thinsp;3.92\u003c/td\u003e\n \u003ctd align=\"char\"\u003e0.6\u0026thinsp;\u0026plusmn;\u0026thinsp;3.55\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\u0026lt;\u0026thinsp;0.01\u003c/td\u003e\n \u003ctd align=\"char\"\u003e0.14\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003ekidney_R\u003c/td\u003e\n \u003ctd align=\"left\"\u003eD\u003csub\u003emean\u003c/sub\u003e (Gy)\u003c/td\u003e\n \u003ctd align=\"char\"\u003e0.65\u0026thinsp;\u0026plusmn;\u0026thinsp;3.48\u003c/td\u003e\n \u003ctd align=\"char\"\u003e1.13\u0026thinsp;\u0026plusmn;\u0026thinsp;4.24\u003c/td\u003e\n \u003ctd align=\"char\"\u003e0.63\u0026thinsp;\u0026plusmn;\u0026thinsp;3.70\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\u0026lt;\u0026thinsp;0.01\u003c/td\u003e\n \u003ctd align=\"char\"\u003e0.39\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"4\"\u003eGI\u003c/td\u003e\n \u003ctd align=\"left\"\u003eGI\u003csub\u003e40Gy\u003c/sub\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e1.55\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08\u003c/td\u003e\n \u003ctd align=\"char\"\u003e1.72\u0026thinsp;\u0026plusmn;\u0026thinsp;0.13\u003c/td\u003e\n \u003ctd align=\"char\"\u003e1.53\u0026thinsp;\u0026plusmn;\u0026thinsp;0.10\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\u0026lt;\u0026thinsp;0.01\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\u0026lt;\u0026thinsp;0.01\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eGI\u003csub\u003e30Gy\u003c/sub\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e2.30\u0026thinsp;\u0026plusmn;\u0026thinsp;0.13\u003c/td\u003e\n \u003ctd align=\"char\"\u003e2.90\u0026thinsp;\u0026plusmn;\u0026thinsp;0.26\u003c/td\u003e\n \u003ctd align=\"char\"\u003e2.28\u0026thinsp;\u0026plusmn;\u0026thinsp;0.15\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\u0026lt;\u0026thinsp;0.01\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\u0026lt;\u0026thinsp;0.01\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eGI\u003csub\u003e20Gy\u003c/sub\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e4.64\u0026thinsp;\u0026plusmn;\u0026thinsp;0.29\u003c/td\u003e\n \u003ctd align=\"char\"\u003e5.42\u0026thinsp;\u0026plusmn;\u0026thinsp;0.43\u003c/td\u003e\n \u003ctd align=\"char\"\u003e4.71\u0026thinsp;\u0026plusmn;\u0026thinsp;0.33\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\u0026lt;\u0026thinsp;0.01\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\u0026lt;\u0026thinsp;0.01\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eGI\u003csub\u003e10Gy\u003c/sub\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e9.54\u0026thinsp;\u0026plusmn;\u0026thinsp;1.33\u003c/td\u003e\n \u003ctd align=\"char\"\u003e9.55\u0026thinsp;\u0026plusmn;\u0026thinsp;1.47\u003c/td\u003e\n \u003ctd align=\"char\"\u003e9.30\u0026thinsp;\u0026plusmn;\u0026thinsp;1.23\u003c/td\u003e\n \u003ctd align=\"char\"\u003e0.61\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\u0026lt;\u0026thinsp;0.01\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eP\u003csup\u003e+\u003c/sup\u003e indicated a comparison between A-TOMO and M-TOMO;\u003c/p\u003e\n\u003cp\u003eP\u003csup\u003e\u0026minus;\u003c/sup\u003e indicated a comparison between A-TOMO and A-VMAT.\u003c/p\u003e\n\u003cp\u003eFH_L standed for FemoralHead_L, and FH_R standed for FemoralHead_R\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eWorkflow Efficiency and Treatment Delivery Time\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRegarding the workflow efficiency, the execution time for the A-TOMO planning script was approximately only 20 minutes, whereas that for the A-VMAT planning script took slightly longer, about 30 minutes. In terms of treatment delivery efficiency, as reported by the TPS and shown in Table\u0026nbsp;\u003cspan\u003e5\u003c/span\u003e, the dose delivery time for A-TOMO plans was slightly increased compared to M-TOMO plans. In contrast, A-VMAT plans demonstrated superior dose delivery efficiency, requiring less than 3 minutes, which was significantly less than the time required for TOMO plans.