Automated Radiotherapy Planning for Patient-Specific Exploration of the Trade-Off Between Tumor Dose Coverage and Predicted Radiation-Induced Toxicity—A Proof of Principle Study for Prostate Cancer

Abstract

Background: Currently, radiation-oncologists generally evaluate a single treatment plan for each patient that is possibly adapted by the planner prior to final approval. There is no systematic exploration of patient-specific trade-offs between planning aims, using a set of treatment plans with a-priori defined (slightly) different balances. To this purpose, we developed an automated workflow and explored its use for prostate cancer. Materials and Methods: For each of the 50 study patients, seven plans were generated, including the so-called clinical plan, with currently clinically desired ≥99% dose coverage for the low-dose planning target volume (PTV Low). The six other plans were generated with different, reduced levels of PTV Low coverage, aiming at reductions in rectum dose and consequently in predicted grade≥2 late gastro-intestinal (GI) normal tissue complication probabilities (NTCPs), while keeping other dosimetric differences small. The applied NTCP model included diabetes as a non-dosimetric predictor. All plans were generated with a clinically applied, in-house developed algorithm for automated multi-criterial plan generation. Results: With diabetes, the average NTCP reduced from 24.9 ± 4.5% for ≥99% PTV Low coverage to 17.3 ± 2.6% for 90%, approaching the NTCP (15.4 ± 3.0%) without diabetes and full PTV Low coverage. Apart from intended differences in PTV Low coverage and rectum dose, other differences between the clinical plan and the six alternatives were indeed minor. Obtained NTCP reductions were highly patient-specific (ranging from 14.4 to 0.1%), depending on patient anatomy. Even for patients with equal NTCPs in the clinical plan, large differences were found in NTCP reductions. Conclusions: A clinically feasible workflow has been proposed for systematic exploration of patient-specific trade-offs between various treatment aims. For each patient, automated planning is used to generate a limited set of treatment plans with Bijman et al. Automated Planning for Trade-Off Exploration well-defined variations in the balances between the aims. For prostate cancer, trade-offs between PTV Low coverage and predicted GI NTCP were explored. With relatively small coverage reductions, significant NTCP reductions could be obtained, strongly depending on patient anatomy. Coverage reductions could also make up for enhanced NTCPs related to diabetes as co-morbidity, again dependent on the patient. The proposed system can play an important role in further personalization of patient care.

Personalized Treatment Planning Automation in Prostate Cancer Radiation Oncology: A Comprehensive Dosimetric Study

“…On the contrary, in their multi-center study ( 37 ), the Erasmus-ICycle engine was used for MCO-based automation of prostate cancer planning in four Centers, reporting an overall dosimetric superiority of automated plans in terms of rectal dose reduction. The same research group used the ICycle engine to explore the patient-specific trade-offs between planning aims in prostate cancer ( 38 ). The authors reported significant NTCP reductions for rectal toxicity and underlined the role of automated approach for personalization of patient care.…”

Automated multi-criterial planning with beam angle optimization to establish non-coplanar VMAT class solutions for nasopharyngeal carcinoma

“…To the best of our knowledge, the use of automated planning for development and evaluation of VMAT CSs has never been investigated for NPC. Many studies have demonstrated superior plan quality for automated planning compared to manual planning, for many tumor sites [14,[16][17][18][19][20][21][22][23][24][25][26][27]. Especially for development of CSs, high plan quality is required, as the aim is to in principle treat all future patients with the developed CS.…”

“…All plans used for this study were automatically generated with Erasmus-iCycle [14,15]. For many tumor sites, this system has demonstrated superiority of automatically generated plans, over manually generated plans [14,[16][17][18][19][20][21][22][23][24][25][26][27].…”

“…Subsequently, a commercial treatment planning system is used for automated segmentation of the generated FMO plan, mimicking the FMO dose distribution [11,14,[27][28][29]. For VMAT, an FMO plan with equi-angular IMRT beams is generated with Erasmus-iCycle, which is then automatically segmented in the commercial TPS for VMAT delivery [14,[16][17][18][19][20][21][22][23][25][26][27]29].…”

Can the use of knowledge-based planning systems improve stereotactic radiotherapy planning? A systematic review

“…Radiotherapy planning aims to create a treatment that ensures adequate tumour volume irradiation to deliver the highest expected therapeutic ratio while limiting the dose to organs at risk (OARs) to maintain an acceptable toxicity profile. 4 There are secondary aims of ensuring a highly conformal prescription dose to the PTV and limiting the amount of radiation fall off away from the PTV.…”