Automated Field-In-Field (FIF) Plan Framework Combining Scripting Application Programming Interface and User-Executed Program for Breast Forward IMRT

Automated treatment planning for whole breast irradiation with individualized tangential IMRT fields

PURPOSES

This study aimed to develop and validate algorithms for automating intensity modulated radiation therapy (IMRT) planning in breast cancer patients, with a focus on patient anatomical characteristics.

MATERIAL AND METHODS

We retrospectively selected 400 breast cancer patients without lymph node involvement for automated treatment planning. Automation was achieved using the Eclipse Scripting Application Programming Interface (ESAPI) integrated into the Eclipse Treatment Planning System. We employed three beam insertion geometries and three optimization strategies, resulting in 3600 plans, each delivering a 40.05 Gy dose in 15 fractions. Gantry angles in the tangent fields were selected based on a criterion involving the minimum intersection area between the Planning Target Volume (PTV) and the ipsilateral lung in the Beam's Eye View projection. ESAPI was also used to gather patient anatomical data, serving as input for Random Forest models to select the optimal plan. The Random Forest classification considered both beam insertion geometry and optimization strategy. Dosimetric data were evaluated in accordance with the Radiation Therapy Oncology Group (RTOG) 1005 protocol.

RESULTS

Overall, all approaches generated high-quality plans, with approximately 94% meeting the acceptable dose criteria for organs at risk and/or target coverage as defined by RTOG guidelines. Average automated plan generation time ranged from 6 min and 37 s to 9 min and 22 s, with the mean time increasing with additional fields. The Random Forest approach did not successfully enable automatic planning strategy selection. Instead, our automated planning system allows users to choose from the tested geometry and strategy options.

CONCLUSIONS

Although our attempt to correlate patient anatomical features with planning strategy using machine learning tools was unsuccessful, the resulting dosimetric outcomes proved satisfactory. Our algorithm consistently produced high-quality plans, offering significant time and efficiency advantages.

Association of radiotherapy for stage I-III breast cancer survivors and second primary malignant cancers: a population-based study

PURPOSE

With life span extending, breast cancer survivors may face the possibility of developing second primary cancers (SPCs). The objective of this research is to investigate the risk factors, risk attribute to radiotherapy and the survivalship for SPCs.

METHODS

A total of 445 523 breast cancer patients were enrolled from Surveillance, Epidemiology, and End Results database in 2000-2018. The risk factors for SPCs development were confirmed by competing risk model, and then were integrated to the nomogram establishment. The cumulative incidence of SPCs including SBC (second breast cancer), SGC (second gynecological cancer), and SLC (second lung cancer) were estimated. The radiotherapy-associated risk for SPCs were evaluated by Poisson regression in radiotherapy and no-radiotherapy. Propensity score matching was used to reduce possible bias for survival comparison.

RESULTS

There were 57.63% patients in radiotherapy. The risk factors for developing SPCs were age, year, race, tumor size, stage, radiotherapy, grade, surgery, and histology. The cumulative incidence of SPCs was 7.75% in no-radiotherapy and 10.33% in radiotherapy. SLC, SBC, and SGC also appeared the similar results. The increased risk of developing SPCs were associated with radiotherapy in majority subgroups. The dynamic radiotherapy-associated risk for SPCs by age slightly increased risk was observed. Regardless radiotherapy or no-radiotherapy, the 10-year overall survival for SBC (radiotherapy: 59.41%; no-radiotherapy: 55.53%) and SGC (radiotherapy: 48.61%; no-radiotherapy: 35.53%) were worse than that among matched patients with only primary cancers.

CONCLUSIONS

Breast cancer survivors remained a high radiotherapy-associated risk for developing SPCs. The prognosis in radiotherapy was better than in no-radiotherapy for some specific SPCs. Largely attention should be paid to these patients.