\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable id=\"Tab5\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 5\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eTreatment delivery parameters\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"6\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003eBeam delivery parameters\u003c/th\u003e\n \u003cth align=\"left\"\u003eA - TOMO\u003c/th\u003e\n \u003cth align=\"left\"\u003eM - TOMO\u003c/th\u003e\n \u003cth align=\"left\"\u003eA - VMAT\u003c/th\u003e\n \u003cth align=\"left\"\u003eP\u003csup\u003e+\u003c/sup\u003e\u003c/th\u003e\n \u003cth align=\"left\"\u003eP\u003csup\u003e\u0026minus;\u003c/sup\u003e\u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eDelivery time(min)\u003c/td\u003e\n \u003ctd align=\"left\"\u003e7.76\u0026thinsp;\u0026plusmn;\u0026thinsp;0.95\u003c/td\u003e\n \u003ctd align=\"left\"\u003e5.99\u0026thinsp;\u0026plusmn;\u0026thinsp;1.42\u003c/td\u003e\n \u003ctd align=\"left\"\u003e2.63\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026lt;\u0026thinsp;0.01\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026lt;\u0026thinsp;0.01\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eP\u003csup\u003e+\u003c/sup\u003e indicated a comparison between A-TOMO and M-TOMO;\u003c/p\u003e\n\u003cp\u003eP\u003csup\u003e\u0026minus;\u003c/sup\u003e indicated a comparison between A-TOMO and A-VMAT.\u003c/p\u003e"},{"header":"IV. Discussion","content":"\u003cp\u003eIn this study, we successfully developed and validated the first automated Tomotherapy planning method for cervical cancer. This automated planning strategy not only significantly improved the quality of the plans but also enhanced work efficiency. The automated planning method we proposed could automatically generate personalized dose distributions based on the unique anatomical characteristics of different patients, offering greater generalization. The entire process required almost no manual intervention and was compatible with commercial TPS, which was easily applicable in clinical routine.\u003c/p\u003e\n\u003cp\u003eA fundamental principle of radiotherapy is ensuring adequate dose coverage to the target while minimizing the dose to OARs and normal tissue, thereby reducing unnecessary radiation-induced harm to the patient. Our study results demonstrated that A-TOMO plans significantly improved target dose conformity without sacrificing homogeneity, and reduced the dose to OARs and normal tissues compared to clinical M-TOMO plans. This was particularly true for critical indexes such as the V\u003csub\u003e50Gy\u003c/sub\u003e%, V\u003csub\u003e40Gy\u003c/sub\u003e% and D\u003csub\u003emean\u003c/sub\u003e for the rectum and bladder, as well as the D\u003csub\u003emean\u003c/sub\u003e for the bowel bag. Although no statistical significance was observed in the V\u003csub\u003e45Gy\u003c/sub\u003e (cc) for the bowel bag between the two types of plans, the mean value for A-TOMO plans was 141.83cc, lower than the 146.39cc for the M-TOMO plans. Studies indicated that further reducing the dose to the small intestine and rectum could help decrease gastrointestinal toxicity\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e24\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eThe improvements in plan quality are influenced by the experience and effort of different dosimetrists and could also be affected by differences between TPS. Notably, differences exist between the Precison TPS and RayStation TPS, with Precison TPS lacking the equivalent uniform dose (EUD) function and allowing a maximum of only three optimization objectives for each ROI. Moreover, in the TOMO optimization process within Precison TPS, each voxel is assigned to only one ROI. Considering that the quality of M-TOMO plans could inevitably be influenced by subjective human factors, we compared A-TOMO plans with A-VMAT plans. The study results showed that the quality of these two types of plans was comparable, further affirming the plan quality of A-TOMO planning. A study by Panda et al.\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/sup\u003e suggested that VMAT and TOMO were equivalent in treating cervical cancer. A finding corroborated by the experimental data from Simone et al.\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/sup\u003e, also aligned with our study results. These indicated that the A-TOMO planning method we developed further standardized TOMO planning, effectively showcasing the capabilities of TOMO technique.