Measurement uncertainty analysis of low-dose-rate prostate seed brachytherapy: post-implant dosimetry

Artificial intelligence applications in brachytherapy: A literature review

PURPOSE

Artificial intelligence (AI) has the potential to simplify and optimize various steps of the brachytherapy workflow, and this literature review aims to provide an overview of the work done in this field.

METHODS AND MATERIALS

We conducted a literature search in June 2022 on PubMed, Embase, and Cochrane for papers that proposed AI applications in brachytherapy.

RESULTS

A total of 80 papers satisfied inclusion/exclusion criteria. These papers were categorized as follows: segmentation (24), registration and image processing (6), preplanning (13), dose prediction and treatment planning (11), applicator/catheter/needle reconstruction (16), and quality assurance (10). AI techniques ranged from classical models such as support vector machines and decision tree-based learning to newer techniques such as U-Net and deep reinforcement learning, and were applied to facilitate small steps of a process (e.g., optimizing applicator selection) or even automate the entire step of the workflow (e.g., end-to-end preplanning). Many of these algorithms demonstrated human-level performance and offer significant improvements in speed.

CONCLUSIONS

AI has potential to augment, automate, and/or accelerate many steps of the brachytherapy workflow. We recommend that future studies adhere to standard reporting guidelines. We also stress the importance of using larger sample sizes and reporting results using clinically interpretable measures.

Feasibility and Clinical Value of CT-Guided 125I Brachytherapy for Pain Palliation in Patients With Breast Cancer and Bone Metastases After External Beam Radiotherapy Failure

Objectives: To evaluate the feasibility and clinical value of CT-guided iodine-125 (¹²⁵I) brachytherapy for pain palliation in patients with breast cancer and bone metastases after external beam radiotherapy failure. Methods: From January 2014 to July 2016, a total of 90 patients, who had received the standard therapies for bone metastases but still suffered moderate-to-severe pain, were retrospectively studied. About 42 patients were treated with both ¹²⁵I brachytherapy and bisphosphonates (Group A), and 48 patients were treated with bisphosphonates alone (Group B). Results: In Group A, 45 ¹²⁵I brachytherapy procedures were performed in 42 patients with 69 bone metastases; the primary success rate of ¹²⁵I seed implantation was 92.9%, without severe complications. Regarding pain progression of the two groups, Group A exhibited significant relief in "worst pain," "least pain," "average pain," and "present pain" 3-day after treatment and could achieve a 12-week-remission for "worst pain," "least pain," "average pain," and "present pain." The morphine-equivalent 24-h analgesic dose at 3 days, 4 weeks, 8 weeks, and 12 weeks was 91 ± 27, 53 ± 13, 31 ± 17, and 34 ± 12 mg for Group A, and 129 ± 21, 61 ± 16, 53 ± 15, and 105 ± 23 mg for Group B. Group A experienced a lower incidence of analgesic-related adverse events and better quality of life than Group B. Conclusion: The CT-guided ¹²⁵I brachytherapy is a feasible and an effective treatment for the palliation of pain caused by bone metastases from breast cancer after external beam radiotherapy failure.

Deformable registration of x ray and MRI for postimplant dosimetry in low dose rate prostate brachytherapy

PURPOSE

Dosimetric assessment following permanent prostate brachytherapy (PPB) commonly involves seed localization using CT and prostate delineation using coregistered MRI. However, pelvic CT leads to additional imaging dose and requires significant resources to acquire and process both CT and MRI. In this study, we propose an automatic postimplant dosimetry approach that retains MRI for soft-tissue contouring, but eliminates the need for CT and reduces imaging dose while overcoming the inconsistent appearance of seeds on MRI with three projection x rays acquired using a mobile C-arm.

METHODS

Implanted seeds are reconstructed using x rays by solving a combinatorial optimization problem and deformably registered to MRI. Candidate seeds are located in MR images using local hypointensity identification. X ray-based seeds are registered to these candidate seeds in three steps: (a) rigid registration using a stochastic evolutionary optimizer, (b) affine registration using an iterative closest point optimizer, and (c) deformable registration using a local feature point search and nonrigid coherent point drift. The algorithm was evaluated using 20 PPB patients with x rays acquired immediately postimplant and T2-weighted MR images acquired the next day at 1.5 T with mean 0.8 × 0.8 × 3.0 mm 3 voxel dimensions. Target registration error (TRE) was computed based on the distance from algorithm results to manually identified seed locations using coregistered CT acquired the same day as the MRI. Dosimetric accuracy was determined by comparing prostate D90 determined using the algorithm and the ground truth CT-based seed locations.