Improving normal tissue sparing using scripting in endometrial cancer radiation therapy planning

The aim of this study was to improve the protection of organs at risk (OARs), decrease the total planning time and maintain sufficient target doses using scripting endometrial cancer external beam radiation therapy (EBRT) planning. Computed tomography (CT) data of 14 endometrial cancer patients were included in this study. Manual and automatic planning with scripting were performed for each CT. Scripts were created in the RayStation™ (RaySearch Laboratories AB, Stockholm, Sweden) planning system using a Python code. In scripting, seven additional contours were automatically created to reduce the OAR doses. The scripted and manual plans were compared to each other in terms of planning time, dose-volume histogram (DVH) parameters, and total monitor unit (MU) values. While the mean total planning time for manual planning was 368 ± 8 s, it was only 55 ± 2 s for the automatic planning with scripting (p < 0.001). The mean doses of OARs decreased with automatic planning (p < 0.001). In addition, the maximum doses (D2% and D1%) for bilateral femoral heads and the rectum were significantly reduced. It was observed that the total MU value increased from 1146 ± 126 (manual planning) to 1369 ± 95 (scripted planning). It is concluded that scripted planning has significant time and dosimetric advantages over manual planning for endometrial cancer EBRT planning.

Clinical application of a template-guided automated planning routine

PURPOSE

Determine the dosimetric quality and the planning time reduction when utilizing a template-based automated planning application.

METHODS

A software application integrated through the treatment planning system application programing interface, QuickPlan, was developed to facilitate automated planning using configurable templates for contouring, knowledge-based planning structure matching, field design, and algorithm settings. Validations are performed at various levels of the planning procedure and assist in the evaluation of readiness of the CT image, structure set, and plan layout for automated planning. QuickPlan is evaluated dosimetrically against 22 hippocampal-avoidance whole brain radiotherapy patients. The required times to treatment plan generation are compared for the validations set as well as 10 prospective patients whose plans have been automated by QuickPlan.

RESULTS

The generations of 22 automated treatment plans are compared against a manual replanning using an identical process, resulting in dosimetric differences of minor clinical significance. The target dose to 2% volume and homogeneity index result in significantly decreased values for automated plans, whereas other dose metric evaluations are nonsignificant. The time to generate the treatment plans is reduced for all automated plans with a median difference of 9' 50″ ± 4' 33″.

CONCLUSIONS

Template-based automated planning allows for reduced treatment planning time with consistent optimization structure creation, treatment field creation, plan optimization, and dose calculation with similar dosimetric quality. This process has potential expansion to numerous disease sites.

Automated field-in-field whole brain radiotherapy planning

PURPOSE

We developed and tested an automatic field-in-field (FIF) solution for whole-brain radiotherapy (WBRT) planning that creates a homogeneous dose distribution by minimizing hotspots, resulting in clinically acceptable plans.

METHODS

A configurable auto-planning algorithm was developed to automatically generate FIF WBRT plans independent of the treatment planning system. Configurable parameters include the definition of hotspots, target volume, maximum number of subfields, and minimum number of monitor units per field. This algorithm iteratively identifies a hotspot, creates two opposing subfields, calculates the dose, and optimizes the beam weight based on user-configured constraints of dose-volume histogram coverage and least-squared cost functions. The algorithm was retrospectively tested on 17 whole-brain patients. First, an in-house landmark-based automated beam aperture technique was used to generate the treatment fields and initial plans. Second, the FIF algorithm was employed to optimize the plans using physician-defined goals of 99.9% of the brain volume receiving 100% of the prescription dose (30 Gy in 10 fractions) and a target hotspot definition of 107% of the prescription dose. The final auto-optimized plans were assessed for clinical acceptability by an experienced radiation oncologist using a five-point scale.

RESULTS

The FIF algorithm reduced the mean (± SD) plan hotspot percentage dose from 35.0 Gy (116.6%) ± 0.6 Gy (2.0%) to 32.6 Gy (108.8%) ± 0.4 Gy (1.2%). Also, it decreased the mean (± SD) hotspot V107% [cm³ ] from 959 ± 498 cm³ to 145 ± 224 cm³ . On average, plans were produced in 16 min without any user intervention. Furthermore, 76.5% of the auto-plans were clinically acceptable (needing no or minor stylistic edits), and all of them were clinically acceptable after minor clinically necessary edits.

CONCLUSIONS

This algorithm successfully produced high-quality WBRT plans and can improve treatment planning efficiency when incorporated into an automatic planning workflow.

Automation of radiation treatment planning for rectal cancer

Purpose

To develop an automated workflow for rectal cancer three‐dimensional conformal radiotherapy (3DCRT) treatment planning that combines deep learning (DL) aperture predictions and forward‐planning algorithms.