\u003c/p\u003e\n\u003cp\u003eDose-volume parameters are simplified substitutes for potential biological effects and didn\u0026rsquo;t necessarily reflect the entire treatment region\u0026apos;s dose distribution\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e29\u003c/span\u003e]\u003c/sup\u003e. Combining dose-volume constraints with EUD could yield better dose distributions\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e30\u003c/span\u003e]\u003c/sup\u003e. We set the EUD objective value to 0, as a previous study \u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e31\u003c/span\u003e]\u003c/sup\u003e. Additionally, studies showed that automated plans based on predicted EUD values were superior in quality to manual plans\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e33\u003c/span\u003e]\u003c/sup\u003e. We analyzed the relationship between the overlap of bladder, rectum, bowel bag and PTV with EUD (A\u0026thinsp;=\u0026thinsp;1) in A-TOMO plans, as shown in Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e. The Pearson correlation coefficients for bladder and rectum were 0.88 and 0.84, respectively, with R\u0026sup2; values of 0.78 and 0.70, indicating a strong linear relationship. It suggested that automated planning based on the predicted EUD method had research potential and was worth further exploration.\u003c/p\u003e\n\u003cp\u003eOne of the significant features of the automated planning was its high efficiency, and our study results were consistent with this view\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e34\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e36\u003c/span\u003e]\u003c/sup\u003e. In our study, A-TOMO and A-VMAT planning only required about 20 minutes and 30 minutes to complete, respectively. However, as described by dosimetrists, the M-TOMO plans required repeated adjustments and could take several hours from start to submission. Our script set two consecutive rounds of iteration, each with 100 iterations, to seek the final solution. Although we had not yet delved into the possibility of achieving an optimal solution with fewer iterations, this exploration might further shorten the script execution time. In the future, incorporating contouring into the automated planning process could realize a more complete automated planning workflow, further saving time and improving efficiency.\u003c/p\u003e\n\u003cp\u003eAlthough the A-TOMO plan improved work efficiency, the treatment delivery time was longer compared to M-TOMO plans. This could be due to several reasons, including more stringent dose constraints were set and the DTF parameter in A-TOMO plans was set to 1.7, which made the execution time of each rotation approximately 20 seconds. A previous study indicated that increasing the DTF, while improving plan quality, also led to longer dose delivery times\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e37\u003c/span\u003e]\u003c/sup\u003e. Additionally, compared to TOMO plans, VMAT plans had shorter dose delivery times, consistent with previous studies\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e38\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eCurrently, the quality of our developed A-TOMO plan had been preliminarily validated in our treatment center. To further demonstrate its effectiveness and applicability, we plan to collaborate with multiple treatment centers for broader validation. This cross-center collaboration will provide a detailed assessment of our A-TOMO planning method, ensuring its stability and reliability in different settings. Through future multi-center collaboration, we aim to provide a comprehensive and precise validation platform for the automation of cervical cancer radiation therapy planning. Moreover, given the A-TOMO planning method\u0026apos;s ability to generate high-quality and consistent plans, it has great potential to serve as a superior data source for training automated planning systems based on the atlas model. Compared to methods that use traditional manual planning as the training set, we anticipate not only improving the model\u0026apos;s performance but also creating higher-quality plans.\u003c/p\u003e"},{"header":"V. Conclusion","content":"\u003cp\u003eWe have successfully developed an automated Tomotherapy planning method in RayStation TPS for external beam radiotherapy of cervical cancer. This method not only effectively improved the quality of the plans but also significantly enhanced work efficiency. Compared to M-TOMO plans, the A-TOMO plans achieved a higher level of plan quality while significantly reducing the dose to OARs. Moreover, A-TOMO plans demonstrated dose distributions similar to those of A-VMAT plans, further validating their quality and feasibility.