RESULTS

The mean ± standard deviation TREs across 20 patients including 1774 seeds were 2.23 ± 0.52 mm (rigid), 1.99 ± 0.49 mm (rigid + affine), and 1.76 ± 0.43 mm (rigid + affine + deformable). The corresponding mean ± standard deviation D90 errors were 5.8 ± 4.8%, 3.4 ± 3.4%, and 2.3 ± 1.9%, respectively. The mean computation time of the registration algorithm was 6.1 s.

CONCLUSION

The registration algorithm accuracy and computation time are sufficient for clinical PPB postimplant dosimetry.

The impact of body mass index on dosimetric quality in low-dose-rate prostate brachytherapy

Purpose

Low-dose-rate (LDR) brachytherapy has been established as an effective and safe treatment option for men with low and intermediate risk prostate cancer. In this retrospective analysis, we sought to study the effect of body mass index (BMI) on post-implant dosimetric quality.

Material and methods

After institutional approval, records of patients with non-metastatic prostate cancer treated in Puerto Rico with LDR brachytherapy during 2008-2013 were reviewed. All patients were implanted with ¹²⁵I seeds to a prescription dose of 145 Gy. Computed tomography (CT) based dosimetry was performed 1 month after implant. Patients with at least 1 year of prostate-specific antigen (PSA) follow-up were included. Factors predictive of adequate D₉₀ coverage (≥ 140 Gy) were compared via the Pearson χ² or Wilcoxon rank-sum test as appropriate.

Results

One-hundred and four patients were included in this study, with 53 (51%) patients having a D₉₀ ≥ 140 Gy. The only factor associated with a dosimetric coverage detriment (D₉₀ < 140 Gy) was BMI ≥ 25 kg/m² (p = 0.03). Prostate volume (p = 0.26), initial PSA (p = 0.236), age (p = 0.49), hormone use (p = 0.93), percent of cores positive (p = 0.95), risk group (p = 0.24), tumor stage (p = 0.66), and Gleason score (p = 0.61) did not predict D₉₀.

Conclusions

In this study we show that BMI is a significant pre-implant predictor of D₉₀ (< 140 Gy vs. ≥ 140 Gy). Although other studies have reported that prostate volume also affects D₉₀, our study did not find this correlation to be statistically significant, likely because all of our patients had a prostate volume < 50 cc. Our study suggests that in patients with higher BMI values, more rigorous peri-implant dosimetric parameters may need to be applied in order to achieve a target D₉₀ > 140 Gy.

Evaluation of a Machine-Learning Algorithm for Treatment Planning in Prostate Low-Dose-Rate Brachytherapy

PURPOSE

This work presents the application of a machine learning (ML) algorithm to automatically generate high-quality, prostate low-dose-rate (LDR) brachytherapy treatment plans. The ML algorithm can mimic characteristics of preoperative treatment plans deemed clinically acceptable by brachytherapists. The planning efficiency, dosimetry, and quality (as assessed by experts) of preoperative plans generated with an ML planning approach was retrospectively evaluated in this study.

METHODS AND MATERIALS

Preimplantation and postimplantation treatment plans were extracted from 100 high-quality LDR treatments and stored within a training database. The ML training algorithm matches similar features from a new LDR case to those within the training database to rapidly obtain an initial seed distribution; plans were then further fine-tuned using stochastic optimization. Preimplantation treatment plans generated by the ML algorithm were compared with brachytherapist (BT) treatment plans in terms of planning time (Wilcoxon rank sum, α = 0.05) and dosimetry (1-way analysis of variance, α = 0.05). Qualitative preimplantation plan quality was evaluated by expert LDR radiation oncologists using a Likert scale questionnaire.

RESULTS

The average planning time for the ML approach was 0.84 ± 0.57 minutes, compared with 17.88 ± 8.76 minutes for the expert planner (P=.020). Preimplantation plans were dosimetrically equivalent to the BT plans; the average prostate V150% was 4% lower for ML plans (P=.002), although the difference was not clinically significant. Respondents ranked the ML-generated plans as equivalent to expert BT treatment plans in terms of target coverage, normal tissue avoidance, implant confidence, and the need for plan modifications. Respondents had difficulty differentiating between plans generated by a human or those generated by the ML algorithm.

CONCLUSIONS

Prostate LDR preimplantation treatment plans that have equivalent quality to plans created by brachytherapists can be rapidly generated using ML. The adoption of ML in the brachytherapy workflow is expected to improve LDR treatment plan uniformity while reducing planning time and resources.