Methods

We designed an algorithm to automate the clinical workflow for 3DCRT planning with field aperture creations and field‐in‐field (FIF) planning. DL models (DeepLabV3+ architecture) were trained, validated, and tested on 555 patients to automatically generate aperture shapes for primary (posterior–anterior [PA] and opposed laterals) and boost fields. Network inputs were digitally reconstructed radiographs, gross tumor volume (GTV), and nodal GTV. A physician scored each aperture for 20 patients on a 5‐point scale (>3 is acceptable). A planning algorithm was then developed to create a homogeneous dose using a combination of wedges and subfields. The algorithm iteratively identifies a hotspot volume, creates a subfield, calculates dose, and optimizes beam weight all without user intervention. The algorithm was tested on 20 patients using clinical apertures with varying wedge angles and definitions of hotspots, and the resulting plans were scored by a physician. The end‐to‐end workflow was tested and scored by a physician on another 39 patients.

Results

The predicted apertures had Dice scores of 0.95, 0.94, and 0.90 for PA, laterals, and boost fields, respectively. Overall, 100%, 95%, and 87.5% of the PA, laterals, and boost apertures were scored as clinically acceptable, respectively. At least one auto‐plan was clinically acceptable for all patients. Wedged and non‐wedged plans were clinically acceptable for 85% and 50% of patients, respectively. The hotspot dose percentage was reduced from 121% (σ = 14%) to 109% (σ = 5%) of prescription dose for all plans. The integrated end‐to‐end workflow of automatically generated apertures and optimized FIF planning gave clinically acceptable plans for 38/39 (97%) of patients.

Conclusion

We have successfully automated the clinical workflow for generating radiotherapy plans for rectal cancer for our institution.

Automated hybrid volumetric modulated arc therapy (HVMAT) for whole-breast irradiation with simultaneous integrated boost to lumpectomy area : A treatment planning study

PURPOSE

To develop an automated treatment planning approach for whole breast irradiation with simultaneous integrated boost using an automated hybrid VMAT class solution (HVMAT).

MATERIALS AND METHODS

Twenty-five consecutive patients with left breast cancer received 50 Gy (2 Gy/fraction) to the whole breast and an additional simultaneous 10 Gy (2.4 Gy/fraction) to the tumor cavity. Ipsilateral lung, heart, and contralateral breast were contoured as main organs-at-risk. HVMAT plans were inversely optimized by combining two open fields with a VMAT semi-arc beam. Open fields were setup to include the whole breast with a 2 cm flash region and to carry 80% of beams weight. HVMAT plans were compared with three tangential techniques: conventional wedged-field tangential plans (SWF), field-in-field forward planned tangential plans (FiF), and hybrid-IMRT plans (HMRT). Dosimetric differences among the plans were evaluated using Kruskal-Wallis one-way analysis of variance. Dose accuracy was validated using the PTW Octavius-4D phantom together with the 1500 2D-array.

RESULTS

No significant differences were found among the four techniques for both targets coverage. HVMAT plans showed consistently better PTVs dose contrast, conformity, and homogeneity (p < 0.001 for all metrics) and statistically significant reduction of high-dose breast irradiation. V55 and V60 decreased by 30.4, 26.1, and 20.8% (p < 0.05) and 12.3, 9.9, and 6.0% (p < 0.05) for SWF, FIF, and HMRT, respectively. Pretreatment dose verification reported a gamma pass-rate greater than the acceptance threshold of 95% for all HVMAT plans. In addition, HVMAT reduced the time for full planning optimization to about 20 min.

CONCLUSIONS

HVMAT plans resulted in superior target dose conformity and homogeneity compared to other tangential techniques. Due to fast planning time HVMAT can be applied for all patients, minimizing the impact on human or departmental resources.

Assessing the performance of an automated breast treatment planning software

Purpose

To assess the dosimetric performance of an automated breast planning software.

Methods

We retrospectively reviewed 15 breast cancer patients treated with tangent fields according to the RTOG 1005 protocol and 30 patients treated off‐protocol. Planning with electronic compensators (eComps) via manual, iterative fluence editing was compared to an automated planning program called EZFluence (EZF) (Radformation, Inc.). We compared the minimum dose received by 95% of the volume (D95%), D90%, the volume receiving at least 105% of prescription (V105%), V95%, the conformity index of the V95% and PTV volumes (CI95%), and total monitor units (MUs). The PTV_Eval structure generated by EZF was compared to the RTOG 1005 breast PTV_Eval structure.