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003e\u003cstrong\u003eTOMO:\u0026nbsp;\u003c/strong\u003eTomotherapy\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eVMAT:\u0026nbsp;\u003c/strong\u003evolumetric modulated arc therapy\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eM-TOMO:\u0026nbsp;\u003c/strong\u003eManual TOMO\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eA-TOMO:\u0026nbsp;\u003c/strong\u003eAutomated TOMO\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eA-VMAT:\u0026nbsp;\u003c/strong\u003eAutomated VMAT\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTPS:\u0026nbsp;\u003c/strong\u003eTreatment Planning Systems\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eOARs:\u0026nbsp;\u003c/strong\u003eOrgans At Risk\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePTV:\u0026nbsp;\u003c/strong\u003ePlanning Target Volume\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRTOG:\u003c/strong\u003e Radiotherapy Oncology Group\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCTV:\u0026nbsp;\u003c/strong\u003eClinical Target Volume\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNCCN:\u0026nbsp;\u003c/strong\u003eNational Comprehensive Cancer Network\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eQUANTEC:\u0026nbsp;\u003c/strong\u003eQuantitative Analysis of Normal Tissue Effects in the Clinic\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDVH:\u0026nbsp;\u003c/strong\u003eDose Volume Histogram\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eROI:\u0026nbsp;\u003c/strong\u003eRegion Of Interest\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDTF:\u0026nbsp;\u003c/strong\u003eDelivery Time Factor\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMLC:\u0026nbsp;\u003c/strong\u003eMulti-Leaf Collimator\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCI:\u0026nbsp;\u003c/strong\u003eConformity Index\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHI:\u0026nbsp;\u003c/strong\u003eHomogeneity Index\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGI:\u0026nbsp;\u003c/strong\u003eGradient Index\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEUD:\u0026nbsp;\u003c/strong\u003eEquivalent Uniform Dose\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003cstrong\u003e：\u003c/strong\u003eFH, ZT and SJ\u0026nbsp;conceived the project.\u0026nbsp;FH, YX , SH , TL, YY\u0026nbsp;collected and analyzed the data. YC, HH,\u0026nbsp;YS, WW and ZY provided clinical expertise and definitive\u003c/p\u003e\n\u003cp\u003esupervision of the paper.\u0026nbsp;FH\u0026nbsp;drafted the preliminary manuscript and all co‐authors revised and approved the final manuscript for submission.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFundings\u003c/strong\u003e\u003cstrong\u003e：\u003c/strong\u003eThe Science \u0026amp; Technology Development Fund of Tianjin Education Commission for Higher Education (Grant no. 2021ZD034)\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBray, F., J. Ferlay, I. Soerjomataram, R. L. Siegel, L. A. Torre, and A. Jemal. \u0026quot;Global Cancer Statistics 2018: Globocan Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries.\u0026quot; [In eng]. \u003cem\u003eCA Cancer J Clin \u003c/em\u003e68, no. 6 (Nov 2018): 394-424. https://doi.org/10.3322/caac.21492.[27]\u003c/li\u003e\n\u003cli\u003eRose, P. G., B. N. Bundy, E. B. 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Nagata. \u0026quot;Impact of Delivery Time Factor on Treatment Time and Plan Quality in Tomotherapy.\u0026quot; [In eng]. \u003cem\u003eSci Rep \u003c/em\u003e13, no. 1 (Jul 27 2023): 12207. https://doi.org/10.1038/s41598-023-39047-z.[96]\u003c/li\u003e\n\u003cli\u003eW\u0026uuml;thrich, D., Z. Wang, M. Zeverino, J. Bourhis, F. Bochud, and R. Moeckli. \u0026quot;Comparison of Volumetric Modulated Arc Therapy and Helical Tomotherapy for Prostate Cancer Using Pareto Fronts.\u0026quot; [In eng]. \u003cem\u003eMed Phys \u003c/em\u003e (Dec 6 2023). https://doi.org/10.1002/mp.16868.[59]\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"radiation-oncology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"raon","sideBox":"Learn more about [Radiation Oncology](http://ro-journal.biomedcentral.com/)","snPcode":"13014","submissionUrl":"https://submission.nature.com/new-submission/13014/3","title":"Radiation Oncology","twitterHandle":"@OncoBioMed","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Automated, Tomotherpay, Volumetric Modulated Arc Therapy, radiotherapy, Treatment planning, Cervial cancer","lastPublishedDoi":"10.21203/rs.3.rs-4328154/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4328154/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003ePurpose\u003c/h2\u003e \u003cp\u003eThis study aimed to develop an automated Tomotherapy (TOMO) planning method for cervical cancer treatment, and to validate its feasibility and effectiveness.