Comparison of biological-based and dose volume-based intensity-modulated radiotherapy plans generated using the same treatment planning system

Comparing biological and physical cost functions in VMAT planning for pediatric nasopharyngeal cancer

BACKGROUND

Volumetric Modulated Arc Therapy (VMAT) is an option for the delivery of Radiotherapy treatment technique for pediatric nasopharyngeal cancer, VMAT is the most common treatment technique for pediatric nasopharyngeal cancer. The use of a combination of both biological and physical parameters in VMAT planning optimization may produce better target coverage and sparing of critical organs. This work was to compare Biological Cost Functions (BCFs) and Physical Cost Functions (PCFs) in the VMAT of pediatric nasopharyngeal cancer patients.

METHOD

VMAT plans for 20 nasopharyngeal pediatric cancer patients were created using Monaco 5.11® treatment planning system (TPS). Three VMAT plans were retrospectively generated for each patient using BCFs, PCFs and mixed plan with a total dose of 61.2 Gy in 34 fractions to planning target volume (PTV). All plans were adjusted to deliver 95% of the prescribed dose to 95% of the PTV. The calculated plans were qualitatively and quantitatively evaluated using the dose-volume histogram (DVH).

RESULTS

The coverage of the target and the maximum dose for the three plans were nearly the same, and better sparing was achieved in the serial organs (spinal cord and brain stem) with PCFs. On the contrary, more dose spring was observed using the BCFs in the organs at risk (OARs) that were not involved in the dose optimization, such as the optic nerve maximum dose, with a significant p-value (0.035 and 0.0001) respectively. Using the PCFs, both parotids received a lower mean dose, but not for the oral cavity, which had a lower mean dose using BCFs (p=<0.0001). The same values of tumor control probability (TCP) were found for both cost functions in PTVs and normal tissue complications probability (NTCP) (99%). The values reported were as follows: spinal cord = 0.5%, brain stem = 19.1%, and brain = 90.7% for BCFs, compared to spinal cord = 0.3%, brain stem = 14.9%, and brain = 90.7% for PCFs. The delivery time was found to be less in BCFs (p=0.005).

CONCLUSION

The BCFs are superior to the PCFs in conformity index and time of radiation delivery. However, PCFs were better at dose sparing for the serial organs and achieving a sharper falloff dose around the involved volumes. A patient-specific clinical compromise is recommended to gain the best plan that meets the clinical goals.

Rapid Progress in Intelligent Radiotherapy and Future Implementation

Radiotherapy is one of the major approaches to cancer treatment. Artificial intelligence in radiotherapy (shortly, Intelligent radiotherapy) mainly involves big data, deep learning, extended reality, digital twin, radiomics, Internet plus and Internet of Things (IoT), which establish an automatic and intelligent network platform consisting of radiotherapy preparation, target volume delineation, treatment planning, radiation delivery, quality assurance (QA) and quality control (QC), prognosis judgment and post-treatment follow-up. Intelligent radiotherapy is an interdisciplinary frontier discipline in infancy. The review aims to summary the important implements of intelligent radiotherapy in various areas and put forward the future of unmanned radiotherapy center.

Split Common Coincidence Point Problem: A Formulation Applicable to (Bio)Physically-Based Inverse Planning Optimization

Inverse planning is a method of radiotherapy treatment planning where the care team begins with the desired dose distribution satisfying prescribed clinical objectives, and then determines the treatment parameters that will achieve it. The variety in symmetry, form, and characteristics of the objective functions describing clinical criteria requires a flexible optimization approach in order to obtain optimized treatment plans. Therefore, we introduce and discuss a nonlinear optimization formulation called the split common coincidence point problem (SCCPP). We show that the SCCPP is a suitable formulation for the inverse planning optimization problem with the flexibility of accommodating several biological and/or physical clinical objectives. Also, we propose an iterative algorithm for approximating the solution of the SCCPP, and using Bregman techniques, we establish that the proposed algorithm converges to a solution of the SCCPP and to an extremum of the inverse planning optimization problem. We end with a note on useful insights on implementing the algorithm in a clinical setting.

Cone-Beam-CT Guided Adaptive Radiotherapy for Locally Advanced Non-small Cell Lung Cancer Enables Quality Assurance and Superior Sparing of Healthy Lung

Purpose

To evaluate the potential of cone-beam-CT (CB-CT) guided adaptive radiotherapy (ART) for locally advanced non-small cell lung cancer (NSCLC) for sparing of surrounding organs-at-risk (OAR).

Materials and Methods

In 10 patients with locally advanced NSCLC, daily CB-CT imaging was acquired during radio- (n = 4) or radiochemotherapy (n = 6) for simulation of ART. Patients were treated with conventionally fractionated intensity-modulated radiotherapy (IMRT) with total doses of 60–66 Gy (pPlan) (311 fraction CB-CTs). OAR were segmented on every daily CB-CT and the tumor volumes were modified weekly depending on tumor changes. Doses actually delivered were recalculated on daily images (dPlan), and voxel-wise dose accumulation was performed using a deformable registration algorithm. For simulation of ART, treatment plans were adapted using the new contours and re-optimized weekly (aPlan).

Results

CB-CT showed continuous tumor regression of 1.1 ± 0.4% per day, leading to a residual gross tumor volume (GTV) of 65.3 ± 13.4% after 6 weeks of radiotherapy (p = 0.005). Corresponding PTVs decreased to 83.7 ± 7.8% (p = 0.005). In the actually delivered plans (dPlan), both conformity (p = 0.005) and homogeneity (p = 0.059) indices were impaired compared to the initial plans (pPlan). This resulted in higher actual lung doses than planned: V_20Gy was 34.6 ± 6.8% instead of 32.8 ± 4.9% (p = 0.066), mean lung dose was 19.0 ± 3.1 Gy instead of 17.9 ± 2.5 Gy (p = 0.013). The generalized equivalent uniform dose (gEUD) of the lung was 18.9 ± 3.1 Gy instead of 17.8 ± 2.5 Gy (p = 0.013), leading to an increased lung normal tissue complication probability (NTCP) of 15.2 ± 13.9% instead of 9.6 ± 7.3% (p = 0.017). Weekly plan adaptation enabled decreased lung V_20Gy of 31.6 ± 6.2% (−3.0%, p = 0.007), decreased mean lung dose of 17.7 ± 2.9 Gy (−1.3 Gy, p = 0.005), and decreased lung gEUD of 17.6 ± 2.9 Gy (−1.3 Gy, p = 0.005). Thus, resulting lung NTCP was reduced to 10.0 ± 9.5% (−5.2%, p = 0.005). Target volume coverage represented by conformity and homogeneity indices could be improved by weekly plan adaptation (CI: p = 0.007, HI: p = 0.114) and reached levels of the initial plan (CI: p = 0.721, HI: p = 0.333).

Conclusion

IGRT with CB-CT detects continuous GTV and PTV changes. CB-CT-guided ART for locally advanced NSCLC is feasible and enables superior sparing of healthy lung at high levels of plan conformity.