Results

The average D95% was significantly greater for the EZF plans, 95.0%, vs. the original plans 93.2% (P = 0.022). CI95% was less for the EZF plans, 1.18, than the original plans, 1.48 (P = 0.09). D90% was only slightly greater for EZF, averaging at 98.3% for EZF plans and 97.3% for the original plans (P = 0.0483). V105% (cc) was, on average, 27.8cc less in the EZF breast plans, which was significantly less than for those manually planned. The average number of MUs for the EZF plans, 453, was significantly less than original protocol plans, 500 (P = 8 × 10⁻⁶). The average difference between the protocol PTV volume and the EZF PTV volume was 196 cc, with all but two cases having a larger EZF PTV volume (P = 0.020).

Conclusion

EZF improved dose homogeneity, coverage, and MU efficiency vs. manually produced eComp plans. The EZF‐generated PTV eval is based on the volume encompassed by the tangents, and is not appropriate for dosimetric comparison to constraints for RTOG 1005 PTV eval. EZF produced dosimetrically similar or superior plans to manual, iteratively derived plans and may also offer time and efficiency benefits.

A Step Toward Making VMAT TBI More Prevalent: Automating the Treatment Planning Process

PURPOSE

Our purpose was to automate the treatment planning process for total body irradiation (TBI) with volumetric modulated arc therapy (VMAT).

METHODS AND MATERIALS

Two scripts were developed to facilitate autoplanning: the binary plug-in script automating the creation of optimization structures, plan generation, beam placement, and setting of the optimization constraints and the stand-alone executable performing successive optimizations. Ten patients previously treated in our clinic with VMAT TBI were used to evaluate the efficacy of the proposed autoplanning process. Paired t tests were used to compare the dosimetric indices of the produced auto plans to the manually generated clinical plans. In addition, 3 physicians were asked to evaluate the manual and autoplans for each patient in a blinded retrospective review.

RESULTS

No significant differences were observed between the manual and autoplan global D_max (P < .893), planning target volume V110% (P < .734), kidneys D_mean (P < .351), and bowel D_max (P < .473). Significant decreases in the D_mean to the lungs and lungs-1cm (ie, lungs with 1-cm inner margin) volumes of 5.4% ± 6.4% (P < .024) and 6.8% ± 7.4% (P < .017), respectively, were obtained with the autoplans compared with the manual plans. The autoplans were selected 77% of the time by the reviewing physicians as equivalent or superior to the manual plans. The required time for treatment planning was estimated to be 2 to 3 days for the manual plans compared with approximately 3 to 5 hours for the autoplans.

CONCLUSIONS

Large reductions in planning time without sacrificing plan quality were obtained using the developed autoplanning process compared with manual planning, thus reducing the required effort of the treatment planning team. Superior lung sparing with the same target coverage and similar global D_max were observed with the autoplans as compared with the manual treatment plans. The developed scripts have been made open-source to improve access to VMAT TBI at other institutions and clinics.

Automated Hypofractionated IMRT treatment planning for early-stage breast Cancer

Background

Hypofractionated whole-breast irradiation is a standard adjuvant therapy for early-stage breast cancer. This study evaluates the plan quality and efficacy of an in-house-developed automated radiotherapy treatment planning algorithm for hypofractionated whole-breast radiotherapy.

Methods

A cohort of 99 node-negative left-sided breast cancer patients completed hypofractionated whole-breast irradiation with six-field IMRT for 42.56 Gy in 16 daily fractions from year 2016 to 2018 at a tertiary center were re-planned with an in-house-developed algorithm. The automated plan-generating C#-based program is developed in a Varian ESAPI research mode. The dose-volume histogram (DVH) and other dosimetric parameters of the automated and manual plans were directly compared.

Results

The average time for generating an autoplan was 5 to 6 min, while the manual planning time ranged from 1 to 1.5 h. There was only a small difference in both the gantry angles and the collimator angles between the autoplans and the manual plans (ranging from 2.2 to 5.3 degrees). Autoplans and manual plans performed similarly well in hotspot volume and PTV coverage, with the autoplans performing slightly better in the ipsilateral-lung-sparing dose parameters but were inferior in contralateral-breast-sparing. The autoplan dosimetric quality did not vary with different breast sizes, but for manual plans, there was worse ipsilateral-lung-sparing (V_4Gy) in larger or medium-sized breasts than in smaller breasts. Autoplans were generally superior than manual plans in CI (1.24 ± 0.06 vs. 1.30 ± 0.09, p < 0.01) and MU (1010 ± 46 vs. 1205 ± 187, p < 0.01).