\u003c/p\u003e\u003ch2\u003eMaterials and Methods\u003c/h2\u003e \u003cp\u003eThe study enrolled 30 cervical cancer patients treated with TOMO at our center. Utilizing scripting and Python environment within the RayStation (RaySearch Labs, Sweden) treatment planning system (TPS), we developed automated planning methods for TOMO and volumetric modulated arc therapy (VMAT) techniques. The clinical manual TOMO (M-TOMO) plans for the 30 patients were re-optimized using automated planning scripts for both TOMO and VMAT, creating automated TOMO (A-TOMO) and automated VMAT (A-VMAT) plans. we compared it with M-TOMO and A-VMAT plans. The primary evaluated relevant dosimetric parameters and treatment plan efficiency were assessed using the two-sided Wilcoxon signed-rank test for statistical analysis,with a P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 indicating statistical significance.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eA-TOMO plans maintained similar target dose uniformity compared to M-TOMO plans, with improvements in target conformity and faster dose drop-off outside the target, and demonstrated significant statistical differences (P\u003csup\u003e+\u003c/sup\u003e\u0026lt;0.01). A-TOMO plans also significantly outperformed M-TOMO plans in reducing V\u003csub\u003e50Gy\u003c/sub\u003e, V\u003csub\u003e40Gy\u003c/sub\u003e, and D\u003csub\u003emean\u003c/sub\u003e for the bladder and rectum, as well as D\u003csub\u003emean\u003c/sub\u003e for the bowel bag, femoral heads, and kidneys (all P\u003csup\u003e+\u003c/sup\u003e\u0026lt;0.05). Additionally, A-TOMO plans demonstrated better consistency in plan quality. Furthermore, the quality of A-TOMO plans was comparable to or superior than A-VMAT plans. In terms of efficiency, A-TOMO significantly reduced the time required for treatment planning to approximately 20 minutes.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eWe have successfully developed an A-TOMO planning method for cervical cancer. Compared to M-TOMO plans, A-TOMO plans improved target conformity and reduced radiation dose to OARs. Additionally, the quality of A-TOMO plans was on par with or surpasses that of A-VMAT plans. The A-TOMO planning method significantly improved the efficiency of treatment planning.\u003c/p\u003e","manuscriptTitle":"Development and validation of an automated Tomotherapy planning method for cervical cancer","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-05-08 14:44:22","doi":"10.21203/rs.3.rs-4328154/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-05-27T06:22:59+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-05-24T15:05:19+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-05-24T07:44:53+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"339497555974616955168526868640524778469","date":"2024-05-14T08:50:55+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"209833994737853869482218600946301700944","date":"2024-05-14T01:17:34+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-05-13T14:53:03+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-05-09T10:34:52+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-05-01T08:57:21+00:00","index":"","fulltext":""},{"type":"submitted","content":"Radiation Oncology","date":"2024-04-26T08:06:30+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"radiation-oncology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"raon","sideBox":"Learn more about [Radiation Oncology](http://ro-journal.biomedcentral.com/)","snPcode":"13014","submissionUrl":"https://submission.nature.com/new-submission/13014/3","title":"Radiation Oncology","twitterHandle":"@OncoBioMed","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"36874da4-5623-4835-9591-cf4f58f40867","owner":[],"postedDate":"May 8th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2024-06-10T06:26:15+00:00","versionOfRecord":[],"versionCreatedAt":"2024-05-08 14:44:22","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4328154","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4328154","identity":"rs-4328154","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}