Conclusions

Our study presents a well-designed standardized fully automated planning algorithm for optimized whole-breast radiotherapy treatment plan generation. A large cohort of 99 patients were re-planned and retrospectively analyzed. The automated plans demonstrated similar or even better dosimetric quality and efficacy in comparison with the manual plans. Our result suggested that the autoplanning algorithm has great clinical applicability potential.

Automated planning of whole breast irradiation using hybrid IMRT improves efficiency and quality

Purpose

To develop an automated workflow for whole breast irradiation treatment planning using hybrid intensity modulated radiation therapy (IMRT) approach and to demonstrate that this workflow can improve planning quality and efficiency when compared to manual planning.

Methods

The auto planning framework was built based on scripting with MIM and Pinnacle systems. MIM workflows were developed to automatically segment normal structures and targets, identify landmarks for beam placement, select beam energies, and set beam configurations. Pinnacle scripts were generated from the MIM workflow to create hybrid IMRT plans automatically. Each hybrid IMRT plan included two prescriptions: a three‐dimensional (3D) prescription consisted of two open tangent beams, and an IMRT prescription consisted of two step‐and‐shoot IMRT beams. The 3D prescription delivered a full prescription dose to the maximum dose point, and the IMRT prescription was optimized to deliver a uniform dose to the entire breast while sparing dose to the normal structures. For 30 patients, the auto plans were compared with clinically accepted manual plans using the paired sample t‐test.

Results

The auto planning process took approximately 8 min to complete. The mean dice coefficients between auto‐segmentation and manual contours were 0.98, 0.94 and 0.88 for the lungs, heart, and PTVeval_Breast, respectively. The MUs of the auto plans was on average 13% higher than that of the manual plans. Auto planning improved plan quality significantly: percentage volume receiving 95% of the prescription dose (V95%) of the PTVeval_Breast increased from 91.5% to 93.2% (P = 0.001), V105% of the PTVeval_Breast decreased from 7.2% to 1.2% (P = 0.013), V20Gy of the ipsilateral lung decreased from 13.1% to 10.4% (P = 0.001) and mean heart dose for left‐sided breast patients decreased from 1.2 Gy to 0.9 Gy (P < 0.001).

Conclusion

An automated treatment planning process can make the planning process efficient with improved plan quality.

Integration of biological factors in the treatment plan evaluation in breast cancer radiotherapy

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This study suggests a PQM methodology for breast cancer radiotherapy evaluation.

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The risk/benefit balance estimation includes tumor biology and smoking status.

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Smoking status influenced risk/benefit balance for different treatment techniques.

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Survival benefit dominated for all patients with high-risk breast cancer.

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Survival benefit for smokers with low- or intermediate- risk cancer was not seen.

Background/purpose

Tumor biology and patient smoking status have clear effects on the benefit of breast radiotherapy. This study developed treatment evaluation strategies that integrated dosimetry, tumor aggressiveness and smoking status for patients undergoing hypo-fractionated whole breast irradiation with simultaneous integrated boost.

Materials/methods

The evaluation method Plan Quality Metrics (PQM) was adapted for breast cancer. Radiotherapy (RT) benefit was assessed for three levels of tumor aggressiveness; RT risk was estimated using mean dose to organs at risk and published Excess Relative Risk per Gy data for lung cancer and cardiac mortality for smokers and non-smokers. Risk for contralateral breast cancer was also evaluated. PQM and benefit/risk was applied to four patient groups (n = 10 each). Plans using 3D conformal radiotherapy (3DCRT), 3DCRT plus intensity-modulated radiation therapy (IMRT), 3DCRT plus volumetric modulated arc therapy (VMAT) and VMAT were evaluated for each patient.

Results

3DCRT-IMRT hybrid planning resulted in higher PQM score (median 87.0 vs. 3DCRT 82.4, p < 0.01), better dose conformity, lower doses to the heart, lungs and contralateral breast. Survival benefit was most predominant for patients with high-risk breast cancer (>7% and >4.5% gain for non-smokers and smokers). For smokers with intermediate- or low-risk breast cancer, RT induced mortality risk dominated for all techniques. When considering the risk of local recurrence, RT benefitted also smokers (>5% and >2% for intermediate- and low-risk cancer).

Conclusions

PQM methodology was suggested for breast cancer radiotherapy evaluation. Further validation is needed. RT was beneficial for all patients with high risk of recurrence. A survival benefit for smokers with low or intermediate risk of recurrence could not be confirmed.